Alexander J. Norton

1 Carbon allocation governs how plants partition assimilates among leaves, roots, stems, and reproductive organs, directly shaping growth and yield. Most crop models represent this partitioning using fixed or empirically derived coefficients, which limits their ability to predict how allocation responds to genotype, environment, or their interaction. Eco-evolutionary optimality theory offers an alternative where the allocation strategy is allowed to emerge from the marginal costs and benefits of investing carbon in each organ. While this approach has been applied successfully to trees, it has not previously been developed for herbaceous plants. Here, we present DAESIM2-Plant, a mechanistic plant growth model that couples physiological processes including photosynthesis, stomatal conductance, plant hydraulics, and canopy radiative transfer, with an eco-evolutionary optimal allocation scheme for leaves and roots. This is applied to wheat by coupling it to a source–sink grain production module. Using a series of idealized sensitivity experiments, we show that the model reproduces well-documented patterns of plasticity in carbon allocation: diminishing returns on leaf investment as canopy closure and water limitation are approached, a shift in allocation toward roots under drier conditions, and a root:shoot response that depends jointly on soil moisture and the plant’s existing root:leaf balance. Simulating a full growing season across a gradient of soil moisture levels reveals a threshold-like response in canopy development, biomass accumulation, and grain yield, with yield constrained by the same assimilate supply that governs vegetative growth during the critical period and grain filling. This provides a promising basis for generalizing model behaviour across a wide range of environmental conditions. Evaluating these results against experimental and field trial data remains an important next step.

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.

The integration of a nitrogen cycle represents a recent advancement in Earth system models (ESMs). However, diverse formulations introduce uncertainty in the nitrogen effect on the carbon cycle, leaving the global carbon–nitrogen coupling effect unclear. In this study, we present CNit v1.0, a newly developed carbon–nitrogen cycle model designed for integration with MAGICC (Model for the Assessment of Greenhouse Gas Induced Climate Change), a widely used reduced-complexity model. CNit v1.0 has been calibrated to two land surface models (CABLE and OCN) and (the land component of) a set of Coupled Model Intercomparison Project Phase 6 (CMIP6) ESMs. CNit v1.0 is able to capture the dynamics of the more complex models' carbon–nitrogen cycle at the global-mean, annual scale. The emulation results suggest a consistent nitrogen limitation on net primary production (NPP) in CMIP6 ESMs, persisting throughout the simulations (i.e., over the period 1850–2100) in most models. The emulation provides a way to disentangle diverse nitrogen effects on carbon pool turnovers in CMIP6 ESMs, with our results suggesting that nitrogen deficiency generally inhibits litter production and decomposition while enhancing soil respiration (from a multi-model mean perspective). However, this disentanglement is limited due to a lack of simulations from CMIP6 ESMs which would allow us to fully separate the nitrogen and carbon responses. The results imply a potential reduction in land carbon sequestration in the future due to nitrogen deficiency. Future studies will use CNit to further investigate the carbon–nitrogen coupling effect, including uncertainty, in future climate projections.

Reliable, robust, and consistent data are essential foundations for analysis of carbon cycle feedbacks. Here, we consider the data from multiple Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We identify a mass conservation issue in the reported carbon and nitrogen data, with a few exceptions for specific models and reporting levels. The accumulated mass imbalance in the reported data can amount to hundreds of gigatons of carbon or nitrogen by the end of the simulated period, largely exceeding the total carbon–nitrogen pool size changes over the same period. Nitrogen mass imbalance is evident across all reported organic and inorganic pools, with mineral nitrogen exhibiting the most significant cumulative mass imbalance. Due to a lack of detail in the reported data, we cannot uniquely identify the cause of this imbalance. However, we postulate that the carbon mass imbalance primarily arises from missing fluxes in the reported data and inconsistencies between these data and the definitions provided by the C4MIP protocol (e.g., land-use and fire emissions), rather than from an underlying mass conservation issue in the models themselves. Our findings suggest that future CMIP reporting protocols should consider incorporating mass conservation into their data validation processes so that such issues are caught before users have to deal with them, rather than forcing all users to handle this issue in their own way. In addition, attention from model groups to the detailed diagnostic request and definitions, along with their own quality control, will also help to avoid such issues in future. Given that no additional CMIP6 data are currently being published and none are expected in the future, we recommend that data users that rely on a closed carbon–nitrogen cycle address potential flux imbalances by using the workarounds provided in this study.

