Sampling biases in CMIP5 decadal forecasts

Recent studies examining the fidelity of decadal hindcast experiments from phase 5 of the Coupled Model Intercomparison Project have highlighted the need for larger ensembles of forecasts, compared to the initial five yearly spaced initializations, to help correct for model biases (drift). This study quantifies differences in the two drift estimates in sea surface temperature (SST) and SST anomaly (SSTA) predictions, between experiments initialized every 5years and those initialized every year. The effect of the recommended mean drift correction, on the two sets of predictions, is also analyzed. Our results indicate that differences between the SST drift estimates are largest over the tropical Pacific. Moreover, this difference is large for Nino 3.4 and almost negligible for the global average SSTA. Drift correction as per the mean drift from the 5year case leads to spurious peaks in the drift-corrected Nino 3.4 (and the tropical Pacific) and sporadic improvements in skill. This problem with Nino 3.4 stems from an aliasing that occurs during the drift calculation that results from a combination of the timing of major El Nino events in relation to the initialization dates. The study recommends accounting for such sampling effects while considering any subset of the full data set.