Status: this preprint is currently under review for the journal ESSD.
Global Carbon Budget 2025
Pierre Friedlingstein,Michael O'Sullivan,Matthew W. Jones,Robbie M. Andrew,Dorothee C. E. Bakker,Judith Hauck,Peter Landschützer,Corinne Le Quéré,Hongmei Li,Ingrid T. Luijkx,Glen P. Peters,Wouter Peters,Julia Pongratz,Clemens Schwingshackl,Stephen Sitch,Josep G. Canadell,Philippe Ciais,Kjetil Aas,Simone R. Alin,Peter Anthoni,Leticia Barbero,Nicholas R. Bates,Nicolas Bellouin,Alice Benoit-Cattin,Carla F. Berghoff,Raffaele Bernardello,Laurent Bopp,Ida B. M. Brasika,Matthew A. Chamberlain,Naveen Chandra,Frédéric Chevallier,Louise P. Chini,Nathan O. Collier,Thomas H. Colligan,Margot Cronin,Laique Djeutchouang,Xinyu Dou,Matt P. Enright,Kazutaka Enyo,Michael Erb,Wiley Evans,Richard A. Feely,Liang Feng,Daniel J. Ford,Adrianna Foster,Filippa Fransner,Thomas Gasser,Marion Gehlen,Thanos Gkritzalis,Jefferson Goncalves De Souza,Giacomo Grassi,Luke Gregor,Nicolas Gruber,Bertrand Guenet,Özgür Gürses,Kirsty Harrington,Ian Harris,Jens Heinke,George C. Hurtt,Yosuke Iida,Tatiana Ilyina,Akihiko Ito,Andrew R. Jacobson,Atul K. Jain,Tereza Jarníková,Annika Jersild,Fei Jiang,Steve D. Jones,Etsushi Kato,Ralph F. Keeling,Kees Klein Goldewijk,Jürgen Knauer,Yawen Kong,Jan Ivar Korsbakken,Charles Koven,Taro Kunimitsu,Xin Lan,Junjie Liu,Zhiqiang Liu,Zhu Liu,Claire Lo Monaco,Lei Ma,Gregg Marland,Patrick C. McGuire,Galen A. McKinley,Joe Melton,Natalie Monacci,Erwan Monier,Eric J. Morgan,David R. Munro,Jens D. Müller,Shin-Ichiro Nakaoka,Lorna R. Nayagam,Yosuke Niwa,Tobias Nutzel,Are Olsen,Abdirahman M. Omar,Naiqing Pan,Sudhanshu Pandey,Denis Pierrot,Zhangcai Qin,Pierre A. G. Regnier,Gregor Rehder,Laure Resplandy,Alizée Roobaert,Thais M. Rosan,Christian Rödenbeck,Jörg Schwinger,Ingunn Skjelvan,T. Luke Smallman,Victoria Spada,Mohanan G. Sreeush,Qing Sun,Adrienne J. Sutton,Colm Sweeney,Didier Swingedouw,Roland Séférian,Shintaro Takao,Hiroaki Tatebe,Hanqin Tian,Xiangjun Tian,Bronte Tilbrook,Hiroyuki Tsujino,Francesco Tubiello,Erik van Ooijen,Guido van der Werf,Sebastiaan J. van de Velde,Anthony Walker,Rik Wanninkhof,Xiaojuan Yang,Wenping Yuan,Xu Yue,and Jiye Zeng
Abstract. 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).
