Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and
their redistribution among the atmosphere, ocean, and terrestrial biosphere
in a changing climate – the “global carbon budget” – is important to
better understand the global carbon cycle, support the development of
climate policies, and project future climate change. Here we describe and
synthesize data sets and methodology to quantify the five major components
of the global carbon budget and their uncertainties. Fossil CO2
emissions (EFOS) are based on energy statistics and cement production
data, while emissions from land-use change (ELUC), mainly
deforestation, are based on land use and land-use change data and
bookkeeping models. Atmospheric CO2 concentration is measured directly
and its growth rate (GATM) is computed from the annual changes in
concentration. The ocean CO2 sink (SOCEAN) and terrestrial
CO2 sink (SLAND) are estimated with global process models
constrained by observations. The resulting carbon budget imbalance
(BIM), the difference between the estimated total emissions and the
estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a
measure of imperfect data and understanding of the contemporary carbon
cycle. All uncertainties are reported as ±1σ. For the last
decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr-1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr-1 when the cement carbonation sink is included), and
ELUC was 1.6 ± 0.7 GtC yr-1. For the same decade, GATM was 5.1 ± 0.02 GtC yr-1 (2.4 ± 0.01 ppm yr-1), SOCEAN 2.5 ± 0.6 GtC yr-1, and SLAND 3.4 ± 0.9 GtC yr-1, with a budget
imbalance BIM of -0.1 GtC yr-1 indicating a near balance between
estimated sources and sinks over the last decade. For the year 2019 alone, the
growth in EFOS was only about 0.1 % with fossil emissions increasing
to 9.9 ± 0.5 GtC yr-1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr-1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr-1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr-1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was
5.4 ± 0.2 GtC yr-1 (2.5 ± 0.1 ppm yr-1), SOCEAN
was 2.6 ± 0.6 GtC yr-1, and SLAND was 3.1 ± 1.2 GtC yr-1, with a BIM of 0.3 GtC. The global atmospheric CO2
concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary
data for 2020, accounting for the COVID-19-induced changes in emissions,
suggest a decrease in EFOS relative to 2019 of about -7 % (median
estimate) based on individual estimates from four studies of -6 %, -7 %,
-7 % (-3 % to -11 %), and -13 %. Overall, the mean and trend in the
components of the global carbon budget are consistently estimated over the
period 1959–2019, but discrepancies of up to 1 GtC yr-1 persist for the
representation of semi-decadal variability in CO2 fluxes. Comparison of
estimates from diverse approaches and observations shows (1) no consensus
in the mean and trend in land-use change emissions over the last decade, (2)
a persistent low agreement between the different methods on the magnitude of
the land CO2 flux in the northern extra-tropics, and (3) an apparent
discrepancy between the different methods for the ocean sink outside the
tropics, particularly in the Southern Ocean. This living data update
documents changes in the methods and data sets used in this new global
carbon budget and the progress in understanding of the global carbon cycle
compared with previous publications of this data set (Friedlingstein et al.,
2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014,
2013). The data presented in this work are available at 10.18160/gcp-2020 (Friedlingstein et al., 2020).
Introduction
The concentration of carbon dioxide (CO2) in the atmosphere has
increased from approximately 277 parts per million (ppm) in 1750 (Joos and
Spahni, 2008), the beginning of the Industrial Era, to 409.85 ± 0.1 ppm in 2019 (Dlugokencky and Tans, 2020; Fig. 1). The atmospheric CO2
increase above pre-industrial levels was, initially, primarily caused by the
release of carbon to the atmosphere from deforestation and other land-use
change activities (Ciais et al., 2013). While emissions from fossil fuels
started before the Industrial Era, they became the dominant source of
anthropogenic emissions to the atmosphere from around 1950 and their
relative share has continued to increase until the present. Anthropogenic
emissions occur on top of an active natural carbon cycle that circulates
carbon between the reservoirs of the atmosphere, ocean, and terrestrial
biosphere on timescales from sub-daily to millennia, while exchanges with
geologic reservoirs occur at longer timescales (Archer et al., 2009).
Surface average atmospheric CO2 concentration (ppm).
The 1980–2019 monthly data are from NOAA/ESRL (Dlugokencky and Tans, 2020)
and are based on an average of direct atmospheric CO2 measurements from
multiple stations in the marine boundary layer (Masarie and Tans, 1995). The
1958–1979 monthly data are from the Scripps Institution of Oceanography,
based on an average of direct atmospheric CO2 measurements from the
Mauna Loa and South Pole stations (Keeling et al., 1976). To take into
account the difference of mean CO2 and seasonality between the
NOAA/ESRL and the Scripps station networks used here, the Scripps surface
average (from two stations) was de-seasonalized and harmonized to match the
NOAA/ESRL surface average (from multiple stations) by adding the mean
difference of 0.542 ppm, calculated here from overlapping data during
1980–2012.
The global carbon budget presented here refers to the mean, variations, and
trends in the perturbation of CO2 in the environment, referenced to the
beginning of the Industrial Era (defined here as 1750). This paper describes
the components of the global carbon cycle over the historical period with a
stronger focus on the recent period (since 1958, onset of atmospheric
CO2 measurements), the last decade (2010–2019), the last year (2019),
and the current year (2020). We quantify the input of CO2 to the
atmosphere by emissions from human activities, the growth rate of
atmospheric CO2 concentration, and the resulting changes in the storage
of carbon in the land and ocean reservoirs in response to increasing
atmospheric CO2 levels, climate change and variability, and other
anthropogenic and natural changes (Fig. 2). An understanding of this
perturbation budget over time and the underlying variability and trends of
the natural carbon cycle is necessary to understand the response of natural
sinks to changes in climate, CO2, and land-use change drivers, and to
quantify the permissible emissions for a given climate stabilization target.
Note that this paper quantifies the historical global carbon budget but
does not estimate the remaining future carbon emissions consistent with a
given climate target, often referred to as the “remaining carbon budget”
(Millar et al., 2017; Rogelj et al., 2016, 2019).
Schematic representation of the overall perturbation of the global carbon cycle caused by anthropogenic activities, averaged globally for the decade 2010–2019. See legends for the corresponding arrows and units. The uncertainty in the atmospheric CO2 growth rate is very small (±0.02 GtC yr-1) and is neglected for the figure. The anthropogenic perturbation occurs on top of an active carbon cycle, with fluxes and stocks represented in the background and taken from Ciais et al. (2013) for all numbers, with the ocean gross fluxes updated to 90 GtC yr-1 to account for the increase in atmospheric CO2 since publication, and except for the carbon stocks in coasts which is from a literature review of coastal marine sediments (Price and Warren, 2016). Cement carbonation sink of 0.2 GtC yr-1 is included in EFOS.
