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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: ||2018|
|Publisher: ||© American Meteorological Society (AMS)|
|Citation: ||ZHANG, W. ... et al., 2018. Improved ENSO forecasting using Bayesian updating and the North American Multi Model Ensemble (NMME). Journal of Climate, doi:10.1175/JCLI-D-17-0073.1.|
|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: ||Closed access until 10th February 2018. [© American Meteorological Society (AMS)]|
|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:||Closed Access (Geography)|
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