Madden-Julian Oscillation (MJO)

Current Status

The purpose of this page is to provide ways to assess and view the current MJO status.

Phase Diagrams

ROMI phase diagram for the latest 90 days

RMM phase diagram for the latest 90 days

ROMI phase diagram for the latest 90 days

ROMI is computed using OLR only and as such is a purely convective index of the MJO. The EOFs depend on the day of year, latitude and longitude. To plot the diagram in the same phase space as the Wheeler-Hendon RMM index, the sign of ROMI PC1 is reversed and the PC ordering is switched, so that ROMI(PC2) is analogous to RMM(PC1) and -ROMI(PC1) is analogous to RMM(PC2).

RMM phase diagram for the latest 90 days

The RMM is provided by the Bureau of Meteorology Australia. RMM is computed using interpolated OLR from PSL and 850hPa and 200hPa zonal winds from the NCEP reanalysis. The EOFs only depend on longitude.

VPM phase diagram for the latest 90 days.

rMII phase diagram for the latest 90 days

VPM phase diagram for the latest 90 days.

The VPM index was computed using zonal wind and velocity potential based on the NCEP/NCAR Reanalysis Version 1. The VPM PC signs were adjusted to match the RMM index signs for the phase plot.

rMII phase diagram for the latest 90 days

rMII phase diagram for the latest 90 days. rMII was computed using interpolated OLR and 850hPa and 200hPa zonal winds from ERA5. The EOFs depend on the day of year, latitude and longitude. PC signs are consistent with the RMM index for the phase plots.

Atmosphere:

10N-10S: Subseasonal OLR Anomalies

OLR

200mb Zonal Wind Anomalies

R1 200mb uwnd

Precipitation Anomalies latest 90 days

90 day precip anomaly R1 (to be replaced by global cpc pcp)

Satellite picture:

Infrared global composite

MJO Indices

Daily Timeseries | Composite Maps | Animations | ASCII EOF's | Other MJO Indices and Information

PSL is creating a set of MJO timeseries that quantify current and historic MJO activity.
The links and descriptions are below as well as links to some other MJO timeseries created at other institutions.
A description of the timeseries format is available.

NOTE: ROMI is now provided in near real-time.
The OMI has been updated through May 20 2024. These PC amplitudes will differ slightly from the previous version since the sample used for the 20-96 day filter is longer (1979-2021), resulting in slightly different values for the filtered OLR used to calculate OMI. In some cases this may also result in a phase difference (by one phase) from the previous versions. The EOFs used are still from 1979-2012 (see below). The previous (1979-2021) version of OMI can be obtained here.

NOTE: When comparing OMI directly with RMM, to obtain the proper phase the sign of OMI PC1 and the PC ordering should be reversed, so that OMI(PC2) is analogous to RMM(PC1) and -OMI(PC1) is analogous to RMM (PC2).

Daily MJO index time series from 1979

Index	Description	Obtain timeseries
ERA5 OMI The ERA5 OLR MJO Index	Projection of 20-96 day filtered ERA5 OLR, including all eastward and westward wave numbers onto the daily spatial EOF patterns of 30-96 day eastward filtered OLR from the ERA5 dataset. EOFs are calculated using data from 1940 to "present".	ERA5 OMI values
OMI The OLR MJO Index	Projection of 20-96 day filtered OLR, including all eastward and westward wave numbers onto the daily spatial EOF patterns of 30-96 day eastward filtered OLR.	OMI values
OOMI The Original OLR MJO Index	Projection of 30-96 day eastward only filtered OLR onto the spatial EOF patterns of 30-96 day eastward filtered OLR. This results in a smoother index than OMI due to more restrictive filtering.	OOMI values
ROMI The Real-time OLR MJO Index	Projection of 9 day running average OLR anomalies onto the daily spatial EOF patterns of 30-96 day eastward filtered OLR. OLR anomalies are calculated by first subtracting the previous 40 day mean OLR. The running average is tapered as the target date is approached.	ROMI values
FMO The Filtered OLR MJO index.	Univariate EOF of normalized 20-96 day filtered OLR averaged from 15S-15N, by longitude. The same spatial EOF pattern is used for the entire year (see below).	FMO values.
VPM The Velocity Potential MJO index.	Calculated in the same way as the Wheeler-Hendon RMM, except using 200 hPa Velocity Potential instead of OLR, along with U200 and U850 in a combined EOF (see link to Ventrice et al. 2013 below).	VPM values
RMII The realtime Multivariate Index for tropical Intraseasonal oscillations.	Projection of 9 day running average anomalies onto the daily spatial multivariate EOFs of 20-96 day eastward filtered OLR, U850 and U200. Anomalies are calculated by first subtracting the previous 40 day mean. The running average is tapered as the target date is approached.	rMII data currently unavailable. We are working on having this back on the website in the near future."
REOMI The Rotated EOFs OLR Madden Julian Index.	Projection of 20-96 day filtered OLR, including all eastward and westward wave numbers onto the rotated daily spatial EOF patterns of 30-96 day eastward filtered OLR. EOFs are calculated using OLR from 1979-2012. PCs are calculated from 1979-2022. EOFs are rotated to reduce noise and potential degeneracy issues as detailed in Weidman et al., 2022.	REOMI values
KRMM The Koopman Real-time MultiVariate Madden Julian Index	Calculated following the Wheeler-Hendon RMM, but using Koopman spectral analysis to compute eigenfunctions. The leading mode of intraseasonal variability is rotated to maximize correlation with the standard RMM. See link to Lintner et al. 2023 for further discussion of the Koopman spectral analysis and methodological details.	KRMM values