The components of the CO2 budget that are reported annually in this
paper include the following separate estimates for the CO2 emissions: (1) fossil
fuel combustion and oxidation from all energy and industrial processes, also
including cement production and carbonation (EFOS; GtC yr-1);
(2) the emissions resulting from deliberate human activities on land,
including those leading to land-use change (ELUC; GtC yr-1); (3) their partitioning among the growth rate of atmospheric CO2
concentration (GATM; GtC yr-1); (4) the sink of CO2 in the ocean (SOCEAN; GtC yr-1); and (5) the sink of CO2 on land (SLAND; GtC yr-1). The CO2 sinks as defined here
conceptually include the response of the land (including inland waters and
estuaries) and ocean (including coasts and territorial seas) to elevated
CO2 and changes in climate, rivers, and other environmental conditions,
although in practice not all processes are fully accounted for (see Sect. 2.7). Global emissions and their partitioning among the atmosphere, ocean, and land are in reality in balance. Due to combination of imperfect spatial
and/or temporal data coverage, errors in each estimate, and smaller terms
not included in our budget estimate (discussed in Sect. 2.7), their sum
does not necessarily add up to zero. We estimate a budget imbalance
(BIM), which is a measure of the mismatch between the estimated
emissions and the estimated changes in the atmosphere, land, and ocean, with
the full global carbon budget as follows:
EFOS+ELUC=GATM+SOCEAN+SLAND+BIM.GATM is usually reported in ppm yr-1, which we convert to units of
carbon mass per year, GtC yr-1, using 1 ppm = 2.124 GtC (Ballantyne
et al., 2012; Table 1). All quantities are presented in units of gigatonnes of carbon (GtC, 1015 gC), which is the same as petagrams of carbon
(PgC; Table 1). Units of gigatonnes of CO2 (or billion tonnes of
CO2) used in policy are equal to 3.664 multiplied by the value in units
of GtC.
Factors used to convert carbon in various units (by convention, unit 1 = unit 2× conversion).
Unit 1Unit 2ConversionSourceGtC (gigatonnes of carbon)ppm (parts per million)a2.124bBallantyne et al. (2012)GtC (gigatonnes of carbon)PgC (petagrams of carbon)1SI unit conversionGtCO2 (gigatonnes of carbon dioxide)GtC (gigatonnes of carbon)3.66444.01/12.011 in mass equivalentGtC (gigatonnes of carbon)MtC (megatonnes of carbon)1000SI unit conversion
a Measurements of atmospheric CO2 concentration have units of dry-air mole fraction; “ppm” is an abbreviation for micromole mol-1, dry air.
b The use of a factor of 2.124 assumes that all the atmosphere is well mixed within 1 year. In reality, only the troposphere is well mixed and the growth rate of CO2 concentration in the less well-mixed stratosphere is not measured by sites from the NOAA network. Using a factor of 2.124 makes the approximation that the growth rate of CO2 concentration in the stratosphere equals that of the troposphere on a yearly basis.
We also include a quantification of EFOS by country, computed with both
territorial and consumption-based accounting (see Sect. 2), and discuss
missing terms from sources other than the combustion of fossil fuels (see
Sect. 2.7).
The global CO2 budget has been assessed by the Intergovernmental Panel
on Climate Change (IPCC) in all assessment reports (Prentice et al., 2001;
Schimel et al., 1995; Watson et al., 1990; Denman et al., 2007; Ciais et
al., 2013), and by others (e.g. Ballantyne et al., 2012). The Global Carbon
Project (GCP, https://www.globalcarbonproject.org, last access: 16 November 2020)
has coordinated this cooperative community effort for the annual publication
of global carbon budgets for the year 2005 (Raupach et al., 2007; including
fossil emissions only), year 2006 (Canadell et al., 2007), year 2007
(published online; GCP, 2007), year 2008 (Le Quéré et al., 2009),
year 2009 (Friedlingstein et al., 2010), year 2010 (Peters et al., 2012b),
year 2012 (Le Quéré et al., 2013; Peters et al., 2013), year 2013
(Le Quéré et al., 2014), year 2014 (Le Quéré et al., 2015a;
Friedlingstein et al., 2014), year 2015 (Jackson et al., 2016; Le
Quéré et al., 2015b), year 2016 (Le Quéré et al., 2016),
year 2017 (Le Quéré et al., 2018a; Peters et al., 2017), year 2018
(Le Quéré et al., 2018b; Jackson et al., 2018), and most recently the
year 2019 (Friedlingstein et al., 2019; Jackson et al., 2019; Peters et al.,
2020). Each of these papers updated previous estimates with the latest
available information for the entire time series.
We adopt a range of ±1 standard deviation (σ) to report the
uncertainties in our estimates, representing a likelihood of 68 % that the
true value will be within the provided range if the errors have a Gaussian
distribution and no bias is assumed. This choice reflects the difficulty of
characterizing the uncertainty in the CO2 fluxes between the atmosphere
and the ocean and land reservoirs individually, particularly on an annual
basis, as well as the difficulty of updating the CO2 emissions from
land-use change. A likelihood of 68 % provides an indication of our
current capability to quantify each term and its uncertainty given the
available information. For comparison, the Fifth Assessment Report of the
IPCC (AR5; Ciais et al., 2013) generally reported a likelihood of 90 % for
large data sets whose uncertainty is well characterized, or for long time
intervals less affected by year-to-year variability. Our 68 % uncertainty
value is near the 66 % which the IPCC characterizes as “likely” for values
falling into the ±1σ interval. The uncertainties reported
here combine statistical analysis of the underlying data and expert
judgement of the likelihood of results lying outside this range. The
limitations of current information are discussed in the paper and have been
examined in detail elsewhere (Ballantyne et al., 2015; Zscheischler et al.,
2017). We also use a qualitative assessment of confidence level to
characterize the annual estimates from each term based on the type, amount,
quality, and consistency of the evidence as defined by the IPCC (Stocker et
al., 2013).
This paper provides a detailed description of the data sets and methodology
used to compute the global carbon budget estimates for the industrial
period, from 1750 to 2019, and in more detail for the period since 1959. It
also provides decadal averages starting in 1960 including the most recent
decade (2010–2019), results for the year 2019, and a projection for the year
2020. Finally it provides cumulative emissions from fossil fuels and
land-use change since the year 1750, the pre-industrial period, and since
the year 1850, the reference year for historical simulations in IPCC AR6
(Eyring et al., 2016). This paper is updated every year using the format of
“living data” to keep a record of budget versions and the changes in new
data, revision of data, and changes in methodology that lead to changes in
estimates of the carbon budget. Additional materials associated with the
release of each new version will be posted at the GCP website (http://www.globalcarbonproject.org/carbonbudget, last access:
16 November 2020), with fossil fuel emissions also available through the
Global Carbon Atlas (http://www.globalcarbonatlas.org, last access: 16
November 2020). With this approach, we aim to provide the highest
transparency and traceability in the reporting of CO2, the key driver
of climate change.
Methods
Multiple organizations 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 detailed descriptions of each component are provided
elsewhere.
How to cite the individual components of the global carbon budget presented here.
ComponentPrimary referenceGlobal fossil CO2 emissions (EFOS), total and by fuel typeThis paperNational territorial fossil CO2 emissions (EFOS)CDIAC source: Gilfillan et al. (2020)UNFCCC (2020)National consumption-based fossil CO2 emissions (EFOS) by country (consumption)Peters et al. (2011b) updated as described in this paperNet land-use change flux (ELUC)Average from Houghton and Nassikas (2017), Hansis et al. (2015), Gasser et al. (2020), all updated as described in this paperGrowth rate in atmospheric CO2 concentration (GATM)Dlugokencky and Tans (2020)Ocean and land CO2 sinks (SOCEAN and SLAND)This paper for SOCEAN and SLAND and references in Table 4 for individual models.
This is the 15th version of the global carbon budget and the ninth revised
version in the format of a living data update in Earth System Science Data.
