Multiple organisations and research groups around the world generated the
original measurements and data used to complete the global carbon budget. The
effort presented here is thus mainly one of synthesis, where results from
individual groups are collated, analysed, and evaluated for consistency. We
facilitate access to original data with the understanding that primary data
sets will be referenced in future work (see Table 2 for how to cite the
data sets). Descriptions of the measurements, models, and methodologies
follow below and in-depth descriptions of each component are described
elsewhere (e.g. Andres et al., 2012; Houghton et al., 2012).
This is the tenth version of the “global carbon budget” (see Introduction
for details) and the fourth revised version of the “global carbon budget
living data update”. It is an update of Le Quéré et al. (2015),
including data to year 2014 (inclusive) and a projection for fossil fuel
emissions for year 2015. The main changes from Le Quéré et al. (2015)
are (1) the use of national emissions for EFF from the United
Nations Framework Convention on Climate Change (UNFCCC) where available;
(2) the projection of EFF for 2015 is based on national emissions
projections for China and USA, as well as GDP corrected for
recent changes in the carbon intensity of the global economy for the rest of
the world; and (3) that we apply minimum criteria of realism to select ocean data
products and process models. The main methodological differences between
annual carbon budgets are summarised in Table 3.
CO2 emissions from fossil fuels and industry
(EFF)
Emissions from fossil fuels and industry and their
uncertainty
The calculation of global and national CO2 emissions from fossil fuels,
including gas flaring and cement production (EFF), 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 (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 themselves and reported to the UNFCCC for the period 1990–2012
(42 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
periodically audited for each country through an established international
methodology overseen by the UNFCCC. We also use global and national emissions
estimated by CDIAC (Boden et al., 2013). The CDIAC emission estimates are the
only data set that extends back in time to 1751 with consistent and
well-documented emissions from fossil fuels, cement production, and gas
flaring for all countries and their uncertainty (Andres et al., 2014, 2012, 1999); this makes the data set a unique resource for research
of the carbon cycle during the fossil fuel era.
The global emissions presented here are from CDIAC's analysis, which
provides an internally consistent global estimate including bunker fuels,
minimising the effects of lower-quality energy trade data. Thus the
comparison of global emissions with previous annual carbon budgets is not
influenced by the use of data from UNFCCC national reports.
During the period 1959–2011, the emissions from fossil fuels estimated by
CDIAC are based primarily on energy data provided by the UN Statistics
Division (UN, 2014a, b; Table 4). When necessary, fuel masses/volumes are
converted to fuel energy content using coefficients provided by the UN and
then to CO2 emissions using conversion factors that take into account
the relationship between carbon content and energy (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 content) are available
at the country level (UN, 2014a). In general, CO2 emissions for
equivalent primary energy consumption are about 30 % higher for coal
compared to oil, and 70 % higher for coal compared to natural gas
(Marland et al., 2007). All estimated fossil fuel emissions are based on the
mass flows of carbon and assume that the fossil carbon emitted as CO or
CH4 will soon be oxidised to CO2 in the atmosphere and can be
accounted for with CO2 emissions (see Sect. 2.7).
Our emissions totals for the UNFCCC-reporting countries were recorded as in
the UNFCCC submissions, which have a slightly larger system boundary than
CDIAC. Additional emissions come from carbonates other than in cement
manufacture, and thus UNFCCC totals will be slightly higher than CDIAC totals
in general, although there are multiple sources for differences. We use the
CDIAC method to report emissions 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 UNFCCC.
For the most recent 2–3 years when the UNFCCC estimates and UN statistics
used by CDIAC are not yet available (or there was insufficient time to
process and verify them), we generated preliminary estimates based on the BP
annual energy review by applying the growth rates of energy consumption
(coal, oil, gas) for 2013–2014 to the UNFCCC national emissions in 2012, and
for 2012–2014 for the CDIAC national and global emissions in 2011 (BP,
2015). BP's sources for energy statistics overlap with those of the UN data,
but are compiled more rapidly from about 70 countries covering about 96 %
of global emissions. We use the BP values only for the year-to-year rate of
change, because the rates of change are less uncertain than the absolute
values and we wish to avoid discontinuities in the time series when linking the
UN-based data with the BP data. These preliminary estimates are replaced by
the more complete UNFCCC or CDIAC data based on UN statistics when they
become available. Past experience and work by others (Andres et al., 2014;
Myhre et al., 2009) show that projections based on the BP rate of change are
within the uncertainty provided (see Sect. 3.2 and the Supplement from Peters
et al., 2013).
Estimates of emissions from cement production by CDIAC are based on data on
growth rates of cement production from the US Geological Survey up to year
2013 (van Oss, 2013), and up to 2014 for the top 18 countries (representing
85 % of global production; USGS, 2015). For countries without data in
2014 we use the 2013 values (zero growth). Some fraction of the CaO and MgO
in cement is returned to the carbonate form during cement weathering, but this
is generally regarded to be small and is ignored here.
Estimates of emissions from gas flaring by CDIAC are 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 emission
years 2012–2014, flaring is assumed constant from 2011 (emission year)
UN-based data. 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 CO2 or vented as CH4.
Fugitive emissions of CH4 from the so-called upstream sector (e.g. coal
mining and natural gas distribution) are not included in the accounts of
CO2 emissions except to the extent that they are captured in the UN
energy data and counted as gas “flared or lost”.
The published CDIAC data set includes 250 countries and regions. This
expanded list includes countries that no longer exist, such as the USSR and
East Pakistan. For the carbon budget, we reduce the list to 216 countries by
reallocating emissions to the currently defined territories. This involved
both aggregation and disaggregation, and does not change global emissions.
Examples of aggregation include merging East and West Germany to the
currently defined Germany. Examples of disaggregation include reallocating
the emissions from former USSR to the resulting independent countries. For
disaggregation, we use the emission shares when the current territory first
appeared. The disaggregated estimates should be treated with care when
examining countries' emissions trends prior to their disaggregation. For the
most recent years, 2012–2014, the BP statistics are more aggregated, but we
retain the detail of CDIAC by applying the growth rates of each aggregated
region in the BP data set to its constituent individual countries in CDIAC.
Estimates of CO2 emissions show that the global total of emissions is
not equal to the sum of emissions from all countries. This is largely
attributable to 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
emissions from international bunker fuels are calculated based on where the
fuels were loaded, but they are not included with national emissions
estimates. Other differences occur because globally the sum of imports in all
countries is not equal to the sum of exports and because of differing
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 of the annual emissions from fossil fuels and industry for
the globe has been estimated at ±5 % (scaled down from the published
±10 % at ±2σ to the use of ±1σ bounds
reported here; Andres et al., 2012). This is consistent with a more detailed
recent analysis of uncertainty of ±8.4 % at ±2σ (Andres
et al., 2014) and at the high end of the range of ±5–10 % at
±2σ reported by Ballantyne et al. (2015). This includes an
assessment of uncertainties in the amounts of fuel consumed, the carbon and
heat contents of fuels, and the combustion efficiency. While in the budget we
consider a fixed uncertainty of ±5 % for all years, in reality the
uncertainty, as a percentage of the emissions, is growing with time because
of the larger share of global emissions from non-Annex B countries (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 et al.,
2008), and important potential biases have been identified that suggest China's
emissions could be overestimated in published studies (Liu et al., 2015).
Generally, emissions from mature economies with good statistical bases have
an uncertainty of only a few percent (Marland, 2008). Further research is
needed before we can quantify the time evolution of the uncertainty and its
temporal error 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 biases of energy
statistics and to the accounting method used by each country. We assign a
medium confidence to the results presented here because they are based on
indirect estimates of emissions using energy data (Durant et al., 2010).