A python routine to calculate the OMI has been developed for use on real-time and model data, and can be accessed via GitHub at: https://github.com/cghoffmann/mjoindices and also at Zenodo: https://doi.org/10.5281/zenodo.3613752. For the REOMI, code is in the same repository. using the parameter eofs_postprocessing_type="eof_rotation" in the main method for calculating EOFs: omi.omi_calculator.calc_eofs_from_olr(). No other changes should be necessary from the standard OMI calculation. Details of the implementation of this software are outlined in the journal article: Hoffmann CG, Kiladis GN, Gehne M, von Savigny C 2021 A Python Package to Calculate the OLR-Based Index of the Madden-Julian-Oscillation (OMI) in Climate Science and Weather Forecasting. Journal of Open Research Software, 9:9. DOI: https://doi.org/10.5334/jors.331/ (PDF)

The ERA5 based OMI was computed using the python package by: Hoffmann CG, Kiladis GN, Gehne M, von Savigny C 2021 A Python Package to Calculate the OLR-Based Index of the Madden-Julian-Oscillation (OMI) in Climate Science and Weather Forecasting. Journal of Open Research Software, 9:9. DOI: https://doi.org/10.5334/jors.331. The EOFs for the ERA5 based OMI were computed using data from 1940-2023. Using the years 1979-2018 to compute ERA5 EOFs gives good agreement with the observed OMI EOFs in terms of spatial correlation (see below). The EOFs for the time period 1940-2023 have less agreement, but still a pattern correlation above 0.9 for most of the year.

The rMII code and MII values are available upon request from the lead author (Shuguang Wang: wangsg@outlook.com).

For more information for all indices other than the VPM and rMII, please read the article "A comparison of OLR and circulation based indices for tracking the MJO". We ask that if you use the timeseries in you research that, please cite that paper, e.g.:

Kiladis G.N., J. Dias, K.H. Straub, M.C Wheeler, S.N. Tulich, K. Kikuchi, K.M. Weickmann, M.J. Ventrice. A comparison of OLR and circulation based indices for tracking the MJO. Monthly Weather Review, May 2014, 142 1697-1715.

For the VPM, please cite:

Ventrice et al. A Modified Multivariate Madden-Julian Oscillation Index Using Velocity Potential. Monthly Weather Review, December 2013, 141, p. 4197-4120.

For the rMII, please cite

Wang et al: Multivariate Index for Tropical Intraseasonal Oscillations Based on the Seasonally-Varying Modal Structures: JGR Atmospheres, Feb 2022, Vol 127, pp 1-19. https://doi.org/10.1029/2021JD035961.

For the KRMM, please cite

Lintner, B. R., D. Giannakis, M. Pike, J. Slawinska (2023). Identification of the Madden–Julian Oscillation with data-driven Koopman spectral analysis. Geophys. Res. Lett., 50, e2023GL102743. https://doi.org/doi.org/10.1029/2023GL102743. (copy is here)

Python routines to calculate OMI

MATLAB routines to calculate KRMM

courtesy of Dimitrios Giannakis at:https://github.com/dg227/NLSA/tree/master/pubs/LintnerEtAl23_GRL

Composite streamfunction and OLR patterns for RMM and OMI based on the events that exceed 1 standard deviation for each phase of the PC combination.

These are based on data from 1979 through 2012, and the number of events in each composite is given as "N= " at the bottom of each plot. Blue shading denotes negative OLR anomalies (regions of convection) and red positive (suppressed), with two levels of shading at +- 10 and +- 6 W/m**2. Streamfunction contour interval is 5 X 10**5 m**2/s at 200 hPa, and 2 X 10**5 m**2/s at 850 hPa. To facilitate comparison with RMM, these composites are constructed by reversing the sign of OMI PC1 and the OMI PC ordering, so that OMI(PC2) is analogous to RMM(PC1) and -OMI(PC1) is analogous to RMM(PC2), as described in Kiladis et al. 2014.

200mb OMI DJF

MJO EOF Patterns

OMI EOF patterns.

The ASCII EOF values are available via ftp/downloads for EOF1 and EOF2 . They can be read using the code read.eof.f. User can retrieve the files via the web or using an (anonymous) ftp client at the address ftp2.psl.noaa.gov. Then, cd to /Datasets.other/MJO/.

OMI EOF Animations

An animation of the daily OMI EOF's (realtime player format).
A javascript animation of the daily OMI EOF's.

VPM EOF patterns.

The VPM EOFs are computed using zonal wind and velocity potential based on NCEP reanalysis version 1 from 1979 to 2012.

FMO EOF patterns.

The ASCII FMO EOF values are available from here.

FMO Spatial EOFs

Comparison of OMI EOFs from ERA5 vs Observed OLR

Pattern correlation between observed OMI EOFs from 1979-2012 and ERA5 OMI EOFs from 1979-2018 for each day of the year. EOF patterns are correlated at 0.95 or above and PC time series are correlated at above 0.97 and are deemed sufficiently similar. The ERA5 based OMI was computed using the python package by: Hoffmann CG, Kiladis GN, Gehne M, von Savigny C 2021 A Python Package to Calculate the OLR-Based Index of the Madden-Julian-Oscillation (OMI) in Climate Science and Weather Forecasting. Journal of Open Research Software, 9:9. DOI: https://doi.org/10.5334/jors.331/ (PDF).

Other MJO indices

The All-season Real-time Multivariate (RMM) MJO Index (Wheeler-Hendon) from the The Centre for Australian Weather and Climate Research.

Bimodal ISO Index for historical data analysis and real time monitoring from the International Pacific Research Center.
Carl Schreck's analysis of the RMM components.

Forecasts

rMII phase diagram for the latest last 30 days and the latest CFS forecast ensemble.
rMII was computed as above for the last 30 days. Each CFSv2 forecast ensemble member was then used to compute an rMII forecast for the next 40 days.

Primer

What is an MJO and how does it affect the weather?

a. What is an MJO?

