The Mountain Glacier forecast framework

• Applicant: Dr. Johannes Fürst
• Subject Area: Physical Geography ; Geophysics
• Term: since 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 431767937

General glacier retreat around the globe has become iconic to illustrate the effects of global warming and we anticipate that this demise will continue and significantly add to global sea-level rise. Ice loss can also induce shifts in regional water resources, putting some places at high risk of year-around freshwater availability. Better projections of glacier retreat are therefore essential in terms of coastal impact, water management and hydro-glaciological risks.The overall aim of the project is to provide a framework to improve past and future glacier volume projections. The process-based framework will be based on the open-source ice-flow model Elmer/Ice and will allow to reduce three potentially important sources of uncertainty. Anticipating the continuously growing body of information from satellite remote sensing, we will improve and develop robust data assimilation methods to infer the basal topography beneath the ice and calibrate ice-flow parameters for a better representation of the initial state and ice dynamics. The flow model will be coupled to an enhanced surface mass balance model that exhibits a stable melt relation over relevant multi-decadal timescales. Based on ensemble simulations, the framework will allow to better quantify the interaction and propagation of these uncertainties.As a proof of concept, the framework will be applied to hindcast and forecast glacier evolutions in the French Alps and in Cordillera Darwin (Chile). These sites have been chosen because for both glacier sites ice-flow is considered important and because of the availability of in-situ and remotely sensed observations. The two sites will further serve as challenging test setups to scrutinise the applicability, performance and robustness of the modelling framework under different climatic conditions and dynamic settings.

• DFG Programme: Research Grants
• International Connection: France
• Cooperation Partner: Fabien Gillet-Chaulet, Ph.D.