ESSD - Global Carbon Budget 2021

ESSD - Global Carbon Budget 2021
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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
26 Apr 2022
Data description paper |
26 Apr 2022
Global Carbon Budget 2021
Global Carbon Budget 2021
Global Carbon Budget 2021
Pierre Friedlingstein et al.
Pierre Friedlingstein
Matthew W. Jones
Michael O'Sullivan
Robbie M. Andrew
Dorothee C. E. Bakker
Judith Hauck
Corinne Le Quéré
Glen P. Peters
Wouter Peters
Julia Pongratz
Stephen Sitch
Josep G. Canadell
Philippe Ciais
Rob B. Jackson
Simone R. Alin
Peter Anthoni
Nicholas R. Bates
Meike Becker
Nicolas Bellouin
Laurent Bopp
Thi Tuyet Trang Chau
Frédéric Chevallier
Louise P. Chini
Margot Cronin
Kim I. Currie
Bertrand Decharme
Laique M. Djeutchouang
Xinyu Dou
Wiley Evans
Richard A. Feely
Liang Feng
Thomas Gasser
Dennis Gilfillan
Thanos Gkritzalis
Giacomo Grassi
Luke Gregor
Nicolas Gruber
Özgür Gürses
Ian Harris
Richard A. Houghton
George C. Hurtt
Yosuke Iida
Tatiana Ilyina
Ingrid T. Luijkx
Atul Jain
Steve D. Jones
Etsushi Kato
Daniel Kennedy
Kees Klein Goldewijk
Jürgen Knauer
Jan Ivar Korsbakken
Arne Körtzinger
Peter Landschützer
Siv K. Lauvset
Nathalie Lefèvre
Sebastian Lienert
Junjie Liu
Gregg Marland
Patrick C. McGuire
Joe R. Melton
David R. Munro
Julia E. M. S. Nabel
Shin-Ichiro Nakaoka
Yosuke Niwa
Tsuneo Ono
Denis Pierrot
Benjamin Poulter
Gregor Rehder
Laure Resplandy
Eddy Robertson
Christian Rödenbeck
Thais M. Rosan
Jörg Schwinger
Clemens Schwingshackl
Roland Séférian
Adrienne J. Sutton
Colm Sweeney
Toste Tanhua
Pieter P. Tans
Hanqin Tian
Bronte Tilbrook
Francesco Tubiello
Guido R. van der Werf
Nicolas Vuichard
Chisato Wada
Rik Wanninkhof
Andrew J. Watson
David Willis
Andrew J. Wiltshire
Wenping Yuan
Chao Yue
Xu Yue
Sönke Zaehle
and
Jiye Zeng
Pierre Friedlingstein
CORRESPONDING AUTHOR
p.friedlingstein@exeter.ac.uk
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
Laboratoire de Météorologie Dynamique, Institut Pierre-Simon Laplace, CNRS-ENS-UPMC-X, Paris, France
Matthew W. Jones
Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Michael O'Sullivan
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
Robbie M. Andrew
CICERO Center for International Climate Research, Oslo 0349, Norway
Dorothee C. E. Bakker
School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Judith Hauck
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, 27570 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
Glen P. Peters
CICERO Center for International Climate Research, Oslo 0349, Norway
Wouter Peters
Wageningen University, Environmental Sciences Group, P.O. Box 47, 6700AA, Wageningen, the Netherlands
University of Groningen, Centre for Isotope Research, Groningen, the Netherlands
Julia Pongratz
Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 München, Germany
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
Stephen Sitch
College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, 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, 91198 Gif-sur-Yvette, France
Rob 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
Peter Anthoni
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany
Nicholas R. Bates
Bermuda Institute of Ocean Sciences (BIOS), 17 Biological Lane, Ferry Reach, St. Georges, GEO1, Bermuda
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, UK
Laurent Bopp
Laboratoire de Météorologie Dynamique, Institut Pierre-Simon Laplace, CNRS-ENS-UPMC-X, Paris, France
Thi Tuyet Trang Chau
Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
Frédéric Chevallier
Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 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
Kim I. Currie
NIWA, Union Place West, Dunedin, New Zealand
Bertrand Decharme
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Laique M. Djeutchouang
Department of Oceanography, University of Cape Town, Cape Town, 7701, South Africa
SOCCO, Council for Scientific and Industrial Research, Cape Town, 7700, South Africa
Xinyu Dou
Department of Earth System Science, Tsinghua University, Beijing, China
Wiley Evans
Hakai Institute, Heriot Bay, BC, Canada
Richard A. Feely
National Oceanic & Atmospheric Administration, Pacific Marine Environmental Laboratory (NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USA
Liang Feng
National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
Thomas Gasser
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1
2361 Laxenburg, Austria
Dennis Gilfillan
North Carolina School for Science and Mathematics, Durham, NC, USA
Thanos Gkritzalis
Flanders Marine Institute (VLIZ), InnovOceanSite, Wandelaarkaai 7, 8400 Ostend, Belgium
Giacomo Grassi
European Commission, Joint Research Centre, 21027 Ispra (VA), Italy
Luke Gregor
Environmental Physics Group, ETH Zürich, Institute of Biogeochemistry and Pollutant Dynamics and Center for Climate Systems Modeling (C2SM), 8092 Zurich, Switzerland
Nicolas Gruber
Environmental Physics Group, ETH Zürich, Institute of Biogeochemistry and Pollutant Dynamics and Center for Climate Systems Modeling (C2SM), 8092 Zurich, Switzerland
Özgür Gürses
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, 27570 Bremerhaven, Germany
Ian Harris
NCAS-Climate, Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
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, Bundesstr. 53, 20146 Hamburg, Germany
Ingrid T. Luijkx
Wageningen University, Environmental Sciences Group, P.O. Box 47, 6700AA, Wageningen, the Netherlands
Atul Jain
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61821, USA
Steve D. Jones
Geophysical Institute, University of Bergen, Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
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
Utrecht University, Faculty of Geosciences, Department IMEW, Copernicus Institute of Sustainable Development, Heidelberglaan 2, P.O. Box 80115, 3508 TC, Utrecht, the Netherlands
Jürgen Knauer
CSIRO Oceans and Atmosphere, Canberra, ACT 2101, Australia
Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia
Jan Ivar Korsbakken
CICERO Center for International Climate Research, Oslo 0349, Norway
Arne Körtzinger
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Peter Landschützer
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
Siv K. Lauvset
Bjerknes Centre for Climate Research, Bergen, Norway
NORCE Norwegian Research Centre, Jahnebakken 5, 5007 Bergen, Norway
Nathalie Lefèvre
LOCEAN/IPSL laboratory, Sorbonne Université, CNRS/IRD/MNHN, Paris, France
Sebastian Lienert
Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Junjie Liu
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Gregg Marland
Research Institute for Environment, Energy, and Economics, Appalachian State University, Boone, NC, USA
Department of Geological and Environmental Sciences, Appalachian State University, Boone, NC, USA
Patrick C. McGuire
Department of Meteorology, Department of Geography & Environmental Science, National Centre for Atmospheric Science, University of Reading, Reading, UK
Joe R. Melton
Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
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
Julia E. M. S. Nabel
Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
Max Planck Institute for Biogeochemistry, Jena, Germany
Shin-Ichiro Nakaoka
Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Yosuke Niwa
Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki, 305-0052, Japan
Tsuneo Ono
Japan Fisheries Research and Education Agency, 2-12-4 Fukuura, Kanazawa-Ku, Yokohama 236-8648, Japan
Denis Pierrot
National Oceanic & Atmospheric Administration/Atlantic Oceanographic & Meteorological Laboratory (NOAA/AOML), Miami, FL 33149, USA
Benjamin Poulter
NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD 20771, USA
Gregor Rehder
Leibniz Institute for Baltic Sea Research Warnemuende (IOW), Seestrasse 15, 18119 Rostock, Germany
Laure Resplandy
Princeton University, Department of Geosciences and Princeton Environmental Institute, Princeton, NJ, USA
Eddy Robertson
Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
Christian Rödenbeck
Max Planck Institute for Biogeochemistry, Jena, Germany
Thais M. Rosan
College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
Jörg Schwinger
NORCE Norwegian Research Centre, Jahnebakken 5, 5007 Bergen, Norway
Bjerknes Centre for Climate Research, Bergen, Norway
Clemens Schwingshackl
Ludwig-Maximilians-Universität München, Luisenstr. 37, 80333 München, Germany
Roland Séférian
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
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
Toste Tanhua
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
Pieter P. Tans
National Oceanic and Atmospheric Administration, Earth System Research Laboratory (NOAA ESRL), Boulder, CO 80305, USA
Hanqin Tian
School of Forestry and Wildlife Sciences, Auburn University, 602 Ducan Drive, Auburn, AL 36849, USA
Bronte Tilbrook
CSIRO Oceans and Atmosphere, P.O. Box 1538, Hobart, Tasmania 7001, Australia
Australian Antarctic Partnership Program, University of Tasmania, Hobart, Australia
Francesco Tubiello
Statistics Division, Food and Agriculture Organization of the United Nations, Via Terme di Caracalla, Rome 00153, Italy
Guido R. van der Werf
Faculty of Earth and Life Sciences, VU University, Amsterdam, the Netherlands
Nicolas Vuichard
Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
Chisato Wada
Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
Rik Wanninkhof
National Oceanic & Atmospheric Administration/Atlantic Oceanographic & Meteorological Laboratory (NOAA/AOML), Miami, FL 33149, USA
Andrew J. Watson
College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
David Willis
Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Andrew J. Wiltshire
Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
Wenping Yuan
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong 510245, China
Chao Yue
Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91198 Gif-sur-Yvette, France
Xu Yue
School of Environmental Science and Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing, China
Sönke Zaehle
Max Planck Institute for Biogeochemistry, Jena, Germany
Jiye Zeng
Earth System Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
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 datasets and methodology 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 first
time, an approach is shown to reconcile the difference in our
LUC
estimate with the one from national greenhouse gas inventories, supporting
the assessment of collective countries' climate progress.
For the year 2020,
FOS
declined by 5.4 % relative to 2019, with
fossil emissions at 9.5
0.5 GtC yr
−1
(9.3
0.5 GtC yr
−1
when the cement carbonation sink is included), and
LUC
was 0.9
0.7 GtC yr
−1
, for a total anthropogenic CO
emission of
10.2
0.8 GtC yr
−1
(37.4
2.9 GtCO
). Also, for
2020,
ATM
was 5.0
0.2 GtC yr
−1
(2.4
0.1 ppm yr
−1
),
OCEAN
was 3.0
0.4 GtC yr
−1
, and
LAND
was 2.9
1 GtC yr
−1
, with a
IM
of
0.8 GtC yr
−1
. The
global atmospheric CO
concentration averaged over 2020 reached 412.45
0.1 ppm. Preliminary data for 2021 suggest a rebound in
FOS
relative to 2020 of
4.8 % (4.2 % to 5.4 %) globally.
Overall, the mean and trend in the components of the global carbon budget
are consistently estimated over the period 1959–2020, 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 changes emissions, (2) a low
agreement between the different methods on the magnitude of the land
CO
flux in the northern extra-tropics, 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 datasets used in this new global carbon budget and the progress in understanding
of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le
Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The
data presented in this work are available at
(Friedlingstein et al., 2021).
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Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B., Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T., Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., Séférian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, 2022.
Received: 28 Oct 2021
Discussion started: 04 Nov 2021
Revised: 15 Mar 2022
Accepted: 15 Mar 2022
Published: 26 Apr 2022
Executive summary
Global fossil CO
emissions (excluding cement carbonation) in 2021 are
returning towards their 2019 levels after decreasing 5.4 % in 2020. The
2020 decrease was 0.52 GtC yr
−1
(1.9 GtCO
yr
−1
), bringing
2020 emissions to 9.5
0.5 GtC yr
−1
(34.8
1.8 GtCO
yr
−1
), comparable to the emissions level of 2012. Preliminary estimates
based on data available in March 2022 suggest fossil CO
emissions
rebounded 4.8 % in 2021 (4.2 % to 5.4 %), bringing emissions to 9.9 GtC yr
−1
(36.4 GtCO
yr
−1
), back to about the same level as
in 2019 (10.0
0.5 GtC yr
−1
, 36.7
1.8 GtCO
yr
−1
). Emissions from coal and gas in 2021 are expected to have
rebounded above 2019 levels, while emissions from oil were still below their
2019 level. Emissions are expected to have been 5.7 % higher in 2021 than
in 2019 in China, reaching 3.0 GtC (11.1 GtCO
), and also higher in
India with a 3.2 % increase in 2021 relative to 2019, reaching 0.74 GtC
(2.7 GtCO
). In contrast, projected 2021 emissions in the United States
(1.4 GtC, 5.0 GtCO
), European Union (0.8 GtC, 2.8 GtCO
), and the
rest of the world (4.0 GtC, 14.8 GtCO
, in aggregate) remained
respectively 4.5 %, 5.3 %, and 4.0 % below their 2019 levels. These
changes in 2021 emissions reflect the stringency of the COVID-19 confinement
levels in 2020 and the pre-covid background trends in emissions in these
countries.
Fossil CO
emissions significantly decreased in 23 countries during the
decade 2010–2019. Altogether, these 23 countries contribute to about 2.5 GtC yr
−1
fossil fuel CO
emissions over the last decade, only about
one-quarter of world CO
fossil emissions.
