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The Indian Ocean basin (IOB) mode is the dominant mode of the interannual sea surface temperature (SST) variability in the Indian Ocean, with the Indian Ocean dipole (IOD) as the second mode. An IOB event normally occurs after an El Niño or a concurrent IOD–El Niño event, the dynamics of which are traditionally believed as forced by ENSO through the Walker circulation anomalies over the tropical Indian Ocean. A strong IOB in 2020 took place after the strongest 2019 IOD on record but independent of El Niño, which challenges the traditional atmospheric bridge dynamics of the IOB event. In this study, the dynamics of the 2020 IOB event are investigated using the numerical seasonal climate prediction system of the National Marine Environmental Forecasting Center of China. It is found that the initialization of the Indian Ocean subsurface temperature during the 2019 IOD event has led to the outburst of the 2020 IOB event successfully, the dynamics of which are the propagation and the western boundary reflection of the equatorial and off-equatorial Rossby waves, inducing heat content recharge over the tropical Indian Ocean upper thermocline. In comparison, experiments of SST initialization over the tropical Indian Ocean, with the subsurface temperature in a climatological state, were unable to reproduce the onset of the 2020 IOB event, suggesting that the local air–sea interaction within the Indian Ocean basin is of secondary importance. The numerical experiments suggest that the thermocline ocean wave dynamics play an important role in forcing the IOB event. The revealed thermocline dynamics are potentially useful in climate prediction associated with IOB events. [ABSTRACT FROM AUTHOR]

The influence of strong Indian Ocean Dipole (IOD) events on the evolution of the following year's La Niña events is investigated using the National Marine Environmental Forecasting Center (NMEFC) operational seasonal forecasting system. The observation results show that when the strong IOD occurs, the tropical Pacific can be in different sea surface temperature states. As such prediction system can well reproduce the air-sea evolution of the 1998/1999 and 2020/2021 La Niña events, the ocean temperature initializations in December during above events were perturbed with the system to assess the role of the oceanic channel and atmospheric bridge across the maritime continent in the forecasting of the La Niña events 1 year later. In the case of the neutral state of the tropical Pacific at the peak of the 2019 positive IOD, pacemaker experiments have demonstrated that the Indian Ocean subsurface temperature initialization in December 2019 is critically important for the successful prediction of the 2020/2021 La Niña. Experiments of sea surface temperature initialization in December 2019, with only the Indo-Pacific atmospheric bridge at work, failed to predict the 2020/2021 La Niña 1 year in advance. The comparison underlined the dominant role of the oceanic channel dynamics in the evolution of the 2020/2021 La Niña. Forecasting experiments beginning from the 1997/98 El Niño with and without the IOD initializations suggest that the delayed feedback of the tropical Pacific coupled system is more important in the forecasting of the 1998/1999 La Niña, with the Indo-Pacific oceanic channel dynamics playing a secondary yet very important role. Our study has underscored the importance of the Indo-Pacific oceanic channel dynamics in ENSO prediction and suggested the effectiveness of IOD subsurface temperature initialization in La Niña predictions at the 1-year lead time. [ABSTRACT FROM AUTHOR]

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 Ocean 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 Arabian Sea Warm Pool using the latest 5-day SODA reanalysis dataset. We quantified the contributions of sea surface heat flux, advection, and vertical entrainment to the sea surface temperature using mixed-layer heat budget analysis. 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 had the largest impact on the mixed layer temperature with a contribution of up to 85 %. Its impact was divided into the net heat flux at the sea surface (0.41–0.50 °C/5 day) and the short-wave radiation loss penetrating the mixed layer (from –0.08 °C/5 day to –0.17 °C/5 day). During the decay phase, the cooling effect of the vertical entrainment (from –0.05 °C/5 day to –0.18 °C/5 day) on the temperature variation increased and dominated the temperature variation jointly with the sea surface heat flux. We also found that the ASWP has strong interannual variability related to the consistent warming of the Indian Ocean basin. The lead lag correlation indicated that ASWP had a good synchronous correlation with the Indian Ocean Dipole. ASWP had the largest correlation coefficient at a lag of 5–7 months of niño3.4 index, showing the characteristics of modulation by ENSO. The ASWP was more significant (insignificant) in the following year before the summer monsoon after an El Niño (La Niña) event that peaked in the previous winter. [ABSTRACT FROM AUTHOR]

The late spring rainfall may account for 15% of the annual total rainfall, which is crucial to early planting in southeastern China. A better understanding of the precipitation variations in the late spring and its predictability not only greatly increase our knowledge of related mechanisms, but it also benefits society and the economy. Four models participating in the North American Multi-Model Ensemble (NMME) were selected to study their abilities to forecast the late spring rainfall over southeastern China and the major sources of heavy rainfall from the perspective of the sea surface temperature (SST) field. We found that the models have better abilities to forecast the heavy rainfall over the middle and lower reaches of the Yangtze River region (MLYZR) with only a 1-month lead time, but they failed for a 3-month lead time since the occurrence of the heavy rainfall was inconsistent with the observations. The observations indicate that the warm SST anomalies in the tropical eastern Indian Ocean are vital to the simultaneously heavy rainfall in the MLYZR in May, but an El Niño event is not a necessary condition for determining the heavy rainfall over the MLYZR. The heavy rainfall over the MLYZR in May is always accompanied by warming of the northeastern Indian Ocean and of the northeastern South China Sea (NSCS) from April to May in the models and observations, respectively. In the models, El Niño events may promote the warming processes over the northeastern Indian Ocean, which leads to heavy rainfall in the MLYZR. However, in the real world, El Niño events are not the main reason for the warming of the NSCS, and further research on the causes of this warming is still needed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]