Global Carbon Budget 2016

. Accurate assessment of anthropogenic carbon dioxide (CO 2 ) 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


Introduction
The concentration of carbon dioxide (CO 2 ) 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 399.4 ± 0.1 ppm in 2015 (Dlugokencky and 5 Tans, 2016). The Mauna Loa station, which holds the longest running record of direct measurements of atmospheric CO 2 concentration (Tans and Keeling, 2014), went above 400 ppm for the first time in May 2013 (Scripps, 2013). The global monthly average concentration was above 400 ppm in March 10 through May 2015 and again since November 2015 (Dlugokencky and Tans, 2016; Fig. 1). The atmospheric CO 2 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 . While emissions from fossil fuels started before the industrial era, they only became the dominant source of anthropogenic emissions to the atmosphere from around 1920, and their relative share has continued to increase until present. Anthropogenic emissions occur on top of an active 20 natural carbon cycle that circulates carbon between the reservoirs of the atmosphere, ocean, and terrestrial biosphere on timescales from sub-daily to millennia, while exchanges with geologic reservoirs occur at longer timescales (Archer et al., 2009). 25 The global carbon budget presented here refers to the mean, variations, and trends in the perturbation of CO 2 in the atmosphere, referenced to the beginning of the industrial era. It quantifies the input of CO 2 to the atmosphere by emissions from human activities, the growth rate of atmospheric 30 CO 2 concentration, and the resulting changes in the storage of carbon in the land and ocean reservoirs in response to increasing atmospheric CO 2 levels, climate and variability, and other anthropogenic and natural changes (Fig. 2). An understanding of this perturbation budget over time and the under-35 lying variability and trends of the natural carbon cycle is necessary to understand the response of natural sinks to changes 4 C. Le Quéré et al.: Global Carbon Budget 2016196019701980 2000 Scripps Institution of Oceanography (Keeling et al., 1976) NOAA/ESRL  NOAA/ESRL Figure 1. Surface average atmospheric CO 2 concentration, deseasonalised (ppm). The 1980-2016 monthly data are from NOAA/ESRL  and are based on an average of direct atmospheric CO 2 measurements from multiple stations in the marine boundary layer (Masarie and Tans, 1995). The 1958The -1979 monthly data are from the Scripps Institution of Oceanography, based on an average of direct atmospheric CO 2 measurements from the Mauna Loa and South Pole stations (Keeling et al., 1976). To take into account the difference of mean CO 2 between the NOAA/ESRL and the Scripps station networks used here, the Scripps surface average (from two stations) was 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 mean seasonal cycle is also shown from 1980 (in pink). in climate, CO 2 , and land-use-change drivers, and the permissible emissions for a given climate stabilisation target.
The components of the CO 2 budget that are reported annually in this paper include separate estimates for the CO 2 emissions from (1) fossil fuel combustion and oxidation and 5 cement production (E FF ; GtC yr −1 ) and (2) the emissions resulting from deliberate human activities on land leading to land-use change (E LUC ; GtC yr −1 ), as well as their partitioning among (3) the growth rate of atmospheric CO 2 concentration (G ATM ; GtC yr −1 ), and the uptake of CO 2 by the 10 "CO 2 sinks" in (4) the ocean (S OCEAN ; GtC yr −1 ) and (5) on land (S LAND ; GtC yr −1 ). The CO 2 sinks as defined here include the response of the land and ocean to elevated CO 2 and changes in climate and other environmental conditions. The global emissions and their partitioning among the atmo- 15 sphere, ocean, and land are in balance: (1) G ATM is usually reported in ppm yr −1 , which we convert to units of carbon mass per year, GtC yr −1 , using 1 ppm = 2.12 GtC (Ballantyne et al., 2012;Prather et al., 20 2012; Table 1). We also include a quantification of E FF by country, computed with both territorial and consumptionbased accounting (see Sect. 2TS2).

2006-2015
Equation (1) partly omits two kinds of processes. The first is the net input of CO 2 to the atmosphere from the chemical 25 oxidation of reactive carbon-containing gases from sources other than the combustion of fossil fuels (e.g. fugitive anthropogenic CH 4 emissions, industrial processes, and biogenic emissions from changes in vegetation, fires, wetlands), primarily methane (CH 4 ), carbon monoxide (CO), and volatile 30 organic compounds such as isoprene and terpene (Gonzalez-Gaya et al., 2016). CO emissions are currently implicit in E FF , while fugitive anthropogenic CH 4 emissions are not and thus their inclusion would result in a small increase in E FF . The second is the anthropogenic perturbation to carbon 35 cycling in terrestrial freshwaters, estuaries, and coastal areas, which modifies lateral fluxes from land ecosystems to the open ocean; the evasion of CO 2 flux from rivers, lakes, and estuaries to the atmosphere; and the net air-sea anthropogenic CO 2 flux of coastal areas (Regnier et al., 2013). The 40 inclusion of freshwater fluxes of anthropogenic CO 2 would affect the estimates of, and partitioning between, S LAND and S OCEAN in Eq. (1) in complementary ways, but it would not affect the other terms. These flows are omitted in the absence of annual information on the natural vs. anthropogenic per-45 Table 1. Factors used to convert carbon in various units (by convention, unit 1 = unit 2 · conversion).

Unit 1 Unit 2 Conversion Source
GtC (gigatonnes of carbon) ppm (parts per million) a 2.12 b Ballantyne et al. (2012) GtC (gigatonnes of carbon) PgC (petagrams of carbon) 1 SI unit conversion GtCO 2 (gigatonnes of carbon dioxide) GtC (gigatonnes of carbon) 3.664 44.01/12.011 in mass equivalent GtC (gigatonnes of carbon) MtC (megatonnes of carbon) 1000 SI unit conversion a Measurements of atmospheric CO 2 concentration have units of dry-air mole fraction. "ppm" is an abbreviation for micromole per mole of dry air. b The use of a factor of 2.12 assumes that all the atmosphere is well mixed within one year. In reality, only the troposphere is well mixed and the growth rate of CO 2 concentration in the less well-mixed stratosphere is not measured by sites from the NOAA network. Using a factor of 2.12 makes the approximation that the growth rate of CO 2 concentration in the stratosphere equals that of the troposphere on a yearly basis.
turbation terms of these loops of the carbon cycle, and they are discussed in Sect. 2.7. The CO 2 budget has been assessed by the Intergovernmental Panel on Climate Change (IPCC) in all assessment reports Denman et al., 2007;Prentice et 5 al., 2001;Schimel et al., 1995;Watson et al., 1990), as well as by others (e.g. Ballantyne et al., 2012). These assessments included budget estimates for the decades of the 1980s and 1990s (Denman et al., 2007) and, most recently, the period 2002-2011 . The IPCC methodology has 10 been adapted and used by the Global Carbon Project (GCP, http://www.globalcarbonproject.org), which has 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 15 et al., 2007), year 2007 (published online;GCP, 2007), year (Le Quéré et al., 2009), year 2009(Friedlingstein et al., 2010, year 2010 (Peters et al., 2012b), year 2012 (Le Peters et al., 2013), year 2013 (Le Quéré et al., 2014), year 2014 (Friedlingstein et al., 2014;Le Quéré et al., 20 2015b), and most recently year 2015 Le Quéré et al., 2015a). 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. 25 We adopt a range of ±1 standard deviation (σ ) to report the uncertainties in our estimates, representing a likelihood of 68 % that the true value will be within the provided range if the errors have a Gaussian distribution. This choice reflects the difficulty of characterising the uncertainty in the CO 2 30 fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty of updating the CO 2 emissions from land-use change. A likelihood of 68 % provides an indication of our current capability to quantify each term and its uncertainty 35 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 40 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 45 the paper and have been examined in detail elsewhere (Ballantyne et al., 2015).
All quantities are presented in units of gigatonnes of carbon (GtC, 10 15 gC), which is the same as petagrams of carbon (PgC; Table 1). Units of gigatonnes of CO 2 (or billion 50 tonnes of CO 2 ) 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 period pre-industrial (1750) to 2015 55 and in more detail for the period 1959 to 2015. We also provide decadal averages starting in 1960 including the last decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), results for the year 2015, and a projection of E FF for year 2016. Finally, we provide cumulative emissions from fossil fuels and land-use change 60 since year 1750, the pre-industrial period, and since year 1870, the reference year for the cumulative carbon estimate used by the IPCC (AR5) based on the availability of global temperature data . This paper will be updated every year using the format of "living 65 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) 70 website (http://www.globalcarbonproject.org/carbonbudget), with fossil fuel emissions also available through the Global Carbon Atlas (http://www.globalcarbonatlas.org). With this approach, we aim to provide the highest transparency and traceability in the reporting of CO 2 , the key driver of climate 75 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 80 thus mainly one of synthesis, where results from individual groups are collated, analysed, and evaluated for consistency.  Ocean and land CO 2 sinks (S OCEAN and S LAND ) This paper for S OCEAN and S LAND and references in Table 6 for individual models We facilitate access to original data with the understanding that primary data sets will be referenced in future work (see Table 2 for how to cite the data sets). Descriptions of the measurements, models, and methodologies follow below and in-depth descriptions of each component are described else-5 where. This is the 11th version of the global carbon budget and the fifth revised version in the format of a living data update. It builds on the latest published global carbon budget of Le Quéré et al. (2015a). The main changes are (1) the inclusion 10 of data to year 2015 (inclusive) and a projection for fossil fuel emissions for year 2016; (2) the introduction of a projection for the full carbon budget for year 2016 using our fossil fuel projection, combined with preliminary data  and analysis by others (Betts et al., 2016) of the growth rate in atmospheric CO 2 concentration; and (3) the use of BP data from 1990 (BP, 2016b) to estimate emissions in China to ensure all recent revisions in Chinese statistics are incorporated. The main methodological differences between annual carbon budgets are summarised in Table 3. 20 2.1 CO 2 emissions from fossil fuels and industry (E FF )

