1
Global Carbon Budget 2024
1
Pierre Friedlingstein [1,2], Michael O'Sullivan [1], Matthew W. Jones [3], Robbie M. Andrew [4], Judith Hauck
2
[5,6], Peter Landschützer [7], Corinne Le Quéré [3], Hongmei Li [8,9], Ingrid T. Luijkx [10], Are Olsen [11,12],
3
Glen P. Peters [4], Wouter Peters [10,13], Julia Pongratz [14,9], Clemens Schwingshackl [14], Stephen Sitch
4
[1], Josep G. Canadell [15], Philippe Ciais [16], Robert B. Jackson [17], Simone R. Alin [18], Almut Arneth
5
[19], Vivek Arora [20], Nicholas R. Bates [21], Meike Becker [11,12], Nicolas Bellouin [22], Carla F. Berghoff
6
[23], Henry C. Bittig [24], Laurent Bopp [2], Patricia Cadule [2], Katie Campbell [25], Matthew A.
7
Chamberlain [26], Naveen Chandra [27], Frédéric Chevallier [16], Louise P. Chini [28], Thomas Colligan [29],
8
Jeanne Decayeux [30], Laique M. Djeutchouang [31,32], Xinyu Dou [33], Carolina Duran Rojas [1], Kazutaka
9
Enyo [34], Wiley Evans [25], Amanda R. Fay [35], Richard A. Feely [18], Daniel. J. Ford [1], Adrianna Foster
10
[36], Thomas Gasser [37], Marion Gehlen [16], Thanos Gkritzalis [7], Giacomo Grassi [38], Luke Gregor [39],
11
Nicolas Gruber [39], Özgür Gürses [5], Ian Harris [40], Matthew Hefner [41,42], Jens Heinke [43], George C.
12
Hurtt [28], Yosuke Iida [34], Tatiana Ilyina [44,8,9], Andrew R. Jacobson [45], Atul K. Jain [46], Tereza
13
Jarníková [47], Annika Jersild [29], Fei Jiang [48], Zhe Jin [49,50], Etsushi Kato [51], Ralph F. Keeling [52],
14
Kees Klein Goldewijk [53], Jürgen Knauer [54,15], Jan Ivar Korsbakken [4], Siv K. Lauvset [55,12], Nathalie
15
Lefèvre [56], Zhu Liu [33], Junjie Liu [57,58], Lei Ma [28], Shamil Maksyutov [59], Gregg Marland [41,42],
16
Nicolas Mayot [60], Patrick C. McGuire [61], Nicolas Metzl [56], Natalie M. Monacci [62], Eric J. Morgan
17
[52], Shin
-
Ichiro Nakaoka [59], Craig Neill [26], Yosuke Niwa [59], Tobias Nützel [14], Lea Olivier [5],
18
Tsuneo Ono [63], Paul I. Palmer [64,65], Denis Pierrot [66], Zhangcai Qin [67], Laure Resplandy [68], Alizée
19
Roobaert [7], Thais M. Rosan [1], Christian Rödenbeck [69], Jörg Schwinger [55,12], T. Luke Smallman
20
[64,65], Stephen M. Smith [70], Reinel Sospedra
-
Alfonso [71], Tobias Steinhoff [72,55], Qing Sun [73],
21
Adrienne J. Sutton [18], Roland Séférian [30], Shintaro Takao [59], Hiroaki Tatebe [74,75], Hanqin Tian [76],
22
Bronte Tilbrook [26,77], Olivier Torres [2], Etienne Tourigny [78], Hiroyuki Tsujino [79], Francesco Tubiello
23
[80], Guido van der Werf [10], Rik Wanninkhof [66], Xuhui Wang [50], Dongxu Yang [81], Xiaojuan Yang
24
[82], Zhen Yu [83], Wenping Yuan [84], Xu Yue [85], Sönke Zaehle [69], Ning Zeng [86, 29], Jiye Zeng [59].
25
26
1. Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4QF, UK
27
2. Laboratoire de Météorologie Dynamique, Institut Pierre
-
Simon Laplace, CNRS, Ecole Normale Supérieure,
28
Université PSL, Sorbonne Université, Ecole Polytechnique, Paris, France
29
3. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia,
30
Norwich Research Park, Norwich NR4 7TJ, UK
31
4. CICERO Center for International Climate Research, Oslo 0349, Norway
32
5. Alfred
-
Wegener
-
Institut, Helmholtz
-
Zentrum für Polar
-
und Meeresforschung, Am Handelshafen 12, 27570
33
Bremerhaven, Germany
34
6. Universität Bremen, Bremen, Germany
35
7. Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400, Ostend, Belgium
36
8. Helmholtz
-
Zentrum Hereon, Max
-
Planck
-
Straße 1, 21502 Geesthacht, Germany
37
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
2
9. Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany
38
10. Wageningen University, Environmental Sciences Group, P.O. Box 47, 6700AA, Wageningen, The
39
Netherlands
40
11. Geophysical Institute, University of Bergen, Allégaten 70, 5007 Bergen, Norway
41
12. Bjerknes Centre for Climate Research, Bergen, Norway
42
13. University of Groningen, Centre for Isotope Research, Groningen, The Netherlands
43
14. Ludwig
-
Maximilians
-
Universität München, Luisenstr. 37, 80333 München, Germany
44
15. CSIRO Environment, Canberra, ACT 2101, Australia
45
16. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA
-
CNRS
-
UVSQ, Université
46
Paris
-
Saclay, F
-
91198 Gif
-
sur
-
Yvette, France
47
17. Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for
48
Energy, Stanford University, Stanford, CA 94305
–
2210, United States of America
49
18. National Oceanic and Atmospheric Administration, Pacific Marine Environmental Laboratory
50
(NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA
51
19. Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric
52
Environmental Research, 82467 Garmisch
-
Partenkirchen, Germany
53
20. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria,
54
BC, Canada
55
21. ASU
-
BIOS, Bermuda Institute of Ocean Sciences, 31 Biological Lane, Ferry Reach, St. Georges,, GE01,
56
Bermuda
57
22. Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
58
23. Instituto Nacional de Investigación y Desarrollo Pesquero, Paseo Victoria Ocampo Nº1, Escollera Norte,
59
B7602HSA, Mar del Plata, Argentina
60
24. Leibniz Institute for Baltic Sea Research Warnemuende (IOW), Seestrasse 15, 18119 Rostock, Germany
61
25. Hakai Institute, British Columbia, V0P 1H0, Canada
62
26. CSIRO Environment, Castray Esplanade, Hobart, Tasmania 7004, Australia
63
27. Research Institute for Global Change, JAMSTEC, 3173
-
25 Showa
-
machi, Kanazawa, Yokohama, 236
-
0001,
64
Japan
65
28. Department of
Geographical Sciences, University of Maryland, College Park, Maryland 20742, USA
66
29. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
67
30. Centre National de Recherches Météorologiques, Université de Toulouse, Météo
