20865
Article
Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences
Current research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery.
2021-5
Bull. Amer. Meteor. Soc.
102
E1016-E1032
0
10.1175/BAMS-D-20-0031.1
Current research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery.
Boukabara
S.-A.
Krasnopolsky
V.
Penny
S. G.
Stewart
J. Q.
al.
et
20946
Article
Mapping of Directional Ocean Wave Spectra in Hurricanes and Other Environments
The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.
2021-11
IEEE Trans. Geosci. Remote Sens.
59
9007-9020
0
10.1109/TGRS.2020.3042904
The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.
PopStefanija
I.
Fairall
C. W.
Walsh
E. J.
20975
Article
Soil Moisture Retrieval Using Reflection Coefficients: Numerical Experiments
Ready access to small, inexpensive, unmanned aerial vehicles (UAVs) allows for small-scale electromagnetic propagation and scattering experiments using airborne antennas. We consider a low-power, copter-mounted transmit antenna radiating at frequencies on the order of a hundred megahertz, corresponding to wavelength on the order of meters, which at some horizontal distance generates a vertical interference pattern due to the interaction of a direct and ground-reflected wave. The depth of the interference fringes, which is directly related to the modulus of the soil reflection coefficient, can be measured by a second copter-mounted receiving antenna. By varying the geometry of the copter pair, and/or the transmitted signal frequency, the soil moisture profile up to a depth of a few meters can, in principle, be retrieved. In this article, we present the results of numerical experiments designed to evaluate the sensitivity of the angle- and frequency-dependence of the measured reflection coefficient to the soil moisture profile. Our simulations indicate that the retrieval errors are small suggesting that the technique is feasible.
2021-11
IEEE Trans. Geosci. Remote Sens.
59
8957-8967
0
10.1109/TGRS.2020.3037012
Ready access to small, inexpensive, unmanned aerial vehicles (UAVs) allows for small-scale electromagnetic propagation and scattering experiments using airborne antennas. We consider a low-power, copter-mounted transmit antenna radiating at frequencies on the order of a hundred megahertz, corresponding to wavelength on the order of meters, which at some horizontal distance generates a vertical interference pattern due to the interaction of a direct and ground-reflected wave. The depth of the interference fringes, which is directly related to the modulus of the soil reflection coefficient, can be measured by a second copter-mounted receiving antenna. By varying the geometry of the copter pair, and/or the transmitted signal frequency, the soil moisture profile up to a depth of a few meters can, in principle, be retrieved. In this article, we present the results of numerical experiments designed to evaluate the sensitivity of the angle- and frequency-dependence of the measured reflection coefficient to the soil moisture profile. Our simulations indicate that the retrieval errors are small suggesting that the technique is feasible.
Voronovich
A. G.
Lataitis
R.
20993
Article
CROSSINN: A Field Experiment to Study the Three-Dimensional Flow Structure in the Inn Valley, Austria
While the exchange of mass, momentum, moisture, and energy over horizontally homogeneous, flat terrain is mostly driven by vertical turbulent mixing, thermally and dynamically driven mesoscale flows substantially contribute to the Earth–atmosphere exchange in the atmospheric boundary layer over mountainous terrain (MoBL). The interaction of these processes acting on multiple scales leads to a large spatial variability in the MoBL, whose observational detection requires comprehensive instrumentation and a sophisticated measurement strategy. We designed a field campaign that targets the three-dimensional flow structure and its impact on the MoBL in a major Alpine valley. Taking advantage of an existing network of surface flux towers and remote sensing instrumentation in the Inn Valley, Austria, we added a set of ground-based remote sensing instruments, consisting of Doppler lidars, a ceilometer, a Raman lidar, and a microwave radiometer, and performed radio soundings and aircraft measurements. The objective of the Cross-Valley Flow in the Inn Valley Investigated by Dual-Doppler Lidar Measurements (CROSSINN) experiment is to determine the mean and turbulent characteristics of the flow in the MoBL under different synoptic conditions and to provide an intensive dataset for the future validation of mesoscale and large-eddy simulations. A particular challenge is capturing the two-dimensional kinematic flow in a vertical plane across the whole valley using coplanar synchronized Doppler lidar scans, which allows the detection of cross-valley circulation cells. This article outlines the scientific objectives, instrument setup, measurement strategy, and available data; summarizes the synoptic conditions during the measurement period of 2.5 months; and presents first results.
2021-1
Bull. Amer. Meteor. Soc.
102
E38-E60
0
10.1175/BAMS-D-19-0283.1
While the exchange of mass, momentum, moisture, and energy over horizontally homogeneous, flat terrain is mostly driven by vertical turbulent mixing, thermally and dynamically driven mesoscale flows substantially contribute to the Earth–atmosphere exchange in the atmospheric boundary layer over mountainous terrain (MoBL). The interaction of these processes acting on multiple scales leads to a large spatial variability in the MoBL, whose observational detection requires comprehensive instrumentation and a sophisticated measurement strategy. We designed a field campaign that targets the three-dimensional flow structure and its impact on the MoBL in a major Alpine valley. Taking advantage of an existing network of surface flux towers and remote sensing instrumentation in the Inn Valley, Austria, we added a set of ground-based remote sensing instruments, consisting of Doppler lidars, a ceilometer, a Raman lidar, and a microwave radiometer, and performed radio soundings and aircraft measurements. The objective of the Cross-Valley Flow in the Inn Valley Investigated by Dual-Doppler Lidar Measurements (CROSSINN) experiment is to determine the mean and turbulent characteristics of the flow in the MoBL under different synoptic conditions and to provide an intensive dataset for the future validation of mesoscale and large-eddy simulations. A particular challenge is capturing the two-dimensional kinematic flow in a vertical plane across the whole valley using coplanar synchronized Doppler lidar scans, which allows the detection of cross-valley circulation cells. This article outlines the scientific objectives, instrument setup, measurement strategy, and available data; summarizes the synoptic conditions during the measurement period of 2.5 months; and presents first results.
Adler
B.
Gohm
A.
Kalthoff
N.
Babić
N.
Corsmeier
U.
Lehner
M.
Rotach
M. W.
Haid
M.
Markmann
P.
Gast
E.
Tsaknakis
G.
Georgoussis
G.
20994
Article
Subseasonal predictability of the North Atlantic Oscillation
Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on 'subseasonal' (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO's association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.
2021-3
Environ. Res. Lett.
16
44024
0
10.1088/1748-9326/abe781
Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on 'subseasonal' (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO's association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.
Albers
J. R.
Newman
M.
20995
Article
Atmospheric radiative profiles during EUREC4A
The couplings among clouds, convection, and circulation in trade-wind regimes remain a fundamental puzzle that limits our ability to constrain future climate change. Radiative heating plays an important role in these couplings. Here we calculate the clear-sky radiative profiles from 2001 in-situ soundings (978 dropsondes and 1023 radiosondes) collected during the EUREC4A field campaign, which took place south and east of Barbados in January–February 2020. We describe the method used to calculate these radiative profiles and present preliminary results sampling variability at multiple scales, from the variability across all soundings to groupings by diurnal cycle and mesoscale organization state, as well as individual soundings associated with elevated moisture layers. This clear-sky radiative profiles data set can provide important missing detail to what can be learned from calculations based on passive remote sensing and help in investigating the role of radiation in dynamic and thermodynamic variability in trade-wind regimes. All data are archived and freely available for public access on AERIS (Albright et al. (2020), https://doi.org/10.25326/78).
2021-2
Earth Syst. Sci. Data
13
617-630
0
10.5194/essd-13-617-2021
The couplings among clouds, convection, and circulation in trade-wind regimes remain a fundamental puzzle that limits our ability to constrain future climate change. Radiative heating plays an important role in these couplings. Here we calculate the clear-sky radiative profiles from 2001 in-situ soundings (978 dropsondes and 1023 radiosondes) collected during the EUREC4A field campaign, which took place south and east of Barbados in January–February 2020. We describe the method used to calculate these radiative profiles and present preliminary results sampling variability at multiple scales, from the variability across all soundings to groupings by diurnal cycle and mesoscale organization state, as well as individual soundings associated with elevated moisture layers. This clear-sky radiative profiles data set can provide important missing detail to what can be learned from calculations based on passive remote sensing and help in investigating the role of radiation in dynamic and thermodynamic variability in trade-wind regimes. All data are archived and freely available for public access on AERIS (Albright et al. (2020), https://doi.org/10.25326/78).
Albright
A. L.
Fildier
B.
Touzé-Peiffer
L.
Pincus
R.
Vial
J.
Muller
C.
20996
Article
Are Long-Term Changes in Mixed Layer Depth Influencing North Pacific Marine Heatwaves?
N/A
2021-1
Bull. Amer. Meteor. Soc.
102
S59-S66
0
10.1175/BAMS-D-20-0144.1
N/A
Amaya
D. J.
Alexander
M. A.
Capotondi
A.
Deser
C.
Karnauskas
K.
Miller
A. J.
Mantua
N.
20997
Article
Amazonian mesoscale convective systems: Life cycle and propagation characteristics
Convective system tracking was performed using 30‐min GOES‐13 infrared imagery over the Amazon region during 2014 and 2015. A total of 116,701 convective systems were identified and statistics on the probability of occurrence of track area, lifetime, and system velocity were analysed. Maps of the total and seasonal geographic density of trajectories and the geographic density of clusters at genesis, during propagation, and at dissipation were also assessed. The mean area and lifetime of the tracked systems was 4 × 104 km2 and 3 hr, respectively. The top 10% largest systems had areas >8 × 104 km2 and the top 10% longest lived systems lasted >7 hr. The geographical distribution of clusters identified on the coast and within the Amazon basin varied seasonally and their life cycle tracking showed that they are typically distinct from one another (i.e., it is relatively rare for systems to start at the coast and propagate 1,500 km to the centre of the basin). Although the average system velocity indicated a predominantly westward motion, a large spread in the direction of propagation was found. In particular, the probability of a meridional component of motion was generally the same for northward or southward directions and 35% of the zonal propagation was associated with eastward movement. The presence of Kelvin waves accounted for some of the eastward system motion, in addition to increasing the area and lifetime of storms compared to when Kelvin waves were not present.
2021-2
Int. J. Climatol.
41
3968-3981
0
10.1002/joc.7053
Convective system tracking was performed using 30‐min GOES‐13 infrared imagery over the Amazon region during 2014 and 2015. A total of 116,701 convective systems were identified and statistics on the probability of occurrence of track area, lifetime, and system velocity were analysed. Maps of the total and seasonal geographic density of trajectories and the geographic density of clusters at genesis, during propagation, and at dissipation were also assessed. The mean area and lifetime of the tracked systems was 4 × 104 km2 and 3 hr, respectively. The top 10% largest systems had areas >8 × 104 km2 and the top 10% longest lived systems lasted >7 hr. The geographical distribution of clusters identified on the coast and within the Amazon basin varied seasonally and their life cycle tracking showed that they are typically distinct from one another (i.e., it is relatively rare for systems to start at the coast and propagate 1,500 km to the centre of the basin). Although the average system velocity indicated a predominantly westward motion, a large spread in the direction of propagation was found. In particular, the probability of a meridional component of motion was generally the same for northward or southward directions and 35% of the zonal propagation was associated with eastward movement. The presence of Kelvin waves accounted for some of the eastward system motion, in addition to increasing the area and lifetime of storms compared to when Kelvin waves were not present.
Anselmo
E. M.
Machado
L. A. T.
Schumacher
C.
Kiladis
G. N.
20998
Article
A Stochastic Parameterization of Organized Tropical Convection Using Cellular Automata for Global Forecasts in NOAA
In the atmosphere, convection can organize from smaller scale updrafts into more coherent structures on various scales. In bulk‐plume cumulus convection parameterizations, this type of organization has to be represented in terms of how the resolved flow would “feel” convection if more coherent structures were present on the subgrid. This type of subgrid organization acts as building blocks for larger scale tropical convective organization known to modulate local and remote weather. In this work a parameterization for subgrid (and cross‐grid) organization in a bulk‐plume convection scheme is proposed using the stochastic, self‐organizing, properties of cellular automata (CA). We investigate the effects of using a CA which can interact with three different components of the bulk‐plume scheme that modulate convective activity: entrainment, triggering, and closure. The impacts of the revised schemes are studied in terms of the model's ability to organize convectively coupled equatorial waves (CCEWs). The differing impacts of adopting the stochastic CA scheme, as compared to the widely used Stochastically Perturbed Physics Tendency (SPPT) scheme, are also assessed. Results show that with the CA scheme, precipitation is more spatially and temporally organized, and there is a systematic shift in equatorial wave phase speed not seen with SPPT. Previous studies have noted a linear relationship between Gross Moist Stability (GMS) and Kelvin wave phase speed. Analysis of GMS in this study shows an increase in Kelvin wave phase speed and an increase in GMS with the CA scheme, which is tied to a shift from large‐scale precipitation to convective precipitation.
2021-1
J. Adv. Model. Earth Syst.
13
e202MS002260
0
10.1029/2020MS002260
In the atmosphere, convection can organize from smaller scale updrafts into more coherent structures on various scales. In bulk‐plume cumulus convection parameterizations, this type of organization has to be represented in terms of how the resolved flow would “feel” convection if more coherent structures were present on the subgrid. This type of subgrid organization acts as building blocks for larger scale tropical convective organization known to modulate local and remote weather. In this work a parameterization for subgrid (and cross‐grid) organization in a bulk‐plume convection scheme is proposed using the stochastic, self‐organizing, properties of cellular automata (CA). We investigate the effects of using a CA which can interact with three different components of the bulk‐plume scheme that modulate convective activity: entrainment, triggering, and closure. The impacts of the revised schemes are studied in terms of the model's ability to organize convectively coupled equatorial waves (CCEWs). The differing impacts of adopting the stochastic CA scheme, as compared to the widely used Stochastically Perturbed Physics Tendency (SPPT) scheme, are also assessed. Results show that with the CA scheme, precipitation is more spatially and temporally organized, and there is a systematic shift in equatorial wave phase speed not seen with SPPT. Previous studies have noted a linear relationship between Gross Moist Stability (GMS) and Kelvin wave phase speed. Analysis of GMS in this study shows an increase in Kelvin wave phase speed and an increase in GMS with the CA scheme, which is tied to a shift from large‐scale precipitation to convective precipitation.
Bengtsson
L.
Dias
J.
Tulich
S. N.
Gehne
M.
Bao
J.-W.
20999
Article
Projections of physical conditions in the Gulf of Maine in 2050
The Gulf of Maine (GoM) is currently experiencing its warmest period in the instrumental record. Two high-resolution numerical ocean models were used to downscale global climate projections to produce four estimates of ocean physical properties in the GoM in 2050 for the “business as usual” carbon emission scenario. All simulations project increases in the GoM mean sea surface temperature (of 1.1 °C–2.4 °C) and bottom temperature (of 1.5 °C–2.1 °C). In terms of mean vertical structure, all simulations project temperature increases throughout the water column (surface-to-bottom changes of 0.2 °C–0.5 °C). The GoM volume-averaged changes in temperature range from 1.5 °C to 2.3 °C. Translated to rates, the sea surface temperature projections are all greater than the observed 100-year rate, with two projections below and two above the observed 1982–2013 rate. Sea surface salinity changes are more variable, with three of four simulations projecting decreases. Bottom salinity changes vary spatially and between projections, with three simulations projecting varying increases in deeper waters but decreases in shallower zones and one simulation projecting a salinity increase in all bottom waters. In terms of mean vertical structure, salinity structure varies, with two simulations projecting surface decreases that switch sign with depth and two projecting increases throughout the (subsurface) water column. Three simulations show a difference between coastal and deeper waters whereby the coastal zone is projected to be systematically fresher than deeper waters, by as much as 0.2 g kg–1. Stratification, 50 m to surface, is projected to increase in all simulations, with rates ranging from 0.003 to 0.006 kg m–4 century–1 which are lower than the observed change on the Scotian Shelf. The results from these simulations can be used to assess potential acidification and ecosystem changes in the GoM.
2021-5
Elementa Sci. Anthrop.
9
55
0
10.1525/elementa.2020.20.00055
The Gulf of Maine (GoM) is currently experiencing its warmest period in the instrumental record. Two high-resolution numerical ocean models were used to downscale global climate projections to produce four estimates of ocean physical properties in the GoM in 2050 for the “business as usual” carbon emission scenario. All simulations project increases in the GoM mean sea surface temperature (of 1.1 °C–2.4 °C) and bottom temperature (of 1.5 °C–2.1 °C). In terms of mean vertical structure, all simulations project temperature increases throughout the water column (surface-to-bottom changes of 0.2 °C–0.5 °C). The GoM volume-averaged changes in temperature range from 1.5 °C to 2.3 °C. Translated to rates, the sea surface temperature projections are all greater than the observed 100-year rate, with two projections below and two above the observed 1982–2013 rate. Sea surface salinity changes are more variable, with three of four simulations projecting decreases. Bottom salinity changes vary spatially and between projections, with three simulations projecting varying increases in deeper waters but decreases in shallower zones and one simulation projecting a salinity increase in all bottom waters. In terms of mean vertical structure, salinity structure varies, with two simulations projecting surface decreases that switch sign with depth and two projecting increases throughout the (subsurface) water column. Three simulations show a difference between coastal and deeper waters whereby the coastal zone is projected to be systematically fresher than deeper waters, by as much as 0.2 g kg–1. Stratification, 50 m to surface, is projected to increase in all simulations, with rates ranging from 0.003 to 0.006 kg m–4 century–1 which are lower than the observed change on the Scotian Shelf. The results from these simulations can be used to assess potential acidification and ecosystem changes in the GoM.
Brickman
D.
Alexander
M. A.
Pershing
A. J.
Scott
J. D.
Wang
Z.
21000
Article
Assessment of Flood Forecast Products for a Coupled Tributary-Coastal Model
Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users.
2021-1
Water
13
312
0
10.3390/w13030312
Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users.
Cifelli
R.
Johnson
L. E.
Kim
J.
Coleman
T.
Pratt
G.
al.
et
21001
Article
The De-Icing Comparison Experiment (D-ICE): a study of broadband radiometric measurements under icing conditions in the Arctic
Surface-based measurements of broadband shortwave (solar) and longwave (infrared) radiative fluxes using thermopile radiometers are made regularly around the globe for scientific and operational environmental monitoring. The occurrence of ice on sensor windows in cold environments – whether snow, rime, or frost – is a common problem that is difficult to prevent as well as difficult to correct in post-processing. The Baseline Surface Radiation Network (BSRN) community recognizes radiometer icing as a major outstanding measurement uncertainty. Towards constraining this uncertainty, the De-Icing Comparison Experiment (D-ICE) was carried out at the NOAA Atmospheric Baseline Observatory in Utqiaġvik (formerly Barrow), Alaska, from August 2017 to July 2018. The purpose of D-ICE was to evaluate existing ventilation and heating technologies developed to mitigate radiometer icing. D-ICE consisted of 20 pyranometers and 5 pyrgeometers operating in various ventilator housings alongside operational systems that are part of NOAA's Barrow BSRN station and the US Department of Energy Atmospheric Radiation Measurement (ARM) program North Slope of Alaska and Oliktok Point observatories. To detect icing, radiometers were monitored continuously using cameras, with a total of more than 1 million images of radiometer domes archived. Ventilator and ventilator–heater performance overall was skillful with the average of the systems mitigating ice formation 77 % (many >90 %) of the time during which icing conditions were present. Ventilators without heating elements were also effective and capable of providing heat through roughly equal contributions of waste energy from the ventilator fan and adiabatic heating downstream of the fan. This provided ∼0.6 ∘C of warming, enough to subsaturate the air up to a relative humidity (with respect to ice) of ∼105 %. Because the mitigation technologies performed well, a near complete record of verified ice-free radiometric fluxes was assembled for the duration of the campaign. This well-characterized data set is suitable for model evaluation, in particular for the Year of Polar Prediction (YOPP) first Special Observing Period (SOP1). We used the data set to calculate short- and long-term biases in iced sensors, finding that biases can be up to +60 W m−2 (longwave) and −211 to +188 W m−2 (shortwave). However, because of the frequency of icing, mitigation of ice by ventilators, cloud conditions, and the timing of icing relative to available sunlight, the biases in the monthly means were generally less than the aggregate uncertainty attributed to other conventional sources in both the shortwave and longwave.
2021-2
Atmos. Meas. Tech.
14
1205-1224
0
10.5194/amt-14-1205-2021
Surface-based measurements of broadband shortwave (solar) and longwave (infrared) radiative fluxes using thermopile radiometers are made regularly around the globe for scientific and operational environmental monitoring. The occurrence of ice on sensor windows in cold environments – whether snow, rime, or frost – is a common problem that is difficult to prevent as well as difficult to correct in post-processing. The Baseline Surface Radiation Network (BSRN) community recognizes radiometer icing as a major outstanding measurement uncertainty. Towards constraining this uncertainty, the De-Icing Comparison Experiment (D-ICE) was carried out at the NOAA Atmospheric Baseline Observatory in Utqiaġvik (formerly Barrow), Alaska, from August 2017 to July 2018. The purpose of D-ICE was to evaluate existing ventilation and heating technologies developed to mitigate radiometer icing. D-ICE consisted of 20 pyranometers and 5 pyrgeometers operating in various ventilator housings alongside operational systems that are part of NOAA's Barrow BSRN station and the US Department of Energy Atmospheric Radiation Measurement (ARM) program North Slope of Alaska and Oliktok Point observatories. To detect icing, radiometers were monitored continuously using cameras, with a total of more than 1 million images of radiometer domes archived. Ventilator and ventilator–heater performance overall was skillful with the average of the systems mitigating ice formation 77 % (many >90 %) of the time during which icing conditions were present. Ventilators without heating elements were also effective and capable of providing heat through roughly equal contributions of waste energy from the ventilator fan and adiabatic heating downstream of the fan. This provided ∼0.6 ∘C of warming, enough to subsaturate the air up to a relative humidity (with respect to ice) of ∼105 %. Because the mitigation technologies performed well, a near complete record of verified ice-free radiometric fluxes was assembled for the duration of the campaign. This well-characterized data set is suitable for model evaluation, in particular for the Year of Polar Prediction (YOPP) first Special Observing Period (SOP1). We used the data set to calculate short- and long-term biases in iced sensors, finding that biases can be up to +60 W m−2 (longwave) and −211 to +188 W m−2 (shortwave). However, because of the frequency of icing, mitigation of ice by ventilators, cloud conditions, and the timing of icing relative to available sunlight, the biases in the monthly means were generally less than the aggregate uncertainty attributed to other conventional sources in both the shortwave and longwave.
Cox
C. J.
Morris
S. M.
Uttal
T.
Burgener
R.
al.
et
21002
Article
Assessing the vertical structure of Arctic aerosols using balloon-borne measurements
The rapidly warming Arctic is sensitive to perturbations in the surface energy budget, which can be caused by clouds and aerosols. However, the interactions between clouds and aerosols are poorly quantified in the Arctic, in part due to (1) limited observations of vertical structure of aerosols relative to clouds and (2) ground-based observations often being inadequate for assessing aerosol impacts on cloud formation in the characteristically stratified Arctic atmosphere. Here, we present a novel evaluation of Arctic aerosol vertical distributions using almost 3 years' worth of tethered balloon system (TBS) measurements spanning multiple seasons. The TBS was deployed at the U.S. Department of Energy Atmospheric Radiation Measurement Program's facility at Oliktok Point, Alaska. Aerosols were examined in tandem with atmospheric stability and ground-based remote sensing of cloud macrophysical properties to specifically address the representativeness of near-surface aerosols to those at cloud base. Based on a statistical analysis of the TBS profiles, ground-based aerosol number concentrations were unequal to those at cloud base 86 % of the time. Intermittent aerosol layers were observed 63 % of the time due to poorly mixed below-cloud environments, mostly found in the spring, causing a decoupling of the surface from the cloud layer. A uniform distribution of aerosol below cloud was observed only 14 % of the time due to a well-mixed below-cloud environment, mostly during the fall. The equivalent potential temperature profiles of the below-cloud environment reflected the aerosol profile 89 % of the time, whereby a mixed or stratified below-cloud environment was observed during a uniform or layered aerosol profile, respectively. In general, a combination of aerosol sources, thermodynamic structure, and wet removal processes from clouds and precipitation likely played a key role in establishing observed aerosol vertical structures. Results such as these could be used to improve future parameterizations of aerosols and their impacts on Arctic cloud formation and radiative properties.
2021-2
Atmos. Chem. Phys.
21
1737-1757
0
10.5194/acp-21-1737-2021
The rapidly warming Arctic is sensitive to perturbations in the surface energy budget, which can be caused by clouds and aerosols. However, the interactions between clouds and aerosols are poorly quantified in the Arctic, in part due to (1) limited observations of vertical structure of aerosols relative to clouds and (2) ground-based observations often being inadequate for assessing aerosol impacts on cloud formation in the characteristically stratified Arctic atmosphere. Here, we present a novel evaluation of Arctic aerosol vertical distributions using almost 3 years' worth of tethered balloon system (TBS) measurements spanning multiple seasons. The TBS was deployed at the U.S. Department of Energy Atmospheric Radiation Measurement Program's facility at Oliktok Point, Alaska. Aerosols were examined in tandem with atmospheric stability and ground-based remote sensing of cloud macrophysical properties to specifically address the representativeness of near-surface aerosols to those at cloud base. Based on a statistical analysis of the TBS profiles, ground-based aerosol number concentrations were unequal to those at cloud base 86 % of the time. Intermittent aerosol layers were observed 63 % of the time due to poorly mixed below-cloud environments, mostly found in the spring, causing a decoupling of the surface from the cloud layer. A uniform distribution of aerosol below cloud was observed only 14 % of the time due to a well-mixed below-cloud environment, mostly during the fall. The equivalent potential temperature profiles of the below-cloud environment reflected the aerosol profile 89 % of the time, whereby a mixed or stratified below-cloud environment was observed during a uniform or layered aerosol profile, respectively. In general, a combination of aerosol sources, thermodynamic structure, and wet removal processes from clouds and precipitation likely played a key role in establishing observed aerosol vertical structures. Results such as these could be used to improve future parameterizations of aerosols and their impacts on Arctic cloud formation and radiative properties.
Creamean
J. M.
de Boer
G.
Hagen
T.
Mei
F.
Dexheimer
D.
Shupe
M. D.
Solomon
A.
McComiskey
A.
21003
Article
Spatial and temporal variability of ozone along the Colorado Front Range occurring over 2 days with contrasting wind flow
Transport of pollution into pristine wilderness areas is of concern for both federal and state agencies. Assessing such transport in complex terrain is a challenge when relying solely on data from standard federal or state air quality monitoring networks because of the sparsity of network monitors beyond urban areas. During the Front Range air quality study, conducted in the summer of 2008 in the vicinity of Denver, CO, research-grade surface air quality data, vertical wind profiles and mixing heights obtained by radar wind profilers, and ozone profile data obtained by an airborne ozone differential absorption lidar augmented the local regulatory monitoring networks. Measurements from this study were taken on 2 successive days at the end of July 2008. On the first day, the prevailing winds were downslope westerly, advecting pollution to the east of the Front Range metropolitan areas. On this day, chemistry measurements at the mountain and foothills surface stations showed seasonal background ozone levels of approximately 55–68 ppbv (nmol mol–1 by volume). The next day, upslope winds prevailed, advecting pollution from the Plains into the Rocky Mountains and across the Continental Divide. Mountain stations measured ozone values greater than 90 ppbv, comparable to, or greater than, nearby urban measurements. The measurements show the progression of the ozone-enriched air into the mountains and tie the westward intrusion into high-elevation mountain sites to the growth of the afternoon boundary layer. Thus, under deep upslope flow conditions, ozone-enriched air can be advected into wilderness areas of the Rocky Mountains. Our findings highlight a process that is likely to be an important ozone transport mechanism in mountainous terrain adjacent to ozone source areas when the right circumstances come together, namely a deep layer of light winds toward a mountain barrier coincident with a deep regional boundary layer.
2021-5
Elementa Sci. Anthrop.
9
146
0
10.1525/elementa.2020.00146
Transport of pollution into pristine wilderness areas is of concern for both federal and state agencies. Assessing such transport in complex terrain is a challenge when relying solely on data from standard federal or state air quality monitoring networks because of the sparsity of network monitors beyond urban areas. During the Front Range air quality study, conducted in the summer of 2008 in the vicinity of Denver, CO, research-grade surface air quality data, vertical wind profiles and mixing heights obtained by radar wind profilers, and ozone profile data obtained by an airborne ozone differential absorption lidar augmented the local regulatory monitoring networks. Measurements from this study were taken on 2 successive days at the end of July 2008. On the first day, the prevailing winds were downslope westerly, advecting pollution to the east of the Front Range metropolitan areas. On this day, chemistry measurements at the mountain and foothills surface stations showed seasonal background ozone levels of approximately 55–68 ppbv (nmol mol–1 by volume). The next day, upslope winds prevailed, advecting pollution from the Plains into the Rocky Mountains and across the Continental Divide. Mountain stations measured ozone values greater than 90 ppbv, comparable to, or greater than, nearby urban measurements. The measurements show the progression of the ozone-enriched air into the mountains and tie the westward intrusion into high-elevation mountain sites to the growth of the afternoon boundary layer. Thus, under deep upslope flow conditions, ozone-enriched air can be advected into wilderness areas of the Rocky Mountains. Our findings highlight a process that is likely to be an important ozone transport mechanism in mountainous terrain adjacent to ozone source areas when the right circumstances come together, namely a deep layer of light winds toward a mountain barrier coincident with a deep regional boundary layer.
Darby
L. S.
Senff
C. J.
Alvarez II
R. J.
Banta
R. M.
Bianco
L.
Helmig
D.
White
A. B.
21005
Article
Mountain waves can impact wind power generation
Mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River basin in the lee of the Cascade Range bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting (WRF) model simulations also produce mountain waves and are compared to satellite, lidar, and sodar observations. Simulated mountain wave wavelengths and wave propagation speeds (group velocities) are analyzed using the fast Fourier transform. We found that not all mountain waves exhibit the same speed and conclude that the speed of propagation, magnitudes of wind speeds, or wavelengths are important parameters for forecasters to recognize the risk for mountain waves and associated large drops or surges in power. When analyzing wind farm power output and nacelle wind speeds, we found that even small oscillations in wind speed caused by mountain waves can induce oscillations between full-rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. For the wind plant analyzed in this paper, mountain-wave-induced fluctuations translate to approximately 11 % of the total wind farm output being influenced by mountain waves. Oscillations in measured wind speeds agree well with WRF simulations in timing and magnitude. We conclude that mountain waves can impact wind turbine and wind farm power
2021-1
Wind Energ. Sci.
