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.
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 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 (Dlugokencky and Tans, 2016) and
analysis by others (Betts et al., 2016) of the growth rate in atmospheric
CO2 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.
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 1990–2014
(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
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 and Andres, 2016). 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.
Data sources used to compute each component of the global
carbon budget.
Component
Process
Data source
Data reference
EFF (global and CDIAC national)
Fossil fuel combustion and gas flaring
UN Statistics Division to 2013
UN (2015a, b)
BP for 2014–2015
BP (BP, 2016b)
Cement production
US Geological Survey
USGS (2016a, b)
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 frompeat fires and climate–landmanagement interactions(1997–2013)
Global Fire EmissionsDatabase (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 (2016) 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) McNeil et al. (2003) Mikaloff Fletcher et al. (2006) as assessed by the IPCC in Denman et al. (2007)
Impact of increasing atmospheric CO2, climate, and variability
Ocean models
Table 6
SLAND
Response of land vegetation to increasing atmospheric CO2 concentration, climate and variability, and other environmental changes
Budget residual
The global emissions presented here are based on 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 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 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, 2015a). 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).
Recent revisions in energy data for China (Korsbakken et al., 2016) 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 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 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 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 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 1–2 years when the UNFCCC estimates (1 year) and UN
statistics (2 years) used by CDIAC are not yet available, we generated
preliminary estimates based on the BP annual energy review by applying the
growth rates of energy consumption (coal, oil, gas) for 2015 to the national
and global emissions from the UN national data in 2014, and for 2014 and 2015
to the CDIAC national and global emissions in 2013. 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 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 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 (USGS, 2016a). For 2014 and 2015 we use estimates of cement production
made by the USGS for the top 18 countries (representing 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.
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 recent
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 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 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
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 the former USSR to the resulting independent countries. For
disaggregation, 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 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 inconsistent
national reporting, 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 down from the published
±10 % at ±2σ to the use of ±1σ bounds
reported here; Andres et al., 2012). This is consistent with a more detailed
recent analysis of uncertainty of ±8.4 % at ±2σ (Andres
et al., 2014) and at the high end of the range of ±5–10 % at
±2σ reported by Ballantyne et al. (2015). This includes an
assessment of uncertainties in the amounts of fuel consumed, the carbon and
heat contents of fuels, and the combustion efficiency. While we consider a fixed
uncertainty of ±5 % for all years, 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 suggesting 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, 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 biases of energy
statistics and to the accounting method used by each country. We assign a
medium confidence to the results presented here because they are based on
indirect estimates of emissions using energy data (Durant et al., 2011).
There is only limited and indirect evidence for emissions, although there is
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 are another attribution point of view that allocates global
emissions to products that are consumed within a country; these inventories 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 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 2014 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). Our analysis is based on the economic and trade
data from the Global Trade and Analysis Project (GTAP; Narayanan et al.,
2015), and we make detailed estimates for the years 1997 (GTAP version 5),
2001 (GTAP6), and 2004, 2007, and 2011 (GTAP9.1) (using the methodology of
Peters et al., 2011b). The results cover 57 sectors and up to 141 countries
and regions. The detailed results are then extended into an annual
time series from 1990 to the latest year of the GDP data (2014 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, 2015c) 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
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 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 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 (Andrew et al., 2013), 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 un-harmonised 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 affects the growth rate by about
0.3 % yr-1 (1/365) and causes growth rates to go up approximately
0.3 % if the first year is a leap year and down 0.3 % if the second
year is a leap year.
The relative growth rate of EFF over time periods of greater than
1 year can be 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 % yr-1) in the case of
EFF.
Emissions projections
Energy statistics from BP are normally available around June for the previous
year. To gain insight on emission trends for the current year (2016), 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) and the rest of the world.
We specifically estimate emissions in China because the data indicate a
significant 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., 2015a), resulting from a rapid deceleration in
emissions growth against continued growth in economic output. This departure
could be temporary (Jackson et al., 2016). Our 2016 estimate for China uses
(1) coal consumption estimates from the China Coal Industry Association for
January through September (CCIA, 2016), (2) estimated consumption of natural
gas (IEW, 2016; NDRC, 2016a) and domestic 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 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 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.
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
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 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.
