2024 Abstracts

NGEE Arctic: Integrating Boots-on-the-Ground Observations with the Virtual World of Models to Answer Big Science Questions Across the Arctic


Colleen Iversen1* (iversencm@ornl.gov), Charles Abolt3, Katrina Bennett3, W. Robert Bolton1, Amy Breen4, Ryan Crumley3, Baptiste Dafflon2, Eugenie Euskirchen4, David E. Graham1, Susan Heinz1, Forrest M. Hoffman1, Jennifer Holm2, Charles Koven2, Hannah Mevenkamp4, Scott Painter1, William J. Riley2, Alistair Rogers2, Vladimir E. Romanovsky4, Verity G. Salmon1, Fernanda Santos1, Benjamin N. Sulman1, Neslihan Taş2, Michele Thornton1, Peter E. Thornton1, Margaret Torn2, Terri Velliquette1, Daryl Yang1, Stan D. Wullschleger1, NGEE Arctic team


1Oak Ridge National Laboratory, Oak Ridge, TN; 2Lawrence Berkeley National Laboratory, Berkeley, CA; 3Los Alamos National Laboratory, Los Alamos, NM; 4University of Alaska–Fairbanks, AK



The Arctic is a historically understudied biome that holds at least twice as much carbon as the atmosphere. Climate change and Arctic amplification are warming the Arctic faster than the rest of the world, and increasingly frequent wildfires and widespread permafrost degradation are impacting ecosystem structure and function. The Next-Generation Ecosystem Experiments (NGEE) Arctic is a model-driven, multiscale research project with a goal of improving understanding and model representation of complex interactions among climate, landforms, hydrology, biogeochemistry, vegetation, snow, and permafrost in Arctic tundra. NGEE Arctic emphasizes iterative collaboration among interdisciplinary teams of empiricists and modelers to incorporate experiments into models—underscored by open science and a safe, inclusive project culture.

Observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Alaska have improved the representation of tundra processes in the Energy Exascale Earth System Model. The project’s modeling approach was initially driven by the recognition that the current generation of models failed to capture many processes controlling climate feedbacks in Arctic tundra. Model improvements have emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The team is now leveraging Arctic-informed models to make novel predictions. For example, researchers found that improved model representation of the unique characteristics of tundra plants improved model prediction of carbon-climate feedbacks and underscored the need for dynamic vegetation. In turn, improved model representation of ground subsidence predicted that a sinking tundra surface is unlikely to trigger runaway permafrost thaw. Furthermore, machine learning algorithms trained on thousands of observations improved model representation of snow distribution across the landscape. The next phase of NGEE Arctic proposes to confront models trained on and evaluated against observations from Arctic Alaska with current observations and projected climate responses across the Arctic.