Emissions from fossil fuels and industry and their uncertainty
The calculation of global and national CO 2 emissions from fossil fuels, including gas flaring and cement production 25 (E FF ), relies primarily on energy consumption data, specifically data on hydrocarbon fuels, collated and archived by several organisations (Andres et al., 2012). These include the Carbon Dioxide Information Analysis Center (CDIAC), the International Energy Agency (IEA), the United Nations 30 (UN), the United States Department of Energy (DoE) Energy Information Administration (EIA), and more recently also the Planbureau voor de Leefomgeving (PBL) Netherlands Environmental Assessment Agency. Where available, we use national emissions estimated by the countries them-35 selves and reported to the UNFCCC for the period 1990-2014 (40 countries). We assume that national emissions reported to the UNFCCC are the most accurate because national experts have access to additional and country-specific information, and because these emission estimates are peri-40 odically audited for each country through an established international methodology overseen by the UNFCCC. We also use global and national emissions estimated by CDIAC (Boden and Andres, 2016). The CDIAC emission estimates are the only data set that extends back in time to 1751 with con-45 sistent and well-documented emissions from fossil fuels, cement production, and gas flaring for all countries and their uncertainty (Andres et al., 2014(Andres et al., , 2012(Andres et al., , 1999; this makes the data set a unique resource for research of the carbon cycle during the fossil fuel era. 50 The global emissions presented here are from CDIAC's analysis, which provides an internally consistent global estimate including bunker fuels, minimising the effects of lowerquality energy trade data. Thus, the comparison of global emissions with previous annual carbon budgets is not influ-55 enced by the use of national data from UNFCCC reports. During the period 1959-2013, the emissions from fossil fuels estimated by CDIAC are based primarily on energy data provided by the UN Statistics Division (UN, 2015a, b;Table 4). When necessary, fuel masses/volumes are converted 60 to fuel energy content using coefficients provided by the UN and then to CO 2 emissions using conversion factors that take into account the relationship between carbon content and en- Table 3.
Main methodological changes in the global carbon budget since first publication. Unless specified below, the methodology was identical to that described in the current paper.
Furthermore, methodological changes introduced in one year are kept for the following years unless noted. Empty cells mean there were no methodological changes introduced that year.  Impact of increasing atmospheric CO 2 , climate, and variability Ocean models Table 6 S LAND Response of land vegetation to increasing atmospheric CO 2 concentration, climate and variability, and other environmental changes Budget residual ergy (heat) content of the different fuel types (coal, oil, gas, gas flaring) and the combustion efficiency (to account, for example, for soot left in the combustor or fuel otherwise lost or discharged without oxidation). Most data on energy consumption and fuel quality (carbon content and heat con-5 tent) are available at the country level (UN, 2015a). In general, CO 2 emissions for equivalent primary energy consumption are about 30 % higher for coal compared to oil, and 70 % higher for coal compared to natural gas . 10 Recent revisions in energy data for China  have not yet fully propagated to the UN energy statistics used by CDIAC but are available through the BP energy statistics (BP, 2016b). We thus use the BP energy statistics (BP, 2016b) and estimate the emissions by fuel type 15 using the BP methodology (BP, 2016a) to be consistent with the format of the CDIAC data. Emissions in China calculated from the BP statistics differ from those provided by CDIAC emissions mostly between 1997 and 2009. The revised emissions are higher by 5 % on average between 1990 and 2015 20 for a total additional emissions of 2.0 GtC during that period (41.3 GtC using the BP statistics and methodology compared to 39.3 provided by CDIAC). The two estimates converge to similar values from 2011 onwards (< 2 % difference). We propagate these new estimates for China through 25 to the global total to ensure consistency.
Our emission totals for the UNFCCC-reporting countries were recorded as in the UNFCCC submissions, which have a slightly larger system boundary than CDIAC. Additional emissions come from carbonates other than in cement manufacture, and thus UNFCCC totals will be slightly higher than CDIAC totals in general, although there are multiple sources of differences. We use the CDIAC method to report emis-5 sions by fuel type (e.g. all coal oxidation is reported under "coal", regardless of whether oxidation results from combustion as an energy source), which differs slightly from UN-FCCC.
For the most recent 1-2 years when the UNFCCC esti- to-year rate of change, because the rates of change are less uncertain than the absolute values and to avoid discontinuities in the time series when linking the UN-based data with the BP data. These preliminary estimates are replaced by the more complete UNFCCC or CDIAC data based on UN statis-25 tics when they become available. Past experience and work by others (Andres et al., 2014;Myhre et al., 2009) show that projections based on the BP rate of change are within the uncertainty provided (see Sect. 3.2 and Supplement from Peters et al., 2013).

30
Estimates of emissions from cement production by CDIAC are based on data on growth rates of cement production from the US Geological Survey up to year 2013 (USGS, 2016a). For 2014 and 2015 we use estimates of cement production made by the USGS for the top 18 countries (rep-35 resenting 85 % of global production; USGS, 2016b), while for all other countries we use the 2013 values (zero growth). Some fraction of the CaO and MgO in cement is returned to the carbonate form during cement weathering, but this is neglected here. 40 Estimates of emissions from gas flaring by CDIAC are calculated in a similar manner to those from solid, liquid, and gaseous fuels and rely on the UN energy statistics to supply the amount of flared or vented fuel. For the most recent 1-2 emission years, flaring is assumed constant from the most re- 45 cent available year of data (2014 for countries that report to the UNFCCC, and 2013 for the remainder). The basic data on gas flaring report atmospheric losses during petroleum production and processing that have large uncertainty and do not distinguish between gas that is flared as CO 2 or vented as 50 CH 4 . Fugitive emissions of CH 4 from the so-called upstream sector (e.g. coal mining and natural gas distribution) are not included in the accounts of CO 2 emissions except to the extent that they are captured in the UN energy data and counted as gas "flared or lost". 55 The published CDIAC data set includes 255 countries and regions. This list includes countries that no longer exist, such as the USSR and East Pakistan. For the carbon budget, we reduce the list to 219 countries by reallocating emissions to the currently defined territories. This involved both aggrega-60 tion and disaggregation, and does not change global emissions. Examples of aggregation include merging East and West Germany to the currently defined Germany. Examples of disaggregation include reallocating the emissions from the former USSR to the resulting independent countries. For dis-65 aggregation, we use the emission shares when the current territories first appeared. The disaggregated estimates should be treated with care when examining countries' emissions trends prior to their disaggregation. For the most recent years, 2014 and 2015, the BP statistics are more aggregated, but we 70 retain the detail of CDIAC by applying the growth rates of each aggregated region in the BP data set to its constituent individual countries in CDIAC.
Estimates of CO 2 emissions show that the global total of emissions is not equal to the sum of emissions from all coun-75 tries. This is largely attributable to emissions that occur in international territory, in particular the combustion of fuels used in international shipping and aviation (bunker fuels), where the emissions are included in the global totals but are not attributed to individual countries. In practice, the emis-80 sions from international bunker fuels are calculated based on where the fuels were loaded, but they are not included with national emissions estimates. Other differences occur because globally the sum of imports in all countries is not equal to the sum of exports and because of inconsistent national re-85 porting, differing treatment of oxidation of non-fuel uses of hydrocarbons (e.g. as solvents, lubricants, feedstocks), and changes in stock (Andres et al., 2012).
The uncertainty in the annual emissions from fossil fuels and industry for the globe has been estimated at ±5 % (scaled 90 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). 95 This includes an assessment of uncertainties in the amounts of fuel consumed, the carbon and heat contents of fuels, and the combustion efficiency. While in the budget we consider a fixed uncertainty of ±5 % for all years, in reality the uncertainty, as a percentage of the emissions, is growing with 100 time because of the larger share of global emissions from non-Annex B countries (emerging economies and developing countries) with less precise statistical systems (Marland et al., 2009). For example, the uncertainty in Chinese emissions has been estimated at around ±10 % (for ±1σ ; Gregg 105 et al., 2008), and important potential biases have been identified suggesting China's emissions could be overestimated in published studies (Liu et al., 2015TS4). Generally, emissions from mature economies with good statistical bases have an uncertainty of only a few percent (Marland, 2008). Further 110 research is needed before we can quantify the time evolution of the uncertainty, as well as its temporal error correlation structure. We note that even if they are presented as 1σ estimates, uncertainties in emissions are likely to be mainly country-specific systematic errors related to underlying bi-5 ases 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., 2010). There is only limited and indirect evidence for emissions, 10 although there is a high agreement among the available estimates within the given uncertainty (Andres et al., 2014(Andres et al., , 2012, and emission estimates are consistent with a range of other observations , even though their regional and national partitioning is more uncertain (Francey 15 et al., 2013).

Emissions embodied in goods and services
National emission inventories take a territorial (production) perspective and "include greenhouse gas emissions and removals taking place within national territory and offshore ar-20 eas over which the country has jurisdiction" (Rypdal et al., 2006). That is, emissions are allocated to the country where and when the emissions actually occur. The territorial emission inventory of an individual country does not include the emissions from the production of goods and services pro- 25 duced in other countries (e.g. food and clothes) that are used for consumption. Consumption-based emission inventories for an individual country are another attribution point of view that allocates global emissions to products that are consumed within a country; these inventories are conceptually 30 calculated as the territorial emissions minus the "embedded" territorial emissions to produce exported products plus the emissions in other countries to produce imported products (consumption = territorial − exports + imports). The difference between the territorial-and consumption-based emis-35 sion inventories is the net transfer (exports minus imports) of emissions from the production of internationally traded products. Consumption-based emission attribution results (e.g. Davis and Caldeira, 2010) provide additional information to territorial-based emissions that can be used to understand 40 emission drivers (Hertwich and Peters, 2009), quantify emission transfers by the trade of products between countries (Peters et al., 2011b), and potentially design more effective and efficient climate policy (Peters and Hertwich, 2008).
We estimate consumption-based emissions from 1990 to 45 2014 by enumerating the global supply chain using a global model of the economic relationships between economic sectors within and between every country Peters et al., 2011a). Our analysis is based on the economic and trade data from the Global Trade and Analysis  Peters et al., 2011b). We estimate the sector-level CO 2 emissions using our own calculations based on the GTAP data and methodology, include flaring and cement emissions from CDIAC, and then scale the national totals (excluding bunker fuels) to match 65 the CDIAC estimates from the most recent carbon budget. We do not include international transportation in our estimates of national totals, but include them in the global total. The time series of trade data provided by GTAP covers the period 1995-2013 and our methodology uses the trade 70 shares as this data set. For the period 1990-1994 we assume the trade shares of 1995, while for 2014 we assume the trade shares of 2013.
Comprehensive analysis of the uncertainty in consumption emissions accounts is still lacking in the literature, although 75 several analyses of components of this uncertainty have been made (e.g. Dietzenbacher et al., 2012;Inomata and Owen, 2014;Karstensen et al., 2015;Moran and Wood, 2014). For this reason we do not provide an uncertainty estimate for these emissions, but based on model comparisons and sen-80 sitivity analysis, they are unlikely to be larger than for the territorial emission estimates (Peters et al., 2012a). Uncertainty is expected to increase for more detailed results, and to decrease with aggregation (Peters et al., 2011b; e.g. the results for Annex B countries will be more accurate than the 85 sector results for an individual country).
The consumption-based emissions attribution method considers the CO 2 emitted to the atmosphere in the production of products, but not the trade in fossil fuels (coal, oil, gas). It is also possible to account for the carbon trade in fossil fu-90 els , but we do not present those data here. Peters et al. (2012a) additionally considered trade in biomass.
The consumption data do not modify the global average terms in Eq. (1) but are relevant to the anthropogenic car-95 bon cycle as they reflect the trade-driven movement of emissions across the Earth's surface in response to human activities. Furthermore, if national and international climate policies continue to develop in an un-harmonised way, then the trends reflected in these data will need to be accommodated 100 by those developing policies.

