Winter temperatures over the Korean Peninsula and East Asia: development of a new index and its application to seasonal forecast
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This study proposes a new index for monitoring and predicting winter temperatures of the Korean Peninsula based on the dominant atmospheric winter teleconnection patterns. The utilization of this index is further extended to the East Asian Winter Monsoon (EAWM) index because the new index is found to well represent the main feature of the EAWM circulation. Among the teleconnection patterns, the East Atlantic (EA) and Western Pacific (WP) patterns are found to be most strongly correlated with winter temperatures via their partial association with changes in sea level pressure (SLP) around the Korean Peninsula, i.e., the EA and WP patterns are associated with SLP variation over the Siberian High region and the Kuroshio extension region to the east of Japan, respectively. On the basis of this relationship, the two regions representing the northwest-to-southeast SLP gradients are determined to define the new index. It is found that the new index can represent the Korean winter temperatures consistently well regardless of their considerable decadal changes. When compared with the existing SLP-based EAWM indices, the new index shows the best performance in delineating winter air temperatures, not only in the Korean Peninsula but also in the entire East Asian region. We also assess the prediction skill of the new index with seasonal coupled forecast models of the APEC Climate Center of Korea and its capability to predict winter temperatures. This assessment shows that the new index has potential for operationally predicting and monitoring winter temperatures in Korea and the whole of East Asia.
KeywordsTeleconnection EAWM Winter temperature prediction
Authors acknowledge that the GHCN Gridded V2 data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. The authors also acknowledge that the APCC Multi Model Ensemble (MME) Producing Centers for making their data available for analysis and the APEC Climate Center for collecting and archiving them and for organizing APCC MME prediction. Authors thank anonymous reviewers for constructive comments. This research was supported by the APEC Climate Center.
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