Estimating ENSO predictability based on multi-model hindcasts

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• 作者：Arun Kumar ; Zeng-Zhen Hu ; Bhaskar Jha ; Peitao Peng
• 关键词：ENSO ; Predictability estimation ; Multi ; model hindcasts ; NMME
• 刊名：Climate Dynamics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：48
• 期：1-2
• 页码：39-51
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Geophysics/Geodesy; Climatology; Oceanography;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-0894
• 卷排序：48

文摘

Based on hindcasts of seasonal forecast systems participating in the North American Multi-Model Ensemble, the seasonal dependence of predictability of the El Niño–Southern Oscillation (ENSO) was estimated. The results were consistent with earlier analyses in that the predictability of ENSO was highest in winter and lowest in spring and summer. Further, predictability as measured by the relative amplitude of predictable and unpredictable components was dominated by the ensemble mean instead of the spread (or dispersion) among ensemble members. This result was consistent with previous analysis that most of ENSO predictability resides in the shift of the probability density function (PDF) of ENSO sea surface temperature (SST) anomalies (i.e., changes in the first moment of the PDF that is associated with the ensemble mean of ENSO SST anomalies) rather than due to changes in the spread of the PDF. The analysis establishes our current best estimate of ENSO predictability that can serve as a benchmark for quantifying further improvements resulting from advances in observing, assimilation, and seasonal prediction systems.