6
45-60
0
10.5194/wes-6-45-2021
Mountains can modify the weather downstream of the terrain. In particular, when stably stratified air ascends a mountain barrier, buoyancy perturbations develop. These perturbations can trigger mountain waves downstream of the mountains that can reach deep into the atmospheric boundary layer where wind turbines operate. Several such cases of mountain waves occurred during the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River basin in the lee of the Cascade Range bounding the states of Washington and Oregon in the Pacific Northwest of the United States. Signals from the mountain waves appear in boundary layer sodar and lidar observations as well as in nacelle wind speeds and power observations from wind plants. Weather Research and Forecasting (WRF) model simulations also produce mountain waves and are compared to satellite, lidar, and sodar observations. Simulated mountain wave wavelengths and wave propagation speeds (group velocities) are analyzed using the fast Fourier transform. We found that not all mountain waves exhibit the same speed and conclude that the speed of propagation, magnitudes of wind speeds, or wavelengths are important parameters for forecasters to recognize the risk for mountain waves and associated large drops or surges in power. When analyzing wind farm power output and nacelle wind speeds, we found that even small oscillations in wind speed caused by mountain waves can induce oscillations between full-rated power of a wind farm and half of the power output, depending on the position of the mountain wave's crests and troughs. For the wind plant analyzed in this paper, mountain-wave-induced fluctuations translate to approximately 11 % of the total wind farm output being influenced by mountain waves. Oscillations in measured wind speeds agree well with WRF simulations in timing and magnitude. We conclude that mountain waves can impact wind turbine and wind farm power
Draxl
C.
Worsnop
R. P.
Xia
G.
Pichugina
Y. L.
Chand
D.
Lundquist
J. K.
Sharp
J.
Wedam
G.
Wilczak
J. M.
Berg
L. K.
21006
Article
An Assessment of Early 20th Century Antarctic Pressure Reconstructions using Historical Observations
While gridded seasonal pressure reconstructions poleward of 60°S extending back to 1905 have been recently completed, their skill has not been assessed prior to 1958. To provide a more thorough evaluation of the skill and performance in the early 20th century, these reconstructions are compared to other gridded datasets, historical data from early Antarctic expeditions, ship records, and temporary bases.
Overall, the comparison confirms that the reconstruction uncertainty of 2–4 hPa (evaluated after 1979) over the Southern Ocean is a valid estimate of the reconstruction error in the early 20th century. Over the interior and near the coast of Antarctica, direct comparisons with historical data are challenged by elevation‐based reductions to sea level pressure. In a few cases, a simple linear adjustment of the reconstruction to sea level matches the historical data well, but in other cases, the differences remain greater than 10 hPa. Despite these large errors, comparisons with continuous multi‐season observations demonstrate that aspects of the interannual variability are often still captured, suggesting that the reconstructions have skill representing variations on this timescale, even if it is difficult to determine how well they capture the mean pressure at these higher elevations. Additional comparisons with various 20th century reanalysis products demonstrate the value of assimilating the historical observations in these datasets, which acts to substantially reduce the reanalysis ensemble spread, and bring the reanalysis ensemble mean within the reconstruction and observational uncertainty.
2021-1
Int. J. Climatol.
41
E672-E689
0
10.1002/joc.6718
While gridded seasonal pressure reconstructions poleward of 60°S extending back to 1905 have been recently completed, their skill has not been assessed prior to 1958. To provide a more thorough evaluation of the skill and performance in the early 20th century, these reconstructions are compared to other gridded datasets, historical data from early Antarctic expeditions, ship records, and temporary bases.
Overall, the comparison confirms that the reconstruction uncertainty of 2–4 hPa (evaluated after 1979) over the Southern Ocean is a valid estimate of the reconstruction error in the early 20th century. Over the interior and near the coast of Antarctica, direct comparisons with historical data are challenged by elevation‐based reductions to sea level pressure. In a few cases, a simple linear adjustment of the reconstruction to sea level matches the historical data well, but in other cases, the differences remain greater than 10 hPa. Despite these large errors, comparisons with continuous multi‐season observations demonstrate that aspects of the interannual variability are often still captured, suggesting that the reconstructions have skill representing variations on this timescale, even if it is difficult to determine how well they capture the mean pressure at these higher elevations. Additional comparisons with various 20th century reanalysis products demonstrate the value of assimilating the historical observations in these datasets, which acts to substantially reduce the reanalysis ensemble spread, and bring the reanalysis ensemble mean within the reconstruction and observational uncertainty.
Fogt
R. L.
Belak
C. P.
Jones
J. M.
Slivinski
L. C.
Compo
G. P.
21007
Article
Coupled Ocean–Atmosphere Covariances in Global Ensemble Simulations: Impact of an Eddy-Resolving Ocean
Patterns of correlations between the ocean and the atmosphere are examined using a high-resolution (1/12° ocean and ice, 1/3° atmosphere) ensemble of data assimilative, coupled, global, ocean–atmosphere forecasts. This provides a unique perspective into atmosphere–ocean interactions constrained by assimilated observations, allowing for the contrast of patterns of coupled processes across regions and the examination of processes affected by ocean mesoscale eddies. Correlations during the first 24 h of the coupled forecast between the ocean surface temperature and atmospheric variables, and between the ocean mixed layer depth and surface winds are examined as a function of region and season. Three distinct coupling regimes emerge: 1) regions characterized by strong sea surface temperature fronts, where uncertainty in the ocean mesoscale influences ocean–atmosphere exchanges; 2) regions with intense atmospheric convection over the tropical oceans, where uncertainty in the modeled atmospheric convection impacts the upper ocean; and 3) regions where the depth of the seasonal mixed layer (MLD) determines the magnitude of the coupling, which is stronger when the MLD is shallow and weaker when the MLD is deep. A comparison with models at lower horizontal (1/12° vs 1° and 1/4°) and vertical (1- vs 10-m depth of the first layer) ocean resolution reveals that coupling in the boundary currents, the tropical Indian Ocean, and the warm pool regions requires high levels of horizontal and vertical resolution. Implications for coupled data assimilation and short-term forecasting are discussed.
2021-5
Mon. Wea. Rev.
149
1193-1209
0
10.1175/MWR-D-20-0352.1
Patterns of correlations between the ocean and the atmosphere are examined using a high-resolution (1/12° ocean and ice, 1/3° atmosphere) ensemble of data assimilative, coupled, global, ocean–atmosphere forecasts. This provides a unique perspective into atmosphere–ocean interactions constrained by assimilated observations, allowing for the contrast of patterns of coupled processes across regions and the examination of processes affected by ocean mesoscale eddies. Correlations during the first 24 h of the coupled forecast between the ocean surface temperature and atmospheric variables, and between the ocean mixed layer depth and surface winds are examined as a function of region and season. Three distinct coupling regimes emerge: 1) regions characterized by strong sea surface temperature fronts, where uncertainty in the ocean mesoscale influences ocean–atmosphere exchanges; 2) regions with intense atmospheric convection over the tropical oceans, where uncertainty in the modeled atmospheric convection impacts the upper ocean; and 3) regions where the depth of the seasonal mixed layer (MLD) determines the magnitude of the coupling, which is stronger when the MLD is shallow and weaker when the MLD is deep. A comparison with models at lower horizontal (1/12° vs 1° and 1/4°) and vertical (1- vs 10-m depth of the first layer) ocean resolution reveals that coupling in the boundary currents, the tropical Indian Ocean, and the warm pool regions requires high levels of horizontal and vertical resolution. Implications for coupled data assimilation and short-term forecasting are discussed.
Frolov
S.
Reynolds
C. A.
Alexander
M. A.
Flatau
M.
Barton
N. P.
Hogan
P.
Rowley
C.
21008
Article
Assessing potential of sparse‐input reanalyses for centennial‐scale land surface air temperature homogenisation
Observations from the historical meteorological observing network contain many artefacts of non‐climatic origin which must be accounted for prior to using these data in climate applications. State‐of‐the‐art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability – a condition that is met for most station series since the mid‐20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse‐input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land‐based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially‐interpolated sparse‐input reanalysis products to neighbour‐based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station‐minus‐reanalysis and station‐minus‐neighbour series. This shows that neighbour‐based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA‐CIRES‐DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long‐term global average estimates where a small number of long‐term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.
2021-1
Int. J. Climatol.
41
E3000-E3020
0
10.1002/joc.6898
Observations from the historical meteorological observing network contain many artefacts of non‐climatic origin which must be accounted for prior to using these data in climate applications. State‐of‐the‐art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability – a condition that is met for most station series since the mid‐20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse‐input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land‐based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially‐interpolated sparse‐input reanalysis products to neighbour‐based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station‐minus‐reanalysis and station‐minus‐neighbour series. This shows that neighbour‐based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA‐CIRES‐DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long‐term global average estimates where a small number of long‐term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.
Gillespie
I. M.
Haimberger
L.
Compo
G. P.
Thorne
P. W.
21009
Article
Combining Radar Attenuation and Partial Beam Blockage Corrections for Improved Quantitative Application
Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP).
2021-1
J. Hydrometeor.
22
139-153
0
10.1175/JHM-D-20-0121.1
Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP).
Gou
Y.
Chen
H.
21010
Article
Improving Ensemble Weather Prediction System Initialization: Disentangling the Contributions from Model Systematic Errors and Initial Perturbation Size
Characteristics of the European Centre for Medium-Range Weather Forecast’s (ECMWF’s) 0000 UTC diagnosed 2-m temperatures (T2m) from 4D-Var and global ensemble forecasts initial conditions were examined in 2018 over the contiguous United States at 1/2° grid spacing. These were compared against independently generated, upscaled high-resolution T2m analyses that were created with a somewhat novel data assimilation methodology, an extension of classical optimal interpolation (OI) to surface data analysis. The analysis used a high-resolution, spatially detailed climatological background and was statistically unbiased. Differences of the ECMWF 4D-Var T2m initial states from the upscaled OI reference were decomposed into a systematic component and a residual component. The systematic component was determined by applying a temporal smoothing to the time series of differences between the ECMWF T2m analyses and the OI analyses. Systematic errors at 0000 UTC were commonly 1 K or more and larger in the mountainous western United States, with the ECMWF analyses cooler than the reference. The residual error is regarded as random in character and should be statistically consistent with the spread of the ensemble of initial conditions after inclusion of OI analysis uncertainty. This analysis uncertainty was large in the western United States, complicating interpretation. There were some areas suggestive of an overspread initial ensemble, with others underspread. Assimilation of more observations in the reference OI analysis would reduce analysis uncertainty, facilitating more conclusive determination of initial-condition ensemble spread characteristics.
2021-1
Mon. Wea. Rev.
149
77-90
0
10.1175/MWR-D-20-0119.1
Characteristics of the European Centre for Medium-Range Weather Forecast’s (ECMWF’s) 0000 UTC diagnosed 2-m temperatures (T2m) from 4D-Var and global ensemble forecasts initial conditions were examined in 2018 over the contiguous United States at 1/2° grid spacing. These were compared against independently generated, upscaled high-resolution T2m analyses that were created with a somewhat novel data assimilation methodology, an extension of classical optimal interpolation (OI) to surface data analysis. The analysis used a high-resolution, spatially detailed climatological background and was statistically unbiased. Differences of the ECMWF 4D-Var T2m initial states from the upscaled OI reference were decomposed into a systematic component and a residual component. The systematic component was determined by applying a temporal smoothing to the time series of differences between the ECMWF T2m analyses and the OI analyses. Systematic errors at 0000 UTC were commonly 1 K or more and larger in the mountainous western United States, with the ECMWF analyses cooler than the reference. The residual error is regarded as random in character and should be statistically consistent with the spread of the ensemble of initial conditions after inclusion of OI analysis uncertainty. This analysis uncertainty was large in the western United States, complicating interpretation. There were some areas suggestive of an overspread initial ensemble, with others underspread. Assimilation of more observations in the reference OI analysis would reduce analysis uncertainty, facilitating more conclusive determination of initial-condition ensemble spread characteristics.
Hamill
T. M.
Scheuerer
M.
21011
Article
The Modulation of Daily Southern Africa Precipitation by El Niño–Southern Oscillation across the Summertime Wet Season
The spatiotemporal evolution of daily southern Africa precipitation characteristics, and associated atmospheric circulation, related to El Niño and La Niña is examined across the region’s November–April wet season. Precipitation characteristics are examined in terms of monthly changes in daily average precipitation, the number of precipitation days, and the number of heavy precipitation days in three independently constructed estimates of observed precipitation during 1983–2018. Mechanisms related to precipitation changes, including contributions from mass divergence, water vapor transports, and transient eddies, are diagnosed using the atmospheric moisture budget based on the ERA5 reanalysis. El Niño is related to precipitation anomalies that build during December–March, the core of the rainy season, culminating in significantly below average values stretching across a semiarid region from central Mozambique to southeastern Angola. A broad anticyclone centered over Botswana drives these precipitation anomalies primarily through anomalous mass divergence, with moisture advection and transient eddies playing secondary roles. La Niña is related to significantly above average daily precipitation characteristics over all Africa south of 20°S in February and much less so during the other five months. February precipitation anomalies are primarily driven through mass divergence due to a strong anomalous cyclonic circulation, whereas a similar circulation is more diffuse during the other months. The spatiotemporal evolutions of anomalies in daily precipitation characteristics across southern Africa related to El Niño and La Niña are not equal and opposite. The robustness of an asymmetric evolution, which could have implications for subseasonal forecasts, needs to be confirmed with analysis of additional empirical data and established with climate model experimentation.
2021-2
J. Climate
34
1115-1134
0
10.1175/JCLI-D-20-0379.1
The spatiotemporal evolution of daily southern Africa precipitation characteristics, and associated atmospheric circulation, related to El Niño and La Niña is examined across the region’s November–April wet season. Precipitation characteristics are examined in terms of monthly changes in daily average precipitation, the number of precipitation days, and the number of heavy precipitation days in three independently constructed estimates of observed precipitation during 1983–2018. Mechanisms related to precipitation changes, including contributions from mass divergence, water vapor transports, and transient eddies, are diagnosed using the atmospheric moisture budget based on the ERA5 reanalysis. El Niño is related to precipitation anomalies that build during December–March, the core of the rainy season, culminating in significantly below average values stretching across a semiarid region from central Mozambique to southeastern Angola. A broad anticyclone centered over Botswana drives these precipitation anomalies primarily through anomalous mass divergence, with moisture advection and transient eddies playing secondary roles. La Niña is related to significantly above average daily precipitation characteristics over all Africa south of 20°S in February and much less so during the other five months. February precipitation anomalies are primarily driven through mass divergence due to a strong anomalous cyclonic circulation, whereas a similar circulation is more diffuse during the other months. The spatiotemporal evolutions of anomalies in daily precipitation characteristics across southern Africa related to El Niño and La Niña are not equal and opposite. The robustness of an asymmetric evolution, which could have implications for subseasonal forecasts, needs to be confirmed with analysis of additional empirical data and established with climate model experimentation.
Hoell
A.
Gaughan
A. E.
Magadzire
T.
Harrison
L.
21012
Article
Explaining the Spatial Pattern of U.S. Extreme Daily Precipitation Change
Observed United States trends in the annual maximum 1-day precipitation (RX1day) over the last century consist of 15%–25% increases over the eastern United States (East) and 10% decreases over the far western United States (West). This heterogeneous trend pattern departs from comparatively uniform observed increases in precipitable water over the contiguous United States. Here we use an event attribution framework involving parallel sets of global atmospheric model experiments with and without climate change drivers to explain this spatially diverse pattern of extreme daily precipitation trends. We find that RX1day events in our model ensembles respond to observed historical climate change forcing differently across the United States with 5%–10% intensity increases over the East but no appreciable change over the West. This spatially diverse forced signal is broadly similar among three models used, and is positively correlated with the observed trend pattern. Our analysis of model and observations indicates the lack of appreciable RX1day signals over the West is likely due to dynamical effects of climate change forcing—via a wintertime atmospheric circulation anomaly that suppresses vertical motion over the West—largely cancelling thermodynamic effects of increased water vapor availability. The large magnitude of eastern U.S. RX1day increases is unlikely a symptom of a regional heightened sensitivity to climate change forcing. Instead, our ensemble simulations reveal considerable variability in RX1day trend magnitudes arising from internal atmospheric processes alone, and we argue that the remarkable observed increases over the East has most likely resulted from a superposition of strong internal variability with a moderate climate change signal. Implications for future changes in U.S. extreme daily precipitation are discussed.
2021-4
J. Climate
34
2759-2775
0
10.1175/JCLI-D-20-0666.1
Observed United States trends in the annual maximum 1-day precipitation (RX1day) over the last century consist of 15%–25% increases over the eastern United States (East) and 10% decreases over the far western United States (West). This heterogeneous trend pattern departs from comparatively uniform observed increases in precipitable water over the contiguous United States. Here we use an event attribution framework involving parallel sets of global atmospheric model experiments with and without climate change drivers to explain this spatially diverse pattern of extreme daily precipitation trends. We find that RX1day events in our model ensembles respond to observed historical climate change forcing differently across the United States with 5%–10% intensity increases over the East but no appreciable change over the West. This spatially diverse forced signal is broadly similar among three models used, and is positively correlated with the observed trend pattern. Our analysis of model and observations indicates the lack of appreciable RX1day signals over the West is likely due to dynamical effects of climate change forcing—via a wintertime atmospheric circulation anomaly that suppresses vertical motion over the West—largely cancelling thermodynamic effects of increased water vapor availability. The large magnitude of eastern U.S. RX1day increases is unlikely a symptom of a regional heightened sensitivity to climate change forcing. Instead, our ensemble simulations reveal considerable variability in RX1day trend magnitudes arising from internal atmospheric processes alone, and we argue that the remarkable observed increases over the East has most likely resulted from a superposition of strong internal variability with a moderate climate change signal. Implications for future changes in U.S. extreme daily precipitation are discussed.
Hoerling
M. P.
Smith
L.
Quan
X.-W.
Eischeid
J. K.
Barsugli
J. J.
Diaz
H. F.
21013
Article
Flash Drought in CMIP5 Models
‘Flash drought’ (FD) describes the rapid onset of drought on sub-seasonal times scales. It is of particular interest for agriculture as it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the Standardized Precipitation Index (SPI); the Evaporative Demand Drought Index (EDDI), derived from evaporative demand (E0); and the Evaporative Stress Index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r2 > 0.5) between drought indices and upper level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant inter-model spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E0 playing a secondary role.
2021-6
J. Hydrometeor.
22
1439-1454
0
10.1175/JHM-D-20-0262.1
‘Flash drought’ (FD) describes the rapid onset of drought on sub-seasonal times scales. It is of particular interest for agriculture as it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the Standardized Precipitation Index (SPI); the Evaporative Demand Drought Index (EDDI), derived from evaporative demand (E0); and the Evaporative Stress Index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r2 > 0.5) between drought indices and upper level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant inter-model spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E0 playing a secondary role.
Hoffman
F.
Gallant
A. J. E.
Hobbins
M. T.
21014
Article
The development of rainfall retrievals from radar at Darwin
The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data.
The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 1<R<10mmh−1. These rates primarily originate from stratiform clouds and weak convection (median drop diameters less than 1.5 mm). The dual-polarization specific differential phase and differential reflectivity increase in usefulness for rainfall estimators in times with R>10mmh−1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1<R<10mmh−1 (stratiform rain) and when R>10mmh−1 (deeper convective rain).
2021-1
Atmos. Meas. Tech.
14
53-69
0
10.5194/amt-14-53-2021
The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data.
The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 1<R<10mmh−1. These rates primarily originate from stratiform clouds and weak convection (median drop diameters less than 1.5 mm). The dual-polarization specific differential phase and differential reflectivity increase in usefulness for rainfall estimators in times with R>10mmh−1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1<R<10mmh−1 (stratiform rain) and when R>10mmh−1 (deeper convective rain).
Jackson
R.
Collis
S.
Louf
V.
Protat
A.
Wang
D.
Giangrande
S.
Thompson
E. J.
Dolan
B.
Powell
S. W.
21015
Article
Assessing ENSO Summer Teleconnections, Impacts, and Predictability in North America
During the summer when an El Niño event is transitioning to a La Niña event, the extratropical teleconnections exert robust warming anomalies over the U.S. Midwest threatening agricultural production. This study assesses the performance of current climate models in capturing the prominent observed extratropical responses over North America during the transitioning La Niña summer, based on atmospheric general circulation model experiments and coupled models from the North American Multimodel Ensemble (NMME). The ensemble mean of the SST-forced experiments across the transitioning La Niña summers does not capture the robust warming in the Midwest. The SST-forced experiments do not produce consistent subtropical western Pacific (WP) negative precipitation anomalies and this leads to the poor simulations of extratropical teleconnections over North America. In the NMME models, with active air–sea interaction, the negative WP precipitation anomalies show better agreement across the models and with observations. However, the downstream wave train pattern and the resulting extratropical responses over North America exhibit large disagreement across the models and are consistently weaker than in observations. Furthermore, in these climate models, an anomalous anticyclone does not robustly translate into a warm anomaly over the Midwest, in disagreement with observations. This work suggests that, during the El Niño to La Niña transitioning summer, active air–sea interaction is important in simulating tropical precipitation over the WP. Nevertheless, skillful representations of the Rossby wave propagation and land–atmosphere processes in climate models are also essential for skillful simulations of extratropical responses over North America.
2021-5
J. Climate
34
3629-3643
0
10.1175/JCLI-D-20-0761.1
During the summer when an El Niño event is transitioning to a La Niña event, the extratropical teleconnections exert robust warming anomalies over the U.S. Midwest threatening agricultural production. This study assesses the performance of current climate models in capturing the prominent observed extratropical responses over North America during the transitioning La Niña summer, based on atmospheric general circulation model experiments and coupled models from the North American Multimodel Ensemble (NMME). The ensemble mean of the SST-forced experiments across the transitioning La Niña summers does not capture the robust warming in the Midwest. The SST-forced experiments do not produce consistent subtropical western Pacific (WP) negative precipitation anomalies and this leads to the poor simulations of extratropical teleconnections over North America. In the NMME models, with active air–sea interaction, the negative WP precipitation anomalies show better agreement across the models and with observations. However, the downstream wave train pattern and the resulting extratropical responses over North America exhibit large disagreement across the models and are consistently weaker than in observations. Furthermore, in these climate models, an anomalous anticyclone does not robustly translate into a warm anomaly over the Midwest, in disagreement with observations. This work suggests that, during the El Niño to La Niña transitioning summer, active air–sea interaction is important in simulating tropical precipitation over the WP. Nevertheless, skillful representations of the Rossby wave propagation and land–atmosphere processes in climate models are also essential for skillful simulations of extratropical responses over North America.
Jong
B.-T.
Ting
M.
Seager
R.
21016
Article
Impact of precipitation and increasing temperatures on drought trends in eastern Africa
In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits.
Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate.
We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit.
2021-1
Earth Syst. Dynam.
12
17-35
0
10.5194/esd-12-17-2021
In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits.
Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate.
We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit.
Kew
S. F.
Philip
S. Y.
Hauser
M.
Hobbins
M. T.
Wanders
N.
van Oldenborgh
G. J.
van der Wiel
Karin
Veldkamp
T. I. E.
Kimutal
J.
Funk
C.
Otto
F. E. L.
21017
Article
Evaluation of the CMORPH high-resolution precipitation product for hydrological applications over South Korea
The use of a high-resolution satellite-based precipitation product on a global-scale is attractive to hydrological applications. However, it should be systematically evaluated from various perspectives as the quality property is dependent on the geographical and topographical features of a region. This study aims to comprehensively assess the Climate Prediction Center morphing technique (CMORPH) precipitation product over South Korea. Two evaluation approaches, the general evaluation using statistical metrics and the detection evaluation (to measure skill in detecting precipitation and non-precipitation) using categorical metrics, are employed based on an 18-year long-term period of record. As a result, the CMORPH product tended to underestimate precipitation over South Korea, and the level of the underestimation varied with the seasons and regions. The overall quality was adequate for hydrological applications that require precipitation data at the annual-to-daily resolution but not at hourly resolution. Skill in detecting hourly precipitation in a storm event was 60% of the rain-gauge data, and the accuracies of total volume and peak value were 45% and 40%, respectively. The quality in the coastal regions and islands was not as good as in inland areas at low altitudes. The accuracy in a wet season was better than that in a dry/winter season. Notably, the CMORPH precipitation product was not suitable for snowfall data. Ultimately, the CMORPH product at hourly resolution needs a correction process using the local measurement systems for enhancing the quality property over South Korea.
2021-8
Atmos. Res.
258
105650
0
10.1016/j.atmosres.2021.105650
The use of a high-resolution satellite-based precipitation product on a global-scale is attractive to hydrological applications. However, it should be systematically evaluated from various perspectives as the quality property is dependent on the geographical and topographical features of a region. This study aims to comprehensively assess the Climate Prediction Center morphing technique (CMORPH) precipitation product over South Korea. Two evaluation approaches, the general evaluation using statistical metrics and the detection evaluation (to measure skill in detecting precipitation and non-precipitation) using categorical metrics, are employed based on an 18-year long-term period of record. As a result, the CMORPH product tended to underestimate precipitation over South Korea, and the level of the underestimation varied with the seasons and regions. The overall quality was adequate for hydrological applications that require precipitation data at the annual-to-daily resolution but not at hourly resolution. Skill in detecting hourly precipitation in a storm event was 60% of the rain-gauge data, and the accuracies of total volume and peak value were 45% and 40%, respectively. The quality in the coastal regions and islands was not as good as in inland areas at low altitudes. The accuracy in a wet season was better than that in a dry/winter season. Notably, the CMORPH precipitation product was not suitable for snowfall data. Ultimately, the CMORPH product at hourly resolution needs a correction process using the local measurement systems for enhancing the quality property over South Korea.
Kim
J.
Han
H.
21018
Article
The Record-Breaking 1933 Atlantic Hurricane Season
The 1933 Atlantic hurricane season was extremely active, with 20 named storms and 11 hurricanes including 6 major (Category 3+; one-minute maximum sustained winds >=96 kt) hurricanes occurring. The 1933 hurricane season also generated the most Accumulated Cyclone Energy (an integrated metric that accounts for frequency, intensity, and duration) of any Atlantic hurricane season on record. A total of 8 hurricanes tracked through the Caribbean in 1933 -the most on record. In addition, two Category 3 hurricanes made landfall in the United States just 23 hours apart: the Treasure Coast hurricane in southeast Florida followed by the Cuba-Brownsville hurricane in south Texas. This manuscript examines large-scale atmospheric and oceanic conditions that likely led to such an active hurricane season. Extremely weak vertical wind shear was prevalent over both the Caribbean and the tropical Atlantic throughout the peak months of the hurricane season, likely in part due to a weak-to-moderate La Niña event. These favorable dynamic conditions, combined with above-normal tropical Atlantic sea surface temperatures, created a very conducive environment for hurricane formation and intensification. The Madden-Julian oscillation was relatively active during the summer and fall of 1933, providing sub-seasonal conditions that were quite favorable for tropical cyclogenesis during mid-to-late August and late September to early October. The current early June and August statistical models used by Colorado State University would have predicted a very active 1933 hurricane season. A better understanding of these extremely active historical Atlantic hurricane seasons may aid in anticipation of future hyperactive seasons.
2021-3
Bull. Amer. Meteor. Soc.
102
E446-E463
0
10.1175/BAMS-D-19-0330.1
The 1933 Atlantic hurricane season was extremely active, with 20 named storms and 11 hurricanes including 6 major (Category 3+; one-minute maximum sustained winds >=96 kt) hurricanes occurring. The 1933 hurricane season also generated the most Accumulated Cyclone Energy (an integrated metric that accounts for frequency, intensity, and duration) of any Atlantic hurricane season on record. A total of 8 hurricanes tracked through the Caribbean in 1933 -the most on record. In addition, two Category 3 hurricanes made landfall in the United States just 23 hours apart: the Treasure Coast hurricane in southeast Florida followed by the Cuba-Brownsville hurricane in south Texas. This manuscript examines large-scale atmospheric and oceanic conditions that likely led to such an active hurricane season. Extremely weak vertical wind shear was prevalent over both the Caribbean and the tropical Atlantic throughout the peak months of the hurricane season, likely in part due to a weak-to-moderate La Niña event. These favorable dynamic conditions, combined with above-normal tropical Atlantic sea surface temperatures, created a very conducive environment for hurricane formation and intensification. The Madden-Julian oscillation was relatively active during the summer and fall of 1933, providing sub-seasonal conditions that were quite favorable for tropical cyclogenesis during mid-to-late August and late September to early October. The current early June and August statistical models used by Colorado State University would have predicted a very active 1933 hurricane season. A better understanding of these extremely active historical Atlantic hurricane seasons may aid in anticipation of future hyperactive seasons.
Klotzbach
P. J.
Schreck III
C. J.
Compo
G. P.
Bowen
S. G.
Gibney
E. J.
Oliver
E. C. J.
Bell
M. M.
21019
Article
The 2019 Southern Hemisphere stratospheric polar vortex weakening and its impacts
This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August and mid-September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first-ever-observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by ∼50% in the midstratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-1 activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the southern annular mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July.
2021-2
Bull. Amer. Meteor. Soc.
102
E1150–E1171
0
10.1175/BAMS-D-20-0112.1
This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August and mid-September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first-ever-observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by ∼50% in the midstratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-1 activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the southern annular mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July.
Lim
E.-P.
Hendon
H. H.
Butler
A. H.
Thompson
D. W. J.
Lawrence
Z. D.
al.
et
21020
Article
Making sense of flash drought: definitions, indicators, and where we go from here
The topic of “Flash Drought” is rapidly gaining attention within both the research and drought management communities. This literature review aims to synthesize the research todate and provide a basis for future research on the topic. Specifically, our review is focused on documenting the range of definitions of “flash drought” being proposed in the research community. We found that the term first appeared in the peer-reviewed literature in 2002, and by 2020 has become an area of active research. Within that 18-year span, “flash drought” has been given 29 general descriptions, and 20 papers have provided measurable, defining criteria used to distinguish a flash drought from other drought. Of these papers, 11 distinguish flash drought as a rapid-onset drought event while eight distinguish flash drought as a short-term or short-lived, yet severe, drought event and one paper considers flash drought as both a short-lived and rapid onset event. Of the papers that define a flash drought by its rate of onset, the rate proposed ranges from 5 days to 8 weeks. Currently, there is not a universally accepted definition or criteria for
“flash drought,” despite recent research that has called for the research community to adopt the principle of rapid-intensification of drought conditions.