-
France, CNRS UMR 3589,
68
Toulouse, France
69
31. School for Climate Studies, Stellenbosch University, Private Bag X1, Matieland, Stellenbosch, 7602, South
70
Africa
71
32. Southern Ocean Carbon
–
Climate Observatory, CSIR, Rosebank, Cape Town, 7700, South Africa
72
33. Department of Earth System Science, Tsinghua University, Beijing, China
73
34. Japan Meteorological Agency, 3
-
6
-
9 Toranomon, Minato City, Tokyo 105
-
8431, Japan
74
35. Columbia University and Lamont
-
Doherty Earth Observatory, New York, NY, USA
75
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
3
36. Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80305,
76
USA
77
37. International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A
-
2361 Laxenburg, Austria
78
38. European Commission, Joint Research Centre, 21027 Ispra (VA), Italy
79
39. Environmental Physics Group, ETH Zürich, Institute of Biogeochemistry and Pollutant Dynamics and
80
Center for Climate Systems Modeling (C2SM),
Zürich, Switzerland
81
40. NCAS
-
Climate, Climatic Research Unit, School of Environmental Sciences, University of East Anglia,
82
Norwich Research Park, Norwich, NR4 7TJ, UK
83
41. Research Institute for Environment, Energy, and Economics, Appalachian State University, Boone, North
84
Carolina, USA
85
42. Department of Geological and
Environmental Sciences, Appalachian State University, Boone, North
86
Carolina, USA
87
43. Potsdam Institute for Climate Impact Research (PIK), member of the Leibniz Association, P.O. Box 60 12
88
03, 14412 Potsdam, Germany
89
44. Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany
90
45. Cooperative Institute for Research in Environmental Sciences, CU Boulder and NOAA Global Monitoring
91
Laboratory, Boulder, USA
92
46. Department of Climate, Meteorology and Atmospheric Sciences, University of Illinois, Urbana, IL 61821,
93
USA
94
47. University of East Anglia, Norwich, UK
95
48. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International
96
Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
97
49. State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan
98
Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
99
50. Institute of Carbon Neutrality, Sino
-
French Institute for Earth System Science, College of Urban and
100
Environmental Sciences, Peking University, Beijing 100871, China
101
51. Institute of Applied Energy (IAE), Minato
-
ku, Tokyo 105
-
0003, Japan
102
52. University of California, San Diego, Scripps Institution of Oceanography, La Jolla, CA 92093
-
0244, USA
103
53. Utrecht University, Faculty of Geosciences, Department IMEW, Copernicus Institute of Sustainable
104
Development, Heidelberglaan 2, P.O. Box 80115, 3508 TC, Utrecht, the Netherlands
105
54. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales,
106
Australia
107
55. NORCE Norwegian Research Centre, Jahnebakken 5, 5007 Bergen, Norway
108
56. LOCEAN/IPSL laboratory, Sorbonne Université, CNRS/IRD/MNHN, Paris, 75252, France
109
57. Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
110
58. California Institute of Technology, Pasadena, CA, USA
111
59. Earth System Division, National Institute for Environmental Studies, 16
-
2 Onogawa, Tsukuba, Ibaraki, 305
-
112
8506 Japan
113
60. Sorbonne Université, Laboratoire d'Océanographie de Villefranche, Villefranche
-
sur
-
Mer, France
114
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
4
61. Department of Meteorology & National Centre for Atmospheric Science (NCAS), University of Reading,
115
Reading, UK
116
62. University of Alaska Fairbanks, College of Fisheries and Ocean Sciences, Fairbanks, AK, 99709, USA
117
63. Fisheries Research and Education Agency, 2
-
12
-
4 Fukuura, Kanazawa
-
Ku, Yokohama 236
-
8648, Japan
118
64. National Centre for Earth Observation, University of Edinburgh, EH9 3FF, UK
119
65. School of GeoSciences, University of Edinburgh, EH9 3FF, UK
120
66. NOAA Atlantic Oceanographic and Meteorological Laboratory (NOAA/AOML), 4301 Rickenbacker
121
Causeway, Miami, Florida 33149, USA
122
67. School of Atmospheric Sciences, Sun Yat
-
sen University, Zhuhai 519000, China
123
68. Princeton University, Department of Geosciences and Princeton Environmental Institute, Princeton, NJ,
124
USA
125
69. Max Planck Institute for Biogeochemistry, P.O. Box 600164, Hans
-
Knöll
-
Str. 10, 07745 Jena, Germany
126
70. Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
127
71. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria,
128
British Columbia, Canada
129
72. GEOMAR Helmholtz Centre for OCean Research Kiel, Wischhofstr. 1
-
3, 24148 Kiel, Germany
130
73. Institute for Climate and Environmental Physics, Bern, Switzerland
131
74. Research Center for Environmental Modeling and Application, Japan Agency for Marine
-
Earth Science and
132
Technology, Yokohama, Japan
133
75. Advanced Institute for Marine Ecosystem Change, Japan Agency for Marine
-
Earth Science and Technology,
134
Yokohama, Japan
135
76. Schiller Institute of Integrated Science and Society, Department of Earth and Environmental Sciences,
136
Boston College, Chestnut Hill, MA 02467, USA
137
77. Australian Antarctic Partnership Program, University of Tasmania, Hobart, Australia
138
78. Barcelona Supercomputing Center, Barcelona, Spain
139
79. JMA Meteorological Research Institute, Tsukuba, Ibaraki, Japan
140
80. Statistics Division, Food and Agriculture Organization of the United Nations, Via Terme di Caracalla, Rome
141
00153, Italy
142
81. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
143
82. Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Lab, Oak
144
Ridge, TN 37831, USA.
