Evolution of a Surge Cycle of the Bering‐Bagley Glacier System From Observations and Numerical Modeling

Abstract

The Bering‐Bagley Glacier System (BBGS), Alaska, Earth's largest temperate surging glacier, surged in 2008–2013. We use numerical modeling and satellite observations to investigate how surging in a large and complex glacier system differs from surging in smaller glaciers for which our current understanding of the surge phenomenon is based. With numerical simulations of a long quiescent phase and a short surge phase in the BBGS, we show that surging is more spatiotemporally complex in larger glaciers with multiple reservoir areas forming during quiescence which interact in a cascading manner when ice accelerates during the surge phase. For each phase, we analyze the simulated elevation‐change and ice‐velocity pattern, infer information on the evolving basal drainage system through hydropotential analysis, and supplement these findings with observational data such as CryoSat‐2 digital elevation maps. During the quiescent simulation, water drainage paths become increasingly lateral and hydropotential wells form indicating an expanding storage capacity of subglacial water. These results are attributed to local bedrock topography characterized by large subglacial ridges that dam the down‐glacier flow of ice and water. In the surge simulation, we model surge evolution through Bering Glacier's trunk by imposing a basal friction representation that mimics a propagating surge wave. As the surge progresses, drainage efficiency further degrades in the active surging‐zone from its already inefficient, end‐of‐quiescence state. Results from this study improve our knowledge of surging in large and complex systems which generalizes to glacial accelerations observed in outlet glaciers of Greenland, thus reducing uncertainty in modeling sea‐level rise.

Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges

“…Only five days later, on 2016-06-25, the crevassed area had already expanded in both upglacier and across-flow directions (Figure 9b), as indicated by the increased region of ice classified as crevassed. This rapid change matches existing knowledge that a surge is a catastrophic event, which expands rapidly in the acceleration phase [18,20,128,137,[172][173][174][175][176][177]. At this point, two new crevasse classes occur, (2) multidirectional and (4) shear/chaos.…”

“…[135]), however, during a surge the large-scale non-linear dynamics complicate velocity determination [20,136], particularly on short time scales relevant to a surge. Therefore, crevasse observations are our most reliable source of dynamical information during peak surge activity and can be used to derive and optimize basal sliding laws for modeling a surge phase [137].…”

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

“…Only five days later, on 2016-06-25, the crevassed area had already expanded in both upglacier and across-flow directions (Figure 9b), as indicated by the increased region of ice classified as crevassed. This rapid change matches existing knowledge that a surge is a catastrophic event, which expands rapidly in the acceleration phase [18,20,126,135,[169][170][171][172][173][174]. At this point, two new crevasse classes occur:…”

“…Ice velocity observations are popularly used to constrain unknown model parameters (e.g., [133]); however, during a surge, the large-scale non-linear dynamics complicate velocity determination [20,134], particularly on short time scales relevant to a surge. Therefore, crevasse observations are our most reliable source of dynamical information during peak surge activity and can be used to derive and optimize basal sliding laws for modeling a surge phase [135].…”