Improved EOF-based bias correction method for seasonal forecasts and its application in IAP AGCM4.1

Abstract

An effective improvement on the empirical orthogonal function (EOF)–based bias correction method for seasonal forecasts is proposed in this paper, by introducing a stepwise regression method into the process of EOF time series correction. Using 30-year (1981–2010) hindcast results from IAP AGCM4.1 (the latest version of this model), the improved method is validated for the prediction of summer (June–July–August) rainfall anomalies in Southeast China. The results in terms of the pattern correction coefficient (PCC) of rainfall anomalies shows that the 30-year-averaged prediction skill improves from 0.01 to 0.06 with the original correction method, and to 0.29 using the improved method. The applicability in real-time prediction is also investigated, using 2016 summer rainfall prediction as a test case. With a PCC of 0.59, the authors find that the new correction method significantly improves the prediction skill; the PCC using the direct prediction of the model is −0.04, and using the old bias correction method it is 0.37.