# The global carbon budget 1959–2011

C. Le Quéré, R. Andres, T. Boden, T. Conway, R. Houghton, J. House, G. Marland, G. Peters, G. van Der Werf, A. Ahlström, et al.

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C. Le Quéré, R. Andres, T. Boden, T. Conway, R. Houghton, et al. The global carbon budget 1959–2011. Earth System Science Data, 2013, 5 (1), pp.165-185. 10.5194/essd-5-165-2013. hal-03208397

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The global carbon budget 1959–2011

C. Le Quéré¹, R. J. Andres², T. Boden², T. Conway³, R. A. Houghton⁴, J. I. House⁵, G. Marland⁶,
G. P. Peters⁷, G. R. van der Werf⁸, A. Ahlström⁹, R. M. Andrew⁷, L. Bopp¹⁰, J. G. Canadell¹¹, P. Ciais¹⁰,
S. C. Doney¹², C. Enright¹, P. Friedlingstein¹³, C. Huntingford¹⁴, A. K. Jain¹⁵, C. Jourdain¹,*, E. Kato¹⁶,
R. F. Keeling¹⁷, K. Klein Goldewijk¹⁸,¹⁹,²⁰, S. Levis²¹, P. Levy¹⁴, M. Lomas²², B. Poulter¹⁰,
M. R. Raupach¹¹, J. Schwinger²³,²⁴, S. Sitch²⁵, B. D. Stocker²⁶,²⁷, N. Viovy¹⁰, S. Zaehle²⁸, and N. Zeng²⁹

¹Tyndall Centre for Climate Change Research, University of East Anglia, Norwich Research Park,
Norwich, NR4 7TJ, UK

²Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, Oak Ridge,
Tennessee, USA

³National Oceanic & Atmosphere Administration, Earth System Research Laboratory (NOAA/ESRL), Boulder,
Colorado 80305, USA

⁴Woods Hole Research Centre (WHRC), Falmouth, Massachusetts 02540, USA

⁵Cabot Institute, Dept. of Geography, University of Bristol, Bristol, UK

⁶Research Institute for Environment, Energy, and Economics, Appalachian State University, Boone,
North Carolina 28608, USA

⁷Center for International Climate and Environmental Research – Oslo (CICERO), Oslo, Norway

⁸Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, the Netherlands

⁹Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

¹⁰Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, CE Orme des Merisiers,
91191 Gif sur Yvette Cedex, France

¹¹Global Carbon Project, CSIRO Marine and Atmospheric Research, Canberra, Australia

¹²Woods Hole Oceanographic Institution (WHOI), Woods Hole, Massachusetts 02543, USA

¹³College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
¹⁴Centre for Ecology and Hydrology (CEH), Wallingford, OX10 8BB, UK

¹⁵Department of Atmospheric Sciences, University of Illinois, Illinois, USA

¹⁶Center for Global Environmental Research (CGER), National Institute for Environmental Studies (NIES),
Tsukuba, Japan

¹⁷University of California, San Diego, Scripps Institution of Oceanography, La Jolla, California
92093-0244, USA

¹⁸PBL Netherlands Environmental Assessment Agency, The Hague/Bilthoven, the Netherlands

¹⁹Department Innovation and Environmental Sciences (IMEW) Utrecht University, Utrecht, the Netherlands
²⁰Institute for History and Culture (OGC), Utrecht University, Utrecht, the Netherlands

²¹National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA

²²Centre for Terrestrial Carbon Dynamics (CTCD), Sheffield University, UK

²³Geophysical Institute, University of Bergen, Bergen, Norway

²⁴Bjerknes Centre for Climate Research, Bergen, Norway

²⁵College of Life and Environmental Sciences, University of Exeter, EX4 4RJ, Exeter, UK

²⁶Climate and Environmental Physics, Physics Institute, University of Bern, 3012 Bern, Switzerland

²⁷Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland

²⁸Max-Planck-Institut für Biogeochemie, P.O. Box 600164, Hans-Knöll-Str. 10, 07745 Jena, Germany

²⁹Department of Atmospheric and Oceanic Science, University of Maryland, Maryland, USA

*now at: Food and Agriculture Organization of the United Nations (FAO), Rome, Italy

Correspondence to: C. Le Quéré (c.lequere@uea.ac.uk)

Received: 20 November 2012 – Published in Earth Syst. Sci. Data Discuss.: 2 December 2012

Revised: 11 March 2013 – Accepted: 14 March 2013 – Published: 8 May 2013
**Abstract.** Accurate assessments of anthropogenic carbon dioxide (CO₂) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO₂ emissions from fossil fuel combustion and cement production (E<sub>FF</sub>) are based on energy statistics, while emissions from Land-Use Change (E<sub>LUC</sub>), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO₂ concentration is measured directly and its rate of growth (G<sub>ATM</sub>) is computed from the concentration. The mean ocean CO₂ sink (S<sub>OCEAN</sub>) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO₂ sink (S<sub>LAND</sub>) is estimated by the difference of the other terms. For the last decade available (2002–2011), E<sub>FF</sub> was 8.3 ± 0.4 PgC yr<sup>-1</sup>, E<sub>LUC</sub> 1.0 ± 0.5 PgC yr<sup>-1</sup>, G<sub>ATM</sub> 4.3 ± 0.1 PgC yr<sup>-1</sup>, S<sub>OCEAN</sub> 2.5 ± 0.5 PgC yr<sup>-1</sup>, and S<sub>LAND</sub> 2.6 ± 0.8 PgC yr<sup>-1</sup>. For year 2011 alone, E<sub>FF</sub> was 9.5 ± 0.5 PgC yr<sup>-1</sup>, 3.0 percent above 2010, reflecting a continued trend in these emissions; E<sub>LUC</sub> was 0.9 ± 0.5 PgC yr<sup>-1</sup>, approximately constant throughout the decade; G<sub>ATM</sub> was 3.6 ± 0.2 PgC yr<sup>-1</sup>, S<sub>OCEAN</sub> was 2.7 ± 0.5 PgC yr<sup>-1</sup>, and S<sub>LAND</sub> was 4.1 ± 0.9 PgC yr<sup>-1</sup>. G<sub>ATM</sub> was low in 2011 compared to the 2002–2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO₂ concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that E<sub>FF</sub> will have increased by 2.6% (1.9–3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future.

All data presented here can be downloaded from
the Carbon Dioxide Information Analysis Center
(doi:10.3334/CDIAC/GCP_V2013).

# 1 Introduction

The concentration of carbon dioxide (CO₂) in the atmosphere has increased from approximately 278 parts per million (ppm) in 1750, the beginning of the Industrial Era, to 391.31 at the end of 2011 (Conway and Tans, 2012). This increase was caused initially mainly by the anthropogenic release of carbon to the atmosphere from deforestation and other land-use change activities. Emissions from fossil fuel combustion started before the Industrial Era and became the dominant source of anthropogenic emissions to the atmosphere from around 1920 until present. Anthropogenic emissions occur on top of an active natural carbon cycle that circulates carbon between the atmosphere, ocean, and terrestrial biosphere reservoirs on timescales from days to many millenia, while geologic reservoirs have even longer timescales (Archer et al., 2009).

The “global carbon budget” presented here refers to the mean, variations, and trends in the anthropogenic perturbation of CO₂ in the atmosphere. It quantifies the input of CO₂ to the atmosphere by emissions from human activities, the growth of CO₂ in the atmosphere, and the resulting changes in land and ocean carbon fluxes directly in response to increasing atmospheric CO₂ levels and indirectly in response to climate change and climate variability, and other anthropogenic and natural changes. An understanding of this perturbation budget over time and the underlying variability and trends of the natural carbon cycle are necessary to understand and quantify climate-carbon feedbacks. This also allows potentially earlier detection of any approaching discontinuities or tipping points of the carbon cycle in response to anthropogenic changes (Falkowski et al., 2000).

The components of the CO₂ budget that are reported in this paper include separate estimates for (1) the CO₂ emissions from fossil fuel combustion and cement production (E<sub>FF</sub>); (2) the CO₂ emissions resulting from deliberate human activities on land, including land use; land-use change and forestry (shortened to LUC hereafter; E<sub>LUC</sub>), (3) the growth rate of CO₂ in the atmosphere (G<sub>ATM</sub>); and (4) the uptake of CO₂ by the “CO₂ sinks” in the ocean (S<sub>OCEAN</sub>) and on land (S<sub>LAND</sub>). The CO₂ sinks as defined here include the response of the land and ocean to elevated CO₂ and changes in climate and other environmental conditions. The emissions and their partitioning among the atmosphere, ocean and land are in balance:

$$E_{\text{FF}} + E_{\text{LUC}} = G_{\text{ATM}} + S_{\text{OCEAN}} + S_{\text{LAND}}. \quad (1)$$

Equation (1) subsumes, and partly omits, two kinds of processes. The first is the net input of CO₂ to the atmosphere from the chemical oxidation of reactive carbon-containing gases, primarily methane (CH₄), carbon monoxide (CO), and volatile organic compounds such as terpene and isoprene, which we quantify here for the first time. The second process involves anthropogenic perturbations to carbon cycling in inland freshwaters, estuaries, and coastal areas that modify
both lateral fluxes transported from land ecosystems to the open ocean, “vertical” CO₂ fluxes by outgassing in rivers and estuaries, and the air-sea net exchange of CO₂ in coastal areas (Battin et al., 2008; Aufdenkampe et al., 2011). These flows are omitted in the absence of details on the natural versus anthropogenic terms of these facets of the carbon cycle. The inclusion of these fluxes of anthropogenic CO₂ would affect the estimates of S_LAND and perhaps S_OCEAN in Eq. (1), but not G_ATM.

