ESSD - Global Carbon Budget 2022

ESSD - Global Carbon Budget 2022
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© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Collection:
Global climate system data products
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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Data description paper
11 Nov 2022
Data description paper |
11 Nov 2022
Global Carbon Budget 2022
Global Carbon Budget 2022
Global Carbon Budget 2022
Pierre Friedlingstein et al.
Pierre Friedlingstein
Michael O'Sullivan
Matthew W. Jones
Robbie M. Andrew
Luke Gregor
Judith Hauck
Corinne Le Quéré
Ingrid T. Luijkx
Are Olsen
Glen P. Peters
Wouter Peters
Julia Pongratz
Clemens Schwingshackl
Stephen Sitch
Josep G. Canadell
Philippe Ciais
Robert B. Jackson
Simone R. Alin
Ramdane Alkama
Almut Arneth
Vivek K. Arora
Nicholas R. Bates
Meike Becker
Nicolas Bellouin
Henry C. Bittig
Laurent Bopp
Frédéric Chevallier
Louise P. Chini
Margot Cronin
Wiley Evans
Stefanie Falk
Richard A. Feely
Thomas Gasser
Marion Gehlen
Thanos Gkritzalis
Lucas Gloege
Giacomo Grassi
Nicolas Gruber
Özgür Gürses
Ian Harris
Matthew Hefner
Richard A. Houghton
George C. Hurtt
Yosuke Iida
Tatiana Ilyina
Atul K. Jain
Annika Jersild
Koji Kadono
Etsushi Kato
Daniel Kennedy
Kees Klein Goldewijk
Jürgen Knauer
Jan Ivar Korsbakken
Peter Landschützer
Nathalie Lefèvre
Keith Lindsay
Junjie Liu
Zhu Liu
Gregg Marland
Nicolas Mayot
Matthew J. McGrath
Nicolas Metzl
Natalie M. Monacci
David R. Munro
Shin-Ichiro Nakaoka
Yosuke Niwa
Kevin O'Brien
Tsuneo Ono
Paul I. Palmer
Naiqing Pan
Denis Pierrot
Katie Pocock
Benjamin Poulter
Laure Resplandy
Eddy Robertson
Christian Rödenbeck
Carmen Rodriguez
Thais M. Rosan
Jörg Schwinger
Roland Séférian
Jamie D. Shutler
Ingunn Skjelvan
Tobias Steinhoff
Qing Sun
Adrienne J. Sutton
Colm Sweeney
Shintaro Takao
Toste Tanhua
Pieter P. Tans
Xiangjun Tian
Hanqin Tian
Bronte Tilbrook
Hiroyuki Tsujino
Francesco Tubiello
Guido R. van der Werf
Anthony P. Walker
Rik Wanninkhof
Chris Whitehead
Anna Willstrand Wranne
Rebecca Wright
Wenping Yuan
Chao Yue
Xu Yue
Sönke Zaehle
Jiye Zeng
and
Bo Zheng
Pierre Friedlingstein
CORRESPONDING AUTHOR
p.friedlingstein@exeter.ac.uk
Faculty of Environment, Science and Economy, University of Exeter,
Exeter EX4 4QF, UK
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, CNRS,
Ecole Normale Supérieure/Université PSL, Sorbonne Université, Ecole
Polytechnique, Paris, 75231, France
Michael O'Sullivan
Faculty of Environment, Science and Economy, University of Exeter,
Exeter EX4 4QF, UK
Matthew W. Jones
Tyndall Centre for Climate Change Research, School of Environmental
Sciences, University of
East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Robbie M. Andrew
CICERO Center for International Climate Research, Oslo 0349, Norway
Luke Gregor
Environmental Physics Group, Institute of
Biogeochemistry and Pollutant Dynamics
and Center for Climate Systems Modeling (C2SM), ETH Zürich, Zurich, Switzerland
Judith Hauck
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und
Meeresforschung, Postfach 120161,
27515 Bremerhaven, Germany
Corinne Le Quéré
Tyndall Centre for Climate Change Research, School of Environmental
Sciences, University of
East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Ingrid T. Luijkx
Environmental Sciences Group, Wageningen University, P.O. Box 47,
6700AA, Wageningen, the
Netherlands
Are Olsen
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Glen P. Peters
CICERO Center for International Climate Research, Oslo 0349, Norway
Wouter Peters
Environmental Sciences Group, Wageningen University, P.O. Box 47,
6700AA, Wageningen, the
Netherlands
Centre for Isotope Research, University of Groningen, Groningen, the
Netherlands
Julia Pongratz
Department für Geographie, Ludwig-Maximilians-Universität Munich, Luisenstr. 37, 80333
München, Germany
Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Clemens Schwingshackl
Department für Geographie, Ludwig-Maximilians-Universität Munich, Luisenstr. 37, 80333
München, Germany
Stephen Sitch
Faculty of Environment, Science and Economy, University of Exeter,
Exeter EX4 4QF, UK
Josep G. Canadell
CSIRO Oceans and Atmosphere, Canberra, ACT 2101, Australia
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ,
Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Robert B. Jackson
Department of Earth System Science, Woods Institute for the
Environment, and Precourt
Institute for Energy, Stanford University, Stanford, CA 94305–2210, USA
Simone R. Alin
National Oceanic & Atmospheric Administration, Pacific Marine
Environmental Laboratory
(NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA
Ramdane Alkama
Joint Research Centre, European Commission, 21027 Ispra (VA), Italy
Almut Arneth
Karlsruhe Institute of Technology, Institute of Meteorology and
Climate Research/Atmospheric
Environmental Research, 82467 Garmisch-Partenkirchen, Germany
Vivek K. Arora
Canadian Centre for Climate Modelling and Analysis, Climate Research
Division, Environment
and Climate Change Canada, Victoria, BC, Canada
Nicholas R. Bates
Bermuda Institute of Ocean Sciences (BIOS), 17 Biological Lane, St.
Georges, GE01, Bermuda
Department of Ocean and Earth Science, University of Southampton,
European Way,
Southampton SO14 3ZH, UK
Meike Becker
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Nicolas Bellouin
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Henry C. Bittig
Leibniz Institute for Baltic Sea Research Warnemuende (IOW),
Seestrasse 15, 18119 Rostock,
Germany
Laurent Bopp
Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, CNRS,
Ecole Normale Supérieure/Université PSL, Sorbonne Université, Ecole
Polytechnique, Paris, 75231, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ,
Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Louise P. Chini
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742,
USA
Margot Cronin
Marine Institute, Galway, Ireland
Wiley Evans
Hakai Institute, Heriot Bay, BC, Canada
Stefanie Falk
Department für Geographie, Ludwig-Maximilians-Universität Munich, Luisenstr. 37, 80333
München, Germany
Richard A. Feely
National Oceanic & Atmospheric Administration, Pacific Marine
Environmental Laboratory
(NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA
Thomas Gasser
International Institute for Applied Systems Analysis (IIASA),
Schlossplatz 1, 2361 Laxenburg, Austria
Marion Gehlen
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ,
Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Thanos Gkritzalis
Flanders Marine Institute (VLIZ), InnovOceanSite, Jacobsenstraat 1, 8400, Ostend, Belgium
Lucas Gloege
Lamont-Doherty Earth Observatory and Department of Earth and
Environmental Sciences,
Columbia University, New York, NY 10027, USA
Open Earth Foundation, Marina del Rey, CA 90292, USA
Giacomo Grassi
Joint Research Centre, European Commission, 21027 Ispra (VA), Italy
Nicolas Gruber
Environmental Physics Group, Institute of
Biogeochemistry and Pollutant Dynamics
and Center for Climate Systems Modeling (C2SM), ETH Zürich, Zurich, Switzerland
Özgür Gürses
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und
Meeresforschung, Postfach 120161,
27515 Bremerhaven, Germany
Ian Harris
NCAS-Climate, Climatic Research Unit, School of Environmental
Sciences, University of East
Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Matthew Hefner
Research Institute for Environment, Energy, and Economics,
Appalachian State University,
Boone, NC 28608, USA
Department of Geological and Environmental Sciences, Appalachian
State University, Boone,
NC 28608, USA
Richard A. Houghton
Woodwell Climate Research Center, Falmouth, MA 02540, USA
George C. Hurtt
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742,
USA
Yosuke Iida
Atmosphere and Ocean Department, Japan Meteorological Agency,
Minato-Ku, Tokyo 105-8431, Japan
Tatiana Ilyina
Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Atul K. Jain
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61821, USA
Annika Jersild
Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Koji Kadono
Atmosphere and Ocean Department, Japan Meteorological Agency,
Minato-Ku, Tokyo 105-8431, Japan
Etsushi Kato
Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan
Daniel Kennedy
National Center for Atmospheric Research, Climate and Global
Dynamics, Terrestrial Sciences
Section, Boulder, CO 80305, USA
Kees Klein Goldewijk
Department IMEW, Faculty of Geosciences,
Copernicus Institute of
Sustainable Development, Utrecht University, Heidelberglaan 2, P.O. Box 80115, 3508 TC, Utrecht,
the Netherlands
Jürgen Knauer
Hawkesbury Institute for the Environment, Western Sydney University,
Penrith, NSW 2751, Australia
Climate Science Centre, CSIRO Oceans and Atmosphere, Canberra, ACT 2601, Australia
Jan Ivar Korsbakken
CICERO Center for International Climate Research, Oslo 0349, Norway
Peter Landschützer
Max Planck Institute for Meteorology, 20146 Hamburg, Germany
Flanders Marine Institute (VLIZ), InnovOceanSite, Jacobsenstraat 1, 8400, Ostend, Belgium
Nathalie Lefèvre
LOCEAN/IPSL laboratory, Sorbonne Université, CNRS/IRD/MNHN, Paris, 75252,
France
Keith Lindsay
National Center for Atmospheric Research, Climate and Global
Dynamics, Oceanography
Section, Boulder, CO 80305, USA
Junjie Liu
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Zhu Liu
Department of Earth System Science, Tsinghua University, Beijing,
China
Gregg Marland
Research Institute for Environment, Energy, and Economics,
Appalachian State University,
Boone, NC 28608, USA
Department of Geological and Environmental Sciences, Appalachian
State University, Boone,
NC 28608, USA
Nicolas Mayot
Tyndall Centre for Climate Change Research, School of Environmental
Sciences, University of
East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Matthew J. McGrath
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL,
CEA-CNRS-UVSQ,
Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Nicolas Metzl
LOCEAN/IPSL laboratory, Sorbonne Université, CNRS/IRD/MNHN, Paris, 75252,
France
Natalie M. Monacci
University of Alaska Fairbanks, College of Fisheries and Ocean
Sciences, P.O. Box 757220, Fairbanks, AK 99775-7220, USA
David R. Munro
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, CO 80305, USA
National Oceanic & Atmospheric Administration/Global Monitoring
Laboratory (NOAA/GML),
Boulder, CO 80305, USA
Shin-Ichiro Nakaoka
Earth System Division, National Institute for Environmental Studies
(NIES), 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan
Yosuke Niwa
Earth System Division, National Institute for Environmental Studies
(NIES), 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan
Hawkesbury Institute for the Environment, Western Sydney University,
Penrith, NSW 2751, Australia
Kevin O'Brien
Cooperative Institute for Climate, Ocean and Ecosystem Studies
(CICOES), University of
Washington, Seattle, WA 98195, USA
National Oceanic & Atmospheric Administration, Pacific Marine
Environmental Laboratory
(NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA
Tsuneo Ono
Japan Fisheries Research and Education Agency, 2-12-4 Fukuura,
Kanazawa-Ku, Yokohama 236-8648, Japan
Paul I. Palmer
National Centre for Earth Observation, University of Edinburgh, Edinburgh, EH9 3FE, UK
School of Geosciences, University of Edinburgh, Edinburgh, EH9 3FE, UK
Naiqing Pan
College of Forestry, Wildlife and Environment, Auburn University,
Auburn, AL 36849, USA
Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA
Denis Pierrot
National Oceanic & Atmospheric Administration/Atlantic
Oceanographic & Meteorological
Laboratory (NOAA/AOML), Miami, FL 33149, USA
Katie Pocock
Hakai Institute, Heriot Bay, BC, Canada
Benjamin Poulter
NASA Goddard Space Flight Center, Biospheric Sciences Laboratory,
Greenbelt, MD
20771, USA
Laure Resplandy
Princeton University, Department of Geosciences and Princeton
Environmental Institute,
Princeton, NJ 08544, USA
Eddy Robertson
Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
Christian Rödenbeck
Max Planck Institute for Biogeochemistry, P.O. Box 600164,
Hans-Knöll-Str. 10, 07745 Jena,
Germany
Carmen Rodriguez
University of Miami, RSMAS, 4600 Rickenbacker Causeway, Miami, FL
33149, USA
Thais M. Rosan
Faculty of Environment, Science and Economy, University of Exeter,
Exeter EX4 4QF, UK
Jörg Schwinger
NORCE Norwegian Research Centre, Jahnebakken 5, 5007 Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Roland Séférian
CNRM, Université de Toulouse, Météo-France, CNRS,
Toulouse, 31057, France
Jamie D. Shutler
Faculty of Environment, Science and Economy, University of Exeter,
Exeter EX4 4QF, UK
Ingunn Skjelvan
NORCE Norwegian Research Centre, Jahnebakken 5, 5007 Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Tobias Steinhoff
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Qing Sun
Climate and Environmental Physics, Physics Institute and Oeschger
Centre for Climate Change Research, University of Bern, Bern, Switzerland
Adrienne J. Sutton
National Oceanic & Atmospheric Administration, Pacific Marine
Environmental Laboratory
(NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA
Colm Sweeney
National Oceanic & Atmospheric Administration/Global Monitoring
Laboratory (NOAA/GML),
Boulder, CO 80305, USA
Shintaro Takao
Earth System Division, National Institute for Environmental Studies
(NIES), 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan
Toste Tanhua
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Pieter P. Tans
National Oceanic & Atmospheric Administration, Global Monitoring Laboratory (NOAA GML), Boulder, CO 80305, USA
Institute of Arctic and Alpine Research, University of Colorado,
Boulder, CO 80309, USA
Xiangjun Tian
State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources
(TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing,
100101, China
Hanqin Tian
Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA
Bronte Tilbrook
CSIRO Oceans and Atmosphere, P.O. Box 1538, Hobart, TAS 7001,
Australia
Australian Antarctic Partnership Program, University of Tasmania,
Hobart, TAS 7001, Australia
Hiroyuki Tsujino
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki
305-0052, Japan
Francesco Tubiello
Statistics Division, Food and Agriculture Organization of the United
Nations, Via Terme di
Caracalla, Rome 00153, Italy
Guido R. van der Werf
Department of Earth
Sciences, Faculty of Science, Vrije
Universiteit, 1081 Amsterdam, the
Netherlands
Anthony P. Walker
Environmental Sciences Division and Climate Change Science
Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Rik Wanninkhof
National Oceanic & Atmospheric Administration/Atlantic
Oceanographic & Meteorological
Laboratory (NOAA/AOML), Miami, FL 33149, USA
Chris Whitehead
Sitka Tribe of Alaska, 456 Katlian Street, Sitka, AK 99835, USA
Anna Willstrand Wranne
Swedish Meteorological and Hydrological Institute, Sven
Källfeltsgata 15, 426 68 Västra Frölunda, Sweden
Rebecca Wright
Tyndall Centre for Climate Change Research, School of Environmental
Sciences, University of
East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Wenping Yuan
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai,
Guangdong 510245, China
Chao Yue
Institute of Soil and Water Conservation, Northwest A&F
University, Yangling, Shaanxi 712100, China
Xu Yue
School of Environmental Science and Engineering, Nanjing University of Information Science
and Technology (NUIST), Nanjing 211544, China
Sönke Zaehle
Max Planck Institute for Biogeochemistry, P.O. Box 600164,
Hans-Knöll-Str. 10, 07745 Jena,
Germany
Jiye Zeng
Earth System Division, National Institute for Environmental Studies
(NIES), 16-2 Onogawa,
Tsukuba, Ibaraki 305-8506, Japan
Bo Zheng
Institute of Environment and Ecology, Tsinghua Shenzhen
International Graduate School,
Tsinghua University, Shenzhen 518055, China
Publisher's note
: In the originally published article, a section numbering error occurred starting below Sect. 2.4 with the section
Land CO
sink
and ending with the section
Processes not included in the global carbon budget
. We corrected the article accordingly. Nothing else has changed.
Abstract
Accurate assessment of anthropogenic carbon dioxide (CO
) 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 synthesize data sets and methodologies to
quantify the five major components of the global carbon budget and their
uncertainties. Fossil CO
emissions (
FOS
) are based on energy
statistics and cement production data, while emissions from land-use change
LUC
), mainly deforestation, are based on land use and land-use change
data and bookkeeping models. Atmospheric CO
concentration is measured
directly, and its growth rate (
ATM
) is computed from the annual
changes in concentration. The ocean CO
sink (
OCEAN
) is estimated
with global ocean biogeochemistry models and observation-based
data products. The terrestrial CO
sink (
LAND
) is estimated with
dynamic global vegetation models. The resulting carbon budget imbalance
IM
), the difference between the estimated total emissions and the
estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a
measure of imperfect data and understanding of the contemporary carbon
cycle. All uncertainties are reported as
For the year 2021,
FOS
increased by 5.1 % relative to 2020, with
fossil emissions at 10.1
0.5 GtC yr
−1
(9.9
0.5 GtC yr
−1
when the cement carbonation sink is included), and
LUC
was 1.1
0.7 GtC yr
−1
, for a total anthropogenic CO
emission
(including the cement carbonation sink) of 10.9
0.8 GtC yr
−1
(40.0
2.9 GtCO
). Also, for 2021,
ATM
was 5.2
0.2 GtC yr
−1
(2.5
0.1 ppm yr
−1
),
OCEAN
was 2.9
0.4 GtC yr
−1
, and
LAND
was 3.5
0.9 GtC yr
−1
, with a
IM
of
0.6 GtC yr
−1
(i.e. the total estimated sources were too low or
sinks were too high). The global atmospheric CO
concentration averaged over
2021 reached 414.71
0.1 ppm. Preliminary data for 2022 suggest an
increase in
FOS
relative to 2021 of
1.0 % (0.1 % to 1.9 %)
globally and atmospheric CO
concentration reaching 417.2 ppm, more
than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean
and trend in the components of the global carbon budget are consistently
estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr
−1
persist for the representation of annual to semi-decadal
variability in CO
fluxes. Comparison of estimates from multiple
approaches and observations shows (1) a persistent large uncertainty in the
estimate of land-use change emissions, (2) a low agreement between the
different methods on the magnitude of the land CO
flux in the northern
extratropics, and (3) a discrepancy between the different methods on the
strength of the ocean sink over the last decade. This living data update
documents changes in the methods and data sets used in this new global
carbon budget and the progress in understanding of the global carbon cycle
compared with previous publications of this data set. The data presented in
this work are available at
(Friedlingstein et al., 2022b).
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Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora, V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L., Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, 2022.
Received: 26 Sep 2022
Discussion started: 29 Sep 2022
Revised: 14 Oct 2022
Accepted: 14 Oct 2022
Published: 11 Nov 2022
Executive summary
Global fossil CO
emissions (including cement
carbonation) further increased in 2022, being now slightly above their
pre-COVID-19 pandemic 2019 level. The 2021 emission increase was 0.46 GtC yr
−1
(1.7 GtCO
yr
−1
), bringing 2021 emissions to 9.9
0.5 GtC yr
−1
(36.3
1.8 GtCO
yr
−1
), same as the 2019
emissions level. Preliminary estimates based on data available suggest
fossil CO
emissions continued to increase by 1.0 % in 2022 relative
to 2021 (0.1 % to 1.9 %), bringing emissions of 10.0 GtC yr
−1
(36.6 GtCO
yr
−1
), slightly above the 2019 level.
Emissions from coal, oil, and gas in 2022 are expected to be above their
2021 levels (by 1.0 %, 2.2 % and
0.2 % respectively). Regionally,
emissions in 2022 are expected to have decreased by 0.9 % in China
(3.1 GtC, 11.4 GtCO
) and 0.8 % in the European Union (0.8 GtC, 2.8 GtCO
) but increased by 1.5 % in the United States (1.4 GtC, 5.1 GtCO
), 6 % in India (0.8 GtC, 2.9 GtCO
), and 1.7 % in the
rest of the world (4.2 GtC, 15.4 GtCO
).
Fossil CO
emissions decreased in 24
countries during the decade 2012–2021. Altogether, these 24 countries
contributed about 2.4 GtC yr
−1
(8.8 GtCO
) fossil fuel
CO
emissions over the last decade, about a quarter of global CO
fossil emissions.
Global CO
emissions from land use, land-use
change, and forestry (LUC) averaged at 1.2
0.7 GtC yr
−1
(4.5
2.6 GtCO
yr
−1
) for the 2012–2021 period with a preliminary
projection for 2022 of 1.1
0.7 GtC yr
−1
(3.9
2.6 GtCO
yr
−1
). A
small decrease over the past 2 decades is not robust given the large model
uncertainty. Emissions from deforestation, the main driver of global gross
sources, remain high at 1.8
0.4 GtC yr
−1
over the 2012–2021
period, highlighting the strong potential for
emissions reductions when halting deforestation. Sequestration of 0.9
0.3 GtC yr
−1
through afforestation or reafforestation and forestry offsets half of the
deforestation emissions. Emissions from other land-use transitions and from
peat drainage and peat fire add further small contributions. The highest
emitters during 2012–2021 in descending order were Brazil, Indonesia, and
the Democratic Republic of the Congo, with these three countries contributing
more than half of the global total land-use emissions.
The remaining carbon budget for a 50 % likelihood to limit global
warming to 1.5, 1.7, and 2
C has,
respectively, reduced to 105 GtC (380 GtCO
), 200 GtC
(730 GtCO
), and 335 GtC (1230 GtCO
) from the beginning of 2023, equivalent to 9,
18, and 30 years, assuming 2022 emissions levels. Total anthropogenic
emissions were 11.0 GtC yr
−1
(40.2 GtCO
yr
−1
) in 2021, with a
preliminary estimate of 11.1 GtC yr
−1
(40.5 GtCO
yr
−1
) for 2022.
The remaining carbon budget to keep global temperatures below these climate targets has shrunk by 32 GtC (121 GtCO
) since the IPCC AR6
Working Group 1 assessment based on data up to 2019. Reaching zero CO
emissions by 2050 entails a total anthropogenic CO
emissions linear
decrease by about 0.4 GtC (1.4 GtCO
) each year, comparable to the
decrease during 2020, highlighting the scale of the action needed.
The concentration of CO
in the atmosphere is
set to reach 417.2 ppm in 2022, 51 % above pre-industrial levels. The
atmospheric CO
growth was 5.2
0.02 GtC yr
−1
during the
decade 2012–2021 (48 % of total CO
emissions) with a preliminary
2022 growth rate estimate of around 5.3 GtC yr
−1
(2.5 ppm).
The ocean CO
sink resumed a more rapid
growth in the past 2 decades after low or no growth during the 1991–2002
period. However, the growth of the ocean CO
sink in the past decade
has an uncertainty of a factor of 3, with estimates based on data
products and estimates based on models showing an ocean sink trend of
0.7 GtC yr
−1
per decade and
0.2 GtC yr
−1
per decade since 2010, respectively. The discrepancy in the trend originates from all
latitudes but is largest in the Southern Ocean. The ocean CO
sink was
2.9
0.4 GtC yr
−1
during the decade 2012–2021 (26 % of total
CO
emissions), with a similar preliminary estimate of 2.9 GtC yr
−1
for 2022.
The land CO
sink continued to increase
during the 2012–2021 period primarily in response to increased atmospheric
CO
, albeit with large interannual
variability. The land CO
sink was 3.1
0.6 GtC yr
−1
during the decade 2012–2021 (29 % of total CO
emissions), 0.4 GtC yr
−1
larger than during the previous decade (2000–2009), with a
preliminary 2022 estimate of around 3.4 GtC yr
−1
. Year-to-year
variability in the land sink is about 1 GtC yr
−1
and dominates the
year-to-year changes in the global atmospheric CO
concentration,
implying that small annual changes in anthropogenic emissions (such as the
fossil fuel emission decrease in 2020) are hard to detect in the atmospheric
CO
observations.
Introduction
The concentration of carbon dioxide (CO
) in the atmosphere has
increased from approximately 278 parts per million (ppm) in 1750 (Gulev et
al., 2021), the beginning of the Industrial Era, to 414.7
0.1 ppm in
2021 (Dlugokencky and Tans, 2022; Fig. 1). The atmospheric CO
increase above pre-industrial levels was, initially, primarily caused by the
release of carbon to the atmosphere from deforestation and other land-use
change activities (Canadell et al., 2021). While emissions from fossil fuels
started before the Industrial Era, they became the dominant source of
anthropogenic emissions to the atmosphere from around 1950, and their
relative share has continued to increase until present. Anthropogenic
emissions occur on top of an active natural carbon cycle that circulates
carbon between the reservoirs of the atmosphere, ocean, and terrestrial
biosphere on timescales from sub-daily to millennia, while exchanges with
geologic reservoirs occur at longer timescales (Archer et al., 2009).
Figure 1
Surface average atmospheric CO
concentration (ppm).
Since 1980, monthly data are from NOAA/GML (Dlugokencky and Tans, 2022) and
are based on an average of direct atmospheric CO
measurements
from multiple stations in the marine boundary layer (Masarie and Tans,
1995). The 1958–1979 monthly data are from the Scripps Institution of
Oceanography, based on an average of direct atmospheric CO
measurements from the Mauna Loa and South Pole stations (Keeling et al.,
1976). To account for the difference in mean CO
and
seasonality between the NOAA/GML and the Scripps station networks used here,
the Scripps surface average (from two stations) was de-seasonalized and
adjusted to match the NOAA/GML surface average (from multiple stations) by
adding the mean difference of 0.667 ppm, calculated here from overlapping
data during 1980–2012.
The global carbon budget (GCB) presented here refers to the mean,
variations, and trends in the perturbation of CO
in the environment,
referenced to the beginning of the Industrial Era (defined here as 1750).
This paper describes the components of the global carbon cycle over the
historical period with a stronger focus on the recent period (since 1958, the
onset of atmospheric CO
measurements), the last decade (2012–2021),
the last year (2021), and the current year (2022). Finally, it provides
cumulative emissions from fossil fuels and land-use change since the year
1750 (the pre-industrial period) and since the year 1850 (the reference
year for historical simulations in IPCC AR6) (Eyring et al., 2016).
We quantify the input of CO
to the atmosphere by emissions from human
activities; the growth rate of atmospheric CO
concentration; and the
resulting changes in the storage of carbon in the land and ocean reservoirs
in response to increasing atmospheric CO
levels, climate change and
variability, and other anthropogenic and natural changes (Fig. 2). An
understanding of this perturbation budget over time and the underlying
variability and trends of the natural carbon cycle is necessary to
understand the response of natural sinks to changes in climate, CO
, and
land-use change drivers and to quantify emissions compatible with a given
climate stabilization target.
Figure 2
Schematic representation of the overall perturbation of the global carbon cycle caused by anthropogenic activities averaged globally for the decade 2012–2021. See legends for the corresponding arrows and units. The uncertainty in the atmospheric CO
growth rate is very small (
0.02 GtC yr
−1
) and is neglected for the figure. The anthropogenic perturbation occurs on top of an active carbon cycle, with fluxes and stocks represented in the background and taken from Canadell et al. (2021) for all numbers, except for the carbon stocks in coasts, which are from a literature review of coastal marine sediments (Price and Warren, 2016).
The components of the CO
budget that are reported annually in this
paper include separate and independent estimates for the CO
emissions
from (1) fossil fuel combustion and oxidation from all energy and industrial
processes, including cement production and carbonation (
FOS
; GtC yr
−1
), and (2) the emissions resulting from deliberate human activities
on land, including those leading to land-use change (
LUC
; GtC yr
−1
) and their partitioning among (3) the growth rate of atmospheric
CO
concentration (
ATM
; GtC yr
−1
) and the uptake of
CO
(the “CO
sinks”) in (4) the ocean (
OCEAN
; GtC yr
−1
) and (5) on land (
LAND
; GtC yr
−1
). The CO
sinks
as defined here conceptually include the response of the land (including
inland waters and estuaries) and ocean (including coastal and marginal seas)
to elevated CO
and changes in climate and other environmental
conditions, although in practice not all processes are fully accounted for
(see Sect. 2.7). 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 Sect. 2.7), the independent estimates (1) to (5) above do
not necessarily add up to zero. We therefore (i) additionally assess a set
of global atmospheric inversion system results that by design close the
global carbon balance (see Sect. 2.6) and (i) estimate a budget imbalance
IM
), which is a measure of the mismatch between the estimated
emissions and the estimated changes in the atmosphere, land, and ocean, as
follows:
(1)
IM
FOS
LUC
ATM
OCEAN
LAND
ATM
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). All quantities are presented in units of gigatonnes
of carbon (GtC, 10
15
gC), which is the same as petagrams of carbon
(PgC; Table 1). Units of gigatonnes of CO
(or billion tonnes of
CO
) used in policy are equal to 3.664 multiplied by the value in units
of GtC.
