# 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, et al.

► To cite this version:

Pierre Friedlingstein, Michael O'Sullivan, Matthew W Jones, Robbie M Andrew, Dorothee C E Bakker, et al.. Global Carbon Budget 2025. 2025. hal-05370337

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1 Global Carbon Budget 2025

2 Pierre Friedlingstein[1,2], Michael O'Sullivan[1], Matthew W. Jones[3], Robbie M. Andrew[4],

3 Dorothee C. E. Bakker[5], Judith Hauck[6,7], Peter Landschützer[8], Corinne Le Quéré[3], Hongmei

4 Li[9,10], Ingrid T. Luijkx[11], Glen P. Peters[4], Wouter Peters[11,12], Julia Pongratz[13,10], Clemens

5 Schwingshackl[13], Stephen Sitch[1], Josep G. Canadell[14], Philippe Ciais[15], Kjetil Aas[4], Simone

6 R. Alin[16], Peter Anthoni[17], Leticia Barbero[18], Nicholas R. Bates[19,20], Nicolas Bellouin[21],

7 Alice Benoit-Cattin[22], Carla F. Berghoff[23], Raffaele Bernardello[24], Laurent Bopp[2], Ida Bagus

8 Mandhara Brasika[1,25], Matthew A. Chamberlain[26], Naveen Chandra[27], Frédéric Chevallier[15],

9 Louise P. Chini[28], Nathan O. Collier[29], Thomas H. Colligan[30], Margot Cronin[31], Laique M.

10 Djeutchouang[32,33,34], Xinyu Dou[35], Matt P. Enright[19,20], Kazutaka Enyo[36], Michael

11 Erb[37,38], Wiley Evans[39], Richard A. Feely[16], Liang Feng[40,41], Daniel J. Ford[1], Adrianna

12 Foster[42], Filippa Fransner[43,44], Thomas Gasser[45], Marion Gehlen[15], Thanos Gkritzalis[8],

13 Jefferson Goncalves De Souza[1], Giacomo Grassi[46], Luke Gregor[47,48], Nicolas Gruber[47],

14 Bertrand Guenet[49], Özgür Gürses[6], Kirsty Harrington[50], Ian Harris[51], Jens Heinke[52], George

15 C. Hurtt[28], Yosuke Iida[36], Tatiana Ilyina[53,9,10], Akihiko Ito[54], Andrew R. Jacobson[55,56],

16 Atul K. Jain[57], Tereza Jarníková[3], Annika Jersild[30], Fei Jiang[58], Steve D. Jones[8], Etsushi

17 Kato[59], Ralph F. Keeling[60], Kees Klein Goldewijk[61], Jürgen Knauer[62], Yawen Kong[63,64],

18 Jan Ivar Korsbakken[4], Charles Koven[65], Taro Kunimitsu[4], Xin Lan[55,56], Junjie Liu[66,67],

19 Zhiqiang Liu[68], Zhu Liu[69], Claire Lo Monaco[70], Lei Ma[28], Gregg Marland[37,38], Patrick C.

20 McGuire[71], Galen A. McKinley[72], Joe R. Melton[73], Natalie Monacci[74], Erwan Monier[75,76],

21 Eric J. Morgan[60], David R. Munro[55,56], Jens D. Müller[77], Shin-Ichiro Nakaoka[78], Lorna R.

22 Nayagam[78], Yosuke Niwa[78], Tobias Nutzel[13], Are Olsen[43,44], Abdirahman M. Omar[79,44],

23 Naiqing Pan[80], Sudhanshu Pandey[66], Denis Pierrot[81], Zhangcai Qin[82], Pierre Regnier[83],

24 Gregor Rehder[84], Laure Resplandy[85], Alizée Roobaert[8], Thais M. Rosan[1], Christian

25 Rödenbeck[86], Jörg Schwinger[44,79], Ingunn Skjelvan[79,44], T. Luke Smallman[40,41], Victoria

26 Spada[87], Mohanan G. Sreeush[6], Qing Sun[88], Adrienne J. Sutton[16], Colm Sweeney[56], Didier

27 Swingedouw[89], Roland Séférian[90], Shintaro Takao[78], Hiroaki Tatebe[91,92], Hanqin Tian[80],

28 Xiangjun Tian[93,94], Bronte Tilbrook[26,95], Hiroyuki Tsujino[96], Francesco Tubiello[97], Erik van

29 Ooijen[26], Guido R. van der Werf[11], Sebastiaan J. van de Velde[98,99], Anthony P. Walker[100],

30 Rik Wanninkhof[81], Xiaojuan Yang[100], Wenping Yuan[101], Xu Yue[102], Jiye Zeng[78]

31

32 1: Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK

33 2: Laboratoire de Météorologie Dynamique, Institut Pierre-Simon Laplace, CNRS, École Normale

34 Supérieure, Université PSL, Sorbonne Université, École Polytechnique, Paris, France

35 3: Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East

36 Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
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<table>
  <tr>
    <td>37</td>
    <td>4: CICERO Center for International Climate Research, Oslo 0349, Norway</td>
  </tr>
  <tr>
    <td>38</td>
    <td>5: School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK</td>
  </tr>
  <tr>
    <td>39</td>
    <td>6: Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven</td>
  </tr>
  <tr>
    <td>40</td>
    <td>7: Universität Bremen, FB02 Biology/Chemistry Bremen, Germany</td>
  </tr>
  <tr>
    <td>41</td>
    <td>8: Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400, Ostend, Belgium</td>
  </tr>
  <tr>
    <td>42</td>
    <td>9: Helmholtz-Zentrum Hereon, Max-Planck-Straße 1, 21502 Geesthacht, Germany</td>
  </tr>
  <tr>
    <td>43</td>
    <td>10: Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany</td>
  </tr>
  <tr>
    <td>44</td>
    <td>11: Wageningen University, Environmental Sciences Group, P.O. Box 47, 6700AA, Wageningen, The Netherlands</td>
  </tr>
  <tr>
    <td>45</td>
    <td>12: University of Groningen, Centre for Isotope Research, Groningen, The Netherlands</td>
  </tr>
  <tr>
    <td>46</td>
    <td>13: Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 München, Germany</td>
  </tr>
  <tr>
    <td>47</td>
    <td>14: CSIRO Oceans and Atmosphere, Canberra, ACT 2101, Australia</td>
  </tr>
  <tr>
    <td>48</td>
    <td>15: Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, F-91198 Gif-sur-Yvette, France</td>
  </tr>
  <tr>
    <td>49</td>
    <td>16: National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory (NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA</td>
  </tr>
  <tr>
    <td>50</td>
    <td>17: Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany</td>
  </tr>
  <tr>
    <td>51</td>
    <td>18: 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</td>
  </tr>
  <tr>
    <td>52</td>
    <td>19: Arizona State University, Tempe, Arizona, AZ 85287-5502, USA</td>
  </tr>
  <tr>
    <td>53</td>
    <td>20: Bermuda Institute of Ocean Sciences (BIOS), 17 Biological Lane, St. Georges, GE01, Bermuda</td>
  </tr>
  <tr>
    <td>54</td>
    <td>21: Department of Meteorology, University of Reading, Reading, RG6 6BB, UK</td>
  </tr>
  <tr>
    <td>55</td>
    <td>22: Marine and Freshwater Research Institute, Reykjavik, Iceland</td>
  </tr>
  <tr>
    <td>56</td>
    <td>23: Instituto Nacional de Investigación y Desarrollo Pesquero (INIDEP), Paseo Victoria Ocampo N°1, B7602HSA, Mar del Plata, Argentina</td>
  </tr>
  <tr>
    <td>57</td>
    <td>24: Barcelona Supercomputing Center, Barcelona, Spain</td>
  </tr>
  <tr>
    <td>58</td>
    <td>25: Faculty of Marine Science &amp; Fisheries, Udayana University, Denpasar, Bali, 80361, Indonesia</td>
  </tr>
  <tr>
    <td>59</td>
    <td>26: CSIRO Environment, Castray Esplanade, Hobart, Tasmania 7004, Australia</td>
  </tr>
  <tr>
    <td>60</td>
    <td>27: Research Institute for Global Change, JAMSTEC, 3173-25 Showa-machi, Kanazawa, Yokohama, 236-0001, Japan</td>
  </tr>
  <tr>
    <td>61</td>
    <td>28: Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA</td>
  </tr>
  <tr>
    <td>62</td>
    <td>29: Computer Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA</td>
  </tr>
  <tr>
    <td>63</td>
    <td>30: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>64</td>
    <td>31: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>65</td>
    <td>32: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>66</td>
    <td>33: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>67</td>
    <td>34: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>68</td>
    <td>35: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>69</td>
    <td>36: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>70</td>
    <td>37: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>71</td>
    <td>38: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>72</td>
    <td>39: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
  <tr>
    <td>73</td>
    <td>40: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</td>
  </tr>
</table>
<figure><img src="image_4.png" /></figure>

74 31: Marine Institute, Rinville, Oranmore, Co Galway H91 R673, Ireland

75 32: School for Climate Studies, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch,

76 7602, South Africa

77 33: Southern Ocean Carbon - Climate Observatory, CSIR, Rosebank, Cape Town, 7700, South Africa

78 34: Engr. Computer Science, University of California, Davis, CA 95616, USA

79 35: Department of Earth System Science, Stanford University, Stanford, CA 94305, USA

80 36: Japan Meteorological Agency, 3-6-9 Toranomon, Minato City, Tokyo, 105-8431, Japan

81 37: Research Institute for Environment, Energy, and Economics, Appalachian State University, Boone,

82 North Carolina, USA

83 38: Department of Geological and Environmental Sciences, Appalachian State University, Boone,

84 North Carolina, USA

85 39: Hakai Institute, 1713 Hyacinthe Bay Rd, Heriot Bay, BC, V0P 1H0, Canada

86 40: National Centre for Earth Observation, University of Edinburgh, Edinburgh, EH9 3FE, UK

87 41: School of Geosciences, University of Edinburgh, Edinburgh, EH9 3FE, UK

88 42: Climate and Global Dynamics Laboratory, National Center for Atmospheric Research

89 43: Geophysical Institute, University of Bergen, Allégaten 70, 5007 Bergen, Norway

90 44: Bjerknes Centre for Climate Research, Bergen, Norway

91 45: International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1 A-2361 Laxenburg,

92 Austria

93 46: European Commission, Joint Research Centre (JRC), Ispra, Italy

94 47: Environmental Physics Group, Institute of Biogeochemistry and Pollutant Dynamics and Center for

95 Climate Systems Modeling (C2SM), ETH Zürich, Zurich, Switzerland

96 48: Swiss Data Science Center, 8050 Zurich, Switzerland

97 49: Laboratoire de Géologie, Ecole Normale Supérieure, CNRS UMR 8538, Institut Pierre-Simon

98 Laplace, PSL Research University, Paris, France

99 50: Smith School for Enterprise and the Environment, University of Oxford, Oxford, UK

100 51: NCAS-Climate, Climatic Research Unit, School of Environmental Sciences, University of East

101 Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK

102 52: Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box

103 60 12 03, 14412 Potsdam, Germany

104 53: Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany

105 54: Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan

106 55: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado

107 Boulder, Boulder, CO 80305, USA

108 56: National Oceanic and Atmospheric Administration Global Monitoring Laboratory (NOAA/GML),

109 325 Broadway R/GML, Boulder, CO 80305, USA
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110 57: Department of Climate, Meteorology and Atmospheric Sciences, University of Illinois, Urbana, IL

111 61801, USA

112 58: Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information

113 Technology, International Institute for Earth System Science, Nanjing University, Nanjing, 210023,

114 China.

115 59: Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan

116 60: University of California, San Diego, Scripps Institution of Oceanography, La Jolla, CA 92093-

117 0244, USA

118 61: Utrecht University, Faculty of Geosciences, Department IMEW, Copernicus Institute of Sustainable

119 Development, Heidelberglaan 2, P.O. Box 80115, 3508 TC, Utrecht, the Netherlands

120 62: School of Life Sciences, Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007,

121 Australia

122 63: State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research

123 Institute, Chinese Academy of Sciences, Beijing 100101, China

124 64: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System

125 Science, Tsinghua University, Beijing 100084, China

126 65: Lawrence Berkeley National Laboratory, USA

127 66: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

128 67: Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA,

129 USA

130 68: CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute

131 of Meteorological Sciences, Chongqing 401147, China

132 69: Department of Earth System Science, Tsinghua University, Beijing, China

133 70: LOCEAN Laboratory (Sorbonne Université, CNRS, IRD, MNHN), 4 Place Jussieu, F-75005 Paris,

134 France

135 71: Department of Meteorology and National Centre for Atmospheric Science, University of Reading,

136 Reading, UK

137 72: Lamont-Doherty Earth Observatory, Columbia University, New York, NY, USA

138 73: Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada

139 74: College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, Alaska 99775-

140 7220, USA

141 75: Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA

142 76: Climate Adaptation Research Center, University of California, Davis, CA 95616, USA

143 77: Carbon to Sea Initiative, Washington D.C., USA

144 78: Earth System Division, National Institute for Environmental Studies (NIES), 16-2 Onogawa,

145 Tsukuba Ibaraki, 305-8506, Japan

146 79: NORCE Research, Jahnebakken 5, 5007 Bergen, Norway
<figure><img src="image_6.png" /></figure>

<table><tr><td>147</td><td>80: Schiller Institute of Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA</td></tr><tr><td>148</td><td>81: National Oceanic and Atmospheric Administration, Atlantic Oceanographic & Meteorological Laboratory (NOAA/AOML), 4301 Rickenbacker Causeway, Miami, FL 33149, USA</td></tr><tr><td>150</td><td>82: School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, China</td></tr><tr><td>151</td><td>83: Department of Geoscience, Environment & Society-BGEOSYS, Université Libre de Bruxelles, 1050 Brussels, Belgium</td></tr><tr><td>154</td><td>84: Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Seestrasse 15, 18119 Rostock, Germany</td></tr><tr><td>156</td><td>85: Princeton University, Department of Geosciences and Princeton Environmental Institute, Princeton, NJ, USA</td></tr><tr><td>158</td><td>86: Max Planck Institute for Biogeochemistry, P.O. Box 600164, Hans-Köll-Str. 10, 07745 Jena, Germany</td></tr><tr><td>160</td><td>87: Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, Canada</td></tr><tr><td>162</td><td>88: Institute for Climate and Environmental Physics, University of Bern, Bern, Switzerland</td></tr><tr><td>163</td><td>89: Environnements et Paléoenvironnements Océaniques et Continentaux (EPOC) UMR CNRS 5805</td></tr><tr><td>164</td><td>EPOC - OASU, Université de Bordeaux, Allée Geoffroy Saint Hilaire, 33615 Pessac, France</td></tr><tr><td>165</td><td>90: Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, UMR 3589, Toulouse, France</td></tr><tr><td>167</td><td>91: Research Center for Environmental Modeling and Application, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan</td></tr><tr><td>169</td><td>92: Advanced Institute for Marine Ecosystem Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan</td></tr><tr><td>171</td><td>93: State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China</td></tr><tr><td>173</td><td>94: University of Chinese Academy of Sciences, Beijing 101408, China</td></tr><tr><td>174</td><td>95: Australian Antarctic Partnership Program, University of Tasmania, Hobart, Australia</td></tr><tr><td>175</td><td>96: JMA Meteorological Research Institute, Tsukuba, Ibaraki, Japan</td></tr><tr><td>176</td><td>97: Statistics Division, Food and Agriculture Organization of the United Nations, Via Terme di Caracalla, Rome 00153, Italy</td></tr><tr><td>178</td><td>98: Earth Sciences New Zealand, Wellington, New Zealand</td></tr><tr><td>179</td><td>99: Department of Marine Science, University of Otago, Dunedin, New Zealand</td></tr><tr><td>180</td><td>100: Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA</td></tr><tr><td>182</td><td>101: Institute of Carbon Neutrality, College of Urban and Environmental Sciences, Peking University, Beijing 100091, China</td></tr></table>
<figure><img src="image_7.png" /></figure>

102: School of Environmental Science and Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing, 210044, China

185

186

Correspondence to: Pierre Friedlingstein (p.friedlingstein@exeter.ac.uk)

188 **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 (E fos) are based on energy statistics and cement production data. Emissions from land-use change (E luc) 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 (G atm) is computed from the annual changes in concentration. The global net uptake of CO₂ by the ocean (S ocean, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based CO₂ products. The global net uptake of CO₂ by the land (S land, 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 (B atm), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ.

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, 0.2GtC/yr), E luc 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, 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 atm (-1.7 GtC yr⁻¹), suggesting that the total sink or G atm 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 E fos 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

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218
<figure><img src="image_8.png" /></figure>

219 magnitude of the land CO₂ flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink.

221 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).

225

226 Executive Summary

227 Global fossil CO₂ emissions (including cement carbonation) are expected to further increase in 2025 by 1.1% relative to 2024 (range 0.2% to 2.2%), bringing fossil emissions to an expected 10.4 GtC yr⁻<fnref n="1" /> (38.1 GtCO₂ yr⁻<fnref n="1" />)<fnref n="1" />. Emissions from coal, oil and gas in 2025 are all expected to be above their 2024 levels (by 0.8%, 1.0% and 1.3% respectively). These preliminary estimates are based on available data for 2025. Consolidated data confirm a growth of 1.1% in 2024 relative to 2023, with fossil CO₂ emissions of 10.3 ± 0.5 GtC yr⁻<fnref n="1" /> (37.8 ± 1.8 GtCO₂ yr⁻<fnref n="1" />) in 2024.

233 Regionally, fossil emissions are projected to grow in 2025 in the USA and the European Union, reversing long-term decreases, and also in China and India, albeit more slowly compared to recent trends. The increase in fossil emissions in 2025 relative to 2024 is projected to be 0.4% (-0.9% to 2.0%) for China, 1.9% (-0.2% to 4.1%) for the United States, 1.4% (-0.3% to 3.1%) for India, and 0.4% (-2.1% to 2.8%) for the European Union. Projected emissions in Japan, provided this year for the first time, are for a decrease of -2.2% (-8.1% to 3.7%). Emissions are also projected to increase by 6.8% for international aviation but to remain flat for international shipping, and to increase by 1.1% (-1.1% to 3.3%) for the rest of the world in aggregate. The global carbon intensity of energy has consistently decreased over the past decade (-0.7% yr⁻<fnref n="1" />), indicating decarbonisation of the energy system in China (-1.4% yr⁻<fnref n="1" />), the European Union (-1.5% yr⁻<fnref n="1" />), and the USA (-1.3% yr⁻<fnref n="1" />), but no decline in India and an increase in the rest of the world in aggregate. These trends are not sufficient to offset the growth in global energy demand, which is driven by growing GDP globally and a weakening decline in energy per GDP, particularly in China and the US.

246 Fossil CO₂ emissions decreased (p<0.05) in 35 countries with growing economies (p<0.05) during the decade 2015-2024. The list of decarbonising countries nearly doubled since the previous decade (2005-2014; 18 countries). The 35 decarbonising countries contribute 2.7 GtC yr⁻<fnref n="1" /> (9.7 GtCO₂) fossil fuel CO₂ emissions over the last decade in aggregate, representing 27% of the world CO₂ fossil

<footnote n="1">¹ All growth rates use a leap year adjustment that corrects for the extra day in 2024.</footnote>
<figure><img src="image_9.png" /></figure>

250 emissions.

251 **Global net CO₂ emissions from land use averaged 1.4 ± 0.7 GtC yr⁻¹ (5.0 ± 2.6 GtCO₂ yr⁻¹) for the 2015-2024 period.** Emissions from deforestation, the main driver of global gross emissions, remain high at around 1.9 GtC yr⁻¹ over the 2015-2024 period, highlighting the strong potential of halting deforestation for emissions reductions. Sequestration of 1.3 GtC yr⁻¹ through re-/afforestation and forest regrowth in shifting cultivation cycles offsets two third of the deforestation emissions. In addition, smaller net emissions are due to wood harvest & forest management, peat drainage and peat fire.
252
253
254
255
256
257 Regionally, the highest net emitters during 2015-2024 were Brazil, Indonesia, and the Democratic Republic of the Congo, with these 3 countries contributing more than half of global land-use CO₂ emissions. Net carbon dioxide removals (CDR) from re-/afforestation were highest in China, the USA, and the EU27, with combined removals of 0.3 GtC/yr during 2015-2024.
257
258
259
260

261 **Since the late-1990s, emissions from land use show a statistically significant decrease at a rate of around 0.2 GtC per decade, with a larger drop within the most recent decade. Preliminary data for 2025 suggest emissions decreased to around 1.1 GtC (4.1 GtCO₂ yr⁻¹), mainly attributable to the end of the El Niño conditions.**

265 **Total anthropogenic emissions (fossil and land use, including the carbonation sink) were 11.6 GtC yr⁻¹ (42.4 GtCO₂ yr⁻¹) in 2024, with a slightly lower preliminary estimate of 11.5 GtC yr⁻¹ (42.2 GtCO₂ yr⁻¹) for 2025.** Total anthropogenic emissions have grown more slowly over the last decade (0.3% yr⁻¹ over the 2015-2024 period) compared to the previous decade (1.9% yr⁻¹ over 2005-2014).
266
267
268

269 **The remaining carbon budget from the beginning of 2026 for a 50% likelihood to limit warming to 1.5°C is nearly exhausted (50 GtC, 170 GtCO₂ left, equivalent to around 4 years at the 2025 emissions levels), consistent with the warming of the planet attributed to human activities, which reached 1.36°C in 2024.** Each additional cumulative emission of about 180 GtCO₂ will lead to approximately 0.1°C of warming. The remaining carbon budgets to limit warming to 1.7°C and 2°C above the 1850-1900 level have been reduced to 145 GtC (525 GtCO₂, 12 years) and 290 GtC (1055 GtCO₂, 25 years) respectively.
270
271
272
273
274
275

276 **The concentration of CO₂ in the atmosphere is set to reach 425.7 ppm in 2025, 52% above pre-industrial levels.** The atmospheric CO₂ growth rate was 5.6 ± 0.02 GtC yr⁻¹ (2.6 ppm) during the decade 2015-2024 (50% of total CO₂ emissions). 2024 had a record-high growth rate of 7.9 ± 0.02 GtC yr⁻¹ (3.7 ppm) mainly due to the 2023/2024 El Niño conditions. The preliminary 2025 growth rate estimate is 4.9 GtC (2.3 ppm).
277
278
279
280

281 **The ocean sink, the global net uptake of CO₂ by the ocean, has taken up 29% of the total emissions in the past decade, after being re-evaluated upwards based on new evidence and process understanding.** The ocean sink has been stagnant since 2016, largely in response to climate variability modulating the growing sink trend, but further affected by the ocean heatwave
282
283
284
<figure><img src="image_10.png" /></figure>

of 2023-2024 in the Northern Hemisphere. The ocean CO₂ sink was 3.2 ± 0.4 GtC yr⁻¹ during the decade 2015-2024, with a preliminary estimate of 3.2 GtC yr⁻¹ for 2025, slightly below 2024 levels, due to the end of the El Niño conditions in 2024 and associated reduced atmospheric CO₂ growth rate.