The CO₂ budget has been assessed by the Intergovernmental Panel on Climate Change (IPCC) in all assessment reports (Watson et al., 1990; Schimel et al., 1995; Prentice et al., 2001; Denman et al., 2007), and by others (Conway and Tans, 2012). These included budget estimates for the decades of the 1980s, 1990s and, most recently, the period 2000–2005. The IPCC methodology has been adapted and used by the Global Carbon Project (GCP, www.globalcarbonproject.org), who have coordinated a cooperative community effort for the annual publication of global carbon budgets up to year 2005 (Raupach et al., 2007; including fossil emissions only), year 2006 (Canadell et al., 2007), year 2007 (published online; http://lgmacweb.env.uea.ac.uk/lequere/co2/2007/carbonbudget_2007.htm), year 2008 (Le Quéré et al., 2009), year 2009 (Friedlingstein et al., 2010), and most recently, year 2010 (Peters et al., 2012a). Each of these papers updated previous estimates with the latest available information for the entire time series. From 2008, these publications projected fossil fuel emissions for one additional year using the projected World Gross Domestic Product and estimated changes in the carbon intensity of the economy.

We adopt a range of ±1 standard deviation (sigma) to report the uncertainties in our annual estimates, representing a likelihood of 68% that the true value lies within the provided range, assuming that the errors have a Gaussian distribution. This choice reflects the difficulty of characterising the uncertainty in the CO₂ fluxes between the atmosphere and the ocean and land reservoirs individually, as well as the difficulty to update the CO₂ emissions from LUC, particularly on an annual basis. A 68% likelihood provides an indication of our current capability to quantify each term and its uncertainty given the available information. For comparison, the Fourth Assessment Report of the IPCC (AR4) generally reported 90% uncertainty for large datasets whose uncertainty is well characterised, or for long time intervals less affected by year-to-year variability. This includes, for instance, attribution statements associated with recorded warming levels since the pre-industrial period. The 90% number corresponds to the IPCC language of “very likely” or “very high confidence represents at least a 9 out of 10 chance”; our 68% value is near the 66% which the IPCC reports as “likely”. 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.

All units are presented in petagrammes of carbon (PgC, 10¹⁵ gC), which is the same as gigatonnes of carbon (GtC). Units of gigatonnes of CO₂ (or billion tonnes of CO₂) used in policy circles are equal to 3.67 multiplied by the value in units of PgC.

This paper provides a detailed description of the datasets and methodology used to compute the global carbon budget and associated uncertainties for the period 1959–2011. It presents the global carbon budget estimates by decade since the 1960s, including the last decade (2002–2011), the results for the year 2011, and a projection of E FF for year 2012. It is intended that this paper will be updated every year using the format of “living reviews” to help keep track of new versions of the budget that result from new data, revision of data, and changes in methodology. 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). With this approach, we aim to provide transparency and traceability in reporting indicators and drivers of climate change.

## 2 Methods

The original data and measurements used to complete the global carbon budget are generated by multiple organisations and research groups around the world. The effort presented here is thus mainly one of synthesis, where results from individual groups are collated, analysed and evaluated for consistency. Descriptions of the measurements, models, and methodologies follow below and in depth descriptions of each component are described elsewhere (e.g. Andres et al., 2012; Houghton et al., 2012).

### 2.1 CO₂ emissions from fossil fuel combustion and cement production (E FF)

#### 2.1.1 Fossil fuel and cement emissions and their uncertainty

The calculation of global and national CO₂ emissions from fossil fuel combustion, including gas flaring and cement production (E FF), relies primarily on energy data, specifically data on hydrocarbon fuels, collated and archived by several organisations (Andres et al., 2012), including the Carbon Dioxide Information Analysis Center (CDIAC), the International Energy Agency (IEA), the United Nations (UN), and the United States Department of Energy (DoE) Energy Information Administration (EIA). We use the emissions estimated by the CDIAC (http://cdiac.ornl.gov) which are based primarily on energy data provided by the UN Statistics Division (UN, 2012a, b; Table 1), and are typically available 2–3 yr after the close of a given year. CDIAC also provides the only dataset that extends back in time to 1751 with consistent and well-documented emissions from all fossil fuels, cement production, and gas flaring for all countries; this makes
<table><caption>**Table 1.** Data sources used to compute each component of the global carbon budget.</caption><thead><tr><th>Component</th><th>Process</th><th>Data source</th><th>Data reference</th></tr></thead><tbody><tr><td rowspan="3"><i>E</i><sub>FF</sub></td><td rowspan="2">Fossil fuel combustion and gas flaring</td><td>UN Statistics Division to 2009</td><td>UN (2012a, b)</td></tr><tr><td>BP for 2010–2011</td><td>BP (2012)</td></tr><tr><td>Cement production</td><td>US Geological Survey</td><td>van Oss (2011)</td></tr><tr><td rowspan="2"><i>E</i><sub>LUC</sub></td><td rowspan="2">Land cover change (deforestation, afforestation, and forest regrowth)</td><td>Global Trade and Analysis Project (GTAP)</td><td>Narayanan et al. (2012)</td></tr><tr><td>Forest Resource Assessment (FRA) of the Food and Agriculture Organisation (FAO)</td><td>FAO (2010)</td></tr><tr><td></td><td>Wood harvest</td><td>FAO Statistics Division</td><td>FAOSTAT (2010)</td></tr><tr><td></td><td>Shifting agriculture</td><td>FAO FRA and Statistics Division</td><td>FAOSTAT (2010)</td></tr><tr><td></td><td>Peat fires and interannual variability from climate–land management interactions</td><td>Global Fire Emissions Database (GFED3)</td><td>van der Werf et al. (2010)</td></tr><tr><td rowspan="2"><i>G</i><sub>ATM</sub></td><td rowspan="2">Change in CO<sub>2</sub> concentration</td><td>1959–1980: CO<sub>2</sub> Program at Scripps Institution of Oceanography and other research groups</td><td>Keeling et al. (1976)</td></tr><tr><td>1980–2011: US National Oceanic and Atmospheric Administration Earth System Research Laboratory</td><td>Conway and Tans (2012) and Ballantyne et al. (2012)</td></tr><tr><td rowspan="2"><i>S</i><sub>OCEAN</sub></td><td rowspan="2">Uptake of anthropogenic CO<sub>2</sub></td><td>1990–1999 average: indirect estimates based on CFCs, atmospheric O<sub>2</sub>, and other tracer observations</td><td>Manning and Keeling (2006); McNeil et al. (2003); Mikaloff Fletcher et al. (2006) as assessed by the IPCC (Denman et al., 2007)</td></tr><tr><td>Impact of increasing atmospheric CO<sub>2</sub>, and climate change and variability</td><td>Ocean models</td><td>Le Quéré et al. (2009) and Table 3</td></tr><tr><td><i>S</i><sub>LAND</sub></td><td>Response of land vegetation to: increasing atmospheric CO<sub>2</sub> concentration<br>Climate change and variability<br>Other environmental changes</td><td>Budget residual</td><td></td></tr></tbody></table>

the dataset a unique resource for research of the carbon cycle during the fossil fuel era. For this paper, we use CDIAC emissions data from the period 1959–2009, and preliminary estimates based on the BP annual energy review for extrapolation of emissions in 2010 and 2011 (BP, 2012). BP’s sources for energy statistics overlap with those of the UN data but are compiled more rapidly, using a smaller group

of mostly developed countries and assumptions for missing data. We use the BP values only for the year-to-year rate of change, because the rates of change are less uncertain than the absolute values. The preliminary estimates are replaced by the more complete CDIAC data when available. Past experience shows that projections based on the BP rate of change provide reliable estimates for the two most recent
years when full data are not yet available from the UN (see Sect. 3.2).

Emissions from cement production are based on cement data from the US Geological Survey (van Oss, 2011) up to year 2009, and from preliminary data for 2010 and 2011 (US Geological Survey, 2012). Emission estimates from gas flaring are calculated in a similar manner as those from solid, liquid, and gaseous fuels, and rely on the UN Energy Statistics to supply the amount of flared fuel. For emission years 2010 and 2011, flaring estimates are assumed constant from the emission year 2009 UN-based data. The basic data on gas flaring have large uncertainty. Fugitive emissions of CH₄ from the so-called upstream sector (coal mining, oil extraction, gas extraction and distribution) are not included in the accounts of CO₂ emissions except to the extent that they get captured in the UN energy data and counted as gas “flared or lost”. The UN data are not able to distinguish between gas that is flared or vented.

When necessary, fuel masses/volumes are converted to fuel energy content using coefficients provided by the UN and then to CO₂ emissions using conversion factors that take into account the relationship between carbon content and heat content of the different fuel types (coal, oil, gas, gas flaring) and the combustion efficiency (to account, for example, for soot left in the combustor or fuel otherwise lost or discharged without oxidation). In general, CO₂ emissions for equivalent energy consumptions are about 30% higher for coal compared to oil, and 70% higher for coal compared to gas (Marland et al., 2007). These calculations are based on the mass flows of carbon and assume that the carbon discharged, such as CO or CH₄, will soon be oxidized to CO₂ in the atmosphere and hence counts the carbon mass with CO₂ emissions.

Emissions are estimated for 1959–2011 for 129 countries and regions. The disaggregation of regions (e.g. the former Soviet Union prior to 1992) is based on the shares of emissions in the first year after the countries were disaggregated.

Estimates of CO₂ emissions show that the global total of emissions is not equal to the sum of emissions from all countries. This is largely attributable to combustion of fuels used in international shipping and aviation, where the emissions are included in the global totals but are not attributed to individual countries. In practice, the emissions from international bunker fuels are calculated based on where the fuels were loaded, but they are not included with national emissions estimates. Smaller differences also occur because globally, the sum of imports in all countries is not equivalent to the sum of exports, due to differing treatment of oxidation of non-fuel uses of hydrocarbons (e.g. as solvents, lubricants, feedstocks, etc.).

