“The Madden Julian Oscillation (MJO) is of central importance in subseasonal to seasonal forecasts but remains difficult to predict. An outstanding problem is that models have difficulty simulating or predicting the propagation of the MJO across the Maritime Continent. This deficiency results in a “prediction barrier.” Overcoming this barrier is a challenge because its precise cause or causes are unknown. Proposed causes include poor representation of th diurnal cycle, biases in mean climate and failure to capture precursor signals. Our project seeks to improve both understanding and prediction of the MJO, focusing on the relation of the MJO to the Maritime Continent. We propose a systematic analysis of forecast and reforecast ensembles from the Seasonal-to-Subseasonal (S2S) prediction project dataset. Success in forecasting MJO propagation across the Maritime Continent varies between different runs in each ensemble as well as across models. Relating forecast success, as well as MJO characteristics, to other variables across the ensembles will identify which variables and processes are most important for determining the success of the forecast and for achieving a good representation of the MJO itself.
Interactions between the diurnal cycle, the MJO, and the mean climatology in structuring deep convection over the Maritime Continent are of particular interest. The moist static energy budget will be adopted to interpret results from a thermodynamic prospective. Dynamic precursors pertinent to the MJO will be extensively explored to evaluate their roles in the MJO forecasts and relation to model biases. All the analysis of these variables will be carried out based on a “seamless” verification approach by which variables are averaged with varying time windows to facilitate smooth transition from daily weather forecast to seasonal climate prediction time scales.
Relevance to NOAA’s goal and to the competition: Our proposal targets Competition 5: Research to Advance Prediction of Subseasonal to Seasonal Phenomena. This competition focuses on the predictability and prediction of S2S phenomena in the context of key underlying physical processes and dynamical processes. Our proposed statistical analysis specifically addresses this issue. Our project is well within the scope of the NOAA’s MAPP program, whose goal is to advance understanding and prediction of variability and changes in Earth’s climate system and infuse research advances into NOAA’s service. One particular focus of the MAPP is to improve intraseasonal and interannual climate prediction. Our proposed research is designed to improve understanding of the MJO prediction barrier over the Maritime Continent, and to help identify ensemble forecast error, and sources of that error, in NOAA’s forecast systems. By transforming knowledge learned from the basic research to operational practice, our project will benefit the general public by improving subseasonal to seasonal forecasts and providing better weather service.”