The Madden-Julian Oscillation is a mode of sub-seasonal atmospheric variability that influences the location and strength of tropical precipitation as in this classic schematic (left)

from Rol Madden and Paul Julian. Typically the convectively active stage of an MJO starts over the equatorial Indian Ocean and moves slowly eastward at 3-5 m/s toward the west and central Pacific Ocean. The active stage is followed by a convectively suppressed stage and together they give rise to precipitation anomalies (i.e., departures from normal) that have a "dipole" structure. The "cycle" repeats itself approximately every 5 days. Sometimes the debris from a previous event starts a new MJO over the Indian Ocean.

The MJO was first described in 1971 but a field project held over the west equatorial Pacific during 1992-93 raised awareness of the MJO as a coherent phenomenon, possibly useful for weekly predictions of tropical precipitation and extratropical weather patterns. The MJO is best defined over the oceanic warm pool, which extends from the Indian Ocean to the central Pacific and where complex variations of precipitation occur from day to day. The warm pool is generally defined by sea surface temperatures that are > 28C. As a result of this intense activity, the MJO is sometimes difficult to see in a sequence of satellite pictures of the Tropics. This link shows a satellite animation of a strong MJO that occurred during December 2003 to January 2004. Note the non-steady or transient nature of the convection, some of which can be seen more clearly when the data are averaged in space or time. Ways to monitor the MJO are illustrated later.

b) How can an MJO affect the weather?

When an MJO moves eastward through the Indo-Pacific Ocean region, it produces large, slowly changing departures from the normal tropical precipitation. These departures change the atmospheric circulation and, through the phenomenon of Rossby wave dispersion, the changes can propagate into higher latitudes. The accompanying picture

Rossby Wave Propagation

from Sardeshmukh and Hoskins illustrates the phenomenon for the situation where the "base state" atmospheric circulation is extremely simple. The picture shows upper atmospheric ridges and troughs emanating from a region of "precipitation" forcing over the west Pacific and extending poleward into both hemispheres. The circulation features can alter the path and intensity of synoptic waves in mid-latitudes and thereby can affect the weather. The actual "base state" through which these Rossby waves propagate is very complicated, involving jet streams, storm tracks and other regional circulation features. These features channel or interact with the Rossby wave energy and result in a pattern of troughs and ridges that is highly variable from case to case. This makes it difficult to predict the effect of an MJO, especially in regions far removed (e.g., North America) from the MJO's centers of action over the oceanic warm pool. The MJO signal is fairly small north of 30N on average.

c) How do MJOs interact with slower climate processes like ENSO?

MJOs may play a role in the transition to an El Nino or La Nina.This means they help determine details of the timing and/or amplitude of a warming or cooling event. However, the transitional stage of an ENSO event is complicated and the MJO's role is still being investigated. MJOs were prominent during the 1996-97 northern winter at the early stage of the 1997-98 El Nino and this led to heightened awareness of a possible MJO-ENSO link.The 1996-97 MJO activity was by some measures the greatest in the ~30 year record of outgoing longwave radiation (OLR, a proxy for deep tropical convection). On the other hand, 1997-98 had the weakest activity in the record.The two years are contrasted here using a time-longitude or Hovmoller diagram of total OLR for 1996-97 and 1997-98


1996-97 OLR	1997-98 OLR

Once SST anomalies associated with an ENSO are in place, the MJO life cycle is influenced by the SST anomalies. When or if an MJO develops, the simplest effect during El Nino is a farther eastward movement of the convection into the central Pacific whereas during La Nina the convection anomalies barely get into the western Pacific. In both cases the MJO still "starts" over the Indian Ocean.

Despite this influence on the MJO life cycle, it is unclear whether overall MJO activity is influenced by the phase of the ENSO cycle. The natural variation of MJO activity is too large and observed datasets are too short to provide a definitive answer to this question. Transient convection at all time scales increases when sea surface temperatures (SST) reach ~ 29C (84F). On the other hand, there is a significant relationship between MJO activity and SST anomalies over the western Pacific Ocean (140-180E).

Scatter plot of SST vs. MJO activity

When ENSO is in a neutral stage during the northern fall season and anomalously warm SST are present in the west Pacific Ocean, stronger MJO activity follows during the winter season. The 2003-04 northern winter was a good illustration of this relationship.

d) How can the atmosphere and ocean signals associated with the MJO be determined?

The MJO life cycle can be broken down into stages using an index based, for example, on the location of the MJO's tropical convection anomaly. The atmosphere's observed large-scale circulation and/or local weather anomalies can then be averaged over many cases to produce a "composite" `anomaly for each stage. Time filtering is usually applied to exclude higher and lower frequency variability. The results produce an estimate of the MJO signal or influence based on the observational record. The composite anomalies of circulation and weather are generally weak in the extratropics and moderately strong in the tropics and subtropics. They will be described in more detail later.

e) How do MJOs interact with faster weather processes like synoptic scale waves and wavetrains?

The composite anomalies, assuming they are statistically significant and large enough, produce persistent (1-3 week) changes in the atmospheric flow due to the MJO. These changes can influence the development and propagation of synoptic-scale weather systems, i.e. they influence the storm track. For example, during one stage Pacific Ocean storms tend to be stronger and farther south when they make landfall on the U.S. west coast. At the opposite stage the storm or wave energy may split and move south into the tropics and north into Canada, favoring storms over the central U.S. Plains. Because the MJO extratropical signal is weak there are large variations of the actual circulation or weather observed in individual cases. Other processes may overwhelm or mask the MJO signal.

