Akihiko Ito

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their partitioning among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, supporting 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 (EFOS) are based on energy and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use data. The global atmospheric CO2 growth rate (GATM) is computed from changes in concentration measured at surface stations. The global net uptake of CO2 by the ocean (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND) 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. The sum of all sources and sinks results in the carbon budget imbalance (BIM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2025, EFOS increased by 1.2% relative to 2024, with fossil emissions at 10.5 ± 0.5 GtC yr-1 (including the cement carbonation sink, 0.2 GtC yr-1), ELUC was 1.2 ± 0.7 GtC yr-1, for total anthropogenic CO2 emissions of 11.7 ± 0.9 GtC yr-1 (42.8 ± 3.2 GtCO2 yr-1). Also, for 2025, GATM was 4.4 ± 0.2 GtC yr-1 (2.06 ± 0.1 ppm yr-1), 3.6 GtC lower than the record high 2024 growth rate. SOCEAN was 3.2 ± 0.7 GtC yr-1 and SLAND was 3.6 ± 0.9 GtC yr-1, with a small BIM (0.5 GtC yr-1). The global atmospheric CO2 concentration averaged over 2025 reached 425.6 ± 0.1 ppm. Preliminary data for 2026 suggest a decrease in EFOS relative to 2025 of 0.40% (–1.5% to +0.6%) globally, and atmospheric CO2 concentration increasing by 2.2 ppm reaching 427.7 ppm, 54% 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 1959–2025, with a small positive overall budget imbalance, (0.6 GtC yr-1 averaged over the period 1959–2025), 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 estimates on the magnitude of the land CO2 flux, particularly in the northern extra-tropics, and (3) a discrepancy between the different estimates on the mean ocean sink and its trend. This living data update documents changes in methods and datasets applied to this most-recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2026 (Friedlingstein et al., 2026b).

Fire is a key Earth system process influencing vegetation distribution and the land carbon cycle, yet its representation within dynamic global vegetation (DGVM) models is generally poor, adding to the high level of inter-model uncertainty around simulated burned area (BA), fire emissions, and hence land carbon sink estimates. Here, we address this uncertainty by applying a satellite-based long-term reconstruction of burned area from 1901 to 2020 to five DGVM models, in order to assess the impacts of diagnostic BA on global carbon cycle estimates. By diagnosing BA, we reduced the inter-model standard deviation in global fire emissions by 59 % compared to the prognostic BA simulations, and significantly increased estimate agreement with observations spatially and regionally. We also moderately improved net land carbon sink estimates, reducing bias by 0.3 PgC year^-1 against top-down constraints on average over 2001–2020, however the inter-model spread persisted. Additionally, regional simulated fire combustion rate (fire carbon emissions per unit BA) magnitudes and trends did not align with observations under diagnostic BA, influencing simulated land sink trends. This indicates poor representation of fire dynamics, specifically around the representation of fuel loads and combustion completeness, fire-induced mortality, fire intensity, and vegetation recovery processes. We also explore land sink uncertainty associated with various diagnostic BA reconstructions using the JULES DGVM. By performing two JULES simulations using two reconstructed BA datasets derived from distinct satellite products (FireCCI5.1 and GFED5), we found a substantial difference in the land carbon sink (0.8 PgC year^-1) between the two simulations over 2001–2020. This shows that the underlying uncertainty in BA mapping and reconstructed BA trends can add a large source of uncertainty to modern day land sink estimates. Using a counterfactual simulation, we show that this uncertainty likely stems from differences in reconstructed pre-satellite era burned area dynamics leaving legacy impacts on both fire emission and ecosystem productivity estimates. Our results show that although diagnostic burned area can greatly improve fire emission realism and constrain DGVMs, an enhanced representation of post-fire ecosystem impacts, fire intensity and fuel loads is needed in order to capture regional impacts of fire on the carbon cycle.

