Topoclimatic forcing and non-linear dynamics in the climate change adaption of glaciers in High Asia (TopoCliF)

高亚洲地区的冰川是超过10亿人口的重要水源，众所周知，冰川对气候变化的反应非常不同。然而，这种异质性背后的空间格局、强迫机制和敏感性仍然知之甚少。最近的研究强调了个别山谷和山脊尺度上的地形气候效应的作用，这意味着非线性融化动力学的巨大潜力。迄今为止，缺乏足够的工具来分析这种容易产生大数据的中尺度现象，导致大规模遥感研究与个别冰川的实地调查之间存在数据差距。冰川平衡线海拔（ELA）是一个综合现象，反映了影响冰川质量平衡（MB）的所有地形和气候因素的交叉总和；因此，ela非常适合作为地形气候影响的指标。拟议的项目将采用一种新的遥感方法，专门用于检索整个造山带的ELA和多时间ELA变化计算数据集，并且具有前所未有的详细程度。人工神经网络将应用于研究气候驱动因素，如全球辐射、温度、降水和风（由高亚洲精细分析提供的数据；HAR），以及地形因素，如坡向、坡角和峰顶高度（来自数字高程模型；DEM）如何控制高亚洲地区的ELAs。对于高亚洲的每个山脉，将根据良好的数据可用性选择至少一个基准设置。在这里，将在单个冰川的尺度上通过应用数值模式来研究这些过程，以获得详细的地表能和MB数据。由此产生的MBs随后将用于模拟所调查冰川对每月温度和降水异常的敏感性（来自HAR数据）。初步调查表明，如果ELA持续上升，喜马拉雅冰川堆积区的近平面地表将有可能发生非线性融化动力学。这些高海拔表面的表面积和地形配置将通过基于dem的GIS分析对整个高亚洲地区的冰川进行量化。当ELA达到特定地表的高度时，将通过测量地表与现代ELA之间的剩余高度缓冲来确定引爆点。随后可以利用之前处理的敏感性数据评估与海拔缓冲相关的温度和降水偏移。最终，将根据不同排放情景下的气候变化预估，估算各个冰川ela超过临界点海拔阈值的剩余时间。总之，所提出的跨学科和多方法方法的结果将大大有助于解开高亚洲冰川的地形气候强迫，并量化其非线性融化动力学的潜力。

Glaciers in High Asia, a substantial resource of water supply for more than a billion people, are known to react highly heterogeneous to climate change. However, spatial patterns, forcing mechanisms, and sensitivities underlying this heterogeneity are still poorly understood. Recent studies highlight the role of topoclimatic effects at the scale of individual valleys and ridges, implying substantial potential for non-linear melt dynamics. To date, adequate tools to analyze such big data-prone mesoscale phenomena are lacking, resulting in a data gap between large-scale remote sensing studies and field-based investigations at individual glaciers. The equilibrium line altitude (ELA) of a glacier is an integrating phenomenon, reflecting a cross total of all topographic and climatic factors affecting the mass balance (MB); ELAs are thus eminently suited indicators for topoclimatic effects. The proposed project will apply a novel remote sensing approach, specifically designed to retrieve datasets of ELA and multitemporale ELA change calculations for whole orogens and with unprecedented level of detail. An artificial neural network will be applied to investigate how climatic drivers, such as global radiation, temperature, precipitation, and wind (data provided by the High Asia Refined analysis; HAR), and topographic factors, such as aspect, slope angle, and summit altitude (derived from digital elevation models; DEM) control ELAs in High Asia. For each mountain range in High Asia at least one benchmark settings will be selected basing on good data availability. Here, processes will be investigated at the scale of individual glaciers by applying a numerical model to obtain detailed surface energy and MB data. Resulting MBs will subsequently be used to model the sensitivity of the investigated glaciers to monthly anomalies in temperature and precipitation (from HAR data). Preliminary surveys showed that near-planar surfaces in accumulation areas of Himalayan glaciers entail great potential for non-linear melt dynamics if ELA rise continues. Surface areas and topographic configurations of such high-elevation surfaces will be quantified by DEM-based GIS analyses for glaciers throughout High Asia. Tipping points, at which ELA reaches the altitude of particular surfaces, will be identified by measuring remaining altitudinal buffers between the surfaces and modern ELAs. Temperature and precipitation offsets correlating to the altitudinal buffers may subsequently be assessed using the sensitivity data processed before. Ultimately, time remaining until ELAs exceed the tipping point elevation thresholds at individual glaciers will be estimated based on climate change projections under different emission scenarios. In summary, the results of the proposed interdisciplinary and poly-methodological approach will contribute substantially to disentangling the topoclimatic forcing of High Asia's glaciers and to quantify their potential for non-linear melt dynamics.