There is only limited and indirect evidence for emissions, although there is
a high agreement among the available estimates within the given uncertainty
(Andres et al., 2014, 2012), and emission estimates are
consistent with a range of other observations (Ciais et al., 2013), even
though their regional and national partitioning is more uncertain (Francey 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 areas over which the country has jurisdiction”
(Rypdal et al., 2006). That is, emissions are allocated to the country where
and when the emissions actually occur. The territorial emission inventory of
an individual country does not include the emissions from the production of
goods and services produced in other countries (e.g. food and clothes) that
are used for consumption. Consumption-based emission inventories for an
individual country constitute another attribution point of view that allocates global
emissions to products that are consumed within a country, and are
conceptually calculated as the territorial emissions minus the “embedded”
territorial emissions to produce exported products plus the emissions in
other countries to produce imported products
(consumption = territorial - exports + imports). The difference
between the territorial- and consumption-based emission 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 emission drivers
(Hertwich and Peters, 2009), quantify emission (virtual) transfers by the
trade of products between countries (Peters et al., 2011b), and potentially
design more effective and efficient climate policy (Peters and Hertwich,
2008).
We estimate consumption-based emissions by enumerating the global supply
chain using a global model of the economic relationships between economic
sectors within and between every country (Andrew and Peters, 2013; Peters et
al., 2011a). Due to availability of the input data, detailed estimates are
made for the years 1997, 2001, 2004, 2007, and 2011 (using the methodology of
Peters et al., 2011b) using economic and trade data from the Global Trade and
Analysis Project version 9 (GTAP; Narayanan et al., 2015). The results cover
57 sectors and 140 countries and regions. The results are extended into an
annual time series from 1990 to the latest year of the fossil fuel emissions
or GDP data (2013 in this budget), using GDP data by
expenditure in current exchange rate of US dollars (USD; from the UN National
Accounts Main Aggregates Database; UN, 2014c) and time series of trade data
from GTAP (based on the methodology in Peters et al., 2011b).
We estimate the sector-level CO2
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 the CDIAC estimates from the most
recent carbon budget. We do not include international transportation in our
estimates of national totals, but we do include them in the global total. The
time series of trade data provided by GTAP covers the period 1995–2011 and
our methodology uses the trade shares as this data set. For the period
1990–1994 we assume the trade shares of 1995, while for 2012 and 2013 we
assume the trade shares of 2011.
Comprehensive analysis of the uncertainty of consumption emissions accounts
is still lacking in the literature, although several analyses of components
of this uncertainty have been made (e.g. Dietzenbacher et al., 2012; Inomata
and Owen, 2014; Karstensen et al., 2015; Moran and Wood, 2014). For this
reason we do not provide an uncertainty estimate for these
emissions, but based on model comparisons and sensitivity 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 sector results for an
individual country).
The consumption-based emissions attribution method considers the CO2
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 fuels (Davis et al., 2011), 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 carbon 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 unharmonised way, then the trends reflected in
these data will need to be accommodated 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 in the first year: EFFt0+1-EFFt0EFFt0×100%yr-1.
This is the simplest method to characterise a 1-year growth compared to the
previous year and is widely used. We apply a leap-year adjustment to ensure
valid interpretations of annual growth rates. This would affect the growth rate by
about 0.3 % yr-1 (1/365) and causes growth rates to go up
approximately 0.3 % if the first year is a leap year and down 0.3 %
if the second year is a leap year.
The relative growth rate of EFF over time periods of greater than
1 year can be re-written using its logarithm equivalent as follows:
1EFFdEFFdt=d(lnEFF)dt.
Here we calculate relative growth rates in emissions for multi-year periods
(e.g. a decade) by fitting a linear trend to ln(EFF) in Eq. (2),
reported in percent per year. We fit the logarithm of EFF rather
than EFF directly because this method ensures that computed growth
rates satisfy Eq. (6). This method differs from previous papers (Canadell et
al., 2007; Le Quéré et al., 2009; Raupach et al., 2007) that computed
the fit to EFF and divided by average EFF directly, but
the difference is very small (< 0.05 %) in the case of EFF.
Emissions projections
Energy statistics from BP are normally available around June for the previous
year. To gain insight into emission trends for the current year (2015), we
provide an assessment of global emissions for EFF by combining
individual assessments of emissions for China and the USA (the two biggest
emitting countries), as well as the rest of the world.
We specifically estimate emissions in China because the evidence suggests a
departure from the long-term trends in the carbon intensity of the economy
used in emissions projections in previous global carbon budgets (e.g. Le
Quéré et al., 2015), resulting from significant drops in industrial
production against continued growth in economic output. This departure could
be temporary (Jackson et al., 2015). Our 2015 estimate for China uses
(1) apparent consumption of coal for January to August estimated using
production data from the National Bureau of Statistics (2015b), imports and
exports of coal from China Customs Statistics (General Administration of
Customs of the People's Republic of China, 2015a, b), and from partial data
on stock changes from industry sources (China Coal Industry Association,
2015; China Coal Resource, 2015); (2) apparent consumption of oil and gas for
January to June from the National Energy Administration (2015); and
(3) production of cement reported for January to August (National Bureau of
Statistics of China, 2015b). Using these data, we estimate the change in
emissions for the corresponding months in 2015 compared to 2014 assuming
constant emission factors. We then assume that the relative changes during
the first 6–8 months will 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 2015.
These are discussed further in Sect. 3.2.1. We tested our new method using
data available in October 2014 to make a 2014 projection of coal consumption
and cement production, both of which changed substantially in 2014. For the
apparent consumption of coal we would have projected a change of -3.2 %
in coal use for 2014, compared to -2.9 % reported by the National Bureau of Statistics of China in February
2015, while for the production of cement we would have projected a change of
+3.5 %, compared to a realised change of +2.3 %. In both cases,
the projection is consistent with the sign of the realised change. This new
method should be more reliable as it is based on actual data, even if they
are preliminary. Note that the growth rates we project for China are
unaffected by recent upwards revisions of Chinese energy consumption
statistics (National Bureau of Statistics of China, 2015a), as all data used
here dates from after the revised period. The revisions do, however, affect the
absolute value of the time series up to 2013, and hence the absolute value
for 2015 extrapolated from that time series using projected growth rates.
Further, because the revisions will increase China's share of total global
emissions, the projected growth rate of global emissions will also be
affected slightly. This effect is discussed in the Results section.
For the USA, we use the forecast of the US Energy Information Administration
(EIA) “Short-term energy outlook” (October 2015) for emissions from fossil
fuels. This is based on an energy forecasting model which is revised monthly,
and takes into account heating-degree days, household expenditures by fuel
type, energy markets, policies, and other effects. We combine this with our
estimate of emissions from cement production using the monthly US cement data
from USGS for January–July, assuming changes in cement production over the
first 7 months apply throughout the year. We estimate an uncertainty
range using the revisions of historical October forecasts made by the EIA 1
year later. These revisions were less than 2 % during 2009–2014 (when a forecast
was done), except for 2011, when it was -4.0 %. We thus use a
conservative uncertainty range of -4.0 to +1.8 % around the central
forecast.
For the rest of the world, we use the close relationship between the growth
in GDP and the growth in emissions (Raupach et al., 2007) to project
emissions for the current year. This is based on the so-called Kaya identity
(also called IPAT identity, the acronym standing for human impact (I) on
the environment, which is equal to the product of population (P),
affluence (A), and technology (T)), whereby EFF
(GtC yr-1) is decomposed by the product of GDP (USD yr-1) and the
fossil fuel carbon intensity of the economy (IFF; GtC USD-1)
as follows:
EFF=GDP×IFF.
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
dEFFdt=d(GDP×IFF)dt
and applying the rules of calculus
dEFFdt=dGDPdt×IFF+GDP×dIFFdt.
Finally, dividing Eq. (5) by (3) gives
1EFFdEFFdt=1GDPdGDPdt+1IFFdIFFdt,
where the left-hand term is the relative growth rate of EFF and
the right-hand terms are the relative growth rates of GDP and IFF,
respectively, which can simply be added linearly to give 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 of
a calendar year, making assumptions on the growth rate of IFF
allows us to make projections of the annual change in CO2 emissions well
before the end of a calendar year. The IFF is based on GDP in
constant PPP (purchasing power parity) from the IEA up to 2012 (IEA/OECD,
2014) and extended using the IMF growth rates for 2013 and 2014 (IMF, 2015).
Experience of the past year has highlighted that the interannual variability
in IFF is the largest source of uncertainty in the GDP-based
emissions projections. We thus use the standard deviation of the annual
IFF for the period 2005–2014 as a measure of uncertainty,
reflecting ±1σ as in the rest of the carbon budget. This is
±1.4 % yr-1 for the rest of the world (global emissions minus
China and USA).
