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Global Carbon Budget 2015


Abstract and Figures

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005–2014), EFF was 9.0 ± 0.5 GtC yr−1, ELUC was 0.9 ± 0.5 GtC yr−1, GATM was 4.4 ± 0.1 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 3.0 ± 0.8 GtC yr−1. For the year 2014 alone, EFF grew to 9.8 ± 0.5 GtC yr−1, 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr−1 that took place during 2005–2014. Also, for 2014, ELUC was 1.1 ± 0.5 GtC yr−1, GATM was 3.9 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and SLAND was 4.1 ± 0.9 GtC yr−1. GATM was lower in 2014 compared to the past decade (2005–2014), reflecting a larger SLAND for that year. The global atmospheric CO2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in EFF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of EFF and assumed constant ELUC for 2015, cumulative emissions of CO2 will reach about 555 ± 55 GtC (2035 ± 205 GtCO2) for 1870–2015, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2015).
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Earth Syst. Sci. Data, 7, 349–396, 2015
© Author(s) 2015. CC Attribution 3.0 License.
Global Carbon Budget 2015
C. Le Quéré1, R. Moriarty1, R. M. Andrew2, J. G. Canadell3, S. Sitch4, J. I. Korsbakken2,
P. Friedlingstein5, G. P. Peters2, R. J. Andres6, T. A. Boden6, R. A. Houghton7, J. I. House8,
R. F. Keeling9, P. Tans10, A. Arneth11, D. C. E. Bakker12, L. Barbero13,14, L. Bopp15, J. Chang15,
F. Chevallier15, L. P. Chini16, P. Ciais15, M. Fader17, R. A. Feely18, T. Gkritzalis19, I. Harris20,
J. Hauck21, T. Ilyina22, A. K. Jain23, E. Kato24, V. Kitidis25, K. Klein Goldewijk26, C. Koven27,
P. Landschützer28, S. K. Lauvset29, N. Lefèvre30, A. Lenton31, I. D. Lima32, N. Metzl30, F. Millero33,
D. R. Munro34, A. Murata35, J. E. M. S. Nabel22, S. Nakaoka36, Y. Nojiri36, K. O’Brien37, A. Olsen38,39,
T. Ono40, F. F. Pérez41, B. Pfeil38,39, D. Pierrot13,14, B. Poulter42, G. Rehder43, C. Rödenbeck44, S. Saito45,
U. Schuster4, J. Schwinger29, R. Séférian46, T. Steinhoff47, B. D. Stocker48,49, A. J. Sutton37,18,
T. Takahashi50, B. Tilbrook51, I. T. van der Laan-Luijkx52,53, G. R. van der Werf54, S. van Heuven55,
D. Vandemark56, N. Viovy15, A. Wiltshire57, S. Zaehle44, and N. Zeng58
1Tyndall Centre for Climate Change Research, University of East Anglia, Norwich Research Park,
Norwich NR4 7TJ, UK
2Center for International Climate and Environmental Research – Oslo (CICERO), Oslo, Norway
3Global Carbon Project, CSIRO Oceans and Atmosphere, GPO Box 3023, Canberra, ACT 2601, Australia
4College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QE, UK
5College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
6Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, Oak Ridge, TN, USA
7Woods Hole Research Center (WHRC), Falmouth, MA 02540, USA
8Cabot Institute, Department of Geography, University of Bristol, Bristol BS8 1TH, UK
9University of California, San Diego, Scripps Institution of Oceanography, La Jolla,
CA 92093-0244, USA
10National Oceanic & Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL),
Boulder, CO 80305, USA
11Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU),
Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
12Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia,
Norwich NR4 7TJ, UK
13Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine and Atmospheric
Science, University of Miami, Miami, FL 33149, USA
14National Oceanic & Atmospheric Administration/Atlantic Oceanographic & Meteorological Laboratory
(NOAA/AOML), Miami, FL 33149, USA
15Laboratoire des Sciences du Climat et de l’Environnement, Institut Pierre-Simon Laplace,
CEA-CNRS-UVSQ, CE Orme des Merisiers, 91191 Gif sur Yvette CEDEX, France
16Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
17Institut Méditerranéen de Biodiversité et d’Ecologie marine et continentale, Aix-Marseille Université, CNRS,
IRD, Avignon Université, Technopôle Arbois-Méditerranée, Bâtiment Villemin, BP 80,
13545 Aix-en-Provence CEDEX 04, France
18National Oceanic & Atmospheric Administration/Pacific Marine Environmental Laboratory (NOAA/PMEL),
7600 Sand Point Way NE, Seattle, WA 98115, USA
19Flanders Marine Institute, InnovOcean site, Wandelaarkaai 7, 8400 Ostend, Belgium
20Climatic Research Unit, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
21Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung, Am Handelshafen 12,
27570 Bremerhaven, Germany
Published by Copernicus Publications.
350 C. Le Quéré et al.: Global Carbon Budget 2015
22Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany
23Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61821, USA
24Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan
25Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
26PBL Netherlands Environmental Assessment Agency, The Hague/Bilthoven and Utrecht University,
Utrecht, the Netherlands
27Earth Sciences Division, Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley,
CA 94720, USA
28Environmental Physics Group, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich,
Universitätstrasse 16, 8092 Zurich, Switzerland
29Uni Research Climate, Bjerknes Centre for Climate Research, Allegt. 55, 5007 Bergen, Norway
30Sorbonne Universités (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, LOCEAN/IPSL Laboratory, 4 place
Jussieu, 75005 Paris, France
31CSIRO Oceans and Atmosphere, P.O. Box 1538 Hobart, Tasmania, Australia
32Woods Hole Oceanographic Institution (WHOI), Woods Hole, MA 02543, USA
33Department of Ocean Sciences, RSMAS/MAC, University of Miami, 4600 Rickenbacker Causeway, Miami,
FL 33149, USA
34Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research,
University of Colorado Campus Box 450 Boulder, CO 80309-0450, USA
35Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 2-15 Natsushimacho, Yokosuka,
Kanagawa Prefecture 237-0061, Japan
36Center for Global Environmental Research, National Institute for Environmental Studies (NIES),
16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
37Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle,
WA 98115, USA
38Geophysical Institute, University of Bergen, Allégaten 70, 5007 Bergen, Norway
39Bjerknes Centre for Climate Research, Allégaten 70, 5007 Bergen, Norway
40National Research Institute for Fisheries Science, Fisheries Research Agency 2-12-4 Fukuura, Kanazawa-Ku,
Yokohama 236-8648, Japan
41Instituto de Investigaciones Marinas (CSIC), C/Eduardo Cabello, 6. Vigo. Pontevedra, 36208, Spain
42Department of Ecology, Montana State University, Bozeman, MT 59717, USA
43Leibniz Institute for Baltic Sea Research Warnemünde, Seestr 15, 18119 Rostock, Germany
44Max Planck Institut für Biogeochemie, P.O. Box 600164, Hans-Knöll-Str. 10, 07745 Jena, Germany
45Marine Division, Global Environment and Marine Department, Japan Meteorological Agency,
1-3-4 Otemachi, Chiyoda-ku, Tokyo 100-8122, Japan
46Centre National de Recherche Météorologique–Groupe d’Etude de l’Atmosphère Météorologique
(CNRM-GAME), Météo-France/CNRS, 42 Avenue Gaspard Coriolis, 31100 Toulouse, France
47GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
48Climate and Environmental Physics, and Oeschger Centre for Climate Change Research, University of Bern,
Bern, Switzerland
49Imperial College London, Life Science Department, Silwood Park, Ascot, Berkshire SL5 7PY, UK
50Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
51CSIRO Oceans and Atmosphere and Antarctic Climate and Ecosystems Co-operative Research Centre,
Hobart, Australia
52Department of Meteorology and Air Quality, Wageningen University, P.O. Box 47, 6700AA Wageningen,
the Netherlands
53ICOS-Carbon Portal, c/o Wageningen University, P.O. Box 47, 6700AA Wageningen, the Netherlands
54Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, the Netherlands
55Royal Netherlands Institute for Sea Research, Landsdiep 4, 1797 SZ ’t Horntje (Texel), the Netherlands
56University of New Hampshire, Ocean Process Analysis Laboratory, 161 Morse Hall, 8 College Road,
Durham, NH 03824, USA
57Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK
58Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
Correspondence to: C. Le Quéré (
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 351
Received: 2 November 2015 – Published in Earth Syst. Sci. Data Discuss.: 2 November 2015
Revised: 25 November 2015 – Accepted: 26 November 2015 – Published: 7 December 2015
Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution
among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cy-
cle, support the development of climate policies, and project future climate change. Here we describe data sets
and a methodology to quantify all major components of the global carbon budget, including their uncertainties,
based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation
by a broad scientific community. We discuss changes compared to previous estimates as well as consistency
within and among components, alongside methodology and data limitations. CO2emissions from fossil fuels
and industry (EFF) are based on energy statistics and cement production data, while emissions from land-use
change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activ-
ity associated with deforestation, and models. The global atmospheric CO2concentration is measured directly
and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2
sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated
with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2
measurements. The global residual terrestrial CO2sink (SLAND) is estimated by the difference of the other terms
of the global carbon budget and compared to results of independent dynamic global vegetation models forced
by observed climate, CO2, and land-cover change (some including nitrogen–carbon interactions). We compare
the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for
three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to charac-
terise the annual estimates of each component of the global carbon budget. For the last decade available (2005–
2014), EFF was 9.0±0.5 GtCyr1,ELUC was 0.9 ±0.5GtC yr1,GATM was 4.4±0.1 GtCyr1,SOCEAN was
2.6±0.5 GtCyr1, and SLAND was 3.0 ±0.8GtC yr1. For the year 2014 alone, EFF grew to 9.8 ±0.5GtCyr1,
0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average
growth of 2.2% yr1that took place during 2005–2014. Also, for 2014, ELUC was 1.1 ±0.5 GtC yr1,GATM
was 3.9 ±0.2GtCyr1,SOCEAN was 2.9 ±0.5GtC yr1, and SLAND was 4.1 ±0.9GtCyr1.GATM was lower in
2014 compared to the past decade (2005–2014), reflecting a larger SLAND for that year. The global atmospheric
CO2concentration reached 397.15±0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the
growth in EFF will be near or slightly below zero, with a projection of 0.6 [range of 1.6 to +0.5]%, based
on national emissions projections for China and the USA, and projections of gross domestic product corrected
for recent changes in the carbon intensity of the global economy for the rest of the world. From this projec-
tion of EFF and assumed constant ELUC for 2015, cumulative emissions of CO2will reach about 555±55GtC
(2035±205 GtCO2) for 1870–2015, about 75% from EFF and 25 % from ELUC. This living data update docu-
ments changes in the methods and data sets used in this new carbon budget compared with previous publications
of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the
Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2015).
1 Introduction
The concentration of carbon dioxide (CO2) in the atmo-
sphere has increased from approximately 277 parts per mil-
lion (ppm) in 1750 (Joos and Spahni, 2008), the beginning
of the industrial era, to 397.15ppm in 2014 (Dlugokencky
and Tans, 2015). Daily averages went above 400ppm for
the first time at Mauna Loa station in May 2013 (Scripps,
2013). This station holds the longest running record of direct
measurements of atmospheric CO2concentration (Tans and
Keeling, 2014). The global monthly average concentration
was above 400ppm in March through May 2015 for the first
time (Dlugokencky and Tans, 2015; Fig. 1), while at Mauna
Loa the seasonally corrected monthly average concentration
reached 400ppm in March 2015 and continued to rise. The
atmospheric CO2increase above pre-industrial levels was,
initially, primarily caused by the release of carbon to the at-
mosphere from deforestation and other land-use-change ac-
tivities (Ciais et al., 2013). While emissions from fossil fuels
started before the industrial era, they only became the dom-
inant source of anthropogenic emissions to the atmosphere
from around 1920, and their relative share has continued to
increase until present. Anthropogenic emissions occur on top
of an active natural carbon cycle that circulates carbon be-
tween the atmosphere, ocean, and terrestrial biosphere reser-
voirs on timescales from days to millennia, while exchanges
with geologic reservoirs occur at longer timescales (Archer
et al., 2009). Earth Syst. Sci. Data, 7, 349–396, 2015
352 C. Le Quéré et al.: Global Carbon Budget 2015
1960 1970 1980 1990 2000 2010
Time (yr)
Atmospheric CO2 concentration (ppm)
Seasonally corrected trend:
Monthly mean:
Scripps Institution of Oceanography (Keeling et al., 1976)
NOAA/ESRL (Dlugokencky & Tans, 2015)
Figure 1. Surface average atmospheric CO2concentration, de-
seasonalised (ppm). The 1980–2015 monthly data are from
NOAA/ESRL (Dlugokencky and Tans, 2015) and are based on
an average of direct atmospheric CO2measurements from mul-
tiple stations in the marine boundary layer (Masarie and Tans,
1995). The 1958–1979 monthly data are from the Scripps Institu-
tion of Oceanography, based on an average of direct atmospheric
CO2measurements from the Mauna Loa and South Pole stations
(Keeling et al., 1976). To take into account the difference of mean
CO2between the NOAA/ESRL and the Scripps station networks
used here, the Scripps surface average (from two stations) was har-
monised to match the NOAA/ESRL surface average (from multiple
stations) by adding the mean difference of 0.542ppm, calculated
here from overlapping data during 1980–2012. The mean seasonal
cycle is also shown from 1980.
The global carbon budget presented here refers to the
mean, variations, and trends in the perturbation of CO2in the
atmosphere, referenced to the beginning of the industrial era.
It quantifies the input of CO2to the atmosphere by emissions
from human activities, the growth of CO2in the atmosphere,
and the resulting changes in the storage of carbon in the land
and ocean reservoirs in response to increasing atmospheric
CO2levels, climate, and variability, and other anthropogenic
and natural changes (Fig. 2). An understanding of this per-
turbation 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, CO2and
land-use-change drivers, and the permissible emissions for a
given climate stabilisation target.
The components of the CO2budget that are reported annu-
ally in this paper include separate estimates for (1) the CO2
emissions from fossil fuel combustion and oxidation and ce-
ment production (EFF; GtC yr1), (2) the CO2emissions re-
sulting from deliberate human activities on land leading to
land-use change (ELUC; GtC yr1), (3) the growth rate of
CO2in the atmosphere (GATM; GtC yr1), and the uptake of
CO2by the “CO2sinks” in (4) the ocean (SOCEAN; GtC yr1)
and (5) on land (SLAND; GtC yr1). The CO2sinks as defined
here include the response of the land and ocean to elevated
Figure 2. Schematic representation of the overall perturbation of
the global carbon cycle caused by anthropogenic activities, av-
eraged globally for the decade 2005–2014. The arrows represent
emission from fossil fuels and industry (EFF), emissions from de-
forestation and other land-use change (ELUC), the growth of carbon
in the atmosphere (GATM) and the uptake of carbon by the “sinks”
in the ocean (SOCEAN) and land (SLAND) reservoirs. All fluxes are
in units of GtC yr1, with uncertainties reported as ±1σ(68% con-
fidence that the real value lies within the given interval) as described
in the text. This figure is an update of one prepared by the Inter-
national Geosphere-Biosphere Programme for the Global Carbon
Project (GCP), first presented in Le Quéré (2009).
CO2and changes in climate and other environmental condi-
tions. The global emissions and their partitioning among the
atmosphere, ocean, and land are in balance:
GATM is usually reported in ppmyr1, which we convert
to units of carbon mass, GtCyr1, using 1 ppm=2.12 GtC
(Ballantyne et al., 2012; Prather et al., 2012; Table 1). We
also include a quantification of EFF by country, computed
with both territorial- and consumption-based accounting (see
Sect. 2.1.1).
Equation (1) partly omits two kinds of processes. The first
is the net input of CO2to the atmosphere from the chemical
oxidation of reactive carbon-containing gases from sources
other than fossil fuels (e.g. fugitive anthropogenic CH4emis-
sions, industrial processes, and changes in biogenic emis-
sions from changes in vegetation, fires, wetlands), primar-
ily methane (CH4), carbon monoxide (CO), and volatile or-
ganic compounds such as isoprene and terpene. CO emis-
sions are currently implicit in EFF while anthropogenic CH4
emissions are not and thus their inclusion would result in a
small increase in EFF. The second is the anthropogenic per-
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 353
Table 1. Factors used to convert carbon in various units (by convention, unit 1 =unit 2·conversion).
Unit 1 Unit 2 Conversion Source
GtC (gigatonnes of carbon) ppm (parts per million)a2.12bBallantyne et al. (2012)
GtC (gigatonnes of carbon) PgC (petagrams of carbon) 1 SI unit conversion
GtCO2(gigatonnes of carbon dioxide) GtC (gigatonnes of carbon) 3.664 44.01/12.011 in mass equivalent
GtC (gigatonnes of carbon) MtC (megatonnes of carbon) 1000 SI unit conversion
aMeasurements of atmospheric CO2concentration have units of dry-air mole fraction. “ppm” is an abbreviation for micromole per mole of dry air. bThe use of a factor
of 2.12 assumes that all the atmosphere is well mixed within one year. In reality, only the troposphere is well mixed and the growth rate of CO2in the less well-mixed
stratosphere is not measured by sites from the NOAA network. Using a factor of 2.12 makes the approximation that the growth rate of CO2in the stratosphere equals
that of the troposphere on a yearly basis and reflects the uncertainty in this value.
turbation to carbon cycling in terrestrial freshwaters, estuar-
ies, and coastal areas, which modifies lateral fluxes from land
ecosystems to the open ocean; the evasion CO2flux from
rivers, lakes, and estuaries to the atmosphere; and the net air–
sea anthropogenic CO2flux of coastal areas (Regnier et al.,
2013). The inclusion of freshwater fluxes of anthropogenic
CO2would affect the estimates of, and partitioning between,
SLAND and SOCEAN in Eq. (1) in complementary ways, but
would not affect the other terms. These flows are omitted in
absence of annual information on the natural versus anthro-
pogenic perturbation terms of these loops of the carbon cycle,
and they are discussed in Sect. 2.7.
