State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China.
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.
Sci Total Environ. 2019 Feb 15;651(Pt 2):2866-2873. doi: 10.1016/j.scitotenv.2018.10.126. Epub 2018 Oct 15.
Snow depth plays an essential role in the water and energy balance of the land surface. It is of special importance in arid and semi-arid regions of Central Asia. Owing to the limited availability of field observations, the spatial and temporal variations of snow depth are still poorly known. Using the Japanese 55-year (JRA-55) and the ERA-Interim reanalysis snow depth products, we considered four global climate models (GCMs) applied in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), examining how they represent snow depth in Central Asia during the period 1986-2005 in terms of spatial and temporal characteristics. We also investigated changes of winter (January-March) snow depth in Central Asia, at 1.5 °C and 2 °C global warming levels. Finally, the joint probabilistic behavior of winter temperature and precipitation at 1.5 °C and 2 °C global warming are investigated using the kernel density estimator (KDE). The result shows that the snow depth climatology of Central Asia is generally well simulated in both spatial pattern and temporal (inter-annual and inter-seasonal) pattern. All models approximately simulate the winter maximum and the summer minimum values of snow depth but tend to overestimate the amplitude during October-December. Only the trend in HadGEM2-ES matches fairly well to the JRA-55 reanalysis snow depth. When comparing the projections of spatial distribution of winter snow depth, distinctive spatial pattern is noted at both 1.5 °C and 2 °C global warming levels, when the snow depth is shown to increase in northeastern and to decrease in midwestern regions of Central Asia. According to the joint probability distributions of precipitation and temperature, Central Asia will tend to experience a warmer and wetter winter at both 1.5 °C and 2 °C global warming levels, which can be associated with an increase in snow depth in the northeastern regions.
积雪深度在陆面的水量和能量平衡中起着至关重要的作用。在中亚的干旱和半干旱地区，它尤为重要。由于实地观测的有限性，积雪深度的时空变化仍然知之甚少。利用日本 55 年（JRA-55）和 ERA-Interim 再分析积雪深度产品，我们考虑了应用于部门间影响模型比较计划（ISI-MIP）的四个全球气候模型（GCM），考察了它们在 1986-2005 年期间如何代表中亚的积雪深度，包括空间和时间特征。我们还研究了中亚冬季（1 月至 3 月）积雪深度的变化，在 1.5°C 和 2°C 的全球变暖水平下。最后，利用核密度估计器（KDE）研究了 1.5°C 和 2°C 的全球变暖下冬季温度和降水的联合概率行为。结果表明，中亚的积雪深度气候学在空间格局和时间（年际和季节间）格局上都得到了很好的模拟。所有模型都大致模拟了冬季最大值和夏季最小值的积雪深度，但在 10 月至 12 月期间往往会高估幅度。只有 HadGEM2-ES 模型的趋势与 JRA-55 再分析积雪深度相当吻合。在比较冬季积雪深度的空间分布预测时，在 1.5°C 和 2°C 的全球变暖水平下，都注意到了明显的空间模式，即中亚东北部的积雪深度增加，而中西部的积雪深度减少。根据降水和温度的联合概率分布，中亚在 1.5°C 和 2°C 的全球变暖水平下都将经历一个更温暖、更湿润的冬季，这可能与东北部地区积雪深度的增加有关。
