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Abstract Evapotranspiration (ET) is co‐regulated by subsurface water availability, atmospheric demand for water, and radiation. Spatial differences in the limiting factors on ET that emerge along the soil‐plant‐atmosphere continuum result in distinct ecohydrological regimes with differing sensitivities to atmospheric and subsurface drivers. However, different components of the soil‐plant‐atmosphere continuum are not equally well understood. Deep subsurface water access is particularly difficult to measure and model, but can sustain ET under drought conditions when shallow soil moisture appears to be acutely limiting. Here, we exploited this principle to identify ecosystems that rely on deep subsurface water availability. We first used a plant hydraulic model to determine the expected ET behavior for plants with deep water access. We then examined 19 flux towers and found that responsiveness of ET to atmospheric conditions during dry periods was indicative of some ecosystems with deep water access. We used the divergent sensitivities of ET to vapor pressure deficit, radiation, and shallow soil moisture to identify distinct ecohydrological regimes in gridded data covering the continental U.S. We diagnosed deep water usage in ecosystems where ET remained sensitive to atmospheric conditions despite being insensitive to shallow soil moisture variability. Further, we found that drought stress, plant hydraulic traits, and ecosystem biophysical variables mediated the sensitivity of ET to aboveground and belowground conditions. 

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 Terrestrial photosynthesis requires the evaporation of water (transpiration) in exchange for CO₂needed to form sugars. The water for transpiration is drawn up through plant roots, stem, and branches via a water potential gradient. However, this flow of water—or sap ascent—requires energy to lift the water to the canopy and to overcome the resistance of the plant’s water transporting xylem. Here, we use a combination of field measurements of plant physiology (hydraulic conductivity) and state‐of‐the‐science global estimates of transpiration to calculate how much energy is passively harvested by plants to power the sap ascent pump across the world’s terrestrial vegetation. Globally, we find that 0.06 W/m²is consumed in sap ascent over forest dominated ecosystems or 9.4 PWh/yr (equal to global hydropower energy production). Though small in comparison to other components of the Earth’s surface energy budget, sap ascent work in forests represents 14.2% of the energy compared to the energy consumed to create sugars through photosynthesis, with values up to 18% in temperate rainforests. The power needed for sap ascent generally increases with photosynthesis, but is moderated by both climate and plant physiology, as the most work is consumed in regions with large transpiration fluxes (such as the moist tropics) and in areas where vegetation has low conductivity (such as temperate rainforests dominated by conifer trees). Here, we present a bottom‐up analysis of sap ascent work that demonstrates its significant role in plant function across the globe. 

Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models. 

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.