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Effective ENSO Amplitude Forecasts Based on Oceanic and Atmospheric Preconditions

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  • 1 aCIC-FEMD/ILCEC, Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
  • | 2 bDepartment of Atmospheric Sciences, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu, Hawaii
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Abstract

Current climate models have relatively high skills in predicting El Niño–Southern Oscillation (ENSO) phase (i.e., El Niño, neutral, and La Niña), once leaping over the spring predictability barrier. However, it is still a big challenge to realistically forecast the ENSO amplitude, for instance, whether a predicted event will be strong, moderate, or weak. Here we demonstrate that the accumulated westerly wind events (WWEs)/easterly wind surges (EWSs) and oceanic recharged/discharged states are both of importance in accurate ENSO amplitude forecasts. El Niño and La Niña events exhibit asymmetric temporal and spatial features in the atmospheric and oceanic preconditions. El Niño amplitude at the peak season is closely associated with the accumulated WWEs over the eastern equatorial Pacific from the previous December to May and the recharged state in the western equatorial Pacific during February. In contrast, the amplitude of La Niña events is sensitive to the accumulated EWSs over the equatorial western Pacific from the previous November to April and the discharged state extending from the equatorial western to central Pacific during February. Considering these asymmetric atmospheric and oceanic preconditions of El Niño and La Niña cases, a statistical model is established to accurately forecast the ENSO amplitude at its mature phase during 1982–2018, which is validated to be robust based on a 1-yr cross-validation and independent sample tests. The feasibility and the limitation of the established statistical model are also discussed by examining its practical utility.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wenjun Zhang, zhangwj@nuist.edu.cn

Abstract

Current climate models have relatively high skills in predicting El Niño–Southern Oscillation (ENSO) phase (i.e., El Niño, neutral, and La Niña), once leaping over the spring predictability barrier. However, it is still a big challenge to realistically forecast the ENSO amplitude, for instance, whether a predicted event will be strong, moderate, or weak. Here we demonstrate that the accumulated westerly wind events (WWEs)/easterly wind surges (EWSs) and oceanic recharged/discharged states are both of importance in accurate ENSO amplitude forecasts. El Niño and La Niña events exhibit asymmetric temporal and spatial features in the atmospheric and oceanic preconditions. El Niño amplitude at the peak season is closely associated with the accumulated WWEs over the eastern equatorial Pacific from the previous December to May and the recharged state in the western equatorial Pacific during February. In contrast, the amplitude of La Niña events is sensitive to the accumulated EWSs over the equatorial western Pacific from the previous November to April and the discharged state extending from the equatorial western to central Pacific during February. Considering these asymmetric atmospheric and oceanic preconditions of El Niño and La Niña cases, a statistical model is established to accurately forecast the ENSO amplitude at its mature phase during 1982–2018, which is validated to be robust based on a 1-yr cross-validation and independent sample tests. The feasibility and the limitation of the established statistical model are also discussed by examining its practical utility.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Wenjun Zhang, zhangwj@nuist.edu.cn
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