It builds on the latest published global carbon budget of Friedlingstein et
al. (2019). The main changes are (1) the inclusion of data of the year 2019 and
a projection for the global carbon budget for year 2020; (2) the inclusion
of gross carbon fluxes associated with land-use changes; and (3) the
inclusion of cement carbonation in the fossil fuel and cement component of
the budget (EFOS). The main methodological differences between recent
annual carbon budgets (2015–2019) are summarized in Table 3 and previous
changes since 2006 are provided in Table A7.
Main methodological changes in the global carbon budget since 2016. Methodological changes introduced in one year are kept for the following years unless noted. Empty cells mean there were no methodological changes introduced that year. Table A7 lists methodological changes from the first global carbon budget publication up to 2015.
Publication yearFossil fuel emissions LUC emissionsReservoirs Uncertainty andother changesGlobalCountry (territorial)Country (consumption)AtmosphereOceanLand20162 yearsof BP dataAdded three small countries; China's emissions from 1990 from BP data (this release only)Preliminary ELUC using FRA-2015 shown for comparison; use of 5 DGVMsBased on 7modelsBased on 14 modelsDiscussion of projection for full budget for current yearLe Quéré et al. (2016)2017Projection includes India-specific dataAverage of two bookkeeping models; use of 12 DGVMsBased on eightmodels that match the observed sink for the 1990s; no longer normalizedBased on 15 models that meet observation-based criteria (see Sect. 2.5)Land multi-model average now used in main carbon budget, with the carbon imbalance presented separately; new table of key uncertaintiesLe Quéré et al. (2018a) GCB20172018Revision in cement emissions; Projection includes EU-specific dataAggregation of overseas territories into governing nations for total of 213 countriesUse of 16DGVMsUse of four atmospheric inversionsBased on seven modelsBased on 16 models; revised atmospheric forcing from CRUNCEP to CRU-JRA-55Introduction ofmetrics for evaluation of individual models using observationsLe Quéré et al. (2018b) GCB20182019Global emissions calculated as sum of all countries plus bunkers, rather than taken directly from CDIAC.Use of 15DGVMs*Use of threeatmospheric inversionsBased on ninemodelsBased on 16 modelsFriedlingstein et al. (2019) GCB20192020Cement carbonation now included in the EFOS estimate, reducing EFOS by about 0.2 GtC yr-1 for the last decadeIndia's emissions from Andrew (2020b); Corrections to Netherland Antilles and Aruba and Soviet emissions before 1950 as per Andrew (2020a; China's coal emissions in 2019 derived from official statistics, emissions now shown for EU27 instead of EU28. Projection for 2020 based on assessment of four approaches.Average of three bookkeeping models; use of 17 DGVMs*Use of six atmospheric inversionsBased on nine models. River flux revised and partitioned NH, tropics, SHBased on 17 models(this study) GCB2020
*ELUC is still estimated based on bookkeeping models, as in 2018 (Le Quéré et al., 2018b), but the number of DGVMs used to characterize the uncertainty has changed.
Fossil CO2 emissions (EFOS)Emissions estimates
The estimates of global and national fossil CO2 emissions (EFOS)
include the combustion of fossil fuels through a wide range of activities
(e.g. transport, heating and cooling, industry, fossil industry own use, and
natural gas flaring), the production of cement, and other process emissions
(e.g. the production of chemicals and fertilizers) as well as CO2
uptake during the cement carbonation process. The estimates of EFOS in
this study rely primarily on energy consumption data, specifically data on
hydrocarbon fuels, collated and archived by several organizations (Andres et
al., 2012; Andrew, 2020a). We use four main data sets for historical
emissions (1750–2019):
Global and national emission estimates for coal, oil, natural gas, and peat fuel extraction from the Carbon Dioxide Information Analysis Center (CDIAC) for the time period 1750–2017 (Gilfillan et al., 2020), as it is the only data set that extends back to 1750 by country.
Official national greenhouse gas inventory reports annually for 1990–2018 for the 42 Annex I countries in the UNFCCC (UNFCCC, 2020). We assess these to be the most accurate estimates because they are compiled by experts within countries that have access to the most detailed data, and they are periodically reviewed.
The BP Statistical Review of World Energy (BP, 2020), as these are the most up-to-date estimates of national energy statistics.
Global and national cement emissions updated from Andrew (2019) to include the latest estimates of cement production and clinker ratios.
In the following section we provide more details for each data set and
describe the additional modifications that are required to make the data set
consistent and usable.
CDIAC. The CDIAC estimates have been updated annually up to the year 2017, derived
primarily from energy statistics published by the United Nations (UNSD,
2020). Fuel masses and volumes are converted to fuel energy content using
country-level coefficients provided by the UN and then converted to
CO2 emissions using conversion factors that take into account the
relationship between carbon content and energy (heat) content of the
different fuel types (coal, oil, natural gas, natural gas flaring) and the
combustion efficiency (Marland and Rotty, 1984; Andrew, 2020a). Following
Andrew (2020a), we make corrections to emissions from coal in the Soviet
Union during World War II, amounting to a cumulative reduction of 53 MtC
over 1942–1943, and corrections to emissions from oil in the Netherlands
Antilles and Aruba prior to 1950, amounting to a cumulative reduction of 340 MtC over 23 years.
UNFCCC. Estimates from the national greenhouse gas inventory reports submitted to
the United Nations Framework Convention on Climate Change (UNFCCC) follow
the IPCC guidelines (IPCC, 2006, 2019) but have a slightly larger
system boundary than CDIAC by including emissions coming from carbonates
other than in cement manufacture. We reallocate the detailed UNFCCC sectoral
estimates to the CDIAC definitions of coal, oil, natural gas, cement, and
others to allow more consistent comparisons over time and between countries.
Specific country updates. For India, the data reported by CDIAC are
for the fiscal year running from April to March (Andrew, 2020a), and various
interannual variations in emissions are not supported by official data.
Given that India is the world's third-largest emitter and that a new data
source is available that resolves these issues, we replace CDIAC estimates
with calendar-year estimates through 2019 by Andrew (2020b). For Norway, CDIAC's
method of apparent energy consumption results in large errors,
and we therefore overwrite emissions before 1990 with estimates derived from
official Norwegian statistics.
BP. For the most recent year(s) for which the UNFCCC and CDIAC estimates are not yet available, we generate preliminary estimates using energy consumption
data (in exajoules, EJ) from the BP Statistical Review of World Energy (Andres et al.,
2014; BP, 2020; Myhre et al., 2009). We apply the BP growth rates by fuel
type (coal, oil, natural gas) to estimate 2019 emissions based on 2018
estimates (UNFCCC Annex I countries), and to estimate 2018–2019 emissions
based on 2017 estimates (remaining countries except India). BP's data set
explicitly covers about 70 countries (96 % of global energy emissions),
and for the remaining countries we use growth rates from the sub-region the
country belongs to. For the most recent years, natural gas flaring is
assumed to be constant from the most recent available year of data (2018 for Annex
I countries, 2017 for the remainder). We apply two exceptions to this update
using BP data. The first is for China's coal emissions, for which we use
growth rates reported in official preliminary statistics for 2019 (NBS,
2020b). The second exception is for Australia, for which BP reports a growth
rate of natural gas consumption in Australia of almost 30 %, which is
incorrect, and we use a figure of 2.2 % derived from Australia's own
reporting (Department of the Environment and Energy, 2020).