Data sources used to compute each component of the global
carbon budget. National emissions from UNFCCC are provided directly and thus no additional data sources need citing in this table.
Component
Process
Data source
Data reference
EFF (globaland CDIAC
Fossil fuel combustion and oxidation and gas flaring
UN Statistics Division to 2011
UN (2014a, b)
national)
BP for 2012–2014
BP (2015)
Cement production
US Geological Survey
van Oss (2015) USGS (2015)
ELUC
Land-cover change (deforestation, afforestation, and forest regrowth)
Forest Resource Assessment (FRA) of the Food and Agriculture Organization (FAO)
FAO (2010)
Wood harvest
FAO Statistics Division
FAOSTAT (2010)
Shifting agriculture
FAO FRA and Statistics Division
FAO (2010) FAOSTAT (2010)
Interannual variability from peat fires and climate – land management interactions (1997–2013)
Global Fire Emissions Database (GFED4)
Giglio et al. (2013)
GATM
Change in atmospheric CO2 concentration
1959–1980: CO2 Program at Scripps Institution of Oceanography and other research groups
Keeling et al. (1976)
1980–2015: US National Oceanic and Atmospheric Administration Earth System Research Laboratory
Dlugokencky and Tans (2015) Ballantyne et al. (2012)
SOCEAN
Uptake of anthropogenic CO2
1990–1999 average: indirect estimates based on CFCs, atmospheric O2, and other tracer observations
Manning and Keeling (2006) Keeling et al. (2011) McNeil et al. (2003) Mikaloff Fletcher et al. (2006) as assessed by the IPCC in Denman et al. (2007)
Impact of increasing atmospheric CO2, climate, and variability
Ocean models
Table 6
SLAND
Response of land vegetation to: Increasing atmospheric CO2concentration Climate and variability Other environmental changes
Budget residual
The 2015 projection for the world is made of the sum of the projections for
China, the USA, and the rest of the world. The uncertainty is added quadratically among the
three regions. The uncertainty here reflects the best of our expert opinion.
CO2 emissions from land use, land-use change, and forestry
(ELUC)
Land-use-change emissions reported here (ELUC) include CO2
fluxes from deforestation, afforestation, logging (forest degradation and
harvest activity), shifting cultivation (cycle of cutting forest for
agriculture and then abandoning), and regrowth of forests following wood harvest
or abandonment of agriculture. Only some land management activities (Table 5)
are included in our land-use-change emissions estimates (e.g. emissions or
sinks related to management and management changes of established pasture and
croplands are not included). Some of these activities lead to emissions of
CO2 to the atmosphere, while others lead to CO2 sinks.
ELUC is the net sum of all anthropogenic activities considered. Our
annual estimate for 1959–2010 is from a bookkeeping method (Sect. 2.2.1)
primarily based on net forest area change and biomass data from the Forest
Resource Assessment (FRA) of the Food and Agriculture Organization (FAO),
which is only available at intervals of 5 years. We use the FAO FRA 2010
here (Houghton et al., 2012). Interannual variability in emissions due to
deforestation and degradation has been coarsely estimated from
satellite-based fire activity in tropical forest areas (Sect. 2.2.2; Giglio
et al., 2013; van der Werf et al., 2010). The bookkeeping method is used to
quantify the ELUC over the time period of the available data, and
the satellite-based deforestation fire information to incorporate interannual
variability (ELUC flux annual anomalies) from tropical
deforestation fires. The satellite-based deforestation and degradation fire
emissions estimates are available for years 1997–2014. We calculate the
global annual anomaly in deforestation and degradation fire emissions in
tropical forest regions for each year, compared to the 1997–2010 period, and
add this annual flux anomaly to the ELUC estimated using the
bookkeeping method that is available up to 2010 only and assumed constant at
the 2010 value during the period 2011–2014. We thus assume that all land
management activities apart from deforestation and degradation do not vary
significantly on a year-to-year basis. Other sources of interannual
variability (e.g. the impact of climate variability on regrowth fluxes) are
accounted for in SLAND. In addition, we use results from dynamic
global vegetation models (see Sect. 2.2.3 and Table 6) that calculate net
land-use-change CO2 emissions in response to land-cover-change
reconstructions prescribed to each model in order to help quantify the uncertainty in
ELUC and to explore the consistency of our understanding. The
three methods are described below, and differences are discussed in
Sect. 3.2.
Comparison of the processes included in the ELUC of
the global carbon budget and the DGVMs. See Table 6 for model references. All
models include deforestation and forest regrowth after abandonment of
agriculture (or from afforestation activities on agricultural land).
Bookkeeping
CLM4.5BGC
ISAM
JSBACH
JULES
LPJ-GUESS
LPJ
LPJmL
OCNv1.r240
ORCHIDEE
VISIT
Wood harvest and forest degradationa
yes
yes
yes
yes
no
no
no
no
yes
no
yesb
Shifting cultivation
yes
yes
no
yes
no
no
no
no
no
no
yes
Cropland harvest
yes
yes
yes
yesc
no
yes
no
yes
yes
yes
yes
Peat fires
no
yes
no
no
no
no
no
no
no
no
no
Fire simulation and/or suppression
for US only
yes
no
yes
no
yes
yes
yes
no
no
yes
Climate and variability
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
CO2 fertilisation
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Carbon–nitrogen interactions, including N deposition
no
yes
yes
no
no
no
no
no
yes
no
no
a Refers to the routine harvest of established managed forests rather
than pools of harvested products. b Wood stems are harvested
according to the land-use data. c Carbon from crop harvest is entirely
transferred into the litter pools.
Bookkeeping method
Land-use-change CO2 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,
including possible forest regrowth after deforestation. The approach tracks the
CO2 emitted to the atmosphere immediately during deforestation, and over
time due to the follow-up decay of soil and vegetation carbon in different
pools, including wood product pools after logging and deforestation. 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
forest management such as wood harvest and, in the USA, fire management. In
addition to tracking logging debris on the forest floor, the bookkeeping
method tracks the fate of carbon contained in harvested wood products that is
eventually emitted back to the atmosphere as CO2, although a detailed
treatment of the lifetime in each product pool is not performed (Earles et
al., 2012). Harvested wood products are partitioned into three pools with
different turnover times. All fuelwood is assumed burnt 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 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 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
integrates satellite data in more recent assessments (Table 4). Changes in land
cover other than forest are based on annual, national changes in cropland and
pasture areas reported by the FAO Statistics Division (FAOSTAT, 2010).
Land-use-change country data are aggregated by regions. The carbon stocks on
land (biomass and soils), and their response functions subsequent to land-use
change, are based on FAO data averages per land-cover type, per biome, and per
region. Similar results were obtained using forest biomass carbon density
based on satellite data (Baccini et al., 2012). The bookkeeping method does
not include land ecosystems' transient response to changes in climate,
atmospheric CO2, and other environmental factors, but the growth/decay
curves are based on contemporary data that will implicitly reflect the
effects of CO2 and climate at that time. Results from the bookkeeping
method are available from 1850 to 2010.
Fire-based interannual variability in ELUC
Land-use-change-associated CO2 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 complementary to the bookkeeping approach. They do not provide a direct
estimate of ELUC as they do not include non-combustion processes
such as respiration, wood harvest, wood products, or forest regrowth. Legacy
emissions such as decomposition from on-ground debris and soils are not
included in this method either. However, fire estimates provide some insight
into the year-to-year variations in the subcomponent of the total
ELUC flux that result from immediate CO2 emissions during
deforestation caused, for example, by the interactions between climate and
human activity (e.g. there is more burning and clearing of forests in dry
years) that are not represented by other methods. The “deforestation fire
emissions” assume an important role of fire in removing biomass in the
deforestation process, and thus can be used to infer gross instantaneous
CO2 emissions from deforestation 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 versus other types of fires, and this information can be merged
with information on biomass stocks and the fraction of the biomass lost in a
deforestation fire to estimate CO2 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 ELUC.