145
83. School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology,
146
Nanjing 210044, PR. China
147
84. Institute of Carbon Neutrality, College of Urban and Environmental Sciences, Peking University, Beijing
148
100091, China
149
85. School of Environmental Science and
Engineering, Nanjing University of Information Science and
150
Technology (NUIST), Nanjing, 210044, China
151
86. Department of Atmospheric and Oceanic Science, University of Maryland, Maryland, USA
152
153
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
5
154
155
Correspondence to
: Pierre Friedlingstein
(p.friedlingstein@exeter.ac.uk)
156
1.
Abstract
157
Accurate assessment of anthropogenic carbon dioxide (CO
2
) emissions and their redistribution among the
158
atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global
159
carbon cycle, support the development of climate policies, and project future climate change. Here we describe
160
and synthesise datasets and methodologies to quantify the five major components of the global carbon budget
161
and their uncertainties. Fossil CO
2
emissions (E
FOS
) are based on energy statistics and cement production data,
162
while emissions from land
-
use change (E
LUC
) are based on land
-
use and land
-
use change data and bookkeeping
163
models. Atmospheric CO
2
concentration is measured directly, and its growth rate (G
ATM
) is computed from the
164
annual changes in concentration. The ocean CO
2
sink (S
OCEAN
) is estimated with global ocean biogeochemistry
165
models and observation
-
based
f
CO
2
-
products. The terrestrial CO
2
sink (S
LAND
) is estimated with dynamic
166
global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric
167
inversions, atmospheric oxygen measurements and Earth System Models. The
sum of all sources and sinks
168
results in the
carbon budget imbalance (B
IM
), a measure of imperfect data and incomplete understanding of the
169
contemporary carbon cycle. All uncertainties are reported as ±1σ.
170
For the year 2023, E
FOS
increased by 1.3% relative to 2022, with fossil emissions at 10.1 ± 0.5 GtC yr
-
1
(10.3 ±
171
0.5 GtC yr
-
1
when the cement carbonation sink is not included), E
LUC
was 1.0 ± 0.7 GtC yr
-
1
, for a total
172
anthropogenic CO
2
emission (including the cement carbonation sink) of 11.1 ± 0.9 GtC yr
-
1
(40.6 ± 3.2 GtCO
2
173
yr
-
1
). Also, for 2023, G
ATM
was 5.9 ± 0.2 GtC yr
-
1
(2.79 ± 0.1 ppm yr
-
1
), S
OCEAN
was 2.9 ± 0.4 GtC yr
-
1
and
174
S
LAND
was 2.3 ± 1.0 GtC yr
-
1
, with a
near zero B
IM
(
-
0.02 GtC yr
-
1
). The global atmospheric CO
2
concentration
175
averaged over 2023 reached
419.3 ± 0.1
ppm. Preliminary data for 2024, suggest an increase in E
FOS
relative to
176
2023 of +0.8% (
-
0.3% to 1.9%) globally, and atmospheric CO
2
concentration increased by 2.8 ppm reaching
177
422.5 ppm, 52% above pre
-
industrial level (around 278 ppm in 1750). Overall, the mean and trend in the
178
components of the global carbon budget are consistently estimated over the period 1959
-
2023, with a near
-
zero
179
overall budget imbalance, although discrepancies of up to around 1 GtC yr
-
1
persist for the representation of
180
annual to semi
-
decadal variability in CO
2
fluxes. Comparison of estimates from multiple approaches and
181
observations shows: (1) a persistent large uncertainty in the estimate of land
-
use changes emissions, (2) a low
182
agreement between the different methods on the magnitude of the land CO
2
flux in the northern extra
-
tropics,
183
and (3) a discrepancy between the different methods on the mean ocean sink.
184
This living data update documents changes in methods and datasets applied to this most
-
recent global carbon
185
budget as well as evolving community understanding of the global carbon cycle.
The data presented in this
186
work are available at
https://doi.org/10.18160/GCP
-
2024 (Friedlingstein et al., 2024)
.
187
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
6
2.
Executive Summary
188
Global fossil CO
2
emissions (including cement carbonation) are expected to further increase in 2024 by
189
0.8%.
The 2023 emission increase was 0.14 GtC yr
-
1
(0.5 GtCO
2
yr
-
1
) relative to 2022, bringing 2023 fossil CO
2
190
emissions to 10.1 ± 0.5 GtC yr
-
1
(36.8 ± 1.8 GtCO
2
yr
-
1
). Preliminary estimates based on data available suggest
191
fossil CO
2
emissions to increase further in 2024, by 0.8% relative to 2023 (
-
0.3% to 1.9%), bringing emissions
192
to 10.2 GtC yr
-
1
(37.4 GtCO
2
yr
-
1
).
1
193
Emissions from coal, oil and gas in 2024 are expected to be slightly above their 2023 levels (by 0.2%, 0.9% and
194
2.4% respectively). Regionally, fossil emissions in 2024 are expected to decrease by 3.8% in the European
195
Union reaching 0.7 GtC (2.4 GtCO
2
), and by 0.6% in the United States (1.3 GtC, 4.9 GtCO
2
). Emissions in
196
China are expected to increase in 2024 by 0.2%, reaching 3.3 GtC, (12.0 GtCO
2
). Fossil emissions are also
197
expected to increase by 4.6% in India (0.9 GtC, 3.2 GtCO
2
) and by 1.1% for the rest of the world (4.0 GtC, 14.5
198
GtCO
2
) in 2024. Emissions from international aviation and shipping (IAS) are also expected to increase by 7.8%
199
(0.3 GtC, 1.2 GtCO
2
) in 2024.
200
Fossil CO
2
emissions decreased significantly in 22 countries with significantly growing economies during
201
the decade 2014
-
2023.
Altogether, these 22 countries contribute about 2.2 GtC yr
-
1
(8.1 GtCO
2
) fossil fuel CO
2
202
emissions over the last decade, representing about 23% of world CO
2
fossil emissions.
203
Global CO
2
emissions from land
-
use, land
-
use change, and forestry (LULUCF) averaged 1.1 ± 0.7 GtC yr
-
204
1
(4.1 ± 2.6 GtCO
2
yr
-
1
) for the 2014
-
2023 period with a similar preliminary projection for 2024 of 1.1 ±
205
0.7 GtC yr
-
1
(4.2 ± 2.6 GtCO
2
yr
-
1
). Since the late
-
1990s, emissions from LULUCF show a statistically
206
significant decrease at a rate of around 0.2 GtC per decade.