The uncertainty of the annual fossil fuel and cement emissions for the globe has been estimated at ±5% (scaled down from the published ±10% at ±2 sigma to the use of ±1 sigma bounds reported here; Andres et al., 2012). This includes an assessment of the amounts of fuel consumed, the carbon con-

tents of fuels, and the combustion efficiency. While in the budget we consider a fixed uncertainty of ±5% for all years, in reality the uncertainty, as a percentage of the emissions, is growing with time because of the larger share of global emissions from non-Annex B countries with weaker statistical systems (Marland et al., 2009). For example, the uncertainty in Chinese emissions estimates has been estimated at around ±10% (±1 sigma; Gregg et al., 2008). Generally, emissions from mature economies with good statistical bases have an uncertainty of only a few percent (Marland, 2008). Further research is needed before we can quantify the time evolution of the uncertainty.

### 2.1.2 Emissions embodied in goods and services

National emissions inventories take a territorial (production) perspective by “include[ing] all greenhouse gas emissions and removals taking place within national (including administered) territories and offshore areas over which the country has jurisdiction” (from the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories). That is, emissions are allocated to the country where and when the emissions actually occur. The emission inventory of an individual country does not include the emissions from the production of goods and services produced in other countries (e.g. food and clothes) that are used for national consumption. The difference between the standard territorial emission inventories and consumption-based emission inventories is the net transfer (exports minus imports) of emissions from the production of internationally traded goods and services. Complementary emission inventories that allocated emissions to the final consumption of goods and services (e.g. Davis and Caldeira, 2010) provide additional information that can be used to understand emission drivers, quantify emission leakages between countries, and potentially design more effective and efficient climate policy.

We estimate consumption-based emissions by enumerating the global supply chain using a global model of the economic relationships between sectors in every country (Peters et al., 2011a). Due to availability of the input data, detailed estimates are made for the years 1997, 2001, 2004, and 2007 (an extension of Peters et al., 2011b) using economic and trade data from the Global Trade and Analysis Project (GTAP; Narayanan et al., 2012). The results cover 57 sectors and up to 129 countries and regions. The results are extended into an annual time series from 1990 to the latest year of the fossil-fuel emissions or GDP data (2010 in this budget), using GDP data by expenditure (from the UN Main Aggregates database; UN, 2012c) and time series of trade data from GTAP (Narayanan et al., 2012). We do not provide an uncertainty estimate for these emissions, but based on model comparisons and sensitivity analysis, they are unlikely to be significantly larger than for the territorial emission estimates (Peters et al., 2012b). Uncertainty is expected to increase for more detailed results (Peters et al., 2011b; e.g. the results for
Annex B will be more accurate than the sector results for an individual country).

It is important to note that the consumption-based emissions defined here consider directly the carbon embodied in traded goods and services, but not the trade in unoxidised fossil fuels (coal, oil, gas). In our consumption-based inventory, emissions from traded fossil fuels accrue to the country where the fuel is burned or consumed, not the exporting country from which it was extracted (Davis et al., 2011).

The consumption-based emission inventories in this carbon budget have several improvements over previous versions (Peters et al., 2011b, 2012a). The detailed estimates for 2004 and 2007 are based on an updated version of the GTAP database (Narayanan et al., 2012). We estimate the sector level CO₂ emissions using our own calculations based on the GTAP data and methodology, but scale the national totals to match the CDIAC estimates from the carbon budget. We do not include international transportation in our estimates. The time series of trade data provided by GTAP covers the period 1995–2009 and our methodology uses the trade shares of this dataset. For the period 1990–1994 we assume the trade shares of 1995, while in 2010 we assume the trade shares of 2008, since 2009 was heavily affected by the global financial crisis. We identified errors in the trade shares of Taiwan and the Netherlands in 2008 and 2009, and for these two countries, the trade shares for 2008–2010 are based on the 2007 trade shares.

These data do not contribute to the global average terms in Eq. (1), but are relevant to the anthropogenic carbon cycle, as they reflect the movement of carbon across the Earth's surface in response to human needs (both physical and economic). Furthermore, if national and international climate policies continue to develop in an unharmonious way, then the trends reflected in these data will need to be accommodated by those developing policies.

### 2.1.3 Emissions projections for the current year

Energy statistics are normally available around June for the previous year. We use the close relationship between the growth in world Gross Domestic Product (GDP) and the growth in global emissions (Raupach et al., 2007) to project emissions for the current year. This is based on the so-called Kaya (also called IPAT) identity, whereby $E_{FF}$ is decomposed by the product of GDP and the fossil fuel carbon intensity of the economy ($I_{FF}$) as follows:

$$E_{FF} = GDP \cdot I_{FF}; \qquad (2)$$

taking a time derivative of this equation gives:

$$\frac{dE_{FF}}{dt} = \frac{d(GDP \cdot I_{FF})}{dt}; \qquad (3)$$

and applying the rules of calculus, assuming that GDP and $I_{FF}$ are independent:

$$\frac{dE_{FF}}{dt} = \frac{dGDP}{dt} \cdot I_{FF} + GDP \cdot \frac{dI_{FF}}{dt}; \qquad (4)$$

finally, dividing Eqs. (4) by (2) gives:

$$\frac{1}{E_{FF}} \frac{dE_{FF}}{dt} = \frac{1}{GDP} \frac{dGDP}{dt} + \frac{1}{I_{FF}} \frac{dI_{FF}}{dt}, \qquad (5)$$

where the left hand term is the relative growth rate of $E_{FF}$, and the right hand terms are the relative growth rates of GDP and $I_{FF}$, respectively, which can simply be added linearly to give overall growth rate. The growth rates are reported in percent below by multiplying each term by 100. Because preliminary estimates of annual change in GDP are made well before the end of a calendar year, making assumptions on the growth rate of $I_{FF}$ allows us to make projections of the annual change in CO₂ emissions well before the end of a calendar year.

### 2.1.4 Growth rate in emissions

We report the annual growth rate in emissions for adjacent years in percent by calculating the difference between the two years and then comparing to the emissions in the first year: $[(E_{FF}(t_0 + 1) - E_{FF}(t_0))/E_{FF}(t_0)] \cdot 100$. This is the simplest method to characterise a one-year growth compared to the previous year. This has strong links with the more general way in which society presents economic change in journalistic circles, most often a comparison of present-day economic activity compared to the previous year.

The growth rate of $E_{FF}$ over time periods of greater than one year can be re-written using its logarithm equivalent as follows:

$$\frac{1}{E_{FF}} \frac{dE_{FF}}{dt} = \frac{d(\ln E_{FF})}{dt}. \qquad (6)$$

Here we calculate growth rates in emissions for multi-year periods (e.g. a decade) by fitting a linear trend to $\ln(E_{FF})$ in Eq. (6), reported in percent per year. We fit the logarithm of $E_{FF}$ rather than $E_{FF}$ directly because this method ensures that computed growth rates satisfy Eq. (5). This method differs from previous papers (Canadell et al., 2007; Le Quéré et al., 2009), who computed the fit to $E_{FF}$ and divided by average $E_{FF}$ directly, but the difference is very small (< 0.05 percent) in the case of $E_{FF}$.

## 2.2 CO₂ emissions from land use, land-use change and forestry ($E_{LUC}$)

Net LUC emissions reported in our annual budget ($E_{LUC}$) include CO₂ fluxes from afforestation, deforestation, logging (forest degradation and harvest activity), shifting cultivation (cycle of cutting forest for agriculture then abandoning), regrowth of forests following wood harvest or abandonment of agriculture, fire-based peatland emissions and other land management practices (Table 2). Our annual estimate combines information from a bookkeeping model (Sect. 2.2.1) primarily based on forest area change and biomass data from
<table><caption>**Table 2.** Comparison of the processes included in the *E*<sub>LUC</sub> of the global carbon budget and the DGVMs. See Table 3 for model references.</caption><thead><tr><th></th><th>CO<sub>2</sub> budget</th><th>VISIT</th><th>ISAM-HYDE</th><th>LPJmL</th><th>LPJ-Bern</th></tr></thead><tbody><tr><td>Deforestation, afforestation, forest regrowth after abandonment of agriculture</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Wood harvest and forest degradation</td><td>yes</td><td>no</td><td>yes</td><td>no</td><td>no</td></tr><tr><td>Shifting cultivation</td><td>yes</td><td>yes</td><td>no</td><td>no</td><td>no</td></tr><tr><td>Cropland harvest</td><td>yes</td><td>no</td><td>no</td><td>no</td><td>yes</td></tr><tr><td>Peat fires</td><td>from 1997</td><td>no</td><td>no</td><td>no</td><td>no</td></tr><tr><td>Fire suppression</td><td>for US only</td><td>no</td><td>no</td><td>no</td><td>no</td></tr><tr><td>Management-Climate interactions</td><td>from 1997</td><td>no</td><td>no</td><td>no</td><td>no</td></tr><tr><td>Climate change and variability</td><td>no</td><td>climate change is present but decadal mean response is used for regrowing uptake</td><td>climate variability present but not corresponding to observed years</td><td>yes</td><td>yes</td></tr><tr><td>CO<sub>2</sub> fertilisation</td><td>no</td><td>yes</td><td>yes</td><td>yes</td><td>yes</td></tr><tr><td>Nitrogen dynamics</td><td>no</td><td>no</td><td>yes</td><td>no</td><td>no</td></tr></tbody></table>

the Forest Resource Assessment (FRA) of the Food and Agriculture Organisation (FAO; Houghton, 2003) published at intervals of five years, with annual emissions estimated from satellite-based fire activity in deforested areas (Sect. 2.2.2; van der Werf et al., 2010). The bookkeeping model is used mainly to quantify the mean *E*<sub>LUC</sub> over the time period of the available data, and the satellite-based method to distribute these emissions annually. The satellite-based emissions are available from year 1997 onwards only. We calculate the global anomaly in satellite-based emissions over deforested regions, compared to the 1997–2011 time period, and add this to average *E*<sub>LUC</sub> estimated using the bookkeeping method. We thus assume that all land management activities apart from deforestation do not vary significantly on a year-to-year basis. Other sources of interannual variability (e.g. the impact of climate variability on regrowth) are accounted for in *S*<sub>LAND</sub>. We also use independent estimates from Dynamic Global Vegetation Models (Sect. 2.2.3) to help quantify the uncertainty in global *E*<sub>LUC</sub>.