Fire emissions associated with deforestation and tropical peat burning are
based on the Global Fire Emissions Database (GFED4; accessed October 2015)
described in van der Werf et al. (2010) but with updated burned area (Giglio
et al., 2013) as well as burned area from relatively small fires that are
detected by satellite as thermal anomalies but not mapped by the burned area
approach (Randerson et al.,
2012). The burned area information is used as input data in a modified
version of the satellite-driven Carnegie–Ames–Stanford Approach (CASA)
biogeochemical model to estimate 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 emissions or regrowth from land-use change prior to the
availability of satellite data. Comparing coincident CO emissions and their
atmospheric fate with satellite-derived CO concentrations allows for some
validation of this approach (e.g. van der Werf et al., 2008). Results from
the fire-based method to estimate land-use-change emissions anomalies added
to the bookkeeping mean ELUC estimate are available from 1997 to
2014. Our combination of land-use-change CO2 emissions where the
variability in annual CO2 deforestation emissions is diagnosed from
fires assumes that year-to-year variability is dominated by variability in
deforestation.
Dynamic global vegetation models (DGVMs)
Land-use-change CO2 emissions have been estimated using an ensemble of
10 DGVMs. New model experiments up to year 2014 have been coordinated by the
project “Trends and drivers of the regional-scale sources and sinks of
carbon dioxide” (TRENDY; Sitch et al., 2015). We use only models that have
estimated land-use-change CO2 emissions and the terrestrial residual
sink following the TRENDY protocol (see Sect. 2.5.2), thus providing better
consistency in the assessment of the causes of carbon fluxes on land. Models
use their latest configurations, summarised in Tables 5 and 6.
The DGVMs were forced with historical changes in land-cover distribution,
climate, atmospheric CO2 concentration, and N deposition. As further
described below, each historical DGVM simulation was repeated with a
time-invariant pre-industrial land-cover distribution, allowing for estimation of,
by difference with the first simulation, the dynamic evolution of biomass and
soil carbon pools in response to prescribed land-cover change. All DGVMs
represent deforestation and (to some extent) regrowth, the most important
components of ELUC, but they do not represent all processes
resulting directly from human activities on land (Table 5). DGVMs represent
processes of vegetation growth and mortality, as well as decomposition of
dead organic matter associated with natural cycles, and include the
vegetation and soil carbon response to increasing atmospheric CO2 levels
and to climate variability and change. In addition, three models explicitly
simulate the coupling of C and N cycles and account for atmospheric N
deposition (Table 5). The DGVMs are independent of the other budget terms
except for their use of atmospheric CO2 concentration to calculate the
fertilisation effect of CO2 on primary production.
References for the process models and data products included
in Figs. 6–8.
Model/data name
Reference
Change from Le Quéré et al. (2015)
Dynamic global vegetation models
CLM4.5BGCa
Oleson et al. (2013)
No change
ISAM
Jain et al. (2013)b
We accounted for crop harvest for C3 and C4 crops based on Arora and Boer (2005) and agricultural soil carbon loss due to tillage (Jain et al., 2005)
JSBACH
Reick et al. (2013)c
Not applicable (first use of this model)
JULESe
Clark et al. (2011)e
Updated JULES version 4.3 compared to v3.2 for last year's budget. A number of small code changes, but no change in major science sections with the exception of an update in the way litter flux is calculated.
LPJ-GUESS
B. Smith et al. (2014)
Implementation of C / N interactions in soil and vegetation, including a complete update of the soil organic matter scheme
LPJf
Sitch et al. (2003)
No change
LPJmL
Bondeau et al. (2007)g
Not applicable (first use of this model)
OCNv1.r240
Zaehle et al. (2011)h
Revised photosynthesis parameterisation allowing for temperature acclimation as well as cold and heat effects on canopy processes. Revised grassland phenology. Included wood harvest as a driver to simulate harvest and post-harvest regrowth. Using Hurtt land-use data set
ORCHIDEE
Krinner et al. (2005)
Revised parameters values for photosynthetic capacity for boreal forests (following assimilation of FLUXNET data), updated parameters values for stem allocation, maintenance respiration and biomass export for tropical forests (based on literature) and, CO2 down-regulation process added to photosynthesis.
VISIT
Kato et al. (2013)i
No change
Data products for land-use-change emissions
Bookkeeping
Houghton et al. (2012)
No change
Fire-based emissions
van der Werf et al. (2010)
No change
Ocean biogeochemistry models
NEMO-PlankTOM5
Buitenhuis et al. (2010)j
No change
NEMO-PISCES (IPSL)k
Aumont and Bopp (2006)
No change
CCSM-BEC
Doney et al. (2009)
No change; small differences in the mean flux are caused by a change in how global and annual means were computed
MICOM-HAMOCC (NorESM-OC)
Assmann et al. (2010)l,m
Revised light penetration formulation and parameters for ecosystem module, revised salinity restoring scheme enforcing salt conservation, new scheme enforcing global freshwater balance, and model grid changed from displaced pole to tripolar
MPIOM-HAMOCC
Ilyina et al. (2013)
No change
NEMO-PISCES (CNRM)
Séférian et al. (2013)n
No change
CSIRO
Oke et al. (2013)
No change
MITgcm-REcoM2
Hauck et al. (2013)o
Not applicable (first use of this model)
Data products for ocean CO2 flux
Landschützerp
Landschützer et al. (2015)
No change
Jena CarboScopep
Rödenbeck et al. (2014)
Updated to version oc_1.2gcp2015
Atmospheric inversions for total CO2 fluxes (land-use change + land + ocean CO2 fluxes)
CarbonTracker
Peters et al. (2010)
Updated to version CTE2015. Updates include using CO2 observations from obspack_co2_1_GLOBALVIEWplus_v1.0_2015-07-30 (NOAA/ESRL, 2015b), prior SiBCASA biosphere and fire fluxes on 3-hourly resolution and fossil fuel emissions for 2010–2014 scaled to updated global totals.
Jena CarboScope
Rödenbeck et al. (2003)
Updated to version s81_v3.7
MACCq
Chevallier et al. (2005)
Updated to version 14.2. Updates include a change of the convection scheme and a revised data selection.
a Community Land Model 4.5.
b See also El-Masri et al. (2013).
c See also Goll et al. (2015).
d Joint UK Land Environment Simulator.
e See also Best et al. (2011).
f Lund–Potsdam–Jena.
g The LPJmL (Lund–Potsdam–Jena managed Land) version used also includes
developments described in Rost et al. (2008; river routing and
irrigation), Fader et al. (2010; agricultural management),
Biemans et al. (2011; reservoir management), Schaphoff et al. (2013; permafrost and 5 layer hydrology),
and Waha et al. (2012; sowing data) (sowing dates).
h See also Zaehle et al. (2010) and
Friend (2010).
i See also Ito and Inatomi (2012).
j With no nutrient restoring below the mixed layer depth.
k Referred to as LSCE in previous carbon budgets.
l With updates to the physical model as described in Tjiputra et
al. (2013).
m Further information (e.g. physical evaluation) for these models can
be found in Danabasoglu et al. (2014).
n Using winds from Atlas et al. (2011).
o A few changes have been applied to the ecosystem model. (1) The
constant Fe : C ratio was substituted by a constant Fe : N ratio. (2) A
sedimentary iron source was implemented. (3) the following parameters were
changed: CHL_N_max = 3.78, Fe2N = 0.033, deg_CHL_d = 0.1, Fe2N_d = 0.033, ligandStabConst = 200,
constantIronSolubility = 0.02.
p Updates using SOCATv3 plus new 2012–2014 data.
q The MACCv14.2 CO2 inversion system, initially described by
Chevallier et al. (2005), relies on the global tracer
transport model LMDZ (see also Supplement of Chevallier, 2015;
Hourdin et al., 2006).