Daily monitoring of many individual cases has produced qualitative evidence for interaction between the circulation induced by flare-ups of convection within the MJO's convective envelope and synoptic scale waves or wavetrains passing by in mid-latitudes and the subtropics. These daily interactions are large amplitude and sometimes contribute to the rapid initiation of the composite MJO signal and/or major transitions in weather patterns.Until GCMs are able to simulate MJOs, we rely on daily monitoring and a subseasonal synoptic model to provide an early indication of such situations.

f) How is the MJO signal extracted from climate forecasts?

This section will discuss how the MJO signal is pulled from models and forecasts. Ideally, the MJO signal in the forecasts in the group will be displayed here. Advantages and disadvantages of the different methods will be discussed.

Project on EOFs

Modes of variability are extracted from the data which describe the MJO. Then, the forecasted time series of a variable like velocity potential is projected onto those modes and an amplitude and phase is extracted.

References

2022

Amaya, D. J., M. G. Jacox, J. Dias, M. A. Alexander, K. Karnauskas, J. D. Scott and M. Gehne (January 2022): Subseasonal-to-seasonal forecast skill in the California Current System and its connection to coastal Kelvin waves. J. Geophys. Res. Oceans, 127 (1), e2021JC017892, https://doi.org/10.1029/2021JC017892..
Bengtsson, L., L. Gerard, J. Han, M. Gehne, W. Li and J. Dias (December 2022): A Prognostic-Stochastic and Scale-Adaptive Cumulus Convection Closure for Improved Tropical Variability and Convective Gray-Zone Representation in NOAA’s Unified Forecast System (UFS). Mon. Wea. Rev., 150 (12), 3211–3227, https://doi.org/10.1175/MWR-D-22-0114.1.
Berrington, A. H., N. Sakaeda, J. Dias and G. N. Kiladis (August 2022): Relationships Between the Eastward Propagation of the Madden-Julian Oscillation and its Circulation Structure. J. Geophys. Res. Atmos., 127 (16), e2021JD035806, https://doi.org/10.1029/2021JD035806.
Cheng, Y.-M., S. N. Tulich, G. N. Kiladis and J. Dias (October 2022): Two extratropical pathways to forcing tropical convective disturbances. J. Climate, 35 (20), 2987–3009, https://doi.org/10.1175/JCLI-D-22-0171.1.
Hsiao, W.-T., E. A. Barnes, E. D. Maloney, S. N. Tulich, J. Dias and G. N. Kiladis (March 2022): Role of the Tropics and its Extratropical Teleconnections in State-Dependent Improvements of U.S. West Coast UFS Precipitation Forecasts. Geophys. Res. Lett., 49 (5), e2021GL096447, https://doi.org/10.1029/2021GL096447.
Knippertz, P., M. Gehne, G. N. Kiladis, K. Kikuchi, A. R. Satheesh, P. E. Roundy, G.-Y. Yang, J. Dias, A. H. Fink, J. Methven, A. Schlueter, F. Sielmann and M. C. Wheeler (July 2022): The intricacies of identifying equatorial waves. Q. J. R. Meteorol. Soc., 148 (747), 2814-2852, https://doi.org/10.1002/qj.4338.
Wang, S., Z. K. Martin, A. H. Sobel, M. K. Tippett, J. Dias and G. N. Kiladis (February 2022): A multivariate index for tropical intraseasonal oscillations based on seasonally-varying modal structures. J. Geophys. Res. Atmos., 127 (4), e2021JD035961, https://doi.org/10.1029/2021JD035961.
Wolding, B., S. W. Powell, F. Ahmed, J. Dias, M. Gehne, G. N. Kiladis and J. D. Neelin (July 2022): Tropical Thermodynamic–Convection Coupling in Observations and Reanalyses. J. Atmos. Sci., 79 (7), 1781–1803, https://doi.org/10.1175/JAS-D-21-0256.1.

2021

Dias, J., S. N. Tulich, M. Gehne and G. N. Kiladis(September 2021): Tropical Origins of Weeks 2–4 Forecast Errors during the Northern Hemisphere Cool Season. Mon. Wea. Rev., 149 (9), 2975–2991, https://doi.org/10.1175/MWR-D-21-0020.1.
Gehne, M., B. Wolding, J. Dias and G. N. Kiladis (September 2021): Diagnostics of Tropical Variability for Numerical Weather Forecasts. Wea. Forecasting, 37 (9), 1661–1680, https://doi.org/10.1175/WAF-D-21-0204.1.
Haynes, P., P. Hitchcock, M. Hitchman, S. Yoden, H. H. Hendon, G. N. Kiladis, K. Kodera and I. Simpson (August 2021): The Influence of the Stratosphere on the Tropical Troposphere. J. Meteor. Soc. Japan, 99 (4), 803-845, https://doi.org/10.2151/jmsj.2021-040.
Hoffmann, C. G., G. N. Kiladis, M. Gehne and C. von Savigny (May 2021): A Python Package to Calculate the OLR-Based Index of the Madden- Julian-Oscillation (OMI) in Climate Science and Weather Forecasting. J. Open Res. Software, 9 (1), 9, https://doi.org/10.5334/jors.331.
Tulich, S. N. and G. N. Kiladis (September 2021): On the Regionality of Moist Kelvin Waves and the MJO: The Critical Role of the Background Zonal Flow. J. Adv. Model. Earth Syst., 13 (9), e2021MS002528, https://doi.org/10.1029/2021MS002528.

2020

Sakaeda, N., J. Dias and G. N. Kiladis (September 2020): The unique characteristics and potential mechanisms of the MJO-QBO relationship. J. Geophys. Res. Atmos., 125 (17), e2020JD033196, https://doi.org/10.1029/2020JD033196.
Wolding, B., J. Dias, G. N. Kiladis, E. Maloney and M. Branson (May 2020): Interactions between Moisture and Tropical Convection. Part II: The Convective Coupling of Equatorial Waves . J. Atmos. Sci., 77 (5), 1801-1819, https://doi.org/10.1175/JAS-D-19-0226.1.
Wolding, B., J. Dias, G. N. Kiladis, F. Ahmed, S. W. Powell, E. Maloney and M. Branson (May 2020): Interactions between Moisture and Tropical Convection. Part I: The Coevolution of Moisture and Convection. J. Atmos. Sci., 77 (5), 1783-1799, https://doi.org/10.1175/JAS-D-19-0225.1.

