U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Predicting Long-Term Carbon Sequestration in Response to CO2 Enrichment
Published: April 27, 2015
Posted: November 25, 2015

Large uncertainty exists in model projections of the land carbon sink response to increasing atmospheric carbon dioxide (CO2). Free-Air CO2 Enrichment (FACE) experiments lasting a decade or more have investigated ecosystem responses to a step change in atmospheric CO2 concentration. To interpret FACE results in the context of gradual increases in atmospheric CO2 over decades to centuries, a recent study used a suite of seven models to simulate the Duke Forest and Oak Ridge FACE experiments extended for 300 years of CO2 enrichment. It also determined key modeling assumptions that drive divergent projections of terrestrial carbon uptake and evaluated whether these assumptions can be constrained by experimental evidence. All models simulated increased terrestrial carbon pools resulting from CO2 enrichment, though there was substantial variability in quasi-equilibrium carbon sequestration and rates of change. In two of two models that assume that plant nitrogen uptake is solely a function of soil nitrogen supply, the net primary production response to elevated CO2 became progressively nitrogen limited. In four of five models assuming that nitrogen uptake is a function of both soil nitrogen supply and plant nitrogen demand, elevated CO2 led to reduced ecosystem nitrogen losses and thus progressively relaxed nitrogen limitation. Many allocation assumptions resulted in increased wood allocation relative to leaves and roots, which reduced the vegetation turnover rate and increased carbon sequestration. In addition, self-thinning assumptions had a substantial impact on carbon sequestration in two models. Accurate representation of nitrogen process dynamics (in particular nitrogen uptake), allocation, and forest self-thinning is key to minimizing uncertainty in projections of future carbon sequestration in response to elevated atmospheric CO2.

Reference: Walker, A. P., S. Zaehle, B. E. Medlyn, M. G. De Kauwe, S. Asao, T. Hickler, W. Parton, D. M. Ricciuto, Y.-P. Wang, D. Wårlind, and R. J. Norby. 2015. “Predicting Long–Term Carbon Sequestration in Response to CO2 Enrichment: How and Why Do Current Ecosystem Models Differ?” Global Biogeochemical Cycles 29(4), 476–95. DOI: 10.1002/2014GB004995. (Reference link)

Contact: Jared DeForest, SC-23, (301) 903-3251, Daniel Stover, SC-23.1, (301) 903-0289
Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Terrestrial Ecosystem Science
  • Research Area: Carbon Cycle, Nutrient Cycling
  • Research Area: Free Air CO2 Enrichment (FACE)

Division: SC-23.1 Climate and Environmental Sciences Division, BER


BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)