Optimizing carbon cycle parameters drastically improves terrestrial biosphere model underestimates of dryland mean net CO$_2$ flux and its inter‐annual variability

International audienceDrylands occupy ∼40% of the land surface and are thought to dominate global carbon (C) cycle inter-annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO$_2$ fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site-years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or "pivot" between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long-term carbon-climate feedbacks. Plain Language Summary Drylands occupy ∼40% of the land surface and are thought to dominate the inter-annual variability and long-term trend of the global carbon cycle. Therefore, it is imperative that global terrestrial biosphere models (TBMs) are able to accurately predict dryland vegetation and carbon cycle processes. However, models have not been widely tested or calibrated against in situ dryland ecosystem CO$_2$ fluxes. Here, we address this gap using a data assimilation system and daily net CO 2 flux data from 12 southwest US Ameriflux sites spanning forest, shrub and grass dryland ecosystems to optimize the carbon cycle related parameters of the ORCHIDEE TBM. We find that before parameter optimization, the model drastically underestimates both the mean annual magnitude and interannual variability of net CO$_2$ flux. By testing different optimization scenarios, we showed that optimizing model parameters related to phenology dramatically improves the model's ability to capture the net CO$_2$ flux inter-annual variability. At the high elevation forested sites, optimizing parameters related to C allocation, respiration and biomass and soil C turnover reduces the model underestimate in simulated mean annual NEE. Our study demonstrates that all global TBMs need to be calibrated specifically for dryland ecosystems before they are used to determine dryland contributions to global carbon cycle variability and long-term carbon-climate feedbacks