Abstract
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.
| Original language | English |
|---|---|
| Article number | 094023 |
| Journal | Environmental Research Letters |
| Volume | 16 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 2021 |
Funding
NM was supported by a grant from the Macrosystems Program in the Emerging Frontiers Section of the US National Science Foundation (NSF Award 1065790). DG received support from the French Agence Nationale de la Recherche (ANR) Convergence Lab Changement climatique et usage des terres (CLAND). AK was supported by Oak Ridge National Laboratories (ORNL). ORNL is managed by UTBattelle, LLC, for the DOE under Contract DE-AC05-1008 00OR22725. Partial funding for some of the AmeriFlux sites run by RLS and MEL was provided by the US Department of Energy’s Office of Science, NSF funding to the Sevilleta LTER program, and the USDA. USDA is an equal opportunity employer. PK and TM were funded by the NOAA OAR/ARL Climate Research Program. SS was supported by NERC Driving-C project (NE/R00062X/1). SZ was supported by the European Union’s Horizon 2020 research and innovation program under the Grant Agreement No. 821003 (4C-project). The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. DLL was supported by the U.S. Department of Agriculture NIFA Award 2015-67003-23485. We would like to thank the ORCHIDEE Project Team for developing and maintaining the ORCHIDEE code and for providing the ORCHIDEE version 2.0 used in this study. Finally, we thank the two anonymous referees for their comprehensive and useful reviews.
Keywords
- Drylands
- Dynamic global vegetation models
- Global carbon cycle
- Inter-annual variability