Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1789-2022
https://doi.org/10.5194/gmd-15-1789-2022
Model evaluation paper
 | 
02 Mar 2022
Model evaluation paper |  | 02 Mar 2022

CARDAMOM-FluxVal version 1.0: a FLUXNET-based validation system for CARDAMOM carbon and water flux estimates

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

Related authors

Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, Ricardo Dalagnol, and Wei Li
Geosci. Model Dev., 18, 4915–4933, https://doi.org/10.5194/gmd-18-4915-2025,https://doi.org/10.5194/gmd-18-4915-2025, 2025
Short summary
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025,https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Water vapour isotopes over West Africa as observed from space: which processes control tropospheric H2O ∕ HDO pair distributions?
Christopher Johannes Diekmann, Matthias Schneider, Peter Knippertz, Tim Trent, Hartmut Boesch, Amelie Ninja Roehling, John Worden, Benjamin Ertl, Farahnaz Khosrawi, and Frank Hase
Atmos. Chem. Phys., 25, 5409–5431, https://doi.org/10.5194/acp-25-5409-2025,https://doi.org/10.5194/acp-25-5409-2025, 2025
Short summary
Implementing Riverine Biogeochemical Inputs in ECCO-Darwin: a Critical Step Forward for a Pioneering Data-Assimilative Global-Ocean Biogeochemistry Model
Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, and Hong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1707,https://doi.org/10.5194/egusphere-2025-1707, 2025
Short summary
The role of the tropical carbon balance in determining the large atmospheric CO2 growth rate in 2023
Liang Feng, Paul Palmer, Luke Smallman, Jingfeng Xiao, Paulo Cristofanelli, Ove Hermansen, John Lee, Casper Labuschagne, Simonetta Montaguti, Steffen Noe, Stephen Platt, Xinrong Ren, Martin Steinbacher, and Irene Xueref-Remy
EGUsphere, https://doi.org/10.5194/egusphere-2025-1793,https://doi.org/10.5194/egusphere-2025-1793, 2025
Short summary

Cited articles

Anderson, M. C., Kustas, W. P., and Norman, J. M.: Upscaling Flux Observations from Local to Continental Scales Using Thermal Remote Sensing, Agron. J., 99, 240–254, https://doi.org/10.2134/agronj2005.0096S, 2007. 
Bacour, C., Maignan, F., Peylin, P., MacBean, N., Bastrikov, V., Joiner, J., Köhler, P., Guanter, L., and Frankenberg, C.: Differences Between OCO-2 and GOME-2 SIF Products From a Model-Data Fusion Perspective, J. Geophys. Res.-Biogeo., 124, 3143–3157, https://doi.org/10.1029/2018JG004938, 2019. 
Bloom, A. A. and Williams, M.: Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological “common sense” in a model–data fusion framework, Biogeosciences, 12, 1299–1315, https://doi.org/10.5194/bg-12-1299-2015, 2015. 
Bloom, A. A., Exbrayat, J.-F., Velde, I. R. van der, Feng, L., and Williams, M.: The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016. 
Download
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.
Share