Global CO
emissions from land use, land-use change, and forestry (LUC)
converge based on revised data of land-use change and show a small decrease
over the past two decades. Near-constant gross emissions estimated at 3.8
0.6 GtC yr
−1
in the 2011–2020 decade are only partly offset by
growing carbon removals on managed land of 2.7
0.4 GtC yr
−1
resulting in the net emissions in managed land of 1.1
0.7 GtC yr
−1
(4.1
2.6 GtCO
yr
−1
). These net emissions decreased
by 0.2 GtC in 2020 compared to 2019 levels, with large uncertainty.
Preliminary estimates for emissions in 2021 suggest a 0.1 GtC decrease for
2021, giving net emissions of 0.8 GtC yr
−1
(2.9 GtCO
yr
−1
). The
small decrease in net LUC emissions amidst large uncertainty prohibits
robust conclusions concerning trend changes of total anthropogenic
emissions. For the first time, we link the global carbon budget models'
estimates to the official country reporting of national greenhouse gases
inventories. While the global carbon budget distinguishes anthropogenic from
natural drivers of land carbon fluxes, country reporting is area-based and
attributes part of the natural terrestrial sink on managed land to the
land-use sector. Accounting for this redistribution, the two approaches are
shown to be consistent with each other.

The remaining carbon budget for a 50 % likelihood to limit global warming
to 1.5, 1.7, and 2
C has respectively
reduced to 120 GtC (420 GtCO
), 210 GtC (770 GtCO
) and 350 GtC
(1270 GtCO
) from the beginning of 2022, equivalent to 11, 20, and 32 years, assuming 2021 emissions levels. Total anthropogenic emissions were
10.4 GtC yr
−1
(38.0 GtCO
yr
−1
) in 2020, with a preliminary
estimate of 10.7 GtC yr
−1
(39.3 GtCO
yr
−1
) for 2021. The
remaining carbon budget to keep global temperatures below these climate
targets has shrunk by 21 GtC (77 GtCO
) since the release of the IPCC
AR6 Working Group 1 assessment. Reaching 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 during 2020,
highlighting the scale of the action needed.
The concentration of CO
in the atmosphere is set to reach 414.7 ppm in
2021, 50 % above pre-industrial levels. The atmospheric CO
growth
was 5.1
0.02 GtC yr
−1
during the decade 2011–2020 (47 % of
total CO
emissions) with a preliminary 2021 growth rate estimate of
around 5 GtC yr
−1
The ocean CO
sink resumed a more rapid growth in the past decade 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 increase of 0.9 and 0.3 GtC yr
−1
since 2010, respectively. The discrepancy in the trend originates from all
latitudes but is largest in the Southern Ocean. The ocean CO
sink was
2.8
0.4 GtC yr
−1
during the decade 2011–2020 (26 % of total
CO
emissions), with a preliminary 2021 estimate of around 2.9 GtC yr
−1
The land CO
sink continued to increase during the 2011–2020 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 2011–2020 decade (29 % of total CO
emissions),
0.5 GtC yr
−1
larger than during the previous decade (2000–2009), with a
preliminary 2021 estimate of around 3.3 GtC yr
−1
. Year-to-year
variability in the land sink is about 1 GtC yr
−1
, making small annual
changes in anthropogenic emissions hard to detect in global atmospheric
CO
concentration.
Introduction
The concentration of carbon dioxide (CO
) in the atmosphere has
increased from approximately 277 parts per million (ppm) in 1750 (Joos and
Spahni, 2008), the beginning of the industrial era, to 412.4
0.1 ppm
in 2020 (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., 2022). 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 the 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 millennial, while exchanges with
geologic reservoirs occur on longer timescales (Archer et al., 2009).
Figure 1
Surface average atmospheric CO
concentration (ppm). Since
1980, monthly data are from NOAA/ESRL (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 of mean CO
and seasonality between the NOAA/ESRL and the
Scripps station networks used here, the Scripps surface average (from two
stations) was de-seasonalized and adjusted to match the NOAA/ESRL 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,
onset of atmospheric CO
measurements), the last decade (2011–2020),
the last year (2020), and the current year (2021). 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 2011–2020. 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. (2022) for all numbers, except for the carbon stocks in coasts which is 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, also including cement production and carbonation (
FOS
; GtC yr
−1
); (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 coasts and territorial
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 reality in balance. 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 (a) additionally assess a set of global atmospheric
inverse model results that by design close the global carbon balance (see
Sect. 2.6), and (b) 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 petagrammes 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 micromole per mole of dry air.
The use of a factor of 2.124 assumes that all 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 is equal to that of the troposphere on a yearly basis.
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We also include a quantification of
FOS
by country, computed with both
territorial and consumption-based accounting (see Sect. 2), and discuss
missing terms from sources other than the combustion of fossil fuels (see
Sect. 2.7).
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., 2022), and by others (e.g. Ballantyne et al.,
2012). The Global Carbon Project (GCP,
, last
access: 11 March 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), and
more recently the year 2020 (Friedlingstein et al., 2020; Le Quéré
et al., 2021). 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 datasets, and expert judgement of the likelihood of
results lying outside this range. The limitations of current information are
discussed in the paper and have been examined in detail elsewhere
(Ballantyne et al., 2015; Zscheischler et al., 2017). We also use a
qualitative assessment of confidence level to 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 datasets and methodology
used to compute the global carbon budget estimates for the industrial
period, from 1750 to 2020, and in more detail for the period since 1959. It
also provides decadal averages starting in 1960 including the most recent
decade (2011–2020), results for the year 2020, and a projection for the year
2021. 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). This paper is updated every year using the format of
“living data” to keep a record of budget versions and the changes in new
data, revision of data, and changes in methodology that lead to changes in
estimates of the carbon budget. Additional materials associated with the
release of each new version will be posted at the Global Carbon Project
(GCP) website (
, last access:
11 March 2022), with fossil fuel emissions also available through the Global
Carbon Atlas (
, last access: 11 March 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 datasets will be referenced in future work (see Table 2 for
how to cite the datasets). 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 16th version of the global carbon budget and the 10th 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. (2020). The main changes are as follows: the inclusion of (1) data to year 2020 and
a projection for the global carbon budget for year 2021, (2) a Kaya analysis
to identify the driving factors behind the recent trends in fossil fuel
emissions (changes in population, GDP per person, energy use per GDP, and
CO
emissions per unit energy), (3) an estimate of the ocean sink from
models and data products combined, (4) an assessment of the relative
contributions of increased atmospheric CO
and climate change in
driving the land and ocean sinks, and (5) an assessment of the current
trends in anthropogenic emissions and implications for the remaining carbon
budget for specific climate targets. The main methodological differences
between recent annual carbon budgets (2016–2020) are summarized in Table 3
and previous changes since 2006 are provided in Table A7.
Table 3
Main methodological changes in the global carbon budget since 2017. 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. Table A7 lists methodological changes from the first global carbon budget publication up to 2016.
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2.1
Fossil CO
emissions (
FOS
2.1.1
Historical period 1850–2020
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 emissions
sources are not estimated or not fully covered: coverage of emissions from
lime production are 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) due to a 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 dataset, derived largely from UN energy data, forms the
foundation, and we extend emissions to year Y-1 using energy growth rates
reported by BP. 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 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 datasets. Datasets such as those
published by BP energy company, 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, datasets such as PRIMAP-hist, CEDS, EDGAR, and GCP's
dataset 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 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. Since carbonation is a function of both
current and previous cement production, we extend these estimates by 1 year to 2020 by using the growth rate derived from the smoothed cement
emissions (10-year smoothing) fitted to the carbonation data.
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 use. 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
2021 projection
We provide a projection of global CO
emissions in 2021 by combining
separate projections for China, the USA, the 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 (2021), 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 production. For the EU we use monthly energy data from
Eurostat to derive estimates of monthly CO
emissions through July,
with coal emissions extended first through September using a statistical
relationship with reported electricity generation from coal and other
factors, then through December assuming normal seasonal patterns. EU
emissions from natural gas – a strongly seasonal cycle – are extended
through December using bias-adjusted Holt–Winters exponential smoothing
(Chatfield, 1978). 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 two of the largest producers, Germany and
Poland. India's projected emissions are derived from estimates through
August (September for coal) 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 2021
projection can be found in Appendix C1.
2.2
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.
Three bookkeeping approaches (updated estimates 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 model (DGVM) 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., 2018). Projections for 2021 are based on fire activity
from tropical deforestation and degradation as well as 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 GHG inventories (NGHGIs) 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 NGHGIs 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, compared to
LAND
, both anthropogenic and natural
sources on managed land (Tubiello et al., 2021). Using the approach outlined
in Grassi et al. (2021), here we map as additional information the two
definitions to each other, to provide a comparison of the anthropogenic
carbon budget to the official country reporting to the climate convention.
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
The rate of growth of the atmospheric CO
concentration is provided
for years 1959–2020 by the US National Oceanic and Atmospheric
Administration Earth System Research Laboratory (NOAA/ESRL; 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 each
station with a smoothed curve 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 1 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).
Starting in 2020, NOAA/ESRL now 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 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 (versus 250–520 ppm
for WMO-CO
-X2007). In addition, NOAA/ESRL 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 versus 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/ESRL, 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. Comparing the
estimates of
ATM
made by Dlugokencky and Tans (2020), used in the
Global Carbon Budget 2020 (Friedlingstein et al., 2020), with updated
estimates from Dlugokencky and Tans (2022), used here, we find that
ATM
reduced on average by
0.06 GtC yr
−1
during 2010–2019 and by
0.01 GtC yr
−1
during 1959–2019 due to the new calibration. These
changes are well within the uncertainty ranges reported below. Hence the
change in analytical procedures made by NOAA/ESRL has a negligible impact on
the atmospheric growth rate
ATM
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, etc. (Dlugokencky and Tans, 2022). The
latter uncertainty was estimated by NOAA/ESRL with a Monte Carlo method by
constructing 100 “alternative” networks (Masarie and Tans, 1995; NOAA/ESRL,
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. Preliminary estimates suggest this effect
would increase the annual uncertainty, but a full analysis is not yet
available. 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), but recognize further exploration of
this uncertainty is required. At this time, 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 277
3 ppm or
286
3 ppm, respectively, based on a cubic spline fit to ice core
data (Joos and Spahni, 2008). 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).
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)
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
estimates are primarily from Gilfillan and Marland (2021), with
uncertainty of about
5 % (
). The
LUC
estimates are from three bookkeeping models (Table 4) with uncertainties of
about
0.7 GtC yr
−1
. The
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.
Table 4
References for the process models,
CO
-based ocean data products, and atmospheric inversions. All models and products are updated with new data to the end of year 2020, and the atmospheric forcing for the DGVMs has been updated as described in Sect. 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 Decharme et al. (2019) and Seferian et al. (2019).
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 shortwave 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 Buitenhuis et al. (2013).
See also Séférian et al. (2019).
See also Schourup-Kristensen et al. (2014).
See also Remaud et al. (2018).
See also Rodenbeck et al. (2003).
See also Feng et al. (2009) and Palmer et al. (2019).
See also Niwa et al. (2020).
Download XLSX
2.3.2
2021 projection
We provide an assessment of
ATM
for 2021 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
2022. 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' monthly growth rates.
2.4
Ocean CO
sink
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 eight 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
further use two 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 0.61 GtC yr
−1
from river input to the ocean (the average of 0.45
0.18 GtC yr
−1
by Jacobson et al., 2007, and 0.78
0.41 GtC yr
−1
by Resplandy et al., 2018), 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: 0.16 GtC yr
−1
, tropics: 0.15 GtC yr
−1
, south: 0.30 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
modelling study (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).
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. Data products are adjusted to represent the
full ocean area by a simple scaling approach when coverage is below 98 %.
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.
We assign an uncertainty of
0.4 GtC yr
−1
to the ocean sink
based on a combination of random (ensemble standard deviation) and
systematic uncertainties (GOBMs bias in anthropogenic carbon accumulation,
previously reported uncertainties in
CO
-based data products; see
Sect. 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 results are consistent in that the
interannual variability in the GOBMs and data-based estimates are all
generally small compared to the variability in the growth rate of
atmospheric CO
concentration. 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.
The ocean CO
sink forecast for the year 2021 is based on the annual
historical and estimated 2021 atmospheric CO
concentration
(Dlugokencky and Tans, 2021), historical and estimated 2021 annual global
fossil fuel emissions from this year's carbon budget, and the spring (March,
April, May) oceanic Niño index (ONI) (NCEP, 2021). Using a
non-linear regression approach, i.e. a feed-forward neural network,
atmospheric CO
, the ONI, and the 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 2020
from this year's carbon budget. Using this relationship, the 2021
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 runs 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
2021 prediction.
2.5
Terrestrial CO
sink
The terrestrial land sink (
LAND
) is thought to be due to the combined
effects of fertilization by rising atmospheric CO
and N 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 17 DGVMs (Table A1). As
described in Appendix C4, DGVM simulations include all climate variability
and CO
effects over land, with 12 DGVMs also including the effect of N
inputs. 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.
Like the ocean forecast, the land CO
sink (
LAND
) forecast is
based on the annual historical and estimated 2021 atmospheric CO
concentration (Dlugokencky and Tans 2021), historical and estimated 2021
annual global fossil fuel emissions from this year's carbon budget, and the
summer (June, July, August) ONI (NCEP, 2021). All training data are
again used to best match
LAND
from 1959 through 2020 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 step 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 2020 (1.0 GtC yr
−1
) to
estimate the overall uncertainty of the 2021 prediction.