2021-1
J. Appl. Serv. Climatol.
2021
1-19
0
10.46275/joasc.2021.02.001
The topic of “Flash Drought” is rapidly gaining attention within both the research and drought management communities. This literature review aims to synthesize the research todate and provide a basis for future research on the topic. Specifically, our review is focused on documenting the range of definitions of “flash drought” being proposed in the research community. We found that the term first appeared in the peer-reviewed literature in 2002, and by 2020 has become an area of active research. Within that 18-year span, “flash drought” has been given 29 general descriptions, and 20 papers have provided measurable, defining criteria used to distinguish a flash drought from other drought. Of these papers, 11 distinguish flash drought as a rapid-onset drought event while eight distinguish flash drought as a short-term or short-lived, yet severe, drought event and one paper considers flash drought as both a short-lived and rapid onset event. Of the papers that define a flash drought by its rate of onset, the rate proposed ranges from 5 days to 8 weeks. Currently, there is not a universally accepted definition or criteria for
“flash drought,” despite recent research that has called for the research community to adopt the principle of rapid-intensification of drought conditions.
Lisonbee
J.
Woloszyn
M.
Skumanich
M.
21022
Article
A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau
Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.
2021-1
Hydrol. Earth Syst. Sci.
25
359-374
0
10.5194/hess-25-359-2021
Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.
Ma
Y.
Sun
X.
Chen
H.
Hong
Y.
Zhang
Y.
21023
Article
Cool season precipitation projections for California and the Western United States in NA-CORDEX models
Understanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.
2021-1
Clim. Dyn.
56
3081-3102
0
10.1007/s00382-021-05632-z
Understanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.
Mahoney
K. M.
Scott
J. D.
Alexander
M. A.
McCrary
R.
Hughes
M.
Swales
D.
Bukovsky
M.
21024
Article
Distinguishing between Warm and Stratiform Rain Using Polarimetric Radar Measurements
Modeled statistical differential reflectivity–reflectivity (i.e., ZDR–Ze) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, ZDR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A ZDR–Ze relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational ZDR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean ZDR–Ze relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations.
2021-1
Remote Sens.
13
214
0
10.3390/rs13020214
Modeled statistical differential reflectivity–reflectivity (i.e., ZDR–Ze) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, ZDR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A ZDR–Ze relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational ZDR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean ZDR–Ze relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations.
Matrosov
S. Y.
21025
Article
Polarimetric Radar Variables in Snowfall at Ka- and W-Band Frequency Bands: A Comparative Analysis
Dual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (KDP) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band ZDR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed ZDR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.
2021-1
J. Atmos. Oceanic Technol.
38
91-101
0
10.1175/JTECH-D-20-0138.1
Dual-frequency millimeter-wavelength radar observations in snowfall are analyzed in order to evaluate differences in conventional polarimetric radar variables such as differential reflectivity (ZDR) specific differential phase shift (KDP) and linear depolarization ratio (LDR) at traditional cloud radar frequencies at Ka and W bands (~35 and ~94 GHz, correspondingly). Low radar beam elevation (~5°) measurements were performed at Oliktok Point, Alaska, with a scanning fully polarimetric radar operating in the horizontal–vertical polarization basis. This radar has the same gate spacing and very close beam widths at both frequencies, which largely alleviates uncertainties associated with spatial and temporal data matching. It is shown that observed Ka- and W-band ZDR differences are, on average, less than about 0.5 dB and do not have a pronounced trend as a function of snowfall reflectivity. The observed ZDR differences agree well with modeling results obtained using integration over nonspherical ice particle size distributions. For higher signal-to-noise ratios, KDP data derived from differential phase measurements are approximately scaled as reciprocals of corresponding radar frequencies indicating that the influence of non-Rayleigh scattering effects on this variable is rather limited. This result is also in satisfactory agreement with data obtained by modeling using realistic particle size distributions. Observed Ka- and W-band LDR differences are strongly affected by the radar hardware system polarization “leak” and are generally less than 4 dB. Smaller differences are observed for higher depolarizations, where the polarization “leak” is less pronounced. Realistic assumptions about particle canting and the system polarization isolation lead to modeling results that satisfactorily agree with observational dual-frequency LDR data.
Matrosov
S. Y.
21026
Article
Convectively Coupled Kelvin Waves Over Tropical South America
Rainfall over tropical South America is known to be modulated by convectively coupled Kelvin waves (CCKWs). In this work, the origin and dynamical features of South American Kelvin waves are revisited using satellite-observed brightness temperature, radiosonde, and reanalysis datasets. Two main types of CCKWs over the Amazon are considered: Kelvin waves with a Pacific precursor, and Kelvin waves with a precursor originating over South America. Amazonian CCKW’s associated with a preexisting Kelvin convection in the eastern Pacific account for about 35% of the total events. The cases with South American precursors are associated with either pressure surges in the western Amazon from extratropical wave train activity, responsible for 40% of the total events, or “in situ” convection that locally excites CCKWs, accounting for the remaining 25%. The analysis also suggests that CCKWs associated with different precursors are sensitive to Pacific sea surface temperature. Kelvin wave events with a Pacific precursor are more common during ENSO warm events, while Kelvin waves with extratropical South American precursors show stronger activity during La Niña events. This study also explores other triggering mechanisms of CCKWs over the Amazon. These mechanisms are associated with: 1) extratropical Rossby wave trains not necessarily of extratropical South American origin; 2) CCKWs initiated in response to the presence of the southern and/or double Intertropical Convergence Zone (ITCZ) in the Eastern Pacific Ocean; 3) and possible interaction between CCKWs and other equatorial waves in the Amazon.
2021-8
J. Climate
34
6531–6547
0
10.1175/JCLI-D-20-0662.1
Rainfall over tropical South America is known to be modulated by convectively coupled Kelvin waves (CCKWs). In this work, the origin and dynamical features of South American Kelvin waves are revisited using satellite-observed brightness temperature, radiosonde, and reanalysis datasets. Two main types of CCKWs over the Amazon are considered: Kelvin waves with a Pacific precursor, and Kelvin waves with a precursor originating over South America. Amazonian CCKW’s associated with a preexisting Kelvin convection in the eastern Pacific account for about 35% of the total events. The cases with South American precursors are associated with either pressure surges in the western Amazon from extratropical wave train activity, responsible for 40% of the total events, or “in situ” convection that locally excites CCKWs, accounting for the remaining 25%. The analysis also suggests that CCKWs associated with different precursors are sensitive to Pacific sea surface temperature. Kelvin wave events with a Pacific precursor are more common during ENSO warm events, while Kelvin waves with extratropical South American precursors show stronger activity during La Niña events. This study also explores other triggering mechanisms of CCKWs over the Amazon. These mechanisms are associated with: 1) extratropical Rossby wave trains not necessarily of extratropical South American origin; 2) CCKWs initiated in response to the presence of the southern and/or double Intertropical Convergence Zone (ITCZ) in the Eastern Pacific Ocean; 3) and possible interaction between CCKWs and other equatorial waves in the Amazon.
Mayta
V. C.
Kiladis
G. N.
Dias
J.
Silva Dias
P. L.
Gehne
M.
21027
Article
Characteristics of Long-Duration Heavy Precipitation Events along the West Coast of the United States
Prolonged periods (e.g., several days or more) of heavy precipitation can result in sustained high-impact flooding. Herein, an investigation of long-duration heavy precipitation events (HPEs), defined as periods comprising ≥3 days with precipitation exceeding the climatological 95th percentile, is conducted for 1979–2019 for the U.S. West Coast, specifically Northern California. An objective flow-based categorization method is applied to identify principal large-scale flow patterns for the events. Four categories are identified and examined through composite analyses and case studies. Two of the categories are characterized by a strong zonal jet stream over the eastern North Pacific, while the other two are characterized by atmospheric blocking over the central North Pacific and the Bering Sea–Alaska region, respectively. The composites and case studies demonstrate that the flow patterns for the HPEs tend to remain in place for several days, maintaining strong baroclinicity and promoting occurrences of multiple cyclones in rapid succession near the West Coast. The successive cyclones result in persistent water vapor flux and forcing for ascent over Northern California, sustaining heavy precipitation. For the zonal jet patterns, cyclones affecting the West Coast tend to occur in the poleward jet exit region in association with cyclonic Rossby wave breaking. For the blocking patterns, cyclones tend to occur in association with anticyclonic Rossby wave breaking on the downstream flank of the block. For Bering Sea–Alaska blocking cases, cyclones can move into this region in conjunction with cyclonically breaking waves that extend into the eastern North Pacific from the upstream flank of the block.
2021-7
Mon. Wea. Rev.
149
2255–2277
0
10.1175/MWR-D-20-0336.1
Prolonged periods (e.g., several days or more) of heavy precipitation can result in sustained high-impact flooding. Herein, an investigation of long-duration heavy precipitation events (HPEs), defined as periods comprising ≥3 days with precipitation exceeding the climatological 95th percentile, is conducted for 1979–2019 for the U.S. West Coast, specifically Northern California. An objective flow-based categorization method is applied to identify principal large-scale flow patterns for the events. Four categories are identified and examined through composite analyses and case studies. Two of the categories are characterized by a strong zonal jet stream over the eastern North Pacific, while the other two are characterized by atmospheric blocking over the central North Pacific and the Bering Sea–Alaska region, respectively. The composites and case studies demonstrate that the flow patterns for the HPEs tend to remain in place for several days, maintaining strong baroclinicity and promoting occurrences of multiple cyclones in rapid succession near the West Coast. The successive cyclones result in persistent water vapor flux and forcing for ascent over Northern California, sustaining heavy precipitation. For the zonal jet patterns, cyclones affecting the West Coast tend to occur in the poleward jet exit region in association with cyclonic Rossby wave breaking. For the blocking patterns, cyclones tend to occur in association with anticyclonic Rossby wave breaking on the downstream flank of the block. For Bering Sea–Alaska blocking cases, cyclones can move into this region in conjunction with cyclonically breaking waves that extend into the eastern North Pacific from the upstream flank of the block.
Moore
B. J.
White
A. B.
Gottas
D. J.
21028
Article
A Dynamically Downscaled Ensemble of Future Projections for the California Current System
Given the ecological and economic importance of eastern boundary upwelling systems like the California Current System (CCS), their evolution under climate change is of considerable interest for resource management. However, the spatial resolution of global earth system models (ESMs) is typically too coarse to properly resolve coastal winds and upwelling dynamics that are key to structuring these ecosystems. Here we use a high-resolution (0.1°) regional ocean circulation model coupled with a biogeochemical model to dynamically downscale ESMs and produce climate projections for the CCS under the high emission scenario, Representative Concentration Pathway 8.5. To capture model uncertainty in the projections, we downscale three ESMs: GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A-MR, which span the CMIP5 range for future changes in both the mean and variance of physical and biogeochemical CCS properties. The forcing of the regional ocean model is constructed with a “time-varying delta” method, which removes the mean bias of the ESM forcing and resolves the full transient ocean response from 1980 to 2100. We found that all models agree in the direction of the future change in offshore waters: an intensification of upwelling favorable winds in the northern CCS, an overall surface warming, and an enrichment of nitrate and corresponding decrease in dissolved oxygen below the surface mixed layer. However, differences in projections of these properties arise in the coastal region, producing different responses of the future biogeochemical variables. Two of the models display an increase of surface chlorophyll in the northern CCS, consistent with a combination of higher nitrate content in source waters and an intensification of upwelling favorable winds. All three models display a decrease of chlorophyll in the southern CCS, which appears to be driven by decreased upwelling favorable winds and enhanced stratification, and, for the HadGEM2-ES forced run, decreased nitrate content in upwelling source waters in nearshore regions. While trends in the downscaled models reflect those in the ESMs that force them, the ESM and downscaled solutions differ more for biogeochemical than for physical variables.
2021-4
Front. Mar. Sci.
0
10.3389/fmars.2021.612874
Given the ecological and economic importance of eastern boundary upwelling systems like the California Current System (CCS), their evolution under climate change is of considerable interest for resource management. However, the spatial resolution of global earth system models (ESMs) is typically too coarse to properly resolve coastal winds and upwelling dynamics that are key to structuring these ecosystems. Here we use a high-resolution (0.1°) regional ocean circulation model coupled with a biogeochemical model to dynamically downscale ESMs and produce climate projections for the CCS under the high emission scenario, Representative Concentration Pathway 8.5. To capture model uncertainty in the projections, we downscale three ESMs: GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A-MR, which span the CMIP5 range for future changes in both the mean and variance of physical and biogeochemical CCS properties. The forcing of the regional ocean model is constructed with a “time-varying delta” method, which removes the mean bias of the ESM forcing and resolves the full transient ocean response from 1980 to 2100. We found that all models agree in the direction of the future change in offshore waters: an intensification of upwelling favorable winds in the northern CCS, an overall surface warming, and an enrichment of nitrate and corresponding decrease in dissolved oxygen below the surface mixed layer. However, differences in projections of these properties arise in the coastal region, producing different responses of the future biogeochemical variables. Two of the models display an increase of surface chlorophyll in the northern CCS, consistent with a combination of higher nitrate content in source waters and an intensification of upwelling favorable winds. All three models display a decrease of chlorophyll in the southern CCS, which appears to be driven by decreased upwelling favorable winds and enhanced stratification, and, for the HadGEM2-ES forced run, decreased nitrate content in upwelling source waters in nearshore regions. While trends in the downscaled models reflect those in the ESMs that force them, the ESM and downscaled solutions differ more for biogeochemical than for physical variables.
Pozo Buil
M.
Jacox
M. G.
Fiechter
J.
Bograd
S. J.
Curchitser
E. N.
Edwards
C. A.
Rykaczewski
R. R.
Stock
C. A.
21031
Article
Seasonal Predictability of Global and North American Coastal Sea Surface Temperature and Height Anomalies
A Linear Inverse Model (LIM) is constructed to evaluate predictability of seasonal sea surface temperature (SST) and height (SSH) anomalies over the ice‐free global ocean. Its ensemble‐mean hindcast skill is also compared to that of the North American Multi‐Model Ensemble (NMME) for 1982‐2010. Both have similar skill for dominant modes of SST variability, but regional NMME SST skill is somewhat higher in many locations. However, the LIM has considerably more Atlantic and Southern Ocean SSH skill. Skill is generally comparable along the North American coastline, but LIM skill is greater for several highly productive coastal zones and East Coast tide gauge stations. Diverse, often predictable ENSO events drive teleconnections providing predictability in the North Pacific and along the US West Coast. Predictability in the Atlantic and along the US East Coast is associated with Gulf Stream strength modulation. Overall, the LIM shows potential for seasonal prediction of coastal ocean conditions.
2021-5
Geophys. Res. Lett.
48
e2020GL091886
0
10.1029/2020GL091886
A Linear Inverse Model (LIM) is constructed to evaluate predictability of seasonal sea surface temperature (SST) and height (SSH) anomalies over the ice‐free global ocean. Its ensemble‐mean hindcast skill is also compared to that of the North American Multi‐Model Ensemble (NMME) for 1982‐2010. Both have similar skill for dominant modes of SST variability, but regional NMME SST skill is somewhat higher in many locations. However, the LIM has considerably more Atlantic and Southern Ocean SSH skill. Skill is generally comparable along the North American coastline, but LIM skill is greater for several highly productive coastal zones and East Coast tide gauge stations. Diverse, often predictable ENSO events drive teleconnections providing predictability in the North Pacific and along the US West Coast. Predictability in the Atlantic and along the US East Coast is associated with Gulf Stream strength modulation. Overall, the LIM shows potential for seasonal prediction of coastal ocean conditions.
Shin
S.-I.
Newman
M.
21032
Article
Impact of Annual Cycle on ENSO Variability and Predictability
Low-order linear inverse models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere–ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclostationary linear inverse model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multimodel Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST–SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result from explicitly accounting for not only the annual cycle of the background state, but also that of the stochastic forcing.
2021-1
J. Climate
34
171-193
0
10.1175/JCLI-D-20-0291.1
Low-order linear inverse models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere–ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclostationary linear inverse model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multimodel Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST–SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result from explicitly accounting for not only the annual cycle of the background state, but also that of the stochastic forcing.
Shin
S.-I.
Sardeshmukh
P. D.
Newman
M.
Penland
C.
Alexander
M. A.
21033
Article
An evaluation of the performance of the 20th Century Reanalysis version 3
The performance of a new historical reanalysis, the NOAA-CIRES-DOE 20th Century Reanalysis Version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the 20th century. Upper-air fields from 20CRv3 in the late 20th century and early 21st century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500hPa geopotential heights from 20CRv3 for 1979-2015 is comparable to that of modern operational 3- to 4-day forecasts. Finally, 20CRv3 performs well on climate timescales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric layer temperatures that correlate well with independent products in the 20th century, placing recent trends in a longer historical context.
2021-2
J. Climate
34
1417-1438
0
10.1175/JCLI-D-20-0505.1
The performance of a new historical reanalysis, the NOAA-CIRES-DOE 20th Century Reanalysis Version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the 20th century. Upper-air fields from 20CRv3 in the late 20th century and early 21st century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500hPa geopotential heights from 20CRv3 for 1979-2015 is comparable to that of modern operational 3- to 4-day forecasts. Finally, 20CRv3 performs well on climate timescales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric layer temperatures that correlate well with independent products in the 20th century, placing recent trends in a longer historical context.
Slivinski
L. C.
Compo
G. P.
Sardeshmukh
P. D.
Whitaker
J. S.
McColl
C.
Allan
R. J.
Brohan
P.
Yin
X.
Smith
C. A.
Spencer
L. J.
al.
et
21034
Article
Snow Particle Size Distribution From a 2-D Video Disdrometer and Radar Snowfall Estimation in East China
In this study, as part of an effort to study snowfall characteristics and quantify winter precipitation in East China, we investigated the microphysical properties of snowfall, including size, shape, density, and terminal velocity using a 2-D video disdrometer (2-DVD) and a weighing precipitation gauge in Nanjing (NJ), East China during the winters of 2015-2019. We obtained larger snow density and terminal velocity values than those reported in the literature for this region. Higher snow density could account for higher snowflake terminal velocity, after removing the effects of observation altitude and surface temperature. We then fit the snow particle size distributions (PSDs) to the gamma model and explored the interrelationships among the model parameters and snowfall rate (SR). The relationship between radar reflectivity factor (Zₑ) and SR was derived based on snow PSD measurements and the snow density relation. Using this Zₑ-SR relationship, the estimated liquid-equivalent SRs are obtained from S-band NJ radar data collected during several snowfall events. Radar-inferred SRs showed reasonable agreement with those measured on the ground, with a mean absolute error of 16% for the collected snowfall events in NJ.
2021-1
IEEE Trans. Geosci. Remote Sens.
59E
196-207
0
10.1109/TGRS.2020.2990920
In this study, as part of an effort to study snowfall characteristics and quantify winter precipitation in East China, we investigated the microphysical properties of snowfall, including size, shape, density, and terminal velocity using a 2-D video disdrometer (2-DVD) and a weighing precipitation gauge in Nanjing (NJ), East China during the winters of 2015-2019. We obtained larger snow density and terminal velocity values than those reported in the literature for this region. Higher snow density could account for higher snowflake terminal velocity, after removing the effects of observation altitude and surface temperature. We then fit the snow particle size distributions (PSDs) to the gamma model and explored the interrelationships among the model parameters and snowfall rate (SR). The relationship between radar reflectivity factor (Zₑ) and SR was derived based on snow PSD measurements and the snow density relation. Using this Zₑ-SR relationship, the estimated liquid-equivalent SRs are obtained from S-band NJ radar data collected during several snowfall events. Radar-inferred SRs showed reasonable agreement with those measured on the ground, with a mean absolute error of 16% for the collected snowfall events in NJ.
Tao
R.
Zhao
K.
Huang
H.
Wen
L.
Zhang
G.
Zhou
A.
Chen
H.
21035
Article
Predicting atmospheric optical properties for radiative transfer computations using neural networks
The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m−2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy.
2021-2
Philos. Trans. R. Soc. A
379
20200095
0
10.1098/rsta.2020.0095
The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m−2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy.
Veerman
M. A.
Pincus
R.
Stoffer
R.
van Leeuwen
C. M.
Podareanu
D.
van Heerwaarden
C. C.
21036
Article
In the eye of the storm
The airborne NOAA Wide Swath Radar Altimeter (WSRA) is a 16 GHz digital beamforming radar altimeter that produces a topographic map of the waves as the aircraft advances. The wave topography is transformed by a two-dimensional FFT into directional wave spectra. The WSRA operates unattended on the aircraft and provides continuous real-time reporting of several data products: (1) significant wave height, (2) directional ocean wave spectra, (3) the wave height, wavelength, and direction of propagation of the primary and secondary wave fields, (4) rainfall rate and (5) sea surface mean square slope (mss). During hurricane flights the data products are transmitted in real-time from the NOAA WP-3D aircraft through a satellite data link to a ground station and on to the National Hurricane Center (NHC) for use by the forecasters for intensity projections and incorporation in hurricane wave models. The WSRA is the only instrument that can quickly provide high-density measurements of the complex wave topography over a large area surrounding the eye of the storm.
2021-5
J. Phys. Oceanogr.
51
1835-1842
0
10.1175/JPO-D-20-0219.1
The airborne NOAA Wide Swath Radar Altimeter (WSRA) is a 16 GHz digital beamforming radar altimeter that produces a topographic map of the waves as the aircraft advances. The wave topography is transformed by a two-dimensional FFT into directional wave spectra. The WSRA operates unattended on the aircraft and provides continuous real-time reporting of several data products: (1) significant wave height, (2) directional ocean wave spectra, (3) the wave height, wavelength, and direction of propagation of the primary and secondary wave fields, (4) rainfall rate and (5) sea surface mean square slope (mss). During hurricane flights the data products are transmitted in real-time from the NOAA WP-3D aircraft through a satellite data link to a ground station and on to the National Hurricane Center (NHC) for use by the forecasters for intensity projections and incorporation in hurricane wave models. The WSRA is the only instrument that can quickly provide high-density measurements of the complex wave topography over a large area surrounding the eye of the storm.
Walsh
E. J.
Fairall
C. W.
PopStefanija
I.
21037
Article
The Continuum of Northeast Pacific Marine Heatwaves and Their Relationship to the Tropical Pacific
Some questions remain concerning the record‐breaking 2013–2015 Northeast Pacific marine heatwave (MHW) event: was it exceptional or merely the most pronounced of a group of similar events, and was its intensity and multiyear duration driven by internal extratropical processes or did the tropics play an important role? By analyzing the statistical behavior of the historical MHWs within the ERSST.v3 data set over the 1950–2019 period, we find that Northeast Pacific MHWs occurred over a continuum of intensities and durations, suggesting that these events are a recurrent Pacific phenomenon. These statistics and corresponding composite evolution are dynamically reproduced by a large ensemble simulation of a Pacific Linear Inverse Model, thereby providing a greater range of MHW expressions than the short observational record alone. Consistent with the 2013–2015 event's evolution, we find that overall the tropics influence MHWs primarily by increasing their duration, while MHW intensity is related to the initial extratropical anomalies.
2021-1
Geophys. Res. Lett.
48
202GL090661
0
10.1029/2020GL090661
Some questions remain concerning the record‐breaking 2013–2015 Northeast Pacific marine heatwave (MHW) event: was it exceptional or merely the most pronounced of a group of similar events, and was its intensity and multiyear duration driven by internal extratropical processes or did the tropics play an important role? By analyzing the statistical behavior of the historical MHWs within the ERSST.v3 data set over the 1950–2019 period, we find that Northeast Pacific MHWs occurred over a continuum of intensities and durations, suggesting that these events are a recurrent Pacific phenomenon. These statistics and corresponding composite evolution are dynamically reproduced by a large ensemble simulation of a Pacific Linear Inverse Model, thereby providing a greater range of MHW expressions than the short observational record alone. Consistent with the 2013–2015 event's evolution, we find that overall the tropics influence MHWs primarily by increasing their duration, while MHW intensity is related to the initial extratropical anomalies.
Xu
Tongtong
T.
Newman
M.
Capotondi
A.
Di Lorenzo
E.
21038
Article
On block iterative correction in strongly coupled data assimilation
The on‐going transition to coupled data assimilation (DA) systems encounters substantial technical difficulties associated with the need to merge together different elements of atmospheric and ocean DA systems that typically have had independent development paths for decades. In this study we consider the incorporation of strong coupling in the observation space via successive corrections that involve the application of only uncoupled solvers to a sequence of innovation vectors. The coupled increment is then obtained by projecting a coupled innovation vector on the grid using coupled ensemble correlations. Proposed approach is motivated by the classic block Jacobi matrix iteration applied to the coupled system using the uncoupled solvers as a preconditioner. The method is tested via numerical experiments with the CERA ensemble in a simplified setting.
2021-7
Q. J. R. Meteorol. Soc.
147
2729-2740
0
10.1002/qj.4047
The on‐going transition to coupled data assimilation (DA) systems encounters substantial technical difficulties associated with the need to merge together different elements of atmospheric and ocean DA systems that typically have had independent development paths for decades. In this study we consider the incorporation of strong coupling in the observation space via successive corrections that involve the application of only uncoupled solvers to a sequence of innovation vectors. The coupled increment is then obtained by projecting a coupled innovation vector on the grid using coupled ensemble correlations. Proposed approach is motivated by the classic block Jacobi matrix iteration applied to the coupled system using the uncoupled solvers as a preconditioner. The method is tested via numerical experiments with the CERA ensemble in a simplified setting.
Yaremchuk
M.
Beattie
C.
Frolov
S.
21039
Article
Interannual to Decadal Variability of Tropical Indian Ocean Sea Surface Temperature: Pacific Influence versus Local Internal Variability
The Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal processes has not been well understood particularly on decadal and longer time scales, and the external influence from the tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a linear inverse model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal time scales. On the other hand, the Indian Ocean dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal time scale. The third mode is partly associated with the subtropical Indian Ocean dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian–Pacific Ocean interaction is a two-way process.
2021-4
J. Climate
34
2669-2684
0
10.1175/JCLI-D-20-0807.1
The Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal processes has not been well understood particularly on decadal and longer time scales, and the external influence from the tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a linear inverse model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal time scales. On the other hand, the Indian Ocean dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal time scale. The third mode is partly associated with the subtropical Indian Ocean dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian–Pacific Ocean interaction is a two-way process.
Zhang
L.
Wang
G.
Newman
M.
Han
W.
21046
Article
Subseasonal prediction of springtime Pacific–North American transport using upper-level wind forecasts
Forecasts of Pacific jet variability are used to predict stratosphere-to-troposphere transport (STT) and tropical-to-extratropical moisture export (TME) during boreal spring over the Pacific–North American region. A retrospective analysis first documents the regionality of STT and TME for different Pacific jet patterns. Using these results as a guide, Pacific jet hindcasts, based on zonal-wind forecasts from the European Centre for Medium-Range Weather Forecasting Integrated Forecasting System, are utilized to test whether STT and TME over specific geographic regions may be predictable for subseasonal forecast leads (3–6 weeks ahead of time). Large anomalies in STT to the mid-troposphere over the North Pacific, TME to the west coast of the United States, and TME over Japan are found to have the best potential for subseasonal predictability using upper-level wind forecasts. STT to the planetary boundary layer over the intermountain west of the United States is also potentially predictable for subseasonal leads but likely only in the context of shifts in the probability of extreme events. While STT and TME forecasts match verifications quite well in terms of spatial structure and anomaly sign, the number of anomalous transport days is underestimated compared to observations. The underestimation of the number of anomalous transport days exhibits a strong seasonal cycle, which becomes steadily worse as spring progresses into summer.
2021-5
Weather Clim. Dynam.
2
433-452
0
10.5194/wcd-2-433-2021
Forecasts of Pacific jet variability are used to predict stratosphere-to-troposphere transport (STT) and tropical-to-extratropical moisture export (TME) during boreal spring over the Pacific–North American region. A retrospective analysis first documents the regionality of STT and TME for different Pacific jet patterns. Using these results as a guide, Pacific jet hindcasts, based on zonal-wind forecasts from the European Centre for Medium-Range Weather Forecasting Integrated Forecasting System, are utilized to test whether STT and TME over specific geographic regions may be predictable for subseasonal forecast leads (3–6 weeks ahead of time). Large anomalies in STT to the mid-troposphere over the North Pacific, TME to the west coast of the United States, and TME over Japan are found to have the best potential for subseasonal predictability using upper-level wind forecasts. STT to the planetary boundary layer over the intermountain west of the United States is also potentially predictable for subseasonal leads but likely only in the context of shifts in the probability of extreme events. While STT and TME forecasts match verifications quite well in terms of spatial structure and anomaly sign, the number of anomalous transport days is underestimated compared to observations. The underestimation of the number of anomalous transport days exhibits a strong seasonal cycle, which becomes steadily worse as spring progresses into summer.
Albers
J. R.
Butler
A. H.
Breeden
M. L.
Langford
A. O.
Kiladis
G. N.
21049
Article
Comparison of TOA and BOA LW Radiation Fluxes Inferred From Ground-Based Sensors, A-Train Satellite Observations and ERA Reanalyzes at the High Arctic Station Eureka Over the 2002–2020 Period
This study focuses on the accuracy of longwave radiation flux retrievals at the top and bottom of the atmosphere at Eureka station, Canada, in the high Arctic. We report comparisons between seven products derived from (a) calculations based on a combination of ground-based and space-based lidar and radar observations, (b) standard radiometric observations from the CERES sensor, (c) direct observations at the surface from a broadband radiation station, and (d) the ERA-Interim and ERA5 reanalyzes. Statistical, independent analyses are first performed to look at recurring bias and trends in fluxes at Top and Bottom of the Atmosphere (TOA, BOA). The analysis is further refined by comparing fluxes derived from coincident observations decomposed by scene types. Results show that radiative transfer calculations using ground-based lidar-radar profiles derived at Eureka agree well with TOA LW fluxes observed by CERES and with BOA LW fluxes reference. CloudSat-CALIPSO also shows good agreement with calculations from ground-based sensor observations, with a relatively small bias. This bias is shown to be largely due to low and thick cloud occurrences that the satellites are insensitive to owing to attenuation from clouds above and surface clutter. These conditions of opaque low clouds, cause an even more pronounced bias for CERES BOA flux calculation in winter, due to the deficit of low clouds identified by MODIS. ERA-I and ERA5 fluxes behave differently, the large positive bias observed with ERA-I is much reduced in ERA5. ERA5 is closer to reference observations due to better behavior of low and mid-level clouds and surface temperature.
