Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1789-2022
© 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-15-1789-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
02 Mar 2022
Model evaluation paper |
| 02 Mar 2022
| 02 Mar 2022
CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
A. Anthony Bloom
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Shuang Ma
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Paul Levine
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Alexander Norton
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Nicholas C. Parazoo
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
John T. Reager
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
John Worden
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Gregory R. Quetin
Department of Earth System Science, Stanford University, Stanford, CA
94305, USA
T. Luke Smallman
School of Geosciences, University of Edinburgh, Edinburgh, EH9 3FF,
United Kingdom
National Centre for Earth Observation, University of Edinburgh,
Edinburgh, EH9 3FF, United Kingdom
Mathew Williams
School of Geosciences, University of Edinburgh, Edinburgh, EH9 3FF,
United Kingdom
National Centre for Earth Observation, University of Edinburgh,
Edinburgh, EH9 3FF, United Kingdom
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Sassan Saatchi
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
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7 citations as recorded by crossref.
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- Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground‐Based Observational Constraints C. DiMaria et al. 10.1029/2022JD037822
- Inner Mongolia grasslands act as a weak regional carbon sink: A new estimation based on upscaling eddy covariance observations C. You et al. 10.1016/j.agrformet.2023.109719
- Attributing Past Carbon Fluxes to CO2 and Climate Change: Respiration Response to CO2 Fertilization Shifts Regional Distribution of the Carbon Sink G. Quetin et al. 10.1029/2022GB007478
- Modeling China's terrestrial ecosystem gross primary productivity with BEPS model: Parameter sensitivity analysis and model calibration X. Xing et al. 10.1016/j.agrformet.2023.109789
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5 citations as recorded by crossref.
- Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance A. Norton et al. 10.5194/bg-20-2455-2023
- Optimizing the Isoprene Emission Model MEGAN With Satellite and Ground‐Based Observational Constraints C. DiMaria et al. 10.1029/2022JD037822
- Inner Mongolia grasslands act as a weak regional carbon sink: A new estimation based on upscaling eddy covariance observations C. You et al. 10.1016/j.agrformet.2023.109719
- Attributing Past Carbon Fluxes to CO2 and Climate Change: Respiration Response to CO2 Fertilization Shifts Regional Distribution of the Carbon Sink G. Quetin et al. 10.1029/2022GB007478
- Modeling China's terrestrial ecosystem gross primary productivity with BEPS model: Parameter sensitivity analysis and model calibration X. Xing et al. 10.1016/j.agrformet.2023.109789
2 citations as recorded by crossref.
Latest update: 23 Nov 2024
Short summary
Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Global carbon and water have large uncertainties that are hard to quantify in current regional...
