ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-2141-2018Global Carbon Budget 2018Global Carbon Budget 2018Le QuéréCorinnec.lequere@uea.ac.ukhttps://orcid.org/0000-0003-2319-0452AndrewRobbie M.https://orcid.org/0000-0001-8590-6431FriedlingsteinPierrehttps://orcid.org/0000-0003-3309-4739SitchStephenHauckJudithhttps://orcid.org/0000-0003-4723-9652PongratzJuliahttps://orcid.org/0000-0003-0372-3960PickersPenelope A.https://orcid.org/0000-0002-1923-5163KorsbakkenJan Ivarhttps://orcid.org/0000-0002-2939-9778PetersGlen P.https://orcid.org/0000-0001-7889-8568CanadellJosep G.https://orcid.org/0000-0002-8788-3218ArnethAlmuthttps://orcid.org/0000-0001-6616-0822AroraVivek K.BarberoLeticiahttps://orcid.org/0000-0002-8858-5247BastosAnahttps://orcid.org/0000-0002-7368-7806BoppLaurenthttps://orcid.org/0000-0003-4732-4953ChevallierFrédérichttps://orcid.org/0000-0002-4327-3813ChiniLouise P.https://orcid.org/0000-0002-9070-3505CiaisPhilippehttps://orcid.org/0000-0001-8560-4943DoneyScott C.https://orcid.org/0000-0002-3683-2437GkritzalisThanosGollDaniel S.https://orcid.org/0000-0001-9246-9671HarrisIanHaverdVanessaHoffmanForrest M.https://orcid.org/0000-0001-5802-4134HoppemaMariohttps://orcid.org/0000-0002-2326-619XHoughtonRichard A.https://orcid.org/0000-0002-3298-7028HurttGeorgehttps://orcid.org/0000-0001-7278-202XIlyinaTatianahttps://orcid.org/0000-0002-3475-4842JainAtul K.https://orcid.org/0000-0002-4051-3228JohannessenTrulshttps://orcid.org/0000-0002-1671-3465JonesChris D.https://orcid.org/0000-0002-7141-9285KatoEtsushihttps://orcid.org/0000-0001-8814-804XKeelingRalph F.https://orcid.org/0000-0002-9749-2253GoldewijkKees KleinLandschützerPeterhttps://orcid.org/0000-0002-7398-3293LefèvreNathalieLienertSebastianhttps://orcid.org/0000-0003-1740-918XLiuZhuhttps://orcid.org/0000-0002-8968-7050LombardozziDanicaMetzlNicolasMunroDavid R.https://orcid.org/0000-0002-1373-7402NabelJulia E. M. S.https://orcid.org/0000-0002-8122-5206NakaokaShin-ichirohttps://orcid.org/0000-0002-3870-1721NeillCraigOlsenArehttps://orcid.org/0000-0003-1696-9142OnoTsuenohttps://orcid.org/0000-0003-3472-5731PatraPrabirhttps://orcid.org/0000-0001-5700-9389PeregonAnnaPetersWouterhttps://orcid.org/0000-0001-8166-2070PeylinPhilippePfeilBenjaminPierrotDenishttps://orcid.org/0000-0002-0374-3825PoulterBenjaminhttps://orcid.org/0000-0002-9493-8600RehderGregorhttps://orcid.org/0000-0002-0597-9989ResplandyLaurehttps://orcid.org/0000-0002-1212-3943RobertsonEddyRocherMatthiasRödenbeckChristianhttps://orcid.org/0000-0001-6011-6249SchusterUteSchwingerJörgSéférianRolandhttps://orcid.org/0000-0002-2571-2114SkjelvanIngunnSteinhoffTobiasSuttonAdriennehttps://orcid.org/0000-0002-7414-7035TansPieter P.TianHanqinhttps://orcid.org/0000-0002-1806-4091TilbrookBrontehttps://orcid.org/0000-0001-9385-3827TubielloFrancesco N.https://orcid.org/0000-0003-4617-4690van der Laan-LuijkxIngrid T.https://orcid.org/0000-0002-3990-6737van der WerfGuido R.ViovyNicolashttps://orcid.org/0000-0002-9197-6417WalkerAnthony P.https://orcid.org/0000-0003-0557-5594WiltshireAndrew J.WrightRebeccahttps://orcid.org/0000-0003-2333-6247ZaehleSönkehttps://orcid.org/0000-0001-5602-7956ZhengBohttps://orcid.org/0000-0001-8344-3445Tyndall Centre for Climate Change Research, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UKCICERO Center for International Climate Research, Oslo 0349, NorwayCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UKCollege of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UKAlfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Postfach 120161, 27515 Bremerhaven, GermanyLudwig-Maximilians-Universität Munich, Luisenstr. 37, 80333 Munich, GermanyMax Planck Institute for Meteorology, Hamburg, GermanyCentre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UKGlobal Carbon Project, CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT 2601, AustraliaKarlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, GermanyCanadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment and Climate Change Canada, Victoria, BC, CanadaCooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine and Atmospheric Science, University of Miami, Miami, FL 33149, USANational Oceanic & Atmospheric Administration/Atlantic Oceanographic & Meteorological Laboratory (NOAA/AOML), Miami, FL 33149, USALaboratoire de Météorologie Dynamique, Institut Pierre-Simon Laplace, CNRS-ENS-UPMC-X, Département de Géosciences,
Ecole Normale Supérieure, 24 rue Lhomond, 75005 Paris, FranceLaboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace, CEA-CNRS-UVSQ, CE Orme des Merisiers, 91191 Gif-sur-Yvette CEDEX, FranceDepartment of Geographical Sciences, University of Maryland, College Park, Maryland 20742, USAUniversity of Virginia, Charlottesville, VA 22904, USAFlanders Marine Institute (VLIZ), Wanelaarkaai 7, 8400 Ostend, BelgiumNCAS-Climate, Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UKCSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT 2601, AustraliaComputational Earth Sciences Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USAWoods Hole Research Center (WHRC), Falmouth, MA 02540, USADepartment of Atmospheric Sciences, University of Illinois, Urbana, IL 61821, USAGeophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Allégaten 70, 5007 Bergen, NorwayMet Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UKInstitute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, JapanUniversity of California, San Diego, Scripps Institution of Oceanography, La Jolla, CA 92093-0244, USAPBL Netherlands Environmental Assessment Agency, Bezuidenhoutseweg 30, P.O. Box 30314, 2500 GH, The Hague, the NetherlandsFaculty of Geosciences, Department IMEW, Copernicus Institute of Sustainable Development, Heidelberglaan 2, P.O. Box 80115,
3508 TC, Utrecht, the NetherlandsSorbonne Universités (UPMC, Univ Paris 06), CNRS, IRD, MNHN, LOCEAN/IPSL Laboratory, 75252 Paris, FranceClimate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandNational Center for Atmospheric Research, Climate and Global Dynamics, Terrestrial Sciences Section, Boulder, CO 80305, USADepartment of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado, Campus Box 450, Boulder, CO 80309-0450, USACenter for Global Environmental Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, JapanCSIRO Oceans and Atmosphere, P.O. Box 1538, Hobart, Tasmania, 7001, AustraliaAntarctic Climate and Ecosystem Cooperative Research Centre, University of Tasmania, Hobart, AustraliaNORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, NorwayNational Research Institute for Far Sea Fisheries, Japan Fisheries Research and Education Agency, 2-12-4 Fukuura, Kanazawa-Ku, Yokohama 236-8648, JapanDepartment of Environmental Geochemical Cycle Research, JAMSTEC, Yokohama, JapanDepartment of Meteorology and Air Quality, Wageningen University & Research, P.O. Box 47, 6700AA Wageningen, the NetherlandsCentre for Isotope Research, University of Groningen, Nijenborgh 6, 9747 AG Groningen, the NetherlandsNASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, Maryland 20771, USALeibniz Institute for Baltic Sea Research Warnemünde, 18119 Rostock, GermanyPrinceton University Department of Geosciences and Princeton Environmental Institute Princeton, New Jersey, USACentre National de Recherche Météorologique, Unite mixte de recherche 3589 Météo-France/CNRS, 42 Avenue Gaspard Coriolis, 31100 Toulouse, FranceMax Planck Institute for Biogeochemistry, P.O. Box 600164, Hans-Knöll-Str. 10, 07745 Jena, GermanyGEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105, Kiel, GermanyNational Oceanic & Atmospheric Administration/Pacific Marine Environmental Laboratory (NOAA/PMEL), 7600 Sand Point Way NE, Seattle, WA 98115, USANational Oceanic & Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), Boulder, CO 80305, USASchool of Forestry and Wildlife Sciences, Auburn University, 602 Ducan Drive, Auburn, AL 36849, USAStatistics Division, Food and Agriculture Organization of the United Nations, Via Terme di Caracalla, Rome 00153, ItalyFaculty of Science, Vrije Universiteit, Amsterdam, the NetherlandsEnvironmental Sciences Division & Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USADepartment of Earth System Science, Tsinghua University, Beijing 100084, ChinaCorinne Le Quéré (c.lequere@uea.ac.uk)5December20181042141219427September20184October201819November201819November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/essd-10-2141-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/essd-10-2141-2018.pdf
Accurate assessment of anthropogenic carbon dioxide
(CO2) emissions and their redistribution among the atmosphere,
ocean, and terrestrial biosphere – the “global carbon budget” – 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 methodology to quantify the five major components of
the global carbon budget and their uncertainties. Fossil CO2
emissions (EFF) are based on energy statistics and cement
production data, while emissions from land use and land-use change (ELUC),
mainly deforestation, are based on land use and land-use change data and
bookkeeping models. Atmospheric CO2 concentration is measured
directly and its growth rate (GATM) is computed from the annual
changes in concentration. The ocean CO2 sink (SOCEAN)
and terrestrial CO2 sink (SLAND) are estimated with
global process models constrained by observations. The resulting carbon
budget imbalance (BIM), the difference between the estimated
total emissions and the estimated changes in the atmosphere, ocean, and
terrestrial biosphere, is a measure of imperfect data and understanding of
the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2008–2017), EFF was
9.4±0.5 GtC yr-1, ELUC1.5±0.7 GtC yr-1, GATM4.7±0.02 GtC yr-1,
SOCEAN2.4±0.5 GtC yr-1, and SLAND3.2±0.8 GtC yr-1, with a budget imbalance BIM of
0.5 GtC yr-1 indicating overestimated emissions and/or underestimated
sinks. For the year 2017 alone, the growth in EFF was about 1.6 %
and emissions increased to 9.9±0.5 GtC yr-1. Also for 2017,
ELUC was 1.4±0.7 GtC yr-1, GATM was 4.6±0.2 GtC yr-1, SOCEAN was 2.5±0.5 GtC yr-1, and SLAND was 3.8±0.8 GtC yr-1,
with a BIM of 0.3 GtC. The global atmospheric
CO2 concentration reached 405.0±0.1 ppm averaged over 2017.
For 2018, preliminary data for the first 6–9 months indicate a renewed
growth in EFF of +2.7 % (range of 1.8 % to 3.7 %) based
on national emission projections for China, the US, the EU, and India and
projections of gross domestic product corrected for recent changes in the
carbon intensity of the economy for the rest of the world. The analysis
presented here shows that the mean and trend in the five components of the
global carbon budget are consistently estimated over the period of 1959–2017,
but discrepancies of up to 1 GtC yr-1 persist for the representation
of semi-decadal variability in CO2 fluxes. A detailed comparison
among individual estimates and the introduction of a broad range of
observations show (1) no consensus in the mean and trend in land-use change
emissions, (2) a persistent low agreement among the different methods on
the magnitude of the land CO2 flux in the northern extra-tropics,
and (3) an apparent underestimation of the CO2 variability by ocean
models, originating outside the tropics. This living data update documents
changes in the methods and data sets used in this new global carbon budget
and the progress in understanding the global carbon cycle compared with
previous publications of this data set (Le Quéré et al., 2018, 2016,
2015a, b, 2014, 2013). All results presented here can be downloaded from
https://doi.org/10.18160/GCP-2018.
Introduction
The concentration of carbon dioxide (CO2) in the atmosphere has
increased from approximately 277 parts per million (ppm) in 1750 (Joos and
Spahni, 2008), the beginning of the industrial era, to 405.0±0.1 ppm
in 2017 (Dlugokencky and Tans, 2018; Fig. 1). The atmospheric CO2
increase above pre-industrial levels was, initially, primarily caused by the
release of carbon to the atmosphere from deforestation and other land-use
change activities (Ciais et al., 2013). While emissions from fossil fuels
started before the industrial era, they only became the dominant source of
anthropogenic emissions to the atmosphere around 1950 and their relative
share has continued to increase until present. Anthropogenic emissions occur
on top of an active natural carbon cycle that circulates carbon among the
reservoirs of the atmosphere, ocean, and terrestrial biosphere on timescales
from sub-daily to millennial, while exchanges with geologic reservoirs occur
at longer timescales (Archer et al., 2009).
The global carbon budget presented here refers to the mean, variations, and
trends in the perturbation of CO2 in the environment, referenced to
the beginning of the industrial era. It quantifies the input of CO2
to the atmosphere by emissions from human activities, the growth rate of
atmospheric CO2 concentration, and the resulting changes in the
storage of carbon in the land and ocean reservoirs in response to increasing
atmospheric CO2 levels, climate change, and variability and other
anthropogenic and natural changes (Fig. 2). An understanding of this
perturbation budget over time and the underlying variability and trends in
the natural carbon cycle is necessary to understand the response of natural
sinks to changes in climate, CO2 and land-use change drivers, and
the permissible emissions for a given climate stabilisation target.
Surface average atmospheric CO2 concentration
(ppm). The 1980–2018 monthly data are from NOAA/ESRL (Dlugokencky and Tans,
2018) and are based on an average of direct atmospheric CO2
measurements from multiple stations in the marine boundary layer (Masarie
and Tans, 1995). The 1958–1979 monthly data are from the Scripps Institution
of Oceanography, based on an average of direct atmospheric CO2
measurements from the Mauna Loa and South Pole stations (Keeling et al.,
1976). To take into account the difference of mean CO2 and
seasonality between the NOAA/ESRL and the Scripps station networks used here,
the Scripps surface average (from two stations) was deseasonalised and
harmonised to match the NOAA/ESRL surface average (from multiple stations) by
adding the mean difference of 0.542 ppm, calculated here from overlapping
data during 1980–2012.
The components of the CO2 budget that are reported annually in this
paper include separate estimates for (1) the CO2 emissions from
fossil fuel combustion and oxidation from all energy and industrial
processes and cement production (EFF; GtC yr-1); (2) the
emissions resulting from deliberate human activities on land, including those
leading to land-use change (ELUC; GtC yr-1); and (3) their
partitioning among the growth rate of atmospheric CO2
concentration (GATM; GtC yr-1), the uptake of
CO2 (the “CO2 sinks”) in (4) the ocean
(SOCEAN; GtC yr-1), and (5) the uptake of
CO2 on land (SLAND;
GtC yr-1). The CO2 sinks as defined here conceptually include
the response of the land (including inland waters and estuaries) and ocean
(including coasts and territorial sea) to elevated CO2 and changes
in climate, rivers, and other environmental conditions, although in practice
not all processes are accounted for (see Sect. 2.8). The global emissions and
their partitioning among the atmosphere, ocean, and land are in reality in
balance; however due to imperfect spatial and/or temporal data coverage,
errors in each estimate, and smaller terms not included in our budget
estimate (discussed in Sect. 2.8), their sum does not necessarily add up to
zero. We estimate a budget imbalance (BIM), which is a measure of
the mismatch between the estimated emissions and the estimated changes in the
atmosphere, land, and ocean, with the full global carbon budget as follows:
EFF+ELUC=GATM+SOCEAN+SLAND+BIM.GATM is usually reported in ppm yr-1, which we convert to
units of carbon mass per year, GtC yr-1, using 1 ppm =2.124 GtC
(Table 1). We also include a quantification of EFF by country,
computed with both territorial and consumption-based accounting (see
Sect. 2), and discuss missing terms from sources other than the combustion of
fossil fuels (see Sect. 2.8).
Factors used to convert carbon in various units (by convention, Unit
1 = Unit 2 conversion).
Unit 1Unit 2ConversionSourceGtC (gigatonnes of carbon)ppm (parts per million)a2.124bBallantyne et al. (2012)GtC (gigatonnes of carbon)PgC (petagrams of carbon)1SI unit conversionGtCO2 (gigatonnes of carbon dioxide)GtC (gigatonnes of carbon)3.66444.01/12.011 in mass equivalentGtC (gigatonnes of carbon)MtC (megatonnes of carbon)1000SI unit conversion
a Measurements of atmospheric CO2 concentration have units
of dry-air mole fraction. “ppm” is an abbreviation for micromole mol-1, dry
air.
b The use of a factor of 2.124 assumes that all the atmosphere is well
mixed within 1 year. In reality, only the troposphere is well mixed and the
growth rate of CO2 concentration in the less well-mixed
stratosphere is not measured by sites from the NOAA network. Using a factor
of 2.124 makes the approximation that the growth rate of CO2
concentration in the stratosphere equals that of the troposphere on a yearly
basis.
The CO2 budget has been assessed by the Intergovernmental 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), and by others (e.g. Ballantyne et al., 2012). The IPCC methodology has
been adapted and used by the Global Carbon Project (GCP,
http://www.globalcarbonproject.org/, last access: 30 November 2018), 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), the year 2006 (Canadell et al., 2007), the year
2007 (published online; GCP, 2007), the year 2008 (Le Quéré et al.,
2009), the year 2009 (Friedlingstein et al., 2010), the year 2010 (Peters et al.,
2012b), the year 2012 (Le Quéré et al., 2013; Peters et al., 2013), the year
2013 (Le Quéré et al., 2014), the year 2014 (Friedlingstein et al., 2014;
Le Quéré et al., 2015b), the year 2015 (Jackson et al., 2016; Le
Quéré et al., 2015a), the year 2016 (Le Quéré et al., 2016), and
most recently the year 2017 (Le Quéré et al., 2018; Peters et al., 2017).
Each of these papers updated previous estimates with the latest available
information for the entire time series.
We adopt a range of ±1 standard deviation (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 and no bias is assumed. This choice reflects the difficulty of
characterising the uncertainty in the CO2 fluxes between the
atmosphere and the ocean and land reservoirs individually, particularly on an
annual basis, as well as the difficulty of updating the CO2
emissions from land use and 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 ±1σ interval. The uncertainties reported here combine
statistical analysis of the underlying data and expert judgement of the
likelihood of results lying outside this range. The limitations of current
information are discussed in the paper and have been examined in detail
elsewhere (Ballantyne et al., 2015; Zscheischler et al., 2017). We also use a
qualitative assessment of confidence level to characterise the annual
estimates from each term based on the type, amount, quality, and consistency
of the evidence as defined by the IPCC (Stocker et al., 2013).
All quantities are presented in units of gigatonnes of carbon (GtC,
1015 gC), which is the same as petagrams of carbon (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 budget estimates for the pre-industrial period
(1750) to 2017 and in more detail for the period since 1959. It
also provides decadal averages starting in 1960 including the last decade
(2008–2017), results for the year 2017, and a projection for the year 2018.
Finally it provides cumulative emissions from fossil fuels and land-use
change since the year 1750, the pre-industrial period, and since the year 1870, the
reference year for the cumulative carbon estimate used by the IPCC (AR5)
based on the availability of global temperature data (Stocker et al., 2013).
This paper is updated every year using the format of “living data” to keep
a record of budget versions and the changes in new data, revision of data,
and changes in methodology that lead to changes in estimates of the carbon
budget. Additional materials associated with the release of each new version
will be posted at the Global Carbon Project (GCP) website
(http://www.globalcarbonproject.org/carbonbudget, last access: 30 November 2018), with fossil fuel emissions also available
through the Global Carbon Atlas
(http://www.globalcarbonatlas.org, last access: 30 November 2018). With this approach, we aim to provide the highest transparency and
traceability in the reporting of CO2, the key driver of climate
change.
Methods
Multiple organisations and research groups around the world generated the
original measurements and data used to complete the global carbon budget. The
effort presented here is thus mainly one of synthesis, in which 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 elsewhere.
How to cite the individual components of the global carbon budget
presented here.
ComponentPrimary referenceGlobal fossil CO2 emissions (EFF), total and by fuel typeBoden et al. (2017)National territorial fossil CO2 emissions (EFF)CDIAC source: Boden et al. (2017) UNFCCC (2018)National consumption-based fossil CO2 emissions (EFF) by country (consumption)Peters et al. (2011b) updated as described in this paperLand-use change emissions (ELUC)Average from Houghton and Nassikas (2017) and Hansis et al. (2015), both updated as described in this paperGrowth rate in atmospheric CO2 concentration (GATM)Dlugokencky and Tans (2018)Ocean and land CO2 sinks (SOCEAN and SLAND)This paper for SOCEAN and SLAND and references in Table 4 for individual models
This is the 13th version of the global carbon budget and the seventh revised
version in the format of a living data update. It builds on the latest
published global carbon budget of Le Quéré et al. (2018). The main
changes are (1) the inclusion of data to the year 2017 (inclusive) and a
projection for the global carbon budget for the year 2018; (2) the introduction
of metrics that evaluate components of the individual models used to estimate
SOCEAN and SLAND using observations, as an effort to
document, encourage, and support model improvements through time; (3) the
revisions of the CO2 emissions associated with cement production
based on revised clinker ratios; (4) a projection for fossil fuel emissions
for the 28 European Union member states based on compiled energy statistics; and
(5) the addition of Sect. 2.8.2 on additional emissions from calcination not
included in the budget. The main methodological differences among annual
carbon budgets are summarised in Table 3.
Main methodological changes in the global carbon budget
since first publication. Methodological changes introduced in one year are
kept for the following years unless noted. Empty cells mean there were no
methodological changes introduced that year.
Publication yearaFossil fuel emissions Land-use change emissionsReservoirs Uncertainty & other changesGlobalCountry (territorial)Country(consumption)AtmosphereOceanLandSplit in regions2007 Canadell et al. (2007)ELUC based on FAO FRA 2005; constant ELUC for 20061959–1979 data from Mauna Loa; data after 1980 from global averageBased on one ocean model tuned to reproduce observed 1990s sink±1σ provided for all components2008 (online)Constant ELUC for 20072009 Le Quéré et al. (2009)Split between Annex B and non-Annex BResults from an independent study discussedFire-based emissionanomalies used for 2006–2008Based on four ocean models normalised to observations with constant deltaFirst use of five DGVMs to compare with budget residual2010 Friedlingstein etal. (2010)Projection for current year based on GDPEmissions for topemittersELUC updated with FAO-FRA 20102011 Peters et al. (2012b)Split between Annex B and non-Annex B2012 Le Quéré et al. (2013) Peters et al. (2013)129 countries from1959129 countries and regions from 1990 to 2010 based on GTAP8.0ELUC for 1997–2011 includes interannual anomalies fromfire-based emissionsAll years from global averageBased on five ocean models normalised to observations with ratio10 DGVMs available for SLAND; first use of four models to compare with ELUC2013 Le Quéré et al. (2014)250 countriesb134 countries and regions (1990–2011) based on GTAP8.1, with detailed estimates for the years 1997, 2001, 2004, and 2007ELUC for 2012 estimated from 2001–2010 averageBased on six models compared with two data products to the year 2011Coordinated DGVM experiments for SLAND and ELUCConfidence levels; cumulative emissions; budget from 17502014 Le Quéré et al. (2015b)3 years of BP data3 years of BP dataExtended to 2012 with updated GDP dataELUC for 1997–2013 includes interannual anomalies from fire-based emissionsBased on seven modelsBased on 10 modelsInclusion of breakdown of the sinks in three latitude bands and comparison with three atmospheric inversions2015 Le Quéré et al. (2015a) Jackson et al. (2016)Projection for current year based on Jan–Aug dataNational emissions from UNFCCC extended to 2014 also providedDetailed estimates introduced for 2011 based on GTAP9Based on eight modelsBased on 10 models with assessment of minimum realismThe decadal uncertainty for the DGVM ensemble mean now uses ±1σ of the decadal spread across models2016 Le Quéré et al. (2016)2 years of BP dataAdded three small countries; China's (RMA) emissions from 1990 from BP data (this release only)Preliminary ELUC using FRA-2015 shown for comparison; use of five DGVMsBased on seven modelsBased on 14modelsDiscussion of projection for full budget for current year
Continued.
Publication yearaFossil fuel emissions Land-use change emissionsReservoirs Uncertainty & other changesGlobalCountry (territorial)Country(consumption)AtmosphereOceanLand2017 Le Quéré et al. (2018)Projection includes India-specific dataAverage of two bookkeeping models; use of 12 DGVMsBased on eight models that match the observed sink for the 1990s; no longer normalisedBased on 15 models that meet observation-based criteria (see Sect. 2.6)Land multi-model average now used in main carbon budget, with the carbon imbalance presented separately; new table of key uncertainties2018 (this study)Revision in cement emissions; projection includes EU-specific dataAggregation of overseas territories into governing nations for total of 213 countriesbUse of 16DGVMscUse of fouratmospheric inversionsBased on seven modelsBased on 16 models; revised atmospheric forcing from CRUNCEP to CRU–JRA-55Introduction of metrics for evaluation of individual models using observationsIntroduction of Resplandy et al. (2018) correction for riverine fluxes
a The 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 budget year (Carbon Budget 2012) refers to the
initial publication year.
b The CDIAC database has about 250 countries, but we show data for 213
countries since we aggregate and disaggregate some countries to be
consistent with current country definitions (see Sect. 2.1.1 for more
details).
cELUC is still estimated based on bookkeeping models as in
2017, but the number of DGVMs used to characterise the uncertainty has
changed.
Fossil CO2 emissions (EFF)Emission estimates
The estimates of global and national fossil CO2 emissions
(EFF) include the combustion of fossil fuels through a wide range
of activities (e.g. transport, heating, and cooling, industry, fossil
industry's
own use, and gas flaring), the production of cement, and other process
emissions (e.g. the production of chemicals and fertilisers). The estimates
of EFF rely primarily on energy consumption data, specifically
data on hydrocarbon fuels, collated and archived by several organisations
(Andres et al., 2012). We use four main data sets for historical emissions
(1751–2017).
We use global and national emission estimates for coal, oil, and gas from CDIAC
for the time period of 1751–2014 (Boden et al., 2017), as it is the only data
set that extends back to 1751 by country.
We use official UNFCCC national inventory reports for 1990–2016 for the 42 Annex I
countries in the UNFCCC (UNFCCC, 2018). We assess these to be the most
accurate estimates because they are compiled by experts within countries that
have access to detailed energy data, and they are periodically reviewed.
We use the BP Statistical Review of World Energy (BP, 2018), as these are the
most up-to-date estimates of national energy statistics.
We use global and national cement emissions updated from Andrew (2018), which include revised emission factors.
In the following section we provide more details for each data set and
describe the additional modifications that are required to make the data set
consistent and usable.
CDIAC. The CDIAC estimates have been updated annually to the
year 2014, derived primarily from energy statistics published by the United
Nations (UN, 2017b). Fuel masses and volumes are converted to fuel energy
content using country-level coefficients provided by the UN and then
converted to CO2 emissions using conversion factors that take into
account the relationship between carbon content and energy (heat) content of
the different fuel types (coal, oil, gas, gas flaring) and the combustion
efficiency (Marland and Rotty, 1984).