2021-6
J. Geophys. Res. Atmos.
126
e2020JD033615
0
10.1029/2020JD033615
This study focuses on the accuracy of longwave radiation flux retrievals at the top and bottom of the atmosphere at Eureka station, Canada, in the high Arctic. We report comparisons between seven products derived from (a) calculations based on a combination of ground-based and space-based lidar and radar observations, (b) standard radiometric observations from the CERES sensor, (c) direct observations at the surface from a broadband radiation station, and (d) the ERA-Interim and ERA5 reanalyzes. Statistical, independent analyses are first performed to look at recurring bias and trends in fluxes at Top and Bottom of the Atmosphere (TOA, BOA). The analysis is further refined by comparing fluxes derived from coincident observations decomposed by scene types. Results show that radiative transfer calculations using ground-based lidar-radar profiles derived at Eureka agree well with TOA LW fluxes observed by CERES and with BOA LW fluxes reference. CloudSat-CALIPSO also shows good agreement with calculations from ground-based sensor observations, with a relatively small bias. This bias is shown to be largely due to low and thick cloud occurrences that the satellites are insensitive to owing to attenuation from clouds above and surface clutter. These conditions of opaque low clouds, cause an even more pronounced bias for CERES BOA flux calculation in winter, due to the deficit of low clouds identified by MODIS. ERA-I and ERA5 fluxes behave differently, the large positive bias observed with ERA-I is much reduced in ERA5. ERA5 is closer to reference observations due to better behavior of low and mid-level clouds and surface temperature.
Blanchard
Y.
Pelon
J.
Cox
C. J.
Delanoë
J.
Eloranta
E. W.
Uttal
T.
21050
Article
Tropical Origins of Weeks 2–4 Forecast Errors during the Northern Hemisphere Cool Season
A set of 30-day reforecast experiments, focused on the Northern Hemisphere (NH) cool season (November–March), is performed to quantify the remote impacts of tropical forecast errors on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The approach is to nudge the model toward reanalyses in the tropics and then measure the change in skill at higher latitudes as a function of lead time. In agreement with previous analogous studies, results show that midlatitude predictions tend to be improved in association with reducing tropical forecast errors during weeks 2–4, particularly over the North Pacific and western North America, where gains in subseasonal precipitation anomaly pattern correlations are substantial. It is also found that tropical nudging is more effective at improving NH subseasonal predictions in cases where skill is relatively low in the control reforecast, whereas it tends to improve fewer cases that are already relatively skillful. By testing various tropical nudging configurations, it is shown that tropical circulation errors play a primary role in the remote modulation of predictive skill. A time-dependent analysis suggests a roughly 1-week lag between a decrease in tropical errors and an increase in NH predictive skill. A combined tropical nudging and conditional skill analysis indicates that improved Madden–Julian oscillation (MJO) predictions throughout its life cycle could improve weeks 3–4 NH precipitation predictions.
2021-9
Mon. Wea. Rev.
149
2975–2991
0
10.1175/MWR-D-21-0020.1
A set of 30-day reforecast experiments, focused on the Northern Hemisphere (NH) cool season (November–March), is performed to quantify the remote impacts of tropical forecast errors on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The approach is to nudge the model toward reanalyses in the tropics and then measure the change in skill at higher latitudes as a function of lead time. In agreement with previous analogous studies, results show that midlatitude predictions tend to be improved in association with reducing tropical forecast errors during weeks 2–4, particularly over the North Pacific and western North America, where gains in subseasonal precipitation anomaly pattern correlations are substantial. It is also found that tropical nudging is more effective at improving NH subseasonal predictions in cases where skill is relatively low in the control reforecast, whereas it tends to improve fewer cases that are already relatively skillful. By testing various tropical nudging configurations, it is shown that tropical circulation errors play a primary role in the remote modulation of predictive skill. A time-dependent analysis suggests a roughly 1-week lag between a decrease in tropical errors and an increase in NH predictive skill. A combined tropical nudging and conditional skill analysis indicates that improved Madden–Julian oscillation (MJO) predictions throughout its life cycle could improve weeks 3–4 NH precipitation predictions.
Dias
J.
Tulich
S. N.
Gehne
M.
Kiladis
G. N.
21051
Article
Accounting for Land Model Uncertainty in Numerical Weather Prediction Ensemble Systems: Toward Ensemble-Based Coupled Land–Atmosphere Data Assimilation
The ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.
2021-8
J. Hydrometeor.
22
2089–2104
0
10.1175/JHM-D-21-0016.1
The ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.
Draper
C.
21052
Article
Comparing and Combining Deterministic Surface Temperature Postprocessing Methods over the United States
Common methods for the postprocessing of deterministic 2-meter temperature (T2m) forecasts over US were evaluated from +12 to +120 h lead. Forecast data were extracted from the Global Ensemble Forecast System (GEFS) v12 reforecast data set and thinned to a 1/2-degree grid. Analyzed data from the European Centre/Copernicus reanalysis (ERA5) were used for training and validation. Data from the 2000-2018 period were used for training, and 2019 forecasts were validated. The post-processing methods compared were the raw forecast guidance, a decaying-average bias correction (DAV), quantile mapping (QM), a univariate model output statistics (uMOS) algorithm, and a multi-variate (mvMOS) algorithm. mvMOS used the raw forecast temperature, the DAV adjustment, and the QM adjustment as predictors.
Forecasts from all the post-processing methods reduced the root-mean-square error (RMSE) and bias relative to the raw guidance. QM produced forecasts with slightly higher error than DAV. DAV estimates were the most consistent from day to day. uMOS and mvMOS produced statistically significant lower RMSEs than DAV at forecast leads longer than 1 day, with mvMOS exhibiting the lowest error. Taylor diagrams showed that the MOS methods reduced the variability of the forecasts while improving forecast-analyzed correlations. QM and DAV modified the distribution of forecasts to more closely exhibit those of the analyzed data.
A main conclusion is that the judicious statistical combination of guidance from multiple post-processing methods is capable of producing forecasts with improved error statistics relative to any one individual technique. As each method applied here is algorithmically relatively simple, this suggests that operational deterministic postprocessing could produce improved T2m guidance.
2021-10
Mon. Wea. Rev.
149
3289–3298
0
10.1175/MWR-D-21-0027.1
Common methods for the postprocessing of deterministic 2-meter temperature (T2m) forecasts over US were evaluated from +12 to +120 h lead. Forecast data were extracted from the Global Ensemble Forecast System (GEFS) v12 reforecast data set and thinned to a 1/2-degree grid. Analyzed data from the European Centre/Copernicus reanalysis (ERA5) were used for training and validation. Data from the 2000-2018 period were used for training, and 2019 forecasts were validated. The post-processing methods compared were the raw forecast guidance, a decaying-average bias correction (DAV), quantile mapping (QM), a univariate model output statistics (uMOS) algorithm, and a multi-variate (mvMOS) algorithm. mvMOS used the raw forecast temperature, the DAV adjustment, and the QM adjustment as predictors.
Forecasts from all the post-processing methods reduced the root-mean-square error (RMSE) and bias relative to the raw guidance. QM produced forecasts with slightly higher error than DAV. DAV estimates were the most consistent from day to day. uMOS and mvMOS produced statistically significant lower RMSEs than DAV at forecast leads longer than 1 day, with mvMOS exhibiting the lowest error. Taylor diagrams showed that the MOS methods reduced the variability of the forecasts while improving forecast-analyzed correlations. QM and DAV modified the distribution of forecasts to more closely exhibit those of the analyzed data.
A main conclusion is that the judicious statistical combination of guidance from multiple post-processing methods is capable of producing forecasts with improved error statistics relative to any one individual technique. As each method applied here is algorithmically relatively simple, this suggests that operational deterministic postprocessing could produce improved T2m guidance.
Hamill
T. M.
21053
Article
Flash drought in Australia and its relationship to evaporative demand
Flash droughts can be distinguished by rapid intensification from near-normal soil moisture to drought conditions in a matter of weeks. Here, we provide the first characterisation of a climatology of flash drought across Australia using a suite of indices. The experiment is designed to capture a range of conditions related to drought: evaporative demand describes the atmospheric demand for moisture from the surface; precipitation, the supply of moisture from the atmosphere to the surface; and evaporative stress, the supply of moisture from the surface relative to the demand from the atmosphere. We show that regardless of the definition, flash droughts occur in all seasons. They can terminate as rapidly as they start, but in some cases can last many months, resulting in a seasonal-scale drought. We show that flash-drought variability and its prevalence can be related to phases of the El Niño–Southern Oscillation, highlighting scope for seasonal-scale prediction. Using a case study in southeast Australia, we show that monitoring precipitation is less useful for capturing the onset of flash drought as it occurs. Instead, indices like the Evaporative Demand Drought Index and Evaporative Stress Index are more useful for monitoring flash-drought development.
2021-5
Environ. Res. Lett.
16
064033
0
10.1088/1748-9326/abfe2c
Flash droughts can be distinguished by rapid intensification from near-normal soil moisture to drought conditions in a matter of weeks. Here, we provide the first characterisation of a climatology of flash drought across Australia using a suite of indices. The experiment is designed to capture a range of conditions related to drought: evaporative demand describes the atmospheric demand for moisture from the surface; precipitation, the supply of moisture from the atmosphere to the surface; and evaporative stress, the supply of moisture from the surface relative to the demand from the atmosphere. We show that regardless of the definition, flash droughts occur in all seasons. They can terminate as rapidly as they start, but in some cases can last many months, resulting in a seasonal-scale drought. We show that flash-drought variability and its prevalence can be related to phases of the El Niño–Southern Oscillation, highlighting scope for seasonal-scale prediction. Using a case study in southeast Australia, we show that monitoring precipitation is less useful for capturing the onset of flash drought as it occurs. Instead, indices like the Evaporative Demand Drought Index and Evaporative Stress Index are more useful for monitoring flash-drought development.
Parker
T.
Gallant
A.
Hobbins
M. T.
Hoffmann
D.
21055
Article
Measurements from the RV Ronald H. Brown and related platforms as part of the Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC)
The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.
2021-4
Earth Syst. Sci. Data
13
1759-1790
0
10.5194/essd-13-1759-2021
The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) took place from 7 January to 11 July 2020 in the tropical North Atlantic between the eastern edge of Barbados and 51∘ W, the longitude of the Northwest Tropical Atlantic Station (NTAS) mooring. Measurements were made to gather information on shallow atmospheric convection, the effects of aerosols and clouds on the ocean surface energy budget, and mesoscale oceanic processes. Multiple platforms were deployed during ATOMIC including the NOAA RV Ronald H. Brown (RHB) (7 January to 13 February) and WP-3D Orion (P-3) aircraft (17 January to 10 February), the University of Colorado's Robust Autonomous Aerial Vehicle-Endurant Nimble (RAAVEN) uncrewed aerial system (UAS) (24 January to 15 February), NOAA- and NASA-sponsored Saildrones (12 January to 11 July), and Surface Velocity Program Salinity (SVPS) surface ocean drifters (23 January to 29 April). The RV Ronald H. Brown conducted in situ and remote sensing measurements of oceanic and atmospheric properties with an emphasis on mesoscale oceanic–atmospheric coupling and aerosol–cloud interactions. In addition, the ship served as a launching pad for Wave Gliders, Surface Wave Instrument Floats with Tracking (SWIFTs), and radiosondes. Details of measurements made from the RV Ronald H. Brown, ship-deployed assets, and other platforms closely coordinated with the ship during ATOMIC are provided here. These platforms include Saildrone 1064 and the RAAVEN UAS as well as the Barbados Cloud Observatory (BCO) and Barbados Atmospheric Chemistry Observatory (BACO). Inter-platform comparisons are presented to assess consistency in the data sets. Data sets from the RV Ronald H. Brown and deployed assets have been quality controlled and are publicly available at NOAA's National Centers for Environmental Information (NCEI) data archive (https://www.ncei.noaa.gov/archive/accession/ATOMIC-2020, last access: 2 April 2021). Point-of-contact information and links to individual data sets with digital object identifiers (DOIs) are provided herein.
Quinn
P. K.
Thompson
E. J.
Coffman
D. J.
Baidar
S.
Bariteau
L.
. .
.
de Boer
G.
. .
.
Intrieri
J. M.
Iyer
S.
Fairall
C. W.
. .
.
Moran
K. P.
Noone
D.
Pezoa
S.
Pincus
R.
al.
et
21056
Article
Projecting ocean acidification impacts for the Gulf of Maine to 2050: New tools and expectations
Ocean acidification (OA) is increasing predictably in the global ocean as rising levels of atmospheric carbon dioxide lead to higher oceanic concentrations of inorganic carbon. The Gulf of Maine (GOM) is a seasonally varying region of confluence for many processes that further affect the carbonate system including freshwater influences and high productivity, particularly near the coast where local processes impart a strong influence. Two main regions within the GOM currently experience carbonate conditions that are suboptimal for many organisms—the nearshore and subsurface deep shelf. OA trends over the past 15 years have been masked in the GOM by recent warming and changes to the regional circulation that locally supply more Gulf Stream waters. The region is home to many commercially important shellfish that are vulnerable to OA conditions, as well as to the human populations whose dependence on shellfish species in the fishery has continued to increase over the past decade. Through a review of the sensitivity of the regional marine ecosystem inhabitants, we identified a critical threshold of 1.5 for the aragonite saturation state (Ωa). A combination of regional high-resolution simulations that include coastal processes were used to project OA conditions for the GOM into 2050. By 2050, the Ωa declines everywhere in the GOM with most pronounced impacts near the coast, in subsurface waters, and associated with freshening. Under the RCP 8.5 projected climate scenario, the entire GOM will experience conditions below the critical Ωa threshold of 1.5 for most of the year by 2050. Despite these declines, the projected warming in the GOM imparts a partial compensatory effect to Ωa by elevating saturation states considerably above what would result from acidification alone and preserving some important fisheries locations, including much of Georges Bank, above the critical threshold.
2021-5
Elementa Sci. Anthrop.
9
00062
0
10.1525/elementa.2020.00062
Ocean acidification (OA) is increasing predictably in the global ocean as rising levels of atmospheric carbon dioxide lead to higher oceanic concentrations of inorganic carbon. The Gulf of Maine (GOM) is a seasonally varying region of confluence for many processes that further affect the carbonate system including freshwater influences and high productivity, particularly near the coast where local processes impart a strong influence. Two main regions within the GOM currently experience carbonate conditions that are suboptimal for many organisms—the nearshore and subsurface deep shelf. OA trends over the past 15 years have been masked in the GOM by recent warming and changes to the regional circulation that locally supply more Gulf Stream waters. The region is home to many commercially important shellfish that are vulnerable to OA conditions, as well as to the human populations whose dependence on shellfish species in the fishery has continued to increase over the past decade. Through a review of the sensitivity of the regional marine ecosystem inhabitants, we identified a critical threshold of 1.5 for the aragonite saturation state (Ωa). A combination of regional high-resolution simulations that include coastal processes were used to project OA conditions for the GOM into 2050. By 2050, the Ωa declines everywhere in the GOM with most pronounced impacts near the coast, in subsurface waters, and associated with freshening. Under the RCP 8.5 projected climate scenario, the entire GOM will experience conditions below the critical Ωa threshold of 1.5 for most of the year by 2050. Despite these declines, the projected warming in the GOM imparts a partial compensatory effect to Ωa by elevating saturation states considerably above what would result from acidification alone and preserving some important fisheries locations, including much of Georges Bank, above the critical threshold.
Siedlecki
S. A.
Salisbury
J.
Gledhill
D. K.
Bastidas
C.
Meseck
S.
McGarry
K.
Hunt
C. W.
Alexander
M. A.
Lavoie
D.
Wang
Z. A.
Scott
J. D.
al.
et
21071
Article
Observational case study of a persistent cold air pool and gap flow in the Columbia River Basin
Persistent cold pools form as layers of cold stagnant air within topographical depressions mainly during wintertime when the near-surface air cools and/or the air aloft warms and daytime surface heating is insufficient to mix out the stable layer. An area often affected by persistent cold pools is the Columbia River Basin in the Pacific Northwest, when a high-pressure system east of the Cascade Range promotes radiative cooling and easterly flow. The only major outflow for the easterly flow is through the narrow Columbia River Gorge which cuts through the north-south oriented Cascade Range and often experiences very strong gap flows. Observations collected during the Second Wind Forecast Improvement Project (WFIP2) are used to study a persistent cold pool in the Columbia River Basin between 10-19 Jan 2017 which was associated with a strong gap flow. We used data from various remote sensing and in situ instruments and a optimal estimation physical retrieval to obtain thermodynamic profiles to address the temporal and spatial characteristics of the cold pool and gap flow and to investigate the physical processes involved during formation, maintenance and decay. While large-scale temperature advection occurred during all phases, we found that the cold pool vertical structure was modulated by the existence of low-level clouds and that turbulent shear-induced mixing and downslope wind storms likely played a role during its decay.
2021-8
J. Appl. Meteor. Climatol.
60
1071–1090
0
10.1175/JAMC-D-21-0013.1
Persistent cold pools form as layers of cold stagnant air within topographical depressions mainly during wintertime when the near-surface air cools and/or the air aloft warms and daytime surface heating is insufficient to mix out the stable layer. An area often affected by persistent cold pools is the Columbia River Basin in the Pacific Northwest, when a high-pressure system east of the Cascade Range promotes radiative cooling and easterly flow. The only major outflow for the easterly flow is through the narrow Columbia River Gorge which cuts through the north-south oriented Cascade Range and often experiences very strong gap flows. Observations collected during the Second Wind Forecast Improvement Project (WFIP2) are used to study a persistent cold pool in the Columbia River Basin between 10-19 Jan 2017 which was associated with a strong gap flow. We used data from various remote sensing and in situ instruments and a optimal estimation physical retrieval to obtain thermodynamic profiles to address the temporal and spatial characteristics of the cold pool and gap flow and to investigate the physical processes involved during formation, maintenance and decay. While large-scale temperature advection occurred during all phases, we found that the cold pool vertical structure was modulated by the existence of low-level clouds and that turbulent shear-induced mixing and downslope wind storms likely played a role during its decay.
Adler
B.
Wilczak
J. M.
Bianco
L.
Djalalova
I.
Duncan
J.
Turner
D. D.
21073
Article
Fog Formation Related to Gravity Currents Interacting with Coastal Topography
An interesting mixing-fog event was identified during the C-FOG field campaign, where a cold-frontal airmass arriving from the north-east collided with The Downs peninsula in Ferryland, Newfoundland, Canada, to produce misty/foggy conditions. A comprehensive set of field observations suggests that this collision caused turbulent mixing of nearly saturated ambient air with an almost saturated cold-frontal airmass, creating conditions for mixing fog. To delve into the physical processes underlying this phenomenon, laboratory experiments were performed on the interaction of lock-exchange-induced gravity currents with a rectangular obstacle. Instantaneous velocity and density fields were obtained using particle image velocimetry and planar laser-induced fluorescence. The observations suggest that the obstacle starts affecting the approaching gravity-current propagation at an upstream distance of 2H and, upon collision, the mixing occurs over a length of 0.83H, where H is the depth of the ambient fluid layer. The time for larger-scale turbulent stirring to permeate to the smallest scales of turbulence and activate the condensation nuclei is estimated as 3
2021-7
Boundary-Layer Meteorol.
181
499-521
0
10.1007/s10546-021-00638-w
An interesting mixing-fog event was identified during the C-FOG field campaign, where a cold-frontal airmass arriving from the north-east collided with The Downs peninsula in Ferryland, Newfoundland, Canada, to produce misty/foggy conditions. A comprehensive set of field observations suggests that this collision caused turbulent mixing of nearly saturated ambient air with an almost saturated cold-frontal airmass, creating conditions for mixing fog. To delve into the physical processes underlying this phenomenon, laboratory experiments were performed on the interaction of lock-exchange-induced gravity currents with a rectangular obstacle. Instantaneous velocity and density fields were obtained using particle image velocimetry and planar laser-induced fluorescence. The observations suggest that the obstacle starts affecting the approaching gravity-current propagation at an upstream distance of 2H and, upon collision, the mixing occurs over a length of 0.83H, where H is the depth of the ambient fluid layer. The time for larger-scale turbulent stirring to permeate to the smallest scales of turbulence and activate the condensation nuclei is estimated as 3
Bardoel
S. L.
Muñoz
H.
Grachev
A. A.
Krishnamurthy
R.
Chamorro
L. P.
Fernando
H. J. S.
21074
Article
An Evaluation of CMIP6 Historical Simulations of the Cold Season Teleconnection between Tropical Indo-Pacific Sea Surface Temperatures and Precipitation in Southwest Asia, the Coastal Middle East, and Northern Pakistan and India
The ability of six CMIP6 models to reproduce the observed cold season teleconnection between tropical Indo-Pacific sea surface temperatures (SSTs) and precipitation in Southwest Asia, the coastal Middle East (CME), and northern Pakistan and India (NPI) is examined. The 1979–2014 period is analyzed to maximize observations over both the tropical ocean and the regions. Nine historical simulations for the same period are examined for each model to account for the internal variability of the coupled system. The teleconnection is examined in terms of SSTs, precipitation, 200-hPa geopotential heights, and derived quantities. All the models capture some of the broadest features of the teleconnections, but there is a wide range in the ability of the models to reproduce the magnitude and details. The differences appear related to both the models’ ability to capture the details of the tropical variability, including the position and strength of the precipitation anomalies in the Indo-west Pacific, and the models’ ability to accurately propagate the tropically forced response into the region. The teleconnections to the CME and NPI regions on the eastern and western margins, respectively, of the strongest signal are very similar in structure and have similar results, except that the models’ ability to reproduce the strength and details of the teleconnection is even more limited, consistent with their marginal locations and known influence of other modes of variability. For all three areas, the wide range in model ability to capture the leading teleconnection suggests caution in interpreting climate regional projections.
2021-8
J. Climate
34
6905-6926
0
10.1175/JCLI-D-19-1026.1
The ability of six CMIP6 models to reproduce the observed cold season teleconnection between tropical Indo-Pacific sea surface temperatures (SSTs) and precipitation in Southwest Asia, the coastal Middle East (CME), and northern Pakistan and India (NPI) is examined. The 1979–2014 period is analyzed to maximize observations over both the tropical ocean and the regions. Nine historical simulations for the same period are examined for each model to account for the internal variability of the coupled system. The teleconnection is examined in terms of SSTs, precipitation, 200-hPa geopotential heights, and derived quantities. All the models capture some of the broadest features of the teleconnections, but there is a wide range in the ability of the models to reproduce the magnitude and details. The differences appear related to both the models’ ability to capture the details of the tropical variability, including the position and strength of the precipitation anomalies in the Indo-west Pacific, and the models’ ability to accurately propagate the tropically forced response into the region. The teleconnections to the CME and NPI regions on the eastern and western margins, respectively, of the strongest signal are very similar in structure and have similar results, except that the models’ ability to reproduce the strength and details of the teleconnection is even more limited, consistent with their marginal locations and known influence of other modes of variability. For all three areas, the wide range in model ability to capture the leading teleconnection suggests caution in interpreting climate regional projections.
Barlow
M.
Hoell
A.
Agel
L.
21075
Article
Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project
The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.
2021-8
Boundary-Layer Meteorol.
182
147–172
0
10.1007/s10546-021-00645-x
The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.
Bianco
L.
Muradyan
P.
Djalalova
I.
Wilczak
J. M.
Olson
J. B.
Kenyon
J. S.
Kotamarthi
K.
Lantz
K. O.
Long
C. N.
Turner
D. D.
21077
Article
Measurement report: Properties of aerosol and gases in the vertical profile during the LAPSE-RATE campaign
Unmanned aerial systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, Colorado, USA, on 14–20 July 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably, resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way and, thus, demonstrate the power of UASs as new, promising tools in atmospheric and environmental research.
2021-1
Atmos. Chem. Phys.
21
517-533
0
10.5194/acp-21-517-2021
Unmanned aerial systems (UASs) are increasingly being used as observation platforms for atmospheric applications. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) in Alamosa, Colorado, USA, on 14–20 July 2018 investigated and validated different UASs, measurement sensors and setup configurations. Flight teams from the Finnish Meteorological Institute (FMI) and Kansas State University (KSU) participated in LAPSE-RATE to measure and investigate properties of aerosol particles and gases in the lower atmosphere. During the experiment, the performance of different UAS configurations were investigated and confirmed to operate reliably, resulting in a scientifically sound observational dataset. As an example, concentration of aerosols – including two new particle formation events, CO2 and water vapor, and meteorological parameters in the atmospheric vertical profile were measured during the short experiment. Such observations characterizing atmospheric phenomena of this specific environment would have not been possible in any other way and, thus, demonstrate the power of UASs as new, promising tools in atmospheric and environmental research.
Brus
D.
Gustafsson
J.
Vakkari
V.
Kemppinen
O.
de Boer
G.
Hirsikko
A.
21079
Article
Measurements from mobile surface vehicles during the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE)
Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSERATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost
by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).
2021-1
Earth Syst. Sci. Data
13
155-169
0
10.5194/essd-13-155-2021
Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSERATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost
by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).
de Boer
G.
Waugh
S.
Erwin
A.
Borenstein
S.
Dixon
C.
Shanti
W.
Houston
A.
Agrow
B.
21081
Article
Next-generation regional ocean projections for living marine resource management in a changing climate
Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.
2021-6
ICES J. Mar. Sci.
78
1969–1987
0
10.1093/icesjms/fsab100
Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.
Drenkard
E. J.
Stock
C.
Ross
A. C.
Dixon
K. W.
Adcroft
A.
Alexander
M. A.
. .
.
Jacox
M. G.
al.
et
21082
Article
C-FOG: Life of Coastal Fog
C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.
2021-2
Bull. Amer. Meteor. Soc.
102
E244-E272
0
10.1175/BAMS-D-19-0070.1
C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.
Fernando
H. J. S.
Gultepe
I.
Dorman
C.
Pardyjak
E. R.
. .
.
Grachev
A. A.
al.
et
21083
Article
Projected Shifts in 21st Century Sardine Distribution and Catch in the California Current
Predicting changes in the abundance and distribution of small pelagic fish species in response to anthropogenic climate forcing is of paramount importance due to the ecological and socioeconomic importance of these species, especially in eastern boundary current upwelling regions. Coastal upwelling systems are notorious for the wide range of spatial (from local to basin) and temporal (from days to decades) scales influencing their physical and biogeochemical environments and, thus, forage fish habitat. Bridging those scales can be achieved by using high-resolution regional models that integrate global climate forcing downscaled from coarser resolution earth system models. Here, “end-to-end” projections for 21st century sardine population dynamics and catch in the California Current system (CCS) are generated by coupling three dynamically downscaled earth system model solutions to an individual-based fish model and an agent-based fishing fleet model. Simulated sardine population biomass during 2000–2100 exhibits primarily low-frequency (decadal) variability, and a progressive poleward shift driven by thermal habitat preference. The magnitude of poleward displacement varies noticeably under lower and higher warming conditions (500 and 800 km, respectively). Following the redistribution of the sardine population, catch is projected to increase by 50–70% in the northern CCS and decrease by 30–70% in the southern and central CCS. However, the late-century increase in sardine abundance (and hence, catch) in the northern CCS exhibits a large ensemble spread and is not statistically identical across the three downscaled projections. Overall, the results illustrate the benefit of using dynamical downscaling from multiple earth system models as input to high-resolution regional end-to-end (“physics to fish”) models for projecting population responses of higher trophic organisms to global climate change.
2021-7
Front. Mar. Sci.
8
685241
0
10.3389/fmars.2021.685241
Predicting changes in the abundance and distribution of small pelagic fish species in response to anthropogenic climate forcing is of paramount importance due to the ecological and socioeconomic importance of these species, especially in eastern boundary current upwelling regions. Coastal upwelling systems are notorious for the wide range of spatial (from local to basin) and temporal (from days to decades) scales influencing their physical and biogeochemical environments and, thus, forage fish habitat. Bridging those scales can be achieved by using high-resolution regional models that integrate global climate forcing downscaled from coarser resolution earth system models. Here, “end-to-end” projections for 21st century sardine population dynamics and catch in the California Current system (CCS) are generated by coupling three dynamically downscaled earth system model solutions to an individual-based fish model and an agent-based fishing fleet model. Simulated sardine population biomass during 2000–2100 exhibits primarily low-frequency (decadal) variability, and a progressive poleward shift driven by thermal habitat preference. The magnitude of poleward displacement varies noticeably under lower and higher warming conditions (500 and 800 km, respectively). Following the redistribution of the sardine population, catch is projected to increase by 50–70% in the northern CCS and decrease by 30–70% in the southern and central CCS. However, the late-century increase in sardine abundance (and hence, catch) in the northern CCS exhibits a large ensemble spread and is not statistically identical across the three downscaled projections. Overall, the results illustrate the benefit of using dynamical downscaling from multiple earth system models as input to high-resolution regional end-to-end (“physics to fish”) models for projecting population responses of higher trophic organisms to global climate change.
Fiechter
J.
Pozo Buil
M.
Jacox
M. G.
Alexander
M. A.
Rose
K. A.
21084
Article
JOANNE: Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments
As part of the EUREC4A field campaign which took place over the tropical North Atlantic during January–February 2020, 1215 dropsondes from the HALO and WP-3D aircraft were deployed through 26 flights to characterize the thermodynamic and dynamic environment of clouds in the trade-wind regions. We present JOANNE (Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments), the dataset that contains these dropsonde measurements and the products derived from them. Along with the raw measurement profiles and basic post-processing of pressure, temperature, relative humidity and horizontal winds, the dataset also includes a homogenized and gridded dataset with 10 m vertical spacing. The gridded data are used as a basis for deriving diagnostics of the area-averaged mesoscale circulation properties such as divergence, vorticity, vertical velocity and gradient terms, making use of sondes dropped at regular intervals along a circular flight path. A total of 85 such circles, ∼ 222 km in diameter, were flown during EUREC4A. We describe the sampling strategy for dropsonde measurements during EUREC4A, the quality control for the data, the methods of estimation of additional products from the measurements and the different post-processed levels of the dataset. The dataset is publicly available (https://doi.org/10.25326/246, George et al., 2021b) as is the software used to create it (https://doi.org/10.5281/zenodo.4746312, George, 2021).
