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.
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
Data sets
CARDAMOM-FluxVal Version 1.0 Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi https://doi.org/10.5281/zenodo.4904195
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...