Abstract

This study involves examination of glaciological mass-balance time series, glacier and climatic descriptors, the application of machine learning methods for glaciological clustering, and computation of mass-balance time series based upon the clustering and statistical analyses relative to gridded air temperature datasets. Our analysis revealed an increasingly coherent mass-balance trend but a latitudinal bias of monitoring programs. The glacier classification scheme delivered three clusters, suggesting these correspond to climate-based first-order regimes, as glacier morphometric characteristics weighed little in our multivariate analysis. We combined all available surface mass-balance data from in situ monitoring programs to study temperature sensitivity for each cluster. These aggregated mass-balance time series delivered spatially different statistical relationships to temperature. Results also showed that surface mass balance tends to have a temporal self-correlation of similar to 20 years. Using this temporal window to analyze sensitivity since similar to 1950, we found that in all cases temperature sensitivity, while generally negative, tended to fluctuate through time, with the largest absolute magnitudes occurring in the 1980s and becoming less negative in recent years, revealing that glacier sensitivity is non-stationary. These findings point to a scenario of a coherent signal of change no matter the glacier regime. This work provides new insights into glacier-climate relationships that can guide observational and analytical strategies.