Representation of Stony Surface‐Atmosphere Interactions in WRF Reduces Cold and Wet Biases for the Southern Tibetan Plateau

Abstract

The Tibetan Plateau (TP) is the highest plateau in the world. Due to strong solar heating and complex topography, TP is one of the regions with the strongest land-atmosphere interactions in the world (Koster et al., 2004;Xue et al., 2010) and plays an important role in the formation and development of the

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).…”

The Wet Bias of RegCM4 Over Tibet Plateau in Summer Reduced by Adopting the 3D Sub‐Grid Terrain Solar Radiative Effect Parameterization Scheme

“…The turbulent orographic form drag scheme remains its added‐value in reducing the WBT at the model resolution of 0.03° (Y. Wang et al., 2020). In addition, correctly representing the TP soil type can also restrain the WBT (Yue et al., 2021). Adopting the Community Land Model Version 4.5 (CLM4.5) with a greater level of detail in the sub‐grid land surface processes in climate models can produce a decrease of WBT compared to the other land surface models adopted (H. Gu, Yu, et al., 2020; X. Wang et al., 2015).…”

Lake‐Area Expansion Alters Downwind Precipitation Patterns on the Tibetan Plateau: Insights From the Most Dramatically Expanded Lake

“…The biases of the WRF simulation were anticipated because most global and regional models exhibit wet biases in high‐altitude regions (F. Su et al., 2013; Yue et al., 2021). Additionally, the satellite‐based GPM usually underestimates highly intense precipitation over complex terrain areas (Pradhan et al., 2022).…”

“…In terms of the grid spacing, the 5 km of D2 is a good resolution to balance the simulation accuracy and resources. Because the 5 km is currently one of the highest and commonly used grid spacings for most climate modeling groups (Chen et al., 2018; Ou et al., 2020; Prein et al., 2015), and the model is capable of simulating in the regions of TP with the grid spacing less than or equal to 10 km (Maussion et al., 2011, 2014; Ou et al., 2020; X. Wang et al., 2021; Yue et al., 2021). The physical parameterization schemes of the WRF simulations are presented in Table S1 in Supporting Information , and they have been well verified for use on the TP by X. Wang et al.…”