Received: 31 Oct 2025 – Discussion started: 13 Nov 2025
Competing interests: At least one of the authors is a member of the editorial board of Earth System Science Data.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Pierre Friedlingstein,Michael O'Sullivan,Matthew W. Jones,Robbie M. Andrew,Dorothee C. E. Bakker,Judith Hauck,Peter Landschützer,Corinne Le Quéré,Hongmei Li,Ingrid T. Luijkx,Glen P. Peters,Wouter Peters,Julia Pongratz,Clemens Schwingshackl,Stephen Sitch,Josep G. Canadell,Philippe Ciais,Kjetil Aas,Simone R. Alin,Peter Anthoni,Leticia Barbero,Nicholas R. Bates,Nicolas Bellouin,Alice Benoit-Cattin,Carla F. Berghoff,Raffaele Bernardello,Laurent Bopp,Ida B. M. Brasika,Matthew A. Chamberlain,Naveen Chandra,Frédéric Chevallier,Louise P. Chini,Nathan O. Collier,Thomas H. Colligan,Margot Cronin,Laique Djeutchouang,Xinyu Dou,Matt P. Enright,Kazutaka Enyo,Michael Erb,Wiley Evans,Richard A. Feely,Liang Feng,Daniel J. Ford,Adrianna Foster,Filippa Fransner,Thomas Gasser,Marion Gehlen,Thanos Gkritzalis,Jefferson Goncalves De Souza,Giacomo Grassi,Luke Gregor,Nicolas Gruber,Bertrand Guenet,Özgür Gürses,Kirsty Harrington,Ian Harris,Jens Heinke,George C. Hurtt,Yosuke Iida,Tatiana Ilyina,Akihiko Ito,Andrew R. Jacobson,Atul K. Jain,Tereza Jarníková,Annika Jersild,Fei Jiang,Steve D. Jones,Etsushi Kato,Ralph F. Keeling,Kees Klein Goldewijk,Jürgen Knauer,Yawen Kong,Jan Ivar Korsbakken,Charles Koven,Taro Kunimitsu,Xin Lan,Junjie Liu,Zhiqiang Liu,Zhu Liu,Claire Lo Monaco,Lei Ma,Gregg Marland,Patrick C. McGuire,Galen A. McKinley,Joe Melton,Natalie Monacci,Erwan Monier,Eric J. Morgan,David R. Munro,Jens D. Müller,Shin-Ichiro Nakaoka,Lorna R. Nayagam,Yosuke Niwa,Tobias Nutzel,Are Olsen,Abdirahman M. Omar,Naiqing Pan,Sudhanshu Pandey,Denis Pierrot,Zhangcai Qin,Pierre A. G. Regnier,Gregor Rehder,Laure Resplandy,Alizée Roobaert,Thais M. Rosan,Christian Rödenbeck,Jörg Schwinger,Ingunn Skjelvan,T. Luke Smallman,Victoria Spada,Mohanan G. Sreeush,Qing Sun,Adrienne J. Sutton,Colm Sweeney,Didier Swingedouw,Roland Séférian,Shintaro Takao,Hiroaki Tatebe,Hanqin Tian,Xiangjun Tian,Bronte Tilbrook,Hiroyuki Tsujino,Francesco Tubiello,Erik van Ooijen,Guido van der Werf,Sebastiaan J. van de Velde,Anthony Walker,Rik Wanninkhof,Xiaojuan Yang,Wenping Yuan,Xu Yue,and Jiye Zeng
Status: open (until 01 Mar 2026)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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The global carbon budget provides an important contribution to both science and policy by updating annually information about emissions and sinks of CO2 and their trends. This year’s budget is no exception, with valuable insights including recent increasing trends in AGR in spite of a small increase in EFOS and that cannot be explained by the current models, and highlighting persisting uncertainty in SLand in the northern extra tropics. A notable new development is the inclusion of the RSS correction, which results in a weaker, but possibly more realistic, land sink. The report follows previous years’ structure and content, and is well written and well referenced. Nevertheless, I would like to point out a few aspects that can be improved or that need further clarification or discussion.
1) General comments
1.1) RSS correction
This is an important new addition to the budget, since the DGVM protocol used in previous budgets assumed no legacy emissions from land-use prior to 1850, implying a stronger land sink than what is plausible.
However, in many figures and tables, it is not clear whether the land component fluxes show include or not this correction, making it difficult for the reader to appreciate its impact in all relevant components of the budget.
1.2) Next-year projections
This budget, like previous ones, provides an estimate of the values of emissions and sinks for the next year, in this case 2025. These projections are based in very different methods, that rely on many assumptions and which are not consistent with each other (the ESM projections being the most self-consistent). Since next-year projections have been now given for several budgets, it would be important to discuss how much confidence can be attributed to these projections: how have previous projections performed? Are the respective uncertainty ranges provided here realistic?
