{"title":"The role of sea surface salinity in ENSO forecasting in the 21st century","authors":"Haoyu Wang, Shineng Hu, Cong Guan, Xiaofeng Li","doi":"10.1038/s41612-024-00763-6","DOIUrl":null,"url":null,"abstract":"Significant strides have been made in understanding El Niño-Southern Oscillation (ENSO) dynamics, yet its long-lead prediction remains challenging, especially for the El Niño events after 2000. Sea surface salinity (SSS) is known to affect ENSO development and intensity by influencing ocean stratification and heat redistribution and therefore, when combined with sea surface temperature (SST) data, can potentially enhance ENSO forecast skill. In this study, we develop a deep learning (DL) model that incorporates a multiscale-pyramid structure and spatiotemporal feature extraction blocks, and the model successfully extends effective ENSO forecast lead time to 24 months for 2000–2021 with reduced effect of the spring predictability barrier (SPB). Interpretable methods are then applied to reveal the time-dependent roles of SST and SSS in ENSO forecast. More specifically, SST is critical for short-medium lead forecasts (<1 year), while SSS is important for medium-long lead forecasts (>6 months). Furthermore, we track global SST and SSS spatiotemporal shifts related to subsequent ENSO development, highlighting the importance of ocean inter-basin and tropics-extratropics interactions. With increasing availability of satellite SSS observations, our findings unveil unprecedented potential for advancing ENSO long-lead forecast skills.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00763-6.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Climate and Atmospheric Science","FirstCategoryId":"89","ListUrlMain":"https://www.nature.com/articles/s41612-024-00763-6","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Significant strides have been made in understanding El Niño-Southern Oscillation (ENSO) dynamics, yet its long-lead prediction remains challenging, especially for the El Niño events after 2000. Sea surface salinity (SSS) is known to affect ENSO development and intensity by influencing ocean stratification and heat redistribution and therefore, when combined with sea surface temperature (SST) data, can potentially enhance ENSO forecast skill. In this study, we develop a deep learning (DL) model that incorporates a multiscale-pyramid structure and spatiotemporal feature extraction blocks, and the model successfully extends effective ENSO forecast lead time to 24 months for 2000–2021 with reduced effect of the spring predictability barrier (SPB). Interpretable methods are then applied to reveal the time-dependent roles of SST and SSS in ENSO forecast. More specifically, SST is critical for short-medium lead forecasts (<1 year), while SSS is important for medium-long lead forecasts (>6 months). Furthermore, we track global SST and SSS spatiotemporal shifts related to subsequent ENSO development, highlighting the importance of ocean inter-basin and tropics-extratropics interactions. With increasing availability of satellite SSS observations, our findings unveil unprecedented potential for advancing ENSO long-lead forecast skills.
期刊介绍:
npj Climate and Atmospheric Science is an open-access journal encompassing the relevant physical, chemical, and biological aspects of atmospheric and climate science. The journal places particular emphasis on regional studies that unveil new insights into specific localities, including examinations of local atmospheric composition, such as aerosols.
The range of topics covered by the journal includes climate dynamics, climate variability, weather and climate prediction, climate change, ocean dynamics, weather extremes, air pollution, atmospheric chemistry (including aerosols), the hydrological cycle, and atmosphere–ocean and atmosphere–land interactions. The journal welcomes studies employing a diverse array of methods, including numerical and statistical modeling, the development and application of in situ observational techniques, remote sensing, and the development or evaluation of new reanalyses.