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CMIP5 projected changes in temperature and precipitation in arid and humid basins

Zhu, Boli, Xue, Lianqing, Wei, Guanghui, Zhang, Luocheng, Chen, Xinfang
Theoretical and applied climatology 2019 v.136 no.3-4 pp. 1133-1144
basins, climate change, climate models, data collection, drought, floods, model validation, prediction, space and time, spring, surface temperature, uncertainty, watersheds, winter, China, Yangtze River
Future changes projected in surface temperature and precipitation behave differently in different regions or watersheds and vary greatly in space and time, even within the same region under climate change. This study aims to detect and compare differences in the climatological characteristics in response to climate change in arid and humid areas. Based on the CN05.1 reanalysis gridded dataset (high-resolution climate model validation launched by China Meteorological Administration) and output from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), the projected responses of temperature and precipitation to climate change under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) in the arid basin (the Tarim River Basin, the TRB) and the humid basin (the Yangtze River Basin, the YRB) were characterized. Our results show that the swings of annual temperature and precipitation will extend with the rising emission scenarios in the future, while those are more vulnerable in the TRB. The topography-related changes of temperature and precipitation were further evaluated to explore the uncertainty. The elevation-dependent warming (EDW) may be weakened but still hold in the future at the watershed scale. Change in precipitation will decrease with increase in elevation in the TRB, while it will increase in the YRB. Compared with the general growth in seasonal temperature, precipitation shows more striking changes during spring and winter under climate change. Further researches to improve our understanding of the projected changes in temperature and precipitation are needed for better prediction of extreme weather conditions, such as drought and floods.