Information entropy‐based projection of temperature variability under the warming trend in China

Abstract

AbstractQuantifying the predictability of temperature in the future is a priority to inform policy and adaptation planning. Based on information entropy theory, this study uses global climate models within the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project spatiotemporal changes and variability of temperature in China in the 21st century under different shared socio‐economic pathway scenarios (SSP2‐4.5 and SSP5‐8.5). Results show that the multimodel ensembled values can accurately capture the spatiotemporal pattern of temperature in China. The warming rates of annual mean temperature in China under SSP2‐4.5, SSP5‐8.5 scenarios are 0.42 and 0.61°C·decade−1 in the near‐term (2021–2050), 0.20 and 0.87°C·decade−1 in the long‐term (2071–2100), respectively. Warming is found to be the greatest in winter. Northeast, northwest, and the first region of southwest are anticipated to increase particularly rapidly, with larger increases than the rest of China. The intrayear variability of temperature (the variability of monthly mean temperature within the year or different seasons) in China shows obvious latitudinal differences. The variability of monthly mean temperature exhibits a decreasing trend in parts of northeast and the first region of southwest, and an increasing trend in northwest and parts of north. This is mainly resultant from the variability of monthly mean temperatures within spring and autumn. And this trend is more significant under the SSP5‐8.5 scenario than that under the SSP2‐4.5 scenario. The interannual variability of temperature (the variability of annual or seasonal mean temperature) in China shows significant regional difference, and two obvious regions with extreme variability (northeast and the first region of southwest). The trend of variability increases and then decreases under the SSP2‐4.5 scenario, whereas it keeps increasing under the SSP5‐8.5 scenario. This study helps to understand the spatiotemporal characteristics of temperature variability in China in the 21st century.