Predicting Antarctic Net Snow Accumulation at the Kilometer Scale and Its Impact on Observed Height Changes. Issue 20 (17th October 2022)

• Record Type: Journal Article
• Authors: Medley, B.; Lenaerts, J. T. M.; Dattler, M.; Keenan, E.; Wever, N.
• Is Part Of: Geophysical research letters. Volume 49:Issue 20(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-17
• Subjects: Antarctica -- surface mass balance -- altimetry; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2022GL099330
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Sub‐grid‐scale processes occurring at or near the surface of an ice sheet have a potentially large impact on local and integrated net accumulation of snow via redistribution and sublimation. Given observational complexity, they are either ignored or parameterized over large‐length scales. Here, we train random forest (RF) models to predict variability in net accumulation over the Antarctic Ice Sheet using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the RF models. We find that local net accumulation deviates by as much as 172% of the atmospheric model mean. The correlation in space between the predicted net accumulation variability and satellite‐derived surface‐height change indicates that surface processes operate differently through time, driven largely by the seasonal anomalies in snow accumulation. Plain Language Summary: Snowfall occurring at the surface of an ice sheet is subject to large‐scale but also local processes including blowing snow transport during and after the snowfall event. This snow is blown around subtle topographic features that are largely static over the past handful of decades, which means that the transport of snow at the surface is relatively stable in time.Here, we predict a static view of the small‐scale variations in snow accumulation rate at resolutions finer than the present state‐of‐the‐art atmospheric models. We then use these fine‐scale snow accumulation rates to better interpret satellite derived ice surface elevation changes. Key Points: We predict spatial deviations in net accumulation through synthesis of topographic and atmospheric characteristics on a 1‐km grid Modeled deviations in net accumulation typically range between −41% and 33% and are as large as −172% of the Modern‐Era Retrospective analysis for Research and Applications, Version 2 mean The spatiotemporal magnitude and variability in Ice, Cloud, and land Elevation satellite height changes are driven by spatiotemporal anomalies in net accumulation