2) Specific comments
Abstract
Lines 193-204: I understand the need to describe briefly the datasets used, but since the budget follows the same approach as previous years, I would recommend shortening this description, in order to rather highlight the new additions (for example the RSS correction) and provide more depth on the new insights from this year’s update (for example the very large value of BIM for 2024).
Lines 205-213: it would be helpful to report percent increase for all budget terms.
Lines 216-220: it would seem important to add here some insight about possible causes for the land BIM in 2024.
Introduction
Please state briefly in the introduction what the novel aspects of this year’s budget are (for example the RSS correction), compared to previous ones.
Lines 367-382: these sentences would probably fit better right in the beginning of the introduction, before stating the current budget’s approach.
Results
The use of sign to indicate an increase/decrease “by” is not always consistent but it is needed for clarity. For example, in Lines 996-1006 +3.6 is given for India, but – sign in “EU27 by” and “USA by” is missing. Many other instances can be found throughout the text across all sections, I note some lines here, but a careful editing is needed. Lines: 1011, 1014-1015, 1028, 1039, 1042, 1048, 1055, 1057-61, 1078, 1205 (35TgC “up”), 1228, 1246, 1262, 1302, 1417, 1422, 1432, 1444, 1950-51.
Fossil fuel
Lines 1026: please state briefly what information and assumptions the projections are based on.
Land use change
Lines 1068-1069: clarify which period is covered by vegetation biomass observations.
Lines 1096-1099: it is the first instance when fluxes, rather than emissions are mentioned. It would be helpful for non-expert readers to state the sign convention here.
Lines 1103-1114: this is relevant for decision making, but it overlooks the fact that these models do not represent processes that can reduce the land sink, especially disturbance fluxes, as noted by Roebroek et al. (2023) among others. Please state limitations and assumptions underlying the values given here.
Lines 1111: it is unclear if here you refer to a potential re/afforestation sink, or current CDR.
Lines 1151: clarify here you mean nature-based CDR, right?
Lines 1160: are RECCAP2 the estimates that are shown in Figure 15?
Lines 1194-1196: I find this confusing, since BM models do not include fire emissions due to natural drivers such as El Niño droughts. Shouldn’t this be rather part of the SLand discussion? And do DGVMs capture these emissions?
Atmospheric CO2
Lines 1271: is r for pearson correlation?
Lines 1295-1299: this is an important point. What would be the implications for 2023, then?
Lines 1301: briefly clarify what the “GCB regression method” is based on.
Lines 1303: give reference to “neutral ENSO year”.
Ocean sink
Lines 1365: remove . after Tropical Pacific
Lines 1382: is there a reason uncertainty ranges given using different notations (+- vs [ ] )? If so, clarify.
Lines 1415-1420: it would be more informative to have this point being discussed along with discrepancies between models and observation-based products. Here makes comparison more difficult.
Lines 1443: briefly explain what predictors are used by the FFNN.
Lines 1489-1491: since there seems to be a linear relationship between the two, would it make sense to apply a correction to fCO2? I do not have a strong opinion, but I am curious if the authors have considered this.
Land sink
Lines 1497-98: does the RSS correction affect only the mean values (bias), or are there carry-over effects that could result in different IAV and trends? Please clarify that the RSS correction was only applied to the DGVM multimodel mean, not to the individual DGVM estimates (as far as I understand).
Lines 1505-1506: give value of the residual sink here too.
Lines 1560: is it unclear if the values given here include the RSS correction, please clarify.
Lines 1571-1573: how about fires?
Lines 1574-1586: do the models representing fires estimate a weaker sink?
Partitioning of the fluxes
Lines 1683-1687: it is worth mentioning that, however, the northern land flux does not seem to contribute much to the BIM in 2024.
Lines 1717-1721: are there implications for the BIM value in 2024 that can be drawn from this comparison?
