Preprints
https://doi.org/10.5194/gmd-2021-190
https://doi.org/10.5194/gmd-2021-190

Submitted as: model evaluation paper 09 Jul 2021

Submitted as: model evaluation paper | 09 Jul 2021

Review status: this preprint is currently under review for the journal GMD.

CARDAMOM-FluxVal Version 1.0: a FLUXNET-based Validation System for CARDAMOM Carbon and Water Flux Estimates

Yan Yang1, A. Anthony Bloom1, Shuang Ma1, Paul Levine1, Alexander Norton1, Nicholas C. Parazoo1, John T. Reager1, John Worden1, Gregory R. Quetin2, T. Luke Smallman3,4, Mathew Williams3,4, Liang Xu1, and Sassan Saatchi1 Yan Yang et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109
  • 2Department of Earth System Science, Stanford University, Stanford, CA 94305, U.S.A.
  • 3School of Geosciences, University of Edinburgh, Edinburgh, EH9 3FF, United Kingdom
  • 4National Centre for Earth Observation, Edinburgh EH9 3FF, United Kingdom

Abstract. Land-atmosphere carbon and water exchanges have large uncertainty in land surface and biosphere models. Using observations to reduce land biosphere model structural and parametric errors is a key priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARDAMOM model-data fusion framework have yielded key insights into ecosystem carbon and water cycling. CARDAMOM analyses—informed by co-located C and H2O flux observations—have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. While CARDAMOM model configurations (namely CARDAMOM-compatible biogeochemical model structures) have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints, there has so far been no concerted effort to globally and systematically validate CARDAMOM performance across individual model-data fusion configurations. Here we use the FLUXNET-2015 dataset—an ensemble of 200+ eddy covariance flux tower sites—to formulate a concerted benchmarking framework for CARDAMOM carbon (GPP, NEE) and water (ET) flux estimates (CARDAMOM-FLUXVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET-2015 GPP, NEE and ET data. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land cover types, (ii) monthly, annual and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system—along with CARDAMOM driver files provided—can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development and testing of new CARDAMOM model structures.

Yan Yang et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Yang et al. (gmd-2021-190)', Anonymous Referee #1, 02 Sep 2021 reply
  • RC2: 'Comment on gmd-2021-190', Anonymous Referee #2, 25 Oct 2021 reply

Yan Yang et al.

Data sets

CARDAMOM-FluxVal Version 1.0 Yang, Y., Bloom, A. A., Ma, S., Levine, P., Norton, A., Parazoo, N. C., Reager, J. T., Worden, J., Quetin, G. R., Smallman, T. L., Williams, M., Xu, L., and Saatchi, S. https://doi.org/10.5281/zenodo.4904195

Model code and software

CARDAMOM-FluxVal Version 1.0 Yang, Y., Bloom, A. A., Ma, S., Levine, P., Norton, A., Parazoo, N. C., Reager, J. T., Worden, J., Quetin, G. R., Smallman, T. L., Williams, M., Xu, L., and Saatchi, S. https://doi.org/10.5281/zenodo.4904195

Yan Yang et al.

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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 model, we have utilized the data assimilation capability and designed the benchmarking framework by taking field observations into modeling. Results show that data assimilation improves model performance in different aspects.