UNFCCC. Estimates from the UNFCCC national inventory reports
follow the IPCC guidelines (IPCC, 2006) but have a slightly larger system
boundary than CDIAC by including emissions coming from carbonates other than
in cement manufacturing. We reallocate the detailed UNFCCC estimates to the
CDIAC definitions of coal, oil, gas, cement, and other to allow consistent
comparisons over time and among countries.
BP. For the most recent period when the UNFCCC (2018) and CDIAC
(2015–2017) estimates are not available, we generate preliminary estimates
using the BP Statistical Review of World Energy (Andres et al., 2014; Myhre
et al., 2009; BP, 2018). We apply the BP growth rates by fuel type (coal,
oil, gas) to estimate 2017 emissions based on 2016 estimates (UNFCCC) and to
estimate 2015–2017 emissions based on 2014 estimates (CDIAC). BP's data set
explicitly covers about 70 countries (96 % of global emissions), and for
the remaining countries we use growth rates from the subregion the country
belongs to. For the most recent years, flaring is assumed constant from the
most recent available year of data (2016 for countries that report to the
UNFCCC, 2014 for the remainder).
Cement. Estimates of emissions from cement production are taken
directly from Andrew (2018). Additional calcination and carbonation processes
are not included explicitly here, except in national inventories provided by
UNFCCC, but are discussed in Sect. 2.8.2.
Country mappings. The published CDIAC data set includes 256
countries and regions. This list includes countries that no longer exist,
such as the USSR and Yugoslavia. We reduce the list to 213 countries by
reallocating emissions to the currently defined territories, using
mass-preserving aggregation or disaggregation. Examples of aggregation
include merging East and West Germany to the currently defined Germany.
Examples of disaggregation include reallocating the emissions from the former
USSR to the resulting independent countries. For disaggregation, we use the
emission shares when the current territories first appeared, and thus
historical estimates of disaggregated countries should be treated with
extreme care. In addition, we aggregate some overseas territories (e.g.
Réunion, Guadeloupe) into their governing nations (e.g. France) to align
with UNFCCC reporting.
Global total. Our global estimate is based on CDIAC for fossil
fuel combustion plus Andrew (2018) for cement emissions. This is greater than
the sum of emissions from all countries. 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). The emissions from international bunker fuels are calculated based on
where the fuels were loaded, but we do not include them in the national
emission estimates. Other differences occur (1) because the sum of imports
in all countries is not equal to the sum of exports, and (2) because of
inconsistent national reporting, differing treatment of oxidation of non-fuel
uses of hydrocarbons (e.g. as solvents, lubricants, feedstocks), and
(3) because of changes in fuel stored (Andres et al., 2012).
Uncertainty assessment for EFF
We estimate the uncertainty of the global fossil CO2 emissions at
±5 % (scaled down from the published ±10 % at ±2σ to
the use of ±1σ bounds reported here; Andres et al., 2012). This is
consistent 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 ±2σ reported by Ballantyne et
al. (2015). This includes an assessment of uncertainties in the amounts of
fuel consumed, the carbon and heat contents of fuels, and the combustion
efficiency. While we consider a fixed uncertainty of ±5 % for all
years, the uncertainty as a percentage of the emissions is growing with time
because of the larger share of global emissions from emerging economies and
developing countries (Marland et al., 2009). Generally, emissions from mature
economies with good statistical processes have an uncertainty of only a few
per cent (Marland, 2008), while emissions from developing countries such as
China have uncertainties of around ±10 % (for ±1σ; Gregg et
al., 2008). Uncertainties of emissions are likely to be mainly systematic
errors related to underlying biases of energy statistics and to the
accounting method used by each country.
We assign a medium confidence to the results presented here because they are
based on indirect estimates of emissions using energy data (Durant et al.,
2011). There is only limited and indirect evidence for emissions, although
there is high agreement among the available estimates within the given
uncertainty (Andres et al., 2012, 2014), and emission
estimates are consistent with a range of other observations (Ciais et al.,
2013), even though their regional and national partitioning is more uncertain
(Francey et al., 2013).
Emissions embodied in goods and services
CDIAC, UNFCCC, and BP national emission statistics “include greenhouse gas
emissions and removals taking place within national territory and offshore
areas over which the country has jurisdiction” (Rypdal et al., 2006) and
are called territorial emission inventories. Consumption-based emission
inventories allocate emissions to products that are consumed within a
country and are conceptually calculated as the territorial emissions minus
the “embodied” territorial emissions to produce exported products plus the
emissions in other countries to produce imported products (consumption =
territorial - exports + imports). Consumption-based emission attribution
results (e.g. Davis and Caldeira, 2010) provide additional information to
territorial-based emissions that can be used to understand emission drivers
(Hertwich and Peters, 2009) and quantify emission transfers by the trade of
products between countries (Peters et al., 2011b). The consumption-based
emissions have the same global total but reflect the trade-driven movement
of emissions across the Earth's surface in response to human activities.
We estimate consumption-based emissions from 1990 to 2016 by enumerating the
global supply chain using a global model of the economic relationships
between economic sectors within and among every country (Andrew and Peters,
2013; Peters et al., 2011a). Our analysis is based on the economic and trade
data from the Global Trade and Analysis Project (GTAP; Narayanan et al.,
2015), and we make detailed estimates for the years 1997 (GTAP version 5),
2001 (GTAP6), and 2004, 2007, and 2011 (GTAP9.2), covering 57 sectors and 141
countries and regions. The detailed results are then extended into an annual
time series from 1990 to the latest year of the gross domestic product (GDP)
data (2016 in this budget), using GDP data by expenditure in the current exchange
rate of US dollars (USD; from the UN National Accounts Main Aggregrates
Database; UN, 2017a) and time series of trade data from GTAP (based on the
methodology in Peters et al., 2011b). We estimate the sector-level
CO2 emissions using the GTAP data and methodology, include flaring
and cement emissions from CDIAC, and then scale the national totals
(excluding bunker fuels) to match the emission estimates from the carbon
budget. We do not provide a separate uncertainty estimate for the
consumption-based emissions, but based on model comparisons and sensitivity
analysis, they are unlikely to be significantly different than for the
territorial emission estimates (Peters et al., 2012a).
Growth rate in emissions
We report the annual growth rate in emissions for adjacent years (in per cent
per year) by calculating the difference between the two years and then
normalising to the emissions in the first year:
(EFF(t0+1)-EFF(t0))/EFF(t0)×100%×100/(1year). ×100/(1year). We
apply a leap-year adjustment when relevant to ensure valid interpretations
of annual growth rates. This affects the growth rate by about
0.3 % yr-1 (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 rewritten using its logarithm equivalent as follows:
1EFFdEFFdt=d(lnEFF)dt.
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 per cent per year.
Emission projections
To gain insight into emission trends for the current year (2018), we provide an
assessment of global fossil CO2 emissions, EFF, by
combining individual assessments of emissions for China, the US, the EU, and
India (the four countries/regions with the largest emissions), and the rest
of the world.
Our 2018 estimate for China uses (1) the sum of domestic production (NBS,
2018b) and net imports (General Administration of Customs of the People's
Republic of China, 2018) for coal, oil and natural gas, and production of
cement (NBS, 2018b) from preliminary statistics for January through September
of 2018 and (2) historical relationships between January–September
statistics for both production and imports and full-year statistics for consumption using final data for 2000–2016 (NBS, 2015, 2017) and preliminary
data for 2017 (NBS, 2018a). See also Liu et
al. (2018) and Jackson et al. (2018) for details. The uncertainty is based on
the variance of the difference between the January–September and full-year
data from historical data, as well as typical variance in the preliminary
full-year data used for 2017 and typical changes in the energy content of
coal for the period of 2013–2016 (NBS, 2017, 2015). We note that
developments for the final 3 months this year may be atypical due to the
ongoing trade disputes between China and the US, and this additional
uncertainty has not been quantified. Results and uncertainties are discussed
further in Sect. 3.4.1.
For the US, we use the forecast of the U.S. Energy Information
Administration (EIA) for emissions from fossil fuels (EIA, 2018). This is
based on an energy forecasting model which is updated monthly (last update to
October) and takes into account heating-degree days, household expenditures
by fuel type, energy markets, policies, and other effects. We combine this
with our estimate of emissions from cement production using the monthly US
cement data from the U.S. Geological Survey (USGS) for January–August, assuming changes in cement
production over the first part of the year apply throughout the year. While
the EIA's forecasts for current full-year emissions have on average been
revised downwards, only 10 such forecasts are available, so we
conservatively use the full range of adjustments following revision and
additionally assume symmetrical uncertainty to give ±2.5 % around the
central forecast.
For India, we use (1) monthly coal production and sales data from the
Ministry of Mines (2018), Coal India Limited (CIL, 2018), and Singareni
Collieries Company Limited (SCCL, 2018), combined with import data from the
Ministry of Commerce and Industry (MCI, 2018) and power station stocks data
from the Central Electricity Authority (CEA, 2018); (2) monthly oil
production and consumption data from the Ministry of Petroleum and Natural
Gas (PPAC, 2018a); (3) monthly natural gas production and import data from
the Ministry of Petroleum and Natural Gas (PPAC, 2018b); and (4) monthly
cement production data from the Office of the Economic Advisor
(OEA, 2018). All data were available for January to September or October. We
use Holt–Winters exponential smoothing with multiplicative seasonality
(Chatfield, 1978) on each of these four emission series to project
to the end of the current year. This iterative method produces estimates of
both trend and seasonality at the end of the observation period that are a
function of all prior observations, weighted most strongly to more recent
data, while maintaining some smoothing effect. The main source of uncertainty
in the projection of India's emissions is the assumption of continued trends
and typical seasonality.
For the EU, we use (1) monthly coal supply data from Eurostat for the first
6–9 months of the year (Eurostat, 2018) cross-checked with more recent data
on coal-generated electricity from ENTSO-E for January through October
(ENTSO-E, 2018); (2) monthly oil and gas demand data for January through
August from the Joint Organisations Data Initiative (JODI, 2018); and
(3) cement production assumed to be stable. For oil and gas emissions we apply
the Holt–Winters method separately to each country and energy carrier to
project to the end of the current year, while for coal – which is much less
strongly seasonal because of strong weather variations – we assume the
remaining months of the year are the same as the previous year in each
country.
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 a simplified Kaya identity,
whereby EFF (GtC yr-1) is decomposed by the product of GDP
(USD yr-1) and the fossil fuel carbon intensity of the economy
(IFF; GtC USD-1) as follows:
EFF=GDP×IFF.
Taking a time derivative of Eq. (3) and rearranging gives
1EFFdEFFdt=1GDPdGDPdt+1IFFdIFFdt,
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 the
overall growth rate.
The growth rates are reported in per cent by multiplying each term by 100. As
preliminary estimates 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 CO2 emissions
well before the end of a calendar year. The IFF is based on GDP
in constant PPP (purchasing power parity) from the International Energy
Agency (IEA) up until 2016 (IEA/OECD, 2017) and extended using the International
Monetary Fund (IMF) growth rates for 2016 and 2017 (IMF, 2018). Interannual
variability in IFF is the largest source of uncertainty in the
GDP-based emission projections. We thus use the standard deviation of the
annual IFF for the period of 2007–2017 as a measure of uncertainty,
reflecting a ±1σ as in the rest of the carbon budget. This is ±1.0 % yr-1 for the rest of the world (global emissions minus China,
the
US, the EU, and India).
The 2018 projection for the world is made of the sum of the projections for
China, the US, the EU, India, and the rest of the world. The uncertainty is added
in quadrature among the five regions. The uncertainty here reflects the best
of our expert opinion.
CO2 emissions from land use, land-use change, and forestry
(ELUC)
The net CO2 flux from land use, land-use change, and forestry
(ELUC, called land-use change emissions in the rest of the text)
include CO2 fluxes from deforestation, afforestation, logging and
forest degradation (including harvest activity), shifting cultivation (cycle
of cutting forest for agriculture, then abandoning), and regrowth of forests
following wood harvest or abandonment of agriculture. Only some land
management activities are included in our land-use change emission estimates
(Table A1 in the Appendix). Some of these activities lead to emissions of
CO2 to the atmosphere, while others lead to CO2 sinks.
ELUC is the net sum of emissions and removals due to all
anthropogenic activities considered. Our annual estimate for 1959–2017 is
provided as the average of results from two bookkeeping models (Sect. 2.3.1):
the estimate published by Houghton and Nassikas (2017; hereafter H&N2017)
extended here to 2017 and an estimate using the BLUE model (Bookkeeping of
Land Use Emissions; Hansis et al., 2015). In addition, we use results from
dynamic global vegetation models (DGVMs; see Sect. 2.3.3 and Table 4) to
help quantify the uncertainty in ELUC and thus better
characterise our understanding. The three methods are described below, and
differences are discussed in Sect. 3.2.
References for the process models,
pCO2-based ocean flux products, and atmospheric inversions
included in Figs. 6–8. All models and products are updated with new data to
the end of the year 2017, and the atmospheric forcing for the DGVMs has been updated
as described in Sect. 2.3.2.
Model/data nameReferenceChange from Le Quéré et al. (2018)Bookkeeping models for land-use change emissions BLUEHansis et al. (2015)LUH2 rangelands were treated differently, using the static LUH2 information on forest–non-forest grid cells to determine clearing for rangelands. Additionally effects on degradation of primary to secondary lands due to rangelands on natural (uncleared) vegetation were added to BLUE.H&N2017Houghton and Nassikas (2017)No change.Dynamic global vegetation modelsaCABLE-POPHaverd et al. (2018)Simple crop harvest and grazing implemented. Small adjustments to photosynthesis parameters to compensate for the effect of new climate forcing on GPP.CLASS–CTEMMelton and Arora (2016)20 soil layers used. Soil depth is prescribed following Pelletier et al. (2016).CLM5.0Oleson et al. (2013)No change.DLEMTian et al. (2015)Using observed irrigation data instead of a potential irrigation map.ISAMMeiyappan et al. (2015)Crop harvest and N fertiliser application as described in Song et al. (2016).JSBACHMauritsen et al. (2018)New version of JSBACH (JSBACH 3.2), as used for CMIP6 simulations. Changes include a new fire algorithm, as well as new processes (land nitrogen cycle, carbon storage of wood products). Furthermore, LUH2 rangelands were treated differently, using the static LUH2 information on forest–non-forest grid cells to determine clearing for rangelands.JULESClark et al. (2011)No change.LPJ-GUESSSmith et al. (2014)bNo change.LPJPoulter et al. (2011)cUses monthly litter update (previously annual), three product pools for deforestation flux, shifting cultivation, wood harvest, and inclusion of boreal needleleaf deciduous plant functional type.LPX-BernLienert and Joos (2018)Minor refinement of parameterization. Changed from 1∘×1∘ to 0.5∘×0.5∘ resolution. Nitrogen deposition and fertilisation from NMIP.OCNZaehle and Friend (2010)No change (uses r294).ORCHIDEE-TrunkKrinner et al. (2005)dUpdated soil water stress and albedo scheme; overall C-cycle optimisation (gross fluxes).ORCHIDEE-CNPGoll et al. (2017)First time contribution (ORCHIDEE with nitrogen and phosphorus dynamics).SDGVMWalker et al. (2017)No change.SURFEXv8Joetzjer et al. (2015)Not applicable (not used in 2017).VISITKato et al. (2013)Updated spin-up protocol.Global ocean biogeochemistry models CCSM-BECDoney et al. (2009)No change.MICOM-HAMOCC (NorESM-OC)Schwinger et al. (2016)No drift correction.MITgcm-REcoM2Hauck et al. (2016)No change.MPIOM-HAMOCCMauritsen et al. (2018)Change of atmospheric forcing; CMIP6 model version including modifications and bug fixes in HAMOCC and MPIOM.NEMO-PISCES (CNRM)Berthet et al. (2018)New model version with update to NEMOv3.6 and improved gas exchange.NEMO-PISCES (IPSL)Aumont and Bopp (2006)No change.NEMO-PlankTOM5Buitenhuis et al. (2010)eNo change.pCO2-based flux ocean products LandschützerLandschützer et al. (2016)No change.Jena CarboScopeRödenbeck et al. (2014)No change.Atmospheric inversions CAMSChevallier et al. (2005)No change.CarbonTracker Europe (CTE)van der Laan-Luijkx et al. (2017)Minor changes in the inversion set-up.Jena CarboScopeRödenbeck et al. (2003)No change.MIROCSaeki and Patra (2017)Not applicable (not used in 2017).
a The forcing for all DGVMs has been updated from CRUNCEP to CRU–JRA.
b To account for the differences between the derivation of shortwave
radiation (SWRAD) from CRU cloudiness and SWRAD from CRU–JRA-55, the
photosynthesis scaling parameter αa was modified (-15 %) to yield
similar results.
c Compared to the published version, LPJ wood harvest efficiency
was decreased
so that 50 % of biomass was removed off-site compared to 85 % used in the
2012 budget. Residue management of managed grasslands increased so that
100 % of harvested grass enters the litter pool.
d Compared to the published version, new hydrology and snow scheme; revised
parameter values for photosynthetic capacity for all ecosystem (following
assimilation of FLUXNET data), updated parameters values for stem allocation,
maintenance respiration, and biomass export for tropical forests (based on
literature), and CO2 down-regulation process added to
photosynthesis. Version used for CMIP6.
e No nutrient restoring below the mixed-layer depth.
Bookkeeping models
Land-use change CO2 emissions and uptake fluxes are calculated by
two bookkeeping models. Both are based on the original bookkeeping approach
of Houghton (2003) that keeps track of the carbon stored in vegetation and
soils before and after a land-use change (transitions between various natural
vegetation types, croplands, and pastures). Literature-based response curves
describe decay of vegetation and soil carbon, including transfer to product
pools of different lifetimes, as well as carbon uptake due to regrowth. In
addition, the bookkeeping models represent long-term degradation of primary
forest as lowered standing vegetation and soil carbon stocks in secondary
forests and also include forest management practices such as wood harvests.
The bookkeeping models do not include land ecosystems' transient response to
changes in climate, atmospheric CO2, and other environmental
factors, and the carbon densities are based on contemporary data reflecting
environmental conditions at (and up to) that time. Since carbon densities remain
fixed over time in bookkeeping models, the additional sink capacity that
ecosystems provide in response to CO2 fertilisation and some other
environmental changes is not captured by these models (Pongratz et al., 2014;
see Sect. 2.8.4).
The H&N2017 and BLUE models differ in (1) computational units
(country level vs. spatially explicit treatment of land-use change),
(2) processes represented (see Table A1), and (3) carbon densities assigned
to vegetation and soil of each vegetation type. A notable change of
H&N2017 over the original approach by Houghton et al. (2003) used in
earlier budget estimates is that no shifting cultivation or other back-and-forth transitions below the country level are included. Only a decline in
forest area in a country as indicated by the Forest Resource Assessment of
the FAO that exceeds the expansion of agricultural area as indicated by the FAO
is assumed to represent a concurrent expansion and abandonment of cropland.
In contrast, the BLUE model includes sub-grid-scale transitions at the grid
level among all vegetation types as indicated by the harmonised land-use
change data (LUH2) data set (https://doi.org/10.22033/ESGF/input4MIPs.1127; Hurtt et al., 2011, 2018). Furthermore, H&N2017
assume conversion of natural grasslands to pasture, while BLUE allocates
pasture proportionally on all natural vegetation that exists in a grid cell.
This is one reason for generally higher emissions in BLUE. H&N2017 add
carbon emissions from peat burning based on the Global Fire Emission Database
(GFED4s; van der Werf et al., 2017) and peat drainage based on estimates by
Hooijer et al. (2010) to the output of their bookkeeping model for the
countries of Indonesia and Malaysia. Peat burning and emissions from the
organic layers of drained peat soils, which are not captured by bookkeeping
methods directly, need to be included to represent the substantially larger
emissions and interannual variability due to synergies of land use and
climate variability in Southeast Asia, in particular during El Niño
events. Similarly to H&N2017, peat burning and drainage-related emissions
are also added to the BLUE estimate.
The two bookkeeping estimates used in this study also differ with respect to
the land-use change data used to drive the models. H&N2017 base their
estimates directly on the Forest Resource Assessment of the FAO, which
provides statistics on forest area change and management at intervals of 5
years currently updated until 2015 (FAO, 2015). The data are based on country
reporting to the FAO and may include remote-sensing information in more recent
assessments. Changes in land use other than forests are based on annual
national changes in cropland and pasture areas reported by the FAO (FAOSTAT,
2015). BLUE uses the harmonised land-use change data LUH2 (https://doi.org/10.22033/ESGF/input4MIPs.1127,
Hurtt et al.,
2011, 2018), which describe land-use change, also based on the FAO data, but
downscaled at a quarter-degree spatial resolution, considering sub-grid-scale
transitions among primary forest, secondary forest, cropland, pasture, and
rangeland. The LUH2 data provide a new distinction between rangelands and
pasture. To constrain the models' interpretation on whether rangeland implies
the original natural vegetation to be transformed to grassland or not (e.g.
browsing on shrubland), a new forest mask was provided with LUH2; forest is
assumed to be transformed, while all other natural vegetation remains. This
is implemented in BLUE.
The estimate of H&N2017 was extended here by 2 years (to 2017) by adding
the anomaly of total tropical emissions (peat drainage from Hooijer et
al. (2010), peat burning, and tropical deforestation and degradation
fires (from GFED4s) over the previous decade (2006–2015) to the decadal
average of the bookkeeping result.
Dynamic global vegetation models (DGVMs)
Land-use change CO2 emissions have also been estimated using an
ensemble of 16 DGVM simulations. The DGVMs account for deforestation and
regrowth, the most important components of ELUC, but they do not
represent all processes resulting directly from human activities on land
(Table A1). All DGVMs represent processes of vegetation growth and mortality,
as well as decomposition of dead organic matter associated with natural
cycles, and include the vegetation and soil carbon response to increasing
atmospheric CO2 levels and to climate variability and change. Some
models explicitly simulate the coupling of carbon and nitrogen cycles and
account for atmospheric N deposition (Table A1). The DGVMs are independent
from the other budget terms except for their use of atmospheric CO2
concentration to calculate the fertilisation effect of CO2 on plant
photosynthesis.
The DGVMs used the HYDE land-use change data set (Klein Goldewijk et al.,
2017a, b), which provides annual half-degree fractional data on cropland
and pasture. These data are based on annual FAO statistics of change in
agricultural land area available until 2012. The FAOSTAT land use database is
updated annually, currently covering the period of 1961–2016 (but used here
until
2015 because of the timing of data availability). HYDE-applied annual changes
in FAO data to the year 2012 from the previous release are used to derive new
2013–2015 data. After the year 2015 HYDE extrapolates cropland, pasture, and
urban land use data until the year 2018. Some models also use an update of
the more comprehensive harmonised land-use data set (Hurtt et al., 2011),
which further includes fractional data on primary and secondary forest
vegetation, as well as all underlying transitions between land-use states
(Hurtt et al., 2018; Table A1). This new data set is of quarter-degree
fractional areas of land use states and all transitions between those states,
including a new wood harvest reconstruction, new representation of shifting
cultivation, crop rotations, and management information including irrigation and
fertiliser application. The land-use states now include five different crop
types in addition to the pasture–rangeland split discussed before. Wood
harvest patterns are constrained with Landsat tree cover loss data.
DGVMs implement land-use change differently (e.g. an increased cropland
fraction in a grid cell can be at the expense of either grassland or shrubs,
or forest, the latter resulting in deforestation; land cover fractions of the
non-agricultural land differ among models). Similarly, model-specific
assumptions are applied to convert deforested biomass or deforested area and
other forest product pools into carbon, and different choices are made
regarding the allocation of rangelands as natural vegetation or pastures.
The DGVM model runs were forced by either the merged monthly CRU and 6-hourly
JRA-55 data set or by the monthly CRU data set, both providing observation-based temperature, precipitation, and incoming surface radiation on a
0.5∘×0.5∘ grid and updated to 2017 (Harris et al.,
2014). The combination of CRU monthly data with 6-hourly forcing is updated
this year from NCEP to JRA-55 (Kobayashi et al., 2015), adapting the
methodology used in previous years (Viovy, 2016) to the specifics of the
JRA-55 data. The forcing data also include global atmospheric CO2,
which changes over time (Dlugokencky and Tans, 2018) and gridded time-dependent N deposition (as used in some models; Table A1).
Two sets of simulations were performed with the DGVMs. Both applied
historical changes in climate, atmospheric CO2 concentration, and N
deposition. The two sets of simulations differ, however, with respect to land
use: one set applies historical changes in land use, the other a
time-invariant pre-industrial land cover distribution and pre-industrial wood
harvest rates. By difference of the two simulations, the dynamic evolution of
vegetation biomass and soil carbon pools in response to land use change can
be quantified in each model (ELUC). We only retain model outputs
with positive ELUC, i.e. a positive flux to the atmosphere,
during the 1990s (Table A1). Using the difference between these two DGVM
simulations to diagnose ELUC means the DGVMs account for the loss
of additional sink capacity (around 0.3 GtC yr-1; see Sect. 2.8.4),
while the bookkeeping models do not.
Uncertainty assessment for ELUC
Differences between the bookkeeping models and DGVM models originate from
three main sources: the different methodologies, the underlying land use/land
cover data set, and the different processes represented (Table A1). We
examine the results from the DGVM models and from the bookkeeping method and
use the resulting variations as a way to characterise the uncertainty in
ELUC.
Comparison of results from the bookkeeping method and
budget residuals with results from the DGVMs and inverse estimates for
different periods, the last decade, and the last year available. All values are in
GtC yr-1. The DGVM uncertainties represent ±1σ of the
decadal or annual (for 2017 only) estimates from the individual DGVMs: for
the inverse models the range of available results is given.
Mean (GtC yr-1) ±1σ1960–19691970–19791980–19891990–19992000–20092008–20172017Land-use change emissions (ELUC) Bookkeeping methods1.5±0.71.2±0.71.2±0.71.4±0.71.3±0.71.5±0.71.4±0.7DGVMs1.5±0.71.4±0.71.5±0.71.3±0.61.4±0.61.9±0.62.0±0.7Terrestrial sink (SLAND) Residual sink from global budget (EFF+ELUC-GATM-SOCEAN)1.8±0.91.8±0.91.5±0.92.6±0.92.9±0.93.5±1.04.1±1.0DGVMs1.2±0.52.1±0.41.8±0.62.4±0.52.7±0.73.2±0.73.8±0.8Total land fluxes (SLAND-ELUC) Budget constraint (EFF-GATM-SOCEAN)0.3±0.50.6±0.60.4±0.61.2±0.61.6±0.62.1±0.72.7±0.7DGVMs-0.3±0.60.7±0.50.3±0.61.1±0.51.3±0.51.3±0.51.8±0.5Inversions*–/–/––/–/–-0.2–0.10.5–1.10.8–1.51.4–2.41.2–3.1
* Estimates are corrected for the pre-industrial influence of river fluxes
and adjusted to common EFF (Sect. 2.8.2). Two inversions are
available for the 1980s and 1990s. Two additional inversions are available
from 2001 and used from the decade of the 2000s (Table A3).
The ELUC estimate from the DGVMs multi-model mean is consistent
with the average of the emissions from the bookkeeping models (Table 5).
