Abstract The assimilation of carbon dioxide by plants can be predicted by the Farquhar, von Caemmerer and Berry model of photosynthesis. This largely mechanistic model is central to understanding how plants influence Earth’s climate. However, it represents the use of light by photosynthesis using an empirical formulation. Johnson and Berry proposed an alternative mechanistic formulation based on the functioning of the cytochrome b6f complex that includes key steps in light harvesting and electron transport. We compared both formulations using photosynthetic light response measurements from 146 C3 species spanning arctic to tropical biomes and implemented them in the terrestrial biosphere model ELM-FATES to simulate global photosynthesis. The Johnson and Berry formulation better fitted the measured response of leaf-level photosynthesis to light, and predicted lower photosynthetic rates at intermediate light levels, which decreased global estimations of terrestrial photosynthesis by 8%. Our findings support adopting the Johnson and Berry formulation to improve model representation of global carbon cycle modeling.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesise datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E-FOS) are based on energy and cement production data. Emissions from land-use change (E-LUC) are estimated by bookkeeping models based on land-use data. The global atmospheric CO2 growth rate (G(ATM)) is computed from changes in concentration measured at surface stations. The global net uptake of CO2 by the ocean (S-OCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO(2)-products. The global net uptake of CO2 by the land (S-LAND) is estimated with dynamic global vegetation models. Additional lines of evidence are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. This year, we introduced corrections on the E-LUC, S-OCEAN and S-LAND estimates. The sum of all sources and sinks results in the carbon budget imbalance (B-IM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the year 2024, E-FOS increased by 1.1 % relative to 2023, with fossil emissions at 10.3 +/- 0.5 GtC yr(-1) (including the cement carbonation sink, 0.2 GtC yr(-1)), E-LUC was 1.3 +/- 0.7 GtC yr(-1), for total anthropogenic CO2 emissions of 11.6 +/- 0.9 GtC yr(-1) (42.4 +/- 3.2 GtCO(2) yr(-1)). Also, for 2024, G(ATM) was 7.9 +/- 0.2 GtC yr(-1) (3.73 +/- 0.1 ppm yr(-1)), 2.2 GtC above the 2023 growth rate. S-OCEAN was 3.4 +/- 0.4 GtC yr(-1) and S-LAND was 1.9 +/- 1.1 GtC yr(-1), leaving a large negative B-IM (-1.7 GtC yr(-1)), suggesting that the total sink or G(ATM) is strongly overestimated in 2024. The global atmospheric CO2 concentration averaged over 2024 reached 422.8 +/- 0.1 ppm. Preliminary data for 2025 suggest an increase in E-FOS relative to 2024 of +1.0 % (0.2 % to 1.7 %) globally, and atmospheric CO2 concentration increasing by 2.1 ppm reaching 425.6 ppm, 53 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2024, with a near-zero overall budget imbalance, although discrepancies of up to around 1 GtC yr(-1) persist for the representation of annual to decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink.

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico's National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI's reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (>= 200 Mg/ ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESACCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCIBIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

Natural disturbances are major drivers of large-scale forest dynamics. However, the representation of forest disturbances remains oversimplified and poorly constrained in terrestrial biosphere models, compromising predictive performance under rapidly changing disturbance regimes. In this review, we first summarize the general mechanisms underlying vegetation responses to major disturbance agents across spatio-temporal scales, including occurrence regimes, immediate structural impacts, post-disturbance community reorganization, and long-term ecosystem feedbacks. Predictable patterns of the processes provide the ecological foundation of disturbance modeling. Subsequently, we synthesize progress and challenges toward more mechanistic disturbance modeling, organized by three generic modules: occurrence, impact, and post-disturbance dynamics. Future model developments should implement dynamic disturbance regimes, model lethal and nonlethal structural damage with trait-based hazard functions, and represent key ecological processes determining post-disturbance ecosystem dynamics - including different regeneration strategies, trait plasticity in response to rapid microenvironmental changes, and disturbance legacy effects. Finally, we discuss observational constraints on mechanistic disturbance models. Model parameterization and development can benefit from advancing disturbance detection and attribution via multi-modal and multi-platform remote sensing, ground measurements, and disturbance manipulation experiments. Altogether, mechanistic, scalable, and observation-constrained simulation of disturbance is essential for reducing uncertainties in forecasting global change impacts on ecosystems and carbon cycle feedbacks.

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific "opportunities" describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.

