Improving ENSO prediction in CFSv2 with an analogue‐based correction method

Abstract

ABSTRACTThis study focuses on improving the El Niño–Southern Oscillation (ENSO) prediction in Climate Forecast System version 2 (CFSv2) of the National Centers for Environmental Prediction through the development and application of an analogue‐based correction method. We show that errors in sea surface temperature (SST) forecasts in CFSv2 in the tropical Pacific are strongly correlated with the observed SST anomaly index in the Niño3.4 region at the forecast initiation time, indicating that SST forecast errors in CFSv2 is similar in cases in which the corresponding initial SST states are also similar. Therefore, the analogue‐based correction method is developed, in which SST forecast errors in CFSv2 can be corrected empirically using historical forecast errors, which are calculated by the same model, initiated from states that are analogues of the present initial state. Results show that the corrected SST anomaly forecasts have improved skills, as measured by the temporal correlation coefficient and root‐mean‐square error, compared with uncorrected forecasts. This is true for several different Niño SST indices, at most of lead months, and for most of initiation calendar months. Particular improvement is found for forecasts of the SST anomaly indices that are specially used to depict the two different ENSO flavours/types. With regard to the Niño3.4 index, the analogue‐based correction method also predicted the 2014/2016 El Niño event relatively successfully. The results indicate that the analogue‐based correction method provides an effective means of empirically improving ENSO prediction in CFSv2.