2021-11
Earth Syst. Sci. Data
13
5253-5272
0
10.5194/essd-13-5253-2021
As part of the EUREC4A field campaign which took place over the tropical North Atlantic during January–February 2020, 1215 dropsondes from the HALO and WP-3D aircraft were deployed through 26 flights to characterize the thermodynamic and dynamic environment of clouds in the trade-wind regions. We present JOANNE (Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments), the dataset that contains these dropsonde measurements and the products derived from them. Along with the raw measurement profiles and basic post-processing of pressure, temperature, relative humidity and horizontal winds, the dataset also includes a homogenized and gridded dataset with 10 m vertical spacing. The gridded data are used as a basis for deriving diagnostics of the area-averaged mesoscale circulation properties such as divergence, vorticity, vertical velocity and gradient terms, making use of sondes dropped at regular intervals along a circular flight path. A total of 85 such circles, ∼ 222 km in diameter, were flown during EUREC4A. We describe the sampling strategy for dropsonde measurements during EUREC4A, the quality control for the data, the methods of estimation of additional products from the measurements and the different post-processed levels of the dataset. The dataset is publicly available (https://doi.org/10.25326/246, George et al., 2021b) as is the software used to create it (https://doi.org/10.5281/zenodo.4746312, George, 2021).
George
G.
Stevens
B.
Bony
S.
Pincus
R.
Fairall
C. W.
al.
et
21086
Article
Atmospheric Turbulence Measurements in Coastal Zone with and without Fog
Measurements of atmospheric turbulence at a site in Ferryland (Newfoundland) during the C-FOG (Coastal-Fog) field campaign in September–October 2018 are used to study meteorological parameters, turbulence statistics, internal boundary layers, and scaling laws for turbulent mixing in the coastal zone. We observe stable/unstable shallow internal boundary layers with a region of unstable/stable stratification above with onshore flow from a relatively warm/cold sea onto the cold/heated land during the night/day. This study compares surface fluxes and other turbulence statistics as well as different scaling laws with and without fog. While both complexity of the coastal landforms and foggy conditions nominally violate assumptions underlying Monin–Obukhov similarity theory (MOST), our observations show that the non-dimensional standard deviations of the velocity components and the dissipation rate of turbulence kinetic energy obey MOST reasonably well for all measurement levels, stability conditions, and wind directions for both fog and no fog cases. However, the data scatter for the normalized dissipation rate is somewhat greater compared with the normalized standard deviations of the wind components. The bias and relatively larger scatter of normalized standard deviations for scalars in near-neutral conditions is likely associated with the underlying inhomogeneous coastal surface. According to the C-FOG data, during a fog event the moisture flux data become irregular and the latent heat flux is often negative (downward). Our observations also demonstrate poor agreement between normalized standard deviations of specific humidity with MOST for foggy conditions; its statistical dependence on the MOST stability parameter is weak at best in fog.
2021-8
Boundary-Layer Meteorol.
181
395-422
0
10.1007/s10546-021-00655-9
Measurements of atmospheric turbulence at a site in Ferryland (Newfoundland) during the C-FOG (Coastal-Fog) field campaign in September–October 2018 are used to study meteorological parameters, turbulence statistics, internal boundary layers, and scaling laws for turbulent mixing in the coastal zone. We observe stable/unstable shallow internal boundary layers with a region of unstable/stable stratification above with onshore flow from a relatively warm/cold sea onto the cold/heated land during the night/day. This study compares surface fluxes and other turbulence statistics as well as different scaling laws with and without fog. While both complexity of the coastal landforms and foggy conditions nominally violate assumptions underlying Monin–Obukhov similarity theory (MOST), our observations show that the non-dimensional standard deviations of the velocity components and the dissipation rate of turbulence kinetic energy obey MOST reasonably well for all measurement levels, stability conditions, and wind directions for both fog and no fog cases. However, the data scatter for the normalized dissipation rate is somewhat greater compared with the normalized standard deviations of the wind components. The bias and relatively larger scatter of normalized standard deviations for scalars in near-neutral conditions is likely associated with the underlying inhomogeneous coastal surface. According to the C-FOG data, during a fog event the moisture flux data become irregular and the latent heat flux is often negative (downward). Our observations also demonstrate poor agreement between normalized standard deviations of specific humidity with MOST for foggy conditions; its statistical dependence on the MOST stability parameter is weak at best in fog.
Grachev
A. A.
Krishnamurthy
R.
Fernando
H. J. S.
Fairall
C. W.
Bardoel
S. L.
Wang
S.
21088
Article
Controls on surface aerosol particle number concentrations and aerosol-limited cloud regimes over the central Greenland Ice Sheet
This study presents the first full annual cycle (2019–2020) of ambient surface aerosol particle number concentration measurements (condensation nuclei > 20 nm, N20) collected at Summit Station (Summit), in the centre of the Greenland Ice Sheet (72.58∘ N, −38.45∘ E; 3250 ma.s.l.). The mean surface concentration in 2019 was 129 cm−3, with the 6 h mean ranging between 1 and 1441 cm−3. The highest monthly mean concentrations occurred during the late spring and summer, with the minimum concentrations occurring in February (mean: 18 cm−3). High-N20 events are linked to anomalous anticyclonic circulation over Greenland and the descent of free-tropospheric aerosol down to the surface, whereas low-N20 events are linked to anomalous cyclonic circulation over south-east Greenland that drives upslope flow and enhances precipitation en route to Summit. Fog strongly affects particle number concentrations, on average reducing N20 by 20 % during the first 3 h of fog formation. Extremely-low-N20 events (< 10 cm−3) occur in all seasons, and we suggest that fog, and potentially cloud formation, can be limited by low aerosol particle concentrations over central Greenland.
2021-10
Atmos. Chem. Phys.
21
15351–15374
0
10.5194/acp-21-15351-2021
This study presents the first full annual cycle (2019–2020) of ambient surface aerosol particle number concentration measurements (condensation nuclei > 20 nm, N20) collected at Summit Station (Summit), in the centre of the Greenland Ice Sheet (72.58∘ N, −38.45∘ E; 3250 ma.s.l.). The mean surface concentration in 2019 was 129 cm−3, with the 6 h mean ranging between 1 and 1441 cm−3. The highest monthly mean concentrations occurred during the late spring and summer, with the minimum concentrations occurring in February (mean: 18 cm−3). High-N20 events are linked to anomalous anticyclonic circulation over Greenland and the descent of free-tropospheric aerosol down to the surface, whereas low-N20 events are linked to anomalous cyclonic circulation over south-east Greenland that drives upslope flow and enhances precipitation en route to Summit. Fog strongly affects particle number concentrations, on average reducing N20 by 20 % during the first 3 h of fog formation. Extremely-low-N20 events (< 10 cm−3) occur in all seasons, and we suggest that fog, and potentially cloud formation, can be limited by low aerosol particle concentrations over central Greenland.
Guy
H.
Brooks
I. M.
Carslaw
K. S.
Murray
B. J.
Walden
V. P.
Shupe
M. D.
Pettersen
C.
Turner
D. D.
Cox
C. J.
Neff
W. D.
Bennartz
R.
Neely
R. R.
21091
Article
A review of River Herring science in support of species conservation and ecosystem restoration
River herring—a collective name for the Alewife Alosa pseudoharengus and Blueback Herring A. aestivalis—play a crucial role in freshwater and marine ecosystems along the Eastern Seaboard of North America. River herring are anadromous and return to freshwater habitats in the tens to hundreds of millions to spawn, supplying food to many species and providing nutrients to freshwater ecosystems. After two and a half centuries of habitat loss, habitat degradation, and overfishing, river herring are at historic lows. In 2013, National Oceanic and Atmospheric Administration Fisheries established the Technical Expert Working Group (TEWG) to synthesize information about river herring and to provide recommendations to advance the science related to their restoration. This paper was composed largely by the chairs of the TEWG subgroups and represents a review of the current state of knowledge of river herring, with an emphasis on identification of threats and discussion of recent research and management actions related to understanding and reducing these threats. Important research needs are then identified and discussed. Finally, current knowledge is synthesized, considering the relative importance of different threats. This synthesis identifies dam removal and increased stream connectivity as critical to river herring restoration. Better understanding and accounting for predation, climate change, and fisheries are also important for restoration. Finally, there is recent evidence that the effects of human development and contamination on habitat quality may be more important threats than previously recognized. Given the range of threats, an ecosystem approach is needed to be successful with river herring restoration. To facilitate this ecosystem approach, collaborative forums such as the TEWG (renamed the Atlantic Coast River Herring Collaborative Forum in 2020) are needed to share and synthesize information among river herring managers, researchers, and community groups from across the species’ range.
2021-12
Mar. Coast. Fish.
13
627-664
0
10.1002/mcf2.10174
River herring—a collective name for the Alewife Alosa pseudoharengus and Blueback Herring A. aestivalis—play a crucial role in freshwater and marine ecosystems along the Eastern Seaboard of North America. River herring are anadromous and return to freshwater habitats in the tens to hundreds of millions to spawn, supplying food to many species and providing nutrients to freshwater ecosystems. After two and a half centuries of habitat loss, habitat degradation, and overfishing, river herring are at historic lows. In 2013, National Oceanic and Atmospheric Administration Fisheries established the Technical Expert Working Group (TEWG) to synthesize information about river herring and to provide recommendations to advance the science related to their restoration. This paper was composed largely by the chairs of the TEWG subgroups and represents a review of the current state of knowledge of river herring, with an emphasis on identification of threats and discussion of recent research and management actions related to understanding and reducing these threats. Important research needs are then identified and discussed. Finally, current knowledge is synthesized, considering the relative importance of different threats. This synthesis identifies dam removal and increased stream connectivity as critical to river herring restoration. Better understanding and accounting for predation, climate change, and fisheries are also important for restoration. Finally, there is recent evidence that the effects of human development and contamination on habitat quality may be more important threats than previously recognized. Given the range of threats, an ecosystem approach is needed to be successful with river herring restoration. To facilitate this ecosystem approach, collaborative forums such as the TEWG (renamed the Atlantic Coast River Herring Collaborative Forum in 2020) are needed to share and synthesize information among river herring managers, researchers, and community groups from across the species’ range.
Hare
J. A.
Borggaard
D. L.
Alexander
M. A.
. .
.
Scott
J. D.
al.
et
21099
Article
Observations of Clouds, Aerosols, Precipitation, and Surface Radiation over the Southern Ocean: An Overview of CAPRICORN, MARCUS, MICRE, and SOCRATES
Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.
2021-4
Bull. Amer. Meteor. Soc.
102
E894-E928
0
10.1175/BAMS-D-20-0132.1
Weather and climate models are challenged by uncertainties and biases in simulating Southern Ocean (SO) radiative fluxes that trace to a poor understanding of cloud, aerosol, precipitation, and radiative processes, and their interactions. Projects between 2016 and 2018 used in situ probes, radar, lidar, and other instruments to make comprehensive measurements of thermodynamics, surface radiation, cloud, precipitation, aerosol, cloud condensation nuclei (CCN), and ice nucleating particles over the SO cold waters, and in ubiquitous liquid and mixed-phase clouds common to this pristine environment. Data including soundings were collected from the NSF–NCAR G-V aircraft flying north–south gradients south of Tasmania, at Macquarie Island, and on the R/V Investigator and RSV Aurora Australis. Synergistically these data characterize boundary layer and free troposphere environmental properties, and represent the most comprehensive data of this type available south of the oceanic polar front, in the cold sector of SO cyclones, and across seasons. Results show largely pristine environments with numerous small and few large aerosols above cloud, suggesting new particle formation and limited long-range transport from continents, high variability in CCN and cloud droplet concentrations, and ubiquitous supercooled water in thin, multilayered clouds, often with small-scale generating cells near cloud top. These observations demonstrate how cloud properties depend on aerosols while highlighting the importance of dynamics and turbulence that likely drive heterogeneity of cloud phase. Satellite retrievals confirmed low clouds were responsible for radiation biases. The combination of models and observations is examining how aerosols and meteorology couple to control SO water and energy budgets.
McFarquhar
G. M.
Bretherton
C. S.
Marchand
R.
Protat
A.
. .
.
Fairall
C. W.
al.
et
21102
Article
Forecasts of Opportunity for Northern California Soil Moisture
Soil moisture anomalies underpin a number of critical hydrological phenomena with socioeconomic consequences, yet systematic studies of soil moisture predictability are limited. Here, we use a data-adaptive technique, Linear Inverse Modeling, which has proved useful as an indication of predictability in other fields, to investigate the predictability of soil moisture in northern California. This approach yields a model of soil moisture at 10 stations in the region, with results that indicate the possibility of skillful forecasts at each for lead times of 1–2 weeks. An important advantage of this model is the a priori identification of forecasts of opportunity—conditions under which the model’s forecasts may be expected to have particularly high skill. Given that forecast errors (and inversely, their skill) can be estimated in advance, these findings have the potential to greatly increase the utility of soil moisture forecasts for practical applications including drought and flood forecasting.
2021-7
Land
10
713
0
10.3390/land10070713
Soil moisture anomalies underpin a number of critical hydrological phenomena with socioeconomic consequences, yet systematic studies of soil moisture predictability are limited. Here, we use a data-adaptive technique, Linear Inverse Modeling, which has proved useful as an indication of predictability in other fields, to investigate the predictability of soil moisture in northern California. This approach yields a model of soil moisture at 10 stations in the region, with results that indicate the possibility of skillful forecasts at each for lead times of 1–2 weeks. An important advantage of this model is the a priori identification of forecasts of opportunity—conditions under which the model’s forecasts may be expected to have particularly high skill. Given that forecast errors (and inversely, their skill) can be estimated in advance, these findings have the potential to greatly increase the utility of soil moisture forecasts for practical applications including drought and flood forecasting.
Penland
C.
Fowler
M. D.
Jackson
D. L.
Cifelli
R.
21104
Article
Climate Impacts in the Gulf of Maine Ecosystem: A Review of Observed and Expected Changes in 2050 from Rising Temperatures
The Gulf of Maine has recently experienced its warmest 5-year period (2015–2020) in the instrumental record. This warming was associated with a decline in the signature subarctic zooplankton species, Calanus finmarchicus. The temperature changes have also led to impacts on commercial species such as Atlantic cod (Gadus morhua) and American lobster (Homarus americanus) and protected species including Atlantic puffins (Fratercula arctica) and northern right whales (Eubalaena glacialis). The recent period also saw a decline in Atlantic herring (Clupea harengus) recruitment and an increase in novel harmful algal species, although these have not been attributed to the recent warming. Here, we use an ensemble of numerical ocean models to characterize expected ocean conditions in the middle of this century. Under the high CO2 emissions scenario (RCP8.5), the average temperature in the Gulf of Maine is expected to increase 1.1°C to 2.4°C relative to the 1976–2005 average. Surface salinity is expected to decrease, leading to enhanced water column stratification. These physical changes are likely to lead to additional declines in subarctic species including C. finmarchicus, American lobster, and Atlantic cod and an increase in temperate species. The ecosystem changes have already impacted human communities through altered delivery of ecosystem services derived from the marine environment. Continued warming is expected to lead to a loss of heritage, changes in culture, and the necessity for adaptation.
2021-8
Elementa Sci. Anthrop.
9
00076
0
10.1525/elementa.2020.00076
The Gulf of Maine has recently experienced its warmest 5-year period (2015–2020) in the instrumental record. This warming was associated with a decline in the signature subarctic zooplankton species, Calanus finmarchicus. The temperature changes have also led to impacts on commercial species such as Atlantic cod (Gadus morhua) and American lobster (Homarus americanus) and protected species including Atlantic puffins (Fratercula arctica) and northern right whales (Eubalaena glacialis). The recent period also saw a decline in Atlantic herring (Clupea harengus) recruitment and an increase in novel harmful algal species, although these have not been attributed to the recent warming. Here, we use an ensemble of numerical ocean models to characterize expected ocean conditions in the middle of this century. Under the high CO2 emissions scenario (RCP8.5), the average temperature in the Gulf of Maine is expected to increase 1.1°C to 2.4°C relative to the 1976–2005 average. Surface salinity is expected to decrease, leading to enhanced water column stratification. These physical changes are likely to lead to additional declines in subarctic species including C. finmarchicus, American lobster, and Atlantic cod and an increase in temperate species. The ecosystem changes have already impacted human communities through altered delivery of ecosystem services derived from the marine environment. Continued warming is expected to lead to a loss of heritage, changes in culture, and the necessity for adaptation.
Pershing
A. J.
Alexander
M. A.
Brady
D. C.
Brickman
D.
. .
.
Scott
J. D.
al.
et
21105
Article
Downscaling snow deposition using historic snow depth patterns: diagnosing limitations from snowfall biases, winter snow losses, and interannual snow pattern repeatability
Repeatable snow depth patterns have been identified in many regions between years with similar meteorological characteristics. This suggests that snow patterns from previous years could adjust snow deposition in space as a substitution for unmodeled snow processes. Here, we tested a pattern-based snow deposition downscaling routine which assumes (a) a spatially consistent relationship between snow deposition and snow depth, (b) interannually repeatable snow patterns, and (c) unbiased mean snowfall. We investigated these assumptions, and future avenues for improvement, in water-year 2014 over the California Tuolumne River Watershed. 6 km snowfall from an atmospheric model was downscaled to 25 m resolution using snow depth patterns from seven different years, and was compared to a more common terrain-based downscaling method. Snow depth patterns were influenced not only by snow accumulation, but also snowmelt, snow sublimation, and snow density, resulting in pattern-based snow deposition downscaling that was too spatially heterogeneous. However, snow depth simulated using terrain-based downscaling was too spatially homogeneous, and less spatially correlated with observations (r = 0.27), than simulations with pattern-based downscaling using snow depth patterns from the simulation season (r = 0.76), or from a different year (r = 0.52). Overall, modeled snow depth errors at peak-snowpack timing were driven more by atmospheric model snowfall biases than different downscaling methods. In order of most- to least-importance, future research should focus on bias-correcting coarse-scale snowfall estimates, correcting snow deposition patterns for winter snow losses and snow density spatial variability, and identifying the historic periods of most-similar snow accumulation.
2021-8
Water Resour. Res.
57
e2021WR029999
0
10.1029/2021WR029999
Repeatable snow depth patterns have been identified in many regions between years with similar meteorological characteristics. This suggests that snow patterns from previous years could adjust snow deposition in space as a substitution for unmodeled snow processes. Here, we tested a pattern-based snow deposition downscaling routine which assumes (a) a spatially consistent relationship between snow deposition and snow depth, (b) interannually repeatable snow patterns, and (c) unbiased mean snowfall. We investigated these assumptions, and future avenues for improvement, in water-year 2014 over the California Tuolumne River Watershed. 6 km snowfall from an atmospheric model was downscaled to 25 m resolution using snow depth patterns from seven different years, and was compared to a more common terrain-based downscaling method. Snow depth patterns were influenced not only by snow accumulation, but also snowmelt, snow sublimation, and snow density, resulting in pattern-based snow deposition downscaling that was too spatially heterogeneous. However, snow depth simulated using terrain-based downscaling was too spatially homogeneous, and less spatially correlated with observations (r = 0.27), than simulations with pattern-based downscaling using snow depth patterns from the simulation season (r = 0.76), or from a different year (r = 0.52). Overall, modeled snow depth errors at peak-snowpack timing were driven more by atmospheric model snowfall biases than different downscaling methods. In order of most- to least-importance, future research should focus on bias-correcting coarse-scale snowfall estimates, correcting snow deposition patterns for winter snow losses and snow density spatial variability, and identifying the historic periods of most-similar snow accumulation.
Pflug
J.
Hughes
M.
Lundquist
J. K.
21106
Article
Observations from the NOAA P-3 aircraft during ATOMIC
The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC), part of the larger experiment known as Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC4A), was held in the western Atlantic during the period 17 January–11 February 2020. This paper describes observations made during ATOMIC by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados. The aircraft obtained 95 h of observations over 11 flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment, slow descents and ascents to measure the distribution of water vapor and its isotopic composition, stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer, and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (NOAA Aircraft Operations Center and NOAA Physical Sciences Laboratory, 2020, https://doi.org/10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (National Center for Atmospheric Research, 2020, https://doi.org/10.25921/c5yx-7w29); in situ observations of aerosol, cloud, and precipitation size distributions (Leandro and Chuang, 2020, https://doi.org/10.25921/vwvq-5015); profiles of seawater temperature made with Airborne eXpendable BathyThermographs (AXBTs; NOAA Physical Sciences Laboratory, 2020a, https://doi.org/10.25921/pe39-sx75); radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band radar (NOAA Physical Sciences Laboratory, 2020c, https://doi.org/10.25921/n1hc-dc30); estimates of cloud presence, the cloud-top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10 m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (NOAA Physical Sciences Laboratory, 2020b, https://doi.org/10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (Prosensing, Inc., 2020, https://doi.org/10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions.
2021-7
Earth Syst. Sci. Data
13
3281–3296
0
10.5194/essd-13-3281-2021
The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC), part of the larger experiment known as Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC4A), was held in the western Atlantic during the period 17 January–11 February 2020. This paper describes observations made during ATOMIC by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados. The aircraft obtained 95 h of observations over 11 flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment, slow descents and ascents to measure the distribution of water vapor and its isotopic composition, stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer, and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (NOAA Aircraft Operations Center and NOAA Physical Sciences Laboratory, 2020, https://doi.org/10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (National Center for Atmospheric Research, 2020, https://doi.org/10.25921/c5yx-7w29); in situ observations of aerosol, cloud, and precipitation size distributions (Leandro and Chuang, 2020, https://doi.org/10.25921/vwvq-5015); profiles of seawater temperature made with Airborne eXpendable BathyThermographs (AXBTs; NOAA Physical Sciences Laboratory, 2020a, https://doi.org/10.25921/pe39-sx75); radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band radar (NOAA Physical Sciences Laboratory, 2020c, https://doi.org/10.25921/n1hc-dc30); estimates of cloud presence, the cloud-top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10 m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (NOAA Physical Sciences Laboratory, 2020b, https://doi.org/10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (Prosensing, Inc., 2020, https://doi.org/10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions.
Pincus
R.
Fairall
C. W.
Bailey
A.
Chen
H.
Chuang
P. Y.
de Boer
G.
. .
.
Pezoa
S.
PopStefanija
I.
Thompson
E. J.
al.
et
21107
Article
Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System
Errors in soil moisture adversely impact the modeling of land-atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This study investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly-coupled land-atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture vs. in situ measurements by ~0.1-0.26 over that of ADAS estimates; the unbiased root-mean-square error (ubRMSE) of LADAS soil moisture is reduced by 0.002-0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE vs. in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ~700 mb), with relative improvements in bias of 15-25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root-mean-square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ~0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land-atmosphere coupling.
2021-10
IEEE J. Select. Topics Appl. Earth Obs. Remote Sens.
14
10628-10643
0
10.1109/JSTARS.2021.3118595
Errors in soil moisture adversely impact the modeling of land-atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This study investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly-coupled land-atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture vs. in situ measurements by ~0.1-0.26 over that of ADAS estimates; the unbiased root-mean-square error (ubRMSE) of LADAS soil moisture is reduced by 0.002-0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE vs. in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ~700 mb), with relative improvements in bias of 15-25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root-mean-square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ~0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land-atmosphere coupling.
Reichle
R. H.
Zhang
S. Q.
Liu
Q.
Draper
C.
Kolassa
J.
Todling
R.
21109
Article
EUREC4A
The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.
2021-8
Earth Syst. Sci. Data
13
4067-4119
0
10.5194/essd-13-4067-2021
The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.
Stevens
B.
Bony
S.
Farrell
D.
Ament
F.
Blyth
A.
Fairall
C. W.
. .
.
de Boer
G.
. .
.
Pincus
R.
. .
.
Intrieri
J. M.
. .
.
Thompson
E. J.
al.
et
21111
Article
Ensemble forecasting greatly expands the prediction horizon for internal “weather” of the ocean
Mesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.
2021-5
Nat. Commun. Earth Environ.
2
89
0
10.1038/s43247-021-00151-5
Mesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.
Thoppil
P.
Frolov
S.
Rowley
C.
Reynolds
C.
al.
et
21112
Article
On the Regionality of Moist Kelvin Waves and the MJO: The Critical Role of the Background Zonal Flow
A global model with superparameterized physics is used to shed light on the observed regionality of convectively coupled Kelvin waves and the Madden-Julian Oscillation (MJO). A series of aquaplanet simulations over zonally uniform sea-surface temperatures is performed, in which the axisymmetric structure of the background zonal flow urn:x-wiley:19422466:media:jame21430:jame21430-math-0001 is altered through nudging, while maintaining a quasi-fixed rainfall climatology. Results show that nudging urn:x-wiley:19422466:media:jame21430:jame21430-math-0002 at the equator to match profiles typical of the Indo-Pacific or eastern Pacific sectors yields eastward-moving tropical rain spectra typical of those sectors. Two different mechanistic pathways are identified as being responsible for this mean-flow dependence, in addition to Doppler shifting effects. The first is through shifts of the Rossby wave critical line in the subtropical upper troposphere that affect the lateral forcing of Kelvin-mode circulations at the equator by eastward and equatorward-propagating eddies impinging on the tropics from higher latitudes. The second is through changes in the strength of the mean cyclonic shear in the lower tropical troposphere that affect the degree to which intraseasonal fluctuations in Kelvin-mode zonal winds modulate the activity of higher-frequency equatorial Rossby-type eddies. In cases where the mean low-level cyclonic shear is enhanced, the strength of this modulation, referred to as “shear-induced eddy modulation” or SIEM, is also seen to be enhanced, such that MJO-like modes of variability are rendered either unstable or near neutral, depending on the strength of the shear.
2021-9
J. Adv. Model. Earth Syst.
13
e2021MS002528
0
10.1029/2021MS002528
A global model with superparameterized physics is used to shed light on the observed regionality of convectively coupled Kelvin waves and the Madden-Julian Oscillation (MJO). A series of aquaplanet simulations over zonally uniform sea-surface temperatures is performed, in which the axisymmetric structure of the background zonal flow urn:x-wiley:19422466:media:jame21430:jame21430-math-0001 is altered through nudging, while maintaining a quasi-fixed rainfall climatology. Results show that nudging urn:x-wiley:19422466:media:jame21430:jame21430-math-0002 at the equator to match profiles typical of the Indo-Pacific or eastern Pacific sectors yields eastward-moving tropical rain spectra typical of those sectors. Two different mechanistic pathways are identified as being responsible for this mean-flow dependence, in addition to Doppler shifting effects. The first is through shifts of the Rossby wave critical line in the subtropical upper troposphere that affect the lateral forcing of Kelvin-mode circulations at the equator by eastward and equatorward-propagating eddies impinging on the tropics from higher latitudes. The second is through changes in the strength of the mean cyclonic shear in the lower tropical troposphere that affect the degree to which intraseasonal fluctuations in Kelvin-mode zonal winds modulate the activity of higher-frequency equatorial Rossby-type eddies. In cases where the mean low-level cyclonic shear is enhanced, the strength of this modulation, referred to as “shear-induced eddy modulation” or SIEM, is also seen to be enhanced, such that MJO-like modes of variability are rendered either unstable or near neutral, depending on the strength of the shear.
Tulich
S. N.
Kiladis
G. N.
21114
Article
Inconsistent Global Kinetic Energy Spectra in Reanalyses and Models
Global upper-tropospheric kinetic energy (KE) spectra in several global atmospheric circulation datasets are examined. The datasets considered include ERA-Interim, JRA-55, and ERA5 and two versions of NOAA GFS analyses at horizontal resolutions ranging from 0.7° to 0.12°. The mesoscale portions of the spectra are found to be highly inconsistent. This is shown to be mainly due to inconsistencies in the scale-dependent numerical damping and in the large contributions to the global mesoscale KE from the KE in convective regions and near orography. The spectra also generally have a steeper mesoscale slope than the −5/3 slope of the observational Nastrom–Gage spectrum pursued at many modeling centers. The sensitivity of the slope in global models to 1) stochastically perturbing diabatic tendencies and 2) decreasing the horizontal hyperviscosity coefficient is explored in large ensembles of 10-day forecasts made with the NCEP GFS (0.7° grid) model. Both changes lead to larger mesoscale KE and a flatter spectral slope. The effect is stronger in the modified hyperviscosity experiment. These results show that (i) despite assimilating vastly more observations than used in the original Nastrom–Gage studies, current high-resolution global analyses still do not converge to a single “true” global mesoscale KE spectrum, and (ii) model KE spectra can be made flatter not just by increasing model resolution but also by perturbing model physics and decreasing horizontal diffusion. Such sensitivities and lack of consensus on the spectral slope also raise the possibility that the true global mesoscale spectral slope may not be a precisely −5/3 slope.
2021-8
J. Atmos. Sci.
78
2589–2603
0
10.1175/JAS-D-20-0294.1
Global upper-tropospheric kinetic energy (KE) spectra in several global atmospheric circulation datasets are examined. The datasets considered include ERA-Interim, JRA-55, and ERA5 and two versions of NOAA GFS analyses at horizontal resolutions ranging from 0.7° to 0.12°. The mesoscale portions of the spectra are found to be highly inconsistent. This is shown to be mainly due to inconsistencies in the scale-dependent numerical damping and in the large contributions to the global mesoscale KE from the KE in convective regions and near orography. The spectra also generally have a steeper mesoscale slope than the −5/3 slope of the observational Nastrom–Gage spectrum pursued at many modeling centers. The sensitivity of the slope in global models to 1) stochastically perturbing diabatic tendencies and 2) decreasing the horizontal hyperviscosity coefficient is explored in large ensembles of 10-day forecasts made with the NCEP GFS (0.7° grid) model. Both changes lead to larger mesoscale KE and a flatter spectral slope. The effect is stronger in the modified hyperviscosity experiment. These results show that (i) despite assimilating vastly more observations than used in the original Nastrom–Gage studies, current high-resolution global analyses still do not converge to a single “true” global mesoscale KE spectrum, and (ii) model KE spectra can be made flatter not just by increasing model resolution but also by perturbing model physics and decreasing horizontal diffusion. Such sensitivities and lack of consensus on the spectral slope also raise the possibility that the true global mesoscale spectral slope may not be a precisely −5/3 slope.
