Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES

Se-Hwan YANG; LI Chaofan; and LU Riyu

doi:10.1007/s00376-013-3172-2

Abstract:

Winter rainfall over South China shows strong interannual variability, which accounts for about half of the total winter rainfall over South China. This study investigated the predictability of winter (December-January-February; DJF) rainfall over South China using the retrospective forecasts of five state-of-the-art coupled models included in the ENSEMBLES project for the period 1961-2006. It was found that the ENSEMBLES models predicted the interannual variation of rainfall over South China well, with the correlation coefficient between the observed/station-averaged rainfall and predicted/area-averaged rainfall being 0.46. In particular, above-normal South China rainfall was better predicted, and the correlation coefficient between the predicted and observed anomalies was 0.64 for these wetter winters. In addition, the models captured well the main features of SST and atmospheric circulation anomalies related to South China rainfall variation in the observation. It was further found that South China rainfall, when predicted according to predicted DJF Nio 3.4 index and the ENSO-South China rainfall relationship, shows a prediction skill almost as high as that directly predicted, indicating that ENSO is the source for the predictability of South China rainfall.

摘要: Winter rainfall over South China shows strong interannual variability, which accounts for about half of the total winter rainfall over South China. This study investigated the predictability of winter (December-January-February; DJF) rainfall over South China using the retrospective forecasts of five state-of-the-art coupled models included in the ENSEMBLES project for the period 1961-2006. It was found that the ENSEMBLES models predicted the interannual variation of rainfall over South China well, with the correlation coefficient between the observed/station-averaged rainfall and predicted/area-averaged rainfall being 0.46. In particular, above-normal South China rainfall was better predicted, and the correlation coefficient between the predicted and observed anomalies was 0.64 for these wetter winters. In addition, the models captured well the main features of SST and atmospheric circulation anomalies related to South China rainfall variation in the observation. It was further found that South China rainfall, when predicted according to predicted DJF Ni?o3.4 index and the ENSO-South China rainfall relationship, shows a prediction skill almost as high as that directly predicted, indicating that ENSO is the source for the predictability of South China rainfall.

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Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES

1. State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

2. University of the Chinese Academy of Sciences, Beijing 100049

3. Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Manuscript received: 2013-09-11

Manuscript revised: 2013-11-29

Fund Project: We thank the anonymous reviewers for their comments and suggestions, which greatly helped us to improve the presentation of this paper.This work was supported by the National Natural Science Foundation of China (Grant Nos. 41305067 and 41320104007).

Abstract: Winter rainfall over South China shows strong interannual variability, which accounts for about half of the total winter rainfall over South China. This study investigated the predictability of winter (December-January-February; DJF) rainfall over South China using the retrospective forecasts of five state-of-the-art coupled models included in the ENSEMBLES project for the period 1961-2006. It was found that the ENSEMBLES models predicted the interannual variation of rainfall over South China well, with the correlation coefficient between the observed/station-averaged rainfall and predicted/area-averaged rainfall being 0.46. In particular, above-normal South China rainfall was better predicted, and the correlation coefficient between the predicted and observed anomalies was 0.64 for these wetter winters. In addition, the models captured well the main features of SST and atmospheric circulation anomalies related to South China rainfall variation in the observation. It was further found that South China rainfall, when predicted according to predicted DJF Nio 3.4 index and the ENSO-South China rainfall relationship, shows a prediction skill almost as high as that directly predicted, indicating that ENSO is the source for the predictability of South China rainfall.

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Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES

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