Lines 1753-1795: this is a very informative addition. However, RECCAP2 provides estimates of regional budgets using additional datasets (possibly with higher confidence that global products). Therefore, it would be worth comparing the estimates here with the values provided by RECCAP2 for each region, or at least highlight those regions where results from RECCAP2 indicate large discrepancies between global datasets and refined regional products.
Closing the global carbon cycle
Lines 1873: th should be superscript
Lines 1876: with “historical” do you mean “pre-industrial”? Does this refer to the RSS correction?
Lines 1891-1893: is this only because of the RSS correction, or are there other reasons?
Lines 1894-1898: the discussion of the disagreements in AGR between the surface network and satellite-based GRESO should be linked here. What is the effect of these disagreements on BIM for 2024?
Lines 1910-1913: I understand a full attribution is out of scope in this already long report, but Bastos et al. (2021) showed that the BIM strongly correlated with differences between inversions and global models, so maybe something could be said about the BIM based on Figure 14? At first glance some of the peaks in BIM in Figure 4 seem to correspond to large differences between inversions and GCB estimates of the total and land fluxes shown in Figure 14, especially in the tropics.
Lines 2024-2032: for traceability, it would be useful to have a table reporting major corrections/updates to the data and their implications.
Lines 2057-2082: there are several satellite-based biomass products that now allow to derive at least some components of ELUC, for example Xu et al. (2021). Are these planned to be incorporated in future budgets, if not, what are the limitations?
Lines 2011-2116: provide values for the estimates by atmospheric inversions, oxygen-based estimates and Randerson et al. (2025) for comparison.
Figures and Tables
Figure 13: do the DGVM estimates shown here include the RSS correction? Please clarify or, ideally, include both the uncorrected and corrected estimates.
Figure 14: does the gray shade for DGVMs correspond to the land-flux with or without RSS correction?
Figure 15 has very poor quality, please increase resolution.
Data availability
I agree that making the data available will contribute to greater understanding and new scientific insights of how the carbon cycle works, as stated in Section 7. However, in that spirit, why are gridded data provided openly for atmospheric inversions only, and not for the other datasets underlying the budget, especially those used in the main figures? Providing all gridded data openly would contribute to speed up understanding of uncertainties and knowledge gaps, by making it accessible to the broader scientific community. Furthermore, this hampers reproducibility of some of the main results of this paper (Figures 6, 11, 12, 15), and does not seem to fully align with ESSD core principles as stated in the journal’s page.
Supplement
It is difficult to extract from the supplementary information the key aspects of the protocol used for DGVM and BM models (starting year, period covered, forcing, adjustments, etc.), since they are referred to in between lengthy descriptions of specific aspects of the datasets. These lengthy descriptions are useful since they contain very relevant information, but I would suggest summarizing the modelling protocol and adjustments performed in a table (similar to Table S2 for GOBMs).
Line 857: I suggest adding a subsection header for the RSS correction here (or highlight as a paragraph), making it easier for the readers to find the relevant information.