Fire is a key Earth system process influencing vegetation distribution and the land carbon cycle, yet its representation within dynamic global vegetation (DGVM) models is generally poor, adding to the high level of inter-model uncertainty around simulated burned area (BA), fire emissions, and hence land carbon sink estimates. Here, we address this uncertainty by applying a satellite-based long-term reconstruction of burned area from 1901 to 2020 to five DGVM models, in order to assess the impacts of diagnostic BA on global carbon cycle estimates. By diagnosing BA, we reduced the inter-model standard deviation in global fire emissions by 59 % compared to the prognostic BA simulations, and significantly increased estimate agreement with observations spatially and regionally. We also moderately improved net land carbon sink estimates, reducing bias by 0.3 PgC year^-1 against top-down constraints on average over 2001–2020, however the inter-model spread persisted. Additionally, regional simulated fire combustion rate (fire carbon emissions per unit BA) magnitudes and trends did not align with observations under diagnostic BA, influencing simulated land sink trends. This indicates poor representation of fire dynamics, specifically around the representation of fuel loads and combustion completeness, fire-induced mortality, fire intensity, and vegetation recovery processes. We also explore land sink uncertainty associated with various diagnostic BA reconstructions using the JULES DGVM. By performing two JULES simulations using two reconstructed BA datasets derived from distinct satellite products (FireCCI5.1 and GFED5), we found a substantial difference in the land carbon sink (0.8 PgC year^-1) between the two simulations over 2001–2020. This shows that the underlying uncertainty in BA mapping and reconstructed BA trends can add a large source of uncertainty to modern day land sink estimates. Using a counterfactual simulation, we show that this uncertainty likely stems from differences in reconstructed pre-satellite era burned area dynamics leaving legacy impacts on both fire emission and ecosystem productivity estimates. Our results show that although diagnostic burned area can greatly improve fire emission realism and constrain DGVMs, an enhanced representation of post-fire ecosystem impacts, fire intensity and fuel loads is needed in order to capture regional impacts of fire on the carbon cycle.

Peatlands cover only ~3 % of Earth’s land surface yet store ~30 % of global soil carbon (C), making them critical components of the terrestrial C cycle and influential regulators of C-climate feedbacks. However, their responses to climate warming and elevated CO₂ remain highly uncertain, in part because Earth system models represent peatland processes with varying levels of complexity and realism, and because peat C pools turn over on centennial to millennial timescales that challenge model evaluation. Here, we present results from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) Model Intercomparison Project (SPRUCE-MIP), which evaluates 15 terrestrial ecosystem models against observations from a long-term whole-ecosystem warming and CO₂-enrichment experiment at an ombrotrophic bog in northern Minnesota, USA. Models were driven by observed meteorology from in situ warming treatments spanning +0 to +9 °C, at either ambient or elevated CO₂ (+500 ppm). Simulated net ecosystem exchange (NEE), net primary productivity (NPP), heterotrophic respiration (HR), and methane (CH₄) fluxes were benchmarked against multi-year observations. Model predictions exhibit a large spread in both baseline C balance, ranging from strong sinks to strong sources under +0 °C warming, and temperature sensitivity, indicating substantial uncertainty in predicting peatland C responses to environmental forcing. Across the ensemble, models fall into four distinct functional response types: (i) models that simulate peatlands as net C sources across all warming and CO₂ conditions; (ii) models that transition from C sinks to sources under warming irrespective of CO₂ level; (iii) models that transition only under ambient CO₂ but remain C sinks under elevated CO₂, reflecting strong CO₂ fertilization effects; and (iv) models that maintain persistent sinks or near-neutral C balance even under extreme warming. While most models predict enhanced NPP under elevated CO₂ across all warming treatments, SPRUCE observations show little or no NPP enhancement under elevated CO₂ at the +0 and +2.25 °C warming levels, with a positive CO₂ fertilization effect emerging only under stronger warming. This discrepancy highlights persistent model biases in representing interactions between warming and CO₂ responses. Process-based analysis further indicates that divergence in modeled responses is associated with differences in vegetation structure (light competition and moss representation), nutrient cycling (nitrogen and phosphorus), and dynamic peat representation. These structural differences systematically influence ecosystem productivity, heterotrophic respiration, and net C balance across the model ensemble. An illustrative parameter sensitivity analysis using the SPRUCE-specific Energy Exascale Earth System Model (E3SM) Land Model (ELM-SPRUCE) further shows that parameter choices can also modulate the magnitude of simulated responses. Together, these results demonstrate that peatland responses to warming and elevated CO₂ are highly sensitive to model structure, parameterization, and process coupling, highlighting the need for improved representation of key peatland processes to reduce uncertainty in Earth system projections. The SPRUCE experimental framework provides a unique benchmark for evaluation and improving process representation and constraining near-term peatland response to environmental change, thereby strengthening confidence in longer-term C-climate projections.