The CO2budget has been assessed by the Intergovern-
mental Panel on Climate Change (IPCC) in all assessment
reports (Ciais et al., 2013; Denman et al., 2007; Prentice
et al., 2001; Schimel et al., 1995; Watson et al., 1990), as
well as by others (e.g. Ballantyne et al., 2012). These as-
sessments included budget estimates for the decades of the
1980s, 1990s (Denman et al., 2007) and, most recently, the
period 2002–2011 (Ciais et al., 2013). The IPCC methodol-
ogy has been adapted and used by the Global Carbon Project
(GCP,, which has coordinated
a cooperative community effort for the annual publication
of global carbon budgets up to the year 2005 (Raupach et
al., 2007; including fossil emissions only), 2006 (Canadell
et al., 2007), 2007 (published online; GCP, 2007), 2008 (Le
Quéré et al., 2009), 2009 (Friedlingstein et al., 2010), 2010
(Peters et al., 2012b), 2012 (Le Quéré et al., 2013; Peters et
al., 2013), 2013 (Le Quéré et al., 2014), and most recently
2014 (Friedlingstein et al., 2014; Le Quéré et al., 2015). The
carbon budget year refers to the initial year of publication.
Each of these papers updated previous estimates with the
latest available information for the entire time series. From
2008, these publications projected fossil fuel emissions for
one additional year using the projected world gross domestic
product (GDP) and estimated trends in the carbon intensity
of the global economy.
We adopt a range of ±1 standard deviation (σ) 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. This choice reflects
the difficulty of characterising the uncertainty in the CO2
fluxes between the atmosphere and the ocean and land reser-
voirs individually, particularly on an annual basis, as well as
the difficulty of updating the CO2emissions 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. For comparison, the Fifth
Assessment Report of the IPCC (AR5) generally reported a
likelihood of 90% for large data sets whose uncertainty is
well characterised, or for long time intervals less affected by
year-to-year variability. Our 68 % uncertainty value is near
the 66% which the IPCC characterises as “likely” for values
falling into the ±1σinterval. The uncertainties reported here
combine statistical analysis of the underlying data and ex-
pert 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 (Bal-
lantyne et al., 2015).
All quantities are presented in units of gigatonnes of car-
bon (GtC, 1015 gC), which is the same as petagrams of car-
bon (PgC; Table 1). Units of gigatonnes of CO2(or billion
tonnes of CO2) used in policy are equal to 3.664 multiplied
by the value in units of GtC.
This paper provides a detailed description of the data sets
and methodology used to compute the global carbon bud-
get estimates for the period pre-industrial (1750) to 2014
and in more detail for the period 1959 to 2014. We also
provide decadal averages starting in 1960 and including the
last decade (2005–2014), results for the year 2014, and a
projection of EFF for year 2015. Finally we provide cu-
mulative emissions from fossil fuels and land-use change
since year 1750, the pre-industrial period, and since year
1870, the reference year for the cumulative carbon esti-
mate used by the IPCC (AR5) based on the availability
of global temperature data (Stocker et al., 2013). This pa-
per is intended to be 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 on the GCP website (http:
// Data associ-
ated with this release are also available through the Global Earth Syst. Sci. Data, 7, 349–396, 2015
354 C. Le Quéré et al.: Global Carbon Budget 2015
Carbon Atlas ( With this
approach, we aim to provide the highest transparency and
traceability in the reporting of CO2, the key driver of climate
2 Methods
Multiple organisations and research groups around the world
generated the original measurements and data used to com-
plete the global carbon budget. The effort presented here is
thus mainly one of synthesis, where results from individual
groups are collated, analysed, and evaluated for consistency.
We facilitate access to original data with the understanding
that primary data sets will be referenced in future work (see
Table 2 for how to cite the data sets). Descriptions of the
measurements, models, and methodologies follow below and
in-depth descriptions of each component are described else-
where (e.g. Andres et al., 2012; Houghton et al., 2012).
This is the tenth version of the “global carbon budget” (see
Introduction for details) and the fourth revised version of the
“global carbon budget living data update”. It is an update
of Le Quéré et al. (2015), including data to year 2014 (in-
clusive) and a projection for fossil fuel emissions for year
2015. The main changes from Le Quéré et al. (2015) are
(1) the use of national emissions for EFF from the United Na-
tions Framework Convention on Climate Change (UNFCCC)
where available; (2) the projection of EFF for 2015 is based
on national emissions projections for China and USA, as well
as GDP corrected for recent changes in the carbon intensity
of the global economy for the rest of the world; and (3) that
we apply minimum criteria of realism to select ocean data
products and process models. The main methodological dif-
ferences between annual carbon budgets are summarised in
Table 3.
2.1 CO2emissions from fossil fuels and industry (EFF)
2.1.1 Emissions from fossil fuels and industry and their
The calculation of global and national CO2emissions from
fossil fuels, including gas flaring and cement production
(EFF), relies primarily on energy consumption data, specif-
ically data on hydrocarbon fuels, collated and archived by
several organisations (Andres et al., 2012). These include
the Carbon Dioxide Information Analysis Center (CDIAC),
the International Energy Agency (IEA), the United Nations
(UN), the United States Department of Energy (DoE) En-
ergy Information Administration (EIA), and more recently
also the Planbureau voor de Leefomgeving (PBL) Nether-
lands Environmental Assessment Agency. Where available,
we use national emissions estimated by the countries them-
selves and reported to the UNFCCC for the period 1990–
2012 (42 countries). We assume that national emissions re-
ported to the UNFCCC are the most accurate because na-
tional experts have access to additional and country-specific
information, and because these emission estimates are peri-
odically audited for each country through an established in-
ternational methodology overseen by the UNFCCC. We also
use global and national emissions estimated by CDIAC (Bo-
den et al., 2013). The CDIAC emission estimates are the only
data set that extends back in time to 1751 with consistent and
well-documented emissions from fossil fuels, cement pro-
duction, and gas flaring for all countries and their uncertainty
(Andres et al., 2014, 2012, 1999); this makes the data set a
unique resource for research of the carbon cycle during the
fossil fuel era.
The global emissions presented here are from CDIAC’s
analysis, which provides an internally consistent global esti-
mate including bunker fuels, minimising the effects of lower-
quality energy trade data. Thus the comparison of global
emissions with previous annual carbon budgets is not influ-
enced by the use of data from UNFCCC national reports.
During the period 1959–2011, the emissions from fossil
fuels estimated by CDIAC are based primarily on energy data
provided by the UN Statistics Division (UN, 2014a, b; Ta-
ble 4). When necessary, fuel masses/volumes are converted
to fuel energy content using coefficients provided by the UN
and then to CO2emissions using conversion factors that take
into account the relationship between carbon content and en-
ergy (heat) content of the different fuel types (coal, oil, gas,
gas flaring) and the combustion efficiency (to account, for
example, for soot left in the combustor or fuel otherwise
lost or discharged without oxidation). Most data on energy
consumption and fuel quality (carbon content and heat con-
tent) are available at the country level (UN, 2014a). In gen-
eral, CO2emissions for equivalent primary energy consump-
tion are about 30% higher for coal compared to oil, and
70% higher for coal compared to natural gas (Marland et
al., 2007). All estimated fossil fuel emissions are based on
the mass flows of carbon and assume that the fossil carbon
emitted as CO or CH4will soon be oxidised to CO2in the at-
mosphere and can be accounted for with CO2emissions (see
Sect. 2.7).
Our emissions totals for the UNFCCC-reporting countries
were recorded as in the UNFCCC submissions, which have
a slightly larger system boundary than CDIAC. Additional
emissions come from carbonates other than in cement manu-
facture, and thus UNFCCC totals will be slightly higher than
CDIAC totals in general, although there are multiple sources
for differences. We use the CDIAC method to report emis-
sions by fuel type (e.g. all coal oxidation is reported under
“coal”, regardless of whether oxidation results from combus-
tion as an energy source), which differs slightly from UN-
For the most recent 2–3 years when the UNFCCC esti-
mates and UN statistics used by CDIAC are not yet avail-
able (or there was insufficient time to process and verify
them), we generated preliminary estimates based on the BP
annual energy review by applying the growth rates of en-
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 355
Table 2. How to cite the individual components of the global carbon budget presented here.
Component Primary reference
Global emissions from fossil fuels and industry (EFF), total and
by fuel type Boden et al. (2015; CDIAC:
National territorial emissions from fossil fuels and industry
(EFF)CDIAC source: Boden et al. (2015; CDIAC: http:
UNFCCC source (2015;
items/8108.php; accessed May 2015)
National consumption-based emissions from fossil fuels and in-
dustry (EFF) by country (consumption) Peters et al. (2011b) updated as described in this paper
Land-use-change emissions (ELUC) Houghton et al. (2012) combined with Giglio et al. (2013)
Atmospheric CO2growth rate (GATM) Dlugokencky and Tans (2015; NOAA/ESRL: http://www.esrl.; accessed 12 October
Ocean and land CO2sinks (SOCEAN and SLAND) This paper for SOCEAN and SLAND and references in Table 6
for individual models.
ergy consumption (coal, oil, gas) for 2013–2014 to the UN-
FCCC national emissions in 2012, and for 2012–2014 for the
CDIAC national and global emissions in 2011 (BP, 2015).
BP’s sources for energy statistics overlap with those of the
UN data, but are compiled more rapidly from about 70 coun-
tries covering about 96% of global emissions. We use the
BP values only for the year-to-year rate of change, because
the rates of change are less uncertain than the absolute values
and we wish to avoid discontinuities in the time series when
linking the UN-based data with the BP data. These prelimi-
nary estimates are replaced by the more complete UNFCCC
or CDIAC data based on UN statistics when they become
available. Past experience and work by others (Andres et al.,
2014; Myhre et al., 2009) show that projections based on the
BP rate of change are within the uncertainty provided (see
Sect. 3.2 and the Supplement from Peters et al., 2013).
Estimates of emissions from cement production by
CDIAC are based on data on growth rates of cement pro-
duction from the US Geological Survey up to year 2013
(van Oss, 2013), and up to 2014 for the top 18 countries
(representing 85% of global production; USGS, 2015). For
countries without data in 2014 we use the 2013 values (zero
growth). Some fraction of the CaO and MgO in cement is re-
turned to the carbonate form during cement weathering, but
this is generally regarded to be small and is ignored here.
Estimates of emissions from gas flaring by CDIAC are cal-
culated in a similar manner to those from solid, liquid, and
gaseous fuels, and rely on the UN Energy Statistics to supply
the amount of flared or vented fuel. For emission years 2012–
2014, flaring is assumed constant from 2011 (emission year)
UN-based data. The basic data on gas flaring report atmo-
spheric losses during petroleum production and processing
that have large uncertainty and do not distinguish between
gas that is flared as CO2or vented as CH4. Fugitive emis-
sions of CH4from the so-called upstream sector (e.g. coal
mining and natural gas distribution) are not included in the
accounts of CO2emissions except to the extent that they are
captured in the UN energy data and counted as gas “flared or
The published CDIAC data set includes 250 countries and
regions. This expanded list includes countries that no longer
exist, such as the USSR and East Pakistan. For the carbon
budget, we reduce the list to 216 countries by reallocating
emissions to the currently defined territories. This involved
both aggregation and disaggregation, and does not change
global emissions. Examples of aggregation include merging
East and West Germany to the currently defined Germany.
Examples of disaggregation include reallocating the emis-
sions from former USSR to the resulting independent coun-
tries. For disaggregation, we use the emission shares when
the current territory first appeared. The disaggregated esti-
mates should be treated with care when examining countries’
emissions trends prior to their disaggregation. For the most
recent years, 2012–2014, the BP statistics are more aggre-
gated, but we retain the detail of CDIAC by applying the
growth rates of each aggregated region in the BP data set
to its constituent individual countries in CDIAC.
Estimates of CO2emissions show that the global total of
emissions is not equal to the sum of emissions from all coun-
tries. This is largely attributable to emissions that occur in
international territory, in particular the combustion of fuels
used in international shipping and aviation (bunker fuels),
where the emissions are included in the global totals but are
not attributed to individual countries. In practice, the emis-
sions from international bunker fuels are calculated based
on where the fuels were loaded, but they are not included
with national emissions estimates. Other differences occur
because globally the sum of imports in all countries is not
equal to the sum of exports and because of differing treat-
ment of oxidation of non-fuel uses of hydrocarbons (e.g. as Earth Syst. Sci. Data, 7, 349–396, 2015
356 C. Le Quéré et al.: Global Carbon Budget 2015
Table 3. Main methodological changes in the global carbon budget since first publication. Unless specified below, the methodology was identical to that described in the current paper.
Furthermore, methodological changes introduced in one year are kept for the following years unless noted. Empty cells mean there were no methodological changes introduced that
Publication yearaFossil fuel emissions LUC emissions Reservoirs Uncertainty &
other changes
Global Country
(territorial) Country
(consumption) Atmosphere Ocean Land
Raupach et al. (2007) Split in regions
Canadell et al. (2007) ELUC based on
FAO-FRA 2005;
constant ELUC for
1959–1979 data
from Mauna Loa;
data after 1980
from global aver-
Based on one ocean
model tuned to re-
produced observed
1990s sink
±1σprovided for
all components
2008 (online) Constant ELUC for
Le Quéré et al. (2009) Split between An-
nex B and non-
Annex B
Results from an
independent study
Fire-based emis-
sion anomalies
used for 2006–
Based on four
ocean models
normalised to
observations with
constant delta
First use of five
DGVMs to com-
pare with budget
Friedlingstein et al. (2010) Projection for cur-
rent year based on
Emissions for top
emitters ELUC updated with
FAO-FRA 2010
Peters et al. (2012b) Split between An-
nex B and non-
Annex B
Le Quéré et al. (2013)
Peters et al. (2013)
129 countries from
1959 129 countries
and regions from
1990–2010 based
on GTAP8.0
ELUC for 1997–
2011 includes
interannual anoma-
lies from fire-based
All years from
global average Based on five ocean
models normalised
to observations
with ratio
Ten DGVMs avail-
able for SLAND;
first use of four
models to compare
with ELUC
Le Quéré et al. (2014) 250 countriesb134 countries and
regions 1990–2011
based on GTAP8.1,
with detailed es-
timates for years
1997, 2001, 2004,
and 2007
ELUC for 2012 es-
timated from 2001–
2010 average
Based on six mod-
els compared with
two data products
to year 2011
DGVM experi-
ments for SLAND
and ELUC
Confidence levels;
cumulative emis-
budget from 1750
Le Quéré et al. (2015) Three years of BP
data Three years of BP
data Extended to 2012
with updated GDP
ELUC for 1997–
2013 includes
interannual anoma-
lies from fire-based
Based on seven
models compared
with three data
products to year
Based on 10 models Inclusion of break-
down of the sinks in
three latitude bands
and comparison
with three atmo-
spheric inversions
(this study) National emissions
from UNFCCC ex-
tended to 2014 also
provided (along
with CDIAC)
Detailed estimates
introduced for 2011
based on GTAP9
Based on eight
models compared
with two data
products to year
Based on 10 models
with assessment of
minimum realism
The decadal un-
certainty for the
DGVM ensemble
mean now uses
±1σof the decadal
spread across
aThe naming convention of the budgets has changed. Up to and including 2010, the budget year (Carbon Budget 2010) represented the latest year of the data. From 2012, the budgetyear (Carbon Budget 2012) refers to the initial publication year. bThe CDIAC database has
about 250 countries, but we show data for about 216 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2.1.1 for more details).
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 357
solvents, lubricants, feedstocks), and changes in stock (An-
dres et al., 2012).
The uncertainty of the annual emissions from fossil fuels
and industry for the globe has been estimated at ±5 % (scaled
down from the published ±10% at ±2σto the use of ±1σ
bounds reported here; Andres et al., 2012). This is consis-
tent with a more detailed recent analysis of uncertainty of
±8.4% at ±2σ(Andres et al., 2014) and at the high end
of the range of ±5–10% at ±2σreported 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 in the budget we
consider a fixed uncertainty of ±5% for all years, in reality
the uncertainty, as a percentage of the emissions, is grow-
ing with time because of the larger share of global emissions
from non-Annex B countries (emerging economies and de-
veloping countries) with less precise statistical systems (Mar-
land et al., 2009). For example, the uncertainty in Chinese
emissions has been estimated at around ±10% (for ±1σ;
Gregg et al., 2008), and important potential biases have been
identified that suggest China’s emissions could be overes-
timated in published studies (Liu et al., 2015). Generally,
emissions from mature economies with good statistical bases
have an uncertainty of only a few percent (Marland, 2008).