References cited
Caspar T. J. Roebroek et al. Releasing global forests from human management: How much more carbon could be stored? Science380, 749-753(2023). DOI:10.1126/science.add5878
James T. Randerson et al.The weak land carbon sink hypothesis. Sci.Adv.11, eadr5489(2025). DOI:10.1126/sciadv.adr5489
Bastos, A et al. (2020), Sources of uncertainty in regional and global terrestrial CO2 exchange estimates. Global Biogeochemical Cycles, 34, e2019GB006393. https://doi.org/10.1029/2019GB006393
Liang Xu et al. Changes in global terrestrial live biomass over the 21st century.Sci. Adv.7,eabe9829(2021).DOI:10.1126/sciadv.abe9829
Pierre Friedlingstein,Michael O'Sullivan,Matthew W. Jones,Robbie M. Andrew,Dorothee C. E. Bakker,Judith Hauck,Peter Landschützer,Corinne Le Quéré,Hongmei Li,Ingrid T. Luijkx,Glen P. Peters,Wouter Peters,Julia Pongratz,Clemens Schwingshackl,Stephen Sitch,Josep G. Canadell,Philippe Ciais,Kjetil Aas,Simone R. Alin,Peter Anthoni,Leticia Barbero,Nicholas R. Bates,Nicolas Bellouin,Alice Benoit-Cattin,Carla F. Berghoff,Raffaele Bernardello,Laurent Bopp,Ida B. M. Brasika,Matthew A. Chamberlain,Naveen Chandra,Frédéric Chevallier,Louise P. Chini,Nathan O. Collier,Thomas H. Colligan,Margot Cronin,Laique Djeutchouang,Xinyu Dou,Matt P. Enright,Kazutaka Enyo,Michael Erb,Wiley Evans,Richard A. Feely,Liang Feng,Daniel J. Ford,Adrianna Foster,Filippa Fransner,Thomas Gasser,Marion Gehlen,Thanos Gkritzalis,Jefferson Goncalves De Souza,Giacomo Grassi,Luke Gregor,Nicolas Gruber,Bertrand Guenet,Özgür Gürses,Kirsty Harrington,Ian Harris,Jens Heinke,George C. Hurtt,Yosuke Iida,Tatiana Ilyina,Akihiko Ito,Andrew R. Jacobson,Atul K. Jain,Tereza Jarníková,Annika Jersild,Fei Jiang,Steve D. Jones,Etsushi Kato,Ralph F. Keeling,Kees Klein Goldewijk,Jürgen Knauer,Yawen Kong,Jan Ivar Korsbakken,Charles Koven,Taro Kunimitsu,Xin Lan,Junjie Liu,Zhiqiang Liu,Zhu Liu,Claire Lo Monaco,Lei Ma,Gregg Marland,Patrick C. McGuire,Galen A. McKinley,Joe Melton,Natalie Monacci,Erwan Monier,Eric J. Morgan,David R. Munro,Jens D. Müller,Shin-Ichiro Nakaoka,Lorna R. Nayagam,Yosuke Niwa,Tobias Nutzel,Are Olsen,Abdirahman M. Omar,Naiqing Pan,Sudhanshu Pandey,Denis Pierrot,Zhangcai Qin,Pierre A. G. Regnier,Gregor Rehder,Laure Resplandy,Alizée Roobaert,Thais M. Rosan,Christian Rödenbeck,Jörg Schwinger,Ingunn Skjelvan,T. Luke Smallman,Victoria Spada,Mohanan G. Sreeush,Qing Sun,Adrienne J. Sutton,Colm Sweeney,Didier Swingedouw,Roland Séférian,Shintaro Takao,Hiroaki Tatebe,Hanqin Tian,Xiangjun Tian,Bronte Tilbrook,Hiroyuki Tsujino,Francesco Tubiello,Erik van Ooijen,Guido van der Werf,Sebastiaan J. van de Velde,Anthony Walker,Rik Wanninkhof,Xiaojuan Yang,Wenping Yuan,Xu Yue,and Jiye Zeng
Pierre Friedlingstein,Michael O'Sullivan,Matthew W. Jones,Robbie M. Andrew,Dorothee C. E. Bakker,Judith Hauck,Peter Landschützer,Corinne Le Quéré,Hongmei Li,Ingrid T. Luijkx,Glen P. Peters,Wouter Peters,Julia Pongratz,Clemens Schwingshackl,Stephen Sitch,Josep G. Canadell,Philippe Ciais,Kjetil Aas,Simone R. Alin,Peter Anthoni,Leticia Barbero,Nicholas R. Bates,Nicolas Bellouin,Alice Benoit-Cattin,Carla F. Berghoff,Raffaele Bernardello,Laurent Bopp,Ida B. M. Brasika,Matthew A. Chamberlain,Naveen Chandra,Frédéric Chevallier,Louise P. Chini,Nathan O. Collier,Thomas H. Colligan,Margot Cronin,Laique Djeutchouang,Xinyu Dou,Matt P. Enright,Kazutaka Enyo,Michael Erb,Wiley Evans,Richard A. Feely,Liang Feng,Daniel J. Ford,Adrianna Foster,Filippa Fransner,Thomas Gasser,Marion Gehlen,Thanos Gkritzalis,Jefferson Goncalves De Souza,Giacomo Grassi,Luke Gregor,Nicolas Gruber,Bertrand Guenet,Özgür Gürses,Kirsty Harrington,Ian Harris,Jens Heinke,George C. Hurtt,Yosuke Iida,Tatiana