## 2.2.1 Bookkeeping method

*E*<sub>LUC</sub> calculated using a bookkeeping method (Houghton, 2003) keeps track of the carbon stored in vegetation and soils before deforestation or other land-use change, and the changes in forest age classes, or cohorts, of disturbed lands after land-use change. It tracks the CO<sub>2</sub> emitted to the atmosphere over time due to decay of soil and vegetation carbon in different pools, including wood products, pools after logging

and deforestation. It also tracks the regrowth of vegetation and build-up of soil carbon pools following land-use change. It considers transitions between forests, pastures and cropland, shifting cultivation, degradation of forests where a fraction of the trees is removed, abandonment of agricultural land, and forest management such as logging and fire management. In addition to tracking logging debris on the forest floor, the bookkeeping model tracks the fate of carbon contained in harvested wood products that is eventually emitted back to the atmosphere as CO<sub>2</sub>, although a detailed treatment of the lifetime in each product pool is not performed (Earles et al., 2012). Harvested wood products are partitioned into three pools with different turnover times. All fuelwood is assumed to be burned in the year of harvest (1.0 yr<sup>-1</sup>). Pulp and paper products are oxidized at a rate of 0.1 yr<sup>-1</sup>. Timber is assumed to be oxidized at a rate of 0.01 yr<sup>-1</sup>, and elemental carbon decays at 0.001 yr<sup>-1</sup>. The general assumptions about partitioning wood products among these pools are based on national harvest data.

The primary land cover change and biomass data for the bookkeeping model analysis is the FAO FRA 2010 (FAO, 2010; Table 1), which is based on countries' self-reporting of statistics on forest cover change and management partially combined with satellite data in more recent assessments. Changes in land cover other than forest are based on annual, national changes in cropland and pasture areas reported by the FAO Statistics Division (FAOSTAT, 2010). The LUC dataset is non-spatial and aggregated by regions. The carbon stocks on land (biomass and soils), and their response
<table><caption>**Table 3.** References for the process models included in Fig. 3.</caption><thead><tr><th>Model name</th><th>Reference</th></tr></thead><tbody><tr><td colspan="2">Dynamic Global Vegetation Models providing E<sub>LUC</sub></td></tr><tr><td>VISIT</td><td>Kato et al. (2013) Climate forcing is changed to use CRU TS3.10.01 up to the year 2009</td></tr><tr><td>ISAM-HYDE</td><td>Jain et al. (2013)</td></tr><tr><td>LPJmL</td><td>Poulter et al. (2010)</td></tr><tr><td>LPJ-Bern</td><td>Stocker et al. (2011); Strassmann et al. (2008)</td></tr><tr><td colspan="2">Dynamic Global Vegetation Models providing S<sub>LAND</sub></td></tr><tr><td>Community Land Model 4CN</td><td>Lawrence et al. (2011)</td></tr><tr><td>Hyland</td><td>Levy et al. (2004)</td></tr><tr><td>JULES</td><td>Clark et al. (2011); Cox (2001)</td></tr><tr><td>LPJ</td><td>Sitch et al. (2003)</td></tr><tr><td>LPJ-GUESS</td><td>Smith et al. (2001); Ahlström et al. (2012) and references therein</td></tr><tr><td>O-CN</td><td>Zaehle et al. (2011)</td></tr><tr><td>Orchidee</td><td>Krinner et al. (2005)</td></tr><tr><td>Sheffield-DGVM</td><td>Woodward and Lomas (2004)</td></tr><tr><td>VEGAS</td><td>Zeng et al. (2005)</td></tr><tr><td colspan="2">Ocean Biogeochemistry Models providing S<sub>OCEAN</sub></td></tr><tr><td>NEMO-PlankTOM5</td><td>Buitenhuis et al. (2010) with no nutrient restoring below the mixed layer depth</td></tr><tr><td>LSCE</td><td>Aumont and Bopp (2006)</td></tr><tr><td>CCSM-BEC</td><td>Doney et al. (2009)</td></tr><tr><td>MICOM-HAMOCC</td><td>Assmann et al. (2010) with updates to the physical model as described in Tjiputra et al. (2013)</td></tr></tbody></table>

functions subsequent to LUC, are based on averages per land cover type, per biome and per region. Similar results were obtained using forest biomass carbon density based on satellite data (Baccini et al., 2012). The bookkeeping model does not include land ecosystems' transient response to changes in climate, atmospheric CO₂ and other environmental factors, but the growth/decay curves are based on contemporary data that will implicitly reflect the effects of CO₂ and climate at that time. Results from the bookkeeping method are available from 1850 to 2010.

## 2.2.2 Fire-based method

LUC CO₂ emissions calculated from satellite-based fire activity in deforested areas (van der Werf et al., 2010) provide information that is complementary to the bookkeeping approach. Although they do not provide a direct estimate of E_LUC, as they do not include processes such as respiration, wood harvest, wood products or forest regrowth, they do provide insight on the year-to-year variations in E_LUC that result from the interactions between climate and human activity (e.g. there is more burning and clearing of forests in dry years). The “deforestation fire emissions” assumes an important role of fire in removing biomass in the deforestation process, and thus can be used to infer direct CO₂ emissions

from deforestation using satellite-derived data on fire activity in regions with active deforestation (legacy emissions such as decomposition from ground debris or soils are missed by this method). The method requires information on the fraction of total area burned associated with deforestation versus other types of fires, and can be merged with information on biomass stocks and the fraction of the biomass lost in a deforestation fire to estimate CO₂ emissions. The satellite-based fire emissions are limited to the tropics, where fires result mainly from human activities. Tropical deforestation is the largest and most variable single contributor to E_LUC.

Here we used annual estimates from the Global Fire Emissions Database (GFED3), available from http://www.globalfiredata.org. Burned area from (Giglio et al., 2010) is merged with active fire retrievals to mimic more sophisticated assessments of deforestation rates in the pan-tropics (van der Werf et al., 2010). This information is used as input data in a modified version of the satellite-driven CASA biogeochemical model to estimate carbon emissions, keeping track of what fraction was due to deforestation (van der Werf et al., 2010). The CASA model uses different assumptions to compute delay functions compared to the bookkeeping model, and does not include historical emissions or regrowth from land-use change prior to the availability of satellite data.
Comparing coincident CO emissions and their atmospheric fate with satellite-derived CO concentrations allows for some validation of this approach (e.g. van der Werf et al., 2008). In this paper, we only use emissions based on deforestation fires to quantify the interannual variability in $E_{LUC}$. Results from the fire-based method are available from 1997 to 2011.

### 2.2.3 Dynamic Global Vegetation Models (DGVMs) and uncertainty assessment for LUC

Net LUC CO₂ emissions have also been estimated using DGVMs that explicitly represent some processes of vegetation growth, mortality and decomposition associated with natural cycles and also provide a response to prescribed land cover change and climate and CO₂ drivers (Table 2). The DGVMs calculate the dynamic evolution of biomass and soil carbon pools that are affected by environmental variability and change in addition to LUC transitions each year. They are independent from the other budget terms except for their use of atmospheric CO₂ concentration to calculate the fertilization effect of CO₂ on primary production. The DGVMs do not exactly provide $E_{LUC}$ as defined in this paper because they represent fewer processes resulting directly from human activities on land, but include the vegetation and soil response to increasing atmospheric CO₂ levels, to climate variability and change (in three models), in addition to atmospheric N deposition in the presence of nitrogen limitation (in one model; Table 2). Nevertheless all methods represent deforestation, afforestation and regrowth, three of the most important components of $E_{LUC}$, and thus the model spread can help quantify the uncertainty in $E_{LUC}$.

The DGVMs used here prescribe land cover change from the HYDE spatially gridded datasets updated to 2009 (Goldewijk et al., 2011; Hurtt et al., 2011), which is based on FAO statistics of change in agricultural areas (FAOSTAT, 2010) with assumptions made about change in forest or other land cover as a result of agricultural area change. The changes in agricultural areas are then implemented within each model (for instance, an increased cropland fraction in a grid cell can either use pasture land, or forest, the latter resulting into deforestation). This differs with the dataset used in the bookkeeping method (Houghton, 2003 and updates), which is based on forest area change statistics (FAO, 2010). The DGVMs also represent a different methodology of calculating carbon fluxes, and thus provide an independent assessment of LUC emissions to the bookkeeping results (Sect. 2.2.1).

Differences between estimates thus originate from three main sources, firstly the land cover change dataset, secondly different approaches in models, and thirdly different process boundaries (Table 2). Four different DGVM estimates are presented here and used to explore the uncertainty in LUC annual emissions (Jain et al., 2013; Kato et al., 2013; Poulter et al., 2010; Stocker et al., 2011). While many published DGVM LUC emissions estimates exist, these model runs

were driven by a consistently updated HYDE LUC dataset up to year 2009.