Emissions from deforestation, the main driver of
207
global gross sources
,
remain
high at around 1.7 GtC yr
-
1
over the 2014
-
2023 period, highlighting the strong
208
potential of halting deforestation for emissions reductions. Sequestration of 1.2 GtC yr
-
1
through re
-
209
/afforestation and forestry offsets two third of the deforestation emissions. Further, smaller emissions are due to
210
other land
-
use transitions and peat drainage and peat fire. The highest emitters during 2014
-
2023 in descending
211
order were Brazil, Indonesia, and the Democratic Republic of the Congo, with these 3 countries contributing
212
more than half of global land
-
use CO
2
emissions.
213
Total anthropogenic emissions (
fossil and LULUCF, including the carbonation sink)
were 11.1 GtC yr
-
1
214
(40.6 GtCO
2
yr
-
1) in 2023, with a marginally higher preliminary estimate of 11.3 GtC yr
-
1
(41.6 GtCO
2
yr
-
215
1
) for 2024.
Total anthropogenic emissions have been stable over the last decade (zero growth rate over
216
the 2014
-
2023 period), much slower than over the previous decade (2004
-
2013) with an average growth
217
rate of 2.0% yr
-
1
.
218
The remaining carbon budget for a 50% likelihood to limit global warming to 1.5°C, 1.7°C and 2°C above
219
the 1850
-
1900 level has respectively been reduced to 65 GtC (235 GtCO
2
), 160 GtC (585 GtCO
2
) and 305
220
1
All 2024 growth rates use a leap year adjustment that corrects for the extra day in 2024.
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
7
GtC (1110 GtCO
2
) from the beginning of 2025, equivalent to around 6, 14 and 27 years, assuming 2024
221
emissions levels.
222
The concentration of CO
2
in the atmosphere is set to reach 422.5 ppm in 2024, 52% above pre
-
industrial
223
levels.
The atmospheric CO
2
growth was 5.2 ± 0.02 GtC yr
-
1
(2.5 ppm) during the decade 2014
-
2023 (48% of
224
total CO
2
emissions) with a preliminary 2024 growth rate estimate of around 5.9 GtC (2.8
ppm).
225
The ocean CO
2
sink has been stagnant since 2016 after rapid growth during 2002
-
2016, largely in
226
response to large inter
-
annual climate variability.
The ocean CO
2
sink was 2.9 ± 0.4
GtC yr
-
1
during the
227
decade 2014
-
2023 (26% of total
CO
2
emissions). A slightly higher value of 3.0
GtC yr
-
1
is preliminarily
228
estimated for 2024, which marks an increase in the sink since 2023 due to the prevailing El Niño and neutral
229
conditions in 2024.
230
The land CO
2
sink continued to increase during the 2014
-
2023 period primarily in response to increased
231
atmospheric CO
2
, albeit with large interannual variability.
The land CO
2
sink was 3.2 ± 0.9 GtC yr
-
1
during
232
the 2014
-
2023 decade (30% of total CO
2
emissions). The land sink in 2023 was 2.3 ± 1 GtC yr
-
1
, 1.6 GtC lower
233
than in 2022, and the lowest estimate since 2015. This reduced sink is primarily driven by a response of tropical
234
land ecosystems to the onset of the 2023
-
2024 El
Niño event, combined with large wildfires in Canada in 2023.
235
The preliminary 2024 estimate is around 3.2 GtC yr
-
1
, similar to the decadal average, consistent with a land sink
236
emerging from the El
Niño state.
237
So far in 2024, global fire CO
2
emissions have been 11
-
32% higher than the 2014
-
2023 average due to
238
high fire activity in both North and South America, reaching 1.6
-
2.2 GtC during January
-
September.
In
239
Canada, emissions through September were 0.2
-
0.3 GtC yr
-
1
, down from 0.5
-
0.8 GtC yr
-
1
in 2023 but still more
240
than twice the 2014
-
2023 average. In Brazil, fires through September emitted 0.2
-
0.3 GtC yr
-
1
, 91
-
118% above
241
the 2014
-
2023 average due to intense drought. These fire emissions estimates should not be directly compared
242
with the land use emissions or the land sink, because they represent a gross carbon flux to the atmosphere and
243
do not account for post
-
fire recovery or distinguish between natural, climate
-
driven, and land
-
use
-
related fires.
244
245
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
8
246
1
Introduction
247
The
concentration of carbon dioxide (CO
2
) in the atmosphere has increased from approximately 278 parts per
248
million (ppm) in 1750 (Gulev et al., 2021), the beginning of the Industrial Era, to
419.3 ± 0.1 ppm in 2023
(Lan
249
et al., 2024; Figure 1). The atmospheric CO
2
increase above pre
-
industrial levels was, initially, primarily caused
250
by the release of carbon to the atmosphere from deforestation and other land
-
use change activities (Canadell et
251
al., 2021). While emissions from fossil fuels started before the Industrial Era, they became the dominant source
252
of anthropogenic emissions to the atmosphere from around 1950 and their relative share has continued to
253
increase until present. Anthropogenic emissions occur on top of an active natural carbon cycle that circulates
254
carbon between the reservoirs of the atmosphere, ocean, and terrestrial biosphere on time scales from sub
-
daily
255
to millennial, while exchanges with geologic reservoirs occur on longer timescales (Archer et al., 2009).
256
The global carbon budget (GCB) presented here refers to the mean, variations, and trends in the perturbation of
257
CO
2
in the environment, referenced to the beginning of the Industrial Era (defined here as 1750). This paper
258
describes the components of the global carbon cycle over the historical period with a stronger focus on the
259
recent period (since 1958, onset of robust atmospheric CO
2
measurements), the last decade (2014
-
2023), the last
260
year (2023) and the current year (2024). Finally, it provides cumulative emissions from fossil fuels and land
-
use
261
change since the year 1750, and since the year 1850 (the reference year for historical simulations in IPCC AR6)
262
(Eyring et al., 2016).
