Surface mass balance downscaling through elevation classes in an Earth system model: application to the Greenland ice sheet

• Published in: The Cryosphere
• Main Authors: R. Sellevold, L. van Kampenhout, J. T. M. Lenaerts, B. Noël, W. H. Lipscomb, M. Vizcaino
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: geo; envir; Greenland; Ice Sheet; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-13-3193-2019; https://www.the-cryosphere.net/13/3193/2019/tc-13-3193-2019.pdf; https://doaj.org/article/ea3afcb83c9946ce86b1ff7aba67e505

The modeling of ice sheets in Earth system models (ESMs) is an active area of research with applications to future sea level rise projections and paleoclimate studies. A major challenge for surface mass balance (SMB) modeling with ESMs arises from their coarse resolution. This paper evaluates the elevation class (EC) method as an SMB downscaling alternative to the dynamical downscaling of regional climate models. To this end, we compare EC-simulated elevation-dependent surface energy and mass balance gradients from the Community Earth System Model 1.0 (CESM1.0) with those from the regional climate model RACMO2.3. The EC implementation in CESM1.0 combines prognostic snow albedo, a multilayer snow model, and elevation corrections for two atmospheric forcing variables: temperature and humidity. Despite making no corrections for incoming radiation and precipitation, we find that the EC method in CESM1.0 yields similar SMB gradients to RACMO2.3, in part due to compensating biases in snowfall, surface melt, and refreezing gradients. We discuss the sensitivity of the results to the lapse rate used for the temperature correction. We also evaluate the impact of the EC method on the climate simulated by the ESM and find minor cooling over the Greenland ice sheet and Barents and Greenland seas, which compensates for a warm bias in the ESM due to topographic smoothing. Based on our diagnostic procedure to evaluate the EC method, we make several recommendations for future implementations.