Next Generation Ecosystem Experiments in the Arctic

Overview

The Next-Generation Ecosystem Experiments in the Arctic project seeks to improve the representation of critical processes in ultra high-resolution Earth system models by focusing on systems that are globally important, sensitive to change, and understudied or inadequately represented in models. An Arctic-informed Earth system model can better predict key processes such as permafrost stability and land surface subsidence, the navigability of waterways, and disturbances such as wildfire and rain-on-snow. The model can also predict water availability for a variety of uses, including energy production, ice road construction, and drinking water. Near-term understanding of changes in the distribution and magnitude of important Earth system processes can inform placement and maintenance of critical Arctic infrastructure vital to America’s energy, national, and economic security.

Project history

Between 2012 and 2024, more than 100 scientists spanning four national laboratories and the University of Alaska Fairbanks worked on NGEE Arctic. In Phases 1–3, the team conducted a series of collaborative investigations across a gradient of permafrost landscapes in coordination with landowners from multiple Native Corporations. These landscapes included polygonal tundra underlaid by continuous, cold permafrost on the coastal plain at the Barrow Environmental Observatory (BEO) outside of Utqiaġvik, Alaska, as well as tundra hillslopes underlaid by discontinuous, warmer permafrost on the Seward Peninsula, Alaska, that span a range of coastal and interior climates and glaciation histories.

Phase 1 of the project lasted between 2012 and 2014. The team’s modeling approach was driven by the recognition that the current generation of Earth system models fails to capture many processes controlling the cycling of carbon, water, nutrients, and energy in Arctic tundra. Initial model improvements emphasized the unique aspects of cold, continuous permafrost environments and improved mechanistic model representation of coupled surface and subsurface thermal hydrology, biogeochemistry, and snow.

In Phase 2, lasting between 2015 and 2018, the team’s multiscale modeling approach informed hypotheses about which reductions in model complexity were possible while retaining predictive power, and, in turn, which processes required the use of fine-scale model parameterizations to inform intermediate-scale models. Researchers confronted their improved models with unique aspects of hillslope environments on warmer, discontinuous permafrost on the Seward Peninsula of Alaska.

In Phase 3 (2019–2024), the team leveraged NGEE Arctic observations, synthesis products and model scaling over space and time under a framework of six science questions to inform the conceptualization and parameterization of six integrated model modules in an ‘Arctic-informed’ land surface model, ELM.

Phase 4 of the project, which wraps in 2027, uses computational, artificial intelligence, remote-sensing and data synthesis tools to test their newly-developed predictive understanding under novel conditions across the Arctic. 

Researchers