2019

Camberlin, P., W. Gitau, G. N. Kiladis, E. Bosire and B. Pohl (November 2019): Intraseasonal to Interannual Modulation of Diurnal Precipitation Distribution Over Eastern Africa. J. Geophys. Res. Atmos., 124 (22), 11863-11886, https://doi.org/10.1029/2019JD031167.
Dias, J. and G. N. Kiladis (April 2019): The Influence of Tropical Forecast Errors on Higher Latitude Predictions. Geophys. Res. Lett., 46 (8), 4450-4459, https://doi.org/10.1029/2019GL082812.

2018

Dias, J., M. Gehne, G. N. Kiladis, N. Sakaeda, P. Bechtold and T. Haiden (June 2018): Equatorial Waves and the Skill of NCEP and ECMWF Numerical Weather Prediction Systems. Mon. Wea. Rev., 146, 1763-1784, https://doi.org/10.1175/MWR-D-17-0362.1 .
Hoell A. (November 2018): Middle East and Southwest Asia Daily Precipitation Characteristics Associated with the Madden–Julian Oscillation during Boreal Winter. J. Climate, 31, 8843-8860. doi:10.1175/JCLI-D-18-0059.1
Capotondi A. and P. D. Sardeshmukh (October 2018): The Nature of the Stochastic Wind Forcing of ENSO. J. Climate, 31, 8081-8099. doi:10.1175/JCLI-D-17-0842.1
Vera C. S., P. L. M. Gonzalez, B. Liebmann and G. N. Kiladis (November 2018): Seasonal cycle of precipitation variability in South America on intraseasonal timescales. Clim. Dyn., ONLINE, doi:10.1007/s00382-017-3994-1
Kikuchi K., G. N. Kiladis, J. Dias and T. Nasuno (June 2018): Convectively coupled equatorial waves within the MJO during CINDY/DYNAMO: slow Kelvin waves as building blocks. Clim. Dyn., doi:10.1007/s00382-017-3869-5
Sakaeda N., S. W. Powell, J. Dias and G. N. Kiladis (April 2018): The Diurnal Variability of Precipitating Cloud Populations during DYNAMO. J. Atmos. Sci., 75, 1307-1326. doi:10.1175/JAS-D-17-0312.1

2017

Alvarez M. S., C. S. Vera and G. N. Kiladis (November 2017): MJO Modulating the Activity of the Leading Mode of Intraseasonal Variability in South America. Atmosphere, 8 (12), p. 232. doi:10.3390/atmos8120232
Dias J., N. Sakaeda, G. N. Kiladis and K. Kikuchi (August 2017): Influences of the MJO on the space-time organization of tropical convection. J. Geophys. Res. Atmos., 122 (15), 8012-8032. doi:10.1002/2017JD026526
Sakaeda N., G. N. Kiladis and J. Dias (April 2017): The Diurnal Cycle of Tropical Cloudiness and Rainfall Associated with the Madden-Julian Oscillation. J. Climate, 145, 1401-1412. doi:10.1175/JCLI-D-16-0788.1
Sossa A., B. Liebmann, I. Bladé, D. Allured, H. H. Hendon, P. Peterson and A. Hoell (March 2017): Statistical Connection between the Madden–Julian Oscillation and Large Daily Precipitation Events in West Africa. J. Climate, 30, 1999-2010. doi:10.1175/JCLI-D-16-0144.1
Cannon F. , L. M. V. Carvalho, C. Jones, A. Hoell, J. Norris, G. N. Kiladis and A. A. Tahir (February 2017): The influence of tropical forcing on extreme winter precipitation in the western Himalaya. Clim. Dyn., 48 (3), 1213-1232. doi:10.1007/s00382-016-3137-0

2016

Alvarez M. S., C. S. Vera, G. N. Kiladis and B. Liebmann (January 2016): Influence of the Madden Julian Oscillation on precipitation and surface air temperature in South America. Clim. Dyn., 46 (1), 245-262. doi:10.1007/s00382-015-2581-6.
Cannon F. , L. M. V. Carvalho, C. Jones, A. Hoell, J. Norris, G. N. Kiladis and A. A. Tahir (April 2016): The influence of tropical forcing on extreme winter precipitation in the western Himalaya. Clim. Dyn., ONLINE, doi:10.1007/s00382-016-3137-0
Valéds-Pineda R., J. B. Valdés, H. F. Diaz and R. Pizarro-Tapia (June 2016): Analysis of spatio-temporal changes in annual and seasonal precipitation variability in South America-Chile and related ocean-atmosphere circulation patterns. Int. J. Climatol., 36 (8), 2979-3001. doi:10.1002/joc.4532
van der Linden R., A. H. Fink, J. G. Pinto, T. Phan-Van and G. N. Kiladis (August 2016): Modulation of Daily Rainfall in Southern Vietnam by the Madden–Julian Oscillation and Convectively Coupled Equatorial Waves. J. Climate, 29, 5801-5820. doi:10.1175/JCLI-D-15-0911.1