Cement. Estimates of emissions from cement production are updated from Andrew
(2019). Other carbonate decomposition processes are not included explicitly
here, except in national inventories provided by Annex I countries, but are
discussed in Sect. 2.7.2.
Country mappings. The published CDIAC data set includes 257 countries and regions. This list
includes countries that no longer exist, such as the USSR and Yugoslavia. We
reduce the list to 214 countries by reallocating emissions to currently
defined territories, using mass-preserving aggregation or disaggregation.
Examples of aggregation include merging East and West Germany to the
currently defined Germany. Examples of disaggregation include reallocating
the emissions from the former USSR to the resulting independent countries.
For disaggregation, we use the emission shares when the current territories
first appeared (e.g. USSR in 1992), and thus historical estimates of
disaggregated countries should be treated with extreme care. In the case of
the USSR, we were able to disaggregate 1990 and 1991 using data from the
International Energy Agency (IEA). In addition, we aggregate some overseas
territories (e.g. Réunion, Guadeloupe) into their governing nations
(e.g. France) to align with UNFCCC reporting.
Global total. The global estimate is the sum of the individual countries' emissions and
international aviation and marine bunkers. The CDIAC global total differs from the sum of the countries and bunkers since (1) the sum of imports in all
countries is not equal to the sum of exports because of reporting
inconsistencies, (2) changes in stocks, and (3) the share of non-oxidized
carbon (e.g. as solvents, lubricants, feedstocks) at the global level
is assumed to be fixed at the 1970s average while it varies in the country-level data based on energy data (Andres et al., 2012). From the 2019 edition
CDIAC now includes changes in stocks in the global total (Dennis Gilfillan, personal communication,
2020), removing one contribution to this discrepancy. The
discrepancy has grown over time from around zero in 1990 to over 500 MtCO2 in recent years, consistent with the growth in non-oxidized
carbon (IEA, 2019). To remove this discrepancy we now calculate the global
total as the sum of the countries and international bunkers.
Cement carbonation. From the moment it is created, cement begins to absorb CO2 from the
atmosphere, a process known as “cement carbonation”. We estimate this
CO2 sink as the average of two studies in the literature (Cao et al.,
2020; Guo et al., 2020). Both studies use the same model, developed by Xi et al. (2016), with different parameterizations and input data, with the estimate
of Guo and colleagues being a revision of Xi et al. (2016). The trends of the
two studies are very similar. Modelling cement carbonation requires
estimation of a large number of parameters, including the different types of
cement material in different countries, the lifetime of the structures
before demolition, of cement waste after demolition, and the volumetric
properties of structures, among others (Xi et al., 2016). Lifetime is an
important parameter because demolition results in the exposure of new
surfaces to the carbonation process. The most significant reasons for
differences between the two studies appear to be the assumed lifetimes of
cement structures and the geographic resolution, but the uncertainty bounds
of the two studies overlap. In the present budget, we include the cement
carbonation carbon sink in the fossil CO2 emission component
(EFOS), unless explicitly stated otherwise.
Uncertainty assessment for EFOS
We estimate the uncertainty of the global fossil CO2 emissions at
±5 % (scaled down from the published ± 10 % at ±2σ to the use of ±1σ bounds reported here; Andres et
al., 2012). This is consistent with a more detailed analysis of uncertainty
of ±8.4 % at ±2σ (Andres et al., 2014) and at the
high end of the range of ±5 %–10 % at ±2σ reported by
Ballantyne et al. (2015). This includes an assessment of uncertainties in
the amounts of fuel consumed, the carbon and heat contents of fuels, and the
combustion efficiency. While we consider a fixed uncertainty of ±5 % for all years, the uncertainty as a percentage of the emissions is
growing with time because of the larger share of global emissions from
emerging economies and developing countries (Marland et al., 2009).
Generally, emissions from mature economies with good statistical processes
have an uncertainty of only a few per cent (Marland, 2008), while emissions
from strongly developing economies such as China have uncertainties of
around ±10 % (for ±1σ; Gregg et al., 2008; Andres et
al., 2014). Uncertainties of emissions are likely to be mainly systematic
errors related to underlying biases of energy statistics and to the
accounting method used by each country.
Emissions embodied in goods and services
CDIAC, UNFCCC, and BP national emission statistics “include greenhouse gas
emissions and removals taking place within national territory and offshore
areas over which the country has jurisdiction” (Rypdal et al., 2006) and
are called territorial emission inventories. Consumption-based emission
inventories allocate emissions to products that are consumed within a
country and are conceptually calculated as the territorial emissions minus
the “embodied” territorial emissions to produce exported products plus the
emissions in other countries to produce imported products (consumption = territorial - exports + imports). Consumption-based emission attribution
results (e.g. Davis and Caldeira, 2010) provide additional information to
territorial-based emissions that can be used to understand emission drivers
(Hertwich and Peters, 2009) and quantify emission transfers by the trade of
products between countries (Peters et al., 2011b). The consumption-based
emissions have the same global total but reflect the trade-driven movement
of emissions across the Earth's surface in response to human activities.
We estimate consumption-based emissions from 1990–2018 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) and 2001 (GTAP6) as well as 2004, 2007, and 2011 (GTAP9.2), covering 57
sectors and 141 countries and regions. The detailed results are then
extended into an annual time series from 1990 to the latest year of the
gross domestic product (GDP) data (2018 in this budget), using GDP data by
expenditure in the current exchange rate of US dollars (USD; from the UN
National Accounts Main Aggregates Database; UN, 2019) and time series of
trade data from GTAP (based on the methodology in Peters et al., 2011b). We
estimate the sector-level CO2 emissions using the GTAP data and
methodology, include flaring and cement emissions from CDIAC, and then scale
the national totals (excluding bunker fuels) to match the emission estimates
from the carbon budget. We do not provide a separate uncertainty estimate
for the consumption-based emissions, but based on model comparisons and
sensitivity analysis, they are unlikely to be significantly different than
for the territorial emission estimates (Peters et al., 2012a).
Growth rate in emissions
We report the annual growth rate in emissions for adjacent years (in percent
per year) by calculating the difference between the two years and then
normalizing to the emissions in the first year:
(EFOS(t0+1)-EFOS(t0))/EFOS(t0)×100 %. We apply a leap-year
adjustment where relevant to ensure valid interpretations of annual growth
rates. This affects the growth rate by about 0.3 % yr-1 (1/366) and
causes calculated growth rates to go up by approximately 0.3 % if the first
year is a leap year and down by 0.3 % if the second year is a leap year.
The relative growth rate of EFOS over time periods of greater than 1 year can be rewritten using its logarithm equivalent as follows:
1EFOSdEFOSdt=d(lnEFOS)dt.
Here we calculate relative growth rates in emissions for multi-year periods
(e.g. a decade) by fitting a linear trend to ln(EFOS) in Eq. (2), reported
in percent per year.
Emissions projections
To gain insight into emission trends for 2020, we provide an assessment of
global fossil CO2 emissions, EFOS, by combining individual
assessments of emissions for China, the USA, the EU, India (the four
countries/regions with the largest emissions), and the rest of the world.