2.6
The atmospheric perspective
The worldwide 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
extra-tropics. 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 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 six inversion systems that are described in
Table A4. Each system is rooted in Bayesian inversion principles but uses
slightly different methodologies. These differences concern the selection of
atmospheric CO
data and the choice of a priori fluxes to refine with
these data. 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). Multiple inversion systems (UoE, CTE, and CAMS) were previously
tested with satellite xCO
retrievals from GOSAT or OCO-2 measurements,
but their results at the larger scales (as discussed in this work) did not
deviate substantially from their in situ counterparts and are therefore not
separately included. One inversion this year (CMS-Flux) used ACOS-GOSAT v9
retrievals between July 2009 and December 2014 and OCO-2 b10 retrievals between
January to December 2015, 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 three accounts: (1) global total fossil fuel emissions, (2) riverine CO
transport, and (3) cement carbonation CO
uptake. Details are given
below. We note that with these adjustments the inverse results no longer
represent the net atmosphere–surface exchange over land–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 GOBMs estimates.
The inversion systems prescribe global fossil fuel emissions based on the
GCP's Gridded Fossil Emissions Dataset version 2021.2 (GCP-GridFEDv2021.2;
Jones et al., 2021b), which is an update to 2019 of the first version of
GCP-GridFED presented by Jones et al. (2021a). GCP-GridFEDv2021.2 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 1959–2020, which were compiled
following the methodology described in Sect. 2.1 with all datasets
available on 14 August 2021 (Robbie Andrew, personal communication, 2021). Small differences between the
systems due to for instance regridding to the transport model resolution are
corrected for 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 five out of six inversions are part of the
suite of the ocean process model 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 done for the observation-based estimates of
OCEAN
(see Appendix C3). Furthermore, the inversions did not
include a cement carbonation sink (see Sect. 2.1), and therefore this GCB
component is implicitly part of their total land sink estimate. In the
numbers presented in this budget, each year's global carbonation sink from
cement was subtracted from each year's estimated land sink in each
inversion, distributed proportionally to fossil fuel emissions per region
(north, tropics, and south).
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, have been used to assess model performance (with space–time
observational coverage sparse in the SH and tropics, and denser in NH
mid-latitudes; Table A6). The six models are compared to the independent
aircraft CO
measurements between 2 and 7 km above sea level between
2001 and 2020. Results are shown in Fig. B4 and discussed in Sect. 3.7.
With a relatively small ensemble (
=6
) 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 of other carbonates to CO
emissions 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 2020; the
six decades in which we have atmospheric concentration records from Mauna
Loa (1960–2020), with a specific focus on last year (2020); and the projection
for the current year (2021). 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 1850–2020
Cumulative fossil CO
emissions for 1850–2020 were 455
25 GtC,
including the cement carbonation sink (Fig. 3, Table 8).
In this period, 46 % of fossil CO
emissions came from coal, 35 %
from oil, 14 % from natural gas, 3 % from decomposition of carbonates,
and 1 % from flaring.
In 1850, the UK accounted for 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
1850–2020, US cumulative emissions amounted to 110 GtC (25 % of world
total), the EU's to 80 GtC (18 %), and China's to 60 GtC (14 %).
There are three additional global datasets 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 datasets are not
independent. 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 datasets. However, in general these four
datasets are in relative agreement with total historical global emissions
of fossil CO
Figure 4
Components of the global carbon budget and their uncertainties as
a function of time, presented individually for
(a)
fossil CO
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 2021 and
the red error bars the uncertainty in the projections (see methods).
Figure 5
Fossil CO
emissions for
(a)
the globe, including an
uncertainty of
5 % (grey shading) and a projection through the
year 2021 (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, and cement, and cement minus cement
carbonation (dashed); and
(d)
per capita emissions the world and for the
large emitters as in panel
(b)
. Territorial emissions are primarily from
Gilfillan and Marland (2021) except national data for the USA and EU27 for
1990–2018, which are reported by the countries to the UNFCCC as detailed in
the text; consumption-based emissions are updated from Peters et al. (2011b). See Sect. 2.1 and Appendix C1 for details of the calculations
and data sources.
3.1.2
Recent period 1960–2020
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 9.5
0.5 GtC yr
−1
during 2011–2020 (Table 6, Figs. 2, 4 and 5). The
growth rate in these emissions decreased between the 1960s and the 1990s,
from 4.3 % yr
−1
in the 1960s (1960–1969), 3.2 % yr
−1
in the
1970s (1970–1979), and 1.6 % yr
−1
in the 1980s (1980–1989), to 0.9 % yr
−1
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 % yr
−1
, decreasing to 0.6 % yr
−1
for the last decade (2011–2020).
China's emissions increased by
1.0 % yr
−1
on average over the last
10 years, dominating the global trend, followed by India's emissions increase
by
3.9 % yr
−1
, while emissions decreased in EU27 by
1.9 % yr
−1
, and in the USA by
1.1 % yr
−1
. Figure 6 illustrates the
spatial distribution of fossil fuel emissions for the 2011–2020 period.
Figure 6
The 2011–2020 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/red (green/blue) colours represent a flux
from (into) the land–ocean to (from) the atmosphere. All units are in kgC m
−2
yr
−1
. Note the different scales in each panel.
FOS
data
shown are from GCP-GridFEDv2021.2.
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 GOBMs
simulation A; no adjustment for bias and drift is applied to the gridded fields
(see Sect. 2.4).
LAND
data shown are the average of DGVMs for
simulation S2 (see Sect. 2.5).
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 2011–2020 (Fig. 5).
3.1.3
Final year 2020
Global fossil CO
emissions were 5.4 % lower in 2020 than in 2019,
because of the COVID-19 pandemic, with a decline of 0.5 GtC to reach 9.5
0.5 GtC (9.3
0.5 GtC when including the cement carbonation
sink) in 2020 (Fig. 5), distributed among coal (40 %), oil (32 %),
natural gas (21 %), cement (5 %), and others (2 %). Compared to the
previous year, 2020 emissions from coal, oil, and gas declined by 4.4 %,
9.7 %, and 2.3 % respectively, while emissions from cement increased by
0.8 %. All growth rates presented are adjusted for the leap year, unless
stated otherwise.
In 2020, the largest absolute contributions to global fossil CO
emissions were from China (31 %), the USA (14 %), the EU27 (7 %), 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 (2.9 % of the total).
Growth rates for these countries from 2019 to 2020 were
1.4 % (China),
10.6 % (USA),
10.9 % (EU27), and
7.3 % (India), with
7.0 % for
the rest of the world. The per capita fossil CO
emissions in 2020 were
1.2 tC person
−1
yr
−1
for the globe, and were 3.9 (USA), 2.0
(China), 1.6 (EU27) and 0.5 (India) tC per person per year for the four
highest emitting countries (Fig. 5).
The COVID-19-induced decline in emissions of
5.4 % in 2020 is close to
the projected decline of
6.7 %, which was the median of four approaches,
published in Friedlingstein et al. (2020) (Table 7). Of the four approaches, the “GCP”
method was closest at
5.8 %. That method was based on national emissions
projections for China, the USA, the EU27, and India using reported monthly
activity data when available and projections of gross domestic product
corrected for trends in fossil fuel intensity (
FOS
) for the rest of
the world. Of the regions, the projection for the EU27 was the least accurate,
and the reasons for this are discussed by Andrew (2021).
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
. The DGVM uncertainties represent
of the decadal or annual (for 2020 only) estimates from
the individual DGVMs: for the inverse models 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, for the cement carbonation sink, and 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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3.1.4
Year 2021 projection
Globally, we estimate that global fossil CO
emissions will rebound
4.8 % in 2021 (4.2 % to 5.4 %) to 9.9 GtC (36.4 GtCO
), returning to
near their 2019 emission levels of 10.0 GtC (36.7 GtCO
). Global
increases in 2021 emissions per fuel types are
6.3 % (range 5.5 % to
7.0 %) for coal,
4.0 % (range 2.6 % to 5.4 %) for oil,
3.8 %
(range 2.8 % to 4.8 %) for natural gas, and
3.2 % (range 1.7 % to
4.6 %) for cement.
For China, projected fossil emissions in 2021 are expected to increase by
4.3 % (range 3.0 % to 5.4 %) compared with 2020 emissions, bringing
2021 emissions for China to around 3.0 GtC yr
−1
(11.1 GtCO
yr
−1
). Chinese emissions appear to have risen in both 2020 and 2021
despite the economic disruptions of COVID-19. Increases in fuel-specific
projections for China are
4.1 % for coal,
4.4 % for oil,
12.8 % natural gas, and a decrease of 0.1 % for cement.
For the USA, the Energy Information Administration (EIA) emissions
projection for 2021 combined with cement clinker data from USGS gives an
increase of 6.8 % (range 6.6 % to 7.0 %) compared to 2020, bringing
USA 2021 emissions to around 1.4 GtC yr
−1
(5.0 GtCO
yr
−1
). This
is based on separate projections for coal of
17.1 %, oil
9.0 %,
natural gas
0.8 %, and cement
0.3 %.
For the European Union, our projection for 2021 is for an increase of
6.3 % (range 4.3 % to 8.3 %) over 2020, with 2021 emissions around 0.8 GtC yr
−1
(2.8 GtCO
yr
−1
). This is based on separate
projections for coal of
14.6 %, oil
3.7 %, natural gas
4.6 %,
and cement
0.3 %.
For India, our projection for 2021 is an increase of 11.2 % (range of
10.7 % to 11.7 %) over 2020, with 2021 emissions around 0.7 GtC yr
−1
(2.7 GtCO
yr
−1
). This is based on separate projections
for coal of
13.9 %, oil
3.4 %, natural gas
4.8 %, and cement
21.6 %.
For the rest of the world, the expected growth rate for 2021 is 3.2 %
(range 2.0 % to 4.3 %). This is computed using the GDP projection for
the world (excluding China, the USA, the EU, and India) of 4.4 % made by
the IMF (2022) and a decrease in
FOS
of
1.7 % yr
−1
, which is
the average over 2011–2020. The uncertainty range is based on the standard
deviation of the interannual variability in
FOS
during 2011–2020 of
0.6 % yr
−1
and our estimates of uncertainty in the IMF's GDP forecast
of 0.6 %. The methodology allows independent projections for coal, oil,
natural gas, cement, and other components, which add to the total emissions
in the rest of the world. The fuel-specific projected 2021 growth rates for
the rest of the world are:
3.2 % (range 0.7 % to 5.8 %) for coal,
2.3 % (
0.3 % to
4.9 %) for oil,
4.1 % (2.6 % to 5.7 %)
for natural gas, and
4.8 % (
2.7 % to
6.9 %) for cement.
Independently, the IEA has published two forecasts of global fossil energy
CO
emissions (i.e. a subset of fossil CO
emissions), the first in
April (4.8 %; IEA, 2021a) which was then revised in October to 4 % (IEA, 2021b).
In March 2022 they also published a new, preliminary estimate of 6 %
growth (IEA, 2021a). Carbon Monitor produces estimates of global emissions
with low temporal lag, and their estimates suggest that emissions were
5.1 % higher than in 2020 (Carbon Monitor, 2022).
Table 6
Decadal mean in the five components of the anthropogenic
CO
budget for different periods, and last year available. All values are in
GtC yr
−1
, and uncertainties are reported as
Fossil CO
emissions include cement carbonation. The
table also shows the budget imbalance (
IM
), which
provides a measure of the discrepancies among the nearly independent
estimates and has an uncertainty exceeding
1 GtC yr
−1
. 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 GtC yr
−1
, 4.7
0.2 GtC yr
−1
, 5.5
0.3 GtC yr
−1
, 6.4
0.3 GtC yr
−1
, 7.9
0.4 GtC yr
−1
, and 9.7
0.5 GtC yr
−1
for the decades 1960s to 2010s respectively and to 9.5
0.5 GtC yr
−1
for 2020.
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3.2
Emissions from land-use changes
3.2.1
Historical period 1850–2020
Cumulative CO
emissions from land-use changes (
LUC
) for
1850–2020 were 200
65 GtC (Table 8; Figs. 3, 13). The cumulative
emissions from
LUC
are particularly uncertain, with large spread among
individual estimates of 140 GtC (updated H&N2017), 270 GtC (BLUE), and
195 GtC (OSCAR) for the three bookkeeping models and a similar wide estimate
of 190
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 1960–2020
In contrast to growing fossil emissions, CO
emissions from land use,
land-use change, and forestry have remained relatively constant, at around
1.3
0.7 GtC yr
−1
over the 1970–1999 period, and even show a
slight decrease over the last 20 years, reaching 1.1
0.7 GtC yr
−1
for the 2011–2020 period (Table 6, Fig. 4), but with large spread across
estimates (Table 5, Fig. 7). Emissions have been relatively constant in the DGVMs
ensemble of models since the 1970s, with similar mean values until the 1990s
as the bookkeeping mean and large model spread (Table 5, Fig. 7). The DGVMs
average grows larger than the bookkeeping average in the recent decades and
shows no sign of decreasing emissions, which 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).
Figure 7
CO
exchanges between the atmosphere and the terrestrial
biosphere as used in the global carbon budget (black with
uncertainty in grey shading in all panels).
(a)
CO
emissions from
land-use change (
LUC
) with estimates from the three bookkeeping models
(yellow lines) and DGVMs (green) shown individually, with DGVM
ensemble means (dark green). The dashed line identifies the pre-satellite
period before the inclusion of peatland burning.
(b)
CO
gross sinks
(positive, from regrowth after agricultural abandonment and wood harvesting)
and gross sources (negative, 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 also from emissions
from peat drainage and peat burning) from the three bookkeeping models
(yellow lines). The sum of the gross sinks and sources is
LUC
shown in
panel
(a)
(c)
Land CO
sink (
LAND
) with individual DGVM
estimates (green).
