Ensemble Temperature and Precipitation Projection for Multi-Factorial Interactive Effects of GCMs and SSPs: Application to China
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abstract
Climate change has broadly impacted on the China areas. There will be severe challenges due to the variations of precipitation and temperature in the future. Therefore, a comprehensive understanding of the future climate change over China areas is desired. In this study, future annual precipitation and annual mean temperature under two SSPs over China areas were projected through multiple global climate models. Meanwhile, to explore the sources of uncertainty in projecting future climate change, the multi-factorial analysis was conducted through GCMs (five levels) and SSPs (two levels). This study can help us understand the possible changes in precipitation, temperature, and the potential extreme climate events over the China area. The results indicate that China would have more annual precipitation and higher annual mean temperature in the future. Compared with the historical period, the annual mean temperature would face a continuously increasing trend under SSPs. Regardless of SSP245 or SSP585, the growth rate of annual precipitation and annual mean temperature increase in the northern region (e.g., Northeast China, North China, and Northwest China) are higher than those in the southern parts (e.g., East China, South China, and Central China). The future temperature rise may increase the frequency of heat-related extreme climate events, which needs to be focused on in future research. Moreover, GCM was the main contributing factor to the sources of uncertainty in projecting future precipitation and SSP was the main factor for future temperature. Overall, climate change is an indisputable fact in China. The annual precipitation and annual mean temperature would increase to varying degrees in the future. Reducing the systemic bias of the climate model itself will undoubtedly be the top priority, and it would help to improve the projection and evaluation effects of relevant climate variables.