Growth rate in emissions
We report the annual growth rate in emissions for adjacent years (in percent per year) by calculating the difference between the two years and then comparing to the emissions 105 in the first year: × % yr −1 TS5 . This is the simplest method to characterise a 1-year growth compared to the previous year and is widely used. We apply a leapyear adjustment 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 E FF over time periods of greater than 1 year can be re-written using its logarithm 10 equivalent as follows: (2) Here we calculate relative growth rates in emissions for multi-year periods (e.g. a decade) by fitting a linear trend to ln(E FF ) in Eq.
(2), reported in percent per year. We fit 15 the logarithm of E FF rather than E FF directly because this method ensures that computed growth rates satisfy Eq. (6). This method differs from previous papers (Canadell et al., 2007;Le Quéré et al., 2009;Raupach et al., 2007) that computed the fit to E FF and divided by average E FF directly, but 20 the difference is very small (< 0.05 % yr −1 ) in the case of E FF .

Emissions projections
Energy statistics from BP are normally available around June for the previous year. To gain insight on emission trends for tic production plus net imports of petroleum (NDRC, 2016b) for January through July from the National Development and Reform Commission, and (3) production of cement reported for January to September (NBS, 2016). Using these data, we estimate the change in emissions for the corresponding 45 months in 2016 compared to 2015 assuming a 2 % increase in the energy (and thus carbon) content of coal for 2016 resulting from improvements in the quality of the coal used, in line with the trends reported by the National Bureau of Statistics for recent years. We then assume that the relative changes 50 during the first months will persist throughout the year. The main sources of uncertainty are from the incomplete data on stock changes, the carbon content of coal, and the assumption of persistent behaviour for the rest of the year. These are discussed further in Sect. 3.2.1. 55 For the USA, we use the forecast of the US Energy Information Administration (EIA) for emissions from fossil fuels (EIA, 2016). This is based on an energy forecasting model which is revised monthly, and takes into account heating degree days, household expenditures by fuel type, energy 60 markets, policies, and other effects. We combine this with our estimate of emissions from cement production using the monthly US cement data from USGS for January-July, assuming changes in cement production over the first seven months apply throughout the year. While the EIA's forecasts 65 for current full-year emissions have on average been revised downwards, only seven such forecasts are available, so we conservatively use the full range of adjustments following revision, and additionally assume symmetrical uncertainty to give ±2.3 % around the central forecast.

70
For the rest of the world, we use the close relationship between the growth in GDP and the growth in emissions (Raupach et al., 2007) to project emissions for the current year. This is based on the so-called Kaya identity (also called IPAT identity, the acronym standing for human im-75 pact (I ) on the environment, which is equal to the product of population (P ), affluence (A), and technology (T )), whereby E FF (GtC yr −1 ) is decomposed by the product of GDP (USD yr −1 ) and the fossil fuel carbon intensity of the economy (I FF ; GtC USD −1 ) as follows: ( Such product-rule decomposition identities imply that the relative growth rates of the multiplied quantities are additive. Taking a time derivative of Eq. (3) gives and, applying the rules of calculus, Finally, dividing Eq. (5) by Eq. (3) gives where the left-hand term is the relative growth rate of E FF , 90 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 by multiplying each term by 100. As preliminary estimates of annual change in GDP are made well before the end 95 of a calendar year, making assumptions on the growth rate of  We thus assume that all land management activities apart from deforestation and degradation do not vary significantly on a year-to-year ba-sis. Other sources of interannual variability (e.g. the impact of climate variability on regrowth fluxes) are accounted for 55 in S LAND . In addition, we use results from dynamic global vegetation models (see Sect. 2.2.3 and Table 6) that calculate net land-use-change CO 2 emissions in response to landcover change reconstructions prescribed to each model in order to help quantify the uncertainty in E LUC and to explore 60 the consistency of our understanding. The three methods are described below, and differences are discussed in Sect. Land-use-change CO 2 emissions are calculated by a bookkeeping method approach (Houghton, 2003) that 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, 70 including possible forest regrowth after deforestation. The method tracks the CO 2 emitted to the atmosphere immediately during deforestation, and over time due to the followup decay of soil and vegetation carbon in different pools, including wood products pools after logging and deforesta-75 tion. It also tracks the regrowth of vegetation and associated build-up of soil carbon pools after land-use change. It considers transitions between forests, pastures, and cropland; shifting cultivation; degradation of forests where a fraction of the trees is removed; abandonment of agricultural land; and for-80 est management such as wood harvest and, in the USA, fire management. In addition to tracking logging debris on the forest floor, the bookkeeping method tracks the fate of carbon contained in harvested wood products that is eventually emitted back to the atmosphere as CO 2 , although a detailed treat-85 ment 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 fuel wood is assumed burned in the year of harvest (1.0 yr −1 ). Pulp and paper products are oxidised at a rate of 0.1 yr −1 , timber is 90 assumed to be oxidised at a rate of 0.01 yr −1 , and elemental carbon decays at 0.001 yr −1 . The general assumptions about partitioning wood products among these pools are based on national harvest data (Houghton, 2003). The primary land-cover change and biomass data for the 95 bookkeeping method analysis is the Forest Resource Assessment of the FAO which provides statistics on forest-cover change and management at intervals of 5 years (FAO, 2010). The data are based on countries' self-reporting, some of which include satellite data in more recent assessments (Ta-100 ble 4). Changes in land cover other than forest are based on annual, national changes in cropland and pasture areas reported by the FAO Statistics Division (FAOSTAT, 2010). Land-use-change country data are aggregated by regions. The carbon stocks on land (biomass and soils), and their re-105 sponse functions subsequent to land-use change, are based on Table 5. Comparison of the processes included in the bookkeeping method and DGVMs in their estimates of E LUC and S LAND . See Table 6 for model references. All models include deforestation and forest regrowth after abandonment of agriculture (or from afforestation activities on agricultural land). Processes relevant for E LUC are only described for the DGVMs used with land-cover change in this study ( Fig. 6  yes no yes yes yes no no yes no yes yes no yes c no a Refers to the routine harvest of established managed forests rather than pools of harvested products. b Not in the recent update (Houghton and Nassikas, 2016). c Very limited. Nitrogen uptake is simulated as a function of soil C, and Vcmax CE2 is an empirical function of canopy N. Does not consider N deposition.
FAO data averages per land-cover type, per biome, and per region. Similar results were obtained using forest biomass carbon density based on satellite data (Baccini et al., 2012). The bookkeeping method does not include land ecosystems' transient response to changes in climate, atmospheric 5 CO 2 , and other environmental factors, and the growth/decay curves are based on contemporary data that will implicitly reflect the effects of CO 2 and climate at that time. Published results from the bookkeeping method are available from 1850 to 2010, with preliminary results available to 2015.

Fire-based interannual variability in E LUC
Land-use-change-associated CO 2 CE3 emissions calculated from satellite-based fire activity in tropical forest areas (van der Werf et al., 2010) provide information on emissions due to tropical deforestation and degradation that are comple- 15 mentary to the bookkeeping approach. They do not provide a direct estimate of E LUC as they do not include noncombustion processes such as respiration, wood harvest, wood products, or forest regrowth. Legacy emissions such as decomposition from on-ground debris and soils are not 20 included in this method either. However, fire estimates provide some insight in the year-to-year variations in the subcomponent of the total E LUC flux that result from immediate CO 2 emissions during deforestation caused, for example, by the interactions between climate and human activity (e.g. 25 there is more burning and clearing of forests in dry years) that are not represented by other methods. The "deforestation fire emissions" assume an important role of fire in removing biomass in the deforestation process and thus can be used to infer gross instantaneous CO 2 emissions from defor-30 estation using satellite-derived data on fire activity in regions with active deforestation. The method requires information on the fraction of total area burned associated with deforestation vs. other types of fires, and this information can be merged with information on biomass stocks and the fraction 35 of the biomass lost in a deforestation fire to estimate CO 2 emissions. The satellite-based deforestation fire emissions are limited to the tropics, where fires result mainly from human activities. Tropical deforestation is the largest and most variable single contributor to E LUC .  Zaehle et al. (2011). h Compared to published version: revised parameters values for photosynthetic capacity for boreal forests (following assimilation of FLUXNET data), updated parameters values for stem allocation, maintenance respiration and biomass export for tropical forests (based on literature), and CO 2 down-regulation process added to photosynthesis. i See also Woodward and Lomas (2004) TS8 . Changes from publications include sub-daily photosynthesis downscaling and other adjustments. j See also Ito and Inatomi (2012). k With no nutrient restoring below the mixed layer depth. l Uses winds from Atlas et al. (2011). m The CAMS (Copernicus Atmosphere Monitoring Service) v15.2 CO 2 inversion system, initially described by Chevallier et al. (2005), relies on the global tracer transport model LMDZ (see also Supplement TS9 Chevallier, 2015;Hourdin et al., 2006). mate carbon emissions associated with fires, keeping track of what fraction of fire emissions was due to deforestation (see van der Werf et al., 2010). The CASA model uses different assumptions to compute decay functions compared to the bookkeeping method, and does not include historical emis-5 sions 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). Results from the fire-based method to 10 estimate land-use-change emissions anomalies added to the bookkeeping mean E LUC estimate are available from 1997 to 2015. Our combination of land-use-change CO 2 emissions where the variability in annual CO 2 deforestation emissions is diagnosed from fires assumes that year-to-year variability 15 is dominated by variability in deforestation.

