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

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 analyse 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 setting 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.