Wang
J.-W. A.
Sardeshmukh
P. D.
21117
Article
Quantifying the potential of AQPI gap-filling radar network for streamflow simulation through a WRF-Hydro experiment
It remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, a sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.
2021-7
J. Hydrometeor.
22
1869–1882
0
10.1175/JHM-D-20-0122.1
It remains a challenge to provide accurate and timely flood warnings in many parts of the western United States. As part of the Advanced Quantitative Precipitation Information (AQPI) project, this study explores the potential of using the AQPI gap-filling radar network for streamflow simulation of selected storm events in the San Francisco Bay Area under a WRF-Hydro modeling system. Two types of watersheds including natural and human-affected among the most flood-prone region of the Bay Area are investigated. Based on the high-resolution AQPI X-band radar rainfall estimates, three basic routing configurations, including Grid, Reach, and National Water Model (NWM), are used to quantify the impact of different model physics options on the simulated streamflow. It is found that the NWM performs better in terms of reproducing streamflow volumes and hydrograph shapes than the other routing configurations when reservoirs exist in the watershed. Additionally, the AQPI X-band radar rainfall estimates (without gauge correction) provide reasonable streamflow simulations, and they show better performance in reproducing the hydrograph peaks compared with the gauge-corrected rainfall estimates based on the operational S-band Next Generation Weather Radar network. Also, a sensitivity test reveals that surficial conditions have a significant influence on the streamflow simulation during the storm: the discharge increases to a higher level as the infiltration factor (REFKDT) decreases, and its peak goes down and lags as surface roughness coefficient (Mann) increases. The time delay analysis of precipitation input on the streamflow at the two outfalls of the surveyed watersheds further demonstrates the link between AQPI gap-filling radar observations and streamflow changes in this urban region.
Ma
Y.
Chandrasekar
V.
Chen
H.
Cifelli
R.
21121
Article
Preconditions for extreme wet winters over the contiguous United States
We identify physical factors leading to extreme wet winters over the contiguous U.S. and examine whether preconditions operated during winter 2019 (December 2018 to February 2019) when record precipitation occurred that led to billion-dollar flood disasters along the Missouri and Mississippi Rivers. Models and observations are used to determine the effect of slow-varying forcing that may lead to practical forecast skill for extreme wet winters. Atmospheric models indicate that sea surface temperatures during strong eastern Pacific El Niño events like 1983 and 1998 can drive extreme wet winters over the contiguous U.S. These strong El Niños shift the distribution of contiguous U.S. precipitation to wetter conditions with a mean wetting of 1.5–2.0 standard deviations of the interannual variability. The shift to wetter conditions leads to a fivefold increase in the probability of wet winters of the magnitude observed in 2019. On longer timescales, observations indicate contiguous U.S. winter precipitation has increased over the last century. Analysis of historical coupled model simulations indicate anthropogenically-forced shifts to wetter conditions over the last century of 0.2–0.4 standard deviations of the interannual variability. While increasing the risk of extreme wet winters like 2019, this effect is a limited source of predictability during any particular winter. Concerning 2019 specifically, preconditioning factors of the risk for extreme contiguous U.S. winter wetness were weak or absent and offered little practical early warning. The ongoing central Pacific El Niño that winter did not significantly alter the risk of the wetness, and thus the extreme 2019 conditions are judged not to have been a seasonal forecast of opportunity.
2021-9
Weather Clim. Extremes
33
100333
0
10.1016/j.wace.2021.100333
We identify physical factors leading to extreme wet winters over the contiguous U.S. and examine whether preconditions operated during winter 2019 (December 2018 to February 2019) when record precipitation occurred that led to billion-dollar flood disasters along the Missouri and Mississippi Rivers. Models and observations are used to determine the effect of slow-varying forcing that may lead to practical forecast skill for extreme wet winters. Atmospheric models indicate that sea surface temperatures during strong eastern Pacific El Niño events like 1983 and 1998 can drive extreme wet winters over the contiguous U.S. These strong El Niños shift the distribution of contiguous U.S. precipitation to wetter conditions with a mean wetting of 1.5–2.0 standard deviations of the interannual variability. The shift to wetter conditions leads to a fivefold increase in the probability of wet winters of the magnitude observed in 2019. On longer timescales, observations indicate contiguous U.S. winter precipitation has increased over the last century. Analysis of historical coupled model simulations indicate anthropogenically-forced shifts to wetter conditions over the last century of 0.2–0.4 standard deviations of the interannual variability. While increasing the risk of extreme wet winters like 2019, this effect is a limited source of predictability during any particular winter. Concerning 2019 specifically, preconditioning factors of the risk for extreme contiguous U.S. winter wetness were weak or absent and offered little practical early warning. The ongoing central Pacific El Niño that winter did not significantly alter the risk of the wetness, and thus the extreme 2019 conditions are judged not to have been a seasonal forecast of opportunity.
Hoell
A.
Hoerling
M. P.
Eischeid
J. K.
Barsugli
J. J.
21122
Article
Characteristics and Predictability of Midwestern United States Drought
Characteristics and predictability of drought in the midwestern United States, spanning the from the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916–2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and 3-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for subannual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multiannual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March–November in the NGP and all year in the OV, with a preference for March–May and September–November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is 4 times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons is related to atmospheric wave trains spanning the Pacific–North American sector, longer-lead predictability is limited to the OV in December–February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño–Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwest drought.
2021-11
J. Hydrometeor.
22
3087–3105
0
10.1175/JHM-D-21-0052.1
Characteristics and predictability of drought in the midwestern United States, spanning the from the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916–2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and 3-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for subannual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multiannual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March–November in the NGP and all year in the OV, with a preference for March–May and September–November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is 4 times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons is related to atmospheric wave trains spanning the Pacific–North American sector, longer-lead predictability is limited to the OV in December–February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño–Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwest drought.
Hoell
A.
Ford
T. W.
Woloszyn
M.
Otkin
J. A.
Eischeid
J. K.
21123
Article
Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)
Wildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.
2021-12
Wea. Forecasting
36
2113-2125
0
10.1175/WAF-D-21-0075.1
Wildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.
Worsnop
R. P.
Scheuerer
M.
Di Giuseppe
F.
Barnard
C.
Hamill
T. M.
Vitolo
R. P.
21125
Article
Influence of warming and atmospheric circulation changes on multidecadal European flood variability
European flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (central Europe) and early 20th century (western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we address the contribution of atmospheric circulation and of warming to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in central and western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase.
2021-4
Clim. Past
18
919-933
0
10.5194/cp-18-919-2022
European flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (central Europe) and early 20th century (western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we address the contribution of atmospheric circulation and of warming to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in central and western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase.
Brönnimann
S.
Stucki
P.
Franke
J.
Valler
V.
Brugnara
Y.
Hand
R.
Slivinski
L. C.
Compo
G. P.
Sardeshmukh
P. D.
Lang
M.
Schaefli
B.
21126
Article
Role of geostrophic currents in future changes of coastal upwelling in the California Current system
Given the importance of coastal upwelling in the California Current System (CCS), there is considerable interest in predicting its response to global warming. However, upwelling changes are often treated as synonymous with changes in upwelling-favorable winds, while the role of geostrophic transport is unaccounted for. Here, we examine the respective roles of Ekman and geostrophic transports using the Community Earth System Model Large Ensemble. In some parts of the CCS, geostrophic transports make equal or greater contributions to long-term changes in upwelling than Ekman transports. The combination of the two transports nearly close the momentum budget, and thus reproduce the mean state, interannual variability, and long-term changes in upwelling. These results highlight the importance of accounting for ocean circulation when quantifying upwelling and its variability and change.
2021-2
Geophys. Res. Lett.
48
e2020GL090768
0
10.1029/2020GL090768
Given the importance of coastal upwelling in the California Current System (CCS), there is considerable interest in predicting its response to global warming. However, upwelling changes are often treated as synonymous with changes in upwelling-favorable winds, while the role of geostrophic transport is unaccounted for. Here, we examine the respective roles of Ekman and geostrophic transports using the Community Earth System Model Large Ensemble. In some parts of the CCS, geostrophic transports make equal or greater contributions to long-term changes in upwelling than Ekman transports. The combination of the two transports nearly close the momentum budget, and thus reproduce the mean state, interannual variability, and long-term changes in upwelling. These results highlight the importance of accounting for ocean circulation when quantifying upwelling and its variability and change.
Ding
H.
Alexander
M. A.
Jacox
M. G.
21128
Article
On the Development of GFDL’s decadal prediction system: initialization approaches and retrospective forecast assessment
Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi-decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR-A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR-A1 significantly outperforms CM2.1 in predicting multi-decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.
2021-11
J. Adv. Model. Earth Syst.
13
e2021MS002529
0
10.1029/2021MS002529
Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi-decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR-A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR-A1 significantly outperforms CM2.1 in predicting multi-decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.
Yang
X.
Delworth
T. L.
Zeng
F.
Zhang
L.
Cooke
W. F.
Harrison
M. J.
Rosati
A.
Underwood
S.
Compo
G. P.
McColl
C.
21129
Article
Controls on Wintertime Nonbrightband Rain Rate and Frequency in California’s Northern Coast Ranges
2021-7
J. Hydrometeor.
22
1783–1799
0
10.1175/JHM-D-20-0046.1
Cann
M. D.
White
A. B.
21130
Article
Initialized Earth System prediction from subseasonal to decadal timescales
Initialized Earth System predictions are made by starting a numerical prediction model in a state as consistent as possible to observations and running it forward in time for up to 10 years. Skilful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, ranging from agriculture to water resource management to human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skilful predictions can be made: on S2S timescales, there has been some skill in predicting the Madden–Julian Oscillation and North Atlantic Oscillation; on S2I, in predicting the El Niño–Southern Oscillation; and on S2D, in predicting ocean and atmosphere variability in the North Atlantic region. However, challenges remain, and future work must prioritize reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could enhance predictive skill and, in turn, confidence. As numerical models progress towards Earth System models, initialized predictions are expanding to include prediction of sea ice, air pollution, and terrestrial and ocean biochemistry that can bring clear benefit to society and various stakeholders.
2021-4
Nat. Rev. Earth Environ.
2
340–357
0
10.1038/s43017-021-00155-x
Initialized Earth System predictions are made by starting a numerical prediction model in a state as consistent as possible to observations and running it forward in time for up to 10 years. Skilful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, ranging from agriculture to water resource management to human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skilful predictions can be made: on S2S timescales, there has been some skill in predicting the Madden–Julian Oscillation and North Atlantic Oscillation; on S2I, in predicting the El Niño–Southern Oscillation; and on S2D, in predicting ocean and atmosphere variability in the North Atlantic region. However, challenges remain, and future work must prioritize reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could enhance predictive skill and, in turn, confidence. As numerical models progress towards Earth System models, initialized predictions are expanding to include prediction of sea ice, air pollution, and terrestrial and ocean biochemistry that can bring clear benefit to society and various stakeholders.
Meehl
G. A.
Richter
J. H.
Teng
H.
Capotondi
A.
al.
et
21131
Article
The role of interannual ENSO events in decadal timescale transitions of the Interdecadal Pacific Oscillation
The build-up of decadal timescale upper ocean heat content in the off-equatorial western tropical Pacific can provide necessary conditions for interannual El Niño/Southern Oscillation (ENSO) events to contribute to decadal timescale transitions of tropical Pacific SSTs to the opposite phase of the Interdecadal Pacific Oscillation (IPO). This can be viewed as a corollary to subseasonal westerly wind burst events contributing to El Niño interannual timescale transitions. A long pre-industrial control run with CESM1 is analyzed to show that there is a greater chance of ENSO activity to contribute to an IPO transition when off-equatorial western Pacific Ocean heat content reaches either a maximum (for El Niño to contribute to a transition to positive IPO) or minimum (for La Niña to contribute to a transition to negative IPO) as seen in observations. If above a necessary ocean heat content threshold, the convergence associated with westerly anomaly near-equatorial surface winds associated with El Niño activity can draw that heat content equatorward to sustain anomalously warm western and central Pacific SSTs. These are associated with positive precipitation and convective heating anomalies, a Gill-type response and wind stress curl anomalies that continue to feed warm water into the near-equatorial western Pacific. These conditions then sustain the decadal-timescale transition to positive IPO (with the opposite sign for transition to negative IPO). Associated central equatorial Pacific convective heating anomalies produce SLP and wind stress anomalies in the North and South Pacific that can excite westward-propagating off-equatorial oceanic Rossby waves to contribute to the western Pacific thermocline depth and consequent heat content anomalies.
2021-5
Clim. Dyn.
57
1933–1951
0
10.1007/s00382-021-05784-y
The build-up of decadal timescale upper ocean heat content in the off-equatorial western tropical Pacific can provide necessary conditions for interannual El Niño/Southern Oscillation (ENSO) events to contribute to decadal timescale transitions of tropical Pacific SSTs to the opposite phase of the Interdecadal Pacific Oscillation (IPO). This can be viewed as a corollary to subseasonal westerly wind burst events contributing to El Niño interannual timescale transitions. A long pre-industrial control run with CESM1 is analyzed to show that there is a greater chance of ENSO activity to contribute to an IPO transition when off-equatorial western Pacific Ocean heat content reaches either a maximum (for El Niño to contribute to a transition to positive IPO) or minimum (for La Niña to contribute to a transition to negative IPO) as seen in observations. If above a necessary ocean heat content threshold, the convergence associated with westerly anomaly near-equatorial surface winds associated with El Niño activity can draw that heat content equatorward to sustain anomalously warm western and central Pacific SSTs. These are associated with positive precipitation and convective heating anomalies, a Gill-type response and wind stress curl anomalies that continue to feed warm water into the near-equatorial western Pacific. These conditions then sustain the decadal-timescale transition to positive IPO (with the opposite sign for transition to negative IPO). Associated central equatorial Pacific convective heating anomalies produce SLP and wind stress anomalies in the North and South Pacific that can excite westward-propagating off-equatorial oceanic Rossby waves to contribute to the western Pacific thermocline depth and consequent heat content anomalies.
Meehl
G. A.
Teng
H.
Capotondi
A.
Hu
A.
21132
Article
Changing El Niño–Southern Oscillation in a warming climate
Originating in the equatorial Pacific, the El Niño–Southern Oscillation (ENSO) has highly consequential global impacts, motivating the need to understand its responses to anthropogenic warming. In this Review, we synthesize advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, there is an inter-model consensus of an increase in future ENSO rainfall variability. Now, however, it is apparent that models that best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, thereby, ENSO magnitude under greenhouse warming, as well as an eastward shift and intensification of ENSO-related atmospheric teleconnections — the Pacific–North American and Pacific–South American patterns. Such projected changes are consistent with palaeoclimate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability, though underpinned by increased equatorial Pacific upper-ocean stratification, is strongly influenced by internal variability, raising issues about its quantifiability and detectability. Yet, ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modelling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes and reducing uncertainties in projections.
2021-8
Nat. Rev. Earth Environ.
2
628-644
0
10.1038/s43017-021-00199-z
Originating in the equatorial Pacific, the El Niño–Southern Oscillation (ENSO) has highly consequential global impacts, motivating the need to understand its responses to anthropogenic warming. In this Review, we synthesize advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, there is an inter-model consensus of an increase in future ENSO rainfall variability. Now, however, it is apparent that models that best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, thereby, ENSO magnitude under greenhouse warming, as well as an eastward shift and intensification of ENSO-related atmospheric teleconnections — the Pacific–North American and Pacific–South American patterns. Such projected changes are consistent with palaeoclimate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability, though underpinned by increased equatorial Pacific upper-ocean stratification, is strongly influenced by internal variability, raising issues about its quantifiability and detectability. Yet, ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modelling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes and reducing uncertainties in projections.
Cai
W.
Santoso
A.
Collins
M.
Dewitte
B.
. .
.
Capotondi
A.
al.
et
21133
Article
Removing the Effects of Tropical Dynamics from North Pacific Climate Variability
Teleconnections from the tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño–Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved—including ENSO diversity in the tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific—have a variety of overlapping spatial and temporal scales. Here we use a multivariate linear inverse model to quantify tropical and extratropical multiscale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the tropics are responsible for almost half of the seasonal variance, and almost three-quarters of the decadal variance, along the North American coast and within the Subtropical Front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio–Oyashio Extension (KOE) region is considerably less impacted by the tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific decadal oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.
2021-12
J. Climate
34
9249-9265
0
10.1175/JCLI-D-21-0344.1
Teleconnections from the tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño–Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved—including ENSO diversity in the tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific—have a variety of overlapping spatial and temporal scales. Here we use a multivariate linear inverse model to quantify tropical and extratropical multiscale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the tropics are responsible for almost half of the seasonal variance, and almost three-quarters of the decadal variance, along the North American coast and within the Subtropical Front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio–Oyashio Extension (KOE) region is considerably less impacted by the tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific decadal oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.
Zhao
Y.
Newman
M.
Capotondi
A.
Di Lorenzo
E.
Sun
D.
21134
Article
The influence of Pacific winds on ENSO diversity
The differences in ENSO sea surface temperature (SST) spatial patterns, whether centered in the Eastern Pacific (EP), Central Pacific (CP) or in the eastern-central equatorial region (“canonical”) have been associated to differences in atmospheric teleconnections and global impacts. However, predicting different types of ENSO events has proved challenging, highlighting the need for a deeper understanding of their predictability. Given the key role played by wind variations in the development and evolution of ENSO events, this study examines the relationship between the leading modes of Pacific surface wind speed variability and ENSO diversity using three different state-of-the-art wind products, including satellite observations and atmospheric reanalyses. Although previous studies have associated different ENSO precursors to either EP or CP events, our results indicate that the most prominent of those ENSO precursors are primarily related to canonical and CP events, and show little correlation with EP events. The latter are associated with tropical Pacific conditions favoring equatorial westerly wind and precipitation anomalies that extend all the way to the eastern Pacific. Results over the entire twentieth century period versus those during the satellite era also suggest that the influences from the Southern Hemisphere may be more robust than those from the Northern Hemisphere.
2021-9
Sci. Rep.
11
18672
0
10.1038/s41598-021-97963-4
The differences in ENSO sea surface temperature (SST) spatial patterns, whether centered in the Eastern Pacific (EP), Central Pacific (CP) or in the eastern-central equatorial region (“canonical”) have been associated to differences in atmospheric teleconnections and global impacts. However, predicting different types of ENSO events has proved challenging, highlighting the need for a deeper understanding of their predictability. Given the key role played by wind variations in the development and evolution of ENSO events, this study examines the relationship between the leading modes of Pacific surface wind speed variability and ENSO diversity using three different state-of-the-art wind products, including satellite observations and atmospheric reanalyses. Although previous studies have associated different ENSO precursors to either EP or CP events, our results indicate that the most prominent of those ENSO precursors are primarily related to canonical and CP events, and show little correlation with EP events. The latter are associated with tropical Pacific conditions favoring equatorial westerly wind and precipitation anomalies that extend all the way to the eastern Pacific. Results over the entire twentieth century period versus those during the satellite era also suggest that the influences from the Southern Hemisphere may be more robust than those from the Northern Hemisphere.
Capotondi
A.
Ricciardulli
L.
21136
Article
Evaluation of the Rapid Refresh Numerical Weather Prediction Model over Arctic Alaska
Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.
2021-6
Wea. Forecasting
36
1061–1077
0
10.1175/WAF-D-20-0169.1
Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.
Bray
M. T.
Turner
D. D.
de Boer
G.
21137
Article
Assimilation of a Coordinated Fleet of Uncrewed Aircraft System Observations in Complex Terrain: EnKF System Design and Preliminary Assessment
Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.
2021-5
Mon. Wea. Rev.
149
1459–1480
0
10.1175/MWR-D-20-0359.1
Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation—a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado’s San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.
Jensen
A. A.
Pinto
J. O.
Bailey
S. C. C.
Sobash
R. A.
de Boer
G.
al.
et
21142
Article
Evaluating operational and experimental HRRR model forecasts of atmospheric river events in California
2021-12
Wea. Forecasting
36
1925–1944
0
10.1175/WAF-D-21-0081.1
English
J. M.
Turner
D. D.
Alcott
T.
Moninger
W. R.
Bytheway
J. L.
Cifelli
R.
Marquis
M.
21143
Article
Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars
Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.
We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics.
2021-7
Atmos. Meas. Tech.
14
4893–4913
0
10.5194/amt-14-4893-2021
Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.
We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics.
Oue
M.
Kollias
P.
Matrosov
S. Y.
Battaglia
A.
Ryzhkov
A. V.
21146
Article
A review of decadal climate variability in the tropical Pacific: characteristics, causes, predictability and prospects
BACKGROUND: Tropical Pacific decadal climate variability and change (TPDV) affects the global climate system, extreme weather events, agricultural production, streamflow, marine and terrestrial ecosystems, and biodiversity. Although major international efforts are underway to provide decadal climate predictions, there is still a great deal of uncertainty about the characteristics and causes of TPDV and the accuracy to which it can be simulated and predicted. Here, we critically synthesize what, as of now, is known and not known and provide recommendations to improve our understanding of TPDV and our ability to predict it.
2021-10
Science
374
0
10.1126/science.aay9165
BACKGROUND: Tropical Pacific decadal climate variability and change (TPDV) affects the global climate system, extreme weather events, agricultural production, streamflow, marine and terrestrial ecosystems, and biodiversity. Although major international efforts are underway to provide decadal climate predictions, there is still a great deal of uncertainty about the characteristics and causes of TPDV and the accuracy to which it can be simulated and predicted. Here, we critically synthesize what, as of now, is known and not known and provide recommendations to improve our understanding of TPDV and our ability to predict it.
Power
S.
Lengaigne
M.
Capotondi
A.
Khodri
M.
. .
.
Newman
M.
al.
et
21147
Article
Mesoscale wind patterns over the complex urban terrain around Stuttgart investigated with dual-Doppler lidar profiles
The flow in the atmospheric boundary layer (ABL) over cities is crucial for the urban climate as it controls the exchange of heat, moisture and pollutants within the ABL and with the surroundings. In particular for cities in mountainous terrain, the mesoscale flow shows a high spatial and temporal variability, which poses great challenges to the means of observation. We used the dual-Doppler lidar scan strategy of virtual towers (VT), to measure profiles of the horizontal wind in the ABL over the city of Stuttgart in southwestern Germany. To study the mesoscale variability of the horizontal wind, we placed six VTs in the topographically structured investigation area, which is characterised by a basin-shaped valley opening into a major valley. Comparisons with radiosonde data show that reliable wind information can be retrieved from the VT measurements after careful processing. A statistical analysis reveals a strong dependence of the flow in the valleys below ridge height on the bulk Richardson number (BRN). A critical BRN of 1.25 is identified for the area, below which the flow below ridge height is dynamically unstable and coupled to the ambient flow above. For BRNs greater than 1.25, the flow is dynamically stable and the flow below ridge height is dominated by thermally driven down-valley winds, which are decoupled from the ambient wind. This study shows that the VT technique is applicable in highly complex terrain and a promising tool for the investigation of the flow in areas which are difficult to access by traditional in situ or single lidar measurements, like complex urban terrain.
2021-4
Meteorol. Z.
30
185-200
0
10.1127/metz/2020/1029
The flow in the atmospheric boundary layer (ABL) over cities is crucial for the urban climate as it controls the exchange of heat, moisture and pollutants within the ABL and with the surroundings. In particular for cities in mountainous terrain, the mesoscale flow shows a high spatial and temporal variability, which poses great challenges to the means of observation. We used the dual-Doppler lidar scan strategy of virtual towers (VT), to measure profiles of the horizontal wind in the ABL over the city of Stuttgart in southwestern Germany. To study the mesoscale variability of the horizontal wind, we placed six VTs in the topographically structured investigation area, which is characterised by a basin-shaped valley opening into a major valley. Comparisons with radiosonde data show that reliable wind information can be retrieved from the VT measurements after careful processing. A statistical analysis reveals a strong dependence of the flow in the valleys below ridge height on the bulk Richardson number (BRN). A critical BRN of 1.25 is identified for the area, below which the flow below ridge height is dynamically unstable and coupled to the ambient flow above. For BRNs greater than 1.25, the flow is dynamically stable and the flow below ridge height is dominated by thermally driven down-valley winds, which are decoupled from the ambient wind. This study shows that the VT technique is applicable in highly complex terrain and a promising tool for the investigation of the flow in areas which are difficult to access by traditional in situ or single lidar measurements, like complex urban terrain.
Wittkamp
N.
Adler
B.
Kalthoff
N.
Kiseleva
O.
21149
Article
A Python Package to Calculate the OLR-Based Index of the Madden- Julian-Oscillation (OMI) in Climate Science and Weather Forecasting
The Madden-Julian Oscillation (MJO) is a prominent feature of the intraseasonal variability of the atmosphere. The MJO strongly modulates tropical precipitation and has implications around the globe for weather, climate and basic atmospheric research. The time-dependent state of the MJO is described by MJO indices, which are calculated through sometimes complicated statistical approaches from meteorological variables. One of these indices is the OLR-based MJO Index (OMI; OLR stands for outgoing longwave radiation). The Python package mjoindices, which is described in this paper, provides the first open source implementation of the OMI algorithm, to our knowledge. The package meets state-of-the-art criteria for sustainable research software, like automated tests and a persistent archiving to aid the reproducibility of scientific results. The agreement of the OMI values calculated with this package and the original OMI values is also summarized here. There are several reuse scenarios; the most probable one is MJO-related research based on atmospheric models, since the index values have to be recalculated for each model run.
2021-5
J. Open Res. Software
9
9
0
10.5334/jors.331
The Madden-Julian Oscillation (MJO) is a prominent feature of the intraseasonal variability of the atmosphere. The MJO strongly modulates tropical precipitation and has implications around the globe for weather, climate and basic atmospheric research. The time-dependent state of the MJO is described by MJO indices, which are calculated through sometimes complicated statistical approaches from meteorological variables. One of these indices is the OLR-based MJO Index (OMI; OLR stands for outgoing longwave radiation). The Python package mjoindices, which is described in this paper, provides the first open source implementation of the OMI algorithm, to our knowledge. The package meets state-of-the-art criteria for sustainable research software, like automated tests and a persistent archiving to aid the reproducibility of scientific results. The agreement of the OMI values calculated with this package and the original OMI values is also summarized here. There are several reuse scenarios; the most probable one is MJO-related research based on atmospheric models, since the index values have to be recalculated for each model run.
Hoffmann
C. G.
Kiladis
G. N.
Gehne
M.
von Savigny
C.
21150
Article
The Influence of the Stratosphere on the Tropical Troposphere
Observational and model studies suggest that the stratosphere exerts a significant influence on the tropical troposphere. The corresponding influence, through dynamical coupling, of the stratosphere on the extratropical troposphere has over the last 15-20 years been intensively investigated, with consequent improvement in scientific understanding which is already being exploited by weather forecasting and climate prediction centres. The coupling requires both communication of dynamical effects from stratosphere to troposphere and feedbacks within the troposphere which enhance the tropospheric response. Scientific understanding of the influence of the stratosphere on the tropical troposphere is far less developed. This review summarises the current observational and modelling evidence for that influence, on timescales ranging from diurnal to centennial. The current understanding of potentially relevant mechanisms for communication and for feedbacks within the tropical troposphere and the possible implications of the coupling for weather and climate prediction are discussed. These include opportunities for model validation and for improved subseasonal and seasonal forecasting and the effects, for example, of changes in stratospheric ozone and of potential geoengineering approaches. Outstanding scientific questions are identified and future needs for observational and modelling work to resolve these questions are suggested.
2021-8
J. Meteor. Soc. Japan
99
803-845
0
10.2151/jmsj.2021-040
Observational and model studies suggest that the stratosphere exerts a significant influence on the tropical troposphere. The corresponding influence, through dynamical coupling, of the stratosphere on the extratropical troposphere has over the last 15-20 years been intensively investigated, with consequent improvement in scientific understanding which is already being exploited by weather forecasting and climate prediction centres. The coupling requires both communication of dynamical effects from stratosphere to troposphere and feedbacks within the troposphere which enhance the tropospheric response. Scientific understanding of the influence of the stratosphere on the tropical troposphere is far less developed. This review summarises the current observational and modelling evidence for that influence, on timescales ranging from diurnal to centennial. The current understanding of potentially relevant mechanisms for communication and for feedbacks within the tropical troposphere and the possible implications of the coupling for weather and climate prediction are discussed. These include opportunities for model validation and for improved subseasonal and seasonal forecasting and the effects, for example, of changes in stratospheric ozone and of potential geoengineering approaches. Outstanding scientific questions are identified and future needs for observational and modelling work to resolve these questions are suggested.
Haynes
P.
Hitchcock
P.
Hitchman
M.
Yoden
S.
Hendon
H. H.
Kiladis
G. N.
Kodera
K.
Simpson
I.
21151
Article
Revisiting the Quasi-Biennial Oscillation as Seen in ERA5. Part I: Description and Momentum Budget
The dynamics and momentum budget of the quasi-biennial oscillation (QBO) are examined in ERA5. Because of ERA5’s higher spatial resolution compared to its predecessors, it is capable of resolving a broader spectrum of atmospheric waves and allows for a better representation of the wave–mean flow interactions, both of which are of crucial importance for QBO studies. It is shown that the QBO-induced mean meridional circulation, which is mainly confined to the winter hemisphere, is strong enough to interrupt the tropical upwelling during the descent of the westerly shear zones. Since the momentum advection tends to damp the QBO, the wave forcing is responsible for both the downward propagation and for the maintenance of the QBO. It is shown that half the required wave forcing is provided by resolved waves during the descent of both westerly and easterly regimes. Planetary-scale waves account for most of the resolved wave forcing of the descent of westerly shear zones and small-scale gravity (SSG) waves with wavelengths shorter than 2000 km account for the remainder. SSG waves account for most of the resolved forcing of the descent of the easterly shear zones. The representation of the mean fields in the QBO is very similar in ERA5 and ERA-Interim but the resolved wave forcing is substantially stronger in ERA5. The contributions of the various equatorially trapped wave modes to the QBO forcing are documented in Part II.