However there are large differences among individual DGVMs (standard
deviation at around 0.6–0.7 GtC yr-1; Table 5), between the two
bookkeeping models (average of 0.7 GtC yr-1), and between the current
estimate of H&N2017 and its previous model version (Houghton et al.,
2012). The uncertainty in ELUC of ±0.7 GtC yr-1
reflects our best value judgment that there is at least a 68 % chance (±1σ) that the true land-use change emission lies within the given
range, for the range of processes considered here. Prior to the year 1959,
the uncertainty in ELUC was taken from the standard deviation of
the DGVMs. We assign low confidence to the annual estimates of
ELUC because of the inconsistencies among estimates and of the
difficulties to quantify some of the processes in DGVMs.
Emission projections
We project emissions for both H&N2017 and BLUE for 2018 using the same
approach as for the extrapolation of H&N2017 for 2016–2017. Peat burning
as well as tropical deforestation and degradation are estimated using active
fire data (MCD14ML; Giglio et al., 2016), which scales almost linearly with
GFED (van der Werf et al., 2017) and thus allows for tracking fire emissions
in deforestation and tropical peat zones in near-real time. During most
years, emissions during January–October cover most of the fire season in the
Amazon and Southeast Asia, where a large part of the global deforestation
takes place.
Growth rate in atmospheric CO2 concentration
(GATM)Global growth rate in atmospheric CO2 concentration
The rate of growth of the atmospheric CO2 concentration is provided
by the US National Oceanic and Atmospheric Administration Earth System
Research Laboratory (NOAA/ESRL, 2018; Dlugokencky and Tans, 2018), which is updated
from Ballantyne et al. (2012). For the 1959–1979 period, the global growth
rate is based on measurements of atmospheric CO2 concentration
averaged from the Mauna Loa and South Pole stations, as observed by the
CO2 Program at the Scripps Institution of Oceanography (Keeling et al.,
1976). For the 1980–2017 time period, the global growth rate is based on the
average of multiple stations selected from the marine boundary layer sites
with well-mixed background air (Ballantyne et al., 2012), after fitting each
station with a smoothed curve as a function of time and averaging by
latitude band (Masarie and Tans, 1995). The annual growth rate is estimated
by Dlugokencky and Tans (2018) from the atmospheric CO2 concentration
by taking the average of the most recent December–January months corrected
for the average seasonal cycle and subtracting this same average 1 year
earlier. The growth rate in units of ppm yr-1 is converted to units of
GtC yr-1 by multiplying by a factor of 2.124 GtC per ppm
(Ballantyne et al., 2012).
The uncertainty around the atmospheric growth rate is due to four main
factors. The first factor is the long-term reproducibility of reference gas standards
(around 0.03 ppm for 1σ from the 1980s). The second factor is that small unexplained
systematic analytical errors that may have a duration of several months to
2 years come and go. They have been simulated by randomising both the
duration and the magnitude (determined from the existing evidence) in a Monte
Carlo procedure. The third factor is the network composition of the marine boundary layer
with some sites coming or going, gaps in the time series at each site, etc.
(Dlugokencky and Tans, 2018). The latter uncertainty was estimated by
NOAA/ESRL with a Monte Carlo method by constructing 100 “alternative”
networks (NOAA/ESRL, 2018; Masarie and Tans, 1995). The second and third
uncertainties, summed in quadrature, add up to 0.085 ppm on average
(Dlugokencky and Tans, 2018). Fourth, the uncertainty associated with using
the average CO2 concentration from a surface network to approximate
the true atmospheric average CO2 concentration (mass weighted, in
three dimensions) as needed to assess the total atmospheric CO2 burden.
In reality, CO2 variations measured at the stations will not
exactly track changes in total atmospheric burden, with offsets in magnitude
and phasing due to vertical and horizontal mixing. This effect must be very
small on decadal and longer timescales, when the atmosphere can be
considered well mixed. Preliminary estimates suggest this effect would
increase the annual uncertainty, but a full analysis is not yet available. We
therefore maintain an uncertainty around the annual growth rate based on the
multiple stations' data set ranges between 0.11 and 0.72 GtC yr-1, with
a mean of 0.61 GtC yr-1 for 1959–1979 and 0.18 GtC yr-1 for
1980–2017, when a larger set of stations were available as provided by
Dlugokencky and Tans (2018), but recognise further exploration of this
uncertainty is required. At this time, we estimate the uncertainty of the
decadal averaged growth rate after 1980 at 0.02 GtC yr-1 based on the
calibration and the annual growth rate uncertainty, but stretched over a
10-year interval. For years prior to 1980, we estimate the decadal averaged
uncertainty to be 0.07 GtC yr-1 based on a factor proportional to the
annual uncertainty prior to and after 1980
(0.61/0.18×0.02 GtC yr-1).
We assign a high confidence to the annual estimates of GATM
because 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 atmosphere since
1750 or 1870, we use an atmospheric CO2 concentration of 277±3 ppm or 288±3 ppm, respectively, based on a cubic spline fit to ice
core data (Joos and Spahni, 2008). The uncertainty of ±3 ppm (converted
to ±1σ) is taken directly from the IPCC's assessment (Ciais et
al., 2013). Typical uncertainties in the growth rate in atmospheric
CO2 concentration from ice core data are equivalent to ±0.1–0.15 GtC yr-1 as evaluated from the Law Dome data
(Etheridge et al., 1996) for individual 20-year intervals over the period
from 1870 to 1960 (Bruno and Joos, 1997).
Atmospheric growth rate projection
We provide an assessment of GATM for 2018 based on the observed
increase in atmospheric CO2 concentration at the Mauna Loa station
for January to October and a mean growth rate over the past 5 years for the
months November to December. Growth at Mauna Loa is closely correlated with
the global growth (r=0.95) and is used here as a proxy for global growth,
but the regression is not 1 to 1. We also adjust the projected global growth
rate to take this into account. The assessment method used this year differs
from the forecast method used in Le Quéré et al. (2018) based on the
relationship between annual CO2 growth rate and sea surface
temperatures (SSTs) in the Niño3.4 region of Betts et al. (2016). A
change was introduced because although the observed growth rate for 2017 of
2.2 ppm was within the projection range of 2.5±0.5 ppm of last year
( Le Quéré et al., 2018), the forecast values for 2018 for January to
October are too high by approximately 0.4 ppm above observed values on
average. The reasons for the difference are being investigated. The use of
observed growth at Mauna Loa Observatory, Hawaii, for the first half of the year is thought to be more
robust because of its high correlation with the global growth rate.
Furthermore, additional analysis suggests that the first half of the year
shows more interannual variability than the second half of the year, so that
the exact projection method applied to November–December has only a small
impact (<0.1 ppm) on the projection of the full year. Uncertainty is
estimated from past variability using the standard deviation of the last
5 years' monthly growth rates.
Ocean CO2 sink
Estimates of the global ocean CO2 sink SOCEAN are from
an ensemble of global ocean biogeochemistry models (GOBMs) that meet
observational constraints over the 1990s (see below). We use
observation-based estimates of SOCEAN to provide a qualitative
assessment of confidence in the reported results and to estimate the
cumulative accumulation of SOCEAN over the pre-industrial period.
Observation-based estimates
We use the observational constraints assessed by IPCC of a mean ocean
CO2 sink of 2.2±0.4 GtC yr-1 for the 1990s
(Denman et al., 2007) to verify that the
GOBMs provide a realistic assessment of SOCEAN. This is based on
indirect observations with seven different methodologies and their
uncertainties, using the methods that are deemed most reliable for the
assessment of this quantity (Denman et al., 2007). The IPCC confirmed this
assessment in 2013 (Ciais et al., 2013). The observational-based estimates
use the ocean–land CO2 sink partitioning from observed atmospheric
O2/N2 concentration trends (Manning and Keeling, 2006; updated
in Keeling and Manning 2014), an oceanic inversion method constrained by
ocean biogeochemistry data (Mikaloff Fletcher et al., 2006), and a method
based on a penetration timescale for chlorofluorocarbons (McNeil et al., 2003). The IPCC
estimate of 2.2 GtC yr-1 for the 1990s is consistent with a range of
methods (Wanninkhof et al., 2013).
We also use two estimates of the ocean CO2 sink and its variability
based on interpolations of measurements of surface ocean fugacity of
CO2 (pCO2 corrected for the non-ideal behaviour
of the gas; Pfeil et al., 2013). We refer to these as
pCO2-based flux estimates. The measurements are from the
Surface Ocean CO2 Atlas version 6, which is an update of version 3
(Bakker et al., 2016) and contains quality-controlled data until 2017 (see data
attribution Table A4). The SOCAT v6 data were mapped using 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 pCO2-based flux estimates were adjusted to remove
the pre-industrial ocean source of CO2 to the atmosphere of
0.78 GtC yr-1 from river input to the ocean (Resplandy et al., 2018),
per our definition of SOCEAN. Several other ocean sink products
based on observations are also available but they continue to show large
unresolved discrepancies with observed variability. Here we used the two
pCO2-based flux products that had the best fit to
observations for their representation of tropical and global variability
(Rödenbeck et al., 2015).
We further use results from two diagnostic ocean models of Khatiwala et
al. (2013) and DeVries (2014) to estimate the anthropogenic carbon
accumulated in the ocean prior to 1959. The two approaches assume constant
ocean circulation and biological fluxes, with SOCEAN estimated as
a response in the change in atmospheric CO2 concentration
calibrated to observations. The uncertainty in cumulative uptake of ±20 GtC (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).
Global ocean biogeochemistry models (GOBMs)
The ocean CO2 sink for 1959–2017 is estimated using seven GOBMs
(Table A2). The GOBMs represent the physical, chemical, and biological
processes that influence the surface ocean concentration of CO2 and
thus the air–sea CO2 flux. The GOBMs are forced by meteorological
reanalysis and atmospheric CO2 concentration data available for the
entire time period. They mostly differ in the source of the atmospheric
forcing data (meteorological reanalysis), spin-up strategies, and their
horizontal and vertical resolutions (Table A2). GOBMs do not include the
effects of anthropogenic changes in nutrient supply, which could lead to an
increase in the ocean sink of up to about 0.3 GtC yr-1 over the
industrial period (Duce et al., 2008). They also do not include the
perturbation associated with changes in riverine organic carbon (see
Sect. 2.8.3).
GOBM evaluation and uncertainty assessment for
SOCEAN
The mean ocean CO2 sink for all GOBMs falls within 90 % confidence
of the observed range, or 1.6 to 2.8 GtC yr-1 for the 1990s. Here we
have adjusted the confidence interval to the IPCC confidence interval of
90 % to avoid rejecting models that may be outliers but are still
plausible.
The GOBMs and flux products have been further evaluated using
fCO2 from the SOCAT v6 database. We focused this initial
evaluation on the interannual mismatch metric proposed by Rödenbeck et
al. (2015) for the comparison of flux products. The metric provides a measure
of the mismatch between observations and models or flux products on the
x axis as well as a measure of the amplitude of the interannual variability
on the y axis. A smaller number on the x axis indicates a better fit with
observations. The amplitude of the interannual variability in
SOCEAN (y axis) is calculated as the temporal standard
deviation of the CO2 flux time series.
The calculation for the x axis is carried out as follows: (1) the mismatch between
the observed and the modelled fCO2 is calculated for the
period 1985 to 2017 (except for the IPSL model, which uses 1985 to 2015 due to
data availability), but only for grid points for which actual observations exist.
(2) The interannual variability in this mismatch is calculated as the
temporal standard deviation of the mismatch. (3) To put numbers into
perspective, the interannual variability in the mismatch is reported relative
to the interannual variability in the mismatch between a benchmark
fCO2 field and the observations. The benchmark
fCO2 field is designed to have no interannual variability,
i.e. it is calculated as the mean seasonal cycle at each grid point over the
full period plus the deseasonalised atmospheric fCO2
increase over time. By definition, the interannual variability in the misfit
between benchmark and observations is large as the benchmark field does not
contain any interannual variability from the ocean. A smaller relative
interannual variability mismatch indicates a better fit between observed and
modelled fCO2. This metric is chosen because it is the
most direct measure of the year-to-year variability in SOCEAN in
ocean biogeochemistry models. We apply the metric globally and by latitude
bands. Results are shown in Fig. B1 and discussed in Sect. 3.1.3.
The uncertainty around the mean ocean sink of anthropogenic CO2 was
quantified by Denman et al. (2007) for the 1990s (see Sect. 2.5.1). To
quantify the uncertainty around annual values, we examine the standard
deviation of the GOBM ensemble, which averages between 0.2 and
0.3 GtC yr-1 during 1959–2017. We estimate that the uncertainty in
the annual ocean CO2 sink is about ±0.5 GtC yr-1 from
the combined uncertainty of the mean flux based on observations of ±0.4 GtC yr-1 and the standard deviation across GOBMs of up to ±0.3 GtC yr-1, reflecting the uncertainty in both the mean sink from
observations during the 1990s (Denman et al., 2007; Sect. 2.5.1) and the
interannual variability as assessed by GOBMs.
We examine the consistency between the variability in the model-based and the
pCO2-based flux products to assess confidence in
SOCEAN. The interannual variability in the ocean fluxes
(quantified as the standard deviation) of the two
pCO2-based flux products for 1985–2017 (where they
overlap) is ±0.36 GtC yr-1 (Rödenbeck et al., 2014) and ±0.38 GtC yr-1 (Landschützer et al., 2015), compared to ±0.29 GtC yr-1 for the GOBM ensemble. The standard deviation includes
a component of trend and decadal variability in addition to interannual
variability, and their relative influence differs across estimates.
Individual estimates (both GOBM and flux products) generally produce a higher
ocean CO2 sink during strong El Niño events. The annual
pCO2-based flux products correlate with the ocean
CO2 sink estimated here with a correlation of r=0.75 (0.59 to
0.79 for individual GOBMs) and r=0.80 (0.71 to 0.81) for the
pCO2-based flux products of Rödenbeck et al. (2014)
and Landschützer et al. (2015), respectively (simple linear regression),
with their mutual correlation at 0.73. The agreement between models and the
flux products reflects some consistency in their representation of underlying
variability since there is little overlap in their methodology or use of
observations. The use of annual data for the correlation 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 CO2 sink and its uncertainty
because it is based on multiple lines of evidence, and the results are
consistent in that the interannual variability in the GOBMs and data-based
estimates are all generally small compared to the variability in the growth
rate of atmospheric CO2 concentration.
Terrestrial CO2 sinkDGVM simulations
The terrestrial land sink (SLAND) is thought to be due to the
combined effects of fertilisation by rising atmospheric CO2 and N
deposition on plant growth, as well as the effects of climate change such as
the lengthening of the growing season in northern temperate and boreal areas.
SLAND does not include land sinks directly resulting from land
use and land-use change (e.g. regrowth of vegetation) as these are part of
the land use flux (ELUC), although system boundaries make it
difficult to exactly attribute CO2 fluxes on land between
SLAND and ELUC (Erb et al., 2013).
SLAND is estimated from the multi-model mean of the DGVMs
(Table 4). As described in Sect. 2.3.2, DGVM simulations include all climate
variability and CO2 effects over land, with some DGVMs also
including the effect of N deposition. The DGVMs do not include the
perturbation associated with changes in river organic carbon, which is
discussed in Sect. 2.8.
DGVM evaluation and uncertainty assessment for
SLAND
We apply three criteria for minimum DGVM realism by including only those
DGVMs with (1) steady state after spin-up; (2) net land fluxes
(SLAND–ELUC) that are the atmosphere-to-land carbon
flux over the 1990s ranging between -0.3 and 2.3 GtC yr-1, within
90 % confidence of constraints by global atmospheric and oceanic
observations (Keeling and Manning, 2014; Wanninkhof et al., 2013); and
(3) global ELUC that is a carbon source to the atmosphere over
the 1990s. All 16 DGVMs meet the three criteria.
In addition, the DGVM results are now also evaluated using the International
Land Model Benchmarking System (ILAMB; Collier et al., 2018). This evaluation
is provided here to document, encourage, and support model improvements
through time. ILAMB variables cover key processes that are relevant for the
quantification of SLAND and resulting aggregated outcomes. The
selected variables are vegetation biomass, gross primary productivity, leaf
area index, net ecosystem exchange, ecosystem respiration,
evapotranspiration, and runoff (see Fig. B2 for the results and for the list
of observed databases). Results are shown in Fig. B2 and discussed in
Sect. 3.1.3.
For the uncertainty, we use the standard deviation of the annual
CO2 sink across the DGVMs, which averages to ±0.8 GtC yr-1 for the period from 1959 to 2017. We attach a medium
confidence level to the annual land CO2 sink and its uncertainty
because the estimates from the residual budget and averaged DGVMs match well
within their respective uncertainties (Table 5).
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 CO2 fluxes from all sources, including fossil and land-use
change emissions and land and ocean CO2 fluxes. 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 gradients of
CO2 among stations that are not explained by fossil fuel
emissions.
Four atmospheric inversions (Table A3) used atmospheric CO2 data
until
the end of 2017 (including preliminary values in some cases) to infer the
spatio-temporal distribution of the CO2 flux exchanged between the
atmosphere and the land or oceans. We focus here on the largest and most
consistent sources of information, namely the total land and ocean
CO2 fluxes and their partitioning among the mid- to high-latitude region
of the Northern Hemisphere (30–90∘ N), the tropics
(30∘ S–30∘ N), and the mid- to high-latitude region of the
Southern Hemisphere (30–90∘ S). We also break down those estimates
for the land and ocean regions separately, to further scrutinise the
constraints from atmospheric observations. We use these estimates to comment
on the consistency across various data streams and process-based estimates.
Atmospheric inversions
The four inversion systems used in this release are CarbonTracker Europe
(CTE; van der Laan-Luijkx et al., 2017), Jena CarboScope
(Rödenbeck, 2005), the Copernicus Atmosphere Monitoring Service
(CAMS; Chevallier et al., 2005), and MIROC
(Patra et al., 2018). See Table A3 for version numbers. The
inversions are based on the same Bayesian inversion principles that interpret
the same, for the most part, observed time series (or subsets thereof) but
use different methodologies (Table A3). These differences mainly concern the
selection of atmospheric CO2 data, the used prior fluxes, spatial
breakdown (i.e. grid size), assumed correlation structures, and mathematical
approach. The details of these approaches are documented extensively in the
references provided above. Each system uses a different transport model,
which was demonstrated to be a driving factor behind differences in
atmospheric-based flux estimates, and specifically their distribution across
latitudinal bands (e.g. Gaubert et al., 2018).
The inversions use atmospheric CO2 observations from various flask
and in situ networks, as detailed in Table A3. They prescribe global
EFF, which is scaled to the present study for CAMS and CTE, while
slightly lower EFF values based on alternative emission
compilations were used in CarboScope and MIROC. Since this is known to result
directly in lower total CO2 uptake in atmospheric inversions
(Gaubert et al., 2018; Peylin et al., 2013), we adjusted the land sink of
each inversion estimate (where most of the emissions occur) by its fossil
fuel difference to the CAMS model. These differences amount to as much as
0.7 GtC for certain years (CarboScope inversion region NH) and are thus an
important consideration in an inverse flux comparison.
The land–ocean CO2 fluxes from atmospheric inversions contain
anthropogenic perturbation and natural pre-industrial CO2 fluxes.
Natural pre-industrial fluxes are land CO2 sinks corresponding to
carbon transported to the ocean by rivers. These land CO2 sinks are
compensated for over the globe by ocean CO2 sources corresponding to
the outgassing of riverine carbon inputs to the ocean. We apply the
distribution of land CO2 fluxes in three latitude bands using
estimates from Resplandy et al. (2018), which are constrained by ocean heat
transport to a total sink of 0.78 GtC yr-1. The latitude distribution
of river-induced ocean CO2 sources is derived from a simulation of
the IPSL GOBM using the river flux constrained by heat transport
of Resplandy et al. (2018) as an input. We adjusted the land–ocean fluxes per latitude
band based on these results.
The atmospheric inversions are now evaluated using vertical profiles of
atmospheric CO2 concentrations (Fig. B3). More than 50 aircraft
programmes over the globe, either regular or occasional, have been used in
order to draw a picture of the model performance but the space–time
data coverage is irregular, denser around 2009 or in the 0–45∘ N
latitude band. The four models are compared to independent CO2
measurements made onboard aircraft over many places of the world between 1
and 7 km above sea level, between 2008 and 2016. Results are shown in
Fig. B3 and discussed in Sect. 3.1.3.
Processes not included in the global carbon budget
The contribution of anthropogenic CO and CH4 to the global carbon
budget has been partly neglected in Eq. (1) and is described in Sect. 2.8.1.
The contributions of other carbonates to CO2 emissions are described
in Sect. 2.8.2. The contribution of anthropogenic changes in river fluxes is
conceptually included in Eq. (1) in SOCEAN and in
SLAND, but it is not represented in the process models used to
quantify these fluxes. This effect is discussed in Sect. 2.8.3. Similarly,
the loss of additional sink capacity from reduced forest cover is missing in
the combination of approaches used here to estimate both land fluxes
(ELUC and SLAND) and its potential effect is
discussed and quantified in Sect. 2.8.4.
Contribution of anthropogenic CO and CH4 to the global
carbon budget
Equation (1) only partly includes the net input of CO2 to the
atmosphere from the chemical oxidation of reactive carbon-containing gases
from sources other than the combustion of fossil fuels, such as (1) cement
process emissions, since these do not come from combustion of fossil fuels,
(2) the oxidation of fossil fuels, and (3) the assumption of immediate oxidation
of vented methane in oil production. Equation (1) omits however any other anthropogenic
carbon-containing gases that are eventually oxidised in the atmosphere, such
as anthropogenic emissions of CO and CH4. 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 CH4 that matter for the global carbon budget are the
fugitive emissions of coal, oil, and gas upstream from sectors (see below). These
emissions of CO and CH4 contribute 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
associated with incomplete combustion and their atmospheric oxidation into
CO2 within 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 CH4 are not included in EFF
because these fugitive emissions are not included in the fuel inventories.
Yet they contribute to the annual CO2 growth rate after
CH4 is oxidised into CO2. Anthropogenic emissions of
fossil CH4 represent 15 % of total CH4 emissions
(Kirschke et al., 2013), that is 0.061 GtC yr-1 for the past decade.
Assuming steady state, these emissions are all converted to CO2 by
OH oxidation and thus explain 0.06 GtC yr-1 of the global
CO2 growth rate in the past decade, or 0.07–0.1 GtC yr-1
using the higher CH4 emissions reported recently (Schwietzke et
al., 2016).
Schematic representation of the overall perturbation of the global
carbon cycle caused by anthropogenic activities, averaged globally for the
decade 2008–2017. See legends for the corresponding arrows and units. The
uncertainty in the atmospheric CO2 growth rate is very small (±0.02 GtC yr-1) and is neglected for the figure. The anthropogenic
perturbation occurs on top of an active carbon cycle, with fluxes and stocks
represented in the background and taken from Ciais et al. (2013) for all
numbers, with the ocean fluxes updated to 90 GtC yr-1 to account for
the increase in atmospheric CO2 since publication, and except for
the carbon stocks at the coasts, which are from a literature review of coastal
marine sediments (Price and Warren, 2016).
Other anthropogenic changes in the sources of CO and CH4 from
wildfires, vegetation biomass, wetlands, ruminants, or permafrost changes are
similarly assumed to have a small effect on the CO2 growth rate.
The CH4 emissions and sinks are published and analysed separately
in the Global Methane Budget publication that follows an approach similar to
that
presented here (Saunois et al., 2016).
Contribution of other carbonates to CO2 emissions
The contribution of fossil carbonates other than cement production is not
systematically included in estimates of EFF, except at the
national level at which they are accounted for in the UNFCCC national inventories.
The missing processes include CO2 emissions associated with the
calcination of lime and limestone outside cement production and the
reabsorption of CO2 by the rocks and concrete from carbonation
through their lifetime (Xi et al., 2016). Carbonates are used in various
industries, including in iron and steel manufacture and in agriculture. They
are found naturally in some coals. Carbonation from the cement life cycle,
including demolition and crushing, was estimated by one study to be around
0.25 GtC yr-1 for the year 2013 (Xi et al., 2016). Carbonation emissions
from the cement life cycle would offset calcination emissions from lime and
limestone production. The balance of these two processes is not clear.
Anthropogenic carbon fluxes in the land-to-ocean aquatic
continuum
The approach used to determine the global carbon budget refers to the mean,
variations, and trends in the perturbation of CO2 in the
atmosphere, referenced to the pre-industrial era. Carbon is continuously
displaced from the land to the ocean through the land–ocean aquatic continuum
(LOAC) comprising freshwaters, estuaries, and coastal areas (Bauer et al.,
2013; Regnier et al., 2013). A significant fraction of this lateral carbon
flux is entirely “natural” and is thus a steady-state component of the
pre-industrial carbon cycle. We account for this pre-industrial flux where
appropriate in our study. However, changes in environmental conditions and
land use change have caused an increase in the lateral transport of carbon
into the LOAC – a perturbation that is relevant for the global carbon budget
presented here.
The results of the analysis of Regnier et al. (2013) can be summarised in two
points of relevance for the anthropogenic CO2 budget. First, the
anthropogenic perturbation has increased the organic carbon export from
terrestrial ecosystems to the hydrosphere at a rate of 1.0±0.5 GtC yr-1, mainly owing to enhanced carbon export from soils.
Second, this exported anthropogenic carbon is partly respired through the
LOAC, partly sequestered in sediments along the LOAC, and to a lesser extent
transferred to the open ocean where it may accumulate. The increase in
storage of land-derived organic carbon in the LOAC and open ocean combined is
estimated by Regnier et al. (2013) at 0.65±0.35 GtC yr-1. We do
not attempt to incorporate the changes in LOAC in our study.
The inclusion of freshwater fluxes of anthropogenic CO2 affects the
estimates of, and partitioning between, SLAND and
SOCEAN in Eq. (1), but does not affect the other terms. This
effect is not included in the GOBMs and DGVMs used in our global carbon
budget analysis presented here.
Loss of additional sink capacity
Historical land-cover change was dominated by transitions from vegetation
types that can provide a large sink per area unit (typically forests) to
others less efficient in removing CO2 from the atmosphere
(typically croplands). The resultant decrease in land sink, called the
“loss of sink capacity”, is calculated as the difference between the actual
land sink under changing land cover and the counterfactual land sink under
pre-industrial land cover. An efficient protocol has yet to be designed to
estimate the magnitude of the loss of additional sink capacity in DGVMs.
Here, we provide a quantitative estimate of this term to be used in the
discussion. Our estimate uses the compact Earth system model OSCAR whose land
carbon cycle component is designed to emulate the behaviour of DGVMs
(Gasser et al., 2017). We use OSCAR v2.2.1 (an update
of v2.2 with minor changes) in a probabilistic setup identical to the one of
Arneth et al. (2017) but with a Monte Carlo ensemble of 2000 simulations.
For each, we calculate SLAND and the loss of
additional sink capacity separately. We then constrain the ensemble by weighting each
member to obtain a distribution of cumulative SLAND over
1850–2005 close to the DGVMs used here. From this ensemble, we estimate a
loss of additional sink capacity of 0.4±0.3 GtC yr-1 on average
over 2005–2014 and 20±15 GtC accumulated between 1870 and 2017
(using a linear extrapolation of the trend to estimate the last few years).
