Paul A. Levine

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.

Terrestrial ecosystems mitigate CO2 accumulation in the atmosphere by annually absorbing ~30% of anthropogenic emissions. The degrees to which CO2 and climate drive this absorption are uncertain, which presents a challenge for future planning around carbon mitigation scenarios. To reduce this knowledge gap, we use a Bayesian model–data integration framework (CARDAMOM) to build and analyze a global terrestrial biosphere reanalysis that optimally reconciles multiple lines of Earth Observations with mechanistic model processes. The Earth observations informing the model dynamics include satellite- and inventory-based constraints on distributions and change in terrestrial C storage (e.g., live biomass, soil organic C, and net biosphere exchange of CO2) and mechanisms of that change (e.g., photosynthesis, deforestation, water storage anomalies, or fire). We find that the impact of 2001–2021’s atmospheric CO2 increase on terrestrial C storage (+38 PgC) opposes and far outweighs the impact of climate trends over over the same period ($-$8.2 PgC). Globally, CO2-induced carbon gains occurred primarily in living biomass pools, while climate-induced losses occurred primarily in dead organic C pools, but relative gains and losses vary regionally. The fact that live biomass and dead organic C exhibit distinct and often opposing responses indicates that mechanistically resolving ecosystem function underlying the terrestrial C cycle’s emergent behavior is crucial for estimating the strength and resilience of the land C sink over the coming decades.

Solar-induced fluorescence (SIF) is an important indicator of terrestrial photosynthesis and an increasingly targeted observable in spaceborne remote sensing. Here, we present results from efforts to harmonize data across multiple sensors in order to create long-term records that are suitable for multi-decadal analyses. Nevertheless, discontinuities in harmonized records and non-linearities in the relationship between SIF and gross primary production (GPP) demand the use of model enhanced approaches to bridge the gap between observed SIF and inferred GPP. Bayesian model-data fusion (MDF) provides an increasingly established and effective tool to reconcile different satellite datasets and systematically retrieve otherwise unobserved quantities (i.e., not directly measured by spaceborne sensors) such as biomass, leaf area, and GPP, and more accurately estimate interactions between carbon pools and changing climate.Here, we apply the CARbon DAta–MOdel fraMework (CARDAMOM) MDF system to (i) optimize the parameters and initial states of a terrestrial ecosystem model against global harmonized SIF datasets and ancillary vegetation products to constrain terrestrial photosynthesis and generate reanalyses of GPP, (ii) propagate uncertainty from SIF observations into the GPP reanalyses, and (iii) diagnose these reanalyses to examine the current and evolving state of photosynthesis with climate. Using two SIF products derived from OCO-2 and GOME-2/SCIAMACHY, respectively, we have produced two twenty-year reanalyses of global GPP at a monthly, 0.5-degree resolution. The variability of GPP in these products is well constrained by their respective SIF observations, and reproduces a significant fraction of the observed spatial and interannual variability from globally distributed flux towers. Our results provide a basis for further investigation of photosynthetic carbon uptake as a data product available for the research community.

Tropical land carbon fluxes drive the inter-annual variability (IAV) of the atmospheric CO2 growth rate. However, what mechanistic processes and climate variables drive the IAV of tropical land carbon exchange remains debated. Here, we use a Bayesian framework that ingests a suite of satellite-based carbon and water observables to quantify the relative contributions of different climate variables to the tropical land carbon exchange IAV. We find the dominant climate drivers for carbon exchange depend on climate states. Radiation dominates the IAV of gross primary production (GPP) in wetter tropical forests, especially during La Niña and wet seasons, while VPD dominates GPPIAV in drier forests, with more pronounced influence during El Niño and dry seasons. Precipitation predominantly drives heterotrophic respiration IAV. Net ecosystem exchange IAV is more closely linked to ecosystem respiration than GPP. Our findings yield novel insights into the complex interplay among tropical climate gradients and IAV in climate states and carbon fluxes.

