Evaluation of precipitation in CMIP6 over the Yangtze River Basin

Y Li, D Yan, H Peng, S Xiao - Atmospheric Research, 2021 - Elsevier
Y Li, D Yan, H Peng, S Xiao
Atmospheric Research, 2021Elsevier
Precipitation simulation and projection in global climate models (GCMs) play an important
role in regional water resource management. Since multiple climate simulations from CMIP6
are already available, we evaluated the precipitation performance over the Yangtze River
basin (YRB) from 18 models against observations during 1961–2014, before using the
relevant simulations for scientific investigation and policy making. Our results suggest that
CMIP6 models generally capture the increasing pattern of precipitation from the source …
Abstract
Precipitation simulation and projection in global climate models (GCMs) play an important role in regional water resource management. Since multiple climate simulations from CMIP6 are already available, we evaluated the precipitation performance over the Yangtze River basin (YRB) from 18 models against observations during 1961–2014, before using the relevant simulations for scientific investigation and policy making. Our results suggest that CMIP6 models generally capture the increasing pattern of precipitation from the source region to the lower reaches of the YRB. However, most models overestimate precipitation (wet bias), by about 28.6% for multi-model ensemble mean (MEM). Spatially, the most significant wet bias could reach 70.1% in the source region. Seasonally, the wet bias is more pronounced in the cold season (56.2% in winter and 45.1% in spring). For precipitation change during 1961–2014, most models reproduce the observed increasing trend in the most northwest YRB, but they display large spatial discrepancies in the other regions. In CMIP6 projections (2015–2099), the precipitation over the YRB keep a consistently increasing trend under both scenarios SSP1-2.6 (14.76 mm/10a) and SSP5-8.5 (22.47 mm/10a). From near term (2015–2060) to long term (2061–2099), this wetting trend slows down under SSP1-2.6, but it accelerates under SSP5-8.5.
Elsevier
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