Ilyina,Akihiko Ito,Andrew R. Jacobson,Atul K. Jain,Tereza Jarníková,Annika Jersild,Fei Jiang,Steve D. Jones,Etsushi Kato,Ralph F. Keeling,Kees Klein Goldewijk,Jürgen Knauer,Yawen Kong,Jan Ivar Korsbakken,Charles Koven,Taro Kunimitsu,Xin Lan,Junjie Liu,Zhiqiang Liu,Zhu Liu,Claire Lo Monaco,Lei Ma,Gregg Marland,Patrick C. McGuire,Galen A. McKinley,Joe Melton,Natalie Monacci,Erwan Monier,Eric J. Morgan,David R. Munro,Jens D. Müller,Shin-Ichiro Nakaoka,Lorna R. Nayagam,Yosuke Niwa,Tobias Nutzel,Are Olsen,Abdirahman M. Omar,Naiqing Pan,Sudhanshu Pandey,Denis Pierrot,Zhangcai Qin,Pierre A. G. Regnier,Gregor Rehder,Laure Resplandy,Alizée Roobaert,Thais M. Rosan,Christian Rödenbeck,Jörg Schwinger,Ingunn Skjelvan,T. Luke Smallman,Victoria Spada,Mohanan G. Sreeush,Qing Sun,Adrienne J. Sutton,Colm Sweeney,Didier Swingedouw,Roland Séférian,Shintaro Takao,Hiroaki Tatebe,Hanqin Tian,Xiangjun Tian,Bronte Tilbrook,Hiroyuki Tsujino,Francesco Tubiello,Erik van Ooijen,Guido van der Werf,Sebastiaan J. van de Velde,Anthony Walker,Rik Wanninkhof,Xiaojuan Yang,Wenping Yuan,Xu Yue,and Jiye Zeng
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Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany
Rosenstiel School of Marine Atmospheric and Earth Science, Cooperative Institute for Marine and Atmospheric Studies (CIMAS), University of Miami, 4600 Rickenbacker Causeway, Miami, FL, USA
Nicholas R. Bates
Arizona State University, Tempe, Arizona, AZ 85287-5502, USA
Bermuda Institute of Ocean Sciences (BIOS), 17 Biological Lane, St. Georges, GE01, Bermuda
Environmental Physics Group, Institute of Biogeochemistry and Pollutant Dynamics and Center for Climate Systems Modeling (C2SM), ETH Zürich, Zurich, Switzerland
Swiss Data Science Center, 8050 Zurich, Switzerland
Environmental Physics Group, Institute of Biogeochemistry and Pollutant Dynamics and Center for Climate Systems Modeling (C2SM), ETH Zürich, Zurich, Switzerland
Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
Steve D. Jones
Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400, Ostend, Belgium
Utrecht University, Faculty of Geosciences, Department IMEW, Copernicus Institute of Sustainable Development, Heidelberglaan 2, P.O. Box 80115, 3508 TC, Utrecht, the Netherlands
Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC) UMR CNRS 5805 EPOC - OASU, Université de Bordeaux, Allée Geoffroy Saint Hilaire, 33615 Pessac, France
Schiller Institute of Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA
Xiangjun Tian
State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 101408, China
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to...
The global carbon budget provides an important contribution to both science and policy by updating annually information about emissions and sinks of CO2 and their trends. This year’s budget is no exception, with valuable insights including recent increasing trends in AGR in spite of a small increase in EFOS and that cannot be explained by the current models, and highlighting persisting uncertainty in SLand in the northern extra tropics. A notable new development is the inclusion of the RSS correction, which results in a weaker, but possibly more realistic, land sink. The report follows previous years’ structure and content, and is well written and well referenced. Nevertheless, I would like to point out a few aspects that can be improved or that need further clarification or discussion.
1) General comments
1.1) RSS correction
This is an important new addition to the budget, since the DGVM protocol used in previous budgets assumed no legacy emissions from land-use prior to 1850, implying a stronger land sink than what is plausible.