263
We quantify the input of CO
2
to the atmosphere by emissions from human activities, the growth rate of
264
atmospheric CO
2
concentration, and the resulting changes in the storage of carbon in the land and ocean
265
reservoirs in response to increasing atmospheric CO
2
levels, climate change and variability, and other
266
anthropogenic and natural changes (Figure 2). An understanding of this perturbation budget over time and the
267
underlying variability and trends of the natural carbon cycle is necessary to understand the response of natural
268
sinks to changes in climate, CO
2
and land
-
use change drivers, and to quantify emissions compatible with a given
269
climate stabilisation target.
270
The components of the CO
2
budget that are reported annually in this paper include separate and independent
271
estimates for the CO
2
emissions from (1) fossil fuel combustion and oxidation from all energy and industrial
272
processes; also including cement production and carbonation (E
FOS
; GtC yr
-
1
) and (2) the emissions resulting
273
from deliberate human activities on land, including those leading to land
-
use change (E
LUC
; GtC yr
-
1
); and their
274
partitioning among (3) the growth rate of atmospheric CO
2
concentration (G
ATM
; GtC yr
-
1
), and the uptake of
275
CO
2
(the ‘CO
2
sinks’) in (4) the ocean (S
OCEAN
; GtC yr
-
1
) and (5) on land (S
LAND
; GtC yr
-
1
). The CO
2
sinks as
276
defined here conceptually include the response of the land (including inland waters and estuaries) and ocean
277
(including coastal and marginal seas) to elevated CO
2
and changes in climate and other environmental
278
conditions, although in practice not all processes are fully accounted for (see Section 2.10). Global emissions
279
and their partitioning among the atmosphere, ocean and land are in balance in the real world. Due to the
280
combination of imperfect spatial and/or temporal data coverage, errors in each estimate, and smaller terms not
281
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
9
included in our budget estimate (discussed in Section 2.10), the independent estimates (1) to (5) above do not
282
necessarily add up to zero. We hence estimate a budget imbalance (B
IM
), which is a measure of the mismatch
283
between the estimated emissions and the estimated changes in the atmosphere, land and ocean, as follows:
284
퐵
!"
=
퐸
#$%
+
퐸
&'(
−
(
퐺
)*"
+
푆
$(+),
+
푆
&),-
)
(1)
285
G
ATM
is usually reported in ppm yr
-
1
, which we convert to units of carbon mass per year, GtC yr
-
1
, using 1 ppm
286
= 2.124 GtC (Ballantyne et al., 2012;
Table 1). Units of gigatonnes of CO
2
(or billion tonnes of CO
2
) used in
287
policy are equal to 3.664 multiplied by the value in units of GtC.
288
We also assess a set of additional lines of evidence derived from global atmospheric inversion system results
289
(Section 2.7), observed changes in oxygen concentration (Section 2.8) and Earth System Models (ESMs)
290
simulations (Section 2.9), all of these methods closing the global carbon balance (zero B
IM
).
291
We further quantify E
FOS
and E
LUC
by country, including both territorial and consumption
-
based accounting for
292
E
FOS
(see Section 2), and discuss missing terms from sources other than the combustion of fossil fuels (see
293
Section 2.10, Supplement S1 and S2). We also assess carbon dioxide removal (CDR) (see Sect. 2.2 and 2.3).
294
Land
-
based CDR is significant, but already accounted for in
퐸
&'(
in equation (1) (Sect 3.2.2). Other CDR
295
methods, not based on vegetation, are currently several orders of magnitude smaller than the other components
296
of the budget (Sect. 3.3), hence these are not included in equation (1), or in the global carbon budget tables or
297
figures (with the exception of Figure 2 where CDR is shown primarily for illustrative purpose).
298
The global CO
2
budget has been assessed by the Intergovernmental Panel on Climate Change (IPCC) in all
299
assessment reports (Prentice et al., 2001; Schimel et al., 1995; Watson et al., 1990; Denman et al., 2007; Ciais et
300
al., 2013; Canadell et al., 2021), and by others (e.g. Ballantyne et al., 2012). The Global Carbon Project (GCP,
301
www.globalcarbonproject.org, last access: 28 October 2024) has coordinated this cooperative community effort
302
for the annual publication of global carbon budgets for the year 2005 (Raupach et al., 2007; including fossil
303
emissions only), year 2006 (Canadell et al., 2007), year 2007 (GCP, 2008), year 2008 (Le Quéré et al., 2009),
304
year 2009 (Friedlingstein et al., 2010), year 2010 (Peters et al., 2012a), year 2012 (Le Quéré et al., 2013; Peters
305
et al., 2013), year 2013 (Le Quéré et al., 2014), year 2014 (Le Quéré et al., 2015a; Friedlingstein et al., 2014),
306
year 2015 (Jackson et al., 2016; Le Quéré et al., 2015b), year 2016 (Le Quéré et al., 2016), year 2017 (Le Quéré
307
et al., 2018a; Peters et al., 2017a), year 2018 (Le Quéré et al., 2018b; Jackson et al., 2018), year 2019
308
(Friedlingstein et al., 2019; Jackson et al., 2019; Peters et al., 2020), year 2020 (Friedlingstein et al., 2020; Le
309
Quéré et al., 2021), year 2021 (Friedlingstein et al., 2022a; Jackson et al., 2022), year 2022 (Friedlingstein et al.,
310
2022b), and most recently the year 2023 (Friedlingstein et al., 2023). Each of these papers updated previous
311
estimates with the latest available information for the entire time series.
312
We adopt a range of ±1 standard deviation (σ) to report the uncertainties in our global estimates, representing a
313
likelihood of 68% that the true value will be within the provided range if the errors have a gaussian distribution,
314
and no bias is assumed. This choice reflects the difficulty of characterising the uncertainty in the CO
2
fluxes
315
between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well
316
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
10
as the difficulty of updating the CO
2
emissions from land
-
use change. A likelihood of 68% provides an
317
indication of our current capability to quantify each term and its uncertainty given the available information.
318
The uncertainties reported here combine statistical analysis of the underlying data, assessments of uncertainties
319
in the generation of the datasets, and expert judgement of the likelihood of results lying outside this range. The
320
limitations of current information are discussed in the paper and have been examined in detail elsewhere
321
(Ballantyne et al., 2015; Zscheischler et al., 2017). We also use a qualitative assessment of confidence level to
322
characterise the annual estimates from each term based on the type, amount, quality, and consistency of the
323
different lines of evidence as defined by the IPCC (Stocker et al., 2013).
