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Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real‐time forecasts (2008–2013)
Author(s) -
Min YoungMi,
Kryjov Vladimir N.,
Oh Sang Myeong
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2014jd022230
Subject(s) - predictability , precipitation , climatology , forecast skill , regression , environmental science , stepwise regression , computer science , meteorology , statistics , geography , mathematics , machine learning , geology
Since 2007, the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective (1983–2003) and real‐time (2008–2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single‐model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression‐based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.

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