Dynamic global vegetation models (DGVMs)
Land-use-change CO 2 emissions have been estimated using an ensemble of DGVM simulations. New model experiments up to year 2015 have been coordinated by the project "Trends 20 and drivers of the regional-scale sources and sinks of carbon dioxide" (TRENDY; Sitch et al., 2015). We use only models that have estimated land-use-change CO 2 emissions following the TRENDY protocol (see Sect. 2.5.2). Models use their latest configurations, summarised in Tables 5 and 6. 25 Two sets of simulations were performed with the DGVMs, first forced with historical changes in land-cover distribution, climate, atmospheric CO 2 concentration, and N deposition, and second, as further described below with a time-invariant pre-industrial land-cover distribution, allowing for estima-30 tion of, by difference with the first simulation, the dynamic evolution of biomass and soil carbon pools in response to prescribed land-cover change. Because of the limited availability of the land-use forcing (see below), 14 DGVMs performed historical simulations with time-invariant land-cover 35 distribution, but only 5 DGVMs managed to simulate realistic simulations with time varying land-cover change. These latter DGVMs accounted for deforestation and (to some extent) regrowth, the most important components of E LUC , but they do not represent all processes resulting directly from 40 human activities on land (Table 5). All DGVMs represent processes of vegetation growth and mortality, as well as decomposition of dead organic matter associated with natural cycles, and include the vegetation and soil carbon response to increasing atmospheric CO 2 levels and to climate variabil-45 ity and change. In addition, eight models explicitly simulate the coupling of C and N cycles and account for atmospheric N deposition (Table 5), with three of those models used for land-use-change simulations. The DGVMs are independent of the other budget terms except for their use of atmospheric 50 CO 2 concentration to calculate the fertilisation effect of CO 2 on primary production.
For this global carbon budget, the DGVMs used the HYDE land-use-change data set (Klein Goldewijk et al., 2011), which provides annual, half-degree, fractional data 55 on cropland and pasture. These data are based on annual FAO statistics of change in agricultural area available to 2012 (FAOSTAT, 2010). For the years 2013 to 2015, the HYDE data were extrapolated by country for pastures and cropland separately based on the trend in agricultural area over the 60 previous 5 years. The more comprehensive LUHCE4 data set (Hurtt et al., 2011TS10), which also includes fractional data on primary vegetation and secondary vegetation, as well as all underlying transitions between land-use states, has not been made available yet for this year. Hence, the reduced 65 ensemble of DGVMs that can simulate the LUC flux from the HYDE data set only. The HYDE data are independent of the data set used in the bookkeeping method (Houghton, 2003, and updates), which is based primarily on forest area change statistics (FAO, 2010). The HYDE land-use-change 70 data set does not indicate whether land-use changes occur on forested or non-forested land; it only provides the changes in agricultural areas. Hence, it is implemented differently within each model (e.g. an increased cropland fraction in a grid cell can be at the expense of either grassland or forest, 75 the latter resulting in deforestation; land-cover fractions of the non-agricultural land differ between models). Thus, the DGVM forest area and forest area change over time is not consistent with the Forest Resource Assessment of the FAO forest area data used for the bookkeeping model to calculate 80 E LUC . Similarly, model-specific assumptions are applied to convert deforested biomass or deforested area, and other forest product pools, into carbon in some models ( Table 5).
The DGVM runs were forced by either 6-hourly CRU-NCEP or by monthly CRU temperature, precipitation, and 85 cloud cover fields (transformed into incoming surface radiation) based on observations and provided on a 0.5 • × 0.5 • grid and updated to 2015 Viovy, 2016). The forcing data include both gridded observations of climate and global atmospheric CO 2 , which change over time 90 , and N deposition (as used in some models; Table 5). As mentioned before, E LUC is diagnosed in each model by the difference between a model simulation with prescribed historical land-cover change and a simulation with constant, pre-industrial land-cover distribu-95 tion. Both simulations were driven by changing atmospheric CO 2 , climate, and, in some models, N deposition over the period 1860-2015. Using the difference between these two DGVM simulations to diagnose E LUC is not fully consistent with the definition of E LUC in the bookkeeping method 100 (Gasser and Ciais, 2013;Stocker and Joos, 2015). The DGVM approach to diagnose land-use-change CO 2 emissions would be expected to produce systematically higher E LUC emissions than the bookkeeping approach if all the parameters of the two approaches were the same, which is not 105 the case (see Sect. 2.5.2). Table 7. Comparison of results from the bookkeeping method and budget residuals with results from the DGVMs and inverse estimates for the periods 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009, as well as the last decade and last year available. All values are in GtC yr −1 . The DGVM uncertainties represent ±1σ of the decadal or annual (for 2015 only) estimates from the individual models; for the inverse models all three results are given where available.

Uncertainty assessment for E LUC
Differences between the bookkeeping, the addition of firebased interannual variability to the bookkeeping, and DGVM methods originate from three main sources: the land-coverchange data set, the different approaches used in models, and 5 the different processes represented (Table 5). We examine the results from the DGVMs and of the bookkeeping method to assess the uncertainty in E LUC . The uncertainties in annual E LUC estimates are examined using the standard deviation across models, which averages  (Table 7). The mean of the multi-model E LUC estimates is consistent with a combination of the bookkeeping method and fire-based emissions (Le Quéré et al., 2014), with the multi-model mean and bookkeeping method differing by less than 0.5 GtC yr −1 15 over 85 % of the time. Based on this comparison, we determine that an uncertainty of ±0.5 GtC yr −1 provides a semiquantitative measure of uncertainty for annual emissions and reflects our best value judgement that there is at least 68 % chance (±1σ ) that the true land-use-change emission lies 20 within the given range, for the range of processes considered here. This is consistent with the uncertainty analysis of Houghton et al. (2012), which partly reflects improvements in data on forest area change using data and partly more complete understanding and representation of processes in mod-25 els.
The uncertainties in the decadal E LUC estimates are also examined using the DGVM ensemble, although they are likely correlated between decades. The correlations between decades come from (1) common biases in system bound-30 aries (e.g. not counting forest degradation in some models), (2) common definition for the calculation of E LUC from the difference of simulations with and without land-use change (a source of bias vs. the unknown truth), (3) common and uncertain land-cover change input data which also cause a 35 bias (though if a different input data set is used each decade, decadal fluxes from DGVMs may be partly decorrelated), and (4) model structural errors (e.g. systematic errors in biomass stocks). In addition, errors arising from uncertain DGVM parameter values would be random but they are not 40 accounted for in this study, since no DGVM provided an ensemble of runs with perturbed parameters.
Prior to 1959, the uncertainty in E LUC is taken as ±33 %, which is the ratio of uncertainty to mean from the 1960s in the bookkeeping method (Table 7), the first decade avail-45 able. This ratio is consistent with the mean standard deviation of DGVMs land-use-change emissions over 1870-1958 over the multi-model mean (0.9 GtC).

Growth rate in atmospheric CO
Global growth rate in atmospheric CO 2 concentration TS11 The rate of growth of the atmospheric CO 2 concentration is provided by the US National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL; Dlugokencky and Tans, 2016), which is up-55 dated from Ballantyne et al. (2012). For the 1959-1980 period, the global growth rate is based on measurements of atmospheric CO 2 concentration averaged from the Mauna Loa and South Pole stations, as observed by the CO 2 Program at Scripps Institution of Oceanography (Keeling et al., 1976). For the 1980-2015 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 (2016)  tions 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 GtC yr −1 reported for decadal mean growth rate by 25 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 10 years apart based on a measurement error of 0.35 ppm. This error is 30 based on offsets between NOAA/ESRL measurements and those of the World Meteorological Organization World Data Centre for Greenhouse Gases (NOAA/ESRL, 2015) for the start and end points (the decadal change uncertainty is the 2(0.35 ppm) 2 (10 yr) −1 assuming that each yearly mea-35 surement error is independent). This uncertainty is also used in Table 8.
The contribution of anthropogenic CO and CH 4 is neglected from the global carbon budget (see Sect. 2.7.1). We assign a high confidence to the annual estimates of G ATM be-40 cause they are based on direct measurements from multiple and consistent instruments and stations distributed around the world (Ballantyne et al., 2012).
In order to estimate the total carbon accumulated in the atmosphere since 1750 or 1870, we use an atmospheric CO 2 45 concentration of 277 ± 3 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 . Typical uncertainties in the growth rate in atmospheric CO 2 con-50 centration from ice core data are ±1-1.5 GtC per decade 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).

Ocean CO 2 sink 55
Estimates of the global ocean CO 2 sink are based on a combination of a mean CO 2 sink estimate for the 1990s from observations, as well as a trend and variability in the ocean CO 2 sink for 1959-2015 from seven global ocean biogeochemistry models. We use two observation-based estimates 60 of S OCEAN available for recent decades to provide a qualitative assessment of confidence in the reported results.

Observation-based estimates
A mean ocean CO 2 sink of 2.2 ± 0.4 GtC yr −1 for the 1990s was estimated by the IPCC (Denman et al., 2007) based on 65 indirect observations and their spread: ocean-land CO 2 sink partitioning from observed atmospheric O 2 / N 2 concentration trends (Manning and Keeling, 2006), an oceanic inversion method constrained by ocean biogeochemistry data (Mikaloff Fletcher et al., 2006), and a method based on pen-70 etration timescale for CFCs (McNeil et al., 2003). This is comparable with the sink of 2.0 ± 0.5 GtC yr −1 estimated by Khatiwala et al. (2013) for the 1990s, and with the sink of 1.9 to 2.5 GtC yr −1 estimated from a range of methods for the period 1990-2009 (Wanninkhof et al., 2013), with uncer-75 tainties ranging from ±0.3 to ±0.7 GtC yr −1 . The most direct way for estimating the observation-based ocean sink is from the product of (sea-air pCO 2 difference) × (gas transfer coefficient). Estimates based on sea-air pCO 2 are fully consistent with indirect observations (Wanninkhof et al., 2013), 80 but their uncertainty is larger mainly due to difficulty in capturing complex turbulent processes in the gas transfer coefficient (Sweeney et al., 2007) and because of uncertainties in the pre-industrial river outgas of CO 2 (Jacobson et al., 2007).
Both observation-based estimates (Landschützer et al., 85 2015;Rödenbeck et al., 2014) compute the ocean CO 2 sink and its variability using interpolated measurements of surface ocean fugacity of CO 2 (pCO 2 corrected for the nonideal behaviour of the gas; Pfeil et al., 2013). The measurements were from the Surface Ocean CO 2 Atlas version 4, 90 which is an update of version 3 (Bakker et al., 2016) and contains data to 2015 (see data attribution Table 1a). In contrast to last year's global carbon budget, where preliminary data were used for the past year, data used here are fully quality-controlled following standard SOCAT proce-95 dures. The SOCAT v4 were mapped using a data-driven diagnostic method (Rödenbeck et al., 2013) and a combined self-organising map and feed-forward neural network . The global observation-based estimates were adjusted to remove a background (not part of 100 the anthropogenic ocean flux) ocean source of CO 2 to the atmosphere of 0.45 GtC yr −1 from river input to the ocean (Jacobson et al., 2007) in order to make them comparable to S OCEAN , which only represents the annual uptake of anthropogenic CO 2 by the ocean. Several other data-based prod-105 ucts are available, but they show large discrepancies with Table 8. Decadal mean in the five components of the anthropogenic CO 2 budget for the periods 1960-1969, 1970-1979, 1980-1989, 1990-1999, and 2000-2009, as well as the last decade and last year available. All values are in GtC yr −1 . All uncertainties are reported as ±1σ . A data set containing data for each year during 1959-2014 is available at http://cdiac.ornl.gov/GCP/carbonbudget/2015/. Please follow the terms of use and cite the original data sources as specified in the data set.

Global ocean biogeochemistry models
The trend in the ocean CO 2 sink for 1959-2015 is computed using a combination of seven global ocean biogeochemistry models ( Table 6). The models represent the physical, chemi-25 cal, and biological processes that influence the surface ocean concentration of CO 2 and thus the air-sea CO 2 flux. The models are forced by meteorological reanalysis and atmospheric CO 2 concentration data available for the entire time period. Models do not include the effects of anthropogenic 30 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 indus-trial period (Duce et al., 2008). They compute the air-sea flux of CO 2 over grid boxes of 1 to 4 • in latitude and longitude. The ocean CO 2 sink for each model is normalised to the ob-35 servations by dividing the annual model values by their modelled average over 1990-1999 and multiplying this by the observation-based estimate of 2.2 GtC yr −1 (obtained from Manning and Keeling, 2006;McNeil et al., 2003;Mikaloff Fletcher et al., 2006). The ocean CO 2 sink for each year (t) 40 in GtC yr −1 is therefore where n is the number of models. This normalisation ensures that the ocean CO 2 sink for the global carbon budget is based on observations, whereas the trends and annual values in CO 2 45 sinks are from model estimates. The normalisation based on a ratio assumes that if models over or underestimate the sink in the 1990s, it is primarily due to the process of diffusion, which depends on the gradient of CO 2 . Thus, a ratio is more appropriate than an offset as it takes into account the time 50 dependence of CO 2 gradients in the ocean. The mean uncorrected ocean CO 2 sink from the models for 1990-1999 ranges between 1.7 and 2.4 GtC yr −1 , with a multi-model mean of 2.0 GtC yr −1 .

