Assessing Model Predictions of Carbon Dynamics in Global Drylands

Abstract

This is the final version. Available on open access from Frontiers media via the DOI in this record ; Data Availability Statement; The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The TRENDY-v8 ensemble of simulation outputs is available upon request at https://sites.exeter.ac.uk/trendy. The PML-v2 product script is available online from https://github.com/gee-hydro/gee_PML. The SMOS-IC V2 L-VOD product was provided by Jean-Pierre Wigneron. Processing code is available at https://doi.org/10.5281/zenodo.5511724. Google Earth Engine Repository is available at: https://earthengine.googlesource.com/users/dfawcett/DRIVING_C_RS_publication (requires Google account to access). ; 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'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'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. ; Natural Environment Research Council (NERC) ; ESA ; ANR CLAND Convergence Institute ; European Union Horizon 2020