(d)
Total atmosphere–land CO
fluxes (
LAND
LUC
), with individual DGVMs (green) and their multi-model mean (dark
green).
LUC
is a net term of various gross fluxes, which comprise emissions
and removals. Gross emissions are on average 2–4 times larger than the net
LUC
emissions, and remained largely constant over the last 60 years,
with a moderate increase from an average of 3.4
0.9 GtC yr
−1
for the decade of the 1960s to an average of 3.8
0.6 GtC yr
−1
during 2011–2020 (Fig. 7, Table 5), showing the relevance of land management
such as harvesting or rotational agriculture. Increases in gross removals,
from 1.9
0.4 GtC yr
−1
for the 1960s to 2.7
0.4 GtC yr
−1
for 2011–2020, were 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, influence gross emissions dynamics more than
gross removal dynamics. It is these relative changes to each other that
explain the decrease in net
LUC
emissions over the last two decades
and the last few years. Gross fluxes 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 decrease in net CO
emissions from land-use change over the
last decade (Fig. 7, Table 6), in contrast to earlier estimates of no clear
trend across
LUC
estimates (Friedlingstein et al., 2020; Hong et al.,
2021). The trend in the last decade is now about
4 % yr
−1
, compared
to the
1.8 % yr
−1
reported by Friedlingstein et al. (2020). This
decrease is principally attributable to changes in
LUC
estimates from
BLUE and OSCAR, which relate to changes in the underlying land-use forcing,
LUH2 (Chini et al., 2021; Hurtt et al., 2020), based on HYDE3.3 (Klein
Goldewijk et al., 2017a, b): HYDE3.3 now incorporates updated estimates of
agricultural areas by the FAO and uses multi-annual land-cover maps from
satellite remote sensing (ESA CCI Land Cover) to constrain contemporary land-cover patterns (see Appendix C2.2 for details). These changes lead to
lower global
LUC
estimates in the last two decades compared to earlier
versions of the global carbon budget due most notably to lower emissions
from cropland expansion, particularly in the tropical regions. Rosan et al. (2021) showed that for Brazil, the new HYDE3.3 version is closer to
independent, regional estimates of land-use and land-cover change
(MapBiomas, 2021) with respect to spatial patterns, but it shows less
land-use and land-cover changes than these independent estimates, while
HYDE3.2-based estimates had shown higher changes and lower emissions. The
update in land-use forcing leads to a decrease in estimated emissions in
Brazil across several models after the documented deforestation peak of
2003–2004 that preceded policies and monitoring systems decreasing
deforestation rates (Rosan et al., 2021). However, estimated emissions based
on the new land-use forcing do not reflect the rise in Brazilian
deforestation in the last few years (Silva Junior et al., 2021), and associated
increasing emissions from deforestation would have been missed here. The
update in FAO agricultural areas in Brazil also implied that substantial
interannual variability reported to earlier FAO assessment and captured by
the HYDE3.2 version since 2000 was removed. Due to the asymmetry of (fast)
decay (like clearing by fire) and (slower) regrowth, such reduced
variability is expected to decrease annual emissions. Also, the approach by
Houghton and Nassikas (2017) smooths land-use area changes before
calculating carbon fluxes by a 5-year running mean, hence the three emission
estimates are in better agreement than in previous GCB estimates. However,
differences still exist, which highlight the need for accurate knowledge of
land-use transitions and their spatial and temporal variability. A further
caveat is that global land-use change data for model input does not capture
forest degradation, which often occurs on small scales 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).
Overall, therefore, we assign low confidence to the change towards a
decreasing trend of land-use emissions over the last two decades as seen
compared to the estimate of the global carbon budget 2020 (Friedlingstein et
al., 2020). Our approach aims at using the most up-to-date data and methods,
such as accounting for revisions of living databases of country-level
agricultural statistics from FAO or including satellite remote-sensing
information for spatial allocation. While we start from a well-documented
methodology to provide gridded land-use data (Chini et al., 2021), not all
changes in individual components are always documented, complicating the
explanation of changes from one GCB to the next. The rising number of
pan-tropical or global estimates of carbon stock changes based on satellite
remote sensing of carbon densities and forest cover changes (Fan et al., 2019; Qin et al., 2021; Xu et al., 2022; Feng et al., 2022) may seem a
promising path for independent evaluation of the land-use emissions term.
However, comparison of satellite-derived fluxes to global model estimates is
hampered for several reasons discussed by Pongratz et al. (2021). Most
importantly, satellite-based estimates usually do not distinguish between
anthropogenic drivers and natural forest cover losses (e.g. from drought or
natural wildfires), which have also increased over time in some regions,
including the tropics; ancillary information would be needed to attribute
the observed signal of vegetation or carbon stock change to different
drivers. Further, satellite-based estimates often only provide sub-component
fluxes of
LUC
, excluding soil or product pool changes. Since forest
cover loss is better detectable from space than regrowth, satellite-based
products often limit their estimates to emissions from forest loss,
neglecting carbon uptake from regrowth of forests, as may occur following
wood harvesting, abandonment, or natural disturbances; such products thus
provide a subset of the gross emissions term (Fig. 7b) and cannot be
compared to net emissions. Lastly, satellite-based fluxes typically quantify
committed instead of actual emissions, i.e. legacy CO
fluxes from
potentially slow processes such as slash, soil carbon or product decay, or
forest regrowth are not captured at the time they actually occur but are
attributed to the time of the land-use change event (Pongratz et al., 2021).
Using data on drivers of forest cover loss to isolate fluxes from
agricultural expansion, and looking into gross emissions instead of the net
land-use change flux, Feng et al. (2022) suggest a stronger increase in
global gross emissions (though generally a smaller flux) than the
bookkeeping models do (see gross fluxes in Fig. 7b). This is in line with
Rosan et al. (2021) suggesting that the trend of net emissions in Brazil may
be underestimated by the updated land-use data (though patterns have
improved). Further studies are needed to robustly estimate the trend of
global net land-use emissions. Progress is also needed on accurate
quantifications of land-use dynamics, including less well observable
management types such as shifting cultivation and wood harvesting, and their
distinction from natural disturbances (Pongratz et al., 2021).
The highest land-use emissions occur in the tropical regions of all three
continents, including the Arc of Deforestation in the Amazon basin (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), to a substantial extent for export (Pendrill et
al., 2019). Emission intensity is high in many tropical countries,
particularly of Southeast Asia, due to high rates of land conversion in
regions of carbon-dense and often still pristine, undegraded natural forests
(Hong et al., 2021). Emissions are further increased by peat fires in
equatorial Asia (GFED4s, van der Werf et al., 2017). Uptake due to land-use
change occurs, particularly in Europe, partly related to expanding forest
area as a consequence of the forest transition in the 19th and
20th century and subsequent regrowth of forest (Fig. 6b) (Mather, 2001;
McGrath et al., 2015).
While the mentioned patterns are robust and supported by independent literature, we acknowledge that model spread is substantially larger on regional
than on global level, as has been shown for bookkeeping models (Bastos et al.,
2021) as well as DGVMs (Obermeier et al., 2021). A detailed analysis of
country-level or regional uncertainties globally is beyond the scope of this
study. 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 Petrescu et al.,
2020, for Brazil Rosan et al., 2021).
National GHG inventory data (NGHGI) under the LULUCF sector or data
submitted by countries to FAOSTAT differ from the global models' definition
of
LUC
we adopt here in that in the NGHGI reporting, the natural
fluxes (
LAND
) are counted towards
LUC
when they occur on managed
land (Grassi et al., 2018). In order to compare our results to the NGHGI
approach, we perform a re-mapping of our
LUC
estimate by including the
LAND
over managed forest from the DGVM simulations (following Grassi
et al., 2021) to the bookkeeping
LUC
estimate (see Appendix C2.3).
For the 2011–2020 period, we estimate that 1.5 GtC yr
−1
of
LAND
occurred on managed forests and is then reallocated to
LUC
here, as
done in the NGHGI method. Doing so, our mean estimate of
LUC
is
reduced from a source of 1.1 GtC to a sink of
0.4 GtC, very similar to the
NGHGI estimate of
0.6 GtC (Table A8).
Though estimates between GHGI, FAOSTAT, individual process-based models, and
the mapped budget estimates 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 the UNFCCC Paris Agreement.
3.2.3
Final year 2020
The global CO
emissions from land-use change are estimated as 0.9
0.7 GtC in 2020, 0.2 GtC lower than 2019, which had featured
particularly large peat and tropical deforestation and degradation fires. The
surge in deforestation fires in the Amazon, causing about 30 % higher
emissions from deforestation and degradation fires in 2019 over the previous
decade, continued into 2020 (GFED4.1s, van der Werf et al., 2017). However,
the unusually dry conditions for a non-El Niño year that occurred in
Indonesia in 2019 and led to fire emissions from peat burning, deforestation,
and degradation in equatorial Asia to be about twice as large as the average
over the previous decade (GFED4.1s, van der Werf et al., 2017) ceased in
2020. However, confidence in the annual change remains low. While the
mentioned fires are clearly attributable to land-use activity, foremost
deforestation and peat burning, and may have been reinforced by dry weather
conditions, as was the case in Indonesia in 2019, wildfires also occur
naturally. In particular, the extreme fire events in recent years in
Australia, Siberia, and California were unrelated to land-use change and are
thus not attributed to
LUC
, but to the natural land sink, and are
discussed in Sect. 3.6.2.
Land-use change and related emissions may have been affected by the COVID-19
pandemic (e.g. Poulter et al., 2021). Although emissions from tropical
deforestation and degradation fires have been decreasing from 2019 to 2020
on the global scale, they increased in Latin America (GFED4s; van der Werf
et al., 2017). 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, too, monitoring capacities and legal
enforcement of measures to reduce tropical deforestation have been reduced
due to budget restrictions of environmental agencies or impairments to
ground-based monitoring that prevents 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, 2020; Golar et
al., 2020; Beckman and Countryman, 2021) remain to be seen.
3.2.4
Year 2021 projection
With wet conditions in Indonesia and a below-average fire season in South
America our preliminary estimate of
LUC
for 2021 is substantially
lower than the 2011–2020 average. By the end of September 2021 emissions
from tropical deforestation and degradation fires were estimated to be 222
TgC, down from 347 TgC in 2019 and 288 in 2020 (315 TgC 1997–2020 average).
Peat fire emissions in equatorial Asia were estimated to be 1 TgC, down from
117 TgC in 2019 and 2 TgC in 2020 (74 TgC 1997–2020 average) (GFED4.1s, van
der Werf et al., 2017). Based on the fire emissions until the end of
September, we expect
LUC
emissions of around 0.8 GtC in 2021. 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.
3.3
Total anthropogenic emissions
Cumulative anthropogenic CO
emissions for 1850–2020 totalled 660
65 GtC (2420
240 GtCO
), of which almost 70 % (455 GtC) occurred since 1960 and more than 30 % (205 GtC) since 2000 (Tables 6
and 8). Total anthropogenic emissions more than doubled over the last 60 years, from 4.6
0.7 GtC yr
−1
for the decade of the 1960s to an
average of 10.6
0.8 GtC yr
−1
during 2011–2020.
The total anthropogenic CO
emissions from fossil plus land-use change
amounted to 10.6
0.8 GtC (38.9
2.9 GtCO
) for the
2011–2020 decade, reaching 10.2
0.8 GtC (37.2
2.9 GtCO
) in 2020, while for 2021, we project global total anthropogenic
CO
emissions from fossil and land-use changes to be around 10.7 GtC
(39.3 GtCO
).
During the historical period 1850–2020, 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 (17 % during
the period 1960–2020 and 10 % during 2011–2020).
Table 7
Comparison of the projection with realized fossil CO
emissions (EFOS). The “Actual” values are first the 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),
This study (median of four reported estimates, Sect. 3.1.4).
EU28 until 2019, EU27 from 2020.
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Table 8
Cumulative CO
for different time
periods in gigatonnes of carbon (GtC). All uncertainties are reported as
. 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.
Using projections for the year 2021.
Cumulative
LUC
1750–1849 of 30 GtC based on multi-model mean of Pongratz et al. (2009), Shevliakova et al. (2009), Zaehle et al. (2011), and Van Minnen et al. (2009). 1850–2020 from the mean of BLUE (Hansis et al., 2015), OSCAR (Gasser et al., 2020), and H&N2017 (Houghton and Nassikas, 2017). 1750–2020 uncertainty is estimated from standard deviation of DGVMs over 1870–2020 scaled by 1750–2020 emissions.
Cumulative
LUC
based on BLUE, OSCAR, and H&N2017. Uncertainty is estimated from the standard deviation of DGVM estimates.
Cumulative
LUC
based on BLUE, OSCAR, and H&N2017. Uncertainty is formed from the uncertainty in annual
LUC
over 1959–2020, which is 0.7 GtC yr
−1
multiplied by length of the time series.
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3.4
Atmospheric CO
3.4.1
Historical period 1850–2020
Atmospheric CO
concentration was approximately 277 parts per million
(ppm) in 1750 (Joos and Spahni, 2008), reaching 300 ppm in the 1910s, 350 ppm
in the late 1980s, and 412.44
0.1 ppm in 2020 (Dlugokencky
and Tans, 2022; Fig. 1). The mass of carbon in the atmosphere increased by
48 % from 590 GtC in 1750 to 876 GtC in 2020. 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., 2022).
3.4.2
Recent period 1960–2020
The growth rate in atmospheric CO
level increased from 1.7
0.07 GtC yr
−1
in the 1960s to 5.1
0.02 GtC yr
−1
during
2011–2020 with important decadal variations (Table 6, Figs. 3 and 4).