2015

Chen S., M. Flatau, T. G. Jensen, T. Shinoda, J. Schmidt, P. May, J. Cummings, M. Liu, P. E. Ciesielski, C. W. Fairall, et al. (October 2015): A Study of CINDY/DYNAMO MJO Suppressed Phase. J. Atmos. Sci., 72, 3755-3779. doi:10.1175/JAS-D-13-0348.1
de Szoeke S. P., J. B. Edson, J. R. Marion, C. W. Fairall and L. Bariteau (January 2015): The MJO and Air–Sea Interaction in TOGA COARE and DYNAMO. J. Climate, 28, 597-622. doi:10.1175/JCLI-D-14-00477.1

2014

Moum J. N., S. P. de Szoeke, W. D. Smyth, J. B. Edson, H. L. DeWitt, A. J. Moulin, E. J. Thompson, C. J. Zappa, S. A. Rutledge, R. H. Johnson and C. W. Fairall (August 2014): Air–Sea Interactions from Westerly Wind Bursts During the November 2011 MJO in the Indian Ocean. Bull. Am. Meteorol. Soc., 95 (8), 1185-1199. doi:10.1175/BAMS-D-12-00225.1
Kiladis G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N. Tulich, K. Kikuchi, K. M. Weickmann and M. J. Ventrice (May 2014): A Comparison of OLR and Circulation-Based Indices for Tracking the MJO. Mon. Weather Rev., 142 (5), 1697-1715. doi:10.1175/MWR-D-13-00301.1
Hamill T. M. and G. N. Kiladis (February 2014): Skill of the MJO and Northern Hemispheric Blocking in GEFS Medium-Range Reforecasts. Mon. Weather Rev., 142 (2), 868-885. doi:10.1175/MWR-D-13-00199.1

2013

Ventrice M. J., M. C. Wheeler, H. H. Hendon, C. J. Schreck III, C. D. Thorncroft and G. N. Kiladis (December 2013): A Modified Multivariate Madden–Julian Oscillation Index Using Velocity Potential. Mon. Weather Rev., 141 (12), 4197-4210. doi:10.1175/MWR-D-12-00327.1
Dias J., S. Leroux, S. N. Tulich and G. N. Kiladis (April 2013): How systematic is organized tropical convection within the MJO? Geophys. Res. Lett., 40 (7), 1420-1425. doi:10.1002/grl.50308

2012

Guan B., D. E. Waliser, N. P. Molotch, E. J. Fetzer and P. J. Neiman (February 2012): Does the Madden-Julian Oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Mon. Weather Rev ., 140, 325-345. doi:10.1175/MWR-D-11-00087.1

2011

Riley E. , B. Mapes and S. Tulich (December 2011): Clouds associated with the Madden–Julian Oscillation: A new perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051. doi:10.1175/JAS-D-11-030.1

2010

Gottschalck J., . . . ., K. M. Weickmann, et al. (September 2010): A Framework for Assessing Operational Madden-Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project. Bull. Am. Meteorol. Soc., 91 (9), 1247-1258. doi:10.1175/2010BAMS2816.1
Serra Y. L., G. N. Kiladis and K. I. Hodges (September 2010): Tracking and Mean Structure of Easterly Waves over the Intra-Americas Sea. J. Climate, 23 (18), 4823-4840. doi:10.1175/2010JCLI3223.1
Janicot S., F. Mounier, S. Gervois, B. Sultan and G. N. Kiladis (July 2010): The Dynamics of the West African Monsoon. Part V: The Detection and Role of the Dominant Modes of Convectively Coupled Equatorial Rossby Waves. J. Climate, 23 (14), 4005-4024. doi:10.1175/2010jcli3221.1

2009

Kim D., K. R. Sperber, W. Stern, D. E. Waliser, I.S. Kang, E. Maloney, W. Wang, K. M. Weickmann, J. Benedict, M. Khairoutdinov, M.-I. Lee, R. Neale, M. Suarez, K. Thayer-Calder and G. Zhang (December 2009): Application of MJO Simulation Diagnostics to Climate Models. J. Climate, 22 (23), 6413-6436. doi:10.1175/2009JCLI3063.1
Lin J. L., T. Shinoda, B. Liebmann, T. Qian, W. Han, P. E. Roundy, J. Zhou and Y. Zheng (September 2009): Intraseasonal Variability Associated with Summer Precipitation over South America Simulated by 14 IPCC AR4 Coupled GCMs. Mon. Weather Rev., 137 (9), 2931-2954. doi:10.1175/2009MWR2777.1
Newman M., P. D. Sardeshmukh and M. C. Penland (June 2009): How Important Is Air-Sea Coupling in ENSO and MJO Evolution? J. Climate, 22 (11), 2958-2977. doi:10.1175/2008JCLI2659.1
Waliser D. E., K. R. Sperber, . . . ., K. M. Weickmann and al. et (June 2009): MJO Simulation Diagnostics. J. Climate, 22 (11), 3006-3030. doi:10.1175/2008JCLI2731.1
Weickmann K. and E. Berry (May 2009): The Tropical Madden-Julian Oscillation and the Global Wind Oscillation. Mon. Weather Rev., 137 (5), 1601-1614. doi:10.1175/2008MWR2686.1
Janicot S., F. Mounier, N. M. Hall, S. Leroux, B. Sultan and G. N. Kiladis (March 2009): Dynamics of the West African Monsoon. Part IV: Analysis of 25-90-Day Variability of Convection and the Role of the Indian Monsoon. J. Climate, 22 (6), 1541-1565. doi:10.1175/2008JCLI2314.1

2008

Lin J., B. E. Mapes, K. M. Weickmann, G. N. Kiladis, et al. (June 2008): North American Monsoon and Convectively Coupled Equatorial Waves Simulated by IPCC AR4 Coupled GCMs. J. Climate, 21 (12), 2919-2937. doi:10.1175/2007JCLI1815.1
Shinoda T., P. E. Roundy and G. N. Kiladis (May 2008): Variability of Intraseasonal Kelvin Waves in the Equatorial Pacific Ocean. J. Phys. Oceanogr., 38 (5), 921-944. doi:10.1175/2007JPO3815.1
Haertel P. T., G. Kiladis, A. Denno and T. M. Rickenbach (March 2008): Vertical-Mode Decompositions of 2-Day Waves and the Madden-Julian Oscillation. J. Atmos. Sci., 65 (3), 813-833. doi:10.1175/2007JAS2314.1

