CaltechAUTHORS
Published August 2025 | Version Published
Journal Article Open

Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling

Creators

  • 1. ROR icon Stanford University
  • 2. ROR icon Jet Propulsion Lab
  • 3. ROR icon Columbia University
  • 4. ROR icon University of California, Davis
  • 5. ROR icon University of Edinburgh
  • 6. ROR icon University of California, Santa Barbara
  • 7. ROR icon California Institute of Technology
  • 8. ROR icon Weizmann Institute of Science
  • 9. ROR icon Howard University
  • 10. ROR icon Lawrence Berkeley National Laboratory
  • 11. ROR icon University of California, Los Angeles
  • 12. ROR icon University of Montana
  • 13. ROR icon Oak Ridge National Laboratory
  • 14. ROR icon UK Centre for Ecology & Hydrology
  • 15. ROR icon Australian National University
  • 16. ROR icon University of Southampton

Abstract

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

Copyright and License

© 2025 The Author(s). Global Change Biology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Funding

This work was supported by the RUBISCO Science Focus Area (KP1703), Natural Environment Research Council (NE/X019071/1), Alfred P. Sloan Foundation, NASA Carbon Cycle Science, SECO: Resolving the current and future carbon dynamics of the dry tropics, UT-Battelle (DE-AC05-00OR22725), USDI Park Service (P24AC00910), UK EO Climate Information Service, National Centre for Earth Observation, Division of Environmental Biology (1942133), Future Investigators in NASA Earth and Space Science and Technology (80NSSC21K1593), California Department of Forestry and Fire Protection (8GG20808), Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology, and National Science Foundation (2003205).

Acknowledgement

This paper originated from the 2024 CARDAMOM Community workshop, held at the Caltech Keck Center. M.A.W., T.E.B., A.G.K., and A.A.B. are supported by a grant from NASA CCS. M.A.W. and A.G.K. are also supported by DEB grant 1942133 and by the Alfred P. Sloan Foundation. M.A.W. is supported by NASA grant 80NSSC21K1593 issued through the Future Investigators in NASA Earth and Space Science and Technology (FINESST) program. L.P.K. and T.S.M. are supported by California Department of Forestry and Fire Protection Forest Health Research Program (CAL FIRE agreement #8GG20808). R.K.B. was supported in part by the Resnick Sustainability Institute at Caltech. G.R.Q. and A.T.T. were supported by NSF Award No. 2003205 and USDI Park Service Award No. P24AC00910. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. Copyright 2024. All rights reserved. T.L.S. is supported by the UK's National Centre for Earth Observation (NCEO) and the UK's Natural Environment Research Council project [NERC grant reference number NE/X019071/1], “UK EO Climate Information Service” (UK EOCIS). D.T.M. is funded by the SECO NERC large grant. M.W. is supported by the NCEO, UK EOCIS and SECO. This paper has been partially authored by UT-Battelle LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). E.C.M. was supported by the RUBISCO Science Focus Area (RUBISCO SFA KP1703), which is sponsored by the Regional and Global Model Analysis (RGMA) activity of the Earth and Environmental Systems Modeling (EESM) Program in the Earth and Environmental Systems Sciences Division (EESSD) of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science.

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Additional details

Identifiers

PMCID
PMC12379577
PMID
40856273

Related works

Describes
Journal Article: PMC12379577 (PMCID)
Journal Article: 40856273 (PMID)

Funding

Natural Environment Research Council
NE/X019071/1
Alfred P. Sloan Foundation
UT-Battelle
DE‐AC05‐00OR22725
National Centre for Earth Observation
Division of Environmental Biology
1942133
California Department of Forestry and Fire Protection
8GG20808
Resnick Sustainability Institute
National Science Foundation
2003205
Jet Propulsion Laboratory
National Aeronautics and Space Administration

Dates

Available
2025-08-26
Version of record

Caltech Custom Metadata

Caltech groups
Division of Geological and Planetary Sciences (GPS)
Publication Status
Published