Abstract. Prescribed fire and mechanical thinning are common fuel management and forest restoration practices in fire-prone ecosystems, particularly where long-term fire suppression has led to significant fuel accumulation and altered stand structure. Existing field evidence shows varied outcomes depending on environmental and stand conditions, with long-term, landscape-scale management effects remaining poorly understood. The majority of large-scale models that project long-term effects lack mechanistic management processes and are rarely validated for the ecological processes driving outcomes. To address this, we implemented a new prescribed fire model and adapted a pre-existing wood harvest model to represent restoration thinning in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model. We developed a model validation framework emphasizing the key ecological mediators and expected feedback between management, vegetation, and fire. Running simulations with varying weather conditions under which prescribed fire can occur (burn window) and with the forest thinning model across three sites in California’s mixed conifer forests, we found that FATES successfully captured the observed relative changes in stand structure, tree size distribution, fuel load, and demographic rates following forest health treatments. Additionally, model results aligned with general ecological expectations and reproduced the key differences between thinning and prescribed fire: logging immediately and significantly reduces stem density and basal area but not surface fuel load, while prescribed fire results in nonsignificant to moderate reduction in stem density and basal area but an immediate decrease in surface fuel load. Notably, simulations with a wide, inclusive burn window resulted in cooler fires and caused less change in stem density and basal area than those with a narrow, exclusive burn window, but only at the warmest and driest modeled study site. Our work advances the integration of management activities into Earth system models by introducing a mechanistic, interactive prescribed fire model, and a validation framework grounded in empirical data to assist model evaluations and applications. The model provides a promising tool for projecting the complex climate-vegetation-fire-human interactions in dry conifer forests of the Western U.S. and other fire-adapted ecosystems with similar management history.

The concept of a remaining carbon budget associated with global warming levels has underpinned mitigation efforts since the Paris Agreement. However, as observed temperatures near 1.5 degrees, a number of challenges have emerged for the continued use of carbon budgets to frame mitigation needs. Firstly, while the transient response to cumulative emissions describes the temperature response to constant emissions - Paris-compatible pathways require deep emissions cuts and potentially extended periods of negative emissions, the temperature outcome of which is complicated by zero emissions commitments and non-CO2 responses. Understanding of Zero emissions commitments has been thus far been primarily informed by idealised experiments which terminate emissions during an idealised concentration ramp - but these metrics are subject to unrealistic termination shocks and model-specific emissions pathways. Second, non-CO2 responses remain highly uncertain, and recent satellite observations of global radiative imbalance raise further questions on the adequacy of current modeling platforms to describe the warming which should be expected due to aerosol phaseout. Here, we consider how two novel developments impact carbon budgets beyond estimates presented in the IPCC 6th Assessment. Firstly, we present an ESM ensemble of climate reversibility experiments which provides a more realistic proxy for non-TCRE carbon dynamics during a net zero transition for use in carbon budgets. Secondly, we consider how the inclusion of recent global mean temperature measurements and CERES top of atmosphere radiative flux measurements would impacts the calibration of simple climate models - with subsequent impacts on both estimates of both TCRE and expected warming due to non-CO2 effects. Synthesising this information, we provide an updated estimate of carbon budgets and timing with respect to the 1.5 and 2 degree thresholds.

Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land-use changes, and are essential for projecting carbon cycling and large-scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03-0.60, 0.18-0.70 and 0.35-1.10 kgCm-2 yr-1 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi-model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.

Idealized experiments with coupled climate-carbon Earth system models (ESMs) provide a basis for understanding the response of the carbon cycle to external forcing and for quantifying climate-carbon feedbacks. Here, we analyze globally-averaged results from idealized esm-flat10 experiments and show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean during a period of constant fossil fuel emissions of 10 Pg C yr−1. We hypothesize that this relationship does not depend on emission pathways. Further, as a simplification, we quantify the relationship between cumulative ocean carbon uptake and changes in ocean heat content using a linear approximation. In this way, changes in oceanic heat content and atmospheric CO2 concentration become interdependent variables, reducing the coupled temperature-CO2 system to just one differential equation. The equation can be solved analytically or numerically for the atmospheric CO2 concentration as a function of fossil fuel emissions. This approach leads to a simplified description of global carbon and climate dynamics, which could be used for applications beyond existing analytical frameworks.