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 Eq. (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 2013 (IEA/OECD,
2015) and extended using the IMF growth rates for 2014 and 2015 (IMF, 2016).
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 2006–2015 as a measure of
uncertainty, reflecting a ±1σ as in the rest of the carbon
budget. This is ±1.0 % yr-1 for the rest of the world (global
emissions minus China and USA).
Comparison of the processes included in the bookkeeping method and
DGVMs in their estimates of ELUC and SLAND. 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 ELUC are only
described for the DGVMs used with land-cover change in this study (Fig. 6 top
panel).
Bookkeeping
CABLE
CLASS-CTEM
CLM
DLEM
ISAM
JSBACH
JULES
LPJ-GUESS
LPJ
LPX-Bern
OCN
ORCHIDEE
SDGVM
VISIT
Processes relevant for ELUC
Wood harvest and forest degradationa
yes
yes
no
no
no
yes
Shifting cultivation
yesb
no
no
no
no
no
Cropland harvest
yes
yes
no
yes
no
yes
Peat fires
no
no
no
no
no
no
Processes also relevant for SLAND
Fire simulation and/or suppression
for US only
no
yes
yes
yes
no
yes
no
yes
yes
yes
no
no
yes
yes
Climate and variability
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
CO2 fertilisation
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Carbon–nitrogen interactions, including N deposition
no
yes
no
yes
yes
yes
no
no
yes
no
yes
yes
no
yesc
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 is an empirical function of
canopy N. Does not consider N deposition.
The 2016 projection for the world is made of the sum of the projections for
China, USA, and the rest of the world. The uncertainty is added in quadrature among the
three regions. The uncertainty here reflects the best of our expert opinion.
References for the process models and data products
included in Figs. 6–8. All models and products are updated with new data to
end of year 2015.
Model/data name
Reference
Change from Le Quéré et al. (2015a)
Dynamic global vegetation models
CABLE
Zhang et al. (2013)
Not applicable (not used in 2015)
CLASS-CTEM
Melton and Arora (2016)
Not applicable (not used in 2015)
CLM
Oleson et al. (2013)
No change
DLEM
Tian et al. (2010)
Not applicable (not used in 2015)
ISAM
Jain et al. (2013)
Updated to account for dynamic phenology and dynamic rooting distribution and depth parameterisations for various ecosystem types as described in El Masri et al. (2015). These parameterisations account for light, water, and nutrient stresses while allocating the assimilated carbon to leaf, stem, and root pools.
JSBACH
Reick et al. (2013)a
No change
JULESb
Clark et al. (2011)c
Updated to code release 4.6 and configuration JULES-C-1.1. This version includes improvements to the seasonal cycle of soil respiration.
LPJ-GUESS
Smith et al. (2014)
Use of CRU-NCEP. Crop representation in LPJ-GUESS was adopted from Olin et al. (2015), applying constant fertiliser rate and area fraction under irrigation, as in Elliott et al. (2015).
LPJd
Sitch et al. (2003)e
No change
LPX-Bern
Stocker et al. (2014)f
Not applicable (not used in 2015)
OCN
Zaehle and Friend(2010)g
Updated to v1.r278. Biological N fixation is now simulated dynamically according to the OPT scheme of Meyerholt et al. (2016).
ORCHIDEE
Krinner et al. (2005)h
Updated revision 3687, including a new hydrological scheme with 11 layers and a complete diffusion scheme, a new parameterisation of photosynthesis, an improved scheme for representation of snow, and a new representation of soil albedo based on satellite data.
SDGVM
Woodward et al. (1995)i
Not applicable (not used in 2015)
VISIT
Kato et al. (2013)j
Updated to use CRU-NCEP shortwave radiation data instead of using internally estimated radiation from CRU cloudiness data.
