Predicting the Impact of Climate Change and Fire on Arctic Vegetation: FATES Modeling and Satellite Remote Sensing
Authors
Yanlan Liu1* ([email protected]), Colette Brown2, Jennifer Holm2, Amy Breen3, Verity Salmon4, Charlie Koven2, Alistair Rogers5, Margaret Torn2, Colleen Iversen4
Institutions
1The Ohio State University–Columbus, OH; 2Lawrence Berkeley National Laboratory, Berkeley, CA; 3University of Alaska–Fairbanks, AK; 4Oak Ridge National Laboratory, Oak Ridge, TN; 5Brookhaven National Laboratory, Upton, NY
URLs
Abstract
Climate warming and disturbance are expected to lead to large changes in the distribution of plant communities in the Arctic. Vegetation change is challenging to predict, as it is driven by the interplay of chronic climate trends, such as warming, disturbance by wildfire and thermokarst, and transient demographic processes of recruitment, growth, competition, and mortality. While most simulation models focus on the impacts of chronic climate trends, the roles of demographics and disturbance are poorly represented.
The research team seeks to improve the understanding and modeling of vegetation response to climate, subsurface conditions, and fire. To capture demographics and recovery from disturbance, researchers are developing the E3SM Land Model (ELM) coupled with the Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) for the Arctic. The team simulated vegetation demographic dynamics at the NGEE-Arctic Kougarok Hillslope field site in Alaska under historical (1960 to 2010) and future (2051 to 2100) climates using ELM-FATES. Researchers identified a diverse set of trait combinations that all reproduce in situ observations of plant function type (PFT) composition under historical climate. However, under future climate, these trait combinations lead to drastically divergent compositions and productivity, resulting in trait-induced uncertainties that are three times larger than the effect of climate itself. The variation in PFT composition is primarily explained by traits controlling recruitment, growth strategy, and freeze-induced mortality. The findings highlight that accounting for demographic dynamics and additional observational constraints on key functional traits will contribute to constraining uncertainties in predicted vegetation change and carbon sink strength in northern-high latitudes.
Arctic wildfires, which are increasing in extent and severity, may be an important driver of vegetation shifts. Previous studies have used satellite imagery and machine learning to characterize Arctic land cover change, but not for post-fire recovery. Researchers studied the largest recorded tundra fire—the 2007 Anaktuvuk River Fire in northern Alaska—as a case study. The team used Landsat 7 imagery and a random forest model to generate annual maps of PFTs at 30 m resolution for 2007 to 2020. The model is constrained by ground surveys collected by the Bureau of Land Management. One year after the Anaktuvuk River Fire, shrub cover increased in burned areas but not in unburned areas. The maps in this poster are among the first quantifying decadal Arctic vegetation recovery trajectories at a high spatial resolution, which can be used in future work to identify greening trends, vegetation communities, and the expanding shrub line.