Table 1
Factors used to convert carbon in various units (by convention, Unit 1
Unit 2
conversion).
Measurements of atmospheric CO
concentration have units
of dry-air mole fraction. “ppm” is an abbreviation for
µmol
mol
−1
dry air.
The use of a factor of 2.124 assumes that all of the atmosphere is well mixed within 1 year. In reality, only the troposphere is well mixed, and the growth rate of CO
concentration in the less well-mixed stratosphere is not measured by sites from the NOAA network. Using a factor of 2.124 makes the approximation that the growth rate of CO
concentration in the stratosphere equals that of the troposphere on a yearly basis.
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We also quantify
FOS
and
LUC
by country, including both
territorial and consumption-based accounting for
FOS
(see Sect. 2), and discuss missing terms from sources other than the combustion of
fossil fuels (see Sect. 2.7 and Appendix D1 and D2).
The global CO
budget has been assessed by the Intergovernmental Panel
on Climate Change (IPCC) in all assessment reports (Prentice et al., 2001;
Schimel et al., 1995; Watson et al., 1990; 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,
, last
access: 25 September 2022) 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, 2007), year 2008 (Le Quéré et al.,
2009), year 2009 (Friedlingstein et al., 2010), year 2010 (Peters et al.,
2012b), 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; Le
Quéré et al., 2015b), year 2016 (Le Quéré et al., 2016),
year 2017 (Le Quéré et al., 2018a; Peters et al., 2017), 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), and
more recently the year 2021 (Friedlingstein et al., 2022a; Jackson et al.,
2022). Each of these papers updated previous estimates with the latest
available information for the entire time series.
We adopt a range of
1 standard deviation (
) to report the
uncertainties in our 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
characterizing the uncertainty in the CO
fluxes between the atmosphere
and the ocean and land reservoirs individually, particularly on an annual
basis, as well as the difficulty of updating the CO
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 data sets, 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 characterize the annual
estimates from each term based on the type, amount, quality, and consistency
of the evidence as defined by the IPCC (Stocker et al., 2013).
This paper provides a detailed description of the data sets and methodology
used to compute the global carbon budget estimates for the industrial
period (from 1750 to 2022) and in more detail for the 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, revisions of data, and
changes in methodology that lead to changes in estimates of the carbon
budget. Additional materials associated with the release of each new version
will be posted at the Global Carbon Project (GCP) website
, last access: 25 September
2022), with fossil fuel emissions also available through the Global Carbon
Atlas (
, last access: 25 September 2022).
All underlying data used to produce the budget can also be found at
(last access: 25 September
2022). With this approach, we aim to provide the highest transparency and
traceability in the reporting of CO
, the key driver of climate change.
Methods
Multiple organizations 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 data sets will be referenced in future work (see Table 2 for
how to cite the data sets). Descriptions of the measurements, models, and
methodologies follow below, and detailed descriptions of each component are
provided elsewhere.
Table 2
How to cite the individual components of the global carbon budget presented here.
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This is the 17th version of the global carbon budget and the 11th 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 Friedlingstein et
al. (2022a). The main changes are the inclusion of (1) data to year 2021
and a projection for the global carbon budget for the year 2022, (2) the
inclusion of country-level estimates of
LUC
, and(3) a process-based
decomposition of
LUC
into its main components (deforestation;
afforestation, reafforestation, and wood harvest; emissions from organic soils; and net
flux from other transitions).
The main methodological differences between recent annual carbon budgets
(2018–2022) are summarized in Table 3, and previous changes since 2006 are
provided in Table A7.
Table 3
The main methodological changes in the global carbon budget since 2018. Methodological changes introduced in any given year are kept for the following years unless otherwise noted. Empty cells mean there were no methodological changes introduced that year. Table A7 lists methodological changes from the first global carbon budget publication up to 2017.
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2.1
Fossil CO
emissions (
FOS
2.1.1
Historical period 1850–2021
The estimates of global and national fossil CO
emissions (
FOS
include the oxidation of fossil fuels through both combustion (e.g.
transport, heating) and chemical oxidation (e.g. carbon anode decomposition
in aluminium refining) activities, and the decomposition of carbonates in
industrial processes (e.g. the production of cement). We also include
CO
uptake from the cement carbonation process. Several emission
sources are not estimated or not fully covered: coverage of emissions from
lime production is not global, and decomposition of carbonates in glass and
ceramic production are included only for the “Annex 1” countries of the
United Nations Framework Convention on Climate Change (UNFCCC) for lack of
activity data. These omissions are considered to be minor. Short-cycle
carbon emissions – for example from combustion of biomass – are not included
here but are accounted for in the CO
emissions from land use (see
Sect. 2.2).
Our estimates of fossil CO
emissions are derived using the standard
approach of activity data and emission factors, relying on data collection
by many other parties. Our goal is to produce the best estimate of this
flux, and we therefore use a prioritization framework to combine data from
different sources that have used different methods, while being careful to
avoid double counting and undercounting of emissions sources. The CDIAC-FF
emissions data set, derived largely from UN energy data, forms the
foundation, and we extend emissions to year Y-1 using energy growth rates
reported by the BP energy company. We then proceed to replace estimates using
data from what we consider to be superior sources, for example Annex 1
countries' official submissions to the UNFCCC. All data points are
potentially subject to revision, not just the latest year. For the full details,
see Andrew and Peters (2021).
Other estimates of global fossil CO
emissions exist, and these are
compared by Andrew (2020a). The most common reason for differences in
estimates of global fossil CO
emissions is a difference in which
emissions sources are included in the data sets. Data sets such as those
published by the energy company BP, the US Energy Information
Administration, and the International Energy Agency's “CO
emissions
from fuel combustion” are all generally limited to emissions from combustion
of fossil fuels. In contrast, data sets such as PRIMAP-hist, CEDS, EDGAR, and
GCP's data set aim to include all sources of fossil CO
emissions. See
Andrew (2020a) for detailed comparisons and discussion.
Cement absorbs CO
from the atmosphere over its lifetime, a process
known as “cement carbonation”. We estimate this CO
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 parameterizations 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 2022 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 CO
emission
component (
FOS
).
We use the Kaya Identity for a simple decomposition of CO
emissions
into the key drivers (Raupach et al., 2007). While there are variations
(Peters et al., 2017), we focus here on a decomposition of CO
emissions into population, GDP per person, energy use per GDP, and CO
emissions per energy. Multiplying these individual components together
returns the CO
emissions. Using the decomposition, it is possible to
attribute the change in CO
emissions to the change in each of the
drivers. This method gives a first-order understanding of what causes
CO
emissions to change each year.
2.1.2
The 2022 projection
We provide a projection of global CO
emissions in 2022 by combining
separate projections for China, USA, EU, India, 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,
import and export data from the Customs Administration, and monthly coal
consumption estimates from SX Coal (2022), giving us partial data for the
growth rates to date of natural gas, petroleum, and cement, and of the
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, 2022), 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 CO
emissions through
July, with coal emissions extended through August using a statistical
relationship with reported electricity generation from coal and other
factors. Given the very high uncertainty in European energy markets in 2022,
we forego our usual history-based projection techniques and instead use the
year-to-date growth rate as the full-year growth rate for both coal and
natural gas. EU emissions from oil are derived using the EIA's projection of
oil consumption for Europe. EU cement emissions are based on available
year-to-date data from three of the largest producers, Germany, Poland, and
Spain. India's projected emissions are derived from estimates through July
(August for oil) using the methods of Andrew (2020b) and extrapolated
assuming normal seasonal patterns. Emissions for the rest of the world are
derived using projected growth in economic production from the IMF (2022)
combined with extrapolated changes in emissions intensity of economic
production. More details on the
FOS
methodology and its 2022
projection can be found in Appendix C1.
2.2
CO
emissions from land use, land-use change, and forestry (
LUC
2.2.1
Historical period 1850–2021
The net CO
flux from land use, land-use change, and forestry
LUC
, called land-use change emissions in the rest of the text)
includes CO
fluxes from deforestation, afforestation, logging and
forest degradation (including harvest activity), shifting cultivation (cycle
of cutting forest for agriculture, then abandoning), and regrowth of forests
(following wood harvest or agriculture abandonment). Emissions from peat
burning and drainage are added from external data sets, with peat drainage being
averaged from three spatially explicit independent data sets (see Appendix C2.1).
Three bookkeeping approaches, updated estimates each of BLUE (Hansis et al.,
2015), OSCAR (Gasser et al., 2020), and H&N2017 (Houghton and Nassikas,
2017), were used to quantify gross sources and sinks and the resulting net
LUC
. Uncertainty estimates were derived from the dynamic global
vegetation models (DGVMs) ensemble for the time period prior to 1960, 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
) that the true land-use change emission lies within the
given range for the range of processes considered here. This uncertainty
range had been increased from 0.5 GtC yr
−1
after new bookkeeping models
were included that indicated a larger spread than assumed before (Le
Quéré et al., 2018a). Projections for 2021 are based on fire activity
from tropical deforestation and degradation and emissions from peat
fires and drainage.
Our
LUC
estimates follow the definition of global carbon cycle models
of CO
fluxes related to land-use and land management and differ from
IPCC definitions adopted in national greenhouse gas (GHG) inventories (NGHGI) for reporting
under the UNFCCC, which additionally generally include, through adoption of
the IPCC so-called managed land proxy approach, the terrestrial fluxes
occurring on land defined by countries as managed. This partly includes
fluxes due to environmental change (e.g. atmospheric CO
increase),
which are part of
LAND
in our definition. This causes the global
emission estimates to be smaller for NGHGI than for the global carbon budget
definition (Grassi et al., 2018). The same is the case for the Food
Agriculture Organization (FAO) estimates of carbon fluxes on forest land,
which include both anthropogenic and natural sources on managed land
(Tubiello et al., 2021). We map the two definitions to each other, to
provide a comparison of the anthropogenic carbon budget to the official
country reporting to the climate convention.
2.2.2
The 2022 projection
We project the 2022 land-use emissions for BLUE, the updated H&N2017, and
OSCAR, starting from their estimates for 2021 assuming unaltered peat
drainage, which has low interannual variability but adjusting the highly
variable emissions from peat fires, tropical deforestation, and degradation
as estimated using active fire data (MCD14ML; Giglio et al., 2016). More
details on the
LUC
methodology can be found in Appendix C2.
2.3
Growth rate in atmospheric CO
concentration (
ATM
2.3.1
Historical period 1850–2021
The rate of growth of the atmospheric CO
concentration is provided
for years 1959–2021 by the US National Oceanic and Atmospheric
Administration Global Monitoring Laboratory (NOAA/GML; Dlugokencky and Tans,
2022), which is updated from Ballantyne et al. (2012) and includes recent
revisions to the calibration scale of atmospheric CO
measurements
(Hall et al., 2021). For the 1959–1979 period, the global growth rate is
based on measurements of atmospheric CO
concentration averaged from
the Mauna Loa and South Pole stations, as observed by the CO
Program
at Scripps Institution of Oceanography (Keeling et al., 1976). For the
1980–2020 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 (Ballantyne et al., 2012), 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 Dlugokencky and Tans (2022) from atmospheric CO
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. The 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 ppm
−1
, assuming instantaneous mixing of CO
throughout the atmosphere
(Ballantyne et al., 2012; Table 1).
Since 2020, NOAA/GML provides estimates of atmospheric CO
concentrations with respect to a new calibration scale, referred to as
WMO-CO
-X2019, in line with the recommendation of the World Meteorological
Organization (WMO) Global Atmosphere Watch (GAW) community (Hall et al.,
2021). The “X” in the scale name indicates that it is a mole fraction scale,
how many micro-moles of CO
in a single mole of (dry) air. The word
“concentration” only loosely reflects this. The WMO-CO
-X2019 scale improves
upon the earlier WMO-CO
-X2007 scale by including a broader set of
standards, which contain CO
in a wider range of concentrations that
span the range 250–800 ppm (vs. 250–520 ppm for WMO-CO
-X2007). In
addition, NOAA/GML made two minor corrections to the analytical procedure
used to quantify CO
concentrations, fixing an error in the second
virial coefficient of CO
and accounting for loss of a small amount of
CO
to materials in the manometer during the measurement process. The
difference in concentrations measured using WMO-CO
-X2019 vs.
WMO-CO
-X2007 is
0.18 ppm at 400 ppm and the
observational record of atmospheric CO
concentrations have been
revised accordingly. The revisions have been applied retrospectively in all
cases where the calibrations were performed by NOAA/GML, thus affecting
measurements made by members of the WMO-GAW programme and other regionally
coordinated programmes (e.g. Integrated Carbon Observing System, ICOS).
Changes to the CO
concentrations measured across these networks
propagate to the global mean CO
concentrations. The recalibrated data
were first used to estimate
ATM
in the 2021 edition of the global
carbon budget (Friedlingstein et al., 2022a). Friedlingstein et al. (2022a)
verified that the change of scales from WMO-CO
-X2007 to WMO-CO
-X2019 made
a negligible difference to the value of
ATM
0.06 GtC yr
−1
during 2010–2019 and
0.01 GtC yr
−1
during 1959–2019, well within the
uncertainty range reported below).
The uncertainty around the atmospheric growth rate is due to four main
factors. First, the long-term reproducibility of reference gas standards
(around 0.03 ppm for 1
from the 1980s; Dlugokencky and Tans, 2022).
Second, small unexplained systematic analytical errors that may have a
duration of several months to 2 years come and go. They have been
simulated by randomizing both the duration and the magnitude (determined
from the existing evidence) in a Monte Carlo procedure. Third, the network
composition of the marine boundary layer with some sites coming or going,
gaps in the time series at each site, and so on (Dlugokencky and Tans, 2022). The
latter uncertainty was estimated by NOAA/GML with a Monte Carlo method by
constructing 100 “alternative” networks (Masarie and Tans, 1995; NOAA/GML,
2019). The second and third uncertainties, summed in quadrature, add up to
0.085 ppm on average (Dlugokencky and Tans, 2022). Fourth, the uncertainty
associated with using the average CO
concentration from a surface
network to approximate the true atmospheric average CO
concentration
(mass-weighted, in three dimensions) as needed to assess the total atmospheric
CO
burden. In reality, CO
variations measured at the stations
will not exactly track changes in total atmospheric burden, with offsets in
magnitude and phasing due to vertical and horizontal mixing. This effect
must be very small on decadal and longer timescales, when the atmosphere
can be considered well mixed. The CO
increase in the stratosphere lags the
increase (meaning lower concentrations) that we observe in the marine
boundary layer, while the continental boundary layer (where most of the
emissions take place) leads the marine boundary layer with higher
concentrations. These effects nearly cancel each other. In addition, the
growth rate is nearly the same everywhere (Ballantyne et al., 2012). We
therefore maintain an uncertainty around the annual growth rate based 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 1959–1979 and 0.17 GtC yr
−1
for
1980–2020, when a larger set of stations were available as provided by
Dlugokencky and Tans (2022). We estimate the uncertainty of the decadal
averaged growth rate after 1980 at 0.02 GtC yr
−1
based on the
calibration and the annual growth rate uncertainty but stretched over a
10-year interval. For years prior to 1980, we estimate the decadal averaged
uncertainty to be 0.07 GtC yr
−1
based on a factor proportional to the
annual uncertainty prior and after 1980 (0.02
0.61
0.17
] GtC yr
−1
).
We assign a high confidence to the annual estimates of
ATM
because
they are based on direct measurements from multiple and consistent
instruments and stations distributed around the world (Ballantyne et al.,
2012; Hall et al., 2021).
To estimate the total carbon accumulated in the atmosphere since 1750 or
1850, we use an atmospheric CO
concentration of 278.3
3 ppm or
285.1
3 ppm, respectively (Gulev et al., 2021). For the construction
of the cumulative budget shown in Fig. 3, we use the fitted estimates of
CO
concentration from Joos and Spahni (2008) to estimate the annual
atmospheric growth rate using the conversion factors shown in Table 1. The
uncertainty of
3 ppm (converted to
) is taken
directly from the IPCC's AR5 assessment (Ciais et al., 2013). Typical
uncertainties in the growth rate in atmospheric CO
concentration from
ice core data are equivalent to
0.1–0.15 GtC yr
−1
as evaluated
from the Law Dome data (Etheridge et al., 1996) for individual 20-year
intervals over the period from 1850 to 1960 (Bruno and Joos, 1997).
Table 4
References for the process models, bookkeeping models, ocean data products, and atmospheric inversions. All models and products are updated with new data to the end of year 2021, and the atmospheric forcing for the DGVMs has been updated as described in Appendix C2.2.
See also Asaadi et al. (2018).
See also Tian et al. (2011).
The dynamic carbon allocation scheme was presented by Xia et al. (2015).
See also Jain et al. (2013). Soil biogeochemistry is updated based on Shu et al. (2020).
See also Mauritsen et al. (2019).
See also Sellar et al. (2019) and Burton et al. (2019). JULES-ES is the Earth System configuration of the Joint UK Land Environment Simulator as used in the UK Earth System Model (UKESM).
To account for the differences between the derivation of short-wave radiation from CRU cloudiness and DSWRF from CRUJRA, the photosynthesis scaling parameter
was modified (
15 %) to yield similar results.
Compared to published version, decreased LPJ wood harvest efficiency so that 50 % of biomass was removed off-site compared to 85 % used in the 2012 budget. Residue management of managed grasslands increased so that 100 % of harvested grass enters the litter pool.
See also Zaehle et al. (2011).
See also Zaehle and Friend (2010) and Krinner et al. (2005)
See also Woodward and Lomas (2004).
See also Ito and Inatomi (2012).
See also Séférian et al. (2019).
See also Schourup-Kristensen et al. (2014).
See also Yeager et al. (2022).
See also Bennington et al. (2022).
See also Remaud (2018).
See also Rödenbeck et al. (2003).
See also Feng et al. (2009) and Palmer et al. (2019)
See also Niwa et al. (2020)
See also Tian et al. (2014).
Download XLSX
2.3.2
The 2022 projection
We provide an assessment of
ATM
for 2022 based on the monthly
calculated global atmospheric CO
concentration (GLO) through August
(Dlugokencky and Tans, 2022), and bias-adjusted Holt–Winters exponential
smoothing with additive seasonality (Chatfield, 1978) to project to January 2023. Additional analysis suggests that the first half of the year (the
boreal winter–spring–summer transition) shows more interannual variability
than the second half of the year (the boreal summer–autumn–winter
transition), so that the exact projection method applied to the second half
of the year has a relatively smaller impact on the projection of the full
year. Uncertainty is estimated from past variability using the standard
deviation of the last 5 years of monthly growth rates.
2.4
Ocean CO
sink
2.4.1
Historical period 1850–2021
The reported estimate of the global ocean anthropogenic CO
sink
OCEAN
is derived as the average of two estimates. The first estimate
is derived as the mean over an ensemble of 10 global ocean biogeochemistry
models (GOBMs, Tables 4 and A2). The second estimate is obtained as the
mean over an ensemble of seven observation-based data products (Tables 4 and
A3). An eighth product (Watson et al., 2020) is shown but is not
included in the ensemble average as it differs from the other products by
adjusting the flux to a cool, salty ocean surface skin (see Appendix C3.1
for a discussion of the Watson product). The GOBMs simulate both the natural
and anthropogenic CO
cycles in the ocean. They constrain the
anthropogenic air–sea CO
flux (the dominant component of
OCEAN
by the transport of carbon into the ocean interior, which is also the
controlling factor of present-day ocean carbon uptake in the real world.
They cover the full globe and all seasons and were recently evaluated
against surface ocean carbon observations, suggesting they are suitable to
estimate the annual ocean carbon sink (Hauck et al., 2020). The
data products are tightly linked to observations of
CO
(fugacity of
CO
, which equals
CO
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. Their asset is the assessment of
interannual and spatial variability (Hauck et al., 2020). We use two further
diagnostic ocean models to estimate
OCEAN
over the industrial era
(1781–1958).
The global
CO
-based flux estimates were adjusted to remove the
pre-industrial ocean source of CO
to the atmosphere of 0.65 GtC yr
−1
from river input to the ocean (Regnier et al., 2022) to satisfy
our definition of
OCEAN
(Hauck et al., 2020). The river flux
adjustment was distributed over the latitudinal bands using the regional
distribution of Aumont et al. (2001; north: 0.17 GtC yr
−1
; tropics:
0.16 GtC yr
−1
; south: 0.32 GtC yr
−1
), acknowledging that the
boundaries of Aumont et al. (2001; namely 20
S and 20
N) are not consistent with the boundaries otherwise used in the GCB
(30
S and 30
N). A recent study based on one ocean
biogeochemical model (Lacroix et al., 2020) suggests that more of the
riverine outgassing is located in the tropics than in the Southern Ocean,
and hence this regional distribution is associated with a major uncertainty.
Anthropogenic perturbations of river carbon and nutrient transport to the
ocean are not considered (see Sect. 2.7 and Appendix D3).
We derive
OCEAN
from GOBMs by using a simulation (sim A) with
historical forcing of climate and atmospheric CO
, accounting for model
biases and drift from a control simulation (sim B) with constant atmospheric
CO
and normal-year climate forcing. A third simulation (sim C) with
historical atmospheric CO
increase and normal-year climate forcing is
used to attribute the ocean sink to CO
(sim C minus sim B) and climate
(sim A minus sim C) effects. A fourth simulation (sim D; historical climate
forcing and constant atmospheric CO
) is used to compare the change in
anthropogenic carbon inventory in the interior ocean (sim A minus sim D) to
the observational estimate of Gruber et al. (2019) with the same flux
components (steady state and non-steady state anthropogenic carbon flux).
Data products are adjusted to represent the full ice-free ocean area by a
simple scaling approach when coverage is below 99 %. GOBMs and
data products fall within the observational constraints over the 1990s (2.2
0.7 GtC yr
−1
, Ciais et al., 2013) after applying adjustments.
OCEAN
is calculated as the average of the GOBM ensemble mean and
data product ensemble mean from 1990 onwards. Prior to 1990, it is
calculated as the GOBM ensemble mean plus half of the offset between GOBMs
and data product ensemble means over 1990–2001.
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 (GOBM bias in anthropogenic carbon accumulation,
previously reported uncertainties in
CO
-based data products; see
Appendix C3.3). We assess a medium confidence level to the annual ocean
CO
sink and its uncertainty because it is based on multiple lines of
evidence, it is consistent with ocean interior carbon estimates (Gruber et
al., 2019, see Sect. 3.5.5) and the interannual variability in the GOBMs,
and data-based estimates are largely consistent and can be explained by
climate variability. We refrain from assigning a high confidence because of
the systematic deviation between the GOBM and data product trends since
around 2002. More details on the
OCEAN
methodology can be found in
Appendix C3.
2.4.2
The 2022 projection
The ocean CO
sink forecast for the year 2022 is based on the annual
historical and estimated 2022 atmospheric CO
concentration
(Dlugokencky and Tans, 2022), the historical and estimated 2022 annual global
fossil fuel emissions from this year's carbon budget, and the spring (March,
April, May) Oceanic Niño Index (ONI) (NCEP, 2022). Using a non-linear
regression approach, i.e. a feed-forward neural network, atmospheric
CO
, ONI, and fossil fuel emissions are used as training data to
best match the annual ocean CO
sink (i.e. combined
OCEAN
estimate from GOBMs and data products) from 1959 through 2021 from this
year's carbon budget. Using this relationship, the 2022
OCEAN
can then
be estimated from the projected 2021 input data using the non-linear
relationship established during the network training. To avoid overfitting,
the neural network was trained with a variable number of hidden neurons
(varying between 2–5), and 20 % of the randomly selected training data were
withheld for independent internal testing. 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
training sequences 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
OCEAN
uncertainty (0.4 GtC yr
−1
) to estimate the overall uncertainty of the
2022 projection.
2.5
Land CO
sink
2.5.1
Historical period
The terrestrial land sink (
LAND
) is thought to be due to the combined
effects of fertilization by rising atmospheric CO
and
inputs on
plant growth, as well as the effects of climate change such as the
lengthening of the growing season in northern temperate and boreal areas.
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 flux (
LUC
), although system boundaries make it difficult to exactly
attribute CO
fluxes on land between
LAND
and
LUC
(Erb et al., 2013).
LAND
is estimated from the multi-model mean of 16 DGVMs (Table A1). As
described in Appendix C.4, DGVM simulations include all climate
variability and CO
effects over land. In addition to the carbon cycle
represented in all DGVMs, 11 models also account for the nitrogen cycle and
hence can include the effect of
inputs on
LAND
. The DGVM estimate
of
LAND
does not include the export of carbon to aquatic systems or
its historical perturbation, which is discussed in Appendix D3. See Appendix C4 for DGVM evaluation and uncertainty assessment for
LAND
using
the International Land Model Benchmarking system (ILAMB; Collier et al.,
2018). More details on the
LAND
methodology can be found in Appendix C4.
2.5.2
The 2022 projection
Like for the ocean forecast, the land CO
sink (
LAND
) forecast is
based on the annual historical and estimated 2022 atmospheric CO
concentration (Dlugokencky and Tans, 2021), historical and estimated 2022
annual global fossil fuel emissions from this year's carbon budget, and the
summer (June, July, August) ONI (NCEP, 2022). All training data are again
used to best match
LAND
from 1959 through 2021 from this year's carbon
budget using a feed-forward neural network. To avoid overfitting, the neural
network was trained with a variable number of hidden neurons (varying
between 2–15), larger than for
OCEAN
prediction due to the stronger
land carbon interannual variability. As done for
OCEAN
, a pre-training
selects the optimal number of hidden neurons based on 20 % withheld input
data, and in a second step, an ensemble of 10 forecasts is produced to
provide the mean forecast plus uncertainty. This uncertainty is then
combined with the
LAND
uncertainty for 2021 (0.9 GtC yr
−1
) to
estimate the overall uncertainty of the 2022 projection.
2.6
The atmospheric perspective
The world-wide network of in situ atmospheric measurements and satellite-derived atmospheric CO
column (xCO
) observations put a strong
constraint on changes in the atmospheric abundance of CO
. This is true
globally (hence our large confidence in
ATM
) but also regionally in
regions with sufficient observational density found mostly in the
extratropics. This allows atmospheric inversion methods to constrain the
magnitude and location of the combined total surface CO
fluxes from
all sources, including fossil and land-use change emissions and land and
ocean CO
fluxes. The inversions assume
FOS
to be well known, and
they solve for the spatial and temporal distribution of land and ocean
fluxes from the residual gradients of CO
between stations that are not
explained by fossil fuel emissions. By design, such systems thus close the
carbon balance (
IM
=0
) and thus provide an additional perspective
on the independent estimates of the ocean and land fluxes.
This year's release includes nine inversion systems that are described in
Table A4. Each system is rooted in Bayesian inversion principles but uses
different methodologies. These differences concern the selection of
atmospheric CO
data or xCO
, 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 A4 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). Four inversion systems (CAMS-FT21r2, CMS-flux, GONGGA, THU) used
satellite xCO
retrievals from GOSAT and/or OCO-2, scaled to the WMO
2019 calibration scale. One inversion this year (CMS-Flux) used these xCO
data sets in addition to the in situ observational CO
mole fraction
records.
The original products delivered by the inverse modellers were modified to
facilitate the comparison to the other elements of the budget, specifically
on two accounts: (1) global total fossil fuel emissions, including cement
carbonation CO
uptake, and (2) riverine CO
transport. Details
are given below. We note that with these adjustments the inverse results no
longer represent the net atmosphere–surface exchange over land and ocean areas
as sensed by atmospheric observations. Instead, for land, they become the
net uptake of CO
by vegetation and soils that is not exported by
fluvial systems, similar to the DGVM estimates. For oceans, they become the
net uptake of anthropogenic CO
, similar to the GOBM estimates.
The inversion systems prescribe global fossil fuel emissions based on the
GCP's Gridded Fossil Emissions Dataset versions 2022.1 or 2022.2
(GCP-GridFED; Jones et al., 2022), which are updates to GCP-GridFEDv2021
presented by Jones et al. (2021). GCP-GridFEDv2022 scales gridded estimates
of CO
emissions from EDGARv4.3.2 (Janssens-Maenhout et al., 2019)
within national territories to match national emissions estimates provided
by the GCB for the years 1959–2021, which were compiled following the
methodology described in Sect. 2.1. Small differences between the systems
due to, for instance, regridding to the transport model resolution or use of
different GridFED versions with different cement carbonation sinks (which
were only present starting with GridFEDv2022.1) are adjusted in the
latitudinal partitioning we present to ensure agreement with the estimate
of
FOS
in this budget. We also note that the ocean fluxes used as
prior by six out of the nine inversions are part of the suite of the ocean process
models or
CO
data products listed in Sect. 2.4. Although these fluxes are
further adjusted by the atmospheric inversions, it makes the inversion
estimates of the ocean fluxes not completely independent of
OCEAN
assessed here.