Further research is needed before we can quantify the time
evolution of the uncertainty and its temporal error correla-
tion structure. We note that, even if they are presented as 1σ
estimates, uncertainties in emissions are likely to be mainly
country-specific systematic errors related to underlying bi-
ases of energy statistics and to the accounting method used
by each country. We assign a medium confidence to the re-
sults presented here because they are based on indirect esti-
mates of emissions using energy data (Durant et al., 2010).
There is only limited and indirect evidence for emissions,
although there is a high agreement among the available es-
timates within the given uncertainty (Andres et al., 2014,
2012), and emission estimates are consistent with a range of
other observations (Ciais et al., 2013), even though their re-
gional and national partitioning is more uncertain (Francey
et al., 2013).
2.1.2 Emissions embodied in goods and services
National emission inventories take a territorial (production)
perspective and “include greenhouse gas emissions and re-
movals taking place within national territory and offshore
areas over which the country has jurisdiction” (Rypdal et
al., 2006). That is, emissions are allocated to the country
where and when the emissions actually occur. The territo-
rial emission inventory of an individual country does not in-
clude the emissions from the production of goods and ser-
vices produced in other countries (e.g. food and clothes) that
are used for consumption. Consumption-based emission in-
ventories for an individual country constitute another attri-
bution point of view that allocates global emissions to prod-
ucts that are consumed within a country, and are conceptually
calculated as the territorial emissions minus the “embedded”
territorial emissions to produce exported products plus the
emissions in other countries to produce imported products
(consumption=territorial exports+imports). The differ-
ence between the territorial- and consumption-based emis-
sion inventories is the net transfer (exports minus imports) of
emissions from the production of internationally traded prod-
ucts. 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), quantify emis-
sion (virtual) transfers by the trade of products between
countries (Peters et al., 2011b), and potentially design more
effective and efficient climate policy (Peters and Hertwich,
We estimate consumption-based emissions by enumerat-
ing the global supply chain using a global model of the eco-
nomic relationships between economic sectors within and
between every country (Andrew and Peters, 2013; Peters et
al., 2011a). Due to availability of the input data, detailed es-
timates are made for the years 1997, 2001, 2004, 2007, and
2011 (using the methodology of Peters et al., 2011b) using
economic and trade data from the Global Trade and Analysis
Project version 9 (GTAP; Narayanan et al., 2015). The results
cover 57 sectors and 140 countries and regions. The results
are extended into an annual time series from 1990 to the lat-
est year of the fossil fuel emissions or GDP data (2013 in this
budget), using GDP data by expenditure in current exchange
rate of US dollars (USD; from the UN National Accounts
Main Aggregates Database; UN, 2014c) and time series of
trade data from GTAP (based on the methodology in Peters
et al., 2011b).
We estimate the sector-level CO2emissions using our own
calculations based on the GTAP data and methodology, in-
clude flaring and cement emissions from CDIAC, and then
scale the national totals (excluding bunker fuels) to match the
CDIAC estimates from the most recent carbon budget. We
do not include international transportation in our estimates
of national totals, but we do include them in the global to-
tal. The time series of trade data provided by GTAP covers
the period 1995–2011 and our methodology uses the trade
shares as this data set. For the period 1990–1994 we assume
the trade shares of 1995, while for 2012 and 2013 we assume
the trade shares of 2011.
Comprehensive analysis of the uncertainty of consumption
emissions accounts is still lacking in the literature, although
several analyses of components of this uncertainty have been
made (e.g. Dietzenbacher et al., 2012; Inomata and Owen,
2014; Karstensen et al., 2015; Moran and Wood, 2014). For
this reason we do not provide an uncertainty estimate for
these emissions, but based on model comparisons and sen-
sitivity analysis, they are unlikely to be larger than for the
territorial emission estimates (Peters et al., 2012a). Uncer-
tainty is expected to increase for more detailed results, and Earth Syst. Sci. Data, 7, 349–396, 2015
358 C. Le Quéré et al.: Global Carbon Budget 2015
to decrease with aggregation (Peters et al., 2011b; e.g. the
results for Annex B countries will be more accurate than the
sector results for an individual country).
The consumption-based emissions attribution method con-
siders the CO2emitted to the atmosphere in the production
of products, but not the trade in fossil fuels (coal, oil, gas).
It is also possible to account for the carbon trade in fossil
fuels (Davis et al., 2011), but we do not present those data
here. Peters et al. (2012a) additionally considered trade in
The consumption data do not modify the global average
terms in Eq. (1) but are relevant to the anthropogenic car-
bon cycle as they reflect the trade-driven movement of emis-
sions across the Earth’s surface in response to human activ-
ities. Furthermore, if national and international climate poli-
cies continue to develop in an unharmonised way, then the
trends reflected in these data will need to be accommodated
by those developing policies.
2.1.3 Growth rate in emissions
We report the annual growth rate in emissions for adjacent
years (in percent per year) by calculating the difference be-
tween the two years and then comparing to the emissions
in the first year: EFF(t0+1)EFF(t0)
EFF(t0)×100%yr1. This is the
simplest method to characterise a 1-year growth compared to
the previous year and is widely used. We apply a leap-year
adjustment to ensure valid interpretations of annual growth
rates. This would affect the growth rate by about 0.3%yr1
(1/365) and causes 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 EFF over time periods of
greater than 1 year can be re-written using its logarithm
equivalent as follows:
Here we calculate relative growth rates in emissions for
multi-year periods (e.g. a decade) by fitting a linear trend
to ln(EFF) in Eq. (2), reported in percent per year. We fit
the logarithm of EFF rather than EFF directly because this
method ensures that computed growth rates satisfy Eq. (6).
This method differs from previous papers (Canadell et al.,
2007; Le Quéré et al., 2009; Raupach et al., 2007) that com-
puted the fit to EFF and divided by average EFF directly, but
the difference is very small (<0.05%) in the case of EFF.
2.1.4 Emissions projections
Energy statistics from BP are normally available around June
for the previous year. To gain insight into emission trends for
the current year (2015), we provide an assessment of global
emissions for EFF by combining individual assessments of
emissions for China and the USA (the two biggest emitting
countries), as well as the rest of the world.
We specifically estimate emissions in China because the
evidence suggests a departure from the long-term trends in
the carbon intensity of the economy used in emissions pro-
jections in previous global carbon budgets (e.g. Le Quéré et
al., 2015), resulting from significant drops in industrial pro-
duction against continued growth in economic output. This
departure could be temporary (Jackson et al., 2015). Our
2015 estimate for China uses (1) apparent consumption of
coal for January to August estimated using production data
from the National Bureau of Statistics (2015b), imports and
exports of coal from China Customs Statistics (General Ad-
ministration of Customs of the People’s Republic of China,
2015a, b), and from partial data on stock changes from indus-
try sources (China Coal Industry Association, 2015; China
Coal Resource, 2015); (2) apparent consumption of oil and
gas for January to June from the National Energy Admin-
istration (2015); and (3) production of cement reported for
January to August (National Bureau of Statistics of China,
2015b). Using these data, we estimate the change in emis-
sions for the corresponding months in 2015 compared to
2014 assuming constant emission factors. We then assume
that the relative changes during the first 6–8 months will per-
sist throughout the year. The main sources of uncertainty are
from the incomplete data on stock changes, the carbon con-
tent of coal, and the assumption of persistent behaviour for
the rest of 2015. These are discussed further in Sect. 3.2.1.
We tested our new method using data available in Octo-
ber 2014 to make a 2014 projection of coal consumption and
cement production, both of which changed substantially in
2014. For the apparent consumption of coal we would have
projected a change of 3.2 % in coal use for 2014, compared
to 2.9% reported by the National Bureau of Statistics of
China in February 2015, while for the production of cement
we would have projected a change of +3.5%, compared to
a realised change of +2.3%. In both cases, the projection
is consistent with the sign of the realised change. This new
method should be more reliable as it is based on actual data,
even if they are preliminary. Note that the growth rates we
project for China are unaffected by recent upwards revisions
of Chinese energy consumption statistics (National Bureau
of Statistics of China, 2015a), as all data used here dates from
after the revised period. The revisions do, however, affect the
absolute value of the time series up to 2013, and hence the
absolute value for 2015 extrapolated from that time series
using projected growth rates. Further, because the revisions
will increase China’s share of total global emissions, the pro-
jected growth rate of global emissions will also be affected
slightly. This effect is discussed in the Results section.
For the USA, we use the forecast of the US Energy Infor-
mation Administration (EIA) “Short-term energy outlook”
(October 2015) for emissions from fossil fuels. This is based
on an energy forecasting model which is revised monthly,
and takes into account heating-degree days, household ex-
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 359
penditures by fuel type, energy markets, policies, and other
effects. We combine this with our estimate of emissions from
cement production using the monthly US cement data from
USGS for January–July, assuming changes in cement pro-
duction over the first 7 months apply throughout the year. We
estimate an uncertainty range using the revisions of historical
October forecasts made by the EIA 1 year later. These revi-
sions were less than 2% during 2009–2014 (when a forecast
was done), except for 2011, when it was 4.0 %. We thus use
a conservative uncertainty range of 4.0 to +1.8% around
the central forecast.
For the 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 the so-called Kaya identity (also
called IPAT identity, the acronym standing for human im-
pact (I) on the environment, which is equal to the prod-
uct of population (P), affluence (A), and technology (T)),
whereby EFF (GtCyr1) is decomposed by the product of
GDP (USDyr1) and the fossil fuel carbon intensity of the
economy (IFF; GtC USD1) as follows:
Such product-rule decomposition identities imply that the
relative growth rates of the multiplied quantities are additive.
Taking a time derivative of Eq. (3) gives
and applying the rules of calculus
Finally, dividing Eq. (5) by (3) gives
where the left-hand term is the relative growth rate of EFF
and the right-hand terms are the relative growth rates of GDP
and IFF, respectively, which can simply be added linearly to
give overall growth rate. The growth rates are reported in per-
cent by multiplying each term by 100 %. As preliminary esti-
mates of annual change in GDP are made well before the end
of a calendar year, making assumptions on the growth rate of
IFF allows us to make projections of the annual change in
CO2emissions well before the end of a calendar year. The
IFF is based on GDP in constant PPP (purchasing power par-
ity) from the IEA up to 2012 (IEA/OECD, 2014) and ex-
tended using the IMF growth rates for 2013 and 2014 (IMF,
2015). Experience of the past year has highlighted that the
interannual variability in IFF is the largest source of uncer-
tainty in the GDP-based emissions projections. We thus use
the standard deviation of the annual IFF for the period 2005–
2014 as a measure of uncertainty, reflecting ±1σas in the
rest of the carbon budget. This is ±1.4% yr1for the rest of
the world (global emissions minus China and USA).
The 2015 projection for the world is made of the sum of
the projections for China, the USA, and the rest of the world.
The uncertainty is added quadratically among the three re-
gions. The uncertainty here reflects the best of our expert
2.2 CO2emissions from land use, land-use change,
and forestry (ELUC)
Land-use-change emissions reported here (ELUC) include
CO2fluxes from deforestation, afforestation, logging (forest
degradation and harvest activity), shifting cultivation (cycle
of cutting forest for agriculture and then abandoning), and
regrowth of forests following wood harvest or abandonment
of agriculture. Only some land management activities (Ta-
ble 5) are included in our land-use-change emissions esti-
mates (e.g. emissions or sinks related to management and
management changes of established pasture and croplands
are not included). Some of these activities lead to emissions
of CO2to the atmosphere, while others lead to CO2sinks.
ELUC is the net sum of all anthropogenic activities consid-
ered. Our annual estimate for 1959–2010 is from a book-
keeping method (Sect. 2.2.1) primarily based on net forest
area change and biomass data from the Forest Resource As-
sessment (FRA) of the Food and Agriculture Organization
(FAO), which is only available at intervals of 5 years. We use
the FAO FRA 2010 here (Houghton et al., 2012). Interannual
variability in emissions due to deforestation and degradation
has been coarsely estimated from satellite-based fire activity
in tropical forest areas (Sect. 2.2.2; Giglio et al., 2013; van
der Werf et al., 2010). The bookkeeping method is used to
quantify the ELUC over the time period of the available data,
and the satellite-based deforestation fire information to incor-
porate interannual variability (ELUC flux annual anomalies)
from tropical deforestation fires. The satellite-based defor-
estation and degradation fire emissions estimates are avail-
able for years 1997–2014. We calculate the global annual
anomaly in deforestation and degradation fire emissions in
tropical forest regions for each year, compared to the 1997–
2010 period, and add this annual flux anomaly to the ELUC
estimated using the bookkeeping method that is available up
to 2010 only and assumed constant at the 2010 value during
the period 2011–2014. We thus assume that all land manage-
ment activities apart from deforestation and degradation do
not vary significantly on a year-to-year basis. Other sources
of interannual variability (e.g. the impact of climate variabil-
ity on regrowth fluxes) are accounted for in SLAND. In ad-
dition, we use results from dynamic global vegetation mod-
els (see Sect. 2.2.3 and Table 6) that calculate net land-use-
change CO2emissions in response to land-cover-change re-
constructions prescribed to each model in order to help quan-
tify the uncertainty in ELUC and to explore the consistency of Earth Syst. Sci. Data, 7, 349–396, 2015
360 C. Le Quéré et al.: Global Carbon Budget 2015
Table 4. Data sources used to compute each component of the global carbon budget. National emissions from UNFCCC are provided directly
and thus no additional data sources need citing in this table.
Component Process Data source Data reference
EFF (global
and CDIAC Fossil fuel combustion and oxida-
tion and gas flaring UN Statistics Division to 2011 UN (2014a, b)
national) BP for 2012–2014 BP (2015)
Cement production US Geological Survey van Oss (2015)
USGS (2015)
ELUC Land-cover change (deforestation,
afforestation, and forest regrowth) Forest Resource Assessment (FRA)
of the Food and Agriculture Orga-
nization (FAO)
FAO (2010)
Wood harvest FAO Statistics Division FAOSTAT (2010)
Shifting agriculture FAO FRA and Statistics Division FAO (2010)
FAOSTAT (2010)
Interannual variability from peat
fires and climate – land manage-
ment interactions (1997–2013)
Global Fire Emissions Database
(GFED4) Giglio et al. (2013)
GATM Change in atmospheric CO2con-
centration 1959–1980: CO2Program at
Scripps Institution of Oceanogra-
phy and other research groups
Keeling et al. (1976)
1980–2015: US National Oceanic
and Atmospheric Administration
Earth System Research Laboratory
Dlugokencky and Tans (2015)
Ballantyne et al. (2012)
SOCEAN Uptake of anthropogenic CO21990–1999 average: indirect esti-
mates based on CFCs, atmospheric
O2, and other tracer observations
Manning and Keeling (2006)
Keeling et al. (2011)
McNeil et al. (2003)
Mikaloff Fletcher et al. (2006) as
assessed by the IPCC in Denman et
al. (2007)
Impact of increasing atmospheric
CO2, climate, and variability Ocean models Table 6
SLAND Response of land vegetation to:
Increasing atmospheric CO2
Climate and variability
Other environmental changes
Budget residual
our understanding. The three methods are described below,
and differences are discussed in Sect. 3.2.
2.2.1 Bookkeeping method
Land-use-change CO2emissions are calculated by a book-
keeping method approach (Houghton, 2003) that keeps track
of the carbon stored in vegetation and soils before defor-
estation or other land-use change, and the changes in for-
est age classes, or cohorts, of disturbed lands after land-use
change, including possible forest regrowth after deforesta-
tion. The approach tracks the CO2emitted to the atmosphere
immediately during deforestation, and over time due to the
follow-up decay of soil and vegetation carbon in different
pools, including wood product pools after logging and defor-
estation. It also tracks the regrowth of vegetation and asso-
ciated build-up of soil carbon pools after land-use change. It
considers transitions between forests, pastures, and cropland;
shifting cultivation; degradation of forests where a fraction of
the trees is removed; abandonment of agricultural land; and
forest management such as wood harvest and, in the USA,
fire management. In addition to tracking logging debris on
the forest floor, the bookkeeping method tracks the fate of
carbon contained in harvested wood products that is even-
tually emitted back to the atmosphere as CO2, although a
detailed treatment of the lifetime in each product pool is not
performed (Earles et al., 2012). Harvested wood products are
partitioned into three pools with different turnover times. All
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 361
Table 5. Comparison of the processes included in the ELUC of the global carbon budget and the DGVMs. See Table 6 for model references.
All models include deforestation and forest regrowth after abandonment of agriculture (or from afforestation activities on agricultural land).
Wood harvest and forest degradationayes yes yes yes no no no no yes no yesb
Shifting cultivation yes yes no yes no no no no no no yes
Cropland harvest yes yes yes yescno yes no yes yes yes yes
Peat fires no yes no no no no no no no no no
Fire simulation and/or suppression for US only yes no yes no yes yes yes no no yes
Climate and variability no yes yes yes yes yes yes yes yes yes yes
CO2fertilisation no yes yes yes yes yes yes yes yes yes yes
Carbon–nitrogen interactions, including N deposition no yes yes no no no no no yes no no
aRefers to the routine harvest of established managed forests rather than pools of harvested products. bWood stems are harvested according to the land-use data. cCarbon from crop
harvest is entirely transferred into the litter pools.
fuelwood is assumed burnt in the year of harvest (1.0yr1).