However, in many figures and tables, it is not clear whether the land component fluxes show include or not this correction, making it difficult for the reader to appreciate its impact in all relevant components of the budget.
1.2) Next-year projections
This budget, like previous ones, provides an estimate of the values of emissions and sinks for the next year, in this case 2025. These projections are based in very different methods, that rely on many assumptions and which are not consistent with each other (the ESM projections being the most self-consistent). Since next-year projections have been now given for several budgets, it would be important to discuss how much confidence can be attributed to these projections: how have previous projections performed? Are the respective uncertainty ranges provided here realistic?
2) Specific comments
Abstract
Lines 193-204: I understand the need to describe briefly the datasets used, but since the budget follows the same approach as previous years, I would recommend shortening this description, in order to rather highlight the new additions (for example the RSS correction) and provide more depth on the new insights from this year’s update (for example the very large value of BIM for 2024).
Lines 205-213: it would be helpful to report percent increase for all budget terms.
Lines 216-220: it would seem important to add here some insight about possible causes for the land BIM in 2024.
Introduction
Please state briefly in the introduction what the novel aspects of this year’s budget are (for example the RSS correction), compared to previous ones.
Lines 367-382: these sentences would probably fit better right in the beginning of the introduction, before stating the current budget’s approach.
Results
The use of sign to indicate an increase/decrease “by” is not always consistent but it is needed for clarity. For example, in Lines 996-1006 +3.6 is given for India, but – sign in “EU27 by” and “USA by” is missing. Many other instances can be found throughout the text across all sections, I note some lines here, but a careful editing is needed. Lines: 1011, 1014-1015, 1028, 1039, 1042, 1048, 1055, 1057-61, 1078, 1205 (35TgC “up”), 1228, 1246, 1262, 1302, 1417, 1422, 1432, 1444, 1950-51.
Fossil fuel
Lines 1026: please state briefly what information and assumptions the projections are based on.
Land use change
Lines 1068-1069: clarify which period is covered by vegetation biomass observations.
Lines 1096-1099: it is the first instance when fluxes, rather than emissions are mentioned. It would be helpful for non-expert readers to state the sign convention here.
Lines 1103-1114: this is relevant for decision making, but it overlooks the fact that these models do not represent processes that can reduce the land sink, especially disturbance fluxes, as noted by Roebroek et al. (2023) among others. Please state limitations and assumptions underlying the values given here.
Lines 1111: it is unclear if here you refer to a potential re/afforestation sink, or current CDR.
Lines 1151: clarify here you mean nature-based CDR, right?
Lines 1160: are RECCAP2 the estimates that are shown in Figure 15?
Lines 1194-1196: I find this confusing, since BM models do not include fire emissions due to natural drivers such as El Niño droughts. Shouldn’t this be rather part of the SLand discussion? And do DGVMs capture these emissions?
Atmospheric CO2
Lines 1271: is r for pearson correlation?
Lines 1295-1299: this is an important point. What would be the implications for 2023, then?
Lines 1301: briefly clarify what the “GCB regression method” is based on.
Lines 1303: give reference to “neutral ENSO year”.
Ocean sink
Lines 1365: remove . after Tropical Pacific
Lines 1382: is there a reason uncertainty ranges given using different notations (+- vs [ ] )? If so, clarify.
Lines 1415-1420: it would be more informative to have this point being discussed along with discrepancies between models and observation-based products. Here makes comparison more difficult.
Lines 1443: briefly explain what predictors are used by the FFNN.
Lines 1489-1491: since there seems to be a linear relationship between the two, would it make sense to apply a correction to fCO2? I do not have a strong opinion, but I am curious if the authors have considered this.
Land sink
Lines 1497-98: does the RSS correction affect only the mean values (bias), or are there carry-over effects that could result in different IAV and trends? Please clarify that the RSS correction was only applied to the DGVM multimodel mean, not to the individual DGVM estimates (as far as I understand).
Lines 1505-1506: give value of the residual sink here too.
Lines 1560: is it unclear if the values given here include the RSS correction, please clarify.