To facilitate comparisons to the independent
OCEAN
and
LAND
, we
used the same corrections for transport and outgassing of carbon transported
from land to ocean, as has been done for the observation-based estimates of
OCEAN
(see Appendix C3).
The atmospheric inversions are evaluated using vertical profiles of
atmospheric CO
concentrations (Fig. B4). More than 30 aircraft
programmes over the globe, either regular programmes or repeated surveys over at
least 9 months (except for Southern Hemisphere, SH, programmes), have been used to assess system
performance (with space–time observational coverage sparse in the SH and
tropics, and denser in Northern Hemisphere, NH, mid-latitudes; Table A6). The nine systems are
compared to the independent aircraft CO
measurements between 2 and 7 km above sea level between 2001 and 2021. Results are shown in Fig. B4 and
discussed in Appendix C5.2
With a relatively small ensemble (
=9
) of systems that moreover share some
a priori fluxes used with one another, or with the process-based models, it
is difficult to justify using their mean and standard deviation as a metric
for uncertainty across the ensemble. We therefore report their full range
(min–max) without their mean. More details on the atmospheric inversions
methodology can be found in Appendix C5.
2.7
Processes not included in the global carbon budget
The contribution of anthropogenic CO and CH
to the global carbon
budget is not fully accounted for in Eq. (1) and is described in Appendix D1. The contributions to CO
emissions of decomposition of carbonates
not accounted for is described in Appendix D2. The contribution of
anthropogenic changes in river fluxes is conceptually included in Eq. (1) in
OCEAN
and in
LAND
, but it is not represented in the process
models used to quantify these fluxes. This effect is discussed in Appendix D3. Similarly, the loss of additional sink capacity from reduced forest
cover is missing in the combination of approaches used here to estimate both
land fluxes (
LUC
and
LAND
) and its potential effect is discussed
and quantified in Appendix D4.
Results
For each component of the global carbon budget, we present results for three
different time periods: the full historical period, from 1850 to 2021, the
6 decades in which we have atmospheric concentration records from Mauna
Loa (1960–2021); a specific focus on the last year (2021); and the projection
for the current year (2022). Subsequently, we assess the combined
constraints from the budget components (often referred to as a bottom-up
budget) against the top-down constraints from inverse modelling of
atmospheric observations. We do this for the global balance of the last
decade, as well as for a regional breakdown of land and ocean sinks by broad
latitude bands.
3.1
Fossil CO
emissions
3.1.1
Historical period 1850–2021
Cumulative fossil CO
emissions for 1850–2021 were 465
25 GtC,
including the cement carbonation sink (Fig. 3, Table 8, all cumulative
numbers are rounded to the nearest 5 GtC).
In this period, 46 % of fossil CO
emissions came from coal, 35 %
from oil, 15 % from natural gas, 3 % from decomposition of carbonates,
and 1 % from flaring.
In 1850, the UK contributed 62 % of global fossil CO
emissions. In
1891 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
1850–2021, US cumulative emissions amounted to 115 GtC (24 % of world
total), the EU's to 80 GtC (17 %), and China's to 70 GtC (14 %).
In addition to the estimates of fossil CO
emissions that we provide
here (see Sect. 2), there are three additional global data sets with long
time series that include all sources of fossil CO
emissions: CDIAC-FF
(Gilfillan and Marland, 2021), CEDS version v_2021_04_21 (Hoesly et al., 2018; O'Rourke et
al., 2021), and PRIMAP-hist version 2.3.1 (Gütschow et al., 2016, 2021),
although these data sets are not entirely independent of each other
(Andrew, 2020a). CDIAC-FF has the lowest cumulative emissions over 1750–2018
at 437 GtC, GCP has 443 GtC, CEDS 445 GtC, PRIMAP-hist TP 453 GtC, and
PRIMAP-hist CR 455 GtC. CDIAC-FF excludes emissions from lime production,
while neither CDIAC-FF nor GCP explicitly include emissions from
international bunker fuels prior to 1950. CEDS has higher emissions from
international shipping in recent years, while PRIMAP-hist has higher
fugitive emissions than the other data sets. However, in general these four
data sets are in relative agreement as to total historical global emissions
of fossil CO
Figure 3
Combined components of the global carbon budget illustrated in
Fig. 2 as a function of time for fossil CO
emissions
FOS
, including a small sink from cement carbonation; grey)
and emissions from land-use change (
LUC
; brown), as well as
their partitioning among the atmosphere (
ATM
; cyan), ocean
OCEAN
; blue), and land (
LAND
; green). Panel
(a)
shows annual estimates of each flux, and panel
(b)
shows the cumulative flux
(the sum of all prior annual fluxes) since the year 1850. The partitioning
is based on nearly independent estimates from observations (for
ATM
) and from process model ensembles constrained by data
(for
OCEAN
and
LAND
) and does not exactly add
up to the sum of the emissions, resulting in a budget imbalance
(BI
), which is represented by the difference between the
bottom red line (mirroring total emissions) and the sum of carbon fluxes in
the ocean, land, and atmosphere reservoirs. All data are in GtC yr
−1
(a)
and GtC
(b)
. The
FOS
estimate is based on a mosaic of different data sets, and has an uncertainty
of
5 % (
). The
LUC
estimate is
from three bookkeeping models (Table 4) with uncertainty of
0.7 GtC yr
−1
. The G
ATM
estimates prior to 1959 are from
Joos and Spahni (2008) with uncertainties equivalent to about
0.1–0.15 GtC yr
−1
and from Dlugokencky and Tans (2022) since
1959 with uncertainties of about
-0.07 GtC yr
−1
during
1959–1979 and
0.02 GtC yr
−1
since 1980. The
OCEAN
estimate is the average from Khatiwala et al. (2013)
and DeVries (2014) with uncertainty of about
30 % prior to 1959,
and the average of an ensemble of models and an ensemble of
CO
data products (Table 4) with uncertainties of about
0.4 GtC yr
−1
since 1959. The
LAND
estimate is the average of an ensemble of models (Table 4) with
uncertainties of about
1 GtC yr
−1
. See the text for
more details of each component and their uncertainties.
3.1.2
Recent period 1960–2021
Global fossil CO
emissions,
FOS
(including the cement
carbonation sink), have increased every decade from an average of 3.0
0.2 GtC yr
−1
for the decade of the 1960s to an average of 9.6
0.5 GtC yr
−1
during 2012–2021 (Table 6, Figs. 2 and 5).
The growth rate in these emissions decreased between the 1960s and the
1990s, from 4.3 % per year in the 1960s (1960–1969), 3.2 % per year in
the 1970s (1970–1979), 1.6 % per year in the 1980s (1980–1989), and
0.9 % per year in the 1990s (1990–1999). After this period, the growth
rate began increasing again in the 2000s at an average growth rate of
3.0 % per year, decreasing to 0.5 % per year for the last decade
(2012–2021). China's emissions increased by
1.5 % per year on average
over the last 10 years, dominating the global trend, and India's emissions
increased by
3.8 % per year, while emissions decreased in EU27 by
1.8 % per year and in the USA by
1.1 % per year. Figure 6
illustrates the spatial distribution of fossil fuel emissions for the
2012–2021 period.
FOS
includes the uptake of CO
by cement via carbonation, which
has increased with increasing stocks of cement products from an average of
20 MtC yr
−1
(0.02 GtC yr
−1
) in the 1960s to an average of 200 MtC yr
−1
(0.2 GtC yr
−1
) during 2012–2021 (Fig. 5).
Figure 4
Components of the global carbon budget and their uncertainties as
a function of time, presented individually for
(a)
fossil CO
and cement carbonation emissions (
FOS
),
(b)
growth rate in
atmospheric CO
concentration (
ATM
),
(c)
emissions from land-use change (
LUC
),
(d)
the land
CO
sink (
LAND
),
(e)
the ocean
CO
sink (
OCEAN
), and
(f)
the budget imbalance that
is not accounted for by the other terms. Positive values of
LAND
and
OCEAN
represent a flux from the
atmosphere to land or the ocean. All data are in GtC yr
−1
with
the uncertainty bounds representing
1 standard deviation in shaded
colour. Data sources are as in Fig. 3. The red dots indicate our
projections for the year 2022, and the red error bars the uncertainty in the
projections (see Sect. 2).
Figure 5
Fossil CO
emissions for
(a)
the globe, including an
uncertainty of
5 % (grey shading) and a projection through the
year 2022 (red dot and uncertainty range);
(b)
territorial (solid lines) and
consumption (dashed lines) emissions for the top three country emitters
(USA, China, India) and for the European Union (EU27);
(c)
global emissions
by fuel type, including coal, oil, gas, cement, and cement minus cement
carbonation (dashed); and
(d)
per capita emissions for the world and for the
large emitters, as in panel
(b)
. Territorial emissions are primarily from a
draft update of Gilfillan and Marland (2021), with the exception of the national data for
Annex I countries for 1990–2020, which are reported to the UNFCCC as
detailed in the text, as well as some improvements in individual countries,
and are extrapolated forward to 2021 using BP Energy Statistics.
Consumption-based emissions are updated from Peters et al. (2011b). See
Sect. 2.1 and Appendix C1 for details about the calculations and data
sources.
3.1.3
Final year 2021
Global fossil CO
emissions were 5.1 % higher in 2021 than in 2020
because of the global rebound from the worst of the COVID-19 pandemic, with
an increase of 0.5 GtC to reach 9.9
0.5 GtC (including the cement
carbonation sink) in 2021 (Fig. 5), distributed among coal (41 %), oil
(32 %), natural gas (22 %), cement (5 %), and others (1 %). Compared
to the previous year, 2021 emissions from coal, oil, and gas increased by
5.7 %, 5.8 %, and 4.8 %, respectively, while emissions from cement
increased by 2.1 %. All growth rates presented are adjusted for the leap
year unless stated otherwise.
In 2021, the largest absolute contributions to global fossil CO
emissions were from China (31 %), the USA (14 %), the EU27 (8 %), and
India (7 %). These four regions account for 59 % of global CO
emissions, while the rest of the world contributed 41 %, including
international aviation and marine bunker fuels (2.8 % of the total).
Growth rates for these countries from 2020 to 2021 were 3.5 % (China),
6.2 % (USA), 6.8 % (EU27), and 11.1 % (India), with
4.5 % for the
rest of the world. The per capita fossil CO
emissions in 2021 were 1.3 tC per person per year for the globe and were 4.0 (USA), 2.2 (China),
1.7 (EU27), and 0.5 (India) tC per person per year for the four highest-emitting countries (Fig. 5).
The post-COVID-19 rebound in emissions of 5.1 % in 2021 is close to the
projected increase of 4.8 % published in Friedlingstein et al. (2022a)
(Table 7). Of the regions, the projection for the “rest of world” region was
least accurate (off by
1.3 %), largely because of poorly projected
emissions from international transport (bunker fuels), which were subject to
very large changes during this period.
3.1.4
Year 2022 projection
Globally, we estimate that global fossil CO
emissions (including
cement carbonation) will grow by 1.0 % in 2022 (0.1 % to 1.9 %) to
10.0 GtC (36.6 GtCO
), exceeding their 2019 emission levels of 9.9 GtC
(36.3 GtCO
). Global increase in 2022 emissions per fuel types are
projected to be
1 % (range 0.2 % to 1.8 %) for coal,
2.2 %
(range 1.1 % to 3.3 %) for oil,
0.2 % (range
1.1 % to 0.7 %)
for natural gas, and
1.6 % (range
3.7 % to
0.5 %) for cement.
For China, projected fossil emissions in 2022 are expected to decline by
0.9 % (range
2.3 % to
0.4 %) compared with 2021 emissions,
bringing 2022 emissions for China to around 3.1 GtC yr
−1
(11.4 GtCO
yr
−1
). Changes in fuel-specific projections for China are
0.1 % for
coal,
2.8 % for oil,
1.1 % for natural gas, and
7.0 % for cement.
For the USA, the Energy Information Administration (EIA) emissions
projection for 2022 combined with cement clinker data from USGS gives an
increase of 1.5 % (range
1 % to
4 %) compared to 2021,
bringing 2022 USA emissions to around 1.4 GtC yr
−1
(5.1 GtCO
yr
−1
). This is based on separate projections for coal of
4.6 %, oil
of
2 %, natural gas of
4.7 %, and cement of
1.2 %.
For the European Union, our projection for 2022 is for a decline of 0.8 %
(range
2.8 % to
1.2 %) over 2021, with 2022 emissions around 0.8 GtC yr
−1
(2.8 GtCO
yr
−1
). This is based on separate projections
for coal of
6.7 %, oil of
0.9 %, and natural gas of
10.0 %, while cement remains
unchanged.
For India, our projection for 2022 is an increase of 6 % (range of
3.9 % to 8 %) over 2021, with 2022 emissions around 0.8 GtC yr
−1
(2.9 GtCO
yr
−1
). This is based on separate projections for coal
of
5.0 %, oil of
10.0 %, natural gas of
4.0 %, and cement of
10.0 %.
For the rest of the world, the expected growth rate for 2022 is 1.7 %
(range 0.1 % to 3.3 %). The fuel-specific projected 2022 growth rates
for the rest of the world are:
1.6 % for coal,
3.1 % for
oil,
0.1 % for natural gas,
3 % for cement.
3.2
Emissions from land-use changes
3.2.1
Historical period 1850–2021
Cumulative CO
emissions from land-use changes (
LUC
) for
1850–2021 were 205
60 GtC (Table 8; Fig. 3; Fig. 14). The
cumulative emissions from
LUC
show a large spread among individual
estimates of 140 GtC (updated H&N2017), 280 GtC (BLUE), and 190 GtC
(OSCAR) for the three bookkeeping models and a similar wide estimate of 185
60 GtC for the DGVMs (all cumulative numbers are rounded to the
nearest 5 GtC). These estimates are broadly consistent with indirect
constraints from vegetation biomass observations, giving a cumulative source
of 155
50 GtC over the 1901–2012 period (Li et al., 2017). However,
given the large spread, a best estimate is difficult to ascertain.
3.2.2
Recent period 1960–2021
In contrast to growing fossil emissions, CO
emissions from land use,
land-use change, and forestry have remained relatively constant over the
1960–1999 period but show a slight decrease of about 0.1 GtC per decade
since the 1990s, reaching 1.2
0.7 GtC yr
−1
for the 2012–2021
period (Table 6) but with large spread across estimates (Table 5, Fig. 7). Different from the bookkeeping average, the DGVM model average grows
slightly larger over the 1970–2021 period and shows no sign of decreasing
emissions in the recent decades (Table 5, Fig. 7). This is, however,
expected as DGVM-based estimates include the loss of additional sink
capacity, which grows with time, while the bookkeeping estimates do not
(Appendix D4).
Table 5
Comparison of results from the bookkeeping method and
budget residuals with results from the DGVMs and inverse estimates for
different periods, the last decade, and the last year available. All values
are in GtC yr
−1
. See Fig. 7 for an explanation of the bookkeeping
component fluxes. The DGVM uncertainties represent
of the decadal or annual (for 2021) estimates from the individual
DGVMs; for the inverse systems the range of available results is given. All
values are rounded to the nearest 0.1 GtC and therefore columns do not
necessarily add to zero.
Estimates are adjusted for the pre-industrial influence of river fluxes and the cement carbonation sink and are also adjusted to common
FOS
(Sect. 2.6). The ranges given include varying numbers (in parentheses) of inversions in each decade (Table A4).
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Figure 6
The 2012–2021 decadal mean components of the global carbon budget,
presented for
(a)
fossil CO
emissions (
FOS
),
(b)
land-use change emissions (
LUC
),
(c)
the ocean
CO
sink (
OCEAN
), and
(d)
the land
CO
sink (
LAND
). Positive values for
FOS
and
LUC
represent a flux to the
atmosphere, whereas positive values of
OCEAN
and
LAND
represent a flux from the atmosphere to the ocean or the
land. In all panels, yellow and red (green and blue) colours represent a flux from
(into) the land and ocean to (from) the atmosphere. All units are in kgC m
−2
yr
−1
. Note the different scales in each
panel.
FOS
data shown is from GCP-GridFEDv2022.2.
LUC
data shown are only from BLUE as the updated H&N2017
and OSCAR do not resolve gridded fluxes.
OCEAN
data shown are
the average of GOBMs and data product means using GOBM simulation A with no
adjustment for bias or drift applied to the gridded fields (see Sect. 2.4).
LAND
data shown are the average of DGVMs for simulation
S2 (see Sect. 2.5).
Figure 7
Net CO
exchanges between the atmosphere and the
terrestrial biosphere related to land-use change.
(a)
Net CO
emissions from land-use change (
LUC
) with estimates from the
three bookkeeping models (yellow lines) and the budget estimate (black with
uncertainty), which is the average of the three
bookkeeping models. Estimates from individual DGVMs (narrow green lines) and
the DGVM ensemble mean (thick green line) are also shown.
(b)
Net
CO
emissions from land-use change from the four countries
with largest cumulative emissions since 1959. Values shown are the average
of the three bookkeeping models, with shaded regions as
uncertainty.
(c)
CO
gross sinks (negative, from regrowth
after agricultural abandonment and wood harvesting) and gross sources
(positive, from decaying material left dead on site, products after clearing
of natural vegetation for agricultural purposes, wood harvesting, and, for
BLUE, degradation from primary to secondary land through usage of natural
vegetation as rangeland and from emissions from peat drainage and peat
burning). Values are shown for the three bookkeeping models (yellow lines)
and for their average (black with
uncertainty). The sum
of the gross sinks and sources is
LUC
shown in panel
(a)
(d)
Sources and sinks aggregated into four components that contribute to the net
fluxes of CO
, including (i) gross sources from
deforestation; (ii) afforestation, reafforestation, and wood harvest (i.e. the net flux on
forest lands comprising slash and product decay following wood harvest and
sinks due to regrowth after wood harvest or after abandonment, including
reforestation and abandonment as parts of shifting cultivation cycles); (iii) emissions from organic soils (peat drainage and peat
fire); and (iv) sources and sinks related to other land-use transitions. The
scale of the fluxes shown is smaller than in panel
(c)
because the
substantial gross sources and sinks from wood harvesting are accounted for
as net flux under (ii). The sum of the component fluxes is
LUC
shown in panel
(a)
LUC
is a net term of various gross fluxes, which comprise emissions
and removals. Gross emissions on average over the 1850–2021 period are 2
(BLUE, OSCAR) to 3 (updated H&N2017) times larger than the net
LUC
emissions. Gross emissions show a moderate increase from an
average of 3.2
0.9 GtC yr
−1
for the decade of the 1960s to an
average of 3.8
0.7 GtC yr
−1
during 2012–2021 (Fig. 7).
Increases in gross removals, from 1.8
0.4 GtC yr
−1
for the
1960s to 2.6
0.4 GtC yr
−1
for 2012–2021, were slightly larger
than the increase in gross emissions. Since 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 land-use dynamics, such as a temporary decrease in deforestation,
influences gross emissions dynamics more than gross removal dynamics. It is
these relative changes to each other that explain the small decrease in net
LUC
emissions over the last 2 decades and the last few years. Gross
fluxes often differ more across the three bookkeeping estimates than net
fluxes, which is expected due to different process representation; in
particular, treatment of shifting cultivation, which increases both gross
emissions and removals, differs across models.
There is a smaller decrease in net CO
emissions from land-use change
in the last few years (Fig. 7) than in last year's estimate
(Friedlingstein et al., 2021), which places our updated estimates between
last year's estimate and the estimate from the GCB2020 (Friedlingstein et
al., 2020). This change is principally attributable to changes in
LUC
estimates from BLUE and OSCAR, which relate to improvements in the
underlying land-use forcing (see Appendix C2.2 for details). These changes
address issues identified with last year's land-use forcing (see
Friedlingstein et al., 2022a) and remove or attenuate several emission peaks in
Brazil and the Democratic Republic of the Congo and lead to higher net
emissions in Brazil in the last decades compared to last year's global
carbon budget (the emissions averaged over the three bookkeeping models for
Brazil for the 2011–2020 period were 168 MtC yr
−1
in GCB2021 as
compared to 289 MtC yr
−1
in GCB2022). A remaining caveat is that global
land-use change data for model input does not capture forest degradation,
which often occurs on small scale or without forest cover changes easily
detectable from remote sensing and poses a growing threat to forest area and
carbon stocks that may surpass deforestation effects (e.g. Matricardi et
al., 2020; Qin et al., 2021). While independent pan-tropical or global
estimates of vegetation cover dynamics or carbon stock changes based on
satellite remote sensing have become available in recent years, a direct
comparison to our estimates is not possible, most importantly because
satellite-based estimates usually do not distinguish between anthropogenic
drivers and natural forest cover losses (e.g. from drought or natural
wildfires) (Pongratz et al., 2021).
We additionally separate the net
LUC
into four component fluxes to
gain further insight into the drivers of emissions: deforestation,
afforestation, reafforestation, and wood harvest (i.e. all fluxes on forest lands);
emissions from organic soils (i.e. peat drainage and peat fires); and fluxes
associated with all other transitions (Fig. 7; Sect. C2.1). On average
over the 2012–2021 period and over the three bookkeeping estimates, fluxes
from deforestation amount to 1.8
0.4 GtC yr
−1
, and from
afforestation, reafforestation, and wood harvest fluxes amount to
0.9
0.3 GtC yr
−1
(Table 5). Emissions from organic soils (0.2
0.1 GtC yr
−1
) and the net
flux from other transitions (0.2
0.1 GtC yr
−1
) are
substantially less important globally. Deforestation is thus the main driver
of global gross sources. The relatively small deforestation flux (1.8
0.4 GtC yr
−1
) in comparison to the gross emission estimate
above (3.8
0.7 GtC yr
−1
) is explained by the fact that
emissions associated with wood harvesting do not count as deforestation as
they do not change the land cover. This split into component fluxes
clarifies the potential for emission reduction and carbon dioxide removal:
the emissions from deforestation could be halted (largely) without
compromising carbon uptake by forests and would contribute to emissions
reduction. By contrast, reducing wood harvesting would have limited
potential to reduce emissions as it would be associated with less forest
regrowth; sinks and sources cannot be decoupled here. Carbon dioxide removal
in forests could instead be increased by afforestation and reafforestation.
Overall, the highest land-use emissions occur in the tropical regions of all
three continents. The top three emitters (both cumulatively 1959–2021 and on
average over 2012–2021) are Brazil (in particular the Amazon Arc of
Deforestation), Indonesia, and the Democratic Republic of the Congo, with
these three countries contributing 0.7 GtC yr
−1
or 58 % of the global
total land-use emissions (average over 2012–2021) (Fig. 6b). This is
related to massive expansion of cropland, particularly in the last few
decades in Latin America, Southeast Asia, and sub-Saharan Africa
(Hong et al., 2021), a substantial part of which has been for export of agricultural
products (Pendrill et al., 2019). Emission intensity is high in many
tropical countries, particularly in 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 partly
due to expanding forest area as a consequence of the forest transition
in the 19th and 20th centuries and the subsequent regrowth of forest, particularly in Europe
(Fig. 6b) (Mather, 2001; McGrath et al., 2015).
While the mentioned patterns are supported by independent literature and
robust, we acknowledge that model spread is substantially larger at regional rather
than global levels, as has been shown for bookkeeping models (Bastos et al.,
2021) and DGVMs (Obermeier et al., 2021). Assessments for individual
regions will be performed as part of REgional Carbon Cycle Assessment and
Processes (RECCAP2; Ciais 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; and for eight selected countries and regions in comparison to inventory data
by Schwingshackl et al., 2022).
National GHG inventory data (NGHGI) under the LULUCF sector or data
submitted by countries to FAOSTAT differ from the global models' definition
of
LUC
that we adopt here in that the natural
fluxes (
LAND
) are counted towards
LUC
when they occur on managed
land in the NGHGI reporting (Grassi et al., 2018). In order to compare our results to the NGHGI
approach, we perform a re-mapping of our
LUC
estimates by adding
LAND
in managed forest from the DGVM simulations (following Grassi et
al., 2021) to the bookkeeping
LUC
estimate (see Appendix C2.3). For
the 2012–2021 period, we estimate that 1.8 GtC yr
−1
of
LAND
occurred in managed forests and is then reallocated to
LUC
here, as has been
done in the NGHGI method. By doing so, our mean estimate of
LUC
is
reduced from a source of 1.2 GtC to a sink of 0.6 GtC, which is very similar to the
NGHGI estimate of a 0.5 GtC sink (Table 9). The re-mapping approach has been
shown to also be generally applicable for country-level data (Grassi et al.,
2022b; Schwingshackl et al., 2022). Country-level analysis suggests, e.g.
that the bookkeeping mean estimates higher deforestation emissions than the
national report in Indonesia but estimates less CO
removal by
afforestation than the national report in China. The fraction of the natural
CO
sinks that the NGHGI estimates include differs substantially across
countries, related to varying proportions of managed vs. all forest areas
(Schwingshackl et al., 2022). Comparing
LUC
and NGHGI on the basis of
the four component fluxes (Grassi et al., 2022b), we find that NGHGI
deforestation emissions are reported to be smaller than the bookkeeping
estimate (1.1 GtC yr
−1
averaged over 2012–2021). A reason for this lies
in the fact that country reports do not (fully) capture the carbon flux
consequences of shifting cultivation. Conversely, carbon uptake in forests
(afforestation, reafforestation, and forestry) is substantially larger than the bookkeeping
estimate (1.75 GtC yr
−1
averaged over 2012–2021), owing to the
inclusion of natural CO
fluxes on managed land in the NGHGI. Emissions
from organic soils and the net flux from other transitions are similar to
the estimates based on the bookkeeping approach and the external peat
drainage and burning data sets. Though estimates between NGHGI, FAOSTAT,
individual process-based models, and the mapped budget still differ
in value and need further analysis, the approach taken here provides a
possibility to relate the global models' and NGHGI approach to each other
routinely and thus link the anthropogenic carbon budget estimates of land
CO
fluxes directly to the Global Stocktake as part of UNFCCC Paris
Agreement.
3.2.3
Final year 2021
The global CO
emissions from land-use change are estimated as 1.1
0.7 GtC in 2021, similar to the 2020 estimate. However, confidence
in the annual change remains low.
Land-use change and related emissions may have been affected by the COVID-19
pandemic (e.g. Poulter et al., 2021). During the period of the pandemic,
environmental protection policies and their implementation may have been
weakened in Brazil (Vale et al., 2021). In other countries monitoring
capacities and legal enforcement of measures to reduce tropical
deforestation have also been reduced due to budget restrictions of environmental
agencies or the impairments of ground-based monitoring intended to prevent land grabs
and tenure conflicts (Brancalion et al., 2020; Amador-Jiménez et al.,
2020). Effects of the pandemic on trends in fire activity or forest cover
changes are hard to separate from those of general political developments
and environmental changes, and the long-term consequences of disruptions in
agricultural and forestry economic activities (e.g. Gruère and Brooks,
2021; Golar et al., 2020; Beckman and Countryman, 2021) remain to be seen.
Overall, there is limited evidence so far that COVID-19 was a key driver of
changes in LULUCF emissions at a global scale. Impacts vary across countries
and deforestation-curbing and enhancing factors may partly compensate each
other (Wunder et al., 2021).
3.2.4
Year 2022 projection
In Indonesia, peat fire emissions are very low, potentially related to a
relatively wet dry season (GFED4.1s, van der Werf et al., 2017). In South
America, the trajectory of tropical deforestation and degradation fires
resembles the long-term average; global emissions from tropical deforestation and degradation fires were estimated to be 206 TgC by 14 October 2020. (GFED4.1s, van der Werf et al., 2017). Our preliminary estimate of
LUC
for 2022 is substantially lower than the 2012–2021 average, which
saw years of anomalously dry conditions in Indonesia and high deforestation
fires in South America (Friedlingstein et al., 2022a). Based on the fire
emissions until 14 October, we expect
LUC
emissions of around 1.1 GtC
in 2022. Note that although our extrapolation is based on tropical
deforestation and degradation fires, degradation attributable to selective
logging, edge effects, or fragmentation will not be captured. Further,
deforestation and fires in deforestation zones may become more disconnected,
partly due 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. More recent
years, however, saw an uptick in the Amazon again (Tyukavina et al., 2022
with update), and more work is needed to understand fire–deforestation
relations.
The fires in Mediterranean Europe in summer 2022 and in the US in spring
2022, though above average for those regions, only contribute a small amount
to global emissions. However, they were unrelated to land-use change and are
thus not attributed to
LUC
but would be part of the natural land
sink.
Land-use dynamics may be influenced by the disruption to the global food
market associated with the war in Ukraine, but scientific evidence so far is
very limited. High food prices, which preceded (but were exacerbated by) the
war (Torero and FAO, 2022), are generally linked to higher deforestation (Angelsen
and Kaimowitz, 1999), while high prices on agricultural inputs such as
fertilizers and fuel, which are also under pressure from embargoes, may
impair yields.