The land sink, which is the global net uptake of CO₂ by the land excluding land use, has taken up 21% of the total emissions in the past decade, after being re-evaluated downwards based on improved process representation. The land sink has been relatively stagnant since 2000, largely in response to climate variability and climate change offsetting the CO₂ induced growth. The land CO₂ sink was 2.4 ± 0.8 GtC yr⁻¹ during the 2015-2024 decade, but was reduced to 1.9 GtC ± 0.8 GtC yr⁻¹ in 2024. The preliminary estimate for the land sink in 2025 is an increase to 3.1 GtC, recovering entirely from its strong drop during the 2023-2024 El Niño conditions.

The effects of climate change and climate variability act to reduce the land and ocean CO₂ sinks by 25% and 7.9% respectively, on average for the 2015-2024 period. The land sink is negatively affected by warming with decreased tropical plant productivity and enhanced ecosystem respiration globally. The ocean sink is negatively affected by altered oceanic circulation and surface warming which decreases CO₂ solubility.

So far in 2025, global fire CO₂ emissions have been approximately 20% lower than the 2015-2024 average due to low fire activity in Africa and the tropics, reaching 1.2-1.4 GtC globally during January-September. This contrasts with the above average fire emissions in 2023 (1.7-2.1 GtC yr⁻¹) and 2024 (1.6-2.2 GtC yr⁻¹) due to extensive fires in Canada (2023) and Brazil and Bolivia (2024). These fire emissions estimates should not be directly compared with the land-use emissions or the land sink, because they represent a gross carbon flux to the atmosphere and do not account for post-fire recovery. They also do not distinguish between natural, climate-driven, and land-use-related fires.

307
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308

309 **1 Introduction**

310 The concentration of carbon dioxide (CO₂) in the atmosphere has increased from approximately 278
311 parts per million (ppm) in 1750 (Gulev et al., 2021), the beginning of the Industrial Era, to 422.8 ± 0.1
312 ppm in 2024 (Lan et al., 2025a; Figure 1). The atmospheric CO₂ increase above pre-industrial levels
313 was, initially, primarily caused by the release of carbon to the atmosphere from deforestation and other
314 land-use change activities (Canadell et al., 2021). While emissions from fossil fuels started before the
315 Industrial Era, they became the dominant source of anthropogenic emissions to the atmosphere from
316 around 1950 and their relative share has continued to increase until present. Anthropogenic emissions
317 occur on top of an active natural carbon cycle that circulates carbon between the reservoirs of the
318 atmosphere, ocean, and terrestrial biosphere on time scales from sub-daily to millennial, while
319 exchanges with geologic reservoirs occur on longer timescales (Archer et al., 2009).

320 The global carbon budget (GCB) presented here refers to the mean, variations, and trends in the
321 perturbation of CO₂ in the environment, referenced to the beginning of the Industrial Era (defined here
322 as 1750). This paper describes the components of the global carbon cycle over the historical period with
323 a stronger focus on the recent period (since 1959, onset of robust atmospheric CO₂ measurements), the
324 last decade (2015-2024), the last year (2024) and the current year (2025). Finally, it provides
325 cumulative emissions from fossil fuels and land-use change since the year 1750, and since the year
326 1850 (the reference year for historical simulations in IPCC).

327 We quantify the input of CO₂ to the atmosphere by emissions from human activities, the growth rate of
328 atmospheric CO₂ concentration, and the resulting changes in the storage of carbon in the land and ocean
329 reservoirs in response to increasing atmospheric CO₂ levels, climate change and variability, and other
330 anthropogenic and natural changes (Figure 2). An understanding of this perturbation budget over time
331 and the underlying variability and trends of the natural carbon cycle is necessary to understand the
332 response of natural sinks to changes in climate, CO₂ and land-use change drivers, and to quantify
333 emissions compatible with a given climate stabilisation target.

334 The components of the CO₂ budget that are reported annually in this paper include separate and
335 independent estimates for the CO₂ emissions from (1) fossil fuel combustion and oxidation from all
336 energy and industrial processes; also including cement production and carbonation (E<sub>FOS</sub>; GtC yr⁻¹) and
337 (2) the emissions resulting from deliberate human activities on land, including those leading to land-use
338 change (E<sub>LUC</sub>; GtC yr⁻¹); and their partitioning among (3) the growth rate of atmospheric CO₂
339 concentration (G<sub>ATM</sub>; GtC yr⁻¹), and the uptake of CO₂ (the ‘CO₂ sinks’) in (4) the ocean (S<sub>OCEAN</sub>; GtC
340 yr⁻¹) and (5) on land (S<sub>LAND</sub>; GtC yr⁻¹). The CO₂ sinks as defined here conceptually include the response
341 of the land (including inland waters and estuaries) and ocean (including coastal and marginal seas) to
<figure><img src="image_12.png" /></figure>

342 elevated CO₂ and changes in climate and other environmental conditions, although in practice not all processes are fully accounted for (see Section 2.10). Note that the term sink means that the net transfer of carbon is from the atmosphere to land or the ocean, but it does not imply any permanence of that sink in the future.

346 Global emissions and their partitioning among the atmosphere, ocean and land are in balance in the real world. Due to the combination of imperfect spatial and/or temporal data coverage, errors in each estimate, and smaller terms not included in our budget estimate (discussed in Section 2.10), the independent estimates (1) to (5) above do not necessarily add up to zero. We hence estimate a budget imbalance (BIM), which is a measure of the mismatch between the estimated emissions and the estimated changes in the atmosphere, land and ocean, as follows:

$$B_{IM} = E_{FOS} + E_{LUC} - (G_{ATM} + S_{OCEAN} + S_{LAND}) \quad (1)$$

353 GATM is usually reported in ppm yr⁻¹, which we convert to units of carbon mass per year, GtC yr⁻¹, using 1 ppm = 2.124 GtC (Ballantyne et al., 2012; Table 1). Units of gigatonnes of CO₂ (or billion tonnes of CO₂) used in policy are equal to 3.664 multiplied by the value in units of GtC.

356 We also assess a set of additional lines of evidence derived from global atmospheric inversion system results (Section 2.7), observed changes in oxygen concentration (Section 2.8) and Earth System Models (ESMs) simulations (Section 2.9), all of these methods closing the global carbon balance (zero BIM).

359 We further quantify EFOS and ELUC by country, including both territorial and consumption-based accounting for EFOS (see Section 2), and discuss missing terms from sources other than the combustion of fossil fuels (see Section 2.10). We also assess carbon dioxide removal (CDR) (see Sect. 2.2 and 2.3). Land-based CDR is significant, but already accounted for in ELUC in equation (1) (Sect 3.2.2). Other CDR methods, not based on vegetation, are currently several orders of magnitude smaller than the other components of the budget (Sect. 3.3), hence these are not included in equation (1), or in the global carbon budget tables or figures (with the exception of Figure 2 where CDR is shown primarily for illustrative purpose).

367 The global CO₂ budget has been assessed by the Intergovernmental Panel on Climate Change (IPCC) in all assessment reports (Watson et al., 1990; Schimel et al., 1995; Prentice et al., 2001; Denman et al., 2007; Ciais et al., 2013; Canadell et al., 2021), and by others (e.g. Ballantyne et al., 2012). The Global Carbon Project (GCP, www.globalcarbonproject.org, last access: 23 October 2025) has coordinated this cooperative community effort for the annual publication of global carbon budgets for the year 2005 (Raupach et al., 2007; including fossil emissions only), year 2006 (Canadell et al., 2007), year 2007 (GCP, 2008), year 2008 (Le Quéré et al., 2009), year 2009 (Friedlingstein et al., 2010), year 2010 (Peters et al., 2012a), year 2012 (Le Quéré et al., 2013; Peters et al., 2013), year 2013 (Le Quéré et al., 2014), year 2014 (Le Quéré et al., 2015a; Friedlingstein et al., 2014), year 2015 (Jackson et al., 2016;
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376 Le Quéré et al., 2015b), year 2016 (Le Quéré et al., 2016), year 2017 (Le Quéré et al., 2018a; Peters et al., 2017a), year 2018 (Le Quéré et al., 2018b; Jackson et al., 2018), year 2019 (Friedlingstein et al., 2019; Jackson et al., 2019; Peters et al., 2020), year 2020 (Friedlingstein et al., 2020; Le Quéré et al., 2021), year 2021 (Friedlingstein et al., 2022a; Jackson et al., 2022), year 2022 (Friedlingstein et al., 2022b), the year 2023 (Friedlingstein et al., 2023), and most recently the year 2024 (Friedlingstein et al., 2025a). Each of these papers updated previous estimates with the latest available information for the entire time series.

383 We adopt a range of ±1 standard deviation (σ) to report the uncertainties in our global estimates, representing a likelihood of 68% that the true value will be within the provided range if the errors have a gaussian distribution, and no bias is assumed. This choice reflects the difficulty of characterising the uncertainty in the CO₂ fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty of updating the CO₂ emissions from land-use change. A likelihood of 68% provides an indication of our current capability to quantify each term and its uncertainty given the available information. The uncertainties reported here combine statistical analysis of the underlying data, assessments of uncertainties in the generation of the datasets, and expert judgement of the likelihood of results lying outside this range. The limitations of current information are discussed in the paper and have been examined in detail elsewhere (Ballantyne et al., 2015; Zscheischler et al., 2017). We also use a qualitative assessment of confidence level to characterise the annual estimates from each term based on the type, amount, quality, and consistency of the different lines of evidence as defined by the IPCC (Stocker et al., 2013).

396 This paper provides a detailed description of the datasets and methodology used to compute the global carbon budget estimates for the industrial period, from 1750 to 2025, and in more detail for the recent period since 1959. This paper is updated every year using the format of 'living data' to keep a record of budget versions and the changes in new data, revision of data, and changes in methodology that lead to changes in estimates of the carbon budget. All underlying data used to produce the budget and additional materials associated with the release of each new version are available via the Global Carbon Budget website (https://globalcarbonbudget.org/, last access: 23 October 2025), with emissions also available through the Global Carbon Atlas (http://www.globalcarbonatlas.org, last access: 23 October 2025). With this approach, we aim to provide the highest transparency and traceability in the reporting of CO₂, the key driver of climate change.

## 2 Methods

407 Multiple organisations and research groups around the world generated the original measurements and data used to complete the global carbon budget. The effort presented here is thus mainly one of synthesis, where results from individual groups are collated, analysed, and evaluated for consistency. We facilitate access to original data with the understanding that primary datasets will be referenced in
<figure><img src="image_14.png" /></figure>

411 future work (see Table 2 for how to cite the datasets, and Section on data availability). Descriptions of
412 the measurements, models, and methodologies follow below, with more detailed descriptions of each
413 component provided as Supplementary Information (Supplement Tables S1 to S5).

414 This is the 20th version of the global carbon budget and the 14th revised version in the format of a living
data update in Earth System Science Data. It builds on the latest published global carbon budget of
415 Friedlingstein et al. (2025a). The main changes this year are: the inclusion of (1) data to year 2024 and
416 a projection for the global carbon budget for year 2025; (2) transient carbon densities in the estimate of
417 ELUC, (3) a correction on SLAND to account for the historical changes in forest cover, (4) a correction on
418 SOCEAN to account for the underestimation of the sink by GOBMs and for the ocean temperature
419 gradients in the /CO2-products estimate, (5) an estimate of the atmospheric CO2 growth rate derived
420 from satellite observations, and (6) decadal estimates of change in the dissolved inorganic carbon
421 inventory in the interior ocean. Other methodological differences are summarised in Table 3 and
422 previous changes since 2006 are provided in Table S9.
423

## 2.1 Fossil CO₂ emissions (EFOS)

### 2.1.1 Historical period 1850-2024

426 The estimates of global and national fossil CO₂ emissions (EFOS) include the oxidation of fossil fuels
427 through both combustion (e.g., transport, heating) and chemical oxidation (e.g. carbon anode
428 decomposition in aluminium refining) activities, and the decomposition of carbonates in industrial
429 processes (e.g. the production of cement). We also include CO₂ uptake from the cement carbonation
430 process. Several emissions sources are not estimated or not fully covered: coverage of emissions from
431 lime production are not global, and decomposition of carbonates in glass and ceramic production are
432 included only for the “Annex 1” countries of the United Nations Framework Convention on Climate
433 Change (UNFCCC) for lack of activity data. These omissions are considered to be minor. Short-cycle
434 carbon emissions - for example from combustion of biomass - are not included here but are accounted
435 for in the CO₂ emissions from land use (see Section 2.2).

436 Our estimates of fossil CO₂ emissions rely on data collection by many other parties. Our goal is to
437 produce the best estimate of this flux, and we therefore use a prioritisation framework to combine data
438 from different sources that have used different methods, while being careful to avoid double counting
439 and undercounting of emissions sources. The CDIAC-FF emissions dataset, derived largely from UN
440 energy data, forms the foundation, and we extend emissions to 2024 using energy growth rates reported
441 by the Energy Institute (a dataset formerly produced by BP). We then proceed to replace estimates
442 using data from what we consider to be superior sources, for example Annex 1 countries’ official
443 submissions to the UNFCCC. All data points are potentially subject to revision, not just the latest year.
444 For full details see Andrew and Peters (2025).
<figure><img src="image_15.png" /></figure>

Other estimates of global fossil CO₂ emissions exist, and these are compared by Andrew (2020a). The most common reason for differences in estimates of global fossil CO₂ emissions is a difference in which emissions sources are included in the datasets. Datasets such as those published by the Energy Institute, the US Energy Information Administration, and the International Energy Agency's 'CO₂ emissions from fuel combustion' are all generally limited to emissions from combustion of fossil fuels. In contrast, datasets such as PRIMAP-hist, CEDS, EDGAR, and GCP's dataset aim to include all sources of fossil CO₂ emissions. See Andrew (2020a) for detailed comparisons and discussion.

Cement products absorb CO₂ from the atmosphere over their lifetimes, a process known as 'cement carbonation'. We estimate this CO₂ sink, from 1931 onwards, as the average of two studies in the literature (Cao et al., 2020; Guo et al., 2021). Both studies use the same model, developed by Xi et al. (2016), with different parameterisations and input data, with the estimate of Guo and colleagues being a revision of Xi et al. (2016). The trends of the two studies are very similar. Since carbonation is a function of both current and previous cement production, we extend these estimates to 2024 by using the growth rate derived from the smoothed cement emissions (10-year smoothing) fitted to the carbonation data. In the present budget, we always include the cement carbonation carbon sink in the fossil CO₂ emission component (EFOS) unless explicitly stated otherwise.

We use the Kaya Identity for a simple decomposition of CO₂ emissions into the key drivers (Raupach et al., 2007). While there are variants (Peters et al., 2017b), we focus here on a decomposition of CO₂ emissions into population, GDP per person, energy use per GDP, and CO₂ emissions per energy. Multiplying these individual components together returns the CO₂ emissions. Using the decomposition, it is possible to attribute the change in CO₂ emissions to the change in each of the drivers. This method gives a first-order understanding of what causes CO₂ emissions to change each year.

## 2.1.2 2025 projection

We provide a projection of global fossil CO₂ emissions in 2025 by combining separate projections for China, USA, EU, India, Japan, and for all other countries combined. The methods are different for each of these. For China we combine monthly fossil fuel production data from the National Bureau of Statistics and trade data from the Customs Administration, giving us partial data for the growth rates to date of natural gas, petroleum, and cement, and of the apparent consumption itself for raw coal. We then use a regression model to project full-year emissions based on historical observations. For the USA our projection is taken directly from the Energy Information Administration's (EIA) Short-Term Energy Outlook (EIA, 2025), combined with the year-to-date growth rate of cement clinker production. For the EU we use monthly energy data from Eurostat to derive estimates of monthly CO₂ emissions (Andrew, 2021), with (i) coal emissions extended using a statistical relationship with reported electricity generation from coal and other factors, (ii) natural gas emissions extended using Holt-Winters, and (iii) oil emissions extended using a regression relationship between our monthly estimates
<figure><img src="image_16.png" /></figure>

480 and the EIA's estimates of oil consumption for Europe, which include a forecast. EU cement emissions
481 are based on available year-to-date data from three of the largest producers, Germany, Poland, and
482 Spain. India's projected emissions are derived from monthly estimates through August/September using
483 the methods of Andrew (2020b) and extrapolated through December assuming seasonal patterns from
484 before 2019. Japan's emissions are also projected based on monthly estimates through July and normal
485 seasonal patterns. Emissions from international transportation (bunkers) are estimated separately for
486 aviation and shipping. Changes in aviation emissions are derived primarily from OECD monthly
487 estimates, extrapolated using the growth rates of global flight miles from Airportia, and then the final
488 months are projected assuming normal patterns from previous years. Shipping emissions are assumed to
489 be unchanged in 2025, based on available sub-annual evidence from multiple sources. Emissions for the
490 rest of the world are derived for coal and cement using projected growth in economic production from
491 the IMF (2025) combined with extrapolated changes in emissions intensity of economic production; for
492 oil using a global constraint from EIA; and for natural gas using a global constraint from IEA. More
493 details on the EFOS methodology and its 2025 projection can be found in Supplement S.1.

494 We compare our 2025 projection with the Carbon Monitor (2025). Carbon Monitor is a dataset of daily
495 emissions constructed using hourly to daily proxy data (e.g., electricity consumption, travel patterns,
496 etc) instead of energy use data (Liu et al., 2020a; Liu et al., 2020b). Emissions estimates from January
497 to August are combined to a new projection for September-December to give a full-year 2025 estimate.
498 The projections are estimated by leveraging seasonal patterns from 2019-2024 daily CO₂ emission data
499 from Carbon Monitor. A regression model is applied separately for individual countries to obtain their
500 respective forecast. First, the seasonality component for each month is assessed based on daily average
501 emissions from 2019 to 2023, excluding 2020 due to the COVID-19 pandemic. Then, a linear
502 regression model is constructed using the calculated seasonal components and the daily average
503 emissions for the months from January to August 2025. The resulting model is used to project carbon
504 emissions for September-December 2025. The uncertainty range is calculated by using historical
505 monthly variance of seasonal components (See Supplement S.1.5).

## 2.2 CO₂ emissions from land-use, land-use change and forestry (E_LUC)

### 2.2.1 Historical period 1850-2024

508 The net CO₂ flux from land-use, land-use change and forestry (E_LUC, called land-use change emissions
509 in the rest of the text) includes CO₂ fluxes from deforestation, afforestation, logging and forest
510 degradation (including harvest activity), shifting cultivation (cycle of cutting forest for agriculture, then
511 abandoning), regrowth of forests (following wood harvest or agriculture abandonment), peat burning,
512 and peat drainage.
<figure><img src="image_17.png" /></figure>

Updated estimates from three bookkeeping models are used to quantify gross emissions, gross removals and the resulting net E_LUC: BLUE (Hansis et al., 2015), LUCE (Qin et al. 2024), and OSCAR (Gasser et al., 2020). Different to previous GCBs, all three bookkeeping models use transient carbon densities, i.e., they consider the effects of environmental changes, such as atmospheric CO₂ increase, on vegetation and soil carbon densities, to estimate E_LUC (Gasser et al., 2020; Dorgeist et al., 2024). The GCB assessments have for a long time also (and initially exclusively) used estimates from the bookkeeping model H&C2023 (Houghton and Castanho, 2023), its predecessor H&N2017 (Houghton and Nassikas, 2017), and earlier versions. However, H&C2023 does not consider transient carbon densities and only provides data up to 2020, which implies the need to extrapolate its data to the time after 2020. As the model does thus not incorporate the most recent developments in bookkeeping modeling, it is not used anymore in GCB2025.

Emissions from peat burning and peat drainage are added from external datasets (see Supplement S.2.2): peat fire emissions from the Global Fire Emission Database (GFED4s; van der Werf et al., 2017) and peat drainage emissions averaged from estimates of the Food Agriculture Organization (Conchedda and Tubiello, 2020; FAO, 2025a) and from simulations with the DGVM ORCHIDEE-PEAT (Qiu et al., 2021) and the DGVM LPX-Bern (Lienert and Joos, 2018; Müller and Joos, 2021).

Uncertainty estimates were derived from the Dynamic Global Vegetation Models (DGVMs) ensemble for the time period prior to 1960, and using for the recent decades an uncertainty range of ±0.7 GtC yr⁻¹, which is a semi-quantitative measure for annual and decadal emissions and reflects our best value judgement that there is at least 68% chance (±1σ) that the true land-use change emission lies within the given range, for the range of processes considered here.

The GCB E_LUC estimates follow the CO₂ flux definition of global carbon cycle models and differ from IPCC definitions adopted in National GHG Inventories (NGHGI) for reporting under the UNFCCC. The latter typically include terrestrial fluxes occurring on all land that countries define as managed, following the IPCC managed land proxy approach (Grassi et al., 2018). This partly includes fluxes due to environmental change (e.g. atmospheric CO₂ increase), which are part of S_LAND in our definition. As a result, global emission estimates are smaller for NGHGI than for the global carbon budget definition (Grassi et al., 2023). The same is the case for the FAO estimates of carbon fluxes on forest land, which include both anthropogenic and natural fluxes on managed land (Tubiello et al., 2025; FAO, 2025b). Using the NGHGI data collected and processed in the LULUCF data hub V3.1 (Melo et al. 2025), we translate the GCB and NGHGI definitions to each other, to provide a comparison of the anthropogenic carbon budget as reported in GCB to the official country reporting to the UNFCCC convention. We further compare these estimates with the net atmosphere-to-land flux from atmospheric inversion systems (see Section 2.7), averaged over managed land only.
<figure><img src="image_18.png" /></figure>

E_LUC contains a range of fluxes that are related to Carbon Dioxide Removal (CDR). CDR is defined as the set of anthropogenic activities that remove CO₂ from the atmosphere, in addition to the Earth's natural processes (such as carbon uptake in response to atmospheric CO₂ increase), and store it in durable form, such as in forest biomass, soils, long-lived products, oceans or geological reservoirs. Here, we quantify vegetation-based CDR that is implicitly or explicitly captured by land-use fluxes (CDR not based on vegetation is discussed in Section 2.3). We quantify CDR through re/afforestation from the three bookkeeping estimates by separating permanent increases in forest cover, which counts as CDR, from forest regrowth in shifting cultivation cycles (not part of CDR; see Supplement S.2.2). It should be noted that the permanence of the storage under climate risks such as fire is increasingly questioned. Other CDR activities related to land use but not fully accounted for in our E_LUC estimate include the transfer of carbon to harvested wood products (HWP), bioenergy with carbon capture and storage (BECCS), and biochar production (Babiker et al., 2022; Smith et al., 2024). The different bookkeeping models all represent HWP but with varying details concerning product usage and their lifetimes. BECCS and biochar are currently only represented in bookkeeping and DGVM models with regard to the CO₂ removal through photosynthesis, without accounting for the durable storage. HWP, BECCS, and biochar are typically counted as CDR once the transfer to the durable storage site occurs and not when the CO₂ is removed from the atmosphere, which complicates a direct comparison to the GCB approach to quantify annual fluxes to and from the atmosphere. We provide estimates for CDR through HWP, BECCS, and biochar based on independent studies in Section 3.2.2. HWP and BECCS estimates reflect updated 2024 data of the State of CDR report (Smith et al., 2024), while biochar estimates correspond to 2023 due to unavailability of newer data. We do not add them to our E_LUC estimate to avoid potential double-counting that arises from the partial consideration of HWP, BECCS, and biochar in the bookkeeping and DGVM models and to avoid inconsistencies from the temporal discrepancy between transfer to storage and removal from the atmosphere. More details on the E_LUC methodology can be found in Supplement S.2.