During the last decade (2011–2020), 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,
(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 45 %, albeit showing a large interannual variability
driven by the year-to-year variability in
ATM
(Fig. 8). The observed
stability of the airborne fraction over the 1960–2020 period indicates that
the ocean and land CO
sinks have been removing on average about 55 %
of the anthropogenic emissions (see Sect. 3.5 and 3.6).
Figure 8
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 grey dashed lines represent the long-term average of
the airborne (44 %), land-borne (28 %), and ocean-borne (24 %)
fractions during 1959–2020.
3.4.3
Final year 2020
The growth rate in atmospheric CO
concentration was 5.0
0.2 GtC (2.37
0.08 ppm) in 2020 (Fig. 4; Dlugokencky and Tans, 2022),
very close to the 2011–2020 average. The 2020 decrease in
FOS
and
LUC
of about 0.7 GtC propagated to an atmospheric CO
growth rate
reduction of 0.38 GtC (0.18 ppm), given the significant interannual
variability of the land carbon sink.
3.4.4
Year 2021 projection
The 2021 growth in atmospheric CO
concentration (
ATM
) is
projected to be about 5.3 GtC (2.49 ppm) based on GLO observations until the
end of December 2021, bringing the atmospheric CO
concentration to an
expected level of 414.67 ppm averaged over the year, 50 % over the
pre-industrial level.
3.5
Ocean sink
3.5.1
Historical period 1850–2020
Cumulated since 1850, the ocean sink adds up to 170
35 GtC, with two-thirds of this amount being taken up by the global ocean since 1960. Over
the historical period, the ocean sink increased in pace with the
anthropogenic emissions exponential increase (Fig. 3b). Since 1850, the
ocean has removed 26 % of total anthropogenic emissions.
3.5.2
Recent period 1960–2020
The ocean CO
sink increased from 1.1
0.4 GtC yr
−1
in the
1960s to 2.8
0.4 GtC yr
−1
during 2011–2020 (Table 6), with
interannual variations of the order of a few tenths of GtC yr
−1
(Fig. 9). The ocean-borne fraction (
OCEAN
FOS
LUC
) has been
remarkably constant, around 25 % on average (Fig. 8). 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 2020. 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 and the Southern Ocean (Fig. 10a). The effect
of climate change is much weaker, reducing the ocean sink globally by 0.12
0.07 GtC yr
−1
or 5 % (2011–2020, range
0.8 % to
7.4 %), and
does not show clear spatial patterns across the GOBMs ensemble (Fig. 10b).
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).
Figure 9
Comparison of the anthropogenic atmosphere–ocean CO
flux
showing the budget values of
OCEAN
(black; with the uncertainty in
grey shading), individual ocean models (teal), and the ocean
CO
-based
data products (cyan; with Watson et al. (2020) in dashed line as not used
for ensemble mean). 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 0.61 GtC yr
−1
to make them comparable to
OCEAN
(see Sect. 2.4). Bar-plot in the lower right illustrates the
number of
CO
observations in the SOCAT v2021 database (Bakker et al.,
2021). Grey bars indicate the number of data points in SOCAT v2020, and
coloured bars the newly added observations in v2021.
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, mode, intermediate, and deep-water masses are formed that
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) dataset,
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. 9; 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. 9, 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., 2022), but
also contributions from the North Atlantic and North Pacific
(Landschützer et al., 2016; DeVries et al., 2019) or a global signal
(McKinley et al., 2020) were proposed.
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 0.24 GtC yr
−1
in the 1990s to 0.66 GtC yr
−1
in the
decade 2011–2020 and reaching 1.1 GtC yr
−1
in 2020. The
OCEAN
trend diverges with a factor-of-2 difference since 2002 (GOBMs: 0.3
0.1 GtC yr
−1
per decade, data products: 0.7
0.2 GtC yr
−1
per decade, best estimate: 0.5 GtC yr
−1
per decade) and with a factor-of-3 since 2010 (GOBMs: 0.3
0.1 GtC yr
−1
per decade,
data products: 0.9
0.3 GtC yr
−1
per decade, best
estimate: 0.6 GtC yr
−1
per decade). The GOBM estimate is lower than
in the previous global carbon budget (Friedlingstein et al., 2020), because
one high-sink model was not available. The effect of two models (CNRM,
MOM6-COBALT) revising their estimates downwards was largely balanced by two
models revising their estimate upwards (FESOM-REcoM, PlankTOM).
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 extra-tropics since
then (Fig. 12). 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), model biases (as indicated by
the large model spread in the south, Fig. 12, and the larger model–data
mismatch, Fig. B2), or uncertainties in the regional river flux adjustment
(Hauck et al., 2020; Lacroix et al., 2020).
Figure 10
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 2011–2020. 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 Appendix Sects. C3.2 and C4.1 for attribution methodology. Units
are in kgC m
−2
yr
−1
(note the non-linear colour scale).
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. 9,
GOBMs:
0.43 GtC yr
−1
, data products:
0.56 GtC yr
−1
). The
reduction of
0.09 GtC yr
−1
(range:
0.30 to
0.12 GtC yr
−1
) in
the ocean CO
sink in 2017 is consistent with the return to normal
conditions after the El Niño in 2015–2016, which caused an enhanced sink
in previous years. After 2017, the GOBMs ensemble mean suggests the ocean
sink levels off at about 2.5 GtC yr
−1
, whereas the data products'
estimate increases by 0.3 GtC yr
−1
over the same period.
3.5.3
Final year 2020
The estimated ocean CO
sink was 3.0
0.4 GtC in 2020. This is
the average of GOBMs and data products, and is a small increase of 0.02 GtC
compared to 2019, in line with the competing effects from an expected sink
strengthening from atmospheric CO
growth and expected sink weakening
from La Niña conditions. There is, however, a substantial difference
between GOBMs and
CO
-based data products in their mean 2020
OCEAN
estimate (GOBMs: 2.5 GtC, data products: 3.5 GtC). While the
GOBMs simulate a stagnation of the sink from 2019 to 2020 (
0.02
0.11 GtC), the data products suggest an increase by 0.06 GtC, although
not significant at the 1
level (
0.13 GtC). Four models and
four data products show an increase in
OCEAN
(GOBMs up to
0.18 GtC,
data product up to
0.21 GtC), while four models and three data products
show no change or a decrease in
OCEAN
(GOBMs down to
0.12 GtC,
data products down to
0.13 GtC; Fig. 9). 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 1–5 years lag (Fig. 9
bottom).
3.5.4
Year 2021 projection
Using a feed-forward neural network method (see Sect. 2.4) we project an
ocean sink of 2.9 GtC for 2021. This is a reduction of the sink by 0.1 GtC
relative to the 2020 value, which we attribute to La Niña conditions in
January to May 2021 and projections of a re-emergence of La Niña later
in the year.
3.5.5
Model evaluation
The evaluation of the ocean estimates (Fig. B2) shows an RMSE from annually
detrended data of 1.3 to 2.8
µatm
for the seven
CO
-based data
products over the globe, relative to the
CO
observations from the
SOCAT v2021 dataset for the period 1990–2020. The GOBMs RMSEs are larger and
range from 3.3 to 5.9
µatm
. 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 1.3 to 3.6
µatm
in the tropics, 1.3
to 2.7
µatm
in the north, and 2.2 to 6.1
µatm
in the south.
Note that the data products are based on the SOCAT v2021 database; hence the
latter are not an independent dataset for the evaluation of the data products.
The GOBM RMSEs are more spread across regions, ranging from 2.7 to 4.3
µatm
in the tropics, 2.9 to 6.9
µatm
in the north, and 6.4 to
9.8
µatm
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. (2020),
owing to one model with upper-end RMSE not being represented this year, and
the reduction of RMSE in one model (MPIOM-HAMOCC6), presumably related to
the inclusion of riverine carbon fluxes.
The additional simulation C allows the steady-state
anthropogenic carbon component (sim C – sim B) to be separated and the model flux
and DIC inventory change to be compared directly to the interior ocean estimate of Gruber
et al. (2019) without further assumptions. The GOBMs ensemble average of
steady-state anthropogenic carbon inventory change 1994–2007 amounts to 2.1 GtC yr
−1
and is significantly lower than the 2.6
0.3 GtC yr
−1
estimated by Gruber et al. (2019). Only the three models with the
highest sink estimate fall within the range reported by Gruber et al. (2019). This suggests that most of the models underestimate anthropogenic
carbon uptake by the ocean likely due to biases in ocean carbon transport
and mixing from the surface mixed layer to the ocean interior.
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
when accounting 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 (Sect. 3.5.2) than in
Gruber et al. (2019).
3.6
Land sink
3.6.1
Historical period 1850–2020
Cumulated since 1850, the terrestrial CO
sink amounts to 195
45 GtC, 30 % of total anthropogenic emissions. Over the historical period,
the sink increased in pace with the anthropogenic emissions exponential
increase (Fig. 3b).
3.6.2
Recent period 1960–2020
The terrestrial CO
sink increased from 1.2
0.5 GtC yr
−1
in the 1960s to 3.1
0.6 GtC yr
−1
during 2010–2019, with
important interannual variations of up to 2 GtC yr
−1
generally showing
a decreased land sink during El Niño events (Fig. 7), responsible for
the corresponding enhanced growth rate in atmospheric CO
concentration. The larger land CO
sink during 2010–2019 compared to
the 1960s is reproduced by all the DGVMs in response to the combined
atmospheric CO
increase and the changes in climate, and 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 in
the models (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. 10). 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. 10a). 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 2011–2020 the land sink is positive in all regions (Fig. 6) with the
exception of central and eastern Brazil, southwest USA and northern Mexico,
southeast Europe and central Asia, South 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. 10 where the effects of CO
(Fig. 10a) and climate (Fig. 10b) as
simulated by the DGVMs are isolated. The negative effect of climate is the
strongest in most of South America, Central America, southwest USA, and
central Europe (Fig. 10b). Globally, climate change reduces the land sink by
0.45
0.39 GtC yr
−1
or 15 % (2011–2020).
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
2019–2020 Black Summer event in Australia (emissions of roughly 0.2 GtC; van
der Velde et al., 2021) and Siberia in 2021, where emissions approached 0.4 GtC or 3 times the 1997–2020 average according to GFED4s. While other
regions, including western USA 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 six decades, with a land-borne fraction
LAND
FOS
LUC
) of
30 % (Fig. 8).
3.6.3
Final year 2020
The terrestrial CO
sink from the DGVM ensemble was 2.9
1.0 GtC in 2020, slightly below the decadal average of 3.1 GtC yr
−1
(Fig. 4, Table 6). We note that the DGVM estimate for 2020 is significantly
larger than the 2.1
0.9 GtC yr
−1
estimate from the residual
sink from the global budget (
FOS
LUC
ATM
OCEAN
(Table 5).
3.6.4
Year 2021 projection
Using a feed-forward neural network method (see Sect. 2.5) we project a
land sink of 3.3 GtC for 2021. This is an increase in the land sink by 0.3 GtC relative to the 2020 value which we attribute to La Niña conditions
in 2021.
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,
GPP, and ecosystem respiration (Fig. B3a). Skill score was lowest
for leaf area index and net ecosystem exchange, with the widest disparity
among models for soil carbon. Further analysis of the results will be
provided separately, focusing on the strengths and weaknesses in the DGVMs
ensemble and its validity for use in the global carbon budget.
3.7
Partitioning the carbon sinks
3.7.1
Global sinks and spread of estimates
In the period 2011–2020, the bottom-up view of total global carbon sinks
provided by the GCB (
OCEAN
LAND
LUC
) agrees
closely with the top-down budget delivered by the atmospheric inversions.
Figure 11 shows both total sink estimates of the last decade split by land
and ocean, which match the difference between
ATM
and
FOS
to
within 0.06–0.17 GtC yr
−1
for inverse models and to 0.3 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 models.
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 models 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 2011–2020 (Table 5). Estimates of total atmosphere-to-land fluxes
LAND
LUC
) from the DGVMs alone (1.6
0.6 GtC yr
−1
) are consistent with this estimate and also with the global carbon
budget constraint (
FOS
ATM
OCEAN
, 1.7
0.8 GtC yr
−1
Table 5). Consistent with the bookkeeping model estimates,
the DGVM-based
LUC
is substantially lower than in Friedlingstein et
al. (2020) due to the improved land-cover forcing (see Sect. 3.2.2),
increasing their total atmosphere-to-land fluxes and hence the consistency
with the budget constraint. For the last decade (2011–2020), the inversions
estimate the net atmosphere-to-land uptake to lie within a range of 1.3 to
2.0 GtC yr
−1
, consistent with the GCB and DGVM estimates of
LAND
LUC
(Fig. 11, Fig. 2, top row).
Figure 11
The 2011–2020 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 six
inversions (purple symbols on secondary
and
axis). 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.
Figure 12
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 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), and over ocean (left column), over land (middle column), and
total (right column). Estimates are shown for process-based models (DGVMs
for land, GOBMs for oceans), inversion models (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
0.06 GtC yr
−1
).
3.7.3
Total atmosphere-to-ocean fluxes
For the 2011–2020 period, the GOBMs (2.5
0.6 GtC yr
−1
) produce
a lower estimate for the ocean sink than the
CO
-based data products
(3.1
0.5 GtC yr
−1
), which shows up in Fig. 11 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 (2.6 to 3.1 GtC yr
−1
) also suggest higher ocean
uptake in the recent decade (Fig. 11, Fig. 12 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 (0.6 GtC yr
−1
and uncertain term that separates the various methods. A recent estimate of
decadal ocean uptake from observed O
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 12 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
).