2007

Sardeshmukh P. D. and P. Sura (December 2007): Multiscale Impacts of Variable Heating in Climate. J. Climate, 20 (23), 5677-5695. doi:10.1175/2007JCLI1411.1
Weickmann K. and E. Berry (February 2007): A Synoptic-Dynamic Model of Subseasonal Atmospheric Variability. Mon. Weather Rev., 135 (2), 449-474. doi:10.1175/MWR3293.1

2006

Straub K. H., G. N. Kiladis and P. E. Ciesielski (December 2006): The role of equatorial waves in the onset of the South China Sea summer monsoon and the demise of El Niño during 1998. Dyn. Atmos. Oceans, 42 (1-4), doi:10.1016/j.dynatmoce.2006.02.005
Roundy P. E. and G. N. Kiladis (October 2006): Observed relationships between oceanic Kelvin waves and atmospheric forcing. J. Climate, 19 (20), 5253-5272. doi:10.1175/JCLI3893.1
Lin J., G. N. Kiladis, B. Mapes, K. M. Weickmann, K. R. Sperber, W. Lin, M. C. Wheeler, S. Schubert, A. Del Genio, L. J. Donner, S. Emori, J.-F. Gueremy, F. Hourdin, P. J. Rasch, E. Roeckner and J. F. Scinocca (June 2006): Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models. Part I: Convective Signals. J. Climate, 19 (12), 2665-2690. doi:10.1175/JCLI3735.1

2005

Lin J.-L., M. Zhang and B. Mapes (July 2005): Zonal Momentum Budget of the Madden-Julian Oscillation: The Source and Strength of Equivalent Linear Damping. J. Atmos. Sci., 62 (7), 2172-2188. doi:10.1175/JAS3471.1

2004

Carvalho, L., C Jones and B. Liebmann (2004), The South Atlantic convergence zone: Intensity, form, persistence, and relationships with intraseasonal to interannual activity and extreme rainfall, Journal of Climate, 17(1), 88-108, 10.1175/1520-0442%282004%29017%3C0088:TSACZI%3E2.0.CO;2. .
Kiladis, G. N. and B. E. Mapes (2006), Convective life cycles and scale interactions in tropical waves, Dynamics of Atmospheres and Ocean, 42(1-4), 1-2, 10.1016/j.dynatmoce.2006.07.001. .
Kiladis, G. N., K. Straub and P. Haertel (2005), Zonal and vertical structure of the Madden-Julian oscillation, J. Atmospheric Sciences, 62(8), 2790-2809, 10.1175/JAS3520.1..
Kim, D., K. Sperber, W. Stern, D. Waliser, I. S. Kang, E. Maloney, W. Wang, K. M. Weickmann, J. Benedict, M. Khairoutdinov, M. I. Lee, R. Neale, M. Suarez, K. Thayer-Calder and G. Zhang (2009), Application of MJO Simulation Diagnostics to Climate Models, Journal of Climate, 22(23), 6413-6436, 10.1175/2009jcli3063.1..
Liebmann, B., G. N. Kiladis, C. Vera, A. Saulo and L. Carvalho (2004), Subseasonal variations of rainfall in South America in the vicinity of the low-level jet east of the Andes and comparison to those in the South Atlantic convergence zone, Journal of Climate, 17(19), 3829-3842, 10.1175/1520-0442%282004%29017%3C3829:SVORIS%3E2.0.CO;2. .
Lin, J. L., G. N. Kiladis, B E Mapes, K. M. Weickmann, K R Sperber, W Lin, M C Wheeler, S D Schubert, A Del Genio, L J Donner, S Emori, J -F Gueremy, F Hourdin, P J Rasch, E Roeckner and J F Scinocca (2006), Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals, Journal of Climate, 19(12), 2665-2690, 10.1175/JCLI3735.1..
Lin, J. L., B. E. Mapes, M. Zhang and M. Newman (2004), Stratiform precipitation, vertical heating profiles, and the Madden-Julian oscillation, J. Atmospheric Sciences, 61(3), 296-309, 10.1175/1520-0469%282004%29061%3C0296:SPVHPA%3E2.0.CO;2..
Lin, J. L., T. Shinoda, B. Liebmann, T. T. Qian, W. Q. Han, P. Roundy, J. Y. Zhou and Y. Zheng (2009), Intraseasonal Variability Associated with Summer Precipitation over South America Simulated by 14 IPCC AR4 Coupled GCMs, Monthly Weather Review, 137(9), 2931-2954, 10.1175/2009mwr2777.1..
Shinoda, T., P. E. Roundy and G. N. Kiladis (2008), Variability of intraseasonal Kelvin waves in the equatorial Pacific Ocean, Journal of Physical Oceanography, 38(5), 921-944, 10.1175/2007jpo3815.1. .
Straub, K. H., G. N. Kiladis and P. E. Ciesielski (2006), The role of equatorial waves in the onset of the South China Sea summer monsoon and the demise of El Nino during 1998, Dynamics of Atmospheres and Ocean, 42(1-4, Sp. Iss. SI), 216-238, 10.1016/j.dynatmoce.2006.02.005. .
Waliser, D., K. Sperber, H. Hendon, D. Kim, M. Wheeler, K. M. Weickmann, C. Zhang, L. Donner, J. Gottschalck, W. Higgins, I. S. Kang, D. Legler, M. Moncrieff, F. Vitart, B. Wang, W. Wang, S. Woolnough, E. Maloney, S. Schubert, W. Stern and O. Clivar Madden-Julian (2009), MJO Simulation Diagnostics, Journal of Climate, 22(11), 3006-3030, 10.1175/2008jcli2731.1. .
Weickmann, K. M. and E. Berry (2009), The Tropical Madden-Julian Oscillation and the Global Wind Oscillation, Monthly Weather Review, 137(5), 1601-1614, 10.1175/2008mwr2686.1..
Weickmann, K. M. and E. Berry (2007), A synoptic-dynamic model of subseasonal atmospheric variability, Monthly Weather Review, 135(2), 449-474, 10.1175/mwr3293.1.