Pulp and paper products are oxidised at a rate of 0.1yr1,
timber is assumed to be oxidised at a rate of 0.01yr1, and
elemental carbon decays at 0.001yr1. The general assump-
tions about partitioning wood products among these pools
are based on national harvest data (Houghton, 2003).
The primary land-cover-change and biomass data for the
bookkeeping method analysis is the Forest Resource As-
sessment of the FAO, which provides statistics on forest-
cover change and management at intervals of 5 years (FAO,
2010). The data are based on countries’ self-reporting, some
of which integrates satellite data in more recent assessments
(Table 4). Changes in land cover other than forest are based
on annual, national changes in cropland and pasture areas
reported by the FAO Statistics Division (FAOSTAT, 2010).
Land-use-change country data are aggregated by regions.
The carbon stocks on land (biomass and soils), and their re-
sponse functions subsequent to land-use change, are based on
FAO data averages per land-cover type, per biome, and per
region. Similar results were obtained using forest biomass
carbon density based on satellite data (Baccini et al., 2012).
The bookkeeping method does not include land ecosystems’
transient response to changes in climate, atmospheric CO2,
and other environmental factors, but the growth/decay curves
are based on contemporary data that will implicitly reflect
the effects of CO2and climate at that time. Results from the
bookkeeping method are available from 1850 to 2010.
2.2.2 Fire-based interannual variability in ELUC
Land-use-change-associated CO2emissions calculated from
satellite-based fire activity in tropical forest areas (van der
Werf et al., 2010) provide information on emissions due to
tropical deforestation and degradation that are complemen-
tary to the bookkeeping approach. They do not provide a di-
rect estimate of ELUC as they do not include non-combustion
processes such as respiration, wood harvest, wood products,
or forest regrowth. Legacy emissions such as decomposi-
tion from on-ground debris and soils are not included in
this method either. However, fire estimates provide some in-
sight into the year-to-year variations in the subcomponent of
the total ELUC flux that result from immediate CO2emis-
sions during deforestation caused, for example, by the in-
teractions between climate and human activity (e.g. there is
more burning and clearing of forests in dry years) that are not
represented by other methods. The “deforestation fire emis-
sions” assume an important role of fire in removing biomass
in the deforestation process, and thus can be used to infer
gross instantaneous CO2emissions from deforestation using
satellite-derived data on fire activity in regions with active
deforestation. The method requires information on the frac-
tion of total area burned associated with deforestation ver-
sus other types of fires, and this information can be merged
with information on biomass stocks and the fraction of the
biomass lost in a deforestation fire to estimate CO2emis-
sions. The satellite-based deforestation fire emissions are
limited to the tropics, where fires result mainly from human
activities. Tropical deforestation is the largest and most vari-
able single contributor to ELUC.
Fire emissions associated with deforestation and tropi-
cal peat burning are based on the Global Fire Emissions
Database (GFED4; accessed October 2015) described in van
der Werf et al. (2010) but with updated burned area (Giglio
et al., 2013) as well as burned area from relatively small
fires that are detected by satellite as thermal anomalies but
not mapped by the burned area approach (Randerson et al.,
2012). The burned area information is used as input data in
a modified version of the satellite-driven Carnegie–Ames–
Stanford Approach (CASA) biogeochemical model to esti-
mate carbon emissions associated with fires, keeping track
of what fraction of fire emissions was due to deforestation
(see van der Werf et al., 2010). The CASA model uses differ- Earth Syst. Sci. Data, 7, 349–396, 2015
362 C. Le Quéré et al.: Global Carbon Budget 2015
ent assumptions to compute decay functions compared to the
bookkeeping method, and does not include historical emis-
sions or regrowth from land-use change prior to the avail-
ability of satellite data. Comparing coincident CO emissions
and their atmospheric fate with satellite-derived CO concen-
trations allows for some validation of this approach (e.g. van
der Werf et al., 2008). Results from the fire-based method to
estimate land-use-change emissions anomalies added to the
bookkeeping mean ELUC estimate are available from 1997
to 2014. Our combination of land-use-change CO2emissions
where the variability in annual CO2deforestation emissions
is diagnosed from fires assumes that year-to-year variability
is dominated by variability in deforestation.
2.2.3 Dynamic global vegetation models (DGVMs)
Land-use-change CO2emissions have been estimated us-
ing an ensemble of 10 DGVMs. New model experiments up
to year 2014 have been coordinated by the project “Trends
and drivers of the regional-scale sources and sinks of carbon
dioxide” (TRENDY; Sitch et al., 2015). We use only models
that have estimated land-use-change CO2emissions and the
terrestrial residual sink following the TRENDY protocol (see
Sect. 2.5.2), thus providing better consistency in the assess-
ment of the causes of carbon fluxes on land. Models use their
latest configurations, summarised in Tables 5 and 6.
The DGVMs were forced with historical changes in land-
cover distribution, climate, atmospheric CO2concentration,
and N deposition. As further described below, each historical
DGVM simulation was repeated with a time-invariant pre-
industrial land-cover distribution, allowing for estimation of,
by difference with the first simulation, the dynamic evolution
of biomass and soil carbon pools in response to prescribed
land-cover change. All DGVMs represent deforestation and
(to some extent) regrowth, the most important components
of ELUC, but they do not represent all processes resulting di-
rectly from human activities on land (Table 5). DGVMs rep-
resent processes of vegetation growth and mortality, as well
as decomposition of dead organic matter associated with nat-
ural cycles, and include the vegetation and soil carbon re-
sponse to increasing atmospheric CO2levels and to climate
variability and change. In addition, three models explicitly
simulate the coupling of C and N cycles and account for at-
mospheric N deposition (Table 5). The DGVMs are indepen-
dent of the other budget terms except for their use of atmo-
spheric CO2concentration to calculate the fertilisation effect
of CO2on primary production.
The DGVMs used a consistent land-use-change data set
(Hurtt et al., 2011), which provided annual, half-degree, frac-
tional data on cropland, pasture, primary vegetation, and sec-
ondary vegetation, as well as all underlying transitions be-
tween land-use states, including wood harvest and shifting
cultivation. This data set used the HYDE (Klein Goldewijk
et al., 2011) spatially gridded maps of cropland, pasture, and
ice/water fractions of each grid cell as an input. The HYDE
data are based on annual FAO statistics of change in agricul-
tural area available to 2012 (FAOSTAT, 2010). For the years
2013 and 2014, the HYDE data were extrapolated by coun-
try for pastures and cropland separately based on the trend
in agricultural area over the previous 5 years. The HYDE
data are independent of the data set used in the bookkeeping
method (Houghton, 2003, and updates), which is based pri-
marily on forest area change statistics (FAO, 2010). Although
the HYDE land-use-change data set indicates whether land-
use changes occur on forested or non-forested land, typi-
cally only the changes in agricultural areas are used by the
models and are implemented differently within each model
(e.g. an increased cropland fraction in a grid cell can either
be at the expense of grassland, or forest, the latter resulting
in deforestation; land-cover fractions of the non-agricultural
land differ between models). Thus the DGVM forest area
and forest area change over time is not consistent with the
Forest Resource Assessment of the FAO forest area data
used for the bookkeeping model to calculate ELUC. Similarly,
model-specific assumptions are applied to convert deforested
biomass or deforested area, and other forest product pools,
into carbon in some models (Table 5).
The DGVM runs were forced by either 6-hourly CRU-
NCEP or by monthly CRU temperature, precipitation, and
cloud cover fields (transformed into incoming surface radi-
ation) based on observations and provided on a 0.5×0.5
grid and updated to 2014 (CRU TS3.23; Harris et al., 2015).
The forcing data include both gridded observations of cli-
mate and global atmospheric CO2, which change over time
(Dlugokencky and Tans, 2015), and N deposition (as used in
three models, Table 5; Lamarque et al., 2010). ELUC is di-
agnosed in each model by the difference between a model
simulation with prescribed historical land-cover change and
a simulation with constant, pre-industrial land-cover distribu-
tion. Both simulations were driven by changing atmospheric
CO2, climate, and in some models N deposition over the
period 1860–2014. Using the difference between these two
DGVM simulations to diagnose ELUC is not fully consis-
tent with the definition of ELUC in the bookkeeping method
(Gasser and Ciais, 2013; Pongratz et al., 2014). The DGVM
approach to diagnose land-use-change CO2emissions would
be expected to produce systematically higher ELUC emis-
sions than the bookkeeping approach if all the parameters
of the two approaches were the same, which is not the case
(see Sect. 2.5.2).
2.2.4 Other published ELUC methods
Other methods have been used to estimate CO2emissions
from land-use change. We describe some of the most impor-
tant methodological differences between the approach used
here and other published methods, and for completion, we
explain why they are not used in the budget.
Different definitions (e.g. the inclusion of fire manage-
ment) for ELUC can lead to significantly different estimates
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 363
Table 6. References for the process models and data products included in Figs. 6–8.
Model/data name Reference Change from Le Quéré et al. (2015)
Dynamic global vegetation models
CLM4.5BGCaOleson et al. (2013) No change
ISAM Jain et al. (2013)bWe accounted for crop harvest for C3 and C4 crops based on Arora and Boer
(2005) and agricultural soil carbon loss due to tillage (Jain et al., 2005)
JSBACH Reick et al. (2013)cNot applicable (first use of this model)
JULESeClark et al. (2011)eUpdated JULES version 4.3 compared to v3.2 for last year’s budget. A num-
ber of small code changes, but no change in major science sections with the
exception of an update in the way litter flux is calculated.
LPJ-GUESS B. Smith et al. (2014) Implementation of C /N interactions in soil and vegetation, including a com-
plete update of the soil organic matter scheme
LPJfSitch et al. (2003) No change
LPJmL Bondeau et al. (2007)gNot applicable (first use of this model)
OCNv1.r240 Zaehle et al. (2011)hRevised photosynthesis parameterisation allowing for temperature acclimation
as well as cold and heat effects on canopy processes. Revised grassland phe-
nology. Included wood harvest as a driver to simulate harvest and post-harvest
regrowth. Using Hurtt land-use data set
ORCHIDEE Krinner et al. (2005) Revised parameters values for photosynthetic capacity for boreal forests (fol-
lowing assimilation of FLUXNET data), updated parameters values for stem al-
location, maintenance respiration and biomass export for tropical forests (based
on literature) and, CO2down-regulation process added to photosynthesis.
VISIT Kato et al. (2013)iNo change
Data products for land-use-change emissions
Bookkeeping Houghton et al. (2012) No change
Fire-based emissions van der Werf et al. (2010) No change
Ocean biogeochemistry models
NEMO-PlankTOM5 Buitenhuis et al. (2010)jNo change
NEMO-PISCES (IPSL)kAumont and Bopp (2006) No change
CCSM-BEC Doney et al. (2009) No change; small differences in the mean flux are caused by a change in how
global and annual means were computed
MICOM-HAMOCC (NorESM-OC) Assmann et al. (2010)l,m Revised light penetration formulation and parameters for ecosystem module,
revised salinity restoring scheme enforcing salt conservation, new scheme en-
forcing global freshwater balance, and model grid changed from displaced pole
to tripolar
MPIOM-HAMOCC Ilyina et al. (2013) No change
NEMO-PISCES (CNRM) Séférian et al. (2013)nNo change
CSIRO Oke et al. (2013) No change
MITgcm-REcoM2 Hauck et al. (2013)oNot applicable (first use of this model)
Data products for ocean CO2flux
LandschützerpLandschützer et al. (2015) No change
Jena CarboScopepRödenbeck et al. (2014) Updated to version oc_1.2gcp2015
Atmospheric inversions for total CO2fluxes (land-use change+land+ocean CO2fluxes)
CarbonTracker Peters et al. (2010) Updated to version CTE2015. Updates include using CO2observations
from obspack_co2_1_GLOBALVIEWplus_v1.0_2015-07-30 (NOAA/ESRL,
2015b), prior SiBCASA biosphere and fire fluxes on 3-hourly resolution and
fossil fuel emissions for 2010–2014 scaled to updated global totals.
Jena CarboScope Rödenbeck et al. (2003) Updated to version s81_v3.7
MACCqChevallieret al.(2005) Updated to version 14.2. Updates include a change of the convection scheme
and a revised data selection.
aCommunity Land Model 4.5. bSee also El-Masri et al. (2013). cSee also Goll et al. (2015). dJoint UK Land Environment Simulator. eSee also Best et al. (2011). fLund–Potsdam–Jena. gThe
LPJmL (Lund–Potsdam–Jena managed Land) version used also includes developments described in Rost et al. (2008; river routing and irrigation), Fader et al. (2010; agricultural management),
Biemans et al. (2011; reservoir management), Schaphoff et al. (2013; permafrost and 5 layer hydrology), and Waha et al. (2012; sowing data) (sowing dates). hSee also Zaehle et al. (2010) and
Friend (2010). iSee also Ito and Inatomi (2012). jWith no nutrient restoring below the mixed layer depth. kReferred to as LSCE in previous carbon budgets. lWith updates to the physical model
as described in Tjiputra et al. (2013). mFurther information (e.g. physical evaluation) for these models can be found in Danabasoglu et al. (2014). nUsing winds from Atlas et al. (2011). oA few
changes have been applied to the ecosystem model. (1) The constant Fe :C ratio was substituted by a constant Fe :N ratio. (2) A sedimentary iron source was implemented. (3) the following
parameters were changed: CHL_N_max =3.78, Fe2N=0.033, deg_CHL_d =0.1, Fe2N_d =0.033, ligandStabConst=200, constantIronSolubility =0.02. pUpdates using SOCATv3 plus new
2012–2014 data. qThe MACCv14.2 CO2inversion system, initially described by Chevallier et al. (2005), relies on the global tracer transport model LMDZ (see also Supplement of Chevallier,
2015; Hourdin et al., 2006). Earth Syst. Sci. Data, 7, 349–396, 2015
364 C. Le Quéré et al.: Global Carbon Budget 2015
within models (Gasser and Ciais, 2013; Hansis et al., 2015;
Pongratz et al., 2014) as well as between models and other
approaches (Houghton et al., 2012; P. Smith et al., 2014).
FAO uses the IPCC approach called “Tier 1” (e.g. Tubiello
et al., 2015) to produce a “Land use – forest land” estimate
from the Forest Resources Assessment data used in the book-
keeping method described in Sect. 2.2.1 (MacDicken, 2015).
The Tier 1-type method applies a nationally reported mean
forest carbon stock change (above and below ground liv-
ing biomass) to nationally reported net forest area change,
across all forest land combined (planted and natural forests).
The methods implicitly assume instantaneous loss or gain of
mean forest. Thus the Tier 1 approach provides an estimate of
attributable emissions from the process of land-cover change,
but it does not distribute these emissions through time. It also
captures a fraction of what the global modelling approach
considers residual carbon flux (SLAND), it does not consider
loss of soil carbon, and there are no legacy fluxes. Land-
use fluxes estimated with this method were 0.47GtC yr1in
2001–2010 and 0.22GtC yr1in 2011–2015 (Federici et al.,
2015). This estimate is not directly comparable with ELUC
used here because of the different boundary conditions.
Recent advances in satellite data leading to higher-
resolution area change data (e.g. Hansen et al., 2013) and
estimates of biomass in live vegetation (e.g. Baccini et al.,
2012; Saatchi et al., 2011) have led to several satellite-
based estimates of CO2emissions due to tropical deforesta-
tion (typically gross loss of forest area; Achard and House,
2015). These include estimates of 1.0GtC yr1for 2000 to
2010 (Baccini et al., 2012), 0.8GtC yr1for 2000 to 2005
(Harris et al., 2012), 0.9GtC yr1for 2000 to 2010 for net
area change (Achard et al., 2014), and 1.3GtC yr12000 to
2010 (Tyukavina et al., 2015). These estimates include be-
lowground carbon biomass using a scaling factor. Some esti-
mate soil carbon loss, some assume instantaneous emissions,
some do not account for regrowth fluxes, and none account
for legacy fluxes from land-use change prior to the avail-
ability of satellite data. They are mostly estimates of tropi-
cal deforestation only, and do not capture regrowth flux after
abandonment or planting (Achard and House, 2015). These
estimates are also difficult to compare with ELUC used here
because they do not fully include legacy fluxes and forest re-
2.2.5 Uncertainty assessment for ELUC
Differences between the bookkeeping, the addition of fire-
based interannual variability to the bookkeeping, and DGVM
methods originate from three main sources: the land-cover-
change data set, the different approaches used in models, and
the different processes represented (Table 5). We examine the
results from the 10 DGVMs and of the bookkeeping method
to assess the uncertainty in ELUC.
The uncertainties in annual ELUC estimates are examined
using the standard deviation across models, which averages
0.4GtC yr1from 1959 to 2014 (Table 7). The mean of the
multi-model ELUC estimates is consistent with a combina-
tion of the bookkeeping method and fire-based emissions
(Le Quéré et al., 2014), with the multi-model mean and
bookkeeping method differing by less than 0.5GtC yr1over
85% of the time. Based on this comparison, we assess that
an uncertainty of ±0.5GtC yr1provides a semi-quantitative
measure of uncertainty for annual emissions, and reflects
our best value judgment that there is at least 68% chance
(±1σ) that the true land-use-change emission lies within the
given range, for the range of processes considered here. This
is consistent with the uncertainty analysis of Houghton et
al. (2012), which partly reflects improvements in data on for-
est area change using data, and partly more complete under-
standing and representation of processes in models.