Data products for land-use-change emissions
Bookkeeping
Houghton et al. (2012)
No change
Bookkeeping usingFAO2015
Houghton and Nassikas (2016)
Not applicable (not used in 2015)
Fire-based emissions
van der Werf et al. (2010)
No change
Ocean biogeochemistry models
NEMO-PlankTOM5
Buitenhuis et al. (2010)k
No change
NEMO-PISCES (IPSL)
Aumont and Bopp (2006)
No change
CCSM-BEC
Doney et al. (2009)
No change
MICOM-HAMOCC (NorESM-OC)
Schwinger et al. (2016)
No change
NEMO-PISCES (CNRM)
Séférian et al. (2013)l
No change
CSIRO
Oke et al. (2013)
No change
MITgcm-REcoM2
Hauck et al. (2016)
Nanophytoplankton chlorophyll degradation rate set to 0.1 per day
Data products for ocean CO2 flux
Landschützer
Landschützer et al. (2015)
No change
Jena CarboScope
Rödenbeck et al. (2014)
Updated to version oc_1.4 with longer spin-up/down periods both before and after the data-constrained period.
Atmospheric inversions for total CO2 fluxes (land-use-change + land + ocean CO2 fluxes)
CarbonTracker
Peters et al. (2010)
Updated to version CTE2016-FT with minor changes in the inversion setup.
Jena CarboScope
Rödenbeck et al. (2003)
Updated to version s81_v3.8.
CAMSm
Chevallier et al. (2005)
Updated to version 15.2 with minor changes in the inversion setup.
a See also Goll et al. (2015). b Joint UK
Land Environment Simulator. c See also Best et al. (2011).
d Lund–Potsdam–Jena. e Compared to published version,
decreased LPJ wood harvest efficiency so that 50 % of biomass was removed
off-site compared to 85 % used in the 2012 budget. Residue management of
managed grasslands increased so that 100 % of harvested grass enters the
litter pool. f Compared to published version: changed several model
parameters, due to new tuning with multiple observational constraints. No
mechanistic changes. g See also 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 CO2
down-regulation process added to photosynthesis. i See also
Woodward and Lomas (2004). Changes from publications include sub-daily
light downscaling for calculation of photosynthesis 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 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).
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
are included in our land-use-change emissions estimates (Table 5). 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 are only available at intervals of
5 years. We use the bookkeeping method based on FAO FRA 2010 here (Houghton
et al., 2012) and present preliminary results of an update using the FAO FRA
2015 (Houghton and Nassikas, 2016). Interannual variability in emissions due
to deforestation and degradation have 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 1997–2015. 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
published bookkeeping method that is available up to 2010 only and assumed
constant at the 2010 value during the period 2011–2015. 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. A discussion of other methods to estimate
ELUC was provided in the 2015 update (Le Quéré et al.,
2015a; Sect. 2.2.4).
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 method 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 products 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 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 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
include 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, and the growth/decay
curves are based on contemporary data that will implicitly reflect the
effects of CO2 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 ELUC
CO2 emissions associated with land-use change 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
in the year-to-year variations in the sub-component 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 vs. 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 July 2016)
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 2015. 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
DGVM simulations. New model experiments up to year 2015 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 following the
TRENDY protocol (see Sect. 2.5.2). Models use their latest configurations,
summarised in Tables 5 and 6.
Two sets of simulations were performed with the DGVMs, first forced with
historical changes in land-cover distribution, climate, atmospheric CO2
concentration, and N deposition, and second, as further described below 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.
Because of the limited availability of the land-use forcing (see below),
14 DGVMs performed historical simulations with time-invariant land-cover
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
ELUC, but they do not represent all processes resulting directly
from 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 CO2 levels and to climate
variability 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 CO2 concentration to calculate the fertilisation effect of
CO2 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 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 previous 5 years. The more comprehensive harmonised land-use data set
(Hurtt et al., 2011), 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 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 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, 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).
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.