3.3
Total anthropogenic emissions
Cumulative anthropogenic CO
emissions for 1850–2021 totalled 670
65 GtC (2455
240 GtCO
), of which 70 % (470 GtC)
occurred since 1960 and 33 % (220 GtC) since 2000 (Tables 6 and 8). Total
anthropogenic emissions more than doubled over the last 60 years, from 4.5
0.7 GtC yr
−1
for the decade of the 1960s to an average of 10.8
0.8 GtC yr
−1
during 2012–2021, and reaching 10.9
0.9 GtC (40.0
3.3 GtCO
) in 2021. For 2022, we project global total
anthropogenic CO
emissions from fossil and land-use changes to be also
around 11.1 GtC (40.5 GtCO
). All values here include the cement
carbonation sink (currently about 0.2 GtC yr
−1
).
During the historical period 1850–2021, 30 % of historical emissions were
from land-use change and 70 % from fossil emissions. However, fossil
emissions have grown significantly since 1960 while land-use changes have
not, and consequently the contributions of land-use change to total
anthropogenic emissions were smaller during recent periods (18 % during
the period 1960–2021 and 11 % during 2012–2021).
Table 6
Decadal mean in the five components of the anthropogenic
CO
budget for different periods and the last year available. All values are in
GtC yr
−1
, and uncertainties are reported as
Fossil CO
emissions include cement carbonation. The budget imbalance (
IM
) is also shown, which
provides a measure of the discrepancies among the nearly independent
estimates. A positive imbalance means the emissions are overestimated and/or
the sinks are too small. All values are rounded to the nearest 0.1 GtC, and
therefore columns do not necessarily add to zero.
Fossil emissions excluding the cement carbonation sink amount to 3.1
0.2, 4.7
0.2, 5.5
0.3, 6.4
0.3, 7.9
0.4, and 9.8
0.5 GtC yr
−1
for the decades of the 1960s to 2010s, respectively, 10.1
0.5 GtC yr
−1
for 2021, and 10.2
0.5 GtC yr
−1
for 2022.
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Table 7
Comparison of the projection with realized fossil CO
emissions (
FOS
). The “actual” values are the first estimate available using actual data, and the “projected” values refer to estimates made before the end of the year for each publication. Projections based on a different method from that described here during 2008–2014 are available in Le Quéré et al. (2016). All values are adjusted for leap years.
Jackson et al. (2016) and Le Quéré et al. (2015a).
Le Quéré et al. (2016).
Le Quéré et al. (2018a).
Le Quéré et al. (2018b).
Friedlingstein et al. (2019),
Friedlingstein et al. (2020),
Friedlingstein et al. (2022a),
This study.
EU28 up to 2019 and EU27 from 2020.
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Table 8
Cumulative CO
for different time
periods in gigatonnes of carbon (GtC). Fossil CO
emissions include cement carbonation. The budget imbalance
IM
) provides a measure of the discrepancies among
the nearly independent estimates. All values are rounded to the nearest 5 GtC, and therefore columns do not necessarily add to zero. Uncertainties are
reported as follows:
FOS
is 5 % of cumulative
emissions,
LUC
prior to 1959 is
spread from the
DGVMs,
LUC
post-1959 is
0.7
times the number of years (where
0.7 GtC yr
−1
is the uncertainty on the annual
LUC
flux estimate),
ATM
uncertainty is held constant at 5 GtC for all
time periods,
OCEAN
uncertainty is 20 % of the
cumulative sink (20 % relates to the annual uncertainty of 0.4 GtC yr
−1
which is
20 % of the current ocean sink), and
LAND
is the
spread from the DGVM estimates.
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3.4
Atmospheric CO
3.4.1
Historical period 1850–2021
Atmospheric CO
concentration was approximately 278 ppm in 1750, 300 ppm in the 1910s, 350 ppm in the late 1980s, and
414.71
0.1 ppm in 2021 (Dlugokencky and Tans, 2022); Fig. 1). The mass of carbon in the atmosphere increased by 48 % from 590 GtC in
1750 to 879 GtC in 2021. Current CO
concentrations in the atmosphere
are unprecedented in the last 2 million years, and the current rate of
atmospheric CO
increase is at least 10 times faster than at any other
time during the last 800 000 years (Canadell et al., 2021).
3.4.2
Recent period 1960–2021
The growth rate in atmospheric CO
level increased from 1.7
0.07 GtC yr
−1
in the 1960s to 5.2
0.02 GtC yr
−1
during
2012–2022 with important decadal variations (Table 6, Figs. 3 and
4). During the last decade (2012–2021), the growth rate in atmospheric
CO
concentration continued to increase, albeit with large interannual
variability (Fig. 4).
The airborne fraction (AF), defined as the ratio of atmospheric CO
growth rate to total anthropogenic emissions, i.e.
(2)
AF
ATM
FOS
LUC
provides a diagnostic of the relative strength of the land and ocean carbon
sinks in removing part of the anthropogenic CO
perturbation. The
evolution of AF over the last 60 years shows no significant trend, remaining
at around 44 %, albeit showing a large interannual and decadal variability
driven by the year-to-year variability in
ATM
(Fig. 9). The observed
stability of the airborne fraction over the 1960–2020 period indicates that
the ocean and land CO
sinks have on average been removing about 55 %
of the anthropogenic emissions (see Sect. 3.5 and 3.6).
Figure 8
(a)
The land CO
sink (
LAND
estimated by individual DGVM estimates (green), as well as the budget
estimate (black with
uncertainty), which is the average
of all DGVMs.
(b)
Total atmosphere–land CO
fluxes
LAND
LUC
). The budget estimate of the
total land flux (black with
uncertainty) combines the
DGVM estimate of
LAND
from panel
(a)
with the bookkeeping
estimate of
LUC
from Fig. 7a. Uncertainties are similarly
propagated in quadrature from the budget estimates of
LAND
from panel
(a)
and
LUC
from Fig. 7a. DGVMs also provide
estimates of
LUC
(see Fig. 7a), which can be combined
with their own estimates of the land sink. Hence, panel
(b)
also includes an
estimate for the total land flux for individual DGVMs (thin green lines) and
their multi-model mean (thick green line).
3.4.3
Final year 2021
The growth rate in atmospheric CO
concentration was 5.2
0.2 GtC (2.46
0.08 ppm) in 2021 (Fig. 4; Dlugokencky and Tans, 2022),
slightly above the 2020 growth rate (5.0 GtC) but similar to the 2011–2020
average (5.2 GtC).
3.4.4
Year 2022 projection
The 2022 growth in atmospheric CO
concentration (
ATM
) is
projected to be about 5.3 GtC (2.5 ppm) based on global observations until October 2022, bringing the atmospheric CO
concentration to an expected level of 417.2 ppm averaged over the year, 51 % over the preindustrial
level.
3.5
Ocean sink
3.5.1
Historical period 1850–2021
Cumulated since 1850, the ocean sink adds up to 175
35 GtC, with
more than two-thirds of this amount (120 GtC) being taken up by the global
ocean since 1960. Over the historical period, the ocean sink increased in
pace with the exponential anthropogenic emissions increase (Fig. 3b).
Since 1850, the ocean has removed 26 % of total anthropogenic emissions.
3.5.2
Recent period 1960–2021
The ocean CO
sink increased from 1.1
0.4 GtC yr
−1
in the
1960s to 2.9
0.4 GtC yr
−1
during 2012–2021 (Table 6), with
interannual variations of the order of a few tenths of a gigatonne of carbon per year (Fig. 10). The ocean-borne fraction (
OCEAN
FOS
LUC
) has been
remarkably constant at around 25 % on average (Fig. 9). Variations around
this mean illustrate decadal variability of the ocean carbon sink. So far
there is no indication of a decrease in the ocean-borne fraction from 1960
to 2021. The increase in the ocean sink is primarily driven by the increased
atmospheric CO
concentration, with the strongest CO
-induced
signal in the North Atlantic Ocean and the Southern Ocean (Fig. 11a). The effect
of climate change is much weaker, reducing the ocean sink globally by 0.11
0.09 GtC yr
−1
4.2 %) during 2012–2021 (nine models simulate
a weakening of the ocean sink by climate change with a range of
3.2 to
8.9 %, and
only one model simulates a strengthening by 4.8 %), and it does not show
clear spatial patterns across the GOBM ensemble (Fig. 11b). This is the
combined effect of change and variability in all atmospheric forcing fields,
previously attributed to wind and temperature changes in one model
(Le Quéré et al., 2010).
Table 9
Mapping of global carbon cycle model land flux definitions to the definition of the LULUCF net flux used in national Greenhouse Gas Inventories reported to UNFCCC. See Sect. C2.3 and Table A8 for details on the methodology and a comparison to other data sets.
Download Print Version
Download XLSX
The global net air–sea CO
flux is a residual of large natural and
anthropogenic CO
fluxes into and out of the ocean with distinct
regional and seasonal variations (Figs. 6 and B1). Natural fluxes dominate
on regional scales but largely cancel out when integrated globally (Gruber
et al., 2009). Mid-latitudes in all basins and the high-latitude North
Atlantic dominate the ocean CO
uptake where low temperatures and high
wind speeds facilitate CO
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 CO
uptake at the surface. Outgassing of natural CO
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
CO
exchange (e.g. Takahashi et al., 2009; Gruber et al., 2009). These
patterns are also noticeable in the Surface Ocean CO
Atlas (SOCAT) data set,
where an ocean
CO
value above the atmospheric level indicates
outgassing (Fig. B1). This map further illustrates the data sparsity in
the Indian Ocean and the Southern Hemisphere in general.
Interannual variability of the ocean carbon sink is driven by climate
variability with a first-order effect from a stronger ocean sink during
large El Niño events (e.g. 1997–1998) (Fig. 10; Rödenbeck et al.,
2014; Hauck et al., 2020). The GOBMs show the same patterns of decadal
variability as the mean of the
CO
-based data products, with a
stagnation of the ocean sink in the 1990s and a strengthening since the
early 2000s (Fig. 10, Le Quéré et al., 2007; Landschützer et
al., 2015, 2016; DeVries et al., 2017; Hauck et al., 2020; McKinley et al.,
2020). Different explanations have been proposed for this decadal
variability, ranging from the ocean's response to changes in atmospheric
wind and pressure 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 and its
effects on sea surface temperature and slowed atmospheric CO
growth
rate in the 1990s (McKinley et al., 2020). 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), but
contributions from the North Atlantic and North Pacific oceans
(Landschützer et al., 2016; DeVries et al., 2019) or a global signal
(McKinley et al., 2020) were also proposed.
Although all individual GOBMs and data products fall within the
observational constraint, the ensemble means of GOBMs and data products
adjusted for the riverine flux diverge over time with a mean offset
increasing from 0.28 GtC yr
−1
in the 1990s to 0.61 GtC yr
−1
in the
decade 2012–2021 and reaching 0.79 GtC yr
−1
in 2021. The
OCEAN
positive trend over time has diverged by a factor of 2 since 2002 (GOBMs: 0.28
0.07 GtC yr
−1
per decade; data products: 0.61
0.17 GtC yr
−1
per decade;
OCEAN
: 0.45 GtC yr
−1
per decade) and
by a factor of 3 since 2010 (GOBMs: 0.21
0.14 GtC yr
−1
per decade; data products: 0.66
0.38 GtC yr
−1
per
decade;
OCEAN
: 0.44 GtC yr
−1
per decade). The GOBM
estimate is slightly higher (
0.1 GtC yr
−1
) than in the
previous global carbon budget (Friedlingstein et al., 2022a) because two new
models are included (CESM2, MRI) and four models revised their estimates
upwards (CESM-ETHZ, CNRM, FESOM2-REcoM, PlankTOM). The data product estimate
is higher by about 0.1 GtC yr
−1
compared to Friedlingstein et al. (2022a) as a result of an upward correction in three products (Jena-MLS,
MPI-SOMFFN, OS-ETHZ-Gracer), the submission of LDEO-HPD (which is above
average), the non-availability of the CSIR product, and the small upward
correction of the river flux adjustment.
The discrepancy between the two types of estimates stems mostly from a
larger Southern Ocean sink in the data products prior to 2001 and from a
larger
OCEAN
trend in the northern and southern extratropics since
then (Fig. 13). Note that the location of the mean offset (but not its
trend) depends strongly on the choice of regional river flux adjustment and
would occur in the tropics rather than in the Southern Ocean when using the
data set of Lacroix et al. (2020) instead of Aumont et al. (2001). Other
possible explanations for the discrepancy in the Southern Ocean could be
missing winter observations and data sparsity in general (Bushinsky et al.,
2019, Gloege et al., 2021) or model biases (as indicated by the large model
spread in the Southern Hemisphere, as shown in Fig. 13, and the larger model–data mismatch, as shown in Fig. B2).
In GCB releases until 2021, the ocean sink 1959–1989 was only estimated by
GOBMs due to the absence of
CO
observations. Now, the first
data-based estimates extending back to 1957/58 are becoming available
(Jena-MLS, Rödenbeck et al., 2022, LDEO-HPD, Bennington et al., 2022;
Gloege et al., 2022). These are based on a multi-linear regression of
CO
with environmental predictors (Rödenbeck et al., 2022,
included here) or on model–data
CO
misfits and their relation to
environmental predictors (Bennington et al., 2022). The Jena-MLS estimate
falls well within the range of GOBM estimates and has a correlation of 0.98
with
OCEAN
(1959–2021 and 1959–1989). It agrees well on the
mean
OCEAN
estimate since 1977 with a slightly higher amplitude of
variability (Fig. 10). Until 1976, Jena-MLS is 0.2–0.3 GtC yr
−1
below
the central
OCEAN
estimate. The agreement, especially on phasing of
variability, is impressive, and the discrepancies in the mean flux 1959–1976
could be explained by an overestimated trend of Jena-MLS (Rödenbeck et
al., 2022). Bennington et al. (2022) report a larger flux into the pre-1990
ocean than in Jena-MLS.
The reported
OCEAN
estimate from GOBMs and data products is 2.1
0.4 GtC yr
−1
over the period 1994 to 2007, which is in
agreement with the ocean interior estimate of 2.2
0.4 GtC yr
−1
, which accounts for the climate effect on the natural CO
flux of
0.4
0.24 GtC yr
−1
(Gruber et al., 2019) to match the
definition of
OCEAN
used here (Hauck et al., 2020). This comparison
depends critically on the estimate of the climate effect on the natural
CO
flux, which is smaller from the GOBMs (
0.1 GtC yr
−1
) than in
Gruber et al. (2019). Uncertainties in these two estimates would also
overlap when using the GOBM estimate of the climate effect on the natural
CO
flux.
During 2010–2016, the ocean CO
sink appears to have intensified in
line with the expected increase from atmospheric CO
(McKinley et al.,
2020). This effect is stronger in the
CO
-based data products (Fig. 10, ocean sink 2016 minus 2010, GOBMs:
0.42
0.09 GtC yr
−1
data products:
0.52
0.22 GtC yr
−1
). The reduction of
0.09 GtC yr
−1
(range:
0.39 to
0.01 GtC yr
−1
) in the ocean CO
sink in 2017 is consistent with the return to normal conditions after the El
Niño in 2015/16, which caused an enhanced sink in previous years. After
2017, the GOBM ensemble mean suggests the ocean sink levelling off at about
2.6 GtC yr
−1
, whereas the data product estimate increases by 0.24
0.17 GtC yr
−1
over the same period.
3.5.3
Final year 2021
The estimated ocean CO
sink was 2.9
0.4 GtC in 2021. This is a
decrease of 0.12 GtC compared to 2020, in line with the expected sink
weakening from persistent La Niña conditions. GOBM and data product
estimates consistently result in a stagnation of
OCEAN
(GOBMs:
0.09
0.15 GtC; data products:
0.15
0.24 GtC). Seven models and
six data products show a decrease in
OCEAN
(GOBMs down to
0.31 GtC,
data products down to
0.58 GtC), while three models and two data products
show an increase in
OCEAN
(GOBMs up to 0.15 GtC, data products up to
0.12 GtC; Fig. 10). The data products have a larger uncertainty at the
tails of the reconstructed time series (e.g. Watson et al., 2020).
Specifically, the data products' estimate of the last year is regularly
adjusted in the following release owing to the tail effect and an
incrementally increasing data availability with a 1–5-year lag (Fig. 10
inset).
3.5.4
Year 2022 projection
Using a feed-forward neural network method (see Sect. 2.4) we project an
ocean sink of 2.9 GtC for 2022. This is similar to the year 2021 as the La
Niña conditions persist in 2022.
3.5.5
Model evaluation
The additional simulation D allows us to separate the anthropogenic carbon
component (steady state and non-steady state, sim D
sim A) and compare
the model flux and dissolved inorganic carbon (DIC) inventory change directly to the interior ocean
estimate of Gruber et al. (2019) without further assumptions. The GOBM
ensemble average of anthropogenic carbon inventory changes 1994–2007 amounts
to 2.2 GtC yr
−1
and is thus lower than the 2.6
0.3 GtC yr
−1
estimated by Gruber et al. (2019). Only four models with the
highest sink estimate fall within the range reported by Gruber et al. (2019). This suggests that the majority of the GOBMs underestimate
anthropogenic carbon uptake by 10 %–20 %. Analysis of Earth system models
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 the late starting date of the simulations (mirrored in
atmospheric CO
chosen for the pre-industrial control simulation, Table A2, Bronselaer et al., 2017; Terhaar et al., 2022). Interestingly, and in
contrast to the uncertainties in the surface CO
flux, we find the
largest mismatch in interior ocean carbon accumulation in the tropics
(93 % of the mismatch), with minor contribution from the north (1 %) and
the south (6 %). This highlights the role of interior ocean carbon
redistribution for those inventories (Khatiwala et al., 2009).
Figure 9
The partitioning of total anthropogenic CO
emissions (
FOS
LUC
) across
(a)
the
atmosphere (airborne fraction),
(b)
land (land-borne fraction), and
(c)
ocean (ocean-borne fraction). Black lines represent the central estimate,
and the coloured shading represents the uncertainty. The dashed grey lines
represent the long-term average of the airborne (44 %), land-borne
(30 %), and ocean-borne (25 %) fractions during 1960–2021.
Figure 10
Comparison of the anthropogenic atmosphere–ocean
CO
flux showing the budget values of
OCEAN
(black; with the uncertainty in grey shading), individual ocean models
(royal blue), and the ocean
CO
-based data products (cyan;
with Watson et al. , 2020, shown as a dashed line as it is not used for the ensemble mean).
Only one data product (Jena-MLS) extends back to 1959 (Rödenbeck et al.,
2022). The
CO
-based data products were adjusted for the
pre-industrial ocean source of CO
from river input to the
ocean by subtracting a source of 0.65 GtC yr
−1
to make them
comparable to
OCEAN
(see Sect. 2.4). The bar plot in the lower
right illustrates the number of
CO
observations in the SOCAT
v2022 database (Bakker et al., 2022). Grey bars indicate the number of data
points in SOCAT v2021, and coloured bars show the newly added observations in
v2022.
The evaluation of the ocean estimates (Fig. B2) shows a root-mean-squared error (RMSE) from
annually detrended data of 0.4 to 2.6
atm for the seven
CO
-based data products over the globe, relative to the
CO
observations from the SOCAT v2022 data set for the period 1990–2021. The
GOBM RMSEs are larger and range from 3.0 to 4.8
atm. The RMSEs are
generally larger at high latitudes compared to the tropics, for both the
data products and the GOBMs. The data products have RMSEs of 0.4 to 3.2
atm in the tropics, 0.8 to 2.8
atm in the northern extratropics (
30
N), and 0.8 to
3.6
atm in the southern extratropics (
30
S). Note that the data products are based on the
SOCAT v2022 database; hence, the SOCAT is not an independent data set for the
evaluation of the data products. The GOBM RMSEs are more spread across
regions, ranging from 2.5 to 3.9
atm in the tropics, 3.1 to 6.5
µatm
in the north, and 5.4 to 7.9
atm in the south. The
higher RMSEs occur in regions with stronger climate variability, such as the
northern and southern high latitudes (poleward of the subtropical gyres).
The upper ranges of the model RMSEs have decreased somewhat relative to
Friedlingstein et al. (2022a).
3.6
Land sink
3.6.1
Historical period 1850–2021
Cumulated since 1850, the terrestrial CO
sink amounts to 210
45 GtC, 31 % of total anthropogenic emissions. Over the historical period,
the sink increased in pace with the exponential anthropogenic emissions
increase (Fig. 3b).
3.6.2
Recent period 1960–2021
The terrestrial CO
sink increased from 1.2
0.4 GtC yr
−1
in the 1960s to 3.1
0.6 GtC yr
−1
during 2012–2021, with
important interannual variations of up to 2 GtC yr
−1
generally showing
a decreased land sink during El Niño events (Fig. 8), responsible for
the corresponding enhanced growth rate in atmospheric CO
concentration. The larger land CO
sink during 2012–2021 compared to
the 1960s is reproduced by all the DGVMs in response to the increase in both
atmospheric CO
and nitrogen deposition and the changes in climate
and is consistent with constraints from the other budget terms (Table 5).
Over the period 1960 to present the increase in the global terrestrial
CO
sink is largely attributed to the CO
fertilization effect
(Prentice et al., 2001; Piao et al., 2009), directly stimulating plant
photosynthesis and increased plant water use in water-limited systems, with
a small negative contribution of climate change (Fig. 11). There is a
range of evidence to support a positive terrestrial carbon sink in response
to increasing atmospheric CO
, albeit with uncertain magnitude (Walker
et al., 2021). As expected from theory, the greatest CO
effect is
simulated in the tropical forest regions, associated with warm temperatures
and long growing seasons (Hickler et al., 2008) (Fig. 11a). 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., 2005, Hubau et al., 2020).
During 2012–2021 the land sink is positive in all regions (Fig. 6) with
the exception of eastern Brazil, the southwestern US, southeastern Europe, central
Asia, northern and southern Africa, and eastern Australia, where the negative
effects of climate variability and change (i.e. reduced rainfall)
counterbalance CO
effects. This is clearly visible in Fig. 11 where
the effects of CO
(Fig. 11a) and climate (Fig. 11b) as simulated
by the DGVMs are isolated. The negative effect of climate is the strongest
in most of South America, Central America, the southwestern US, central Europe,
western Sahel, southern Africa, Southeast Asia and southern China, and
eastern Australia (Fig. 11b). Globally, climate change reduces the land
sink by 0.63
0.52 GtC yr
−1
or 17 % (2012–2021).
Figure 11
Attribution of the atmosphere–ocean (
OCEAN
) and
atmosphere–land (
LAND
) CO
fluxes to
(a)
increasing atmospheric CO
concentrations and
(b)
changes in
climate, averaged over the previous decade 2012–2021. All data shown are from
the processed-based GOBMs and DGVMs. The sum of ocean CO
and
climate effects will not equal the ocean sink shown in Fig. 6, which
includes the
CO
-based data products. See Appendices C3.2 and
C4.1 for attribution methodology. Units are in kgC m
−2
yr
−1
(note the non-linear colour scale).
Since 2020 the globe has experienced La Niña conditions, which would be
expected to lead to an increased land carbon sink. A clear peak in the
global land sink is not evident in
LAND
, and we find that a La
Niña-driven increase in tropical land sink is offset by a reduced high
latitude extratropical land sink, which may be linked to the land response
to recent climate extremes. In the past years several regions experienced
record-setting fire events. 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
events include the Black Summer event in Australia in 2019–2020 (emissions of
roughly 0.2 GtC; van der Velde et al., 2021) and events in Siberia in 2021 where
emissions approached 0.4 GtC or 3 times the 1997–2020 average according
to GFED4s. While other regions, including the western US and Mediterranean
Europe, also experienced intense fire seasons in 2021, their emissions are
substantially lower.
Despite these regional negative effects of climate change on
LAND
, the
efficiency of land to remove anthropogenic CO
emissions has remained
broadly constant over the last 6 decades, with a land-borne fraction
LAND
FOS
LUC
)) of
30 % (Fig. 9).
3.6.3
Final year 2021
The terrestrial CO
sink from the DGVMs ensemble was 3.5
0.9 GtC in 2021, slightly above the decadal average of 3.1
0.6 GtC yr
−1
(Fig. 4, Table 6). We note that the DGVM estimate for 2021 is
larger than, but within the uncertainty of, the 2.8
0.9 GtC yr
−1
estimate from the residual sink from the global budget
FOS
LUC
ATM
OCEAN
) (Table 5).
3.6.4
Year 2022 projection
Using a feed-forward neural network method we project a land sink of 3.4 GtC
for 2022, very similar to the 2021 estimate. As for the ocean sink, we
attribute this to the persistence of La Niña conditions in 2022.
3.6.5
Model evaluation
The evaluation of the DGVMs (Fig. B3) shows generally high skill scores
across models for runoff and to a lesser extent for vegetation biomass,
gross primary production (or productivity; GPP), and ecosystem respiration (Fig. B3, left panel). Skill score was
lowest for leaf area index and net ecosystem exchange, with the widest
disparity among models for soil carbon. These conclusions are supported by a
more comprehensive analysis of DGVM performance in comparison with benchmark
data (Seiler et al., 2022). Furthermore, results show how DGVM differences
are often of similar magnitude compared with the range across observational
data sets.
3.7
Partitioning the carbon sinks
3.7.1
Global sinks and spread of estimates
In the period 2012–2021, the bottom-up view of total global carbon sinks
provided by the GCB,
OCEAN
for the ocean and
LAND
LUC
for the land (to be comparable to inversions), agrees closely with the
top-down global carbon sinks delivered by the atmospheric inversions. Figure 12 shows both total sink estimates of the last decade split by ocean and
land (including
LUC
), which match the difference between
ATM
and
FOS
to within 0.01–0.12 GtC yr
−1
for inverse systems, and to 0.34 GtC yr
−1
for the GCB mean. The latter represents the
IM
discussed
in Sect. 3.8, which by design is minimal for the inverse systems.
Figure 12
The 2012–2021 decadal mean net atmosphere–ocean and
atmosphere–land fluxes derived from the ocean models and
CO
products (
axis, right- and left-pointing blue triangles, respectively) and
from the DGVMs (
axis, green symbols) and the same fluxes estimated from
the inversions (purple symbols on secondary
and
axes). The grey central
point is the mean (
of
OCEAN
and
LAND
LUC
) as assessed in this budget. The
shaded distributions show the density of the ensemble of individual
estimates. The grey diagonal band represents the fossil fuel emissions minus
the atmospheric growth rate from this budget (
FOS
ATM
). Note that positive values are CO
sinks.
The distributions based on the individual models and data products reveal
substantial spread but converge near the decadal means quoted in Tables 5
and 6. Sink estimates for
OCEAN
and from inverse systems are mostly
non-Gaussian, while the ensemble of DGVMs appears more normally distributed,
justifying the use of a multi-model mean and standard deviation for their
errors in the budget. Noteworthy is that the tails of the distributions
provided by the land and ocean bottom-up estimates would not agree with the
global constraint provided by the fossil fuel emissions and the observed
atmospheric CO
growth rate (
FOS
ATM
). This
illustrates the power of the atmospheric joint constraint from
ATM
and
the global CO
observation network it derives from.
3.7.2
Total atmosphere-to-land fluxes
The total atmosphere-to-land fluxes (
LAND
LUC
), calculated
here as the difference between
LAND
from the DGVMs and
LUC
from
the bookkeeping models, amounts to a 1.9
0.9 GtC yr
−1
sink
during 2012–2021 (Table 5). Estimates of total atmosphere-to-land fluxes
LAND
LUC
) from the DGVMs alone (1.5
0.5 GtC yr
−1
) are consistent with this estimate and also with the global carbon
budget constraint (
FOS
ATM
OCEAN
, 1.5
0.6 GtC yr
−1
Table 5). For the last decade (2012–2021), the inversions
estimate the net atmosphere-to-land uptake to lie within a range of 1.1 to
1.7 GtC yr
−1
, consistent with the GCB and DGVM estimates of
LAND
LUC
(Fig. 13 top row).
3.7.3
Total atmosphere-to-ocean fluxes
For the 2012–2021 period, the GOBMs (2.6
0.5 GtC yr
−1
) produce
a lower estimate for the ocean sink than the
CO
-based data products
(3.2
0.6 GtC yr
−1
), which shows up in Fig. 12 as a separate
peak in the distribution from the GOBMs (triangle symbols pointing right)
and from the
CO
-based products (triangle symbols pointing left).
Atmospheric inversions (2.7 to 3.3 GtC yr
−1
) also suggest higher ocean
uptake in the last decade (Fig. 13 top row). In interpreting these
differences, we caution that the riverine transport of carbon taken up on
land and outgassing from the ocean is a substantial (0.65 GtC yr
−1
) and
uncertain term that separates the various methods. A recent estimate of
decadal ocean uptake from observed
ratios (Tohjima et al.,
2019) also points towards a larger ocean sink, albeit with large uncertainty
(2012–2016: 3.1
1.5 GtC yr
−1
).