The uncertainties in the decadal ELUC estimates are also
examined using the DGVM ensemble, although they are
likely correlated between decades. The correlations between
decades come from (1) common biases in system bound-
aries (e.g. not counting forest degradation in some models);
(2) common definition for the calculation of ELUC from the
difference of simulations with and without land-use change
(a source of bias vs. the unknown truth); (3) common and
uncertain land-cover-change input data which also cause a
bias, though if a different input data set is used each decade,
decadal fluxes from DGVMs may be partly decorrelated; and
(4) model structural errors (e.g. systematic errors in biomass
stocks). In addition, errors arising from uncertain DGVM pa-
rameter values would be random, but they are not accounted
for in this study, since no DGVM provided an ensemble of
runs with perturbed parameters.
Prior to 1959, the uncertainty in ELUC is taken as ±33%,
which is the ratio of uncertainty to mean from the 1960s (Ta-
ble 7), the first decade available. This ratio is consistent with
the mean standard deviation of DGMVs’ land-use-change
emissions over 1870–1958 (0.38GtC) over the multi-model
mean (1.1GtC).
2.3 Atmospheric CO2growth rate (GATM)
Global atmospheric CO2growth rate estimates
The atmospheric CO2growth rate is provided by the US Na-
tional Oceanic and Atmospheric Administration Earth Sys-
tem Research Laboratory (NOAA/ESRL; Dlugokencky and
Tans, 2015), which is updated from Ballantyne et al. (2012).
For the 1959–1980 period, the global growth rate is based on
measurements of atmospheric CO2concentration averaged
from the Mauna Loa and South Pole stations, as observed
by the CO2Program at Scripps Institution of Oceanogra-
phy (Keeling et al., 1976). For the 1980–2014 time period,
the global growth rate is based on the average of multi-
ple 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 func-
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 365
Table 7. Comparison of results from the bookkeeping method and budget residuals with results from the DGVMs and inverse estimates
for the periods 1960–1969, 1970–1979, 1980–1989, 1990–1999, 2000–2009, the last decade, and the last year available. All values are in
GtCyr1. The DGVM uncertainties represents ±1σof the decadal or annual (for 2014 only) estimates from the 10 individual models; for
the inverse models all three results are given where available.
Mean (GtC yr1)
1960–1969 1970–1979 1980–1989 1990–1999 2000–2009 2005–2014 2014
Land-use-change emissions (ELUC)
Bookkeeping method 1.5 ±0.5 1.3±0.5 1.4 ±0.5 1.6±0.5 1.0 ±0.5 0.9 ±0.5 1.1 ±0.5
DGVMsa1.2±0.4 1.2 ±0.4 1.3 ±0.4 1.2 ±0.4 1.2±0.4 1.4 ±0.4 1.4 ±0.5
Residual terrestrial sink (SLAND)
Budget residual 1.7 ±0.7 1.7±0.8 1.6 ±0.8 2.6±0.8 2.4 ±0.8 3.0 ±0.8 4.1 ±0.9
DGVMsa1.1±0.6 2.1 ±0.3 1.7 ±0.4 2.3 ±0.3 2.7±0.4 3.0 ±0.5 3.6 ±0.9
Total land fluxes (SLAND ELUC)
(EFF GATM SOCEAN)0.2 ±0.5 0.4 ±0.6 0.2±0.6 1.0 ±0.6 1.5 ±0.6 2.1 ±0.7 3.0±0.7
DGVMsa0.1±0.6 0.9 ±0.4 0.5 ±0.5 1.1 ±0.5 1.5±0.4 1.6 ±0.4 2.3 ±0.9
Inversions (CTE2015/Jena
CarboScope/MACC)b–/–/– –/–/– –/0.3b/0.8b–/1.1b/1.8b–/1.6b/2.4b2.0b/2.0b/3.3b2.8b/2.6b/4.2b
aNote that the decadal uncertainty calculation for the DGVMs is smaller here compared to previous global carbon budgets because it uses ±1σof the decadal estimates for the DGVMs,
compared to the average of the annual ±1σestimates in previous years. It thus represents the true model range for their decadal estimates. This change was introduced to be consistent
with the decadal uncertainty calculations in Table 8. bEstimates are not corrected for the influence of river fluxes, which would reduce the fluxes by 0.45GtCyr1when neglecting the
anthropogenic influence on land (Sect. 7.2.2). CTE2015 refers to Peters et al. (2010), Jena CarboScope to Rödenbeck et al. (2014), and MACC to Chevallier et al. (2005); see Table 6.
tion of time, and averaging by latitude band (Masarie and
Tans, 1995). The annual growth rate is estimated by Dlu-
gokencky and Tans (2015) from atmospheric CO2concen-
tration 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 ppmyr1is converted to units of GtCyr1by
multiplying by a factor of 2.12 GtCppm1(Ballantyne et al.,
2012) for consistency with the other components.
The uncertainty around the annual growth rate based
on the multiple stations data set ranges between 0.11 and
0.72GtC yr1, with a mean of 0.61GtC yr1for 1959–1979
and 0.19GtC yr1for 1980–2014, when a larger set of sta-
tions were available (Dlugokencky and Tans, 2015). It is
based on the number of available stations, and thus takes
into account both the measurement errors and data gaps at
each station. This uncertainty is larger than the uncertainty
of ±0.1GtC yr1reported for decadal mean growth rate by
the IPCC because errors in annual growth rate are strongly
anti-correlated in consecutive years leading to smaller er-
rors for longer timescales. The decadal change is com-
puted from the difference in concentration 10 years apart
based on a measurement error of 0.35ppm. This error is
based on offsets between NOAA/ESRL measurements and
those of the World Meteorological Organization World Data
Centre for Greenhouse Gases (NOAA/ESRL, 2015a) for
the start and end points (the decadal change uncertainty is
q2(0.35ppm)2(10yr)1assuming that each yearly mea-
surement error is independent). This uncertainty is also used
in Table 8.
The contribution of anthropogenic CO and CH4is ne-
glected from the global carbon budget (see Sect. 2.7.1). We
assign a high confidence to the annual estimates of GATM be-
cause they are based on direct measurements from multiple
and consistent instruments and stations distributed around
the world (Ballantyne et al., 2012).
In order to estimate the total carbon accumulated in the at-
mosphere since 1750 or 1870, we use an atmospheric CO2
concentration of 277 ±3 or 288±3ppm, respectively, based
on a cubic spline fit to ice core data (Joos and Spahni, 2008).
The uncertainty of ±3ppm (converted to ±1σ) is taken di-
rectly from the IPCC’s assessment (Ciais et al., 2013). Typi-
cal uncertainties in the atmospheric growth rate from ice core
data are ±1–1.5GtC per decade as evaluated from the Law
Dome data (Etheridge et al., 1996) for individual 20-year in-
tervals over the period from 1870 to 1960 (Bruno and Joos,
2.4 Ocean CO2sink
Estimates of the global ocean CO2sink are based on a com-
bination of a mean CO2sink estimate for the 1990s from
observations, and a trend and variability in the ocean CO2
sink for 1959–2014 from eight global ocean biogeochemistry
models. We use two observation-based estimates of SOCEAN
available for recent decades to provide a qualitative assess-
ment of confidence in the reported results. Earth Syst. Sci. Data, 7, 349–396, 2015
366 C. Le Quéré et al.: Global Carbon Budget 2015
Table 8. Decadal mean in the five components of the anthropogenic CO2budget for the periods 1960–1969, 1970–1979, 1980–1989, 1990–
1999, 2000–2009, the last decade, and the last year available. All values are in GtCyr1. All uncertainties are reported as ±1σ. A data set
containing data for each year during 1959–2014 is available at Please follow the terms of use
and cite the original data sources as specified on the data set.
Mean (GtC yr1)
1960–1969 1970–1979 1980–1989 1990–1999 2000–2009 2005–2014 2014
Fossil fuels and industry (EFF) 3.1 ±0.2 4.7 ±0.2 5.5±0.3 6.4 ±0.3 7.8 ±0.4 9.0 ±0.5 9.8 ±0.5
Land-use-change emissions
(ELUC)1.5 ±0.5 1.3±0.5 1.4 ±0.5 1.6 ±0.5 1.0±0.5 0.9 ±0.5 1.1 ±0.5
Atmospheric growth rate
(GATM)1.7 ±0.1 2.8±0.1 3.4 ±0.1 3.1 ±0.1 4.0±0.1 4.4 ±0.1 3.9 ±0.2
Ocean sink (SOCEAN)1.1 ±0.5 1.5±0.5 2.0 ±0.5 2.2 ±0.5 2.3±0.5 2.6 ±0.5 2.9 ±0.5
Residual terrestrial sink
(SLAND)1.7 ±0.7 1.7±0.8 1.6 ±0.8 2.6 ±0.8 2.4±0.8 3.0 ±0.8 4.1 ±0.9
The uncertainty in SOCEAN for the 1990s is directly based on observations, while that for other decades combines the uncertainty from observations with the model spread
(Sect. 2.4.3).
2.4.1 Observation-based estimates
A mean ocean CO2sink of 2.2±0.4 GtCyr1for the 1990s
was estimated by the IPCC (Denman et al., 2007) based on
indirect observations and their spread: ocean/land CO2sink
partitioning from observed atmospheric O2/N2concentra-
tion trends (Manning and Keeling, 2006), an oceanic in-
version method constrained by ocean biogeochemistry data
(Mikaloff Fletcher et al., 2006), and a method based on pen-
etration timescale for CFCs (McNeil et al., 2003). This is
comparable with the sink of 2.0±0.5 GtCyr1estimated by
Khatiwala et al. (2013) for the 1990s, and with the sink of
1.9 to 2.5 GtC yr1estimated from a range of methods for the
period 1990–2009 (Wanninkhof et al., 2013), with uncertain-
ties ranging from ±0.3 to ±0.7GtC yr1. The most direct
way for estimating the observation-based ocean sink is from
the product of (sea–air pCO2difference)×(gas transfer co-
efficient). Estimates based on sea–air pCO2are fully con-
sistent with indirect observations (Wanninkhof et al., 2013),
but their uncertainty is larger mainly due to difficulty in cap-
turing complex turbulent processes in the gas transfer coeffi-
cient (Sweeney et al., 2007) and because of uncertainties in
the pre-industrial river-induced outgassing of CO2(Jacobson
et al., 2007).
Both observation-based estimates compute the ocean CO2
sink and its variability using interpolated measurements of
surface ocean fugacity of CO2(pCO2corrected for the non-
ideal behaviour of the gas; Pfeil et al., 2013). The measure-
ments were from the Surface Ocean CO2Atlas (SOCAT v3;
Bakker et al., 2014, 2015), which contains 14.5 million data
to the end of 2014. This was extended with 1.4 million ad-
ditional measurements over years 2013–2014 (see data attri-
bution Table A1 in Appendix A), submitted to SOCAT but
not yet fully quality controlled following standard SOCAT
procedures. Revisions and corrections to previously reported
measurements were also included where they were available.
All new data were subjected to an automated quality con-
trol system to detect and remove the most obvious errors
(e.g. incorrect reporting of metadata such as position, wrong
units, clearly unrealistic data). The combined SOCAT v3 and
preliminary new 2013–2014 measurements were mapped us-
ing a data-driven diagnostic method (Rödenbeck et al., 2013)
and a combined self-organising map and feed-forward neural
network (Landschützer et al., 2014). The global observation-
based estimates were adjusted to remove a background (not
part of the anthropogenic ocean flux) ocean source of CO2
to the atmosphere of 0.45GtC yr1from river input to the
ocean (Jacobson et al., 2007) in order to make them compa-
rable to SOCEAN, which only represents the annual uptake of
anthropogenic CO2by the ocean. Several other data-based
products are available, but they partly show large discrepan-
cies with observed variability that need to be resolved. Here
we used the two data products that had the best fit to obser-
vations, distinctly better than most in their representation of
tropical and global variability (Rödenbeck et al., 2015).
We use the data-based product of Khatiwala et al. (2009)
updated by Khatiwala et al. (2013) to estimate the anthro-
pogenic carbon accumulated in the ocean during 1765–
1958 (60.2GtC) and 1870–1958 (47.5 GtC), and assume an
oceanic uptake of 0.4GtC for 1750–1765 (for which time no
data are available) based on the mean uptake during 1765–
1770. The estimate of Khatiwala et al. (2009) is based on
regional disequilibrium between surface pCO2and atmo-
spheric CO2, and a Green’s function utilising transient ocean
tracers like CFCs and 14C to ascribe changes through time.
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 367
It does not include changes associated with changes in ocean
circulation, temperature, and climate, but these are thought
to be small over the time period considered here (Ciais et
al., 2013). The uncertainty in cumulative uptake of ±20GtC
(converted to ±1σ) 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).
2.4.2 Global ocean biogeochemistry models
The trend in the ocean CO2sink for 1959–2014 is computed
using a combination of eight global ocean biogeochemistry
models (Table 6). The models represent the physical, chemi-
cal, and biological processes that influence the surface ocean
concentration of CO2and thus the air–sea CO2flux. The
models are forced by meteorological reanalysis and atmo-
spheric CO2concentration data available for the entire time
period. Models do not include the effects of anthropogenic
changes in nutrient supply. They compute the air–sea flux of
CO2over grid boxes of 1 to 4in latitude and longitude. The
ocean CO2sink for each model is normalised to the obser-
vations by dividing the annual model values by their average
over 1990–1999 and multiplying this with the observation-
based estimate of 2.2GtC yr1(obtained from Manning and
Keeling, 2006; McNeil et al., 2003; Mikaloff Fletcher et al.,
2006). The ocean CO2sink for each year (t) in GtCyr1is
where nis the number of models. This normalisation en-
sures that the ocean CO2sink for the global carbon budget is
based on observations, whereas the trends and annual values
in CO2sinks are from model estimates. The normalisation
based on a ratio assumes that if models over- or underesti-
mate the sink in the 1990s, it is primarily due to the process
of diffusion, which depends on the gradient of CO2. Thus a
ratio is more appropriate than an offset as it takes into ac-
count the time dependence of CO2gradients in the ocean.
The mean uncorrected ocean CO2sink from the eight mod-
els for 1990–1999 ranges between 1.6 and 2.4 GtCyr1, with
a multi-model mean of 1.9GtC yr1.
2.4.3 Uncertainty assessment for SOCEAN
The uncertainty around the mean ocean sink of anthro-
pogenic CO2was quantified by Denman et al. (2007) for the
1990s (see Sect. 2.4.1). To quantify the uncertainty around
annual values, we examine the standard deviation of the nor-
malised model ensemble. We use further information from
the two data-based products to assess the confidence level.
The average standard deviation of the normalised ocean
model ensemble is 0.13GtC yr1during 1980–2010 (with a
maximum of 0.27), but it increases as the model ensemble
goes back in time, with a standard deviation of 0.22 GtCyr1
across models in the 1960s. We estimate that the uncer-
tainty in the annual ocean CO2sink is about ±0.5GtC yr1
from the fractional uncertainty of the data uncertainty of
±0.4GtC yr1and standard deviation across models of up to
±0.27GtC yr1, reflecting both the uncertainty in the mean
sink from observations during the 1990s (Denman et al.,
2007; Sect. 2.4.1) and in the interannual variability as as-
sessed by models.
We examine the consistency between the variability
in the model-based and the data-based products to as-
sess confidence in SOCEAN. The interannual variability in
the ocean fluxes (quantified as the standard deviation) of
the two data-based estimates for 1986–2014 (where they
overlap) is ±0.38GtC yr1(Rödenbeck et al., 2014) and
±0.40GtC yr1(Landschützer et al., 2015), compared to
±0.27GtC yr1for the normalised model ensemble. The
standard deviation includes a component of trend and
decadal variability in addition to interannual variability, and
their relative influence differs across estimates. The phase is
generally consistent between estimates, with a higher ocean
CO2sink during El Niño events. The annual data-based esti-
mates correlate with the ocean CO2sink estimated here with
a correlation of r=0.51 (0.34 to 0.58 for individual mod-
els), and r=0.71 (0.54 to 0.72) for the data-based estimates
of Rödenbeck et al. (2014) and Landschützer et al. (2015),
respectively (simple linear regression), but their mutual cor-
relation is only 0.55. The use of annual data for the correla-
tion may reduce the strength of the relationship because the
dominant source of variability associated with El Niño events
is less than 1 year. We assess a medium confidence level to
the annual ocean CO2sink and its uncertainty because they
are based on multiple lines of evidence, and the results are
consistent in that the interannual variability in the model and
data-based estimates are all generally small compared to the
variability in atmospheric CO2growth rate. Nevertheless the
various results do not show agreement in interannual vari-
ability on the global scale or for the relative roles of the an-
nual and decadal variability compared to the trend.
