Assessment of the Sea-surface Temperature Predictability based on Multimodel Hindcasts

Based on historical forecasts of several individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability and resolution. The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the CFSv2 forecasts in forecasting COLD, NEUTRAL, and WARM events. Except in the North Atlantic Ocean (NA), poor performance in the NA for the forecast of the COLD events was caused mainly by the underestimate for the SSTA of Fgoals-f, which brought about large and finally a low BSS value. These results remind us the importance of evaluation before applying the MME.