GSA Connects 2021 in Portland, Oregon

Paper No. 111-8
Presentation Time: 3:40 PM


RUPPER, Summer1, BREWER, Simon1, FORSTER, Richard1 and SCHAEFER, Joerg M.2, (1)Geography, University of Utah, Salt Lake City, UT 84112, (2)Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027

Glaciers across much of High Mountain Asia (HMA) have been losing mass at an accelerating rate driven largely by accelerating warming, threatening this valuable water resource. Glacier response to warming over the past ~20-50 years, however, is spatially heterogenous. Differences in response to climate change arise due to differences in glacier sensitivity, glacier feedbacks, glacier response times, and magnitude of climate change. It is inherently difficult to disentangle these four aspects of glacier response to climate change. Yet, they are crucial to understanding the spatial patterns in glacier changes and improving studies focused on attribution, reconstruction, and projections of glacier change. We leverage the new wealth of satellite observations, combined with numerical modeling and statistical approaches, to gain insight into the mechanisms driving the spatially heterogeneous glacier changes across HMA. Specifically, we run a set of idealized numerical modeling experiments to assess the relative role of each of these four aspects in driving glacier change. We define and model three separate quantities of glacier change as part of these experiments - reference glacier sensitivity, equilibrium glacier sensitivity, and transient glacier response. The results show that transient glacier response to a constant climate forcing varies significantly with time, driven by temporally-evolving response times and feedbacks. Importantly, the magnitude of these transient rates of change over multiple decades do not necessarily equate to the equilibrium glacier sensitivity. We then apply a Bayesian spatially varying coefficient model to examine the regional importance of glacier attributes, climate change, and glacier response times. The results of the statistical analyses show that, superimposed on the warming-induced mass loss dominating the majority of the HMA region, are strong spatial gradients in the other dominant driving factors. Importantly, the results show how glacier attributes may act to modulate glacier response times, leading to spatial variability in glacier response over short observation periods. These analyses are a step toward a better understanding of the variables dominating glacier response to climate change in HMA, and improving the modeling of historical and future glacier response and the downstream impacts.