Sea Surface Temperature Boundary Condition: prior to 1981(20CRv3.SI): 8 members of pentad interpolated to daily Simple Ocean Data Assimilation with Sparse Input (SODAsi) version 3 (SODAsi.3, Giese et al. 2016) seasonally adjusted to the 1981-2010 HadISST2.2 climatology. Regions where sea ice was ever indicated in HadISST2.3 are filled with: HadISST2.2 daily (1963+); HadISST2.1 monthly interpolated to daily (1850-1962); or the 1861–1891 HadISST2.1 climatology (1849 and earlier). 1981 and later (20CRv3.MO): 8 members of pentad interpolated to daily HadISST2.2 sea surface temperatures.
Sea Ice Concentration Boundary Condition: monthly HadISST2.3 sea ice (Slivinski et al. 2019; Titchner & Rayner 2014; Walsh et al. 2015).
Model: NCEP GFS v14.0.1; Noah land surface model and thermodynamic ice model as described in Compo et al. (2011).
Model Resolution: 20CRv3 is run at a resolution of T254 (approximately 75km at the equator) with 64 vertical levels up to .3mb and 80 individual ensemble members.
Assimilation: Ensemble Filter as in 20CRv2 and 20CRv2c, and as described in Compo et al. (2011), Compo et al. (2006), Whitaker et al. (2004), but additionally using a 4-dimensional incremental analysis update (Lei & Whitaker 2016).
Streams: As in Compo et al. 2011, every 5th year is produced in parallel for a continuous 5 year stream that started from September of stream year-1. Stream years are 1835, 1840,..., 2005, 2010. Released data start in January of stream year + 1, e.g., 1836 to 1840, 1841 to 1845, ..., 2010 to 2015. Stream year 2010 will be extended beyond 2015.
Version 14.0.1 of the GFS became operational at NCEP in fall 2017. Several adjustments were made to the operational model prior to implementation in the 20CRv3 system. First, the operational model includes an ensemble run at a resolution of T574, with a single deterministic forecast run at a resolution of T1534. The version of the GFS used in 20CRv3 is run at a resolution of T254. Second, the dry air mass is specified to be 98.305 kPa (Trenberth and Smith, 2005). Third, as in 20CRv2c, sea ice concentrations are allowed down to 0.15. Fourth, the radiation interacts with CMIP5 ozone from 1850 onwards (Cionni et al., 2011)); prior to 1850, it uses 1850-level CMIP5 ozone. The model still advances a prognostic ozone determined from a gas-phase parameterization of linearized ozone production and loss (McCormack et al., 2006) implemented by NCEP/EMC (Moorthi, pers. comm.) This prognostic ozone is output during model forecasts, but is not used in the internal radiation computations. This was done to prevent spurious trends associated with the fact that the prognostic ozone scheme was developed for conditions that existed in the late 20th century, including ozone depletion and the ‘ozone hole’ associated with CFC emissions. Next, the solar forcing in 20CRv3 is determined from the Total Solar Irradiance Reconstruction based on NRLTSI2 (Coddington et al., 2016). Volcanic aerosols in 20CRv3 are prescribed as per Crowley and Unterman (2013). A hybrid eddy-diffusivity mass-flux boundary layer parameterization was used as described by Han et al. (2016), but the dissipative heating in tropical cyclones that is used operationally was turned off for 20CRv3 (Pegion, pers. comm.) The coefficients that determine the auto-conversion from ice to snow were decreased from the operational values of (6e-4, 3e-4) to (2e-4, 2e-4) as the larger values were found to give a substantial warm bias in the global mid-troposphere. These lower values appear to be more consistent with the reduced spatial resolution used here. The model uses two stochastic physics schemes: Stochastically Perturbed Parametrization Tendencies (SPPT; Palmer et al. (2009); Shutts et al. (2011)) and specific humidity perturbations (SHUM; Tompkins and Berner (2008)), which perturbs the humidity fields directly (see Wang et al. (2019) for a description of the GFS implementation). The snow depth and lower 3 soil moisture levels are both subject to a 60-day relaxation to monthly climatology (Saha et al., 2010). The prescribed CO2 and other physical parameterizations are unchanged from 20CRv2c and are described by Compo et al. (2011); Saha et al. (2010) and summarized by Fujiwara et al. (2017).
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez‐Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Le Blancq, F., Lee, T., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K. and Wyszyński, P. (2019), Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q J R Meteorol Soc. (accepted) doi:10.1002/qj.3598 and open access NOAA IR.
Giese, B.S., H.F. Seidel, G.P. Compo, and P.D. Sardeshmukh, 2016: An ensemble of ocean reanalyses for 1815-2013 with sparse observational input. J. Geophys. Res. Oceans, 121, 6891-6910, doi:10.1002/2016JC012079.
