Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia

Abstract

The seasonal snowpack plays an essential role in the water resources budget of the global mountainous area and provides freshwater supply for over 1/6 of the world's population (Barnett et al., 2005;, which has a profound effect on the food production of irrigated agriculture and snowmelt runoff regimes in the snow-dominated basin (Qin et al., 2020). It also strongly affects regional climate system, alpine phenology, and biogeochemical processes through regulation of the land-atmospheric exchanges of water and energy (Arndt et al., 2020;Tomaszewska et al., 2020;Zhang, 2005). In addition, snow attracts recreational activities and is an important resource of winter tourism (Deng et al., 2019), but it also causes snow-related disasters, such as snow avalanches and snowmelt flooding (Ballesteros-Cánovas et al., 2018;Schweizer et al., 2003). Although the role of snow is irrefutable, present approaches show a large uncertainty regarding snow mass estimation in a global mountainous area due to the presence of orographic barriers, its strong vertical and horizontal variability, diverse vegetation cover, and representative sites for snow measurement (Dong, 2018;Dozier et al., 2016;Mudryk et al., 2015). The accuracy of snow mass map based on in-situ observations interpolation methods depends on the number and representativeness of the ground observations, while a sparse network of snow observations usually exists in the mountainous environment, especially in the area with dense vegetation and a complex topography (Dozier et al., 2016;Mortimer et al., 2020). The passive microwave sensors could provide a nearly real-time global snow mass

Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation

“…The uncertainty of the snow simulation in the RCMs was influenced by the quality of the simulated precipitation and temperature (Liu et al ., 2019; Wrzesien et al ., 2019). An underestimation in the total precipitation during the cold season was revealed in the low altitude regions of the western and southern Tianshan Mountains, and the intersection between the northern and eastern Tianshan Mountains (Yang et al ., 2021). On the other hand, possible reasons of the overestimation in snow depth at mid‐high altitude regions were mainly caused by the severely overestimated precipitation of the model and the undercatch of the snowfall in the gauge observations (Bonekamp et al ., 2018).…”

Interplay Between Urbanization and Irrigation on Summer Climate in the Huang‐Huai‐Hai Plain, China

“…The LUCs in the ECOCLIMP II dataset were divided into 573 ecosystems according to the land cover with climate maps (Champeaux et al., 2005). Due to the outdated land‐use cover data, the ECOCLIMAP could not correctly reveal the real land cover status (He, Luo, et al., 2021; T. Yang et al., 2021). The previous comparison indicated that the Climate Change Initiative Land cover dataset (CCI‐LC) from ESA (with high overall accuracy) could better exhibit a homogeneous land cover change in China, particularly in the farming zone of the northern part (Yang et al., 2017).…”

“…The LUCs in the ECOCLIMP II dataset were divided into 573 ecosystems according to the land cover with climate maps (Champeaux et al, 2005). Due to the outdated land-use cover data, the ECOCLIMAP could not correctly reveal the real land cover status T. Yang et al, 2021).…”

“…However, in‐situ observations are confined to a fixed area and do not reflect the spatial differences (Khosa et al., 2019; Metzger et al., 2018), and remote sensing data are insufficient to represent the land‐atmosphere interaction process (He et al., 2018). Moreover, great uncertainties are induced since the climatic responses caused by the terrestrial surface's biogeophysical properties (i.e., land‐surface parameters) may vary across different locations and seasons (Yang et al., 2021). The regional climate models provide a possibility to understand the complex processes and mechanisms (Berckmans et al., 2019; Shi et al., 2014).…”

“…Numerical climate models are powerful tools to investigate the regional climate change processes due to their ability to provide detailed spatiotemporal information (Giannaros et al, 2018;Langendijk et al, 2021;T. Yang et al, 2021).…”