## 2.2.2 2025 Projection

We project the 2025 land-use emissions for BLUE, OSCAR, and LUCE based on their E_LUC estimates for 2023 and adding the anomalies in carbon emissions from peat fires in equatorial Asia and tropical deforestation and degradation fires (2025 emissions relative to 2023 emissions) from GFED4s (van der Werf et al., 2017) estimated using active fire data (MCD14ML; Giglio et al., 2016). 2023 is used as base year since the E_LUC estimate for the year 2024 is informed by extrapolated land-use data (Supplement S.2.1). Peat drainage is assumed to be unaltered as it has low interannual variability.

## 2.3 Carbon Dioxide Removal (CDR) not based on vegetation

Some CDR involves CO₂ fluxes via land use, which is included in our estimate of E_LUC (re/afforestation) or provided separately in Section 3.2.2 (biochar, HWP, and BECCS). Other CDR
<figure><img src="image_19.png" /></figure>

occurs through CO₂ fluxes directly from the air to the geosphere, which is reported in Section 3.3. The majority of this derives from bio-oil storage in geological reservoirs, enhanced weathering through the application of crushed rock to soils, and the production of solid mineral products with CO₂ captured from the atmosphere, with smaller contributions from Direct Air Carbon Capture and Storage (DACCS), sinking of terrestrial (e.g., straw) or marine (e.g., macroalgae) biomass into the deep ocean through human intervention, and others like intentional ocean or river alkalinity enhancement. For these methods, we use updated 2024 data (Smith et al., 2024), which compiles and harmonises reported CDR from a combination of existing databases, surveys, and novel research. Currently, there are no internationally agreed methods for reporting these types of CDR, implying that estimates are based on self-disclosure by projects following their own protocols or protocols produced by third party registries. As such, the fractional uncertainty on these numbers should be viewed as substantial, and numbers are liable to change in future years as protocols are harmonised and improved.

## 2.4 Growth rate in atmospheric CO₂ concentration ($G_{ATM}$)

### 2.4.1 Historical period 1850-2024

The rate of growth of the atmospheric CO₂ concentration is provided for years 1959-2024 by the US National Oceanic and Atmospheric Administration Global Monitoring Laboratory (NOAA/GML; Lan et al., 2025a), which includes recent revisions to the calibration scale of atmospheric CO₂ measurements (WMO-CO₂-X2019; Hall et al., 2021). For the 1959-1979 period, the global growth rate is based on measurements of atmospheric CO₂ concentration averaged from the Mauna Loa and South Pole stations, as observed by the CO₂ Program at Scripps Institution of Oceanography (SIO, Keeling et al., 1976). For the 1980-2024 time period, the global growth rate is based on the average of multiple stations selected from the marine boundary layer sites with well-mixed background air (Lan et al., 2024), after fitting a smooth curve through the data for each station as a function of time, and averaging by latitude band (Masarie and Tans, 1995). The annual growth rate is estimated by Lan et al. (2025a) from atmospheric CO₂ concentration by taking the average of the most recent December-January months corrected for the average seasonal cycle and subtracting this same average one year earlier. To obtain $G_{ATM}$, the observation-based growth rate in units of ppm yr⁻¹ is converted to units of GtC yr⁻¹ by multiplying by a factor of 2.124 GtC per ppm, assuming instantaneous mixing of CO₂ throughout the atmosphere (Ballantyne et al., 2012; Table 1). There is high confidence in the observations because they are based on direct measurements from stations distributed around the world (Lan et al., 2024) with all CO₂ measurements consistently measured against the same CO₂ standard scale (WMO X2019) defined by a suite of gas standards (Hall et al., 2021). However, the conversion to estimates of $G_{ATM}$ in GtC/yr incurs large uncertainty on annual time scale as discussed next.

The uncertainty around $G_{ATM}$ is due to three main factors. First, the network composition of the marine boundary layer sites with some sites coming or going, gaps in the time series at each site, etc. This
<figure><img src="image_20.png" /></figure>

617 uncertainty was estimated with a bootstrap method by constructing 100 "alternative" networks (Steele
618 et al., 1992; Masarie and Tans, 1995; Lan et al., 2025a). Second, the analytical uncertainty that
619 describes the short- and long-term uncertainties associated with the CO₂ analyzers. A Monte Carlo
620 method was used to estimate the total analytical uncertainty by randomly selecting errors to add to each
621 observation from a normal distribution of combined short- and long-term uncertainties. Prior to the
622 1980s when analyzers were less precise and CO₂ measurement scale was slightly less well defined,
623 larger analytical errors were assigned to account for these factors. However, the network uncertainty
624 remains the larger term of uncertainty. The first and second uncertainties are reported as 1-sigma
625 standard deviations (i.e., 68% confidence interval), and summed in quadrature to determine the global
626 surface growth rate uncertainty, which averaged to 0.085 ppm (Lan et al., 2024b). Third, the uncertainty
627 associated with using the average CO₂ concentration from a surface network to approximate the true
628 atmospheric average CO₂ concentration (mass-weighted, in 3 dimensions) as needed to assess the total
629 atmospheric CO₂ burden. In reality, CO₂ variations measured at the stations will not exactly track
630 changes in total atmospheric burden, with offsets in magnitude and phasing due to vertical and
631 horizontal mixing (Pandey et al., 2025). This effect must be very small on decadal and longer time
632 scales, when the atmosphere can be considered well mixed. The long-term CO₂ increase in the
633 stratosphere lags the increase (meaning lower concentrations) that we observe in the marine boundary
634 layer, while the continental boundary layer (where most of the emissions take place) leads the marine
635 boundary layer with higher concentrations. These effects nearly cancel each other on decadal time
636 scales, when the growth rate is nearly the same everywhere (Ballantyne et al., 2012). We therefore
637 maintain an uncertainty around the annual growth rate based on the multiple stations dataset ranges
638 between 0.11 and 0.72 GtC yr⁻¹, with a mean of 0.61 GtC yr⁻¹ for 1959-1979 and 0.17 GtC yr⁻¹ for
639 1980-2024, when more measurement sites were available (Lan et al., 2025a). We estimate the
640 uncertainty of the decadal averaged growth rate after 1980 at 0.02 GtC yr⁻¹ based on the annual growth
641 rate uncertainty but stretched over a 10-year interval. For years prior to 1980, we estimate the decadal
642 averaged uncertainty to be 0.07 GtC yr⁻¹ based on a factor proportional to the annual uncertainty prior
643 and after 1980 (0.02 * [0.61/0.17] GtC yr⁻¹).

644 To estimate the total carbon accumulated in the atmosphere since 1750 or 1850, we use an atmospheric
645 CO₂ concentration of 278.3 ± 3 ppm or 285.1 ± 3 ppm, respectively (Gulev et al., 2021). For the
646 construction of the historical budget shown in Figure 3, we use the fitted estimates of CO₂
647 concentration from Joos and Spahni (2008) to estimate the annual atmospheric growth rate (see
648 Supplement S.7). The uncertainty of ±3 ppm (converted to ±1σ) is taken directly from the IPCC's AR5
649 assessment (Ciais et al., 2013). Typical uncertainties in the growth rate in atmospheric CO₂
650 concentration from ice core data are equivalent to ±0.1-0.15 GtC yr⁻¹ as evaluated from the Law Dome
651 data (Etheridge et al., 1996) for individual 20-year intervals over the period from 1850 to 1960 (Bruno
652 and Joos, 1997).
<figure><img src="image_21.png" /></figure>

653 **2.4.2 Satellite-based (2015-present)**

654 Further opportunities to estimate global CO₂ growth rates are offered by space-based platforms such as
655 GOSAT (since 2010) and OCO-2 (since 2015). Their recorded short-wave infrared spectra allow
656 retrieval of column CO₂ abundances (XCO₂) over cloud-free scenes over land and ocean with footprints
657 of 80 (GOSAT) and 3 (OCO-2) km² respectively. The columns are not full integrals; the sensitivity to
658 surface and lower-tropospheric CO₂ mole fractions is much higher than to upper tropospheric and
659 stratospheric CO₂. However, for the purpose of calculating atmospheric growth rates, the sensitivity is
660 not limiting, as the pressure-weighted sensitivity is still above 0.5 in the stratosphere (Pandey et al.
661 2024) and priors used in OCO-2 account for the slow stratospheric-tropospheric exchanges using the
662 age of air (Laughner et al. 2023). Here, we use OCO-2 based whole atmosphere growth rates using the
663 Growth Rate from Satellite Observations (GRESO) approach (Pandey et al., 2024). We specifically
664 note that their retrievals are evaluated against surface-based remote sensing (Total Column Carbon
665 Observing Network) data, which in turn are tied to atmospheric observations (from aircraft and using
666 aircore) (Wunch et al., 2017). Furthermore, OCO-2 retrievals use a priori CO₂ mole fraction profiles
667 tied to NOAA GML in-situ flask records at Mauna Loa (MLO) and American Samoa (SMO)
668 measurement stations (Laughner et al., 2023). For details on evaluation, bias-correction, and
669 spatiotemporal coverage of OCO-2 we refer to O’Dell et al (2018).

670 Relative to surface observations, the GRESO product typically reflects tropical growth rate anomalies
671 earlier, and sees whole atmosphere carbon stock changes with a lower latency. The GRESO growth
672 rates presented here use both land and ocean observations of OCO-2 providing global sampling of the
673 atmosphere. We used GRESO post 2015 to present an alternative quantification of the atmospheric
674 carbon stock changes in GATM, using the reported 1-sigma uncertainty of 0.08 ppm/yr. The mean
675 differences between GRESO and surface-data derived GATM is 0.05±0.26 ppm/yr over the 2015-2024
676 (n=10 years) period.

677 **2.4.3 2025 projection**

678 We provide an assessment of G<sub>ATM</sub> for 2025 as the average of two methods. First, the GCB regression
679 method models monthly global-average atmospheric CO<sub>2</sub> concentrations and derives the increment and
680 annual average from these. The model uses lagged observations of concentration (Lan et al., 2025a):
681 both a 12-month lag, and the lowest lag that will allow model prediction to produce an estimate for the
682 following January, recalling that the G<sub>ATM</sub> increment is derived from December/January pairs. The
683 largest driver of interannual changes is the ENSO signal (Betts et al., 2016), so the monthly ENSO 3.4
684 index (Huang et al., 2023) is included in the model. Given the natural lag between sea-surface
685 temperatures and effects on the biosphere, and in turn effects on globally mixed atmospheric CO<sub>2</sub>
686 concentration, a lagged ENSO index is used, and we use both a 5-month and a 6-month lag. The
687 combination of the two lagged ENSO values helps reduce possible effects of noise in a single month.
<figure><img src="image_22.png" /></figure>

688 To help characterise the seasonal variation, we add month as a categorical variable. Finally, we flag the
689 period affected by the Pinatubo eruption (August 1991 - November 1993) as a categorical variable.

690 The second method uses the multi-model mean and uncertainty of the 2025 GATM estimated by the
691 ESMs prediction system (see Section 2.9). We then take the average of the GCB regression and ESMs
692 GATM estimates, with their respective uncertainty combined quadratically.

693 Similarly, the projection of the 2025 global average CO₂ concentration (in ppm), is calculated as the
694 average of the estimates from the two methods. For the GCB regression method, it is the annual average
695 of global concentration over the 12 months of 2025; for the ESMs, it is the observed global average
696 CO₂ concentration for 2024 plus the annual increase in 2025 of the global average CO₂ concentration,
697 which is an average of NOAA/GML measurements from January to June (Lan et al., 2025) and
698 predictions of the ESMs multi-model mean from July to December (see Section 2.9).

699 **2.5 Ocean CO₂ sink**

700 **2.5.1 Historical period 1850-2024**

701 The reported estimate of the global ocean anthropogenic CO₂ sink SOCEAN is derived as the average of
702 two estimates. The first estimate is derived as the mean over an ensemble of ten global ocean
703 biogeochemistry models (GOBMs, Table 4 and Table S2). The second estimate is obtained as the mean
704 over an ensemble of nine surface ocean fCO₂-observation-based data-products (Table 4 and Table S3).

705 The GOBMs simulate both the natural and anthropogenic CO₂ cycles in the ocean. They constrain the
706 anthropogenic air-sea CO₂ flux (the dominant component of SOCEAN) by the transport of carbon into the
707 ocean interior, which is also the controlling factor of present-day ocean carbon uptake in the real world.
708 They cover the full globe and all seasons and were evaluated against surface ocean carbon observations,
709 suggesting they are suitable to estimate the annual ocean carbon sink (Hauck et al., 2020). We derive
710 SOCEAN from GOBMs by using a simulation (sim A) with historical forcing of climate and atmospheric
711 CO₂ from GCB (Section 2.4), accounting for model biases and drift from a control simulation (sim B)
712 with constant atmospheric CO₂ and normal year climate forcing. A third simulation (sim C) with
713 historical atmospheric CO₂ increase and normal year climate forcing is used to attribute the ocean sink
714 to CO₂ (sim C minus sim B) and climate (sim A minus sim C) effects. A fourth simulation (sim D;
715 historical climate forcing and constant atmospheric CO₂) is used to compare the change in
716 anthropogenic carbon inventory in the interior ocean (sim A minus sim D) to the observation-based
717 estimates of Gruber et al. (2019) and Müller et al. (2023) with the same flux components (steady state
718 and non-steady state anthropogenic carbon flux).

719 As there is accumulating evidence for a 10-20% underestimation of SOCEAN by the GOBMs based on
720 ocean interior data (section 3.6.5), atmospheric oxygen (section 3.6.2), atmospheric inversions (section
<figure><img src="image_23.png" /></figure>

3.8) and supported by eddy-covariance measurements (Dong et al., 2024), we scale up the global

GOBM multi-model mean estimates by 10% for estimating SOCEAN (Friedlingstein et al., 2025b) (see

Supplement S3.3). Analysis of Earth System Models and GOBMs indicate that an underestimation by

about 10% may be due to biases in ocean carbon transport and mixing from the surface mixed layer to

the ocean interior (Goris et al., 2018, Terhaar et al., 2021, Bourgeois et al., 2022, Terhaar et al., 2022),

biases in the chemical buffer capacity (Revelle factor) of the ocean (Vaittinada Ayar et al., 2022;

Terhaar et al., 2022) and partly due to a late starting date of the simulations (mirrored in atmospheric

CO₂ chosen for the pre-industrial control simulation, Table S2, Bronselaer et al., 2017, Terhaar et al.,

2022; 2024). GOBMs are evaluated against key metrics and their capability to reproduce various

statistical measures of physical and biogeochemical fields using the International Ocean Model

Benchmarking (IOMB) Scheme (Fu et al., 2022, Text S3.3).

The /CO₂-products are tightly linked to observations of /CO₂ (fugacity of CO₂, which equals pCO₂

corrected for the non-ideal behaviour of the gas; Pfeil et al., 2013), which carry imprints of temporal

and spatial variability, but are also sensitive to uncertainties in gas-exchange parameterizations and

data-sparsity (Fay et al., 2021, Gloege et al., 2021, Hauck et al., 2023a). Their asset is the assessment of

the mean spatial pattern of variability and its seasonality (Hauck et al., 2020, Gloege et al. 2021, Hauck

et al., 2023a). To benchmark biases and trends derived from the /CO₂-products, we update a model

subsampling exercise following Hauck et al. (2023a, see section S3) and use independent measurements

from the SOCAT (Surface Ocean CO₂ Atlas) flag E dataset (uncertainty less than 10 µatm) (Bakker et

al., 2016) and calculated /CO₂ from the Global Ocean Data Analysis Project GLODAP (Lauvset et al.,

2024). New in GCB2025, we now also include the UExP-FNN-U product in the ensemble mean, which

was previously shown but not included. We include it since new evidence emerged from field and

modelling studies, recommending the adjustment of the /CO₂ to the surface skin layer where the gas

exchange takes place (Dong et al., 2024, Ford et al., 2024, Bellenger et al., 2022). Furthermore, a

second /CO₂-product submitted temperature adjusted /CO₂ (JMA-MLR) this year, so we now include

these corrected estimates in the ensemble mean. Additionally, following Friedlingstein et al (2025b),

we now also add 0.18 PgC yr⁻¹ (multiplied by 7/9 as two products already include a temperature

correction) to the multi /CO₂-product average in the calculation of SOCEAN to account for the warm

layer and cool skin effect (See Supplement S3.1). This correction is based on a recent field study (Ford

et al., 2024) and broadly consistent in magnitude with a GOBM study (Bellenger et al 2022).

The global /CO₂-based flux estimates were adjusted to remove the pre-industrial natural ocean source of

CO₂ to the atmosphere of 0.65 ± 0.3 GtC yr⁻¹, arising from the transfer of carbon from land to ocean via

rivers (Regnier et al., 2022), to satisfy our definition of SOCEAN (Hauck et al., 2020). This CO₂

outgassing adjustment was distributed over the latitudinal bands using the regional distribution of

Lacroix et al. (2020; North: 0.14 GtC yr⁻¹, Tropics: 0.42 GtC yr⁻¹, South: 0.09 GtC yr⁻¹).

Acknowledging that this distribution is based on only one model, the advantage is that a gridded field is
<figure><img src="image_24.png" /></figure>

available, and the adjustment can be calculated for the three latitudinal bands and the RECCAP regions (REgional Carbon Cycle Assessment and Processes (RECCAP2; Ciais et al., 2020, Poulter et al., 2022, DeVries et al., 2023). This dataset suggests that more of the river-induced outgassing is located in the tropics than in the Southern Ocean and is thus opposed to the previously used dataset of Aumont et al. (2001). Accordingly, the regional distribution is associated with an additional uncertainty in addition to the large uncertainty around the global estimate (Crisp et al., 2022; Gruber et al., 2023). Anthropogenic perturbations of river carbon and nutrient transport to the ocean are not represented in the process models used to quantify SOCEAN, but an a-posteriori correction is applied to the global SOCEAN estimate (see Section 2.10 and Supplement S.8.3). We calculate SOCEAN as the average of the GOBM ensemble mean and the $f/CO_2$-product ensemble mean from 1990 onwards. For the 1959-1989 period, it is calculated as the GOBM ensemble mean plus half of the offset between GOBMs and $f/CO_2$-products ensemble means over 1990-2001. In addition, two diagnostic ocean models are used to estimate SOCEAN over the industrial era (1781-1958, Khatiwala et al. (2013) and DeVries (2014)).

SOCEAN is evaluated against the change in the total dissolved inorganic carbon inventory in the interior ocean from the observation-based estimates of Keppler et al. (2023) and Ehmen et al. (2025, in review) with the same flux components.

We assign an uncertainty of ± 0.4 GtC yr⁻¹ to the ocean sink based on a combination of random (ensemble standard deviation) and systematic uncertainties (GOBMs bias in anthropogenic carbon accumulation, previously reported uncertainties in $f/CO_2$-products; see Supplement S.3.4). While this approach is consistent within the GCB, an independent uncertainty assessment of the $f/CO_2$-products alone suggests a somewhat larger uncertainty of up to 0.7 GtC yr⁻¹ (Ford et al. 2024). We assess a medium confidence level to the annual ocean $CO_2$ sink and its uncertainty because it is based on multiple lines of evidence, it is consistent with ocean interior carbon estimates (see Section 3.6.5) and the interannual variability in the GOBMs and data-based estimates is largely consistent and can be explained by climate variability. We refrain from assigning a high confidence because of the deviation between the GOBM and $f/CO_2$-product trends between around 2002 and 2020 and the higher SOCEAN estimate from $O_2:N_2$ (section 2.8). More details on the SOCEAN methodology can be found in Supplement S.3.

## 2.5.2 2025 Projection

The SOCEAN forecast for the year 2025 is based on (a) the historical (Lan et al., 2025a) and our 2025 estimate of atmospheric $CO_2$ concentration, (b) the historical and our 2025 estimate of global fossil emissions, and (c) the boreal spring (March, April, May) Oceanic Niño Index (ONI) (NCEP, 2025). Using a non-linear regression approach, i.e., a feed-forward neural network, atmospheric $CO_2$, ONI, and the fossil emissions are used as training data to best match SOCEAN from 1959 through 2024 from this year's carbon budget. Using this relationship, the 2025 SOCEAN can then be estimated from the projected
<figure><img src="image_25.png" /></figure>

792 2025 input data using the non-linear relationship established during the network training. To avoid overfitting, the neural network training was done using a Monte Carlo approach, with a variable number of artificial neurons (varying between 2-5) and 20% of the randomly selected training data were withheld for independent internal testing.

796 Based on the best output performance (tested using the 20% withheld input data), the best performing number of neurons was selected. In a second step, we trained the network 10 times using the best number of neurons identified in step 1 and different sets of randomly selected training data. The mean of the 10 trainings is considered our best forecast, whereas the standard deviation of the 10 ensembles provides a first order estimate of the forecast uncertainty. This uncertainty is then combined with the SOCEAN uncertainty (0.4 GtC yr⁻¹) to estimate the overall uncertainty of the 2025 projection. As an additional line of evidence, we also assess the 2025 atmosphere-ocean carbon flux from the ESM prediction system (see Section 2.9).

## 2.6 Land CO₂ sink

### 2.6.1 Historical Period 1850-2024

806 The terrestrial land sink ($S_{LAND}$) is thought to be due to the combined effects of rising atmospheric CO₂, increasing N inputs, and climate change, on plant growth and terrestrial carbon storage. $S_{LAND}$ does not include land sinks directly resulting from land-use and land-use change (e.g., regrowth of vegetation) as these are part of the land-use change emissions ($E_{LUC}$), although system boundaries make it difficult to attribute exactly CO₂ fluxes on land between $S_{LAND}$ and $E_{LUC}$ (Erb et al., 2013).

811 $S_{LAND}$ is estimated from the multi-model mean of 22 DGVMs (Table 4 and Table S1). DGVMs simulations include all climate variability and CO₂ effects over land. In addition to the carbon cycle represented in all DGVMs, 15 models also account for the nitrogen cycle and hence can include the effect of N inputs on $S_{LAND}$. The DGVMs estimates of $S_{LAND}$ do not explicitly include the export of carbon to aquatic systems or its historical perturbation, which is discussed in Supplement S.8.3. DGVMs need to meet several criteria to be included in this assessment (see Supplement S.4.2). In addition, we use the International Land Model Benchmarking system (ILAMB; Collier et al., 2018) for the DGVMs evaluation (see Supplement S.4.2), with an additional comparison of DGVMs with a data-informed, Bayesian model-data fusion framework (CARDAMOM) (Bloom and Williams, 2015; Bloom et al., 2016). The uncertainty on $S_{LAND}$ is taken from the DGVMs standard deviation.

