Laure Resplandy

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 deoxygenation is a consequence of anthropogenic ocean warming. Global historical deoxygenation estimates are based on gap-filled reconstructions from sparse and unevenly distributed observations and span a wide and uncertain range between 0.8-2.4% (equivalent to 0.23-0.7 micromol/kg/decade oxygen loss. Here, we quantify errors in historical deoxygenation estimates arising from observational subsampling and reconstruction by applying six state-of-the-art reconstruction frameworks to 14 Earth System Models (ESMs) subsampled at historical observation locations from 1965-2021, and comparing the reconstructed oxygen changes with their fully sampled counterparts. We find that five out of six reconstruction frameworks reduce deoxygenation estimates between 64% (i.e., 0.12 micromol/kg/decade) to 13% (i.e., 0.02 micromol/kg/decade), with one framework increasing the deoxygenation estimate by 7% (i.e., 0.01 micromol/kg/decade). The largest errors occur in poorly sampled regions, including the deep ocean (below 2000 m) and the tropical oceans. We leverage across-ESM relationships between reconstructed and fully sampled oxygen trends to correct the subsampling-reconstruction error in observation-based estimates. The correction in the upper 2000 m is small due to regional compensations. Model-based corrections weaken deoxygenation estimates in the tropical Atlantic (by 25%) and the tropical Pacific (by 19%), but strengthen deoxygenation in other regions including the Southern Ocean (by 37%). In contrast, corrections below 2000 m indicate a 69% [15-117%, 95% confidence interval] stronger deoxygenation, equivalent to an additional oxygen loss of 0.14 [0.03 to 0.25] micromol/kg/decade. Our results call for targeted observations in poorly-sampled regions to better quantify future ocean deoxygenation.

Mesoscale eddies modulate the regional and seasonal variability of ocean biogeochemistry, but their chaotic dynamics make individual eddies unpredictable beyond a few weeks. Large ensembles of high-resolution models can reproduce these mesoscale statistics, but are computationally prohibitive. We present SamudraBGC, a machine-learning emulator trained on an idealized double-gyre North Atlantic configuration of a 9 km ocean biogeochemical model. SamudraBGC reproduces the contrasts between the oligotrophic subtropical gyre, the productive subpolar gyre, and the jet, and the seasonal to interannual variability. Four choices mattered most: a Helmholtz velocity decomposition to produce coherent eddies, log-transformation of biogeochemical tracers to capture their extremes, a gradient loss to preserve fronts, and vertical compression to improve fidelity at depth. A 50-member ensemble broadly reproduces the spread and growth rates of the dynamical ensemble -- using $\sim$0.1 GPU-hours per model year against $\sim$3,600 CPU-hours for the dynamical model -- paving the way towards ensemble-based data-assimilation of ocean biogeochemistry.

The ocean carbon sink has absorbed approximately 29 % of anthropogenic carbon dioxide (CO₂) emissions in the last decade, mitigating climate change. Over time, the ocean carbon sink has grown nearly proportionally to atmospheric CO₂ concentrations. Natural variability also modulates the ocean carbon sink, but the decadal-timescale mechanisms of this variability are not well-understood. Using LDEO-Hybrid Physics Data, a unique observation-based product that merges observations with hindcast models, we assess the decadal variability of global air-sea CO₂ fluxes for 1959–2024, with a focus on the dominant regional contributions. The dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals between the tropical Pacific and the Southern Ocean. This synchronicity is modulated by Pacific Decadal Variability (PDV) and the interannually varying El Niño-Southern Oscillation (ENSO) as measured by the Multivariate ENSO Index. When PDV and MEI indices are positive, the Southern Ocean experiences stronger westerly winds and deeper mixed layers. However, fully explaining the weakening of the Southern Ocean carbon sink in recent decades, which had predominantly negative PDV and MEI, will require additional constraints, particularly for biological processes.

The ocean carbon sink has absorbed approximately 29 % of anthropogenic carbon dioxide (CO₂) emissions in the last decade, mitigating climate change. Over time, the ocean carbon sink has grown nearly proportionally to atmospheric CO₂ concentrations. Natural variability also modulates the ocean carbon sink, but the decadal-timescale mechanisms of this variability are not well-understood. Using LDEO-Hybrid Physics Data, a unique observation-based product that merges observations with hindcast models, we assess the decadal variability of global air-sea CO₂ fluxes for 1959–2024, with a focus on the dominant regional contributions. The dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals between the tropical Pacific and the Southern Ocean. This synchronicity is modulated by Pacific Decadal Variability (PDV) and the interannually varying El Niño-Southern Oscillation (ENSO) as measured by the Multivariate ENSO Index. When PDV and MEI indices are positive, the Southern Ocean experiences stronger westerly winds and deeper mixed layers. However, fully explaining the weakening of the Southern Ocean carbon sink in recent decades, which had predominantly negative PDV and MEI, will require additional constraints, particularly for biological processes.

