Inter-annual variability and skill of tropical rainfall and SST in APCC seasonal forecast models

International audienceThe present study explored the performance of the current coupled models obtained from the Asia Pacific Economic Cooperation (APEC) Climate Centre (APCC) in representing the tropical Indo-Pacific sea surface temperature (SST) and rainfall during boreal summer season (June through September; JJAS). We have used the retrospective/hindcast runs for 28 years from 1983 to 2010 initialized in May. The mean SST bias in the tropical Indo-Pacific Oceans showed large diversity among the models in JJAS. In the case of the rainfall, most of the models displayed a strong dry bias over the major continental regions and wet biases over the tropical oceans. The majority of the models simulated the Inter-annual variability (IAV) of JJAS rainfall and SST reasonably well over the equatorial Pacific region, where the models are close to observed IAV and maximum signal to noise ratio (SNR). It is found that the models display, low IAV of rainfall and SST over the Indian Ocean with low SNR values, resulting in less predictive skill as compared to the tropical Pacific region. Similarly, all models showed a higher skill in summer rainfall prediction over the oceanic regions compared to the Asian land region, where SNR is very low. Further analysis suggested that the models have greater skill in predicting El Niño-Southern Oscillation (ENSO). The category wise analysis showed that models could predict 60–70% of the extreme ENSO events, but the normal events are represented only by 50%. It is noted that the models predict many false alarms for El Niño resulting in a higher frequency of El Niño occurrence. This is mainly responsible for stronger ENSO and the Asian Monsoon teleconnections in the models than in the observations. Meanwhile, the category wise rainfall skill for extended Indian monsoon region (EMR) displayed 50–60% accuracy for the extreme monsoon years and is around 50% for normal years. However, models such as CCSM3, CFSV2, and CANCM3 have displayed higher rainfall skills over EMR as compared to the other models possibly due to better representation of teleconnections spatial patterns between EMR rainfall and SST anomalies over Indo-Pacific Oceans