E3SM Land Model Simulated Snow Seasonality in NGEE Arctic’s Alaskan Seward Peninsula Study Region Affected by Topography, Plant Functional Types, and Meteorological Forcings
Authors
Fengming Yuan1* (yuanf@ornl.gov), Peter Thornton1* (thorntonpe@ornl.gov), Benjamin Sulman1, Bailey Murphy1, Jitendra Kumar1, Amy Breen2, Verity Salmon1, Shih-Chieh Kao1, Michele Thornton1, Claire Bachand3, Katrina Bennett3, Colleen Iversen1, NGEE Arctic data team
Institutions
1Oak Ridge National Laboratory, Oak Ridge, TN; 2University of Alaska–Fairbanks, AK; 3Los Alamos National Laboratory, Los Alamos, NM
URLs
Abstract
In the Arctic, snow processes are of great challenge in Earth system models, especially in highly heterogeneous areas like the Alaskan Seward Peninsula where there is a lack of reliable input data consistent in both space and time series. In this study, the team presents some initial high-resolution (1 km×1 km) land-surface modeling results in that region using the E3SM Land Model (ELM) and data collected by or for the Next-Generation Ecosystem Experiments Arctic project.
Researchers integrated a few high-resolution surface properties for the region, including: (1) a newly developed tundra plant functional type dataset, derived from field investigations and remote sensing, with field-based model development and parameterizations (Sulman et al. 2021); (2) topography derived from the Arctic Digital Elevation Model (https://www.pgc.umn.edu/data/arcticdem/); (3) soil thickness (https://daac.ornl.gov/SOILS/guides/Global_Soil_Regolith_Sediment.html); and (4) soil physical properties (https://soilgrids.org). The model is driven by three forcing datasets: half-degree resolution Global Soil Wetness Project Phase 3 (GSWP3) Version 2, half-degree resolution CRU-JRA5 v2.3, and 1 km×1 km GSWP3-Daymet (https://daymet.ornl.gov).
The preliminary data analysis in general shows reasonable and more realistic snow coverage on the ground and snow-free seasonality compared to a global high-resolution dataset of snow coverage from the U.S. National Ice Center Ice Mapping System (https://usicecenter.gov/Resources/ImsInfo). Those improvements are associated with detailed topography, soil, and vegetation features, even with coarse forcings. The 1-km resolution Daymet forcing can further improve the modeled snow distribution pattern in terrain. Further improvement of snow fence effects in tall shrubby land would be another critical step for ELM modeling in high-latitude regions.