2021-3
J. Atmos. Sci.
78
673–691
0
10.1175/JAS-D-20-0248.1
The dynamics and momentum budget of the quasi-biennial oscillation (QBO) are examined in ERA5. Because of ERA5’s higher spatial resolution compared to its predecessors, it is capable of resolving a broader spectrum of atmospheric waves and allows for a better representation of the wave–mean flow interactions, both of which are of crucial importance for QBO studies. It is shown that the QBO-induced mean meridional circulation, which is mainly confined to the winter hemisphere, is strong enough to interrupt the tropical upwelling during the descent of the westerly shear zones. Since the momentum advection tends to damp the QBO, the wave forcing is responsible for both the downward propagation and for the maintenance of the QBO. It is shown that half the required wave forcing is provided by resolved waves during the descent of both westerly and easterly regimes. Planetary-scale waves account for most of the resolved wave forcing of the descent of westerly shear zones and small-scale gravity (SSG) waves with wavelengths shorter than 2000 km account for the remainder. SSG waves account for most of the resolved forcing of the descent of the easterly shear zones. The representation of the mean fields in the QBO is very similar in ERA5 and ERA-Interim but the resolved wave forcing is substantially stronger in ERA5. The contributions of the various equatorially trapped wave modes to the QBO forcing are documented in Part II.
Pahlavan
H. A.
Fu
Q.
Wallace
J. M.
Kiladis
G. N.
21152
Article
Revisiting the Quasi-Biennial Oscillation as Seen in ERA5. Part II: Evaluation of Waves and Wave Forcing
This paper describes stratospheric waves in ERA5 and evaluates the contributions of different types of waves to the driving of the quasi-biennial oscillation (QBO). Because of its higher spatial resolution compared to its predecessors, ERA5 is capable of resolving a broader spectrum of waves. It is shown that the resolved waves contribute to both eastward and westward accelerations near the equator, mainly by the way of the vertical flux of zonal momentum. The eastward accelerations by the resolved waves are mainly due to Kelvin waves and small-scale gravity (SSG) waves with zonal wavelengths smaller than 2000 km, whereas the westward accelerations are forced mainly by SSG waves, with smaller contributions from inertio-gravity and mixed Rossby–gravity waves. Extratropical Rossby waves disperse upward and equatorward into the tropical region and impart a westward acceleration to the zonal flow. They appear to be responsible for at least some of the irregularities in the QBO cycle.
2021-3
J. Atmos. Sci.
78
693–707
0
10.1175/JAS-D-20-0249.1
This paper describes stratospheric waves in ERA5 and evaluates the contributions of different types of waves to the driving of the quasi-biennial oscillation (QBO). Because of its higher spatial resolution compared to its predecessors, ERA5 is capable of resolving a broader spectrum of waves. It is shown that the resolved waves contribute to both eastward and westward accelerations near the equator, mainly by the way of the vertical flux of zonal momentum. The eastward accelerations by the resolved waves are mainly due to Kelvin waves and small-scale gravity (SSG) waves with zonal wavelengths smaller than 2000 km, whereas the westward accelerations are forced mainly by SSG waves, with smaller contributions from inertio-gravity and mixed Rossby–gravity waves. Extratropical Rossby waves disperse upward and equatorward into the tropical region and impart a westward acceleration to the zonal flow. They appear to be responsible for at least some of the irregularities in the QBO cycle.
Pahlavan
H. A.
Wallace
J. M.
Fu
Q.
Kiladis
G. N.
21158
Article
The Development of the NCEP Global Ensemble Forecast System Version 12
The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core - the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25-km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 days to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the Stochastically Perturbed Physics Tendencies (SPPT) scheme and Stochastic Kinetic Energy Backscatter (SKEB) scheme.
Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error-spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over Northern America. GEFSv12 was implemented on September 23, 2020.
2021-6
Wea. Forecasting
37
1069–1084
0
10.1175/WAF-D-21-0112.1
The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core - the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25-km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 days to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the Stochastically Perturbed Physics Tendencies (SPPT) scheme and Stochastic Kinetic Energy Backscatter (SKEB) scheme.
Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error-spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over Northern America. GEFSv12 was implemented on September 23, 2020.
Zhou
X.
Zhu
Y.
Hou
D.
Fu
B.
Li
W.
Guan
H.
Sinsky
E.
Kolczynski
W.
Xue
X.
Luo
Y.
Peng
J.
Yang
B.
Tallapragada
V.
Pegion
P.
21159
Article
Deep learning for bias correction of satellite retrievals of orographic precipitation
The performance of various composite satellite precipitation products is severely limited by their individual passive microwave (PMW)-based retrieval uncertainties since the PMW sensors have difficulties in resolving heavy rain and/or shallow orographic precipitation systems. Characterizing the error struc- ture of PMW retrievals is crucial to improving precipitation mapping at different space-time scales. To this end, this paper introduces a machine learning framework to quantify the un- certainties associated with satellite precipitation products with an emphasis on orographic precipitation. A deep convolutional neural network (CNN) is designed, which utilizes the ground- based Stage IV precipitation estimates as target labels in the training phase, to reduce biases involved in the precipitation product derived using the NOAA/Climate Prediction Center morphing technique (CMORPH). The products before and after bias correction are evaluated using four independent precipitation events over the coastal mountain region in the western United States, and the impact of topography on satellite-based precipitation retrievals is quantified. Experimental results show that the orographic gradients have a strong impact on precipitation retrievals in complex terrain regions. The accuracy of CMORPH is dramatically enhanced after applying the proposed machine learning-based bias correction technique. Using Stage IV data as references, the overall correlation (CC), normalized mean error (NME), and normalized mean absolute error (NMAE) of CMORPH are improved from 0.55, 32%, 63%, to 0.88, -2%, 39%, respectively, after bias correction for the independent case studies presented in this article. Such a machine learning scheme also has great potential for improved fusion of other or future satellite precipitation retrievals.
2021-8
IEEE Trans. Geosci. Remote Sens.
60
4104611
0
10.1109/TGRS.2021.3105438
The performance of various composite satellite precipitation products is severely limited by their individual passive microwave (PMW)-based retrieval uncertainties since the PMW sensors have difficulties in resolving heavy rain and/or shallow orographic precipitation systems. Characterizing the error struc- ture of PMW retrievals is crucial to improving precipitation mapping at different space-time scales. To this end, this paper introduces a machine learning framework to quantify the un- certainties associated with satellite precipitation products with an emphasis on orographic precipitation. A deep convolutional neural network (CNN) is designed, which utilizes the ground- based Stage IV precipitation estimates as target labels in the training phase, to reduce biases involved in the precipitation product derived using the NOAA/Climate Prediction Center morphing technique (CMORPH). The products before and after bias correction are evaluated using four independent precipitation events over the coastal mountain region in the western United States, and the impact of topography on satellite-based precipitation retrievals is quantified. Experimental results show that the orographic gradients have a strong impact on precipitation retrievals in complex terrain regions. The accuracy of CMORPH is dramatically enhanced after applying the proposed machine learning-based bias correction technique. Using Stage IV data as references, the overall correlation (CC), normalized mean error (NME), and normalized mean absolute error (NMAE) of CMORPH are improved from 0.55, 32%, 63%, to 0.88, -2%, 39%, respectively, after bias correction for the independent case studies presented in this article. Such a machine learning scheme also has great potential for improved fusion of other or future satellite precipitation retrievals.
Chen
H.
Sun
L.
Cifelli
R.
Xie
P.
21160
Article
University of Colorado and Black Swift Technologies RPAS-based measurements of the lower atmosphere during LAPSE-RATE
Between 14 and 20 July 2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30 h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914 m a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27 May 2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020).
2021-6
Earth Syst. Sci. Data
13
2515–2528
0
10.5194/essd-13-2515-2021
Between 14 and 20 July 2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30 h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914 m a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27 May 2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020).
de Boer
G.
Dixon
C.
Borenstein
S.
Lawrence
D. A.
Elston
J.
al.
et
21163
Article
Liquid Containing Clouds at the North Slope of Alaska Demonstrate Sensitivity to Local Industrial Aerosol Emissions
Cloud condensation nucleus control alter cloud solar albedo through cloud droplet size. Here, we leverage anthropogenic emissions at the North Slope of Alaska as a natural laboratory to study relationships between aerosols and Arctic liquid-containing clouds. Averaging 14 years of MODIS observations, we found a reduction in temporally averaged cloud effective radius (urn:x-wiley:00948276:media:grl62868:grl62868-math-0001) of up to 1.0 μm related to localized pollution. Pronounced regional gradients in cloud frequency of occurrence and liquid water path prohibit the detection of potential changes of these variables. Observed changes of urn:x-wiley:00948276:media:grl62868:grl62868-math-0003 alter radiative fluxes and increase cloud-reflected shortwave radiation by up to 0.8 W m−2 in the Prudhoe Bay area for the period covered by observations (April–September). Due to the frequent occurrence of liquid-containing clouds, this implies that enhanced local emissions in Arctic regions can impact climate processes.
2021-9
Geophys. Res. Lett.
48
e2021GL094307
0
10.1029/2021GL094307
Cloud condensation nucleus control alter cloud solar albedo through cloud droplet size. Here, we leverage anthropogenic emissions at the North Slope of Alaska as a natural laboratory to study relationships between aerosols and Arctic liquid-containing clouds. Averaging 14 years of MODIS observations, we found a reduction in temporally averaged cloud effective radius (urn:x-wiley:00948276:media:grl62868:grl62868-math-0001) of up to 1.0 μm related to localized pollution. Pronounced regional gradients in cloud frequency of occurrence and liquid water path prohibit the detection of potential changes of these variables. Observed changes of urn:x-wiley:00948276:media:grl62868:grl62868-math-0003 alter radiative fluxes and increase cloud-reflected shortwave radiation by up to 0.8 W m−2 in the Prudhoe Bay area for the period covered by observations (April–September). Due to the frequent occurrence of liquid-containing clouds, this implies that enhanced local emissions in Arctic regions can impact climate processes.
Maahn
M.
Goren
T.
Shupe
M. D.
de Boer
G.
21165
Article
Potential Caveats in Land Surface Model Evaluations Using the U.S. Drought Monitor: Role of Base Periods and Drought Indicators
The US drought monitor (USDM) has been widely used as an observational reference for evaluating land surface model (LSM) simulation of drought. This study investigates potential caveats in such evaluation when the USDM and LSMs use different base periods and drought indices to identify drought. The retrospective national water model (NWM) v2.0 simulation (1993–2018) was used to exemplify the evaluation, supplemented by North American land data assimilation system phase 2 (NLDAS-2). Over their common period (2000–2018), in distinct contrast with the USDM which shows high drought occurrence (>50%) in the western half of the continental US (CONUS) and the southeastern US with low occurrence (<30%) elsewhere, the NWM and NLDAS-2 based on soil moisture percentiles (SMPs) consistently show higher drought occurrence (30%–40%) in the central and southeastern US than the rest of the CONUS. Much of the differences between the LSMs and USDM, particularly the strong LSM underestimation of drought occurrence in the western and southeastern US, are not attributed to the LSM deficiencies, but rather the lack of long-term drought in the LSM simulations due to their relatively short lengths. Specifically, the USDM integrates drought indices with century-long periods of record, which enables it to capture both short-term (<6 months) drought and long-term (⩾6 months) drought, whereas the relatively short retrospective simulations of the LSMs allows them to adequately capture short-term drought but not long-term drought. In addition, the USDM integrates many drought indices whereas the NWM results are solely based on the SMP, further adding to the inconsistency. The high occurrence of long-term drought in the western and southeastern US in the USDM is further found to be driven collectively by the post-2000 long-term warm sea surface temperature (SST) trend, cold Pacific decadal oscillation and warm Atlantic multi-decadal oscillation, all of which are typical leading patterns of global SST variability that can induce drought conditions in the western, central, and southeastern US. Our findings highlight the effects of the above caveats and suggest that LSM evaluation should stay qualitative when the caveats are considerable.
2021-12
Environ. Res. Lett.
17
014011
0
10.1088/1748-9326/ac3f63
The US drought monitor (USDM) has been widely used as an observational reference for evaluating land surface model (LSM) simulation of drought. This study investigates potential caveats in such evaluation when the USDM and LSMs use different base periods and drought indices to identify drought. The retrospective national water model (NWM) v2.0 simulation (1993–2018) was used to exemplify the evaluation, supplemented by North American land data assimilation system phase 2 (NLDAS-2). Over their common period (2000–2018), in distinct contrast with the USDM which shows high drought occurrence (>50%) in the western half of the continental US (CONUS) and the southeastern US with low occurrence (<30%) elsewhere, the NWM and NLDAS-2 based on soil moisture percentiles (SMPs) consistently show higher drought occurrence (30%–40%) in the central and southeastern US than the rest of the CONUS. Much of the differences between the LSMs and USDM, particularly the strong LSM underestimation of drought occurrence in the western and southeastern US, are not attributed to the LSM deficiencies, but rather the lack of long-term drought in the LSM simulations due to their relatively short lengths. Specifically, the USDM integrates drought indices with century-long periods of record, which enables it to capture both short-term (<6 months) drought and long-term (⩾6 months) drought, whereas the relatively short retrospective simulations of the LSMs allows them to adequately capture short-term drought but not long-term drought. In addition, the USDM integrates many drought indices whereas the NWM results are solely based on the SMP, further adding to the inconsistency. The high occurrence of long-term drought in the western and southeastern US in the USDM is further found to be driven collectively by the post-2000 long-term warm sea surface temperature (SST) trend, cold Pacific decadal oscillation and warm Atlantic multi-decadal oscillation, all of which are typical leading patterns of global SST variability that can induce drought conditions in the western, central, and southeastern US. Our findings highlight the effects of the above caveats and suggest that LSM evaluation should stay qualitative when the caveats are considerable.
Wang
H.
Xu
L.
Hughes
M.
Chelliah
M.
DeWitt
D. G.
Fuchs
B.
Jackson
D. L.
21170
Article
Doppler Lidar Evaluation of HRRR Model Skill at Simulating Summertime Wind Regimes in the Columbia River Basin during WFIP2
Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.
2021-10
Wea. Forecasting
36
1961–1983
0
10.1175/WAF-D-21-0012.1
Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.
Banta
R. M.
Pichugina
Y. L.
Darby
L. S.
Brewer
W. A.
Olson
J. B.
Kenyon
J. S.
Baidar
S.
Benjamin
S. G.
Fernando
H. J. S.
Lantz
K. O.
Lundquist
J. K.
McCarty
B. J.
Marke
T.
Sandberg
S. P.
Sharp
J.
Shaw
W. J.
Turner
D. D.
Wilczak
J. M.
Worsnop
R. P.
Stoelinga
M. T.
21172
Article
ENSO diversity shows robust decadal variations that must be captured for accurate future projections
El Niño-Southern Oscillation (ENSO) shows a large diversity of events that is modulated by climate variability and change. The representation of this diversity in climate models limits our ability to predict their impact on ecosystems and human livelihood. Here, we use multiple observational datasets to provide a probabilistic description of historical variations in event location and intensity, and to benchmark models, before examining future system trajectories. We find robust decadal variations in event intensities and locations in century-long observational datasets, which are associated with perturbations in equatorial wind-stress and thermocline depth, as well as extra-tropical anomalies in the North and South Pacific. Some climate models are capable of simulating such decadal variability in ENSO diversity, and the associated large-scale patterns. Projections of ENSO diversity in future climate change scenarios strongly depend on the magnitude of decadal variations, and the ability of climate models to reproduce them realistically over the 21st century.
2021-10
Nat. Commun. Earth Environ.
2
212
0
10.1038/s43247-021-00285-6
El Niño-Southern Oscillation (ENSO) shows a large diversity of events that is modulated by climate variability and change. The representation of this diversity in climate models limits our ability to predict their impact on ecosystems and human livelihood. Here, we use multiple observational datasets to provide a probabilistic description of historical variations in event location and intensity, and to benchmark models, before examining future system trajectories. We find robust decadal variations in event intensities and locations in century-long observational datasets, which are associated with perturbations in equatorial wind-stress and thermocline depth, as well as extra-tropical anomalies in the North and South Pacific. Some climate models are capable of simulating such decadal variability in ENSO diversity, and the associated large-scale patterns. Projections of ENSO diversity in future climate change scenarios strongly depend on the magnitude of decadal variations, and the ability of climate models to reproduce them realistically over the 21st century.
Dieppois
B.
Capotondi
A.
Pohl
B.
Chun
K. P.
Monerie
P.-A.
Eden
J.
21178
Article
Seasonal Performance of a Nonhydrostatic Global Atmospheric Model on a Cubed-Sphere Grid
The Korean Integrated Model (KIM), a recently developed nonhydrostatic global atmospheric model over a cubed-sphere grid, was deployed in April 2020 as an operational weather forecasting model. As its application extends to research and predictions longer than the weather time scale, we evaluated the ability of the KIM on seasonal ensemble simulation for the boreal winter and summer cases with respect to seasonal mean biases. The results are compared with those obtained from a conventional hydrostatic spectral model, which has been widely used for seasonal simulations and in climate research. To isolate the origin of the error sources, the same physics packages is used in both the KIM and the reference models. The simulated mean states are very close to the reanalysis for the selected cases. Most large-scale fields from the KIM are comparable to those from the reference model, which implies that the general features of large-scale variables and precipitation are highly governed by physical parameterizations, and that the physics-dynamics coupling is stable in a long-term simulation. Large-scale tropical circulations, such as the Hadley and Walker circulations, need to be improved for applications related to future changes and climate projections. Moreover, the results reveal that the simulated global precipitation band is misplaced and the heat fluxes over oceans are relatively misrepresented near the eastern boundaries of tropical and subtropical regions. This analysis suggests the necessity of realistic atmosphere-ocean interactions that reflect ocean overturning circulation via ocean coupling as well as the refinement of deep and shallow convection schemes.
2021-4
Earth Space Sci.
8
e2021EA001643
0
10.1029/2021EA001643
The Korean Integrated Model (KIM), a recently developed nonhydrostatic global atmospheric model over a cubed-sphere grid, was deployed in April 2020 as an operational weather forecasting model. As its application extends to research and predictions longer than the weather time scale, we evaluated the ability of the KIM on seasonal ensemble simulation for the boreal winter and summer cases with respect to seasonal mean biases. The results are compared with those obtained from a conventional hydrostatic spectral model, which has been widely used for seasonal simulations and in climate research. To isolate the origin of the error sources, the same physics packages is used in both the KIM and the reference models. The simulated mean states are very close to the reanalysis for the selected cases. Most large-scale fields from the KIM are comparable to those from the reference model, which implies that the general features of large-scale variables and precipitation are highly governed by physical parameterizations, and that the physics-dynamics coupling is stable in a long-term simulation. Large-scale tropical circulations, such as the Hadley and Walker circulations, need to be improved for applications related to future changes and climate projections. Moreover, the results reveal that the simulated global precipitation band is misplaced and the heat fluxes over oceans are relatively misrepresented near the eastern boundaries of tropical and subtropical regions. This analysis suggests the necessity of realistic atmosphere-ocean interactions that reflect ocean overturning circulation via ocean coupling as well as the refinement of deep and shallow convection schemes.
Kim
J.-E. E.
Koo
M.-S.
Yoo
C.
Hong
S.-Y.
21181
Article
Waterfall low-frequency vibrations and infrasound: implications for avian migration and hazard detection
Many researchers have suggested that birds may use natural infrasound sources for navigation and hazard avoidance. However, there is a need to define the sound levels and frequencies to characterize potential infrasound sources. This paper summarizes new measurements from Niagara Falls which define a stable, powerful infrasound source that could be detected by birds on a regional scale of over 400 km. Measurements made in the vicinity of Niagara Falls show that exceptional infrasonic pressure levels can occur in the regions of large waterfalls (> 100 Pa at a range of about 500 m). This paper reviews investigator assessments of avian use of infrasound. A review of the results of Cornell researchers on pigeon hearing provides a basis for estimating avian detection ranges of waterfalls. It is possible that migrating birds use sounds from waterfalls as beacons- a component of their “navigation toolbox” as well as infrasound for hazard avoidance.
2021-9
J. Comp. Physiol. A
207
685–700
0
10.1007/s00359-021-01510-5
Many researchers have suggested that birds may use natural infrasound sources for navigation and hazard avoidance. However, there is a need to define the sound levels and frequencies to characterize potential infrasound sources. This paper summarizes new measurements from Niagara Falls which define a stable, powerful infrasound source that could be detected by birds on a regional scale of over 400 km. Measurements made in the vicinity of Niagara Falls show that exceptional infrasonic pressure levels can occur in the regions of large waterfalls (> 100 Pa at a range of about 500 m). This paper reviews investigator assessments of avian use of infrasound. A review of the results of Cornell researchers on pigeon hearing provides a basis for estimating avian detection ranges of waterfalls. It is possible that migrating birds use sounds from waterfalls as beacons- a component of their “navigation toolbox” as well as infrasound for hazard avoidance.
Bedard
A. J.
21187
Article
Atmospheric aerosol, gases, and meteorological parameters measured during the LAPSE-RATE campaign by the Finnish Meteorological Institute and Kansas State University
Small unmanned aerial systems (sUASs) are becoming very popular as affordable and reliable observation platforms. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), conducted in the San Luis Valley (SLV) of Colorado (USA) between 14 and 20 July 2018, gathered together numerous sUASs, remote-sensing equipment, and ground-based instrumentation. Flight teams from the Finnish Meteorological Institute (FMI) and the Kansas State University (KSU) co-operated during LAPSE-RATE to measure and investigate the properties of aerosol particles and gases at the surface and in the lower atmosphere. During LAPSE-RATE the deployed instrumentation operated reliably, resulting in an observational dataset described below in detail. Our observations included aerosol particle number concentrations and size distributions, concentrations of CO2 and water vapor, and meteorological parameters.
All datasets have been uploaded to the Zenodo LAPSE-RATE community archive (https://zenodo.org/communities/lapse-rate/, last access: 21 August 2020). The dataset DOIs for FMI airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3993996, Brus et al. (2020a), and those for KSU airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3736772, Brus et al. (2020b).
2021-6
Earth Syst. Sci. Data
13
2909–2922
0
10.5194/essd-13-2909-2021
Small unmanned aerial systems (sUASs) are becoming very popular as affordable and reliable observation platforms. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), conducted in the San Luis Valley (SLV) of Colorado (USA) between 14 and 20 July 2018, gathered together numerous sUASs, remote-sensing equipment, and ground-based instrumentation. Flight teams from the Finnish Meteorological Institute (FMI) and the Kansas State University (KSU) co-operated during LAPSE-RATE to measure and investigate the properties of aerosol particles and gases at the surface and in the lower atmosphere. During LAPSE-RATE the deployed instrumentation operated reliably, resulting in an observational dataset described below in detail. Our observations included aerosol particle number concentrations and size distributions, concentrations of CO2 and water vapor, and meteorological parameters.
All datasets have been uploaded to the Zenodo LAPSE-RATE community archive (https://zenodo.org/communities/lapse-rate/, last access: 21 August 2020). The dataset DOIs for FMI airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3993996, Brus et al. (2020a), and those for KSU airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3736772, Brus et al. (2020b).
Brus
D.
Gustafsson
J.
Kemppinen
O.
de Boer
G.
Hirsikko
A.
21192
Article
Seasonal predictability of sea ice and bottom temperature across the eastern Bering Sea shelf
Seasonal sea ice plays a key role in shaping the ecosystem dynamics of the eastern Bering Sea shelf. In particular, it leads to the formation of a characteristic pool of cold water that covers the bottom of the shelf from winter through summer; the extent of this cold pool is often used as a management index for distribution, productivity, recruitment, and survival of commercially important fish and shellfish species. Here, we quantify our ability to seasonally forecast interannual variability in Bering Sea bottom temperature and sea ice extent. Retrospective forecast simulations from two global forecast models are downscaled using a regional ocean model; the retrospective forecast simulations include 9-month to 12-month forecasts spanning 1982–2010. We find that dynamic forecasting can predict summer bottom temperatures across the eastern Bering Sea shelf with lead times of up to 4 months. The majority of the prediction skill derives from the persistence signal, and a persistence forecast is comparably skillful to the dynamic forecast at these lead times. However, forecast skill of sea ice advance and retreat is low when a forecast model is initialized before or during the ice season (October–February); this limits the ability of either dynamic or persistence models to predict summer bottom temperatures when initialized across the late fall to early spring months.
2021-11
J. Geophys. Res. Oceans
126
e2021JC017545
0
10.1029/2021JC017545
Seasonal sea ice plays a key role in shaping the ecosystem dynamics of the eastern Bering Sea shelf. In particular, it leads to the formation of a characteristic pool of cold water that covers the bottom of the shelf from winter through summer; the extent of this cold pool is often used as a management index for distribution, productivity, recruitment, and survival of commercially important fish and shellfish species. Here, we quantify our ability to seasonally forecast interannual variability in Bering Sea bottom temperature and sea ice extent. Retrospective forecast simulations from two global forecast models are downscaled using a regional ocean model; the retrospective forecast simulations include 9-month to 12-month forecasts spanning 1982–2010. We find that dynamic forecasting can predict summer bottom temperatures across the eastern Bering Sea shelf with lead times of up to 4 months. The majority of the prediction skill derives from the persistence signal, and a persistence forecast is comparably skillful to the dynamic forecast at these lead times. However, forecast skill of sea ice advance and retreat is low when a forecast model is initialized before or during the ice season (October–February); this limits the ability of either dynamic or persistence models to predict summer bottom temperatures when initialized across the late fall to early spring months.
Kearney
K. A.
Alexander
M. A.
Aydin
K.
Cheng
W.
Hervieux
G.
Ortiz
I.
21199
Article
Diagnostics of Tropical Variability for Numerical Weather Forecasts
Tropical precipitation and circulation are often coupled and span a vast spectrum of scales from a few to several thousands of kilometers and from hours to weeks. Current operational numerical weather prediction (NWP) models struggle with representing the full range of scales of tropical phenomena. Synoptic to planetary scales are of particular importance because improved skill in the representation of tropical larger scale features such as convectively coupled equatorial waves (CCEWs) have potential to reduce forecast error propagation from the tropics to the midlatitudes. Here we introduce diagnostics from a recently developed tropical variability diagnostics toolbox, where we focus on two recent versions of NOAA’s Unified Forecast System (UFS): operational GFSv15 forecasts and experimental GFSv16 forecasts from April through October 2020. The diagnostics include space-time coherence spectra to identify preferred scales of coupling between circulation and precipitation, pattern correlations of Hovmöller diagrams to assess model skill in zonal propagation of precipitating features, CCEW skill assessment, plus a diagnostic aimed at evaluating moisture - convection coupling in the tropics. Results show that the GFSv16 forecasts are slightly more realistic than GFSv15 in their coherence between precipitation and model dynamics at synoptic to planetary scales scales, with modest improvements in moisture convection coupling. However, this slightly improved performance does not necessarily translate to improvements in traditional precipitation skill scores. The results highlight the utility of these diagnostics in the pursuit of better understanding of NWP model performance in the tropics, while also demonstrating the challenges in translating model advancements into improved skill.
2021-9
Wea. Forecasting
37
1661–1680
0
10.1175/WAF-D-21-0204.1
Tropical precipitation and circulation are often coupled and span a vast spectrum of scales from a few to several thousands of kilometers and from hours to weeks. Current operational numerical weather prediction (NWP) models struggle with representing the full range of scales of tropical phenomena. Synoptic to planetary scales are of particular importance because improved skill in the representation of tropical larger scale features such as convectively coupled equatorial waves (CCEWs) have potential to reduce forecast error propagation from the tropics to the midlatitudes. Here we introduce diagnostics from a recently developed tropical variability diagnostics toolbox, where we focus on two recent versions of NOAA’s Unified Forecast System (UFS): operational GFSv15 forecasts and experimental GFSv16 forecasts from April through October 2020. The diagnostics include space-time coherence spectra to identify preferred scales of coupling between circulation and precipitation, pattern correlations of Hovmöller diagrams to assess model skill in zonal propagation of precipitating features, CCEW skill assessment, plus a diagnostic aimed at evaluating moisture - convection coupling in the tropics. Results show that the GFSv16 forecasts are slightly more realistic than GFSv15 in their coherence between precipitation and model dynamics at synoptic to planetary scales scales, with modest improvements in moisture convection coupling. However, this slightly improved performance does not necessarily translate to improvements in traditional precipitation skill scores. The results highlight the utility of these diagnostics in the pursuit of better understanding of NWP model performance in the tropics, while also demonstrating the challenges in translating model advancements into improved skill.
Gehne
M.
Wolding
B.
Dias
J.
Kiladis
G. N.
21200
Article
Robust forecasting using predictive generalized synchronization in reservoir computing
Reservoir computers (RCs) are a class of recurrent neural networks (RNNs) that can be used for forecasting the future of observed time series data. As with all RNNs, selecting the hyperparameters in the network to yield excellent forecasting presents a challenge when training on new inputs. We analyze a method based on predictive generalized synchronization (PGS) that gives direction in designing and evaluating the architecture and hyperparameters of an RC. To determine the occurrences of PGS, we rely on the auxiliary method to provide a computationally efficient pre-training test that guides hyperparameter selection. We provide a metric for evaluating the RC using the reproduction of the input system’s Lyapunov exponents that demonstrates robustness in prediction.
2021-12
Chaos
31
0
10.1063/5.0066013
Reservoir computers (RCs) are a class of recurrent neural networks (RNNs) that can be used for forecasting the future of observed time series data. As with all RNNs, selecting the hyperparameters in the network to yield excellent forecasting presents a challenge when training on new inputs. We analyze a method based on predictive generalized synchronization (PGS) that gives direction in designing and evaluating the architecture and hyperparameters of an RC. To determine the occurrences of PGS, we rely on the auxiliary method to provide a computationally efficient pre-training test that guides hyperparameter selection. We provide a metric for evaluating the RC using the reproduction of the input system’s Lyapunov exponents that demonstrates robustness in prediction.
Platt
J. A.
Wong
A.
Clark
R.
Penny
S. G.
Abarbanel
H. D. I.
21236
Article
Easterly waves in the east Pacific during the OTREC 2019 field campaign
2021-11
J. Atmos. Sci.
78
4071-4088
0
10.1175/JAS-D-21-0128.1
Huaman
L.
Maloney
E. D.
Schumacher
C.
Kiladis
G. N.
21358
Article
Editorial: Climate Science Advances to Address 21st Century Weather and Climate Extremes
N/A
2021-6
Front. Clim.
3
680291
0
10.3389/fclim.2021.680291
N/A
Funk
C.
Hoell
A.
