Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4313-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4313-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
03 Jun 2022
Development and technical paper |
| 03 Jun 2022
| 03 Jun 2022
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA
Shawn P. Serbin
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA
Julien Lamour
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA
Kenneth J. Davidson
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA
Department of Ecology and Evolution, Stony Brook University, Stony
Brook, NY, USA
Kim S. Ely
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA
Alistair Rogers
Department of Environmental and Climate Sciences, Brookhaven National Laboratory, Upton, NY, USA
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Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022, https://doi.org/10.5194/gmd-15-3233-2022, 2022
Short summary
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We present a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon
reanalysisproduct that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
Short summary
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Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
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Short summary
Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Stomatal conductance is the rate of water release from leaves’ pores. We implemented an...
