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|Title: ||Improved ENSO forecasting using Bayesian updating and the North American Multi Model Ensemble (NMME)|
|Authors: ||Zhang, Wei|
Vecchi, Gabriel A.
|Issue Date: ||2017|
|Publisher: ||© American Meteorological Society (AMS)|
|Citation: ||ZHANG, W. ... et al., 2017. Improved ENSO forecasting using Bayesian updating and the North American Multi Model Ensemble (NMME). Journal of Climate, 30(22), pp. 9007-9025.|
|Abstract: ||This study assesses the forecast skill of eight North American Multi Model Ensemble (NMME) models in predicting Niño3/3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows strong dependence on lead (initial) month and target month, and is quite promising in terms of correlation, root mean square error (RMSE), the standard deviation ratio (SDRatio) and probabilistic Brier Skill Score, especially at short lead months. However, the skill decreases in target months from late spring to summer due to the “Spring Predictability Barrier.” When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Niño3/3.4 in terms of correlation, RMSE, and SDRatio. For Niño3.4, the BU-Model outperforms NMME- EM forecasts for almost all leads (1-12; particularly for short leads) and target months (from January to December). However, for Niño3, the BU-Model does not outperform NMME-EM forecasts for leads 7-11 and target months from June to October in terms of correlation and RMSE. Last, we test further potential improvements by preselecting “good” models (BU-Model-0.3) and by using principal components analysis to remove the multicollinearity among models, but these additional methodologies do not outperform the BU-Model, which produces the best forecasts of Niño3/3.4 for the 2015/2016 El Niño event.|
|Description: ||© Copyright 2017 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Permission to place a copy of this work on this server has been provided by the AMS. The AMS does not guarantee that the copy provided here is an accurate copy of the published work|
|Sponsor: ||This study was partly supported by NOAA's 21 Climate Program Office's Modeling, Analysis, Predictions, and Projections Program, Grant #NA15OAR4310073, and Award NA14OAR4830101 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. GV also acknowledges funding by the National Science Foundation under CAREER Grant AGS-1349827, and the Broad Agency Announcement Program and the Engineer Research and Development Center–Cold Regions Research and Engineering Laboratory under Contract W913E5-16-C-0002.|
|Version: ||Accepted for publication|
|Publisher Link: ||https://doi.org/10.1175/JCLI-D-17-0073.1|
|Appears in Collections:||Published Articles (Geography and Environment)|
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