Uncertainty assessment for S OCEAN 55
The uncertainty around the mean ocean sink of anthropogenic CO 2 was quantified by Denman et al. (2007) for the 1990s (see Sect. 2.4.1). To quantify the uncertainty around annual values, we examine the standard deviation of the normalised model ensemble. We use further information from 60 the two data-based products to assess the confidence level.
The average standard deviation of the normalised ocean model ensemble is 0.16 GtC yr −1 during 1980-2010 (with a maximum of 0.33), but it increases as the model ensemble goes back in time, with a standard deviation of 0.22 GtC yr −1 across models in the 1960s. We estimate that the uncer-5 tainty in the annual ocean CO 2 sink is about ±0.5 GtC yr −1 from the fractional uncertainty in the data uncertainty of ±0.4 GtC yr −1 and standard deviation across models of up to ±0.33 GtC yr −1 , reflecting both the uncertainty in the mean sink from observations during the 1990s (Denman et al.,10 2007; Sect. 2.4.1) and in the interannual variability as assessed by models. We examine the consistency between the variability in the model-based and the data-based products to assess confidence in S OCEAN . The interannual variability of 15 the ocean fluxes (quantified as the standard deviation) of the two data-based estimates for 1986-2015 (where they overlap) is ± 0.34 GtC yr −1  and ± 0.41 GtC yr −1 , compared to ±0.29 GtC yr −1 for the normalised model ensemble. 20 The standard deviation includes a component of trend and decadal variability in addition to interannual variability, and their relative influence differs across estimates. The phase is generally consistent between estimates, with a higher ocean CO 2 sink during El Niño events. The annual data-based esti-25 mates correlate with the ocean CO 2 sink estimated here with a correlation of r = 0.71 (0.51 to 0.77 for individual models), and r = 0.81 (0.66 to 0.79) for the data-based estimates of Rödenbeck et al. (2014) and Landschützer et al. (2015), respectively (simple linear regression), with their mutual corre-30 lation at 0.65. The agreement is better for decadal variability than for interannual variability. 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 one year. We assess a medium confi-35 dence level to the annual ocean CO 2 sink and its uncertainty because they are based on multiple lines of evidence, and the results are consistent in that the interannual variability in the model and data-based estimates are all generally small compared to the variability in the growth rate of atmospheric CO 2 40 concentration.

Terrestrial CO 2 sink
The difference between, on the one hand, fossil fuel (E FF ) and land-use-change emissions (E LUC ) and, on the other hand, the growth rate in atmospheric CO 2 concentration 45 (G ATM ) and the ocean CO 2 sink (S OCEAN ) is attributable to the net sink of CO 2 in terrestrial vegetation and soils (S LAND ), within the given uncertainties (Eq. 1). Thus, this sink can be estimated as the residual of the other terms in the mass balance budget, as well as directly calculated us- 50 ing DGVMs. The residual land sink (S LAND ) is thought to be in part because of the fertilising effect of rising atmospheric CO 2 on plant growth, N deposition, and effects of cli-mate change such as the lengthening of the growing season in northern temperate and boreal areas. S LAND does not include 55 gross land sinks directly resulting from land-use change (e.g. regrowth of vegetation) as these are estimated as part of the net land-use flux (E LUC ). System boundaries make it difficult to exactly attribute CO 2 fluxes on land between S LAND and E LUC (Erb et al., 2013), and by design most of the uncertain-60 ties in our method are allocated to S LAND for those processes that are poorly known or represented in models.

Residual of the budget
For 1959-2015, the terrestrial carbon sink was estimated from the residual of the other budget terms by rearranging 65 Eq. (1): The uncertainty in S LAND is estimated annually from the root sum of squares of the uncertainty in the right-hand terms assuming the errors are not correlated. The uncertainty av-70 erages to ±0.8 GtC yr −1 over 1959-2015 (Table 7). S LAND estimated from the residual of the budget includes, by definition, all the missing processes and potential biases in the other components of Eq. (8).

DGVMs 75
A comparison of the residual calculation of S LAND in Eq. (8) with estimates from DGVMs as used to estimate E LUC in Sect. 2.2.3, but here excluding the effects of changes in land cover (using a constant pre-industrial land-cover distribution), provides an independent estimate of the consistency of 80 S LAND with our understanding of the functioning of the terrestrial vegetation in response to CO 2 and climate variability ( Table 7). As described in Sect. 2.2.3, the DGVM runs that exclude the effects of changes in land cover include all climate variability and CO 2 effects over land, but they do not 85 include reductions in CO 2 sink capacity associated with human activity directly affecting changes in vegetation cover and management, which by design is allocated to E LUC . This effect has been estimated to have led to a reduction in the terrestrial sink by 0.5 GtC yr −1 since 1750 (Gitz and Ciais, 90 2003). The models in this configuration estimate the mean and variability of S LAND based on atmospheric CO 2 and climate, and thus both terms can be compared to the budget residual. We apply three criteria for minimum model realism by including only those models with (1) steady state after 95 spin-up; (2) where available, net land fluxes (S LAND −E LUC ) that are a carbon sink over the 1990s as constrained by global atmospheric and oceanic observations (Keeling and Manning, 2014;Wanninkhof et al., 2013); and (3) where available, global E LUC that is a carbon source over the 1990s. 100 Fourteen models met criteria (1), and five of the models that provided E LUC met all three criteria.
The standard deviation of the annual CO 2 sink across the DGVMs' averages to ±0.8 GtC yr −1 for the period 1959 to 2015. The model mean, over different decades, correlates with the budget residual with r = 0.68 (0.51 to r = 0.77 for individual models). The standard deviation is similar to 5 that of the five model ensembles presented in Le Quéré et al. (2009), but the correlation is improved compared to r = 0.54 obtained in the earlier study. The DGVM results suggest that the sum of our knowledge on annual CO 2 emissions and their partitioning is plausible (see Sect. 4TS12), 10 and provide insight on the underlying processes and regional breakdown. However, as the standard deviation across the DGVMs (0.8 GtC yr −1 on average) is of the same magnitude as the combined uncertainty due to the other components (E FF , E LUC , G ATM , S OCEAN ; Table 7), the DGVMs do not 15 provide further reduction of uncertainty in the annual terrestrial CO 2 sink compared to the residual of the budget (Eq. 8). Yet, DGVM results are largely independent of the residual of the budget, and it is worth noting that the residual method and ensemble mean DGVM results are consistent within their re-20 spective uncertainties. We attach a medium confidence level to the annual land CO 2 sink and its uncertainty because the estimates from the residual budget and averaged DGVMs match well within their respective uncertainties, and the estimates based on the residual budget are primarily dependent 25 on E FF and G ATM , both of which are well constrained.

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 CO 2 fluxes from all 30 sources, including fossil and land-use-change emissions and land and ocean CO 2 fluxes. The inversions assume E FF to be well known, and they solve for the spatial and temporal distribution of land and ocean fluxes from the residual gradients of CO 2 between stations that are not explained by emissions.

35
Inversions used atmospheric CO 2 data to the end of 2015 (including preliminary values in some cases), and three atmospheric CO 2 inversions (Table 6) to infer the total CO 2 flux over land regions, and the distribution of the total land and ocean CO 2 fluxes for the mid-to high-latitude Northern 40 Hemisphere (30-90 • N), tropics (30 • S-30 • N) and mid-to high-latitude region of the Southern Hemisphere (30-90 • S). We focus here on the largest and most consistent sources of information and use these estimates to comment on the consistency across various data streams and process-based esti- 45 mates.

Atmospheric inversions
TS13 The three inversion systems used in this release are the CarbonTracker (Peters et al., 2010), the Jena CarboScope (Rödenbeck, 2005), and CAMS (Chevallier et al., 2005). See 50 Table 6 for version numbers. They 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 that represent some of the many approaches used in the field. This mainly concerns the time 55 resolution of the estimates (i.e. weekly or monthly), spatial breakdown (i.e. grid size), assumed correlation structures, and mathematical approach. The details of these approaches are documented extensively in the references provided. Each system uses a different transport model, which 60 was demonstrated to be a driving factor behind differences in atmospheric-based flux estimates, and specifically their global distribution (Stephens et al., 2007).
The three inversions use atmospheric CO 2 observations from various flask and in situ networks. They prescribe spa-65 tial and global E FF that can vary from that presented here. Given theirCE6 prescribed map of E FF , each inversion estimates natural fluxes from a similar set of surface CO 2 measurement stations, and CarbonTracker additionally uses two 75 sites of aircraft CO 2 vertical profiles over the Amazon and Siberia, regions where surface observations are sparse. The atmospheric transport models of each inversion are TM5 for CarbonTracker, TM3 for Jena CarboScope, and LMDZ for CAMS. These three models are based on the same ECMWF 80 wind fields. The three inversions use different prior natural fluxes, which partly influences their optimised fluxes. CAMS assumes that the prior land flux is zero on the annual mean in each grid cell of the transport model, so that any sink or source on land is entirely reflecting the information brought 85 by atmospheric measurements. CarbonTracker simulates a small prior sink on land from the SIBCASA model that results from regrowth following fire disturbances of an otherwise net zero biosphere. Jena CarboScope assumes a prior sink on land as well from the LPJ model. Inversion results 90 for the sum of natural ocean and land fluxes (Fig. 8) are more constrained in the Northern Hemisphere (NH) than in the tropics, because of the higher measurement stations density in the NH.
Finally, results from atmospheric inversions include the 95 natural CO 2 fluxes from rivers (which need to be taken into account to allow comparison to other sources) and chemical oxidation of reactive carbon-containing gases (which are neglected here). These inverse estimates are not truly independent of the other estimates presented here as the atmo-100 spheric observations include a set of observations used to estimate the global growth rate in atmospheric CO 2 concentration (Sect. 2.3). However, they provide new information on the regional distribution of fluxes.
We focus the analysis on two known strengths of the in-105 verse approach: the derivation of the year-to-year changes in total land fluxes (S LAND − E LUC ) consistent with the whole network of atmospheric observations, and the spatial breakdown of land and ocean fluxes (S LAND − E LUC + S OCEAN ) across large regions of the globe. The spatial breakdown is discussed in Sect.  (Kirschke et al., 2013) that is 0.061 GtC yr −1 for the past decade. Assuming steady state, these emissions are all converted to CO 2 by OH oxidation and thus explain 0.06 GtC yr −1 of the global CO 2 growth rate in the past decade, or 0.07-0.1 GtC yr −1 using higher CH 4 emissions reported recently (Schwietzke et al., 2016).