ResultsGlobal carbon budget mean and variability for 1959–2017
The global carbon budget averaged over the last half-century is shown in
Fig. 3. For this time period, 82 % of the total emissions (EFF+ELUC) were caused by fossil CO2 emissions and 18 % by
land-use change. The total emissions were partitioned among the atmosphere
(45 %), ocean (24 %), and land (30 %). All components except land-use
change emissions have grown since 1959, with important interannual
variability in the growth rate in atmospheric CO2 concentration and
in the land CO2 sink (Fig. 4) and some decadal variability in all
terms (Table 6). Differences with previous budget releases are documented in
Fig. B4.
Combined components of the global carbon budget illustrated in
Fig. 2 as a function of time, for fossil CO2 emissions
(EFF; grey) and emissions from land-use change (ELUC;
brown), as well as their partitioning among the atmosphere (GATM;
blue), ocean (SOCEAN; turquoise), and land (SLAND;
green). The partitioning is based on nearly independent estimates from
observations (for GATM) and from process model ensembles
constrained by data (for SOCEAN and SLAND) and does
not exactly add up to the sum of the emissions, resulting in a budget
imbalance, which is represented by the difference between the bottom pink line
(reflecting total emissions) and the sum of the ocean, land, and atmosphere.
All time series are in GtC yr-1. GATM and
SOCEAN prior to 1959 are based on different methods.
EFF values are primarily from Boden et al. (2017), with uncertainty of
about ±5 % (±1σ); ELUC values are from two bookkeeping
models (Table 2) with uncertainties of about ±50 %; GATM
prior to 1959 is from Joos and Spahni (2008) with uncertainties equivalent to
about ±0.1–0.15 GtC yr-1 and from Dlugokencky and Tans (2018)
from 1959 with uncertainties of about ±0.2 GtC yr-1;
SOCEAN prior to 1959 is averaged from Khatiwala et al. (2013) and
DeVries (2014) with uncertainty of about ±30 % and from a multi-model
mean (Table 4) from 1959 with uncertainties of about ±0.5 GtC yr-1; SLAND is a multi-model mean (Table 4) with
uncertainties of about ±0.9 GtC yr-1. See the text for more
details of each component and their uncertainties.
Components of the global carbon budget and their uncertainties as a
function of time, presented individually for (a) fossil
CO2 emissions (EFF), (b) emissions from
land-use change (ELUC), (c) the budget imbalance that is
not accounted for by the other terms, (d) growth rate in atmospheric
CO2 concentration (GATM), (e) the land
CO2 sink (SLAND, positive indicates a flux from the
atmosphere to the land), and (f) the ocean CO2 sink
(SOCEAN, positive indicates a flux from the atmosphere to the
ocean). All time series are in GtC yr-1 with the uncertainty bounds
representing ±1σ in shaded colours. Data sources are as in Fig. 3.
The black dots in (a) show values for 2015–2017 that originate from
a different data set to the remainder of the data (see text). The dashed line
in (b) identifies the pre-satellite period before the inclusion of
peatland burning.
Decadal mean in the five components of the anthropogenic
CO2 budget for different periods and the last year available. All
values are in GtC yr-1, and uncertainties are reported as ±1σ. The table also shows the budget imbalance (BIM),
which provides a measure of the discrepancies among the nearly independent
estimates and has an uncertainty exceeding ±1 GtC yr-1. A
positive imbalance means the emissions are overestimated and/or the sinks are
too small.
Mean (GtC yr-1) 1960–19691970–19791980–19891990–19992000–20092008–20172017Total emissions (EFF+ELUC)Fossil CO2 emissions (EFF)3.1±0.24.7±0.25.4±0.36.3±0.37.8±0.49.4±0.59.9±0.5Land-use change emissions (ELUC)1.5±0.71.2±0.71.2±0.71.4±0.71.3±0.71.5±0.71.4±0.7Total emissions4.7±0.75.8±0.76.6±0.87.6±0.89.0±0.810.8±0.811.3±0.9PartitioningGrowth rate in atmospheric CO2 concentration (GATM)1.7±0.072.8±0.073.4±0.023.1±0.024.0±0.024.7±0.024.6±0.2Ocean sink (SOCEAN)1.0±0.51.3±0.51.7±0.52.0±0.52.1±0.52.4±0.52.5±0.5Terrestrial sink (SLAND)1.2±0.52.1±0.41.8±0.62.4±0.52.7±0.73.2±0.73.8±0.8Budget imbalanceBIM=EFF+ELUC-(GATM+SOCEAN+SLAND)(0.6)(-0.3)(-0.3)(0.2)(0.2)(0.5)(0.3)CO2 emissions
Global fossil CO2 emissions have increased every decade from an
average of 3.1±0.2 GtC yr-1 in the 1960s to an average of 9.4±0.5 GtC yr-1 during 2008–2017 (Table 6, Figs. 2 and 5). The
growth rate in these emissions decreased between the 1960s and the 1990s,
from 4.5 % yr-1 in the 1960s (1960–1969) to 2.8 % yr-1 in the
1970s (1970–1979), 1.9 % yr-1 in the 1980s (1980–1989), and
1.0 % yr-1 in the 1990s (1990–1999). After this period, the growth
rate began increasing again in the 2000s at an average growth rate of
3.2 % yr-1, decreasing to 1.5 % yr-1 for the last decade
(2008–2017), with a 3-year period of no or low growth during 2014–2016
(Fig. 5).
Fossil CO2 emissions for (a) the globe, including
an uncertainty of ±5 % (grey shading), and the emissions extrapolated
using BP energy statistics (black dots); (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 (solid lines) and consumption (dashed lines)
emissions for the top three country emitters (US – olive; China – salmon;
India – purple) and for the European Union (EU; turquoise for the 28 member
states of the EU as of 2012); and (d) per capita emissions for the
top three country emitters, the EU (all colours as in panel c),
and the world (black). In (b–c), the dots show the data that were
extrapolated from BP energy statistics for 2014–2016. All time series are in
GtC yr-1 except the per capita emissions (d), which are in
tonnes of carbon per person per year (tC person-1 yr-1).
Territorial emissions are primarily from Boden et al. (2017) except national
data for the US and EU28 (the 28 member states of the EU) for 1990–2016,
which are reported by the countries to the UNFCCC as detailed in the text;
consumption-based emissions are updated from Peters et al. (2011a). See
Sect. 2.1.1 for details of the calculations and data sources.
In contrast, CO2 emissions from land use, land-use change, and
forestry have remained relatively constant, at around 1.3±0.7 GtC yr-1 over the past half-century but with large spread across
estimates (Fig. 6). These emissions are also relatively constant in the DGVM
ensemble of models, except during the last decade when they increase to 1.9±0.6 GtC yr-1. However, there is no agreement on this recent
increase between the two bookkeeping models, each suggesting an opposite
trend (Fig. 6).
CO2 exchanges between the atmosphere and the terrestrial
biosphere as used in the global carbon budget (black with ±1σ
uncertainty in grey shading), for (a)CO2 emissions from
land-use change (ELUC), also individually showing the two
bookkeeping models (two brown lines) and the DGVM model results (green) and
their multi-model mean (dark green). The dashed line identifies the
pre-satellite period before the inclusion of peatland burning.
(b) Land CO2 sink (SLAND) with individual
DGVMs (green); (c) total land CO2 fluxes
(b–a) with individual DGVMs (green) and
their multi-model mean (dark green).
Partitioning among the atmosphere, ocean, and land
The growth rate in atmospheric CO2 level increased from 1.7±0.07 GtC yr-1 in the 1960s to 4.7±0.02 GtC yr-1 during
2008–2017 with important decadal variations (Table 6 and Fig. 2). Both ocean
and land CO2 sinks increased roughly in line with the atmospheric
increase, but with significant decadal variability on land (Table 6), and
possibly in the ocean (Fig. 7).
The ocean CO2 sink increased from 1.0±0.5 GtC yr-1 in
the 1960s to 2.4±0.5 GtC yr-1 during 2008–2017, with
interannual variations of the order of a few tenths of GtC yr-1
generally showing an increased ocean sink during large El Niño events
(i.e. 1997–1998) (Fig. 7; Rödenbeck et al., 2014). Although there is
some coherence among the GOBMs and pCO2-based flux
products regarding the mean, there is poor agreement for interannual
variability, and the ocean models underestimate decadal variability
(Sect. 2.5.3 and Fig. 7; DeVries et al., 2017).
Comparison of the anthropogenic atmosphere–ocean CO2 flux
showing the budget values of SOCEAN (black; with ±1σ
uncertainty in grey shading), individual ocean models (blue), and the two
ocean pCO2-based flux products (dark blue; see Table 4).
Both pCO2-based flux products were adjusted for the
pre-industrial ocean source of CO2 from river input to the ocean,
which is not present in the ocean models, by adding a sink of
0.78 GtC yr-1
(Resplandy et al., 2018), to make them comparable to
SOCEAN. This adjustment does not take into account the
anthropogenic contribution to river fluxes (see Sect. 2.8.3).
The terrestrial CO2 sink increased from 1.2±0.5 GtC yr-1 in the 1960s to 3.2±0.7 GtC yr-1 during
2008–2017, with important interannual variations of up to 2 GtC yr-1
generally showing a decreased land sink during El Niño events (Fig. 6),
responsible for the corresponding enhanced growth rate in atmospheric
CO2 concentration. The larger land CO2 sink during
2008–2017 compared to the 1960s is reproduced by all the DGVMs in response
to the combined atmospheric CO2 increase and changes in climate,
and consistent with constraints from the other budget terms (Table 5).
Estimates of total atmosphere-to-land fluxes
(SLAND–ELUC) from the DGVMs are consistent with the
budget constraints (Table 5), except during 2008–2017, when the DGVM
ensemble estimates a total atmosphere-to-land flux of 1.3±0.5 GtC yr-1, likely below the budget constraints of 2.1±0.7 GtC yr-1 and outside the range of the inversions (Table 5). This
comparison suggests that the DGVMs could overestimate ELUC
emissions and/or underestimate the terrestrial sink SLAND during
the last decade.
Model evaluation
The evaluation of ocean estimates (Fig. B1) shows a relative interannual
mismatch of 15 % and 17 % for the two pCO2-based flux
products over the globe, relative to the pCO2 observations
from the SOCAT v6 database for the period 1985–2017. A 0 % mismatch would
indicate a perfect model, and a field with no interannual variability would
result in a 100 % mismatch. A mismatch larger than 100 % is possible when
the method produces a larger mismatch than the benchmark field with no
interannual variability (see Sect. 2.5.3). This mismatch by the
pCO2-based flux products is improved compared with earlier
published versions of these two flux products of around 20 %–25 % for
the 1992–2009 time period (Rödenbeck et al., 2015), likely because of
the larger data availability after 2009. The GOBMs show a global relative
interannual mismatch between 50 % and 60 %, with one model at 94 % and
one at 193 %. The GOBM mismatch is of the same order as the mismatch
calculated in an ensemble of 14 flux products but larger than the two flux
products used in this report (Fig. 5 in Rödenbeck et al., 2015). The
mismatch is generally larger at high latitudes compared to the tropics, for
both the flux products and the GOBMs. The two flux products have a similar
mismatch of around 10 %–15 % in the tropics, around 25 % in the north,
and 30 %–55 % in the south. The GOBM mismatch is more spread across
regions, ranging from 29 % to 178 % in the tropics, 70 % to 192 % in
the north, and 108 % to 304 % in the south. The higher mismatch occurs in
regions with stronger climate variability, such as the northern and southern
high latitudes (poleward of the subtropical gyres) and the equatorial
Pacific. The latter is also apparent in the model mismatch but is hidden in
Fig. B1 due to the averaging over 30∘ S to 30∘ N (see also
Sect. 4).
The evaluation of the DGVMs (Fig. B2) shows generally high skill scores
across models for runoff, and to a lesser extent for vegetation biomass, GPP,
and ecosystem respiration (Fig. B2, left panel). The skill score was lowest for
leaf area index and net ecosystem exchange, with the widest disparity among
models for soil carbon. Further analysis of the results will be provided
separately, focusing on the strengths and weaknesses in the DGVM ensemble and
its validity for use in the global carbon budget.
The evaluation of the atmospheric inversions (Fig. B3) shows long-term mean
biases in the free troposphere better than 0.8 ppm in absolute values for
each product. CAMS and CTE biases show some dependency on latitude (a trend
of -0.0018±0.0005 and 0.0043±0.0004 ppm per degree for CAMS and
CTE, respectively). These latitude-dependent biases may reveal biases in the
surface fluxes (e.g. Houweling et al., 2015) but the link is not
straightforward and will be analysed separately. The biases for MIROC and
CarboScope behave similarly together in relative values, but they are less
regular than the two other products, which hampers the interpretation. Lesser
model performance for specific aircraft programmes, like for the 4-year
DISCOVER-AQ campaign in the continental US
(https://discover-aq.larc.nasa.gov/, last access: 28 November 2018), contributes to this variability.
Budget imbalance
The carbon budget imbalance (BIM; Eq. 1) quantifies the mismatch
between the estimated total emissions and the estimated changes in the
atmosphere, land, and ocean reservoirs. The mean budget imbalance from 1959 to
2017 is small (0.14 GtC yr-1) and shows no trend over the full time
series. The process models (GOBMs and DGVMs) have been selected to match
observational constraints in the 1990s but no further constraints have been
applied to their representation of trend and variability. Therefore, the
near-zero mean and trend in the budget imbalance are indirect evidence of a
coherent community understanding of the emissions and their partitioning on
those timescales (Fig. 4). However, the budget imbalance shows substantial
variability on the order of ±1 GtC yr-1, particularly over
semi-decadal timescales, although most of the variability is within the
uncertainty of the estimates. The positive carbon imbalance during the 1960s,
early 1990s, and in the last decade suggests that either the emissions were
overestimated or the sinks were underestimated during these periods. The
reverse is true for the 1970s and around 1995–2000 (Fig. 4).
We cannot attribute the cause of the variability in the budget imbalance with
our analysis, only to note that the budget imbalance is unlikely to be
explained by errors or biases in the emissions alone because of its large
semi-decadal variability component, a variability that is untypical of
emissions and has not changed in the past 50 years in spite of a nearly
tripling in emissions (Fig. 4). Errors in SLAND and
SOCEAN are more likely to be the main cause for the budget
imbalance. For example, underestimation of SLAND by DGVMs has
been reported following the eruption of Mount Pinatubo in 1991 possibly due
to missing responses to changes in diffuse radiation (Mercado et al., 2009)
or other yet unknown factors, and DGVMs are suspected to overestimate the land
sink in response to the wet decade of the 1970s (Sitch et al., 2008). Decadal
and semi-decadal variability in the ocean sink has been also reported
recently (DeVries et al., 2017; Landschützer et al., 2015), with the
pCO2-based ocean flux products suggesting a smaller-than-expected ocean CO2 sink in the 1990s and a larger-than-expected
sink in the 2000s (Fig. 7), possibly caused by changes in ocean circulation
(DeVries et al., 2017) not captured in coarse-resolution GOBMs used here
(Dufour et al., 2013). The absence of internal variability could also be at
fault. Internal variability is not captured by single realisations of coarse-resolution model simulations
(Li and Ilyina, 2018) and is thought to be
largest in regions with strong seasonal and interannual climate variability,
i.e. the high-latitude ocean regions (poleward of the subtropical gyres) and
the equatorial Pacific (McKinley et al., 2016). Some of these errors could be
driven by errors in the climatic forcing data, particularly precipitation
(for SLAND) and wind (for SOCEAN), rather than in the
models.
Global carbon budget for the last decade (2008–2017)
The global carbon budget averaged over the last decade (2008–2017) is shown
in Figs. 2 and 9. For this time period, 87 % of the total emissions
(EFF+ELUC) were from fossil CO2 emissions
(EFF) and 13 % were from land-use change (ELUC). The
total emissions were partitioned among the atmosphere (44 %), ocean
(22 %), and land (29 %), with a remaining unattributed budget imbalance
(5 %).
CO2 emissions
Global fossil CO2 emissions grew at a rate of 1.5 % yr-1
for the last decade (2008–2017). China's emissions increased by
+3.0 % yr-1 on average (increasing by +0.64 GtC yr-1
during the 10-year period), dominating the global trends, followed by India's
emissions increase by +5.2 % yr-1 (increasing by
+0.25 GtC yr-1), while emissions decreased in the EU28 by
-1.8 % yr-1 (decreasing by -0.17 GtC yr-1), and in the US
by 0.9 % yr-1 (decreasing by -0.18 GtC yr-1). In the past
decade, fossil CO2 emissions decreased significantly (at the 95 %
level) in 25 countries: Aruba, Barbados, Croatia, Czech Republic, North
Korea, Denmark, France, Greece, Greenland, Iceland, Ireland, Malta,
the Netherlands, Romania, Slovakia, Slovenia, Sweden, Switzerland, Syria,
Trinidad and Tobago, Ukraine, the United Kingdom, the US, Uzbekistan, and Venezuela.
Notable was Germany, whose emissions did not decrease significantly.
In contrast, there is no apparent trend in CO2 emissions from
land-use change (Fig. 6), though the data are very uncertain, with the two
bookkeeping estimates showing opposite trends over the last decade. Larger
emissions are expected increasingly over time for DGVM-based estimates as
they include the loss of additional sink capacity, while the bookkeeping
estimates do not. The LUH2 data set also features large dynamics in land use in
particular in the tropics in recent years, causing higher emissions in DGVMs
and BLUE than in H&N.
Partitioning among the atmosphere, ocean, and land
The growth rate in atmospheric CO2 concentration increased during
2008–2017, in contrast to more constant levels the previous decade and
reflecting a similar decrease in the land sink compared to an increase in the
previous decade, albeit with large interannual variability (Fig. 4). During
the same period, the ocean CO2 sink appears to have intensified, an
effect which is particularly apparent in the pCO2-based
flux products (Fig. 7) and is thought to originate at least in part in the
Southern Ocean (Landschützer et al., 2015).
The budget imbalance (Table 6) and the residual sink from the global budget
(Table 5) include an error term due to the inconsistency that arises from
using ELUC from bookkeeping models but SLAND from
DGVMs. This error term includes the fundamental differences between
bookkeeping models and DGVMs, most notably the loss of additional sink
capacity. Other differences include an incomplete account of land-use
change
practices and processes in DGVMs, while they are all accounted for in
bookkeeping models by using observed carbon densities, and bookkeeping error
of keeping present-day carbon densities fixed in the past. That the budget
imbalance shows no clear trend towards larger values over time is an
indication that the loss of additional sink capacity plays a minor role
compared to other errors in SLAND or SOCEAN
(discussed in Sect. 3.1.4).
Regional distribution
Figure 8 shows the partitioning of the total atmosphere-to-surface fluxes
excluding fossil CO2 emissions (SLAND+SOCEAN-ELUC) according to the multi-model average of the process models
in the ocean and on land (GOBMs and DGVMs) and to the atmospheric
inversions. Figure 8 provides information on the regional distribution of
those fluxes by latitude bands. The global mean total atmosphere-to-surface
CO2 fluxes from process models for 2008–2017 is 3.7±1.2 GtC yr-1. This is below but still within the uncertainty range of
a global mean atmosphere-to-surface flux of 4.6±0.5 GtC yr-1
inferred from the carbon budget (EFF–GATM in Eq. 1;
Table 6). The total atmosphere-to-surface CO2 fluxes from the four
inversions are very similar, ranging from 4.7 to 5.0 GtC yr-1,
consistent with the carbon budget as expected from the constraints on the
inversions and the adjustments to the same EFF distribution (see
Sect. 2.7).
CO2 fluxes between the atmosphere and the surface
(SOCEAN+SLAND-ELUC) by latitude bands for
the (top) globe (second row), north (north of 30∘ N), (third row) tropics
(30∘ S–30∘ N), and (bottom) south (south of 30∘ S)
and (left) total, (middle) land only (SLAND-ELUC), and
(right) ocean only. Positive values indicate a flux from the atmosphere to
the land and/or ocean. Estimates from the combination of the process models
for the land and oceans are shown (black for the total, green for the land,
blue for the ocean) with ±1σ of the model ensemble (in grey).
Results from the atmospheric inversions (pink lines) and from
the pCO2-based flux products (dark blue lines) are also shown.
In the south (south of 30∘ S), the atmospheric inversions suggest an
atmosphere-to-surface flux for 2008–2017 of around 1.6–1.7 GtC yr-1,
close to the process models' estimate of 1.4±0.7 GtC yr-1
(Fig. 8). The interannual variability in the south is low because of the
dominance of ocean area with low variability compared to land areas. The
split between land (SLAND-ELUC) and ocean
(SOCEAN) shows a small contribution to variability in the south
coming from the land, with no consistency between the DGVMs and the
inversions or among inversions. This is expected due to the difficulty of
separating exactly the land and oceanic fluxes when viewed from atmospheric
observations alone. The oceanic variability in the south is estimated to be
significant in the two flux products and in at least one of the inversions,
with decadal variability in around 0.5 GtC yr-1. The GOBMs do not
reproduce this variability.
In the tropics (30∘ S–30∘ N), both the atmospheric
inversions and process models suggest the total carbon balance in this region
is close to neutral on average over the past decade, with
atmosphere-to-surface fluxes for the 2008–2017 average ranging between
-0.4 and +0.4 GtC yr-1. The agreement between inversions and
models is significantly better for the last decade than for any previous
decade, although the reasons for this better agreement are still unclear.
Both the process models and the inversions consistently allocate more
year-to-year variability in CO2 fluxes to the tropics compared to
the north (north of 30∘ N; Fig. 8). The split between the land and
ocean indicates the land is the origin of most of the tropical variability,
consistently among models (both for the land and for the ocean) and
inversions. The oceanic variability in the tropics is similar among models
and with the two ocean flux products, reflected in their lower observational
mismatch (Sect. 3.1.3). While the inversions indicate that atmosphere-to-land
CO2 fluxes are more variable than atmosphere-to-ocean CO2
fluxes in the tropics, the correspondence between the inversions and the
ocean flux products or GOBMs is much poorer.
In the north (north of 30∘ N), the inversions and process models
show less agreement on the magnitude of the atmosphere-to-land flux, with the
ensemble mean of the process models suggesting a total Northern Hemisphere
sink for 2008–2017 of 2.2±0.6 GtC yr-1, likely below the
estimates from the inversions ranging from 2.6 to 3.6 GtC yr-1
(Fig. 8). The discrepancy in the north-tropics distribution of CO2
fluxes between the inversions and models arises from the differences in mean
fluxes over the northern land. This discrepancy is also evidenced over the
previous decade and highlights not only persistent issues with the
quantification of the drivers of the net land CO2 flux (Arneth et
al., 2017; Huntzinger et al., 2017) but also the distribution of
atmosphere-to-land fluxes between the tropics and higher latitudes that is
particularly marked in previous decades, as highlighted previously (Stephens
et al., 2007; Baccini et al., 2017; Schimel et al., 2015).
Differences between inversions may be related for example to differences in
their interhemispheric transport, and other inversion settings (Table A3).
Separate analysis has shown that the influence of the chosen prior land and
ocean fluxes is minor compared to other aspects of each inversion. In
comparison to the previous global carbon budget publication, the fossil fuel
inputs were adjusted to match those of EFF used in this analysis
(see Sect. 2.7), therefore removing differences due to
prior fossil emissions. Differences between inversions and the ensemble of process models in
the north cannot be simply explained. They could either reflect a bias in the
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 the tropics for the DGVMs. The estimated
contribution of the north and its uncertainty from process models is
sensitive to both the ensemble of process models used and the specifics of
each inversion.
Resolving the differences in the Northern Hemisphere land sink will require
the consideration and inclusion of larger volumes of semi-continuous
observations from tall towers close to the surface CO2 exchange. Some
of these data are becoming available but not used in the current inverse
models, sometimes due to the short records and sometimes because the coarse
transport models cannot adequately represent these time series. Improvements
in model resolution and atmospheric transport realism together with expansion
of the observational record (also in the data-sparse Boreal Eurasian area)
will help anchor the mid-latitude fluxes per continent. In addition, new
metrics could potentially differentiate between the more and less realistic
realisations of the Northern Hemisphere land sink shown in Fig. 8.
Budget imbalance
The budget imbalance was +0.5 GtC yr-1 on average over 2008–2017.
Although the uncertainties are large in each term, the sustained imbalance
over this last decade suggests an overestimation of the emissions and/or an
underestimation of the sinks. An origin in the land and/or ocean sink may be
more likely, given the large variability in the land sink and the suspected
underestimation of decadal variability in the ocean sink. An underestimate of
SLAND would also reconcile model results with inversion
estimates for fluxes in the total land during the past decade (Fig. 8;
Table 5). However, we cannot exclude that the budget imbalance over the last
decade could partly be due to an overestimation of CO2 emissions
from land-use change, given their large uncertainty, as has been suggested
elsewhere (Piao et al., 2018). More integrated use of observations in the
Global Carbon Budget, either on their own or for further constraining model
results, should help resolve some of the budget imbalance (Peters et al.,
2017; Sect. 4).
Global carbon budget for the year 2017CO2 emissions
Preliminary estimates of global fossil CO2 emissions based on BP
energy statistics are for emissions growing by 1.6 % between 2016 and 2017
to 9.9±0.5 GtC in 2017 (Fig. 5), distributed among coal (40 %), oil
(35 %), gas (20 %), cement (4 %), and gas flaring (0.7 %). Compared to
the previous year, emissions from coal increased by 1.6 %, while emissions
from oil, gas, and cement increased by 1.7 %, 3.0 %, and 1.2 %,
respectively. All growth rates presented are adjusted for the leap year,
unless stated otherwise.
The growth in emissions of 1.6 % in 2017 is within the range of the
projected growth of 2.0 % (range of 0.8 to 3.0 %) published in Le
Quéré et al. (2018) based on national emission projections for
China, the US, and India and projections of gross domestic product corrected
for IFF trends for the rest of the world. The growth in emissions
in 2017 for China, the US, and the rest of the world is also within their
previously projected range, while the growth in India was slightly above the
projection (Table 7).
Comparison of the projection with realised fossil
CO2 emissions (EFF). The “Actual” values are the first
estimate available using actual data, and the “Projected” values refer to
the estimate made before the end of the year for each publication. Projections
based on a different method from that described here during 2008–2014 are
available in Le Quéré et al. (2016). All values are adjusted for
leap years.
World China US EU28 India Rest of the world ProjectedActualProjectedActualProjectedActualProjectedActualProjectedActualProjectedActual2015a-0.6 % (-1.6 to 0.5)0.06 %-3.9 % (-4.6 to -1.1)-0.7 %-1.5 % (-5.5 to 0.3)-2.5 %––––1.2 % (-0.2 to 2.6)+1.2 %2016b-0.2 % (-1.0 to +1.8)0.2 %-0.5 % (-3.8 to +1.3)-0.3 %-1.7 % (-4.0 to +0.6)-2.1 %––––+1.0 % (-0.4 to +2.5)+1.3 %2017c+2.0 % (+0.8 to +3.0)+1.6 %+3.5 (+0.7 to +5.4)+1.5 %-0.4 % (-2.7 to +1.0)-0.5 %––+2.0 % (+0.2 to +3.8)+3.9 %+1.6 % (0.0 to +3.2)+1.9 %2018d+2.7 % (+1.8 to +3.7)–+4.7 (+2.0 to +7.4)–+2.5 % (+0.5 to +4.5)–-0.7 % (-2.6 to +1.3)–+6.3 % (+4.3 to +8.3)–+1.8 % (+0.5 to +3.0)–
a Jackson et al. (2016) and Le Quéré et
al. (2015a). b Le Quéré et al. (2016). c Le
Quéré et al. (2018). d This study.