The ocean carbon sink has absorbed approximately 29 % of anthropogenic carbon dioxide (CO₂) emissions in the last decade, mitigating climate change. Over time, the ocean carbon sink has grown nearly proportionally to atmospheric CO₂ concentrations. Natural variability also modulates the ocean carbon sink, but the decadal-timescale mechanisms of this variability are not well-understood. Using LDEO-Hybrid Physics Data, a unique observation-based product that merges observations with hindcast models, we assess the decadal variability of global air-sea CO₂ fluxes for 1959–2024, with a focus on the dominant regional contributions. The dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals between the tropical Pacific and the Southern Ocean. This synchronicity is modulated by Pacific Decadal Variability (PDV) and the interannually varying El Niño-Southern Oscillation (ENSO) as measured by the Multivariate ENSO Index. When PDV and MEI indices are positive, the Southern Ocean experiences stronger westerly winds and deeper mixed layers. However, fully explaining the weakening of the Southern Ocean carbon sink in recent decades, which had predominantly negative PDV and MEI, will require additional constraints, particularly for biological processes.

Mesoscale eddies modulate the regional and seasonal variability of ocean biogeochemistry, but their chaotic dynamics make individual eddies unpredictable beyond a few weeks. Large ensembles of high-resolution models can reproduce these mesoscale statistics, but are computationally prohibitive. We present SamudraBGC, a machine-learning emulator trained on an idealized double-gyre North Atlantic configuration of a 9 km ocean biogeochemical model. SamudraBGC reproduces the contrasts between the oligotrophic subtropical gyre, the productive subpolar gyre, and the jet, and the seasonal to interannual variability. Four choices mattered most: a Helmholtz velocity decomposition to produce coherent eddies, log-transformation of biogeochemical tracers to capture their extremes, a gradient loss to preserve fronts, and vertical compression to improve fidelity at depth. A 50-member ensemble broadly reproduces the spread and growth rates of the dynamical ensemble -- using $\sim$0.1 GPU-hours per model year against $\sim$3,600 CPU-hours for the dynamical model -- paving the way towards ensemble-based data-assimilation of ocean biogeochemistry.

The ocean carbon sink has absorbed approximately 29 % of anthropogenic carbon dioxide (CO₂) emissions in the last decade, mitigating climate change. Over time, the ocean carbon sink has grown nearly proportionally to atmospheric CO₂ concentrations. Natural variability also modulates the ocean carbon sink, but the decadal-timescale mechanisms of this variability are not well-understood. Using LDEO-Hybrid Physics Data, a unique observation-based product that merges observations with hindcast models, we assess the decadal variability of global air-sea CO₂ fluxes for 1959–2024, with a focus on the dominant regional contributions. The dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals between the tropical Pacific and the Southern Ocean. This synchronicity is modulated by Pacific Decadal Variability (PDV) and the interannually varying El Niño-Southern Oscillation (ENSO) as measured by the Multivariate ENSO Index. When PDV and MEI indices are positive, the Southern Ocean experiences stronger westerly winds and deeper mixed layers. However, fully explaining the weakening of the Southern Ocean carbon sink in recent decades, which had predominantly negative PDV and MEI, will require additional constraints, particularly for biological processes.

The ocean carbon sink has absorbed approximately 29 % of anthropogenic carbon dioxide (CO₂) emissions in the last decade, mitigating climate change. Over time, the ocean carbon sink has grown nearly proportionally to atmospheric CO₂ concentrations. Natural variability also modulates the ocean carbon sink, but the decadal-timescale mechanisms of this variability are not well-understood. Using LDEO-Hybrid Physics Data, a unique observation-based product that merges observations with hindcast models, we assess the decadal variability of global air-sea CO₂ fluxes for 1959–2024, with a focus on the dominant regional contributions. The dominant mode of decadal air-sea CO₂ flux variability exhibits strong synchronous signals between the tropical Pacific and the Southern Ocean. This synchronicity is modulated by Pacific Decadal Variability (PDV) and the interannually varying El Niño-Southern Oscillation (ENSO) as measured by the Multivariate ENSO Index. When PDV and MEI indices are positive, the Southern Ocean experiences stronger westerly winds and deeper mixed layers. However, fully explaining the weakening of the Southern Ocean carbon sink in recent decades, which had predominantly negative PDV and MEI, will require additional constraints, particularly for biological processes.