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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. 

Abstract Boreal‐Arctic regions are key stores of organic carbon (C) and play a major role in the greenhouse gas balance of high‐latitude ecosystems. The carbon‐climate (C‐climate) feedback potential of northern high‐latitude ecosystems remains poorly understood due to uncertainty in temperature and precipitation controls on carbon dioxide (CO₂) uptake and the decomposition of soil C into CO₂and methane (CH₄) fluxes. While CH₄fluxes account for a smaller component of the C balance, the climatic impact of CH₄outweighs CO₂(28–34 times larger global warming potential on a 100‐year scale), highlighting the need to jointly resolve the climatic sensitivities of both CO₂and CH₄. Here, we jointly constrain a terrestrial biosphere model with in situ CO₂and CH₄flux observations at seven eddy covariance sites using a data‐model integration approach to resolve the integrated environmental controls on land‐atmosphere CO₂and CH₄exchanges in Alaska. Based on the combined CO₂and CH₄flux responses to climate variables, we find that 1970‐present climate trends will induce positive C‐climate feedback at all tundra sites, and negative C‐climate feedback at the boreal and shrub fen sites. The positive C‐climate feedback at the tundra sites is predominantly driven by increased CH₄emissions while the negative C‐climate feedback at the boreal site is predominantly driven by increased CO₂uptake (80% from decreased heterotrophic respiration, and 20% from increased photosynthesis). Our study demonstrates the need for joint observational constraints on CO₂and CH₄biogeochemical processes—and their associated climatic sensitivities—for resolving the sign and magnitude of high‐latitude ecosystem C‐climate feedback in the coming decades. 

Abstract. At the leaf level, stomata control the exchange of water and carbon across the air–leaf interface. Stomatal conductance is typically modeledempirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predictingcarbon and water fluxes at both the leaf and tree levels. However, how well the optimization models perform atlarger scales has not been extensively evaluated. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such asbig-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves nor the entirecanopy. As a result, they are unable to directly link canopy optical properties with light distribution within the canopy to remote sensing dataobserved from afar. Here, we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the landcomponent of a new Earth system model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and waterfluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes andsolar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIFretrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model,in particular, showed increased skill in predicting the water flux given the lower error (ca. 14.2 % and 21.8 % improvement for thegymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest andfrom ca. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for bothforests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. Theever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will helpparameterize the land model and improve future Earth system modeling in general. 

Abstract Terrestrial ecosystems annually absorb % of anthropogenic C emissions. The degrees to which contemporary and climate trends drive this absorption are uncertain, as are the governing mechanisms. To reduce uncertainty, we use Bayesian model‐data integration (CARbon DAta MOdel fraMework) to retrieve a terrestrial biosphere reanalysis where Earth Observations optimally inform mechanistic model processes: observations include satellite‐ and inventory‐based constraints on distributions and change in terrestrial C (including live biomass, dead organic C, and land‐atmosphere exchanges) and underlying mechanisms (including photosynthesis, deforestation, water storage anomalies, and fire). We find that the impact of 2001–2021's atmospheric increase on terrestrial C (+39.4 PgC) opposes and far outweighs the impact of climate trends over this period (10.5 PgC). Globally, C gains are mostly attributable to live biomass growth (+31.2 PgC), while ‐induced dead organic C gains (+7.8 PgC) are compensated by climate‐induced losses (8.8 PgC). The distribution of compensating dead C changes induces an aggregate shift in dead C from high‐ and mid‐latitudes (3.5 PgC) to tropical ecosystems (+2.6 PgC). We additionally find global residence time reductions attributable to (2.6%) and climate (1.3%) reflected across latitudes, irrespective of reservoir C changes. In aggregate, these changes reveal an acceleration and redistribution of terrestrial C stores in response to and climate trends, which together reflect a gradual but fundamental reorganization of the terrestrial C cycle. Tracking this reorganization—through robust and continual diagnosis of ecosystem function—is essential for accurately resolving the compensating dynamics governing the strength and resilience of the terrestrial C sink. 

Abstract We stand at the threshold of a transformative era in Earth observation, marked by space‐borne visible‐to‐shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter‐ and intra‐annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub‐seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High‐Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub‐seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open‐source processing methods and cloud‐based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high‐frequency time series data from space‐borne VSWIR imaging spectrometers.