Mitchell
D.
21029
Book_Section
Chapter 10. Financing Weather and Climate Risks in the United States
N/A
2021-1
Chapman and Hall/CRC Press
217-247
394
9780815392378
N/A
Pulwarty
R. S.
Easterling
D. R.
Adkins
J.
Smith
A.
Smith
A.
Smith
A.
Smith
A.
Smith
A.
Smith
A.
Smith
A.
21205
Book_Section
Composite Atmospheric Profiling
Each observation system that vertically profiles one or several atmospheric parameters by performing in-situ or remote-sensing measurements has its own distinct characteristics in terms of vertical range and resolution, temporal resolution, and height-dependent error. Combining several profiling instruments of the same type or different types—known as composite atmospheric profiling—bypasses the limitations of using those instruments separately, allowing additional information on the state of the atmosphere to be retrieved, thus enhancing the retrieval accuracy and the temporal and/or spatial resolution of atmospheric parameters. New quantities with added value can also be obtained using this approach. In this chapter, the state of the art in composite vertical atmospheric profiling is discussed. It is described how multiple systems that measure different parameters and have different height ranges as well as vertical, temporal, and spatial resolutions can be optimally combined. Some examples from recent research are presented to demonstrate the successful implementation of composite profiling techniques.
2021-12
Springer, Cham
Springer Handbooks
1295-1319
0
10.1007/978-3-030-52171-4_47
Each observation system that vertically profiles one or several atmospheric parameters by performing in-situ or remote-sensing measurements has its own distinct characteristics in terms of vertical range and resolution, temporal resolution, and height-dependent error. Combining several profiling instruments of the same type or different types—known as composite atmospheric profiling—bypasses the limitations of using those instruments separately, allowing additional information on the state of the atmosphere to be retrieved, thus enhancing the retrieval accuracy and the temporal and/or spatial resolution of atmospheric parameters. New quantities with added value can also be obtained using this approach. In this chapter, the state of the art in composite vertical atmospheric profiling is discussed. It is described how multiple systems that measure different parameters and have different height ranges as well as vertical, temporal, and spatial resolutions can be optimally combined. Some examples from recent research are presented to demonstrate the successful implementation of composite profiling techniques.
Kottmeier
C.
Adler
B.
Kalthoff
N.
Löhnert
U.
Görsdorf
U.
Görsdorf
U.
21072
Report
Chapter 9: Quasi-Biennial Oscillation
N/A
2021-6
SPARC Reanalysis Intercomparison Project (S-RIP) Final Report
10
389-488
0
N/A
Antsey
J. A.
Gray
L. J.
Fujiwara
M.
Ivanciu
I.
Kiladis
G. N.
Kim
Y.-H.
Schenzinger
V.
Tegtmeier
S.
Tegtmeier
S.
Tegtmeier
S.
Tegtmeier
S.
21057
Dataset
ATOMIC CU-RAAVEN UAS: Lower-atmospheric meteorological and surface properties from the CU-RAAVEN unmanned aircraft system (UAS) over the Tropical Atlantic Ocean, near Barbados: Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign 2020-01-24 to 2020-02-16 (NCEI Accession 0225373)
The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the NOAA Ship Ronald H. Brown, as well as uncrewed vehicles and platforms launched from Barbados and two ships. This dataset contains uncrewed aircraft meteorological and ocean surface data in netcdf files.
2021-2
NOAA National Centers for Environmental Information
0
10.25921/jhnd-8e58
The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the NOAA Ship Ronald H. Brown, as well as uncrewed vehicles and platforms launched from Barbados and two ships. This dataset contains uncrewed aircraft meteorological and ocean surface data in netcdf files.
de Boer
G.
Borenstein
S.
Calmer
R.
Rhodes
M.
Choate
C.
Hamilton
J.
Intrieri
J. M.
21058
Dataset
ATOMIC Wave Gliders: Near-surface meteorology, air-sea fluxes, surface ocean waves, and near-surface ocean parameters (currents, temperature, salinity) estimated from in-situ and remote sensing instruments aboard two Wave Gliders launched and recovered from NOAA Ship Ronald H. Brown in the North Atlantic Ocean, near Barbados: Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign 2020-01-09 to 2020-02-11 (NCEI Accession 0225336)
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset consists of Wave Glider data in netcdf files.
2021-2
NOAA National Centers for Environmental Information
0
10.25921/dvys-1f29
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset consists of Wave Glider data in netcdf files.
Thomson
J.
Thompson
E. J.
Iyer
S.
Drushka
K.
de Klerk
A.
21060
Dataset
ATOMIC ship navigation, meteorology, seawater, fluxes: Near-surface meteorology, air-sea fluxes, surface ocean waves, and near surface ocean parameters (temperature, salinity, currents) and primary dataset of ship location and navigation estimated from in-situ and remote sensing instruments aboard NOAA Ship Ronald H. Brown in the North Atlantic Ocean, near Barbados: Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign 2020-01-09 to 2020-02-12 (NCEI Accession 0225427)
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains meteorological, sea surface, and navigation data in netcdf files.
2021-2
NOAA National Centers for Environmental Information
0
10.25921/etxb-ht19
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains meteorological, sea surface, and navigation data in netcdf files.
Thompson
E. J.
Fairall
C. W.
Pezoa
S.
Bariteau
L.
21061
Dataset
ATOMIC ship skin SST radiometer ROSR: Ocean skin surface temperature estimated from remote sensing of infrared radiation by the Remote Ocean Surface Radiometer (ROSR) aboard NOAA Ship Ronald H. Brown in the North Atlantic Ocean, near Barbados: Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign 2020-01-09 to 2020-01-26 (NCEI Accession 0225402)
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains Remote Ocean Surface Radiometer (ROSR) data in a netcdf file.
2021-2
NOAA National Centers for Environmental Information
0
10.25921/nwx9-rd07
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains Remote Ocean Surface Radiometer (ROSR) data in a netcdf file.
Thompson
E. J.
21062
Dataset
ATOMIC ship W-band radar: Vertical Profiles of cloud, vertical velocity, and precipitation parameters estimated from a motion-stabilized vertically-pointing W-band (94 GHz) Doppler radar aboard NOAA Ship Ronald H. Brown in the North Atlantic Ocean, near Barbados: Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign 2020-01-29 to 2020-02-13, (NCEI Accession 0225423)
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains W-band radar data in netcdf files.
2021-2
NOAA National Centers for Environmental Information
0
10.25921/44cy-kr53
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains W-band radar data in netcdf files.
Thompson
E. J.
Zuidema
P.
Fairall
C. W.
Pezoa
S.
Moran
K. P.
Bariteau
L.
21063
Dataset
ATOMIC ship ceilometer: Cloud base height and vertical profiles of visible light backscattered from aerosols and clouds in the atmospheric boundary layer estimated from a vertically-pointing lidar remote sensing instrument aboard NOAA Ship Ronald H. Brown in the North Atlantic Ocean, near Barbados: Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign 2010-01-09 to 2010-02-12 (NCEI Accession 0225425)
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains ceilometer data in netcdf files.
2021-2
NOAA National Centers for Environmental Information
0
10.25921/jbz6-e918
The Atlantic Tradewind OceanAtmosphere Mesoscale Interaction Campaign (ATOMIC) was a field campaign held January-February 2020 in the tropical North Atlantic east of Barbados. The campaign, the U.S. complement to the European field campaign called EUREC4A, was aimed at better understanding cloud and air-sea interaction processes. ATOMIC included measurements from a NOAA WP-3D Orion "Hurricane Hunter" aircraft, the research ship Ronald H. Brown, and unpiloted vehicles launched from Barbados and from the Ronald H. Brown. This dataset contains ceilometer data in netcdf files.
Thompson
E. J.
Fairall
C. W.
Pezoa
S.
Bariteau
L.
21186
Dataset
CU RAAVEN data for WiscoDISCO21
This dataset includes data collected using the RAAVEN uncrewed aircraft system between 21-26 May, 2021 over coastal Wisconsin (USA) around the Chiwaukee Prairie State Natural Area. The aircraft was operated between the surface and 500 m AGL over the coastal land and lake environments, making measurements of temperature, relative humidity, winds, surface and sky IR temperature, turbulence, aircraft position and aircraft attitude. In total, 12 flights were completed totaling nearly 24 flight hours.
2021-7
Zenodo
0
10.5281/zenodo.5142491
This dataset includes data collected using the RAAVEN uncrewed aircraft system between 21-26 May, 2021 over coastal Wisconsin (USA) around the Chiwaukee Prairie State Natural Area. The aircraft was operated between the surface and 500 m AGL over the coastal land and lake environments, making measurements of temperature, relative humidity, winds, surface and sky IR temperature, turbulence, aircraft position and aircraft attitude. In total, 12 flights were completed totaling nearly 24 flight hours.
de Boer
G.
Borenstein
S.
Hamilton
J.
Rhodes
M.
Choate
C.
Cleary
P.
21208
Dataset
10-meter (m) meteorological flux tower measurements (Level 1 Raw), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September, 2020
Raw (Level 1) measurements from the 10-meter (m) meteorological and flux tower (TOWER) deployed at the Met City location within the Central Observatory of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. At times, measurements from a second meteorological mast (MAST) located nearby are also included in the data set. The collective TOWER systems measured many parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. Measurements of meteorology and high-resolution 3-dimensional winds were observed at nominal heights of 2-, 6-, and 10-m, with some measurements at either 30- or 23-m when the MAST was also operational. The measurements are included in two netCDF files per day. The "slow" files are for 1-sec samples of the measured variables, including near-surface meteorology, surface temperature, surface height change, net surface heat flux, and position. The "fast" files are for data at 10 and 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with only technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
2021-12
Arctic Data Center
0
10.18739/A2VM42Z5F
Raw (Level 1) measurements from the 10-meter (m) meteorological and flux tower (TOWER) deployed at the Met City location within the Central Observatory of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. At times, measurements from a second meteorological mast (MAST) located nearby are also included in the data set. The collective TOWER systems measured many parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. Measurements of meteorology and high-resolution 3-dimensional winds were observed at nominal heights of 2-, 6-, and 10-m, with some measurements at either 30- or 23-m when the MAST was also operational. The measurements are included in two netCDF files per day. The "slow" files are for 1-sec samples of the measured variables, including near-surface meteorology, surface temperature, surface height change, net surface heat flux, and position. The "fast" files are for data at 10 and 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with only technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
Cox
C. J.
Gallagher
M. R.
Shupe
M. D.
Persson
P. O. G.
Solomon
A.
al.
et
21209
Dataset
Atmospheric Surface Flux Station #30 measurements (Level 1 Raw), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #30 (ASFS30) deployed at various locations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. The ASFS measured all parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. These measurements are included in two netCDF files per day. The "slow" files are for 1-minute averages of measured variables, including near-surface meteorology, surface height change, upwelling and downwelling shortwave and longwave radiative fluxes, net surface heat flux, and position. The "fast" files are for data at 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
2021-12
Arctic Data Center
Arctic Data
0
10.18739/A20C4SM1J
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #30 (ASFS30) deployed at various locations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. The ASFS measured all parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. These measurements are included in two netCDF files per day. The "slow" files are for 1-minute averages of measured variables, including near-surface meteorology, surface height change, upwelling and downwelling shortwave and longwave radiative fluxes, net surface heat flux, and position. The "fast" files are for data at 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
Cox
C. J.
Gallagher
M. R.
Shupe
M. D.
Persson
P. O. G.
Solomon
A.
al.
et
21210
Dataset
Atmospheric Surface Flux Station #50 measurements (Level 1 Raw), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #50 (ASFS50) deployed at various locations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. The ASFS measured all parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. These measurements are included in two netCDF files per day. The "slow" files are for 1-minute averages of measured variables, including near-surface meteorology, surface height change, upwelling and downwelling shortwave and longwave radiative fluxes, net surface heat flux, and position. The "fast" files are for data at 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
2021-12
Arctic Data Center
0
10.18739/A2445HD46
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #50 (ASFS50) deployed at various locations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. The ASFS measured all parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. These measurements are included in two netCDF files per day. The "slow" files are for 1-minute averages of measured variables, including near-surface meteorology, surface height change, upwelling and downwelling shortwave and longwave radiative fluxes, net surface heat flux, and position. The "fast" files are for data at 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
Cox
C. J.
Gallagher
M. R.
Shupe
M. D.
Persson
P. O. G.
Solomon
A.
al.
et
21211
Dataset
Atmospheric Surface Flux Station #40 measurements (Level 1 Raw), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 - September 2020
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #40 (ASFS40) deployed at a remote, uncrewed sampling site during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. The ASFS measured all parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. These measurements are included in two netCDF files per day. The "slow" files are for 1-minute averages of measured variables, including near-surface meteorology, surface height change, upwelling and downwelling shortwave and longwave radiative fluxes, net surface heat flux, and position. The "fast" files are for data at 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
2021-12
0
10.18739/A2CJ87M7G
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #40 (ASFS40) deployed at a remote, uncrewed sampling site during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition drifting with the central Arctic sea ice from October 2019 through September 2020. The ASFS measured all parameters of the surface energy budget, surface momentum flux, near-surface meteorology, and local position. These measurements are included in two netCDF files per day. The "slow" files are for 1-minute averages of measured variables, including near-surface meteorology, surface height change, upwelling and downwelling shortwave and longwave radiative fluxes, net surface heat flux, and position. The "fast" files are for data at 20-Hertz (Hz) resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. Moreover, the data files contain all measurements, including data collected during system testing that are not intended for scientific use. A detailed documentation of the measurement conditions will be provided in a forthcoming data publication. For scientific purposes, we recommend using Level 3 data files when they are available, as these will include full quality control as well as higher-order derived products.
Cox
C. J.
Gallagher
M. R.
Shupe
M. D.
Persson
P. O. G.
Solomon
A.
al.
et
21218
Dataset
HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign, A1 level data
A1 level data from HELiX Uncrewed Aircraft System correspond to the raw data collected in the Central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality-controlled B1 level data are also available in the Arctic Data Center. Users are encouraged to primarily use the B1 level data for analysis. A1 level data include hemispheric irradiance measurements from Kipp and Zonen pyranometers and thermodynamic parameters from Vaisala RSS421 sensors. Autopilot positions and attitudes, along with gimbal attitudes are also provided. Each field of measurements has its own time stamped based on a common clock and associated acquisition frequency. No synchronization or Universal Coordinated Time (UTC) are provided at the A1 level. This dataset is used to create the B1 level data at 10 hertz (Hz) with quality-controlled flags. More information on the data and method can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
2021-12
Arctic Data Center
0
10.18739/A2697000S
A1 level data from HELiX Uncrewed Aircraft System correspond to the raw data collected in the Central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality-controlled B1 level data are also available in the Arctic Data Center. Users are encouraged to primarily use the B1 level data for analysis. A1 level data include hemispheric irradiance measurements from Kipp and Zonen pyranometers and thermodynamic parameters from Vaisala RSS421 sensors. Autopilot positions and attitudes, along with gimbal attitudes are also provided. Each field of measurements has its own time stamped based on a common clock and associated acquisition frequency. No synchronization or Universal Coordinated Time (UTC) are provided at the A1 level. This dataset is used to create the B1 level data at 10 hertz (Hz) with quality-controlled flags. More information on the data and method can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
Calmer
R.
de Boer
G.
Hamilton
J.
Borenstein
S.
Cox
C. J.
al.
et
21219
Dataset
HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign, B1 level data
The dataset is derived from HELiX Uncrewed Aircraft System flights that were conducted in the Central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Universal Coordinated Time (UTC), downwelling and upwelling shortwave radiation measurements, and position and attitude from the Uncrewed Aircraft System (UAS). Temperature, relative humidity and pressure from two different sensors are also provided. A quality control flag is associated with each scientific measurement. A flight flag is also included to indicate the different phases of the flight - on the ground, take-off/landing phases, and in flight. All the data have been synchronized and interpolated at 10 hertz (Hz). The purpose of this dataset is to provide information on albedo over different features of the sea ice (snow, melt pond, ocean). Three flight patterns were implemented during the campaign with the HELiX, a grid pattern at constant altitude (15 meters or 7 meters above ground level), hovering flights ( 2-5 minutes hovering over identified sea ice features at low altitude ~ 3 meters above ground level), and profiles up to 400 meters above ground level. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Albedo measurements have been validated with surface-based measurements and details can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
2021-12
Arctic Data Center
0
10.18739/A2GH9BB0Q
The dataset is derived from HELiX Uncrewed Aircraft System flights that were conducted in the Central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Universal Coordinated Time (UTC), downwelling and upwelling shortwave radiation measurements, and position and attitude from the Uncrewed Aircraft System (UAS). Temperature, relative humidity and pressure from two different sensors are also provided. A quality control flag is associated with each scientific measurement. A flight flag is also included to indicate the different phases of the flight - on the ground, take-off/landing phases, and in flight. All the data have been synchronized and interpolated at 10 hertz (Hz). The purpose of this dataset is to provide information on albedo over different features of the sea ice (snow, melt pond, ocean). Three flight patterns were implemented during the campaign with the HELiX, a grid pattern at constant altitude (15 meters or 7 meters above ground level), hovering flights ( 2-5 minutes hovering over identified sea ice features at low altitude ~ 3 meters above ground level), and profiles up to 400 meters above ground level. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Albedo measurements have been validated with surface-based measurements and details can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
Calmer
R.
de Boer
G.
Hamilton
J.
Lawrence
D.
Borenstein
S.
al.
et
21220
Dataset
DataHawk2 Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, A1 level.
This dataset is derived from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the MOSAiC expedition. The data include Universal Coordinated Time (UTC), aircraft position and attitude, atmospheric thermodynamic conditions (pressure, temperature, humidity) from various sensors, approximate brightness temperature of the surface and overlying atmosphere, and estimated horizontal winds. A flight flag is included to indicate when the aircraft is in flight. All the data have been synchronized and quality controlled, and are provided at their native frequency logged on board the aircraft’s secure digital (SD) card. Data interpolated to a common 10 hertz (Hz) clock are provided in the B1 level files, and are available in the Arctic Data Center at doi:10.18739/A2VQ2SB8S. Users are encouraged to primarily use the B1 level data for analysis. More information on the data and methods used for synchronization and quality control can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
2021-12
Arctic Data Center
arc
0
10.18739/A2R20RX6S
This dataset is derived from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the MOSAiC expedition. The data include Universal Coordinated Time (UTC), aircraft position and attitude, atmospheric thermodynamic conditions (pressure, temperature, humidity) from various sensors, approximate brightness temperature of the surface and overlying atmosphere, and estimated horizontal winds. A flight flag is included to indicate when the aircraft is in flight. All the data have been synchronized and quality controlled, and are provided at their native frequency logged on board the aircraft’s secure digital (SD) card. Data interpolated to a common 10 hertz (Hz) clock are provided in the B1 level files, and are available in the Arctic Data Center at doi:10.18739/A2VQ2SB8S. Users are encouraged to primarily use the B1 level data for analysis. More information on the data and methods used for synchronization and quality control can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
Jozef
G.
de Boer
G.
Cassano
J.
Calmer
R.
Hamilton
J.
al.
et
21221
Dataset
DataHawk2 Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, B1 level
This dataset is derived from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Coordinated Universal Time (UTC), aircraft position and attitude, atmospheric thermodynamic conditions (pressure, temperature, humidity) from various sensors, approximate brightness temperature of the surface and overlying atmosphere, and estimated horizontal winds. A flight flag is included to indicate when the aircraft is in flight. All the data have been synchronized, quality controlled, and interpolated at 10 hertz (Hz). Data at their native frequency are provided in the A1 level files, and are available in the Arctic Data Center at doi:10.18739/A2R20RX6S. The purpose of this dataset is to provide information on the thermodynamic and kinematic states of the lower atmosphere, and provide detailed observations of turbulence between the surface and one kilometer. Two flight patterns were implemented during the campaign with the DataHawk2: an orbital profile extending from the ice surface to 1000 meter(m) or cloud base if lower, and a “racetrack” pattern where the aircraft was held at a constant altitude while sampling horizontally between two circles. The latter was used to collect data on the spatial variability of thermodynamic properties over the ice surface, particularly over inhomogeneities in the surface such as leads. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Thermodynamic and kinematic measurements have been validated with radiosonde-based measurements. More information on the data and methods used for synchronization and quality control can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
2021-12
Arctic Data Center
arct
0
10.18739/A2VQ2SB8S
This dataset is derived from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Coordinated Universal Time (UTC), aircraft position and attitude, atmospheric thermodynamic conditions (pressure, temperature, humidity) from various sensors, approximate brightness temperature of the surface and overlying atmosphere, and estimated horizontal winds. A flight flag is included to indicate when the aircraft is in flight. All the data have been synchronized, quality controlled, and interpolated at 10 hertz (Hz). Data at their native frequency are provided in the A1 level files, and are available in the Arctic Data Center at doi:10.18739/A2R20RX6S. The purpose of this dataset is to provide information on the thermodynamic and kinematic states of the lower atmosphere, and provide detailed observations of turbulence between the surface and one kilometer. Two flight patterns were implemented during the campaign with the DataHawk2: an orbital profile extending from the ice surface to 1000 meter(m) or cloud base if lower, and a “racetrack” pattern where the aircraft was held at a constant altitude while sampling horizontally between two circles. The latter was used to collect data on the spatial variability of thermodynamic properties over the ice surface, particularly over inhomogeneities in the surface such as leads. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Thermodynamic and kinematic measurements have been validated with radiosonde-based measurements. More information on the data and methods used for synchronization and quality control can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
Jozef
G.
de Boer
G.
Cassano
J.
Calmer
R.
Hamilton
J.
al.
et
21280
Dataset
HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, multispectral imagery data
Data are available for download at: https://arcticdata.io/data/10.18739/A2RV0D21Z/
This dataset consists of multispectral imagery data products produced from HELiX uncrewed aircraft system (UAS) flights that were conducted over or near sea ice during the MOSAiC expedition. These data were produced from raw multispectral imagery acquired by the Helix’s gimbal-mounted RedEdge-MX camera. Additional data from the Helix UAS’ other sensors, which consist of hemispheric irradiance measurements from two Kipp and Zonen pyranometers and thermodynamic parameters from two Vaisala RSS421 sensors can be found in Radiance Calmer, Gijs de Boer, Jonathan Hamilton, Dale Lawrence, Steve Borenstein, et al. 2021. HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate campaign, A1 level data. Arctic Data Center. doi:10.18739/A2M90243X (A1 data) or Radiance Calmer, Gijs de Boer, Jonathan Hamilton, Dale Lawrence, Steve Borenstein, et al. 2021. HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign. Arctic Data Center. doi:10.18739/A2GH9BB0Q (B1 data). Three main flight types were conducted with the Helix: a grid pattern, hover, and profile. Pix4D was used to produce five (one for each channel of the camera) orthomosaics, reflectance maps, and colorized index maps for all flight types. A video of the images taken during the flight, including an image scale, UTC time, and altitude overlay was produced for each profile flight. More information on the data and methods can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Ob
2021-12
Arctic Data Center
0
10.18739/A2RV0D21Z
Data are available for download at: https://arcticdata.io/data/10.18739/A2RV0D21Z/
This dataset consists of multispectral imagery data products produced from HELiX uncrewed aircraft system (UAS) flights that were conducted over or near sea ice during the MOSAiC expedition. These data were produced from raw multispectral imagery acquired by the Helix’s gimbal-mounted RedEdge-MX camera. Additional data from the Helix UAS’ other sensors, which consist of hemispheric irradiance measurements from two Kipp and Zonen pyranometers and thermodynamic parameters from two Vaisala RSS421 sensors can be found in Radiance Calmer, Gijs de Boer, Jonathan Hamilton, Dale Lawrence, Steve Borenstein, et al. 2021. HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate campaign, A1 level data. Arctic Data Center. doi:10.18739/A2M90243X (A1 data) or Radiance Calmer, Gijs de Boer, Jonathan Hamilton, Dale Lawrence, Steve Borenstein, et al. 2021. HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign. Arctic Data Center. doi:10.18739/A2GH9BB0Q (B1 data). Three main flight types were conducted with the Helix: a grid pattern, hover, and profile. Pix4D was used to produce five (one for each channel of the camera) orthomosaics, reflectance maps, and colorized index maps for all flight types. A video of the images taken during the flight, including an image scale, UTC time, and altitude overlay was produced for each profile flight. More information on the data and methods can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Ob
Hamilton
J.
de Boer
G.
Calmer
R.
Lawrence
D.
Argrow
B.
al.
et
21341
Dataset
Raw files for sea ice drift tracks from the Distributed Network of autonomous buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition 2019 - 2021
This is the raw position data for ice-tethered buoys deployed in the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition Distributed Network (DN).
The largest ever network of autonomous ice-tethered buoys was deployed as a DN surrounding the MOSAiC Central Observatory (CO). This extensive network of 112 Global Positioning System (GPS) buoys and 12 multi-instrumented ice stations captured the annual cycle of Arctic sea ice drift and deformation for the first time as the DN traversed the Transpolar Drift Stream. GPS position data from buoys deployed during the year-long MOSAiC experiment capture sea ice drift and deformation at spatial scales ranging from 100s of meters to 200 kilometers (km) from late September 2019 into 2021.
2021-12
Arctic Data Center
0
10.18739/A2KD1QM54
This is the raw position data for ice-tethered buoys deployed in the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition Distributed Network (DN).
The largest ever network of autonomous ice-tethered buoys was deployed as a DN surrounding the MOSAiC Central Observatory (CO). This extensive network of 112 Global Positioning System (GPS) buoys and 12 multi-instrumented ice stations captured the annual cycle of Arctic sea ice drift and deformation for the first time as the DN traversed the Transpolar Drift Stream. GPS position data from buoys deployed during the year-long MOSAiC experiment capture sea ice drift and deformation at spatial scales ranging from 100s of meters to 200 kilometers (km) from late September 2019 into 2021.
Bliss
A.
Hutchings
J.
Anderson
P.
. .
.
Costa
D. M.
. .
.
Cox
C. J.
. .
.
Gallagher
M. R.
. .
.
Morris
S. M.
. .
.
Osborn
J.
. .
.
Persson
P. O. G.
. .
.
Shupe
M. D.
. .
.
Uttal
T.
al.
et
21342
Dataset
Sea ice drift tracks from the Distributed Network of autonomous buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition 2019 - 2021
The largest ever network of autonomous ice-tethered buoys was deployed as a Distributed Network (DN) surrounding the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory (CO). This extensive network of 112 Global Positioning System (GPS) buoys and 12 multi-instrumented ice stations captured the annual cycle of Arctic sea ice drift and deformation for the first time as the DN traversed the Transpolar Drift Stream. GPS position data from buoys deployed during the year-long MOSAiC experiment capture sea ice drift and deformation at spatial scales ranging from 100s of meters to 200 kilometers (km) from late September 2019 into 2021.
This dataset contains 216 quality-controlled drift tracks from buoys deployed at sites within a 45 km radius of the MOSAiC CO. Initial deployments began 26 September 2019 (Leg 1) with new deployments of buoys in mid-March-April 2020 (Leg 3), and August-September 2020 (leg 5).
2021-12
Arctic Data Center
0
10.18739/A2Q52FD8S
The largest ever network of autonomous ice-tethered buoys was deployed as a Distributed Network (DN) surrounding the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory (CO). This extensive network of 112 Global Positioning System (GPS) buoys and 12 multi-instrumented ice stations captured the annual cycle of Arctic sea ice drift and deformation for the first time as the DN traversed the Transpolar Drift Stream. GPS position data from buoys deployed during the year-long MOSAiC experiment capture sea ice drift and deformation at spatial scales ranging from 100s of meters to 200 kilometers (km) from late September 2019 into 2021.
This dataset contains 216 quality-controlled drift tracks from buoys deployed at sites within a 45 km radius of the MOSAiC CO. Initial deployments began 26 September 2019 (Leg 1) with new deployments of buoys in mid-March-April 2020 (Leg 3), and August-September 2020 (leg 5).
Bliss
A.
Hutchings
J.
Anderson
P.
. .
.
Costa
D. M.
. .
.
Cox
C. J.
. .
.
Gallagher
M. R.
. .
.
Morris
S. M.
. .
.
Osborn
J.
. .
.
Persson
P. O. G.
. .
.
Shupe
M. D.
. .
.
Uttal
T.
al.
et
21347
Dataset
HELiX Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Campaign
The dataset is derived from HELiX Uncrewed Aircraft System flights that were conducted in the Central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Universal Coordinated Time (UTC), downwelling and upwelling shortwave radiation measurements, and position and attitude from the Uncrewed Aircraft System (UAS). Temperature, relative humidity and pressure from two different sensors are also provided. A quality control flag is associated with each scientific measurement. A flight flag is also included to indicate the different phases of the flight - on the ground, take-off/landing phases, and in flight. All the data have been synchronized and interpolated at 10 hertz (Hz). The purpose of this dataset is to provide information on albedo over different features of the sea ice (snow, melt pond, ocean). Three flight patterns were implemented during the campaign with the HELiX, a grid pattern at constant altitude (15 meters or 7 meters above ground level), hovering flights ( 2-5 minutes hovering over identified sea ice features at low altitude ~ 3 meters above ground level), and profiles up to 400 meters above ground level. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Albedo measurements have been validated with surface-based measurements and details can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
2021-12
Arctic Data Center
0
10.18739/A22J6857H
The dataset is derived from HELiX Uncrewed Aircraft System flights that were conducted in the Central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Universal Coordinated Time (UTC), downwelling and upwelling shortwave radiation measurements, and position and attitude from the Uncrewed Aircraft System (UAS). Temperature, relative humidity and pressure from two different sensors are also provided. A quality control flag is associated with each scientific measurement. A flight flag is also included to indicate the different phases of the flight - on the ground, take-off/landing phases, and in flight. All the data have been synchronized and interpolated at 10 hertz (Hz). The purpose of this dataset is to provide information on albedo over different features of the sea ice (snow, melt pond, ocean). Three flight patterns were implemented during the campaign with the HELiX, a grid pattern at constant altitude (15 meters or 7 meters above ground level), hovering flights ( 2-5 minutes hovering over identified sea ice features at low altitude ~ 3 meters above ground level), and profiles up to 400 meters above ground level. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Albedo measurements have been validated with surface-based measurements and details can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep.
Calmer
R.
de Boer
G.
Hamilton
J.
Lawrence
D.
Borenstein
S.
Cox
C. J.
al.
et