821 New to GCB2025 is a correction applied to the $S_{LAND}$ to account for its overestimation resulting from the assumption of pre-industrial land-use in the DGVM simulations, when in reality a large portion of the land surface has been converted to pasture and cropland, with a lower sink capacity. This bias is termed the Replaced Sinks and Sources (RSS) (Gitz and Ciais, 2003; Sitch et al., 2005, Pongratz et al.,
<figure><img src="image_26.png" /></figure>

2009; Gasser et al., 2021; Obermeier et al., 2021; Dorgeist et al., 2024). The correction applied on S<sub>LAND</sub> utilises results from a subset of DGVMs that were able to supply Net Biome Productivity estimates at a Plant Functional Type basis combined with time-varying PFT area fractions from the simulation with land use and land use change considered (O’Sullivan et al., 2025; Friedlingstein et al. 2025b), see S.4.1 for details on methodology. S<sub>LAND</sub> is reduced by 19% globally when accounting for the RSS. More details on the S<sub>LAND</sub> methodology can be found in Supplement S.4.

## 2.6.2 2025 Projection

In previous versions of the GCB, the land sink projection was similar to the ocean sink forecast, based on a non-linear regression approach with a feed-forward neural network. This approach, however, resulted in a large uncertainty and underestimated the land sink variability in the past. This year, we update the projection and simply calculate the land sink for 2025 as the residual of the projection of the other components of the carbon cycle (S<sub>LAND</sub>=E<sub>FOS</sub>+E<sub>ELUC</sub>-G<sub>ATM</sub>-S<sub>OCEAN</sub>).

## 2.7 Atmospheric inversion estimate

The world-wide network of in-situ atmospheric measurements and satellite derived atmospheric CO₂ column (XCO₂) observations put a strong constraint on changes in the atmospheric abundance of CO₂. This allows atmospheric inversion methods to constrain the magnitude and location of the combined total surface CO₂ fluxes from all sources, including fossil and land-use change emissions and land and ocean CO₂ fluxes. The inversions assume E<sub>FOS</sub> to be well known, and they solve for the spatial and temporal distribution of land and ocean fluxes from the residual gradients of CO₂ between stations that are not explained by fossil fuel emissions. By design, such systems thus close the carbon balance (B<sub>IM</sub> = 0) and provide an additional perspective on the independent estimates of the ocean and land fluxes.

This year’s release includes fourteen inversion systems that are described in Table S4. Each system is rooted in Bayesian inversion principles but uses different methodologies. These differences concern the selection of atmospheric CO₂ data or XCO₂, and the choice of a-priori fluxes to refine. They also differ in spatial and temporal resolution, assumed correlation structures, and mathematical approach of the models (see references in Table S4 for details). Importantly, the systems use a variety of transport models, which was demonstrated to be a driving factor behind differences in atmospheric inversion-based flux estimates, and specifically their distribution across latitudinal bands (Gaubert et al., 2019; Schuh et al., 2019). Six inversion systems used surface observations from the global measurement network (Schuldt et al., 2024, 2025). Eight inversion systems used satellite XCO₂ retrievals from GOSAT and/or OCO-2, scaled to the WMO 2019 calibration scale, of which three inversions this year used these XCO₂ datasets in addition to the in-situ observational CO₂ mole fraction records.
<figure><img src="image_27.png" /></figure>

857 The original products delivered by the inverse modellers were modified to facilitate the comparison to
858 the other elements of the budget, specifically on two accounts: (1) global total fossil emissions
859 including cement carbonation CO₂ uptake, and (2) riverine CO₂ transport. We note that with these
860 adjustments the inverse results no longer represent the net atmosphere-surface exchange over
861 land/ocean areas as sensed by atmospheric observations. Instead, for land, they become the net uptake
862 of CO₂ by vegetation and soils that is not exported by fluvial systems, similar to the DGVMs estimates.
863 For oceans, they become the net uptake of anthropogenic CO₂, similar to the GOBMs estimates.

864 The inversion systems prescribe global fossil emissions based on e.g. the GCP's Gridded Fossil
865 Emissions Dataset versions 2025.1 (GCP-GridFED; Jones et al., 2025), which are updates to GCP-
866 GridFEDv2021 presented by Jones et al. (2021b). GCP-GridFEDv2025.1 scales gridded estimates of
867 CO₂ emissions from EDGARv4.3.2 (Janssens-Maenhout et al., 2019) within national territories to
868 match national emissions estimates provided by the GCB for the years 1959-2024, which were
869 compiled following the methodology described in Section 2.1. Small differences between the systems
870 due to for instance regridding to the transport model resolution, or use of different fossil fuel emissions
871 than GCP-GridFEDv2025.1, are adjusted in the latitudinal partitioning we present, to ensure agreement
872 with the estimate of E<sub>FOS</sub> in this budget. We also note that the ocean fluxes used as prior by 8 out of 14
873 inversions are part of the suite of the ocean process model or fCO₂-products listed in Section 2.5.
874 Although these fluxes are further adjusted by the atmospheric inversions (except for Jena CarboScope),
875 it makes the inversion estimates of the ocean fluxes not completely independent of S<sub>OCEAN</sub> assessed
876 here.

877 To facilitate comparisons to the independent S<sub>OCEAN</sub> and S<sub>LAND</sub>, we used the same adjustments for
878 transport and outgassing of carbon transported from land to ocean, as done for the observation-based
879 estimates of S<sub>OCEAN</sub> (see Supplement S.5.1).

880 The atmospheric inversions are evaluated using vertical profiles of atmospheric CO₂ concentrations
881 (Figure S13). More than 50 aircraft programs over the globe, either regular programs or repeated
882 surveys over at least 9 months (except for SH programs), have been used to assess system performance
883 (with space-time observational coverage sparse in the SH and tropics, and denser in NH mid-latitudes;
884 Table S8). The fourteen systems are compared to the independent aircraft CO₂ measurements between 2
885 and 7 km above sea level between 2001 and 2024. Results are shown in Figure S13 and discussed in
886 Supplement S.5.2.

887 We note that as of GCB2025, the ensemble of inverse models covering a full decade is deemed
888 sufficiently large to report the 1-sigma standard deviation as uncertainty, following the convention of
889 the other components in GCB. More details on the atmospheric inversion methodology can be found in
890 Supplement S.5.
<figure><img src="image_28.png" /></figure>

891 **2.8 Atmospheric oxygen based estimate**

892 Long-term atmospheric O₂ and CO₂ observations allow estimation of the global ocean and land carbon sinks, due to the coupling of O₂ and CO₂ with distinct exchange ratios for fossil fuel emissions and land uptake, and uncoupled O₂ and CO₂ ocean exchange (Keeling and Manning, 2014). The global ocean and net land carbon sinks were calculated following methods and constants used in Keeling and Manning (2014) but modified to also include the effective O₂ source from metal refining (Battle et al., 2023). For the exchange ratio of the net land sink at value of 1.05 is used, following Resplandy et al. (2019). For fossil fuels, the following values are used: gas: 1.95 (+/-) 0.04, liquid: 1.44, (+/-) 0.03, solid: 1.17 (+/-) 0.03, cement: 0 (+/-) 0, gas flaring: 1.98 (+/-) 0.07 (Keeling, 1988). Atmospheric O₂ is observed as δ(O₂/N₂) and combined with CO₂ mole fraction observations into Atmospheric Potential Oxygen (APO, Stephens et al., 1998). The APO observations from 1990 to 2024 were taken from a weighted average of flask records from three stations in the Scripps O₂ program network (Alert, Canada (ALT), La Jolla, California (LJO), and Cape Grim, Australia (CGO), weighted per Keeling and Manning (2014). Observed CO₂ was taken from the globally averaged marine surface annual mean growth rate from the NOAA/GML Global Greenhouse Gas Reference Network (Lan et al., 2025a). The O₂ source from ocean warming is based on ocean heat content from updated data from NOAA/NCEI (Levitus et al., 2012). The effective O₂ source from metal refining is based on production data from Bray (2020), Flanagan (2021), and Tuck (2022). Uncertainty was determined through a Monte Carlo approach with 20,000 iterations, using uncertainties prescribed in Keeling and Manning (2014), including observational uncertainties from Keeling et al. (2007) and autoregressive errors in fossil fuel emissions (Ballantyne et al., 2015). The reported uncertainty is 1 standard deviation of the ensemble. As for the atmospheric inversions, the O₂ based estimates also close the carbon balance (B<sub>IM</sub> = 0) by design and provides another independent estimate of the ocean and land fluxes. Note that the O₂ method requires a correction for global air-sea O₂ flux, which has the largest uncertainty at annual time scales, but which is still non-negligible for decadal estimates (Nevison et al., 2008).

916 **2.9 Earth System Models estimate**

917 Reconstructions and predictions from decadal prediction systems based on Earth system models (ESMs) provide a novel line of evidence in assessing the atmosphere-land and atmosphere-ocean carbon fluxes in the past decades and predicting their changes for the current year and years to come. By assimilating physical atmospheric and oceanic data products into the ESMs, the models are able to reproduce the historical variations of the atmosphere-sea CO₂ fluxes, atmosphere-land CO₂ fluxes, and atmospheric CO₂ growth rate (Li et al., 2016, 2019; Lovenduski et al., 2019a,b; Ilyina et al., 2021; Li et al., 2023). Furthermore, the ESM-based predictions have proven their skill in predicting the air-sea CO₂ fluxes for up to 6 years, the air-land CO₂ fluxes and atmospheric CO₂ growth for 2 years (Lovenduski et
<figure><img src="image_29.png" /></figure>

al., 2019a,b; Ilyina et al., 2021; Li et al., 2023). The reconstructions from the fully coupled model simulations ensure a closed budget within the Earth system, i.e., no budget imbalance term.

Six ESMs have performed the set of prediction simulations. The ensemble size of initialized prediction simulations is 10, and the ensemble mean for each individual model is used here. Each ESM uses a different assimilation method and combination of data products incorporated in the system, more details on the models configuration can be found in Table 4 and Supplement Table S5. Reconstructions of atmosphere-ocean CO₂ fluxes (SOCEAN) and atmosphere-land CO₂ fluxes (SLAND-ELUC) for the time period from 1960-2024 are assessed here. Predictions of the atmosphere-ocean CO₂ flux, atmosphere-land CO₂ flux, and atmospheric CO₂ growth for 2025 are calculated based on the predictions at lead year 1. The predictions of atmosphere-ocean CO₂ flux and atmosphere-land CO₂ flux are bias corrected by removing the climatological mean and linear trend biases from 1981-2021 referring to GCB2022 (Friedlingstein et al., 2022), the atmospheric CO₂ growth (GATM) is then calculated as the residual of the CO₂ emissions subtracting the carbon sinks into the ocean and the land. With the ESMs simulations, we also compute the GATM in another way based on monthly atmospheric CO₂ concentrations at 1000hPa level over the oceans to be comparable to the NOAA/GML global measurements of atmospheric CO₂ concentrations (Lan et al., 2025a). The bias correction is done referring to NOAA/GML monthly CO₂ concentrations in recent 10 years from 2015-2024 regarding mean state and linear trend, a shorter period is used because a non-linear trend for the whole period is observed from both NOAA/GML measurements and model simulations. The 2025 GATM is then calculated by the increment of atmospheric CO₂ concentration in December 2025 minus that in December 2024 multiplied by 2.124 to convert the unit from ppm to GtC. The prediction of 2025 atmospheric CO₂ concentration is an average of the available NOAA/GML measurements from January to June (Lan et al., 2025) and ESMs' predictions from July to December. Note that the two methods, i.e., one based on the residual of carbon sources and sinks and another based on atmospheric concentration increment, of calculation of GATM result in different magnitudes, which suggests the consideration of variable ratio rather than a constant value in converting CO₂ concentration to mass. More details on methods of bias correction of decadal predictions can be found in Kharin et al. (2012), Boer et al. (2016) and Li et al. (2023). The ESMs are used here to support the assessment of SOCEAN and net atmosphere-land CO₂ flux (SLAND - ELUC) over the 1960-2024 period, and to provide an estimate of the 2025 prediction of GATM and atmospheric CO₂ concentration.

## 2.10 Processes not fully included in the global carbon budget

The contribution of anthropogenic CO and CH₄ to the global carbon budget is not fully accounted for in Eq. (1) and is described in Supplement S.8.1. The contributions to CO₂ emissions of decomposition of carbonates not accounted for is described in Supplement S.8.2. The contribution of anthropogenic changes in river fluxes is conceptually included in Eq. (1) in SOCEAN and in SLAND, but it is not represented in the process models used to quantify these fluxes. However, a correction of -0.07 GtC/yr
<figure><img src="image_30.png" /></figure>

960 is applied a posteriori to the S<sub>LAND</sub> estimate for the 2015-2024 period, as proposed in Friedlingstein et al. (2025b). This effect is discussed in Supplement S.8.3. Similarly, the Replaced Sinks and Sources effect from reduced forest cover over the historical period is missing from the DGVMs used here to estimate S<sub>LAND</sub>. However, a correction (-19%) is applied a posteriori to the S<sub>LAND</sub> estimate from DGVMs, based on O'Sullivan et al. (2025; see Section 2.6 and Supplement S.4.1).

965 **3 Results**

966 For each component of the global carbon budget, we present results for three different time periods: the full historical period, from 1850 to 2024, the decades in which we have atmospheric concentration records from Mauna Loa (1959-2024), a specific focus on last year (2024), and the projection for the current year (2025). Subsequently, we assess the estimates of the budget components of the last decades against the top-down constraints from inverse modelling of atmospheric observations and the land/ocean partitioning derived from the atmospheric O₂ measurements. Atmospheric inversions further allow for an assessment of the budget components with a regional breakdown of land and ocean sinks.

973

974 **3.1 Fossil CO₂ Emissions**

975 **3.1.1 Historical period 1850-2024**

976 Cumulative fossil CO₂ emissions for 1850-2024 were 495 ± 25 GtC, including the cement carbonation sink (Table 8, with all cumulative numbers rounded to the nearest 5GtC). In this period, 46% of global fossil CO₂ emissions came from coal, 35% from oil, 15% from natural gas, 3% from decomposition of carbonates for cement production, and 1% from flaring. In 1850, the UK stood for 62% of global fossil CO₂ emissions. In 1893 the combined cumulative emissions of the current members of the European Union reached and subsequently surpassed the level of the UK. Since 1917 US cumulative emissions have been the largest. Over the entire period 1850-2024, US cumulative emissions amounted to 120 GtC (24% of world total), the EU's to 80 GtC (16%), China's to 80 GtC (15%), and India's to 18 GtC (4%).

985 In addition to the estimates of fossil CO₂ emissions that we provide here (see Section 2.1), there are three global datasets with long time series that include all sources of fossil CO₂ emissions: CDIAC-FF (Erb and Marland, 2025), CEDS version 2024_07_08 (Hoesly et al., 2024) and PRIMAP-hist version 2.6 (Gütschow et al., 2016; Gütschow et al., 2024), although these datasets are not entirely independent from each other (Andrew, 2020a). CEDS has cumulative emissions over 1750-2022 at 480 GtC, CDIAC-FF has 481 GtC, GCP 484 GtC, PRIMAP-hist CR 490 GtC, and PRIMAP-hist TR 492 GtC. CDIAC-FF excludes emissions from lime production. CEDS estimates higher emissions from international shipping in recent years, while PRIMAP-hist has higher fugitive emissions than the other
<figure><img src="image_31.png" /></figure>

993 datasets. However, in general these four datasets are in relative agreement as to total historical global

emissions of fossil CO₂.

995 **3.1.2 Recent period 1959-2024**

996 Global fossil CO₂ emissions, E<sub>FOS</sub> (including the cement carbonation sink), have increased every decade
997 from an average of 3.0 ± 0.2 GtC yr<sup>-1</sup> for the decade of the 1960s to an average of 9.8 ± 0.5 GtC yr<sup>-1</sup>
998 during 2015-2024 (Table 7, Figure 2 and Figure 4). The growth rate in these emissions decreased
999 between the 1960s and the 1990s, from 4.3% yr<sup>-1</sup> in the 1960s (1960-1969), 3.1% yr<sup>-1</sup> in the 1970s
1000 (1970-1979), 1.5% yr<sup>-1</sup> in the 1980s (1980-1989), to 1.0% yr<sup>-1</sup> in the 1990s (1990-1999). After this
1001 period, the growth rate began increasing again in the 2000s at an average growth rate of 2.8% yr<sup>-1</sup>,
1002 decreasing to 1.2% yr<sup>-1</sup> in the 2010s, and 0.8% yr<sup>-1</sup> for the last decade (2015-2024). China's emissions
1003 increased by +2.5% yr<sup>-1</sup> on average over the last 10 years dominating the global trend, and India's
1004 emissions increased by +3.6% yr<sup>-1</sup>, while emissions decreased in EU27 by 2.5% yr<sup>-1</sup>, and in the USA by
1005 1.2% yr<sup>-1</sup> (Figure 5a). Figure 6 illustrates the spatial distribution of fossil fuel emissions for the 2015-
1006 2024 period.

1007 E<sub>FOS</sub> reported here includes the uptake of CO₂ by cement via carbonation which has increased with
1008 increasing stocks of cement products, from an average of 20 MtC yr<sup>-1</sup> (0.02 GtC yr<sup>-1</sup>) in the 1960s to an
1009 average of 210MtC yr<sup>-1</sup> (0.21 GtC yr<sup>-1</sup>) during 2015-2024 (Figure 5b).

1010 **3.1.3 Final year 2024**

1011 Global fossil CO₂ emissions were slightly higher, 1.1%, in 2024 than in 2023, with an increase of 0.11
1012 GtC to reach 10.3 ± 0.5 GtC (including the 0.22 GtC cement carbonation sink) in 2024 (Figure 4),
1013 distributed among coal (41%), oil (32%), natural gas (21%), cement (4%), flaring (1%), and others
1014 (1%). Compared to 2023, the 2024 emissions from coal, oil, and gas increased by 0.8%, 1.3%, and
1015 2.4% respectively, while emissions from cement decreased by 4.6%. All annual growth rates presented
1016 are adjusted for the leap year, unless stated otherwise.

1017 In 2024, the largest absolute contributions to global fossil CO₂ emissions were from China (32%), the
1018 USA (13%), India (8%), and the EU27 (6%). These four regions account for 59% of global fossil CO₂
1019 emissions, while the rest of the world contributed 41%, including international aviation and marine
1020 bunker fuels (3% of the total). Growth rates for these countries from 2023 to 2024 were +0.7% (China),
1021 -0.6% (USA), -2.6% (EU27), and +4.0% (India), with +1.3% for the rest of the world, including
1022 international aviation and marine bunker fuels (+9.8%). The per-capita fossil CO₂ emissions in 2024
1023 were 1.3 tC person⁻¹ yr⁻¹ for the globe, and were 3.9 (USA), 2.4 (China), 1.5 (EU27) and 0.6 (India) tC
1024 person⁻¹ yr⁻¹ for the four highest emitters (Figure 5c).
<figure><img src="image_32.png" /></figure>

1025 **3.1.4 Year 2025 Projection**

1026 Globally, we estimate that global fossil CO₂ emissions (including cement carbonation, -0.21 GtC) will grow by 1.1% in 2025 (0.2% to +2.2%) to 10.4 GtC (38.1 GtCO₂), an historical record high<fnref n="2" />. The estimates of changes in 2025 emissions per fuel types, relative to 2024, are projected to be 0.8% (range -0.1% to 1.9%) for coal, +1.0% (range 0.3% to 1.8%) for oil, +1.3% (range 0.0% to 2.5%) for natural gas, and 0.5% (range -1.5% to 2.6%) for cement (Figure 5b).

1031 For China, projected fossil emissions in 2025 are expected to increase slightly by 0.4% (range -0.9% to 2.0%) compared with 2024 emissions, bringing 2024 emissions for China around 3.4 GtC yr⁻¹ (12.3 GtCO₂ yr⁻¹). Our projected changes by fuel for China are +0.3% for coal, +2.1% for oil, +1.3% for natural gas, and -2.8% for cement.

1035 For the USA, using the Energy Information Administration (EIA) emissions projection for 2025 combined with cement clinker data from USGS, we project an increase of 1.9% (range -0.2% to 4.1%) compared to 2024, bringing USA 2025 emissions to around 1.4 GtC yr⁻¹ (5.0 GtCO₂ yr⁻¹). Our projected changes by fuel are 7.5% for coal, 1.1% for oil, +1.1% for natural gas, and -8.0% for cement.

1039 For India, our projection for 2025 is an increase of 1.4% (range of -0.3% to 3.1%) over 2024, with 2025 emissions around 0.9 GtC yr⁻¹ (3.2 GtCO₂ yr⁻¹). Our projected changes by fuel are +1.7% for coal, +0.1% for oil, -6.4% for natural gas, and +9.9% for cement.

1042 For the European Union, our projection for 2025 is for an increase of 0.4% (range -2.1% to 2.8%) relative to 2024, with 2025 emissions around 0.7 GtC yr⁻¹ (2.4 GtCO₂ yr⁻¹). Our projected changes by fuel are -0.3% for coal, 0.6% for oil, +0.9% for natural gas, and -4.1% for cement.

1045 New this year, we provide a projection for Japan, with a 2025 decrease of 2.2% (range of -8.1% to +3.7%), with 2025 emissions around 0.3 GtC yr⁻¹ (0.9 GtCO₂ yr⁻¹). Our projected changes by fuel are -3.1% for coal, -0.8% for oil, -2.7% for natural gas, and -3.0% for cement.

1048 International aviation and shipping are projected to increase by 3.7% in 2025, reaching 0.3 GtC yr⁻¹ (1.2 GtCO₂ yr⁻¹), with international aviation projected to be up 6.8% over 2024, and international shipping projected to be flat (0%). For the rest of the world, the expected change for 2025 is an increase of 1.1% (range -1.1% to 3.3%) with 2025 emissions around 3.7 GtC yr⁻¹ (13.7 GtCO₂ yr⁻¹). The fuel-specific projected 2025 growth rates for the rest of the world are: +1.0% for coal, +0.5% for oil, +1.8% for natural gas, +2.4% for cement.

1054 Compared to the GCB estimate, Carbon Monitor projects a global fossil CO₂ emissions increase of 0.3% (-1.8% to 2.4%) for 2025, lower than GCB but with overlapping uncertainties. In contrast to

<footnote n="2">² Growth rates in this section use a leap year adjustment that corrects for the extra day in 2024.</footnote>
<figure><img src="image_33.png" /></figure>

1056 GCB, Carbon Monitor projects the 2025 emissions from China to decline by -2.4% (range -4.7% to -0.2%). Conversely, Carbon Monitor projects the 2025 emissions to increase by 3.0% (0% to 6.0%) for the USA, by +0.3% (-1.9% to +2.5%) for India, by 1.6% (-2.8% to 5.8%) for the EU27, and by 0.4% (-3.3% to 4.0%) for Japan. The Carbon Monitor projects international aviation and shipping to increase by 0.7% (-0.9% to 2.2%) in 2025. For the rest of the world, the expected change is an increase of 1.5% (range 0.3% to 2.7%).

