Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions

The most predictable components of the El Niño-Southern Oscillation (ENSO) evolution in real-time multi-model predictions are identified by applying an empirical orthogonal function analysis of the model data that maximizes the signal-to-noise ratio (MSN EOF). The normalized Niño3.4 index is analyze...

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Main Authors:	Zheng, Zhihai, Hu, Zeng-Zhen, L’Heureux, Michelle
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075933/

id	pubmed-5075933
recordtype	oai_dc
spelling	pubmed-50759332016-10-28 Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions Zheng, Zhihai Hu, Zeng-Zhen L’Heureux, Michelle Article The most predictable components of the El Niño-Southern Oscillation (ENSO) evolution in real-time multi-model predictions are identified by applying an empirical orthogonal function analysis of the model data that maximizes the signal-to-noise ratio (MSN EOF). The normalized Niño3.4 index is analyzed for nine 3-month overlapping seasons. In this sense, the first most predictable component (MSN EOF1) is the decaying phase of ENSO during the Northern Hemisphere spring, followed by persistence through autumn and winter. The second most predictable component of ENSO evolution, with lower prediction skill and smaller explained variance than MSN EOF1, corresponds to the growth during spring and then persistence in summer and autumn. This result suggests that decay phase of ENSO is more predictable than the growth phase. Also, the most predictable components and the forecast skills in dynamical and statistical models are similar overall, with some differences arising during spring season initial conditions. Finally, the reconstructed predictions, with only the first two MSN components, show higher skill than the model raw predictions. Therefore this method can be used as a diagnostic for model comparison and development, and it can provide a new perspective for the most predictable components of ENSO. Nature Publishing Group 2016-10-24 /pmc/articles/PMC5075933/ /pubmed/27775016 http://dx.doi.org/10.1038/srep35909 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zheng, Zhihai Hu, Zeng-Zhen L’Heureux, Michelle
spellingShingle	Zheng, Zhihai Hu, Zeng-Zhen L’Heureux, Michelle Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions
author_facet	Zheng, Zhihai Hu, Zeng-Zhen L’Heureux, Michelle
author_sort	Zheng, Zhihai
title	Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions
title_short	Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions
title_full	Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions
title_fullStr	Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions
title_full_unstemmed	Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions
title_sort	predictable components of enso evolution in real-time multi-model predictions
description	The most predictable components of the El Niño-Southern Oscillation (ENSO) evolution in real-time multi-model predictions are identified by applying an empirical orthogonal function analysis of the model data that maximizes the signal-to-noise ratio (MSN EOF). The normalized Niño3.4 index is analyzed for nine 3-month overlapping seasons. In this sense, the first most predictable component (MSN EOF1) is the decaying phase of ENSO during the Northern Hemisphere spring, followed by persistence through autumn and winter. The second most predictable component of ENSO evolution, with lower prediction skill and smaller explained variance than MSN EOF1, corresponds to the growth during spring and then persistence in summer and autumn. This result suggests that decay phase of ENSO is more predictable than the growth phase. Also, the most predictable components and the forecast skills in dynamical and statistical models are similar overall, with some differences arising during spring season initial conditions. Finally, the reconstructed predictions, with only the first two MSN components, show higher skill than the model raw predictions. Therefore this method can be used as a diagnostic for model comparison and development, and it can provide a new perspective for the most predictable components of ENSO.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5075933/
_version_	1613694120472084480

Predictable Components of ENSO Evolution in Real-time Multi-Model Predictions

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