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Progress and Prospects of Research on Subseasonal to Seasonal Variability and Prediction of the East Asian Monsoon 被引量:2
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作者 Congwen ZHU Boqi LIU +3 位作者 Lun LI Shuangmei MA Ning JIANG Yuhan YAN 《Journal of Meteorological Research》 SCIE CSCD 2022年第5期677-690,共14页
Subseasonal to seasonal(S2S)variability represents the atmospheric disturbance on the 10–90-day timescale,which is an important bridge linking weather and climate.In 2015,China Meteorological Administration(CMA)liste... Subseasonal to seasonal(S2S)variability represents the atmospheric disturbance on the 10–90-day timescale,which is an important bridge linking weather and climate.In 2015,China Meteorological Administration(CMA)listed the S2S prediction project that was initiated by WMO programs three years ago as one of its key tasks.After five years of research,significant progress has been made on the mechanisms of the East Asian monsoon(EAM)S2S variability,related impact of climate change,as well as the predictability on the S2S timescale of numerical models.The S2S variability of the EAM is closely linked to extreme persistent climate events in China and is an important target for seasonal climate prediction.However,under the influence of global warming and the interactions among climate systems,the S2S variability of the EAM is so complex that its prediction remains a great challenge.This paper reviews the past achievement and summarizes the recent progress in research of the EAM S2S variability and prediction,including characteristics of the main S2S modes of the EAM,their impact on the extreme events in China,effects of external and internal forcing on the S2S variability,as well as uncertainties of climate models in predicting the S2S variability,with a focus on the progress achieved by the S2S research team of the Chinese Academy of Meteorological Sciences.The present bottlenecks,future directions,and critical research recommendations are also analyzed and presented. 展开更多
关键词 East Asian monsoon(EAM) subseasonal to seasonal(S2S)timescale change mechanism predictability of climate models
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Has the Prediction of the South China Sea Summer Monsoon Improved Since the Late 1970s? 被引量:2
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作者 FAN Yi FAN Ke1 TIAN Baoqiang 《Journal of Meteorological Research》 SCIE CSCD 2016年第6期833-852,共20页
Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models (CGCMs) from the ENSEMBLES (Ensemble-based Predictions of Climate Changes and Their Impacts) and DEME- TER (Developm... Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models (CGCMs) from the ENSEMBLES (Ensemble-based Predictions of Climate Changes and Their Impacts) and DEME- TER (Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction) projects, it is found that the prediction of the South China Sea summer monsoon (SCSSM) has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s. 展开更多
关键词 South China Sea summer monsoon prediction Ensemble-based predictions of climate Chan-ges and Their Impacts Development of a European Multimodel Ensemble System for Seasonalto Interannual prediction year-to-year increment prediction approach
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