1062 For traceability, Table S6 provides a comparison of annual projections from GCB since 2015 with the actual emissions assessed in the subsequent GCB annual report.

## 3.2 Emissions from Land-Use Change

### 3.2.1 Historical period 1850-2024

1066 Cumulative net CO₂ emissions from land-use change (ELUC) for 1850-2024 are 250 ± 60 GtC (Table 8) with a large spread among individual bookkeeping estimates of 210 GtC (OSCAR), 240 GtC (LUCE) and 290 GtC (BLUE). DGVMs show a lower estimate of 160 ± 60 GtC. Vegetation biomass observations provide independent constraints on the ELUC estimates (Li et al., 2017). Over the 1901-2012 period, the GCB bookkeeping models' cumulative ELUC amounts to 180 GtC [155 to 210 GtC] and the DGVM cumulative ELUC to 121 ± 45 GtC, both not significantly different from the observation-based estimate of 155 ± 50 GtC (Li et al., 2017). The substantially lower cumulative ELUC estimates from DGVMs compared to GCB2024 are due to the correction for the RSS bias, which is implemented in this assessment (see Section 2.6.1; O'Sullivan et al., 2025).

### 3.2.2 Recent period 1959-2024

1076 In contrast to growing fossil emissions, net CO₂ emissions from land use, land-use change, and forestry remained relatively constant (around 1.8 GtC yr⁻¹) over the 1959-1999 period. Since then, they have shown a statistically significant decrease of about 0.2 GtC per decade (p<0.001), reaching 1.4 ± 0.7 GtC yr⁻¹ for the 2015-2024 period (Table 7), with a spread from 1.3 to 1.6 GtC yr⁻¹ across the three bookkeeping estimates (Table 5, Figure 4b). Different from the bookkeeping average, the DGVM average grows slightly larger over the 1980-2010 period, but as bookkeeping models, DGVMs show decreasing emissions in the most recent decade, 2015-2024 (Table 5).

1083 Compared to GCB2024, the overall trends in ELUC remained similar but the ELUC estimates increased. The main reason for the larger estimates in this assessment is that the H&C2023 model, included up to GCB2024, is not included anymore (see Section 2.2). H&C2023 had the lowest ELUC estimates among all bookkeeping models (Figure S3). Larger ELUC estimates in the last few decades (compared to GCB2024) are also due to the consideration of transient instead of constant carbon densities by all three bookkeeping models (Section 2.2.1). The inclusion of transient carbon densities increases gross fluxes
<figure><img src="image_34.png" /></figure>

1089 for all E_LUC components in the last few decades (Figure S3). Net E_LUC increases because emissions from deforestation are still the dominating term.

1091 We separate E_LUC into five component fluxes to gain further insight into the drivers of net emissions: deforestation, forest (re-)growth, wood harvest and other forest management, peat drainage and peat fires, and all other transitions (Figure 7b; Supplement S.2.2). We further decompose the deforestation and the forest (re-)growth term into contributions from shifting cultivation vs permanent forest cover changes (Figure 7c). Averaged over the 2015-2024 period and over the three bookkeeping estimates, fluxes from deforestation amount to 1.9 [1.5 to 2.3] GtC yr⁻¹ (Table 5), of which 1.1 [0.9, 1.2] GtC yr⁻¹ are from permanent deforestation. Fluxes from forest (re-)growth amount to -1.3 [-1.5, -1.0] GtC yr⁻¹ (Table 5), of which -0.6 [-0.7, -0.5] GtC yr⁻¹ are from re/afforestation and the remainder from forest regrowth in shifting cultivation cycles. Wood harvest and other forest management causes net emissions of 0.4 [0.1, 0.7] GtC yr⁻¹ (with substantial gross fluxes largely compensating each other; see Figure S2). Emissions from peat drainage and peat fires (0.2 [0.2, 0.3] GtC yr⁻¹) and the net flux from other transitions (0.1 [0.1, 0.1] GtC yr⁻¹) are of smaller magnitude globally (Table 5).

1103 The split into component fluxes clarifies the potentials for emission reduction and carbon dioxide removal: emissions from permanent deforestation - the largest of our component fluxes - could be halted (largely) without compromising carbon uptake by forests, contributing substantially to emissions reduction. By contrast, reducing wood harvesting would have limited potential to reduce net emissions as it would be associated with less forest regrowth; removals and emissions cannot be decoupled here on long timescales. A similar conclusion applies to removals and emissions from shifting cultivation, which we have therefore separated out. Carbon Dioxide Removal (CDR) in forests could instead be increased by permanently increasing the forest cover through re/afforestation, for which we estimate a removal of -0.6 GtC yr⁻¹ from the atmosphere averaged over 2015-2024. This value is similar to independent estimates derived from NGHGIs for CDR in managed forests (through re/afforestation plus forest management) for 2013-2022 (-0.5 GtC yr⁻¹, Pongratz et al., 2024). Re/afforestation constitutes the vast majority of all current CDR (Pongratz et al., 2024). Though they cannot be compared directly to annual fluxes from the atmosphere and are thus not included in our estimate of E_LUC, CDR through transfers between non-atmospheric reservoirs such as in durable HWPs, biochar, or BECCS comprise much smaller amounts of carbon. 218 MtC yr⁻¹ have been estimated to be transferred to HWPs, averaged over 2013-2022 (Pongratz et al., 2024). The net flux of HWPs, considering the re-release of CO₂ through their decay, amounts to 91 MtC yr⁻¹ over that period (Pongratz et al., 2024). Note that some double-counting between the CDR through HWPs and the CDR through re/afforestation exists if the HWPs are derived from newly forested areas. “Blue carbon”, i.e. coastal wetland management such as restoration of mangrove forests, saltmarshes and seagrass meadows, though at the interface of land and ocean carbon fluxes, are counted towards the land-use sector as well. Currently, bookkeeping
<figure><img src="image_35.png" /></figure>

1124 models do not include blue carbon; however, current CDR deployment in coastal wetlands is small globally, less than 0.003 MtC yr⁻¹ (Powis et al., 2023).

1125 The statistically significant decrease in E<sub>LUC</sub> since the late-1990s, including the larger drop within the most recent decade, is due to the combination of decreasing emissions from deforestation (in particular permanent deforestation) and increasing removals from forest regrowth (with those from re/afforestation stagnating globally in the last decade). Net emissions in 2015-2024 are 23% lower than in the late-1990s (1995-2004) and 19% lower than in 2005-2014. The steep drop in E<sub>LUC</sub> after 2015 is due to the combined effect from a peak in peat fire emissions in 2015 and a long-term decline in deforestation emissions in many countries over the 2010-2020 period. The processes behind gross removals, foremost forest regrowth and soil recovery, are all slow, while gross emissions include a large instantaneous component. Short-term changes in gross emissions dynamics, such as a temporary decrease in deforestation, influences E<sub>LUC</sub> faster than a change in gross removals, which rather act on longer-term timescales. Component fluxes often differ more across the bookkeeping estimates than the net flux (Figure 7b), which is expected due to different process representation; in particular, the treatment of shifting cultivation, which increases both gross emissions and gross removals, differs across models, but also net and gross wood harvest fluxes show high uncertainty (Figure S2). By contrast, models agree relatively well for emissions from permanent deforestation.

1141 Overall, highest net land-use emissions occur in the tropical regions of all three continents. The top three emitters over 2015-2024 are Brazil (in particular the Amazon Arc of Deforestation), Indonesia, and the Democratic Republic of the Congo, with these 3 countries contributing 0.8 GtC yr⁻¹ or 57% of the global net land-use emissions (average over 2015-2024; Figure 6b, Figure 7a). This is related to massive expansion of cropland (FAO, 2025c), particularly in the last few decades in Latin America, Southeast Asia, and sub-Saharan Africa (Hong et al., 2021), to a substantial part for export of agricultural products (Pendrill et al., 2019). Emission intensity is high in many tropical countries, particularly of Southeast Asia, due to high rates of land conversion in regions of carbon-dense and often still pristine, undegraded natural forests (Hong et al., 2021). Emissions are further increased by peat fires in equatorial Asia (GFED4s, van der Werf et al., 2017). Uptake due to land-use change occurs in several regions of the world (Figure 6b) particularly due to re/afforestation. Highest CDR in the last decade is seen in China, the USA, and the EU27, partly related to expanding forest area as a consequence of the forest transition in the 19th and 20th century and subsequent regrowth of forest (Mather 2001; McGrath et al., 2015). Substantial uptake through re/afforestation also exists in other regions such as Brazil, Russia, or Indonesia, where, however, emissions from deforestation and other land-use changes dominate the net land-use flux. While the mentioned patterns are robust and supported by independent literature, we acknowledge that model spread is substantially larger on regional than global levels, as has been shown for bookkeeping models (Bastos et al., 2021) as well as DGVMs (Obermeier et al., 2021). Assessments for individual regions are being performed as part of the
<figure><img src="image_36.png" /></figure>

1160 REgional Carbon Cycle Assessment and Processes 2 initiative (RECCAP2; Ciais et al., 2020, Poulter et al., 2022) or already exist for selected regions (e.g., for Europe by Petrescu et al., 2020; for Brazil by Rosan et al., 2021; for China by Zhu et al., 2025; and for 8 selected countries/regions in comparison to inventory data by Schwingshackl et al., 2022).

1164 The NGHGI data under the land-use, land-use change, and forestry (LULUCF) sector (Melo et al. 2025) and the LULUCF estimates from FAOSTAT (FAO, 2025d) differ from the global models' definition of E_LUC (see Section 2.2.1). In the NGHGI reporting, natural land fluxes (S\_LAND) are counted towards E_LUC when they occur on managed land (Grassi et al., 2018) whereas FAOSTAT LULUCF estimates generally include natural fluxes in managed and unmanaged forests (Tubiello et al., 2021). To compare our results to the NGHGI approach, we perform a translation of our E_LUC estimates by adding S\_LAND in managed forest from the DGVMs simulations (1.9 GtC yr\_-1 in 2015-2024) to the bookkeeping E_LUC estimate (following the methodology described in Grassi et al., 2023; see Supplement S.2.4).
1165
1166
1167
1168
1169
1170
1171 Adding this sink changes E_LUC from being a source of 1.4 GtC yr\_-1 to a sink of 0.5 GtC yr\_-1, much closer to the NGHGI estimate reporting a sink of 1.0 GtC yr\_-1 (Figure 8a, Table S10). Remaining differences in the bookkeeping and NGHGI estimates are mainly stemming from fluxes due to other transitions and permanent deforestation, whereas peat emissions and the net flux in managed forests agree well (Figure 8b,c). We further apply a mask of managed land to the net atmosphere-to-land flux estimate from atmospheric inversions to obtain inverse estimates that are comparable to the NGHGI estimates and to the translated E_LUC estimates from bookkeeping models (see Supplement S.2.4). The inversion-based net flux in managed land indicates a sink of 0.7 GtC yr\_-1 for 2015-2024, which is in broad agreement with the NGHGI and the translated E_LUC estimates (Figure 8, Table S10).
1172
1173
1174
1175
1176
1177
1178
1179
1180 Additionally, the interannual variability of the inversion estimates and the translated E_LUC estimates show a remarkable agreement (Pearson correlation of 0.71 in 2000-2024), which supports the suggested translation approach. Though estimates of NGHGI, FAOSTAT, and atmospheric inversions and the translated bookkeeping estimates still differ in value and need further analysis, the approach suggested by Grassi et al. (2023), which we adopt here, provides a feasible way to relate the global models' and NGHGI approach to each other and thus link the anthropogenic carbon budget estimates of land-use CO₂ fluxes directly to the Global Stocktake, as part of the UNFCCC Paris Agreement. The translation approach has been shown to be generally applicable also at the country-level (Grassi et al., 2023; Schwingshackl et al., 2022).
1181
1182
1183
1184
1185
1186
1187
1188
1189

1190 **3.2.3 Final year 2024**

1191 The global CO₂ emissions from land-use change are estimated as 1.3 ± 0.7 GtC in 2024, similar to the 2023 estimate. However, confidence in the annual change remains low, as the land-use forcing underlying the E_LUC estimates was informed directly by data only up to and including 2023, with a trend extrapolation to 2024 (see Supplement Section S.2.1). The anomalously large fire emissions in South
<figure><img src="image_37.png" /></figure>

America in 2024 that resulted from the impacts of the 2023/2024 droughts linked to El Niño are thus not yet fully reflected in the 2024 E_LUC estimate, even if they led to permanent deforestation. The extent of degradation was anomalously high in the Amazon in 2024 and partly masked relatively low deforestation rates (https://terrabrasilis.dpi.inpe.br). However, this is not captured by our approach that is based on forest cover changes. For degradation it is currently impossible to be separated into effects of land-use activity versus effects of climate variability.

### 3.2.4 Year 2025 Projection

In equatorial Asia, peat fire emissions remained low (1 TgC in 2025 through October 15 2025, after 2 TgC in 2024; GFED4.1s, van der Werf et al., 2017), as did deforestation and degradation fires (4 TgC in 2025; 8 TgC in 2024). South America saw a big reduction in emissions from deforestation and degradation fires, from 334 TgC in 2024 to 35 TgC up until October 15 2025. Pantropical 2025 fire emission estimates from deforestation and degradation through October 15 are 105 TgC, which is not just a massive drop compared to the anomalously high 2024 emissions, but also only about one third of the long-term average (1997-2024). The reduction in emissions is mainly attributable to anomalously low deforestation and degradation fire emissions in South America, likely connected to the ceasing of the El Niño conditions and their impacts on ecosystems.

Since the E_LUC estimate for the year 2024 is not informed directly by data on land-use dynamics (Section 3.2.3), we estimate the 2025 projection based on fire anomalies over the year 2023. We expect E_LUC emissions of around 1.1 GtC (4.1 GtCO₂) in 2025, 0.1 GtC below the 2024 level (0.3 GtC below the 2015-2024 average). Note that the confidence in the 2025 E_LUC projection remains low, as it is based on deforestation, degradation and peat fire emissions, which are only a proxy for land-use change. Projections in past GCB assessments are only partially confirmed by updated E_LUC estimates based on bookkeeping models in the following GCB assessments (Figure S4). Although our extrapolation includes tropical deforestation and degradation fires, the degradation attributable to selective logging, edge-effects or fragmentation is not captured. Further, deforestation and fires in deforestation zones may become more disconnected, partly due to changes in legislation in some regions. For example, Van Wees et al. (2021) found that the contribution from fires to forest loss decreased in the Amazon and in Indonesia over the period of 2003-2018.

### 3.3 CDR not based on vegetation

Besides the CDR through land use (Sec. 3.2), the atmosphere-to-geosphere flux of carbon resulting from carbon dioxide removal (CDR) activities in 2024 is estimated at 0.0088 MtC/yr. This results primarily from 0.002 MtC/yr and 0.00022 MtC/yr of mineralisation and enhanced weathering projects respectively, 0.003 MtC/yr of bio-oil geological storage, and 0.0004 MtC/yr of DACCS (Pongratz et al., 2024). This represents a decrease of 20% in the anthropogenic sink compared to the 0.011 MtC/yr
<figure><img src="image_38.png" /></figure>

estimate for 2023, and it remains roughly a million times smaller than current fossil CO₂ emissions.
Note that the lower DACCS estimates reflect more accurate annual data from registries, indicating that
operational projects are currently removing less CO₂ than their expected capacities. Similarly, reported
CDR via Enhanced Rock Weathering decreased in 2024; however, this may not indicate a real decline
in activity. The decline likely reflects a transition toward third party verification, with some removals
not yet appearing on registries. Other novel CDR methods, including ocean and river alkalinity
enhancement (0.0002 MtC/yr and 0.00001 MtC/yr in 2024 respectively), and biomass direct storage
(compressing waste biomass to store underground, 0.0008 MtC/yr), jointly contributed just under 0.004
MtC/yr according to updated 2024 estimates of the State of CDR report (Smith et al., 2024). CDR
through intentional biomass sinking into the deep ocean was reported at 0.001 MtC/yr for 2023, but no
activities have been identified for 2024.

## 3.4 Total anthropogenic emissions

Cumulative anthropogenic CO₂ emissions (fossil, including cement carbonation, and land-use change) for 1850-2024 totalled 745 ± 65 GtC (2730 ± 240 GtCO₂), of which 71% (530 GtC) occurred since 1959 and 35% (260GtC) since 2000 (Table 7 and 8). Total anthropogenic emissions more than doubled since the 1960s, from 4.9 ± 0.7 GtC yr⁻¹ for the decade of the 1960s to an average of 11.2 ± 0.9 GtC yr⁻¹ during 2015-2024, and reaching 11.6 ± 0.9 GtC (42.4 ± 3.2 GtCO₂) in 2024. However, total anthropogenic CO₂ emissions have been almost stable over the last decade (0.3% yr⁻¹ average growth rate over the 2015-2024 period), much lower than the 1.9% growth rate over the previous decade (2005-2014). This slower growth is due both to the reduced growth in E fos between the two decades, and to the decrease in emissions from E luc over the past decade.

During the historical period 1850-2024, 67% of historical emissions were from fossil emissions and 33% from land-use change. However, fossil emissions have grown significantly since the 1960s while net land-use change emissions have not, and consequently the contributions of land-use change to total anthropogenic emissions were smaller during recent periods, 21% during the period 1959-2024 and down to 12% over the last decade (2015-2024).

For 2025 we project global total anthropogenic CO₂ emissions from fossil and land-use changes to be around 11.5 GtC (42.2 GtCO₂), marginally below the 2024 level due to lower net land-use emissions (-0.13 GtC) compensating the growth in fossil emission (+0.09 GtC).

## 3.5 Atmospheric CO₂

### 3.5.1 Historical period 1850-2024

Atmospheric CO₂ concentration was approximately 278 parts per million (ppm) in 1750, reaching 300 ppm in the late 1900s, 350 ppm in the late 1980s, and reaching 422.80 ± 0.1 ppm in 2024 (Lan et al.,
<figure><img src="image_39.png" /></figure>

1262 2025a; Figure 1). The mass of carbon in the atmosphere increased by 51% from 590 GtC in 1750 to 898 GtC in 2024. Current CO₂ concentrations in the atmosphere are unprecedented in the last 2 million years and the current rate of atmospheric CO₂ increase is at least 10 times faster than at any other time during the last 800,000 years (Canadell et al., 2021).

1266 **3.5.2 Recent period 1959-2024**

1267 The growth rate in atmospheric CO₂ level increased from 1.7 ± 0.07 GtC yr⁻¹ in the 1960s to 5.6 ± 0.02 GtC yr⁻¹ during 2015-2024 with important decadal variations (Table 7, Figure 3 and Figure 4c). During the last decade (2015-2024), the growth rate in atmospheric CO₂ concentration continued to increase, albeit with large interannual variability (Figure 9). This interannual variability is highly consistent between the surface-based CO₂ growth rate and GRESO, the XCO₂-based growth rate (r= 0.84), but substantial differences are seen in 2019 and 2022-2024. These reflect differences in the total atmospheric carbon stock seen within the set boundaries of Jan-1 and Dec-31 used to create an annual growth rate, as discussed in Section 2.4.2. Especially with large anomalies in tropical regions and close to January 1st, the calculation becomes sensitive to when the change is detected. This affected the 2023 and 2024 growth rates most strongly as they originated from tropical regions, and late in the calendar year (see next section). Figure 9 also shows the total annual fluxes (in GtC yr⁻¹) from the inverse models, which are derived to match the observed atmospheric increase while numerically accounting for the atmospheric transport-related delay in detection. The inversely modeled values generally match the GRESO growth rate better than the surface observations-based growth rate, indicating that the latter is more strongly influenced by the delay between the fluxes occurring and the actual observation of these signals. Note that this effect is mainly important on the annual timescale, and not over longer periods.

1284 **3.5.3 Final year 2024**

1285 The growth rate in the surface based atmospheric CO₂ concentration was 7.9 ± 0.2 GtC (3.73 ± 0.08 ppm) in 2024 (Figure 4c; Lan et al., 2025a), well above the 2023 growth rate (5.7 ± 0.2 GtC, 2.7 ± 0.08 ppm) or the 2015-2024 average (5.6 ± 0.02 GtC, 2.6 ± 0.008 ppm), as to be expected during an El Niño year. The 2024 atmospheric CO₂ growth rate was the largest over the 1959-2024 atmospheric observational record, more than 1.5 GtC above the growth rates of 1998, 2015, and 2016 and 1998, all strong El Niño years.

1291 In contrast, the satellite-based GRESO growth rate was 6.8±0.2 GtC/yr (3.20±0.09 ppm/yr) for 2024 which was also above its 2023 growth rate (6.5±0.2 GtC/yr, 3.06±0.07 ppm/yr) and the highest on its 10-year record. But it shows a more equal split of the anomaly between both years (2023-2024). This likely reflects more accurately that a substantial fraction of the flux anomaly occurred in 2023, but this was only detected by the surface network in 2024. For the interpretation of the annual global budget
<figure><img src="image_40.png" /></figure>

this has strong implications, as 2024 surface fluxes would need to add up to a smaller anomaly (by >1 GtC/yr) than when using the surface-based annual growth rate. Hence the budget imbalance in 2024 (Table 7) would be reduced from -1.7 GtC to -0.6 GtC if the GRESO growth rate was used for the budget estimate.

### 3.5.4 Year 2025 Projection

Based on a projection of the 2025 surface fluxes, we estimate the annual growth in atmospheric CO₂ (GATM) to be about 4.9 GtC (equivalent to a 2.3 ppm increase in the global mean concentration), in line with a neutral ENSO year. This is the average of the GCB regression method (4.4 GtC, 2.1 ppm) and ESMs the multi-model mean (5.5 GtC, 2.6 ppm). The 2025 atmospheric CO₂ concentration, averaged over the year, is expected to reach the level of 425.7 ppm, 53% over the pre-industrial level (278 ppm).

### 3.6 Ocean Sink

#### 3.6.1 Historical period 1850-2024

Cumulated since 1850, the ocean sink SOCEAN adds up to 200 ± 35 GtC, with more than 70% of this amount (145 ± 25 GtC) being taken up by the global ocean since 1959. Over the historical period, the ocean sink increased in pace with the anthropogenic emissions exponential increase (Figure 3). Since 1850, the ocean has removed 27% of total anthropogenic emissions.

#### 3.6.2 Recent period 1959-2024

SOCEAN increased from 1.3 ± 0.4 GtC yr⁻¹ in the 1960s to 3.2 ± 0.4 GtC yr⁻¹ during 2015-2024 (Table 7), with interannual variations of the order of a few tenths of GtC yr⁻¹ (Figure 4d, Figure 10b). The ocean-borne fraction (SOCEAN/(EFOS+ELUC)) has been remarkably constant around 27% on average, with variations around this mean illustrating the decadal variability of the ocean carbon sink. So far, there is no evidence of a sustained decrease in the ocean-borne fraction from 1959 to 2024 (Figure S16).