We examine the standard deviation of the annual estimates to assess the uncertainty in $E_{LUC}$. The standard deviation across models in each year ranged from 0.09 to 0.70 PgC yr⁻¹, with an average of 0.42 PgC yr⁻¹ from 1960 to 2009. One of the four models (Jain et al., 2013) was used with three different LUC datasets (including HYDE and FAO FRA2005; Jain et al., 2013; Meiyappan and Jain, 2012). The standard deviation for decadal means in these three model runs was ±0.19 PgC yr⁻¹ for 1990 to 2005, and ranged from 0.06 to 0.70 PgC yr⁻¹ for annual estimates with an average of ±0.27 PgC yr⁻¹ from 1960 to 2005. Assuming the two sources of uncertainty are independent, we can combine them using standard error propagation rules. Taking the quadratic sum of the mean annual standard deviation across the four DGVMs (0.42 PgC yr⁻¹) and the standard deviation due to different land cover change datasets (0.27 PgC yr⁻¹) we get a combined standard deviation of 0.5 PgC yr⁻¹.

We use the combined standard deviation ±0.5 PgC yr⁻¹ as a quantitative measure of uncertainty for annual emissions, and to reflect our best value judgment that there is at least 68% chance (±1 sigma) that the true LUC emission lies within the given range, for the range of processes considered here. However, we note that missing processes such as the decomposition of drained tropical peatlands (Ballhorn et al., 2009; Hooijer et al., 2010) could introduce biases which are not quantified here, while the inclusion of the impact of climate variability on land processes by some DGVMs (Table 2) may inflate the standard deviation in annual estimates of LUC emissions compared to our definition of $E_{LUC}$. The uncertainty of ±0.5 PgC yr⁻¹ is slightly lower than that of ±0.7 PgC yr⁻¹ estimated in the 2010 CO₂ budget release (Friedlingstein et al., 2010) based on expert assessment of the available estimates. A more recent expert assessment of uncertainty for the decadal mean based on a larger set of published model and uncertainty studies estimated ±0.5 PgC yr⁻¹ (Houghton et al., 2012) which partly reflects improvements in data on forest area change using satellite data, and partly more complete understanding and representation of processes in models. We adopt ±0.5 PgC yr⁻¹ here for the decadal averages presented Table 4.

The errors in the decadal mean estimates from the DGVM ensemble are likely correlated between decades. They come from (1) system boundaries (e.g. not counting forest degradation in some models), which cause a bias that makes decadal estimates perfectly correlated (Gasser and Ciais, 2013; Table 2); (2) common land cover change input data which cause a bias, though if a different input dataset is used each decade, decadal fluxes from DGVMs may be partly decorrelated; (3) model structural errors, which cause bias that correlate decadal estimates. In addition, errors arising from uncertain DGVM parameter values would be random but they are not accounted for in this study, since no DGVM provided an ensemble of runs with perturbed parameters.
<table><caption>**Table 4.** Decadal mean in the five components of the anthropogenic CO₂ budget for the periods 1960–1969, 1970–1979, 1980–1989, 1990–1999, 2000–2009 and the last decade available. All values are in PgC yr⁻¹. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval).</caption><thead><tr><th rowspan="2"></th><th colspan="6">mean (PgC yr<sup>-1</sup>)</th></tr><tr><th>1960–1969</th><th>1970–1979</th><th>1980–1989</th><th>1990–1999</th><th>2000–2009</th><th>2002–2011</th></tr></thead><tbody><tr><td colspan="7"><strong>Emissions</strong></td></tr><tr><td>Fossil fuel combustion and cement production (E<sub>FF</sub>)</td><td>3.1 ± 0.2</td><td>4.7 ± 0.2</td><td>5.5 ± 0.3</td><td>6.4 ± 0.3</td><td>7.8 ± 0.4</td><td>8.3 ± 0.4</td></tr><tr><td>Land-Use Change emissions (E<sub>LUC</sub>)</td><td>1.5 ± 0.5</td><td>1.3 ± 0.5</td><td>1.4 ± 0.5</td><td>1.6 ± 0.5</td><td>1.0 ± 0.5</td><td>1.0 ± 0.5</td></tr><tr><td colspan="7"><strong>Partitioning</strong></td></tr><tr><td>Atmospheric growth rate (G<sub>ATM</sub>)</td><td>1.7 ± 0.1</td><td>2.8 ± 0.1</td><td>3.4 ± 0.1</td><td>3.1 ± 0.1</td><td>4.0 ± 0.1</td><td>4.3 ± 0.1</td></tr><tr><td>Ocean sink (S<sub>OCEAN</sub>)</td><td>1.2 ± 0.5</td><td>1.5 ± 0.5</td><td>1.9 ± 0.5</td><td>2.2 ± 0.4</td><td>2.4 ± 0.5</td><td>2.5 ± 0.5</td></tr><tr><td>Residual terrestrial sink (S<sub>LAND</sub>)</td><td>1.7 ± 0.7</td><td>1.7 ± 0.8</td><td>1.6 ± 0.8</td><td>2.7 ± 0.8</td><td>2.4 ± 0.8</td><td>2.6 ± 0.8</td></tr></tbody></table>

## 2.3 Atmospheric CO₂ growth rate (G<sub>ATM</sub>)

### 2.3.1 Global atmospheric CO₂ growth rate estimates

The atmospheric CO₂ growth rate is provided by the US National Oceanic and Atmospheric Administration Earth System Research Laboratory (Conway and Tans, 2012), which is updated from Ballantyne et al. (2012). For the 1959–1980 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 Scripps Institution of Oceanography (Keeling et al., 1976). For the 1980–2011 time period, the global growth rate is based on the average of multiple stations selected from the marine boundary layer sites (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 from atmospheric CO₂ concentration by taking the average of the most recent and December–January months corrected for the average seasonal cycle and subtracting this same average one year earlier. The growth rate in units of ppm yr⁻¹ is converted to fluxes by multiplying by a factor of 2.123 PgC per ppm (Enting et al., 1994) for comparison with the other components.

The uncertainty around the annual growth rate based on the multiple stations dataset ranges between 0.11 and 0.72 PgC yr⁻¹, with a mean of 0.61 PgC yr⁻¹ for 1959–1980 and 0.18 PgC yr⁻¹ for 1980–2011, when a larger set of stations were available. It is based on the number of available stations, and thus takes into account both the measurement errors and data gaps at each station. This uncertainty is larger than the uncertainty of ±0.1 PgC yr⁻¹ reported for decadal mean growth rate by the IPCC because errors in annual growth rate are strongly anti-correlated in consecutive

years leading to smaller errors for longer timescales. The decadal change is computed from the difference in concentration ten years apart based on measurement error of 0.35 ppm (based on offsets between NOAA/ESRL measurements and those of the World Meterological Organisation World Data Center for Greenhouse Gases; NOAA/ESRL, 2012) for the start and end points (the decadal change uncertainty is the sqrt(2 × (0.35 ppm)²)/10 yr assuming that each yearly measurement error is independent). This uncertainty is also used in Table 4.

### 2.3.2 Assessing the contribution of anthropogenic CO and CH₄ to the global anthropogenic CO₂ budget

Emissions of CO and CH₄ to the atmosphere are assumed to be mainly balanced by natural land CO₂ sinks for all biogenic carbon compounds, but small imbalances arise through anthropogenic emissions of fugitive fossil fuel CH₄ and CO, and changes in oxidation rates, e.g. in response to climate variability. These contributions are omitted in Eq. (1), but quantified in this section to highlight the current understanding about their magnitude, and identify the sources of uncertainty. Emissions of CO from combustion processes are included with E<sub>FF</sub> and E<sub>LUC</sub> (for example, CO emissions from fires associated with LUC are included in E<sub>LUC</sub>). However, fugitive anthropogenic emissions of fossil CH₄ (e.g. gas leaks) from the coal, oil and gas upstream sectors are not counted in E<sub>FF</sub> because these leaks are not inventoried in the fossil fuel statistics as they are not consumed as fuel.

In the absence of anthropogenic change, natural sources of CO and CH₄ from wildfires and CH₄ wetlands are assumed to be balanced by CO₂ uptake by photosynthesis on continental and long timescales (e.g. decadal or longer). Anthropogenic land-use change (e.g. biomass burning for forest
clearing or land management, wetland management) and the indirect anthropogenic effects of climate change on wildfires and wetlands result in an imbalance of sources and sinks of carbon. For the purposes of this study, we assume wildfire and wetland emissions of CO and CH₄ are in balance, and that the non-industrial anthropogenic biogenic sources are captured within estimates of emissions of CO₂ from LUC (included in Sect. 2.2). Peatland draining results in a reduction of CH₄ emissions and an increase in CO₂ (not included in modelled estimates presented here). Thus, none of the CO and CH₄ sources above are included in the (anthropogenic) CO₂ budget of this study.

By contrast to biogenic sources, CO and CH₄ emissions from fossil fuel use are not balanced by any recent CO₂ uptake by photosynthesis, and hence represent a net addition of fossil carbon to the atmosphere. This is implicitly included in this study as estimates of CO₂ emissions are based on the total carbon content of the fuel, and the measured CO₂ growth rate includes CO₂ from CO.

