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

作者：M. Worden, T. Eren Bilir, A. Anthony Bloom, Jianing Fang, Lily Klinek, Alexandra G. Konings, Paul A. Levine, David T. Milodowski, Gregory R. Quetin, T. Luke Smallman, Yinon M. Bar‐On, Renato K. Braghiere, Cédric H. David, Nina A. Fischer, Pierre Gentine, T Green, A. D. G. Jones, Junjie Liu, Marcos Longo, Shuang Ma, Troy S. Magney, Elias Massoud, Vasileios Myrgiotis, Alexander Norton, Nick Parazoo, Elahe Tajfar, Anna T. Trugman, Mathew Williams, Sarah Worden, Wenli Zhao, Songyan Zhu · 发表于：Global Change Biology · 年份：2025 · DOI：10.1111/gcb.70462 · 被引用次数：6 · 研究领域：Hydrology and Watershed Management Studies、Soil Geostatistics and Mapping、Peatlands and Wetlands Ecology

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 pr...