The DGVMs used a consistent land-use-change data set (Hurtt et al., 2011),
which provided annual, half-degree, fractional data on cropland, pasture,
primary vegetation, and secondary vegetation, as well as all underlying
transitions between land-use states, including wood harvest and shifting
cultivation. This data set used the HYDE (Klein Goldewijk et al., 2011)
spatially gridded maps of cropland, pasture, and ice/water fractions of each
grid cell as an input. The HYDE data are based on annual FAO statistics of
change in agricultural area available to 2012 (FAOSTAT, 2010). For the years
2013 and 2014, the HYDE data were extrapolated by country for pastures and
cropland separately based on the trend in agricultural area over the previous
5 years. The HYDE data are independent of the data set used in the
bookkeeping method (Houghton, 2003, and updates), which is based primarily on
forest area change statistics (FAO, 2010). Although the HYDE land-use-change
data set indicates whether land-use changes occur on forested or non-forested
land, typically only the changes in agricultural areas are used by the models
and are implemented differently within each model (e.g. an increased cropland
fraction in a grid cell can either be at the expense of grassland, or forest,
the latter resulting in deforestation; land-cover fractions of the
non-agricultural land differ between models). Thus the DGVM forest area and
forest area change over time is not consistent with the Forest Resource
Assessment of the FAO forest area data used for the bookkeeping model to
calculate ELUC. Similarly, model-specific assumptions are applied
to convert deforested biomass or deforested area, and other forest product
pools, into carbon in some models (Table 5).
The DGVM runs were forced by either 6-hourly CRU-NCEP or by monthly CRU
temperature, precipitation, and cloud cover fields (transformed into incoming
surface radiation) based on observations and provided on a
0.5∘ × 0.5∘ grid and updated to 2014 (CRU TS3.23;
Harris et al., 2015). The forcing data include both gridded observations of
climate and global atmospheric CO2, which change over time (Dlugokencky
and Tans, 2015), and N deposition (as used in three models, Table 5; Lamarque et
al., 2010). ELUC is 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 distribution. Both
simulations were driven by changing atmospheric CO2, climate, and in
some models N deposition over the period 1860–2014. Using the difference
between these two DGVM simulations to diagnose ELUC is not fully
consistent with the definition of ELUC in the bookkeeping method
(Gasser and Ciais, 2013; Pongratz et al., 2014). The DGVM approach to
diagnose land-use-change CO2 emissions would be expected to produce
systematically higher ELUC emissions than the bookkeeping approach
if all the parameters of the two approaches were the same, which is not the
case (see Sect. 2.5.2).
Other published ELUC methods
Other methods have been used to estimate CO2 emissions from land-use
change. We describe some of the most important methodological differences
between the approach used here and other published methods, and for
completion, we explain why they are not used in the budget.
Different definitions (e.g. the inclusion of fire management) for
ELUC can lead to significantly different estimates within models
(Gasser and Ciais, 2013; Hansis et al., 2015; Pongratz et al., 2014) as well
as between models and other approaches (Houghton et al., 2012; P. Smith et
al., 2014). FAO uses the IPCC approach called “Tier 1” (e.g. Tubiello
et al., 2015) to produce a “Land use – forest land” estimate from the
Forest Resources Assessment data used in the bookkeeping
method described in Sect. 2.2.1 (MacDicken, 2015). The Tier 1-type method
applies a nationally reported mean forest carbon stock change (above and
below ground living biomass) to nationally reported net forest area change,
across all forest land combined (planted and natural forests). The methods
implicitly assume instantaneous loss or gain of mean forest. Thus the
Tier 1 approach provides an estimate of attributable emissions from the
process of land-cover change, but it does not distribute these emissions
through time. It also captures a fraction of what the global modelling
approach considers residual carbon flux (SLAND), it does not
consider loss of soil carbon, and there are no legacy fluxes. Land-use fluxes
estimated with this method were 0.47 GtC yr-1 in 2001–2010 and
0.22 GtC yr-1 in 2011–2015 (Federici et al., 2015). This estimate is
not directly comparable with ELUC used here because of the
different boundary conditions.
Recent advances in satellite data leading to higher-resolution area change
data (e.g. Hansen et al., 2013) and estimates of biomass in live vegetation
(e.g. Baccini et al., 2012; Saatchi et al., 2011) have led to several satellite-based estimates of CO2
emissions due to tropical deforestation (typically gross loss of forest area;
Achard and House, 2015). These include estimates of 1.0 GtC yr-1 for
2000 to 2010 (Baccini et al., 2012), 0.8 GtC yr-1 for 2000 to 2005
(Harris et al., 2012),
0.9 GtC yr-1 for 2000 to 2010 for net area change (Achard et al.,
2014), and 1.3 GtC yr-1 2000 to 2010 (Tyukavina et al., 2015). These
estimates include belowground carbon biomass using a scaling factor. Some
estimate soil carbon loss, some assume instantaneous emissions, some do not
account for regrowth fluxes, and none account for legacy fluxes from land-use
change prior to the availability of satellite data. They are mostly estimates
of tropical deforestation only, and do not capture regrowth flux after
abandonment or planting (Achard and House, 2015). These estimates are also
difficult to compare with ELUC used here because they do not fully
include legacy fluxes and forest regrowth.
Comparison of results from the bookkeeping method and budget
residuals with results from the DGVMs and inverse estimates for the periods
1960–1969, 1970–1979, 1980–1989, 1990–1999, 2000–2009, the last decade, and the last
year available. All values are in GtC yr-1. The DGVM uncertainties
represents ±1σ of the decadal or annual (for 2014 only)
estimates from the 10 individual models; for the inverse models all three
results are given where available.
Mean (GtC yr-1)
1960–1969
1970–1979
1980–1989
1990–1999
2000–2009
2005–2014
2014
Land-use-change emissions (ELUC)
Bookkeeping method
1.5 ± 0.5
1.3 ± 0.5
1.4 ± 0.5
1.6 ± 0.5
1.0 ± 0.5
0.9 ± 0.5
1.1 ± 0.5
DGVMsa
1.2 ± 0.4
1.2 ± 0.4
1.3 ± 0.4
1.2 ± 0.4
1.2 ± 0.4
1.4 ± 0.4
1.4 ± 0.5
Residual terrestrial sink (SLAND)
Budget residual
1.7 ± 0.7
1.7 ± 0.8
1.6 ± 0.8
2.6 ± 0.8
2.4 ± 0.8
3.0 ± 0.8
4.1 ± 0.9
DGVMsa
1.1 ± 0.6
2.1 ± 0.3
1.7 ± 0.4
2.3 ± 0.3
2.7 ± 0.4
3.0 ± 0.5
3.6 ± 0.9
Total land fluxes (SLAND - ELUC)
Budget (EFF - GATM - SOCEAN)
0.2 ± 0.5
0.4 ± 0.6
0.2 ± 0.6
1.0 ± 0.6
1.5 ± 0.6
2.1 ± 0.7
3.0 ± 0.7
DGVMsa
-0.1 ± 0.6
0.9 ± 0.4
0.5 ± 0.5
1.1 ± 0.5
1.5 ± 0.4
1.6 ± 0.4
2.3 ± 0.9
Inversions (CTE2015/JenaCarboScope/MACC)b
–/–/–
–/–/–
–/0.3b/0.8b
–/1.1b/1.8b
–/1.6b/2.4b
2.0b/2.0b/3.3b
2.8b/2.6b/4.2b
a Note that the decadal uncertainty calculation for the DGVMs is smaller
here compared to previous global carbon budgets because it uses ±1σ of the decadal estimates for the DGVMs, compared to the average
of the annual ±1σ estimates in previous years. It thus
represents the true model range for their decadal estimates. This change was
introduced to be consistent with the decadal uncertainty calculations in
Table 8.
b Estimates are not corrected for the influence of river fluxes, which would
reduce the fluxes by 0.45 GtC yr-1 when neglecting the anthropogenic
influence on land (Sect. 7.2.2). CTE2015 refers to Peters et al. (2010),
Jena CarboScope to Rödenbeck et al. (2014), and MACC to Chevallier et al. (2005); see
Table 6.
Uncertainty assessment for ELUC
Differences between the bookkeeping, the addition of fire-based interannual
variability to the bookkeeping, and DGVM methods originate from three main
sources: the land-cover-change data set, the different approaches used in
models, and the different processes represented (Table 5). We examine the
results from the 10 DGVMs and of the bookkeeping method to assess the
uncertainty in ELUC.
