The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices have been widely applied to diagnose and track the progression of the MJO. Although it has been well demonstrated that the MJO contributes to the leading signals in these indices, the RMM indices vary erratically from day to day. These variations are associated with noise in the outgoing longwave radiation (OLR) and wind data used to generate the indices. This note demonstrates that some of this “noise” evolves systematically and is associated with other types of propagating modes that project onto the RMM eigenmodes. OLR and zonal wind data are filtered in the wavenumber–frequency domain for the MJO, convectively coupled equatorial Rossby (ER) waves, and convectively coupled Kelvin waves. The filtered data are then projected onto the RMM modes. An example phase space associated with these projections is presented. Linear regression is then applied to isolate the wave signals from random variations in the same bands of the wavenumber–frequency domain, and the regressed data are projected onto the RMM EOFs. Results demonstrate the magnitudes of the contributions of the systematically evolving signals associated with these waves to variations in the RMM principal components, and how these contributions vary with the longitude of the active moist deep convection coupled to the waves.

In this study, the contribution of low-frequency (.100 days), Madden-Julian oscillation (MJO), and convectively coupled equatorial wave (CCEW) variability to the skill in predicting convection and winds in the tropics at weeks 1-3 is examined. We use subseasonal forecasts from the Navy Earth System Model (NESM); NCEP Climate Forecast System, version 2 (CFSv2); and ECMWF initialized in boreal summer 1999-2015. A technique for performing wavenumber-frequency filtering on subseasonal forecasts is introduced and applied to these datasets. This approach is better able to isolate regional variations in MJO forecast skill than traditional global MJO indices. Biases in the mean state and in the activity of the MJO and CCEWs are smallest in the ECMWF model. The NESM overestimates cloud cover as well as MJO, equatorial Rossby, and mixed Rossbygravity/tropical depression activity over the west Pacific. The CFSv2 underestimates convectively coupled Kelvin wave activity. The predictive skill of the models at weeks 1-3 is examined by decomposing the forecasts into wavenumber-frequency signals to determine the modes of variability that contribute to forecast skill. All three models have a similar ability to simulate low-frequency variability but large differences in MJO skill are observed. The skill of the NESM and ECMWF model in simulating MJO-related OLR signals at week 2 is greatest over two regions of high MJO activity, the equatorial Indian Ocean and Maritime Continent, and the east Pacific. The MJO in the CFSv2 is too slow and too weak, which results in lower MJO skill in these regions.

The Madden‐Julian oscillation (MJO) is the leading source of global subseasonal predictability; however, many dynamical forecasting systems struggle to predict MJO propagation through the Maritime Continent. Better understanding the biases in simulated physical processes associated with MJO propagation is the key to improve MJO prediction. In this study, MJO prediction skill, propagation processes, and mean state biases are evaluated in reforecasts from models participating in the Subseasonal Experiment (SubX) and Subseasonal to Seasonal (S2S) prediction projects. SubX and S2S reforecasts show MJO prediction skill out to 4.5 weeks based on the Real‐time Multivariate MJO index consistent with previous studies. However, a closer examination of these models' representation of MJO propagation through the Maritime Continent reveals that they fail to predict the MJO convection, associated circulations, and moisture advection processes beyond 10 days with most of models underestimating MJO amplitude. The biases in the MJO propagation can be partly associated with the following mean biases across the Indo‐Pacific: a drier low troposphere, excess surface precipitation, more frequent occurrence of light precipitation rates, and a transition to stronger precipitation rates at lower humidity than in observations. This indicates that deep convection occurs too frequently in models and is not sufficiently inhibited when tropospheric moisture is low, which is likely due to the representation of entrainment.

This paper explores a three-way interaction between an African easterly wave (AEW), the diurnal cycle of convection over the Guinea Highlands (GHs), and a convectively coupled atmospheric equatorial Kelvin wave (CCKW). These interactions resulted in the genesis of Tropical Storm Debby over the eastern tropical Atlantic during late August 2006. The diurnal cycle of convection downstream of the GHs during the month of August is explored. Convection associated with the coherent diurnal cycle is observed off the coast of West Africa during the morning. Later, convection initiates over and downstream of the GHs during the afternoon. These convective features were pronounced during the passage of the pre-Debby AEW. The superposition between the convectively active phase of a strong CCKW and the pre-Debby AEW occurred shortly after merging with the diurnally varying convection downstream of the GHs. The CCKW-AEW interaction preceded tropical cyclogenesis by 18 h. The CCKW provided a favorable environment for deep convection. An analysis of highamplitude CCKWs over the tropical Atlantic and West Africa during the Northern Hemisphere boreal summer highlights a robust relationship between CCKWs and the frequency of tropical cyclogenesis. Tropical cyclogenesis is found to be less frequent immediately prior to the passage of the convectively active phase of the CCKW, more frequent during the passage, and most frequent just after the passage.