ENSO Prediction

Chapter 10 ENSO Prediction Michelle L. L'Heureux, Michelle L. L'Heureux National Oceanic and Atmospheric Administration, NWS/NCEP/Climate Prediction Center, College Park, MD, USASearch for more papers by this authorAaron F. Z. Levine, Aaron F. Z. Levine Department of Atmospheric Sciences, University of Washington, Seattle, WA, USASearch for more papers by this authorMatthew Newman, Matthew Newman University of Colorado/CIRES, NOAA/ESRL Physical Sciences Division, Boulder, CO, USASearch for more papers by this authorCatherine Ganter, Catherine Ganter Australian Bureau of Meteorology, Melbourne, VIC, AustraliaSearch for more papers by this authorJing-Jia Luo, Jing-Jia Luo Institute for Climate and Application Research (ICAR)/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, ChinaSearch for more papers by this authorMichael K. Tippett, Michael K. Tippett Department of Applied Physics and Applied Mathematics, Columbia University, NY, USASearch for more papers by this authorTimothy N. Stockdale, Timothy N. Stockdale European Centre for Medium-Range Weather Forecasts, Reading, UKSearch for more papers by this author Michelle L. L'Heureux, Michelle L. L'Heureux National Oceanic and Atmospheric Administration, NWS/NCEP/Climate Prediction Center, College Park, MD, USASearch for more papers by this authorAaron F. Z. Levine, Aaron F. Z. Levine Department of Atmospheric Sciences, University of Washington, Seattle, WA, USASearch for more papers by this authorMatthew Newman, Matthew Newman University of Colorado/CIRES, NOAA/ESRL Physical Sciences Division, Boulder, CO, USASearch for more papers by this authorCatherine Ganter, Catherine Ganter Australian Bureau of Meteorology, Melbourne, VIC, AustraliaSearch for more papers by this authorJing-Jia Luo, Jing-Jia Luo Institute for Climate and Application Research (ICAR)/CICFEM/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, ChinaSearch for more papers by this authorMichael K. Tippett, Michael K. Tippett Department of Applied Physics and Applied Mathematics, Columbia University, NY, USASearch for more papers by this authorTimothy N. Stockdale, Timothy N. Stockdale European Centre for Medium-Range Weather Forecasts, Reading, UKSearch for more papers by this author Book Editor(s):Michael J. McPhaden, Michael J. McPhadenSearch for more papers by this authorAgus Santoso, Agus SantosoSearch for more papers by this authorWenju Cai, Wenju CaiSearch for more papers by this author First published: 23 October 2020 https://doi.org/10.1002/9781119548164.ch10Citations: 2Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary The El Niño-Southern Oscillation (ENSO) is a coupled ocean-atmosphere phenomenon of variability that is a leading source of seasonal climate prediction skill across the globe. The first ENSO prediction was made in the mid-1970s, but it was another 10–15 years before operational centers, using simple, coupled climate models, began to make routine ENSO predictions. These early forecast models were succeeded in the 1990s by more sophisticated dynamical and statistical models, which created the basis for real-time seasonal outlooks over the globe. These models, and more recent multimodel ensembles, also inform our understanding and estimates of the predictability and prediction skill of ENSO, which varies seasonally and from decade to decade. ENSO predictability largely stems from slowly evolving oceanic conditions, with short-term atmospheric fluctuations often limiting predictability on seasonal timescales. Despite improved models and better initializations, prediction skill remains low for forecasts passing through the boreal spring, the so-called spring prediction barrier. Furthermore, prediction skill and predictability have varied significantly over the past couple decades. Higher skill and predictability are evident during periods of larger amplitude ENSO events (e.g., Eastern Pacific El Niño), whereas lower skill/predictability is associated with lower amplitude events (e.g., Central Pacific El Niño). These natural variations in our ability to predict ENSO, together with challenges during 2014–2016, motivate the search for understanding of how anthropogenic warming will influence seasonal ENSO prediction. Citing Literature El Niño Southern Oscillation in a Changing Climate RelatedInformation