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Evaluation of continental carbon cycle simulations with North American flux tower observations

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https://ir.library.oregonstate.edu/concern/articles/mw22v7379

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  • 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-level model 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 MOD17+, 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 process-based model structure with relevant constraining data could significantly improve model performance.
  • Keywords: model–data comparison, flux towers, terrestrial biosphere models, carbon fluxes
  • Keywords: model–data comparison, flux towers, terrestrial biosphere models, carbon fluxes
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  • Brett M. Raczka, Kenneth J. Davis, Deborah Huntzinger, Ronald P. Neilson, Benjamin Poulter, Andrew D. Richardson, Jingfeng Xiao, Ian Baker, Philippe Ciais, Trevor F. Keenan, Beverly Law, Wilfred M. Post, Daniel Ricciuto, Kevin Schaefer, Hanqin Tian, Enrico Tomelleri, Hans Verbeeck, and Nicolas Viovy 2013. Evaluation of continental carbon cycle simulations with North American flux tower observations. Ecological Monographs 83:531–556. doi:10.1890/12-0893.1
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  • 83
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  • 4
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  • This research was supported by the U.S. Department of Energy’s Office of Science through the Northeastern Regional Center of the National Institute for Climatic Change Research and through NASA’s Terrestrial Ecology Program. We also acknowledge the DOE Office of Science for support of the three NACP Interim Synthesis workshops. NASA provided support for the Modeling and Synthesis Thematic Data Center that processed the model output. Ameriflux measurement and data protocols, QA, and coordination of data activities were supported by the U.S. Department of Energy’s Office of Science (Science Team Research, Grant Number DE-FG02-04ER63911). J. Xiao was partly supported by National Science Foundation (NSF) through MacroSystems Biology (award number 1065777) and National Aeronautics and Space Administration (NASA) through Carbon Monitoring System (award number NNX11AL32G).
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