During the 2015–2016 El Niño, the Amazon basin released almost one gigaton of carbon (GtC) into the atmosphere due to extreme temperatures and drought. The link between the drought impact and recovery of the total carbon pools and its biogeochemical drivers is still unknown. With satellite‐constrained net carbon exchange and its component fluxes including gross primary production and fire emissions, we show that the total carbon loss caused by the 2015–2016 El Niño had not recovered by the end of 2018. Forest ecosystems over the Northeastern (NE) Amazon suffered a cumulative total carbon loss of ∼0.6 GtC through December 2018, driven primarily by a suppression of photosynthesis whereas southeastern savannah carbon loss was driven in part by fire. We attribute the slow recovery to the unexpected large carbon loss caused by the severe atmospheric aridity coupled with a water storage deficit during drought. We show the attenuation of carbon uptake is three times higher than expected from the pre‐drought sensitivity to atmospheric aridity and ground water supply. Our study fills an important knowledge gap in our understanding of the unexpectedly enhanced response of carbon fluxes to atmospheric aridity and water storage deficit and its impact on regional post‐drought recovery as a function of the vegetation types and climate perturbations. Our results suggest that the disproportionate impact of water supply and demand could compromise resiliency of the Amazonian carbon balance to future increases in extreme events.

Large spatio‐temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water‐balance (evapotranspiration minus precipitation, or ET − P) is typically measured at basin‐scales, limited primarily by river‐discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrained ET. We use observations of the isotopic composition of water vapor to quantify the spatio‐temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time‐varying ET − P over four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi‐year record, seasonality, and interannual variability of ET − P from both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity‐based estimates with our water vapor isotope‐based ET − P to calculate time and space averaged ET and net river discharge within the Congo Basin. These quadrant‐scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averaged ET/P > 70%), consistent with models and visible light‐based ET estimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance of ET in modulating the Congo water cycle relative to other water sources.

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.

Water stress regulates land‐atmosphere carbon dioxide (CO₂) exchanges in the tropics; however, its role remains poorly characterized due to the confounding roles of radiation, temperature and canopy dynamics. In particular, uncertainty stems from the relative roles of plant‐available water (supply) and atmospheric water vapor deficit (demand) as mechanistic drivers of photosynthetic carbon (C) uptake variability. Using satellite measurements of gravity, CO₂ and fluorescence to constrain a mechanistic carbon‐water cycle model from 2001 to 2018, we found that the interannual variability (IAV) of water stress on photosynthetic C uptake was 52% greater than the combined effects of other factors. Surprisingly, the dominance of water stress on C uptake IAV was greater in the wet tropics (94%) than in the dry tropics (26%). Plant‐available water supply and atmospheric demand both contributed to the IAV of water stress on photosynthetic C uptake across the tropics, but the IAV of demand effects was 21% greater than the IAV of supply effects (33% greater in the wet tropics and 6% greater in the dry tropics). We found that the IAV of water stress on C uptake was 24% greater than the IAV of the combination of other factors in the net land‐atmosphere C sink in the whole tropics, 26% greater in the wet tropics, and 7% greater in the dry tropics. Given the recent trends in tropical precipitation and atmospheric humidity, our findings indicate that water stress——from both supply and demand——will likely dominate the climate response of land C sink across tropical ecosystems in the coming decades.

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 CO₂ and methane (CH₄) fluxes. While CH₄ fluxes account for a smaller component of the C balance, the climatic impact of CH₄ outweighs CO₂ (28–34 times larger global warming potential on a 100‐year scale), highlighting the need to jointly resolve the climatic sensitivities of both CO₂ and CH₄. Here, we jointly constrain a terrestrial biosphere model with in situ CO₂ and CH₄ flux observations at seven eddy covariance sites using a data‐model integration approach to resolve the integrated environmental controls on land‐atmosphere CO₂ and CH₄ exchanges in Alaska. Based on the combined CO₂ and CH₄ flux 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 CH₄ emissions while the negative C‐climate feedback at the boreal site is predominantly driven by increased CO₂ uptake (80% from decreased heterotrophic respiration, and 20% from increased photosynthesis). Our study demonstrates the need for joint observational constraints on CO₂ and CH₄ biogeochemical processes—and their associated climatic sensitivities—for resolving the sign and magnitude of high‐latitude ecosystem C‐climate feedback in the coming decades.