{"id":452,"date":"2025-12-26T19:56:21","date_gmt":"2025-12-26T19:56:21","guid":{"rendered":"https:\/\/atmos.ucla.edu\/dkondras\/?page_id=452"},"modified":"2026-04-04T00:10:59","modified_gmt":"2026-04-04T00:10:59","slug":"enso-prediction","status":"publish","type":"page","link":"https:\/\/atmos.ucla.edu\/dkondras\/enso-prediction\/","title":{"rendered":"ENSO Prediction"},"content":{"rendered":"\n

Last updated: 04\/03\/26<\/p>\n\n\n\n

The\u00a0NINO-34 stochastic ensemble forecast\u00a0by\u00a0Empirical Model Reduction approach\u00a0(Kondrashov et al. 2005) is based on SST data from\u00a0January 1950 through March\u00a020256(blue),\u00a0and\u00a0ensemble mean (red)\u00a0predicts \u00a0return to El-nino conditions by the end of 2026.\u00a0The error bars (black)\u00a0correspond to one standard deviation of the ensemble plume. You can also check a\u00a0multi-model plume of Nino-34 forecasts<\/a>\u00a0from different statistical and dynamical models maintained by IRI, and\u00a0compare predictions<\/a>\u00a0for the past 22 months,\u00a0including also the UCLA-TCD model<\/a>. Independent\u00a0analysis of real-time 2002-2024\u00a0forecast skill of IRI multi-model plume of Nino-34 forecasts\u00a0shows that UCLA-TCD model is highly competitive, see\u00a0\u00a0see Barnston et al. (2012, Fig. 6)\u00a0<\/a>for\u00a0\u00a0skill during the 2002\u20132011 interval and\u00a0Zhao et al. (2024, Fig. 2m) f<\/a>or 2002\u20132022, respectively.<\/p>\n\n\n\n

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References<\/strong><\/p>\n\n\n\n

    \n
  1. Kondrashov, D., S. Kravtsov, A. W. Robertson and M. Ghil, 2005: A hierarchy of data-based ENSO models. J. Climate, 18, 4425\u20134444, doi: 10.1175\/JCLI3567.1<\/a><\/li>\n\n\n\n
  2. 2. Kravtsov S., D. Kondrashov, and M. Ghil, 2005: Multi-level regression modeling of nonlinear processes: Derivation and applications to climatic variability. J. Climate, 18, 4404\u20134424, doi: 10.1175\/JCLI3544.1<\/a><\/li>\n\n\n\n
  3. S. Kravtsov, D. Kondrashov and M. Ghil, 2009: Empirical Model Reduction and the Modeling Hierarchy in Climate Dynamics, invited chapter in Stochastic Physics and Climate Modeling, (T. Palmer and P. Williams, Eds.) Cambridge Univ. Press<\/a>, pp. 35\u201372.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Last updated: 04\/03\/26 The\u00a0NINO-34 stochastic ensemble forecast\u00a0by\u00a0Empirical Model Reduction approach\u00a0(Kondrashov et al. 2005) is based on SST data from\u00a0January 1950 through March\u00a020256(blue),\u00a0and\u00a0ensemble mean (red)\u00a0predicts \u00a0return to El-nino conditions by the end of 2026.\u00a0The error bars (black)\u00a0correspond to one standard deviation of the ensemble plume. You can also check a\u00a0multi-model plume of Nino-34 forecasts\u00a0from different statistical…<\/p>\n","protected":false},"author":141,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-452","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/pages\/452","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/users\/141"}],"replies":[{"embeddable":true,"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/comments?post=452"}],"version-history":[{"count":6,"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/pages\/452\/revisions"}],"predecessor-version":[{"id":489,"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/pages\/452\/revisions\/489"}],"wp:attachment":[{"href":"https:\/\/atmos.ucla.edu\/dkondras\/wp-json\/wp\/v2\/media?parent=452"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}