The Next-Generation Ecosystem Experiments (NGEE Arctic) is a multi phase project (2012—2024) to improve our predictive understanding of carbon (C)-rich Arctic system processes and feedbacks to climate. This is achieved through experiments, observations, and synthesis of existing datasets that strategically inform model process representation and parameterization, and that enhance the knowledge base required for model initialization, calibration, and evaluation.
In Phase 1 (2012—2014), NGEE Arctic tested and applied a multiscale measurement and modeling framework in coastal tundra on the North Slope of Alaska. Field plots, transects, and synoptic surveys near Utqiaġvik (formerly Barrow) were chosen to represent a cold, continuous permafrost region at the northern extent of an ecological and climatic gradient. Much of our research focused on subgrid heterogeneity in thermal-hydrology, biogeochemistry, and vegetation as influenced by topography, landscape position, and drainage networks. These efforts provided datasets, derived products, and knowledge designed to meet project requirements for model initialization, parameterization, process representation, and evaluation.
Building upon research conducted in the first 3 years of the project, in Phase 2 (2015—2019) we maintained research at Utqiaġvik and established a set of research sites near Nome in western Alaska (i.e., Seward Peninsula). These field sites are characterized by their proximity to the transition from boreal forest to tundra, as well as by warm, discontinuous permafrost, higher annual precipitation, and well-defined watersheds with strong topographic gradients. We used variation in the structure and organization of the Seward Peninsula landscape to guide a series of process-level investigations (Questions 1 through 3) that were nested at scales ranging from soil core to plot, landscape, and watershed levels. Knowledge from those studies identified mechanisms controlling C, water, nutrient, and energy fluxes, which was used to address two integrated questions regarding the future of the Arctic in a changing climate (Questions 4 and 5).
- Question 1: How does landscape organization control permafrost evolution and associated C and nutrient fluxes in a changing environment?
- Question 2: What will control rates of CO2 and CH4 fluxes across a range of permafrost conditions?
- Question 3: How do plant functional traits change across environmental gradients, and what are the consequences for carbon, water, and nutrient fluxes?
- Question 4: What controls the distribution of shrubs, and how will shrub distributions and associated climate feedbacks shift with warming in the 21st century?
- Question 5: Where, when, and why will the Arctic become wetter or drier, and what are the implications for climate forcing?
- Question 6: What controls the vulnerability of Arctic ecosystems to disturbance, and how do disturbances alter the structure and function of these ecosystems?
Disturbances, both natural and anthropogenic, have the potential to affect profound changes on Arctic ecosystem processes. Question 6 was added in Phase 3 (2019—2024) given the strong need to understand how shrub distribution and disturbance processes in tundra ecosystems may drive future biophysical feedbacks to climate. We will sample across a heterogeneous landscape, the result of thermokarst features and a patchwork of historic tundra wildfires, to measure the controls on storage, processing, and release of C, nutrients, water, and sediments, and how those controls change in response to disturbance severity and time since disturbance.
We focus our modeling efforts in Phase 3 on a series of process-based improvements within the E3SM Land Model (ELM, including its dynamic biogeography component ELM-FATES). These improvements to ELM use NGEE Arctic findings and syntheses from the broader Arctic science community to anchor new model development in current system understanding, and to evaluate new ELM processes and parameterizations against independent observations at spatial and temporal scales appropriate to the use of ELM and E3SM for future climate prediction.