In 2017, the largest absolute contributions to global CO2 emissions
were from China (27 %), the US (15 %), the EU (28 member states;
10 %), and India (7 %) while the rest of the world contributed 42 %. The
percentages are the fraction of the global emissions including bunker fuels
(3.1 %). These four regions account for 59 % of global CO2
emissions. Growth rates for these countries from 2016 to 2017 were +1.5 %
(China), -0.5 % (US), +1.2 % (EU28), and +3.9 % (India), with
+1.9 % for the rest of the world. The per capita CO2 emissions
in 2017 were 1.1 tC person-1 yr-1 for the globe and were 4.4
(US), 2.0 (China), 1.9 (EU28), and 0.5 (India) tC person-1 yr-1
for the four highest emitting countries (Fig. 5).
In 2016 (the last year available), the largest absolute contributions to
global CO2 emissions from a consumption perspective were China
(25 %), the US (16 %), the EU (12 %), and India (6 %). The difference
between territorial and consumption emissions (the net emission transfer via
international trade) has generally increased from 1990 to around 2005 and
remained relatively stable afterwards until the last year available (2016;
Fig. 5).
The global CO2 emissions from land-use change are estimated as 1.4±0.7 GtC in 2017, close to the previous decade but with low confidence
in the annual change. This brings the total CO2 emissions from
fossil fuels plus land-use change (EFF+ELUC) to 11.3±0.9 GtC (41.2±3 GtCO2).
Partitioning among the atmosphere, ocean, and land
The growth rate in atmospheric CO2 concentration was 4.6±0.2 GtC in 2017 (2.16±0.09 ppm; Fig. 4; Dlugokencky and Tans, 2018).
This is near the 2008–2017 average of 4.7±0.1 GtC yr-1 and
reflects the return to normal conditions after the El Niño of 2015–2016.
The estimated ocean CO2 sink was 2.5±0.5 GtC in 2017. All
models and data products estimate a small reduction or no change in the sink
(average of 0.1, ranging from +0.02 to -0.4 GtC), consistent with the
return to normal conditions after the El Niño, which caused an enhanced
sink in previous years (Fig. 7).
The terrestrial CO2 sink from the model ensemble was 3.8±0.8 GtC in 2017, above the decadal average (Fig. 4) and consistent with
constraints from the rest of the budget (Table 5).
The budget imbalance was +0.3 GtC in 2017, indicating, as for the last
decade, a small overestimation of the emissions and/or underestimation of the
sinks for that year. This imbalance is indicative only, given the large
uncertainties in the estimation of the BIM.
Global carbon budget projection for the year 2018CO2 emissions
Based on available data as of 7 November 2018 (see Sect. 2.1.5), fossil
CO2 emissions (EFF) for 2018 are projected to increase
by +2.7 % (range of 1.8 % to +3.7 %; Table 7). Our method contains
several assumptions that could influence the estimate beyond the given range,
and as such, it has an indicative value only. Within the given assumptions,
global emissions would be 10.1±0.5 GtC (37.1±1.8GtCO2) in 2018. The interpretation of the 2018 emission
projection is provided elsewhere (Figueres et al., 2018; Jackson et al.,
2018).
Cumulative changes during 1870–2017 and mean fluxes during
2008–2017 for the anthropogenic perturbation as defined in the legend.
For China, the expected change is for an increase in emissions of +4.7 %
(range of +2.0 % to +7.4 %) in 2018 compared to 2017. This is based
on estimated growth in coal (+4.5 %; the main fuel source in China), oil
(+3.6 %), natural gas (+17.7 %) consumption, and cement production
(+1.0 %). The uncertainty range considers the variations in the
difference between preliminary January–September data and final full-year
data, the uncertainty in the preliminary data used for the 2017 base, and
uncertainty in the evolution of energy density and carbon content of coal.
See also Liu et al. (2018) for further analysis of China's projected
emissions.
Cumulative CO2
for different time periods in gigatonnes of carbon (GtC). All uncertainties are reported as ±1σ.
The budget imbalance provides a measure of the discrepancies among the nearly
independent estimates. Its uncertainty exceeds ±60 GtC. The method used
here does not capture the loss of additional sink capacity from reduced
forest cover, which is about 20 GtC and would exacerbate the budget
imbalance (see Sect. 2.8.4). All values are rounded to the nearest 5 GtC and
therefore columns do not necessarily add to zero.
Units of Gt C1750–20171850–20051850–20141959–20171870–20171870–2018aEmissionsFossil CO2 emissions (EFF)430±20320±15400±20350±20425±20435±20Land-use change CO2 emissions (ELUC)235±95185±70195±7580±40190±75190±75Total emissions660±95500±75595±80430±45615±80625±80PartitioningGrowth rate in atmospheric CO2concentration (GATM)275±5200±5235±5190±5250±5255±5Ocean sink (SOCEAN)165±20125±20b150±20100±20150±20155±20Terrestrial sink (SLAND)215±50160±45185±50130±30190±50195±50Budget imbalanceBIM=EFF+ELUC-(GATM+SOCEAN+SLAND)(5)(20)(25)(10)(25)(25)
a Using projections for the year 2018 (Sect. 3.3).
b This value was incorrectly reported as 145 in Le Quéré et
al. (2018).
For the US, the EIA emission projection for 2018 combined with cement data
from USGS give an increase of 2.5 % (range of +0.5 to +4.5 %)
compared to 2017.
For the European Union, our projection for 2018 is for a decrease of
-0.7 % (range of -2.6 % to +1.3 %) over 2017. This is based on
estimates for coal of -1.2 %, oil of +1.2 %, gas of -2.9 %, and
stable cement emissions.
For India, our projection for 2018 is for an increase of +6.3 % (range of
4.3 % to +8.3 %) over 2017. This is based on separate projections for
coal (+7.1 %), oil (+2.9 %), gas (+6.0 %), and cement
(+13.4 %).
For the rest of the world, the expected growth for 2018 is +1.8 % (range
of +0.5 % to +3.0 %). This is computed using the GDP projection for
the world excluding China, the US, the EU, and India of 2.8 % made by the IMF
(IMF, 2018) and a decrease in IFF of -1.0 % yr-1, which is
the average from 2008 to 2017. The uncertainty range is based on the standard
deviation of the interannual variability in IFF during 2008–2017
of ±0.7 % yr-1 and our estimate of uncertainty in the IMF's GDP
forecast of ±0.5 %.
Preliminary estimates of fire emissions in deforestation zones indicate that
emissions from land-use change (ELUC) for 2018 were below average
until October and are expected to range between 0.1 and 0.2 lower than the
2008–2017 average. We therefore expect ELUC emissions of around
1.2 GtC in 2018, for total CO2 emissions of 11.3±0.9 GtC
(41.5±3GtCO2).
Partitioning among the atmosphere, ocean, and land
The 2018 growth in atmospheric CO2 concentration (GATM)
is projected to be 4.9±0.7 GtC (2.3±0.3 ppm) based on MLO
observations until the end of October 2018, bringing the atmospheric
CO2 concentration to an expected level of 407 ppm averaged over
the year. Combining projected EFF, ELUC, and
GATM suggests a combined land and ocean sink (SLAND+SOCEAN) of about 6.5 GtC for 2018. Although each term has large
uncertainty, the oceanic sink SOCEAN has generally low
interannual variability and is likely to remain close to its 2017 value of
around 2.5 GtC, leaving a rough estimated land sink SLAND of
around 4.0 GtC. If realised, it would be among the largest SLAND
values
over the historical period. However, the possible onset of an El Niño at
the end of 2018 could reduce SLAND, with GATM
returning to a high growth rate towards the end of the year.
Cumulative sources and sinks
Cumulative historical sources and sinks are estimated as in Eq. (1) with
semi-independent estimates for each term and a global carbon budget
imbalance. Cumulative fossil CO2 emissions for 1870–2017 were 425±20 GtC for EFF and 190±75 GtC for ELUC
(Table 8; Fig. 9), for a total of 615±80 GtC. The cumulative emissions
from ELUC are particularly uncertain, with large spread among
individual estimates of 135 GtC (Houghton) and 240 GtC (BLUE) for the two
bookkeeping models and a similar wide estimate of 180±75 GtC for the
DGVMs. These estimates are consistent with indirect constraints from
vegetation biomass observations
(Li et al., 2017), but given
the large spread a best estimate is difficult to ascertain.
Major known sources of uncertainties in each component of
the global carbon budget, defined as input data or processes that have a
demonstrated effect of at least ±0.3 GtC yr-1.
Source of uncertaintyTimescale (years)LocationStatusEvidenceFossil CO2 emissions (EFF; Sect. 2.1) Energy statisticsannual to decadalmainly Chinasee Sect. 2.1Korsbakken et al. (2016)Carbon content of coaldecadalmainly Chinasee Sect. 2.1Liu et al. (2015)Emissions from land-use change (ELUC; Sect. 2.3) Land cover and land-use changestatisticscontinuousglobal; in particular tropicssee Sect. 2.3Houghton et al. (2012)Sub-grid-scale transitionsannual to decadalglobalsee Table A1Wilkenskjeld et al. (2014)Vegetation biomassannual to decadalglobal; in particular tropicssee Table A1Houghton et al. (2012)Wood and crop harvestannual to decadalglobal; SE Asiasee Table A1Arneth et al. (2017)Peat burningamulti-decadal trendglobalsee Table A1van der Werf et al. (2010)Loss of additional sink capacitymulti-decadal trendglobalnot included;Sect. 2.8.4Gitz and Ciais (2003)Atmospheric growth rate (GATM) → no demonstrated uncertainties larger than ±0.3 GtC yr-1bOcean sink (SOCEAN) Variability in oceanic circulationcsemi-decadal to decadalglobal; in particular Southern Oceansee Sect. 2.5.2DeVries et al. (2017)Internal variabilityannual to decadalhigh latitudes; equatorial Pacificno ensembles/coarse resolutionMcKinley et al. (2016)Anthropogenic changes in nutrient supplymulti-decadal trendglobalnot includedDuce et al. (2008)Land sink (SLAND) Strength of CO2 fertilisationmulti-decadal trendglobalsee Sect. 2.6Wenzel et al. (2016)Response to variability in temperature and rainfallannual to decadalglobal; in particular tropicssee Sect. 2.6Cox et al. (2013)Nutrient limitation and supplymulti-decadal trendglobalsee Sect. 2.6Zaehle et al. (2011)Response to diffuse radiationannualglobalsee Sect. 2.6Mercado et al. (2009)
a As a result of interactions between land use and
climate.
b The uncertainties in GATM have been estimated as ±0.2 GtC yr-1, although the conversion of the growth rate into a global
annual flux assuming instantaneous mixing throughout the atmosphere
introduces additional errors that have not yet been quantified.
c Could in part be due to uncertainties in atmospheric forcing
(Swart et al., 2014).
Emissions were partitioned among the atmosphere (250±5 GtC), ocean
(150±20 GtC), and the land (190±50 GtC). The use of nearly
independent estimates for the individual terms shows a cumulative budget
imbalance of 25 GtC during 1870–2017 (Fig. 2), which, if correct, suggests
emissions are too high by the same proportion or the land or ocean sinks are
underestimated. The bulk of the imbalance is likely to originate largely from
the large estimation of ELUC between the mid-1920s and the mid-1960s, which is unmatched by a growth in atmospheric CO2
concentration as recorded in ice cores (Fig. 3). The known loss of additional
sink capacity of about 20 GtC due to reduced forest cover has not been
accounted for in our method and would further exacerbate the budget imbalance
(Sect. 2.8.4).
Cumulative emissions through to the year 2018 increase to 625±80 GtC
(2290±290GtCO2), with about a 70 % contribution from
EFF and about a 30 % contribution from ELUC.
Cumulative emissions and their partitioning for different periods are
provided in Table 8.
Given the large and persistent uncertainties in cumulative emissions, we
suggest extreme caution is needed if using cumulative emission estimates to
determine the remaining carbon budget to stay below the given temperature
limit (Rogelj et al., 2016). We suggest estimating the remaining carbon
budget by integrating scenario data from the current time to some time in the
future (Millar et al., 2017).
Discussion
Each year when the global carbon budget is published, each flux component is
updated for all previous years to consider corrections that are the result of
further scrutiny and verification of the underlying data in the primary input
data sets. Annual estimates may improve with improvements in data quality and
timeliness (e.g. to eliminate the need for extrapolation of forcing data such as
land use). Of the various terms in the global budget, only the fossil
CO2 emissions and the growth rate in atmospheric CO2
concentration are based primarily on empirical inputs supporting annual
estimates in this carbon budget. Although it is an imperfect measure, the
carbon budget imbalance provides a strong indication of the limitations in
observations, in understanding or full representation of processes in models,
and/or in the integration of the carbon budget components.
The persistent unexplained variability in the carbon budget imbalance limits
our ability to verify reported emissions (Peters et al., 2017) and suggests
we do not yet have a complete understanding of the underlying carbon cycle
processes. Resolving most of this unexplained variability should be possible
through different and complementary approaches. First, as intended with our
annual updates, the imbalance as an error term is reduced by improvements of
individual components of the global carbon budget that follow from improving
the underlying data and statistics and by improving the models through the
resolution of some of the key uncertainties detailed in Table 9. Second,
additional clues to the origin and processes responsible for the current
imbalance could be obtained through a closer scrutiny of carbon variability
in light of other Earth system data (e.g. heat balance, water balance), and
the use of a wider range of biogeochemical observations to better understand
the land–ocean partitioning of the carbon imbalance (e.g. oxygen, carbon
isotopes). Finally, additional information could also be obtained through
higher resolution and process knowledge at the regional level and through
the introduction of inferred fluxes such as those based on satellite
CO2 retrievals. The limit of the resolution of the carbon budget
imbalance is yet unclear but most certainly not yet reached given the
possibilities for improvements that lie ahead.
The assessment of the GOBMs used for SOCEAN with flux products
based on observations highlights substantial discrepancy at mid-latitudes and high
latitudes. Given the good data coverage of pCO2
observations in the Northern Hemisphere (Bakker et al., 2016), this
discrepancy points to an underestimation of variability in the GOBMs
globally,
and consequently the variability in SOCEAN appears to be
underestimated. The size of this underestimate (order of 0.5 GtC yr-1)
could account for some of the budget imbalance, but not all. Increasing model
resolution and incorporating internal variability (Li and Ilyina, 2018) have
been suggested as ways to increase model variability (Sect. 3.1.4).
The assessment of the net land–atmosphere exchange derived from land sink and
net land use change flux with atmospheric inversions also shows substantial
discrepancy, particularly for the estimate of the total land flux over the
northern extra-tropics in the past decade. This discrepancy highlights the
difficulty to quantify complex processes (CO2 fertilisation,
nitrogen deposition, climate change and variability, land management, etc.)
that collectively determine the net land CO2 flux. Resolving the
differences in the Northern Hemisphere land sink will require the
consideration and inclusion of larger volumes of observations (Sect. 3.2.3).
Estimates of ELUC suffer from a range of intertwined issues,
including the poor quality of historical land cover and land-use change maps,
the rudimentary representation of management processes in most models, and
the confusion in methodologies and boundary conditions used across methods
(e.g. Pongratz et al., 2014; Arneth et al., 2017, and Sect. 2.8.4 on the loss
of sink capacity). Uncertainties in current and historical carbon stocks in
soils and vegetation also add uncertainty in the land-use change flux estimates. Unless a
major effort to resolve these issues is made, little progress is expected in
the resolution of ELUC. This is particularly concerning given the
growing importance of ELUC for climate mitigation strategies and
the large issues in the quantification of the cumulative emissions over the
historical period that arise from large uncertainties in ELUC.
To move towards the resolution of the carbon budget imbalance, this year we
have introduced metrics for the evaluation of the ocean and land models and
atmospheric inversions. These metrics expand the use of observations in the
global carbon budget, helping (1) to support improvements in the ocean and
land carbon models that produce the sink estimates and (2) to constrain the
representation of key underlying processes in the models and to allocate the
regional partitioning of the CO2 fluxes. This is an initial step
towards the introduction of a broader range of observations that we hope will
support continued improvements in the annual estimates of the global carbon
budget.
We assessed elsewhere (Peters et al., 2017) that a sustained decrease of
-1 % in global emissions could be detected at the 66 % likelihood level
after a decade only. Similarly, a change in behaviour of the land and/or
ocean carbon sink would take as long to detect, and much longer if it emerges
more slowly. Reducing the carbon imbalance, regionalising the carbon budget,
and integrating multiple variables are powerful ways to shorten the detection
limit and ensure the research community can rapidly identify growing issues
of concern in the evolution of the global carbon cycle under the current
rapid and unprecedented changing environmental conditions.
The data presented here are made available in the belief
that their wide dissemination will lead to greater understanding and new
scientific insights into how the carbon cycle works, how humans are altering
it, and how we can mitigate the resulting human-driven climate change. The
free availability of these data does not constitute permission for
publication of the data. For research projects, if the data are essential to
the work, or if an important result or conclusion depends on the data,
co-authorship may need to be considered. Full contact details and information
on how to cite the data shown here are given at the
top of each page in the accompanying database and summarised in Table 2.
The accompanying database includes two Excel files organised in the following
spreadsheets (accessible with the free viewer
https://support.microsoft.com/en-gb/help/273711/how-to-obtain-the-latest-excel-viewer, last access: 28 November 2018):
File Global_Carbon_Budget_2018v1.0.xlsx (Global Carbon Project, 2018) includes the following:
summary;
the global carbon budget (1959–2017),
global CO2 emissions from fossil fuels and cement production by
fuel type and the per capita emissions (1959–2017),
CO2 emissions from land-use change from the individual methods
and models (1959–2017),
ocean CO2 sink from the individual ocean models and
pCO2-based products (1959–2017),
terrestrial CO2 sink from the DGVMs (1959–2017),
additional information on the carbon balance prior to 1959 (1750–2017).
File National_Carbon_Emissions_2018v1.0.xlsx (Global Carbon Project, 2018) includes the following:
summary
territorial country CO2 emissions from fossil CO2
emissions (1959–2017) from CDIAC with UNFCCC data overwritten where
available, extended to 2017 using BP data;
consumption country CO2 emissions from fossil CO2
emissions and emission transfer from the international trade of goods and
services (1990–2016) using CDIAC/UNFCCC data (worksheet 3 above) as
reference;
emission transfers (consumption minus territorial emissions; 1990–2016);
country definitions;
details of disaggregated countries;
details of aggregated countries.
National emission data are also available from the Global Carbon Atlas
(http://www.globalcarbonatlas.org/, last access: 28 November 2018).
Conclusions
The estimation of global CO2 emissions and sinks is a major effort
by the carbon cycle research community that requires a careful compilation
and synthesis of measurements, statistical estimates, and model results. The
delivery of an annual carbon budget serves two purposes. First, there is a
large demand for up-to-date information on the state of the anthropogenic
perturbation of the climate system and its underpinning causes. A broad
stakeholder community relies on the data sets associated with the annual
carbon budget including scientists, policymakers, businesses, journalists,
and non-governmental organisations engaged in adapting to and mitigating
human-driven climate change. Second, over the last decade we have seen
unprecedented changes in the human and biophysical environments (e.g. changes
in the growth of fossil fuel emissions, Earth's temperatures, and strength of
the carbon sinks), which call for frequent assessments of the state of the
planet and a growing understanding of and improved capacity to anticipate the
evolution of the carbon cycle in the future. Building this scientific
understanding to meet the extraordinary climate mitigation challenge requires
frequent, robust, and transparent data sets and methods that can be
scrutinised and replicated. This paper via living data helps to keep
track of new budget updates.
Supplementary tables
Comparison of the processes included (Y) or not (N) in the
bookkeeping and dynamic global vegetation models for their estimates of
ELUC and SLAND. See Table 4 for model references. All
models include deforestation and forest regrowth after abandonment of
agriculture (or from afforestation activities on agricultural
land).
Bookkeeping models DGVMs H&N2017BLUECABLE-POPCLASS-CTEMCLM5.0DLEMISAMJSBACHJULESLPJ-GUESSLPJLPX-BernOCNORCHIDEE-CNPORCHIDEE-TrunkSDGVMSURFEXVISITProcesses relevant for ELUCWood harvest and forest degradationaYYYNYYYYNYYNdYNYNNYShifting cultivation/sub-grid-scale transitionsNbYYNYNNYNYYNdNNNNNYCropland harvest (removed, r,or added to litter, l)Y(r)hY(r)hY(r)Y(l)Y(r)YYY(r,l)NY(r)Y(l)Y(r)Y(r,l)Y(r)Y(r)Y(r)NY(r)Peat firesYYNNYNNNNNNNNNNNNNFire as a management toolYhYhNNNNNNNNNNNNNNNNN fertilisationYhYhNNYYYNNYNYYYNNNNTillageYhYhYYeNNNNNYNNNNYgNNNIrrigationYhYhNNYYYNNYNNNNNNYgNWetland drainageYhYhNNNNNNNNNNNNNNNNErosionYhYhNNNNNNNNNNNNNNNYSoutheast Asia peat drainageYYNNNNNNNNNNNNNNNNGrazing and mowing harvest(removed, r, or added to litter, l)Y(r)hY(r)hY(r)NNNY(l)Y(l)NY(r)Y(l)NY(r,l)NNNNNProcesses relevant also for SLANDFire simulationUS onlyNNYYYNYNYYYNNNYYYClimate and variabilityNNYYYYYYYYYYYYYYYYCO2 fertilisationNfNfYYYYYYYYYYYYYeYYYCarbon–nitrogen interactions, including N depositionNhNhYNdYYYYNYNYYYNYcNiN
a Refers to the routine harvest of established managed forests rather
than pools of harvested products.
b No back-and-forth transitions between vegetation types at the
country level, but if forest loss based on FRA exceeded agricultural
expansion based on the FAO, then this amount of area is interpreted as shifting
cultivation.
c Limited. Nitrogen uptake is simulated as a function of soil C, and
photosynthesis is directly related to canopy N. Does not consider N
deposition.
d Although C–N cycle interactions are not represented, the model
includes a parameterization of down-regulation of photosynthesis as
CO2 increases to emulate nutrient constraints
(Arora et al., 2009).
e Tillage is represented over croplands by increased soil carbon
decomposition rate and reduced humification of litter to soil carbon.
f Bookkeeping models include the effect of CO2 fertilisation as
captured by observed carbon densities, but not as an effect that is transient in
time.
g A 20 % reduction of active soil organic carbon (SOC) pool turnover
time for C3 crops and 40 % reduction for C4 crops.
h Process captured implicitly by use of observed carbon densities.
i Simple parameterization of nitrogen limitation based on Yin
(2002; assessed on FACE experiments).
Comparison of the processes and model set-up for the
global ocean biogeochemistry models for their estimates of
SOCEAN. See Table 4 for model references.
CCSM-BECNorESM-OCMITgcm-REcoM2MPIOM-HAMOCCNEMO3.6-PISCESv2-gas (CNRM)NEMO-PISCES (IPSL)NEMO-PlankTOM5AtmosphericforcingNCEPCORE-I (spin-up)/NCEP with CORE-II correctionsJRA-55NCEP/NCEP+ERA-20C (spin-up)NCEPNCEPNCEPInitialisation of carbon chemistryGLODAPGLODAP v1 + spin-up 1000 yearsGLODAP, then spin-up 116 years (two cycles JRA-55)spin-up with ERA20CGLODAPv2 + 300 years onlineGLODAP from 1948 onwardsGLODAP + spin-up30 yearsPhysical ocean modelPOP version 1.4.3MICOMMITgcm 65nMPIOMNEMOv3.6-GELATOv6-eORCA1L75NEMOv3.2-ORCA2L31NEMOv2.3-ORCA2Resolution3.6∘ long, 0.8 to 1.8∘ lat1∘ long, 0.17 to 0.25 lat; 51 isopycnic layers + two bulk mixed layers2∘ long, 0.38–2∘ lat, 30 levels1.5∘; 40 levels1∘ long, 0.3 to 1∘ lat 75 levels, 1 m at surface2∘ long, 0.3 to 1.5∘ lat; 31 levels2∘ long, 0.3 to 1.5∘ lat; 31 levels
Comparison of the inversion set-up and input fields for
the atmospheric inversions. Atmospheric inversions see the full CO2
fluxes, including the anthropogenic and pre-industrial fluxes. Hence they
need to be adjusted for the pre-industrial flux of CO2 from the
land to the ocean that is part of the natural carbon cycle before they can be
compared with SOCEAN and SLAND from process models.
See Table 4 for references.
CarbonTracker Europe (CTE)Jena CarboScopeCAMSMIROCVersion numberCTE2018s85oc_v4.2v17r1tdi84_2018Observations Atmospheric observationsHourly resolution (well-mixed conditions) ObsPack GLOBALVIEWplus v3.2 & NRTv4.2aFlasks and hourly (outliers removed by 2σ criterion)Daily averages of well-mixed conditions – ObsPack GLOBALVIEWplus v3.2a & NRT v4.2, WDCGG, RAMCES, and ICOS ATCFlask and continuous data at remote sites from ObsPack GLOBALVIEWplus v3.2 and v4.0Prior fluxes Biosphere and firesSiBCASA–GFED4sbNo priorORCHIDEE (climatological), GFEDv4 & GFASClimatological CASA with 3-hourly downscalingOceanOcean inversion by Jacobson et al. (2007)pCO2-based ocean flux product oc_v1.6 (update of Rödenbeck et al., 2014)Landschützer et al. (2015)Takahashi et al. (2009)Fossil fuelsEDGAR+IER, scaled to CDIACCDIAC (extended after 2013 with GCP totals)EDGAR scaled to CDIACEDGARv4.3.2 (2012 map after 2013)Transport and optimisation Transport modelTM5TM3LMDZ v5AMIROC4–ACTMWeather forcingECMWFNCEPECMWFJRA-55Resolution (degrees)Global: 3∘×2∘, Europe: 1∘×1∘, North America: 1∘×1∘Global: 4∘×5∘Global: 3.75∘×1.875∘Global: 2.8∘×2.8∘OptimisationEnsemble Kalman filterConjugate gradient (re-ortho-normalisation)cVariationalMatrix method, 84 regions
a GLOBALVIEW (2016); CarbonTracker Team (2017).
b Van der Velde et al. (2014).
c Ocean prior not optimised.
Attribution of fCO2 measurements for the
year 2017 included in SOCAT v6 (Bakker et al., 2016) to inform ocean
pCO2-based flux products.
Funding supporting the production of the various
components of the global carbon budget in addition to the authors'
supporting institutions (see also acknowledgements).
