Author Correction: El Niño-Southern Oscillation complexity.

In this Review, the middle initial of author Kim M. Cobb was omitted. The original Review has been corrected online.

El Niño-Southern Oscillation complexity.

El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño-Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.

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.