Reliable, robust, and consistent data are essential foundations for analysis of carbon cycle feedbacks. Here, we consider the data from multiple Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We identify a mass conservation issue in the reported carbon and nitrogen data, with a few exceptions for specific models and reporting levels. The accumulated mass imbalance in the reported data can amount to hundreds of gigatons of carbon or nitrogen by the end of the simulated period, largely exceeding the total carbon/nitrogen pool size changes over the same period. Nitrogen mass imbalance is evident across all reported organic and inorganic pools, with mineral nitrogen exhibiting the most significant cumulative mass imbalance. Due to a lack of details in the reported data, we cannot uniquely identify the cause of this imbalance. However, we postulate that the carbon cycle imbalance in the reported data primarily stems from missing fluxes in the reported data and the inconsistency between the reported data and the definitions provided by the C4MIP protocol (e.g., land use and fire emissions), rather than from an underlying mass conservation issue in the models themselves. Our findings suggest that future CMIP reporting protocols should consider incorporating mass conservation into their data validation processes so that such issues are caught before users have to deal with them, rather than forcing all users to handle this issue in their own way. In addition, attention from model groups to the detailed diagnostic request and definitions, along with their own quality control will also help to avoid such issues in future. Given that CMIP6 data is no longer being reported, we recommend that data users that rely on a closed carbon/nitrogen cycle address potential flux imbalances by using the workarounds provided in this study.

Reliable, robust, and consistent data are essential foundations for analysis of carbon cycle feedbacks. Here, we consider the data from multiple Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We identify a mass conservation issue in the reported carbon and nitrogen data, with a few exceptions for specific models and reporting levels. The accumulated mass imbalance in the reported data can amount to hundreds of gigatons of carbon or nitrogen by the end of the simulated period, largely exceeding the total carbon/nitrogen pool size changes over the same period. Nitrogen mass imbalance is evident across all reported organic and inorganic pools, with mineral nitrogen exhibiting the most significant cumulative mass imbalance. Due to a lack of details in the reported data, we cannot uniquely identify the cause of this imbalance. However, we postulate that the carbon cycle imbalance in the reported data primarily stems from missing fluxes in the reported data and the inconsistency between the reported data and the definitions provided by the C4MIP protocol (e.g., land use and fire emissions), rather than from an underlying mass conservation issue in the models themselves. Our findings suggest that future CMIP reporting protocols should consider incorporating mass conservation into their data validation processes so that such issues are caught before users have to deal with them, rather than forcing all users to handle this issue in their own way. In addition, attention from model groups to the detailed diagnostic request and definitions, along with their own quality control will also help to avoid such issues in future. Given that CMIP6 data is no longer being reported, we recommend that data users that rely on a closed carbon/nitrogen cycle address potential flux imbalances by using the workarounds provided in this study.

Reliable, robust, and consistent data are essential foundations for analysis of carbon cycle feedbacks. Here, we consider the data from multiple Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We identify a mass conservation issue in the reported carbon and nitrogen data, with a few exceptions for specific models and reporting levels. The accumulated mass imbalance in the reported data can amount to hundreds of gigatons of carbon or nitrogen by the end of the simulated period, largely exceeding the total carbon/nitrogen pool size changes over the same period. Nitrogen mass imbalance is evident across all reported organic and inorganic pools, with mineral nitrogen exhibiting the most significant cumulative mass imbalance. Due to a lack of details in the reported data, we cannot uniquely identify the cause of this imbalance. However, we postulate that the carbon cycle imbalance in the reported data primarily stems from missing fluxes in the reported data and the inconsistency between the reported data and the definitions provided by the C4MIP protocol (e.g., land use and fire emissions), rather than from an underlying mass conservation issue in the models themselves. Our findings suggest that future CMIP reporting protocols should consider incorporating mass conservation into their data validation processes so that such issues are caught before users have to deal with them, rather than forcing all users to handle this issue in their own way. In addition, attention from model groups to the detailed diagnostic request and definitions, along with their own quality control will also help to avoid such issues in future. Given that CMIP6 data is no longer being reported, we recommend that data users that rely on a closed carbon/nitrogen cycle address potential flux imbalances by using the workarounds provided in this study.

