Characterizing the performance of ecosystem models across time scales: A spectral analysis of the North American Carbon Program site-level synthesis - Dietze, Michael C.; Vargas, Rodrigo; Richardson, Andrew D.; Stoy, Paul C.; Barr, Alan G.; Anderson, Ryan S.; Arain, M. Altaf; Baker, Ian T.; Black, T. Andrew; Chen, Jing M.; Ciais, Philippe; Flanagan, Lawrence B.; et al. (Journal of Geophysical Research, 2011)

A model‐data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis - Schaefer, Kevin; Schwalm, Christopher R.; Williams, Chris; Arain, M. Altaf; Barr, Alan; Chen, Jing M.; Davis, Kenneth J.; Dimitrov, Dimitre; Hilton, Timothy W.; Hollinger, David Y.; Humphreys, Elyn; Poulter, Benjamin; et al. (Journal of Geophysical Research: Biogeosciences, 2012)
Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low‐temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf‐to‐canopy scaling and better values of model parameters that control the maximum potential GPP, such as ε max (LUE), V cmax (unstressed Rubisco catalytic capacity) or J max (the maximum electron transport rate). , Key Points Gross primary productivity (GPP) from 26 models tested at 39 flux tower sites Simulated light use efficiency controls model performance Models overpredict GPP under dry conditions

A model‐data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis - Schwalm, Christopher R.; Williams, Christopher A.; Schaefer, Kevin; Anderson, Ryan; Arain, M. Altaf; Baker, Ian; Barr, Alan; Black, T. Andrew; Chen, Guangsheng; Chen, Jing Ming; Ciais, Philippe; Davis, Kenneth J.; et al. (Journal of Geophysical Research: Biogeosciences, 2010)
Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.