Results from the carbon-land model intercomparison project (C-LAMP) and availability of the data on the earth system grid (ESG)

Hoffman, Forrest M.; Covey, CC; Randerson, J. T.; Thornton, P. E.; Lee, Young Ho; Rosenbloom, N. A.; Stöckli, RC; Running, SW; Bernholdt, D. E.; Williams, DN

doi:10.1088/1742-6596/78/1/012026

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…These observations have been used most commonly for the evaluation of site-level model simulations (e.g., Thornton et al 2002, Hanson et al 2004), but can also serve as a test for regional model performance (e.g., Hoffman et al 2007, Potter et al 2007) or as a tool to evaluate regional flux maps (e.g., Ciais et al 2005, Jung et al 2011. Furthermore, flux tower data combined with statistical scaling approaches can produce regional flux maps (Xiao et al 2008, Beer et al 2010, Carvalhais et al 2010.…”

Section: Introductionmentioning

confidence: 99%

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

Raczka

Davis

Huntzinger

et al. 2013

Ecological Monographs

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Abstract. Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO 2 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 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.

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Section: Introductionmentioning

confidence: 99%

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

Raczka

Davis

Huntzinger

et al. 2013

Ecological Monographs

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“…The intercomparison of data from sensors, using Bayesian techniques when multiple sensors of the same type are deployed (Ni & Pottie, 2007), or between sensors and remote sensing data (Grassotti et al ., 2003), can be part of data QA. In addition, a model–measurement intercomparison can allow the international scientific community to evaluate the performance of models compared with field observations (Hoffman et al ., 2007) to ensure long‐term and cross‐network comparability.…”

Section: Challenges For Sensor Network Developmentmentioning

confidence: 99%

Environmental sensor networks in ecological research

et al. 2009

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Summary Environmental sensor networks offer a powerful combination of distributed sensing capacity, real‐time data visualization and analysis, and integration with adjacent networks and remote sensing data streams. These advances have become a reality as a combined result of the continuing miniaturization of electronics, the availability of large data storage and computational capacity, and the pervasive connectivity of the Internet. Environmental sensor networks have been established and large new networks are planned for monitoring multiple habitats at many different scales. Projects range in spatial scale from continental systems designed to measure global change and environmental stability to those involved with the monitoring of only a few meters of forest edge in fragmented landscapes. Temporal measurements have ranged from the evaluation of sunfleck dynamics at scales of seconds, to daily CO2 fluxes, to decadal shifts in temperatures. Above‐ground sensor systems are partnered with subsurface soil measurement networks for physical and biological activity, together with aquatic and riparian sensor networks to measure groundwater fluxes and nutrient dynamics. More recently, complex sensors, such as networked digital cameras and microphones, as well as newly emerging sensors, are being integrated into sensor networks for hierarchical methods of sensing that promise a further understanding of our ecological systems by revealing previously unobservable phenomena.

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“…The work presented in this paper can be seen as an example of the server-side processing the nextgeneration ESG plans to support. Because of community interest in the next generation of biogeochemistry models, our work has focused on visualization of the data held in the C-LAMP [5] archive, which is physically located at ORNL, but the technique described is broadly applicable.…”

Section: Earth System Gridmentioning

confidence: 99%

“…We draw our research motivation from the current thrust to generate accurate simulations of the global carbon cycle that model the interactions and feedbacks between the terrestrial biosphere and the climate system. The Carbon-Land Model Intercomparison Project (C-LAMP, www.climatemodeling.org/c-lamp [5] was initiated to allow the international scientific community to thoroughly test and intercompare such terrestrial biogeochemistry models through a set of carefully crafted experiments. Well-defined metrics have been established for comparison of model results against best-available observational datasets, and models are graded on their scientific performance with respect to these metrics.…”

Section: Introductionmentioning

confidence: 99%