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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. Plain Language Summary: The Bering‐Bagley Glacier System (BBGS), Alaska, Earth's largest temperate surging glacier, recently surged in 2008–2013. A surge glacier cycles between a long period of normal flow and a short period of accelerated flow where large‐scale deformations, such as crevasses, occur. This paper focuses on investigating a surge in a large and complex system rather than a small glacier where most studies on surges have been conducted. We use a numerical model to simulate glacier evolution for both the quiescent phase and the initial surge phase of the BBGS. For each phase, we analyze the simulated elevation‐change and ice‐velocity, and infer information on the evolving hydrologic drainage system. During the quiescent phase, ice‐mass builds up at locations consistent with those observed and water drainage paths become longer with expanding capacity to store subglacial water. These results are attributed to local bedrock topography characterized by large subglacial ridges that act to dam the down‐glacier flow of ice and water. In the surge simulation, we model surge evolution through Bering Glacier by implementing a new friction representation that mimics a propagating wave. As the surge progresses through the glacier, drainage efficiency further degrades in the areas of fast‐moving ice. Key Points: Using a full‐Stokes approach informed by satellite observations, a quiescent and surge phase of the Bering‐Bagley Glacier System (BBGS) are simulatedLocal bed topography controls the formation of several reservoir areas, which lengthen down‐glacier drainage paths during quiescenceA friction representation for the surge phase is implemented based on observed properties of kinematic surge waves in the BBGS [ABSTRACT FROM AUTHOR]

We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in spring led to early melt onset, an extended melt season, and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find the melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % ± 6 % on 24 June 2020, followed by a slow decrease to 8 % ± 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, it decreased to a minimum of 70 % on 30 July and then became more variable, ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m ± 0.17 m in early June and peaked at 0.97 m ± 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season. [ABSTRACT FROM AUTHOR]

We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in Spring led to early melt onset, an extended melt season and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from ICESat-2 using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % +/- 6 % on 24 June 2020, followed by a slow decrease to 8 % +/- 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, decreased to a minimum of 70 % on July 30, then became more variable ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m +/- 0.17 m in early June, peaked at 0.97 m +/- 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season. [ABSTRACT FROM AUTHOR]

The objective of this paper is to characterize spatial properties of snow-depth structures and their role as indicators of sea-ice properties and sea-ice-morphogenetic processes, and to provide quantitative measures of sea-ice properties that may be utilized in analyses of passive-microwave data. Snow-depth data collected near Point Barrow, Alaska, as part of the AMSR-Ice03 Field Validation Campaign for Advanced Microwave Scanning Radiometer (AMSR)-E-Sea-Ice Products from the NASA Earth Observing System Aqua satellite, are analyzed and compared to P-3 Polarimetric Scanning Radiometer (PSR) data, a proxy for AMSR-E brightness temperatures. The approach taken in the analysis is geostatistical characterization. Vario functions of first and second order are calculated for the snow-depth profiles, then geostatistical classification parameters are extracted and combined into feature vectors, on which the characterization is based. The complexity of sea ice requires a generalization of the method by introduction of the hyperparameter concept. Results include a quantitative characterization of sea-ice provinces from field transects in the Beaufort Sea, Chukchi Sea, and Elson Lagoon, which represent a good subset of Arctic sea-ice types, an internal segmentation of the longer profiles, and a derivation of surface-roughness length and of sea-ice-type complexity. PSR data reflect complexity of spatial snow-depth structures as captured in multidimensional feature vectors and, less directly, snow-depth and surface-roughness length. These results indicate that passive-microwave data in general may be affected by spatial snow depth and surface roughness, with a dependence on scale and quantified by geostatistical classification. Index Terms--Classification, morphogenesis, remote sensing, roughness, sea ice, spatial data analysis.

Aircraft-acquired passive microwave data, laser radar height observations, RADARSAT synthetic aperture radar imagery, and in situ measurements obtained during the AMSR-Ice03 experiment are used to investigate relationships between microwave emission and ice characteristics over several space scales. The data fusion allows delineation of the shore-fast ice and pack ice in the Barrow area, AK, into several ice classes. Results show good agreement between observed and Polarimetric Scanning Radiometer (PSR)-derived snow depths over relatively smooth ice, with larger differences over ridged and rubbled ice. The PSR results are consistent with the effects on snow depth of the spatial distribution and nature of ice roughness, ridging, and other factors such as ice age. Apparent relationships exist between ice roughness and the degree of depolarization of emission at 10, 19, and 37 GHz. This depolarization would yield overestimates of total ice concentration using polarization-based algorithms, with indications of this seen when the NT-2 algorithm is applied to the PSR data. Other characteristics of the microwave data, such as effects of grounding of sea ice and large contrast between sea ice and adjacent land, are also apparent in the PSR data. Overall, the results further demonstrate the importance of macroscale ice roughness conditions such as ridging and rubbling on snow depth and microwave emissivity. Index Terms--Passive microwave, roughness, sea ice, shore-fast ice, snow depth.