324
This paper provides a detailed description of the datasets and methodology used to compute the global carbon
325
budget estimates for the industrial period, from 1750 to 2024, and in more detail for the period since 1959. This
326
paper is updated every year using the format of ‘living data’ to keep a record of budget versions and the changes
327
in new data, revision of data, and changes in methodology that lead to changes in estimates of the carbon
328
budget. Additional materials associated with the release of each new version will be posted at the Global Carbon
329
Project (GCP) website (
http://www.globalcarbonproject.org/carbonbudget
, last access: 28 October 2024
), with
330
fossil fuel emissions also available through the Global Carbon Atlas (http://www.globalcarbonatlas.org, last
331
access: 28 October 2024)
. All underlying data used to produce the budget can also be found at
332
https://globalcarbonbudget.org/
(
last access: 28 October 2024).
With this approach, we aim to provide the
333
highest transparency and traceability in the reporting of CO
2
, the key driver of climate change.
334
2
Methods
335
Multiple organisations and research groups around the world generated the original measurements and data used
336
to complete the global carbon budget. The effort presented here is thus mainly one of synthesis, where results
337
from individual groups are collated, analysed, and evaluated for consistency. We facilitate access to original
338
data with the understanding that primary datasets will be referenced in future work (see Table 2 for how to cite
339
the datasets, and Section on data availability). Descriptions of the measurements, models, and methodologies
340
follow below, with more detailed descriptions of each component provided as Supplementary Information (S1 to
341
S5).
342
This is the
19
th
version of the global carbon budget and the
13
th
revised version in the format of a living data
343
update in Earth System Science Data. It builds on the latest published global carbon budget of
Friedlingstein et
344
al. (2023)
. The main changes this year are: the inclusion of (1) data to year 2023 and a projection for the global
345
carbon budget for year 2024; and (2) an estimate of the 2024 projection of fossil emissions from Carbon
346
Monitor. Other methodological differences between recent annual carbon budgets
(2020 to 2024)
are
347
summarised in Table 3 and previous changes since 2006 are provided in Table S9.
348
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
11
2.1
Fossil CO
2
emissions (E
FOS
)
349
2.1.1
Historical period 1850
-
2023
350
The
estimates of global and national fossil CO
2
emissions (E
FOS
) include the oxidation of fossil fuels through
351
both combustion (e.g., transport, heating) and chemical oxidation (e.g. carbon anode decomposition in
352
aluminium refining) activities, and the decomposition of carbonates in industrial processes (e.g. the production
353
of cement). We also include CO
2
uptake from the cement carbonation process. Several emissions sources are not
354
estimated or not fully covered: coverage of emissions from lime production are not global, and decomposition of
355
carbonates in glass and ceramic production are included only for the “Annex 1” countries of the United Nations
356
Framework Convention on Climate Change (UNFCCC) for lack of activity data. These omissions are
357
considered to be minor. Short
-
cycle carbon emissions
-
for example from combustion of biomass
-
are not
358
included here but are accounted for in the CO
2
emissions from land use (see Section 2.2).
359
Our estimates of fossil CO
2
emissions rely on data collection by many other parties. Our goal is to produce the
360
best estimate of this flux, and we therefore use a prioritisation framework to combine data from different
361
sources that have used different methods, while being careful to avoid double counting and undercounting of
362
emissions sources. The CDIAC
-
FF emissions dataset, derived largely from UN energy data, forms the
363
foundation, and we extend emissions to 2023 using energy growth rates reported by the Energy Institute (a
364
dataset formerly produced by BP). We then proceed to replace estimates using data from what we consider to be
365
superior sources, for example Annex 1 countries’ official submissions to the UNFCCC. All data points are
366
potentially subject to revision, not just the latest year. For full details see Andrew and Peters (2024).
367
Other estimates of global fossil CO
2
emissions exist, and these are compared by Andrew (2020a). The most
368
common reason for differences in estimates of global fossil CO
2
emissions is a difference in which emissions
369
sources are included in the datasets. Datasets such as those published by the Energy Institute, the US Energy
370
Information Administration, and the International Energy Agency’s ‘CO
2
emissions from fuel combustion’ are
371
all generally limited to emissions from combustion of fossil fuels. In contrast, datasets such as PRIMAP
-
hist,
372
CEDS, EDGAR, and GCP’s dataset aim to include all sources of fossil CO
2
emissions. See Andrew (2020a) for
373
detailed comparisons and discussion.
374
Cement absorbs CO
2
from the atmosphere over its lifetime, a process known as ‘cement carbonation’. We
375
estimate this CO
2
sink, from 1931 onwards, as the average of two studies in the literature (Cao et al., 2020; Guo
376
et al., 2021). Both studies use the same model, developed by Xi et al. (2016), with different parameterisations
377
and input data, with the estimate of Guo and colleagues being a revision of Xi et al. (2016). The trends of the
378
two studies are very similar. Since carbonation is a function of both current and previous cement production, we
379
extend these estimates to 2023 by using the growth rate derived from the smoothed cement emissions (10
-
year
380
smoothing) fitted to the carbonation data. In the present budget, we always include the cement carbonation
381
carbon sink in the fossil CO
2
emission component (E
FOS
).
382
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
12
We use the Kaya Identity for a simple decomposition of CO
2
emissions into the key drivers (Raupach et al.,
383
2007). While there are variations (Peters et al., 2017a), we focus here on a decomposition of CO
2
emissions into
384
population, GDP per person, energy use per GDP, and CO
2
emissions per energy. Multiplying these individual
385
components together returns the CO
2
emissions. Using the decomposition, it is possible to attribute the change
386
in CO
2
emissions to the change in each of the drivers. This method gives a first
-
order understanding of what
387
causes CO
2
emissions to change each year.
