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.
Highlights
Terrestrial ecosystems act as a global sink of carbon, C, absorbing ∼30% of anthropogenic emissions
Evaluation of dynamic global vegetation models (DGVMs) annual net ecosystem exchange (NEE) Across all sites, the DGVMs generally underestimate the mean annual NEE (as seen by model values clustering around a y-axis value of 0.0 in figure 1(a) instead of on the 1:1 line, median slope values of 0 in figure 1(b), and high mean bias errors, MBE, in figure S2(a))
Given most of the models have similar representations of the main C cycle processes, we suggest that these site level simulations with ORCHIDEE v2.0 demonstrate that inaccurate forcing or vegetation and soil characteristics are likely not the cause of DGVM underestimates in mean annual NEE and inter-annual variability (IAV); we need to analyze the models further to determine the root causes of these model-data discrepancies
Summary
Terrestrial ecosystems act as a global sink of carbon, C, absorbing ∼30% of anthropogenic emissions. Analyses of atmospheric CO2 inversions, satellite data, and dynamic global vegetation model (DGVM) simulations indicate that dryland ecosystems dominate both the trend and IAV in the global C sink due to the sensitivity of vegetation growth to changes in water availability [7,8,9,10,11]. While well-tested in mesic ecosystems [12,13,14,15,16], DGVMs (which often form the land component of earth system models, ESMs, used in IPCC climate change projections) have been rarely tested against net CO2 flux data from dryland regions (though see [17,18,19] for evaluations of modeled gross CO2 uptake). DGVMs have performed poorly in comparison to satellite-based observations of seasonal to decadal trends in dryland vegetation dynamics [20,21,22], suggesting that DGVM estimates of gross CO2 uptake (and net CO2 exchange) may be inaccurate
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