This is not the case for anthropogenic fossil CH₄ emission from fugitive emissions during natural gas extraction and transport, and from the coal and oil industry (gas leaks). This emission of carbon to the atmosphere is not included in the fossil fuel CO₂ emissions described in Sect. 2.1. This CH₄ emission is estimated at 0.09 Pg C yr⁻¹ (Kirschke et al., 2013). Fossil CH₄ emissions are assumed to be oxidized with a lifetime of 12.4 yr, the e-folding time of an atmospheric perturbation removal (Prater et al., 2012). After one year, 92% of these emissions remain in the atmosphere as CH₄ and contribute to the observed CH₄ global growth rate, whereas the rest (8%) get oxidized into CO₂, and contribute to the CO₂ growth rate. Given that anthropogenic fossil fuel CH₄ emissions represent a fraction of 15% of the total global CH₄ source (Kirschke et al., 2013), we assumed that a fraction of 0.15 times 0.92 of the observed global growth rate of CH₄ of 6 Tg C-CH₄ yr⁻¹ (units of C in CH₄ form) during 2000–2009 is due to fossil CH₄ sources. Therefore, annual fossil fuel CH₄ emissions contribute 0.8 Tg C-CH₄ yr⁻¹ to the CH₄ growth rate and 0.8 Tg C-CO₂ yr⁻¹ (units of C in CO₂ form) to the CO₂ growth rate. Summing up the effect of fossil fuel CH₄ emissions from each previous year during the past 10 yr, a fraction of which is oxidized into CO₂ in the current year, this defines a contribution of 5 Tg C-CO₂ yr⁻¹ to the CO₂ growth rate, or about 0.1%. Thus the effect of anthropogenic fossil CH₄ fugitive emissions and their oxidation to anthropogenic CO₂ in the atmosphere can be assessed to have a negligible effect on the observed CO₂ growth rate, although they do contribute significantly to the global CH₄ growth rate.

## 2.4 Ocean CO₂ sink

A mean ocean CO₂ sink of 2.2 ± 0.4 PgC yr⁻¹ for the 1990s was estimated by the IPCC (Denman et al., 2007) based on three data-based methods (Mikaloff Fletcher et al., 2006; Ta-

ble 1). Here we adopt this mean CO₂ sink (Manning and Keeling, 2006; McNeil et al., 2003), and compute the trends in the ocean CO₂ sink for 1959–2011 using a combination of five global ocean biogeochemistry models (Table 3). The models represent the physical, chemical and biological processes that influence the surface ocean concentration of CO₂ and thus the air-sea CO₂ flux. The models are forced by meteorological reanalysis data and atmospheric CO₂ concentration available for the entire time period. They compute the air-sea flux of CO₂ over grid boxes of 1 to 4 degrees in latitude and longitude. The ocean CO₂ sink for each model is normalised to the observations, by dividing the annual model values by their observed average over 1990–1999, and multiplying this by 2.2 PgC yr⁻¹. This normalisation ensures that the ocean CO₂ sink for the global carbon budget is based on observations, and that the trends and annual values in CO₂ sinks are consistent with model estimates. The ocean CO₂ sink for each year (t) is therefore:

$$S_{\text{OCEAN}}(t) = \frac{1}{n} \sum_{m} S_{\text{OCEAN}}^{m}(t) \cdot 2.2 \, \text{PgC yr}^{-1}, \quad (7)$$

where n is the number of models. We use the four models published in Le Quéré et al. (2009), including updates of Aumont and Bopp (2006), Doney et al. (2009), and Buitenhuys et al. (2010) available to 2011, the model results from Galbraith et al. (2010) available to 2008, and one further model estimate updated from Assman et al. (2010) also available to 2011. The mean ocean CO₂ sink from these models for 1990–1999 ranges between 1.55 and 2.59 PgC yr⁻¹. The standard deviation of the ocean model ensemble averages to 0.14 PgC yr⁻¹ during 1980–2011 (with a maximum of 0.22), but it increases as the model ensemble goes back in time, with a standard deviation of 0.3 PgC yr⁻¹ across models in the 1960s and 0.49 PgC yr⁻¹ in year 1959. We estimate that the uncertainty in the annual ocean CO₂ sink is about ±0.5 PgC yr⁻¹ from the quadratic sum of the data uncertainty of ±0.4 PgC yr⁻¹ and standard deviation across model of up to ±0.3 PgC yr⁻¹, reflecting both the uncertainty in the mean sink and in the interannual variability as assessed by models.

## 2.5 Terrestrial CO₂ sink

The difference between the fossil fuel (E<sub>FF</sub>) and LUC net emissions (E<sub>LUC</sub>), the atmospheric growth rate (G<sub>ATM</sub>) and the ocean CO₂ sink (S<sub>OCEAN</sub>) is attributable to the net sink of CO₂ in terrestrial vegetation and soils (S<sub>LAND</sub>), within the given uncertainties. Thus, this sink can be estimated either as the residual of the other terms in the mass balance budget but also directly calculated using DGVMs. Note the S<sub>LAND</sub> term does not include gross land sinks directly resulting from LUC (e.g. regrowth of vegetation) as these are estimated as part of the net land use flux (E<sub>LUC</sub>). The residual land sink (S<sub>LAND</sub>) is in part due to the fertilising effect of rising atmospheric CO₂ on plant growth, N deposition and climate
change effects such as prolonged growing seasons in northern temperate areas.

## 2.5.1 Residual of the budget

For 1959–2011, the terrestrial carbon sink was estimated from the residual of the other budget terms:

$$S_{\text{LAND}} = E_{\text{FF}} + E_{\text{LUC}} - (G_{\text{ATM}} + S_{\text{OCEAN}}). \quad (8)$$

The uncertainty in $S_{\text{LAND}}$ is estimated annually from the quadratic sum of the uncertainty in the right-hand terms assuming the errors are not correlated. The uncertainty averages to $\pm 0.8 \text{ PgC yr}^{-1}$ over 1959–2011, increasing with time to $\pm 0.93 \text{ PgC yr}^{-1}$ in 2011. $S_{\text{LAND}}$ estimated from the residual of the budget will include, by definition, all the missing processes and potential biases in the other components of Eq. (8).

## 2.5.2 DGVMs

A comparison of the residual calculation of $S_{\text{LAND}}$ in Eq. (8) with outputs from DGVMs similar to those described in Sect. 2.2.3, but designed to quantify $S_{\text{LAND}}$ rather than $E_{\text{LUC}}$, provides an independent estimate of the consistency of $S_{\text{LAND}}$ with our understanding of the functioning of the terrestrial vegetation in response to CO$_2$ and climate variability. An ensemble of nine DGVMs are presented here, coordinated by the project “trends and drivers of the regional-scale sources and sinks of carbon dioxide (Trendy)” (Table 3). These DGVMs were forced with changing climate and atmospheric CO$_2$ concentration, and a fixed contemporary cropland distribution. These models thus include all climate variability and CO$_2$ effects over land, but do not include the trend in CO$_2$ sink capacity associated with human activity directly affecting changes in vegetation cover and management. This effect has been estimated to have lead to a reduction in the terrestrial sink by $0.5 \text{ PgC yr}^{-1}$ since 1750 (Gitz and Ciais, 2003) but it is neglected here. The models estimate the mean and variability of $S_{\text{LAND}}$ based on atmospheric CO$_2$ and climate, and thus both terms can be compared to the budget residual.

The standard deviation of the annual CO$_2$ sink across the nine DGVMs ranges from $\pm 0.2$ to $\pm 1.3 \text{ PgC yr}^{-1}$, with an average of $\pm 0.7 \text{ PgC yr}^{-1}$ for the period 1960 to 2009. This is an improvement from the $0.95 \text{ PgC yr}^{-1}$ presented in Le Quéré et al. (2009) using an ensemble of five models. As this standard deviation across the DGVM models and around the mean trends is of the same magnitude as the combined uncertainty due to the other components ($E_{\text{FF}}$, $E_{\text{LUC}}$, $G_{\text{ATM}}$, $S_{\text{OCEAN}}$), the DGVMs do not provide further constrains on the terrestrial CO$_2$ sink compared to the residual of the budget (Eq. 7). However they confirm that the sum of our knowledge on annual CO$_2$ emissions and their partitioning is plausible (see Discussion), and they enable the attribution of the

fluxes to the underlying processes and provide a breakdown of the regional contributions (not shown here).

# 3 Results

## 3.1 Global carbon budget averaged over decades and its variability

The global carbon budget averaged over the last decade (2002–2011) is shown in Fig. 1. For this time period, 89% of the total emissions ($E_{\text{FF}} + E_{\text{LUC}}$) were caused by fossil fuel combustion and cement production, and 11% by land-use change. The total emissions were partitioned among the atmosphere (46%), ocean (27%) and land (28%). All components except land-use change emissions have grown since 1959 (Figs. 2 and 3), with important interannual variability in the atmospheric growth rate caused primarily by variability in the land CO$_2$ sink (Fig. 3), and some decadal variability in all terms (Table 4).

Global CO$_2$ emissions from fossil fuel combustion and cement production have increased every decade from an average of $3.1 \pm 0.2 \text{ PgC yr}^{-1}$ in the 1960s to $8.3 \pm 0.4 \text{ PgC yr}^{-1}$ during 2002–2011 (Table 4). The growth rate in these emissions decreased between the 1960s and the 1990s, from $4.5 \text{ % yr}^{-1}$ in the 1960s, $2.9 \text{ % yr}^{-1}$ in the 1970s, $1.9 \text{ % yr}^{-1}$ in the 1980s, $1.0 \text{ % yr}^{-1}$ in the 1990s, and increased again since year 2000 at an average of $3.1 \text{ % yr}^{-1}$. In contrast, CO$_2$ emissions from LUC have remained constant at around $1.5 \pm 0.5 \text{ PgC yr}^{-1}$ during 1960–1999, and decreased to $1.0 \pm 0.5 \text{ PgC yr}^{-1}$ since year 2000. The decreased emissions from LUC since 2000 is also reproduced by the DGVMs (Fig. 5).