388
2.1.2
2024
projection
389
We provide a projection of global fossil CO
2
emissions in 2024 by combining separate projections for China,
390
USA, EU, India, and for all other countries combined. The methods are different for each of these. For China we
391
combine monthly fossil fuel production data from the National Bureau of Statistics and trade data from the
392
Customs Administration, giving us partial data for the growth rates to date of natural gas, petroleum, and
393
cement, and of the apparent consumption itself for raw coal. We then use a regression model to project full
-
year
394
emissions based on historical observations. For the USA our projection is taken directly from the Energy
395
Information Administration’s (EIA) Short
-
Term Energy Outlook (EIA, 2024), combined with the year
-
to
-
date
396
growth rate of cement clinker production. For the EU we use monthly energy data from Eurostat to derive
397
estimates of monthly CO
2
emissions through July, with coal emissions extended through September using a
398
statistical relationship with reported electricity generation from coal and other factors. For natural gas we use
399
Holt
-
Winters to project the last four months of the year. EU emissions from oil are derived using the EIA’s
400
projection of oil consumption for Europe. EU cement emissions are based on available year
-
to
-
date data from
401
three of the largest producers, Germany, Poland, and Spain. India’s projected emissions are derived from
402
estimates through August (July for coal) using the methods of Andrew (2020b) and extrapolated assuming
403
seasonal patterns from before 2019. Emissions from international transportation (bunkers) are estimated
404
separately for aviation and shipping. Changes in aviation emissions are derived primarily from OECD monthly
405
estimates, extrapolated using the growth rates of global flight miles from Airportia, and then the final months
406
are projected assuming normal patterns from previous years. Changes in shipping emissions are derived from
407
OECD monthly estimates for global shipping. Emissions for the rest of the world are derived for coal and
408
cement using projected growth in economic production from the IMF (2023) combined with extrapolated
409
changes in emissions intensity of economic production; for oil using a global constraint from EIA; and for
410
natural gas using a global constraint from IEA. More details on the E
FOS
methodology and its 2024 projection
411
can be found in Supplement S.1.
412
For the first time this year, we cross check our 2024 projection with a 2024 projection from Carbon Monitor.
413
Carbon Monitor is an open access dataset (
https://carbonmonitor.org/
) of daily emissions constructed using
414
hourly to daily proxy data (e.g., electricity consumption, travel patterns, etc) instead of energy use data.
415
Available Carbon Monitor estimated emissions from January to August are combined to a new projection for
416
September to December to give a full year 2024 estimate. The September to December projections are estimated
417
by leveraging seasonal patterns from 2019
-
2023 daily CO
2
emission data from Carbon Monitor. A regression
418
model is applied separately for individual countries to obtain their respective 4
-
month forecast. First, the
419
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
13
seasonality component for each month is assessed based on daily average emissions from 2019 to 2023,
420
excluding 2020 due to the COVID
-
19 pandemic. Then, a linear regression model is constructed using the
421
calculated seasonal components and the daily average emissions for the months from January to August 2024.
422
The resulting model is used to project carbon emissions for the remaining months of 2024. The uncertainty
423
range is calculated by using historical monthly variance of seasonal components.
424
2.2
CO
2
emissions from land
-
use, land
-
use change and forestry (E
LUC
)
425
2.2.1
Historical period 1850
-
2023
426
The net CO
2
flux from land
-
use, land
-
use change and forestry (E
LUC
, called land
-
use change emissions in the
427
rest of the text) includes CO
2
fluxes from deforestation, afforestation, logging and forest degradation (including
428
harvest activity), shifting cultivation (cycle of cutting forest for agriculture, then abandoning), regrowth of
429
forests (following wood harvest or agriculture abandonment), peat burning, and peat
drainage.
430
Four bookkeeping approaches (updated estimates each of BLUE (Hansis et al., 2015), OSCAR (Gasser et al.,
431
2020), and H&C2023 (Houghton and Castanho, 2023), and new estimates of LUCE (Qin et al. 2024) were used
432
to
quantify gross emissions and gross removals and the resulting net E
LUC
. Emissions from peat burning and peat
433
drainage are added from external datasets, peat drainage being averaged from three spatially explicit
434
independent datasets (see Supplement S.2.1). Uncertainty estimates were derived from the Dynamic Global
435
Vegetation Models (DGVMs) ensemble for the time period prior to 1960, and using for the recent decades an
436
uncertainty range of ±0.7 GtC yr
-
1
, which is a semi
-
quantitative measure for annual and decadal emissions and
437
reflects our best value judgement that there is at least 68% chance (±1σ) that the true land
-
use change emission
438
lies within the given range, for the range of processes considered here.
439
The GCB E
LUC
estimates follow the CO
2
flux definition of global carbon cycle models and differ from IPCC
440
definitions adopted in National GHG Inventories (NGHGI) for reporting under the UNFCCC. The latter
441
typically include terrestrial fluxes occurring on all land that countries define as managed, following the IPCC
442
managed land proxy approach (Grassi et al., 2018). This partly includes fluxes due to environmental change
443
(e.g. atmospheric CO
2
increase), which are part of S
LAND
in our definition. As a result, global emission estimates
444
are smaller for NGHGI than for the global carbon budget definition (Grassi et al., 2023). The same is the case
445
for the Food Agriculture Organization (FAO) estimates of carbon fluxes on forest land, which include both
446
anthropogenic and natural fluxes on managed land (Tubiello et al., 2021). We translate the GCB and NGHGI
447
definitions to each other, to provide a comparison of the anthropogenic carbon budget as reported in GCB to the
448
official country reporting to the UNFCCC convention. We further compare these estimates with the net
449
atmosphere
-
to
-
land flux from atmospheric inversion systems (see Section 2.7), averaged over managed land
450
only.
451
E
LUC
contains a range of fluxes that are related to
Carbon Dioxide Removal (CDR). CDR is defined as the set of
452
anthropogenic activities that remove CO
2
from the atmosphere, additional to the Earth’s natural processes, and
453
store it in durable form,
such as in forest biomass and soils, long
-
lived products, or in geological or ocean
454
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
14
reservoirs. Here, we quantify vegetation
-
based CDR that is implicitly or explicitly captured by land
-
use fluxes
455
(CDR not based on vegetation is discussed in Section 2.3; IPCC, 2023). We quantify re/afforestation from the
456
four bookkeeping estimates by separating forest regrowth in shifting cultivation cycles from permanent
457
increases in forest cover (see Supplement S.2.1). The latter count as CDR, but it should be noted that the
458
permanence of the storage under climate risks such as fire is increasingly questioned. Other CDR activities
459
contained in E
LUC
include the transfer of carbon to harvested wood products (HWP), bioenergy with carbon
460
capture and storage (BECCS); and biochar production. Note that the different bookkeeping models represent
461
HWP with varying details concerning product usage and their lifetimes. Bookkeeping and TRENDY models
462
currently only represent BECCS and biochar with regard to the CO
2
removal through photosynthesis, but do not
463
account for the durable storage. HWP, BECCS, and biochar are typically counted as CDR when the transfer to
464
the durable storage site occurs and not when the CO
2
is removed from the atmosphere, which complicates a
465
direct comparison to the GCB approach to quantify annual fluxes to and from the atmosphere. Estimates for
466
CDR through HWP, BECCS, and biochar are thus not indicated in this budget, but can be found elsewhere (see
467
Section 3.2.2).
