Advances and challenges of operational seasonal prediction in Pacific Island Countries.

Seasonal climate forecasts play a critical role in building a climate-resilient society in the Pacific Island Countries (PICs) that are highly exposed to high-impact climate events. To assist the PICs National Meteorological and Hydrological Services in generating reliable national climate outlooks, we developed a hybrid seasonal prediction system, the Pacific Island Countries Advanced Seasonal Outlook (PICASO), which has the strengths of both statistical and dynamical systems. PICASO is based on the APEC Climate Center Multi-Model Ensemble (APCC-MME), tailored to generate station-level rainfall forecasts for 49 stations in 13 countries by applying predictor optimization and the large-scale relationship-based Bayesian regression approaches. Overall, performance is improved and further stabilized temporally and spatially relative to not only APCC-MME but also other existing operational prediction systems in the Pacific. Gaps and challenges in operationalization of the PICASO system and its incorporation into operational climate services in the PICs are discussed.

Toward a better multi-model ensemble prediction of East Asian and Australasian precipitation during non-mature ENSO seasons.

An effective and reliable way for better predicting the seasonal Australasian and East Asian precipitation variability in a multi-model ensemble (MME) prediction system is newly designed, in relation to the performance of predicting El Niño-Southern Oscillation (ENSO) and its impact. While ENSO is a major predictability source of global and regional precipitation variation, the prediction skill of precipitation is not solely due to typical ENSO alone, of which variability and predictability exhibit strong seasonality. The first mode of ENSO variability has large variance with high prediction skill for boreal winter and small variance with low skill for spring and summer, while the second mode shows the opposite phase. The regional prediction skills for Australasian and East Asian precipitation also show such seasonal dependence, with low skill and large spread of individual models' skills during the boreal spring to summer and high skill and small spread during winter. Using the individual models' reproducibility of the association between ENSO and regional precipitation, the prediction skills of the MME with selected models can improve at regional levels, compared to those for all-inclusive MME, during boreal spring to summer. While typical ENSO as a predictability source may still dominate during boreal winter, consideration of complex ENSO structure and its diverse impact can lead to a better prediction of regional precipitation variability during non-mature phase of ENSO seasons.

How do the strength and type of ENSO affect SST predictability in coupled models.

The effects of amplitude and type of the El Niño-Southern Oscillation (ENSO) on sea surface temperature (SST) predictability on a global scale were investigated, by examining historical climate forecasts for the period 1982-2006 from air-sea coupled seasonal prediction systems. Unlike in previous studies, SST predictability was evaluated in different phases of ENSO and for episodes with various strengths. Our results reveal that the seasonal mean Niño 3.4 index is well predicted in a multi-model ensemble (MME), even for four-month lead predictions. However, coupled models have particularly low skill in predicting the global SST pattern during weak ENSO events. During weak El Niño events, which are also El Niño Modoki in this period, a number of models fail to reproduce the associated tri-pole SST pattern over the tropical Pacific. During weak La Niña periods, SST signals in the MME tend to be less persistent than observations. Therefore, a good ENSO forecast does not guarantee a good SST prediction from a global perspective. The strength and type of ENSO need to be considered when inferring global SST and other climate impacts from model-predicted ENSO information.