Scaling Knowledge of Arctic Tundra Processes from Field Studies and Fine-Scale Models to an Earth System Model
Peter Thornton1* (firstname.lastname@example.org), Colleen Iversen1, Stan Wullschleger1, Charles Abolt2, Scott Painter1, Ethan Coon1, Yu Zhang2, Rutuja Chitra-Tarak2, Katrina Bennett2, Benjamin Sulman1, Fengming Yuan1, Verity Salmon1, Amy Breen3, Alistair Rogers4, Jitendra Kumar1, William Riley5, Elizabeth Herndon1, Teri O’Meara1
1Climate Change Science Institute, Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN; 2Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM; 3International Arctic Research Center, University of Alaska–Fairbanks, AK; 4Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY; 5Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA
The overarching objective for the NGEE-Arctic project is to deliver an improved predictive understanding of Arctic tundra processes at the scale of a high-resolution Earth System Model (ESM) gridcell. To achieve that goal, researchers have identified six Integrated Modeling (IM) efforts, each of which results in improved prediction capability implemented within the DOE’s Energy Exascale Earth System Model (E3SM). During phase three of NGEE-Arctic, researchers are synthesizing observations, experimentation, and fine-scale modeling results to arrive at new ELM parameterizations suitable for a high-resolution ESM gridcell. The product of each IM effort is an updated code module that extends the default capability of the E3SM Land Model (ELM). Researchers are currently targeting a 1 km × 1 km ELM gridcell size for this work, which is the most highly resolved land grid that is currently planned for continental-to-global scale simulations with E3SM. The six IM efforts focus on improved representations of the following important drivers of energy-water-carbon-biogeochemistry interactions in tundra landscapes: (1) fractional inundated area; (2) hillslope processes; (3) snow-terrain-vegetation interactions; (4) tundra-specific plant functional types; (5) dynamic biogeography; and (6) biogeochemistry in variably saturated soils. Researchers are making extensive use of the existing multilevel hierarchical subgrid capability of ELM to capture new knowledge about variability at scales finer than the 1 km grid, and the team is putting in place strategic extensions of that subgrid capability as needed to represent Arctic tundra ecosystems. The results for each IM effort is shown, demonstrating how observations, experimentation, and fine-scale modeling are producing improved performance within ELM compared to a baseline implementation without Arctic-relevant parameterizations.