Evaluation of Temperature and Precipitation Simulations in CMIP6 Models Over the Tibetan Plateau

Abstract

 CMIP6 models reasonably reproduce the spatial patterns and temporal variations of mean and extreme temperature and precipitation. CMIP6 models continue to suffer from cold bias in temperature and wet bias in precipitation similar to its predecessor CMIP5. Most CMIP6 models underestimate the observed trends in mean and extreme temperature and precipitation.

Why Do CMIP6 Models Fail to Simulate Snow Depth in Terms of Temporal Change and High Mountain Snow of China Skillfully?

“…The abnormally large SD errors in the TP could thus be caused by the clearly larger precipitation overestimates and temperature underestimates there compared with XJ and NPM (Figure 3b). A notably wet bias in precipitation (Cui et al., 2021) and cold bias in temperature (You et al., 2021) of CMIP6 models on the TP have also been observed by other recent studies (Lalande et al., 2021; Lun et al., 2021; Zhu & Yang, 2020), and thought possibly to be caused by inaccurate soil‐type configuration in the models (Yue et al., 2021) and inadequate considerations of convective moisture transport (P. Li et al., 2021) or orographic drag (Zhou et al., 2019).…”

Global Daily Actual and Snow‐Free Blue‐Sky Land Surface Albedo Climatology From 20‐Year MODIS Products

“…As the next‐generation global climate model (GCM), CMIP6 is featured in the 2021 Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6). Several basic outcomes from CMIP6 have recently been assessed using site measurements and satellite observations, such as the planetary albedo (Jian et al., 2020), snow cover (Zhu et al., 2021), sea ice extent (Shu et al., 2020), water fluxes (Li et al., 2021), cloud fraction (Vignesh et al., 2020), air temperature and precipitation (Cui et al., 2021), and surface upward longwave radiation (Xu et al., 2021). However, as an essential climate driver, land surface albedo from CMIP6 GCMs remains to be explored.…”

Accounting for Topographic Effects on Snow Cover Fraction and Surface Albedo Simulations Over the Tibetan Plateau in Winter

“…Therefore, numerical simulations are necessary for snow cover and climate research. However, a common “cold bias” exists in the near‐surface temperature simulations in current global climate models and regional climate models (Cui et al., 2021; Meng et al., 2018; Su et al., 2013; Y. Gao et al., 2015). This “cold bias” may be attributed to precipitation ice radiative effects (Lee et al., 2019), “wet bias” (Duan et al., 2013), overestimated snow cover (Lalande et al., 2021; X. Chen et al., 2017), aerosols in the snow (Usha et al., 2020) and other causes.…”

“…Therefore, numerical simulations are necessary for snow cover and climate research. However, a common "cold bias" exists in the near-surface temperature simulations in current global climate models and regional climate models (Cui et al, 2021;Meng et al, 2018;Su et al, 2013;Y. Gao et al, 2015).…”