Our analysis this year is different to previous editions of the Global
Carbon Budget, as there have been several independent studies estimating
2020 global CO2 emissions in response to restrictions related to the
COVID-19 pandemic, and the highly unusual nature of the year makes the
projection much more difficult. We consider three separate studies (Le
Quéré et al., 2020; Forster et al., 2020; Liu et al., 2020), in
addition to building on the method used in our previous editions. We
separate each method into two parts: first we estimate emissions for the
year to date (YTD) and, second, we project emissions for the rest of the
year 2020. Each method is presented in the order it was published.
UEA: Le Quéré et al. (2020)
YTD. Le Quéré et al. (2020) estimated the effect of COVID-19 on
emissions using observed changes in activity using proxy data (such as
electricity use, coal use, steel production, road traffic, aircraft
departures, etc.), for six sectors of the economy as a function of
confinement levels, scaled to the globe based on policy data in response to
the pandemic. The analyses employed baseline emissions by country for the
latest year available (2018 or 2019) from the Global Carbon Budget 2019 to
estimate absolute daily emission changes and covered 67 countries
representing 97 % of global emissions. Here we use an update through to 13
November. The parameters for the changes in activity by sector were updated
for the industry and aviation sectors, to account for the slow recovery in
these sectors observed since the first peak of the pandemic. Specific
country-based parameters were used for India and the USA, which improved the
match to the observed monthly emissions (from Sect. “Global Carbon Budget Estimates”). By design,
this estimate does not include the background seasonal variability in
emissions (e.g. lower emissions in Northern Hemisphere summer; Jones et al., 2020), nor the trends in emissions that would be caused by other factors
(e.g. reduced use of coal in the EU and the US). To account for the
seasonality in emissions where data are available, the mean seasonal
variability over 2015–2019 was calculated from available monthly emissions
data for the USA, EU27, and India (data from Sect. “Global Carbon Budget Estimates”) and added to
the UEA estimate for these regions in Fig. B5. The uncertainty provided
reflects the uncertainty in activity parameters.
Projection. A projection is used to fill the data from 14 November to the
end of December, assuming countries where confinement measures were at
level 1 (targeted measures) on 13 November remain at that level until the
end of 2020. For countries where confinement measures were at more stringent
levels of 2 and 3 (see Le Quéré et al., 2020) on 13 November, we assume
that the measures ease by one level after their announced end date and then
remain at that level until the end of 2020.
Priestley Centre: Forster et al. (2020)
YTD. Forster et al. (2020) estimated YTD emissions based primarily on Google
mobility data. The mobility data were used to estimate daily fractional
changes in emissions from power, surface transport, industry, residential,
and public and commercial sectors. The analyses employed baseline emissions
for 2019 from the Global Carbon Project to estimate absolute emission
changes and covered 123 countries representing over 99 % of global
emissions. For a few countries – most notably China and Iran – Google data
were not available and so data were obtained from the high-reduction estimate
from Le Quéré et al. (2020). We use an updated version of Forster et al. (2020) in which emission-reduction estimates were extended through 3 November.
Projection. The estimates were projected from the start of November to the
end of December with the assumption that the declines in emissions from
their baselines remain at 66 % of the level over the last 30 d with
estimates.
Carbon Monitor: Liu et al. (2020)
YTD. Liu et al. (2020) estimated YTD emissions using emission data and
emission proxy activity data including hourly to daily electrical power
generation data and carbon emission factors for each different electricity
source from the national electricity operation systems of 31 countries,
real-time mobility data (TomTom city congestion index data of 416 cities
worldwide calibrated to reproduce vehicle fluxes in Paris and FlightRadar24
individual flight location data), monthly industrial production data
(calculated separately by cement production, steel production, chemical
production, and other industrial production of 27 industries) or indices
(primarily the industrial production index) from the national statistics of 62
countries and regions, and monthly fuel consumption data corrected for the
daily population-weighted air temperature in 206 countries using predefined
heating and temperature functions from EDGAR for residential, commercial, and
public buildings' heating emissions, to finally calculate the global fossil
CO2 emissions, as well as the daily sectoral emissions from power sector,
industry sector, transport sector (including ground transport, aviation, and
shipping), and residential sector respectively. We use an updated version of
Liu et al. (2020) with data extended through the end of September.
Projection. Liu et al. (2020) did not perform a projection and only
presented YTD results. For purposes of comparison with other methods, we use
a simple approach to extrapolating their observations by assuming the
remaining months of the year change by the same relative amount compared to
2019 in the final month of observations.
Global Carbon Budget estimates
Previous editions of the Global Carbon Budget (GCB) have estimated YTD
emissions and performed projections, using sub-annual energy consumption
data from a variety of sources depending on the country or region. The YTD
estimates have then been projected to the full year using specific methods
for each country or region. This year we make some adjustments to this
approach, as described below, with detailed descriptions provided in
Appendix C.
China. The YTD estimate is based on monthly data from China's National Bureau
of Statistics and Customs, with the projection based on the relationship
between previous monthly data and full-year data to extend the 2020 monthly
data to estimate full-year emissions.
USA. The YTD and projection are taken directly from the US Energy
Information Agency.
EU27. The YTD estimates are based on monthly consumption data of coal, oil,
and gas converted to CO2 and scaled to match the previous year's emissions.
We use the same method for the EU27 as for Carbon Monitor described above to
generate a full-year projection.
India. YTD estimates are updated from Andrew (2020b), which calculates
monthly emissions directly from detailed energy and cement production data.
We use the same method for India as for Carbon Monitor, described above, to
generate a full-year projection.
Rest of the world. There is no YTD estimate, while the 2020 projection is based
on a GDP estimate from the IMF combined with average improvements in carbon
intensity observed in the last 10 years, as in previous editions of the
Global Carbon Budget (e.g. Friedlingstein et al., 2019).
Synthesis
In the results section we present the estimates from the four different
methods, showing the YTD estimates to the last common historical data point
in each data set and the projections for 2020.
CO2 emissions from land use, land-use change, and forestry
(ELUC)
The net CO2 flux from land use, land-use change, and forestry
(ELUC, called land-use change emissions in the rest of the text)
includes CO2 fluxes from deforestation, afforestation, logging and
forest degradation (including harvest activity), shifting cultivation (cycle
of cutting forest for agriculture, then abandoning), and regrowth of forests
following wood harvest or abandonment of agriculture. Emissions from peat
burning and drainage are added from external data sets (see Sect. 2.2.1). Only some
land-management activities are included in our land-use change emissions
estimates (Table A1). Some of these activities lead to emissions of CO2
to the atmosphere, while others lead to CO2 sinks. ELUC is the net
sum of emissions and removals due to all anthropogenic activities
considered. Our annual estimate for 1959–2019 is provided as the average of
results from three bookkeeping approaches (Sect. 2.2.1): an estimate using
the bookkeeping of land use emissions model (Hansis et al., 2015; hereafter
BLUE), the estimate published by Houghton and Nassikas (2017; hereafter
HandN2017) and the estimate published by Gasser et al. (2020) using the
compact Earth system model OSCAR, the latter two updated to 2019. All three
data sets are then extrapolated to provide a projection for 2020 (Sect. 2.2.4). In addition, we use results from dynamic global vegetation models
(DGVMs; see Sect. 2.2.2 and Table 4) to help quantify the uncertainty in
ELUC (Sect. 2.2.3) and thus better characterize our
understanding. Note that we use the scientific ELUC definition,
which counts fluxes due to environmental changes on managed land towards
SLAND, as opposed to the national greenhouse gas inventories under the
UNFCCC, which include them in ELUC and thus often report smaller
land-use emissions (Grassi et al., 2018; Petrescu et al., 2020).