Lines 1571-1573: how about fires?
Lines 1574-1586: do the models representing fires estimate a weaker sink?
Partitioning of the fluxes
Lines 1683-1687: it is worth mentioning that, however, the northern land flux does not seem to contribute much to the BIM in 2024.
Lines 1717-1721: are there implications for the BIM value in 2024 that can be drawn from this comparison?
Lines 1753-1795: this is a very informative addition. However, RECCAP2 provides estimates of regional budgets using additional datasets (possibly with higher confidence that global products). Therefore, it would be worth comparing the estimates here with the values provided by RECCAP2 for each region, or at least highlight those regions where results from RECCAP2 indicate large discrepancies between global datasets and refined regional products.
Closing the global carbon cycle
Lines 1873: th should be superscript
Lines 1876: with “historical” do you mean “pre-industrial”? Does this refer to the RSS correction?
Lines 1891-1893: is this only because of the RSS correction, or are there other reasons?
Lines 1894-1898: the discussion of the disagreements in AGR between the surface network and satellite-based GRESO should be linked here. What is the effect of these disagreements on BIM for 2024?
Lines 1910-1913: I understand a full attribution is out of scope in this already long report, but Bastos et al. (2021) showed that the BIM strongly correlated with differences between inversions and global models, so maybe something could be said about the BIM based on Figure 14? At first glance some of the peaks in BIM in Figure 4 seem to correspond to large differences between inversions and GCB estimates of the total and land fluxes shown in Figure 14, especially in the tropics.
Lines 2024-2032: for traceability, it would be useful to have a table reporting major corrections/updates to the data and their implications.
Lines 2057-2082: there are several satellite-based biomass products that now allow to derive at least some components of ELUC, for example Xu et al. (2021). Are these planned to be incorporated in future budgets, if not, what are the limitations?
Lines 2011-2116: provide values for the estimates by atmospheric inversions, oxygen-based estimates and Randerson et al. (2025) for comparison.
Figures and Tables
Figure 13: do the DGVM estimates shown here include the RSS correction? Please clarify or, ideally, include both the uncorrected and corrected estimates.
Figure 14: does the gray shade for DGVMs correspond to the land-flux with or without RSS correction?
Figure 15 has very poor quality, please increase resolution.
Data availability
I agree that making the data available will contribute to greater understanding and new scientific insights of how the carbon cycle works, as stated in Section 7. However, in that spirit, why are gridded data provided openly for atmospheric inversions only, and not for the other datasets underlying the budget, especially those used in the main figures? Providing all gridded data openly would contribute to speed up understanding of uncertainties and knowledge gaps, by making it accessible to the broader scientific community. Furthermore, this hampers reproducibility of some of the main results of this paper (Figures 6, 11, 12, 15), and does not seem to fully align with ESSD core principles as stated in the journal’s page.
Supplement
It is difficult to extract from the supplementary information the key aspects of the protocol used for DGVM and BM models (starting year, period covered, forcing, adjustments, etc.), since they are referred to in between lengthy descriptions of specific aspects of the datasets. These lengthy descriptions are useful since they contain very relevant information, but I would suggest summarizing the modelling protocol and adjustments performed in a table (similar to Table S2 for GOBMs).
Line 857: I suggest adding a subsection header for the RSS correction here (or highlight as a paragraph), making it easier for the readers to find the relevant information.
References cited
Caspar T. J. Roebroek et al. Releasing global forests from human management: How much more carbon could be stored? Science380, 749-753(2023). DOI:10.1126/science.add5878
James T. Randerson et al.The weak land carbon sink hypothesis. Sci.Adv.11, eadr5489(2025). DOI:10.1126/sciadv.adr5489
Bastos, A et al. (2020), Sources of uncertainty in regional and global terrestrial CO2 exchange estimates. Global Biogeochemical Cycles, 34, e2019GB006393. https://doi.org/10.1029/2019GB006393
Liang Xu et al. Changes in global terrestrial live biomass over the 21st century.Sci. Adv.7,eabe9829(2021).DOI:10.1126/sciadv.abe9829