3.7.4
Regional breakdown and interannual variability
Figure 13 also shows the latitudinal partitioning of the total
atmosphere-to-surface fluxes excluding fossil CO
emissions
OCEAN
LAND
LUC
) according to the multi-model
average estimates from GOBMs and ocean
CO
-based products
OCEAN
) and DGVMs (
LAND
LUC
) and from atmospheric
inversions (
OCEAN
and
LAND
LUC
).
Figure 13
CO
fluxes between the atmosphere and the Earth's
surface separated between land and oceans globally and in three latitude
bands. The ocean flux is
OCEAN
, and the land flux is the net of
atmosphere–land fluxes from the DGVMs. The latitude bands are (top row)
global, (second row) north (
30
N),
(third row) tropics (30
S–30
N), and
(bottom row) south (
30
S), showing values over ocean (left
column) and land (middle column) and in total (right column). Estimates are shown
for process-based models (DGVMs for land, GOBMs for oceans), inversion
systems (land and ocean), and
CO
-based data products (ocean
only). Positive values indicate a flux from the atmosphere to the land or
the ocean. Mean estimates from the combination of the process models for the
land and oceans are shown (black line) with
1 standard deviation
(1
) of the model ensemble (grey shading). For the total uncertainty
in the process-based estimate of the total sink, uncertainties are summed in
quadrature. Mean estimates from the atmospheric inversions are shown (purple
lines) with their full spread (purple shading). Mean estimates from the
CO
-based data products are shown for the ocean domain (light
blue lines) with their
spread (light blue shading). The
global
OCEAN
(upper left) and the sum of
OCEAN
in all three regions represents the anthropogenic atmosphere-to-ocean flux
based on the assumption that the pre-industrial ocean sink was 0 GtC yr
−1
when riverine fluxes are not considered. This assumption
does not hold at the regional level, where pre-industrial fluxes can be
significantly different from zero. Hence, the regional panels for
OCEAN
represent a combination of natural and anthropogenic
fluxes. Bias correction and area weighting were only applied to global
OCEAN
; hence, the sum of the regions is slightly different
from the global estimate (
0.05 GtC yr
−1
).
North
Despite being one of the most densely observed and studied regions of our
globe, annual mean carbon sink estimates in the northern extratropics
(north of 30
N) continue to differ. The atmospheric inversions
suggest an atmosphere-to-surface sink (
OCEAN
LAND
LUC
) for 2012–2021 of 2.0 to 3.2 GtC yr
−1
, which is higher than
the process models' estimate of 2.2
0.4 GtC yr
−1
(Fig. 13).
The GOBMs (1.2
0.2 GtC yr
−1
),
CO
-based data products
(1.4
0.1 GtC yr
−1
), and inversion systems (0.9 to 1.4 GtC yr
−1
) produce consistent estimates of the ocean sink. Thus, the
difference mainly arises from the total land flux (
LAND
LUC
) estimate, which is 1.0
0.4 GtC yr
−1
in the DGVMs
compared to 0.6 to 2.0 GtC yr
−1
in the atmospheric inversions (Fig. 13, second row).
Discrepancies in the northern land fluxes conform with persistent issues
surrounding the quantification of the drivers of the global net land
CO
flux (Arneth et al., 2017; Huntzinger et al., 2017; O'Sullivan et
al., 2022) and the distribution of atmosphere-to-land fluxes between the
tropics and high northern latitudes (Baccini et al., 2017; Schimel et al.,
2015; Stephens et al., 2007; Ciais et al., 2019; Gaubert et al., 2019).
In the northern extratropics, the process models, inversions, and
CO
-based data products consistently suggest that most of the
variability stems from the land (Fig. 13). Inversions generally estimate
similar interannual variations (IAVs) over land to DGVMs (0.30–0.37 vs.
0.17–0.69 GtC yr
−1
, averaged over 1990–2021), and they have higher
IAV in ocean fluxes (0.05–0.09 GtC yr
−1
) relative to GOBMs (0.02–0.06 GtC yr
−1
, Fig. B2) and
CO
-based data products (0.03–0.09 GtC yr
−1
).
Tropics
In the tropics (30
S–30
N), both the atmospheric
inversions and process models estimate a total carbon balance
OCEAN
LAND
LUC
) that is close to neutral over the past
decade. The GOBMs (0.06
0.34 GtC yr
−1
),
CO
-based data
products (0.00
0.06 GtC yr
−1
), and inversion systems (
0.2 to
0.5 GtC yr
−1
) all indicate an approximately neutral tropical ocean flux
(see Fig. B1 for spatial patterns). DGVMs indicate a net land sink
LAND
LUC
) of 0.5
0.3 GtC yr
−1
, whereas the
inversion systems indicate a net land flux between
0.9 and 0.7 GtC yr
−1
, albeit with high uncertainty (Fig. 13, third row).
The tropical lands are the origin of most of the atmospheric CO
interannual variability (Ahlström et al., 2015), and this is consistent among the
process models and inversions (Fig. 13). The interannual variability in
the tropics is similar among the ocean data products (0.07–0.16 GtC yr
−1
) and the GOBMs (0.07–0.16 GtC yr
−1
, Fig. B2), which is the
highest ocean sink variability of all regions. The DGVMs and inversions
indicate that atmosphere-to-land CO
fluxes are more variable than
atmosphere-to-ocean CO
fluxes in the tropics, with interannual
variability of 0.5 to 1.1 and 0.8 to 1.0 GtC yr
−1
for DGVMs and
inversions, respectively.
South
In the southern extratropics (south of 30
S), the atmospheric
inversions suggest a total atmosphere-to-surface sink
OCEAN
LAND
LUC
) for 2012–2021 of 1.6 to 1.9 GtC yr
−1
, slightly higher than the process models' estimate of 1.4
0.3 GtC yr
−1
(Fig. 13). An approximately neutral total land flux
LAND
LUC
) for the southern extratropics is estimated by both
the DGVMs (0.02
0.06 GtC yr
−1
) and the inversion systems (sink
of
0.2 to 0.2 GtC yr
−1
). This means nearly all carbon uptake is due to
oceanic sinks south of 30
S. The Southern Ocean flux in the
CO
-based data products (1.8
0.1 GtC yr
−1
) and inversion
estimates (1.6 to 1.9 GtC yr
−1
) is higher than in the GOBMs (1.4
0.3 GtC yr
−1
) (Fig. 13, bottom row). This discrepancy in the mean flux is
likely explained by the uncertainty in the regional distribution of the
river flux adjustment (Aumont et al., 2001; Lacroix et al., 2020) applied to
CO
-based data products and inverse systems to isolate the
anthropogenic
OCEAN
flux. Other possibly contributing factors are that
the data products potentially underestimate the winter CO
outgassing
south of the Polar Front (Bushinsky et al., 2019) and potential model
biases. CO
fluxes from this region are more sparsely sampled by all
methods, especially in wintertime (Fig. B1). Dominant biases in Earth
system models are related to mode water formation, stratification, and the
chemical buffer capacity (Terhaar et al., 2021; Bourgeois et al., 2022;
Terhaar et al., 2022).
The interannual variability in the southern extratropics is low because of
the dominance of ocean areas with low variability compared to land areas.
The split between land (
LAND
LUC
) and ocean (
OCEAN
) 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 exactly separating the
land and oceanic fluxes when viewed from atmospheric observations alone. The
OCEAN
interannual variability was found to be higher in the
CO
-based data products (0.09 to 0.19 GtC yr
−1
) compared to GOBMs
(0.03 to 0.06 GtC yr
−1
) in 1990–2021 (Fig. B2). Model subsampling
experiments recently illustrated that observation-based products may
overestimate decadal variability in the Southern Ocean carbon sink by 30 %
due to data sparsity, based on one data product with the highest decadal
variability (Gloege et al., 2021).
Tropical vs. northern land uptake
A continuing conundrum is the partitioning of the global atmosphere–land
flux between the Northern Hemisphere land and the tropical land (Stephens et
al., 2017; Pan et al., 2011; Gaubert et al., 2019). It is of importance
because each region has its own history of land-use change, climate drivers,
and the impact of increasing atmospheric CO
and nitrogen deposition.
Quantifying the magnitude of each sink is a prerequisite to understanding
how each individual driver impacts the tropical and mid- and 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 ranges from 0.1 to
2.9 GtC yr
−1
across this year's inversion ensemble with a preference
across models for either a smaller northern land sink with a near-neutral
tropical land flux (medium N–S difference) or a large northern land sink
and a tropical land source (large N–S difference).
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 inversions. Only two
DGVMs have a N–S difference larger than 1.0 GtC yr
−1
. The larger
agreement 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 leads to an anti-correlation between these two regions. The
much smaller spread in the N–S difference between the DGVMs could help to
scrutinize the inverse systems further. For example, a large northern land
sink and a tropical land source in an inversion would suggest a large
sensitivity to CO
fertilization (the dominant factor driving the land
sinks) for northern ecosystems, which would be not mirrored by tropical
ecosystems. Such a combination could be hard to reconcile with the process
understanding gained from the DGVM ensembles and independent measurements
(e.g. free-air CO
enrichment experiments). Such investigations will be
further pursued in the upcoming assessment from REgional Carbon Cycle
Assessment and Processes (RECCAP2; Ciais et al., 2022).
3.8
Closing the global carbon cycle
3.8.1
Partitioning of cumulative emissions and sink fluxes
The global carbon budget over the historical period (1850–2021) is shown in
Fig. 3.
Emissions during the period 1850–2021 amounted to 670
65 GtC and
were partitioned among the atmosphere (275
5 GtC; 41 %), ocean
(175
35 GtC; 26 %), and land (210
45 GtC; 31 %). The
cumulative land sink is almost equal to the cumulative land-use emissions
(200
60 GtC), making the global land nearly neutral over the whole
1850–2021 period.
The use of nearly independent estimates for the individual terms of the
global carbon budget shows a cumulative budget imbalance of 15 GtC (2 % of
total emissions) during 1850–2021 (Fig. 3, Table 8), which, if correct,
suggests that emissions could be slightly too high by the same proportion
(2 %) or that the combined land and ocean sinks are slightly
underestimated (by about 3 %), although these are well within the
uncertainty range of each component of the budget. Nevertheless, part of the
imbalance could originate from the estimation of significant increase in
FOS
and
LUC
between the mid-1920s and the mid-1960s that is
unmatched by a similar growth in atmospheric CO
concentration as
recorded in ice cores (Fig. 3). However, the known loss of additional sink
capacity of 30–40 GtC (over the 1850–2020 period) due to reduced forest
cover has not been accounted for in our method and would exacerbate the
budget imbalance (see Appendix D4).
For the more recent 1960–2021 period where direct atmospheric CO
measurements are available, total emissions (
FOS
LUC
amounted to 470
50 GtC, of which 390
20 GtC (82 %) were
caused by fossil CO
emissions and 85
45 GtC (18 %) by
land-use change (Table 8). The total emissions were partitioned among the
atmosphere (210
5 GtC; 45 %), ocean (120
25 GtC; 26 %),
and land (145
30 GtC; 30 %), with a near-zero (
5 GtC)
unattributed budget imbalance. All components except land-use change
emissions have significantly grown since 1960, with important interannual
variability in the growth rate in atmospheric CO
concentration and in
the land CO
sink (Fig. 4) and some decadal variability in all terms
(Table 6). Differences with previous budget releases are documented in
Fig. B5.
The global carbon budget averaged over the last decade (2012–2021) is shown
in Figs. 2 and 14 (right panel) and Table 6. For this period, 89 % of
the total emissions (
FOS
LUC
) were from fossil CO
emissions (
FOS
), and 11 % were from land-use change (
LUC
). The
total emissions were partitioned among the atmosphere (48 %), ocean
(26 %), and land (29 %), with a near-zero unattributed budget imbalance
3 %). For single years, the budget imbalance can be
larger (Fig. 4). For 2021, the combination of our estimated sources (10.9
0.9 GtC yr
−1
) and sinks (11.6
1.0 GtC yr
−1
) leads
to a
IM
of
0.6 GtC, suggesting a slight underestimation of the
anthropogenic sources and/or an overestimation of the combined land and
ocean sinks.
Figure 14
Cumulative changes over the 1850–2021 period (left) and average
fluxes over the 2012–2021 period (right) for the anthropogenic perturbation
of the global carbon cycle. See the caption of Fig. 3 for key information
and Sect. 2 for full details.
3.8.2
Carbon budget imbalance trend and variability
The carbon budget imbalance (
IM
; Eq. 1, Fig. 4) quantifies the
mismatch between the estimated total emissions and the estimated changes in
the atmosphere, land, and ocean reservoirs. The mean budget imbalance from
1960 to 2021 is very small (4.6 GtC over the period, i.e. average of 0.07 GtC yr
−1
) and shows no trend over the full time series (Fig. 4). The
process models (GOBMs and DGVMs) and data 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 near-zero mean and trend in the budget imbalance is seen as
evidence of a coherent community understanding of the emissions and their
partitioning on those timescales (Fig. 4). However, the budget imbalance
shows substantial variability on the order of
1 GtC yr
−1
particularly over semi-decadal timescales, although most of the variability
is within the uncertainty of the estimates. The positive carbon imbalance
during the 1960s and early 1990s indicates that either the emissions were
overestimated or the sinks were underestimated during these periods. The
reverse is true for the 1970s and to a lesser extent for the 1980s and the
2012–2021 period (Fig. 4, Table 6).
We cannot attribute the cause of the variability in the budget imbalance
with our analysis, we only note that the budget imbalance is unlikely to be
explained by errors or biases in the emissions alone because of its large
semi-decadal variability component, a variability that is untypical of
emissions and which has not changed in the past 60 years despite a near tripling
of emissions (Fig. 4). Errors in
LAND
and
OCEAN
are more
likely to be the main cause for the budget imbalance, especially on
interannual to semi-decadal timescales. For example, underestimation of the
LAND
by DGVMs has been reported following the eruption of Mount
Pinatubo in 1991, possibly due to missing responses to changes in diffuse
radiation (Mercado et al., 2009). Although since GCB2021 we accounted for
aerosol effects on solar radiation quantity and quality (diffuse vs. direct),
most DGVMs only used the former as input (i.e. total solar radiation)
(Table A1). Thus, the ensemble mean may not capture the full effects of
volcanic eruptions, i.e. associated with high light-scattering sulfate
aerosols, on the land carbon sink (O'Sullivan et al., 2021). DGVMs are
suspected to overestimate the land sink in response to the wet decade of the
1970s (Sitch et al., 2008). Quasi-decadal variability in the ocean sink has
also been reported, with all methods agreeing on a smaller than expected
ocean CO
sink in the 1990s and a larger than expected sink in the
2000s (Fig. 10; Landschützer et al., 2016; DeVries et al., 2019; Hauck
et al., 2020; McKinley et al., 2020). Errors in sink estimates could also be
driven by errors in the climatic forcing data, particularly precipitation
for
LAND
and wind for
OCEAN
. Also, the
IM
shows
substantial departure from zero on yearly timescales (Fig. 4e),
highlighting unresolved variability of the carbon cycle, likely in the land
sink (
LAND
), given its large year-to-year variability (Figs. 4d and
8).
Both the budget imbalance (
IM
, Table 6) and the residual land sink
from the global budget (
FOS
LUC
ATM
OCEAN
, Table 5) include an error term due to the inconsistencies that arise from using
LUC
from bookkeeping models and
LAND
from DGVMs, most notably
the loss of additional sink capacity (see Sect. 2.7 and Appendix D4).
Other differences include a better accounting of land-use change practices
and processes in bookkeeping models than in DGVMs or the error in bookkeeping models of having present-day observed carbon densities fixed in the past.
That the budget imbalance shows no clear trend towards larger values over
time is an indication that these inconsistencies probably play a minor role
compared to other errors in
LAND
or
OCEAN
Although the budget imbalance is near zero for the recent decades, it could
be due to compensation of errors. We cannot exclude an overestimation of
CO
emissions, particularly from land-use change, given their large
uncertainty, as has been suggested elsewhere (Piao et al., 2018), combined
with an underestimate of the sinks. A larger DGVM (
LAND
LUC
over the extratropics would reconcile model results with inversion
estimates for fluxes in the total land during the past decade (Fig. 13;
Table 5). Likewise, a larger
OCEAN
is also possible given the higher
estimates from the data products (see Sect. 3.1.2, Figs. 10 and
13), the underestimation of interior ocean anthropogenic carbon accumulation
in the GOBMs (Sect. 3.5.5), and the recently suggested upward adjustments
of the ocean carbon sink in Earth system models (Terhaar et al., 2022) and
in data products, here related to a potential temperature bias and skin
effects (Watson et al., 2020; Dong et al., 2022, Fig. 10). If
OCEAN
were to be based on data products alone, with all data products including
this adjustment, this would result in a 2012–2021
OCEAN
of 3.8 GtC yr
−1
(Dong et al., 2022) or
4 GtC yr
−1
(Watson et
al., 2020), i.e. outside of the range supported by the atmospheric
inversions and with an implied negative
IM
of more than
1 GtC yr
−1
, indicating that a closure of the budget could only be achieved
with either anthropogenic emissions being significantly larger and/or the
net land sink being substantially smaller than estimated here. More
integrated use of observations in the global carbon budget, either on their
own or for further constraining model results, should help resolve some of
the budget imbalance (Peters et al., 2017).
Tracking progress towards mitigation targets
The average growth in global fossil CO
emissions peaked at
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
0.5 % per year over 2012–2021 (including the 2020 global
decline and the 2021 emissions rebound). While this slowdown in global
fossil CO
emissions growth is welcome, it is far from the emission
decrease needed to be consistent with the temperature goals of the Paris
Agreement.
Since the 1990s, the average growth rate of fossil CO
emissions has
continuously declined across the group of developed countries of the
Organization for Economic Co-operation and Development (OECD), with
emissions peaking in around 2005 and now declining at around 1 % per year
(Le Quéré et al., 2021). In the decade 2012–2021, territorial fossil
CO
emissions decreased significantly (at the 95 % confidence level)
in 24 countries whose economies grew significantly (also at the 95 %
confidence level): Belgium, Croatia, Czech Republic, Denmark, Estonia,
Finland, France, Germany, Hong Kong, Israel, Italy, Japan, Luxembourg,
Malta, Mexico, Netherlands, Norway, Singapore, Slovenia, Sweden,
Switzerland, the United Kingdom, the USA, and Uruguay (updated from Le Quéré
et al., 2019). Altogether, these 24 countries emitted 2.4 GtC yr
−1
(8.8 GtCO
yr
−1
) on average over the last decade, about a quarter of
world fossil CO
emissions. Consumption-based emissions also fell
significantly during the final decade for which estimates are available
(2011–2020) in 15 of these countries: Belgium, Denmark, Estonia, Finland,
France, Germany, Hong Kong, Israel, Japan, Luxembourg, Mexico, Netherlands,
Singapore, Sweden, the United Kingdom, and Uruguay. Figure 15 shows that the
emission declines in the USA and the EU27 are primarily driven by increased
decarbonization (CO
emissions per unit energy) in the last decade
compared to the previous, with smaller contributions in the EU27 from
slightly weaker economic growth and slightly larger declines in energy per
GDP. These countries have stable or declining energy use and thus
decarbonization policies replace existing fossil fuel infrastructure (Le
Quéré et al., 2019).
Figure 15
Kaya decomposition of the main drivers of fossil
CO
emissions, considering population, GDP per person, energy
per GDP, and CO
emissions per energy, for China
(a)
, the
USA
(b)
, the EU27
(c)
, India
(d)
, the rest of the world
(e)
, and the world
(f)
. Black dots are the annual fossil
CO
emissions growth rate, coloured bars are the contributions
from the different drivers. A general trend is that population and GDP
growth put upward pressure on emissions, while energy per GDP and more
recently CO
emissions per energy put downward pressure on
emissions. Both the COVID-19-induced changes during 2020 and the recovery in
2021 led to a stark contrast to previous years, with different drivers in
each region.
In contrast, fossil CO
emissions continue to grow in non-OECD
countries, although the growth rate has slowed from almost 6 % per year
during the 2000s to less than 2 % per year in the last decade.
Representing 47 % of non-OECD emissions in 2021, a large part of this
slowdown is due to China, which has seen emissions growth decline from
nearly 10 % per year in the 2000s to 1.5 % per year in the last
decade. Excluding China, non-OECD emissions grew at 3.3 % per year in the
2000s compared to 1.6 % per year in the last decade. Figure 15 shows
that, compared to the previous decade, China has had weaker economic growth
in the last decade and a higher decarbonization rate, with more rapid
declines in energy per GDP that are now back to levels seen during the
1990s. India and the rest of the world have strong economic growth that is
not offset by decarbonization or declines in energy per GDP, driving up
fossil CO
emissions. Despite the high deployment of renewables in some
countries (e.g. India), fossil energy sources continue to grow to meet
growing energy demand (Le Quéré et al., 2019).
Globally, fossil CO
emissions growth is slowing, and this is due to
the emergence of climate policy (Eskander and Fankhauser, 2020; Le Quere et
al., 2019) and technological change, which is leading to a shift from coal to
gas, growth in renewable energies, and reduced expansion of coal
capacity. At the aggregated global level, decarbonization shows a strong and
growing signal in the last decade, with smaller contributions from lower
economic growth and declines in energy per GDP. Despite the slowing growth
in global fossil CO
emissions, emissions are still growing, but these are far from
the reductions needed to meet the ambitious climate goals of the UNFCCC
Paris Agreement.
We update the remaining carbon budget assessed by the IPCC AR6 (Canadell et
al., 2021), accounting for the estimated 2020 to 2022 emissions from fossil
fuel combustion (
FOS
) and land-use changes (
LUC
). From January
2023, the remaining carbon (50 % likelihood) for limiting global warming
to 1.5, 1.7, and 2
C is estimated to
amount to 105, 200, and 335 GtC (380, 730, 1230 GtCO
). These numbers
include an uncertainty based on model spread (as in IPCC AR6), which is
reflected through the percent likelihood of exceeding the given temperature
threshold. These remaining amounts correspond respectively to about 9, 18,
and 30 years from the beginning of 2023 at the 2022 level of total CO
emissions. Reaching net zero CO
emissions by 2050 entails cutting
total anthropogenic CO
emissions by about 0.4 GtC (1.4 GtCO
each year on average, comparable to the decrease observed in 2020 during the
COVID-19 pandemic.
Table 10
Major known sources of uncertainties in each component of the global carbon budget, defined as input data or processes that have a demonstrated effect of at least
0.3 GtC yr
−1
As a result of interactions between land use and climate.
The uncertainties in
ATM
have been estimated as
0.2 GtC yr
−1
, although the conversion of the growth rate into a global annual flux assuming instantaneous mixing throughout the atmosphere introduces additional errors that have not yet been quantified.
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Discussion
Each year when the global carbon budget is published, each flux component is
updated for all previous years to consider corrections that are the result
of further scrutiny and verification of the underlying data in the primary
input data sets. Annual estimates may be updated with improvements in data
quality and timeliness (e.g. to eliminate the need for extrapolation of
forcing data such as land use). Of all terms in the global budget, only the
fossil CO
emissions and the growth rate in atmospheric CO
concentrations are based primarily on empirical inputs supporting annual
estimates in this carbon budget. The carbon budget imbalance, while an
imperfect measure, provides a strong indication of the limitations in
observations in understanding and representing processes in models and/or
in the integration of the carbon budget components.
The persistent unexplained variability in the carbon budget imbalance limits
our ability to verify reported emissions (Peters et al., 2017) and suggests
we do not yet have a complete understanding of the underlying carbon cycle
dynamics on annual to decadal timescales. Resolving most of this unexplained
variability should be possible through different and complementary
approaches. First, as intended with our annual updates, the imbalance as an
error term is reduced by improvements of individual components of the global
carbon budget that follow from improving the underlying data and statistics
and by improving the models through the resolution of some of the key
uncertainties detailed in Table 10. Second, additional clues to the origin
and processes responsible for the variability in the budget imbalance could
be obtained through a closer scrutiny of carbon variability in light of
other Earth system data (e.g. heat balance, water balance) and the use of
a wider range of biogeochemical observations to better understand the
land–ocean partitioning of the carbon imbalance (e.g. oxygen, carbon
isotopes). Finally, additional information could also be obtained through
higher resolution and process knowledge at the regional level and through
the introduction of inferred fluxes such as those based on satellite
CO
retrievals. The limit of the resolution of the carbon budget
imbalance is yet unclear, but has most certainly not yet been reached given the
possibilities for improvements that lie ahead.
Estimates of global fossil CO
emissions from different data sets are in
relatively good agreement when the different system boundaries of these
data sets are considered (Andrew, 2020a). But while estimates of
FOS
are derived from reported activity data requiring much fewer complex
transformations than some other components of the budget, uncertainties
remain, and one reason for the apparently low variation between data sets is
precisely the reliance on the same underlying reported energy data. The
budget excludes some sources of fossil CO
emissions, which available
evidence suggests are relatively small (
1 %). We have added
emissions from lime production in China and the US, but these are still
absent in most other non-Annex I countries and before 1990 in other Annex I
countries.
Estimates of
LUC
suffer from a range of intertwined issues, including
the poor quality of historical land cover and land-use change maps, the
rudimentary representation of management processes in most models, and the
confusion in methodologies and boundary conditions used across methods
(e.g. Arneth et al., 2017; Pongratz et al., 2014; Bastos et al., 2021; see also Appendix D4 on
the loss of sink capacity). Uncertainties in current
and historical carbon stocks in soils and vegetation also add uncertainty in
the
LUC
estimates. Unless a major effort to resolve these issues is
made, little progress is expected in the resolution of
LUC
. This is
particularly concerning given the growing importance of
LUC
for
climate mitigation strategies and the large issues in the quantification of
the cumulative emissions over the historical period that arise from large
uncertainties in
LUC
By adding the DGVM estimates of CO
fluxes due to environmental change
from countries' managed forest areas (part of
LAND
in this budget)
to the budget
LUC
estimate, we successfully reconciled the large gap
between our
LUC
estimate and the land-use flux from NGHGIs using the
approach described in Grassi et al. (2021) for a future scenario and in Grassi
et al. (2022b) using data from the Global Carbon Budget 2021. The updated
data presented here can be used as potential adjustment in the policy
context, e.g. to help assessing the collective countries' progress towards
the goal of the Paris Agreement and avoiding double accounting of the sink
in managed forests. In the absence of this adjustment, collective progress
would hence appear better than it is (Grassi et al., 2021). The need of such
adjustment whenever a comparison between LULUCF fluxes reported by countries
and the global emission estimates of the IPCC is attempted is recommended
also in the recent UNFCCC Synthesis report for the first Global Stocktake
(UNFCCC, 2022). However, this adjustment should be seen as a short-term and
pragmatic fix based on existing data, rather than a definitive solution to
bridge the differences between global models and national inventories.
Additional steps are needed to understand and reconcile the remaining
differences, some of which are relevant at the country level (Grassi et al., 2022b; Schwingshackl et al., 2022).
The comparison of GOBMs, data products, and inversions highlights a substantial
discrepancy in the Southern Ocean (Fig. 13, Hauck et al., 2020). A large
part of the uncertainty in the mean fluxes stems from the regional
distribution of the river flux adjustment term. The current distribution
(Aumont et al., 2001) is based on one model study yielding the largest
riverine outgassing flux south of 20
S, whereas a recent study,
also based on one model, simulates the largest share of the outgassing to
occur in the tropics (Lacroix et al., 2020). The long-standing sparse data
coverage of
CO
observations in the Southern Hemisphere compared to the Northern
Hemisphere (e.g. Takahashi et al., 2009) continues to exist (Bakker et al.,
2016, 2022, Fig. B1) and to lead to substantially higher uncertainty in
the
OCEAN
estimate for the Southern Hemisphere (Watson et al., 2020;
Gloege et al., 2021). This discrepancy, which also hampers model
improvement, points to the need for increased high-quality
CO
observations, especially in the Southern Ocean. At the same time, model
uncertainty is illustrated by the large spread of individual GOBM estimates
(indicated by shading in Fig. 13) and highlights the need for model
improvement. The diverging trends in
OCEAN
from different methods is a
matter of concern, which is unresolved. The assessment of the net
land–atmosphere exchange from DGVMs and atmospheric inversions also shows
substantial discrepancy, particularly for the estimate of the total land
flux over the northern extratropics. This discrepancy highlights the
difficulty to quantify complex processes (CO
fertilization, nitrogen
deposition and fertilizers, climate change and variability, land management,
etc.) that collectively determine the net land CO
flux. Resolving the
differences in the Northern Hemisphere land sink will require the
consideration and inclusion of larger volumes of observations.
We provide metrics for the evaluation of the ocean and land models and the
atmospheric inversions (Figs. B2 to B4). These metrics expand the use of
observations in the global carbon budget, helping (1) to support improvements
in the ocean and land carbon models that produce the sink estimates and (2) to constrain the representation of key underlying processes in the models
and allocate the regional partitioning of the CO
fluxes. However,
GOBMs skills have changed little since the introduction of the ocean model
evaluation. The additional simulation allows for direct comparison with
interior ocean anthropogenic carbon estimates and suggests that the models
underestimate anthropogenic carbon uptake and storage. This is an initial
step towards the introduction of a broader range of observations that we
hope will support continued improvements in the annual estimates of the
global carbon budget.
