Modeling the net carbon balance is challenging due to the knowledge gaps in the variability and processes controlling gross carbon fluxes. Terrestrial carbon cycle modeling is susceptible to several sources of bias, including meteorological uncertainty, model structural uncertainty, and model parametric uncertainty. To determine the impact of these uncertainties, we compare three model‐derived representations of the global terrestrial carbon balance across 1997–2009: (1) observation‐constrained model‐data fusion (CARBon Data Model FraMEwork, CARDAMOM), (2) the reanalysis‐driven Trends in Net Land‐Atmosphere Carbon Exchange (TRENDY) land biosphere model ensemble, and (3) the Coupled Model Intercomparison Project 5 (CMIP5) Earth System Model ensemble. We consider the spread in carbon cycle simulations attributable primarily to parametric uncertainty (CARDAMOM), structural uncertainty (TRENDY), and combined structural and simulated meteorological uncertainty (CMIP5). We find that the spread across the CARDAMOM ensemble long‐term mean—produced by parameter uncertainty—is larger than the spread of TRENDY and CMIP5 for net biosphere exchange (NBE) but similar for gross primary productivity (GPP). The carbon flux dynamics of CARDAMOM compares to models in TRENDY as well as models in TRENDY compare to each other in many regions for NBE seasonal (nine of 12), NBE interannual (11 of 12), and GPP seasonal variability (7 of 12), although not for GPP interannual variability (2 of 12). The simple model structure of CARDAMOM and systematic assimilation of observations is sufficient to produce carbon dynamics within the range of more complex models. These results are encouraging for the use of model‐data fusion products with empirically estimated uncertainty for global carbon cycle studies. Plain Language Summary There is uncertainty in where carbon is going into or leaving the land biosphere on a net basis. Models are used to investigate this movement of carbon, but it is unclear how different approaches compare: using a complex model for which the best parameter sets are difficult to determine or using a simpler model for which it is easier to find the right parameters sets that best match observations. This paper compares collections of relatively complex models with a different approach that systematically combines remote sensing observations with a simpler model. Comparisons across mean values, seasonal variations, and interannual variations of photosynthesis and carbon movement show that the simple model—constrained by observations—is able to recreate most dynamics but also shows a broader range of possibilities than the collections of more complex models. The results suggest that more attention needs to be paid to parameter uncertainty in model settings, not just what processes they include. Including this uncertainty creates a larger spread of carbon cycle outcomes for the present and possibly for the future.
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