The relationship between carbon dioxide emissions and their accumulation in the atmosphere is one of the most important elements of the function of the Earth system. Exertion of control over the terrestrial carbon budget, via afforestation, reforestation, bioenergy production and other methods to enhance land carbon storage (biochar, enhanced weathering) all imply a need to forecast and understand the dynamics of these carbon stores as they evolve in changing atmospheric CO2 and climatic conditions. The dynamics of the carbon cycle, however, are notably complex and require comprehension of models representing the functioning of numerous coupled systems which must produce predictions under these no-analog conditions, and so must necessarily embed process understanding to allow for meaningful extrapolation into the future. Models of the terrestrial biosphere, often embedded in Earth system models, thus contain advanced representations of a large set of processes that are known to impact ecosystem carbon storage. This complexity, however, has presented a substantial barrier to objective calibration using conventional statistical approaches, as the number of model parameters and the computational expense of the models means that comprehensive exploration of the parameter space is effectively unmanageable. Further, many ecosystem processes exhibit non-linear and threshold properties (notably, vegetation death, competitive interactions, fire thresholds) and thus are challenging for methods that assume linearity. Here we propose a method for decomposing the complexity of one such model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) that allows investigation, calibration and comprehension of individual parts of the system in isolation (driven by observed data fields). This ‘modular complexity’ approach allows the full complexity model to be run in a series of ‘modes’ that can operate as domain-specific models for, e.g. ecohydrology, community ecology, biogeochemistry etc. while also allowing the full complexity version to be used for higher order problems, such a predicting global vegetation dynamics under future climate scenarios. We describe a series of investigations using FATES that illustrate the potential for this model decomposition approach and discuss the potential for further application of this philosophy.

Despite its importance for determining global carbon fluxes, leaf respiration remains poorly constrained in land surface models (LSMs). We tested the sensitivity of the Energy Exascale Earth System Model Land Model-Functionally Assembled Terrestrial Ecosystem Simulator (ELM-FATES) to variation in the canopy gradients of leaf maintenance respiration (R-dark). We ran global and point simulations varying the canopy gradient of Rdark to explore the impacts on forest structure, composition, and carbon cycling. In global simulations, steeper canopy gradients of R-dark lead to increased understory survival and leaf biomass. Leaf area index (LAI) increased up to 77% in tropical regions compared with the default parameterization, improving alignment with remotely sensed benchmarks. Global vegetation carbon varied from 308 Pg C to 449 Pg C across the ensemble. In tropical forest simulations, steeper gradients of R-dark had a large impact on successional dynamics. Results show the importance of canopy gradients in leaf traits and fluxes for determining plant carbon budgets and emergent ecosystem properties such as competitive dynamics, LAI, and vegetation carbon. The high-model sensitivity to canopy gradients in R-dark highlights the need for more observations of how leaf traits and fluxes vary along light micro-environments to inform critical dynamics in LSMs.

The proportionality between global mean temperature and cumulative emissions of CO2 predicted in Earth system models (ESMs) is the foundation of carbon budgeting frameworks. Deviations from this behavior could impact estimates of required net-zero timings and negative emissions requirements to meet the Paris Agreement climate targets. However, existing ESM diagnostic experiments do not allow for direct estimation of these deviations as a function of defined emissions pathways. Here, we perform a set of climate model diagnostic experiments for the assessment of transient climate response to cumulative CO2 emissions (TCRE), the Zero Emissions Commitment (ZEC), and climate reversibility metrics in an emissions-driven framework. The emissions-driven experiments provide consistent independent variables simplifying simulation, analysis and interpretation, with emissions rates more comparable to recent levels than existing protocols using model-specific compatible emissions from the CMIP DECK 1pctCO2 experiment, where emissions rates tend to increase during the experiment, such that at the time of CO2 doubling in year 70, emissions are much greater than present-day values. A base experiment, "esm-flat10", has constant emissions of CO2 of 10 GtC per year (near-present-day values), and initial results show that the TCRE estimated in this experiment is about 0.1 K less than that obtained using 1pctCO2. A subset of ESMs exhibit land carbon sinks that saturate during this experiment. A branch experiment, esm-flat10-zec, illustrates that both positive and negative ZEC effects are less pronounced under esm-flat10 than under 1pctCO2 - the magnitude of ZEC50 in ESMs is, on average, reduced by 30 % compared with 1pctCO2 branch experiments. A final experiment, esm-flat10-cdr, assesses climate reversibility under negative emissions, where we find that peak warming may occur before or after net zero and that the asymmetry in temperature at a given level of cumulative emissions between the positive and negative emissions phases is well described by ZEC in most models. Further, we find that existing probabilistic simple climate model (SCM) ensembles tend to overestimate temperature reversibility compared with ESMs, highlighting the need for additional constraints. We propose a set of climate diagnostic indicators to quantify various aspects of climate reversibility. These experiments were suggested as potential candidates in CMIP7 and have since been adopted as "fast track" simulations.

