Christian Rödenbeck

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).

Understanding the dynamics of natural and anthropogenic sources and sinks of greenhouse gases (GHG) is key to projecting anthropogenic climate change and detecting potential climate-carbon feedbacks. While our understanding of the sources and sinks has increased considerably over the past decades, much of the progress has focused on extratropical ecosystems, leaving large parts of the tropics understudied. Tropical ecosystems are assumed to be important carbon sinks and significant sources of methane, but much of the temporal and spatial heterogeneity and trends are still poorly understood. Observational efforts have largely concentrated on key tropical regions such as the Amazon Basin, whereas other tropical areas remain sparsely monitored with in-situ measurements. In particular, northern South America—despite being identified by bottom-up and top-down models as a significant contributor to global GHG budgets—currently lacks atmospheric observations to evaluate these estimates or resolve their spatial and temporal variability. To address this observational gap, here we present the establishment of the Villavicencio Tower (VLO) for atmospheric GHG measurements to provide additional constraints on GHG emission estimates across northern South America. This 26 m tower is located within the Orinoco lowlands, close to the Andean foothills in Colombia. Due to the predominantly easterly winds in the region, the footprint covers the Orinoco basin and the northern Amazon. Weekly duplicated flask measurements are taken, and laboratory analysis of trace gases (CO2, CH4, CO, N2O, SF6, H2) and the stable isotope composition of CO2 (13C and 18O) and CH4 (13C and 2H) are performed, as well as O2/N2 and Ar/N2. The tower started operating on 20 June 2023 and here we present the first 21 months of data. Time series of all measurements are presented as well as a discussion regarding uncertainties. These measurements will allow for improved estimates of GHG emissions across northern South America and to detect long-range transport across the Amazon to the Orinoco river basin.

A major source of uncertainty in inverse modelling of greenhouse gas emissions are deficits in atmospheric transport models, in particular in the description of vertical mixing within the planetary boundary layer (PBL). The properties of radon-222 (Rn) makes it a suitable natural tracer for vertical mixing in the PBL. When comparing the CH ₄ model-data mismatch (MDM), i.e. the differences between the observed and modelled CH ₄ concentrations, with the MDM of Rn, we found substantial correlations for several observation sites in central Europe in 2021 (the median CH ₄ -Rn MDM correlation coefficient is 0.6), indicating that a large part of the CH ₄ and Rn MDM variability can be explained by common errors in the simulated (vertical) transport. We aim to exploit this information in a joint inversion for CH ₄ and Rn by taking into account prior uncertainties and making use of the fact that the transport model error is correlated between the two gases. We use simultaneous CH ₄ and Rn observations from 17 sites across central Europe in 2021. The dual-tracer CH ₄ -Rn inversion yields lower CH ₄ fluxes in several countries covered by the observation sites compared to a single-tracer CH ₄ -only inversion without Rn information. The differences in country-total CH ₄ fluxes between the dual-tracer and single-tracer inversions are on the order of a few percent and depend on the assumed uncertainty for the Rn prior fluxes. These findings underscore the importance of accurate Rn flux maps for fully leveraging the dual-tracer approach and enhancing the reliability of CH ₄ flux estimates.

Tropical South America is one of the world's largest natural methane (CH₄) emitting regions, yet regional methane budgets remain highly uncertain due to poorly constrained wetland emissions and sparse atmospheric observations. Here, we quantify methane emissions from the Amazon, Orinoco, and Paraná river basins for 2010–2019 using the Jena CarboScope atmospheric inverse modelling system. We investigate the variability in the methane budget through a series of sensitivity experiments. Our inversion estimates methane emissions of 4.44 ± 0.88 Tg CH₄ yr^-1 for the Orinoco, 28.60 ± 5.15 Tg CH₄ yr^-1 for the Amazon, and 30.10 ± 3.95 Tg CH₄ yr^-1 for the Paraná basin, together accounting for ~59 % of the total estimated South American methane emissions (105.5 ± 11.10 Tg CH₄ yr^-1). The inversion substantially modifies the seasonality and interannual variability of prior wetland emissions in all three basins. The dominant source of posterior variability differs among basins. The data-sparse Orinoco is most sensitive to the wetland prior, indicating strong dependence on prior information. Regional observations mainly constrain the Amazon, which accounts for 55 % of the variability in South American posterior methane emissions. The Paraná is largely insensitive to regional observations and is constrained primarily by the global network. These results show that uncertainty sources vary across South America. Improving wetland priors and expanding regional atmospheric measurements are essential for reducing uncertainty in regional methane budgets.

