Snow cover and glacier energy and mass balance variability (SG)

降水是最重要的气候要素之一，它将复杂的大气过程与水文循环、雪盖以及冰川和冰帽的物质平衡联系起来。降水量是水资源管理和预防洪水和干旱等自然灾害的一个关键变量。这尤其适用于拟议项目bundle prime的研究区域，该区域包括高亚洲，即，青藏高原及其周围的高山。prime内的研究旨在依靠新的遥感数据和先进的数值建模方法生成和验证改进的网格化降水数据集。在此基础上，我们调查的空间和时间模式和区域特定的边界条件，大尺度的驱动程序和中到本地尺度的过程控制降水变率（ii）。降水类型和变异性的准确性和更好的知识的提高，使我们能够扩大对高亚洲不同次区域的冰川质量平衡和季节性积雪的时空变化的理解（iii）。首先，将根据中分辨率成像分光仪卫星积雪数据和模型运行与雪冰能量和质量平衡耦合模型（COSIMA）的更新版本的组合，对PRIME的其他两个子项目新得出的降水数据集进行评估。为此，COSIMA将扩大到包括冻土，而不是积雪下的冰。结果将允许评估的一致性建模（总理HAR）和遥感派生（总理RS）降雪事件。在这一程序中，单一事件造成的积雪减少的模型将受到中分辨率成像分光仪卫星遥感图像的限制和评价。第二，COSIMA的高级和扩展版本以及新的大气高亚洲精细分析数据将联合用于模拟高亚洲某些冰川的能量和质量通量，并提供质量平衡观测数据以供评价。研究结果将有助于分析COSIMA新模型版本的性能和可靠性，以及高亚洲不同气候背景下冰川的改进输入数据。第三，在prime-SG中，我们将分析冰川质量平衡敏感性的驱动因素及其对大气强迫空间变异的依赖性。分析将以敏感性矩阵和对大气驱动因素的时间模式以及由此产生的不同区域的能量和质量通量的分析为基础。

Precipitation is one of the most important climate elements linking complex atmospheric processes with hydrological cycles, snow cover and mass balances of glaciers and ice caps. Precipitation is a key variable for water resource management and prevention of natural disasters such as floods and droughts. This particularly holds true for the study region of the proposed project bundle prime which encompasses High Asia, i.e., the Tibetan Plateau and its surrounding high-mountain ranges. Research within prime aims at generating and validating improved gridded precipitation data sets relying on new remote sensing data and advanced numerical modelling approaches (i). Based on this we investigate spatial and temporal patterns and region-specific boundary conditions, large-scale drivers and meso- to local-scale processes controlling precipitation variability (ii). The improved accuracy and better knowledge on precipitation type and variability allow to extend our understanding of spatial and temporal variations of glacier mass balances and seasonal snow covers in different sub-regions of High Asia (iii).The sub-project prime-SG shall contribute to the project bundle prime by three major research activities. First, the newly derived precipitation datasets by the other two sub-projects of prime will be evaluated against a combination of MODIS satellite snow cover data and model runs with an updated version of the Coupled Snow and Ice Energy and Mass Balance Modell (COSIMA). For that purpose COSIMA will be extended to include frozen ground instead of ice below the snow cover. The results will allow to evaluate the consistency of modelled (prime-HAR) and remote sensing derived (prime-RS) snowfall events. The modelling of the depletion of the snow cover resulting from single events in this procedure will be constrained and evaluated by MODIS satellite remote sensing imagery. Second, an advanced and extended version of COSIMA and the new atmospheric High Asia Refined Analysis (HAR) data will jointly be used for simulations of energy and mass fluxes on selected glaciers in High Asia with available mass balance observations for evaluation. The results will allow to analyse the performance and reliability of the new COSIMA model version and improved input data for glaciers with different climatic settings in High Asia. Third, within prime-SG, we will analyse the driving factors of glacier mass balance sensitivity and their dependency on the spatial variability of atmospheric forcing. The analysis will be based on both, sensitivity matrices and the analysis of the temporal patterns in atmospheric drivers and resulting energy and mass fluxes in the different regions.