2003

Lin, J., B. Mapes, M. Zhang, and M. Newman, 2003: Stratiform precipitation, vertical heating profiles, and the Madden-Julian Oscillation. J. Atmos. Sci., in press.

2002

Shinoda, T., and H. H. Hendon, 2002: Rectified wind forcing and latent heat flux produced by the Madden-Julian oscillation. J. Climate., 15, 3500-3508. [Abstract]

2001

Shinoda, T., and H. H. Hendon, 2001: Upper ocean heat budget in response to the Madden-Julian Oscillation in the western equatorial Pacific. J. Climate, 14, 4147-4165. [Abstract]
Lee, M.-I., I.-S. Kang, J.-K. Kim, and B. E. Mapes, 2001: Influence of cloud-radiation interaction on simulating tropical intraseasonal oscillation with an atmospheric general circulation model. J. Geophys. Res., 106, 14,219-14,233. [Abstract]

2000

Hendon, H. H., B. Liebmann, M. E. Newman, J. D. Glick, and J. E. Schemm, 2000: Medium range forecast errors associated with active episodes of the Madden-Julian oscillation. Mon. Wea. Rev., 128, 69-86. [Abstract]

1999

Hendon, H. H., C. Zhang, and J. D. Glick, 1999: Interannual variation of the Madden-Julian oscillation during austral summer. J. Climate, 12, 2538-2550. [Abstract]

1998

Hendon, H. H., B. Liebmann, and J. D. Glick, 1998: Oceanic Kelvin waves and the Madden-Julian oscillation. J. Atmos. Sci., 55, 88-101. [Abstract]

1997

Weickmann, K. M., G. N. Kiladis, and P. D. Sardeshmukh, 1997: The dynamics of intraseasonal atmospheric angular momentum oscillations.J. Atmos. Sci., 54, 1445-1461. [Abstract]
Zhang, C., and Hendon, H. H., 1997: On the propagating and standing components of the intraseasonal oscillation in tropical convection. J. Atmos. Sci., 54, 741-752. [Abstract]

1995

Hendon, H. H., 1995: Length of day fluctuations associated with the Madden Julian oscillation. J. Atmos. Sci., 52, 2373-2383. [Abstract]

1994

Weickmann, K., and P. Sardeshmukh, 1994: The atmospheric angular momentum budget associated with a Madden-Julian oscillation. J. Atmos. Sci., 51, 3194-3208. [Abstract]
Hendon, H. H., and B. Liebmann, 1994: Organization of convection within the Madden-Julian Oscillation. J. Geophys. Res., 99, 8073-8083. [Abstract]
Liebmann, B., H. H. Hendon, and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian oceans and the Madden-Julian oscillation. J. Meteor. Soc. Japan, 72, 401-412. [Abstract]

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MJO Research Team

Maria Gehne
PSL | CIRES Associate Scientist

Juliana Dias
PSL Research Scientist

George Kiladis
PSL Research Scientist

Brandon Wolding
PSL | CIRES Research Scientist

Stefan Tulich
PSL | CIRES Research Scientist

Resources

Tutorials and Monitoring

An All-season Real-time Multivariate MJO Index	Forecast and monitoring plots of the MJO that utilize and empirically derived structure of the MJO based on historic data and include predictions for Australia.
What is the MJO, and why do we care?	A short tutorial on the MJO from the Climate.gov ENSO blog.
Multi-Scale MJO Research Page	The Iowa State Tropical Atmospheric Dynamics Group researches the MJO and how it interacts with waves and the mean state of the atmosphere/ocean.
Adrian Matthews MJO page	A very complete page with MJO description, current research topics, illustrations and animations from Adrian Matthews at the University of East Anglia.
MJO monitoring page	From NOAA/CPC. Includes various plots of the current status of the 30-60 day oscillation including OLR anomalies, geopotential height and other animations and satellite photos. Also includes a very nice FAQ on the oscillation mechanism and evolution and it's relationship to other climate processes. They also have a MJO daily index.
U of Wyoming MJO Tutorial	A tutorial on the MJO with schematics, a brief history and a bibliography on the phenomenon.
PSL Spotlight Article: MJO Tutorial	The MJO and it's affect on the onset of an El Niño event.

Related PSL Sites

El Niño/Southern Oscillation (ENSO)
The ENSO/MJO connection
The Maproom
Tropical Pacific OLR Forecast

Links to datasets used in PSL MJO Index Calculations

NCEP/NCAR Reanalysis
OLR
Precipitation
CFSR Ensemble Forecast model

PSL Interactive Data Plotting and Weather/Climate Monitoring Pages

PSL Map Room Includes maps of many variables for many different variables and time scales. Includes links to MJO Composites along with many other climate/weather variables.
Interactive MJO Composites Choose MJO to get list of high/low MJO activity days. Use these days on the page daily composite page to obtain composites for different variables.
Time Section Plots Plots time/latitude and time/longitude plots of NCEP reanalysis and operational data. Plots means and anomalies.
Plot Daily Data Plots maps or crossections of daily and daily averaged data. Includes anomalies, means and climos. Includes OLR.