North
Despite being one of the most densely observed and studied regions of our
globe, annual mean carbon sink estimates in the northern extra-tropics
(north of 30
N) continue to differ by about 0.5 GtC yr
−1
The atmospheric inversions suggest an atmosphere-to-surface sink
OCEAN
LAND
LUC
) for 2011–2020 of 2.0 to 3.4 GtC yr
−1
, which is higher than the process models' estimate of 2.1
0.5 GtC yr
−1
(Fig. 12). The GOBMs (1.1
0.2 GtC yr
−1
),
CO
-based data products (1.3
0.1 GtC yr
−1
), and inversion
models (0.9 to 1.5 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 0.7 to 2.4 GtC yr
−1
in the atmospheric
inversions (Fig. 12, second row).
Discrepancies in the northern land fluxes conforms with persistent issues
surrounding the quantification of the drivers of the global net land
CO
flux (Arneth et al., 2017; Huntzinger et al., 2017) 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 extra-tropics, the process models, inversions, and
CO
-based data products consistently suggest that most of the
variability stems from the land (Fig. 12). Inversions generally estimate
similar interannual variations (IAV) over land to DGVMs (0.28–0.47 vs.
0.20–0.73 GtC yr
−1
, averaged over 1990–2020), and they have higher
IAV in ocean fluxes (0.03–0.19 GtC yr
−1
) relative to GOBMs (0.03–0.05 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 (0.0
0.3 GtC yr
−1
),
CO
-based data
products (0.03
0.2 GtC yr
−1
), and inversion models (
0.2 to 0.2 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 0.6
0.3 GtC yr
−1
, whereas the
inversion models indicate a net land flux between
0.7 and 0.9 GtC yr
−1
, though with high uncertainty (Fig. 12, third row).
The tropical lands are the origin of most of the atmospheric CO
interannual variability (Ahlström et al., 2015), consistently among the
process models and inversions (Fig. 12). The interannual variability in the
tropics is similar among the ocean data products (0.07–0.15 GtC yr
−1
and the models (0.07–0.15 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 0.4 to 1.2 and 0.6 to 1.1 GtC yr
−1
respectively.
South
In the southern extra-tropics (south of 30
S), the atmospheric
inversions suggest a total atmosphere-to-surface sink
OCEAN
LAND
LUC
) for 2011–2020 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. 12). An approximately neutral total land flux
LAND
LUC
) for the southern extra-tropics is estimated by both
the DGVMs (0.02
0.05 GtC yr
−1
) and the inversion models (sink
of
0.1 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 (1.7
0.1 GtC yr
−1
) and inversion
estimates (1.4 to 1.8 GtC yr
−1
) is higher than in the GOBMs (1.4
0.3 GtC yr
−1
) (Fig. 12, bottom row). This might be explained by the
data products potentially underestimating the winter CO
outgassing
south of the Polar Front (Bushinsky et al., 2019), by model biases, or 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 models to isolate the anthropogenic
OCEAN
flux.
CO
fluxes from this region are more sparsely sampled by all methods,
especially in wintertime (Fig. B1).
The interannual variability in the southern extra-tropics is low because of
the dominance of ocean area 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 separating exactly 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 0.14 GtC yr
−1
) compared to GOBMs
(0.04 to 0.06 GtC yr
−1
) in 1990–2020 (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 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- to 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 an equal preference
across models for either a small northern land sink and a tropical land sink
(small N–S difference), a medium northern land sink and a 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 0.5
0.5 GtC yr
−1
, a much narrower range than the one from inversions. Only three
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 models 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 DGVMs 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 (1850–2020) is shown in
Fig. 3.
Emissions during the period 1850–2020 amounted to 660
65 GtC and
were partitioned among the atmosphere (270
5 GtC; 41 %), ocean
(170
35 GtC; 26 %), and the land (195
45 GtC; 30 %). The
cumulative land sink is almost equal to the cumulative land-use emissions
(200
65 GtC), making the global land nearly neutral over the whole
1850–2020 period.
The use of nearly independent estimates for the individual terms shows a
cumulative budget imbalance of 25 GtC (4 %) during 1850–2020 (Fig. 3,
Table 8), which, if correct, suggests that emissions are slightly too high
by the same proportion (4 %) or that the combined land and ocean sinks are
slightly underestimated (by about 7 %). The bulk of the imbalance could
originate from the estimation of large
LUC
between the mid-1920s and
the mid-1960s which is unmatched by a 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
further exacerbate the budget imbalance (Sect. 2.7.4).
For the more recent 1960–2020 period where direct atmospheric CO
measurements are available, 375
20 GtC (82 %) of the total
emissions (
FOS
LUC
) were caused by fossil CO
emissions, and 80
45 GtC (18 %) by land-use change (Table 8). The
total emissions were partitioned among the atmosphere (205
5 GtC;
47 %), ocean (115
25 GtC; 25 %), and the land (135
25 GtC; 30 %), with a near-zero 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 (2011–2020) is shown
in Figs. 2 and 13b and Table 6. For this time period, 90 %
of the total emissions (
FOS
LUC
) were from fossil CO
emissions (
FOS
), and 10 % from land-use change (
LUC
). The
total emissions were partitioned among the atmosphere (47 %), 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 2020, the combination of our sources and sinks
estimates leads to a
IM
of
0.8 GtC, suggesting an underestimation of
the anthropogenic sources (potentially
LUC
), and/or an overestimation
of the combined land and ocean sinks
Figure 13
Cumulative changes over the 1850–2020 period
(a)
and average
fluxes over the 2011–2020 period
(b)
for the anthropogenic perturbation
of the global carbon cycle. See the caption of Fig. 3 for key information
and the methods in text for full details.
3.8.2
Carbon budget imbalance
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 2020 is very small (average of 0.03 GtC yr
−1
) and shows no trend
over the full time series. 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 of 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,
1980s, and for the 2011–2020 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 has not changed in the past 60 years despite a near tripling
in emissions (Fig. 4). Errors in
LAND
and
OCEAN
are more likely
to be the main cause for the budget imbalance. For example, underestimation
of
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 in GCB2021 we have for the first
time 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). 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. 9; 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
The budget imbalance (
IM
) was negative (
0.3 GtC yr
−1
) on average
over 2011–2020, although the B
uncertainty is large (1.1 GtC yr
−1
over the decade). Also, the
IM
shows substantial
departure from zero on yearly timescales (Fig. 4), highlighting unresolved
variability of the carbon cycle, likely in the land sink (
LAND
), given
its large year-to-year variability (Figs. 4e and 7).
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). Other differences
include a better accounting of changing land-use practices and processes in
bookkeeping models than in DGVMs, or the bookkeeping models' error 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
LAND
would reconcile
model results with inversion estimates for fluxes in the total land during
the past decade (Fig. 12; Table 5). Likewise, a larger
OCEAN
is also
possible given the higher estimates from the data products (see Sect. 3.1.2, Figs. 9 and 12) and the recently suggested upward correction of
the ocean carbon sink (Watson et al., 2020, Fig. 9). If
OCEAN
were
to be based on data products alone, with all data products including the
Watson et al. (2020) adjustment, this would result in a 2011–2020
OCEAN
of nearly 4 GtC yr
−1
, outside of the range supported by the
atmospheric inversions, with a 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 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
Fossil CO
emissions growth peaked at
3 % yr
−1
during the
2000s, driven by the rapid growth in Chinese emissions. In the last decade,
however, the growth rate for the preceding 10 years has slowly declined,
reaching a low
0.4 % yr
−1
from 2012–2021 (including the 2020 global
decline and the expected 2021 emissions rebound). While this slowdown in
global fossil CO
emissions growth is welcome, it is far from what is
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
Organisation for Economic Co-operation and Development (OECD), with
emissions peaking in around 2005 and now declining at around 1 % yr
−1
(Le Quéré et al., 2021). In the decade 2010–2019, territorial fossil
CO
emissions decreased significantly (at the 95 % confidence level)
in 23 countries whose economies grew significantly (also at the 95 %
confidence level): Barbados, Belgium, Croatia, Czech Republic, Denmark,
Finland, France, Germany, Israel, Japan, Luxembourg, North Macedonia, Malta,
Mexico, the Netherlands, Slovakia, Slovenia, Solomon Islands, Sweden,
Switzerland, Tuvalu, United Kingdom, and USA (updated from Le
Quéré et al., 2019). Altogether, these 23 countries contributed
2.5 GtC yr
−1
over the last decade, about one-quarter of world CO
fossil emissions. Consumption-based emissions are also falling significantly
in 15 of these countries (Belgium, Croatia, Czech Republic, Denmark,
Finland, France, Germany, Israel, Japan, Mexico, the Netherlands, Slovenia,
Sweden, United Kingdom, and USA). Figure 14 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 so
decarbonization policies replace existing fossil fuel infrastructure (Le
Quéré et al., 2019).
Figure 14
Kaya decomposition of the main drivers of fossil CO
emissions, considering population, GDP per person, energy per GDP, and
CO
emissions per energy use, for China
(a)
, USA
(b)
, 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. The changes during 2020 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 over 5 % yr
−1
during the 2000s to around 2 % yr
−1
in the last decade. A large part
of this slowdown in non-OECD countries is due to China, which has seen
emissions growth declining from nearly 10 % yr
−1
in the 2000s to
2 % yr
−1
in the last decade. Excluding China, non-OECD emissions grew
at 3 % yr
−1
in the 2000s compared to 2 % yr
−1
in the last
decade. Figure 14 shows that compared to the previous decade, China has had
weaker economic growth in the last decade and a larger decarbonization rate,
with more rapid declines in energy per GDP which are now back to levels
during the 1990s. India and the rest of the world have strong economic
growth that is not compensated by decarbonization or declines in energy per
GDP, implying fossil CO
emissions continue to grow. 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 primarily
due to the emergence of climate policy and emission declines in OECD
countries (Eskander and Fankhauser, 2020). 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, 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., 2022), accounting for the 2020 and estimated 2021 emissions from fossil
fuel combustion (
FOS
) and land-use changes (
LUC
). From January
2022, the remaining carbon (50 % likelihood) for limiting global warming
to 1.5, 1.7, and 2
C is estimated to
amount to 120, 210, and 350 GtC (420, 770, 1270 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 11, 20,
and 32 years from the beginning of 2022, at the 2021 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 during 2020.
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 datasets. 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
concentration are based primarily on empirical inputs supporting annual
estimates in this carbon budget. The carbon budget imbalance, yet 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 9. 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 most certainly not yet reached given the
possibilities for improvements that lie ahead.
Table 9
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 result of interactions between land use and climate.
The uncertainties in GATM 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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Estimates of global fossil CO
emissions from different datasets are in
relatively good agreement when the different system boundaries of these
datasets 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 datasets 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. Further changes to
FOS
this year are documented by Andrew
and Peters (2021).
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; see also Sect. 2.7.4 on
the loss of sink capacity; Bastos et al., 2021). 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). This latter estimate has been
used in the recent UNFCCC's Synthesis Report on Nationally Determined
Contribution (UNFCCC, 2021b) to enable the total national emission estimates
to be comparable with those of the IPCC. However, while Grassi et al. (2021)
used only one DGVM, here 17 DGVMs are used, thus providing a more robust
value to 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 for 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 comparison of GOBMs, data products, and inversions highlights substantial
discrepancy in the Southern Ocean (Fig. 12, Hauck et al., 2020). The
long-standing sparse data coverage of
CO
observations in the Southern
compared to the Northern Hemisphere (e.g. Takahashi et al., 2009) continues
to exist (Bakker et al., 2016, 2021, 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. 12) and highlights the need for
model improvement. Further uncertainty stems from the regional distribution
of the river flux adjustment term being based on one model study yielding
the largest riverine outgassing flux south of 20
S (Aumont et
al., 2001), with a recent study questioning this distribution (Lacroix et
al., 2020). 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 extra-tropics. This discrepancy highlights the
difficulty of quantifying 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 to allocate the regional partitioning of the CO
fluxes. However,
GOBM skills have changed little since the introduction of the ocean model
evaluation. An additional simulation this year 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. To continue 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 datasets 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, impact of 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 datasets 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
into 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 two Excel files organized in the
following spreadsheets:
The file Global_Carbon_Budget_2021v1.0.xlsx includes the following:
summary;
the global carbon budget (1959–2020);
the historical global carbon budget (1750–2020);
global CO
emissions from fossil fuels and cement production by fuel
type, and the per capita emissions (1959–2020);
CO
emissions from land-use change from the individual methods and
models (1959–2020);
ocean CO
sink from the individual ocean models and
CO
-based
products (1959–2020);
terrestrial CO
sink from the DGVMs (1959–2020).
The file National_Carbon_Emissions_2021v1.0.xlsx includes the following:
summary;
territorial country CO
emissions from fossil CO
emissions
(1959–2020);
consumption country CO
emissions from fossil CO
emissions and
emissions transfer from the international trade of goods and services
(1990–2019) using CDIAC/UNFCCC data as reference;
emissions transfers (Consumption minus territorial emissions; 1990–2019);
country definitions;
details of disaggregated countries;
details of aggregated countries.
Both spreadsheets are published by the Integrated Carbon Observation System
(ICOS) Carbon Portal and are available at
(Friedlingstein et al., 2021). National
emissions data are also available from the Global Carbon Atlas
, last access: 11 March 2022) and from Our
World in Data (
, last access: 11
March 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. n/a – not applicable
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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. NA – not available
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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 include 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.
Cox et al. (2021); Di Sarra et al. (2021).
van der Velde et al. (2014).
GCP-GridFEDv2021.2 (Jones et al., 2021b) is an update through the year 2020 of the GCP-GridFED dataset presented by Jones et al. (2021a).
