Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events

Abstract This paper provides an updated assessment of the “International Research Institute for Climate and Society’s (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume”. We evaluate 253 real-time forecasts of the Niño 3.4 index issued from February 2002 to February 2023 and examine multimo...

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Main Authors: Muhammad Azhar Ehsan, Michelle L. L’Heureux, Michael K. Tippett, Andrew W. Robertson, Jeffrey Turmelle
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:npj Climate and Atmospheric Science
Online Access:https://doi.org/10.1038/s41612-024-00845-5
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author Muhammad Azhar Ehsan
Michelle L. L’Heureux
Michael K. Tippett
Andrew W. Robertson
Jeffrey Turmelle
author_facet Muhammad Azhar Ehsan
Michelle L. L’Heureux
Michael K. Tippett
Andrew W. Robertson
Jeffrey Turmelle
author_sort Muhammad Azhar Ehsan
collection DOAJ
description Abstract This paper provides an updated assessment of the “International Research Institute for Climate and Society’s (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume”. We evaluate 253 real-time forecasts of the Niño 3.4 index issued from February 2002 to February 2023 and examine multimodal means of dynamical (DYN) and statistical (STAT) models separately. Forecast skill diminishes as lead time increases in both DYN and STAT forecasts, with peak accuracy occurring post-northern hemisphere spring predictability barrier and preceding seasons. The DYN forecasts outperform STAT forecasts with a pronounced advantage in forecasts initiated from late boreal winter through spring. The analysis uncovers an asymmetry in predicting the onset of cold and warm ENSO episodes, with warm episode onsets being better forecasted than cold onsets in both DYN and STAT models. The DYN forecasts are found to be valuable for predicting warm and cold ENSO episode onsets at least several months in advance, while STAT forecasts are less informative about ENSO phase transitions. The results indicate that predicting ENSO onset is challenging and that the ability to do so is both model- and event-dependent.
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spelling doaj-art-9dcfab82354642e99333558e175bc7ed2025-08-20T02:39:52ZengNature Portfolionpj Climate and Atmospheric Science2397-37222024-12-017111210.1038/s41612-024-00845-5Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO eventsMuhammad Azhar Ehsan0Michelle L. L’Heureux1Michael K. Tippett2Andrew W. Robertson3Jeffrey Turmelle4International Research Institute for Climate and Society, Columbia Climate School, Columbia UniversityClimate Prediction Center, National Oceanic and Atmospheric Administration, National Weather Service (NWS), National Centers for Environmental Prediction (NCEP)Department of Applied Physics and Applied Mathematics, Columbia UniversityInternational Research Institute for Climate and Society, Columbia Climate School, Columbia UniversityInternational Research Institute for Climate and Society, Columbia Climate School, Columbia UniversityAbstract This paper provides an updated assessment of the “International Research Institute for Climate and Society’s (IRI) El Niño Southern Oscillation (ENSO) Predictions Plume”. We evaluate 253 real-time forecasts of the Niño 3.4 index issued from February 2002 to February 2023 and examine multimodal means of dynamical (DYN) and statistical (STAT) models separately. Forecast skill diminishes as lead time increases in both DYN and STAT forecasts, with peak accuracy occurring post-northern hemisphere spring predictability barrier and preceding seasons. The DYN forecasts outperform STAT forecasts with a pronounced advantage in forecasts initiated from late boreal winter through spring. The analysis uncovers an asymmetry in predicting the onset of cold and warm ENSO episodes, with warm episode onsets being better forecasted than cold onsets in both DYN and STAT models. The DYN forecasts are found to be valuable for predicting warm and cold ENSO episode onsets at least several months in advance, while STAT forecasts are less informative about ENSO phase transitions. The results indicate that predicting ENSO onset is challenging and that the ability to do so is both model- and event-dependent.https://doi.org/10.1038/s41612-024-00845-5
spellingShingle Muhammad Azhar Ehsan
Michelle L. L’Heureux
Michael K. Tippett
Andrew W. Robertson
Jeffrey Turmelle
Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events
npj Climate and Atmospheric Science
title Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events
title_full Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events
title_fullStr Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events
title_full_unstemmed Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events
title_short Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events
title_sort real time enso forecast skill evaluated over the last two decades with focus on the onset of enso events
url https://doi.org/10.1038/s41612-024-00845-5
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