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The El Niño-Southern Oscillation (ENSO), which originates in the Pacific, is the strongest and most well-known mode of tropical climate variability. Its reach is global, and it can force climate variations of the tropical Atlantic and Indian Oceans by perturbing the global atmospheric circulation. Less appreciated is how the tropical Atlantic and Indian Oceans affect the Pacific. Especially noteworthy is the multidecadal Atlantic warming that began in the late 1990s, because recent research suggests that it has influenced Indo-Pacific climate, the character of the ENSO cycle, and the hiatus in global surface warming. Discovery of these pantropical interactions provides a pathway forward for improving predictions of climate variability in the current climate and for refining projections of future climate under different anthropogenic forcing scenarios.

In this study, we developed a flow‐dependent oceanic initialization system for initializing the oceanic temperature and salinity in the Global Seasonal forecast system version 5 (GloSea5). Our algorithm overcomes the limitation of stationary perturbations for Ensemble Optimal Interpolation (EnOI) by spreading observed information along isopycnal lines to create three‐dimensional snapshot density states. The proposed algorithm, which we call state‐dependent ensemble‐based EnOI (SD‐EnOI), takes into account changes in the background error covariance over time without relying on ensemble model simulations. To evaluate the quality of the oceanic initial conditions (ICs) produced by SD‐EnOI, we compared them with those generated by the Global Ocean Data Assimilation and Prediction System version 1 (GODAPS1) operated by the Korea Meteorological Agency (KMA) throughout January 2017 to December 2017. Our findings show that the thermal construction of the SD‐EnOI ICs is more realistic than that of GODAPS1, particularly in the tropical Pacific region. The strong warm bias in sea surface temperature (SST) and the shallow mixed‐layer depth bias observed in the GODAPS1 ICs are not shown in SD‐EnOI. Due to the more realistic oceanic thermal structure present in the SD‐EnOI ICs, their use in retrospective forecast experiments resulted in a systematic reduction in climatological SST drift in the central‐eastern Pacific for forecasts up to four lead months compared to using GODAPS1 ICs. This demonstrates the significant impact of the initialization process on the quality of dynamical seasonal forecasts. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) anomalies over the North Tropical Atlantic (NTA) during the early boreal spring can trigger El Niño‐Southern Oscillation (ENSO) events in the following boreal winter. However, the future changes in the impact of the NTA on ENSO remain controversial. Here, we show distinct changes in the strength of the NTA−ENSO relationship due to global warming by comparing models from the Coupled Model Intercomparison Project (CMIP) 5 and CMIP6. The impact of the NTA on ENSO under greenhouse warming is notably enhanced in CMIP6 compared to CMIP5. A wetter mean state over the subtropical eastern North Pacific and increased oceanic sensitivity over the equatorial central Pacific are key factors that enhance the impact of the NTA SST on ENSO. Therefore, differences in the mean state under greenhouse warming between the CMIP5 and CMIP6 models can modulate the strength of the NTA−ENSO relationship. Plain Language Summary: The North Tropical Atlantic (NTA) region can affect the development of El Niño events, significantly impacting global weather systems. Our study compares two generations of climate models, CMIP5 and CMIP6, to understand how the influence of the NTA sea surface temperature (SST) on El Niño might change due to global warming. We found that the influence of the NTA SST on El Niño is stronger in the CMIP6 models than in the CMIP5 models. This stronger impact is due to changes in the climatological background states, including a wetter mean state over the subtropical eastern North Pacific and increased oceanic sensitivity over the equatorial central Pacific. Understanding these changes is crucial for improving predictions of future climate variabilities and their potential impacts on weather, ecosystems, and food production worldwide. Key Points: The relationship between the North Tropical Atlantic (NTA) and El Niño‐Southern Oscillation (ENSO) is significantly enhanced by global warming in CMIP6 compared to CMIP5Mean state change under greenhouse warming is responsible for the strengthening of the NTA‐ENSO relationship [ABSTRACT FROM AUTHOR]

Variations in the El Niño/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts.sup.1. Robust, long-lead forecasts would therefore be valuable for managing policy responses. But despite decades of effort, forecasting ENSO events at lead times of more than one year remains problematic.sup.2. Here we show that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observation data, we use transfer learning to train a convolutional neural network (CNN) first on historical simulations.sup.3 and subsequently on reanalysis from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models. A heat map analysis indicates that the CNN model predicts ENSO events using physically reasonable precursors. The CNN model is thus a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms. A statistical forecast model using a deep-learning approach produces useful forecasts of El Niño/Southern Oscillation events with lead times of up to one and a half years., Author(s): Yoo-Geun Ham [sup.1] , Jeong-Hwan Kim [sup.1] , Jing-Jia Luo [sup.2] [sup.3] Author Affiliations: (1) Department of Oceanography, Chonnam National University, Gwangju, South Korea (2) Institute for Climate and [...]