Funder and grant number (where relevant)Author initialsAustralia, Great Barrier Reef FoundationBT, CNAustralia, Integrated Marine Observing System (IMOS)BT, CNAustralian government National Environment Science Program (NESP)JGC, VHEC H2020 (AtlantOS: grant no. 633211)AO, USEC H2020 (CRESCENDO: grant no. 641816)MF, PF, RS, TIEC H2020 European Research Council (ERC) Synergy grant (IMBALANCE-P; grant no. ERC-2013-SyG-610028)DSGEC H2020 ERC (QUINCY; grant no. 647204).SZEC H2020 (RINGO: grant no. 730944; FixO3: grant no. 312463).USEC H2020 project (VERIFY: grant no. 776810)CLQ, GPP, IH, JIK, RMA, PP, PCFrench Institut National des Sciences de l'Univers (INSU) and Institut Polaire Français Paul-Emile Victor (IPEV), Sorbonne Universités (UPMC, Univ Paris 06)NMGerman Federal Ministry for Education and Research (BMBF)GR, MH, TSGerman Federal Ministry of Transport and Digital Infrastructure (BMVI)GR, MH, TSGerman Helmholtz Association in its ATMO programmeAAGerman Helmholtz Association Innovation and Network Fund (VH-NG-1301)JHGerman Research Foundation's Emmy Noether Programme (grant no. PO1751/1-1)JPIntegrated Carbon Observation System (ICOS) RIGR, MH, NL, TG, TJ, TS, IS, USFrench Institut de Recherche pour le Développement (IRD)NLJapan Environment Research and Technology Development Fund of the Ministry of the Environment (grant no. 2-1701)PKPJapan Fisheries Research and Education Agency (FREA), Ministry of Environment (MOE)TOJapan National Institute for Environmental Studies (NIES), Ministry of Environment (MOE)SNNetherlands Organization for Scientific Research (NWO; grant no. SH-312, 16666)IvdLLNorwegian Research Council (grant no. 229771)JSNorwegian Research Council (grant no. ICOS 245927)IS, TJ, BPNorwegian Research Council (grant no. 209701)RMA, JIK, GPPThe Netherlands, Research Foundation – Flanders (FWO contract no. G0H3317N)TGThe Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European CommissionFCSwiss National Science Foundation (grant no. 200020_172476)SLUK BEIS/Defra Met Office Hadley Centre Climate Programme (grant no. GA01101)CDJUK Natural Environment Research Council (SONATA: grant no. NE/P021417/1)CLQ, USUK NERC (RAGNARoCC: grant no. NE/K002473/1)USUK Newton Fund, Met Office Climate Science for Service Partnership Brazil (CSSP Brazil)AWUS Climate Program Office of NOAA (grant no. NA13OAR4310219)LRU.S. Department of Agriculture, National Institute of Food and Agriculture (grant nos. 2015-67003-23489 and 2015-67003-23485)DLLU.S. Department of Commerce, NOAA/OAR's Global Ocean Monitoring & Observing ProgramAS, LB, DPU.S. Department of Commerce, NOAA/OAR's Ocean Acidification ProgramAS, DP, LBU.S. Department of Energy, Oak Ridge National Laboratory (contract no. DE-AC05-00OR22725)APWU.S. Department of Energy, Office of Science and BER programme (grant no. DE-SC000 0016323)ATJU.S. Department of Energy (grant nos. DE-FC03-97ER62402/A010 and DE-SC0012972)DLLUS NASA Interdisciplinary Research in Earth Science programmeBPU.S. NASA (grant no. 80NSSC18K0897)SCDComputing resourcesNorway UNINETT Sigma2, National Infrastructure for High Performance Computing and Data Storage in Norway (NN2980K/NS2980K)JSTGCC under allocations 2017-A0030102201 and 2017-A0030106328 made by GENCIFC, NVJapan National Institute for Environmental Studies computational resourcesEKUEA High Performance Computing Cluster, UKRW, CLQDeutsches Klimarechenzentrum (allocation bm0891)JEMSN, JP
Continued.
Support for aircraft measurements in ObsPackL. V. Gatti, M. Gloor, J. B. Miller: AMAZONICA consortium project was funded by NERC (NE/F005806/1), FAPESP (08/58120-3), GEOCARBON project (283080)Joshua DiGangi, NASA Langley Research Center, principal investigator of the airborne instrument that collected all of the CO2 observations during the Atmospheric Carbon and Transport – America campaigns.Observations from the Atmospheric Carbon and Transport (ACT) – America Earth Venture Suborbital mission were funded by NASA's Earth Science Division (grant NNX15AG76G to Penn State)Jeff Peischl of the University of Colorado/CIRES for the NOAA WP-3D aircraft vertical profile data
Aircraft measurement programmes archived by Cooperative
Global Atmospheric Data Integration Project (CGADIP, 2017) that contribute
to the evaluation of the atmospheric inversions (Fig. B3).
Measurement programme name in ObsPackSpecific DOIData providersAirborne Aerosol Observatory, Bondville, IllinoisSweeney, C.; Dlugokencky, E. J.Alaska Coast GuardSweeney, C.; McKain, K.; Karion, A.; Dlugokencky, E. J.Atmospheric Carbon and Transport – America10.3334/ORNLDAAC/1556Davis, K. J.; Digangi, J. P.; Yang, M.Atmospheric Carbon and Transport – AmericaDavis, K. J.; Sweeney, C.; Dlugokencky, E. J.; Yang, M.Alta FlorestaGatti, L. V.; Gloor, E.; Miller, J. B.Aircraft observation of atmospheric trace gases by JMAghg_obs@met.kishou.go.jpAerosol, Radiation, and Cloud Processes affecting Arctic Climate 2008 (air campaign)Ryerson, T. B.; Peischl, J.; Aikin, K. C.LARC – NASA Langley Research Center AircraftCampaignChen, G.; Digangi, J. P.Beaver Crossing, NebraskaSweeney, C.; Dlugokencky, E. J.California Nexus 2010 (air campaign)Ryerson, T. B.; Peischl, J.; Aikin, K. C.Briggsdale, ColoradoSweeney, C.; Dlugokencky, E. J.Cape May, New JerseySweeney, C.; Dlugokencky, E. J.CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)10.17595/20180208.001Machida, T.; Matsueda, H.; Sawa, Y.; Niwa, Y.Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)Sweeney, C.; Karion, A.; Miller, J. B.; Miller, C. E.; Dlugokencky, E. J.LARC – NASA Langley Research Center AircraftCampaignChen, G.; Digangi, J. P.; Beyersdorf, A.LARC – NASA Langley Research Center AircraftCampaignChen, G.; Digangi, J. P.; Yang, M.Dahlen, North DakotaSweeney, C.; Dlugokencky, E. J.Estevan Point, British ColumbiaSweeney, C.; Dlugokencky, E. J.East Trout Lake, SaskatchewanSweeney, C.; Dlugokencky, E. J.Molokai Island, HawaiiSweeney, C.; Dlugokencky, E. J.Homer, IllinoisSweeney, C.; Dlugokencky, E. J.HIPPO (HIAPER Pole-to-Pole Observations)10.3334/CDIAC/HIPPO_010Wofsy, S. C.; Stephens, B. B.; Elkins, J. W.; Hintsa, E. J.; Moore, F.INFLUX (Indianapolis Flux Experiment)Sweeney, C.; Dlugokencky, E. J.; Shepson, P. B.; Turnbull, J.Park Falls, WisconsinSweeney, C.; Dlugokencky, E. J.Mid-Continent Intensive CampaignSweeney, C.; Dlugokencky, E. J.Marcellus, PennsylvaniaSweeney, C.; Dlugokencky, E. J.Worcester, MassachusettsSweeney, C.; Dlugokencky, E. J.ORCAS (O2/N2 Ratio and CO2 Airborne Southern Ocean Study)10.5065/D6SB445XStephens, B. B.; Sweeney, C.; McKain, K.; Kort, E. A.Poker Flat, AlaskaSweeney, C.; Dlugokencky, E. J.Rio BrancoGatti, L. V.; Gloor, E.; Miller, J. B.RarotongaSweeney, C.; Dlugokencky, E. J.MontzkaSweeney, C.; Dlugokencky, E. J.SantaremSweeney, C.; Dlugokencky, E. J.Charleston, South CarolinaSweeney, C.; Dlugokencky, E. J.LARC – NASA Langley Research Center AircraftCampaignChen, G.; Digangi, J. P.; Beyersdorf, A.Southeast Nexus 2013 (air campaign)Ryerson, T. B.; Peischl, J.; Aikin, K. C.Southern Great Plains, OklahomaSweeney, C.; Dlugokencky, E. J.; Biraud, S.Shale Oil and Natural Gas Nexus 2015 (air campaign)Ryerson, T. B.; Peischl, J.; Aikin, K. C.Harvard University aircraft campaignWofsy, S. C.TabatingaGatti, L. V.; Gloor, E.; Miller, J. B.Sinton, TexasSweeney, C.; Dlugokencky, E. J.Trinidad Head, CaliforniaSweeney, C.; Dlugokencky, E. J.Atmospheric Tomography Mission (ATom)McKain, K.; Sweeney, C.UlaanbaatarSweeney, C.; Dlugokencky, E. J.West Branch, IowaSweeney, C.; Dlugokencky, E. J.Supplementary figures
Evaluation of the GOBMs and flux products using the
interannual mismatch metric for the period from 1985 to 2017, as proposed by
Rödenbeck et al. (2015) and the SOCAT v6 database, versus the amplitude
of the annual variability (taken as the annual standard deviation). Results
are presented for the globe, north (>30∘ N), tropics
(30∘ S–30∘ N), and south (<30∘ S) for the
GOBMs (circles) and for the pCO2-based flux products (star
symbols). The two pCO2-based flux products use the SOCAT
database and therefore are not fully independent from the data (see Sect. 2.5.1).
Evaluation of the DGVM using the International Land Model
Benchmarking system (ILAMB; Collier et al., 2018) (left) absolute skill
scores and (right) skill scores relative to other models. The benchmarking is
carried out with observations for vegetation biomass (Saatchi et al., 2011;
GlobalCarbon, unpublished data; Avitabile et al., 2016), GPP (Jung et al.,
2010; Lasslop et al., 2010), leaf area index (De Kauwe et al., 2011; Myneni
et al., 1997), net ecosystem exchange (Jung et al., 2010; Lasslop et al.,
2010), ecosystem respiration (Jung et al., 2010; Lasslop et al., 2010), soil
carbon (Hugelius et al., 2013; Todd-Brown et al., 2013), evapotranspiration
(De Kauwe et al., 2011), and runoff (Dai and Trenberth, 2002).
For each model–observation comparison a series of error metrics are
calculated, scores are then calculated as an exponential function of each
error metric, and finally for each variable the multiple scores from different
metrics and observational data sets are combined to give the overall variable
scores shown in the left panel. The set of error metrics vary with data set
and can include metrics based on the period mean, bias, root-mean-squared
error, spatial distribution, interannual variability, and seasonal cycle. The
relative skill score shown in the right panel is a Z score, which indicates
in units of standard deviation the model scores relative to the multi-model
mean score for a given variable. Grey boxes represent missing model data.
Evaluation of the atmospheric inversion products. The
mean of the absolute model minus observations is shown for four latitude bands.
The four models are compared to independent CO2 measurements made
onboard aircraft over many places of the world between 1 and 7 km above sea
level. All data between 2008 and 2016 archived in the Cooperative Global
Atmospheric Data Integration Project (CGADIP, 2017) have been used to compute
the biases of the differences in four 45∘ latitude bins. Land and ocean
data are used without distinction. The number of data for each latitude band
is 16 000 (90–45∘ S), 53 000 (45∘ S–0), 64 000
(0–45∘ N), and 122 000 (45–90∘ N), rounded off to the
nearest thousand.
Comparison of global carbon budget components released
annually by GCP since 2006. CO2 emissions from (a) fossil
CO2 emissions (EFF) and (b) land-use change
(ELUC), as well as their partitioning among (c) the
atmosphere (GATM), (d) the land (SLAND),
and (e) the ocean (SOCEAN). See legend for the
corresponding years and Table 3 for references. The budget year corresponds
to the year when the budget was first released. All values are
in GtC yr-1. Grey shading shows the uncertainty bounds representing
±1σ of the current global carbon budget.
CLQ, RMA, PF, SS, JH, JP, GPP, JGC, and WP designed the study, conducted the
analysis, and wrote the paper. RMA and GPP produced the emissions and their
uncertainties, 2018 emission projections, and analysed the emissions data.
RMA, JIK, GPP, ZL, and BZ produced the 2018 projection for China's emissions
and its uncertainty. RFK and PPT provided key atmospheric CO2 data.
RMA and CDJ produced the 2018 projection for atmospheric CO2 growth
rate and its uncertainty. FC, PPa, CR, and IvdLL provided an updated
atmospheric inversion, developed the protocol and produced the evaluation. AB
and RAH provided updated land-use change emissions. LPC, GH, KKG, FNT, and
GRvdW provided forcing data for land-use change. AA, VKA, DSG, VH, AKJ, EK,
SL, DL, JEMSN, PPe, BPo, MR, HT, APW, AJW and SZ provided an update of a
DGVM. IH and NV provided forcing data for the DGVMs. FMH and ER provided the
evaluation of the DGVMs. JH, LBo, SCD, TI, JS, RS and RW provided an update
of a GOBM. PL and CR provided an update of an ocean flux product. Lba, TG,
MH, TJ, NL, NM, DRM, SN, CN, AO, TO, BPf, DP, GR, US, IS, TS, AS and BT
provided ocean pCO2 measurements for the year 2017. LR
provided an updated river flux estimate. CLQ, PAP, RMA and AP revised the
figures, tables, text and/or numbers to ensure the update is clear from the
2017 edition and in phase with the globalcarbonatlas.org.
The authors
declare that they have no conflict of interest.
Acknowledgements
We thank all people and institutions who provided the data used in this
carbon budget; Richard Betts, Erik Buitenhuis, Jinfeng Chang, Shijie Shu, and
Naomi Smith for their involvement in the development, use and analysis of the
models and data products used here; and Fortunat Joos, Samar Khatiwala, and
Timothy DeVries for providing historical data. We thank Rob Jackson and the
Global Carbon Project members for their input throughout the development of
this update. We thank Ed Dlugokencky for providing atmospheric CO2
measurements; Camilla Stegen Landa, Christophe Bernard, and Steve Jones of
the Bjerknes Climate Data Centre and the ICOS Ocean Thematic Centre data
management at the University of Bergen, who helped with gathering information
from the SOCAT community; and Vassilis Kitidis, Pedro M. S. Monteiro, Luke
Gregor, Melchor Gonzáles-Dávila, J. Magdalena Santana-Casiano, Ruben
Negri, and X. Antonio Padin, who contributed to the provision of ocean
pCO2 observations (see Table A1). This is NOAA-PMEL
contribution number 4847. We thank the institutions and funding agencies
responsible for the collection and quality control of the data included in
SOCAT and the support of the International Ocean Carbon Coordination Project
(IOCCP), the Surface Ocean–Lower Atmosphere Study (SOLAS), and the
Integrated Marine Biogeochemistry and Ecosystem Research (IMBER) programme.
We thank the FAO and its member countries for the collection and free
dissemination of data relevant to this work. We thank data providers ObsPack
GLOBALVIEWplus v1.0 and NRT v3.0 for atmospheric CO2 observations
used in CTE2016-FT and the following people for sharing their aircraft data
used in Fig. B3: Toshinobu Machida, Guangsheng Chen, Steven C. Wofsy, Ken
Davis, Joshua DiGangi, Jeff Peischl, Thomas B. Ryerson, Britton Stephens,
Colm Sweeney, Kathryn McKain, and Luciana V. Gatti; University of
Colorado/CIRES for the NOAA WP-3D aircraft vertical profile data; and the
Japan Meteorological Agency. We thank the individuals and institutions that
provided the databases used for the model evaluations introduced here and
Nigel Hawtin for producing Fig. 2.
Finally, we thank all funders who have supported the individual and joint
contributions to this work (see Table A5), as well as the reviewers of this
paper and previous versions, and the many researchers who have provided
feedback.Edited by: David Carlson
Reviewed by: H. Damon Matthews, Albertus J. Dolman, and one
anonymous referee
ReferencesAndres, R. J., Boden, T. A., Bréon, F.-M., Ciais, P., Davis, S., Erickson,
D., Gregg, J. S., Jacobson, A., Marland, G., Miller, J., Oda, T., Olivier, J.
G. J., Raupach, M. R., Rayner, P., and Treanton, K.: A synthesis of carbon
dioxide emissions from fossil-fuel combustion, Biogeosciences, 9, 1845–1871,
10.5194/bg-9-1845-2012, 2012.Andres, R. J., Boden, T., and Higdon, D.: A new evaluation of the
uncertainty associated with CDIAC estimates of fossil fuel carbon dioxide
emission, Tellus B, 66, 23616, 10.3402/tellusb.v66.23616, 2014.Andrew, R. M.: Global CO2 emissions from cement production, Earth
Syst. Sci. Data, 10, 195–217, 10.5194/essd-10-195-2018,
2018.Andrew, R. M. and Peters, G. P.: A multi-region input-output table based on
the Global Trade Analysis Project Database (GTAP-MRIO), Econ. Syst. Res., 25,
99–121, 10.1080/09535314.2012.761953, 2013.Archer, D., Eby, M., Brovkin, V., Ridgwell, A., Cao, L., Mikolajewicz, U.,
Caldeira, K. M., K., Munhoven, G., Montenegro, A., and Tokos, K.: Atmospheric
Lifetime of Fossil Fuel Carbon Dioxide, Annu. Rev. Earth Pl. Sc., 37,
117–134, 10.1146/annurev.earth.031208.100206, 2009.Arneth, A., Sitch, S., Pongratz, J., Stocker, B. D., Ciais, P., Poulter, B.,
Bayer, A. D., Bondeau, A., Calle, L., Chini, L. P., Gasser, T., Fader, M.,
Friedlingstein, P., Kato, E., Li, W., Lindeskog, M., Nabel, J. E. M. S.,
Pugh, T. A. M., Robertson, E., Viovy, N., Yue, C., and Zaehle, S.: Historical
carbon dioxide emissions caused by land-use changes are possibly larger than
assumed, Nat. Geosci., 10, 79–84, 10.1038/ngeo2882, 2017.Arora, V. K., Boer, G. J., Christian, J. R., Curry, C. L., Denman, K. L.,
Zahariev, K., Flato, G. M., Scinocca, J. F., Merryfield, W. J., and Lee, W.
G.: The Effect of Terrestrial Photosynthesis Down Regulation on the
Twentieth-Century Carbon Budget Simulated with the CCCma Earth System Model,
J. Climate, 22, 6066–6088, 10.1175/2009jcli3037.1, 2009.Aumont, O. and Bopp, L.: Globalizing results from ocean in situ iron
fertilization studies, Global Biogeochem. Cy., 20, GB2017,
10.1029/2005GB002591, 2006.Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O.
L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N.
J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley,
E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A.,
Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry,
S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R.,
Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical
biomass map using multiple reference datasets, Glob. Change Biol., 22,
1406–1420, 10.1111/gcb.13139, 2016.Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., and
Houghton, R. A.: Tropical forests are a net carbon source based on
aboveground measurements of gain and loss, Science, 358, 230–233,
10.1126/science.aam5962, 2017.Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen,
A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S.-I., Nojiri,
Y., Schuster, U., Steinhoff, T., Sweeney, C., Takahashi, T., Tilbrook, B.,
Wada, C., Wanninkhof, R., Alin, S. R., Balestrini, C. F., Barbero, L., Bates,
N. R., Bianchi, A. A., Bonou, F., Boutin, J., Bozec, Y., Burger, E. F., Cai,
W.-J., Castle, R. D., Chen, L., Chierici, M., Currie, K., Evans, W.,
Featherstone, C., Feely, R. A., Fransson, A., Goyet, C., Greenwood, N.,
Gregor, L., Hankin, S., Hardman-Mountford, N. J., Harlay, J., Hauck, J.,
Hoppema, M., Humphreys, M. P., Hunt, C. W., Huss, B., Ibánhez, J. S. P.,
Johannessen, T., Keeling, R., Kitidis, V., Körtzinger, A., Kozyr, A.,
Krasakopoulou, E., Kuwata, A., Landschützer, P., Lauvset, S. K., Lefèvre,
N., Lo Monaco, C., Manke, A., Mathis, J. T., Merlivat, L., Millero, F. J.,
Monteiro, P. M. S., Munro, D. R., Murata, A., Newberger, T., Omar, A. M.,
Ono, T., Paterson, K., Pearce, D., Pierrot, D., Robbins, L. L., Saito, S.,
Salisbury, J., Schlitzer, R., Schneider, B., Schweitzer, R., Sieger, R.,
Skjelvan, I., Sullivan, K. F., Sutherland, S. C., Sutton, A. J., Tadokoro,
K., Telszewski, M., Tuma, M., van Heuven, S. M. A. C., Vandemark, D., Ward,
B., Watson, A. J., and Xu, S.: A multi-decade record of high-quality
fCO2 data in version 3 of the Surface Ocean CO2
Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383–413,
10.5194/essd-8-383-2016, 2016.Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P., and White, J.
W. C.: Increase in observed net carbon dioxide uptake by land and oceans
during the last 50 years, Nature, 488, 70–72, 10.1038/nature11299, 2012.Ballantyne, A. P., Andres, R., Houghton, R., Stocker, B. D., Wanninkhof, R.,
Anderegg, W., Cooper, L. A., DeGrandpre, M., Tans, P. P., Miller, J. B.,
Alden, C., and White, J. W. C.: Audit of the global carbon budget: estimate
errors and their impact on uptake uncertainty, Biogeosciences, 12,
2565–2584, 10.5194/bg-12-2565-2015, 2015.Bauer, J. E., Cai, W.-J., Raymond, P. A., Bianchi, T. S., Hopkinson, C. S.,
and Regnier, P. A. G.: The changing carbon cycle of the coastal ocean,
Nature, 504, 61–70, 10.1038/nature12857, 2013.Berthet, S., Séférian, R., Bricaud, C., Chevallier, M., Voldoire,
A., and Ethé, C.: On the benefits of increasing resolution for
biogeochemistry climate modelling, J. Adv. Model. Earth Sy., submitted, 2018.Betts, R. A., Jones, C. D., Knight, J. R., Keeling, R. F., and Kennedy, J.
J.: El Nino and a record CO2 rise, Nat. Clim. Change, 6, 806–810,
10.1038/nclimate3063, 2016.Boden, T. A., Marland, G., and Andres, R. J.: Global, Regional, and National
Fossil-Fuel CO2 Emissions, available at:
http://cdiac.ornl.gov/trends/emis/overview_2014.html (last access: July
2017), Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge,
Tenn., USA, 2017.BP: BP Statistical Review of World Energy June 2018, available at:
https://www.bp.com/content/dam/bp/en/corporate/pdf/energy-economics/statistical-review/bp-stats-review-2018-full-report.pdf,
last access: June 2018.Bruno, M. and Joos, F.: Terrestrial carbon storage during the past 200 years: A monte carlo analysis of CO2 data from ice core and
atmospheric measurements, Global Biogeochem. Cy., 11, 111–124,
10.1029/96GB03611, 1997.Buitenhuis, E. T., Rivkin, R. B., Sailley, S., and Le Quéré, C.:
Biogeochemical fluxes through microzooplankton, Global Biogeochem. Cy., 24,
GB4015, 10.1029/2009GB003601, 2010.Canadell, J. G., Le Quéré, C., Raupach, M. R., Field, C. B.,
Buitenhuis, E. T., Ciais, P., Conway, T. J., Gillett, N. P., Houghton, R. A.,
and Marland, G.: Contributions to accelerating atmospheric CO2
growth from economic activity, carbon intensity, and efficiency of natural
sinks, P. Natl. Acad. Sci. USA, 104, 18866–18870,
10.1073/pnas.0702737104, 2007.Carbontracker Team: Compilation of near real time atmospheric carbon dioxide
data provided by NOAA and EC, obspack_co2_1_NRT_v3.3_2017-04-19; NOAA
Earth System Research Laboratory, Global Monitoring Division,
10.15138/G3G01J, 2017.CEA: Central Electricity Authority (CEA), 2018: Daily Coal –
Archive, Central Electricity Authority, available at:
http://www.cea.nic.in/dailyarchive.html, last access: 7 November 2018.CGADIP: Cooperative Global Atmospheric Data Integration Project
(2017), Multi-laboratory compilation of atmospheric carbon dioxide data for
the period 1957–2016; obspack_co2_1_GLOBALVIEWplus_v3.2_2017-11-02
[Data set], NOAA Earth System Research Laboratory, Global Monitoring
Division, 2017.Chatfield, C.: The Holt-Winters Forecasting Procedure, J. Roy.
Stat. Soc. C-Appl., 27, 264–279, 10.2307/2347162, 1978.Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P.,
Bréon, F.-M., Chédin, A., and Ciais, P.: Inferring CO2
sources and sinks from satellite observations: Method and application to TOVS
data, J. Geophys. Res., 110, D24309, 10.1029/2005JD006390, 2005.Ciais, P., Sabine, C., Govindasamy, B., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le
Quéré, C., Myneni, R., Piao, S., and Thornton, P.: Chapter 6: Carbon
and Other Biogeochemical Cycles, in: Climate Change 2013 The Physical Science
Basis, edited by: Stocker, T., Qin, D., and Platner, G.-K., Cambridge
University Press, Cambridge, 2013.CIL: Coal India Limited, 2018: Production and Offtake Performance of CIL and
Subsidiary Companies, available at:
https://www.coalindia.in/en-us/performance/physical.aspx, last access:
1 November 2018.Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M.
J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O.,
Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land
Environment Simulator (JULES), model description – Part 2: Carbon fluxes and
vegetation dynamics, Geosci. Model Dev., 4, 701–722,
10.5194/gmd-4-701-2011, 2011.Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C.
D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model
Benchmarking (ILAMB) System: Design, Theory, and Implementation, J. Adv.
Model. Earth Sy., 10, 10.1029/2018MS001354, 2018.Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C.,
Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate
change constrained by carbon dioxide variability, Nature, 494, 341–344,
10.1038/nature11882, 2013.Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from
continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3,
660–687, 10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2, 2002.Davis, S. J. and Caldeira, K.: Consumption-based accounting of CO2
emissions, P. Natl. Acad. Sci. USA, 107, 5687–5692,
10.1073/pnas.0906974107, 2010.De Kauwe, M. G., Disney, M. I., Quaife, T., Lewis, P., and Williams, M.: An
assessment of the MODIS collection 5 leaf area index product for a region of
mixed coniferous forest, Remote Sens. Environ., 115, 767–780,
10.1016/j.rse.2010.11.004, 2011.Denman, K. L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P. M.,
Dickinson, R. E., Hauglustaine, D., Heinze, C., Holland, E., Jacob, D.,
Lohmann, U., Ramachandran, S., Leite da Silva Dias, P., Wofsy, S. C., and
Zhang, X.: Couplings Between Changes in the Climate System and
Biogeochemistry, in: Climate Change 2007: The Physical Science Basis.
Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin D.,
Manning M., Marquis M., Averyt K., Tignor M. M. B., Miller, H. L., and Chen
Z. L., Cambridge University Press, Cambridge, UK and New York, USA, 499–587,
2007.DeVries, T.: The oceanic anthropogenic CO2 sink: Storage, air-sea
fluxes, and transports over the industrial era, Global Biogeochem. Cy., 28,
631–647, 10.1002/2013GB004739, 2014.DeVries, T., Holzer, M., and Primeau, F.: Recent increase in oceanic carbon
uptake driven by weaker upper-ocean overturning, Nature, 542, 215–218,
10.1038/nature21068, 2017.Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide,
National Oceanic & Atmospheric Administration, Earth System Research
Laboratory (NOAA/ESRL), available at:
http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html, last access: 4
September 2018.Doney, S. C., Lima, I., Feely, R. A., Glover, D. M., Lindsay, K., Mahowald,
N., Moore, J. K., and Wanninkhof, R.: Mechanisms governing interannual
variability in upper-ocean inorganic carbon system and air–sea CO2
fluxes: Physical climate and atmospheric dust, Deep-Sea Res. Pt. II, 56,
640–655, 10.1016/j.dsr2.2008.12.006, 2009.Duce, R. A., LaRoche, J., Altieri, K., Arrigo, K. R., Baker, A. R., Capone,
D. G., Cornell, S., Dentener, F., Galloway, J., Ganeshram, R. S., Geider, R.
J., Jickells, T., Kuypers, M. M., Langlois, R., Liss, P. S., Liu, S. M.,
Middelburg, J. J., Moore, C. M., Nickovic, S., Oschlies, A., Pedersen, T.,
Prospero, J., Schlitzer, R., Seitzinger, S., Sorensen, L. L., Uematsu, M.,
Ulloa, O., Voss, M., Ward, B., and Zamora, L.: Impacts of atmospheric
anthropogenic nitrogen on the open ocean, Science, 320, 893–897,
10.1126/science.1150369, 2008.Dufour, C. O., Le Sommer, J., Gehlen, M., Orr, J. C., Molines, J. M., Simeon,
J., and Barnier, B.: Eddy compensation and controls of the enhanced
sea-to-air CO2 flux during positive phases of the Southern Annular
Mode, Global Biogeochem. Cy., 27, 950–961, 10.1002/gbc.20090, 2013.Durant, A. J., Le Quéré, C., Hope, C., and Friend, A. D.: Economic
value of improved quantification in global sources and sinks of carbon
dioxide, Philos. T. Roy Soc. A, 269, 1967–1979, 10.1098/rsta.2011.0002,
2011.EIA: U.S. Energy Information Administration, Short-Term Energy and Winter
Fuels Outlook, available at:
http://www.eia.gov/forecasts/steo/outlook.cfm, last access: 7 November
2018.ENTSO-E: The European Network of Transmission System Operators Electricity
Transparency Platform, available at: https://transparency.entsoe.eu/,
last access: 1 November 2018.Erb, K.-H., Kastner, T., Luyssaert, S., Houghton, R. A., Kuemmerle, T.,
Olofsson, P., and Haberl, H.: Bias in the attribution of forest carbon sinks,
Nat. Clim. Change, 3, 854–856, 10.1038/nclimate2004, 2013.Etheridge, D. M., Steele, L. P., Langenfelds, R. L., and Francey, R. J.:
Natural and anthropogenic changes in atmospheric CO2 over the last
1000 years from air in Antarctic ice and firn, J. Geophys. Res., 101,
4115–4128, 10.1029/95JD03410, 1996.Eurostat: Supply and transformation of solid fuels – monthly data
(nrg_101m), available at: https://ec.europa.eu/eurostat/data/database,
last access: 7 November 2018.FAO: Global Forest Resources Assessment 2015, Food and Agriculture
Organization of the United Nations, Rome, Italy, 2015.FAOSTAT: Food and Agriculture Organization Statistics Division, available
at: http://faostat.fao.org/, last access: 2015.Figueres, C., Whiteman, G., Le Quéré, C., and Peters, G. P.: Carbon
emissions rise again, Nature, 564, 27–31, 2018.Francey, R. J., Trudinger, C. M., van der Schoot, M., Law, R. M., Krummel, P.
B., Langenfelds, R. L., Steele, L. P., Allison, C. E., Stavert, A. R.,
Andres, R. J., and Rodenbeck, C.: Reply to “Anthropogenic CO2
emissions”, Nat. Clim. Change, 3, p. 604, 10.1038/nclimate1925, 2013.Friedlingstein, P., Houghton, R. A., Marland, G., Hackler, J., Boden, T. A.,
Conway, T. J., Canadell, J. G., Raupach, M. R., Ciais, P., and Le
Quéré, C.: Update on CO2 emissions, Nat. Geosci., 3,
811–812, 10.1038/ngeo1022, 2010.Friedlingstein, P., Andrew, R. M., Rogelj, J., Peters, G. P., Canadell, J.
G., Knutti, R., Luderer, G., Raupach, M. R., Schaeffer, M., van Vuuren, D.
P., and Le Quéré, C.: Persistent growth of CO2 emissions
and implications for reaching climate targets, Nat. Geosci., 7, 709–715,
10.1038/NGEO2248, 2014.Gasser, T., Ciais, P., Boucher, O., Quilcaille, Y., Tortora, M., Bopp, L.,
and Hauglustaine, D.: The compact Earth system model OSCAR v2.2: description
and first results, Geosci. Model Dev., 10, 271–319,
10.5194/gmd-10-271-2017, 2017.Gaubert, B., Stephens, B. B., Basu, S., Chevallier, F., Deng, F., Kort, E.
A., Patra, P. K., Peters, W., Rödenbeck, C., Saeki, T., Schimel, D., Van
der Laan-Luijkx, I., Wofsy, S., and Yin, Y.: Global atmospheric CO2 inverse
models converging on neutral tropical land exchange but diverging on fossil
fuel and atmospheric growth rate, Biogeosciences Discuss.,
10.5194/bg-2018-384, in review, 2018.GCP: The Global Carbon Budget 2007, available at: http://www.globalcarbonproject.org/carbonbudget/archive.htm (last access: 7
November 2016), 2007.General Administration of Customs of the People's Republic of China: Monthly
statistical reports, available at:
http://www.customs.gov.cn/customs/302249/302274/302277/index.html, last
access: 15 November 2018.Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active
fire detection algorithm and fire products, Remote Sens. Environ., 178,
31–41, 10.1016/j.rse.2016.02.054, 2016.Gitz, V. and Ciais, P.: Amplifying effects of land-use change on future
atmospheric CO2 levels, Global Biogeochem. Cy., 17, 1024,
10.1029/2002GB001963, 2003.Global Carbon Project:
Supplemental data of Global Carbon Budget 2018 (Version 1.1) [Data set],
Global Carbon Project, 10.18160/GCP-2018, 2018.Global Trade, Assistance, and Production: The GTAP 9 Data Base,
available at:
https://www.gtap.agecon.purdue.edu/databases/v9/default.asp, last
access: September 2015.GLOBALVIEW: Cooperative Global Atmospheric Data Integration Project (2016):
Multi-laboratory compilation of atmospheric carbon dioxide data for the
period 1957–2015; obspack_co2_1_GLOBALVIEWplus_v2.1_2016_09_02; NOAA
Earth System Research Laboratory, Global Monitoring Division,
10.15138/G3059Z, 2016.Goll, D. S., Vuichard, N., Maignan, F., Jornet-Puig, A., Sardans, J.,
Violette, A., Peng, S., Sun, Y., Kvakic, M., Guimberteau, M., Guenet, B.,
Zaehle, S., Penuelas, J., Janssens, I., and Ciais, P.: A representation of
the phosphorus cycle for ORCHIDEE (revision 4520), Geosci. Model Dev., 10,
3745–3770, 10.5194/gmd-10-3745-2017, 2017.Gregg, J. S., Andres, R. J., and Marland, G.: China: Emissions pattern of the
world leader in CO2 emissions from fossil fuel consumption and
cement production, Geophys. Res. Lett., 35, L08806, 10.1029/2007GL032887,
2008.Hansis, E., Davis, S. J., and Pongratz, J.: Relevance of methodological
choices for accounting of land use change carbon fluxes, Global Biogeochem.
Cy., 29, 1230–1246, 10.1002/2014GB004997, 2015.Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, 10.1002/joc.3711, 2014.Hauck, J., Kohler, P., Wolf-Gladrow, D., and Volker, C.: Iron fertilisation
and century-scale effects of open ocean dissolution of olivine in a simulated
CO2 removal experiment, Environ. Res. Lett., 11, 024007,
10.1088/1748-9326/11/2/024007, 2016.Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger,
C. M., Canadell, J. G., and Cuntz, M.: A new version of the CABLE land
surface model (Subversion revision r4601) incorporating land use and land
cover change, woody vegetation demography, and a novel optimisation-based
approach to plant coordination of photosynthesis, Geosci. Model Dev., 11,
2995–3026, 10.5194/gmd-11-2995-2018, 2018.Hertwich, E. G. and Peters, G. P.: Carbon Footprint of Nations: A Global,
Trade-Linked Analysis, Environ. Sci. Technol., 43, 6414–6420,
10.1021/es803496a, 2009.Hooijer, A., Page, S., Canadell, J. G., Silvius, M., Kwadijk, J., Wösten,
H., and Jauhiainen, J.: Current and future CO2 emissions from drained
peatlands in Southeast Asia, Biogeosciences, 7, 1505–1514,
10.5194/bg-7-1505-2010, 2010.Houghton, R. A.: Revised estimates of the annual net flux of carbon to the
atmosphere from changes in land use and land management 1850–2000, Tellus B,
55, 378–390, 10.1034/j.1600-0889.2003.01450.x, 2003.Houghton, R. A. and Nassikas, A. A.: Global and regional fluxes of carbon
from land use and land cover change 1850–2015, Global Biogeochem. Cy., 31,
456–472, 10.1002/2016GB005546, 2017.Houghton, R. A., House, J. I., Pongratz, J., van der Werf, G. R., DeFries, R.
S., Hansen, M. C., Le Quéré, C., and Ramankutty, N.: Carbon emissions
from land use and land-cover change, Biogeosciences, 9, 5125–5142,
10.5194/bg-9-5125-2012, 2012.Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A., Chevallier, F.,
Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones, D.,
Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S., Palmer,
P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y., and
Zhuravlev, R.: An intercomparison of inverse models for estimating sources
and sinks of CO2 using GOSAT measurements, J. Geophys. Res.-Atmos.,
120, 5253–5266, 10.1002/2014JD022962, 2015.Hugelius, G., Bockheim, J. G., Camill, P., Elberling, B., Grosse, G., Harden,
J. W., Johnson, K., Jorgenson, T., Koven, C. D., Kuhry, P., Michaelson, G.,
Mishra, U., Palmtag, J., Ping, C.-L., O'Donnell, J., Schirrmeister, L.,
Schuur, E. A. G., Sheng, Y., Smith, L. C., Strauss, J., and Yu, Z.: A new
data set for estimating organic carbon storage to 3 m depth in soils of the
northern circumpolar permafrost region, Earth Syst. Sci. Data, 5, 393–402,
10.5194/essd-5-393-2013, 2013.Huntzinger, D. N., Michalak, A. M., Schwalm, C., Ciais, P., King, A. W.,
Fang, Y., Schaefer, K., Wei, Y., Cook, R. B., Fisher, J. B., Hayes, D.,
Huang, M., Ito, A., Jain, A. K., Lei, H., Lu, C., Maignan, F., Mao, J.,
Parazoo, N., Peng, S., Poulter, B., Ricciuto, D., Shi, X., Tian, H., Wang,
W., Zeng, N., and Zhao, F.: Uncertainty in the response of terrestrial carbon
sink to environmental drivers undermines carbon-climate feedback predictions,
Sci. Rep.-UK, 7, 4765, 10.1038/s41598-017-03818-2, 2017.Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J.,
Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones,
C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K.,
Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van
Vuuren, D. P., and Wang, Y. P.: Harmonization of land-use scenarios for the
period 1500–2100: 600 years of global gridded annual land-use transitions,
wood harvest, and resulting secondary lands, Climatic Change, 109, 117–161,
10.1007/s10584-011-0153-2, 2011.
Hurtt, G., Chini, L., Sahajpa, R., and Frolking, S.: Harmonization of global land-use change
and management for the period 850–2100, Geosci. Model Dev. Discuss., in preparation, 2018.IEA/OECD: CO2 emissions from fuel combustion, International Energy
Agency/Organisation for Economic Cooperation and Development, Paris, 2017.IMF: World Economic Outlook, October 2018: Challenges to Steady Growth,
available at: http://www.imf.org, last access: October 2018.IPCC: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared
by the National Greenhouse Gas Inventories Programme, edited by: Eggleston,
S., Buendia, L., Miwa, K., Ngara, R., and Tanabe, K., Institute for Global
Environmental Strategies (IGES), Japan, 2006.Jackson, R. B., Canadell, J. G., Le Quéré, C., Andrew, R. M.,
Korsbakken, J. I., Peters, G. P., and Nakicenovic, N.: Reaching peak
emissions, Nat. Clim. Change, 6, 7–10, 10.1038/nclimate2892, 2016.Jackson, R. B., Le Quéré, C., Andrew, R. M., Canadell, J. G.,
Korsbakken, J. I., Liu, Z., Peters, G. P., and Zheng, B.: Global Energy
Growth Is Outpacing Decarbonization, Environ. Res. Lett., in press, 2018.JODI: Joint Organisations Data Initiative, available at: https://www.jodidata.org, last access: 7 November 2018.Joetzjer, E., Delire, C., Douville, H., Ciais, P., Decharme, B., Carrer, D.,
Verbeeck, H., De Weirdt, M., and Bonal, D.: Improving the ISBACC
land surface model simulation of water and carbon fluxes and stocks over the
Amazon forest, Geosci. Model Dev., 8, 1709–1727,
10.5194/gmd-8-1709-2015, 2015.Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic
radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105,
1425–1430, 10.1073/pnas.0707386105, 2008.Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J.,
Goulden, M. L., Bonan, G., Cescatti, A., Chen, J. Q., de Jeu, R., Dolman, A.
J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball,
J., Law, B. E., Montagnani, L., Mu, Q. Z., Mueller, B., Oleson, K., Papale,
D., Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N.,
Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline
in the global land evapotranspiration trend due to limited moisture supply,
Nature, 467, 951–954, 10.1038/nature09396, 2010.Kato, E., Kinoshita, T., Ito, A., Kawamiya, M., and Yamagata, Y.: Evaluation
of spatially explicit emission scenario of land-use change and biomass
burning using a process-based biogeochemical model, Journal of Land Use
Science, 8, 104–122, 10.1080/1747423X.2011.628705, 2013.Keeling, C. D., Bacastow, R. B., Bainbridge, A. E., Ekdahl, C. A., Guenther,
P. R., and Waterman, L. S.: Atmospheric carbon dioxide variations at Mauna
Loa Observatory, Hawaii, Tellus, 28, 538–551,
10.1111/j.2153-3490.1976.tb00701.x, 1976.Keeling, R. F. and Manning, A. C.: 5.15 – Studies of Recent Changes in
Atmospheric O2 Content, in: Treatise on Geochemistry: Second
Edition, edited by: Holland, H. D. and Turekian, K. K., Elsevier, Oxford,
385–404, 2014.Khatiwala, S., Primeau, F., and Hall, T.: Reconstruction of the history of
anthropogenic CO2 concentrations in the ocean, Nature, 462,
346–350, 10.1038/nature08526, 2009.Khatiwala, S., Tanhua, T., Mikaloff Fletcher, S., Gerber, M., Doney, S. C.,
Graven, H. D., Gruber, N., McKinley, G. A., Murata, A., Ríos, A. F., and
Sabine, C. L.: Global ocean storage of anthropogenic carbon, Biogeosciences,
10, 2169–2191, 10.5194/bg-10-2169-2013, 2013.Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J., Krummel,
P. B., Lamarque, J., Langenfelds, R. L., Le Quéré, C., Naik, V.,
O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R.
G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T.,
Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa,
S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F.,
Williams, J. E., and Zeng, G.: Three decades of global methane sources and
sinks, Nat. Geosci., 6, 813–823, 10.1038/ngeo1955, 2013.Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic
land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9,
927–953, 10.5194/essd-9-927-2017, 2017a.Klein Goldewijk, K., Dekker, S. C., and van Zanden, J. L.: Per-capita
estimations of long-term historical land use and the consequences for global
change research, Journal of Land Use Science, 12, 313–337,
10.1080/1747423X.2017.1354938, 2017b.Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics, J.
Meteorol. Soc. Jpn., 93, 5–48, 10.2151/jmsj.2015-001, 2015.Korsbakken, J. I., Peters, G. P., and Andrew, R. M.: Uncertainties around
reductions in China's coal use and CO2 emissions, Nat. Clim.
Change, 6, 687–690, 10.1038/nclimate2963, 2016.Krinner, G., Viovy, N., de Noblet, N., Ogée, J., Friedlingstein, P.,
Ciais, P., Sitch, S., Polcher, J., and Prentice, I. C.: A dynamic global
vegetation model for studies of the coupled atmosphere-biosphere system,
Global Biogeochem. Cy., 19, 1–33, 10.1029/2003GB002199, 2005.Landschützer, P., Gruber, N., Bakker, D. C. E., and Schuster, U.: Recent
variability of the global ocean carbon sink, Global Biogeochem. Cy., 28,
927–949, 10.1002/2014GB004853, 2014.Landschützer, P., Gruber, N., Haumann, A., Rödenbeck, C., Bakker, D.
C. E., van Heuven, S., Hoppema, M., Metzl, N., Sweeney, C., Takahashi, T.,
Tilbrook, B., and Wanninkhof, R.: The reinvigoration of the Southern Ocean
carbon sink, Science, 349, 1221–1224, 10.1126/science.aab2620, 2015.Landschützer, P., Gruber, N., and Bakker, D. C. E.: Decadal variations and
trends of the global ocean carbon sink, Global Biogeochem. Cy., 30,
1396–1417, 10.1002/2015GB005359, 2016.Lasslop, G., Reichstein, M., Papale, D., Richardson, A. D., Arneth, A.,
Barr, A., Stoy, P., and Wohlfahrt, G.: Separation of net ecosystem exchange
into assimilation and respiration using a light response curve approach:
critical issues and global evaluation, Glob. Change Biol., 16, 187–208,
10.1111/j.1365-2486.2009.02041.x, 2010.Le Quéré, C., Raupach, M. R., Canadell, J. G., Marland, G., Bopp,
L., Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P.,
Friedlingstein, P., Gurney, K., Houghton, R. A., House, J. I., Huntingford,
C., Levy, P. E., Lomas, M. R., Majkut, J., Metzl, N., Ometto, J. P., Peters,
G. P., Prentice, I. C., Randerson, J. T., Running, S. W., Sarmiento, J. L.,
Schuster, U., Sitch, S., Takahashi, T., Viovy, N., van der Werf, G. R., and
Woodward, F. I.: Trends in the sources and sinks of carbon dioxide, Nat.
Geosci., 2, 831–836, 10.1038/NGEO689, 2009.Le Quéré, C., Andres, R. J., Boden, T., Conway, T., Houghton, R. A.,
House, J. I., Marland, G., Peters, G. P., van der Werf, G. R., Ahlström,
A., Andrew, R. M., Bopp, L., Canadell, J. G., Ciais, P., Doney, S. C.,
Enright, C., Friedlingstein, P., Huntingford, C., Jain, A. K., Jourdain, C.,
Kato, E., Keeling, R. F., Klein Goldewijk, K., Levis, S., Levy, P., Lomas,
M., Poulter, B., Raupach, M. R., Schwinger, J., Sitch, S., Stocker, B. D.,
Viovy, N., Zaehle, S., and Zeng, N.: The global carbon budget 1959–2011,
Earth Syst. Sci. Data, 5, 165–185, 10.5194/essd-5-165-2013,
2013.Le Quéré, C., Peters, G. P., Andres, R. J., Andrew, R. M., Boden, T. A.,
Ciais, P., Friedlingstein, P., Houghton, R. A., Marland, G., Moriarty, R.,
Sitch, S., Tans, P., Arneth, A., Arvanitis, A., Bakker, D. C. E., Bopp, L.,
Canadell, J. G., Chini, L. P., Doney, S. C., Harper, A., Harris, I., House,
J. I., Jain, A. K., Jones, S. D., Kato, E., Keeling, R. F., Klein Goldewijk,
K., Körtzinger, A., Koven, C., Lefèvre, N., Maignan, F., Omar, A., Ono,
T., Park, G.-H., Pfeil, B., Poulter, B., Raupach, M. R., Regnier, P.,
Rödenbeck, C., Saito, S., Schwinger, J., Segschneider, J., Stocker, B. D.,
Takahashi, T., Tilbrook, B., van Heuven, S., Viovy, N., Wanninkhof, R.,
Wiltshire, A., and Zaehle, S.: Global carbon budget 2013, Earth Syst. Sci.
Data, 6, 235–263, 10.5194/essd-6-235-2014, 2014.Le Quéré, C., Moriarty, R., Andrew, R. M., Canadell, J. G., Sitch, S.,
Korsbakken, J. I., Friedlingstein, P., Peters, G. P., Andres, R. J., Boden,
T. A., Houghton, R. A., House, J. I., Keeling, R. F., Tans, P., Arneth, A.,
Bakker, D. C. E., Barbero, L., Bopp, L., Chang, J., Chevallier, F., Chini, L.
P., Ciais, P., Fader, M., Feely, R. A., Gkritzalis, T., Harris, I., Hauck,
J., Ilyina, T., Jain, A. K., Kato, E., Kitidis, V., Klein Goldewijk, K.,
Koven, C., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A.,
Lima, I. D., Metzl, N., Millero, F., Munro, D. R., Murata, A., Nabel, J. E.
M. S., Nakaoka, S., Nojiri, Y., O'Brien, K., Olsen, A., Ono, T., Pérez, F.
F., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Rödenbeck, C., Saito,
S., Schuster, U., Schwinger, J., Séférian, R., Steinhoff, T., Stocker, B.
D., Sutton, A. J., Takahashi, T., Tilbrook, B., van der Laan-Luijkx, I. T.,
van der Werf, G. R., van Heuven, S., Vandemark, D., Viovy, N., Wiltshire, A.,
Zaehle, S., and Zeng, N.: Global Carbon Budget 2015, Earth Syst. Sci. Data,
7, 349–396, 10.5194/essd-7-349-2015, 2015a.Le Quéré, C., Moriarty, R., Andrew, R. M., Peters, G. P., Ciais, P.,
Friedlingstein, P., Jones, S. D., Sitch, S., Tans, P., Arneth, A., Boden, T.
A., Bopp, L., Bozec, Y., Canadell, J. G., Chini, L. P., Chevallier, F.,
Cosca, C. E., Harris, I., Hoppema, M., Houghton, R. A., House, J. I., Jain,
A. K., Johannessen, T., Kato, E., Keeling, R. F., Kitidis, V., Klein
Goldewijk, K., Koven, C., Landa, C. S., Landschützer, P., Lenton, A., Lima,
I. D., Marland, G., Mathis, J. T., Metzl, N., Nojiri, Y., Olsen, A., Ono, T.,
Peng, S., Peters, W., Pfeil, B., Poulter, B., Raupach, M. R., Regnier, P.,
Rödenbeck, C., Saito, S., Salisbury, J. E., Schuster, U., Schwinger, J.,
Séférian, R., Segschneider, J., Steinhoff, T., Stocker, B. D., Sutton, A.
J., Takahashi, T., Tilbrook, B., van der Werf, G. R., Viovy, N., Wang, Y.-P.,
Wanninkhof, R., Wiltshire, A., and Zeng, N.: Global carbon budget 2014, Earth
Syst. Sci. Data, 7, 47–85, 10.5194/essd-7-47-2015, 2015b.Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J.
I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R.
A., Keeling, R. F., Alin, S., Andrews, O. D., Anthoni, P., Barbero, L., Bopp,
L., Chevallier, F., Chini, L. P., Ciais, P., Currie, K., Delire, C., Doney,
S. C., Friedlingstein, P., Gkritzalis, T., Harris, I., Hauck, J., Haverd, V.,
Hoppema, M., Klein Goldewijk, K., Jain, A. K., Kato, E., Körtzinger, A.,
Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D.,
Melton, J. R., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R.,
Nabel, J. E. M. S., Nakaoka, S.-I., O'Brien, K., Olsen, A., Omar, A. M., Ono,
T., Pierrot, D., Poulter, B., Rödenbeck, C., Salisbury, J., Schuster, U.,
Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Sutton, A. J.,
Takahashi, T., Tian, H., Tilbrook, B., van der Laan-Luijkx, I. T., van der
Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., and Zaehle, S.:
Global Carbon Budget 2016, Earth Syst. Sci. Data, 8, 605–649,
10.5194/essd-8-605-2016, 2016.Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Pongratz, J.,
Manning, A. C., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Jackson,
R. B., Boden, T. A., Tans, P. P., Andrews, O. D., Arora, V. K., Bakker, D. C.
E., Barbero, L., Becker, M., Betts, R. A., Bopp, L., Chevallier, F., Chini,
L. P., Ciais, P., Cosca, C. E., Cross, J., Currie, K., Gasser, T., Harris,
I., Hauck, J., Haverd, V., Houghton, R. A., Hunt, C. W., Hurtt, G., Ilyina,
T., Jain, A. K., Kato, E., Kautz, M., Keeling, R. F., Klein Goldewijk, K.,
Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S.,
Lima, I., Lombardozzi, D., Metzl, N., Millero, F., Monteiro, P. M. S., Munro,
D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Nojiri, Y., Padin, X. A., Peregon,
A., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Reimer, J., Rödenbeck,
C., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Tian, H.,
Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G.
R., van Heuven, S., Viovy, N., Vuichard, N., Walker, A. P., Watson, A. J.,
Wiltshire, A. J., Zaehle, S., and Zhu, D.: Global Carbon Budget 2017, Earth
Syst. Sci. Data, 10, 405–448, 10.5194/essd-10-405-2018,
2018.Li, H. and Ilyina, T.: Current and Future Decadal Trends in the Oceanic
Carbon Uptake Are Dominated by Internal Variability, Geophys. Res. Lett., 45,
916–925, 10.1002/2017GL075370, 2018.Li, W., Ciais, P., Peng, S., Yue, C., Wang, Y., Thurner, M., Saatchi, S. S.,
Arneth, A., Avitabile, V., Carvalhais, N., Harper, A. B., Kato, E., Koven,
C., Liu, Y. Y., Nabel, J. E. M. S., Pan, Y., Pongratz, J., Poulter, B., Pugh,
T. A. M., Santoro, M., Sitch, S., Stocker, B. D., Viovy, N., Wiltshire, A.,
Yousefpour, R., and Zaehle, S.: Land-use and land-cover change carbon
emissions between 1901 and 2012 constrained by biomass observations,
Biogeosciences, 14, 5053–5067, 10.5194/bg-14-5053-2017,
2017.Lienert, S. and Joos, F.: A Bayesian ensemble data assimilation to constrain
model parameters and land-use carbon emissions, Biogeosciences, 15,
2909–2930, 10.5194/bg-15-2909-2018, 2018.Liu, Z., Guan, D., Wei, W., Davis, S. J., Ciais, P., Bai, J., Peng, S.,
Zhang, Q., Hubacek, K., Marland, G., Andres, R. J., Crawford-Brown, D., Lin,
J., Zhao, H., Hong, C., Boden, T. A., Feng, K., Peters, G. P., Xi, F., Liu,
J., Li, Y., Zhao, Y., Zeng, N., and He, K.: Reduced carbon emission estimates
from fossil fuel combustion and cement production in China, Nature, 524,
335–338, 10.1038/nature14677, 2015.Liu, Z., Zheng, B., Zhang, Q., and Guan, D.: New dynamics of energy use and
CO2 emissions in China, Nature, available at:
https://arxiv.org/abs/1811.09475 (last access: 28 November 2018), in review, 2018.Manning, A. C. and Keeling, R. F.: Global oceanic and land biotic carbon
sinks from the Scripps atmospheric oxygen flask sampling network, Tellus B,
58, 95–116, 10.1111/j.1600-0889.2006.00175.x, 2006.Marland, G.: Uncertainties in accounting for CO2 from fossil fuels,
J. Ind. Ecol., 12, 136–139, 10.1111/j.1530-9290.2008.00014.x, 2008.Marland, G. and Rotty, R. M.: Carbon-Dioxide Emissions from Fossil-Fuels
– a Procedure for Estimation and Results for 1950–1982, Tellus B, 36,
232–261, 10.1111/j.1600-0889.2011.00530.x, 1984.Marland, G., Hamal, K., and Jonas, M.: How Uncertain Are Estimates of
CO2 Emissions?, J. Ind. Ecol., 13, 4–7,
10.1111/j.1530-9290.2009.00108.x, 2009.Masarie, K. A. and Tans, P. P.: Extension and integration of atmospheric
carbon dioxide data into a globally consistent measurement record, J.