References for the process models, pCO2-based ocean flux products, and atmospheric inversions included in Figs. 6–8. All models and products are updated with new data to the end of the year 2019, and the atmospheric forcing for the DGVMs has been updated as described in Sect. 2.2.2.
Model/data nameReferenceChange from Global Carbon Budget 2019 (Friedlingstein et al., 2019)Bookkeeping models for land-use change emissions BLUEHansis et al. (2015)No changeHandN2017Houghton and Nassikas (2017)No changeOSCARGasser et al. (2020)aNew this yearDynamic global vegetation models CABLE-POPHaverd et al. (2018)No changeCLASSICMelton et al. (2020)Formerly called CLASS-CTEM; evaporation from top soil layer is reduced which increases soil moisture and yields better GPP especially in dry and semi-arid regionsCLM5.0Lawrence et al. (2019)No changeDLEMTian et al. (2015)bUpdated algorithms for land-use change processes.IBISYuan et al. (2014)New this yearISAMMeiyappan et al. (2015)No changeISBA-CTRIPDelire et al. (2020)cUpdated spin-up protocol + model name updated (SURFEXv8 in GCB2017) + inclusion of crop harvesting moduleJSBACHMauritsen et al. (2019)No changeJULES-ESSellar et al. (2019)dNo changeLPJ-GUESSSmith et al. (2014)eBug fixes and output code restructuring.LPJPoulter et al. (2011)fNo changeLPX-BernLienert and Joos (2018)Changed compiler to Intel Fortran from PGI.OCNZaehle and Friend (2010)gNo change (uses r294).ORCHIDEEv3Vuichard et al. (2019)hInclusion of N cycle and CN interactions in ORCHIDEE2.2 (i.e. CMIP6) versionSDGVMWalker et al. (2017)iNo changes from version used in Friedlingstein et al. (2019).VISITKato et al. (2013)jChange to distinguish managed pasture/rangeland information when conversion from natural vegetation to pasture occurs. Add upper limit of deforested biomass from secondary land using the mean biomass density data of LUH2.YIBsYue and Unger (2015)New this yearGlobal ocean biogeochemistry models NEMO-PlankTOM5Buitenhuis et al. (2013)No changeMICOM-HAMOCC (NorESM-OCv1.2)Schwinger et al. (2016)No changeMPIOM-HAMOCC6Paulsen et al. (2017)No changeNEMO3.6-PISCESv2-gas (CNRM)Berthet et al. (2019)kMinor bug fixes and updated spin-up proceduresCSIROLaw et al. (2017)Small bug fixes and revised model-spin-upFESOM-1.4-REcoM2Hauck et al. (2020)lNew physical model this yearMOM6-COBALT (Princeton)Liao et al. (2020)No changeCESM-ETHZDoney et al. (2009)Included water vapour correction when converting from xCO2 to pCO2NEMO-PISCES (IPSL)Aumont et al. (2015)Updated spin-up procedurepCO2-based flux ocean products Landschützer (MPI-SOMFFN)Landschützer et al. (2016)Update to SOCATv2020 measurements and time period 1982–2019; now use of ERA5 winds instead of ERA-InterimRödenbeck (Jena-MLS)Rödenbeck et al. (2014)Update to SOCATv2020 measurements, involvement of a multi-linear regression for extrapolation (combined with an explicitly interannual correction), use of OCIM (deVries et al., 2014) as decadal prior, carbonate chemistry parameterization now time-dependent, grid resolution increased to 2.5×2∘, adjustable degrees of freedom now also covering shallow areas and ArcticCMEMSChau et al. (2020)Update to SOCATv2020 measurements and extend time period 1985–2019. Use the parameterization of air–sea CO2 fluxes as in Wanninkhof (2014) instead of Wanninkhof (1992)CSIR-ML6Gregor et al. (2019)New this yearWatson et al. (2020)Watson et al. (2020)New this year
Continued.
Model/data nameReferenceChange from Global Carbon Budget 2019 (Friedlingstein et al., 2019)Atmospheric inversions CAMSChevallier et al. (2005) with updates given in https://atmosphere.copernicus.eu/ (last access: 16 November 2020)mNo changeCarbonTracker Europe (CTE)van der Laan-Luijkx et al. (2017)Model transport driven by ERA5 reanalysis; GFAS fire emissions applied instead of SIBCASA-GFED; Rödenbeck et al. (2003), ocean fluxes used as priors instead of Jacobson et al. (2007)Jena CarboScopeRödenbeck et al. (2003, 2018)No changeUoE in situFeng et al. (2016)nNew this yearNISMON-CO2Niwa et al. (2017)New this yearMIROC4-ACTMPatra et al. (2018)New this year
a See also Gasser et al. (2017).
b See also Tian et al. (2011).
c See also Decharme et al. (2019) and Seferian et al. (2019).
d JULES-ES is the Earth System configuration of the Joint UK Land Environment Simulator. See also Best et al. (2011), Clark et al. (2011) and Wiltshire et al. (2020).
e To account for the differences between the derivation of shortwave radiation from CRU cloudiness and DSWRF from CRUJRA, the photosynthesis scaling parameter αa was modified (-15 %) to yield similar results.
f Lund–Potsdam–Jena. 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.
g See also Zaehle et al. (2011).
h See Zaehle and Friend (2010) and Krinner et al. (2005).
i See also Woodward and Lomas (2004).
j See also Ito and Inatomi (2012).
k See also Seferian et al. (2019).
l Longer spin-up than in Hauck et al. (2020); see also Schourup-Kristensen et al. (2014).
m See also Remaud et al. (2018).
n See also Feng et al. (2009) and Palmer et al. (2019).
Bookkeeping models
Land-use change CO2 emissions and uptake fluxes are calculated by three
bookkeeping models. These are based on the original bookkeeping approach of
Houghton (2003) that keeps track of the carbon stored in vegetation and
soils before and after a land-use change (transitions between various
natural vegetation types, croplands, and pastures). Literature-based response
curves describe decay of vegetation and soil carbon, including transfer to
product pools of different lifetimes, as well as carbon uptake due to
regrowth. In addition, the bookkeeping models represent long-term
degradation of primary forest as lowered standing vegetation and soil carbon
stocks in secondary forests and also include forest management practices
such as wood harvests.
BLUE and HandN2017 exclude land ecosystems' transient response to changes
in climate, atmospheric CO2, and other environmental factors and base
the carbon densities on contemporary data from literature and inventory
data. Since carbon densities thus remain fixed over time, the additional
sink capacity that ecosystems provide in response to CO2 fertilization
and some other environmental changes is not captured by these models
(Pongratz et al., 2014). On the contrary, OSCAR includes this transient
response, and it follows a theoretical framework (Gasser and Ciais, 2013)
that allows separate bookkeeping of land-use emissions and the loss of
additional sink capacity. Only the former is included here, while the latter
is discussed in Sect. 2.7.4. The bookkeeping models differ in (1) computational units (spatially explicit treatment of land-use change for
BLUE, country-level for HandN2017, 10 regions and 5 biomes for OSCAR), (2) processes represented (see Table A1), and (3) carbon densities assigned to
vegetation and soil of each vegetation type (literature-based for HandN2017
and BLUE, calibrated to DGVMs for OSCAR). A notable change of HandN2017
over the original approach by Houghton (2003) used in earlier budget
estimates is that no shifting cultivation or other back and
forth transitions at a level below country are included. Only a decline in
forest area in a country as indicated by the Forest Resource Assessment of
the FAO that exceeds the expansion of agricultural area as indicated by FAO
is assumed to represent a concurrent expansion and abandonment of cropland.