The uncertainties in annual ELUC estimates are examined using the
standard deviation across models, which averages 0.4 GtC yr-1 from
1959 to 2014 (Table 7). The mean of the multi-model ELUC 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 over 85 %
of the time. Based on this comparison, we assess that an uncertainty of
±0.5 GtC yr-1 provides a semi-quantitative measure of uncertainty
for annual emissions, and reflects our best value judgment that there is at
least 68 % chance (±1σ) that the true land-use-change
emission lies within the given range, for the range of processes considered
here. This is consistent with the uncertainty analysis of Houghton et
al. (2012), which partly reflects improvements in data on forest area change
using data, and partly more complete understanding and representation of
processes in models.
The uncertainties in the decadal ELUC estimates are also examined
using the DGVM ensemble, although they are likely correlated between decades.
The correlations between decades come from (1) common biases in system
boundaries (e.g. not counting forest degradation in some models); (2) common
definition for the calculation of ELUC from the difference of
simulations with and without land-use change (a source of bias vs. the
unknown truth); (3) common and uncertain land-cover-change input data which
also cause a bias, though if a different input data set is used each decade,
decadal fluxes from DGVMs may be partly decorrelated; and (4) model structural
errors (e.g. systematic errors in biomass stocks). In addition, errors
arising from uncertain DGVM parameter values would be random, but they are not
accounted for in this study, since no DGVM provided an ensemble of runs with
perturbed parameters.
Prior to 1959, the uncertainty in ELUC is taken as ±33 %,
which is the ratio of uncertainty to mean from the 1960s (Table 7), the first
decade available. This ratio is consistent with the mean standard deviation
of DGMVs' land-use-change emissions over 1870–1958 (0.38 GtC) over the
multi-model mean (1.1 GtC).
Ocean CO2 sink
Estimates of the global ocean CO2 sink are based on a combination of a
mean CO2 sink estimate for the 1990s from observations, and a trend and
variability in the ocean CO2 sink for 1959–2014 from eight global ocean
biogeochemistry models. We use two observation-based estimates of
SOCEAN available for recent decades to provide a qualitative
assessment of confidence in the reported results.
Decadal mean in the five components of the anthropogenic
CO2 budget for the periods 1960–1969, 1970–1979, 1980–1989, 1990–1999,
2000–2009, the last decade, and the last year available. All values are in 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 on the data set.
Mean (GtC yr-1)
1960–1969
1970–1979
1980–1989
1990–1999
2000–2009
2005–2014
2014
Emissions
Fossil fuels and industry (EFF)
3.1 ± 0.2
4.7 ± 0.2
5.5 ± 0.3
6.4 ± 0.3
7.8 ± 0.4
9.0 ± 0.5
9.8 ± 0.5
Land-use-change emissions(ELUC)
1.5 ± 0.5
1.3 ± 0.5
1.4 ± 0.5
1.6 ± 0.5
1.0 ± 0.5
0.9 ± 0.5
1.1 ± 0.5
Partitioning
Atmospheric growth rate(GATM)
1.7 ± 0.1
2.8 ± 0.1
3.4 ± 0.1
3.1 ± 0.1
4.0 ± 0.1
4.4 ± 0.1
3.9 ± 0.2
Ocean sink (SOCEAN)*
1.1 ± 0.5
1.5 ± 0.5
2.0 ± 0.5
2.2 ± 0.5
2.3 ± 0.5
2.6 ± 0.5
2.9 ± 0.5
Residual terrestrial sink(SLAND)
1.7 ± 0.7
1.7 ± 0.8
1.6 ± 0.8
2.6 ± 0.8
2.4 ± 0.8
3.0 ± 0.8
4.1 ± 0.9
* The uncertainty in SOCEAN for the 1990s is directly based on
observations, while that for other decades combines the uncertainty from
observations with the model spread (Sect. 2.4.3).
Observation-based estimates
A mean ocean CO2 sink of 2.2 ± 0.4 GtC yr-1 for the 1990s
was estimated by the IPCC (Denman et al., 2007) based on indirect
observations and their spread: ocean/land CO2 sink partitioning from
observed atmospheric O2 / N2 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 penetration 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 uncertainties 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 pCO2
difference) × (gas transfer coefficient). Estimates based on sea–air
pCO2 are fully consistent with indirect observations (Wanninkhof et al., 2013), 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-induced outgassing of
CO2 (Jacobson et al., 2007).
Both observation-based estimates compute the ocean CO2 sink and its
variability using interpolated measurements of surface ocean fugacity of
CO2 (pCO2 corrected for the non-ideal behaviour of the gas; Pfeil
et al., 2013). The measurements were from the Surface Ocean CO2 Atlas
(SOCAT v3; Bakker et al., 2014, 2015), which contains 14.5 million data to the
end of 2014. This was extended with 1.4 million additional measurements over
years 2013–2014 (see data attribution Table A1 in
Appendix A), submitted to SOCAT but not yet fully
quality controlled following standard SOCAT procedures. Revisions and
corrections to previously reported measurements were also included where they
were available. All new data were subjected to an automated quality control
system to detect and remove the most obvious errors (e.g. incorrect reporting
of metadata such as position, wrong units, clearly unrealistic data).
The combined SOCAT v3 and preliminary new 2013–2014 measurements were mapped
using a data-driven diagnostic method (Rödenbeck et al., 2013) and a
combined self-organising map and feed-forward neural network
(Landschützer et al., 2014). The global observation-based estimates were
adjusted to remove a background (not part of the anthropogenic ocean flux)
ocean source of CO2 to the atmosphere of 0.45 GtC yr-1 from river
input to the ocean (Jacobson et al., 2007) in order to make them comparable to
SOCEAN, which only represents the annual uptake of anthropogenic
CO2 by the ocean. Several other data-based products are available, but
they partly show large discrepancies with observed variability that need to be
resolved. Here we used the two data products that had the best fit to
observations, distinctly better than most in their representation of tropical
and global variability (Rödenbeck et al., 2015).
We use the data-based product of Khatiwala et al. (2009) updated by Khatiwala
et al. (2013) to estimate the anthropogenic carbon accumulated in the ocean
during 1765–1958 (60.2 GtC) and 1870–1958 (47.5 GtC), and assume an
oceanic uptake of 0.4 GtC for 1750–1765 (for which time no data are
available) based on the mean uptake during 1765–1770. The estimate of
Khatiwala et al. (2009) is based on regional disequilibrium between surface
pCO2 and atmospheric CO2, and a Green's function utilising
transient ocean tracers like CFCs and 14C to ascribe changes through
time. It does not include changes associated with changes in ocean
circulation, temperature, and climate, but these are thought to be small over
the time period considered here (Ciais et al., 2013). The uncertainty in
cumulative uptake of ±20 GtC (converted to ±1σ) is taken
directly from the IPCC's review of the literature (Rhein et al., 2013), or
about ±30 % for the annual values (Khatiwala et al., 2009).
Global ocean biogeochemistry models
The trend in the ocean CO2 sink for 1959–2014 is computed using a
combination of eight global ocean biogeochemistry models (Table 6). The
models represent the physical, chemical, and biological processes that
influence the surface ocean concentration of CO2 and thus the air–sea
CO2 flux. The models are forced by meteorological reanalysis and
atmospheric CO2 concentration data available for the entire time period.
Models do not include the effects of anthropogenic changes in nutrient
supply. They compute the air–sea flux of CO2 over grid boxes of 1 to
4∘ in latitude and longitude. The ocean CO2 sink for each model
is normalised to the observations by dividing the annual model values by
their average over 1990–1999 and multiplying this with 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 CO2 sink for each year (t) in GtC yr-1
is therefore
SOCEAN(t)=1n∑m=1m=nSOCEANm(t)SOCEANm(1990–1999)×2.2GtCyr-1,
where n is the number of models. This normalisation ensures that the ocean
CO2 sink for the global carbon budget is based on observations, whereas
the trends and annual values in CO2 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 CO2. Thus a ratio is more appropriate than an
offset as it takes into account the time dependence of CO2 gradients in
the ocean. The mean uncorrected ocean CO2 sink from the eight models for
1990–1999 ranges between 1.6 and 2.4 GtC yr-1, with a multi-model
mean of 1.9 GtC yr-1.