2.5 Terrestrial CO2sink
The difference between, on the one hand, fossil fuel (EFF)
and land-use-change emissions (ELUC) and, on the other
hand, the growth rate in atmospheric CO2concentration
(GATM) and the ocean CO2sink (SOCEAN) is attributable
to the net sink of CO2in terrestrial vegetation and soils
(SLAND), within the given uncertainties (Eq. 1). Thus, this
sink can be estimated as the residual of the other terms in
the mass balance budget, as well as directly calculated us-
ing DGVMs. The residual land sink (SLAND) is thought to
be in part because of the fertilising effect of rising atmo-
spheric CO2on plant growth, N deposition, and effects of cli-
mate change such as the lengthening of the growing season in Earth Syst. Sci. Data, 7, 349–396, 2015
368 C. Le Quéré et al.: Global Carbon Budget 2015
northern temperate and boreal areas. SLAND does not include
gross land sinks directly resulting from land-use change (e.g.
regrowth of vegetation) as these are estimated as part of the
net land-use flux (ELUC). System boundaries make it difficult
to attribute exactly CO2fluxes on land between SLAND and
ELUC (Erb et al., 2013), and by design most of the uncertain-
ties in our method are allocated to SLAND for those processes
that are poorly known or represented in models.
2.5.1 Residual of the budget
For 1959–2014, the terrestrial carbon sink was estimated
from the residual of the other budget terms by rearranging
Eq. (1):
The uncertainty in SLAND is estimated annually from the root
sum of squares of the uncertainty in the right-hand terms
assuming the errors are not correlated. The uncertainty av-
erages to ±0.8GtC yr1over 1959–2014 (Table 7). SLAND
estimated from the residual of the budget includes, by defi-
nition, all the missing processes and potential biases in the
other components of Eq. (8).
2.5.2 DGVMs
A comparison of the residual calculation of SLAND in Eq. (8)
with estimates from DGVMs as used to estimate ELUC in
Sect. 2.2.3, but here excluding the effects of changes in land
cover (using a constant pre-industrial land-cover distribu-
tion), provides an independent estimate of the consistency of
SLAND with our understanding of the functioning of the ter-
restrial vegetation in response to CO2and climate variability
(Table 7). As described in Sect. 2.2.3, the DGVM runs that
exclude the effects of changes in land cover include all cli-
mate variability and CO2effects over land, but they do not
include reductions in CO2sink capacity associated with hu-
man activity directly affecting changes in vegetation cover
and management, which by design is allocated to ELUC. This
effect has been estimated to have led to a reduction in the
terrestrial sink by 0.5GtC yr1since 1750 (Gitz and Ciais,
2003). The models in this configuration estimate the mean
and variability in SLAND based on atmospheric CO2and cli-
mate, and thus both terms can be compared to the budget
residual. We apply three criteria for minimum model realism
by including only those models with (1) steady state after
spin-up, (2) net land fluxes (SLAND ELUC) that are a car-
bon sink over the 1990s as constrained by global atmospheric
and oceanic observations (McNeil et al., 2003; Manning and
Keeling, 2006; Mikaloff Fletcher et al., 2006), and (3) global
ELUC that is a carbon source over the 1990s. Ten models met
these three criteria.
The annual standard deviation of the CO2sink across the
10 DGVMs averages to ±0.7GtCyr1for the period 1959
to 2014. The model mean, over different decades, corre-
lates with the budget residual with r=0.71 (0.52 to r=0.71
for individual models). The standard deviation is similar to
that of the five model ensembles presented in Le Quéré
et al. (2009), but the correlation is improved compared to
r=0.54 obtained in the earlier study. The DGVM results
suggest that the sum of our knowledge on annual CO2emis-
sions and their partitioning is plausible (see Discussion), and
provide insight into the underlying processes and regional
breakdown. However as the standard deviation across the
DGVMs (e.g. ±0.9GtC yr1for year 2014) is of the same
magnitude as the combined uncertainty due to the other com-
ponents (EFF,ELUC,GATM,SOCEAN; Table 7), the DGVMs
do not provide further reduction of uncertainty on the annual
terrestrial CO2sink compared to the residual of the budget
(Eq. 8). Yet, DGVM results are largely independent of the
residual of the budget, and it is worth noting that the resid-
ual method and ensemble mean DGVM results are consistent
within their respective uncertainties. We attach a medium
confidence level to the annual land CO2sink and its uncer-
tainty because the estimates from the residual budget and av-
eraged DGVMs match well within their respective uncertain-
ties, and the estimates based on the residual budget are pri-
marily dependent on EFF and GATM, both of which are well
2.6 The atmospheric perspective
The worldwide network of atmospheric measurements can
be used with atmospheric inversion methods to constrain the
location of the combined total surface CO2fluxes from all
sources, including fossil and land-use-change emissions and
land and ocean CO2fluxes. The inversions assume EFF to
be well known, and they solve for the spatial and temporal
distribution of land and ocean fluxes from the residual gradi-
ents of CO2between stations that are not explained by emis-
sions. Inversions used atmospheric CO2data to the end of
2014 (including preliminary values in some cases), as well
as three atmospheric CO2inversions (Table 6) to infer the to-
tal CO2flux over land regions and the distribution of the total
land and ocean CO2fluxes for the mid–high-latitude North-
ern Hemisphere (30–90N), tropics (30S–30N) and mid–
high-latitude region of the Southern Hemisphere (30–90S).
We focus here on the largest and most consistent sources of
information and use these estimates to comment on the con-
sistency across various data streams and process-based esti-
Atmospheric inversions
The three inversion systems used in this release are the Car-
bonTracker (Peters et al., 2010), the Jena CarboScope (Rö-
denbeck, 2005), and MACC (Chevallier et al., 2005). They
are based on the same Bayesian inversion principles that in-
terpret the same, for the most part, observed time series (or
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 369
subsets thereof) but use different methodologies that repre-
sent some of the many approaches used in the field. This
mainly concerns the time resolution of the estimates (i.e.
weekly or monthly), spatial breakdown (i.e. grid size), as-
sumed correlation structures, and mathematical approach.
The details of these approaches are documented extensively
in the references provided. Each system uses a different
transport model, which was demonstrated to be a driving fac-
tor behind differences in atmospheric-based flux estimates,
and specifically their global distribution (Stephens et al.,
The three inversions use atmospheric CO2observations
from various flask and in situ networks. They prescribe spa-
tial and global EFF that can vary from that presented here.
The CarbonTracker and MACC inversions prescribed the
same global EFF as in Sect. 2.1.1, during 2010–2014 for
CarbonTracker and during 1979–2014 in MACC. The Jena-
s81_v3.7 inversion uses EFF from EDGAR4.2. Different spa-
tial and temporal distributions of EFF were prescribed in each
Given their prescribed map of EFF, each inversion esti-
mates natural fluxes from a similar set of surface CO2mea-
surement stations, and CarbonTracker additionally uses two
sites of aircraft CO2vertical profiles over the Amazon and
Siberia, regions where surface observations are sparse. The
atmospheric transport models of each inversion are TM5
for CarbonTracker, TM3 for Jena-s81_v3.7, and LMDZ for
MACC. These three models are based on the same ECMWF
wind fields. The three inversions use different prior natural
fluxes, which partly influences their optimised fluxes. MACC
assumes that the prior land flux is zero on the annual mean
in each grid cell of the transport model, so that any sink or
source on land is entirely reflecting the information brought
by atmospheric measurements. CarbonTracker simulates a
small prior sink on land from the SIBCASA model that re-
sults from regrowth following fire disturbances of an other-
wise net zero biosphere. Jena s81_v3.7 assumes a prior on the
long-term mean land sink pattern, using the time-averaged
net ecosystem exchange of the LPJ model. Inversion results
for the sum of natural ocean and land fluxes (Fig. 8) are bet-
ter constrained in the Northern Hemisphere (NH) than in the
tropics, because of the higher measurement stations density
in the NH.
Finally, results from atmospheric inversions include the
natural CO2fluxes from rivers (which need to be taken into
account to allow comparison to other sources) and chemi-
cal oxidation of reactive carbon-containing gases (which are
neglected here). These inverse estimates are not truly inde-
pendent of the other estimates presented here as the atmo-
spheric observations include a set of observations used to es-
timate the global atmospheric growth rate (Sect. 2.3). How-
ever they provide new information on the regional distribu-
tion of fluxes.
We focus the analysis on two known strengths of
the inverse approach: the derivation of the year-to-year
changes in total land fluxes (SLAND ELUC) consistent
with the whole network of atmospheric observations, and
the spatial breakdown of combined land and ocean fluxes
(SOCEAN +SLAND ELUC) across large regions of the globe.
The total land flux correlates well with that estimated from
the budget residual (Eq. 1) with correlations for the annual
time series ranging from r=0.89 to 0.93, and with the
DGVM multi-model mean with correlations for the annual
time series ranging from r=0.71 to 0.80 (r=0.49 to 0.81
for individual DGVMs and inversions). The spatial break-
down is discussed in Sect. 3.1.3.
2.7 Processes not included in the global carbon budget
2.7.1 Contribution of anthropogenic CO and CH4to the
global carbon budget
Anthropogenic emissions of CO and CH4to the atmosphere
are eventually oxidised to CO2and thus are part of the global
carbon budget. These contributions are omitted in Eq. (1), but
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 CH4that matter for the global carbon budget are
the fugitive emissions of coal, oil, and gas upstream sectors
(see below). These emissions of CO and CH4contribute a net
addition of fossil carbon to the atmosphere.
In our estimate of EFF we assumed (Sect. 2.1.1) that all
the fuel burned is emitted as CO2; thus CO anthropogenic
emissions and their atmospheric oxidation into CO2within a
few months are already counted implicitly in EFF and should
not be counted twice (same for ELUC and anthropogenic CO
emissions by deforestation fires). Anthropogenic emissions
of fossil CH4are not included in EFF, because these fugitive
emissions are not included in the fuel inventories. Yet they
contribute to the annual CO2growth rate after CH4gets oxi-
dised into CO2. Anthropogenic emissions of fossil CH4rep-
resent 15% of total CH4emissions (Kirschke et al., 2013)
that is 0.061GtC yr1for the past decade. Assuming steady
state, these emissions are all converted to CO2by OH oxida-
tion and thus explain 0.06 GtCyr1of the global CO2growth
rate in the past decade.
Other anthropogenic changes in the sources of CO and
CH4from wildfires, biomass, wetlands, ruminants, or per-
mafrost changes are similarly assumed to have a small effect
on the CO2growth rate.
2.7.2 Anthropogenic carbon fluxes in the land to ocean
aquatic continuum
The approach used to determine the global carbon budget
considers only anthropogenic CO2emissions and their parti-
tioning among the atmosphere, ocean, and land. In this anal-
ysis, the land and ocean reservoirs that take up anthropogenic Earth Syst. Sci. Data, 7, 349–396, 2015
370 C. Le Quéré et al.: Global Carbon Budget 2015
CO2from the atmosphere are conceived as independent car-
bon storage repositories. This approach thus omits that car-
bon is continuously displaced along the land–ocean aquatic
continuum (LOAC) comprising freshwaters, estuaries, and
coastal areas (Bauer et al., 2013; Regnier et al., 2013). A sig-
nificant fraction of this lateral carbon flux is entirely “natu-
ral” and is thus a steady-state component of the pre-industrial
carbon cycle. The remaining fraction is anthropogenic car-
bon entrained into the lateral transport loop of 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
summarised in three points of relevance to the anthropogenic
CO2budget. First, the anthropogenic carbon input from land
to hydrosphere, FLH, estimated at 1 ±0.5GtC yr1is signifi-
cant compared to the other terms of Eq. (1) (Table 8), and im-
plies that only a portion of the anthropogenic CO2taken up
by land ecosystems remains sequestered in soil and biomass
pools. Second, some of the exported anthropogenic carbon
is stored in the LOAC (1CLOAC, 0.55±0.3GtC yr1) and
some is released back to the atmosphere as CO2(ELOAC,
0.35±0.2 GtCyr1), the magnitude of these fluxes result-
ing from the combined effects of freshwaters, estuaries, and
coastal seas. Third, a small fraction of anthropogenic car-
bon displaced by the LOAC is transferred to the open ocean,
where it accumulates (FHO, 0.1 ±>0.05GtCyr1). The an-
thropogenic perturbation of the carbon fluxes from land to
ocean does not contradict the method used in Sect. 2.5 to
define the ocean sink and residual terrestrial sink. However,
it does point to the need to account for the fate of anthro-
pogenic carbon once it is removed from the atmosphere by
land ecosystems (summarised in Fig. 2). In theory, direct
estimates of changes of the ocean inorganic carbon inven-
tory over time would see the land flux of anthropogenic car-
bon and would thus have a bias relative to air–sea flux esti-
mates and tracer-based reconstructions. However, currently
the value is small enough to be not noticeable relative to the
errors in the individual techniques.
The residual terrestrial sink in a budget that accounts for
the LOAC will be larger than SLAND, as the flux is par-
tially offset by the net source of CO2to the atmosphere,
i.e. ELOAC, of 0.35 ±0.3GtCyr1from rivers, estuaries, and
coastal seas:
The residual terrestrial sink (SLAND) is 3.0 ±0.8GtC yr1
for 2005–2014 as calculated according to Eq. (8; Table 7),
while SLAND+LOAC is 3.3±0.9GtC yr1over the same time
period. A fraction of anthropogenic CO2taken up by land
ecosystems is exported to the LOAC (FLH). With the LOAC
included, we now have
Time (yr)
CO2 flux (GtC yr−1)
Fossil fuels and industry
Land−use change
1900 1950 2000
Figure 3. Combined components of the global carbon budget il-
lustrated in Fig. 2 as a function of time, for emissions from fossil
fuels and industry (EFF; grey) and emissions from land-use change
(ELUC; brown), as well as their partitioning among the atmosphere
(GATM; light blue), land (SLAND; green), and oceans (SOCEAN;
dark blue). All time series are in GtCyr1.GATM and SOCEAN
(and by construction also SLAND) prior to 1959 are based on dif-
ferent methods. The primary data sources for fossil fuels and in-
dustry are from Boden et al. (2013), with uncertainty of about
±5% (±1σ); land-use-change emissions are from Houghton et al.
(2012) with uncertainties of about ±30 %; atmospheric growth rate
prior to 1959 is from Joos and Spahni (2008) with uncertainties
of about ±1–1.5GtC decade1or ±0.1–0.15 GtCyr1(Bruno and
Joos, 1997), and from Dlugokencky and Tans (2015) from 1959
with uncertainties of about ±0.2GtC yr1; the ocean sink prior
to 1959 is from Khatiwala et al. (2013) with uncertainty of about
±30%, and from this study from 1959 with uncertainties of about
±0.5GtC yr1; and the residual land sink is obtained by difference
(Eq. 8), resulting in uncertainties of about ±50 % prior to 1959 and
±0.8GtC yr1after that. See the text for more details of each com-
ponent and their uncertainties.
where 1CTE is the change in annual terrestrial ecosystems
carbon storage, including land vegetation, litter, and soil.
1CTE is 1.4 GtCyr1for the period 2005–2014. It is notably
smaller than what would be calculated in a traditional bud-
get that ignores the LOAC. In this case, the change in car-
bon storage is estimated as 2.1Gt Cyr1from the difference
between SLAND (3.0Gt Cyr1) and ELUC (0.9 Gt C yr1; Ta-
ble 8). All estimates of LOAC are given with low confidence,
because they originate from a single source. The carbon bud-
get presented here implicitly incorporates the fluxes from the
LOAC into SLAND. We do not attempt to separate these fluxes
because the uncertainties in either estimate are too large,
and there is insufficient information available to estimate the
LOAC fluxes on an annual basis.
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 371
1960 1970 1980 1990 2000 2010
12 Fossil fuels and industrya
1960 1970 1980 1990 2000 2010
Time (yr)
CO2 emissions (GtC yr−1)
b Land−use change
1960 1970 1980 1990 2000 2010
10 Atmospheric growthc
1960 1970 1980 1990 2000 2010
10 Ocean sinkd
CO2 partitioning (GtC yr−1)
1960 1970 1980 1990 2000 2010
10 Land sinke
Time (yr)
Figure 4. Components of the global carbon budget and their uncer-
tainties as a function of time, presented individually for (a) emis-
sions from fossil fuels and industry (EFF), (b) emissions from land-
use change (ELUC), (c) atmospheric CO2growth rate (GATM),
(d) the ocean CO2sink (SOCEAN; positive indicates a flux from the
atmosphere to the ocean), and (e) the land CO2sink (SLAND; pos-
itive indicates a flux from the atmosphere to the land). All time se-
ries are in GtCyr1with the uncertainty bounds representing ±1σ
in shaded colour. Data sources are as in Fig. 3. The black dots in
panels (a),(b), and (e) show preliminary values for 2012, 2013, and
2014 that originate from a different data set to the remainder of the
data, as explained in the text.
3 Results
3.1 Global carbon budget averaged over decades and
its variability
The global carbon budget averaged over the last decade
(2005–2014) is shown in Fig. 2. For this time period, 91%
of the total emissions (EFF +ELUC) were caused by fossil
fuels and industry, and 9% by land-use change. The total
emissions were partitioned among the atmosphere (44%),
ocean (26%), and land (30 %). All components except land-
use-change emissions have grown since 1959 (Figs. 3 and
4), with important interannual variability in the atmospheric
growth rate and in the land CO2sink (Fig. 4), as well as some
decadal variability in all terms (Table 8).