The growth rate in atmospheric CO$_2$ increased from $1.7 \pm 0.1 \text{ PgC yr}^{-1}$ in the 1960s to $4.3 \pm 0.1 \text{ PgC yr}^{-1}$ during 2002–2011 with important decadal variations (Table 4). The ocean CO$_2$ sink increased from $1.2 \pm 0.5 \text{ PgC yr}^{-1}$ in the 1960s to $2.5 \pm 0.5 \text{ PgC yr}^{-1}$ during 2002–2011, with decadal variations of the order of a few tenths of PgC yr$^{-1}$. The low uptake anomaly around year 2000 originates from multiple regions in all models (west Equatorial Pacific, Southern Ocean and North Atlantic), and is caused by climate variability. The land CO$_2$ sink increased from $1.7 \pm 0.8 \text{ PgC yr}^{-1}$ in the 1960s to $2.6 \pm 0.8 \text{ PgC yr}^{-1}$ during 2002–2011, with important decadal variations of $1–2 \text{ PgC yr}^{-1}$. The high uptake anomaly around year 1991 is thought to be caused by the effect of the volcanic eruption of Mount Pinatubo, and is reproduced in some of the models only, but not by the model average (Fig. 5).
<figure><img src="image_3.png" /></figure>

**Figure 1.** Schematic representation of the overall perturbation of the global carbon cycle caused by anthropogenic activities, averaged globally for the decade 2002–2011. The arrows represent emission from fossil fuel burning and cement production; emissions from deforestation and other land-use change; and the carbon sinks from the atmosphere to the ocean and land reservoirs. The annual growth of carbon dioxide in the atmosphere is also shown. All fluxes are in units of PgC yr⁻¹, with uncertainties reported as ±1 sigma (68% confidence that the real value lies within the given interval) as described in the text. This Figure is an update of one prepared by the International Geosphere Biosphere Programme for the GCP, first presented in Le Quéré (2009).

## 3.2 Global carbon budget for year 2011 and emissions projection for 2012

Global CO₂ emissions from fossil fuel combustion and cement production reached 9.5 ± 0.5 PgC in 2011 (Fig. 4; see also Peters et al., 2013). The total emissions in 2011 were distributed among coal (43%), oil (34%), gas (18%), cement (4.9%) and gas flaring (0.7%). These first four categories increased by 5.4, 0.7, 2.2, and 2.7% respectively over the previous year, without enough data to calculate the change for gas flaring. Using Eq. (5), we estimate that global CO₂ emissions in 2012 will reach 9.7 ± 0.5 PgC, or 2.6% above 2011 levels (likely range of 1.9–3.5; Peters et al., 2013), and that emissions in 2012 will thus be 58% above emissions in 1990. The expected value is computed using the world GDP projection of 3.3% made by the IMF (October 2012) and a growth rate for I<sub>FF</sub> of –0.7%, which is the average from the previous 10 yr. The uncertainty range is based on 0.2% for GDP growth (the range in IMF estimates published in January, April, July, and October 2012) and the range in I<sub>FF</sub> due to short term trends of –0.1% yr<sup>–1</sup> (2007–2011) and medium term trends of –1.2% yr<sup>–1</sup> (1990–2011); the combined uncertainty range is therefore 1.9% (3.3–1.2–0.2) and 3.5% (3.3–0.1 + 0.2). Projections made for the 2009, 2010,

and 2011 CO₂ budget compared well to the actual CO₂ emissions for that year (Table 5) and were useful to capture the current state of the fossil fuel emissions.

In 2011, global CO₂ emissions were dominated by emissions from China (28% in 2011), the USA (16%), the EU (27 member states; 11%), and India (7%). The per-capita CO₂ emissions in 2011 were 1.4 tC person⁻¹ yr⁻¹ for the globe, and 4.7, 1.8, 2.0 and 0.5 tC person⁻¹ yr⁻¹ for the USA, China, the EU and India, respectively (Fig. 4e).

Territorial-based emissions in Annex B countries have remained stable from 1990–2000, while consumption-based emissions have grown at 0.5% yr⁻¹ (Fig. 4c). In non-Annex B countries territorial-based emissions have grown at 4.4% yr⁻¹, while consumption-based emissions have grown at 4.0% yr⁻¹. In 1990, 65% of global territorial-based emissions were emitted in Annex B countries, while in 2010 this had reduced to 42%. In terms of consumption-based emissions this split was 66% in 1990 and 46% in 2010. The difference between territorial-based and consumption-based emissions (the net emission transfer via international trade) from non-Annex B to Annex B countries has increased from 0.04 PgC yr⁻¹ in 1990 to 0.38 PgC in 2010 (Fig. 4), with an average annual growth rate of 9% yr⁻¹. The increase in net emission transfers of 0.33 PgC from
<table><caption>**Table 5.** Actual CO₂ emissions from fossil fuel combustion and cement production (E<sub>FF</sub>) compared to projections made the previous year based on world GDP and the fossil fuel intensity of GDP (I<sub>FF</sub>). The “Actual” values and the “Projected” value for 2012 refer to those presented in this paper.</caption><thead><tr><th rowspan="2">Component</th><th colspan="2">2009<sup>a</sup></th><th colspan="2">2010<sup>b</sup></th><th colspan="2">2011<sup>c</sup></th><th>2012</th></tr><tr><th>Projected</th><th>Actual</th><th>Projected</th><th>Actual</th><th>Projected</th><th>Actual</th><th>Projected</th></tr></thead><tbody><tr><td>E<sub>FF</sub></td><td>-2.8 %</td><td>-0.3 %</td><td>&gt; 3 %</td><td>5.1 %</td><td>3.1 ± 1.5 %</td><td>3.1 %</td><td>2.6 (1.9–3.5) %</td></tr><tr><td>GDP</td><td>-1.1 %</td><td>0.1 %</td><td>4.8 %</td><td>5.3 %</td><td>4.0 %</td><td>3.9 %</td><td>3.3 %</td></tr><tr><td>I<sub>FF</sub></td><td>-1.7 %</td><td>-0.4 %</td><td>&gt; -1.7 %</td><td>+0.2 %</td><td>-0.9 ± 1.5 %</td><td>-0.8 %</td><td>-0.7 %</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Combined components of the global carbon budget illustrated in Fig. 1 as a function of time, for (top) emissions from fossil fuel combustion and cement production (E<sub>FF</sub>; grey) and emissions from land-use change (E<sub>LUC</sub>; brown), and (bottom) their partitioning among the atmosphere (G<sub>ATM</sub>; light blue), land (S<sub>LAND</sub>; green) and ocean (S<sub>OCEAN</sub>; dark blue). All time series are in PgC yr<sup>-1</sup>. Land-use change emissions include management–climate interactions from year 1997 onwards, where the line changes from dashed to full.</figcaption></figure>

1990–2008 compares with the emission reduction of 0.2 PgC in Annex B countries. These results clearly show a growing net emission transfer via international trade from non-Annex B to Annex B countries. In 2010, the biggest emitters from a territorial-based perspective were China (26 %), USA (17 %), EU (12 %), and India (7 %), while the biggest emitters from a consumption-based perspective were China (22 %), USA (18 %), EU (15 %), and India (6 %).

Global CO₂ emissions from Land-Use Change activities were 0.9 ± 0.5 PgC in 2011, with the decrease of 0.2 PgC yr⁻¹ from the year 2010 estimate based on satellite-detected fire activity.

Atmospheric CO₂ growth rate was 3.6 ± 0.2 PgC in 2011 (1.69 ± 0.09 ppm; Fig. 3). This is slightly below the 2000–

2009 average of 4.0 ± 0.1 PgC yr⁻¹, though the interannual variability in atmospheric growth rate is large.

The ocean CO₂ sink was 2.7 ± 0.5 PgC yr⁻¹ in 2011, a slight increase compared to the sink of 2.5 ± 0.5 PgC yr⁻¹ in 2010 and 2.4 ± 0.5 PgC yr⁻¹ in 2000–2009 (Fig. 3). All models suggest that the ocean CO₂ sink in 2011 was greater than the 2010 sink.

The terrestrial CO₂ sink calculated as the residual from the carbon budget was 4.1 ± 0.9 PgC in 2011, well above the 2.7 ± 0.9 PgC in 2010 and 2.4 ± 0.9 PgC yr⁻¹ in 2000–2009 (Fig. 3). This large sink is consistent with enhanced CO₂ sink during the wet and cold conditions associated with the strong La Niña condition that started in the middle of 2010 and ended in March 2012, as discussed for previous events (Keeling et al., 1995; Peylin et al., 2005). Results from DGVMs are available to year 2010 only (Fig. 5).

## 4 Discussion

Each year when the global carbon budget is published, each component for all previous years is updated to take into account corrections that are due to further scrutiny and verification of the underlying data in the primary input datasets (Fig. 6). The updates have generally been relatively small and generally focused on the most recent past years, except for LUC between 2008 and 2009 when LUC emissions were revised downwards by 0.56 PgC yr⁻¹, and after 1997 for this budget where we introduced an estimate of interannual variability from management–climate interactions. The 2008/2009 revision was the result of the release of FAO 2010, which contained a major update to forest cover change for the period 2000–2005 and provided the data for the following 5 yr to 2010. Updates were at most 0.24 PgC yr⁻¹ for the fossil fuel and cement emissions, 0.19 PgC yr⁻¹ for the atmospheric growth rate, 0.20 PgC yr⁻¹ for the ocean CO₂ sink. The update for the residual land CO₂ sink was also large, with maximum value of 0.71 PgC yr⁻¹, directly reflecting the revision in other terms of the budget. Likewise, the land sink estimated by DGVMs has also reflected the increasing availability of model output to do these calculations.