In contrast, the BLUE and OSCAR models include sub-grid-scale transitions
between all vegetation types. Furthermore, HandN2017 assume conversion of
natural grasslands to pasture, while BLUE and OSCAR allocate pasture
proportionally on all natural vegetation that exists in a grid cell. This is
one reason for generally higher emissions in BLUE and OSCAR. Bookkeeping
models do not directly capture carbon emissions from peat fires, which can
create large emissions and interannual variability due to synergies of
land-use and climate variability in Southeast Asia, in particular during
El-Niño events, nor emissions from the organic layers of drained peat
soils. To correct for this, HandN2017 includes carbon emissions from peat
burning based on the Global Fire Emission Database (GFED4s; van der Werf et
al., 2017), and peat drainage based on estimates by Hooijer et al. (2010)
for Indonesia and Malaysia. We add GFED4s peat fire emissions to BLUE and
OSCAR output but use the newly published global FAO peat drainage emissions
1990–2018 from croplands and grasslands (Conchedda and Tubiello, 2020). We
linearly increase tropical drainage emissions from 0 in 1980, consistent
with HandN2017's assumption, and keep emissions from the often old drained
areas of the extra-tropics constant pre-1990. This adds 8.6 GtC for 1960–2019 for
FAO compared to 5.4 GtC for Hooijer et al. (2010). Peat fires add another
2.0 GtC over the same period.
The three bookkeeping estimates used in this study differ with respect to
the land-use change data used to drive the models. HandN2017 base their
estimates directly on the Forest Resource Assessment of the FAO, which
provides statistics on forest-area change and management at intervals of
5 years currently updated until 2015 (FAO, 2015). The data are based on
country reporting to FAO and may include remote-sensing information in more
recent assessments. Changes in land use other than forests are based on
annual, national changes in cropland and pasture areas reported by FAO
(FAOSTAT, 2015). On the other hand, BLUE uses the harmonized land-use change
data LUH2-GCB2020 covering the entire 850–2019 period (an update to the
previously released LUH2 v2h data set; 10.22033/ESGF/input4MIPs.1127; Hurtt et al., 2020), which
was also used as input to the DGVMs (Sect. 2.2.2). It describes land-use
change, also based on the FAO data as well as the HYDE data set (Klein Goldewijk et
al., 2017a, b), but provided at a quarter-degree spatial resolution,
considering sub-grid-scale transitions between primary forest, secondary
forest, primary non-forest, secondary non-forest, cropland, pasture,
rangeland, and urban land (Hurtt et al., 2020). LUH2-GCB2020 provides a
distinction between rangelands and pasture, based on inputs from HYDE. To
constrain the models' interpretation of whether rangeland implies the
original natural vegetation to be transformed to grassland or not (e.g.
browsing on shrubland), a forest mask was provided with LUH2-GCB2020; forest
is assumed to be transformed to grasslands, while other natural vegetation
remains (in case of secondary vegetation) or is degraded from primary to
secondary vegetation (Ma et al., 2020). This is implemented in BLUE. OSCAR
was run with both LUH2-GCB2019 850–2018 (as used in Friedlingstein et al.,
2019) and FAO/FRA (as used by Houghton and Nassikas, 2017), where the latter
was extended beyond 2015 with constant 2011–2015 average values. The
best-guess OSCAR estimate used in our study is a combination of results for
LUH2-GCB2019 and FAO/FRA land-use data and a large number of perturbed
parameter simulations weighted against an observational constraint.
HandN2017 was extended here for 2016 to 2019 by adding the annual change in
total tropical emissions to the HandN2017 estimate for 2015, including
estimates of peat drainage and peat burning as described above as well as
emissions from tropical deforestation and degradation fires from GFED4.1s
(van der Werf et al., 2017). Similarly, OSCAR was extended from 2018 to
2019. Gross fluxes for HandN2017 and OSCAR were extended to 2019 based on a
regression of gross sources (including peat emissions) to net emissions for
recent years. BLUE's 2019 value was adjusted because the LUH2-GCB2020
forcing for 2019 was an extrapolation of earlier years, thus not capturing
the rising deforestation rates occurring in South America in 2019 and the
anomalous fire season in equatorial Asia (see Sects. 2.2.4 and 3.2.1).
Anomalies of GFED tropical deforestation and degradation and equatorial Asia
peat fire emissions relative to 2018 are therefore added. Resulting dynamics
in the Amazon are consistent with BLUE simulations using directly observed
forest cover loss and forest alert data (Hansen et al., 2013; Hansen et al.,
2016).
For ELUC from 1850 onwards we average the estimates from BLUE,
HandN2017, and OSCAR. For the cumulative numbers starting 1750 an average of
four earlier publications is added (30 ± 20 PgC for 1750–1850, rounded to the
nearest 5; Le Quéré et al., 2016).
For the first time we provide estimates of the gross land-use change fluxes
from which the reported net land-use change flux, ELUC, is derived as a
sum. Gross fluxes are derived internally by the three bookkeeping models:
gross emissions stem from decaying material left dead on site and from
products after clearing of natural vegetation for agricultural purposes,
wood harvesting, emissions from peat drainage and peat burning, and, for
BLUE, additionally from degradation from primary to secondary land through
usage of natural vegetation as rangeland. Gross removals stem from regrowth
after agricultural abandonment and wood harvesting.
Dynamic global vegetation models (DGVMs)
Land-use change CO2 emissions have also been estimated using an
ensemble of 17 DGVM simulations. The DGVMs account for deforestation and
regrowth, the most important components of ELUC, but they do not
represent all processes resulting directly from human activities on land
(Table A1). All DGVMs represent processes of vegetation growth and
mortality, as well as decomposition of dead organic matter associated with
natural cycles, and include the vegetation and soil carbon response to
increasing atmospheric CO2 concentration and to climate variability and
change. Some models explicitly simulate the coupling of carbon and nitrogen
cycles and account for atmospheric N deposition and N fertilizers (Table A1). The DGVMs are independent from the other budget terms except for their
use of atmospheric CO2 concentration to calculate the fertilization
effect of CO2 on plant photosynthesis.
Many DGVMs used the HYDE land-use change data set (Klein Goldewijk et al., 2017a,
b), which provides annual (1700–2019), half-degree, fractional data on
cropland and pasture. The data are based on the available annual FAO
statistics of change in agricultural land area available until 2015. HYDE
version 3.2 used FAO statistics until 2012, which were supplemented using
the annual change anomalies from FAO data for the years 2013–2015 relative to
year 2012. HYDE forcing was also corrected for Brazil for the years 1951–2012.
