Matthew A. Worden

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.

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.

While droughts predominantly induce immediate reductions in plant carbon uptake, they can also exert long‐lasting effects on carbon fluxes through associated changes in leaf area, soil carbon, etc. Among other mechanisms, shifts in carbon allocation due to water stress can contribute to the legacy effects of drought on carbon fluxes. However, the magnitude and impact of these allocation shifts on carbon fluxes and pools remain poorly understood. Using data from a wet tropical flux tower site in French Guiana, we demonstrate that drought‐induced carbon allocation shifts can be reliably inferred by assimilating Net Biosphere Exchange (NBE) and other observations within the CARbon DAta MOdel fraMework. This model‐data fusion system allows inference of optimized carbon and water cycle parameters and states from multiple observational data streams. We then examined how these inferred shifts affected the duration and magnitude of drought's impact on NBE during and after the extreme event. Compared to a static allocation scheme analogous to those typically implemented in land surface models, dynamic allocation reduced average carbon uptake during drought recovery by a factor of 2.8. Additionally, the dynamic model extended the average recovery time by 5 months. The inferred allocation shifts influenced the post‐drought period by altering foliage and fine root pools, which in turn modulated gross primary productivity and heterotrophic respiration for up to a decade. These changes can create a bust‐boom cycle where carbon uptake is enhanced some years after a drought, compared to what would have occurred under drought‐free conditions. Overall, allocation shifts accounted for 65% [45%–75%] of drought legacy effects in modeled NBE. In summary, drought‐induced carbon allocation shifts can play a substantial role in the enduring influence of drought on cumulative land‐atmosphere CO₂ exchanges and should be accounted for in ecosystem models.

Summary: Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait–environment model predictions if timescale is not accounted for. Here, the timescale dependence of trait–environment relationships is investigated by regressing in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales. We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub‐optimal climate timescales reduces the accuracy of the resulting trait–environment relationships. This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long‐term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait–environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.