Compo, G.P., J.S. Whitaker, P.D. Sardeshmukh, N. Matsui, R.J. Allan, X. Yin, B.E. Gleason, R.S. Vose, G. Rutledge, P. Bessemoulin, S. Brönnimann, M. Brunet, R.I. Crouthamel, A.N. Grant, P.Y. Groisman, P.D. Jones, M. Kruk, A.C. Kruger, G.J. Marshall, M. Maugeri, H.Y. Mok, Ø. Nordli, T.F. Ross, R.M. Trigo, X.L. Wang, S.D. Woodruff, and S.J. Worley, 2011: The Twentieth Century Reanalysis Project. Quarterly J. Roy. Meteorol. Soc., 137, 1-28. http://dx.doi.org/10.1002/qj.776
Other Selected References
Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu, F., Bodeker, G. E., Shepherd, T. G., Shindell, D. T. and Waugh, D. W. (2011) Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing. Atmospheric Chemistry and Physics, 11, 11267–11292. URL: https://www.atmos-chem-phys.net/11/11267/2011/.
Coddington, O., Lean, J. L., Pilewskie, P., Snow, M. and Lindholm, D. (2016) A solar irradiance climate data record. Bulletin of the American Meteorological Society, 97, 1265–1282. URL: https://doi.org/10.1175/BAMS-D-14-00265.1.
Compo, G.P., J.S. Whitaker, and P.D. Sardeshmukh, 2006: Feasibility of a 100 year reanalysis using only surface pressure data. Bull. Amer. Met. Soc., 87, 175-190, http://dx.doi.org/10.1175/BAMS-87-2-175.
Fujiwara, M., Wright, J. S., Manney, G. L., Gray, L. J., Anstey, J., Birner, T., Davis, S., Gerber, E. P., Harvey, V. L., Hegglin, M. I., Homeyer, C. R., Knox, J. A., Krüger, K., Lambert, A., Long, C. S., Martineau, P., Molod, A., Monge-Sanz, B. M., Santee, M. L., Tegtmeier, S., Chabrillat, S., Tan, D. G. H., Jackson, D. R., Polavarapu, S., Compo, G. P., Dragani, R., Ebisuzaki, W., Harada, Y., Kobayashi, C., McCarty, W., Onogi, K., Pawson, S., Simmons, A., Wargan, K., Whitaker, J. S. and Zou, C.-Z. (2017) Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems. Atmospheric Chemistry and Physics, 17, 1417–1452. URL: https://www.atmos-chem-phys.net/17/1417/2017/.
Han, J., Witek, M. L., Teixeira, J., Sun, R., Pan, H.-L., Fletcher, J. K. and Bretherton, C. S. (2016) Implementation in the NCEP GFS of a hybrid eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Weather and Forecasting, 31, 341–352. https://doi.org/10.1175/WAF-D-15-0053.1.
McCormack, J., Eckermann, S., Siskind, D. and McGee, T. (2006) CHEM2D-OPP: A new linearized gas-phased ozone photo- chemistry parameterization for high-altitude NWP and climate models. Tech. rep., NAVAL RESEARCH LAB WASHINGTON DC.
Palmer, T., Buizza, R., Doblas-Reyes, F., Jung, T., Leutbecher, M., Shutts, G., Steinheimer, M. and Weisheimer, A. (2009) Stochastic parametrization and model uncertainty. ECMWF Tech. Memo, 598, 1–42.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D. et al. (2010) The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91, 1015–1058.https://doi.org/10.1175/2010BAMS3001.1
Shutts, G., Leutbecher, M., Weisheimer, A., Stockdale, T., Isaksen, L. and Bonavita, M. (2011) Representing model uncertainty: stochastic parameterizations at ECMWF. ECMWF Newsletter, 129, 19–24.
Tompkins, A. and Berner, J. (2008) A stochastic convective approach to account for model uncertainty due to unresolved humidity variability. Journal of Geophysical Research: Atmospheres, 113. https://doi.org/10.1029/2007JD009284
Walsh, J. E., Chapman, W. L. and Fetterer, F. (2015, updated 2016) Gridded monthly sea ice extent and concentration, 1850 onward, version 1. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center.
Wang, J.A., P.D. Sardeshmukh, G.P. Compo, J.S. Whitaker, L.C. Slivinski, C.M. McColl, and P.J. Pegion, 2019: Sensitivities of the NCEP Global Forecast System. Mon. Wea. Rev., 147, 1237–1256, https://doi.org/10.1175/MWR-D-18-0239.1
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For help with the dataset please contact Laura Slivinski, Research Scientist, University of Colorado CIRES: firstname.lastname@example.org; or Gil Compo, Senior Research Scientist, University of Colorado CIRES: email@example.com
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