Matthew A. Chamberlain

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).

Ocean heat content (OHC) is a key indicator of Earth’s energy imbalance and long-term climate change. Within the Marine Environment Reanalyses Evaluation Project (MER-EP), we use recent-generation ocean reanalysis products (ORAs) to assess long-term global OHC changes, spatial warming patterns, and inter-product consistency since 1993. We compare the results with those from objective analysis products (OAs) and earlier-generation ORAs. The results show sustained global ocean warming during 1993–2022, concentrated mainly in the upper 2000 m. The ORA ensemble gives a global 0–2000 m OHC trend of 0.73 [0.49, 0.97] W m-2 (1.65 std), with trend estimates from the OAs falling within the ORA uncertainty range. Warming is more pronounced at mid-to-high latitudes and extends to greater depths at high northern latitudes. Differences among ORAs are largest in the polar oceans, western boundary current regions, frontal systems, and below 2000 m. Inter-product consistency among ORAs improves markedly in the Argo era. In the upper 2000 m, the fraction of ocean area where the common ORA signal exceeds inter-product spread (signal-to-noise ratio > 1) increases by 20.8 percentage points for interannual variability and 21.3 percentage points for long-term trends after 2005. The largest improvement occurs in the 700–2000 m layer. Below 2000 m, inter-product consistency among ORAs remains low throughout the study period. Compared with earlier-generation ORAs, recent-generation ORAs also show a clear improvement in inter-product consistency for both interannual variability and long-term trends in upper-ocean OHC. These results increase confidence in using recent-generation ORAs to estimate global OHC changes and indicate that sustained ocean observations provide an essential foundation for reliably estimating global OHC changes.

Accurate assessment of anthropogenic carbon dioxide (CO ₂ ) 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 CO ₂ 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 CO ₂ growth rate ( G _ATM ) is computed from changes in concentration measured at surface stations. The global net uptake of CO ₂ by the ocean ( S _OCEAN ) is estimated with global ocean biogeochemistry models and observation-based f CO ₂ -products. The global net uptake of CO ₂ 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 σ . 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 CO ₂ 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 CO ₂ 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 CO ₂ 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 CO ₂ 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 CO ₂ flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink. 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-2025 (Friedlingstein et al., 2025c).

Paleo‐proxy records suggest that vegetation at the Last Interglacial (LIG) was different from pre‐industrial (PI), including a greener Sahara and a northward expanded boreal forest at high northern latitudes. Not accounting for these differences may have contributed to an underestimation of temperature and precipitation changes in paleoclimate simulations. Here, we use the LPJ‐GUESS dynamic vegetation model to generate a static vegetation map for the LIG as a boundary condition for the ACCESS‐ESM1.5 model to assess the impact of vegetation changes on climate. Compared to PI, bare ground fractions decrease globally, resulting in a more vegetated state, particularly in northern Africa and at high northern latitudes, where boreal forests expand and tundra shifts northward. As a result, annual mean global surface temperature increases by 0.2C compared to an initial LIG experiment that uses fixed PI vegetation cover, with warming mostly simulated over land due to albedo changes. The strongest warming (C) is located over the western Indian subcontinent, the far eastern region of Siberia, and northeastern North America, all associated with smaller bare ground fractions and albedo. Winds over land are weakened due to higher roughness, though the large‐scale global circulation patterns remain unchanged. Precipitation and monsoon changes are minor. The vegetation change implemented here leads to an annual mean warming of 0.5C over land in the northern hemisphere, which is relatively minor compared to LIG simulations that employ fully coupled dynamic global vegetation models, highlighting the importance of positive climate‐vegetation feedbacks that are not captured within our framework.

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 (EFOS) are based on energy statistics and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use and land-use change data. Atmospheric CO2 concentration is measured at surface stations, and the global atmospheric CO2 growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks 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 2024, EFOS 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), ELUC 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 GtCO2 yr−1). Also, for 2024, GATM was 7.9 ± 0.2 GtC yr−1 (3.73 ± 0.1 ppm yr−1), 2.2 GtC above the 2023 growth rate. SOCEAN was 3.4 ± 0.4 GtC yr−1 and SLAND was 1.9 ± 1.1 GtC yr−1, leaving a large negative BIM (−1.7 GtC yr−1), suggesting that the total sink or GATM 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 EFOS relative to 2024 of +1.1 % (0.2 % to 2.2 %) globally, and atmospheric CO2 concentration increasing by 2.3 ppm reaching 425.7 ppm, 52 % 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. 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-2025 (Friedlingstein et al., 2025c).

The introduction of new processes in biogeochemical models brings new model parameters that must be set. Optimization of the model parameters is crucial to ensure that model performance is based on process representation (i.e., functional forms) rather than poor choices of input parameter values. However, for most biogeochemical models, standard optimization techniques are not viable due to computational cost. Typically, (tens of) thousands of simulations are required to accurately estimate optimal parameter values of complex non-linear models. To overcome this persistent challenge, we apply surrogate machine learning methods to optimize the model parameters of a new version of the World Ocean Model of Biogeochemistry and Trophic dynamics (WOMBAT), which we call WOMBAT-lite. WOMBAT-lite has undergone numerous updates described herein with many new model parameters to prescribe. A computationally inexpensive surrogate machine learning model based on Gaussian process regression was trained on a set of 512 simulations with WOMBAT-lite and was used to produce synthetic results emulating tens of thousands of simulations. These simulations explored model fidelity to 8 observation-based target datasets by varying 26 uncertain parameters across their a priori ranges. The surrogate model, trained on these 512 simulations, facilitated a global sensitivity analysis to identify the most important parameters and facilitated Bayesian parameter optimization. Our approach returned constrained posterior distributions of 13 important parameters that, when sampled and input to WOMBAT-lite, ensured excellent fidelity to the target datasets. This process improved the representation of chlorophyll a concentrations, air–sea carbon dioxide fluxes and patterns of phytoplankton nutrient limitation. We present an optimal parameter set for use by the modeling community. Overall, we show that surrogate-based calibration can deliver optimal parameter values for the biogeochemical components of Earth system models and can improve the simulation of key processes in the global carbon cycle.

The abyssal ocean may provide adequate sequestration for Carbon Capture and Storage and Carbon Dioxide Removal. The transit of carbon from seafloor release to ocean surface can take millennia, as it occurs through many water pathways characterized by long‐tailed transit‐time distributions (TTDs). However, multi‐millennial simulations of TTDs are typically prohibitively expensive. Here, we introduce an idealized but computationally efficient methodological framework for computing TTDs offline from climate‐model archives. We applied this framework to one Earth System Model to estimate the deep ocean sequestration efficiency for the 2030s and 2090s ocean circulations. We found that sequestration is most efficient on abyssal plains isolated from deep branches of the conveyor belt, such as the North Pacific Basins, where less than 10% of the water reaches the surface after 1,000 years. The climate‐warming‐induced slowdown of the 2090s deep‐ocean circulation extends return times by about 30%, which exceeds internal climate variability (%).