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Abstract:
Terrestrial biosphere models can help identify physical processes that control carbon
dynamics, including land–atmosphere CO2 fluxes, and have great potential to predict the terrestrial
ecosystem response to changing climate. The skill of models that provide continental-scale carbon flux
estimates, however, remains largely untested. This paper evaluates the performance of continental-scale
flux estimates from 17 models against observations from 36 North American flux towers. Fluxes
extracted from regional model simulations were compared with co-located flux tower observations at
monthly and annual time increments. Site-levelmodel simulations were used to help interpret sources of
the mismatch between the regional simulations and site-based observations. On average, the regional
model runs overestimated the annual gross primary productivity (5%) and total respiration (15%), and
they significantly underestimated the annual net carbon uptake (64%) during the time period 2000–
2005. Comparison with site-level simulations implicated choices specific to regional model simulations
as contributors to the gross flux biases, but not the net carbon uptake bias. The models performed the
best at simulating carbon exchange at deciduous broadleaf sites, likely because a number of models used
prescribed phenology to simulate seasonal fluxes. The models did not perform as well for crop, grass,
and evergreen sites. The regional models matched the observations most closely in terms of seasonal
correlation and seasonal magnitude of variation, but they have very little skill at interannual correlation
and minimal skill at interannual magnitude of variability. The comparison of site vs. regional-level
model runs demonstrated that (1) the interannual correlation is higher for site-level model runs, but the
skill remains low; and (2) the underestimation of year-to-year variability for all fluxes is an inherent
weakness of the models. The best-performing regional models that did not use flux tower calibration
were CLM-CN, CASA-GFEDv2, and SIB3.1. Two flux tower calibrated, empirical models, EC-MOD
and MOD17þ, performed as well as the best process-based models. This suggests that (1) empirical,
calibrated models can perform as well as complex, process-based models and (2) combining processbased model structure with relevant constraining data could significantly improve model performance.