Tropical South America is one of the world's largest natural methane (CH₄) emitting regions, yet regional methane budgets remain highly uncertain due to poorly constrained wetland emissions and sparse atmospheric observations. Here, we quantify methane emissions from the Amazon, Orinoco, and Paraná river basins for 2010–2019 using the Jena CarboScope atmospheric inverse modelling system. We investigate the variability in the methane budget through a series of sensitivity experiments. Our inversion estimates methane emissions of 4.44 ± 0.88 Tg CH₄ yr^-1 for the Orinoco, 28.60 ± 5.15 Tg CH₄ yr^-1 for the Amazon, and 30.10 ± 3.95 Tg CH₄ yr^-1 for the Paraná basin, together accounting for ~59 % of the total estimated South American methane emissions (105.5 ± 11.10 Tg CH₄ yr^-1). The inversion substantially modifies the seasonality and interannual variability of prior wetland emissions in all three basins. The dominant source of posterior variability differs among basins. The data-sparse Orinoco is most sensitive to the wetland prior, indicating strong dependence on prior information. Regional observations mainly constrain the Amazon, which accounts for 55 % of the variability in South American posterior methane emissions. The Paraná is largely insensitive to regional observations and is constrained primarily by the global network. These results show that uncertainty sources vary across South America. Improving wetland priors and expanding regional atmospheric measurements are essential for reducing uncertainty in regional methane budgets.

Tropical South America is one of the world's largest natural methane (CH₄) emitting regions, yet regional methane budgets remain highly uncertain due to poorly constrained wetland emissions and sparse atmospheric observations. Here, we quantify methane emissions from the Amazon, Orinoco, and Paraná river basins for 2010–2019 using the Jena CarboScope atmospheric inverse modelling system. We investigate the variability in the methane budget through a series of sensitivity experiments. Our inversion estimates methane emissions of 4.44 ± 0.88 Tg CH₄ yr-1 for the Orinoco, 28.60 ± 5.15 Tg CH₄ yr-1 for the Amazon, and 30.10 ± 3.95 Tg CH₄ yr-1 for the Paraná basin, together accounting for ~59 % of the total estimated South American methane emissions (105.5 ± 11.10 Tg CH₄ yr-1). The inversion substantially modifies the seasonality and interannual variability of prior wetland emissions in all three basins. The dominant source of posterior variability differs among basins. The data-sparse Orinoco is most sensitive to the wetland prior, indicating strong dependence on prior information. Regional observations mainly constrain the Amazon, which accounts for 55 % of the variability in South American posterior methane emissions. The Paraná is largely insensitive to regional observations and is constrained primarily by the global network. These results show that uncertainty sources vary across South America. Improving wetland priors and expanding regional atmospheric measurements are essential for reducing uncertainty in regional methane budgets.

A major source of uncertainty in inverse modelling of greenhouse gas emissions are deficits in atmospheric transport models, in particular in the description of vertical mixing within the planetary boundary layer (PBL). The properties of radon-222 (Rn) makes it a suitable natural tracer for vertical mixing in the PBL. When comparing the CH₄ model-data mismatch (MDM), i.e. the differences between the observed and modelled CH₄ concentrations, with the MDM of Rn, we found substantial correlations for several observation sites in central Europe in 2021 (the median CH₄-Rn MDM correlation coefficient is 0.6), indicating that a large part of the CH₄ and Rn MDM variability can be explained by common errors in the simulated (vertical) transport. We aim to exploit this information in a joint inversion for CH₄ and Rn by taking into account prior uncertainties and making use of the fact that the transport model error is correlated between the two gases. We use simultaneous CH₄ and Rn observations from 17 sites across central Europe in 2021. The dual-tracer CH₄-Rn inversion yields lower CH₄ fluxes in several countries covered by the observation sites compared to a single-tracer CH₄-only inversion without Rn information. The differences in country-total CH₄ fluxes between the dual-tracer and single-tracer inversions are on the order of a few percent and depend on the assumed uncertainty for the Rn prior fluxes. These findings underscore the importance of accurate Rn flux maps for fully leveraging the dual-tracer approach and enhancing the reliability of CH₄ flux estimates.

A major source of uncertainty in inverse modelling of greenhouse gas emissions are deficits in atmospheric transport models, in particular in the description of vertical mixing within the planetary boundary layer (PBL). The properties of radon-222 (Rn) makes it a suitable natural tracer for vertical mixing in the PBL. When comparing the CH₄ model-data mismatch (MDM), i.e. the differences between the observed and modelled CH₄ concentrations, with the MDM of Rn, we found substantial correlations for several observation sites in central Europe in 2021 (the median CH₄-Rn MDM correlation coefficient is 0.6), indicating that a large part of the CH₄ and Rn MDM variability can be explained by common errors in the simulated (vertical) transport. We aim to exploit this information in a joint inversion for CH₄ and Rn by taking into account prior uncertainties and making use of the fact that the transport model error is correlated between the two gases. We use simultaneous CH₄ and Rn observations from 17 sites across central Europe in 2021. The dual-tracer CH₄-Rn inversion yields lower CH₄ fluxes in several countries covered by the observation sites compared to a single-tracer CH₄-only inversion without Rn information. The differences in country-total CH₄ fluxes between the dual-tracer and single-tracer inversions are on the order of a few percent and depend on the assumed uncertainty for the Rn prior fluxes. These findings underscore the importance of accurate Rn flux maps for fully leveraging the dual-tracer approach and enhancing the reliability of CH₄ flux estimates.