Subseasonal prediction and predictability of summer rainfall over eastern China in BCC_AGCM2.2

The present study examines subseasonal prediction skills and biases of the summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2) and assesses the predictability of eastern China summer rainfall based on the multi-member forecasts....

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Veröffentlicht in:Climate dynamics 2021-04, Vol.56 (7-8), p.2057-2069
Hauptverfasser: Liu, Yunyun, Hu, Zeng-Zhen, Wu, Renguang, Jha, Bhaskar, Li, Qiaoping, Chen, Lijuan, Yan, Jinghui
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Sprache:eng
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Zusammenfassung:The present study examines subseasonal prediction skills and biases of the summer rainfall over eastern China in the Beijing Climate Center (BCC) Atmospheric General Circulation Model (BCC_AGCM2.2) and assesses the predictability of eastern China summer rainfall based on the multi-member forecasts. The BCC_AGCM2.2 model shows some skill in predicting summer rainfall over eastern China within the lead-times of 0–9 days. However, the subseasonal prediction skill is low on average, which is linked to the ability of the model in predicting the western Pacific subtropical high (WPSH). The low prediction skills may partially be attributed to biases in the model, including a wider meridional span and a weaker intensity of rainbelt in Yangtze River valley, earlier meridional movement, and a further northward shift of WPSH compared to that in the observations. These biases result in an obvious dry bias along the monsoon rainbelt, and a wet bias to both the north and south sides. Moreover, as a major contributor to the predictability, the Pacific-Japan pattern is not well predicted for both its spatial pattern and subseasonal evolution. The low forecast skill of summer rainfall in eastern China seems due to a dominant role of the atmospheric internal variability and a minor influence of the sea surface temperature in extratropical climate variability. Nevertheless, enhanced prediction skills under the assumption of a perfect model imply the potential to improve the prediction skill of the summer rainfall over eastern China through reducing model biases.
ISSN:0930-7575
1432-0894
DOI:10.1007/s00382-020-05574-y