Prediction Skill for the East Asian Winter Monsoon Based on APCC Multi-Models
AbstractThe prediction skill for the East Asian winter monsoon (EAWM) has been analyzed, using the observations and different climate models that participate in the APEC Climate Center (APCC) multi-model ensemble (MME) seasonal forecast. The authors first examined the characteristics of the existing EAWM indices to find a suitable index for the APCC seasonal forecast system. This examination revealed that the selected index shows reasonable prediction skill of EAWM intensity and well-represents the characteristics of wintertime temperature anomalies associated with the EAWM, especially for the extreme cold winters. Although most models capture the main characteristics of the seasonal mean circulation over East Asia reasonably well, they still suffer from difficulty in predicting the interannual variability (IAV) of the EAWM. Fortunately, the POAMA has reasonable skill in capturing the timing and strength of the EAWM IAV and reproduces the EAWM-related circulation anomalies well. The better performance of the POAMA may be attributed to the better skill in simulating the high-latitude forcing including the Siberian High (SH) and Artic Oscillation (AO) and the strong links of the ENSO to the EAWM, compared to other models. View Full-Text
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Shin, S.-H.; Moon, J.-Y. Prediction Skill for the East Asian Winter Monsoon Based on APCC Multi-Models. Atmosphere 2018, 9, 300.
Shin S-H, Moon J-Y. Prediction Skill for the East Asian Winter Monsoon Based on APCC Multi-Models. Atmosphere. 2018; 9(8):300.Chicago/Turabian Style
Shin, Sun-Hee; Moon, Ja-Yeon. 2018. "Prediction Skill for the East Asian Winter Monsoon Based on APCC Multi-Models." Atmosphere 9, no. 8: 300.
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