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Downscaled multi-model superensemble and probabilistic forecasts of seasonal rains over the Asian monsoon belt

This is a study on seasonal climate forecasts for the Asian Monsoon region. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state of the art coupled atmosphere-ocean models. A downscaling component, with respect to observed rainfall estimates uses data sets from TRMM and a dense rain gauge distriburion; this enables the forecasts of each model to be bias corrected to a common 25 km resolution. The downscaling statistics for each model, at each grid location is developed during a training phase of the model forecasts; the forecasts from all of the member models use the downscaling coefficients of the training phase. These forecasts are next used for the construction of a multimodel superensemble. A major result of this paper is on the climatology of the model rainfall. From the downscaled multimodel superensemble which shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. The second part of this study addresses seasonal climate forecasts of Asian monsoon precipitation anomalies. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably carry the highest skill compared to the member models globally and regionally. This relates largely to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts.