Thomas Luke Smallman

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).

The terrestrial carbon (C) cycle is a key global biogeochemical cycle, linked to climate change and regulation, provisioning and regulating ecosystem services and underpinning human livelihoods. To understand climate risks and to support the development of effective climate mitigation strategies requires rigorous, uncertainty bounded, systemic information on C dynamics and their underpinning ecological processes. Significant effort has been invested into attempting to quantify current C stocks and exchanges of C. These data are often incomplete in space and time, contain poorly characterised errors, and lack internal consistency checks, making the construction of a reliable global C budget a significant challenge. Here, we ask what information on contemporary terrestrial carbon dynamics can an integration of current geospatial data confidently provide? We present a global (0.5 × 0.5 deg), multi-decadal (2003–2024) analysis of the terrestrial C-cycle at a monthly time step. Using a Bayesian model-data fusion framework (CARDAMOM) we calibrate a model of terrestrial ecosystems (DALEC) integrating diverse ecological spatio-temporal observations (leaf area, absorbed photosynthetically active radiation, gross primary production, woody biomass, soil C), and forcing (meteorology, atmospheric CO₂, burned area and forest loss). CARDAMOM propagates observational uncertainties through to the retrieved DALEC parameters (i.e. ecosystem properties) and simulates C states and fluxes for each of 55,246 pixels across the vegetated land surface. The resulting analysis is systemic (i.e. consistent between states and fluxes) and can support national and international policy development (e.g., Global Carbon Project). Our analysis shows that current global multi-decadal datasets are inadequate to provide > 95 % confidence on the sign of net exchange across 91 % of the vegetated land surface. At this confidence level we can identify 0.23 % of vegetated land is a net source, with 0.75 % a net sink and 7.3 % as neutral. Although we note that at 68 % confidence we the area identified as likely a sink increases to 30 % without change to the area identified as neutral or a source. We show that uncertainty in net C exchange is strongly correlated with the dynamics of wood (r = 0.8) and soil organic matter (r = 0.7) but poorly correlated with gross fluxes which have historically been a focus of research such as photosynthesis (r = 0.1). New satellite missions such as BIOMASS have more accurate wood biomass sensing, and so should enhance confidence in sink/source detection in the next five years. Soil C dynamics data remain a major challenge, with research and investment required to provide a global constraint on soil C stock changes.

The terrestrial carbon (C) cycle is a key global biogeochemical cycle, linked to climate change and regulation, provisioning and regulating ecosystem services and underpinning human livelihoods. To understand climate risks and to support the development of effective climate mitigation strategies requires rigorous, uncertainty bounded, systemic information on C dynamics and their underpinning ecological processes. Significant effort has been invested into attempting to quantify current C stocks and exchanges of C. These data are often incomplete in space and time, contain poorly characterised errors, and lack internal consistency checks, making the construction of a reliable global C budget a significant challenge. Here, we ask what information on contemporary terrestrial carbon dynamics can an integration of current geospatial data confidently provide? We present a global (0.5 × 0.5 deg), multi-decadal (2003–2024) analysis of the terrestrial C-cycle at a monthly time step. Using a Bayesian model-data fusion framework (CARDAMOM) we calibrate a model of terrestrial ecosystems (DALEC) integrating diverse ecological spatio-temporal observations (leaf area, absorbed photosynthetically active radiation, gross primary production, woody biomass, soil C), and forcing (meteorology, atmospheric CO₂, burned area and forest loss). CARDAMOM propagates observational uncertainties through to the retrieved DALEC parameters (i.e. ecosystem properties) and simulates C states and fluxes for each of 55,246 pixels across the vegetated land surface. The resulting analysis is systemic (i.e. consistent between states and fluxes) and can support national and international policy development (e.g., Global Carbon Project). Our analysis shows that current global multi-decadal datasets are inadequate to provide > 95 % confidence on the sign of net exchange across 91 % of the vegetated land surface. At this confidence level we can identify 0.23 % of vegetated land is a net source, with 0.75 % a net sink and 7.3 % as neutral. Although we note that at 68 % confidence we the area identified as likely a sink increases to 30 % without change to the area identified as neutral or a source. We show that uncertainty in net C exchange is strongly correlated with the dynamics of wood (r = 0.8) and soil organic matter (r = 0.7) but poorly correlated with gross fluxes which have historically been a focus of research such as photosynthesis (r = 0.1). New satellite missions such as BIOMASS have more accurate wood biomass sensing, and so should enhance confidence in sink/source detection in the next five years. Soil C dynamics data remain a major challenge, with research and investment required to provide a global constraint on soil C stock changes.

