Madden–Julian Oscillation Prediction Skill Research Articles

Convective Momentum Transport Associated with the Madden–Julian Oscillation Based on a Reanalysis Dataset

Abstract A better understanding of multiscale interactions within the Madden–Julian oscillation (MJO), including momentum exchanges, is critical for improved MJO prediction skill. In this study, convective momentum transport (CMT) associated with the MJO is analyzed based on the NOAA Climate Forecast System Reanalysis (CFSR). A three-layer vertical structure associated with the MJO, as previously suggested in the mesoscale momentum tendency profile based on global cloud-resolving model simulations, is evident in the subgrid-scale momentum tendency from the CFSR. Positive (negative) subgrid-scale momentum tendency anomalies are found near the surface, negative (positive) anomalies are found in the low to midtroposphere, and positive (negative) anomalies in the upper troposphere are found within and to the west (east) of the MJO convection. This tends to damp the MJO circulation in the free atmosphere, while enhancing MJO winds near the surface. In addition, it could also reduce the MJO eastward propagation speed and lead to the backward tilt with height in the observed MJO structure through a secondary circulation near the MJO center. Further analyses illustrate that this three-layer vertical structure in subgrid-scale momentum tendency largely balances the grid-scale momentum transport of the zonal wind component u, mainly through the transport of seasonal mean u by the MJO-scale vertical motion. Synoptic-scale systems, which were previously proposed to be essential for the u-momentum transport of the MJO, however, are found to play a minor role for the total grid-scale momentum tendency. The above momentum tendency structure is also confirmed with the ECMWF analysis for the Year of Tropical Convection (YOTC) that lends confidence to these above results based on the CFSR.

The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

Abstract Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

Madden-Julian Oscillation prediction skill of a new-generation global model demonstrated using a supercomputer.

Global cloud/cloud system-resolving models are perceived to perform well in the prediction of the Madden–Julian Oscillation (MJO), a huge eastward -propagating atmospheric pulse that dominates intraseasonal variation of the tropics and affects the entire globe. However, owing to model complexity, detailed analysis is limited by computational power. Here we carry out a simulation series using a recently developed supercomputer, which enables the statistical evaluation of the MJO prediction skill of a costly new-generation model in a manner similar to operational forecast models. We estimate the current MJO predictability of the model as 27 days by conducting simulations including all winter MJO cases identified during 2003–2012. The simulated precipitation patterns associated with different MJO phases compare well with observations. An MJO case captured in a recent intensive observation is also well reproduced. Our results reveal that the global cloud-resolving approach is effective in understanding the MJO and in providing month-long tropical forecasts.

Low‐order stochastic model and “past‐noise forecasting” of the Madden‐Julian Oscillation

This paper presents a predictability study of the Madden‐Julian Oscillation (MJO) that relies on combining empirical model reduction (EMR) with the “past‐noise forecasting” (PNF) method. EMR is a data‐driven methodology for constructing stochastic low‐dimensional models that account for nonlinearity, seasonality and serial correlation in the estimated noise, while PNF constructs an ensemble of forecasts that accounts for interactions between (i) high‐frequency variability (noise), estimated here by EMR, and (ii) the low‐frequency mode of MJO, as captured by singular spectrum analysis (SSA). A key result is that—compared to an EMR ensemble driven by generic white noise—PNF is able to considerably improve prediction of MJO phase. When forecasts are initiated from weak MJO conditions, the useful skill is of up to 30 days. PNF also significantly improves MJO prediction skill for forecasts that start over the Indian Ocean.

MJO prediction in the NCEP Climate Forecast System version 2

The Madden–Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999–2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S–15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction.

Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system

Predictions of the Madden–Julian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean–atmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980–2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850 hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21 days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12 days. The prediction limit from POAMA increases by less than 2 days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December–January–February season and minimum skill in June–July–August. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10 days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system.

Experimental Dynamical Forecast of an MJO Event Observed during TOGA-COARE Period

With a hybrid atmosphere-ocean coupled model we carried out an experimental forecast of a well documented Madden-Julian Oscillation (MJO) event that was observed during the period of Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). The observed event, orig- inated in the western Indian Ocean around 6 January 1993, moved eastward with a phase speed of about 6.2 m s −1 and reached the dateline around February 1. The hybrid coupled model reasonably forecasts the MJO initiation in the western Indian Ocean, but the predicted MJO event propagates too slow (~ 4.4 m s −1 ). Results from previous observational studies using unprecedented humidity profiles obtained by NASA Aqua/AIRS satellite sugg- ested that two potential physical processes may be responsible for this model caveat. After improving the cumulus parameterization scheme based on the observa- tions, the model is able to forecast the same event one month ahead. Further sensitivity experiment confirms that the speed-up of model MJO propagation is primarily due to the improved convective scheme. Further, air-sea coupling plays an important role in maintaining the intensity of the predicted MJO. The results here suggest that MJO prediction skill is sensitive to model cumulus