The increase of the ocean sink is primarily driven by the increased atmospheric CO₂ concentration, with the strongest CO₂ induced signal in the North Atlantic and the Southern Ocean (Figure 12a, Figure S10). The effect of climate change is much weaker, reducing the ocean sink globally by 0.20 ± 0.05 GtC yr⁻¹ (-6.2% of SOCEAN) during 2015-2024 (all models simulate a weakening of the ocean sink by climate change, range -4.6 to -12.2%). The climate change effect leading to a reduced ocean sink is evident in all large-scale latitudinal bands (north, tropics, south, Figure 12b, Figure S10). This is the combined effect of change and variability in all atmospheric forcing fields, previously attributed to wind and temperature changes (Le Quéré et al., 2010, Bunsen et al., 2024). The effect of warming is smaller than expected from offline calculation due to a stabilising feedback from limited exchange between surface and deep waters (Bunsen et al., 2024; Müller et al., 2025).
<figure><img src="image_41.png" /></figure>

1328 The global net air-sea CO₂ flux is a residual of large natural and anthropogenic CO₂ fluxes into and out of the ocean with distinct regional and seasonal variations (Figure 6c and Figure S5). Natural fluxes dominate on regional scales, but largely cancel out when integrated globally (Gruber et al., 2009; DeVries et al., 2023). Mid-latitudes in all basins and the high-latitude North Atlantic dominate the ocean CO₂ uptake where low temperatures and high wind speeds facilitate CO₂ uptake at the surface (Takahashi et al., 2009). In these regions, formation of mode, intermediate and deep-water masses transport anthropogenic carbon into the ocean interior, thus allowing for continued CO₂ uptake at the surface. Outgassing of natural CO₂ occurs mostly in the tropics, especially in the equatorial upwelling region, and to a lesser extent in the North Pacific and polar Southern Ocean, mirroring a well-established understanding of regional patterns of air-sea CO₂ exchange (e.g., Takahashi et al., 2009, Gruber et al., 2009; DeVries et al., 2023). These patterns are also noticeable in the Surface Ocean CO₂ Atlas dataset, where an ocean $pCO₂$ value above the atmospheric level indicates outgassing (Figure S5). This map further illustrates the data-sparsity in the Indian Ocean and the Southern Hemisphere in general.

1342 The largest variability in the ocean sink occurs on decadal time-scales (Figure 10b). The ensemble means of GOBMs and $pCO₂$-products show the same patterns of decadal variability, although with a larger amplitude of variability in the $pCO₂$-products than in the GOBMs. The ocean sink stagnated in the 1990s and strengthened between the early 2000s and the mid-2010s (Figure 10b; Le Quéré et al., 2007; Landschützer et al., 2015, 2016; DeVries et al., 2017; Hauck et al., 2020; McKinley et al., 2020, Gruber et al., 2023). Different explanations have been proposed for the decadal variability in the 1990s and 2000s, ranging from the ocean's response to changes in atmospheric wind systems (e.g., Le Quéré et al., 2007, Keppler and Landschützer, 2019), including variations in upper ocean overturning circulation (DeVries et al., 2017) to the eruption of Mount Pinatubo in the 1990s (McKinley et al., 2020, Fay et al., 2023). The main origin of the decadal variability is a matter of debate with a number of studies initially pointing to the Southern Ocean (see review in Canadell et al., 2021 and Gruber et al., 2023), but also contributions from the North Atlantic and North Pacific (Landschützer et al., 2016, DeVries et al., 2019), or a global signal (McKinley et al., 2020) were proposed. The GOBM decomposition into climate and CO₂ effects emphasizes the role of the climate effect (that would include cooling signals from volcanic eruptions) for the stagnation in the 1990s until 2002, which are seemingly more pronounced in the tropics and north than in the south (Figure S10). The atmospheric $pCO₂$ growth rate is a spatially uniform driver that amplifies with large-scale averaging, while climate exerts spatially heterogeneous effects that cancel with averaging. Thus, climate dominates small-scale flux variability, but is matched on the global mean by the impact of the $pCO₂$ growth rate (Fay et al., 2024).

1362 More recently, the sink seems to have entered a phase of stagnation 2016 - 2022, largely in response to large inter-annual climate variability. The first-order effect of interannual variability stems from a
<figure><img src="image_42.png" /></figure>

stronger ocean sink during large El Niño events leading to a reduction in CO₂ outgassing from the Tropical Pacific (e.g., 1997/98, 2015/16) and a weaker sink in neutral years following El Niño (2017) and during La Niña events (2020-2022, Figure 10b; Rödenbeck et al., 2014, Hauck et al., 2020; McKinley et al. 2017). After the triple La Niña event 2020-2022, the ocean sink rebound in 2023 linked to the onset of an El Niño event. Warming in the extratropics, in particular in the Northern Hemisphere, however, weakened the overall increase in the ocean carbon sink (Müller et al., 2025). In the GOBMs, the ocean sink stagnation 2016-2022 is associated with a period of a stronger climate effect reducing the ocean sink, mostly stemming from the tropics, in line with the expected patterns from El Niño and La Niña (Figure S10). The CO₂ effect has also shown a lower growth rate since around 2016.

The unscaled ensemble means of GOBMs, and $f/CO_2$-products (adjusted for the riverine flux) show a mean offset increasing from 0.28 GtC yr$^{-1}$ in the 1990s (first decade available) to 0.59 GtC yr$^{-1}$ in the decade 2015-2024. The offset is larger than in previous GCB versions, because two $f/CO_2$-products (UExP-FNN-U, JMA-MLR), that use an adjusted version of the Surface Ocean CO₂ Atlas data to represent the sea surface $f/CO_2$ within the surface skin layer where gas exchange takes place (Ford et al., 2025), are now included in the $f/CO_2$-product mean. Additionally, an update in the SOCAT dataset with regards to Southern Ocean data led to an increase in the sink strength in three of the $f/CO_2$-products (VLIZ-SOMFFN, LDEO-HPD, CMEMS-LSCE-FFNN - see section S3.1, Fay et al., 2025). In this version of the GCB, the SOCEAN positive trend between GOBMs and $f/CO_2$-products diverges over time by a factor of 1.6 since 2002 (GOBMs: 0.27 ± 0.05 GtC yr$^{-1}$ per decade, $f/CO_2$-products: 0.46 GtC yr$^{-1}$ per decade [0.22 to 0.81 GtC yr$^{-1}$ per decade], SOCEAN: 0.38 GtC yr$^{-1}$ per decade), but the uncertainty ranges overlap. A hybrid approach recently constrained the trend for 2000 to 2022 to 0.42 ± 0.06 GtC yr$^{-1}$ decade$^{-1}$ (Mayot et al., 2024), which aligns with the updated trend of SOCEAN (0.46 GtC yr$^{-1}$ decade$^{-1}$), while the $f/CO_2$-products exhibit a larger (0.54 [0.29, 0.85] GtC yr$^{-1}$ decade$^{-1}$) and the GOBMs a lower trend (0.34 ± 0.05 GtC yr$^{-1}$ per decade) over the same period.

In the current budget, the discrepancy between the two types of estimates stems from a persistently larger ocean sink in the $f/CO_2$-products in the northern and southern extra-tropics since around 2002 (Figure 14). Note that the discrepancy in the mean flux, which was located in the Southern Ocean in GCB2022 and earlier, was reduced due to the choice of the regional river flux adjustment (Lacroix et al., 2020 instead of Aumont et al., 2001). This comes at the expense of a discrepancy in the mean SOCEAN of about 0.2-0.3 GtC yr$^{-1}$ in the tropics. Likely explanations for the discrepancy in the trends and decadal variability in the high-latitudes are data sparsity and uneven data distribution (Bushinsky et al., 2019, Gloege et al., 2021, Hauck et al., 2023a, Mayot et al., 2024). In particular, two $f/CO_2$-products were shown to overestimate the Southern Ocean CO₂ flux trend by 50 and 130% based on current sampling in a model subsampling experiment (Hauck et al., 2023a) and the largest trends in the $f/CO_2$-products occurred in a data devoid region in the North Pacific (Mayot et al., 2024). In Supplement S3 we show that the strength of the trends in $f/CO_2$ products is linked to reconstruction biases of the true
<figure><img src="image_43.png" /></figure>

trend signal. In this respect it is highly worrisome that the coverage of /CO₂ observations has declined since 2016/17 (Dong et al., 2022, 2024; Bakker et al., 2025b) and is now down to that of the mid-2000s (Figure 10b). The temperature correction applied in two products further worsens the agreement in trends between GOBMs and /CO₂-products. Similarly, model biases likely contribute to the discrepancy between GOBMs and /CO₂-products (as indicated by the comparison with Mayot et al., 2024, and by the large model spread in the South, Figure 14).

The adjusted SOCEAN estimate is 2.9 ± 0.4 GtC yr⁻¹ over the period 2004 to 2019, which agrees within the ranges of uncertainty with the ocean interior estimate of 3.2 ± 0.7 GtC yr⁻¹ obtained with the MOBO-DIC approach (Keppler et al., 2023). The SOCEAN estimate of 2.7 ± 0.4 GtC yr⁻¹ over the period 1990 to 2022 is further in excellent agreement with another recent ocean interior estimate of 2.6 ± 0.4 GtC yr⁻¹ obtained with the ResNet-DIC method (Ehmen et al., 2025, in review). The carbon flux components of both observation-based ocean interior estimates match the definition of SOCEAN used here (Hauck et al., 2020). Finally, the decadal SOCEAN estimates agree well with the corresponding ocean interior estimates for all decades from the 1960s to the 2010s, which represent a composite estimate of four observation-based products (Table 6).

The SOCEAN estimate agrees within uncertainties with other lines of evidence from atmospheric oxygen based estimates, atmospheric inversions, and ocean interior observation-based constraints (Table 6). The atmospheric oxygen-based estimate shows a strong growth of >1.5 GtC yr⁻¹ from the 1990s to the recent decade 2015-2024. ESMs with data assimilation result in lower estimates of the ocean sink than all other estimates (Table 6). Also the atmospheric inversions are lower than SOCEAN on average, and cover a similar range as the adjusted GOBMs and /CO₂ products.

### 3.6.3 Final year 2024

The estimated ocean CO₂ sink is 3.4 ± 0.4 GtC for 2024. This is an increase of 0.1 GtC compared to 2023 (Figure 4d). The sink strengthening was expected from the atmospheric CO₂ growth and El Niño conditions in the beginning of the year (January to April). However, the continuation of the anomalous warm conditions in the extratropics, especially of the Atlantic Ocean, led to a weaker than expected increase in the sink (Figure 11c). The GOBMs suggest that the increase is driven by the anomalously high atmospheric CO₂ growth rate and dampened by climate effects, thus suggesting that warming overcompensated the effect from El Niño (Figure S10). GOBMs and /CO₂-products largely agree on patterns of ocean sink anomalies with a reduced sink in parts of the subtropical and subpolar North Atlantic and North Pacific that coincides with regions of anomalously warm sea surface temperatures (Figure S9). GOBM and /CO₂-product ensemble mean estimates consistently result in an ocean sink increase in 2024 (GOBMs: 0.13 ±0.14 GtC, /CO₂-products: 0.08 [-0.34,0.43] GtC). Eight GOBMs and six /CO₂-products show an increase in SOCEAN, while only two GOBMs and three /CO₂-products show a decrease in SOCEAN. The /CO₂-products have a larger uncertainty at the end of the reconstructed time
<figure><img src="image_44.png" /></figure>

1435 series, potentially linked to uncertainties related to fewer available observations in the final year (see
1436 e.g. Watson et al 2020, Pérez et al 2024). Specifically, the /CO₂-products' estimate of the last year is
1437 regularly adjusted in the following release owing to the tail effect and an incrementally increasing data
1438 availability. While the monthly grid cells covered may have a lag of only about a year (Figure 10b
1439 inset), the values within grid cells may change with 1-5 years lag (see absolute number of observations
1440 plotted in previous GCB releases), potentially resulting in annual changes in the flux magnitude from
1441 /CO₂-products.

1442 **3.6.4 Year 2025 projection**

1443 Using a feed-forward neural network method (see Section 2.5.2) we project an ocean sink of 3.3 ±0.4
1444 GtC for 2025, which is a declining sink of 0.1 GtC compared to 2024, and can be explained by the
1445 generally lower ocean CO₂ uptake following the El Niño event ending in 2024, consistent with the
1446 projected recovery of the atmospheric CO₂ growth rate after a record high in 2024. The set of ESMs
1447 predictions support this estimate with a 2025 ocean sink of around 3.1 [3.0, 3.2] GtC. Taking the
1448 average of both estimates, we project an ocean sink of 3.2 ±0.4 GtC for 2025.

1449 **3.6.5 Evaluation of ocean models and /CO₂-products**

1450 The process-based model evaluation draws a generally positive picture with GOBMs scattered around
1451 the observational values for Southern Ocean sea-surface salinity, Southern Ocean stratification index
1452 and surface ocean Revelle factor, albeit outliers exist (Section S3.3 and Table S11). However, the
1453 Atlantic Meridional Overturning Circulation at 26°N is underestimated by 8 out of 10 GOBMs and
1454 overestimated by one GOBM. IOMB summarizes the GOBMs' performance across physical and
1455 biogeochemical data sets and various statistics relative to the GOBM ensemble (Figure S6). All
1456 GOBMs perform better in some variables compared to the other models, and worse in other variables.
1457 Despite the indication that GOBMs underestimate the ocean sink, low sink models do not perform
1458 generally worse than the other models; and similarly, high sink models do not perform better than the
1459 others.

1460 The model simulations allow to separate the anthropogenic carbon component and to compare the
1461 GOBMs DIC inventory change directly to the interior ocean estimates of Gruber et al. (2019), Müller et
1462 al. (2023) and the GOBM anthropogenic surface fluxes to DeVries (2022) without further assumptions
1463 (Table S11). The GOBMs ensemble average of anthropogenic carbon inventory changes 1994-2007
1464 amounts to 2.3 GtC yr⁻¹ and is thus lower than the 2.6 ± 0.3 GtC yr⁻¹ estimated by Gruber et al. (2019)
1465 although within the uncertainty. Five models fall within the range reported by Gruber et al. (2019).
1466 Comparison to the decadal estimates of anthropogenic carbon accumulation (Müller et al., 2023) are
1467 close to the interior ocean data based estimate for the decade 2004-2014 (GOBMs sim D minus sim A,
1468 2.6 ± 0.4 GtC yr⁻¹, Müller et al. 2.7 ± 0.3 GtC yr⁻¹), but do not reproduce the supposedly higher
<figure><img src="image_45.png" /></figure>

1469 anthropogenic carbon accumulation in the earlier period 1994-2004 (GOBMs sim D minus sim A, 2.2 ± 0.3 GtC yr⁻¹, Müller et al. 2.9 ± 0.3 GtC yr⁻¹). Finally, the mean anthropogenic carbon uptake in 1470 GOBMs from 1985 to 2018 amounts to 2.3 GtC yr⁻¹, slightly lower than the corresponding observation- 1471 based uptake rate of 2.4 ± 0.2 Gt yr⁻¹ based on OCIM (DeVries, 2022; DeVries et al., 2023). Taken 1472 together, the comparison to observation-based estimates suggests that the majority of the GOBMs may 1473 underestimate anthropogenic carbon uptake by 10-20% and some models even more and are one line of 1474 evidence that supports the upscaling of GOBMs by 10% (Friedlingstein et al., 2025a, section 2.5.1). 1475 Interestingly, and in contrast to the uncertainties in the surface CO₂ flux, we find the largest mismatch 1476 in interior ocean carbon accumulation in the tropics, with smaller contributions from the north and the 1477 south (Table S11). The large discrepancy in accumulation in the tropics highlights the role of interior 1478 ocean carbon redistribution for those inventories (Khatiwala et al., 2009, DeVries et al., 2023). 1479

1480 Additional benchmarking of the /CO₂-products with independent data generally shows low and 1481 consistent biases and RMSEs, with the exception of the comparison with SOCAT flag E data in the 1482 tropics where the biases range from -6.6 µatm in JMA-MLR to -10.29 µatm in OceanSODA_ETHv2, 1483 (Figure S7) and the Northern Hemisphere, where biases range from -2.68 µatm in VLIZ-SOMFFN to - 1484 40.47 µatm in UExP-FNN-U (Figure S7), although with few measurements covering the latter area. 1485 Furthermore, we evaluate the trends derived from a subset of /CO₂-products by subsampling five 1486 GOBMs used in Friedlingstein et al. (2023; covering the period up to the year 2022) following the 1487 approach of Hauck et al. (2023a) and evaluating the air-sea CO₂ flux trend for the 2001-2021 period, 1488 i.e. the period of strong divergence in the air-sea CO₂ exchange excluding the tail effect, against trend 1489 biases identified by the GOBM reconstruction. The results indicate a relationship between 1490 reconstruction bias and strength of the decadal trends (Figure S8), indicating a tendency of the /CO₂- 1491 products ensemble to overestimate the air-sea CO₂ flux trends in agreement with Mayot et al. (2024). 1492

1492 **3.7 Land Sink**

1493 **3.7.1 Historical period 1850-2024**

1494 Cumulated since 1850, the terrestrial carbon sink S<sub>LAND</sub> amounts to 175 ± 50 GtC, 24% of total 1495 anthropogenic emissions, with more than two thirds of this amount (120 ± 40 GtC) being taken up by 1496 the terrestrial ecosystems since 1959. Over the historical period, the land sink increased in pace with the 1497 anthropogenic emissions exponential increase (Figure 3). As described in section 2, S<sub>LAND</sub> estimate now 1498 includes the RSS correction.

1499 **3.7.2 Recent period 1959-2024**

1500 S<sub>LAND</sub> increased from 0.9 ± 0.3 GtC yr<sup>-1</sup> in the 1960s to 2.4 ± 0.8 GtC yr<sup>-1</sup> during 2015-2024, with 1501 important interannual variations of up to 2 GtC yr<sup>-1</sup> generally showing a decreased land sink during El
<figure><img src="image_46.png" /></figure>

Niño events (Figure 10a), responsible for the corresponding enhanced growth rate in atmospheric CO₂ concentration. The larger land CO₂ sink during 2015-2024 compared to the 1960s is reproduced by all the DGVMs in response to the increase in both atmospheric CO₂, nitrogen deposition, and the changes in climate, and is broadly consistent with the residual estimated from the other budget terms (EFOS+ELUC-GATM-SOCEAN, Table 5).

Over the historical period, the increase in the global terrestrial CO₂ sink is largely attributed to the CO₂ fertilisation effect (Prentice et al., 2001, Piao et al., 2009, Schimel et al., 2015) and increased nitrogen deposition (Huntzinger et al., 2017, O'Sullivan et al., 2019), directly stimulating plant photosynthesis and increased plant water use in water limited systems, with a smaller negative contribution of climate change (Figure 12). There is a range of evidence to support a positive terrestrial carbon sink in response to increasing atmospheric CO₂ (Walker et al., 2021), including a new synthesis of free-air CO₂-enrichment experiments across forests and ages, which concluded that Net Primary Productivity increased by 22% for a common 41% CO₂ enrichment (Norby 2025). As expected from theory, the greatest CO₂ effect is simulated in the tropical forest regions, associated with warm temperatures and long growing seasons (Hickler et al., 2008) (Figure 12a). However, evidence from tropical intact forest plots indicate an overall decline in the land sink across Amazonia (1985-2011), attributed to enhanced mortality offsetting productivity gains (Brienen et al., 2015, Hubau et al., 2020). During 2015-2024 the land sink is positive in all regions (Figure 6d) with the exception of eastern Brazil, Bolivia, northern Venezuela, Southwest USA, central Europe and Central Asia, North and South Africa, and eastern Australia, where the negative effects of climate variability and change (i.e. reduced rainfall and/or increased temperature) counterbalance CO₂ effects. This is clearly visible in Figure 12 where the effects of CO₂ (Figure 12a) and climate (Figure 12b) as simulated by the DGVMs are isolated (see also Figure S12). The negative effect of climate can be seen across the globe, and is particularly strong in most of South America, Central America, Southwest US, Central Europe, western Sahel, southern Africa, Southeast Asia and southern China, and eastern Australia (Figure 12b, Figure S12). Globally, over the 2015-2024 period, climate change reduces the land sink by 0.8 ± 0.7 GtC yr⁻¹ (25% of the CO₂ effect, which is 3.2 ± 1.1 GtC yr⁻¹ for the corresponding period, see Supplement S4.1).

Most DGVMs have similar S<sub>LAND</sub> averaged over 2015-2024, and 14/22 models fall within the 1σ range of the residual land sink [1.4 to 3.3 GtC yr<sup>-1</sup>] (see Table 5), and all models but one are within the 2σ range [0.5 to 4.3 GtC yr<sup>-1</sup>]. The ED model is an outlier, with a land sink estimate of 4.9 GtC yr<sup>-1</sup> for the 2015-2024 period (accounting for the RSS correction), driven by a strong CO<sub>2</sub> fertilisation effect (6.4 GtC yr<sup>-1</sup> in the CO<sub>2</sub> only (S1) simulation). There are no direct global observations of the land sink (S<sub>LAND</sub>), or the CO<sub>2</sub> fertilisation effect, and so we are not yet in a position to rule out models based on component fluxes if their net land sink (S<sub>LAND</sub>-E<sub>LUC</sub>) is within the observational uncertainty provided by O<sub>2</sub> measurements. The important role of non-living carbon pools in carbon budgets has also been recently highlighted from an Earth-observation (EO) perspective, where contrary to DGVMs, EO
<figure><img src="image_47.png" /></figure>

1538 products do not show an increase in biomass, thus inferring a larger role of dead carbon in land carbon cycle dynamics (Bar-On et al., 2025).

