Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes

Abstract

Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental‐scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space‐time patterns that are most consistent with regional CO 2 observational constraints. Here, we leverage atmospheric CO 2 observations and satellite‐observed photosynthetic proxies to understand emergent space‐time patterns in North American carbon fluxes from a large suite of TBMs and data‐driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space‐time variability in atmospheric CO 2 , as is observed by a network of continuous‐monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO 2 variability share a salient feature of growing‐season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing‐season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy‐relevant estimation of North American carbon exchange. In North America, land ecosystems have been known to act as a net sink of carbon, but how carbon fluxes are distributed in space and time remains uncertain. A key unresolved question about space‐time patterns of North American carbon exchange is whether croplands or temperate forests show the strongest uptake rate during the growing season. In this study, we evaluate a large suite of land biosphere models and models that are driven by remotely sensed vegetation indices or ground‐based flux observations to pin down the space‐time patterns in North American carbon exchange. Models are assessed based on how well their carbon flux estimates capture the variability in the atmospheric CO 2 record observed from a network of towers. We find that models with carbon flux estimates that reproduce the observed CO 2 variability well show a hotspot of strong growing‐season carbon uptake in the Midwest US croplands. In contrast, models that reproduce the observed atmospheric CO 2 variability less effectively place the strongest growing‐season uptake in forests. Our findings reveal that peak cropland carbon uptake is underestimated in most models and that a better representation of cropland processes will be needed to obtain robust estimates of North American carbon fluxes. Space‐time variability in North American carbon balance is better resolved in models informed by remote sensing inputs than otherwise Bottom‐up models with strong growing‐season carbon uptake in croplands are more consistent with atmospheric CO 2 observations Most terrestrial biosphere models misrepresent the timing of peak net carbon uptake in croplands