@article{c91b26bcbe034dcbb38ac338d74decc4,
title = "Assessing Model Predictions of Carbon Dynamics in Global Drylands",
abstract = "Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson{\textquoteright}s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson{\textquoteright}s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle.",
keywords = "aboveground biomass, drylands, land surface models (LSM), model evaluation, productivity, vegetation optical depth (VOD)",
author = "Dominic Fawcett and Cunliffe, {Andrew M.} and Stephen Sitch and Michael O{\textquoteright}Sullivan and Karen Anderson and Brazier, {Richard E.} and Hill, {Timothy C.} and Peter Anthoni and Almut Arneth and Arora, {Vivek K.} and Briggs, {Peter R.} and Goll, {Daniel S.} and Jain, {Atul K.} and Xiaojun Li and Danica Lombardozzi and Nabel, {Julia E.M.S.} and Benjamin Poulter and Roland S{\'e}f{\'e}rian and Hanqin Tian and Nicolas Viovy and Wigneron, {Jean Pierre} and Andy Wiltshire and Soenke Zaehle",
note = "Funding Information: Natural Environment Research Council (NERC) (NE/R00062X/1) awarded to RB, AC, SS, KA, and TH. ESA Climate Change Initiative RECCAP2 (contract no. 4000123002/18/I-NB) awarded to SS. SS also received support from NERC SECO grant NE/T01279X/1. DG received support from the ANR CLAND Convergence Institute. RS was supported by the European Union{\textquoteright}s Horizon 2020 research and innovation programme ESM2025—Earth System Models for the Future (Grant Agreement No 101003536). Funding Information: Natural Environment Research Council (NERC) (NE/R00062X/1) awarded to RB, AC, SS, KA, and TH. ESA Climate Change Initiative RECCAP2 (contract no. 4000123002/18/I-NB) awarded to SS. SS also received support from NERC SECO grant NE/T01279X/1. DG received support from the ANR CLAND Convergence Institute. RS was supported by the European Union{\textquoteright}s Horizon 2020 research and innovation programme ESM2025—Earth System Models for the Future (Grant Agreement No 101003536). Funding Information: We thank Cl{\'e}ment Albergel from the European Space Agency (ESA) for constructive feedback on an earlier version of this manuscript. 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. Publisher Copyright: Copyright {\textcopyright} 2022 Fawcett, Cunliffe, Sitch, O{\textquoteright}Sullivan, Anderson, Brazier, Hill, Anthoni, Arneth, Arora, Briggs, Goll, Jain, Li, Lombardozzi, Nabel, Poulter, S{\'e}f{\'e}rian, Tian, Viovy, Wigneron, Wiltshire and Zaehle.",
year = "2022",
month = apr,
day = "27",
doi = "10.3389/fenvs.2022.790200",
language = "English (US)",
volume = "10",
journal = "Frontiers in Environmental Science",
issn = "2296-665X",
publisher = "Frontiers Media S. A.",
}