35
Other anthropogenic changes in the sources of CO and CH 4 from wildfires, biomass, wetlands, ruminants, or permafrost changes are similarly assumed to have a small effect on the CO 2 growth rate.

Anthropogenic carbon fluxes in the land to ocean 40 aquatic continuum
The approach used to determine the global carbon budget considers only anthropogenic CO 2 emissions and their partitioning among the atmosphere, ocean, and land. In this analysis, the land and ocean reservoirs that take up anthropogenic 45 CO 2 from the atmosphere are conceived as independent carbon storage repositories. This approach omits the factCE7 that 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 50 al., 2013;Regnier et al., 2013). A significant fraction of this lateral carbon flux is entirely "natural" and is thus a steadystate component of the pre-industrial carbon cycle. However, changes in environmental conditions and land-use change have caused an increase in the lateral transport of carbon into 55 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 CO 2 budget. First, the anthropogenic perturbation has in-60 creased 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 65 and, to a lesser extent, transferred in 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 . The implication of a substantial LOAC carbon accumula-70 tion is that S LAND corresponds to carbon sequestered both in land ecosystems and in LOAC. We do not attempt to separate these two storage components in our study focused on S LAND .

Global carbon budget averaged over decades and its variability
The global carbon budget averaged over the last decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) is shown in Fig. 2. For this time period, 91% of the total emissions (E FF + E LUC ) were caused by fossil 80 fuels and industry, and 9 % by land-use change. The total emissions were partitioned among the atmosphere (44 %), ocean (26 %), and land (30 %). All components except landuse-change emissions have grown since 1959 (Figs. 3 and 4), with important interannual variability in the growth rate 85 in atmospheric CO 2 concentration and in the land CO 2 sink (Fig. 4) and some decadal variability in all terms (Table 8).

CO 2 emissions
Global CO 2 emissions from fossil fuels and industry have increased every decade from an average of 3.1 ± 0.2 GtC yr −1 90 in the 1960s to an average of 9.3 ± 0.5 GtC yr −1 during 2006-2015 (Table 8 and Fig. 5). The growth rate in these emissions decreased between the 1960s and the 1990s, with 4.5 % yr −1 in the 1960s (1960)(1961)(1962)(1963)(1964)(1965)(1966)(1967)(1968)(1969), 2.8 % yr −1 in the 1970s (1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979), 1.9 % yr −1 in the 1980s (1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989), 95 and 1.1 % yr −1 in the 1990s (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999). After this period, the growth rate began increasing again in the 2000s at an average growth rate of 3.5 % yr −1 , decreasing to 1.8 % yr −1 for the last decade (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015).  Fig. 2 as a function of time, for emissions from fossil fuels and industry (E FF ; grey) and emissions from land-use change (E LUC ; brown), as well as their partitioning among the atmosphere (G ATM ; purple), land (S LAND ; green), and oceans (S OCEAN ; dark blue). All time series are in GtC yr −1 . G ATM and S OCEAN (and by construction also S LAND ) prior to 1959 are based on different methods. The primary data sources for fossil fuels and industry are from Boden and Andres (2016), with uncertainty of about ±5 % (±1σ ); land-use-change emissions are from Houghton et al. (2012) with uncertainties of about ±30 %; growth rate in atmospheric CO 2 concentration prior to 1959 is from Joos and Spahni (2008) with uncertainties of about ±1-1.5 GtC decade −1 or ±0.1-0.15 GtC yr −1 (Bruno and Joos, 1997), and from Dlugokencky and Tans (2016) from 1959 with uncertainties of about ± 0.2 GtC yr −1 ; the ocean sink prior to 1959 is from Khatiwala et al. (2013) with uncertainty of about ± 30%, and from this study from 1959 with uncertainties of about ±0.5 GtC yr −1 ; and the residual land sink is obtained by difference (Eq. 8), resulting in uncertainties of about ±50 % prior to 1959 and ±0.8 GtC yr −1 after that. See the text for more details of each component and their uncertainties. emissions from land-use change is suggested between the 1990s and 2000s by the combination of bookkeeping and fire-based emissions used here (Table 7), but it is highly uncertain due to uncertainty in the underlying land-coverchange data. This decrease is not found in the current ensem-5 ble of the DGVMs (Fig. 6), which are otherwise consistent with the bookkeeping method within their respective uncertainty ( Table 7). The decrease is also not found in the study of tropical deforestation of Achard et al. (2014), where the fluxes in the 1990s were similar to those of the 2000s and 10 outside our uncertainty range. A new study based on FAO data to (Federici et al., 2015 suggests that E LUC decreased during 2011-2015 compared to 2001-2010. 1960 1970 1980 1990 2000  3.1.2 Partitioning among the atmosphere, ocean, and land 15 Emissions are partitioned among the atmosphere, ocean, and land (Eq. 1). The growth rate in atmospheric CO 2 level increased from 1.7 ± 0.1 GtC yr −1 in the 1960s to 4.5 ± 0.1 GtC yr −1 during 2006-2015 with important decadal variations (Table 8). Both ocean and land CO 2 sinks 20 increased roughly in line with the atmospheric increase, but with significant decadal variability on land ( Table 8). The ocean CO 2 sink increased from 1.2 ± 0.5 GtC yr −1 in the 1960s to 2.6 ± 0.5 GtC yr −1 during 2006-2015, with interannual variations of the order of a few tenths of GtC yr −1 25 generally showing an increased ocean sink during El Niño events (i.e. 1982-1983, 1997-1998, 2015-2016) (Fig. 7; Rödenbeck et al., 2014). Although there is some coherence between the ocean models and data products and among data products regarding the mean, decadal variability, and trend, the ocean models and data products show poor agreement for interannual variability (Sect. 2.4.3 and Fig. 7). As shown in Fig. 7, the two data products and most model estimates pro-duce a mean CO 2 sink for the 1990s that is below the mean 5 assessed by the IPCC from indirect (but arguably more reliable) observations (Denman et al., 2007;Sect. 2.4.1). This discrepancy suggests we may need to reassess estimates of the mean ocean carbon sinks, with some implications for the cumulative carbon budget (Landschützer et al., 2016). 10 The residual terrestrial CO 2 sink increased from 1.7 ± 0.7 GtC yr −1 in the 1960s to 3.1 ± 0.9 GtC yr −1 during 2006-2015, with important interannual variations of up to 2 GtC yr −1 generally showing a decreased land sink during El Niño events, overcompensating for the increase 15 in ocean sink and accounting for the enhanced growth rate in atmospheric CO 2 concentration during El Niño events. The high uptake anomaly around year 1991 is thought to be caused by the effect of the volcanic eruption of Mount Pinatubo on climate and is not generally reproduced by the 20 DGVMs, but it is assigned to the land by the two inverse systems that include this period (Fig. 6). The larger land CO 2 sink during 2006-2015 compared to the 1960s is reproduced by all the DGVMs in response to combined atmospheric CO 2 increase, climate, and variability, consistent with the 25 budget residual and reflecting a common knowledge of the processes ( Table 7). The DGVM ensemble mean of 2.8 ± 0.7 GtC yr −1 also reproduces the observed mean for the period 2006-2015 calculated from the budget residual (Table 7).

30
The total CO 2 fluxes on land (S LAND − E LUC ) constrained by the atmospheric inversions show in general very good agreement with the global budget estimate, as expected given the strong constrains of G ATM and the small relative uncertainty assumed on S OCEAN and E FF by inversions. The to-35 tal land flux is of similar magnitude for the decadal average, with estimates for 2006-2015 from the three inversions of 2.2, 2.3, and 3.4 GtC yr −1 compared to 2.1 ± 0.7 GtC yr −1 for the total flux computed with the carbon budget from other terms in Eq. (1) ( Table 7). The total land sink from the 40 three inversions is 1.8, 1.8, and 3.0 GtC yr −1 when including a mean river flux adjustment of 0.45 GtC yr −1 , though the exact adjustment is in fact smaller because the anthropogenic contribution to river fluxes is only a fraction of the total river flux (Sect. 2.7.2). The interannual variability in 45 the inversions also matched the residual-based S LAND closely (Fig. 6). The total land flux from the DGVM multi-model mean also compares well with the estimate from the carbon budget and atmospheric inversions, with a decadal mean of 1.7 ± 0.5 GtC yr −1 (Table 7; [2006][2007][2008][2009][2010][2011][2012][2013][2014][2015], although individ-50 ual models differ by several GtC for some years (Fig. 6).  Figure 6. CO 2 exchanges between the atmosphere and the terrestrial biosphere. (a) Comparison of the global carbon budget values of CO 2 emissions from land-use change (E LUC ; black with ±1σ uncertainty in grey shading), with CO 2 emissions from land-use change showing individual DGVM results (green) and the multimodel mean (olive), as well as fire-based results (orange); land-usechange data prior to 1997 (dashed black) highlights the pre-satellite years; preliminary results using the FAO FRA 2015 (Houghton and Nassikas, 2016) are also shown in dark grey. (b) Land CO 2 sink (S LAND ; black with uncertainty in grey shading) showing individual DGVM results (green) and multi-model mean (olive). (c) CE8 Total land CO 2 fluxes (b-a TS15 ; black with uncertainty in grey shading), from DGVM results (green) and the multi-model mean (olive), and atmospheric inversions Chevallier et al. (2005;CAMSv15.2) in purple; Rödenbeck et al. (2003;Jena CarboScope, s81_v3.8) in violet; Peters et al. (2010;Carbon Tracker, CTE2016-FT) in salmon; see Table 6 and the carbon balance from Eq. (1) (black). Five DGVMs are plotted in (a) and 14 in (b, c); see Table 5 for the list. In (c) the inversions were corrected for the preindustrial land sink of CO 2 from river input by removing a sink of 0.45 GtC yr −1 (Jacobson et al., 2007). This correction does not take into account the anthropogenic contribution to river fluxes (see Sect. 2.7.2).  Table 6). Both data-based products were adjusted for the pre-industrial ocean source of CO 2 from river input to the ocean, which is not present in the models, by adding a sink of 0.45 GtC yr −1 (Jacobson et al., 2007), to make them comparable to S OCEAN . This adjustment does not take into account the anthropogenic contribution to river fluxes (see Sect. 2.7.2). gional distribution of those fluxes by latitude bands (Fig. 8). The global mean CO 2 fluxes from process models for 2006-2015 is 4.2 ± 0.6 GtC yr −1 . This is comparable to the fluxes of 4.8 ± 0.5 GtC yr −1 inferred from the remainder of the carbon budget (E FF − G ATM in Eq. 1; Table 8) within their re-5 spective uncertainties. The total CO 2 fluxes from the three inversions range between 4.6 and 4.9 GtC yr −1 , consistent with the carbon budget as expected from the constraints on the inversions.

