Four decades of pan-Arctic glacier calving fluxes from multimodal satellite imagery

The Arctic has been warming four times faster than the global mean over the last forty years. In response, the mass loss of glaciers has been accelerating and contributing to global sea-level rise. However, many of the mechanisms of this mass loss process are not well understood, especially the calving dynamics of marine-terminating glaciers, in part due to a lack of high-resolution calving front observations. To address this limitation, we developed a novel automated deep learning framework to automatically detect calving fronts in multimodal satellite imagery, including optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 missions. In total, this provides an approximately four-decade time series of calving dynamics across Arctic glaciers and ice caps. The method was tested and developed initially on the Svalbard archipelago, where we identified widespread calving front retreats during the past four decades, including attribution to climate forcing mechanisms. Here, we have extended the analysis across the entire Arctic to produce a time series that starts in the 1980s with quasi-annual temporal resolution, but achieves sub-monthly sampling from 2014 onward, following the launch of Sentinel-1. This provides both seasonal and inter-annual calving dynamics for almost all marine-terminating glaciers in the Arctic, which have been used to explore and understand the drivers of calving processes.