Towards quantifying ice contents in mountain permafrost environments

In its special report on Ocean and Cryosphere in a Changing Climate (SROCC) from 2019, the Intergovernmental Panel on Climate Change (IPCC) highlights clear knowledge gaps concerning the extent and ice content of permafrost in mountain regions. We present results from a study on the distribution of mountain permafrost that includes an improved understanding of its characteristics and an estimation of the sub-surface ice reserves in mountainous regions under climate scenarios. We explore the feature space of mountain permafrost using a range of statistical and machine learning techniques in an uncertainty-aware setting. This space consists of topographic and climatic features such as topographic masks, elevation models, potential incoming solar radiation, seasonal ground temperatures and snow accumulation. We combine these features with existing inventories of rock glaciers, as these are good visible indicators of mountain permafrost, and in addition typically ice-rich. Based on such datasets we create a data-driven model to predict the probability of potential rock glaciers occurrence in order to obtain a first estimate of ground ice content. In addition, output from a numerical permafrost model, the CryoGrid community model, provides synthetic observations. We further investigate the vulnerability of these potentially ground-ice rich areas under climate change by including forcing data from climate models based on various RCP scenarios.