Mean (GtC yr-1)
1960–1969
1970–1979
1980–1989
1990–1999
2000–2009
2006–2015
2015
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
1.0 ± 0.5
1.3 ± 0.5
DGVMsa
1.2 ± 0.3
1.2 ± 0.3
1.2 ± 0.2
1.2 ± 0.2
1.1 ± 0.2
1.3 ± 0.3
1.2 ± 0.4
Residual terrestrial sink (SLAND)
Budget residual
1.7 ± 0.7
1.7 ± 0.8
1.6 ± 0.8
2.6 ± 0.8
2.6 ± 0.8
3.1 ± 0.9
1.9 ± 0.9
DGVMsa
1.2 ± 0.5
2.2 ± 0.5
1.7 ± 0.6
2.3 ± 0.5
2.8 ± 0.6
2.8 ± 0.7
1.0 ± 1.4
Total land fluxes (SLAND-ELUC)
Budget (EFF-GATM-SOCEAN)
0.2 ± 0.5
0.4 ± 0.6
0.1 ± 0.6
1.0 ± 0.6
1.6 ± 0.6
2.2 ± 0.7
0.6 ± 0.7
DGVMsa
-0.2 ± 0.7
1.1 ± 0.5
0.4 ± 0.5
1.1 ± 0.3
1.8 ± 0.4
1.7 ± 0.5
-0.1 ± 1.4
Inversions (CTE2016-FT/JenaCarboScope/CAMS)b
–/–/–
–/–/–
–/0.2b/0.9b
–/1.0b/1.9b
1.5b/1.6b/2.5b
2.2b/2.3b/3.4b
1.9b/2.6b/2.6b
a Note that for DGVMs, the mean reported for the total
land fluxes is not equal to the difference between the means reported for
SLAND and ELUC as a different set of models contributed to
these two estimates (see Sect. 2.2.3). 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. 2.7.2). See Table 6 for model references.
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 2015 (Harris et al.,
2014; Viovy, 2016). The forcing data include both gridded observations of
climate and global atmospheric CO2, which change over time (Dlugokencky
and Tans, 2016), and N deposition (as used in some models; Table 5). As
mentioned before, 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 1860–2015. 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; Stocker and Joos, 2015). 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).
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 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 0.3 GtC yr-1 from
1959 to 2015 (Table 7). The mean of the multi-model ELUC estimates
is consistent with a combination of the bookkeeping method and fire-based
emissions (Table 7), 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 determine that an uncertainty of
±0.5 GtC yr-1 provides a semi-quantitative 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 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 in the bookkeeping
method (Table 7), the first decade available. This ratio is consistent with
the mean standard deviation of DGVMs land-use-change emissions over
1870–1958 (0.32 GtC) over the multi-model mean (0.9 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, as well as a trend and
variability in the ocean CO2 sink for 1959–2015 from seven 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.
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 outgas of
CO2 (Jacobson et al., 2007).
The two observation-based estimates
(Landschützer et al., 2015; Rödenbeck et al.,
2014) used here 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 version 4, 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 procedures. 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 (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 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 for 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 1959–2015 is computed using a
combination of seven 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, which could lead to an increase in the ocean sink of up to about
0.3 GtC yr-1 over the industrial period (Duce et al., 2008). They
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 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 CO2 sink
for each year (t) in GtC yr-1 is therefore
SOCEAN(t)=1n∑m=1m=nSOCEANm(t)SOCEANm(1990–1999)×2.2,
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 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 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
0.16 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
uncertainty in the annual ocean CO2 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., 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 of 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 (Rödenbeck et al., 2014)
and ± 0.41 GtC yr-1 (Landschützer et al., 2015), compared to
±0.29 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 = 0.71 (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 correlation 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 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 the growth rate of atmospheric CO2 concentration.
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 exactly attribute 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 1959–2015, 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
1959–2015 (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 of 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) where
available, net land fluxes (SLAND-ELUC) 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 ELUC that is a carbon source over the
1990s. Fourteen models met criteria (1), and five of the models that provided
ELUC met all three criteria.
The standard deviation of the annual CO2 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
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 Sect. 4),
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 (EFF, ELUC, GATM,
SOCEAN; Table 7), the DGVMs do not provide further reduction of
uncertainty in 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 2015 (including preliminary values in
some cases), and 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- to high-latitude Northern 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 estimates.
Atmospheric inversions
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 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 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 CAMS inversions
prescribed the same global EFF as in Sect. 2.1.1, during
2010–2015 for CarbonTracker and during 1979–2015 in CAMS. The Jena
CarboScope inversion uses EFF from EDGAR (2011) v4.2. Different
spatial and temporal distributions of EFF were prescribed in each
inversion.
Given their prescribed 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 CarboScope, and LMDZ for CAMS. 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. 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 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 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 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 growth rate in atmospheric
CO2 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 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 land and ocean fluxes (SLAND-ELUC+SOCEAN) across large regions of the globe. The
spatial breakdown is discussed in Sect. 3.1.3.