1540 Since 2020 the globe has experienced La Niña conditions which would be expected to lead to an increased land carbon sink. This 3-year long period of La Niña conditions came to an end by the second half of 2023 and transitioned to an El Niño which lasted until mid-2024. A clear transition from maximum to a minimum in the global land sink is evident in $S_{LAND}$, from 2022 to 2023 and we find that an El Niño-driven decrease in tropical land sink is offset by a smaller increase in the high-latitude land sink. In the past years several regions experienced record-setting fire events (see also section 3.8.3).
1541
1542
1543
1544
1545
1546 While global burned area has declined over the past decades mostly due to declining fire activity in savannas (Andela et al., 2017), forest fire emissions are rising and have the potential to counter the negative fire trend in savannas (Zheng et al., 2021). Noteworthy extreme fire events include the 2019-2020 Black Summer event in Australia (emissions of roughly 0.2 GtC; van der Velde et al., 2021), Siberia in 2021, where emissions approached 0.4 GtC or three times the 1997-2020 average according to GFED4s, Canada in 2023 and 2024 (Byrne et al., 2024), and unprecedented wildfires in Brazil and Bolivia in 2024 (partly related to land-use activity, see Sec. 3.2.3 and 3.2.4) (Bourgoin et al., 2025), partially offset by a negative trend in wildfire over Northern Hemisphere Africa. While other regions, including Western US and Mediterranean Europe, also experienced intense fire seasons in 2021 their emissions are substantially lower.
1547
1548
1549
1550
1551
1552
1553
1554
1555

1556 Despite these regional negative effects of climate change on $S_{LAND}$, the efficiency of land to remove anthropogenic CO₂ emissions has remained broadly constant over the last six decades at around 23% (Figure S16).
1557
1558

### 3.7.3 Final year 2024

1560 The terrestrial CO₂ sink from the DGVMs ensemble $S_{LAND}$ was 1.9 ± 0.9 GtC in 2024, 37% below the 2022 La Niña induced strong sink of 3.1 ± 1.0 GtC, and also below the 2015-2024 average of 2.4 ± 0.8 GtC yr⁻¹ (Figure 4e, Table 7). We estimate that the 2024 land sink was the lowest since 2015. The severe reduction in the land sink in 2024 is likely driven by the El Niño conditions, leading to a 66% reduction in $S_{LAND}$ in the tropics (30N-30S) from 2.8 GtC in 2022 to 1.0 GtC in 2024. This is combined with intense wildfires in Canada, Bolivia and Brazil that led to a significant CO₂ source (see also Section 3.8.3). We note that the $S_{LAND}$ estimate for 2024 of 1.9 ± 0.9 GtC is much larger than the 0.3 ± 1.0 GtC yr⁻¹ estimate from the residual sink from the global budget ($E_{FOS}+E_{LUC}-G_{ATM}-S_{OCEAN}$, Table 5), although the residual sink would be substantially larger (at around 1.4 GtC yr⁻¹) if using the satellite-based GRESO in this equation. A large budget imbalance is often associated with El Niño years (e.g. 1986/87, 1997/98, 2005/06, 2023/24), and the possible underestimation in DGVMs of the tropical land response to drought and high temperature extremes. Few DGVMs have explicit representation of
<figure><img src="image_48.png" /></figure>

1572 drought-mortality and only 8 from 22 include some form of forest demography, although recent efforts
1573 are underway to fill these critical research gaps (Eckes-Shephard et al., 2025 Yao et al., 2022, 2023).

1574 An overestimate in S<sub>LAND</sub> in 2024 can in part be attributed to the inability in DGVMs to reproduce
1575 extreme fire emissions in 2024. Globally fire emissions as calculated by GFED were 0.43 GtC/yr higher
1576 in 2024 compared to the decadal average (2015-2024), similar to the anomaly in 2023 (0.44 GtC /yr).
1577 This was mainly due to an increase in fire over forests in Boreal North America (0.16 GtC/yr) and
1578 forests, savannahs and grasslands over Southern Hemisphere South America (0.35 GtC/yr) in 2024,
1579 partly offset by a negative trend in fires over Northern Hemisphere Africa (-0.05 GtC/yr anomaly). In
1580 fact, across the Amazon basin emissions from degradation fires (two thirds Brazil, one third Bolivia)
1581 surpassed those from deforestation fires in 2024 (Bourgoin et al., 2025). In contrast DGVMs simulate
1582 an anomaly of 0.2 GtC/yr in 2024, i.e. an underestimate of 0.23 GtC/yr in fire emissions for 2024.
1583 Finally, a large uncertainty relates to climate forcing datasets, particularly over the critical carbon-rich
1584 tropical forest regions with poor coverage of meteorological stations. For example, there are large
1585 differences in climate forcing (e.g. CRUJRA-3Q and ERA5) and downstream DGVM carbon
1586 simulations over the Congo basin and tropical central and northern Africa in 2024 (Ke et al., 2025).

1587 **3.7.4 Year 2025 projection**

1588 Calculating the land sink as the residual of the other projections for 2025, we project a land sink of 3.1
1589 GtC for 2025, 1.1 GtC larger than the 2024 estimate, consistent with an expected recovery of the land
1590 sink after an El Niño event. The ESMs do not provide an additional estimate of S<sub>LAND</sub> as they only
1591 simulate the net atmosphere-land carbon flux (S<sub>LAND</sub>-E<sub>LUC</sub>).

1592 **3.7.5 Evaluation of land models**

1593 The evaluation of the DGVMs shows generally higher agreement across models for runoff, and to a
1594 lesser extent for GPP, and ecosystem respiration. These conclusions are supported by a more
1595 comprehensive analysis of DGVM performance in comparison with benchmark data (Sitch et al.,
1596 2024). A relative comparison of DGVM performance (Figure S11) suggests several DGVMs (CABLE-
1597 POP, CLASSIC, OCN, ORCHIDEE) may outperform others at multiple carbon and water cycle
1598 benchmarks. However, results from Seiler et al., 2022, also show how DGVM differences are often of
1599 similar magnitude compared with the range across observational datasets. All models score high
1600 enough over the metrics tests to support their use here. There are a few anomalously low scores for
1601 individual metrics from a single model, and these can direct the effort to improve models for use in
1602 future budgets (See also Supplement S.4.2).
<figure><img src="image_49.png" /></figure>

1603 **3.8 Partitioning the carbon sinks**

1604 **3.8.1 Global sinks and spread of estimates**

1605 In the period 2015-2024, the bottom-up view of global net ocean and land carbon sinks provided by the
1606 GCB, SOCEAN for the ocean and SLAND–ELUC for the land, agrees closely with the top-down global
1607 carbon sinks delivered by the atmospheric inversions. This is shown in Figure 13, which visualises the
1608 individual decadal mean atmosphere-land and atmosphere-ocean fluxes from each, along with the
1609 constraints on their sum offered by the global fossil CO2 emissions flux minus the atmospheric growth
1610 rate (EFOS–GATM, 4.2 ± 0.5 Gt C yr⁻¹, Table 7, shown as diagonal line in Figure 13). The GCB estimate
1611 for net atmosphere-to-surface flux (SOCEAN + SLAND - ELUC) during 2015-2024 is 4.2 ± 1.1 Gt C yr⁻¹
1612 (Table 7), implying a zero budget imbalance (BIM) (see Section 3.9). The atmospheric inversions
1613 estimate of the net atmosphere-to-surface flux during 2015-2024 is 4.3 Gt C yr⁻¹, with a < 0.1 GtC yr⁻¹
1614 imbalance, and thus scatter across the diagonal, with inverse models trading land for ocean fluxes in
1615 their solution. The independent constraint on the net atmosphere-to-surface flux based on atmospheric
1616 O₂ by design also closes the balance and is 4.2 ± 0.9 GtC yr⁻¹ over the 2015-2024 period (orange
1617 symbol on Figure 13), while the ESMs estimate for the net atmosphere-to-surface flux over that period
1618 is 4.8 [2.4, 6.0] GtC yr⁻¹ (Tables 5 and 6).

1619 The distributions based on the individual models and /CO₂-products reveal substantial spread but
1620 converge near the decadal means quoted in Tables 5 to 7. Sink estimates for SOCEAN are mostly non-
1621 Gaussian, while the ensemble of DGVMs and inverse models appears more normally distributed
1622 justifying the use of a multi-model mean and standard deviation for their errors in the budget.
1623 Noteworthy is that the tails of the distributions provided by the land and ocean bottom-up estimates
1624 would not agree with the global constraint provided by the fossil fuel emissions and the observed
1625 atmospheric CO₂ growth rate. This illustrates the power of the atmospheric joint constraint from the
1626 global CO₂ observation capacity.

1627 **3.8.1.1 Net atmosphere-to-land flux**

1628 The GCB estimate of the net atmosphere-to-land flux (SLAND – ELUC), calculated as the difference
1629 between SLAND from the DGVMs and ELUC from the bookkeeping models, amounts to a 1.0 ± 1.0 GtC
1630 yr⁻¹ sink during 2015-2024 (Table 5). The estimate of net atmosphere-to-land flux (SLAND – ELUC) from
1631 the DGVMs alone (1.4 ± 0.7 GtC yr⁻¹, Table 5, green symbols on Figure 13) is slightly larger, although
1632 within the uncertainty of the GCB estimate and also within uncertainty of the global carbon budget
1633 constraint (EFOS – GATM – SOCEAN, 1.0 ± 0.6 GtC yr⁻¹; Table 7). Also, for 2015-2024, the inversions
1634 estimate the net atmosphere-to-land flux is a 1.3 ± 0.3 GtC yr⁻¹ sink, similar to the mean of the DGVMs
1635 estimates (purple versus grey symbols on Figure 13). The independent constraint based on atmospheric
1636 O₂ is slightly lower, 0.7 ± 0.8 GtC yr⁻¹ (orange symbol in Figure 13), although its uncertainty overlaps
<figure><img src="image_50.png" /></figure>

1637 with the uncertainty range from other approaches. Last, the ESMs estimate for the net atmosphere-to-land flux during 2014-2023 is a 2.3 [-0.1, 3.6] GtC yr⁻¹ sink, larger than all other estimates (Table 5).

1638 As discussed in Section 3.5.3, the atmospheric growth rate of CO₂ derived from the NOAA surface stations was very high in 2024, 7.9 GtC (3.73 ppm) the largest on the 65 years long observational record. Both DGVMs and inversions assign this large CO₂ growth rate to a continued reduction of the net atmosphere to land flux since 2023, in particular in the tropics (Figures 11 and 14), especially pronounced in the inversions. DGVMs simulate a 2024 global net atmosphere-to-land flux of 1.1 GtC yr⁻¹, a 50% decline relative to the 2.2 GtC yr⁻¹ sink in 2022, primarily driven by the severe reduction in S<sub>LAND</sub> (-37%, see Section 3.7.3). The tropics (30°N-30°S) are recording a dramatic decrease in the net atmosphere-to-land flux from a 1.3 GtC yr⁻¹ sink in 2022 to a 0.2 GtC yr⁻¹ source in 2024. The atmospheric inversions show a continued reduction with the global net atmosphere-to-land flux declining from 2.9 GtC yr⁻¹ in 2022 to 0.8 GtC yr⁻¹ in 2023 to 0.2 GtC yr⁻¹ in 2024 (-94% from 2022 to 2024), with the tropics turning from a 1.3 GtC yr⁻¹ sink in 2022 to a 1.2 GtC yr⁻¹ source in 2024.

### 3.8.1.2 Net atmosphere-to-ocean flux

1650 For the 2015-2024 period, the unadjusted GOBMs (2.7 ± 0.4 GtC yr⁻¹) produce a lower estimate for S<sub>OCEAN</sub> than the unadjusted fCO₂-products with 3.3 [2.9, 3.8] GtC yr⁻¹, which shows up in Figure 13 as separate peaks in the distribution from the GOBMs (dark blue symbols) and from the fCO₂-products (light blue symbols). Atmospheric inversions (3.0 ± 0.3 GtC yr⁻¹) suggest an ocean uptake more in line with the average of the GOBMs and fCO₂-products for the recent decade (Table 6). The inversions are not fully independent as 7 out of 14 inversions covering the last decade use fCO₂-products as ocean priors and one uses a GOBM (Table S4). The independent constraint based on atmospheric O₂ (3.5 ± 0.5 GtC yr⁻¹) is at the high end of the distribution of the other methods. However, as mentioned in section 2.8, the O₂ method requires a correction for global air-sea O₂ flux, which induces a non-negligible uncertainty on the decadal estimates (about 0.5 GtC yr⁻¹). The large growth in the ocean carbon sink from O₂ is compatible with the GOBMs and fCO₂-products estimates when accounting for their uncertainty ranges. Lastly, the ESMs estimate, 2.5 [2.2, 2.8] GtC yr⁻¹, suggest a lower average ocean carbon sink than the other estimates. We caution that the riverine transport of carbon taken up on land and outgassing from the ocean, accounted for here, is a substantial (0.65 ± 0.3 GtC yr⁻¹) and uncertain term (Crisp et al., 2022; Gruber et al., 2023; DeVries et al., 2023) that separates the GOBMs, ESMs and oxygen-based estimates on the one hand from the fCO₂-products and atmospheric inversions on the other hand.

### 3.8.2 Regional partitioning

1668 Figure 14 shows the latitudinal partitioning of the global atmosphere-to-ocean (SOCEAN), atmosphere-to-land (SLAND - ELUC), and their sum (SOCEAN + SLAND - ELUC) according to the estimates from GOBMs
<figure><img src="image_51.png" /></figure>

1671 and ocean /CO₂-products (SOCEAN), DGVMs (SLAND – E_LUC), and from atmospheric inversions (SOCEAN and SLAND – E_LUC). SOCEAN estimates were not adjusted for the regional analysis.

1673 **3.8.2.1 North**

1674 Despite being one of the most densely observed and studied regions of our globe, annual mean carbon
1675 sink estimates in the northern extra-tropics (north of 30°N) continue to differ. The atmospheric
1676 inversions suggest an atmosphere-to-surface sink (SOCEAN + SLAND - E_LUC) for 2015-2024 of 2.7 ± 0.4
1677 GtC yr⁻¹, which is higher than the process models’ estimate of 2.0 ± 0.4 GtC yr⁻¹ (Figure 14). The
1678 GOBMs (1.1 ± 0.2 GtC yr⁻¹), /CO₂-products (1.4 [1.2-1.6] GtC yr⁻¹), and inversion systems (1.2 ± 0.1
1679 GtC yr⁻¹) produce largely consistent estimates of the ocean sink. However, the larger flux in the /CO₂-
1680 products may be related to data sparsity (Mayot et al., 2024). Thus, the difference mainly arises from
1681 the net land flux (SLAND - E_LUC) estimate, which is 0.9 ± 0.3 GtC yr⁻¹ in the DGVMs compared to 1.5 ±
1682 0.4 GtC yr⁻¹ in the atmospheric inversions (Figure 14, second row).

1683 Discrepancies in the northern land fluxes conform with persistent issues surrounding the quantification
1684 of the drivers of the global net land CO₂ flux (Arneth et al., 2017; Huntzinger et al., 2017; O'Sullivan et
1685 al., 2022) and the distribution of atmosphere-to-land fluxes between the tropics and high northern
1686 latitudes (Baccini et al., 2017; Schimel et al., 2015; Stephens et al., 2007; Ciais et al., 2019; Gaubert et
1687 al., 2019; O'Sullivan et al. 2024).

1688 In the northern extra-tropics, the process models, inversions, and /CO₂-products consistently suggest
1689 that most of the interannual variability stems from the land (Figure 14). Inversions generally agree on
1690 the magnitude of interannual variations (IAV) over land, more so than DGVMs (0.27-0.38 vs 0.07-0.55
1691 GtC yr⁻¹, averaged over 1990-2024).

1692 **3.8.2.2 Tropics**

1693 In the tropics (30°S-30°N), both the atmospheric inversions and process models estimate a net carbon
1694 balance (SOCEAN + SLAND - E_LUC) that is relatively close to neutral over the past decade (inversions: 0.05
1695 ± 0.5 GtC yr⁻¹, process models: 0.4±0.6 GtC yr⁻¹). The GOBMs (-0.003 ± 0.3 GtC yr⁻¹), /CO₂-products
1696 (0.3 [0.1, 0.6] GtC yr⁻¹), and inversion systems (0.3 ± 0.1 GtC yr⁻¹) indicate a neutral to positive tropical
1697 ocean flux (see Figure S5 for spatial patterns). DGVMs indicate a net land sink (SLAND - E_LUC) of 0.4
1698 ±0.5 GtC yr⁻¹, whereas the inversion systems indicate a small source in the net land flux although with
1699 larger model spread (-0.2 ± 0.6 GtC yr⁻¹, Figure 14, third row).

1700 A continuing conundrum is the partitioning of the global atmosphere-land flux between the Northern
1701 Hemisphere land and the tropical land (Stephens et al., 2017; Pan et al., 2011; Gaubert et al., 2019). It is
1702 of importance because each region has its own history of land-use change, climate drivers, and impact
1703 of increasing atmospheric CO₂ and nitrogen deposition. Quantifying the magnitude of each sink is a
<figure><img src="image_52.png" /></figure>

prerequisite to understanding how each individual driver impacts the tropical and mid/high-latitude carbon balance. We define the North-South (N-S) difference as net atmosphere-land flux north of 30°N minus the net atmosphere-land flux south of 30°N. For the inversions, the N-S difference is 1.7 ± 1.0 GtC yr⁻¹ across this year’s inversion ensemble. In the ensemble of DGVMs the N-S difference is 0.6 ± 0.5 GtC yr⁻¹, a much narrower range than the one from atmospheric inversions. The smaller spread across DGVMs than across inversions is to be expected as there is no correlation between Northern and Tropical land sinks in the DGVMs as opposed to the inversions where the sum of the two regions being well-constrained by atmospheric observations leads to an anti-correlation between these two regions.

The tropical lands are the origin of most of the atmospheric CO₂ interannual variability (Ahlström et al., 2015), consistently among the process models and inversions (Figure 14). The interannual variability in the tropics is similar among the ocean fCO₂-products (0.06-0.18 GtC yr⁻¹) and the GOBMs (0.07-0.16 GtC yr⁻¹). The DGVMs and inversions indicate that atmosphere-to-land CO₂ fluxes are more variable than atmosphere-to-ocean CO₂ fluxes in the tropics, with interannual variability of 0.3 to 1.13 and 0.96 GtC yr⁻¹ for DGVMs and inversions, respectively for 1990-2024. The year 2024 saw the largest growth rate in the atmosphere so far, largely caused by reductions in the tropical land sink, similar to earlier extreme years, such as during the 2015-2016 El Niño period. The inversions seem to capture the response of the tropical land sink to a larger degree than the DGVMs, with a 2024 tropical land source of 1.2 ± 0.9 GtC yr⁻¹ for the inversions versus 0.2 ± 0.7 for the DGVMs.

### 3.8.2.3 South

In the southern extra-tropics (south of 30°S), the atmospheric inversions suggest a net atmosphere-to-surface sink ($SOCEAN+SLAND-ELUC$) for 2015-2024 of 1.6 ± 0.2 GtC yr⁻¹, similar to the process models' estimate of 1.5 ± 0.4 GtC yr⁻¹ (Figure 14). An approximately neutral net land flux (SLAND-ELUC) for the southern extra-tropics is estimated by both the DGVMs (0.05 ± 0.1 GtC yr⁻¹) and the inversion systems (0.01 ± 0.1 GtC yr⁻¹). This means nearly all carbon uptake is due to oceanic sinks south of 30°S. The Southern Ocean flux in the fCO₂-products (1.6 [1.5, 1.9 GtC] yr⁻¹) and inversion estimates (1.6 ± 0.2 GtC yr⁻¹) is marginally higher than in the GOBMs (1.5 ± 0.4 GtC yr⁻¹) (Figure 14, bottom row). This agreement is subject to the choice of the river flux adjustment (Lacroix et al., 2020, Hauck et al., 2023b). Nevertheless, the time-series of atmospheric inversions and fCO₂-products diverge from the GOBMs. A substantial overestimation of the trends in the fCO₂-products could be explained by sparse and unevenly distributed observations, especially in wintertime (Figure S5; Hauck et al., 2023a; Gloege et al., 2021). Model biases likely contribute as well, with biases in mode water formation, stratification, and the chemical buffer capacity known to play a role in Earth System Models (Terhaar et al., 2021, Bourgeois et al., 2022, Terhaar et al., 2022).
<figure><img src="image_53.png" /></figure>

The interannual variability in the southern extra-tropics is low because of the dominance of ocean areas with low variability compared to land areas. The split between land (SLAND-ELUC) and ocean (SOCEAN) shows a substantial contribution to variability in the south coming from the land, with no consistency between the DGVMs and the inversions or among inversions. This is expected due to the difficulty of separating exactly the land and oceanic fluxes when viewed from atmospheric observations alone. The SOCEAN interannual variability was found to be higher in the $f/CO_2$-products (0.04-0.18 GtC yr$^{-1}$) compared to GOBMs (0.03 to 0.06 GtC yr$^{-1}$) in 1990-2024. Inversions give an interannual variability of 0.13 to 0.15 GtC yr$^{-1}$. Model subsampling experiments recently illustrated that $f/CO_2$-products may overestimate decadal variability in the Southern Ocean carbon sink by 30% and the trend since 2000 by 50-130% due to data sparsity, based on one and two $f/CO_2$-products with strong variability (Gloege et al., 2021, Hauck et al., 2023a). The trend benchmark test using the method of Hauck et al., (2023a) and a subset of 6 $f/CO_2$-products confirms the sensitivity of the decadal trends in $f/CO_2$-products to reconstruction biases, particularly in the Southern Ocean, indicating an overestimation of the ensemble mean trend (See Supplement S.3.4). However, we also find compensating positive biases in the ensemble so that the ensemble mean bias is smaller than the bias from some individual $f/CO_2$-products.

### 3.8.2.4 RECCAP2 regions

Aligning with the RECCAP-2 initiative (Ciais et al., 2022; Poulter et al., 2022; DeVries et al., 2023), we provide a breakdown of this GCB paper estimate of the $E_{LUC}$, $S_{LAND}$, Net land ($S_{LAND} - E_{LUC}$), and SOCEAN fluxes over the 10 land, and 5 ocean RECCAP-2 regions, averaged over the period 2015-2024 (Figure 15). The DGVMs and inversions suggest a positive net land sink in all regions, except for South America, Africa, and Southeast Asia, where the inversions indicate a small net source of respectively -0.1±0.5 GtC yr$^{-1}$, -0.3±0.3 GtC yr$^{-1}$, and -0.1±0.3 GtC yr$^{-1}$ compared to a small sink of 0.1±0.3 GtC yr$^{-1}$, 0.1±0.3 GtC yr$^{-1}$, and 0.03±0.1 GtC yr$^{-1}$ for the DGVMs. The uncertainty in the sign of net tropical carbon fluxes is driven by opposing gross fluxes and relatively large uncertainty in the gross fluxes themselves. For South America, DGVMs estimate $S_{LAND}$ of 0.4±0.4 GtC yr$^{-1}$ and $E_{LUC}$ of 0.3±0.2 GtC yr$^{-1}$. Bookkeeping models suggest a larger $E_{LUC}$ source of around 0.5 GtC yr$^{-1}$. Similarly, in Southeast Asia, the DGVMs estimate an $E_{LUC}$ of 0.2±0.1 GtC yr$^{-1}$, compared with the bookkeeping model estimate of 0.4±0.02 GtC yr$^{-1}$. Therefore, DGVMs may underestimate $E_{LUC}$ in these regions, which could explain the net sink discrepancy with inversions. In Africa, $E_{LUC}$ is similar for DGVMs and bookkeeping models (~0.4 GtC yr$^{-1}$), and DGVMs estimate an $S_{LAND}$ of 0.5±0.2 GtC yr$^{-1}$.

The inversions suggest the largest net land sinks are located in North America (0.4±0.3 GtC yr$^{-1}$), Russia (0.6±0.2 GtC yr$^{-1}$), Europe (0.3±0.2 GtC yr$^{-1}$), and East Asia (0.2±0.3 GtC yr$^{-1}$). This agrees well with the DGVMs in North America (0.4±0.1 GtC yr$^{-1}$), which indicate a large natural land sink ($S_{LAND}$) of 0.4±0.2 GtC yr$^{-1}$, and near-zero net land-use related carbon losses (0.02±0.1 GtC yr$^{-1}$). The
<figure><img src="image_54.png" /></figure>

1771 DGVMs suggest a smaller net land sink in Russia compared to inversions (0.3±0.1 GtC yr⁻¹), and a
1772 similar net sink in East Asia (0.2±0.1 GtC yr⁻¹).

