Westerly wind bursts (WWBs) occurring over the tropical Pacific play an important role on El Niño‐Southern Oscillation (ENSO) dynamics. Currently, climate models have significant biases in their representation of WWBs, which may limit their ability to predict ENSO. In this study, we explore the possibility of improving ENSO prediction by introducing a semistochastic WWBs parameterization scheme into the Community Earth System Model (CESM). Three ensemble hindcasts, namely, the control run with the original CESM, the WWB run with parameterized WWBs, and the Non_WWB run with built‐in WWBs removed, are conducted for the period 1982–2016. We find that CESM with parameterized WWBs enables better ENSO prediction, especially for both amplitude and spatial distribution of eastern Pacific and central Pacific El Niño events. This improvement is related to more realistic representation of WWBs which leads to better prediction of surface wind stress anomalies and thermocline depth anomalies.

The Arabian Sea Warm Pool (ASWP) is a part of the Indian Ocean Warm Pool, formed in the Arabian Sea before the onset of the Indian summer monsoon. The ASWP has a significant impact on climate change in the Indian Peninsula and globally. In this study, we examined the intraseasonal and interannual variability of sea temperature in the ASWP using the latest Simple Ocean Data Assimilation (SODA) reanalysis dataset. We quantified the contributions of sea surface heat flux forcing, horizontal advection, and vertical entrainment to the sea temperature using the mixed-layer heat budget analysis method. We also used a lead–lag correlation method to examine the relationship between the interannual variability of the ASWP and various large-scale modes in the Indo-Pacific Ocean. We found that the ASWP formed in April and decayed in June; its formation and decay processes were asymmetrical, with the decay rate being twice as fast as the formation rate. During the ASWP development phase, the sea surface heat flux forcing had the largest impact on the mixed-layer temperature with a contribution of up to 85 %. Its impact was divided into the net surface heat flux (0.41–0.50 ^∘C per 5 d) and the shortwave radiation loss penetrating the mixed layer (from −0.08 ^∘C per 5 d to −0.17 ^∘C per 5 d). During the decay phase, the cooling effect of the vertical entrainment on the temperature variation increased (from −0.05 ^∘C per 5 d to −0.18 ^∘C per 5 d) and dominated the temperature variation jointly with the sea surface heat flux forcing. We also found that the ASWP has strong interannual variability related to the basin warming of the Indian Ocean. The lead–lag correlation indicated that the ASWP had a good synchronous correlation with the Indian Ocean Dipole. The ASWP had the largest correlation coefficient at a lag of 5–7 months of the Niño3.4 index, showing the characteristics of modulation by the El Niño–Southern Oscillation (ENSO). When the El Niño (La Niña) event peaked in the winter of the previous year, the ASWP that occurred before the summer monsoon was more significant (insignificant) in the following year.

Based on historical forecasts of four 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 (B_rel) and resolution (B_res). 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 Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of B_rel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.

In this study, forecast skill over four different periods of global climate change (1982–1999, 1984–1996, 2000–2018, and 2000–2014) is examined using the hindcasts of five models in the North American Multimodel Ensemble. The deterministic evaluation shows that the forecasting skills of the Niño3.4 and Niño3 indexes are much lower during 2000–2018 than during 1982–1999, indicating that the previously reported decline in forecasting skill continues through 2018. The decreases in skill are most significant for the target months from May to August, especially for medium to long lead times, showing that the forecasts suffer more from the effect of the spring predictability barrier (SPB) post-2000. Relationships between the extratropical Pacific signal and the El Niño-Southern Oscillation (ENSO) weakened after 2000, contributing to a reduction in inherent predictability and skills of ENSO, which may be connected with the forecasting skills decline for medium to long lead times. It is a great challenge to predict ENSO using the memory of the local ocean itself because of the weakening intensity of the warm water volume (WWV) and its relationship with ENSO. These changes lead to a significant decrease in the autocorrelation coefficient of the persistence forecast for short to medium lead months. Moreover, for both the Niño3.4 and Niño3 indexes, after 2000, the models tend to further underestimate the sea surface temperature anomalies (SSTAs) in the El Niño developing year but overestimate them in the decaying year. For the probabilistic forecast, the skills post-2000 are also generally lower than pre-2000 in the tropical Pacific, and in particular, they decayed east of 120° W after 2000. Thus, the advantages of different methods, such as dynamic modeling, statistical methods, and machine learning methods, should be integrated to obtain the best applicability to ENSO forecasts and to deal with the current low forecasting skill phenomenon.