The terrestrial carbon (C) cycle is a key global biogeochemical cycle, linked to climate change and regulation, provisioning and regulating ecosystem services and underpinning human livelihoods. To understand climate risks and to support the development of effective climate mitigation strategies requires rigorous, uncertainty bounded, systemic information on C dynamics and their underpinning ecological processes. Significant effort has been invested into attempting to quantify current C stocks and exchanges of C. These data are often incomplete in space and time, contain poorly characterised errors, and lack internal consistency checks, making the construction of a reliable global C budget a significant challenge. Here, we ask what information on contemporary terrestrial carbon dynamics can an integration of current geospatial data confidently provide? We present a global (0.5 × 0.5 deg), multi-decadal (2003–2024) analysis of the terrestrial C-cycle at a monthly time step. Using a Bayesian model-data fusion framework (CARDAMOM) we calibrate a model of terrestrial ecosystems (DALEC) integrating diverse ecological spatio-temporal observations (leaf area, absorbed photosynthetically active radiation, gross primary production, woody biomass, soil C), and forcing (meteorology, atmospheric CO2, burned area and forest loss). CARDAMOM propagates observational uncertainties through to the retrieved DALEC parameters (i.e. ecosystem properties) and simulates C states and fluxes for each of 55,246 pixels across the vegetated land surface. The resulting analysis is systemic (i.e. consistent between states and fluxes) and can support national and international policy development (e.g., Global Carbon Project). Our analysis shows that current global multi-decadal datasets are inadequate to provide > 95 % confidence on the sign of net exchange across 91 % of the vegetated land surface. At this confidence level we can identify 0.23 % of vegetated land is a net source, with 0.75 % a net sink and 7.3 % as neutral. Although we note that at 68 % confidence we the area identified as likely a sink increases to 30 % without change to the area identified as neutral or a source. We show that uncertainty in net C exchange is strongly correlated with the dynamics of wood (r = 0.8) and soil organic matter (r = 0.7) but poorly correlated with gross fluxes which have historically been a focus of research such as photosynthesis (r = 0.1). New satellite missions such as BIOMASS have more accurate wood biomass sensing, and so should enhance confidence in sink/source detection in the next five years. Soil C dynamics data remain a major challenge, with research and investment required to provide a global constraint on soil C stock changes.

Under historical warming, terrestrial ecosystems within the northern high latitudes have been a net carbon sink, providing vital mitigation against anthropogenic emissions of CO2. However, the long-term stability of this net sink is uncertain due to complex carbon cycle feedbacks in response to future climate change. Here, the PRIME framework is used to probabilistically quantify if and when this region will transition from a net carbon sink to a carbon source in a range of plausible future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), including overshoot (SSP5-3.4-OS). JULES – the land surface model component of PRIME, has the capability to explicitly simulate permafrost physics, dynamic vegetation and fire; key processes within northern high-latitude terrestrial ecosystems that are yet to be coupled together in Earth system models. In a low emission scenario, permafrost carbon emissions increase the risk of northern high latitudes becoming a net carbon source by more than 50 % at 2 °C of warming, and at greater levels of warming in high emission scenarios. Conversely, in all emission scenarios dynamic vegetation is found to limit the sink-to-source transition at all warming levels by enhancing the carbon sink. Fire emissions can further weaken the sink by reducing its resilience to warming. A high temperature overshoot further limits the resilience of the carbon sink due to a reduction in temperatures after the peak, providing less optimal conditions for vegetation growth. These results highlight the importance of vegetation on the strength of the Arctic terrestrial carbon sink under warming and emphasise the need for representing comprehensive terrestrial climate feedbacks in Earth system models to improve projections of the land carbon response in future climate change trajectories.

African tropical moist forests are critical regulators of global carbon cycling, yet their contributions to national carbon budgets remain poorly constrained, impairing effective policy design. Here, we unravel carbon removals and emissions for 18 African tropical countries using multiple approaches over a data-rich period (2015–2019). Across countries, carbon removals (−286 ± 68.4 TgC yr⁻¹) nearly offset land-use change emissions (343.4 ± 100.3 TgC yr⁻¹), with consistent net fluxes between bottom-up and top-down approaches (57.4 ± 121.4 TgC yr⁻¹ versus 99.1 ± 164.5 TgC yr⁻¹). Results vary between countries, with highest removals in intact forests in the Democratic Republic of the Congo (−81.7 ± 64.2 TgC yr⁻¹), lowest land-use change emissions in Gabon (2.6 ± 7.1 TgC yr⁻¹), and highest fossil fuel emissions in Nigeria (31.2 ± 1.6 TgC yr⁻¹). African tropical countries show high carbon removal rates and land-use change emissions but low fossil fuel emissions, contrasting with industrialized countries. (Inter)nationally endorsed conservation policies can reverse national carbon budgets from net sources to sinks.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.

Leaf area index (LAI) is a key biophysical variable which quantifies the surface area for light capture and photosynthetic activity per unit ground area, giving a first order constraint on potential photosynthesis. LAI is tightly coupled to the carbon, energy, and water cycles of the global terrestrial system. Numerous Earth observation (EO) products provide estimates of LAI over time (LAI_EO), delivering valuable information on plant phenology and canopy dynamics. Widely-used LAI_EO, however, consistently exhibit unrealistic seasonality in evergreen needleleaf forests at the northern latitudes. Taking a model-data fusion approach, we show that naïvely assimilating biased, whole-year LAI_EO (i.e., the business-as-usual (BAU) approach) at three well-studied evergreen needleleaf forests in Fennoscandia implies an ecosystem carbon cycle which is unrealistic and inconsistent with independent lines of evidence. We further demonstrate that the model-data fusion framework, CARDAMOM, is capable of diagnosing realistic seasonal amplitudes of LAI by assimilating localised information on leaf lifespan coupled with summer-only LAI_EO (i.e., the alternative (ALT) approach). Important differences arise from the BAU and ALT experiments. The BAU experiment showed highly seasonal canopy dynamics and diagnostic leaf traits erroneously consistent with deciduous species. Conversely, the ALT experiment displayed canopy dynamics and functional characteristics more reflective of evergreen needleleaf species. For BAU, biases in LAI_EO propagated throughout the carbon cycle, especially in the southern, more productive sites. This investigation highlights the need for improved LAI_EO estimates in northern evergreen forests to enhance understanding of carbon cycle processes in this region of rapid warming and large carbon stores, and provides a mechanism for improvement using independent leaf trait data.