Carbon Cycle: Effects of disturbance and atmospheric change on vegetation

Improved understanding of plant processes for prediction and management

Understanding the terrestrial carbon sink and its relationship to climate is critical for predicting and mitigating climate change.  Ecosystem uptake and storage of atmospheric carbon can slow climate change, while increased ecosystem CO2 release due to land use or climate change will accelerate it. Moreover, plant productivity underlies food and fuel production. Ecosystems are increasingly subject to more frequent or extreme disturbances and climate events on the one hand, and more intensive management on the other, all in the context of rapidly rising CO2. Thus there is a pressing need to understand how  plant traits affect these risks, such as wildfire, coastal disturbances, and other perturbations. The projects described here center on understanding and predicting the carbon cycle, identifying the effects of disturbances (e.g., harvesting, fire) on vegetation processes, and using observations to develop theory and benchmark models. EESA scientists are systematically building data and simulation capabilities, developing fundamental understanding across scales, performing model-data integration across disciplines, and leading new partnerships. Two  major areas of  contribution are (1) development of a trait-enabled demographic model integrated in DOE’s Earth system model (FATES-E3SM); and leveraging observations from AmeriFlux, plant-trait databases, remote sensing, and CO2 experiments using artificial intelligence and machine learning.  Our observation and modeling studies are finding that vegetation is more responsive to disturbances like climate extremes and elevated CO2 than expected.

Photo Credit: Berkeley Lab

Recent science & program advances

Development of state-of-the-science next-generation dynamic vegetation model–Functionally Assembled Terrestrial Ecosystem Simulator (FATES)–for integration with ELM and CLM, and application in tropical systems. 

FATES allows explicit representation of disturbance, competition, plant size, and individual plant-scale dynamics in an ESM-tractable formulation. We have also developed and are testing the ability to represent plant hydraulics, selective logging, photosynthesis-associated nutrient constraints, respiration, and allocation. The FATES approach is based on a framework for scaling complexity in land surface models to allow for scaling between simple configurations that allow controlled experiments (e.g, to high-complexity multi-process-resolved representations.

Pioneering a new approach to tropical forest research that closely integrates modeling and experiments (ModEx), and integrates observations from bedrock to canopy. 

EESA is globally recognized for providing well-tested state-of-the-science vegetation dynamics for ESMs, and for addressing high-impact science questions at the land-atmosphere interface.  An international team of ~90 scientists is working in ModEx teams on problems like plant hydraulics and subsurface water dynamics, leaf-to-canopy gas exchange, carbon metabolism and respiration–and their effects on drought- and heat-wave resilience.

Fieldwork on tropical post-disturbance vegetation establishment and recovery shows that forest-atmosphere exchange depends on shifts in vegetation structure and competitive interactions among cohorts, and thus cannot be predicted by conventional land models

AmeriFlux observations combined with machine learning reveal that drought impacts are underestimated by satellite monitoring,  challenging  current scaling approaches

Explored role of wildfire in boreal and tundra systems carbon cycling, and showed that expected 21st century fire regimes will substantially alter forest and tundra vegetation composition and carbon sink potential

Analyzed the prevailing paradigm, used widely in Earth System Models, that leaf photosynthesis is primarily limited by nutrients constrained under fixed allocation. EESA research suggests instead that plants allocate to optimize photosynthetic performance, and we are implementing these ideas in DOE’s E3SM Land Model to quantify the global effect.

Using machine learning, we showed that drought impacts can be larger than is inferred from existing satellite-based estimates for photosynthetic activity. This information, when combined with observations from Free Air CO2 Enrichment (FACE) experiments, suggest that growth in atmospheric CO2 concentrations is having a greater-than-expected impact on the carbon sink.

Using a fully coupled Earth System Model, we demonstrated a positive feedback between earlier leaf-out and warming in the North using an Earth System Model (Xu et al. 2020), highlighting the importance of accurately representing plant phenology in global land models.

Combined satellite observations from 1982-2010, CMIP5 ESM predictions, and functional response analyses of vegetation cover to infer ecosystem response to temperature change, and the likely future decline in temperature limitation of vegetation in the world’s cold regions.

Relevant Projects


EESA benefits from rich partnerships with our collaborators and sponsors. See project & program links above for more information.

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