We assessed before that a sustained decrease of
1 % in global emissions
could be detected at the 66 % likelihood level after a decade only (Peters
et al., 2017). Similarly, a change in behaviour of the land and/or ocean
carbon sink would take as long to detect and much longer if it emerges more
slowly. Continuing with reducing the carbon imbalance on annual to decadal timescales, regionalizing the carbon budget, and integrating multiple variables
are powerful ways to shorten the detection limit and ensure the research
community can rapidly identify issues of concern in the evolution of the
global carbon cycle under the current rapid and unprecedented changing
environmental conditions.
Conclusions
The estimation of global CO
emissions and sinks is a major effort by
the carbon cycle research community that requires a careful compilation and
synthesis of measurements, statistical estimates, and model results. The
delivery of an annual carbon budget serves two purposes. First, there is a
large demand for up-to-date information on the state of the anthropogenic
perturbation of the climate system and its underpinning causes. A broad
stakeholder community relies on the data sets associated with the annual
carbon budget including scientists, policy makers, businesses, journalists,
and non-governmental organizations engaged in adapting to and mitigating
human-driven climate change. Second, over the last decades we have seen
unprecedented changes in the human and biophysical environments (e.g.
changes in the growth of fossil fuel emissions, impacts of the COVID-19 pandemic,
Earth's warming, and strength of the carbon sinks), which call for frequent
assessments of the state of the planet, a better quantification of the
causes of changes in the contemporary global carbon cycle, and an improved
capacity to anticipate its evolution in the future. Building this scientific
understanding to meet the extraordinary climate mitigation challenge
requires frequent, robust, transparent, and traceable data sets and methods
that can be scrutinized and replicated. This paper, via “living data”, helps
to keep track of new budget updates.
Data availability
The data presented here are made available in the belief that their wide
dissemination will lead to greater understanding and new scientific insights
of how the carbon cycle works, how humans are altering it, and how we can
mitigate the resulting human-driven climate change. Full contact details and
information on how to cite the data shown here are given at the top of each
page in the accompanying database and summarized in Table 2.
The accompanying database includes three Excel files organized into the
following spreadsheets.
The file Global_Carbon_Budget_2022v0.1.xlsx includes the following items:
summary;
the global carbon budget (1959–2021);
the historical global carbon budget (1750–2021);
global CO
emissions from fossil fuels and cement production by fuel type and the per capita emissions (1850–2021);
CO
emissions from land-use change from the individual bookkeeping models (1959–2021);
ocean CO
sink from the individual ocean models and
CO
-based products (1959–2021);
terrestrial CO
sink from the individual DGVMs (1959–2021);
cement carbonation CO
sink (1959–2021).
The file National_Fossil_Carbon_Emissions_2022v0.1.xlsx includes the following items:
summary;
territorial country CO
emissions from fossil fuels and cement production (1850–2021);
consumption country CO
emissions from fossil fuels and cement production and emissions transfer from the international trade of goods and services (1990–2020) using CDIAC/UNFCCC data as reference;
emissions transfers (consumption minus territorial emissions; 1990–2020);
country definitions.
The file National_LandUseChange_Carbon_Emissions_2022v0.1xlsx includes the
following items:
summary
territorial country CO
emissions from land-use change (1850–2021) from three bookkeeping models;
All three spreadsheets are published by the Integrated Carbon Observation
System (ICOS) Carbon Portal and are available at
(Friedlingstein et al., 2022b). National
emissions data are also available from the Global Carbon Atlas
, last access: 25 September 2022) and from
Our World in Data (
, last access: 25
September 2022).
Appendix A:
Supplementary tables
Table A1
Comparison of the processes included in the bookkeeping method and DGVMs in their estimates of
LUC
and
LAND
. See Table 4 for model references. All models include deforestation and forest regrowth after abandonment of agriculture (or from afforestation activities on agricultural land). Processes relevant for
LUC
are only described for the DGVMs used with land-cover change in this study. Here we use the term “DGVM” in the broadest sense in terms of global vegetation models which are able to dynamically adjust to imposed land use and land-use change (LULCC).
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Table A2
Comparison of the processes and model set-up for the Global Ocean Biogeochemistry Models for their estimates of
OCEAN
. See Table 4 for model references.
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Table A3
Description of ocean data products used for assessment of
OCEAN
. See Table 4 for references.
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Table A4
Comparison of the inversion set-up and input fields for the atmospheric inversions. Atmospheric inversions see the full CO
fluxes, including the anthropogenic and pre-industrial fluxes. Hence, they need to be adjusted for the pre-industrial flux of CO
from the land to the ocean that is part of the natural carbon cycle before they can be compared with
OCEAN
and
LAND
from process models. See Table 4 for references.
(Schuldt et al., 2021).
(Schuldt et al., 2022).
GCP-GridFED v2021.2, v2021.3, v2022.1, and v2022.2 (Jones et al., 2022) are updates through the year 2021 of the GCP-GridFED dataset presented by Jones et al. (2021).
Ocean prior is not optimized.
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Table A5
Attribution of
CO
measurements for the year 2021 included in SOCATv2022 (Bakker et al., 2016, 2022) to inform ocean
CO
-based data products.
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Table A6
Aircraft measurement programmes archived by Cooperative Global Atmospheric Data Integration Project (CGADIP; Schuldt et al., 2021, 2022) that contribute to the evaluation of the atmospheric inversions (Fig. B4).
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Table A7
Main methodological changes in the global carbon budget since first publication. Methodological changes introduced in one year are kept for the following years unless noted. Empty cells mean there were no methodological changes introduced that year.
Raupach et al. (2007).
Canadell et al. (2007).
GCP (2007).
Le Quéré et al. (2009).
Friedlingstein et al. (2010).
Peters et al. (2012b).
Le Quéré et al. (2013); Peters et al. (2013).
Le Quéré et al. (2014).
Le Quéré et al. (2015a);
Le Quéré et al. (2015b).
Le Quéré et al. (2016).
Le Quéré et al. (2018a).
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Table A8
Mapping of global carbon cycle models land flux definitions to the definition of the LULUCF net flux used in national reporting to UNFCCC. Non-intact lands are used here as proxy for “managed lands” in the country reporting; national greenhouse gas inventories (NGHGI) are gap filled (see Sect. C2.3 for details). Where available, we provide independent estimates of certain fluxes for comparison (values are in GtC yr
−1
).
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Table A9
Funding supporting the production of the various components of the global carbon budget in addition to the authors' supporting institutions (see the Acknowledgements for further details).
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Appendix B:
Supplementary figures
Figure B1
Ensemble mean air–sea CO
flux
from
(a)
global ocean biogeochemistry models and
(b)
CO
-based data products, averaged over the 2012–2021
period (kgC m
−2
yr
−1
).
Positive numbers indicate a flux into the ocean.
(c)
Gridded SOCAT v2022
CO
measurements, averaged over the 2012–2021 period
µatm
). In
(a)
, model simulation A is shown. The data products
represent the contemporary flux, i.e. including outgassing of riverine
carbon, which is estimated to amount to 0.65 GtC yr
−1
globally.
Figure B2
Evaluation of the GOBMs and data products using the root-mean-squared error (RMSE) for the period 1990 to 2021 between the
individual surface ocean
CO
mapping schemes and the
SOCAT v2022 database. The
axis shows the amplitude of the interannual
variability of the air–sea CO
flux (A-IAV), taken as
the standard deviation of the detrended annual time series. Results are
presented for the globe, northern extratropics (
30
N), tropics
(30
S–30
N), and southern extratropics (
30
S) for
the GOBMs (see legend, circles) and for the
CO
-based data products (star symbols). The
CO
-based data products use the SOCAT database and
are therefore not independent of the data (see Sect. 2.4.1).
Figure B3
Evaluation of the DGVMs using the International Land
Model Benchmarking system (ILAMB; Collier et al., 2018) (left) absolute
skill scores and (right) skill scores relative to other models. The
benchmarking is done with observations for vegetation biomass (Saatchi et
al., 2011; and global carbon unpublished data; Avitabile et al., 2016), GPP
(Jung et al., 2010; Lasslop et al., 2010), leaf area index (De Kauwe et al.,
2011; Myneni et al., 1997), ecosystem respiration (Jung et al., 2010;
Lasslop et al., 2010), soil carbon (Hugelius et al., 2013; Todd-Brown et al.,
2013), evapotranspiration (De Kauwe et al., 2011), and runoff (Dai and
Trenberth, 2002). For each model–observation comparison a series of error
metrics are calculated. Scores are then calculated as an exponential
function of each error metric. Finally, for each variable the multiple scores
from different metrics and observational data sets are combined to give the
overall variable scores shown in the left panel. Overall variable scores
increase from 0 to 1 with improvements in model performance. The set of
error metrics vary with data set and can include metrics based on the period
mean, bias, root-mean-squared error, spatial distribution, interannual
variability and seasonal cycle. The relative skill score shown in the right
panel is a
score, which indicates in units of standard deviation the model
scores relative to the multi-model mean score for a given variable. Grey
boxes represent missing model data.
Figure B4
Evaluation of the atmospheric inversion products. The
mean of the model minus observations is shown for four latitude bands in
four periods: (first panel) 2001–2021, (second panel) 2001–2010, (third
panel) 2011–2021, and (fourth panel) 2015–2021. The nine systems are compared to
independent CO
measurements made aboard aircraft
over many areas of the world between 2 and 7 km above sea level. Aircraft
measurements archived in the Cooperative Global Atmospheric Data Integration
Project (Schuldt et al., 2021, 2022) from sites, campaigns, or
programmes that have not been assimilated and cover at least 9 months (except
for SH programmes) between 2001 and 2021 have been used to compute the biases
of the differences in four 45
latitude bins. Land and ocean data
are used without distinction, and observation density varies strongly with
latitude and time, as seen in the lower panels.
Figure B5
Comparison of the estimates of each component of the
global carbon budget in this study (black line) with the estimates released
annually by the GCP since 2006. Grey shading shows the uncertainty bounds
representing
1 standard deviation of the current global carbon
budget based on the uncertainty assessments described in Appendix C.
CO
emissions from
(a)
fossil
CO
emissions (
FOS
) and
(b)
land-use change (
LUC
) and their partitioning
among
(c)
the atmosphere (
ATM
),
(d)
land
LAND
), and
(e)
ocean
OCEAN
). See the legend for the corresponding years and
Tables 3 and A7 for references. The budget year corresponds to the year when
the budget was first released (all values are in GtC yr
−1
).
Figure B6
Differences in the HYDE/LUH2 land-use forcing used for
the global carbon budgets GCB2020 (Friedlingstein et al., 2021), GCB2021
(Friedlingstein et al., 2022a), and GCB2022 (Friedlingstein et al., 2022b).
Shown are year-to-year changes in cropland area
(b)
and pasture
area
(c)
. To illustrate the relevance of the update in the
land-use forcing to the recent trends in
LUC
, the
top panel shows the land-use emission estimate from the bookkeeping model
BLUE (original model output, i.e. excluding peat fire and drainage
emissions).
Appendix C:
Extended methodology
C1
Methodology: fossil fuel CO
emissions (
FOS
C1.1
Cement carbonation
From the moment it is created, cement begins to absorb CO
from the
atmosphere, a process known as “cement carbonation”. We estimate this
CO
sink, from 1931 onwards as the average of two studies in the
literature (Cao et al., 2020; Guo et al., 2021). The Global Cement and
Concrete Association reports a much lower carbonation rate, but this is
based on the highly conservative assumption of 0 % mortar (GCCA, 2021).
Modelling cement carbonation requires estimation of a large number of
parameters, including the different types of cement material in different
countries, the lifetime of the structures before demolition, the lifetime of cement waste
after demolition, and the volumetric properties of structures
(Xi et al., 2016). Lifetime is an important parameter because demolition
results in the exposure of new surfaces to the carbonation process. The main
reasons for differences between the two studies appear to be the assumed
lifetimes of cement structures and the geographic resolution, but the
uncertainty bounds of the two studies overlap.
C1.2
Emissions embodied in goods and services
CDIAC, UNFCCC, and BP national emission statistics “include greenhouse gas
emissions and removals taking place within national territory and offshore
areas over which the country has jurisdiction” (Rypdal et al., 2006) and
are called territorial emission inventories. Consumption-based emission
inventories allocate emissions to products that are consumed within a
country and are conceptually calculated as the territorial emissions minus
the “embodied” territorial emissions to produce exported products plus the
emissions in other countries to produce imported products (consumption is equal to territorial minus exports plus imports). Consumption-based emission attribution
results (e.g. Davis and Caldeira, 2010) provide additional information to
territorial-based emissions that can be used to understand emission drivers
(Hertwich and Peters, 2009) and quantify emission transfers by the trade of
products between countries (Peters et al., 2011b). The consumption-based
emissions have the same global total but reflect the trade-driven movement
of emissions across the Earth's surface in response to human activities. We
estimate consumption-based emissions from 1990–2020 by enumerating the
global supply chain using a global model of the economic relationships
between economic sectors within and between every country (Andrew and
Peters, 2013; Peters et al., 2011a). Our analysis is based on the economic
and trade data from the Global Trade and Analysis Project (GTAP; Narayanan
et al., 2015), and we make detailed estimates for the years 1997 (GTAP
version 5); 2001 (GTAP6); and 2004, 2007, 2011, and 2014 (GTAP10.0a),
covering 57 sectors and 141 countries and regions. The detailed results are
then extended into an annual time series from 1990 to the latest year of the
gross domestic product (GDP) data (2020 in this budget) using GDP data by
expenditure in current exchange rate of US dollars (USD; from the UN
National Accounts main aggregates database; UN, 2021) and time series of
trade data from GTAP (based on the methodology in Peters et al., 2011a). We
estimate the sector-level CO
emissions using the GTAP data and
methodology, add the flaring and cement emissions from our fossil CO
dataset, and then scale the national totals (excluding bunker fuels) to
match the emission estimates from the carbon budget. We do not provide a
separate uncertainty estimate for the consumption-based emissions; however, based
on model comparisons and sensitivity analysis, they are unlikely to be
significantly different than for the territorial emission estimates (Peters
et al., 2012a).
C1.3
Uncertainty assessment for
FOS
We estimate the uncertainty of the global fossil CO
emissions at
5 % (scaled down from the published
10 % at
to the use of
bounds reported here; Andres et al., 2012).
This is consistent with a more detailed analysis of uncertainty of
8.4 % at
(Andres et al., 2014) and at the high end of
the range of
5 %–10 % at
reported by (Ballantyne
et al., 2015). This includes an assessment of uncertainties in the amounts
of fuel consumed, the carbon and heat contents of fuels, and the combustion
efficiency. While we consider a fixed uncertainty of
5 % for all
years, the uncertainty as a percentage of emissions is growing with time
because of the larger share of global emissions from emerging economies and
developing countries (Marland et al., 2009). Generally, emissions from
mature economies with good statistical processes have an uncertainty of only
a few percent (Marland, 2008), while emissions from strongly developing
economies such as China have uncertainties of around
10 % (for
; Gregg et al., 2008; Andres et al., 2014). Uncertainties
in emissions are likely to be mainly systematic errors related to underlying
biases of energy statistics and to the accounting method used by each
country.
C1.4
Growth rate in emissions
We report the annual growth rate in emissions for adjacent years (in percent
per year) by calculating the difference between the 2 years and then
normalizing to the emissions in the first year:
FOS
FOS
FOS
100
%. We apply a leap-year
adjustment where relevant to ensure valid interpretations of annual growth
rates. This affects the growth rate by about 0.3 % per year (
366
) and causes
calculated growth rates to go up approximately 0.3 % if the first year is
a leap year and down 0.3 % if the second year is a leap year.

The relative growth rate of
FOS
over time periods of greater than 1
year can be rewritten using its logarithm equivalent as follows:
(C1)
FOS
FOS
ln
FOS
Here we calculate relative growth rates in emissions for multi-year periods
(e.g. a decade) by fitting a linear trend to
ln (
FOS
in Eq. (2), reported
in percent per year.
C1.5
Emissions projection for 2022
To gain insight into emission trends for 2022, we provide an assessment of
global fossil CO
emissions,
FOS
, by combining individual
assessments of emissions for China, USA, the EU, and India (the four
countries/regions with the largest emissions) and the rest of the world.
The methods are specific to each country or region, as described in detail
below.
China
We use a regression between monthly data for each fossil
fuel and cement and annual data for consumption of fossil fuels or production of cement to project full-year growth in fossil fuel consumption
and cement production. The monthly data for each product consists of the
following elements.
Coal.
This product uses a proprietary estimate for monthly consumption of main coal types from SX Coal.
Oil.
The product uses production data from the National Bureau of Statistics (NBS), plus net imports from the China Customs Administration (i.e. gross supply of oil, not including inventory changes).
Natural gas.
This product uses the same source as for oil.
Cement.
This product uses production data from NBS.
For oil, we use data for production and net imports of refined oil products
rather than crude oil. This choice is made because refined products are one
step closer to actual consumption and because crude oil can be subject to
large market-driven and strategic inventory changes that are not captured by
available monthly data.
For each fuel and cement, we make a Bayesian linear regression between
year-on-year cumulative growth in supply (production for cement) and
full-year growth in consumption (production for cement) from annual
consumption data. In the regression model, the growth rate in annual
consumption (production for cement) is modelled as a regression parameter
multiplied by the cumulative year-on-year growth rate from the monthly data
through July of each year for past years (through 2021). We use broad
Gaussian distributions centred around 1 as priors for the ratios between
annual and through-July growth rates. We then use the posteriors for the
growth rates together with cumulative monthly supply or production data through
July of 2022 to produce a posterior predictive distribution for the
full-year growth rate for fossil fuel consumption and cement production in
2022.
If the growth in supply or production through July were an unbiased estimate of
the full-year growth in consumption or production, the posterior distribution
for the ratio between the monthly and annual growth rates would be centred
around 1. However, in practice the ratios are different from 1 (in most
cases below 1). This is a result of various biassing factors such as uneven
evolution in the first and second half of each year, inventory changes that
are somewhat anti-correlated with production and net imports, differences in
statistical coverage, and other factors that are not captured in the monthly
data.
For fossil fuels, the mean of the posterior distribution is used as the
central estimate for the growth rate in 2022, while the edges of a 68 %
credible interval (analogous to a
confidence interval) are used for
the upper and lower bounds.
For cement, the evolution from January to July has been highly atypical
owing to the ongoing turmoil in the construction sector, and the results of
the regression analysis are heavily biased by equally atypical but different
dynamics in 2021. For this reason, we use an average of the results of the
regression analysis and the plain growth in cement production through July
2022, since this results in a growth rate that seems more plausible and in
line with where the cumulative cement production appears to be headed at the
time of writing.
USA
We use emissions estimated by the U.S. Energy Information
Administration (EIA) in their Short-Term Energy Outlook (STEO) for emissions
from fossil fuels to get both year-to-date (YTD) information and a full-year projection (EIA, 2022).
The STEO also includes a near-term forecast based on an energy forecasting
model that is updated monthly (last update with preliminary data through
August 2022) and takes into account expected temperatures, household
expenditures by fuel type, energy markets, policies, and other effects. We
combine this with our estimate of emissions from cement production using the
monthly US cement clinker production data from USGS for January–June 2022,
assuming changes in cement production over the first part of the year apply
throughout the year.
India
We use monthly emissions estimates for India updated from
Andrew (2020b) through July 2022. These estimates are derived from many
official monthly energy and other activity data sources to produce direct
estimates of national CO
emissions without the use of proxies.
Emissions from coal are then extended to August using a regression
relationship based on power generated from coal, coal dispatches by Coal
India Ltd., the composite Purchasing Managers' Index, time, and days per month. For the last 3–5 months of the year, each series is extrapolated assuming typical trends.
EU
We use a refinement to the methods presented by Andrew (2021),
deriving emissions from monthly energy data reported by Eurostat. Some data
gaps are filled using data from the Joint Organizations Data Initiative
(JODI, 2022). Sub-annual cement production data are limited, but data for
Germany and Poland, the two largest producers, suggest a small decline. For
fossil fuels this provides estimates through July. We extend coal emissions
through August using a regression model built from generation of power from
hard coal, power from brown coal, total power generation, and the number of
working days in Germany and Poland, the two biggest coal consumers in the
EU. These are then extended through the end of the year assuming typical
trends. We extend oil emissions by building a regression model between our
monthly CO
estimates and oil consumption reported by the EIA for
Europe in its Short-Term Energy Outlook (September edition) and then using
this model with EIA's monthly forecasts. For natural gas, the strong
seasonal signal allows the use of the bias-adjusted Holt–Winters exponential
smoothing method (Chatfield, 1978).
Rest of the world
We use the close relationship between the growth
in GDP and the growth in emissions (Raupach et al., 2007) to project
emissions for the current year. This is based on a simplified Kaya Identity,
whereby
FOS
(GtC yr
−1
) is decomposed by the product of GDP (USD yr
−1
) and the fossil fuel carbon intensity of the economy (
FOS
GtC USD
−1
) as follows:
(C2)
FOS
GDP
FOS
Taking a time derivative of Eq. (3) and rearranging gives
(C3)
FOS
FOS
GDP
dGDP
FOS
FOS
where the left-hand term is the relative growth rate of
FOS
, and the
right-hand terms are the relative growth rates of GDP and
FOS
respectively, which can simply be added linearly to give the overall growth
rate.
The
FOS
is based on GDP in constant PPP (purchasing power parity) from
the International Energy Agency (IEA) up to 2017 (IEA/OECD, 2019) and
extended using the International Monetary Fund (IMF) growth rates through
2021 (IMF, 2022). Interannual variability in
FOS
is the largest source
of uncertainty in the GDP-based emissions projections. We thus use the
standard deviation of the annual
FOS
for the period 2012–2021 as a measure
of uncertainty, reflecting a
as in the rest of the carbon
budget. For rest-of-world oil emissions growth, we use the global oil demand
forecast published by the EIA less our projections for the other four
regions and estimate uncertainty as the maximum absolute difference over
the period available for such forecasts using the specific monthly edition
(e.g. August) compared to the first estimate based on more solid data in the
following year (April).
World
The global total is the sum of each of the countries and
regions.
C2
Methodology: CO
emissions from land-use, land-use change, and forestry (
LUC
The net CO
flux from land-use, land-use change, and forestry
LUC
, called land-use change emissions in the rest of the text)
includes CO
fluxes from deforestation, afforestation, logging, and
forest degradation (including harvest activity); shifting cultivation (cycle
of cutting forest for agriculture, then abandoning); and regrowth of forests
following wood harvest or abandonment of agriculture. Emissions from peat
burning and drainage are added from external datasets (see Appendix C2.1
below). Only some land-management activities are included in our land-use
change emissions estimates (Table A1). Some of these activities lead to
emissions of CO
to the atmosphere, while others lead to CO
sinks.
LUC
is the net sum of emissions and removals due to all
anthropogenic activities considered. Our annual estimate for 1960–2021 is
provided as the average of results from three bookkeeping approaches
(Appendix C2.1 below): an estimate using the Bookkeeping of Land Use
Emissions model (Hansis et al., 2015; hereafter BLUE), one using the
compact Earth system model OSCAR (Gasser et al., 2020), with both BLUE and OSCAR
being updated here to new land-use forcing covering the time period until
2021, and an updated version of the estimate published by Houghton and
Nassikas (2017) (hereafter updated H&N2017). All three data sets are then
extrapolated to provide a projection for 2022 (Appendix C2.5 below). In
addition, we use results from dynamic global vegetation models (DGVMs; see
Appendix C2.2 and Table 4) to help quantify the uncertainty in
LUC
(Appendix C2.4) and thus better characterize our understanding.
Note that in this budget, we use the scientific
LUC
definition,
which counts fluxes due to environmental changes on managed land towards
LAND
, as opposed to the national greenhouse gas inventories under the
UNFCCC, which include them in
LUC
and thus often report smaller
land-use emissions (Grassi et al., 2018; Petrescu et al., 2020). However, we
provide a methodology of mapping of the two approaches to each other further
below (Appendix C2.3).
C2.1
Bookkeeping models
Land-use change CO
emissions and uptake fluxes are calculated by three
bookkeeping models. These are based on the original bookkeeping approach of
Houghton (2003) that keeps track of the carbon stored in vegetation and
soils before and after a land-use change (transitions between various
natural vegetation types, croplands, and pastures). Literature-based
response curves describe decay of vegetation and soil carbon, including
transfer to product pools of different lifetimes, as well as carbon uptake
due to regrowth. In addition, the bookkeeping models represent long-term
degradation of primary forest as lowered standing vegetation and soil carbon
stocks in secondary forests and include forest management practices such as
wood harvests.
BLUE and the updated H&N2017 exclude land ecosystems' transient response
to changes in climate, atmospheric CO
, and other environmental factors
and base the carbon densities on contemporary data from literature and
inventory data. Since carbon densities thus remain fixed over time, the
additional sink capacity that ecosystems provide in response to
CO
fertilization and some other environmental changes is not captured
by these models (Pongratz et al., 2014). On the contrary, OSCAR includes
this transient response, and it follows a theoretical framework (Gasser and
Ciais, 2013) that allows separating bookkeeping land-use emissions and the
loss of additional sink capacity. Only the former is included here, while
the latter is discussed in Appendix D4. The bookkeeping models differ in (1) computational units (spatially explicit treatment of land-use change for
BLUE, country-level for the updated H&N2017 and OSCAR), (2) processes
represented (see Table A1), and (3) carbon densities assigned to vegetation
and soil of each vegetation type (based on literature for BLUE and the updated
H&N2017, calibrated to DGVMs for OSCAR). A notable difference between
models exists with respect to the treatment of shifting cultivation. The
update of H&N2017, introduced for the GCB2021 (Friedlingstein et al.,
2022a), changed the approach over the earlier H&N2017 version: H&N2017
had assumed the “excess loss” of tropical forests, i.e. when the Global
Forest Resources Assessment (FRA; FAO 2020) indicated that a forest loss larger
than the increase in agricultural areas from FAO (FAOSTAT 2021) resulted
from converting forests to croplands at the same time older croplands were
abandoned. Those abandoned croplands began to recover to forests after 15 years. The updated H&N2017 now assumes that forest loss in excess of
increases in cropland and pastures represented an increase in shifting
cultivation. When the excess loss of forests was negative, it was assumed
that shifting cultivation was returned to forest. Historical areas in
shifting cultivation were extrapolated taking into account country-based
estimates of areas in fallow in 1980 (FAO/UNEP, 1981) and expert opinion
(from Heinimann et al., 2017). In contrast, the BLUE and OSCAR models
include sub-grid-scale transitions between all vegetation types.
Furthermore, the updated H&N2017 assumes conversion of natural grasslands
to pasture, while BLUE and OSCAR allocate pasture transitions proportionally
on all natural vegetation that exists in a grid cell. This is one reason for
generally higher emissions in BLUE and OSCAR. Bookkeeping models do not
directly capture carbon emissions from peat fires, which can create large
emissions and interannual variability due to synergies of land-use and
climate variability in Southeast Asia, particularly during El-Niño
events, nor do they capture emissions from the organic layers of drained peat soils. To
correct for this, we add peat fire emissions based on the Global Fire
Emission Database (GFED4s; van der Werf et al., 2017) to the bookkeeping
models' output. Emissions are calculated by multiplying the mass of dry
matter emitted by peat fires with the C emission factor for peat fires
indicated in the GFED4s database. Emissions from deforestation fires used to
derive
LUC
projections for 2022 are calculated analogously. As these
satellite-derived estimates of peat fire emissions start in 1997 only, we
follow the approach by Houghton and Nassikas (2017) for earlier years, which
ramps up from zero emissions in 1980 to 0.04 Pg C yr
−1
in 1996,
reflecting the onset of major clearing of peatlands in equatorial Southeast
Asia in the 1980s. Similarly, we add estimates of peat drainage emissions.
In recent years, more peat drainage estimates that provide spatially
explicit data have become available, and we thus extended the number of peat
drainage datasets considered. We employ FAO peat drainage emissions
1990–2019 from croplands and grasslands (Conchedda and Tubiello, 2020),
peat drainage emissions 1700–2010 from simulations with the DGVM
ORCHIDEE-PEAT (Qiu et al., 2021), and peat drainage emissions 1701–2021
from simulations with the DGVM LPX-Bern (Lienert and Joos, 2018; Müller
and Joos, 2021), applying the updated LUH2 forcing as also used by BLUE,
OSCAR, and the DGVMs. We extrapolate the FAO data to 1850–2021 by keeping the
post-2019 emissions constant at 2019 levels, by linearly increasing tropical
drainage emissions between 1980 and 1990 starting from 0 GtC yr
−1
in 1980,
consistent with H&N2017's assumption (Houghton and Nassikas, 2017), and
by keeping pre-1990 emissions from the often old drained areas of the
extratropics constant at 1990 emission levels. ORCHIDEE-PEAT data are
extrapolated to 2011–2021 by replicating the average emissions in 2000–2010
(Chunjing Qiu,, personal communication, 2022). Further, ORCHIDEE-PEAT only provides peat drainage
emissions north of 30
N, and thus we fill the regions south of
30
N using the average peat drainage emissions from FAO and
LPX-Bern. The average of the carbon emission estimates by the three
different peat drainage datasets is added to the bookkeeping models to obtain
net
LUC
and gross sources.