The integration of a nitrogen cycle represents a recent advancement in Earth System Models (ESMs). However, diverse formulations and representations introduce uncertainties and inconsistencies in nitrogen effects on the carbon cycle, leaving the global carbon-nitrogen coupling effect unclear. In this study, we present the newly developed carbon-nitrogen coupling in MAGICC, a reduced complexity model (RCM). We have calibrated this coupled carbon-nitrogen cycle to two land surface models (CABLE and OCN) and (the land component of) a set of CMIP6 ESMs. The new carbon-nitrogen coupled model is able to capture the dynamics of the more complex models' carbon-nitrogen cycle at the global-mean, annual scale. The emulation results suggest a consistent nitrogen limitation on net primary production (NPP) in CMIP6 ESMs, persisting throughout the simulations (i.e. over the period 1850–2100) in most models. The emulation may provide a way to disentangle diverse nitrogen effects on carbon pool turnovers in CMIP6 ESMs, with our results suggesting that nitrogen deficiency generally inhibits litter production and decomposition while enhancing soil respiration (from a multi-model mean perspective). However, this disentanglement is limited due to a lack of simulations from CMIP6 ESMs which would allow us to cleanly separate the nitrogen and carbon responses. The results imply a potential reduction in land carbon sequestration in the future due to nitrogen deficiency. Future studies will use the newly developed model to further investigate the carbon-nitrogen coupling effect and its associated uncertainty.

The integration of a nitrogen cycle represents a recent advancement in Earth System Models (ESMs). However, diverse formulations and representations introduce uncertainties and inconsistencies in nitrogen effects on the carbon cycle, leaving the global carbon-nitrogen coupling effect unclear. In this study, we present the newly developed carbon-nitrogen coupling in MAGICC, a reduced complexity model (RCM). We have calibrated this coupled carbon-nitrogen cycle to two land surface models (CABLE and OCN) and (the land component of) a set of CMIP6 ESMs. The new carbon-nitrogen coupled model is able to capture the dynamics of the more complex models' carbon-nitrogen cycle at the global-mean, annual scale. The emulation results suggest a consistent nitrogen limitation on net primary production (NPP) in CMIP6 ESMs, persisting throughout the simulations (i.e. over the period 1850–2100) in most models. The emulation may provide a way to disentangle diverse nitrogen effects on carbon pool turnovers in CMIP6 ESMs, with our results suggesting that nitrogen deficiency generally inhibits litter production and decomposition while enhancing soil respiration (from a multi-model mean perspective). However, this disentanglement is limited due to a lack of simulations from CMIP6 ESMs which would allow us to cleanly separate the nitrogen and carbon responses. The results imply a potential reduction in land carbon sequestration in the future due to nitrogen deficiency. Future studies will use the newly developed model to further investigate the carbon-nitrogen coupling effect and its associated uncertainty.

The integration of a nitrogen cycle represents a recent advancement in Earth System Models (ESMs). However, diverse formulations and representations introduce uncertainties and inconsistencies in nitrogen effects on the carbon cycle, leaving the global carbon-nitrogen coupling effect unclear. In this study, we present the newly developed carbon-nitrogen coupling in MAGICC, a reduced complexity model (RCM). We have calibrated this coupled carbon-nitrogen cycle to two land surface models (CABLE and OCN) and (the land component of) a set of CMIP6 ESMs. The new carbon-nitrogen coupled model is able to capture the dynamics of the more complex models' carbon-nitrogen cycle at the global-mean, annual scale. The emulation results suggest a consistent nitrogen limitation on net primary production (NPP) in CMIP6 ESMs, persisting throughout the simulations (i.e. over the period 1850–2100) in most models. The emulation may provide a way to disentangle diverse nitrogen effects on carbon pool turnovers in CMIP6 ESMs, with our results suggesting that nitrogen deficiency generally inhibits litter production and decomposition while enhancing soil respiration (from a multi-model mean perspective). However, this disentanglement is limited due to a lack of simulations from CMIP6 ESMs which would allow us to cleanly separate the nitrogen and carbon responses. The results imply a potential reduction in land carbon sequestration in the future due to nitrogen deficiency. Future studies will use the newly developed model to further investigate the carbon-nitrogen coupling effect and its associated uncertainty.