Fire is a key Earth system process influencing vegetation distribution and the land carbon cycle, yet its representation within dynamic global vegetation (DGVM) models is generally poor, adding to the high level of inter-model uncertainty around simulated burned area (BA), fire emissions, and hence land carbon sink estimates. Here, we address this uncertainty by applying a satellite-based long-term reconstruction of burned area from 1901 to 2020 to five DGVM models, in order to assess the impacts of diagnostic BA on global carbon cycle estimates. By diagnosing BA, we reduced the inter-model standard deviation in global fire emissions by 59 % compared to the prognostic BA simulations, and significantly increased estimate agreement with observations spatially and regionally. We also moderately improved net land carbon sink estimates, reducing bias by 0.3 PgC year^-1 against top-down constraints on average over 2001–2020, however the inter-model spread persisted. Additionally, regional simulated fire combustion rate (fire carbon emissions per unit BA) magnitudes and trends did not align with observations under diagnostic BA, influencing simulated land sink trends. This indicates poor representation of fire dynamics, specifically around the representation of fuel loads and combustion completeness, fire-induced mortality, fire intensity, and vegetation recovery processes. We also explore land sink uncertainty associated with various diagnostic BA reconstructions using the JULES DGVM. By performing two JULES simulations using two reconstructed BA datasets derived from distinct satellite products (FireCCI5.1 and GFED5), we found a substantial difference in the land carbon sink (0.8 PgC year^-1) between the two simulations over 2001–2020. This shows that the underlying uncertainty in BA mapping and reconstructed BA trends can add a large source of uncertainty to modern day land sink estimates. Using a counterfactual simulation, we show that this uncertainty likely stems from differences in reconstructed pre-satellite era burned area dynamics leaving legacy impacts on both fire emission and ecosystem productivity estimates. Our results show that although diagnostic burned area can greatly improve fire emission realism and constrain DGVMs, an enhanced representation of post-fire ecosystem impacts, fire intensity and fuel loads is needed in order to capture regional impacts of fire on the carbon cycle.

Peatlands cover only ~3 % of Earth’s land surface yet store ~30 % of global soil carbon (C), making them critical components of the terrestrial C cycle and influential regulators of C-climate feedbacks. However, their responses to climate warming and elevated CO₂ remain highly uncertain, in part because Earth system models represent peatland processes with varying levels of complexity and realism, and because peat C pools turn over on centennial to millennial timescales that challenge model evaluation. Here, we present results from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) Model Intercomparison Project (SPRUCE-MIP), which evaluates 15 terrestrial ecosystem models against observations from a long-term whole-ecosystem warming and CO₂-enrichment experiment at an ombrotrophic bog in northern Minnesota, USA. Models were driven by observed meteorology from in situ warming treatments spanning +0 to +9 °C, at either ambient or elevated CO₂ (+500 ppm). Simulated net ecosystem exchange (NEE), net primary productivity (NPP), heterotrophic respiration (HR), and methane (CH₄) fluxes were benchmarked against multi-year observations. Model predictions exhibit a large spread in both baseline C balance, ranging from strong sinks to strong sources under +0 °C warming, and temperature sensitivity, indicating substantial uncertainty in predicting peatland C responses to environmental forcing. Across the ensemble, models fall into four distinct functional response types: (i) models that simulate peatlands as net C sources across all warming and CO₂ conditions; (ii) models that transition from C sinks to sources under warming irrespective of CO₂ level; (iii) models that transition only under ambient CO₂ but remain C sinks under elevated CO₂, reflecting strong CO₂ fertilization effects; and (iv) models that maintain persistent sinks or near-neutral C balance even under extreme warming. While most models predict enhanced NPP under elevated CO₂ across all warming treatments, SPRUCE observations show little or no NPP enhancement under elevated CO₂ at the +0 and +2.25 °C warming levels, with a positive CO₂ fertilization effect emerging only under stronger warming. This discrepancy highlights persistent model biases in representing interactions between warming and CO₂ responses. Process-based analysis further indicates that divergence in modeled responses is associated with differences in vegetation structure (light competition and moss representation), nutrient cycling (nitrogen and phosphorus), and dynamic peat representation. These structural differences systematically influence ecosystem productivity, heterotrophic respiration, and net C balance across the model ensemble. An illustrative parameter sensitivity analysis using the SPRUCE-specific Energy Exascale Earth System Model (E3SM) Land Model (ELM-SPRUCE) further shows that parameter choices can also modulate the magnitude of simulated responses. Together, these results demonstrate that peatland responses to warming and elevated CO₂ are highly sensitive to model structure, parameterization, and process coupling, highlighting the need for improved representation of key peatland processes to reduce uncertainty in Earth system projections. The SPRUCE experimental framework provides a unique benchmark for evaluation and improving process representation and constraining near-term peatland response to environmental change, thereby strengthening confidence in longer-term C-climate projections.