We review how ‘abrupt thaw’ has been used in published studies, compare these definitions to abrupt processes in other Earth science disciplines, and provide a definitive framework for how abrupt thaw should be used in the context of permafrost science. We address several aspects of permafrost systems necessary for abrupt thaw to occur and propose a framework for classifying permafrost processes as abrupt thaw in the future. Based on a literature review and our collective expertise, we propose that abrupt thaw refers to thaw processes that lead to a substantial persistent environmental change within a few decades. Abrupt thaw typically occurs in ice-rich permafrost but may be initiated in ice-poor permafrost by external factors such as hydrologic change (i.e., increased streamflow, soil moisture fluctuations, altered groundwater recharge) or wildfire. Permafrost thaw alters greenhouse gas emissions, soil and vegetation properties, and hydrologic flow, threatening infrastructure and the cultures and livelihoods of northern communities. The term ‘abrupt thaw’ has emerged in scientific discourse over the past two decades to differentiate processes that rapidly impact large depths of permafrost, such as thermokarst, from more gradual, top-down thaw processes that impact centimeters of near-surface permafrost over years to decades. However, there has been no formal definition for abrupt thaw and its use in the scientific literature has varied considerably. Our standardized definition of abrupt thaw offers a path forward to better understand drivers and patterns of abrupt thaw and its consequences for global greenhouse gas budgets, impacts to infrastructure and land-use, and Arctic policy- and decision-making.

Global continual logging activities have modified the regional landscape and disrupted the energy balance through changing the surface albedo, evapotranspiration and roughness length in both current location (local) and nearby areas through feedback to the atmospheric circulation (non-local). Compared to land use change, less attention has been given to understanding the local biogeophysical effect of the different land use management practices, e.g., wood harvest (logging). Uncertainties from the reconstructed global wood harvest rate forcing data, simplified land heterogeneity and processes representation in the classic big-leaf models largely changed the outcomes. We apply a next generation dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator, FATES), coupled with the land component (ELM) of DOE’s earth system model E3SM to study the local biogeophysical effect and the redistribution of energy after accounting the continuous logging activity on a global scale. In order to account for the uncertainties from forcing data and modeling approaches, we designed 9 parallel experiments with 4 different sets of global wood harvest rates derived from LUH2 reconstructed historical harvest rates combined with 2 different wood harvest methods: area-based harvest and carbon-based harvest. The results highlighted a divergent pattern of the local biogeophysical impact from logging under two dominant stages: regrowth dominant and logging dominant. We found the continuous logging causing up to 5% of the reduction of global canopy coverage and 2% of the increase of albedo. The study also highlighted the uncertainty from the forcing of data sources and modeling approach can lead to a several times difference in the magnitude of local biogeophysical effect.

Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model version 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2,000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive data set, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and CO2 removal. While various CO2 removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable and sustainable approaches to achieve climate targets. Here we show that preserving woody debris in managed forests can remove gigatonnes of CO2 from the atmosphere sustainably based on a carbon cycle analysis using three Earth system models. Woody debris is produced from logging, sawmill wastes and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving annual woody debris production in managed forests has the capacity to remove 769–937 GtCO2 from the atmosphere cumulatively (10.1–12.4 GtCO2 yr−1 on average) from 2025 to 2100, if its residence time is lengthened for 100–2,000 years and after 5

Observations show an increase in the seasonal cycle amplitude of CO2 in northern latitudes over the past half century. Although multiple drivers contribute, observations and inversion models cannot quantitatively account for the factors contributing to the increased CO2 amplitude and older versions of Earth System Models (ESMs) do not simulate it. Here we show that several current generation ESMs are closer to the observed CO2 amplitude and highlight that in the Community Earth System Model (CESM) agricultural nitrogen (N) fertilization increases CO2 amplitude by 1-3 ppm throughout the Northern Hemisphere and up to 9 ppm in agricultural hotspots. While agricultural N fertilization is the largest contributor to the enhanced amplitude (45%) in Northern Hemisphere land-atmosphere carbon fluxes in CESM, higher CO2 concentrations and warmer temperatures also contribute, though to a lesser extent (40% and 18% respectively). Our results emphasize the fundamental role of agricultural management in Northern Hemisphere carbon cycle feedbacks and illustrate that agricultural N fertilization should be considered in future carbon cycle simulations.