Regional distribution
In the south (south of 30 • S), the atmospheric inversions 10 and process models all suggest a CO 2 sink for 2006-2015 of between 1.2 and 1.6 GtC yr −1 (Fig. 8), although the details of the interannual variability are not fully consistent across methods. The interannual variability in the south is low because of the dominance of ocean area with low variability 15 compared to land areas.
In the tropics (30 • S-30 • N), both the atmospheric inversions and process models suggest the carbon balance in this region is close to neutral over the past decade, with fluxes for 2006-2015 ranging between −0.5 and +0.6 GtC yr −1 . Both 20 the process-based models and the inversions consistently allocate more year-to-year variability in CO 2 fluxes to the tropics compared to the north (north of 30 • N; Fig. 8), this variability being dominated by land fluxes.
In the north (north of 30 • N), the inversions and process 25 models are not in agreement on the magnitude of the CO 2 sink with the ensemble mean of the process models suggesting a total Northern Hemisphere sink for 2006-2015 of  Table 6. Where available, the uncertainty in the inversions is also shown.
2.3 ± 0.4 GtC yr −1 , while the three inversions estimate a sink of 2.7, 3.8, and 3.8 GtC yr −1 . The mean difference can only partly be explained by the influence of river fluxes, which is seen by the inversions but not included in the process models, as this flux in the Northern Hemisphere would be 5 less than 0.45 GtC yr −1 , particularly when only the anthropogenic contribution to river fluxes is accounted for. The Car-bonTracker inversion is close to the one standard deviation of the process models for the mean sink during their overlap period. CAMS and Jena CarboScope give a higher sink in the 10 north than the process models, and a correspondingly higher source in the tropics. Differences between CarbonTracker and CAMS, Jena CarboScope may be related to differences in inter-hemispheric mixing time of their transport models, and other inversion settings. Differences between the mean 15 fluxes of CAMS, Jena CarboScope, and the ensemble of process models cannot be explained easily. They could reflect a bias either in these two inversions or in missing processes or biases in the process models, such as the lack of adequate parameterisations for forest management in the north and for 20 forest degradation emissions in tropics for the DGVMs.
The estimated contribution of the north and its uncertainty from process models is sensitive both to the ensemble of process models used and to the specifics of each inversion. All three inversions show substantial differences in variability 25 and/or trend, and one inversion shows a substantial difference in the mean Northern Hemisphere sink.
Growth in emissions in 2015 was not statistically different from zero, at 0.06 % higher than in 2014, in stark contrast with the decadal average of 1.8 % yr −1 (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). 40 Growth in 2015 is in the range of our projection change of −0.6 [−1.6 to +0.5] % made last year (Le Quéré et al., 2015a) based on national emissions projections for China and the USA, and projections of gross domestic product corrected for I FF improvements for the rest of the world. How-45 ever, the specific projection for 2015 for China made last year (likely range of −4.6 to −1.1 %) was for a larger decrease in emissions than realised (−0.7 %). This is due to lower decline in coal production in the last 4 months of the year compared to January-August and to improvements in 50 energy content of coal through improvements in the quality of the coal used which were at the top of the range of improvements considered in our projection.
Territorial emissions in Annex B countries decreased by −0.2 % yr −1 on average during 1990-2014. Trends observed for consumption emissions were less monotonic, with

35
Based on fire activity, the global CO 2 emissions from landuse change are estimated as 1.3 ± 0.5 GtC in 2015, slightly above the 2006-2015 average of 1.0 ± 0.5 GtC yr −1 . The slight rise in E LUC in 2015 is consistent with estimates of peat fires in Asia based on atmospheric data (Yin et al., 40 2016). However, the estimated annual variability is not generally consistent between methods, except that all methods estimate that variability in E LUC is small relative to the variability from S LAND (Fig. 6a). This could be partly due to the design of the DGVM experiments, which use flux dif-45 ferences between simulations with and without land-cover change, and thus their variability may differ due to, for example, fires in forest regions where the contemporary forest cover is smaller than pre-industrial cover used in the "without land-cover change" runs. The growth rate in atmospheric CO 2 concentration was 6.3 ± 0.2 GtC in 2015 (2.97 ± 0.09 ppm; Fig. 4; Dlugokencky and Tans, 2016). This is well above the 2006-2015 55 average of 4.5 ± 0.1 GtC yr −1 and reflects the large interannual variability in the growth rate of atmospheric CO 2 concentration associated with El Niño and La Niña events. The ocean CO 2 sink was 3.0 ± 0.5 GtC yr −1 in 2015, an increase of 0.15 GtC yr −1 over 2015 according to ocean mod-60 els. Five of the seven ocean models produce an increase in the ocean CO 2 sink in 2015 compared to 2014, with nearzero changes in the last two models (Fig. 7). However, the two data products disagree over changes in the last year, with a decrease of −0.4 GtC yr −1 found in Rödenbeck et 65 al. (2014) and an increase of 0.3 GtC yr −1 found in Landschützer et al. (2015). Thus, there is no overall consistency in the annual change in the ocean CO 2 sink, although there is an indication of increasing convergence among products for the assessment of multi-year changes, as suggested by 70 the time-series correlations reported in Sect. 2.4.3 (see also Landschützer et al., 2015). An increase in the ocean CO 2 sink in 2015 would be consistent with the observed El Niño conditions and continued rising atmospheric CO 2 . All estimates suggest an ocean CO 2 sink for 2015 that is larger than their 75 2006-2015 average.
The terrestrial CO 2 sink calculated as the residual from the carbon budget was 1.9 ± 0.9 GtC in 2015, well below the 3.1 ± 0.9 GtC yr −1 averaged over 2006-2015 (Fig. 4), and reflecting the onset of the El Niño conditions in the second half 80 of 2015. The DGVM mean produces a sink of 1.0 ± 1.4 GtC in 2015, also well below the 2006-2015 average (Table 7). Both models and inversions suggest that the lower sink in 2015 primarily originated in the tropics (Fig. 8). Using separate projections for China, the USA, and the rest of the world as described in Sect. 2.1.4, we project a continued low growth in global CO 2 emissions from fossil fuels 90 and cement production in 2016 of +0.5 % (range of −0.7 to +2.1 %) from 2015 levels (Table 9), or +0.2 % (−1.0 to +1.8 %) after adjusting for leap year (see Sect. 2.1.3). Our method is imprecise and contains several assumptions that could influence the results beyond the given range, and 95 as such is indicative only. Within the given assumptions, global emissions remain nearly constant at 9.9 ± 0.5 GtC (36.4 ± 1.8 GtCO 2 ) in 2016, but they are still 63% above emissions in 1990. The drivers of the trends in E FF are discussed elsewhere . 100 For China, the expected change based on available data during January to July or September (see Sect. 2.1.4) is for an Table 9. Actual CO 2 emissions from fossil fuels and industry (E FF ) compared to projections made the previous year based on world GDP (IMF October 2015) and the fossil fuel intensity of GDP (I FF ) based on subtracting the CO 2 and GDP growth rates. The "Actual" values are the latest estimate available and the "Projected" value for 2016 refers to those presented in this paper.  Quéré et al. (2009). b Friedlingstein et al. (2010). c Peters et al. (2013). d Le . e Le Quéré et al. (2014). f Friedlingstein et al. (2014) and

Emission
Le Quéré et al. (2015b). g Jackson et al. (2016) and Le Quéré et al. (2015a). h This study. i These numbers are not adjusted for leap years (see Sect. 2.1.3 for leap-year adjustments).
increase in emissions of −0.2 % (range of −3.5 to +1.6 %) in 2016 compared to 2015, based on estimated decreases in coal consumption (−1.8 %) and estimated growth in apparent oil (+4.0 %) and natural gas (+7.2 %) consumption and in cement production (+2.6 %). The uncertainty range consid-5 ers the spread between different data sources, as well as differences between July/August and end-of-year data observed in 2014 and 2015. The estimated reduction in coal consumption also incorporates an assumed 2 % increase in the energy density of coal -based on increases in the last 2 years, which 10 are assumed to continue given production limits in 2016 that are likely to affect production of low-quality coal more -and the uncertainty range also reflects uncertainty in this figure.
For the USA, the EIA emissions projection for 2016 combined with cement data from USGS gives a decrease of  The growth in atmospheric CO 2 concentration (G ATM ) was projected to be high again in 2016, at 6.7 ± 1.1 GtC (3.15 ± 0.53 ppm) for the Mauna Loa station (Betts et al., 30 2016). Growth at Mauna Loa is closely correlated with the global growth (r = 0. 95 for 1959-2015). Therefore, the global growth rate in atmospheric CO 2 concentration is also expected to be high in 2016. In the 8-month period between December 2015 and August 2016, the observed 35 global growth in atmospheric CO 2 concentration was already 2.3 ppm  after seasonal adjustment, supporting the projection of Betts et al. (2016). Even with a return to El Niño neutral or possible emerging La Niña conditions for the second half of 2016, positive growth 40 in atmospheric CO 2 would still be expected during the last 4 months of the year because of the continuing persistent emissions. For example, during the transitions from El Niño to La Niña of 1986-1987, 1998-1999, and 2010-2011, atmospheric CO 2 growth of 0.3, 0.6, and 0.9 ppm, respectively, 45 was observed in the last 4 months of the year.
Combining projected E FF and G ATM suggests a total for the combined land and ocean (S LAND + S OCEAN − E LUC ) of about 3 GtC only. S OCEAN was 3.0 GtC in 2015 and is expected to slightly increase in 2016 from a delayed response 50 to El Niño conditions (Feely et al., 1999). E LUC was 1.3 GtC Table 10. Cumulative CO 2 emissions for the periods 1750, 1870, and 1870. We also provide the 1850-2005 time period used in a number of model evaluation publications. All uncertainties are reported as ±1σ . All values are rounded to the nearest 5 GtC as in , reflecting the limits of our capacity to constrain cumulative estimates. Thus, some columns will not exactly balance because of rounding errors. in 2015, above the decadal mean average of 1.0 GtC yr −1 , and is expected to return to average or below average in 2016 based on fire activity related to land management so far (up to August). Hence, for 2016, the residual land sink S LAND is expected to be well below its 2006-2015 average and ap-5 proximately balance E LUC . This is consistent with our understanding of the response of the terrestrial vegetation to El Niño conditions and increasing atmospheric CO 2 concentrations, though the uncertainties in G ATM and of the partitioning among S LAND and S OCEAN are substantial.  (Table 10). The cumulative emissions are rounded to the nearest 5 GtC. The total cumulative emissions from fossil and land-use change for 1870-2015 are 560 ± 55 GtC. These emissions were partitioned among the atmosphere (235 ± 5 GtC based on atmospheric measure-20 ments in ice cores of 288 ppm (Sect. "Global growth rate in atmospheric CO 2 concentration"TS16; Joos and Spahni, 2008) and recent direct measurements of 399.1 ppm , ocean (155 ± 20 GtC using Khatiwala et al. , 2013, prior to 1959 and Table 8 otherwise), and 25 land (165 ± 60 GtC by the difference).