Ocean prior not optimized.
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Table A5
Attribution of
CO
measurements for the year 2020 included in SOCATv2021 (Bakker et al., 2016, 2021) 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; Cox et al., 2021) 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)
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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.
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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 also the Acknowledgements).
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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
2011–2020 period (kgC m
−2
yr
−1
). Positive numbers indicate a flux
into the ocean.
(c)
Gridded SOCAT v2021
CO
measurements, averaged over
the 2011–2020 period (
atm). 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 0.615 GtC yr
−1
Figure B2
Evaluation of the GOBMs and data products using the root
mean squared error (RMSE) for the period 1990 to 2020, between the
individual surface ocean
CO
mapping schemes and the SOCAT v2021
database. The
axis shows the amplitude of the interannual variability
(A-IAV, taken as the standard deviation of a detrended time series
calculated as a 12-month running mean over the monthly flux time series,
Rödenbeck et al., 2015). Results are presented for the globe, north
30
N), tropics (30
S–30
N),
and south (
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 therefore are not independent from
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)
(a)
absolute
skill scores and
(b)
skill scores relative to other models. The
benchmarking is done with observations for vegetation biomass (Saatchi et
al., 2011; and GlobalCarbon 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), net ecosystem exchange (Jung et al.,
2010; Lasslop et al., 2010), 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, and finally for each variable the multiple scores from different
metrics and observational datasets are combined to give the overall
variable scores shown in
(a)
. Overall variable scores increase
from 0 to 1 with improvements in model performance. The set of error metrics
vary with dataset 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
(b)
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
three periods:
(a)
2001–2010,
(b)
2011–2020,
(c)
2001–2020. The
six models are compared to independent CO
measurements made onboard
aircraft over many places of the world between 2 and 7 km above sea level.
Aircraft measurements archived in the Cooperative Global Atmospheric Data
Integration Project (CGADIP; Cox et al., 2021) from sites, campaigns, or
programmes that cover at least 9 months between 2001 and 2020 and that have
not been assimilated 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
), as well as their partitioning among
(c)
the
atmosphere (
ATM
),
(d)
the land (
LAND
), and
(e)
the ocean
OCEAN
). See 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
Changes in the HYDE/LUH2 land-use forcing from last
year's global carbon budget (Friedlingstein et al., 2020, in blue) to this
year (orange). 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
(a)
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, 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. 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, of cement waste after demolition, and the
volumetric properties of structures, among others (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. In the present budget, we include the cement
carbonation carbon sink in the fossil CO
emission component
FOS
).
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
territorial – exports
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 1990–2018 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), 2004, 2007, and 2011 (GTAP9.2), 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 (2018 in this budget), using GDP data by
expenditure in the current exchange rate of US dollars (USD; from the UN
National Accounts Main Aggregrates 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, include the flaring and cement emissions from CDIAC, 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, but 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 per cent (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
of 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 two years and then
normalizing to the emissions in the first year:
FOS
+1
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 % yr
−1
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
FOS
To gain insight into emission trends for 2021, 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 and regions with the largest emissions), and the rest of the world. We
provide full year estimates for two datasets: IEA (2021b) and our own
analysis. This approach differs from last year where we used four
independent estimates including our own, because of the unique circumstances
related to the COVID-19 pandemic. This year's analysis is more in line with
earlier budgets.
Previous editions of the Global Carbon Budget (GCB) have estimated year-to-date (YTD) emissions, and performed projections, using sub-annual energy
consumption data from a variety of sources depending on the country or
region. The YTD estimates have then been projected to the full year using
specific methods for each country or region. The methods described in detail
below.
China
We use the growth in total fossil CO
emissions in 2021
reported by the National Bureau of Statistics (NBS) in their 2022
Statistical Communique (NBS, 2022). This report includes growth rates of
energy consumption for coal, oil, and natural gas as well as the growth in
cement production, which are used to determine the changes in emissions from
these four categories.
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 a YTD and a full-year projection (EIA, 2022).
The STEO also includes a near-term forecast based on an energy forecasting
model which is updated monthly (last update with preliminary data through
September 2021) 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 U.S. cement clinker production data from USGS for January–June 2021,
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 August 2021. 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 September using a regression
relationship based on power generated from coal, coal dispatches by Coal
India Ltd., the composite PMI, time, and days per month. For the last 3–4
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 Organisations 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 September 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 (October 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 the following:
(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
2020 (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 2009–2019 as a
measure of uncertainty, reflecting a
as in the rest of
the carbon budget.
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 Sect. 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 1960–2020 is
provided as the average of results from three bookkeeping approaches
(Sect. C2.1 below): an estimate using the Bookkeeping of Land Use
Emissions model (Hansis et al., 2015; hereafter BLUE) and one using the
compact Earth system model OSCAR (Gasser et al., 2020), both BLUE and OSCAR
being updated here to new land-use forcing covering the time period until
2020, and an updated version of the estimate published by Houghton and
Nassikas (2017) (hereafter updated H&N2017). All three datasets are then
extrapolated to provide a projection for 2021 (Sect. C2.5 below). In
addition, we use results from dynamic global vegetation models (DGVMs; see
Sect. 2.5 and Table 4) to help quantify the uncertainty in
LUC
(Sect. 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 (Sect. 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 the separation of 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, regional- or mostly 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 (literature-based for the
updated H&N2017 and BLUE, calibrated to DGVMs for OSCAR). A notable
difference between models exists with respect to the treatment of shifting
cultivation. The update of H&N2017 changed the approach over the earlier
H&N2017 version: H&N2017 had assumed the “excess loss” of tropical
forests (i.e. when FRA indicated a forest loss larger than the increase in
agricultural areas from FAO) 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 lying 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 assume conversion of
natural grasslands to pasture, while BLUE and OSCAR allocate pasture
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 emissions from the organic layers of drained peat
soils. To correct for this, the updated H&N2017 includes carbon emissions
from burning and draining of peatlands in Indonesia, Malaysia, and Papua New
Guinea (based on the Global Fire Emission Database, GFED4s; van der Werf et
al., 2017, for fire and Hooijer et al., 2010, for drainage). Further, estimates of
carbon losses from peatlands in extra-tropical regions are added from Qiu et
al. (2021). We add GFED4s peat fire emissions to BLUE and OSCAR output as
well as the global FAO peat drainage emissions 1990–2018 from croplands and
grasslands (Conchedda and Tubiello, 2020), keeping post-2018 emissions
constant. We linearly increase tropical drainage emissions from 0 in 1980,
consistent with H&N2017's assumption, and keep emissions from the often
old drained areas of the extra-tropics constant pre-1990. This adds 9.0 GtC
for FAO compared to 5.6 GtC for Hooijer et al. (2010). Peat fires add
another 2.0 GtC over the same period.
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
base their estimates directly on the Forest Resource Assessment of the FAO
which provides statistics on forest-area change and management at intervals
of 5 years 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-GCB2021 covering the entire 850–2020 period (an update to the
previously released LUH2 v2h dataset; Hurtt et al., 2017,
2020), which was also used as input to the DGVMs (Sect. C2.2). It
describes land-use change, also based on the FAO data as described in
Sect. C2.2 as well as 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-GCB2021 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-GCB2021 and FAO/FRA (as used by Houghton and
Nassikas, 2017), where emissions from the latter were extended beyond 2015
with constant 2011–2015 average values. The best-guess OSCAR estimate used
in our study is a combination of results for LUH2-GCB2021 and FAO/FRA
land-use data and a large number of perturbed parameter simulations weighted
against an observational constraint. All three bookkeeping estimates were
extended from 2020 to provide a projection for 2021 by adding the annual
change in emissions from tropical deforestation and degradation and peat
burning and drainage to the respective model's estimate for 2020 (van der
Werf et al., 2017; Conchedda and Tubiello, 2020).
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 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, 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 2016–2020 and for the 2021 projection of all three models were
based on a regression of gross sources (including peat emissions) to net
emissions for recent years.
Due to an artefact in the HYDE3.3 dataset expressed as an abrupt shift in
the pattern of pastures and rangelands in 1960, the year 1960 exhibits much
larger gross transitions between natural vegetation and pastures and rangelands
than prior and subsequent years. Although these gross transitions cancel out in
terms of net area changes causing large abrupt transitions, an unrealistic
peak in emissions occurs around 1960 in BLUE and OSCAR. To correct for this,
we replace the estimates for 1959–1961 by the average of 1958 and 1962 in
both BLUE and OSCAR. Abrupt transitions will immediately influence gross
emissions, which have a larger instantaneous component. Processes with
longer timescales, such as slow legacy emissions and regrowth, are
inseparable from the carbon dynamics due to subsequent land-use change
events. We therefore do not adjust gross removals, but only gross emissions
to match the corrected net flux. Since DGVM estimates are only used for an
uncertainty range of
LUC
, which is independent of land-use changes, no
correction is applied to the DGVM data.
C2.2
Dynamic global vegetation models (DGVMs)
Land-use change CO
emissions have also been estimated using an
ensemble of 17 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 from the other budget terms except for their
use of atmospheric CO
concentration to calculate the fertilization
effect of CO
on plant photosynthesis.
DGVMs that do not simulate sub-grid-scale transitions (i.e. net land-use
emissions; see Table A1) used the HYDE land-use change dataset (Klein
Goldewijk et al., 2017a, b), which provides annual (1700–2019),
half-degree, fractional data on cropland and pasture. The data are based on
the available annual FAO statistics of change in agricultural land area
available until 2015. The new HYDE3.3 cropland and grazing land dataset which
now in addition to having FAO country-level statistics is constrained spatially
based on multi-year satellite land-cover maps from ESA CCI LC. Data from
HYDE3.3 are based on a FAO which includes yearly data from 1961 up to and
including the year 2017. After the year 2017 HYDE extrapolates the cropland,
pasture, and urban data linearly based on the trend over the previous 5 years, to generate data until the year 2020. HYDE also 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 2018 map is also used for the 2019–2020
period. The original 300 m 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 sub-grid-scale
transitions (i.e. gross land-use emissions; see Table A1) also use the
LUH2-GCB2021 dataset, an update of the more comprehensive harmonized
land-use dataset (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 new dataset is of quarter-degree
fractional areas of land-use states and all transitions between those
states, including a new wood harvest reconstruction and 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 the pasture–rangeland split discussed
before. Wood harvest patterns are constrained with Landsat-based tree cover
loss data (Hansen et al., 2013). Updates of LUH2-GCB2021 over last year's
version (LUH2-GCB2020) are using the most recent HYDE/FAO release (covering
the time period up to 2021 included). We also use the most recent FAO wood
harvest data for all years from 1961 to 2019. After the year 2019 we
extrapolated the wood harvest data until the year 2020. 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. With the switch from HYDE3.2 to HYDE3.3 changes in the land-use
forcing compared to the version used in the GCB2020 (Friedlingstein et al.,
2020) are pronounced. They are thus compared in Fig. 6b and their relevance
for land-use emissions discussed in Sect. 3.4.2. DGVMs implement land-use
change differently (e.g. an increased cropland fraction in a grid cell can
either be at the expense of grassland or 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 Sect. 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 DGVM 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. 2.7.4, Appendix D4), while 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
LUC
from
bookkeeping models and land use, land-use change, and forestry (LULUCF) from
national GHG inventories (NGHGIs) (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 as 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 the
LAND
) to the original
LUC
flux. This sum is expected to be
conceptually more comparable to LULUCF than simply
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 NBP (net biome productivity) (
LAND
, i.e. due to environmental change and excluding land-use change
fluxes) were obtained with S2 runs from DGVMs up to 2019 from the TRENDY v9
dataset. Results were first masked with the Hansen forest map (Hansen et al., 2013), with a 20 % tree cover and following the FAO definition of forest
(isolated pixels with maximum connectivity less than 0.5 ha are excluded),
and 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, YIBs and ORCHIDEE-CNP) had forest NBP at grid cell level. Two
models (OCN and ISBA-CTRIP) provided forest NEP and simulated disturbances
at pixel level that were used as basis, in addition to forest cover
fraction, to estimate forest NBP. For the other DGVMs, when a grid cell had
forest, all the NBP was allocated to forest.
LULUCF data from NGHGIs are from Grassi et al. (2021) until 2017, updated
until 2019 for UNFCCC Annex I countries. For non-Annex I countries, the
years 2018 and 2019 were assumed to be equal to the average 2013–2017. 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 from 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 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 the separation of 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 diagnosis of 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 used as proxies 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 those based
on DGVMs in combination with the NGHGI managed and unmanaged forest data because
the unmanaged forest area, with about 27 mio km
, is estimated to be
substantially larger by ORCHIDEE-MICT than that, with less than 10 mio km
, by
the NGHGI, while managed forest area is estimated to be smaller (22 compared
to 32 mio km
). Related to this,
LAND
on non-intact lands plus
LUC
is a larger source estimated by ORCHIDEE-MICT compared to the NGHGI.
We also show as comparison FAOSTAT emissions totals (FAO, 2021), which
include emissions from net forest conversion and fluxes on forest land
(Tubiello et al., 2021) as well as CO
emissions from peat drainage and
peat fires.
C2.4
Uncertainty assessment for
LUC
Differences between the bookkeeping models and DGVMs 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/land-cover dataset, and the different
processes represented (Table A1). We examine the results from the DGVMs 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).
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 of quantifying some of
the processes in DGVMs.
C2.5
Emissions projections for
LUC
We project the 2021 land-use emissions for BLUE, the updated H&N2017, and
OSCAR, starting from their estimates for 2020 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). The
latter scale almost linearly with GFED over large areas (van der Werf et
al., 2017) and thus allow for tracking fire emissions in deforestation and
tropical peat zones in near-real time.
