摘要
The evolution of daily synoptic weather patterns is the main driver of day-to-day weather change. These patterns are generally associated with changes in temperature, precipitation, etc., especially during extreme weathers. Evaluating the ability of climate models to reproduce the frequency and intensity of daily synoptic patterns is essential for increasing confidence in future projections. In this study, we investigated the ability of 34 global climate models （GCMs） included in the Coupled Model Intercomparison Project Phase 5 （CMIP5） to simulate synoptic patterns over East Asia and their evolution features in winter and summer. Daily synoptic patterns in sea level pressure and their occurrence frequencies were identified by using an objective clustering algorithm, self-organizing maps （SOMs）. The evaluation consists of correlating the frequencies of these patterns in the 34 CMIP5 models with the frequencies in the NCEP reanalysis during the baseline period of 1980-1999. The results illustrated that most of these models were able to reproduce the synoptic patterns of the NCEP reanalysis. In addition, the frequencies of temporal sea level pressure （SLP） anomaly patterns were reproduced by most of the models over the baseline period, but the frequencies of spatial SLP anomaly patterns were only reproduced by a few GCMs. Overall, the models performed better in summer than in winter. Comprehensive evaluation shows that the four top-performing models for both winter and summer are bcc-csml-l-m, NorESM1-M, MRI-CGCM3, and CCSM4. They show good performance in simulating the daily synoptic patterns in SLP and in reproducing their occurrence frequencies. The results showed that the SOM was an effective tool for differentiating characteristics of synoptic circulation patterns and for evaluating the ability of climate models to simulate the frequency of daily synoptic patterns. The results can also help users to choose a better model for future climate projection and downscaling over East Asia.
The evolution of daily synoptic weather patterns is the main driver of day-to-day weather change. These patterns are generally associated with changes in temperature, precipitation, etc., especially during extreme weathers. Evaluating the ability of climate models to reproduce the frequency and intensity of daily synoptic patterns is essential for increasing confidence in future projections. In this study, we investigated the ability of 34 global climate models （GCMs） included in the Coupled Model Intercomparison Project Phase 5 （CMIP5） to simulate synoptic patterns over East Asia and their evolution features in winter and summer. Daily synoptic patterns in sea level pressure and their occurrence frequencies were identified by using an objective clustering algorithm, self-organizing maps （SOMs）. The evaluation consists of correlating the frequencies of these patterns in the 34 CMIP5 models with the frequencies in the NCEP reanalysis during the baseline period of 1980-1999. The results illustrated that most of these models were able to reproduce the synoptic patterns of the NCEP reanalysis. In addition, the frequencies of temporal sea level pressure （SLP） anomaly patterns were reproduced by most of the models over the baseline period, but the frequencies of spatial SLP anomaly patterns were only reproduced by a few GCMs. Overall, the models performed better in summer than in winter. Comprehensive evaluation shows that the four top-performing models for both winter and summer are bcc-csml-l-m, NorESM1-M, MRI-CGCM3, and CCSM4. They show good performance in simulating the daily synoptic patterns in SLP and in reproducing their occurrence frequencies. The results showed that the SOM was an effective tool for differentiating characteristics of synoptic circulation patterns and for evaluating the ability of climate models to simulate the frequency of daily synoptic patterns. The results can also help users to choose a better model for future climate projection and downscaling over East Asia.
基金
Supported by the National Natural Science Foundation of China(41230528 and 41205162)
National(Key)Basic Research and Development(973)Program of China(2012CB955204)
Priority Academic Program Development of Jiangsu Higher Education Institutions