The three bookkeeping estimates used in this study differ with respect to
the land-use change data used to drive the models. The updated H&N2017
bases its estimates directly on the Forest Resource Assessment of the FAO,
which provides statistics on forest area change and management at intervals
of 5 years and is currently updated until 2020 (FAO, 2020). The data are based on
country reporting to FAO and may include remote-sensing information in more
recent assessments. Changes in land-use other than forests are based on
annual, national changes in cropland and pasture areas reported by FAO
(FAOSTAT, 2021). On the other hand, BLUE uses the harmonized land-use change
data LUH2-GCB2022 covering the entire 850–2021 period (an update to the
previously released LUH2 v2h dataset; Hurtt et al., 2017; Hurtt et al.,
2020), which was also used as input to the DGVMs (Appendix C2.2). It
describes land-use change, also based on the FAO data as described in
Appendix C2.2 and the HYDE3.3 dataset (Klein Goldewijk et al.,
2017a, b), but provided at a quarter-degree spatial resolution,
considering sub-grid-scale transitions between primary forest, secondary
forest, primary non-forest, secondary non-forest, cropland, pasture,
rangeland, and urban land (Hurtt et al., 2020; Chini et al., 2021).
LUH2-GCB2022 provides a distinction between rangelands and pasture, based on
inputs from HYDE. To constrain the models' interpretation on whether
rangeland implies the original natural vegetation to be transformed to
grassland or not (e.g. browsing on shrubland), a forest mask was provided
with LUH2-GCB2021; forest is assumed to be transformed to grasslands, while
other natural vegetation remains (in case of secondary vegetation) or is
degraded from primary to secondary vegetation (Ma et al., 2020). This is
implemented in BLUE. OSCAR was run with both LUH2-GCB2022 and FAO/FRA (as
used with the updated H&N2017), where the drivers of the latter were
linearly extrapolated to 2021 using their 2015–2020 trends. The best-guess
OSCAR estimate used in our study is a combination of results for
LUH2-GCB2022 and FAO/FRA land-use data and a large number of perturbed
parameter simulations weighted against a constraint (the cumulative
LAND
over 1960–2020 of last year's GCB). As the record of the updated
H&N2017 ends in 2020, we extend it to 2021 by adding the difference of
the emissions from tropical deforestation and degradation, peat drainage,
and peat fire between 2020 and 2021 to the model's estimate for 2020 (i.e.
considering the yearly anomalies of the emissions from tropical
deforestation and degradation, peat drainage, and peat fire). The same
method is applied to all three bookkeeping estimates to provide a projection
for 2022.
For
LUC
from 1850 onwards we average the estimates from BLUE, the
updated H&N2017, and OSCAR. For the cumulative numbers starting 1750, an
average of four earlier publications is added (30
20 PgC 1750–1850,
rounded to the nearest 5; Le Quéré et al., 2016).
We provide estimates of the gross land-use change fluxes from which the
reported net land-use change flux,
LUC
, is derived as a sum. Gross
fluxes are derived internally by the three bookkeeping models. Gross
emissions stem from decaying material left dead on site and from products
after clearing of natural vegetation for agricultural purposes or wood
harvesting, emissions from peat drainage and peat burning, and, for BLUE,
additionally from degradation from primary to secondary land through usage
of natural vegetation as rangeland. Gross removals stem from regrowth after
agricultural abandonment and wood harvesting. Gross fluxes for the updated
H&N2017 for 2020 and for the 2022 projection of all three models were
calculated by the change in emissions from tropical deforestation and
degradation and peat burning and drainage as described for the net
LUC
above. As tropical deforestation and degradation and peat burning and
drainage all only lead to gross emissions to the atmosphere, only gross (and
net) emissions are adjusted this way, while gross sinks are assumed to
remain constant over the previous year..
This year, we provide an additional split of the net
LUC
into
component fluxes to better identify reasons for divergence between
bookkeeping estimates and to give more insight into the drivers of sources
and sinks. This split distinguishes between fluxes from deforestation
(including due to shifting cultivation); fluxes from organic soils (i.e.
peat drainage and fires); afforestation, reafforestation, and wood harvest (i.e. fluxes in
forests from slash and product decay following wood harvesting, regrowth
associated with wood harvesting or after abandonment, including
reforestation and in shifting cultivation cycles, and afforestation); and fluxes
associated with all other transitions.
C2.2
Dynamic global vegetation models (DGVMs)
Land-use change CO
emissions have also been estimated using an
ensemble of 16 DGVM simulations. The DGVMs account for deforestation and
regrowth, the most important components of
LUC
, but they do not
represent all processes resulting directly from human activities on land
(Table A1). All DGVMs represent processes of vegetation growth and
mortality, as well as decomposition of dead organic matter associated with
natural cycles, and include the vegetation and soil carbon response to
increasing atmospheric CO
concentration and to climate variability and
change. Most models explicitly simulate the coupling of carbon and nitrogen
cycles and account for atmospheric N deposition and N fertilizers (Table A1). The DGVMs are independent of the other budget terms except for their
use of atmospheric CO
concentration to calculate the fertilization
effect of CO
on plant photosynthesis.
All DGVMs use the LUH2-GCB2022 dataset as input, which includes the HYDE
cropland/grazing land dataset (Klein Goldewijk et al., 2017a, b), and
additional information on land-cover transitions and wood harvest. DGVMs use
annual, half-degree (regridded from 5 min resolution) fractional data on
cropland and pasture from HYDE3.3.
DGVMs that do not simulate subgrid-scale transitions (i.e. net land-use
emissions; see Table A1) used the HYDE information on agricultural area
change. For all countries, with the exception of Brazil and the Democratic
Republic of the Congo, these data are based on the available annual FAO
statistics of change in agricultural land area available from 1961 up to and
including 2017. The FAO retrospectively revised their reporting for the
Democratic Republic of the Congo, which was newly available until 2020. In
addition to FAO country-level statistics, the HYDE3.3 cropland/grazing land
dataset is constrained spatially based on multi-year satellite land cover
maps from ESA CCI LC (see below). After the year 2017, LUH2 extrapolates,
on a grid cell basis, the cropland, pasture, and urban data linearly based on
the trend over the previous 5 years to generate data until the year 2021.
This extrapolation methodology is not appropriate for countries that have
experienced recent rapid changes in the rate of land-use change, e.g. Brazil,
which has experienced a recent upturn in deforestation. Hence, for Brazil we
replace FAO state-level data for cropland and grazing land in HYDE by those
from in-country land cover dataset MapBiomas (collection 6) for 1985–2020
(Souza et al., 2020). ESA-CCI is used to spatially disaggregate as described
below. Similarly, an estimate for the year 2021 is based on the MapBiomas
trend 2015–2020. The pre-1985 period is scaled with the per capita numbers
from 1985 from MapBiomas, and thus this transition is smooth.
HYDE uses satellite imagery from ESA-CCI from 1992–2018 for more detailed
yearly allocation of cropland and grazing land, with the ESA area data
scaled to match the FAO annual totals at country level. The original 300 m spatial resolution data from ESA were aggregated to a 5 arcmin
resolution according to the classification scheme as described in Klein
Goldewijk et al. (2017a).
DGVMs that simulate subgrid scale transitions (i.e. gross land-use
emissions; see Table A1) use more detailed land-use transition and wood
harvest information from the LUH2-GCB2022 data set. LUH2-GCB2022 is an
update of the more comprehensive harmonized land-use data set (Hurtt et al.,
2020) that further includes fractional data on primary and secondary forest
vegetation, as well as all underlying transitions between land-use states
(850-2020; Hurtt et al., 2011, 2017, 2020; Chini et al., 2021; Table A1).
This data set is of quarter-degree fractional areas of land-use states and
all transitions between those states, including a new wood harvest
reconstruction, new representation of shifting cultivation, crop rotations, and
management information, including irrigation and fertilizer application. The
land-use states include five different crop types in addition to splitting
grazing land into managed pasture and rangeland. Wood harvest patterns are
constrained with Landsat-based tree cover loss data (Hansen et al., 2013).
Updates of LUH2-GCB2022 over last year's version (LUH2-GCB2021) are using
the most recent HYDE release (covering the time period up to 2017, revision
to Brazil and the Democratic Republic of the Congo as described above). We
use the same FAO wood harvest data as last year for all dataset years from
1961 to 2019 and extrapolate to the year 2022. The HYDE3.3 population data
are also used to extend the wood harvest time series back in time. Other wood
harvest inputs (for years prior to 1961) remain the same in LUH2. These
updates in the land-use forcing are shown in comparison to the more
pronounced version change from the GCB2020 (Friedlingstein et al., 2020) to
GCB2021, which was discussed in Friedlingstein et al. (2022a) in Fig. B6,
and their relevance for land-use emissions is discussed in Sect. 3.2.2. DGVMs
implement land-use change differently (e.g. an increased cropland fraction
in a grid cell can either be at the expense of grassland, shrubs, or
forest, the latter resulting in deforestation; land cover fractions of the
non-agricultural land differ between models). Similarly, model-specific
assumptions are applied to convert deforested biomass or deforested area
and other forest product pools into carbon, and different choices are made
regarding the allocation of rangelands as natural vegetation or pastures.
The difference between two DGVM simulations (see Appendix C4.1 below), one
forced with historical changes in land use and a second with time-invariant
pre-industrial land cover and pre-industrial wood harvest rates, allows
quantification of the dynamic evolution of vegetation biomass and soil
carbon pools in response to land-use change in each model (
LUC
). Using
the difference between these two DGVMs simulations to diagnose
LUC
means the DGVMs account for the loss of additional sink capacity (around 0.4
0.3 GtC yr
−1
; see Sect. 2.7 and Appendix D4), whereas the
bookkeeping models do not.
As a criterion for inclusion in this carbon budget, we only retain models
that simulate a positive
LUC
during the 1990s, as assessed in the IPCC
AR4 (Denman et al., 2007) and AR5 (Ciais et al., 2013). All DGVMs met this
criterion, although one model was not included in the
LUC
estimate
from DGVMs as it exhibited a spurious response to the transient land cover
change forcing after its initial spin-up.
C2.3
Mapping of national GHG inventory data to
LUC
An approach was implemented to reconcile the large gap between land-use
emissions estimates from bookkeeping models and from national GHG
inventories (NGHGI) (see Table  A8). This gap is due to different approaches
to calculating “anthropogenic” CO
fluxes related to land-use change
and land management (Grassi et al., 2018). In particular, the land sinks due
to environmental change on managed lands are treated as non-anthropogenic in
the global carbon budget, while they are generally considered
anthropogenic in NGHGIs (“indirect anthropogenic fluxes”; Eggleston et
al., 2006). Building on previous studies (Grassi et al., 2021), the approach
implemented here adds the DGVM estimates of CO
fluxes due to
environmental change from countries' managed forest area (part of
LAND
) to the
LUC
flux. This sum is expected to be conceptually
more comparable to LULUCF than
LUC
LUC
data are taken from bookkeeping models, in line with the global carbon
budget approach. To determine
LAND
on managed forest, the following
steps were taken: spatially gridded data of “natural” forest net biome productivity (NBP)
LAND
, i.e. due to environmental change and excluding land-use change
fluxes) were obtained with S2 runs from DGVMs up to 2021 from the TRENDY v11
dataset. Results were first masked with a forest map that is based on Hansen
(Hansen et al., 2013) tree cover data. To do this conversion (“tree” cover
to “forest” cover), we exclude grid cells with less than 20 % tree cover
and isolated pixels with maximum connectivity less than 0.5 ha following the
FAO definition of forest. Forest NBPs are then further masked with the
“intact” forest map for the year 2013, i.e. forest areas characterized by
no remotely detected signs of human activity (Potapov et al., 2017). This
way, we obtained the
LAND
in “intact” and “non-intact” forest area,
which previous studies (Grassi et al., 2021) indicated to be a good proxy,
respectively, for “unmanaged” and “managed” forest area in the NGHGI.
Note that only four models (CABLE-POP, CLASSIC, JSBACH and YIBs) had forest NBP
at grid-cell level. For the other DGVMs, when a grid cell had forest, all
the NBP was allocated to forest. However, since S2 simulations use
pre-industrial forest cover masks that are at least 20 % larger than
today's forest (Hurtt et al., 2020), we corrected this NBP using a ratio between
observed (based on Hansen et al., 2013) and prescribed (from DGVMs) forest cover. This
ratio is calculated for each individual DGVM that provides information on
prescribed forest cover (LPX-Bern, OCN, JULES, VISIT, VISIT-NIES, SDGVM).
For the others (IBIS, CLM5.0, ORCHIDEE, ISAM, DLEM, LPJ-GUESS), a common
ratio (median ratio of all the 10 models that provide information on
prescribed forest cover) is used. The details of the method used are
explained in Alkama (2022).
LULUCF data from NGHGIs are from Grassi et al. (2022a). While Annex I
countries report a complete time series 1990–2020, for non-Annex I countries
gap-filling measures were applied through linear interpolation between two points
and/or through extrapolation backward (till 1990) and forward (till 2020)
using the single closest available data point. For all countries, the estimates of
the year 2021 are assumed to be equal to those of 2020. These data include
all CO
fluxes from land considered managed, which in principle encompasses
all land uses (forest land, cropland, grassland, wetlands, settlements, and
other land), changes among them, and emissions from organic soils and
fires. In practice, although almost all Annex I countries report all land
uses, many non-Annex I countries report only on deforestation and forest
land, and only few countries report on other land uses. In most cases,
NGHGIs include most of the natural response to recent environmental change
because they use direct observations (e.g. national forest inventories)
that do not allow for separating direct and indirect anthropogenic effects
(Eggleston et al., 2006).
To provide additional, largely independent assessments of fluxes on
unmanaged vs. managed lands, we include a DGVM that allows diagnosing fluxes
from unmanaged vs. managed lands by tracking vegetation cohorts of different
ages separately. This model, ORCHIDEE-MICT (Yue et al., 2018), was run using
the same LUH2 forcing as the DGVMs used in this budget (Sect. 2.5) and the
bookkeeping models BLUE and OSCAR (Sect. 2.2). Old-aged forest was
classified as primary forest after a certain threshold of carbon density was
reached again, and the model-internal distinction between primary and
secondary forest was used a proxy for unmanaged vs. managed forests;
agricultural lands are added to the latter to arrive at total managed land.
Table A8 shows the resulting mapping of global carbon cycle models' land flux
definitions to that of the NGHGI (discussed in Sect. 3.2.2). ORCHIDEE-MICT
estimates for
LAND
on intact forests are expected to be higher than based
on DGVMs in combination with the NGHGI managed and unmanaged forest data because
the unmanaged forest area, with about
27×10
km
, is estimated to be
substantially larger by ORCHIDEE-MICT than by
the NGHGI (less than
10×10
km
), while managed forest area is estimated to be smaller (22 compared
to
32×10
km
). Related to this,
LUC
plus
LAND
on non-intact
lands is a larger source estimated by ORCHIDEE-MICT compared to NGHGI. We
also show FAOSTAT emissions totals (FAO, 2021) as a comparison, which include
emissions from net forest conversion and fluxes on forest land (Tubiello et
al., 2021) and CO
emissions from peat drainage and peat fires.
The 2021 data were estimated by including actual 2021 estimates for peatland
drainage and fire and a carry forward from 2020 to 2021 for the forest land
stock change. The FAO data shows a global source of 0.24 GtC yr
−1
averaged over 2012–2021, in contrast to the sink of
0.54 GtC yr
−1
of the gap-filled NGHGI data. Most of this difference is attributable to
different scopes: a focus on carbon fluxes for the NGHGI and a focus on area
and biomass for FAO. In particular, the NGHGI data includes a larger forest
sink for non-Annex 1 countries resulting from a more complete coverage of
non-biomass carbon pools and non-forest land uses. NGHGI and FAO data also
differ in terms of underlying data on forest land (Grassi et al., 2022a).
C2.4
Uncertainty assessment for
LUC
Differences between the bookkeeping models and DGVMs models originate from
three main sources: the different methodologies, which among others lead to
inclusion of the loss of additional sink capacity in DGVMs (see Appendix D1.4), the underlying land-use or land-cover data set, and the different
processes represented (Table A1). We examine the results from the DGVMs
models and of the bookkeeping method and use the resulting variations as a
way to characterize the uncertainty in
LUC
Despite these differences, the
LUC
estimate from the DGVMs multi-model
mean is consistent with the average of the emissions from the bookkeeping
models (Table 5). However, there are large differences among individual DGVMs
(standard deviation at around 0.5 GtC yr
−1
; Table 5), between the
bookkeeping estimates (average difference 1850–2020 BLUE-updated H&N2017
of 0.8 GtC yr
−1
, BLUE-OSCAR of 0.4 GtC yr
−1
, OSCAR-updated
H&N2017 of 0.3 GtC yr
−1
), and between the updated estimate of
H&N2017 and its previous model version (Houghton et al., 2012). A
factorial analysis of differences between BLUE and H&N2017 attributed
them particularly to differences in carbon densities between natural and
managed vegetation or primary and secondary vegetation (Bastos et al.,
2021). Earlier studies additionally showed the relevance of the different
land-use forcing as applied (in updated versions) also in the current study
(Gasser et al., 2020). Ganzenmüller et al. (2022) recently showed that
LUC
estimates with BLUE are substantially smaller when the model is
driven by a new high-resolution land-use dataset (HILDA
). They identified
shifting cultivation and the way it is implemented in LUH2 as a main reason
for this divergence. They further showed that a higher spatial resolution
reduces the estimates of both sources and sinks because successive
transitions are not adequately represented at coarser resolution, which has
the effect that – despite capturing the same extent of transition
areas – overall less area remains pristine at the coarser compared to the
higher resolution.
The uncertainty in
LUC
of
0.7 GtC yr
−1
reflects our best
value judgement that there is at least 68 % chance (
that the true land-use change emission lies within the given range for the
range of processes considered here. Prior to the year 1959, the uncertainty
in
LUC
was taken from the standard deviation of the DGVMs. We assign
low confidence to the annual estimates of
LUC
because of the
inconsistencies among estimates and of the difficulties in quantifying some of
the processes in DGVMs.
C2.5
Emissions projections for
LUC
We project the 2022 land-use emissions for BLUE, the updated H&N2017, and
OSCAR, starting from their estimates for 2021 assuming unaltered peat
drainage, which has low interannual variability, and the highly variable
emissions from peat fires, tropical deforestation and degradation as
estimated using active fire data (MCD14ML; Giglio et al., 2016). These
latter variables scale almost linearly with GFED over large areas (van der Werf et
al., 2017), and thus they allow for tracking fire emissions in deforestation and
tropical peat zones in near-real time.
C3
Methodology: ocean CO
sink
C3.1
Observation-based estimates
We primarily use the observational constraints assessed by IPCC of a mean
ocean CO
sink of 2.2
0.7 GtC yr
−1
for the 1990s (90 %
confidence interval; Ciais et al., 2013) to verify that the GOBMs provide a
realistic assessment of
OCEAN
. This is based on indirect observations
with seven different methodologies and their uncertainties and further
use of the three of these methods that are deemed most reliable for the
assessment of this quantity (Denman et al., 2007; Ciais et al., 2013). The
observation-based estimates use the ocean–land CO
sink partitioning
from observed atmospheric CO
and
concentration trends
(Manning and Keeling, 2006; Keeling and Manning, 2014), an oceanic inversion
method constrained by ocean biogeochemistry data (Mikaloff Fletcher et al.,
2006), and a method based on penetration timescale for chlorofluorocarbons
(McNeil, 2003). The IPCC estimate of 2.2 GtC yr
−1
for the 1990s
is consistent with a range of methods (Wanninkhof et al., 2013). We refrain
from using the IPCC estimates for the 2000s (2.3
0.7 GtC yr
−1
and the period 2002–2011 (2.4
0.7 GtC yr
−1
, Ciais et al., 2013),
as these are based on trends derived mainly from models and one data product
(Ciais et al., 2013). Additional constraints summarized in AR6 (Canadell et
al., 2021) are the interior ocean anthropogenic carbon change (Gruber et
al., 2019) and ocean sink estimates from atmospheric CO
and
(Tohjima et al., 2019), which are used for model evaluation
and discussion, respectively.
We also use eight estimates of the ocean CO
sink and its variability
based on surface ocean
CO
maps obtained by the interpolation of
surface ocean
CO
measurements from 1990 onwards due to severe
restrictions on data availability prior to 1990 (Fig. 10). These estimates
differ in many respects: they use different maps of surface
CO
atmospheric CO
concentrations, wind products, and
gas exchange formulations as specified in Table A3. We refer to them as
CO
-based flux estimates. The measurements underlying the surface
CO
maps are from the Surface Ocean CO
Atlas version 2022
(SOCATv2022; Bakker et al., 2022), which is an update of version 3 (Bakker
et al., 2016) and contains quality-controlled data through 2021 (see data
attribution Table A5). Each of the estimates uses a different method to then
map the SOCAT v2022 data to the global ocean. The methods include a
data-driven diagnostic method combined with a multi-linear regression
approach to extend back to 1957 (Rödenbeck et al., 2022; referred to
here as Jena-MLS), three neural network models (Landschützer et al.,
2014; referred to as MPI-SOMFFN; Chau et al., 2022; Copernicus Marine
Environment Monitoring Service, referred to here as CMEMS-LSCE-FFNN; and
Zeng et al., 2014; referred to as NIES-NN), a cluster regression
approach (Gregor and Gruber, 2021, referred to as OS-ETHZ-GRaCER), a
multi-linear regression method (Iida et al., 2021; referred to as JMA-MLR),
and a method that relates the
CO
misfit between GOBMs and SOCAT to
environmental predictors using the extreme gradient-boosting method (Gloege
et al., 2022). The ensemble mean of the
CO
-based flux estimates is
calculated from these seven mapping methods. Further, we show the flux
estimate of Watson et al. (2020), who also use the MPI-SOMFFN method to map
the adjusted
CO
data to the globe, resulting in a substantially
larger ocean sink estimate owing to a number of adjustments they applied to
the surface ocean
CO
data. Concretely, these authors adjusted the
SOCAT
CO
downward to account for differences in temperature between
the depth of the ship intake and the relevant depth right near the surface,
and they included a further adjustment to account for the cool surface skin
temperature effect. The Watson et al. (2020) flux estimate hence differs from the
others by their choice of adjusting the flux to a cool, salty ocean surface
skin. Watson et al. (2020) showed that this temperature adjustment leads to
an upward correction of the ocean carbon sink, up to 0.9 GtC yr
−1
that, if correct, should be applied to all
CO
-based flux estimates. A
reduction of this adjustment to 0.6 GtC yr
−1
was proposed by Dong et
al. (2022). The impact of the cool skin effect on air–sea CO
flux is
based on established understanding of temperature gradients (as discussed by
Goddijn-Murphy et al 2015) and laboratory observations (Jähne and
Haußecker, 1998; Jähne, 2019), but in situ field observational evidence
is lacking (Dong et al., 2022). The Watson et al. (2020) flux estimate presented
here is therefore not included in the ensemble mean of the
CO
-based
flux estimates. This choice will be re-evaluated in upcoming budgets based
on further lines of evidence.
Typically, data products do not cover the entire ocean due to missing
coastal oceans and sea ice cover. The CO
flux from each
CO
-based product is already at or above 99 % coverage of the
ice-free ocean surface area in two products (Jena-MLS, OS-ETHZ-GRaCER) and
filled by the data provider in three products (using the Fay et al., 2021,
method for JMA-MLR and LDEO-HPD and the Landschützer et al., 2020,
methodology for MPI-SOMFFN). The products that remained below 99 %
coverage of the ice-free ocean (CMEMS-LSCE-FFNN, MPI-SOMFFN, NIES-NN,
UOx-Watson) were scaled by the following procedure.
In previous versions of the GCB, the missing areas were accounted for by
scaling the globally integrated fluxes by the fraction of the global ocean
coverage (361.
9×10
km
based on ETOPO1, Amante and Eakins, 2009; Eakins
and Sharman, 2010) with the area covered by the CO
flux predictions.
This approach may lead to unnecessary scaling when the majority of the
missing data are in the ice-covered region (as is often the case), where
flux is already assumed to be zero. To avoid this unnecessary scaling, we
now scale fluxes regionally (north, tropics, south) to match the ice-free
area (using NOAA's OISSTv2; Reynolds et al., 2002):
(C4)
FCO
reg-scaled
ice
region
FCO
region
FCO
region
In Eq. (C4),
represents area, (
1−ice
) represents the ice-free ocean,
FCO
region
represents the coverage of the data
product for a region, and
FCO
region
is the integrated flux for a region.
We further use results from two diagnostic ocean models, Khatiwala et al. (2013) and DeVries (2014), to estimate the anthropogenic carbon accumulated
in the ocean prior to 1959. The two approaches assume constant ocean
circulation and biological fluxes, with
OCEAN
estimated as a response
in the change in atmospheric CO
concentration calibrated to
observations. The uncertainty in cumulative uptake of
20 GtC
(converted to
) is taken directly from the IPCC's review
of the literature (Rhein et al., 2013) or about
30 % for the
annual values (Khatiwala et al., 2009).
C3.2
Global ocean biogeochemistry models (GOBMs)
The ocean CO
sink for 1959–20121 is estimated using 10 GOBMs (Table A2). The GOBMs represent the physical, chemical, and biological processes
that influence the surface ocean concentration of CO
and thus the
air–sea CO
flux. The GOBMs are forced by meteorological reanalysis and
atmospheric CO
concentration data available for the entire time
period. They mostly differ in the source of the atmospheric forcing data
(meteorological reanalysis), spin-up strategies, and horizontal and
vertical resolutions (Table A2). All GOBMs except two (CESM-ETHZ, CESM2) do
not include the effects of anthropogenic changes in nutrient supply (Duce et
al., 2008). They also do not include the perturbation associated with
changes in riverine organic carbon (see Sect. 2.7 and Appendix D3).
Four sets of simulations were performed with each of the GOBMs. Simulation A
applied historical changes in climate and atmospheric CO
concentration. Simulation B is a control simulation with constant
atmospheric forcing (normal-year or repeated-year forcing) and constant
pre-industrial atmospheric CO
concentration. Simulation C is forced
with historical changes in atmospheric CO
concentration but repeated-year or normal-year atmospheric climate forcing. Simulation D is forced by
historical changes in climate and constant pre-industrial atmospheric
CO
concentration. To derive
OCEAN
from the model simulations, we
subtracted the slope of a linear fit to the annual time series of the
control simulation B from the annual time series of simulation A. Assuming
that drift and bias are the same in simulations A and B, we thereby correct
for any model drift. Further, this difference also removes the natural
steady-state flux (assumed to be 0 GtC yr
−1
globally without rivers),
which is often a major source of biases. This approach works for all model
set-ups, including IPSL, where simulation B was forced with constant
atmospheric CO
but observed historical changes in climate (equivalent
to simulation D). This approach assures that the interannual variability is
not removed from IPSL simulation A.
The absolute correction for bias and drift per model in the 1990s varied
between
0.01 and 0.41 GtC yr
−1
, with seven
models having positive biases, two having negative biases, and one
having essentially no bias (NorESM). The MPI model uses riverine input and
therefore simulates outgassing in simulation B. By subtracting simulation B,
the ocean carbon sink of the MPI model also follows the definition of
OCEAN
. This correction reduces the model mean ocean carbon sink by
0.04 GtC yr
−1
in the 1990s. The ocean models cover 99 % to 101 % of
the total ocean area so that area scaling is not necessary.
C3.3
GOBM evaluation and uncertainty assessment for
OCEAN
The ocean CO
sink for all GOBMs and the ensemble mean falls within
90 % confidence of the observed range, or 1.5 to 2.9 GtC yr
−1
, for the
1990s (Ciais et al., 2013) before and after applying adjustments. An
exception is the MPI model, which simulates a low ocean carbon sink of 1.38 GtC yr
−1
for the 1990s in simulation A owing to the inclusion of
riverine carbon flux. After adjusting to the GCB's definition of
OCEAN
by subtracting simulation B, the MPI model falls into the observed range
with an estimated sink of 1.69 GtC yr
−1
The GOBMs and data products have been further evaluated using the fugacity
of sea surface CO
CO
) from the SOCAT v2022 database (Bakker et
al., 2016, 2022). We focused this evaluation on the root-mean-squared error
(RMSE) between observed and modelled
CO
and on a measure of the
amplitude of the interannual variability of the flux (modified after
Rödenbeck et al., 2015). The RMSE is calculated from detrended, annually
and regionally averaged time series calculated from GOBMs and data product
CO
subsampled to SOCAT sampling points to measure the misfit between
large-scale signals (Hauck et al., 2020). To this end, we apply the
following steps: (i) subsample data points for which there are observations
(GOBMs or data products and SOCAT), (ii) average spatially, (iii) calculate annual mean, (iv) detrend both time series (GOBMs or data products and SOCAT), and (v) calculate RMSE. This year, we do not apply an open-ocean
mask of 400 m but instead a mask based on the minimum area coverage of the
dat -products. This ensures a fair comparison over equal areas. The
amplitude of the
OCEAN
interannual variability (A-IAV) is calculated
as the temporal standard deviation of the detrended annual CO
flux
time series after area scaling (Rödenbeck et al., 2015; Hauck et al.,
2020). These metrics are chosen because RMSE is the most direct measure of
data–model mismatch, and the A-IAV is a direct measure of the variability of
OCEAN
on interannual timescales. We apply these metrics globally and
by latitude bands. Results are shown in Fig. B2 and discussed in Sect. 3.5.5.
