Karakoram Anomaly

This project aims to deepen understanding of how glaciers in the Karakoram region respond to climate change, using Earth Observation data and (highly uncertain) climate data. Primarily, the project will calibrate and force a glacier surface mass balance model to reproduce observed spatial and temporal patterns of glacier mass change. Secondly, glacier sensitivity to climate variability and change will be assessed using satellite-derived snow cover extent will be used for short-term model calibration, while long-term geodetic mass balance records will provide additional constraints. This 'two-pronged' approach to calibration will help refine the uncertain climate inputs - especially precipitation - through glacier-specific model tuning.

To assess future glacier response to climate change in the region, the GloGEM model (Huss and Hock, 2015) is used, and will incorporate key mass balance processes such as debris thickness and spatial variability.

The project will also examine region-wide trends in climate reanalysis data, satellite-derived cloud cover (Cloud_cci), snow cover (Snow_cci), and endorheic lake extent (Lake_cci) to confirm the extent to which anomalies in the Karakoram are consistent and align with the available climate data. A comprehensive uncertainty analysis will accompany all findings to assess the reliability and robustness of the results.

Project plan

The project will start with a review of the scientific literature before collecting the available data required for the project (EO, reanalysis, DEMs, field data). A major initial step will then be to refine the input data to get them suitable for the mass balance model (e.g. file formats, spatial and temporal resolution and range). The model will then first be tested at a more local scale before being applied to the entire study region. In an iterative way we will adjust the input data and the processes and their parameterisations included in the model so that a good match with the observational data is reached. In parallel we will perform a statistical analysis of the input datasets to possibly reveal any peculiarities that might explain further factors of the Karakoram Anomaly. As a last step, the scientific analyses of all results will elucidate the processes and possible reasons behind the Karakoram Anomaly.