3.1.1 CO2emissions
Global CO2emissions from fossil fuels and industry have in-
creased every decade from an average of 3.1±0.2 GtC yr1
in the 1960s to an average of 9.0±0.5GtC yr1during
2005–2014 (Table 8 and Fig. 5). The growth rate in these
emissions decreased between the 1960s and the 1990s, from
4.5% yr1in the 1960s (1960–1969), 2.9% yr1in the
1970s (1970–1979), 1.9% yr1in the 1980s (1980–1989),
and finally to 1.0% yr1in the 1990s (1990–1999), be-
fore it began increasing again in the 2000s at an average
growth rate of 3.2% yr1, decreasing to 2.2 % yr1for the
last decade (2005–2014). In contrast, CO2emissions from
land-use change have remained constant, in our analysis
at around 1.5±0.5 GtCyr1between 1960 and 1999 and
1.0±0.5 GtCyr1during 2000–2014. The decrease in emis-
sions from land-use change between the 1990s and 2000s is
highly uncertain. It is not found in the current ensemble of
the DGVMs (Fig. 6), which are otherwise consistent with
the bookkeeping method within their respective uncertainty
(Table 7). It is also not found in the study of tropical defor-
estation of Achard et al. (2014), where the fluxes in the 1990s
were similar to those of the 2000s and outside our uncertainty
range. A new study based on FAO data to 2015 (Federici et
al., 2015) suggests that ELUC decreased during 2011–2015
compared to 2001–2010.
3.1.2 Partitioning
The growth rate in atmospheric CO2increased from
1.7±0.1 GtCyr1in the 1960s to 4.4 ±0.1GtC yr1during
2005–2014 with important decadal variations (Table 8). Both
ocean and land CO2sinks increased roughly in line with the
atmospheric increase, but with significant decadal variabil-
ity on land (Table 8). The ocean CO2sink increased from
1.1±0.5 GtCyr1in the 1960s to 2.6 ±0.5 GtC yr1dur-
ing 2005–2014, with interannual variations of the order of a
few tenths of GtC yr1generally showing an increased ocean
sink during El Niño (i.e. 1982–1983, 1991–1993, 1997–
1998) events (Fig. 7; Rödenbeck et al., 2014). Although there
is some coherence between the ocean models and data prod-
ucts and among data products, their mutual correlation is
weak and highlights disagreement on the exact amplitude
of the interannual variability, as well as on the relative im-
portance of the trend versus the variability (Sect. 2.4.3 and
Fig. 7). As shown in Fig. 7, the two data products and most
model estimates produce a mean CO2sink for the 1990s that
is below the mean assessed by the IPCC from indirect (but
arguably more reliable) observations (Denman et al., 2007;
Sect. 2.4.1). This discrepancy suggests we may need to re-
assess estimates of the mean ocean carbon sinks.
The land CO2sink increased from 1.7±0.7 GtCyr1in
the 1960s to 3.0±0.8 GtCyr1during 2005–2014, with im-
portant interannual variations of up to 2GtC yr1generally
showing a decreased land sink during El Niño events, over-
compensating for the increase in ocean sink and accounting
for the enhanced atmospheric growth rate during El Niño
events. The high uptake anomaly around year 1991 is thought
to be caused by the effect of the volcanic eruption of Mount Earth Syst. Sci. Data, 7, 349–396, 2015
372 C. Le Quéré et al.: Global Carbon Budget 2015
1960 1970 1980 1990 2000 2010
1960 1970 1980 1990 2000 2010
CO2 emissions (GtC yr−1)
1960 1970 1980 1990 2000 2010
Time (yr)
Annex B
Non−Annex B
Emissions transfers
1960 1970 1980 1990 2000 2010
CO2 emissions (GtC yr−1)
1960 1970 1980 1990 2000 2010
Time (yr)
Per capita emissions (tC person−1 yr−1)
Figure 5. CO2emissions from fossil fuels and industry for (a) the
globe, including an uncertainty of ±5% (grey shading), the emis-
sions extrapolated using BP energy statistics (black dots), and the
emissions projection for year 2015 based on GDP projection (red
dot); (b) global emissions by fuel type, including coal (salmon),
oil (olive), gas (turquoise), and cement (purple), and excluding gas
flaring, which is small (0.6% in 2013); (c) territorial (full line) and
consumption (dashed line) emissions for the countries listed in the
Annex B of the Kyoto Protocol (salmon lines; mostly advanced
economies with emissions limitations) versus non-Annex B coun-
tries (green lines) – also shown are the emissions transfers from
non-Annex B to Annex B countries (light-blue line); (d) territo-
rial CO2emissions for the top three country emitters (USA – olive;
China – salmon; India – purple) and for the European Union (EU28,
the 28 member states of the EU in 2012 – turquoise), and (e) per-
capita emissions for the top three country emitters and the EU (all
colours as in panel d) and the world (black). In panels (b) to (e),
the dots show the preliminary data that were extrapolated from BP
energy statistics for 2012, 2013, and 2014. All time series are in
GtC yr1except the per-capita emissions (panel e), which are in
tonnes of carbon per person per year (tC person1yr1). All territo-
rial emissions are primarily from Boden et al. (2013) except national
data for the USA and EU28 for 1990–2012, which are reported by
the countries to the UNFCCC as detailed in the text; consumption-
based emissions are updated from Peters et al. (2011a).
Pinatubo on climate and is not generally reproduced by the
DGVMs, but it is assigned to the land by the two inverse sys-
tems that include this period (Fig. 6). The larger land CO2
sink during 2005–2014 compared to the 1960s is reproduced
by all the DGVMs in response to combined atmospheric CO2
increase, climate, and variability (3.0±0.5 GtC yr1for the
period 2005–2014 and average change of 1.9GtCyr1rel-
ative to the 1960s), consistent with the budget residual and
reflecting a common knowledge of the processes (Table 7).
The DGVM ensemble mean of 3.0±0.5 GtCyr1also re-
produces the observed mean for the period 2005–2014 cal-
culated from the budget residual (Table 7).
The total CO2fluxes on land (SLAND ELUC) constrained
by the atmospheric inversions show in general very good
agreement with the global budget estimate, as expected given
the strong constraints of GATM and the small relative un-
certainty typically assumed on SOCEAN and EFF by inver-
sions. The total land flux is of similar magnitude for the
decadal average, with estimates for 2005–2014 from the
three inversions of 2.0, 2.0, and 3.3GtCyr1compared to
2.1±0.7 GtCyr1for the total flux computed with the car-
bon budget from other terms in Eq. (1) (Table 7). The
three inversions’ total land sink would be 1.6, 1.6, and
2.9GtC yr1when including a mean river flux adjustment of
0.45GtC yr1, though the exact adjustment would be smaller
when taking into account the anthropogenic contribution to
river fluxes (Sect. 2.7.2). The interannual variability in the
inversions also matched the residual-based SLAND closely
(Fig. 6). The total land flux from the DGVM multi-model
mean also compares well with the estimate from the carbon
budget and atmospheric inversions, with a decadal mean of
1.6±0.4 GtCyr1(Table 7; 2005–2014), although individ-
ual models differ by several GtC for some years (Fig. 6).
3.1.3 Distribution
Figure 8 shows the partitioning of the total surface
fluxes excluding emissions from fossil fuels and industry
(SOCEAN +SLAND ELUC) according to the process models
in the ocean and on land, and to the three atmospheric in-
versions. The total surface fluxes provide information on the
regional distribution of those fluxes by latitude band (Fig. 8).
The global mean CO2fluxes from process models for 2005–
2014 is 4.2±0.5 GtCyr1. This is comparable to the fluxes
of 4.7 ±0.5GtCyr1inferred from the remainder of the car-
bon budget (EFF GATM in Eq. 1; Table 8) within their re-
spective uncertainties. The total CO2fluxes from the three in-
versions range between 4.4 and 4.9 GtCyr1, consistent with
the carbon budget as expected from the constraints on the in-
In the south (south of 30S), the atmospheric inversions
and process models all suggest a CO2sink for 2005–2014 of
between 1.2 and 1.5GtC yr1(Fig. 8), although the details
of the interannual variability are not fully consistent across
methods. The interannual variability in the south is low be-
cause of the dominance of ocean area with low variability
compared to land areas.
In the tropics (30S–30N), both the atmospheric inver-
sions and process models suggest the carbon balance in this
region is close to neutral over the past decade, with fluxes for
2005–2014 ranging between 0.6 and +0.6GtC yr1. The
three inversions consistently allocate more year-to-year vari-
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 373
1960 1970 1980 1990 2000 2010
CO2 (GtC yr−1)
Land−use change
1960 1970 1980 1990 2000 2010
CO2 (GtC yr−1)
Land sink
1960 1970 1980 1990 2000 2010
Time (yr)
CO2 (GtC yr−1)
Total land
Figure 6. (a) Comparison of the atmosphere–land CO2flux show-
ing budget values of ELUC (black). CO2emissions from land-use
change showing individual DGVM results (green) and the multi-
model mean (olive), as well as fire-based results (orange); land-use-
change data prior to 1997 (dashed black) highlight the start of satel-
lite data from that year. (b) Land CO2sink (SLAND; black) showing
individual DGVM results (green) and multi-model mean (olive).
(c) Total land CO2fluxes (ba) from DGVM results (green) and
the multi-model mean (olive); atmospheric inversions of Cheval-
lier et al. (2005; MACC, v14.2) (purple), Rödenbeck et al. (2003;
Jena CarboScope, s81_v3.7) (violet), and Peters et al. (2010; Car-
bon Tracker, vCTE2015) (salmon) (see Table 6); and the carbon
balance from Eq. (1) (black). In (c) the inversions were adjusted for
the pre-industrial land sink of CO2from river input, by adding a
sink of 0.45 GtCyr1(Jacobson et al., 2007). This adjustment does
not take into account the anthropogenic contribution to river fluxes
(see Sect. 2.7.2).
ability in CO2fluxes to the tropics compared to the north
(north of 30N; Fig. 8). This variability is dominated by land
fluxes. Inversions are consistent with each other and with the
mean of process models.
1960 1970 1980 1990 2000 2010
Time (yr)
CO2 (GtC yr−1)
Figure 7. Comparison of the anthropogenic atmosphere–ocean
CO2flux shows the budget values of SOCEAN (black), individ-
ual ocean models before normalisation (blue), and the two ocean-
data-based products (Rödenbeck et al., 2014, in salmon and Land-
schützer et al., 2015, in purple; see Table 6). Both data-based prod-
ucts were adjusted for the pre-industrial ocean source of CO2from
river input to the ocean, which is not present in the models, by
adding a sink of 0.45GtC yr1(Jacobson et al., 2007) so as to
make them comparable to SOCEAN. This adjustment does not take
into account the anthropogenic contribution to river fluxes (see
Sect. 2.7.2).
In the north (north of 30N), the inversions and process
models are not in full agreement on the magnitude of the
CO2sink, with the ensemble mean of the process models
suggesting a total Northern Hemisphere sink for 2005–2014
of 2.3±0.4 GtCyr1, while the three inversions estimate a
sink of 2.5, 3.4, and 3.6GtC yr1. The mean difference can
only partly be explained by the influence of river fluxes,
as this flux in the Northern Hemisphere would be less than
0.45GtC yr1, particularly when the anthropogenic contri-
bution to river fluxes are accounted for. The CarbonTracker
inversion is within 1 standard deviation of the process mod-
els for the mean sink during their overlap period. MACC and
Jena-s81_v3.7 give a higher sink in the north than the pro-
cess models, and a correspondingly higher source in the trop-
ics. Differences between CarbonTracker and MACC, Jena-
s81_v3.7 may be related to differences in inter-hemispheric
mixing time of their transport models, and other inversion
settings. Differences also result from different fossil fuel
emissions assumed in the inversions, as the inversions pri-
marily constrain the sum of fossil fuel and land fluxes. Differ-
ences between the mean fluxes of MACC, Jena-s81_v3.7 and
the ensemble of process models cannot be simply explained.
They could reflect either a bias in these two inversions or
missing processes or biases in the process models, such as the
lack of adequate parameterisations for forest management in
the north and for forest degradation emissions in tropics for
the DGVMs. Earth Syst. Sci. Data, 7, 349–396, 2015
374 C. Le Quéré et al.: Global Carbon Budget 2015
Figure 8. Atmosphere-to-surface CO2flux
(SOCEAN +SLAND ELUC) by latitude bands for the (a) north
(north of 30N), (b) tropics (30S–30N), and (c) south (south
of 30S). Estimates from the combination of the multi-model
means for the land and oceans are shown (turquoise) with ±1σ
of the model ensemble (in grey). Results from the three atmo-
spheric inversions are shown in purple (Chevallier et al., 2005;
MACC, v14.2), violet (Rödenbeck et al., 2003; Jena CarboScope,
s81_v3.7), and salmon (Peters et al., 2010; Carbon Tracker,
vCTE2015); see Table 6.
The estimated contribution of the north from process mod-
els is sensitive both to the ensemble of process models used
and to the specifics of each inversion. Indeed, the process
model results from Le Quéré et al. (2015) included a slightly
different model ensemble (see Table 6) with no assessment
of minimum model realism. The model ensemble from Le
Quéré et al. (2015) showed a larger model spread and smaller
sink (2.0±0.8 GtCyr1for the latest decade), with also dif-
ferent trend in the 1960s. All three inversions show substan-
tial differences in variability and/or trend, and one inversion
substantial difference in the mean northern sink.
3.2 Global carbon budget for year 2014 and emissions
projection for 2015
3.2.1 CO2emissions
Global CO2emissions from fossil fuels and industry reached
9.8±0.5 GtC in 2014 (Fig. 5), distributed among coal
(42%), oil (33 %), gas (19%), cement (5.7 %), and gas flar-
ing (0.6%). The first four categories increased by 0.4, 0.8,
0.4, and 2.5% respectively over the previous year. Due to
lack of data, gas flaring in 2012–2014 is assumed the same
as 2011.
Emissions in 2014 were 0.6% higher than in 2013, an in-
crease well below the decadal average of 2.2%yr1(2005–
2014). Growth in 2014 is lower than our projection of
2.5% yr1made last year (Le Quéré et al., 2015) based on
an estimated GDP growth of 3.3% yr1and a decrease in
IFF of 0.7% yr1(Table 9), and is also outside the pro-
vided likely range of 1.3–3.5%. The latest estimate of GDP
growth for 2014 was 3.4%yr1(IMF, 2015) and hence IFF
improved by 2.8% yr1. This IFF is low compared to re-
cent years (Table 9), but not outside the range of variability
observed in recent decades, suggesting that our uncertainty
range may have been underestimated. Almost half of the
lower growth compared to expectations can be attributed to
a lower growth in emissions than anticipated in China (1.1%
compared to 4.5% in our projection; Friedlingstein et al.,
2014), which primarily reflects structural changes in China’s
economy (Green and Stern, 2015). Similar structural change
occurred following the global financial crisis of 2008–2009
that particularly affected Western economies, which also
made the emissions projections based on GDP temporarily
problematic and outside of the steady behaviour assumed by
the GDP/intensity approach (Peters et al., 2012b). For this
reason we provide an emissions projection with explicit pro-
jection for China based on energy and cement data during
January–August 2015 (see Sect. 2.1.4). Climatic variability
could also have contributed to the lower emissions in China
(from reported high rainfall possibly leading to higher hy-
dropower capacity utilisation), and in Europe and the USA,
where the combined emissions changes account for 37% of
the lower growth compared to expectations (Friedlingstein et
al., 2014).
Using separate projections for China, the USA, and the
rest of the world as described in Sect. 2.1.4, we project
that the growth in global CO2emissions from fossil fu-
els and cement production will be near or slightly below
zero in 2015, with a change of 0.6% (range of 1.6 %
to +0.5%) from 2014 levels. Our method is imprecise and
contains several assumptions that could influence the re-
sults beyond the given range, and as such is indicative only.
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 375
Table 9. Actual CO2emissions from fossil fuels and industry (EFF) compared to projections made the previous year based on world GDP
(IMF October 2015) and the fossil fuel intensity of GDP (IFF) based on subtracting the CO2and GDP growth rates. The “Actual” values are
the latest estimate available, and the “Projected” value for 2015 refers to those presented in this paper. A correction for leap years is applied
(Sect. 2.1.3).
Projected Actual Projected Actual Projected Actual
2009a2.8% 0.5 % 1.1 % 0.0% 1.7% 0.5 %
2010b>3% 5.1% 4.8% 5.4% >1.7 % 0.3 %
2011c3.1±1.5 % 3.4 % 4.0% 4.2 % 0.9±1.5 % 0.8 %
2012d2.6 % (1.9 to 3.5) 1.3 % 3.3 % 3.4% 0.7% 2.1 %
2013e2.1 % (1.1 to 3.1) 1.7 % 2.9 % 3.3% 0.8% 1.6 %
2014f2.5 % (1.3 to 3.5) 0.6 % 3.3 % 3.4% 0.7% 2.8 %
2015g0.6 % (1.6 to 0.5) 3.1% 3.7 % –
aLe Quéré et al. (2009). bFriedlingstein et al. (2010). cPeters et al. (2013). dLe Quéré et al. (2013). eLe Quéré et al. (2014).
fFriedlingstein et al. (2014) and Le Quéré et al. (2015). gThis study.
