Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems
[1];
[4];
[6];
[11];
- Indiana Univ., Bloomington, IN (United States)
- United States Department of Agriculture, Tucson, AZ (United States)
- Univ. Paris-Saclay, Gif-sur-Yvette (France)
- Northern Arizona Univ., Flagstaff, AZ (United States)
- Univ. of New Mexico, Albuquerque, NM (United States)
- NOAA/ARL Atmospheric Turbulence and Diffusion Division, Oak Ridge, TN (United States); Oak Ridge Associated Univ., Oak Ridge, TN (United States)
- NOAA/ARL Atmospheric Turbulence and Diffusion Division, Oak Ridge, TN (United States)
- Canadian Centre for Climate Modelling and Analysis, Victoria (Canada)
- Science Partners, Paris (France)
- Univ. Paris-Saclay, Saint-Aubin (France)
- National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
- Max Planck Inst. for Meteorology, Hamburg (Germany)
- Max Planck Inst. for Meteorology, Hamburg (Germany); Ludwig-Maximilians-Univ. Munich (Germany)
- College of Life and Environmental Sciences, Exeter (United Kingdom)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Max Planck Inst. for Biogeochemistry, Jena (Germany)
- Univ. of Arizona, Tucson, AZ (United States)
Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol. 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models' lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth's land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1840149
- Journal Information:
- Environmental Research Letters, Journal Name: Environmental Research Letters Journal Issue: 9 Vol. 16; ISSN 1748-9326
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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