Investigating extreme snowfall changes in China based on an ensemble of high-resolution regional climate models

Anthropogenic-induced global warming intensifies the water cycle around the world. As a critical sector of the water cycle, snow depth and its related extremes greatly impact agriculture, animal husbandry, and food security, yet lack of investigation. In this study, five high-resolution climate models are selected to simulate and project snow depth and its extremes over China. The simulation capabilities of models in reproducing the basic climate variables in winter are gauged in terms of spatial and temporal patterns over nine subregions. It is found that the driving global climate model (GCM) could contribute to similar patterns while the different regional climate model (RCM) schemes lead to large variations in the snowfall accumulating on the land surface. The warming magnitude is larger under a higher representative concentration pathway (RCP) scenario (2.5°C greater under RCP8.5 than RCP4.5). The distribution of ensemble mean winter precipitation changes are more fragmented because of the relatively low skill in reproducing water-related content in the climate system. The projected precipitation change is larger under RCP8.5 than under RCP4.5 due to the amplification of the hydrological cycle by temperature warming. The projected changes in the ensemble mean snow depth mainly occur over the Tibetan Plateau with a decreasing trend. Only several grids over the Himalayas Mountains and the upper stream of the Yarlung Zangbo River are projected with a slight increase in snow depth. Both the intensity and frequency of extreme snow events are projected to increase in Northeast China and Inner Mongolia which are important agricultural and animal husbandry production areas in China. The reason behind this projection can be explained that the hydrological cycle intensified by temperature warming leads to excessive snowfall stacking up during winter. The changes in extreme snowfall events in the future will have a significant impact on China’s agricultural and animal husbandry production and threaten food security.

人为活动引发的全球变暖加剧了全球水循环。作为水循环的关键组成部分，雪深及其相关极端事件对农牧业与粮食安全具有重大影响，但目前相关研究仍较为匮乏。本研究选取5套高分辨率气候模型，针对中国区域的雪深及其极端事件开展模拟与未来预估。研究通过对比9个分区的冬季基础气候变量时空分布特征，评估了各模型的模拟能力。结果发现，驱动全球气候模型（GCM）会带来相似的模拟结果，而不同的区域气候模型（RCM）方案则会导致陆面积雪的降雪量出现较大差异。在更高的典型浓度路径（RCP）情景下，增温幅度更大：RCP8.5情景下的增温幅度较RCP4.5情景高2.5℃。由于模型对气候系统中水循环相关要素的模拟能力相对较弱，集合平均的冬季降水变化分布更为零散。受温度变暖对水循环的放大作用影响，RCP8.5情景下的预估降水变化幅度高于RCP4.5情景。集合平均的雪深预估变化主要集中在青藏高原地区，整体呈减少趋势；仅喜马拉雅山脉及雅鲁藏布江上游区域的少数网格点，雪深预估会出现小幅增加。作为中国重要的农牧业生产基地，东北地区与内蒙古地区的极端降雪事件的强度与频率均预估会有所上升。该预估结果的成因可解释为：温度变暖加剧的水循环会使得冬季降雪量过剩并累积。未来极端降雪事件的变化将对中国农牧业生产产生显著影响，并威胁粮食安全。