C3
Methodology ocean CO
sink (
OCEAN
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, using the
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 O
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 et al.,
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., 2022)
are the interior ocean anthropogenic carbon change (Gruber et al., 2019) and
ocean sink estimate from atmospheric CO
and O
(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
restriction in data availability prior to 1990 (Fig. 9). These estimates
differ in many respects: they use different maps of surface
CO
different atmospheric CO
concentrations, wind products and different
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 2021
(SOCATv2021; Bakker et al., 2021), which is an update of version 3 (Bakker
et al., 2016) and contains quality-controlled data through 2020 (see data
attribution Table A5). Each of the estimates uses a different method to then
map the SOCAT v2021 data to the global ocean. The methods include a
data-driven diagnostic method (Rödenbeck et al., 2013; 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-FNN), two cluster regression approaches (Gregor et
al., 2019; referred to here as CSIR-ML6; and Gregor and Gruber, 2021,
referred to as OS-ETHZ-GRaCER), and a multi-linear regression method (Iida
et al., 2021; referred to as JMA-MLR). 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,
but resulting in a substantially larger ocean sink estimate, owing to a
number of adjustments they applied to the surface ocean
CO
data and
the gas-exchange parameterization. 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 included a further adjustment to account for the cool surface skin
temperature effect. The Watson et al. 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.
So far, this adjustment is based on a single line of evidence and hence
associated with low confidence until further evidence is available. The
Watson et al. 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.
The CO
flux from each
CO
-based product is either already at or
above 98 % areal coverage (Jena-MLS, OS-ETHZ-GRaCER), filled by the
data provider (using Fay et al., 2021, method for JMA-MLR; and
Landschützer et al., 2020, methodology for MPI-SOMFFN) or scaled for the
remaining products by the ratio of the total ocean area covered by the
respective product to the total ocean area (
361.9×10
km
) from ETOPO1
(Amante and Eakins, 2009; Eakins and Sharman, 2010). In products where the
covered area varies with time (e.g. CMEMS-LSCE-FFNN) we use the maximum
area coverage. The lowest coverage is 93 % (NIES-NN), resulting in a
maximum adjustment factor of 1.08 (Table A3, Hauck et al., 2020).
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 1959–2019 is estimated using eight 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 in their horizontal and
vertical resolutions (Table A2). All GOBMs except one (CESM-ETHZ) 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. 2.7.3).
Three 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. To derive
OCEAN
from
the model simulations, we subtracted 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. Simulation B of IPSL had to be treated
differently as it was forced with constant atmospheric CO
but observed
historical changes in climate. For IPSL, we fitted a linear trend to the
simulation B and subtracted this linear trend from simulation A. 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 0.26 GtC yr
−1
, with six models
having positive biases, and one model having essentially no bias (NorESM).
The remaining model (MPI) uses riverine input and therefore simulates
outgassing in simulation B, i.e. a seemingly negative bias. 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 0.03 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) 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 v2021 database (Bakker et
al., 2016, 2021). 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 open ocean (water depth
400 m) SOCAT
sampling points to measure the misfit between large-scale signals (Hauck et
al., 2020) The amplitude of the
OCEAN
interannual variability (A-IAV)
is calculated as the temporal standard deviation of the detrended CO
flux time series (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 (0.5 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 (0.2 GtC yr
−1
, Jacobson et al., 2007; Resplandy et al.,
2018), and
CO
mapping (0.2 GtC yr
−1
, Landschützer et al.,
2014). Combining these uncertainties as their squared sums, we assign an
uncertainty of
0.6 GtC yr
−1
to the GOBMs ensemble mean and an
uncertainty of
0.5 GtC yr
−1
to the data-product ensemble
mean. These uncertainties are propagated as
OCEAN
0.6
0.5
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 1990–2020 ranges
from 0.16 to 0.26 GtC yr
−1
with the lower estimates by the three
ensemble methods (CSIR-ML6, CMEMS-LSCE-FFNN, OS-ETHZ-GRaCER). The
inter-annual variability in the GOBMs ranges between 0.10 and 0.19 GtC yr
−1
; hence there is overlap with the lower A-IAV estimates of three
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 extra-tropical 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 (1990–2020).
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 (
LAND
C4.1
DGVM simulations
The DGVM runs were forced by either the merged monthly Climate
Research Unit (CRU) and 6-hourly Japanese 55-year Reanalysis (JRA-55) dataset or by the monthly CRU dataset, both providing observation-based
temperature, precipitation, and incoming surface radiation on a
0.5
0.5
grid and updated to 2020 (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.
New to this budget is the revision of incoming short-wave radiation fields
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 shortwave fluxes over the period 1901–2020. 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
Sato et al. (1993) climatology, which has been updated to 2012. Years
2013–2020 are assumed to be background years and so 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 v4.03 dataset
(Harris et al., 2021). 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 conditions. 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 shortwave fluxes.
In summary, the DGVM 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 which 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, and 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
) that is 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
Sect. C2.2. All 17 DGVMs meet these three criteria.
In addition, the DGVM 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 2019. 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
Six atmospheric inversions (details of each 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 atmospheric CO
data and prior fluxes, as well as the
spatial resolution, assumed correlation structures, and mathematical
approach 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 prescribe same global fossil fuel emissions for
FOS
, specifically the GCP's Gridded Fossil Emissions Dataset version
2021 (GCP-GridFEDv2021.2; Jones et al., 2021b), which is an update through
2020 of the first version of GCP-GridFED presented by Jones et al. (2021a).
GCP-GridFEDv2021.2 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 GCP for the years
1959–2020, which were compiled following the methodology described in
Appendix C1 based on all information available on 31 July 2021 (Robbie Andrew, personal communication, 2021). Typically, the GCP-GridFED adopts the seasonal variation in
emissions (the monthly distribution of annual emissions) from EDGAR and
applies small corrections based on heating or cooling degree days to account
for the effects of inter-annual climate variability on the seasonality
emissions (Jones et al., 2021a). However, strategies taken to deal with the
COVID-19 pandemic during 2020 mean that the seasonality of emissions
diverged substantially in 2020 from a typical year. To account for this
change, GCP-GridFEDv2021.2 adopts the national seasonality in emissions from
Carbon Monitor (Liu et al., 2020a, b) during the years 2019–2020 (Jones et al., 2021b).
The consistent use of GCP-GridFEDv2021.2 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 account
for small differences in regridding, and the use of a slightly earlier file
version (GCP-GridFEDv2021.1) for 2000–2018 in CarbonTracker Europe, small
fossil fuel corrections were applied to all inverse models to make the
estimated uptake of atmospheric CO
fully consistent. Finally, we note that
GCP-GridFEDv2021.2 includes emissions from cement production, but it does
not include the cement carbonation CO
sink (Xi et al., 2016; Cao et
al., 2020; Guo et al., 2021) that is applied to the GCB estimate of
FOS
in Table 6.
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
distribution 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. Finally, for the presentation of the
comparison in Fig. 11 we modified the fossil-fuel-corrected and riverine-adjusted
land sinks from the inversions further, by removing a 0.2 GtC yr
−1
CO
sink that is ascribed to cement carbonation in the GCB, rather than
to terrestrial ecosystems. The latter is not applied in the inversion
products released through GCB or the original data portals of these
products.
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, nor 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 0.03–0.08 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
model performance (with space–time data coverage irregular and denser in the
0–45
N latitude band; Table A6). The six models are compared to
the independent aircraft CO
measurements between 2 and 7 km above sea
level between 2001 and 2020. Results are shown in Fig. B4, where the
inversions generally match the atmospheric mole fractions to within 0.6 ppm
at all latitudes, except for CarbonTracker Europe in 2010–2020 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) includes only partly 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, such as anthropogenic emissions of CO and CH
. An attempt
is made in this section 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
, 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). Anthropogenic emissions of fossil
CH
are, however, not included in
FOS
, because these fugitive
emissions are not included in the fuel inventories. Yet they contribute to
the annual CO
growth rate after CH
gets oxidized into
CO
. 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), that is 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, thus explaining 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 min and
max 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 at the national level where they are accounted for in the UNFCCC
national inventories. The missing processes include CO
emissions
associated with the calcination of lime and limestone outside 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 are
estimated to amount to about 1 % of
FOS
(Crippa et al., 2019),
though some of these carbonate emissions are included in our estimates
(e.g. via UNFCCC inventories).
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, MWJ, MOS, CLQ, RMA, DCEB, JH, GPP, WP, JP, 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. DG and GM provided fossil fuel emission
data. JP, TG, CS, and RAH provided the bookkeeping land-use change emissions.
JH, LB, OG, NG, TI, LR, JS, RS, and DW provided an update of the global ocean
biogeochemical models. SRA, TTTC, LD, LG, YI, PL, CR, AJW, and JZ provided an
update of the ocean
CO
data products, with synthesis by JH. MB, NRB,
KIC, MC, WE, RAF, SRA, TG, AK, NL, SKL, DRM, ClS, CoS, SN, CW, TO, DP, GR,
AJS, BT, TT, CW, and RW provided ocean
CO
measurements for the year
2020, with synthesis by DCEB and SDJ. PA, BD, AKJ, DK, EK, JK, SL, PCM, JRM,
JEMSN, BP, HT, NV, AJW, WY, XY, and SZ provided an update of the dynamic
global vegetation models, with synthesis by SS. WP, FC, LF, ITL, JL, YN, and
CR provided an updated atmospheric inversion, developed the protocol, and
produced the evaluation, with synthesis by WP. RMA provided predictions of
the 2021 emissions and atmospheric CO
growth rate. PL provided the
predictions of the 2021 ocean and land sinks. LPC, GCH, KKG, TMS, and GRvdW
provided forcing data for land-use change. GG, FT, and CY provided data for
the land-use change NGHGI mapping. PPT provided key atmospheric CO
data. MWJ produced the historical record of atmospheric CO
concentration and growth rate, including the atmospheric CO
forcing.
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 emissions prior for use in the inversion
models. XD provided seasonal emissions data for years 2019–2020 for the
emission prior. MWJ and MOS developed a new data management pipeline which
automates many aspects of the data collation, analysis, plotting, and
synthesis. PF, MWJ, and MOS revised all figures, tables, text, and/or numbers
to ensure the update was clear from the 2020 edition and in line with the Global Carbon Atlas (
, last access: 11 March 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 have also 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 2021 and the Global Carbon Project members for their
input throughout the development of this publication. We thank Nigel Hawtin
for producing Figs. 2 and 13. We thank Omar Jamil and Freddy
Wordingham for technical support. We thank Ed Dlugokencky for providing
atmospheric CO
measurements. We thank Vivek Arora, Ian G. C. Ashton,
Erik Buitenhuis, Fatemeh Cheginig, Christian Ethé, Marion Gehlen,
Lonneke Goddijn-Murphy, Thomas Holding, Fabrice Lacroix, Enhui Liao, Pedro M. S.
Monteiro, Naiquing Pan, Tristan Quaife, Shijie Shu, Jamie D. Shutler, Jade
Skye, Anthony Walker, and David K. Woolf for their involvement in the
development, use, and analysis of the models and data products used here. We
thank Markus Ritschel, Carmen Rodriguez, Claire Lo Monaco, Nicolas Metzl,
Vassilis Kitidis, Sören Gutekunst, Anne Willstrand Wranne, Tobias
Steinhoff, Jessica N. Cross, Natalie M. Monacci, Alice Benoit-Cattin,
Sólveig R. Ólafsdóttir, Joe Salisbury, Doug Vandemark, and
Christopher W. Hunt, who contributed to the provision of surface ocean
CO
observations for the year 2020 (see Table A5). We also thank
Benjamin Pfeil, Rocío Castaño-Primo, Camilla Landa, and Maren
Karlsen of the Ocean Thematic Centre of the EU Integrated Carbon Observation
System (ICOS) Research Infrastructure; Kevin O'Brien and 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. We thank the
scientists, institutions, and funding agencies responsible for the
collection and quality control of the data in SOCAT as well as the
International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean
Lower Atmosphere Study (SOLAS), and the Integrated Marine Biosphere Research
(IMBeR) programme for their support. We thank data providers ObsPack
GLOBALVIEWplus v6.1 and NRT v6.1.1 for atmospheric CO
observations. We
thank the individuals and institutions that provided the databases used for
the models evaluations used here. We thank Fortunat Joos, Samar Khatiwala,
and Timothy DeVries for providing historical data. Nicolas Vuichard thanks the whole
ORCHIDEE group. 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
. We
thank Kevin Bowman (NASA JPL) for contribution to the CMS-Flux results. Junjie Liu
thanks the Jet Propulsion Laboratory, California Institute of Technology.
This is PMEL contribution 5317. Steve D. Jones thanks the data management team at the
Bjerknes Climate Data Centre. 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) as well as
Tobias Steinhoff for their support. Nathalie Lefèvre thanks the crew of the Cap San
Lorenzo and the US IMAGO of IRD Brest for technical support. Gregor Rehder is grateful
for the skilful technical support of Michael Glockzin and Bernd Sadkowiak. Matthew W. Jones
thanks Anthony J. De-Gol for his technical and conceptual assistance with
the development of GCP-GridFED. We thank Ana Bastos and Joana Melo for
helpful comments on land-use emission estimates. FAOSTAT is funded by FAO
member states through their contributions to the FAO Regular Programme, and data
contributions by national experts are greatly 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), as well as
the reviewers of this paper 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 Hélène Peiro 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
Article
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Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data 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 2021 describes the data sets 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