We quantify the 1
uncertainty around the mean ocean sink of
anthropogenic CO
by assessing random and systematic uncertainties for
the GOBMs and data-products. The random uncertainties are taken from the
ensemble standard deviation (0.3 GtC yr
−1
for GOBMs, 0.3 GtC yr
−1
for data-products). We derive the GOBMs systematic uncertainty
by the deviation of the DIC inventory change 1994–2007 from the Gruber et al. (2019) estimate (0.4 GtC yr
−1
) and suggest these are related to
physical transport (mixing, advection) into the ocean interior. For the
data products, we consider systematic uncertainties stemming from
uncertainty in
CO
observations (0.2 GtC yr
−1
, Takahashi et
al., 2009; Wanninkhof et al., 2013), gas transfer velocity (0.2 GtC yr
−1
, Ho et al., 2011; Wanninkhof et al., 2013; Roobaert et al.,
2018), wind product (0.1 GtC yr
−1
, Fay et al., 2021), river flux
adjustment (0.3 GtC yr
−1
, Regnier et al., 2022, formally 2
uncertainty), and
CO
mapping (0.2 GtC yr
−1
, Landschützer et
al., 2014). Combining these uncertainties as their squared sums, we assign
an uncertainty of
0.5 GtC yr
−1
to the GOBM ensemble mean and
an uncertainty of
0.6 GtC yr
−1
to the data product
ensemble mean. These uncertainties are propagated as
OCEAN
0.5
0.6
GtC yr
−1
and result in an
0.4 GtC yr
−1
uncertainty around the best
estimate of
OCEAN
We examine the consistency between the variability of the model-based and
the
CO
-based data products to assess confidence in
OCEAN
. The
interannual variability of the ocean fluxes (quantified as A-IAV, the
standard deviation after detrending, Fig. B2) of the seven
CO
-based
data products plus the Watson et al. (2020) product for 1990–2021 ranges
from 0.12 to 0.32 GtC yr
−1
, with the lower estimates coming from the two ensemble
methods (CMEMS-LSCE-FFNN, OS-ETHZ-GRaCER). The interannual variability in
the GOBMs ranges between 0.09 and 0.20 GtC yr
−1
; hence, there is overlap
with the lower A-IAV estimates of two data products.
Individual estimates (both GOBMs and data products) generally produce a
higher ocean CO
sink during strong El Niño events. There is
emerging agreement between GOBMs and data products on the patterns of
decadal variability of
OCEAN
, with a global stagnation in the 1990s and
an extratropical strengthening in the 2000s (McKinley et al., 2020; Hauck
et al., 2020). The central estimates of the annual flux from the GOBMs and
the
CO
-based data products have a correlation
of 0.94 (1990–2021).
The agreement between the models and the data products reflects some
consistency in their representation of underlying variability since there is
little overlap in their methodology or use of observations.
C4
Methodology: land CO
sink
C4.1
DGVM simulations
The DGVMs model runs were forced by either the merged monthly Climate
Research Unit (CRU) and 6-hourly Japanese 55-year Reanalysis (JRA-55) data
set or by the monthly CRU data set, with both providing observation-based
temperature, precipitation, and incoming surface radiation data on a
0.5
0.5
grid updated to 2021 (Harris et al.,
2014, 2020). The combination of CRU monthly data with 6-hourly forcing from
JRA-55 (Kobayashi et al., 2015) is performed with methodology used in
previous years (Viovy, 2016) adapted to the specifics of the JRA-55 data.
Introduced in GCB2021 (Friedlingstein et al., 2022a), incoming short-wave
radiation fields are used to take into account aerosol impacts and the division of
total radiation into direct and diffuse components as summarized below.
The diffuse fraction dataset offers 6-hourly distributions of the diffuse
fraction of surface short-wave fluxes over the period 1901–2021. Radiative
transfer calculations are based on monthly averaged distributions of
tropospheric and stratospheric aerosol optical depth and 6-hourly
distributions of cloud fraction. Methods follow those described in the
Methods section of Mercado et al. (2009) but with updated input datasets.
The time series of speciated tropospheric aerosol optical depth is taken
from the historical and RCP8.5 simulations by the HadGEM2-ES climate model
(Bellouin et al., 2011). To correct for biases in HadGEM2-ES, tropospheric
aerosol optical depths are scaled over the whole period to match the global
and monthly averages obtained over the period 2003–2020 by the CAMS
reanalysis of atmospheric composition (Inness et al., 2019), which
assimilates satellite retrievals of aerosol optical depth.
The time series of stratospheric aerosol optical depth is taken from the climatology of
Sato et al. (1993), which has been updated to 2012. The years
2013–2020 are assumed to be background years and thus replicate the background
year 2010. That assumption is supported by the Global Space-based
Stratospheric Aerosol Climatology time series (1979–2016; Thomason et al.,
2018). The time series of cloud fraction is obtained by scaling the 6-hourly
distributions simulated in the Japanese Reanalysis (Kobayashi et al., 2015)
to match the monthly averaged cloud cover in the CRU TS v4.06 dataset
(Harris et al., 2020). Surface radiative fluxes account for
aerosol–radiation interactions from both tropospheric and stratospheric
aerosols and for aerosol–cloud interactions from tropospheric aerosols
(except mineral dust). Tropospheric aerosols are also assumed to exert
interactions with clouds.
The radiative effects of those aerosol–cloud interactions are assumed to
scale with the radiative effects of aerosol–radiation interactions of
tropospheric aerosols using regional scaling factors derived from
HadGEM2-ES. Diffuse fraction is assumed to be 1 in cloudy sky. Atmospheric
constituents other than aerosols and clouds are set to a constant standard
mid-latitude summer atmosphere, but their variations do not affect the
diffuse fraction of surface short-wave fluxes.
In summary, the DGVMs forcing data include time-dependent gridded climate
forcing, global atmospheric CO
(Dlugokencky and Tans, 2022), gridded
land cover changes (see Appendix C2.2), and gridded nitrogen deposition and
fertilizers (see Table A1 for specific models details).
Four simulations were performed with each of the DGVMs. Simulation 0 (S0) is
a control simulation that uses fixed pre-industrial (year 1700) atmospheric
CO
concentrations, cycles early 20th century (1901–1920) climate, and
applies a time-invariant pre-industrial land cover distribution and
pre-industrial wood harvest rates. Simulation 1 (S1) differs from S0 by
applying historical changes in atmospheric CO
concentration and N inputs.
Simulation 2 (S2) applies historical changes in atmospheric CO
concentration, N inputs, and climate, while applying time-invariant
pre-industrial land cover distribution and pre-industrial wood harvest
rates. Simulation 3 (S3) applies historical changes in atmospheric CO
concentration, N inputs, climate, land cover distribution, and wood
harvest rates.
S2 is used to estimate the land sink component of the global carbon budget
LAND
). S3 is used to estimate the total land flux but is not used in
the global carbon budget. We further separate
LAND
into contributions
from CO
S1–S0) and climate (
S2
S1
S0).
C4.2
DGVM evaluation and uncertainty assessment for
LAND
We apply three criteria for minimum DGVM realism by including only those
DGVMs with (1) steady state after spin up, (2) global net land flux
LAND
LUC
), i.e. an atmosphere-to-land carbon flux over the
1990s ranging between
0.3 and 2.3 GtC yr
−1
within 90 % confidence
of constraints by global atmospheric and oceanic observations (Keeling and
Manning, 2014; Wanninkhof et al., 2013), and (3) global
LUC
that is a
carbon source to the atmosphere over the 1990s, as already mentioned in
Appendix C2.2. All DGVMs meet these three criteria.
In addition, the DGVMs results are also evaluated using the International
Land Model Benchmarking system (ILAMB; Collier et al., 2018). This
evaluation is provided here to document, encourage, and support model
improvements through time. ILAMB variables cover key processes that are
relevant for the quantification of
LAND
and resulting aggregated
outcomes. The selected variables are vegetation biomass, gross primary
productivity, leaf area index, net ecosystem exchange, ecosystem
respiration, evapotranspiration, soil carbon, and runoff (see Fig. B3 for
the results and for the list of observed databases). Results are shown in
Fig. B3 and discussed in Sect. 3.6.5.
For the uncertainty for
LAND
, we use the standard deviation of the
annual CO
sink across the DGVMs, averaging to about
0.6 GtC yr
−1
for the period 1959 to 2021. We attach a medium confidence level
to the annual land CO
sink and its uncertainty because the estimates
from the residual budget and averaged DGVMs match well within their
respective uncertainties (Table 5).
C5
Methodology: atmospheric inversions
C5.1
Inversion system simulations
Nine atmospheric inversions (details of each are given in Table A4) were used to infer
the spatio-temporal distribution of the CO
flux exchanged between the
atmosphere and the land or oceans. These inversions are based on Bayesian
inversion principles with prior information on fluxes and their
uncertainties. They use very similar sets of surface measurements of
CO
time series (or subsets thereof) from various flask and in situ
networks. One inversion system also used satellite xCO
retrievals from
GOSAT and OCO-2.
Each inversion system uses different methodologies and input data but is
rooted in Bayesian inversion principles. These differences mainly concern
the selection of the atmospheric CO
data, prior fluxes,
spatial resolution, assumed correlation structures, and mathematical
approaches of the models. Each system uses a different transport model, 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).
The inversion systems all prescribe similar global fossil fuel emissions for
FOS
; specifically, the GCP's Gridded Fossil Emissions Dataset version
2022 (GCP-GridFEDv2022.2; Jones et al., 2022), which is an update through
2021 of the first version of GCP-GridFED presented by Jones et al. (2021),
or another recent version of GCP-GridFED (Table A4). All GCP-GridFED
versions scale gridded estimates of CO
emissions from EDGARv4.3.2
(Janssens-Maenhout et al., 2019) within national territories to match
national emissions estimates provided by the GCP for the years 1959–2021,
which are compiled following the methodology described in Appendix C1.
GCP-GridFEDv2022.2 adopts the seasonality of emissions (the monthly
distribution of annual emissions) from the Carbon Monitor (Liu et al.,
2020a, b; Dou et al., 2022) for Brazil, China, all EU27 countries, the United
Kingdom, the USA, and shipping and aviation bunker emissions. The seasonality
present in Carbon Monitor is used directly for years 2019–2021, while for
years 1959–2018 the average seasonality of 2019 and 2021 are applied
(avoiding the year 2020 during which emissions were most impacted by the
COVID-19 pandemic). For all other countries, seasonality of emissions is
taken from EDGAR (Janssens-Maenhout et al., 2019; Jones et al., 2022), with a
small annual correction to the seasonality present in year 2010 based on
heating or cooling degree days to account for the effects of interannual
climate variability on the seasonality of emissions (Jones et al., 2021).
Earlier versions of GridFED used Carbon Monitor-based seasonality only
from 2019 onwards. In addition, we note that GCP-GridFEDv2022.1
and v2022.2 include emissions from cement production and the cement
carbonation CO
sink (Appendix C1.1), whereas earlier versions of
GCP-GridFED did not include the cement carbonation CO
sink.
The consistent use of recent versions of GCP-GridFED for
FOS
ensures a
close alignment with the estimate of
FOS
used in this budget
assessment, enhancing the comparability of the inversion-based estimate with
the flux estimates deriving from DGVMs, GOBMs, and
CO
-based methods.
To ensure that the estimated uptake of atmospheric CO
by the land and
oceans was fully consistent with the sum of the fossil emissions flux from
GCP-GridFEDv2022.2 and the atmospheric growth rate of CO
, small
corrections to the fossil fuel emissions flux were applied to inversions
systems using other versions of GCP-GridFED.
The land and ocean CO
fluxes from atmospheric inversions contain
anthropogenic perturbation and natural pre-industrial CO
fluxes. On
annual timescales, natural pre-industrial fluxes are primarily land
CO
sinks and ocean CO
sources corresponding to carbon taken up
on land, transported by rivers from land to ocean, and outgassed by the
ocean. These pre-industrial land CO
sinks are thus compensated over
the globe by ocean CO
sources corresponding to the outgassing of
riverine carbon inputs to the ocean, using the exact same numbers and
distributions as described for the oceans in Sect. 2.4. To facilitate the
comparison, we adjusted the inverse estimates of the land and ocean fluxes
per latitude band with these numbers to produce historical perturbation
CO
fluxes from inversions.
C5.2
Inversion system evaluation
All participating atmospheric inversions are checked for consistency with
the annual global growth rate, as both are derived from the global surface
network of atmospheric CO
observations. In this exercise, we use the
conversion factor of 2.086 GtC ppm
−1
to convert the inverted carbon fluxes to
mole fractions, as suggested by Prather (2012). This number is specifically
suited for the comparison to surface observations that do not respond
uniformly (or immediately) to each year's summed sources and sinks. This
factor is therefore slightly smaller than the GCB conversion factor in Table 1 (2.142 GtC ppm
−1
, Ballantyne et al., 2012). Overall, the inversions agree
with the growth rate, with biases between 0.03 and 0.08 ppm (0.06–0.17 GtC yr
−1
) on the decadal average.
The atmospheric inversions are also evaluated using vertical profiles of
atmospheric CO
concentrations (Fig. B4). More than 30 aircraft
programmes over the globe, either regular programmes or repeated surveys over at
least 9 months, have been used in order to draw a robust picture of the
system performance (with space–time data coverage that is irregular and denser in
the 0–45
N latitude band; Table A6). The nine systems are
compared to the independent aircraft CO
measurements between 2 and 7 km above sea level between 2001 and 2021. Results are shown in Fig. B4,
where the inversions generally match the atmospheric mole fractions to
within 0.7 ppm at all latitudes, except for CT Europe in 2011–2021 over the
more sparsely sampled Southern Hemisphere.
Appendix D:
Processes not included in the global carbon budget
D1
Contribution of anthropogenic CO and CH
to the global carbon budget
Equation (1) only partly includes the net input of CO
to the
atmosphere from the chemical oxidation of reactive carbon-containing gases
from sources other than the combustion of fossil fuels, such as (1) cement
process emissions, since these do not come from combustion of fossil fuels,
(2) the oxidation of fossil fuels, and (3) the assumption of immediate oxidation
of vented methane in oil production. However, it omits any other
anthropogenic carbon-containing gases that are eventually oxidized in the
atmosphere, forming a diffuse source of CO
, such as anthropogenic
emissions of CO and CH
. An attempt is made here to estimate
their magnitude and identify the sources of uncertainty. Anthropogenic CO
emissions are from incomplete fossil fuel and biofuel burning and
deforestation fires. The main anthropogenic emissions of fossil CH
that matter for the global (anthropogenic) carbon budget are the fugitive
emissions of coal, oil, and gas sectors (see below). These emissions of CO
and CH
contribute a net addition of fossil carbon to the atmosphere.
In our estimate of
FOS
, we assumed (Sect. 2.1.1) that all the fuel
burned is emitted as CO
, and thus CO anthropogenic emissions associated
with incomplete fossil fuel combustion and its atmospheric oxidation into
CO
within a few months are already counted implicitly in
FOS
and
should not be counted twice (same for
LUC
and anthropogenic CO
emissions by deforestation fires). The diffuse atmospheric source of
CO
deriving from anthropogenic emissions of fossil CH
is not
included in
FOS
. In reality, the diffuse source of CO
from
CH
oxidation contributes to the annual CO
growth. Emissions of
fossil CH
represent 30 % of total anthropogenic CH
emissions
(Saunois et al., 2020; their top-down estimate is used because it is
consistent with the observed CH
growth rate), i.e. 0.083 GtC yr
−1
for the decade 2008–2017. Assuming steady state, an amount equal
to this fossil CH
emission is all converted to CO
by OH
oxidation, and this therefore explains 0.083 GtC yr
−1
of the global CO
growth rate, with an uncertainty range of 0.061 to 0.098 GtC yr
−1
taken from the min–max of top-down estimates in Saunois et al. (2020). If
this min–max range is assumed to be 2
because Saunois et al. (2020) did not account for the internal uncertainty of their minimum and maximum
top-down estimates, it translates into a 1
uncertainty of 0.019 GtC yr
−1
Other anthropogenic changes in the sources of CO and CH
from
wildfires, vegetation biomass, wetlands, ruminants, or permafrost changes
are similarly assumed to have a small effect on the CO
growth rate.
The CH
and CO emissions and sinks are published and analysed
separately in the global methane budget and global carbon monoxide budget
publications, which follow a similar approach to that presented here
(Saunois et al., 2020; Zheng et al., 2019).
D2
Contribution of other carbonates to CO
emissions
Although we do account for cement carbonation (a carbon sink), the
contribution of emissions of fossil carbonates (carbon sources) other than
cement production is not systematically included in estimates of
FOS
except for Annex I countries and lime production in China (Andrew and
Peters, 2021). The missing processes include CO
emissions associated
with the calcination of lime and limestone outside of cement production.
Carbonates are also used in various industries, including in iron and steel
manufacture and in agriculture. They are found naturally in some coals.
CO
emissions from fossil carbonates other than cement not included in
our dataset are estimated to amount to about 0.3 % of
FOS
(estimated
based on Crippa et al., 2019).
D3
Anthropogenic carbon fluxes in the land-to-ocean aquatic continuum
The approach used to determine the global carbon budget refers to the mean,
variations, and trends in the perturbation of CO
in the atmosphere,
referenced to the pre-industrial era. Carbon is continuously displaced from
the land to the ocean through the land–ocean aquatic continuum (LOAC)
comprising freshwaters, estuaries, and coastal areas (Bauer et al., 2013;
Regnier et al., 2013). A substantial fraction of this lateral carbon flux is
entirely “natural” and is thus a steady-state component of the
pre-industrial carbon cycle. We account for this pre-industrial flux where
appropriate in our study (see Appendix C3). However, changes in
environmental conditions and land-use change have caused an increase in the
lateral transport of carbon into the LOAC – a perturbation that is relevant
for the global carbon budget presented here.
The results of the analysis of Regnier et al. (2013) can be summarized in
two points of relevance for the anthropogenic CO
budget. First, the
anthropogenic perturbation of the LOAC has increased the organic carbon
export from terrestrial ecosystems to the hydrosphere by as much as 1.0
0.5 GtC yr
−1
since pre-industrial times, mainly owing to
enhanced carbon export from soils. Second, this exported anthropogenic
carbon is partly respired through the LOAC, partly sequestered in sediments
along the LOAC, and to a lesser extent transferred to the open ocean where
it may accumulate or be outgassed. The increase in storage of land-derived
organic carbon in the LOAC carbon reservoirs (burial) and in the open ocean
combined is estimated by Regnier et al. (2013) at 0.65
0.35 GtC yr
−1
. The inclusion of LOAC-related anthropogenic CO
fluxes
should affect estimates of
LAND
and
OCEAN
in Eq. (1) but does
not affect the other terms. Representation of the anthropogenic perturbation
of LOAC CO
fluxes is, however, not included in the GOBMs and DGVMs used
in our global carbon budget analysis presented here.
D4
Loss of additional land sink capacity
Historical land-cover change was dominated by transitions from vegetation
types that can provide a large carbon sink per area unit (typically,
forests) to others less efficient in removing CO
from the atmosphere
(typically, croplands). The resultant decrease in land sink, called the
“loss of additional sink capacity”, can be calculated as the difference
between the actual land sink under changing land cover and the
counterfactual land sink under pre-industrial land cover. This term is not
accounted for in our global carbon budget estimate. Here, we provide a
quantitative estimate of this term to be used in the discussion. Seven of
the DGVMs used in Friedlingstein et al. (2019) performed additional
simulations with and without land-use change under cycled pre-industrial
environmental conditions. The resulting loss of additional sink capacity
amounts to 0.9
0.3 GtC yr
−1
on average over 2009–2018 and 42
16 GtC accumulated between 1850 and 2018 (Obermeier et al., 2021).
OSCAR, emulating the behaviour of 11 DGVMs, finds values of the loss of
additional sink capacity of 0.7
0.6 GtC yr
−1
and 31
23 GtC for the same time period (Gasser et al., 2020). Since the DGVM-based
LUC
estimates are only used to quantify the uncertainty around the
bookkeeping models'
LUC
, we do not add the loss of additional sink capacity
to the bookkeeping estimate.
Author contributions
PF, MOS, MWJ, RMA, LukG, JH, CLQ, ITL, AO, GPP, WP, JP, ClS, and SS designed
the study, conducted the analysis, and wrote the paper with input from JGC,
PC, and RBJ. RMA, GPP and JIK produced the fossil fuel emissions and their
uncertainties and analysed the emissions data. MH and GM provided fossil
fuel emission data. JP, ThoG, ClS, and RAH provided the bookkeeping land-use
change emissions with synthesis by JP and ClS. JH, LB, ÖG, NG, TI, KL,
NMa, LR, JS, RS, HiT, and ReW provided an update of the global ocean
biogeochemical models. MG, LucG, LukG, YI, AJ, ChR, JDS, and JZ provided an
update of the ocean
CO
data products, with synthesis on both streams
by JH, LukG, and NMa. SRA, NRB, MB, HCB, MC, WE, RAF, ThaG, KK, NL, NMe, NMM,
DRM, SN, TO, DP, KP, ChR, IS, TS, AJS, CoS, ST, TT, BT, RiW, CW, and AW provided
ocean
CO
measurements for the year 2021, with synthesis by AO and KO.
AA, VKA, SF, AKJ, EK, DK, JK, MJM, MOS, BP, QS, HaT, APW, WY, XY, and SZ
provided an update of the dynamic global vegetation models, with synthesis
by SS and MOS. WP, ITL, FC, JL, YN, PIP, ChR, XT, and BZ provided an updated
atmospheric inversion. WP, FC, and ITL developed the protocol and produced
the evaluation. RMA provided predictions of the 2022 emissions and
atmospheric CO
growth rate. PL provided the predictions of the 2022
ocean and land sinks. LPC, GCH, KKG, TMR, and GRvdW provided forcing data for
land-use change. RA, GG, FT, and CY provided data for the land-use change
NGHGI mapping. PPT provided key atmospheric CO
data. MWJ produced the
model atmospheric CO
forcing and the atmospheric CO
growth rate.
MOS and NB produced the aerosol diffuse radiative forcing for the DGVMs. IH
provided the climate forcing data for the DGVMs. ER provided the evaluation
of the DGVMs. MWJ provided the emission priors for use in the inversion
systems. ZL provided seasonal emissions data for most recent years for the
emission prior. MWJ and MOS developed the new data management pipeline, which
automates many aspects of the data collation, analysis, plotting, and
synthesis. PF, MOS, and MMJ coordinated the effort and revised all figures,
tables, text, and/or numbers to ensure the update was clear from the 2021
edition and in line with the
(last access: 25 September 2022).
Competing interests
At least one of the (co-)authors is a member of the editorial board of
Earth System Science Data
. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank all people and institutions who provided the data used in this
Global Carbon Budget 2022 and the Global Carbon Project members for their
input throughout the development of this publication. We thank Nigel Hawtin
for producing Figs. 2 and 14. We thank Thomas Hawes for technical
support with the data management pipeline. We thank Ed Dlugokencky for
providing atmospheric CO
measurements. We thank Ian G. C. Ashton,
Fatemeh Cheginig, Trang T. Chau, Sam Ditkovsky, Christian Ethé, Amanda
R. Fay, Lonneke Goddijn-Murphy, Thomas Holding, Fabrice Lacroix, Enhui Liao,
Galen A. McKinley, Shijie Shu, Richard Sims, Jade Skye, Andrew J. Watson,
David Willis, and David K. Woolf for their involvement in the development,
use, and analysis of the models and data products used here. Daniel Kennedy
thanks all the scientists, software engineers, and administrators who
contributed to the development of CESM2. We thank Joe Salisbury, Doug
Vandemark, Christopher W. Hunt, and Peter Landschützer, who contributed
to the provision of surface ocean CO
observations for the year 2021
(see Table A5). We also thank Benjamin Pfeil, Rocío Castaño-Primo,
and Stephen D. Jones of the Ocean Thematic Centre of the EU Integrated
Carbon Observation System (ICOS) Research Infrastructure; Eugene Burger of
NOAA's Pacific Marine Environmental Laboratory; and Alex Kozyr of NOAA's
National Centers for Environmental Information for their contribution to
surface ocean CO
data and metadata management. This is PMEL
contribution 5434. We thank the scientists, institutions, and funding
agencies responsible for the collection and quality control of the data in
SOCAT and the International Ocean Carbon Coordination Project
(IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS), and the Integrated
Marine Biosphere Research (IMBeR) program for their support. We thank data
providers ObsPack GLOBALVIEWplus v7.0 and NRT v7.2 for atmospheric CO
observations. We thank the individuals and institutions that provided the
databases used for the model evaluations used here. We thank Fortunat Joos,
Samar Khatiwala, and Timothy DeVries for providing historical data. Matthew
J. McGrath thanks the whole ORCHIDEE group. Ian Harris thanks the Japan
Meteorological Agency (JMA) for producing the Japanese 55-year Reanalysis
(JRA-55). Anthony P. Walker thanks ORNL, which is managed by UT-Battelle,
LLC, for the DOE under contract DE-AC05-1008 00OR22725. Yosuke Niwa thanks
CSIRO, EC, EMPA, FMI, IPEN, JMA, LSCE, NCAR, NIES, NILU, NIWA, NOAA, SIO,
and TU/NIPR for providing data for NISMON-CO
. Xiangjun Tian thanks Zhe Jin,
Yilong Wang, Tao Wang, and Shilong Piao for their contributions to the GONGGA
inversion system. Bo Zheng thanks the comments and suggestions from Philippe
Ciais and Frédéric Chevallier. Frédéric Chevallier thanks
Marine Remaud, who maintained the atmospheric transport model for the CAMS
inversion. Paul I. Palmer thanks Liang Feng and acknowledges ongoing support
from the National Centre for Earth Observation. Junjie Liu thanks the Jet
Propulsion Laboratory, California Institute of Technology. Wiley Evans
thanks the Tula Foundation for funding support. Australian ocean CO
data were sourced from Australia's Integrated Marine Observing System
(IMOS); IMOS is enabled by the National Collaborative Research
Infrastructure Strategy (NCRIS). Margot Cronin thanks Anthony English, Clynt
Gregory, and Gordon Furey (P&O Maritime Services) for their support.
Nathalie Lefèvre thanks the crew of the
Cap San Lorenzo
and the US IMAGO
of IRD Brest for technical support. Henry C. Bittig is grateful for the
skilful technical support of Michael Glockzin and Bernd Sadkowiak. Meike Becker and
Are Olsen thank Sparebanken Vest/Agenda Vestlandet for their support for the
observations on the Statsraad Lehmkuhl. Thanos Gkritzalis thanks the
personnel and crew of Simon Stevin. Matthew W. Jones thanks Anthony J.
De-Gol for his technical and conceptual assistance with the development of
GCP-GridFED. FAOSTAT is funded by FAO member states through their
contributions to the FAO Regular Programme; data contributions by national
experts are gratefully acknowledged. The views expressed in this paper are the
authors' only and do not necessarily reflect those of FAO. Finally, we thank
all funders who have supported the individual and joint contributions to
this work (see Table A9), the reviewers of this manuscript and
previous versions, and the many researchers who have provided feedback.
Financial support
For a list of all funders that have
supported this research, please refer to Table A9.
Review statement
This paper was edited by David Carlson and reviewed by H. Damon Matthews, Hélène Peiro, Ana Maria Roxana Petrescu, Michio Kawamiya, and one anonymous referee.
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Articles
Abstract
Executive summary
Introduction
Methods
Results
Tracking progress towards mitigation targets
Discussion
Conclusions
Data availability
Appendix A:
Supplementary tables
Appendix B:
Supplementary figures
Appendix C:
Extended methodology
Appendix D:
Processes not included in the global carbon budget
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References
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Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO
) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO
, the key driver of climate change.
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the...
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Sections
Abstract
Executive summary
Introduction
Methods
Results
Tracking progress towards mitigation targets
Discussion
Conclusions
Data availability
Appendix A:
Supplementary tables
Appendix B:
Supplementary figures
Appendix C:
Extended methodology
Appendix D:
Processes not included in the global carbon budget
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References