Within the given assumptions, global emissions decrease to
9.7±0.5 GtC (35.7±1.8GtCO2) in 2015, but are still 59%
above emissions in 1990.
For China, the expected change based largely on available
data during January to August (see Sect. 2.1.4) is for a de-
crease in emissions of 3.9% (range of 4.6 to 1.1 %) in
2015 compared to 2014. This uncertainty includes a range of
4.6 to 3.2% considering different adjustments for stocks
and no changes in the carbon content of coal, and is based on
estimated decreases in apparent coal consumption (5.3%)
and cement production (5.0%) and estimated growth in
apparent oil (+3.2%) and natural gas (+1.4 %) consump-
tion. However, there are additional uncertainties from the car-
bon content of coal. While China’s Energy Statistical Year-
books indicate declining carbon content over recent years,
preliminary data suggest an increase of up to 3% in 2014.
The Chinese government has introduced measures expressly
to address the declining quality of coal (which also leads to
lower carbon content) by closing lower-quality mines and
placing restrictions on the quality of imported coal. Allow-
ing for a similar increase in 2015 (0 to 3%), we expand
the uncertainty range of China’s emissions growth to 4.6
to 1.1%. Finally, China revised its emissions statistics up-
wards recently, which would affect the absolute value of
emissions for China (but not the trend). With a slightly higher
global contribution for China, our projection of global emis-
sions “growth” for 2015 would decline further from 0.6 to
0.8%, a small difference that falls within our uncertainty
For the USA, the EIA emissions projection for 2015 com-
bined with cement data from USGS gives a decrease of
1.5% (range of 5.5 to +0.3 %) compared to 2014. For
the rest of the world, the expected growth for 2015 of +1.2%
(range of 0.2 to +2.6%) is computed using the GDP
projection for the world excluding China and the USA of
2.3% made by the IMF (2015) and a decrease in IFF of
1.1% yr1, which is the average from 2005 to 2014. The
uncertainty range is based on the standard deviation of the
interannual variability in IFF during 2005–2014 of ±1.4%.
In 2014, the largest contributions to global CO2emis-
sions were from China (27%), the USA (15 %), the EU
(28 member states; 10%), and India (7 %), with the per-
centages compared to the global total including bunker fu-
els (3.0%). These four regions account for 59 % of global
emissions. Growth rates for these countries from 2013 to
2014 were 1.2 % (China), 0.8% (USA), 5.8 % (EU28), and
8.6% (India). The per-capita CO2emissions in 2014 were
1.3tC person1yr1for the globe, and were 4.8 (USA), 1.9
(China), 1.8 (EU28), and 0.5 tCperson1yr1(India) for the
four highest emitting countries (Fig. 5e).
Territorial emissions in Annex B countries have decreased
slightly by 0.1% yr1on average from 1990 to 2013, while
consumption emissions grew at 0.8% yr1to 2007, af-
ter which they have declined at 1.5%yr1(Fig. 5c). In
non-Annex B countries, territorial emissions have grown
at 4.4% yr1, while consumption emissions have grown at
4.1% yr1. In 1990, 66 % of global territorial emissions were
emitted in Annex B countries (34% in non-Annex B, and
2% in bunker fuels used for international shipping and avia-
tion), while in 2013 this had reduced to 38% (58 % in non-
Annex B and 3% in bunker fuels). In terms of consump-
tion emissions this split was 64% in 1990 and 39 % in 2013
(34 to 55% in non-Annex B). The difference between terri-
torial and consumption emissions (the net emission transfer
via international trade) from non-Annex B to Annex B coun-
tries has increased from near zero in 1990 to 0.3GtC yr1
around 2005 and remained relatively stable between 2006
and 2013 (Fig. 5). The increase in net emission transfers
of 0.30GtC yr1between 1990 and 2013 compares with the
emission reduction of 0.37GtC yr1in Annex B countries.
These results show the importance of net emission transfer
via international trade from non-Annex B to Annex B coun- Earth Syst. Sci. Data, 7, 349–396, 2015
376 C. Le Quéré et al.: Global Carbon Budget 2015
tries, and the stabilisation of emissions transfer when aver-
aged over Annex B countries during the past decade. In 2013,
the biggest emitters from a consumption perspective were
China (23% of the global total), USA (16 %), EU28 (12%),
and India (6%).
Based on fire activity, the global CO2emissions from land-
use change are estimated as 1.1 ±0.5GtC in 2014, similar to
the 2005–2014 average of 0.9 ±0.5GtCyr1and the DGVM
estimate for 2014 of 1.4±0.5 GtCyr1. However, the esti-
mated annual variability is not generally consistent between
methods, except that all methods estimate that variability in
ELUC is small relative to the variability from SLAND (Fig. 6a).
This could be partly due to the design of the DGVM exper-
iments, which use flux differences between simulations with
and without land-cover change, and thus may overestimate
variability, e.g. due to fires in forest regions where the con-
temporary forest cover is smaller than pre-industrial cover
used in the “without land-cover-change” runs. The extrapo-
lated land-cover input data for 2013–2014 in the DGVM may
also explain part of the discrepancy.
3.2.2 Partitioning
The atmospheric CO2growth rate was 3.9±0.2 GtC in 2014
(1.83±0.09 ppm; Fig. 4; Dlugokencky and Tans, 2015).
This is below the 2005–2014 average of 4.4±0.1GtC yr1,
though the interannual variability in atmospheric growth rate
is large.
The ocean CO2sink was 2.9±0.5 GtCyr1in 2014, an
increase of 0.1GtC yr1over 2013 according to ocean mod-
els. Seven of the eight ocean models produce an increase in
the ocean CO2sink in 2014 compared to 2013, with the last
model producing a very small reduction. However, of the
two data products available over that period, Rödenbeck et
al. (2014) produce a decrease of 0.1GtC yr1, while Land-
schützer et al. (2015) produce an increase of 0.2GtC yr1.
Thus there is no overall consistency in the annual change in
the ocean CO2sink, although there is an indication of in-
creasing convergence among products for the assessment of
multi-year changes, as suggested by the time-series corre-
lations reported in Sect. 2.4.3 (see also Landschützer et al.,
2015). A small increase in the ocean CO2sink in 2014 would
be consistent with the observed El Niño neutral conditions
and continued rising atmospheric CO2. All estimates suggest
an ocean CO2sink for 2014 that is larger than the 2005–2014
average of 2.6±0.5GtC yr1.
The terrestrial CO2sink calculated as the residual from
the carbon budget was 4.1±0.9 GtC in 2014, 1.1 GtC
higher than the 3.0 ±0.8GtCyr1averaged over 2005–2014
(Fig. 4). This is among the largest SLAND calculated since
1959, equal to year 2011 (Poulter et al., 2014) and 2011.
In contrast to 2011, when La Niña conditions prevailed,
and 1991, when the Pinatubo eruption occurred, the large
SLAND in 2014 occurred under neutral El Niño conditions.
The DGVM mean produced a sink of 3.6±0.9 GtC in 2014,
0.7GtC yr1over the 2005–2014 average (Table 7), smaller
but still consistent with observations (Poulter et al., 2014). In
the DGVM ensemble, 2014 is the fifth largest SLAND, after
1974, 2011, 2004, and 2000. There is no agreement between
models and inversions on the regional origin of the 2014 flux
anomaly (Fig. 8).
Cumulative emissions for 1870–2014 were 400±20GtC
for EFF, and 145 ±50GtC for ELUC based on the bookkeep-
ing method of Houghton et al. (2012) for 1870–1996 and a
combination with fire-based emissions for 1997–2014 as de-
scribed in Sect. 2.2 (Table 10). The cumulative emissions are
rounded to the nearest 5 GtC. The total cumulative emissions
for 1870–2014 are 545±55 GtC. These emissions were par-
titioned among the atmosphere (230±5 GtC based on atmo-
spheric measurements in ice cores of 288 ppm (Sect. “Global
atmospheric CO2growth rate estimates”; Joos and Spahni,
2008) and recent direct measurements of 397.2ppm; Dlugo-
kencky and Tans, 2014), ocean (155 ±20 GtC using Khati-
wala et al., 2013, prior to 1959 and Table 8 otherwise), and
land (160±60 GtC by the difference).
Cumulative emissions for the early period 1750–1869
were 3GtC for EFF, and about 45GtC for ELUC (rounded
to nearest 5), of which 10GtC were emitted in the period
1850–1870 (Houghton et al., 2012) and 30GtC were emit-
ted in the period 1750–1850 based on the average of four
publications (22 GtC by Pongratz et al., 2009; 15GtC by van
Minnen et al., 2009; 64GtC by Shevliakova et al., 2009; and
24GtC by Zaehle et al., 2011). The growth in atmospheric
CO2during that time was about 25GtC, and the ocean up-
take about 20GtC, implying a land uptake of 5 GtC. These
numbers have large relative uncertainties but balance within
the limits of our understanding.
Cumulative emissions for 1750–2014 based on the sum
of the two periods above (before rounding to the nearest
5GtC) were 405 ±20GtC for EFF, and 190±65 GtC for
ELUC, for a total of 590 ±70GtC, partitioned among the
atmosphere (255±5 GtC), ocean (170±20 GtC), and land
(165±70 GtC).
Cumulative emissions through to year 2015 can be es-
timated based on the 2015 projections of EFF (Sect. 3.2),
the largest contributor, and assuming a constant ELUC
of 0.9GtC. For 1870–2015, these are 555 ±55GtC
(2040±200 GtCO2) for total emissions, with about 75%
contribution from EFF (410±20 GtC) and about 25% con-
tribution from ELUC (145±50 GtC). Cumulative emissions
since year 1870 are higher than the emissions of 515 [445
to 585]GtC reported by the IPCC (Stocker et al., 2013) be-
cause they include an additional 43GtC from emissions in
2012–2015 (mostly from EFF). The uncertainty presented
here (±1σ) is smaller than the range of 90% used by IPCC,
but both estimates overlap within their uncertainty ranges.
Earth Syst. Sci. Data, 7, 349–396, 2015
C. Le Quéré et al.: Global Carbon Budget 2015 377
Table 10. Cumulative CO2emissions for the periods 1750–2014, 1870–2014, and 1870–2015 in gigatonnes of carbon (GtC). We also provide
the 1850–2005 time period used in a number of model evaluation publications. All uncertainties are reported as ±1σ. All values are rounded
to the nearest 5GtC as in Stocker et al. (2013), reflecting the limits of our capacity to constrain cumulative estimates. Thus some columns
will not exactly balance because of rounding errors.
Units of GtC 1750–2014 1850–2005 1870–2014 1870–2015
Fossil fuels and industry (EFF) 405 ±20 320 ±15 400±20 410 ±20
Land-use-change emissions (ELUC) 190 ±65 150±55 145 ±50 145 ±50
Total emissions 590 ±70 470±55 545 ±55 555 ±55
Atmospheric growth rate (GATM) 255±5 195 ±5 230 ±5
Ocean sink (SOCEAN) 170 ±20 160±20 155 ±20
Residual terrestrial sink (SLAND) 165 ±70 115±60 160 ±60
The extension to year 2015 uses the emissions projections for fossil fuels and industry for 2015 of 9.7 GtC (Sect. 3.2) and
assumes a constant ELUC flux (Sect. 2.2).
4 Discussion
Each year when the global carbon budget is published, each
component for all previous years is updated to take into ac-
count corrections that are the result of further scrutiny and
verification of the underlying data in the primary input data
sets. The updates have generally been relatively small and fo-
cused on the most recent years, except for land-use change,
where they are more significant but still generally within
the provided uncertainty range (Fig. 9). The difficulty in
accessing land-cover-change data to estimate ELUC is the
key problem to providing continuous records of emissions
in this sector. Current FAO estimates are based on statis-
tics reported at the country level and are not spatially ex-
plicit. Advances in satellite recovery of land-cover change
could help to keep track of land-use change through time
(Achard et al., 2014; Harris et al., 2012). Revisions in ELUC
for the 2008/2009 budget were the result of the release of
FAO (2010), which contained a major update to forest-cover
change for the period 2000–2005 and provided the data for
the following 5 years to 2010 (Fig. 9b). The differences this
year could be attributable to both the different data and the
different methods. Updates to values for any given year in
each component of the global carbon budget were highest at
0.82GtC yr1for the atmospheric growth rate (from a cor-
rection to year 1979), 0.24GtC yr1for fossil fuels and in-
dustry, and 0.52GtCyr1for the ocean CO2sink (from a
change from one to multiple models; Fig. 9). The update
for the residual land CO2sink was also large (Fig. 9e), with
a maximum value of 0.83GtC yr1, directly reflecting revi-
sions in other terms of the budget.
Our capacity to separate the carbon budget components
can be evaluated by comparing the land CO2sink estimated
through two approaches: (1) the budget residual (SLAND),
which includes errors and biases from all components, and
(2) the land CO2sink estimate by the DGVM ensemble,
which is based on our understanding of processes of how
the land responds to increasing CO2, climate, and variabil-
ity. Furthermore, the inverse model estimates which formally
merge observational constraints with process-based models
to close the global budget can provide constraints on the total
land flux. These estimates are generally close (Fig. 6), both
for the mean and for the interannual variability. The annual
estimates from the DGVM over 1959 to 2014 correlate with
the annual budget residual with r=0.71 (Sect. 2.5.2; Fig. 6).
The DGVMs produce a decadal mean and standard deviation
across models of 3.0±0.4 GtCyr1for the period 2005–
2014, fully consistent with the estimate of 3.0 ±0.8GtC yr1
produced with the budget residual (Table 7). New insights
into total surface fluxes arise from the comparison with the
atmospheric inversions, and their regional breakdown al-
ready provides a semi-independent way to validate the re-
sults. The comparison shows a first-order consistency be-
tween inversions and process models but with a lot of dis-
crepancies, particularly for the allocation of the mean land
sink between the tropics and the Northern Hemisphere. Un-
derstanding these discrepancies and further analysis of re-
gional carbon budgets would provide additional information
to quantify and improve our estimates, as has been under-
taken by the project REgional Carbon Cycle Assessment and
Processes (RECCAP; Canadell et al., 2012–2013).
Annual estimates of each component of the global carbon
budgets have their limitations, some of which could be im-
proved with better data and/or better understanding of carbon
dynamics. The primary limitations involve resolving fluxes
on annual timescales and providing updated estimates for re-
cent years for which data-based estimates are not yet avail-
able or only beginning to emerge. Of the various terms in
the global budget, only the burning of fossil fuels and at-
mospheric growth rate terms are based primarily on empir-
ical inputs supporting annual estimates in this carbon bud-
get. The data on fossil fuels and industry are based on sur- Earth Syst. Sci. Data, 7, 349–396, 2015
378 C. Le Quéré et al.: Global Carbon Budget 2015
1960 1970 1980 1990 2000 2010
12 Fossil fuels and industrya
1960 1970 1980 1990 2000 2010
4b Land−use change
Time (yr)
CO2 emissions (GtC yr−1)
1960 1970 1980 1990 2000 2010
8c Atmospheric growth
1960 1970 1980 1990 2000 2010
4d Ocean sink
CO2 partitioning (GtC yr−1)
1960 1970 1980 1990 2000 2010
6e Land sink
Time (yr)
Figure 9. Comparison of global carbon budget components
released annually by GCP since 2006. CO2emissions from
both (a) fossil fuels and industry (EFF) and (b) land-use change
(ELUC), as well as their partitioning among (c) the atmosphere
(GATM), (d) the ocean (SOCEAN), and (e) the land (SLAND). See
legend for the corresponding years, with the 2006 carbon budget
from Raupach et al. (2007), 2007 from Canadell et al. (2007), 2008
released online only, 2009 from Le Quéré et al. (2009), 2010 from
Friedlingstein et al. (2010), 2011 from Peters et al. (2012b), 2012
from Le Quéré et al. (2013), 2013 from Le Quéré et al. (2014), and
2014 from Le Quéré et al. (2015) and this year’s budget (2015; this
study). The budget year generally corresponds to the year when the
budget was first released. All values are in GtCyr1.
vey data in all countries. The other terms can be provided
on an annual basis only through the use of models. While
these models represent the current state of the art, they pro-
vide only simulated changes in primary carbon budget com-
ponents. For example, the decadal trends in global ocean up-
take and the interannual variations associated with El Niño–
Southern Oscillation (i.e. ENSO) are not directly constrained
by observations, although many of the processes controlling
these trends are sufficiently well known that the model-based
trends still have value as benchmarks for further validation.
Data-based products for the ocean CO2sink provide new
ways to evaluate the model results, and could be used di-
rectly as data become more rapidly available and methods
for creating such products improve. However, there are still
large discrepancies among data-based estimates, in large part
due to the lack of routine data sampling, that preclude their
direct use for now (see Rödenbeck et al., 2015). Estimates
of land-use-change emissions and their year-to-year variabil-
ity have even larger uncertainty, and many of the underlying
data are not available as an annual update. Efforts are un-
derway to work with annually available satellite area change
data or FAO-reported data in combination with fire data and
modelling to provide annual updates for future budgets. The
best resolved changes are in atmospheric growth (GATM),
fossil fuel emissions (EFF), and, by difference, the change in
the sum of the remaining terms (SOCEAN +SLAND ELUC).
The variations from year-to-year in these remaining terms
are largely model-based at this time. Further efforts to in-
crease the availability and use o