As a key component of the climate system, terrestrial carbon dynamics are commonly simulated using Dynamic Global Vegetation Models (DGVMs), which provide essential information for estimating the global carbon budget. However, substantial uncertainties persist in DGVM‐based estimates of land‐use change carbon emissions, and their process‐level origins remain poorly constrained. Here, we harmonize DGVM‐based net land‐use emissions (E _NLUC ) from 23 TRENDY2025 DGVMs using a transient, time‐varying PFT‐level land‐cover correction that explicitly removes the loss of additional sink capacity (LASC), and apply a hierarchical Bayesian mixed‐effects variance decomposition framework to characterize between‐DGVM heterogeneity in fitted coefficients associated with land‐use predictors. LASC correction reveals that uncorrected DGVMs artificially converge toward bookkeeping estimates, masking persistent structural deficiencies—most notably the limited representation of wood harvest, forest management, and other peat‐related activities incorporated in bookkeeping models. Between‐DGVM heterogeneity in fitted coefficients is greater for persistent regrowth area than for current deforestation, particularly at decadal‐to‐centennial timescales and in the tropics, where the fitted association with persistent regrowth area varies substantially among models. This uncertainty can propagate into residual land sink estimates, complicating the diagnosis of land–atmosphere carbon exchange. Our results highlight that improving the representation of secondary forest recovery, including sub‐grid land‐use processes and forest management, is critical for reducing uncertainties in land‐use and land‐use change emissions in next‐generation DGVMs.

Fire is a key Earth system process influencing vegetation distribution and the land carbon cycle, yet its representation within dynamic global vegetation (DGVM) models is generally poor, adding to the high level of inter-model uncertainty around simulated burned area (BA), fire emissions, and hence land carbon sink estimates. Here, we address this uncertainty by applying a satellite-based long-term reconstruction of burned area from 1901 to 2020 to five DGVM models, in order to assess the impacts of diagnostic BA on global carbon cycle estimates. By diagnosing BA, we reduced the inter-model standard deviation in global fire emissions by 59 % compared to the prognostic BA simulations, and significantly increased estimate agreement with observations spatially and regionally. We also moderately improved net land carbon sink estimates, reducing bias by 0.3 PgC year^-1 against top-down constraints on average over 2001–2020, however the inter-model spread persisted. Additionally, regional simulated fire combustion rate (fire carbon emissions per unit BA) magnitudes and trends did not align with observations under diagnostic BA, influencing simulated land sink trends. This indicates poor representation of fire dynamics, specifically around the representation of fuel loads and combustion completeness, fire-induced mortality, fire intensity, and vegetation recovery processes. We also explore land sink uncertainty associated with various diagnostic BA reconstructions using the JULES DGVM. By performing two JULES simulations using two reconstructed BA datasets derived from distinct satellite products (FireCCI5.1 and GFED5), we found a substantial difference in the land carbon sink (0.8 PgC year^-1) between the two simulations over 2001–2020. This shows that the underlying uncertainty in BA mapping and reconstructed BA trends can add a large source of uncertainty to modern day land sink estimates. Using a counterfactual simulation, we show that this uncertainty likely stems from differences in reconstructed pre-satellite era burned area dynamics leaving legacy impacts on both fire emission and ecosystem productivity estimates. Our results show that although diagnostic burned area can greatly improve fire emission realism and constrain DGVMs, an enhanced representation of post-fire ecosystem impacts, fire intensity and fuel loads is needed in order to capture regional impacts of fire on the carbon cycle.