Data used in "Assessing the Impact of Ocean In-situ Data Assimilation on MJO Propagation across the Maritime Continent in ECMWF Subseasonal Forecasts"

Despite the well-recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in-situ data assimilation on the propagation of Madden--Julian Oscillation (MJO) across the Maritime Continent in the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in-situ ocean observations in the initial conditions. Besides using the Real-time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in-situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist Static Energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast hinders the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that the atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model.