Our capacity to separate the CO₂ budget components can be evaluated by comparing the land CO₂ sink estimated with

<footnote><sup>a</sup> Le Quéré et al. (2009), <sup>b</sup> Friedlingstein et al. (2010), <sup>c</sup> Peters et al. (2013)</footnote>
<figure><img src="image_5.png" /></figure>

**Figure 3.** Components of the global carbon budget and their uncertainties as a function of time, presented individually for (a) emissions from fossil fuel combustion and cement production ($E_{FF}$), (b) emissions from land-use change ($E_{LUC}$) with management-climate interactions based on fire activities in deforested areas (full line) or not (dashed line), (c) atmospheric CO₂ growth rate ($G_{ATM}$), (d) the ocean CO₂ sink ($S_{OCEAN}$, positive indicates a flux from the atmosphere to the ocean), and (e) the land CO₂ sink ($S_{LAND}$, positive indicates a flux from the atmosphere to the land). All time series are in PgC yr⁻¹ with the uncertainty bounds representing ±1 sigma in shaded colour. The black dots in panels (a) and (e) show the values based on emissions extrapolated using BP energy statistics.

the budget residual ($S_{LAND}$), which includes errors and biases from all components, with the land CO₂ sink estimates by the DGVM ensemble, which are based on our understanding of processes of how the land responds to increasing CO₂ and climate change and variability. The two estimates are generally close (Fig. 5), both for the mean and for the interannual variability. The DGVMs correlate with the budget residual with $r = 0.34$ to 0.45 (median of $r = 0.43$), and $r = 0.48$ for the model mean (Fig. 5). The DGVMs produce a decadal mean and standard deviation across nine models of $2.6 \pm 1.0$ PgC yr⁻¹ for the period 2000–2009, nearly the same as the estimate produced with the budget residual (Table 4). Analysis of regional CO₂ budgets would provide further information to quantify and improve our estimates, as has been undertaken by the Regional Carbon Cycle Assessment and Processes (RECCAP) exercise (Canadell et al., 2011).

Annual estimations of each component of the global carbon budgets have their limitations, some of which could be improved with better data and/or a better understanding of carbon dynamics. The primary limitations involve resolving fluxes on annual timescales and providing updated estimates

for recent years for which data-based estimates are not yet available. Of the various terms in the global budget, only the fossil-fuel burning and atmospheric growth rate terms are based primarily on empirical inputs with annual resolution. The data on fossil fuel consumption and cement production are based on survey data in all countries. The other terms can be provided on an annual basis only through the use of models. While these models represent the current state of the art, they provide only estimates of actual changes. For example, the decadal trends in ocean uptake and the interannual variations associated with El Niño/La Niña (ENSO) are not directly constrained by observations, although many of the processes controlling these trends are sufficiently well known that the model-based trends still have value as benchmarks for further validation. Land-use emissions estimates and their variations from year to year have even larger uncertainty, and much of the underlying data are not available as an annual update. Efforts are underway to work with annually available satellite area change data or FAO reported data in combination with fire data and modelling to provide annual updates for future budgets. The best resolved changes are
<figure><img src="image_6.png" /></figure>

**Figure 4.** CO₂ emissions from fossil fuel combustion and cement production for (a) the globe, including an uncertainty of ±5% (grey shading), the emissions extrapolated using BP energy statistics (black dots) and the emissions projection for year 2012 based on GDP projection (red dot), (b) global emissions by fuel type, including coal (red), oil (black), gas (light blue), and cement (purple), and excluding gas flaring which is small (0.7% in 2011), (c) territorial (full line) and consumption (dashed line) emissions for the countries listed in the Annex B of the Kyoto Protocol (blue lines; mostly advanced economies with emissions limitations) versus non-Annex B countries (red lines), also shown are the emissions transfer from non-Annex B to Annex B countries (black line) (d) territorial CO₂ emissions for the top three country emitters (USA – purple; China – red; India – green) and for the European Union (EU; full blue for the 27 states members of the EU in 2011; dash blue for the 15 states members of the EU in 1997 when the Kyoto Protocol was signed), and (e) per-capita emissions for the top three country emitters and the EU (all colours as in panel d). In panels (b) to (e), the dots show the years where the emissions were extrapolated using BP energy statistics. All time series are in PgC yr⁻¹ except the per-capita emissions (panel e), which are in tonnes of carbon per person per year.

in atmospheric growth ($G_{ATM}$), fossil-fuel emissions ($E_{FF}$), and by difference, the change in the sum of the remaining terms ($S_{OCEAN} + S_{LAND} - E_{LUC}$). The variations from year to year in these remaining terms are largely model-based at this time. Further efforts to increase the availability and use of annual data for estimating the remaining terms with annual to decadal resolution are especially needed.

Our approach also depends on the reliability of the energy and land cover change statistics provided at the country level, and are thus potentially subject to biases. Thus it is critical to develop multiple ways to estimate the carbon balance at the global and regional level, including from the inversion of atmospheric CO₂ concentration, the use of other oceanic and atmospheric tracers, and the compilation of emissions using alternative statistics (e.g. sectors). Multiple approaches go-

ing from global to regional would greatly help improve confidence and reduce uncertainty in CO₂ emissions and their fate.

# 5 Conclusions

The estimation of global CO₂ emissions and sinks is a major effort by the carbon cycle research community that requires a combination of measurements and compilation of statistical estimates and results from models. The delivery of an annual CO₂ budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the datasets associated with the annual CO₂ budget, including scientists, policy makers, businesses, journalists, and the broader civil society increasingly engaged in the climate
<figure><img src="image_7.png" /></figure>

**Figure 5.** Comparison of (top panel) CO₂ emissions from land-use change (LUC), (middle panel) land CO₂ sink (S<sub>LAND</sub>), and (bottom panel) ocean CO₂ sink (S<sub>OCEAN</sub>) between the CO₂ budget values estimated here (black line), and those estimated from process models without any normalisation to observations (Table 3; coloured lines). The thin dotted black lines in the top and middle panels are the model averages. The LUC emissions from the CO₂ budget estimate is dashed before year 1997 to highlight the start of the satellite data from that year, as used to quantify the interannual variability from management–climate interactions based on fire activities in defor-ested areas.

change debate. Second, over the last decade we have seen
important changes in the human and biophysical worlds (e.g.
increase in fossil fuel emissions growth, sea and air warm-
ing, snow and ice melt), which require a more frequent as-
sessment of what we can learn regarding future dynamics
and the needs for climate change mitigation. In very general
terms, both the ocean and the land surface presently mitigate
a large fraction of anthropogenic emissions. Any significant
change in this situation is of great importance to climate pol-
icymaking, as it implies different emissions levels to achieve
warming target aspirations such as remaining below the two-
degrees of global warming since pre-industrial periods. Bet-
ter constraints of carbon cycle models against the contempo-
rary datasets raises the hope that they will be more accurate
at future projections.

<figure><img src="image_8.png" /></figure>

**Figure 6.** Comparison of global carbon budget components released annually by GCP since 2005. CO₂ emissions from both (a) fossil fuel combustion and cement production, and (b) land-use change, and their partitioning among (c) the atmosphere, (d) the ocean, and (e) the land. The different curves were published in (dashed black) Raupach et al. (2007), (dashed red) Canadell et al. (2007), (dark blue) online only, (light blue) Le Quéré et al. (2009), (pink) Friedlingstein et al. (2010), (red) Peters et al. (2012a), and (black) this study. All values are in PgC yr⁻¹.

This all requires more frequent, robust, and transparent
datasets and methods that can be scrutinized and replicated.
After seven annual releases done by the GCP, the effort is
growing and the traceability of the methods has become in-
creasingly complex. Here, we have documented in detail the
datasets and methods used to compile the annual updates
of the global carbon budget, explained the rationale for the
choices made, the limitations of the information, and finally
highlighted need for additional information where gaps exist.

This paper, via “living reviews”, will help to keep track
of new budget updates. The evolution over time of the CO₂
budget is now a key indicator of the anthropogenic pertur-
bation of the climate system and its annual delivery joins a
set of climate indicators to monitor the evolution of human-
induced climate change, such as the annual updates on the
global surface temperature, sea level rise, minimum Arctic
sea ice extent and others.

6 Data access

The accompanying database includes one excel file organised
in seven spreadsheets:

1. The global carbon budget (1959–2011).

2. Global CO₂ emissions from fossil fuel combustion and cement production by fuel type, and the per-capita emissions (1959–2011).

3. Territorial-based (e.g. as reported to the UN Framework Convention on Climate Change) country CO₂ emissions from fossil fuel combustion and cement production (1959–2011).

4. Consumption-based country CO₂ emissions from fossil fuel combustion and cement production and emissions transfer from the international trade of goods and services (1990–2010).

5. CO₂ emissions from land-use change from the individual methods and models (1959–2011).

6. Ocean CO₂ sink from the individual ocean models (1959–2011).

7. Terrestrial residual CO₂ sink from the DGVMs (1959–2010).

**Acknowledgements.** We thank all people and institutions who provided data used in this carbon budget, in particular, G. Hurt, L. Chini, and I. Harris. The observations and modelling analysis were possible thanks to funding from multiple agencies around the world. The UK Natural Environment Research Council provided funding to CLQ and the GCP though their International Opportunities Fund specifically to support this publication (project NE/103002X/1). CLQ, PC, SZ, and JS thank the EU FP7 for funding through projects GEOCarbon (283080), COMBINE (226520) and CARBOCHANGE (264879). GPP and RMA acknowledge support from the Norwegian Research Council (221355/E10). SCD acknowledges support from the US National Science Foundation (NSF AGS-1048827). JH was supported by a Leverhulme Research Fellowship and the Cabot Institute, University of Bristol. RJA and TAB were sponsored by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programs and performed at Oak Ridge National Laboratory (ORNL) under US Department of Energy contract DE-AC05-00OR22725. CH was supported by the Centre for Ecology and Hydrology “Science Budget”. EK was supported by the Global Environment Research

Fund (S-10) of the Ministry of Environment of Japan. GrvdW was supported by the European Research Council. BDS was supported by the Swiss National Science Foundation. AA acknowledges the Mistra-SWECIA programme and the strategic research areas MERGE, BECC and LUCCI. AKJ is funded by the NASA LCLUC Program (No. NNX08AK75G) and the Office of Science (BER), US Department of Energy (DOE-DE-SC0006706).

Edited by: D. Carlson

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