Uncertainty assessment for SOCEAN
The uncertainty around the mean ocean sink of anthropogenic CO2 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
the two data-based products to assess the confidence level. The average
standard deviation of the normalised ocean model ensemble is
0.13 GtC yr-1 during 1980–2010 (with a maximum of 0.27), but it
increases as the model ensemble goes back in time, with a standard deviation
of 0.22 GtC yr-1 across models in the 1960s. We estimate that the
uncertainty in the annual ocean CO2 sink is about
±0.5 GtC yr-1 from the fractional uncertainty of the data
uncertainty of ±0.4 GtC yr-1 and standard deviation across models
of up to ±0.27 GtC yr-1, reflecting both the uncertainty in the
mean sink from observations during the 1990s (Denman et al., 2007;
Sect. 2.4.1) and in the interannual variability as assessed by models.
We examine the consistency between the variability in the model-based and the
data-based products to assess confidence in SOCEAN. The interannual
variability in the ocean fluxes (quantified as the standard deviation) of the
two data-based estimates for 1986–2014 (where they overlap) is
±0.38 GtC yr-1 (Rödenbeck et al., 2014) and
±0.40 GtC yr-1 (Landschützer et al., 2015), compared to
±0.27 GtC yr-1 for the normalised model ensemble. The standard
deviation includes a component of trend and decadal variability in addition
to interannual variability, and their relative influence differs across
estimates. The phase is generally consistent between estimates, with a higher
ocean CO2 sink during El Niño events. The annual data-based
estimates correlate with the ocean CO2 sink estimated here with a
correlation of r=0.51 (0.34 to 0.58 for individual models), and r=0.71 (0.54 to 0.72) for the data-based estimates of Rödenbeck et
al. (2014) and Landschützer et al. (2015), respectively (simple linear
regression), but their mutual correlation is only 0.55. The use of annual
data for the correlation may reduce the strength of the relationship because
the dominant source of variability associated with El Niño events is less
than 1 year. We assess a medium confidence level to the annual ocean
CO2 sink and its uncertainty because they are based on multiple lines of
evidence, and the results are consistent in that the interannual variability
in the model and data-based estimates are all generally small compared to the
variability in atmospheric CO2 growth rate. Nevertheless the various
results do not show agreement in interannual variability on the global scale
or for the relative roles of the annual and decadal variability compared to
the trend.
Terrestrial CO2 sink
The difference between, on the one hand, fossil fuel (EFF) and
land-use-change emissions (ELUC) and, on the other hand, the growth
rate in atmospheric CO2 concentration (GATM) and the ocean
CO2 sink (SOCEAN) is attributable to the net sink of CO2
in terrestrial vegetation and soils (SLAND), within the given
uncertainties (Eq. 1). Thus, this sink can be estimated as the residual of
the other terms in the mass balance budget, as well as directly calculated
using DGVMs. The residual land sink (SLAND) is thought to be in
part because of the fertilising effect of rising atmospheric CO2 on
plant growth, N deposition, and effects of climate change such as the
lengthening of the growing season in northern temperate and boreal areas.
SLAND does not include gross land sinks directly resulting from
land-use change (e.g. regrowth of vegetation) as these are estimated as part
of the net land-use flux (ELUC). System boundaries make it
difficult to attribute exactly CO2 fluxes on land between
SLAND and ELUC (Erb et al., 2013), and by design most of
the uncertainties in our method are allocated to SLAND for those
processes that are poorly known or represented in models.
Residual of the budget
For 1959–2014, the terrestrial carbon sink was estimated from the residual
of the other budget terms by rearranging Eq. (1):
SLAND=EFF+ELUC-(GATM+SOCEAN).
The uncertainty in SLAND is estimated annually from the root sum of
squares of the uncertainty in the right-hand terms assuming the errors are
not correlated. The uncertainty averages to ±0.8 GtC yr-1 over
1959–2014 (Table 7). SLAND 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
A comparison of the residual calculation of SLAND in Eq. (8) with
estimates from DGVMs as used to estimate ELUC in Sect. 2.2.3, but
here excluding the effects of changes in land cover (using a constant
pre-industrial land-cover distribution), provides an independent estimate of
the consistency of SLAND with our understanding of the functioning
of the terrestrial vegetation in response to CO2 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 CO2
effects over land, but they do not include reductions in CO2 sink capacity
associated with human activity directly affecting changes in vegetation cover
and management, which by design is allocated to ELUC. This effect
has been estimated to have led to a reduction in the terrestrial sink by
0.5 GtC yr-1 since 1750 (Gitz and Ciais, 2003). The models in this
configuration estimate the mean and variability in SLAND based on
atmospheric CO2 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 spin-up, (2) net land
fluxes (SLAND - ELUC) that are a carbon sink over the
1990s as constrained by global atmospheric and oceanic observations (McNeil
et al., 2003; Manning and Keeling, 2006; Mikaloff Fletcher et al., 2006), and
(3) global ELUC that is a carbon source over the 1990s. Ten models
met these three criteria.
The annual standard deviation of the CO2 sink across the 10 DGVMs
averages to ±0.7 GtC yr-1 for the period 1959 to 2014. The model
mean, over different decades, correlates with the budget residual with
r = 0.71 (0.52 to r = 0.71 for individual models). The standard deviation is
similar to that of the five model ensembles presented in Le Quéré et
al. (2009), but the correlation is improved compared to r = 0.54 obtained
in the earlier study. The DGVM results suggest that the sum of our knowledge
on annual CO2 emissions and their partitioning is plausible (see
Discussion), and provide insight into the underlying processes and regional
breakdown. However as the standard deviation across the DGVMs (e.g.
±0.9 GtC yr-1 for year 2014) is of the same magnitude as the
combined uncertainty due to the other components (EFF,
ELUC, GATM, SOCEAN; Table 7), the DGVMs do not
provide further reduction of uncertainty on the annual terrestrial CO2
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 respective uncertainties. We attach a medium confidence level to the
annual land CO2 sink and its uncertainty because the estimates from the
residual budget and averaged DGVMs match well within their respective
uncertainties, and the estimates based on the residual budget are primarily
dependent on EFF and GATM, 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 CO2 fluxes from all sources, including fossil and land-use-change emissions and land and ocean CO2 fluxes. The inversions assume
EFF to be well known, and they solve for the spatial and temporal
distribution of land and ocean fluxes from the residual gradients of CO2
between stations that are not explained by emissions. Inversions used
atmospheric CO2 data to the end of 2014 (including preliminary values in
some cases), as well as three atmospheric CO2 inversions (Table 6) to infer the
total CO2 flux over land regions and the distribution of the total land
and ocean CO2 fluxes for the mid–high-latitude Northern Hemisphere
(30–90∘ N), tropics (30∘ S–30∘ N) and mid–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 estimates.
Atmospheric inversions
The three inversion systems used in this release are the CarbonTracker
(Peters et al., 2010), the Jena CarboScope (Rödenbeck, 2005), and MACC
(Chevallier et al., 2005). 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
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 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 CO2 observations from various flask
and in situ networks. They prescribe spatial and global EFF that
can vary from that presented here. The CarbonTracker and MACC inversions
prescribed the same global EFF as in Sect. 2.1.1, during
2010–2014 for CarbonTracker and during 1979–2014 in MACC. The
Jena-s81_v3.7 inversion uses EFF from EDGAR4.2. Different
spatial and temporal distributions of EFF were prescribed in each
inversion.
Given their prescribed map of EFF, each inversion estimates natural
fluxes from a similar set of surface CO2 measurement stations, and
CarbonTracker additionally uses two sites of aircraft CO2 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-s81_v3.7, and LMDZ for MACC. These three
models are based on the same ECMWF wind fields. The three inversions use
different prior natural fluxes, which partly influences their optimised
fluxes. MACC assumes that the prior land flux is zero on the annual mean in
each grid cell of the transport model, so that any sink or source on land is
entirely reflecting the information brought by atmospheric measurements.
CarbonTracker simulates a small prior sink on land from the SIBCASA model
that results from regrowth following fire disturbances of an otherwise net
zero biosphere. Jena s81_v3.7 assumes a prior on the long-term mean land
sink pattern, using the time-averaged net ecosystem exchange of the LPJ model. Inversion results
for the sum of natural ocean and land fluxes (Fig. 8) are better constrained
in the Northern Hemisphere (NH) than in the tropics, because of the higher
measurement stations density in the NH.