1773 There is generally a higher level of agreement in the estimates of regional SOCEAN between the different
1774 data streams (GOBMs, /CO₂-products and atmospheric inversions) on decadal scale, compared to the
1775 agreement between the different land flux estimates. All data streams agree that the largest contribution
1776 to SOCEAN stems from the Southern Ocean due to a combination of high flux density and large surface
1777 area, but with important contributions also from the Atlantic (high flux density) and Pacific (large area)
1778 basins. In the Southern Ocean, GOBMs suggest a sink of 1.1±0.4 GtC yr⁻¹, in line with the /CO₂-
1779 products (1.1 [1.0,1.2] GtC yr⁻¹) and atmospheric inversions (1.1±0.2 GtC yr⁻¹). There is similar
1780 agreement in the Pacific Ocean, with GOBMs, /CO₂-products, and atmospheric inversions indicating a
1781 sink of 0.6±0.1 GtC yr⁻¹, 0.7 [0.6,0.9] GtC yr⁻¹, and 0.6±0.2 GtC yr⁻¹, respectively. However, in the
1782 Atlantic Ocean, GOBMs simulate a sink of 0.5±0.1 GtC yr⁻¹, noticeably lower than both the /CO₂-
1783 products (0.8 [0.7,0.9] GtC yr⁻¹) and atmospheric inversions (0.8±0.1 GtC yr⁻¹). It is important to note
1784 the /CO₂-products and atmospheric inversions have a substantial and uncertain river flux adjustment in
1785 the Atlantic Ocean (0.3 GtC yr⁻¹) that also leads to a mean offset between GOBMs and /CO₂-
1786 products/inversions in the latitude band of the tropics (Figure 14). The Indian Ocean due its smaller size
1787 and the Arctic Ocean due to its size and sea-ice cover that prevents air-sea gas-exchange are responsible
1788 for smaller but non negligible SOCEAN fluxes (Indian Ocean: (0.3±0.1 GtC yr⁻¹, 0.3 [0.3,0.4] GtC yr⁻¹,
1789 and 0.3±0.1 GtC yr⁻¹ for GOBMs, /CO₂-products, and atmospheric inversions, respectively, and Arctic
1790 Ocean: (0.1±0.03 GtC yr⁻¹, 0.2 [0.1,0.3] GtC yr⁻¹, and 0.1±0.04 GtC yr⁻¹ for GOBMs, /CO₂-products,
1791 and atmospheric inversions, respectively). Note that the SOCEAN numbers presented here deviate from
1792 numbers reported in RECCAP-2 where the net air-sea CO₂ flux is reported (i.e. without river flux
1793 adjustment for /CO₂-products and inversions, and with river flux adjustment subtracted from GOBMs
1794 in most chapters, or comparing unadjusted datasets with discussion of uncertain regional riverine fluxes
1795 as major uncertainty, e.g. Sarma et al., 2023, DeVries et al., 2023).

3.8.3 Fire emissions in 2025

1797 Fire emissions so far in 2025 have been below the average of recent decades, chiefly due to the lowest
1798 emissions on record since 2003 across the tropics. Figure S14 shows global and regional emissions
1799 estimates for the period 1st Jan-30th September in each year 2003-2025. Estimates derive from two
1800 global fire emissions products: the global fire emissions database (GFED, version 4.1s; van der Werf et
1801 al., 2017), and the global fire assimilation system (GFAS, operated by the Copernicus Atmosphere
1802 Service; Kaiser et al., 2012). The two products estimate that global emissions from fires were 1.2-1.4
1803 GtC (the range reflecting the difference between the two fire emission datasets, actual uncertainties are
1804 larger and estimates are likely conservative, see Chen et al. (2023)) during January-September 2025.
<figure><img src="image_55.png" /></figure>

1805 These estimates are 19-20% below the 2015-2024 average for the same months (1.5-1.7 GtC). Both the GFED4.1s and GFAS products show the second-lowest January-September fire emissions since 2003 (2022 is the lowest year in both products).

1808 Notably, the year 2025 follows two years with above-average global fire emissions (Figure S14), with global emissions through September totalling 1.7-2.1 GtC in 2023 and 1.6-2.2 GtC in 2024. These high emissions totals were caused by high emissions in North America in 2023 principally in Canada; Jones et al., 2024b), and in both North America and South America in 2024 (principally in Canada and Brazil; Kelley et al., 2025). For example, in January-September 2024, GFED4.1s suggests that global emissions totalled 2.2 GtC, the highest total on record for these months and 32% above the average for the decade prior (2014-2023; GFAS also suggests that emissions were 11% above the average for January-September).

1816 Despite low global fire emissions so far in 2025, a pattern of extremely high fire emissions from Canada has now persisted for three consecutive years commencing with the record-breaking year in 2023 (Jones et al., 2024b, Byrne et al., 2024; Kelley et al., 2025). In January-September 2025, fire emissions from Canada (0.3-0.4 GtC) were slightly greater than in the same months of 2024 (0.2-0.3 GtC yr⁻¹) and around half as large as those in the same months of 2023 (0.5-0.8 GtC). The emissions totals for Canada in January-September 2025 were 2.2 times the average of January-September periods during the prior decade 2015-2024 and 4-6 times greater than the average of those months in 2003-2022 [excluding the record-breaking year in 2023]; Figure S14). According to GFED4.1s, January-September fire emissions from Canada in just the past three years (1.5 GtC) exceeded the country's total January-September fire emissions throughout the 20 years prior (1.2 GtC during 2003-2022). The continued anomaly in Canada propagated to the Northern Hemisphere, where January-September 2025 emissions of 0.4-0.5 GtC were 13-21% above the average of 2015-2024.

1828 Fire emissions anomalies in Africa strongly influence global fire emissions totals because the continent typically contributed near 50% of global fire emissions during 2015-2024 (average of January-September periods). For 2025, fire emissions in Africa through September were 0.4-0.6 GtC, 19-25% below the average of 2015-2024 (0.6-0.8 GtC). Synchronously, emissions through September from South America were around 0.1 GtC in both GFED4.1s and GFAS systems, less than half of the average for 2015-2024 (0.2-0.3 GtC), and emissions from Southeast Asia were around 0.05 GtC, also less than half of the average for 2015-2024 (0.1 GtC in both GFED4.1s and GFAS). Low emissions from the tropical parts of Africa, South America, and Southeast Asia contribute to low emissions across the global tropics so far in 2025 (0.7-0.9 GtC), which were only around two-thirds of the average for 2015-2024 (1.1-1.3 GtC) in both GFED4.1s and GFAS.

1838 We caution that the fire emissions fluxes presented here should not be compared directly with other fluxes of the budget (e.g. S<sub>LAND</sub> or E<sub>LUC</sub>) due to incompatibilities between the observable fire emission
<figure><img src="image_56.png" /></figure>

1840 fluxes and what is quantified in the S<sub>LAND</sub> and E<sub>LUC</sub> components of the budget. The fire emission
1841 estimates from global fire products relate to all fire types that can be observed in Earth Observations
1842 (Giglio et al., 2018; Randerson et al., 2012; Kaiser et al., 2012), including (i) fires occurring as part of
1843 natural disturbance-recovery cycles that would also have occurred in the pre-industrial period (Yue et
1844 al., 2016; Keeley and Pausas, 2019; Zou et al., 2019), (ii) fires occurring above and beyond natural
1845 disturbance-recovery cycle due to changes in climate, CO<sub>2</sub> and N fertilisation and to an increased
1846 frequency of extreme drought and heatwave events (Abatzoglou et al., 2019; Jones et al., 2022; Zheng
1847 et al., 2021; Burton et al., 2024), and (iii) fires occurring in relation to land use and land use change,
1848 such as deforestation fires and agricultural fires (van der Werf et al., 2010; Magi et al., 2012). In the
1849 context of the global carbon budget, fire emissions associated with (ii) should be included in the S<sub>LAND</sub>
1850 component, fire emissions associated with (iii) should already be accounted for in the E<sub>LUC</sub> component,
1851 while fire emissions associated with (i) should not be included in the global carbon budget as part of the
1852 natural carbon cycle. However, it is not currently possible to derive specific estimates for fluxes (i), (ii),
1853 and (iii) using global fire emission products such as GFED or GFAS. In addition, the fire emissions
1854 estimates from global fire emissions products represent a gross flux of carbon to the atmosphere,
1855 whereas the S<sub>LAND</sub> component of the budget is a net flux that should also include post-fire recovery
1856 fluxes. Even if emissions from fires of type (ii) could be separated from those of type (i), these fluxes
1857 may be partially or wholly offset in subsequent years by post-fire fluxes as vegetation recovers,
1858 sequestering carbon from the atmosphere to the terrestrial biosphere (Yue et al., 2016; Jones et al.,
1859 2024c). Increases in forest fire emissions and severity (emissions per unit area) globally during the past
1860 two decades have highlighted the increasing potential for fire emissions fluxes to outweigh post-fire
1861 recovery fluxes, though long-term monitoring of vegetation recovery is required to quantify the net
1862 effect on terrestrial C storage (Jones et al., 2024c).

1863 **3.9 Closing the global carbon cycle**

1864 **3.9.1 Partitioning of cumulative emissions and sink fluxes**

1865 Emissions during the period 1850-2024 amounted to 745 ± 65 GtC and were partitioned among the
1866 atmosphere (290 ± 5 GtC; 39%), ocean (200 ± 40 GtC; 27%), and land (175 ± 50 GtC; 24%). The
1867 cumulative land sink is lower than the cumulative land-use emissions (250 ± 65 GtC), making the
1868 global land a source of 75 ± 80 GtC over the whole 1850-2024 period (Table 8).

1869 The use of nearly independent estimates for the individual terms of the global carbon budget shows a
1870 cumulative budget imbalance of 80 GtC (11% of total emissions) during 1850-2024 (Table 8), which
1871 suggests that emissions could be slightly too high by the same proportion or that the combined land and
1872 ocean sinks are underestimated (by up to 20%). A large part of the B<sub>IM</sub> occurs over the first half of the
1873 20th century (Figure 3, red dashed line) and could originate from the estimation of significant increase
1874 in E<sub>FOS</sub> and E<sub>LUC</sub> between the mid 1920s and the mid 1960s which is unmatched by a similar growth in
<figure><img src="image_57.png" /></figure>

atmospheric CO₂ concentration as recorded in ice cores (Figure 3). Also, we now correct the S<sub>LAND</sub> estimate for historical reduction in forest cover (see Section 2.6). This reduces the S<sub>LAND</sub> estimate by about 40 GtC over the 1850-2024 period, contributing to the increase of the historical budget imbalance in comparison to GCB2024 (Friedlingstein et al., 2025a).

For the more recent 1959-2024 period where direct atmospheric CO₂ measurements are available, total emissions (E<sub>FOS</sub> + E<sub>LUC</sub>) amounted to 530 ± 50 GtC, of which 420 ± 20 GtC (79%) were caused by fossil CO₂ emissions, and 110 ± 45 GtC (21%) by land-use change (Table 8). The total emissions were partitioned among the atmosphere (230 ± 5 GtC; 44%), ocean (145 ± 30 GtC; 27%), and the land (120 ± 30 GtC; 23%), with a budget imbalance of 35 GtC (7% of total emissions). All components except land-use change emissions have significantly grown since the 1960s, with important interannual variability in the growth rate in atmospheric CO₂ concentration primarily mirrored in the land CO₂ sink (Figure 4), and some decadal variability in all terms (Table 7). Differences with previous budget releases are documented in Figure S15.

The global carbon budget averaged over the last decade (2015-2024) is shown in Figure 2 and Table 7. For this period, 88% of the total emissions (E<sub>FOS</sub> + E<sub>LUC</sub>) were from fossil CO₂ emissions (E<sub>FOS</sub>), and 12% from land-use change (E<sub>LUC</sub>). The total emissions were partitioned among the atmosphere (50%), ocean (29%) and land (21%), with a near zero budget imbalance (0.1%, <0.1 GtC yr⁻¹). We note that, compared to GCB2024, we largely reduced the B<sub>IM</sub> over the 2015-2024 period, but at the cost of increasing the B<sub>IM</sub> over the 1959-2024 period.

For single years, the budget imbalance can be larger (Figure 4f). For 2024, the combination of our estimated anthropogenic sources (11.6 ± 0.9 GtC yr⁻¹) and partitioning in atmosphere, land and ocean (13.3 ± 0.9 GtC yr⁻¹) leads to a large negative B<sub>IM</sub> of -1.7 GtC (Table 7), indicating that, despite the lower than average land sink in 2024, the combined land and ocean sink is still too large to explain the record-high atmospheric CO₂ growth rate of 2024.

### 3.9.2 Trend and variability in the carbon budget imbalance

The carbon budget imbalance (B<sub>IM</sub>; Eq. 1, Figure 4f) quantifies the mismatch between the estimated total emissions and the estimated changes in the atmosphere, land, and ocean reservoirs. The budget imbalance from 1959 to 2024 is small (35 GtC cumulated over the period, i.e. 0.5 GtC yr⁻¹ on average) and shows no significant trend over the 1959-2024 period (Figure 4). The process models (GOBMs and DGVMs) and CO₂-products have been selected to match observational constraints in the 1990s, but no further constraints have been applied to their representation of trend and variability. Therefore, the small mean and trend in the budget imbalance can be seen as evidence of a coherent community understanding of the emissions and their partitioning on those time scales (Figure 4). However, the
<figure><img src="image_58.png" /></figure>

1908 budget imbalance shows substantial variability of the order of ±1 GtC yr⁻¹, particularly over semi-
1909 decadal time scales, although most of the variability is within the uncertainty of the estimates.

1910 We cannot attribute the cause of the budget imbalance with our analysis, we only note that the budget
1911 imbalance is unlikely to be explained by errors in the emissions alone because of its large semi-decadal
1912 variability component, a variability that is atypical of emissions, and also because the budget imbalance
1913 has not increased in the past 60 years despite a near tripling in anthropogenic emissions (Figure 4).

1914 Errors in SLAND and SOCEAN are more likely to be the main cause for the budget imbalance, especially on
1915 interannual to semi-decadal timescales. For example, underestimation of the SLAND by DGVMs has
1916 been reported following the eruption of Mount Pinatubo in 1991 possibly due to missing responses to
1917 changes in diffuse radiation (Mercado et al., 2009). Although we account for aerosol effects on solar
1918 radiation quantity and quality (diffuse vs direct), most DGVMs only used the former as input (i.e., total
1919 solar radiation) (Table S1). Thus, the ensemble mean may not capture the full effects of volcanic
1920 eruptions, i.e. associated with high light scattering sulphate aerosols, on the land carbon sink
1921 (O'Sullivan et al., 2021), potentially explaining the large positive BIM in 1991-1992. DGVMs are
1922 suspected to underestimate the land sink reduction in response to El Niño events (see Section 3.7.3),
1923 which could explain the large negative BIM in 1986-1987, 1997-1998 or 2023-2024). Quasi-decadal
1924 variability in the ocean sink has also been reported, with all methods agreeing on smaller than expected
1925 ocean CO2 sink in the 1990s and a larger than expected sink in the 2000s (Figure 10b; Landschützer et
1926 al., 2016, DeVries et al., 2019, Hauck et al., 2020, McKinley et al., 2020, Gruber et al., 2023) and the
1927 climate-driven variability could be substantial but is not well constrained (DeVries et al., 2023, Müller
1928 et al., 2023). Errors in sink estimates could also be partly driven by errors in the climatic forcing data,
1929 particularly precipitation for SLAND and wind for SOCEAN.

1930 Although the budget imbalance is near zero for the most recent decade, it could be due to a
1931 compensation of errors. We cannot exclude an overestimation of CO₂ emissions, particularly from land-
1932 use change, given their large uncertainty, as has been suggested elsewhere (Piao et al., 2018), and/or an
1933 underestimate of the land or ocean sinks. A larger estimate of the atmosphere-land CO₂ flux (SLAND-
1934 E_LUC) over the extra-tropics would reconcile bottom-up model results with inversion estimates (Figure
1935 14). Likewise, a larger SOCEAN is also possible given the higher estimates from the fCO₂-products and
1936 the oxygen based estimates (see Table 6 and Figure 10b), the underestimation of interior ocean
1937 anthropogenic carbon accumulation in the GOBMs (Section 3.6.5, Müller et al., 2023), or known biases
1938 of ocean models (e.g., Terhaar et al., 2022; 2024). More integrated use of observations in the global
1939 carbon budget, either on their own or for further constraining model results, should help resolve some
1940 of the budget imbalance.
<figure><img src="image_59.png" /></figure>

1941 **4 Tracking progress towards mitigation targets**

1942 The average growth in global fossil CO₂ emissions peaked at nearly +3% per year during the 2000s, driven by the rapid growth in emissions in China. In the last decade, however, the global growth rate has slowly declined, reaching a low +0.8% per year over 2015-2024. While this slowdown in global fossil CO₂ emissions growth is welcome, global fossil CO₂ emissions continue to grow, far from the rapid emission decreases needed to be consistent with the temperature goals of the Paris Agreement.

1947 Since the 1990s, the average growth rate of fossil CO₂ emissions has continuously declined across the group of developed countries of the Organisation for Economic Co-operation and Development (OECD), with emissions peaking in around 2005 and declining at 1.5% yr⁻¹ in the decade 2015-2024, compared to a decline of 0.9% yr⁻¹ during the 2005-2014 period (Table 9). In the non-OECD countries, emissions rose 4.6% yr⁻¹ from 2005-2014, but growth has lowered to 2.1% yr⁻¹ from 2015-2024. In the decade 2015-2024, territorial fossil CO₂ emissions decreased significantly (at the 95% confidence level) in 35 countries whose economies grew significantly (also at the 95% confidence level): Andorra, Australia, Austria, Belgium, Bulgaria, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Jordan, Latvia, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, South Korea, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Taiwan, Thailand, United Kingdom, and USA (updated from Le Quéré et al., 2019). Altogether, these 35 countries emitted 2.7 GtC yr⁻¹ (9.7 GtCO₂ yr⁻¹) on average over the last decade, about 27% of world CO₂ fossil emissions. For comparison, over the previous decade (2005-2014), 18 countries showed a significant decrease in territorial fossil CO₂ emissions while significantly growing their economy (Austria, Belgium, Bulgaria, Czechia, France, Germany, Luxembourg, North Macedonia, Netherlands, New Zealand, Romania, Serbia, Slovakia, Sweden, Switzerland, United Kingdom, USA, Uzbekistan). These 18 countries emitted an average 2.0 GtC/yr over 2005–2014, or 20% of the global total.

1964 Decomposing emission changes into the components of growth, a Kaya decomposition, helps give an initial understanding of the drivers of emission changes (Peters et al., 2017b). The reduction in growth in global fossil CO₂ emissions in the last decade (2015-2024, 0.8% yr⁻¹) relative to the previous decade (2005-2014, 2.2% yr⁻¹) is due to slightly weaker economic growth (3.5% yr⁻¹ to 2.4% yr⁻¹), increasing declines in CO₂ emissions per unit energy (0% yr⁻¹ to 0.7% yr⁻¹), and weakening declines in energy per unit GDP (1.6% yr⁻¹ to 1.3% yr⁻¹) (Figure S17). Fossil CO₂ emission declines in the USA and the EU27 are primarily driven by sustained or increasing declines in energy per GDP and CO₂ emissions per unit energy. China has seen emissions growth decline from 6.7% yr⁻¹ in the 2005-2014 decade to 2.5% yr⁻¹ from 2015-2024, driven by weaker economic growth (five percentage points), offset by a considerable weakening in the rate of reduction in the energy per GDP (four percentage points), with sustained improvements in CO₂ emissions per unit energy (Figure S17). India has had strong economic growth that is not offset by declines in energy per GDP or declines in CO₂ emissions per unit energy, driving
<figure><img src="image_60.png" /></figure>

1976 up fossil CO₂ emissions. In the rest of the world, economic growth has slowed considerably in the last decade, and carbon intensity has increased, with declines in energy per GDP unable to counteract these increases leading to growing emissions. Despite the high deployment of renewables in some countries (e.g., China, India), fossil energy sources continue to grow to meet growing energy demand (Le Quéré et al., 2019). In summary, the carbon intensity of energy has consistently decreased over the past decade globally (-0.7% yr⁻¹), indicating decarbonisation of the global energy system, with declines in China (-1.4% yr⁻¹), the European Union (-1.5% yr⁻¹), the USA (-1.3% yr⁻¹), but no change in India and increases (carbonisation) in the Rest of the World. The global decarbonation trends are not sufficient to offset the growth in global energy demand, with energy demand growing faster than expected due to weakening declines in energy per GDP in China, US, and globally.

1986 The slower growth of global fossil CO₂ emissions in the last decade is due in part to the emergence of climate policy (Eskander and Fankhauser 2020; Le Quere et al 2019; Hoppe et al., 2023) and technological change, which is leading to a shift from coal to gas and growth in renewable energies, and reduced expansion of coal capacity. At the aggregated global level, decarbonisation shows a strong and growing signal in the last decade, with smaller contributions from lower economic growth and declines in energy per GDP. Altogether, global fossil CO₂ emissions are still growing, far from the reductions needed to meet the ambitious climate goals of the UNFCCC Paris agreement.

1993 Last, we update the remaining carbon budget (RCB) based on two studies, the IPCC AR6 (Canadell et al., 2021) and the revision of the IPCC AR6 estimates (Forster et al., 2025, Lamboll et al., 2023). We update the RCB assessed by the IPCC AR6 (Canadell et al., 2021), accounting for the 2020 to 2025 GCB estimated anthropogenic emissions from fossil combustion (E<sub>FOS</sub>) and land use change (E<sub>LUC</sub>). From January 2026, the IPCC AR6 RCB (50% likelihood) for limiting global warming to 1.5°C, 1.7°C and 2°C is estimated to amount to 70, 165, and 300 GtC (250, 600, 1100 GtCO₂). The Forster et al. (2025) study proposed a significantly lower RCB than IPCC AR6, with the largest reduction being due to an update of the climate emulator (MAGICC) used to estimate the warming contribution of non-CO₂ agents, and to the warming (i.e. emissions) that occurred over the 2020-2024 period. We update the Forster et al., budget accounting for the 2025 GCB estimated anthropogenic emissions. From January 2026, the Forster et al., (2025) RCB (50% likelihood) for limiting global warming to 1.5°C, 1.7°C and 2°C is estimated to amount to 25, 120, and 275 GtC (90, 450, 1010 GtCO₂), significantly smaller than the updated IPCC AR6 estimate. Both the original IPCC AR6 and Forster et al. (2025) estimates include the Earth System uncertainty on the climate response to cumulative CO₂ emissions, which is reflected through the percent likelihood of exceeding the given temperature threshold, an additional uncertainty of ±220GtCO₂ due to alternative non-CO₂ emission scenarios, and other sources of uncertainties (see Canadell et al., 2021). The two sets of estimates overlap when considering all uncertainties.