Units of GtC
Cumulative emissions for the early period 1750-1869 were 3 GtC for E FF and about 45 GtC for E LUC (rounded to nearest 5), of which 10 GtC was emitted in the period 1850-1870  and 30 GtC in the period 1750-30 1850 based on the average of four publications (22 GtC by Pongratz et al., 2009; GtC by van Minnen et al., 2009;64 GtC by Shevliakova et al., 2009;and 24 GtC by Zaehle et al., 2011). The growth rate in atmospheric CO 2 concentration during that time was about 25 GtC, and the ocean uptake 35 about 20 GtC, implying a land uptake of 5 GtC. These numbers have large relative uncertainties but balance within the limits of our understanding.
Cumulative emissions through to year 2016 can be es-45 timated based on the 2016 projections of E FF (Sect. 3.2), the largest contributor, and assuming a constant E LUC of 1.0 GtC (average of last decade). For 1870-2016, these are 570 ± 55 GtC (2085 ± 205 GtCO 2 ) for total emissions, with about 75 % contribution from E FF (420 ± 20 GtC) and about 50 25 % contribution from E LUC (150 ± 50 GtC). Cumulative emissions since year 1870 are higher than the emissions of 515 [445 to 585] GtC reported in the IPCC  because they include an additional 55 GtC from emissions in 2012-2016 (mostly from E FF ). The uncertainty pre-55 sented here (±1σ ) is smaller than the range of 90 % used by IPCC, but both estimates overlap within their uncertainty ranges.

Discussion
Each year when the global carbon budget is published, each 60 component for all previous years is updated to take into account corrections that are the result of further scrutiny and verification of the underlying data in the primary input data sets. The updates have generally been relatively small and focused on the most recent years, except for land-use change, 65 where they are more significant but still generally within the provided uncertainty range (Fig. 9). The difficulty in accessing land-cover change data to estimate E LUC is the key problem to providing continuous records of emissions in this sector. Current FAO estimates are based on statistics reported 5 at the country level and are not spatially explicit. Advances in satellite recovery of land-cover change could help to keep track of land-use change through time (Achard et al., 2014;Harris et al., 2012). Revisions in E LUC for the 2008/2009 budget were the result of the release of FAO 2010, which con-10 tained a major update to forest cover change for the period 2000-2005 and provided the data for the following 5 years to 2010 (Fig. 9b). The differences this year could be attributable to both the different data and the different methods. Comparison of global carbon budget components released annually by GCP since 2006 show that update differences were highest at 0.82 GtC yr −1 for the growth rate in atmospheric CO 2 concentration (from a one-off correction back to year 1979), 0.24 GtC yr −1 for fossil fuels and industry, and 0.52 GtC yr −1 for the ocean CO 2 sink (from a change from 20 one to multiple models; Fig. 9d). The update for the residual land CO 2 sink was also large (Fig. 9e), with a maximum value of 0.83 GtC yr −1 , directly reflecting revisions in other terms of the budget.
Our capacity to constrain the global carbon budget can be 25 evaluated by adding the five components of Eq. (1) using DGVM estimates for S LAND , thus using largely independent estimates for each component (Fig. 10). This residual global budget represents all the carbon unaccounted currently. Figure 10 shows that the mean global residual is very close to 30 zero, and there is no trend over the entire time period. However, it also highlights periods of multiple years where the sum of the estimates differs significantly from zero. These include an unaccounted flux from the surface to the atmosphere (or underestimated emissions) during 1973-1979 and 1997-35 2001 and an unaccounted sink from the atmosphere to the surface (or overestimated emissions) during 1961-1965 and 1990-1992. This unaccounted variability could come from errors in our estimates of the five components of Equation (1; Li et al., 2016), or from missing factors in the global car-40 bon budget, including but not limited to those discussed in Sect. 2.7. This unaccounted variability limits our ability to verify reported emissions and limits our confidence in the underlying processes regulating the carbon cycle feedbacks with climate change.

45
Another semi-independent way to evaluate the results is provided through the comparison with the atmospheric inversions and their regional breakdown. The comparison shows a first-order consistency between inversions and process models but with substantial discrepancies, particularly for the al- 50 location of the mean sink between the tropics and the Northern Hemisphere. Understanding these discrepancies and further analysis of regional carbon budgets would provide additional information to quantify and improve our estimates, as has been shown by the project REgional Carbon Cycle As-55 sessment and Processes (RECCAP; Canadell et al., 2012). Annual estimates of each component of the global carbon budgets have their limitations, some of which could be improved with better data and/or better understanding of carbon dynamics. The primary limitations involve resolving fluxes 60 on annual timescales and providing updated estimates for recent years for which data-based estimates are not yet available or only beginning to emerge. Of the various terms in the global budget, only the burning of fossil fuels and the growth rate in atmospheric CO 2 concentration terms are based pri-65 marily on empirical inputs supporting annual estimates in this carbon budget. While these models represent the current state of the art, they provide only simulated changes in primary carbon budget components. For example, the decadal trends in global ocean uptake and the interannual varia-70 Figure 10. Unaccounted carbon in the global carbon budget (GtC yr −1 ). This is calculated as the sum of G ATM + S OCEAN − E FF TS19 and E LUC as described in Fig. 4, plus S LAND as estimated with the ensemble of DGVMs as in Fig. 6b. Therefore, the unaccounted carbon represents the fluxes that are missing after accounting for all known processes as quantified in available estimates (see discussion). The uncertainty is the annual uncertainty for the five terms as described in the text, added in quadrature. Positive values indicate an unaccounted surface-to-atmosphere flux of CO 2 or an underestimation of the emissions.
tions associated with the El Niño-Southern Ocean Oscillation (ENSO) are not directly constrained by observations, although many of the processes controlling these trends are sufficiently well known that the model-based trends still have value as benchmarks for further validation. Data-based prod-5 ucts for the ocean CO 2 sink provide new ways to evaluate the model results, and could be used directly as data become more rapidly available and methods for creating such products improve. However, there are still large discrepancies among data-based estimates, in large part due to the lack of routine data sampling, which precludes their direct use for now . Estimates of land-use emissions and their year-to-year variability have even larger uncertainty, and much of the underlying data are not available as an annual update. Efforts are underway to work with an-15 nually available satellite area change data or FAO reported data in combination with fire data and modelling to provide annual updates for future budgets.
Our approach also depends on the reliability of the energy and land-cover change statistics provided at the country 20 level, which are potentially subject to biases. Thus, it is critical to develop multiple ways to estimate the carbon balance at the global and regional level, including estimates from the inversion of atmospheric CO 2 concentration, the use of other oceanic and atmospheric tracers, and the compilation of 25 emissions using alternative statistics (e.g. sectors). It is also important to challenge the consistency of information across observational streams, for example to contrast the coherence of temperature trends with those of CO 2 sink trends. Multiple approaches ranging from global to regional scale would 30 greatly help increase confidence and reduce uncertainty in CO 2 emissions and their fate. TS20 The data presented here are made available in the belief that their wide dissemination will lead to greater under-35 standing 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 40 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 are given at the top of each page in the accompanying database and are summarised in Table 2. 45 The accompanying database includes two Excel files organised in the following spreadsheets (accessible with the free viewer http://www.microsoft.com/en-us/download/ details.aspx?id=10). 50 File Global_Carbon_Budget_2016.xlsx includes the following:

Data availability
1. Summary 2. The global carbon budget  3. Global CO 2 emissions from fossil fuels and cement 55 production by fuel type, and the per capita emissions  4. CO 2 emissions from land-use change from the individual methods and models  5. Ocean CO 2 sink from the individual ocean models and 60 data products  6. Terrestrial residual CO 2 sink from the DGVMs  7. Additional information on the carbon balance prior to 1959 (1750-2015) 65 File National_Carbon_Emissions_2016.xlsx includes the following: 1. Summary 2. Territorial country CO 2 emissions from fossil fuels and industry  from CDIAC, extended to 2015 70 using BP data 3. Territorial country CO 2 emissions from fossil fuels and industry   National emissions data are also available from the Global Carbon Atlas (http://globalcarbonatlas.org).TS21

Conclusions
The estimation of global CO 2 emissions and sinks is a major effort by the carbon cycle research community that requires a 15 combination of measurements and compilation of statistical estimates and results from models. 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 underpin-20 ning causes. A broad stakeholder community relies on the data sets associated with the annual carbon budget, including scientists, policy makers, businesses, journalists, and the broader society increasingly engaged in adapting to and mitigating human-driven climate change. Second, over the last 25 decade we have seen unprecedented changes in the human and biophysical environments (e.g. increase in the growth of fossil fuel emissions, ocean temperatures, and strength of the land sink) -which call for more frequent assessments of the state of the planet and, by implication, a better understanding of the future evolution of the carbon cycle -and 30 the requirements for climate change mitigation and adapta-tionCE11 . Both the ocean and the land surface presently remove a large fraction of anthropogenic emissions. Any significant change in the function of carbon sinks is of great importance to climate policymaking, as they affect the excess 35 carbon dioxide remaining in the atmosphere and therefore the compatible emissions for any climate stabilisation target. Better constraints of carbon cycle models against contemporary data sets raise the capacity for the models to become more accurate at future projections. 40 This all requires more frequent, robust, and transparent data sets and methods that can be scrutinised and replicated. After 11 annual releases from the GCP, the effort is growing and the traceability of the methods has become increasingly complex. Here, we have documented in detail the data sets 45 and methods used to compile the annual updates of the global carbon budget, explained the rationale for the choices made and the limitations of the information, and finally highlighted the need for additional information where gaps exist. This paper via "living data" will help to keep track of new 50 budget updates. The evolution over time of the carbon budget is now a key indicator of the anthropogenic perturbation of the climate system, and its annual delivery joins a set of other climate indicators to monitor the evolution of human-induced climate change, such as the annual updates on the global sur-55 face temperature, sea level rise, and minimum Arctic sea ice extent. The Supplement related to this article is available online at doi: 10.5194/essd-8-1-2016-supplement. analysis of the models and data products used here; F. Joos and S. Khatiwala for providing historical data; and P. Regnier for assistance in describing LOAC fluxes. We thank E. Dlugokencky, who provided the atmospheric CO 2 measurements used here; B. Pfeil, C. Landa, and S. 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 SO-CAT community; D. Bakker for support with the SOCAT coordination; and all those involved in collecting and providing oceanographic CO 2 measurements used here, in particular for the new 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), 25 and the Integrated Marine Biogeochemistry, Ecosystem Research (IMBER) programme. We thank data providers to ObsPack GLOB-ALVIEWplus v1.0 and NRT v3.0 for atmospheric CO 2 observations used in CTE2016-FT. This is NOAA-PMEL contribution number 4576.
J.: Natural and anthropogenic changes in atmospheric CO 2 over the last 1000 years from air in Antarctic ice and firn, J. Geophys.

CE1
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