Process-Based Model Evaluation Using Surface Energy Budget Observations in Central Greenland

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Miller, Nathaniel B., Shupe, Matthew D., Lenaerts, Jan M., Kay, Jennifer E., de Boer, Gijs, Bennartz, Ralf
• Language: unknown
• Published: 2022
• Subjects: Greenland; Ice Sheet
• Online Access: http://www.osti.gov/servlets/purl/1673384; https://www.osti.gov/biblio/1673384; https://doi.org/10.1029/2017jd027377

Energy exchange at the Greenland Ice Sheet surface governs surface temperature variability, a factor critical for representing surface melt. Physical processes link driving forces to subsequent surface energy budget responses, including radiative, turbulent, and ground heat fluxes, and ultimately control surface temperature evolution. A reanalysis product (ERA-Interim, ERA-I), operational model (Climate Forecast System version 2, CFSv2), and climate model (Community Earth System Model, CESM) are evaluated using a comprehensive set of surface energy budget observations and process-based relationships obtained at Summit, Greenland. Simulated downwelling longwave radiation is underestimated, which is linked to a deficiency of liquid-bearing clouds. Lower than observed surface albedo, especially in ERA-I, compensates for summer deficiencies in downwelling longwave raditation. In winter, such deficiencies are compensated by an overestimation of the sensible heat flux. Process-based relationships convey that all three models underestimate the response of surface temperature to changes in radiative forcing, primarily due to an overactive ground heat flux response in ERA-I, turbulent heat fluxes in CFSv2, and sensible heat flux in CESM. Cross-comparison of three distinct models indicate that the ground heat flux response for ERA-I, CFSv2, and CESM are too high, too low and comparatively accurate, respectively, signifying the benefit of using an advanced representation of snow properties. Relatively small biases in CESM surface albedo suggest advances in the representation of cloud microphysics result in more realistic radiative forcing. These results provide insight into model strengths and deficiencies, indicating the importance of representing physical processes when portraying cloud impacts on surface temperature variability.