Geophys. Res.-Atmos., 100, 11593–11610, 10.1029/95jd00859, 1995.Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Popke, D., Gayler, V., Giorgetta, M., Goll, D., Haak, H., Hagemann, S.,
Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenez Cuesta de la
Otero, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S.,
Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner,
K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W., Nabel, J. E. M.
S., Nam, C., Notz, D., Nyawira, S., Paulsen, H., Peters, K., Pincus, R.,
Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T., Rast, S., Redler, R.,
Reick, C., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R.,
Schulzweida, U., Six, K., Stein, L., Stemmler, I., Stevens, B., von Storch,
J., Tian, F., Voigt, A., de Vrese, P., Wieners, K.-H., Wilkenskjeld, S.,
Roeckner, E., and
Winkler, A
Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response
to increasing CO2, J. Adv. Model. Earth Sy.,
in review, 2018.MCI: Ministry of Commerce and Industry, 2018: Foreign Trade Data
Dissemination Portal, available at: http://121.241.212.146, last
access: 26 October 2018.McKinley, G. A., Pilcher, D. J., Fay, A. R., Lindsay, K., Long, M. C., and
Lovenduski, N. S.: Timescales for detection of trends in the ocean carbon
sink, Nature, 530, 469–472, 10.1038/nature16958, 2016.McNeil, B. I., Matear, R. J., Key, R. M., Bullister, J. L., and Sarmiento, J.
L.: Anthropogenic CO2 uptake by the ocean based on the global
chlorofluorocarbon data set, Science, 299, 235–239,
10.1126/science.1077429, 2003.Meiyappan, P., Jain, A. K., and House, J. I.: Increased influence of nitrogen
limitation on CO2 emissions from future land use and land use
change, Global Biogeochem. Cy., 29, 1524–1548, 10.1002/2015GB005086,
2015.Melton, J. R. and Arora, V. K.: Competition between plant functional types in
the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0, Geosci. Model Dev.,
9, 323–361, 10.5194/gmd-9-323-2016, 2016.Mercado, L. M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild,
M., and Cox, P. M.: Impact of changes in diffuse radiation on the global land
carbon sink, Nature, 458, 1014–1018, 10.1038/nature07949, 2009.Mikaloff Fletcher, S. E., Gruber, N., Jacobson, A. R., Doney, S. C.,
Dutkiewicz, S., Gerber, M., Follows, M., Joos, F., Lindsay, K., Menemenlis,
D., Mouchet, A., Müller, S. A., and Sarmiento, J. L.: Inverse estimates
of anthropogenic CO2 uptake, transport, and storage by the oceans,
Global Biogeochem. Cy., 20, GB2002, 10.1029/2005GB002530, 2006.Millar, R. J., Fuglestvedt, J. S., Friedlingstein, P., Rogelj, J., Grubb, M.
J., Matthews, H. D., Skeie, R. B., Forster, P. M., Frame, D. J., and Allen,
A. R.: Emission budgets and pathways consistent with limiting warming to 1.5
degrees C, Nat. Geosci., 10, 741–747, 10.1038/NGEO3031, 2017.Ministry of Mines: Ministry of Mines, 2018: Mineral Production, available
at: http://ibm.nic.in/index.php?c=pages&m=index&id=497, last
access: September 2018.Myhre, G., Alterskjær, K., and Lowe, D.: A fast method for updating
global fossil fuel carbon dioxide emissions, Environ. Res. Lett., 4, 034012,
10.1088/1748-9326/4/3/034012, 2009.Myneni, R. B., Nemani, R. R., and Running, S. W.: Estimation of global leaf
area index and absorbed par using radiative transfer models, IEEE T.
Geosci. Remote 35, 1380–1393, 10.1109/36.649788, 1997.NBS: National Bureau of Statistics, 2015, China Energy Statistical Yearbook
2014, China Statistics Press, Beijing, ISBN 978-7-5037-7499-7, 2015.NBS: National Bureau of Statistics, 2017, China Energy Statistical Yearbook
2017, China Statistics Press, Beijing, ISBN 978-7-5037-8064-6, 2017.NBS: National Bureau of Statistics, 2018, Statistical Communiqué of the
People's Republic of China on the 2017 National Economic and Social
Development, available at:
http://www.stats.gov.cn/english/pressrelease/201802/t20180228_1585666.html,
last access: 15 November 2018a.NBS: National Bureau of Statistics, 2018. National Data – Monthly data,
available at: http://data.stats.gov.cn/easyquery.htm?cn=_A01, last
access: 15 November 2018b.NOAA/ESRL: NOAA Greenhouse Gas Marine Boundary Layer Reference, available at: https://www.esrl.noaa.gov/gmd/ccgg/mbl/mbl.html, last access: 1 August 2018.OEA: Office of the Economic Advisor (OEA), 2018: Index of Eight Core
Industries, Office of the Economic Advisor, available at:
http://eaindustry.nic.in/home.asp, last access: 2 November 2018.Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C.,
Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik,
A., Fisher, R., Heald, C., Kluzek, E., Lamarque, J., Lawrence, P., Leung, L.,
Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Tang, J., and Yang, Z.:
Technical Description of version 4.5 of the Community Land Model (CLM), NCAR,
available at:
http://www.cesm.ucar.edu/models/cesm1.2/clm/CLM45_Tech_Note.pdf (last
access: 28 November 2018), 2013.Patra, P. K., Takigawa, M., Watanabe, S., Chandra, N., Ishijima, K., and
Yamashita, Y.: Improved Chemical Tracer Simulation by MIROC4.0-based
Atmospheric Chemistry-Transport Model (MIROC4-ACTM), Sola, 14, 91–96,
10.2151/sola.2018-016, 2018.Peters, G. P., Andrew, R., and Lennox, J.: Constructing a multi-regional
input-output table using the GTAP database, Econ. Syst. Res., 23, 131–152,
10.1080/09535314.2011.563234, 2011a.Peters, G. P., Minx, J. C., Weber, C. L., and Edenhofer, O.: Growth in
emission transfers via international trade from 1990 to 2008, P. Natl. Acad.
Sci. USA, 108, 8903–8908, 10.1073/pnas.1006388108, 2011b.Peters, G. P., Davis, S. J., and Andrew, R.: A synthesis of carbon in
international trade, Biogeosciences, 9, 3247–3276,
10.5194/bg-9-3247-2012, 2012a.Peters, G. P., Marland, G., Le Quéré, C., Boden, T. A., Canadell, J.
G., and Raupach, M. R.: Correspondence: Rapid growth in CO2
emissions after the 2008–2009 global financial crisis, Nat. Clim. Change, 2,
2–4, 10.1038/nclimate1332, 2012b.Peters, G. P., Andrew, R. M., Boden, T., Canadell, J. G., Ciais, P., Le
Quéré, C., Marland, G., Raupach, M. R., and Wilson, C.: The challenge
to keep global warming below 2∘C, Nat. Clim. Change, 3, 4–6,
10.1038/nclimate1783, 2013.Peters, G. P., Le Quéré, C., Andrew, R. M., Canadell, J. G.,
Friedlingstein, P., Ilyina, T., Jackson, R. B., Joos, F., Korsbakken, J. I.,
McKinley, G. A., Sitch, S., and Tans, P.: Towards real-time verification of
CO2 emissions, Nat. Clim. Change, 7, 848–850,
10.1038/s41558-017-0013-9, 2017.Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki,
T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van
der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget:
results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10,
6699–6720, 10.5194/bg-10-6699-2013, 2013.Pfeil, B., Olsen, A., Bakker, D. C. E., Hankin, S., Koyuk, H., Kozyr, A.,
Malczyk, J., Manke, A., Metzl, N., Sabine, C. L., Akl, J., Alin, S. R.,
Bates, N., Bellerby, R. G. J., Borges, A., Boutin, J., Brown, P. J., Cai,
W.-J., Chavez, F. P., Chen, A., Cosca, C., Fassbender, A. J., Feely, R. A.,
González-Dávila, M., Goyet, C., Hales, B., Hardman-Mountford, N., Heinze,
C., Hood, M., Hoppema, M., Hunt, C. W., Hydes, D., Ishii, M., Johannessen,
T., Jones, S. D., Key, R. M., Körtzinger, A., Landschützer, P., Lauvset,
S. K., Lefèvre, N., Lenton, A., Lourantou, A., Merlivat, L., Midorikawa,
T., Mintrop, L., Miyazaki, C., Murata, A., Nakadate, A., Nakano, Y., Nakaoka,
S., Nojiri, Y., Omar, A. M., Padin, X. A., Park, G.-H., Paterson, K., Perez,
F. F., Pierrot, D., Poisson, A., Ríos, A. F., Santana-Casiano, J. M.,
Salisbury, J., Sarma, V. V. S. S., Schlitzer, R., Schneider, B., Schuster,
U., Sieger, R., Skjelvan, I., Steinhoff, T., Suzuki, T., Takahashi, T.,
Tedesco, K., Telszewski, M., Thomas, H., Tilbrook, B., Tjiputra, J.,
Vandemark, D., Veness, T., Wanninkhof, R., Watson, A. J., Weiss, R., Wong, C.
S., and Yoshikawa-Inoue, H.: A uniform, quality controlled Surface Ocean
CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 5, 125–143,
10.5194/essd-5-125-2013, 2013.Piao, S., Huang, M., Liu, Z., Wang, X., Ciais, P., Canadell, J. G., Wang,
K., Bastos, A., Friedlingstein, P., Houghton, R. A., Le Quéré, C.,
Liu, Y., Myneni, R. B., Peng, S., Pongratz, J., Sitch, S., Yan, T., Wang, Y.,
Zhu, Z., Wu, D., and Wang, T.: Lower land-use emissions responsible for
increased net land carbon sink during the slow warming period, Nat. Geosci.,
11, 739–743, 10.1038/s41561-018-0204-7, 2018.Pongratz, J., Reick, C. H., Houghton, R. A., and House, J. I.: Terminology as
a key uncertainty in net land use and land cover change carbon flux
estimates, Earth Syst. Dynam., 5, 177–195,
10.5194/esd-5-177-2014, 2014.Poulter, B., Frank, D. C., Hodson, E. L., and Zimmermann, N. E.: Impacts of
land cover and climate data selection on understanding terrestrial carbon
dynamics and the CO2 airborne fraction, Biogeosciences, 8,
2027–2036, 10.5194/bg-8-2027-2011, 2011.PPAC: Petroleum, Petroleum Planning and Analysis Cell, Ministry of Petroleum
and Natural Gas, available at: http://eaindustry.nic.in/home.asp, last
access: 17 October 2018a.PPAC: Natural Gas, Petroleum Planning and Analysis Cell, Ministry of
Petroleum and Natural Gas, available at:
http://eaindustry.nic.in/home.asp, last access: 26 October 2018b.Prentice, I. C., Farquhar, G. D., Fasham, M. J. R., Goulden, M. L., Heimann,
M., Jaramillo, V. J., Kheshgi, H. S., Le Quéré, C., Scholes, R. J.,
and Wallace, D. W. R.: The Carbon Cycle and Atmospheric Carbon Dioxide, in:
Climate Change 2001: The Scientific Basis, Contribution of Working Group I to
the Third Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der
Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA., 183–237, 2001.Price, J. T. and Warren, R.: Review of the Potential of “Blue Carbon”
Activities to Reduce Emissions; available at:
http://avoid-net-uk.cc.ic.ac.uk/wp-content/uploads/delightful-downloads/2016/03/Literature-review-of-the-potential-of-blue-carbon-activities-to-reduce-emissions-AVOID2-WPE2.pdf
(last access: 25 July 2018), 2016.Raupach, M. R., Marland, G., Ciais, P., Le Quéré, C., Canadell, J.
G., Klepper, G., and Field, C. B.: Global and regional drivers of
accelerating CO2 emissions, P. Natl. Acad. Sci. USA, 104,
10288–10293, 10.1073/pnas.0700609104, 2007.Regnier, P., Friedlingstein, P., Ciais, P., Mackenzie, F. T., Gruber, N.,
Janssens, I. A., Laruelle, G. G., Lauerwald, R., Luyssaert, S., Andersson, A.
J., Arndt, S., Arnosti, C., Borges, A. V., Dale, A. W., Gallego-Sala, A.,
Goddéris, Y., Goossens, N., Hartmann, J., Heinze, C., Ilyina, T., Joos,
F., La Rowe, D. E., Leifeld, J., Meysman, F. J. R., Munhoven, G., Raymond, P.
A., Spahni, R., Suntharalingam, P., and Thullner M.: Anthropogenic
perturbation of the carbon fluxes from land to ocean, Nat. Geosci., 6,
597–607, 10.1038/NGEO1830, 2013.Resplandy, L., Keeling, R. F., Rodenbeck, C., Stephens, B. B., Khatiwala,
S., Rodgers, K. B., Long, M. C., Bopp, L., and Tans, P. P.: Revision of
global carbon fluxes based on a reassessment of oceanic and riverine carbon
transport, Nat. Geosci., 11, 504–509, 10.1038/s41561-018-0151-3, 2018.Rhein, M., Rintoul, S. R., Aoki, S., Campos, E., Chambers, D., Feely, R. A.,
Gulev, S., Johnson, G. C., Josey, S. A., Kostianoy, A., Mauritzen, C.,
Roemmich, D., Talley, L. D., and Wang, F.: Chapter 3: Observations: Ocean,
in: Climate Change 2013 The Physical Science Basis, Cambridge University
Press, 2013.Rödenbeck, C.: Estimating CO2 sources and sinks from
atmospheric mixing ratio measurements using a global inversion of atmospheric
transport, Max Plank Institute, MPI-BGC, 2005.Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux
history 1982–2001 inferred from atmospheric data using a global inversion of
atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964,
10.5194/acp-3-1919-2003, 2003.Rödenbeck, C., Keeling, R. F., Bakker, D. C. E., Metzl, N., Olsen, A.,
Sabine, C., and Heimann, M.: Global surface-ocean pCO2 and
sea–air CO2 flux variability from an observation-driven ocean
mixed-layer scheme, Ocean Sci., 9, 193–216,
10.5194/os-9-193-2013, 2013.Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar,
N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air
CO2 flux variability from an observation-driven ocean mixed-layer
scheme, Biogeosciences, 11, 4599–4613,
10.5194/bg-11-4599-2014, 2014.Rödenbeck, C., Bakker, D. C. E., Gruber, N., Iida, Y., Jacobson, A. R.,
Jones, S., Landschützer, P., Metzl, N., Nakaoka, S., Olsen, A., Park,
G.-H., Peylin, P., Rodgers, K. B., Sasse, T. P., Schuster, U., Shutler, J.
D., Valsala, V., Wanninkhof, R., and Zeng, J.: Data-based estimates of the
ocean carbon sink variability – first results of the Surface Ocean
pCO2 Mapping intercomparison (SOCOM), Biogeosciences, 12,
7251–7278, 10.5194/bg-12-7251-2015, 2015.Rogelj, J., Schaeffer, M., Friedlingstein, P., Gillett, N. P., van Vuuren,
D. P., Riahi, K., Allen, M., and Knutti, R.: Differences between carbon
budget estimates unravelled, Nat. Clim. Change, 6, 245–252,
10.1038/NCLIMATE2868, 2016.Rypdal, K., Paciomik, N., Eggleston, S., Goodwin, J., Irving, W., Penman,
J., and Woodfield, M.: Chapter 1 Introduction to the 2006 Guidelines, in:
2006 IPCC Guidelines for National Greenhouse Gas Inventories, edited by:
Eggleston, S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K., Institute
for Global Environmental Strategies (IGES), Hayama, Kanagawa, Japan, 2006.Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A.,
Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S.,
White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks
in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108,
9899–9904, 10.1073/pnas.1019576108, 2011.Saeki, T. and Patra, P. K.: Implications of overestimated anthropogenic
CO2 emissions on East Asian and global land CO2 flux
inversion, Geoscience Letters, 4, 9, 10.1186/s40562-017-0074-7, 2017.Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J.
G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S.,
Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe,
M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford,
G., Brovkin, V., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Curry,
C., Frankenberg, C., Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito,
A., Joos, F., Kim, H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F.,
Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S., McDonald, K. C.,
Marshall, J., Melton, J. R., Morino, I., Naik, V., O'Doherty, S., Parmentier,
F.-J. W., Patra, P. K., Peng, C., Peng, S., Peters, G. P., Pison, I.,
Prigent, C., Prinn, R., Ramonet, M., Riley, W. J., Saito, M., Santini, M.,
Schroeder, R., Simpson, I. J., Spahni, R., Steele, P., Takizawa, A.,
Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., van
Weele, M., van der Werf, G. R., Weiss, R., Wiedinmyer, C., Wilton, D. J.,
Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang,
Z., and Zhu, Q.: The global methane budget 2000–2012, Earth Syst. Sci. Data,
8, 697–751, 10.5194/essd-8-697-2016, 2016.SCCL: Singareni Collieries Company Limited (SCCL), 2018: Provisional
Production and Dispatches Performance. Singareni Collieries Company Limited,
available at:
https://scclmines.com/scclnew/performance_production.asp, last access:
1 November 2018.Schimel, D., Alves, D., Enting, I., Heimann, M., Joos, F., Raynaud, D.,
Wigley, T., Prater, M., Derwent, R., Ehhalt, D., Fraser, P., Sanhueza, E.,
Zhou, X., Jonas, P., Charlson, R., Rodhe, H., Sadasivan, S., Shine, K. P.,
Fouquart, Y., Ramaswamy, V., Solomon, S., Srinivasan, J., Albritton, D.,
Derwent, R., Isaksen, I., Lal, M., and Wuebbles, D.: Radiative Forcing of
Climate Change, in: Climate Change 1995 The Science of Climate Change.
Contribution of Working Group I to the Second Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Houghton, J. T., Meira
Rilho, L. G., Callander, B. A., Harris, N., Kattenberg, A., and Maskell, K.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
1995.Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of increasing
CO2 on the terrestrial carbon cycle, P. Natl. Acad. Sci. USA, 112,
436–441, 10.1073/pnas.1407302112, 2015.Schwietzke, S., Sherwood, O. A., Bruhwiler, L. M. P., Miller, J. B., Etiope,
G., Dlugokencky, E. J., Michel, S. E., Arling, V. A., Vaughn, B. H., White,
J. W. C., and Tans, P. P.: Upward revision of global fossil fuel methane
emissions based on isotope database, Nature, 538, 88–91,
10.1038/nature19797, 2016.Schwinger, J., Goris, N., Tjiputra, J. F., Kriest, I., Bentsen, M., Bethke,
I., Ilicak, M., Assmann, K. M., and Heinze, C.: Evaluation of NorESM-OC
(versions 1 and 1.2), the ocean carbon-cycle stand-alone configuration of the
Norwegian Earth System Model (NorESM1), Geosci. Model Dev., 9, 2589–2622,
10.5194/gmd-9-2589-2016, 2016.Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L.,
Betts, R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C. D., Prentice, I.
C., and Woodward, F. I.: Evaluation of the terrestrial carbon cycle, future
plant geography and climate-carbon cycle feedbacks using five Dynamic Global
Vegetation Models (DGVMs), Glob. Change Biol., 14, 2015–2039,
10.1111/j.1365-2486.2008.01626.x, 2008.Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg,
J., and Zaehle, S.: Implications of incorporating N cycling and N limitations
on primary production in an individual-based dynamic vegetation model,
Biogeosciences, 11, 2027–2054, 10.5194/bg-11-2027-2014,
2014.Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W.,
Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S.,
Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M.,
Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and
Denning, A. S.: Weak northern and strong tropical land carbon uptake from
vertical profiles of atmospheric CO2, Science, 316, 1732–1735,
10.1126/science.1137004, 2007.Stocker, T., Qin, D., and Platner, G.-K.: Climate Change 2013 The Physical
Science Basis, Cambridge University Press, 2013.Swart, N. C., Fyfe, J. C., Saenko, O. A., and Eby, M.: Wind-driven changes in
the ocean carbon sink, Biogeosciences, 11, 6107–6117,
10.5194/bg-11-6107-2014, 2014.Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii,
M., Midorikawa, T., Nojiri, Y., Kortzinger, A., Steinhoff, T., Hoppema, M.,
Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C. S., Delille, B., Bates, N. R., and de Baar, H. J. W.:
Climatological mean and decadal change in surface ocean
pCO2, and net sea-air CO2 flux over the global
oceans (vol 56, pg 554, 2009), Deep-Sea Res. Pt. I, 56, 2075–2076,
10.1016/j.dsr.2009.07.007, 2009.Tian, H. Q., Chen, G. S., Lu, C. Q., Xu, X. F., Hayes, D. J., Ren, W., Pan,
S. F., Huntzinger, D. N., and Wofsy, S. C.: North American terrestrial
CO2 uptake largely offset by CH4 and N2O
emissions: toward a full accounting of the greenhouse gas budget, Climatic
Change, 129, 413–426, 10.1007/s10584-014-1072-9, 2015.Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M.,
Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in
soil carbon simulations from CMIP5 Earth system models and comparison with
observations, Biogeosciences, 10, 1717–1736,
10.5194/bg-10-1717-2013, 2013.UN: United Nations Statistics Division: National Accounts Main Aggregates
Database, available at:
http://unstats.un.org/unsd/snaama/Introduction.asp (last access: 2
January 2018), 2017a.UN: United Nations Statistics Division: Energy Statistics, available at:
http://unstats.un.org/unsd/energy/, last access: June 2017b.UNFCCC: National Inventory Submissions,
available at: https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2018,
last access: June 2018.van der Laan-Luijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta,
A., Stanislawska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He, W.,
Chen, H., Masarie, K. A., Krol, M. C., and Peters, W.: The CarbonTracker Data
Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance
2001–2015, Geosci. Model Dev., 10, 2785–2800,
10.5194/gmd-10-2785-2017, 2017.van der Velde, I. R., Miller, J. B., Schaefer, K., van der Werf, G. R., Krol,
M. C., and Peters, W.: Terrestrial cycling of 13CO2 by
photosynthesis, respiration, and biomass burning in SiBCASA, Biogeosciences,
11, 6553–6571, 10.5194/bg-11-6553-2014, 2014.van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M.,
Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen,
T. T.: Global fire emissions and the contribution of deforestation, savanna,
forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10,
11707–11735, 10.5194/acp-10-11707-2010, 2010.van der Werf, G. R., Randerson, J. T., Giglio, L., van Leeuwen, T. T., Chen,
Y., Rogers, B. M., Mu, M., van Marle, M. J. E., Morton, D. C., Collatz, G.
J., Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates
during 1997–2016, Earth Syst. Sci. Data, 9, 697–720,
10.5194/essd-9-697-2017, 2017.Viovy, N.: CRUNCEP data set, available at: ftp://nacp.ornl.gov/synthesis/2009/frescati/temp/land_use_change/original/readme.htm,
last access: June 2016.Walker, A. P., Quaife, T., van Bodegom, P. M., De Kauwe, M. G., Keenan, T.
F., Joiner, J., Lomas, M. R., MacBean, N., Xu, C. G., Yang, X. J., and
Woodward, F. I.: The impact of alternative trait-scaling hypotheses for the
maximum photosynthetic carboxylation rate (V-cmax) on global gross primary
production, New Phytol., 215, 1370–1386, 10.1111/nph.14623, 2017.Wanninkhof, R., Park, G.-H., Takahashi, T., Sweeney, C., Feely, R., Nojiri,
Y., Gruber, N., Doney, S. C., McKinley, G. A., Lenton, A., Le Quéré, C.,
Heinze, C., Schwinger, J., Graven, H., and Khatiwala, S.: Global ocean carbon
uptake: magnitude, variability and trends, Biogeosciences, 10, 1983–2000,
10.5194/bg-10-1983-2013, 2013.Watson, R. T., Rodhe, H., Oeschger, H., and Siegenthaler, U.: Greenhouse
Gases and Aerosols, in: Climate Change: The IPCC Scientific Assessment.
Intergovernmental Panel on Climate Change (IPCC), edited by: Houghton, J. T.,
Jenkins, G. J., and Ephraums, J. J., Cambridge University Press, Cambridge,
1-40, 1990.Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Projected land
photosynthesis constrained by changes in the seasonal cycle of atmospheric
CO2, Nature, 538, 499–501, 10.1038/nature19772, 2016.Wilkenskjeld, S., Kloster, S., Pongratz, J., Raddatz, T., and Reick, C. H.:
Comparing the influence of net and gross anthropogenic land-use and
land-cover changes on the carbon cycle in the MPI-ESM, Biogeosciences, 11,
4817–4828, 10.5194/bg-11-4817-2014, 2014.Xi, F., Davis, S. J., Ciais, P., Crawford-Brown, D., Guan, D., Pade, C.,
Shi, T., Syddall, M., Lv, J., Ji, L., Bing, L., Wang, J., Wei, W., Yang,
K.-H., Lagerblad, B., Galan, I., Andrade, C., Zhang, Y., and Liu, Z.:
Substantial global carbon uptake by cement carbonation, Nat. Geosci., 9,
880–883, 10.1038/ngeo2840, 2016.Yin, X. W.: Responses of leaf nitrogen concentration and specific leaf area
to atmospheric CO2 enrichment: a retrospective synthesis across 62
species, Glob. Change Biol., 8, 631–642,
10.1046/j.1365-2486.2002.00497.x, 2002.Zaehle, S. and Friend, A. D.: Carbon and nitrogen cycle dynamics in the
O-CN land surface model: 1. Model description, site-scale evaluation, and
sensitivity to parameter estimates, Global Biogeochem. Cy., 24, GB1005,
10.1029/2009GB003521, 2010.Zaehle, S., Ciais, P., Friend, A. D., and Prieur, V.: Carbon benefits of
anthropogenic reactive nitrogen offset by nitrous oxide emissions, Na.
Geosci., 4, 601–605, 10.1038/NGEO1207, 2011.Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N.,
Ciais, P., Gans, F., Gruber, N., Hartmann, J., Herold, M., Ichii, K., Jung,
M., Landschützer, P., Laruelle, G. G., Lauerwald, R., Papale, D., Peylin,
P., Poulter, B., Ray, D., Regnier, P., Rödenbeck, C., Roman-Cuesta, R. M.,
Schwalm, C., Tramontana, G., Tyukavina, A., Valentini, R., van der Werf, G.,
West, T. O., Wolf, J. E., and Reichstein, M.: Reviews and syntheses: An
empirical spatiotemporal description of the global surface–atmosphere carbon
fluxes: opportunities and data limitations, Biogeosciences, 14, 3685–3703,
10.5194/bg-14-3685-2017, 2017.