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.