Abstract

Modeling future glacier changes on regional or global scales is challenging due to a scarcity of data for calibrating model parameters. This makes it difficult to derive accurate model parameters and also to convey the model uncertainty associated with those parameters. Previous global-scale models have typically optimized a single glacier or region specific set of model parameters to project global-scale glacier mass changes. As part of the development of a new open source global-scale glacier evolution model this research implements a Markov Chain Monte Carlo method to address this problem. We use geodetic mass-balance observations to determine probability distributions of a set of model parameters at each glacier. We then form an ensemble of model simulations by sampling parameters from these distributions. Using an ensemble allows us to create a range of mass balance changes for each glacier and quantify the model uncertainty. Applying this calibration technique, we use temperature and precipitation projections of one global climate model to model the mass changes until year 2100 of thousands of glaciers in High Mountain Asia. By using climate projections forced by multiple emission scenarios, we compare the uncertainties in changing climate caused by possible emission scenarios with the uncertainties in the glacier evolution model, and evaluate the results of different climate scenarios on glacier evolution.