California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL)
Columbia Univ., New York, NY (United States)
Univ. of California, Davis, CA (United States)
Univ. of Edinburgh, Scotland (United Kingdom)
Univ. of California, Santa Barbara, CA (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL); Weizmann Institute of Science, Rehovot (Israel)
California Institute of Technology (CalTech), Pasadena, CA (United States)
Howard Univ., Washington, DC (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL); Univ. of California, Los Angeles, CA (United States)
Univ. of Montana, Missoula, MT (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
UK Centre for Ecology & Hydrology (UKCEH), Midlothian (United Kingdom)
Australian National Univ., Canberra, ACT (Australia)
Univ. of Edinburgh, Scotland (United Kingdom); Univ. of Southampton (United Kingdom)
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.
Worden, Matthew A., et al. "Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling." Global Change Biology, vol. 31, no. 8, Aug. 2025. https://doi.org/10.1111/gcb.70462
Worden, Matthew A., Bilir, Theresa Eren, Bloom, Alexis Anthony, Fang, Jianing, Klinek, Lily P., Konings, Alexandra G., Levine, Paul A., Milodowski, David T., Quetin, Gregory R., Smallman, T. Luke, Bar‐On, Yinon M., Braghiere, Renato K., David, Cédric H., Fischer, Nina A., Gentine, Pierre, Green, Tim J., Jones, Ayanna, Liu, Junjie, ... Zhu, Songyan (2025). Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling. Global Change Biology, 31(8). https://doi.org/10.1111/gcb.70462
Worden, Matthew A., Bilir, Theresa Eren, Bloom, Alexis Anthony, et al., "Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling," Global Change Biology 31, no. 8 (2025), https://doi.org/10.1111/gcb.70462
@article{osti_3002279,
author = {Worden, Matthew A. and Bilir, Theresa Eren and Bloom, Alexis Anthony and Fang, Jianing and Klinek, Lily P. and Konings, Alexandra G. and Levine, Paul A. and Milodowski, David T. and Quetin, Gregory R. and Smallman, T. Luke and others},
title = {Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling},
annote = {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.},
doi = {10.1111/gcb.70462},
url = {https://www.osti.gov/biblio/3002279},
journal = {Global Change Biology},
issn = {ISSN 1365-2486},
number = {8},
volume = {31},
place = {United States},
publisher = {Wiley},
year = {2025},
month = {08}}
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS); National Science Foundation (NSF); National Aeronautics and Space Administration (NASA)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
3002279
Journal Information:
Global Change Biology, Journal Name: Global Change Biology Journal Issue: 8 Vol. 31; ISSN 1354-1013; ISSN 1365-2486
Sierra, Carlos A.; Quetin, Gregory R.; Metzler, Holger
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 381, Issue 2261https://doi.org/10.1098/rsta.2022.0200