468
2.2.2
2024 Projection
469
We project the 2024 land
-
use emissions for BLUE, H&C2023, OSCAR, and LUCE based on their E
LUC
470
estimates for 2023 and adding the change in carbon emissions from peat fires and tropical deforestation and
471
degradation fires (2024 emissions relative to 2023 emissions) estimated using active fire data (MCD14ML;
472
Giglio et al., 2016). Peat drainage is assumed to be unaltered as it has low interannual variability. More details
473
on the E
LUC
methodology can be found in Supplement S.2.
474
2.3
Carbon Dioxide Removal (CDR) not based on vegetation
475
While some CDR involves CO
2
fluxes via land
-
use and is included in
E
LUC
, (such as afforestation, biochar,
476
HWP, and BECCS) other CDR occurs through fluxes of CO
2
directly from the air to the geosphere. The
477
majority of this
derives from enhanced weathering through the application of crushed rock to soils, with a
478
smaller contribution from Direct Air Carbon Capture and Storage (DACCS). We use data from the State of
479
CDR Report (Smith et al., 2024), which
compiles and harmonises reported removal rates from a combination of
480
existing databases, surveys and novel research. Currently there are no
internationally agreed
methods for
481
reporting these types of CDR, meaning estimates are based on self
-
disclosure by projects following their own
482
protocols. As such, the fractional uncertainty on these numbers should be viewed as substantial, and they are
483
liable to change in future years as protocols are harmonised and improved.
484
2.4
Growth rate in atmospheric CO
2
concentration (G
ATM
)
485
2.4.1
Historical period 1850
-
2023
486
The rate of growth of the atmospheric CO
2
concentration is provided for years 1959
-
2023 by the US National
487
Oceanic and Atmospheric Administration Global Monitoring Laboratory (NOAA/GML; Lan et al., 2024),
488
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.
15
which includes recent revisions to the calibration scale of atmospheric CO
2
measurements (WMO
-
CO2
-
X2019;
489
Hall et al., 2021). For the 1959
-
1979 period, the global growth rate is based on measurements of atmospheric
490
CO
2
concentration averaged from the Mauna Loa and South Pole stations, as observed by the CO
2
Program at
491
Scripps Institution of Oceanography (Keeling et al., 1976). For the 1980
-
2021 time period, the global growth
492
rate is based on the average of multiple stations selected from the marine boundary layer sites with well
-
mixed
493
background air (Ballantyne et al., 2012), after fitting a smooth curve through the data for each station as a
494
function of time, and averaging by latitude band (Masarie and Tans, 1995). The annual growth rate is estimated
495
by Lan et al. (2024) from atmospheric CO
2
concentration by taking the average of the most recent December
-
496
January months corrected for the average seasonal cycle and subtracting this same average one year earlier. The
497
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
498
ppm, assuming instantaneous mixing of CO
2
throughout the atmosphere (Ballantyne et al., 2012; Table 1).
499
The uncertainty around the atmospheric growth rate is due to four main factors. First, the long
-
term
500
reproducibility of reference gas standards (around 0.03 ppm for 1σ from the 1980s; Lan et al., 2024). Second,
501
small unexplained systematic analytical errors that may have a duration of several months to two years come
502
and go. They have been simulated by randomising both the duration and the magnitude (determined from the
503
existing evidence) in a Monte Carlo procedure. Third, the network composition of the marine boundary layer
504
with some sites coming or going, gaps in the time series at each site, etc (Lan et al., 2024). The latter uncertainty
505
was estimated by NOAA/GML with a Monte Carlo method by constructing 100 "alternative" networks (Masarie
506
and Tans, 1995; NOAA/GML, 2019). The second and third uncertainties, summed in quadrature, add up to
507
0.085 ppm on average (Lan et al., 2024). Fourth, the uncertainty associated with using the average CO
2
508
concentration from a surface network to approximate the true atmospheric average CO
2
concentration (mass
-
509
weighted, in 3 dimensions) as needed to assess the total atmospheric CO
2
burden. In reality, CO
2
variations
510
measured at the stations will not exactly track changes in total atmospheric burden, with offsets in magnitude
511
and phasing due to vertical and horizontal mixing. This effect must be very small on decadal and longer time
512
scales, when the atmosphere can be considered well mixed. The CO2 increase in the stratosphere lags the
513
increase (meaning lower concentrations) that we observe in the marine boundary layer, while the continental
514
boundary layer (where most of the emissions take place) leads the marine boundary layer with higher
515
concentrations. These effects nearly cancel each other. In
addition,
the growth rate is nearly the same
516
everywhere (Ballantyne et al., 2012). We therefore maintain an uncertainty around the annual growth rate based
517
on the multiple stations dataset ranges between 0.11 and 0.72 GtC yr
-
1
, with a mean of 0.61 GtC yr
-
1
for 1959
-
518
1979 and 0.17 GtC yr
-
1
for 1980
-
2023, when a larger set of stations were available as provided by Lan et al.
519
(2024). We estimate the uncertainty of the decadal averaged growth rate after 1980 at 0.02 GtC yr
-
1
based on the
520
calibration and the annual growth rate uncertainty but stretched over a 10
-
year interval. For years prior to 1980,
521
we estimate the decadal averaged uncertainty to be 0.07 GtC yr
-
1
based on a factor proportional to the annual
522
uncertainty prior and after 1980 (0.02 * [0.61/0.17] GtC yr
-
1
).
523
We assign a high confidence to the annual estimates of G
ATM
because they are based on direct measurements
524
from multiple and consistent instruments and stations distributed around the world (Ballantyne et al., 2012; Hall
525
et al., 2021).
526
https://doi.org/10.5194/essd-2024-519
Preprint. Discussion started: 13 November 2024
c
©
Author(s) 2024. CC BY 4.0 License.