The performance of CORDEX-EA-II simulations in simulating seasonal temperature and elevation-dependent warming over the Tibetan Plateau

Abstract

To explore the driving mechanisms of elevation-dependent warming (EDW) over the Tibetan Plateau (TP), the output from a suite of numerical experiments with different cumulus parameterization schemes (CPs) under the Coordinated Regional Climate Downscaling Experiments-East Asia (CORDEX-EA-II) project is examined. Results show that all experiments can broadly capture the observed temperature distributions over the TP with consistent cold biases, and the spread in temperature simulations commonly increases with elevation with the maximum located around 4000–5000 m. Such disagreements among the temperature simulations could to a large extent be explained by their spreads in the surface albedo feedback (SAF). All the experiments reproduce the observed EDW below 5000 m in winter but fail to capture the observed EDW above 4500 m in spring. Further analysis suggests that the simulated EDW during winter is mainly caused by the SAF, and the clear-sky downward longwave radiation (LWclr) plays a secondary role in shaping EDW. The models’ inability in simulating EDW during spring is closely related to the SAF and the surface cloud radiative forcing (CRFs). Furthermore, the magnitude and structure of the simulated EDW are sensitive to the choice of CPs. Different CPs generate diverse snow cover fractions, which can modulate the simulated SAF and its effect on EDW. Also, the CPs show great influence on the LWclr via altering the low-level air temperature. Additionally, the mechanism for different temperature changes among the experiments varies with altitudes during summer and autumn, as the diverse temperature changes appear to be caused by the LWclr for the low altitudes while by the SAF for the middle-high altitudes.

Long‐term observed changes of air temperature, relative humidity and vapour pressure deficit in Bolivia, 1950–2019

“…Other authors (e.g., Fan et al, 2015) did not always find significant variations between Tmax and elevation, especially at mid‐elevations, that is, 2000–3500 m (Dimiri et al, 2022), in agreement with our results. For mean air temperatures (Tmean), we found an increase in the warming rate with elevation annually (Figure 13), in agreement with previous studies (e.g., Liu et al, 2009; Niu et al, 2021; Pepin et al, 2015).…”

“…For mean air temperatures (Tmean), we found an increase in the warming rate with elevation annually (Figure 13), in agreement with previous studies (e.g., Liu et al, 2009;Niu et al, 2021;Pepin et al, 2015). Niu et al (2021) pointed out that the relationship between air temperature changes and elevation varies across seasons. In Bolivia, we have found strong seasonal differences in the magnitude of air temperature changes in relation to elevation and they differ between the maximum and minimum air temperatures, with opposite patterns.…”

“…Nevertheless, the elevation ranges examined, however, might also be a contributing factor to the findings. In fact, Niu et al (2021) The weak reduction in the annual RH is associated with the above-mentioned increase in annual air temperatures. These results are in agreement with the findings of You et al (2015), Vicente-Serrano et al (2014) or the calculations made by Ruosteenoja and Räisänen (2013) that projected a decrease of RH by 8%-12% in Europe from June to August from 2070 to 2099 as a response to global warming.…”

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

“…Snow as a critical component of the climate system, with a high albedo and low thermal conductivity, plays a significant role in shaping climate over the TP and its surrounding regions, as well as on the hydrological cycle through mechanical and thermal dynamical effects (Henderson et al., 2018; Li et al., 2018; Shen et al., 2015; Wu et al., 2015; Xiao & Duan, 2016). In recent years, snow cover on the TP has been extensively investigated because it influenced Asian monsoon systems and its high sensitivity to warming (Gao et al., 2019; Ji & Kang, 2013; Jiang et al., 2020; Niu, Tang, Chen, Wang, & Ou, 2021; Niu, Tang, Chen, Wang, Ou, & Fu, 2021; Xiao & Duan, 2016; Zhang & Ma, 2018). Comparing other regions in the world, climate change over the TP is characterized as an earlier start time, a large change, and a rapid change rate (Yao, 1999).…”

Construction of surface air temperature over the Tibetan Plateau based on generative adversarial networks

“…Currently, high-quality temperature datasets have been developed by more and more scientific communities (Gu et al, 2020;He et al, 2020;Niu et al, 2021;Zhou et al, 2021). For instance, the China Meteorological Forcing Dataset (CMFD) is one of the most prevalent climate datasets within China from January 1979 to December 2018 developed by He et al (2020).…”