Finally, results from atmospheric inversions include the natural CO2
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 atmospheric observations include
a set of observations used to estimate the global atmospheric growth rate
(Sect. 2.3). However they provide new information on the regional
distribution of fluxes.
We focus the analysis on two known strengths of the inverse approach: the
derivation of the year-to-year changes in total land fluxes
(SLAND - ELUC) consistent with the whole network of
atmospheric observations, and the spatial breakdown of combined land and ocean fluxes
(SOCEAN + SLAND - ELUC) across large
regions of the globe. The total land flux correlates well with
that estimated from the budget residual (Eq. 1) with correlations for the annual
time series ranging from r=0.89 to 0.93, and with the DGVM multi-model
mean with correlations for the annual time series ranging from r=0.71 to
0.80 (r=0.49 to 0.81 for individual DGVMs and inversions). The spatial
breakdown is discussed in Sect. 3.1.3.
Processes not included in the global carbon budget
Contribution of anthropogenic CO and CH4 to the global
carbon budget
Anthropogenic emissions of CO and CH4 to the atmosphere are eventually
oxidised to CO2 and thus are part of the global carbon budget. These
contributions are omitted in Eq. (1), but an attempt is made in this section
to estimate their magnitude and identify the sources of uncertainty.
Anthropogenic CO emissions are from incomplete fossil fuel and biofuel
burning and deforestation fires. The main anthropogenic emissions of fossil
CH4 that matter for the global carbon budget are the fugitive emissions
of coal, oil, and gas upstream sectors (see below). These emissions of CO and
CH4 contribute a net addition of fossil carbon to the atmosphere.
In our estimate of EFF we assumed (Sect. 2.1.1) that all the fuel
burned is emitted as CO2; thus CO anthropogenic emissions and their
atmospheric oxidation into CO2 within a few months are already counted
implicitly in EFF and should not be counted twice (same for
ELUC and anthropogenic CO emissions by deforestation fires).
Anthropogenic emissions of fossil CH4 are not included in EFF,
because these fugitive emissions are not included in the fuel inventories.
Yet they contribute to the annual CO2 growth rate after CH4 gets
oxidised into CO2. Anthropogenic emissions of fossil CH4 represent
15 % of total CH4 emissions (Kirschke et al., 2013) that is
0.061 GtC yr-1 for the past decade. Assuming steady state, these
emissions are all converted to CO2 by OH oxidation and thus explain
0.06 GtC yr-1 of the global CO2 growth rate in the past decade.
Other anthropogenic changes in the sources of CO and CH4 from
wildfires, biomass, wetlands, ruminants, or permafrost changes are similarly
assumed to have a small effect on the CO2 growth rate.
Anthropogenic carbon fluxes in the land to ocean aquatic
continuum
The approach used to determine the global carbon budget considers only
anthropogenic CO2 emissions and their partitioning among the atmosphere,
ocean, and land. In this analysis, the land and ocean reservoirs that take up
anthropogenic CO2 from the atmosphere are conceived as independent
carbon storage repositories. This approach thus omits that carbon is
continuously displaced along the land–ocean aquatic continuum (LOAC)
comprising freshwaters, estuaries, and coastal areas (Bauer et al., 2013;
Regnier et al., 2013). A significant fraction of this lateral carbon flux is
entirely “natural” and is thus a steady-state component of the pre-industrial
carbon cycle. The remaining fraction is anthropogenic carbon entrained into
the lateral transport loop of the LOAC, a perturbation that is relevant for
the global carbon budget presented here.
The results of the analysis of Regnier et al. (2013) can be summarised in
three points of relevance to the anthropogenic CO2 budget. First, the
anthropogenic carbon input from land to hydrosphere, FLH, estimated
at 1 ± 0.5 GtC yr-1 is significant compared to the other terms
of Eq. (1) (Table 8), and implies that only a portion of the anthropogenic
CO2 taken up by land ecosystems remains sequestered in soil and biomass
pools. Second, some of the exported anthropogenic carbon is stored in the
LOAC (ΔCLOAC, 0.55 ± 0.3 GtC yr-1) and
some is released back to the atmosphere as CO2 (ELOAC,
0.35 ± 0.2 GtC yr-1), the magnitude of these fluxes resulting
from the combined effects of freshwaters, estuaries, and coastal seas. Third,
a small fraction of anthropogenic carbon displaced by the LOAC is transferred
to the open ocean, where it accumulates (FHO,
0.1 ± > 0.05 GtC yr-1). The anthropogenic perturbation of the
carbon fluxes from land to ocean does not contradict the method used in
Sect. 2.5 to define the ocean sink and residual terrestrial sink. However, it
does point to the need to account for the fate of anthropogenic carbon once
it is removed from the atmosphere by land ecosystems (summarised in Fig. 2).
In theory, direct estimates of changes of the ocean inorganic carbon
inventory over time would see the land flux of anthropogenic carbon and would
thus have a bias relative to air–sea flux estimates and tracer-based
reconstructions. However, currently the value is small enough to be not
noticeable relative to the errors in the individual techniques.
Combined components of the global carbon budget illustrated
in Fig. 2 as a function of time, for emissions from fossil fuels and industry
(EFF; grey) and emissions from land-use change (ELUC;
brown), as well as their partitioning among the atmosphere (GATM;
light blue), land (SLAND; green), and oceans (SOCEAN; dark
blue). All time series are in GtC yr-1. GATM and
SOCEAN (and by construction also SLAND) prior to 1959 are
based on different methods. The primary data sources for fossil fuels and
industry are from Boden et al. (2013), with uncertainty of
about ±5 % (±1σ); land-use-change emissions are from
Houghton et al. (2012) with uncertainties of about ±30 %;
atmospheric growth rate prior to 1959 is from Joos and Spahni (2008) with
uncertainties of about ±1–1.5 GtC decade-1 or ±0.1–0.15 GtC yr-1
(Bruno and Joos, 1997), and from Dlugokencky and Tans (2015) 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.
Components of the global carbon budget and their
uncertainties as a function of time, presented individually for (a) emissions
from fossil fuels and industry (EFF), (b) emissions from land-use
change (ELUC), (c) atmospheric CO2 growth rate
(GATM), (d) the ocean CO2 sink (SOCEAN; positive
indicates a flux from the atmosphere to the ocean), and (e) the land CO2
sink (SLAND; positive indicates a flux from the atmosphere to the
land). All time series are in GtC yr-1 with the uncertainty bounds
representing ±1σ in shaded colour. Data sources are as in Fig. 3.
The black dots in panels (a), (b), and (e) show preliminary values for 2012, 2013, and
2014 that originate from a different data set to the remainder of the data,
as explained in the text.
The residual terrestrial sink in a budget that accounts for the LOAC will be
larger than SLAND, as the flux is partially offset by the net
source of CO2 to the atmosphere, i.e. ELOAC, of
0.35 ± 0.3 GtC yr-1 from rivers, estuaries, and coastal seas:
SLAND+LOAC=EFF+ELUC-(GATM+SOCEAN)+ELOAC.
The residual terrestrial sink (SLAND) is
3.0 ± 0.8 GtC yr-1 for 2005–2014 as calculated according to
Eq. (8; Table 7), while SLAND+LOAC is
3.3 ± 0.9 GtC yr-1 over the same time period. A fraction of
anthropogenic CO2 taken up by land ecosystems is exported to the LOAC
(FLH). With the LOAC included, we now have
ΔCTE=SLAND+LOAC-ELUC-FLH,
where ΔCTE is the change in annual terrestrial
ecosystems carbon storage, including land vegetation, litter, and soil.
ΔCTE is 1.4 GtC yr-1 for the period 2005–2014.
It is notably smaller than what would be calculated in a traditional budget
that ignores the LOAC. In this case, the change in carbon storage is
estimated as 2.1 Gt C yr-1 from the difference between
SLAND (3.0 Gt C yr-1) and ELUC
(0.9 Gt C yr-1; Table 8). All estimates of LOAC are given with low
confidence, because they originate from a single source. The carbon budget
presented here implicitly incorporates the fluxes from the LOAC
into SLAND. We do not attempt to separate these fluxes because the
uncertainties in either estimate are too large, and there is insufficient
information available to estimate the LOAC fluxes on an annual basis.