Impacts of Bias‐Corrected ERA5 Initial Snow Depth on Dynamical Downscaling Simulations for the Tibetan Plateau

Abstract

The Tibetan Plateau (TP) is often called the “Water Tower of Asia,” which contains the largest amount of snow and glaciers outside the polar regions. As an important and variable feature of the land surface, snow coverage on the TP has great impacts on regional climate. However, the commonly used ERA5 reanalysis in dynamical downscaling largely overestimates the snow depth for the TP. To improve the representation of snow cover in ERA5, a new ERA5‐driven downscaling data set (High Asia Refined analysis version 2, HAR v2) was generated by the Weather Research and Forecasting (WRF) model with the bias‐corrected snow depth. This study aims to identify and better understand the impact of bias‐corrected initial snow conditions on simulated regional climate, by comparing the HAR v2 with a 5‐year ERA5 forced WRF simulation without bias correction of initial snow depth (referred to as WRF_ERA5). The results show that the bias correction significantly improves the simulation of 2 m air temperature (T2), with regional mean cold bias reduced by 0.2°C–2.4°C, but no significant improvement in precipitation simulation is found. Further comparative analysis reveals that higher snow depth in WRF_ERA5 leads to T2, mean daily precipitation, summer extreme precipitation, and contributions of convective precipitation to summer mean daily precipitation decrease by 0°C–4°C, 0%–60%, 0%–40%, and 0%–10%, respectively, in most areas of the TP. In addition, the bias‐corrected initial snow depth also has impacts on simulated diurnal cycles of precipitation and T2 and leads to peak hours one hour earlier. Overall, this study confirms the importance of snow cover for the climate in the TP.

Performance of the WRF Model at the Convection‐Permitting Scale in Simulating Snowfall and Lake‐Effect Snow Over the Tibetan Plateau

“…The ME values of mean daily T2 relative to the gauge observations and CMFD are shown in Figure S4 in Supporting Information . The results show that the WRF simulations exhibit cold biases at both the station and grid scales, which is consistent with the results of previous studies (e.g., Lin et al., 2021; Prein et al., 2022; Wang et al., 2021). In addition, the cold biases relative to the gauge observations are larger than the cold biases relative to the CMFD, with ME values ranging from −5° to 0°C for most of the stations.…”

“…The results show that the variability of the hourly T2 from the CMFD and all of the WRF simulations agree well with that of the gauge observations from October 4 to 8, 2018. Independent of the PPSs, all of the simulations exhibit an overall cold bias relative to the gauge T2 observations, which is probably linked to the well‐known systematic cold bias of the WRF model in high altitude areas (Bonekamp et al., 2018; Gao et al., 2015; Lin et al., 2021). The cold biases range from −5° to 0°C, and the maximum values occur at the maximum and minimum T2 on each day.…”

“…Besides, the forcing data (i.e., ERA5) largely overestimates the snow depth over the TP (Orsolini et al., 2019), which may also contribute to the cold bias in the WRF simulations. Although it has been found that bias correction of the ERA5 initial snow depth can significantly reduce cold biases when conducting the WRF simulations (Lin et al., 2021; Wang et al., 2021), the cold bias over the TP could not be eliminated. As was previously mentioned, these cold biases may be caused by the systematic cold bias of the WRF model when conducting simulations over high altitude regions (Bonekamp et al., 2018; Gao et al., 2015).…”

“…The integrated multi‐satellite retrievals for the GPM (IMERG) final run product (Huffman et al., 2019) were used in this study, with a temporal resolution of 30 min and a spatial resolution of 0.1° (∼10 km). Although GPM data has been widely used in many studies over the TP (Li, Qi, & Chen, 2022; Lin et al., 2021; Ou et al., 2020), it has a deficiency in terms of detecting snowfall (Behrangi et al., 2014; Immerzeel et al., 2015). Therefore, the GPM precipitation was compared with the gauge observations to confirm if it was suitable for use as a reference at the grid scale from October 4 to 8, 2018, over the TP.…”

Impacts of glacier changes on precipitation in the Tibetan Plateau

“…CC BY 4.0 License. than 50 mm) to the lush southeast (exceeding 2000 mm), coupled with a corresponding decrease in mean annual air temperature from a temperate 20°C in the southeast to a frigid below −6°C in the northwest (Lin et al, 2021b). The rainy season in the TP predominantly occurs between May and September, accounting for over 80% of the annual precipitation (Curio and Scherer, 2016;Lin et al, 2021b).…”