Importance of plant functional type, dynamic vegetation, and fire interactions for process-based modeling of gross carbon uptake across the drylands of western North America

Abstract. Drylands cover ~41 % of the Earth’s land surface and contribute more than one third of the global net primary productivity. Several studies have demonstrated that drylands play a crucial role in global carbon cycle interannual variability. However, drylands are vulnerable to the impacts of climate change. To predict changes in dryland productivity under climate change we depend on dynamic global vegetation models (DGVMs). Compared to more mesic ecosystems, DGVM carbon cycle dynamics have not been widely evaluated against data. Existing studies are mostly focused at site scale; rarely have these models been assessed or benchmarked against dryland carbon flux products at regional to global scales. Global gross primary productivity (GPP) products have poor performance in dryland regions. Only recently upscaled in situ flux products have been developed specifically for drylands. Here, we evaluated GPP inter-annual variability (IAV) simulated by 15 DGVMs from the TRENDY v11 model intercomparison project against theDryFlux GPP, which is newly developed upscaled GPP product that considers dryland- specific ecohydrological responses. Comparing model simulated GPP IAV to DryFlux, we identified two groups of models: a one group of models with generally lower GPP IAV than DryFlux (e.g., lower standard deviation in annual GPP than DryFlux and slope values of the linear regression between each model and the DryFlux product that are less than 1.0) and a second group of models with generally higher GPP IAV than DryFlux. We examined if including a representation of dynamic vegetation (i.e., changes in the spatial distribution of plant functional type (PFT) fractional cover) or fire in the models can explain the inter-model spread and model performance in comparison to DryFlux. Models that do not include a representation of fire and/or dynamic changes in plant functional type distribution over time generally have lower annual GPP variability compared to DryFlux (1st group of models), except for the eastern and southeastern region of the study area with high rainfall variability. We also found that models with dynamic vegetation exhibit high variability in grass fractional cover (that was higher than two independent reference fractional cover datasets), which was strongly correlated with high GPP IAV. Only some models that included fire simulated burnt area annual variability that correlated well with GPP IAV. Other models that included fire simulated low burnt area variability and therefore we did not find any strong relation between burnt area and GPP IAV. Finally, we examined the relationship between the dominant PFT and GPP IAV. We did not find a strong correlation between the spatial mean of the slope of the linear regression between each model and DryFlux annual GPP and their spatial mean woody, grass, or C3 grass fractional cover (although many models with generally low GPP IAV had higher woody plant cover). However, we did find a high correlation between the slope of the linear regression between each model and DryFlux annual GPP and spatial mean C4 grass cover. Therefore, our findings suggest that DGVMs inability to accurately represent the spatial distribution of herbaceous (specifically C4 grass) cover as well as processes controlling dynamically changing vegetation distributions over time (including fire) contribute to poor model performance in capturing annual variability in dryland productivity. Our findings can provide a roadmap for DGVM teams seeking to improve vegetation representation in sparsely vegetated dynamic dryland ecosystems.

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