After the year 2015 HYDE extrapolates cropland, pasture, and urban land-use
data until the year 2019. Some models also use the LUH2-GCB2020 data set, an
update of the more comprehensive harmonized land-use data set (Hurtt et al.,
2011), which further includes fractional data on primary and secondary forest
vegetation, as well as all underlying transitions between land-use states
(1700–2019) (10.22033/ESGF/input4MIPs.1127, Hurtt et al., 2017;
Hurtt et al., 2011, 2020; Table A1). This new data set is of
quarter-degree fractional areas of land-use states and all transitions
between those states, including a new wood harvest reconstruction and new
representation of shifting cultivation, crop rotations, and management
information including irrigation and fertilizer application. The land-use
states include five different crop types in addition to the
pasture–rangeland split discussed before. Wood harvest patterns are
constrained with Landsat-based tree cover loss data (Hansen et al., 2013).
Updates of LUH2-GCB2020 over last year's version (LUH2-GCB2019) are using
the most recent HYDE/FAO release (covering the time period up to and including
2015), which also corrects an error in the version used for the 2018 budget
in Brazil. The FAO wood harvest data have changed for the years 2015 onwards
and so those are now being used in this year's LUH-GCB2020 data set. This
means the LUH-GCB2020 data are identical to LUH-GCB2019 for all years up to
2015 and differ slightly in terms of wood harvest and resulting secondary
area, age, and biomass for years after 2015.
DGVMs implement land-use change differently (e.g. an increased cropland
fraction in a grid cell can either be at the expense of grassland or shrubs,
or forest, the latter resulting in deforestation; land cover fractions of
the non-agricultural land differ between models). Similarly, model-specific
assumptions are applied to convert deforested biomass or deforested areas
and other forest product pools into carbon, and different choices are made
regarding the allocation of rangelands as natural vegetation or pastures.
The DGVM model runs were forced by either the merged monthly Climate
Research Unit (CRU) and 6-hourly Japanese 55-year Reanalysis (JRA-55) data
set or by the monthly CRU data set, both providing observation-based
temperature, precipitation, and incoming surface radiation on a
0.5∘×0.5∘ grid and updated to 2019 (Harris and Jones, 2019; Harris et al.,
2020). The combination of CRU monthly data with 6-hourly forcing from
JRA-55 (Kobayashi et al., 2015) is performed with methodology used in
previous years (Viovy, 2016) adapted to the specifics of the JRA-55 data.
The forcing data also include global atmospheric CO2, which changes
over time (Dlugokencky and Tans, 2020), and gridded, time-dependent N
deposition and N fertilizers (as used in some models; Table A1).
Two sets of simulations were performed with each of the DGVMs. Both applied
historical changes in climate, atmospheric CO2 concentration, and N
inputs. The two sets of simulations differ, however, with respect to
land use: one set applies historical changes in land use, the other a
time-invariant pre-industrial land cover distribution and pre-industrial
wood harvest rates. By difference of the two simulations, the dynamic
evolution of vegetation biomass and soil carbon pools in response to
land-use change can be quantified in each model (ELUC). Using the
difference between these two DGVM simulations to diagnose ELUC means
the DGVMs account for the loss of additional sink capacity (see Sect. 2.7.4), while the bookkeeping models do
not.
As a criterion for inclusion in this carbon budget, we only retain models
that simulate a positive ELUC during the 1990s, as assessed in the IPCC
AR4 (Denman et al., 2007) and AR5 (Ciais et al., 2013). All DGVMs met this
criteria, although one model was not included in the ELUC estimate from
DGVMs as it exhibited a spurious response to the transient land cover change
forcing after its initial spin-up.
Uncertainty assessment for ELUC
Differences between the bookkeeping models and DGVM models originate from
three main sources: the different methodologies, which among other things lead to
inclusion of the loss of additional sink capacity in DGVMs (Sect. 2.7.4),
the underlying land-use and land-cover data set, and the different processes
represented (Table A1). We examine the results from the DGVM models and of
the bookkeeping method and use the resulting variations as a way to
characterize the uncertainty in ELUC.
Despite these differences, the ELUC estimate from the DGVM multi-model
mean is consistent with the average of the emissions from the bookkeeping
models (Table 5). However there are large differences among individual DGVMs
(standard deviation at around 0.5 GtC yr-1; Table 5), between the
bookkeeping estimates (average difference BLUE-HN2017 of 0.7 GtC yr-1,
BLUE-OSCAR of 0.3 GtC yr-1, OSCAR-HN2017 of 0.5 GtC yr-1), and
between the current estimate of HandN2017 and its previous model version
(Houghton et al., 2012). The uncertainty in ELUC of ±0.7 GtC yr-1 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. Prior to
the year 1959, the uncertainty in ELUC was taken from the standard
deviation of the DGVMs. We assign low confidence to the annual estimates of
ELUC because of the inconsistencies among estimates and of the
difficulties in quantifying some of the processes in DGVMs.
Comparison of results from the bookkeeping method and budget residuals with results from the DGVMs and inverse estimates for different periods, the last decade, and the last year available. All values are in GtC yr-1. The DGVM uncertainties represent ±1σ of the decadal or annual (for 2019 only) estimates from the individual DGVMs: for the inverse models the range of available results is given. All values are rounded to the nearest 0.1 GtC and therefore columns do not necessarily add to zero.
* Estimates are adjusted for the pre-industrial influence of river fluxes and adjusted to common EFOS (Sect. 2.6.1). The ranges given include varying numbers (in parentheses) of inversions in each decade (Table A4).
Emissions projections for ELUC
We project the 2020 land-use emissions for BLUE, HandN2017, and OSCAR,
starting from their estimates for 2019 assuming unaltered peat drainage,
which has low interannual variability, and the highly variable emissions
from peat fires, tropical deforestation, and degradation as estimated using
active fire data (MCD14ML; Giglio et al., 2016). Those latter scale almost
linearly with GFED over large areas (van der Werf et al., 2017) and thus
allow for tracking fire emissions in deforestation and tropical peat zones
in near-real time. During most years, emissions during January-September
cover most of the fire season in the Amazon and Southeast Asia, where a
large part of the global deforestation takes place and our estimates capture
emissions until 31 October. By the end of October 2020 emissions from
tropical deforestation and degradation fires were estimated to be 227 TgC,
down from 347 TgC in 2019 (313 TgC 1997–2019 average). Peat fire emissions
in equatorial Asia were estimated to be 1 TgC, down from 117 TgC in 2019 (68 TgC 1997–2019 average). The lower fire emissions for both processes in 2020
compared to 2019 are related to the transition from unusually dry conditions
for a non-El Niño year in Indonesia in 2019, which caused relatively
high emissions, to few fires due to wet conditions throughout 2020. By
contrast, fire emissions in South America remained above-average in 2020,
with the slight decrease since 2019 estimated in GFED4.1s (van der Werf et
al., 2017) being a conservative estimate. This is consistent with slightly
reduced deforestation rates in 2020 compared to 2019 (note that often Amazon
deforestation is reported from August of the previous to July of the current
year; for such reporting, 2020 deforestation will tend to be higher in 2020
than in 2019 by including strong deforestation August–December 2019). Together, this
results in pantropical fire emissions from deforestation, degradation, and
peat burning of about 230 TgC projected for 2020 as compared to 464 TgC in
2019; this is slightly above the 2017 and 2018 values of pantropical fire
emissions. Overall, however, we have low confidence in our projection due to
the large uncertainty range we associate with past ELUC, the dependence of
2020 emissions on legacy fluxes from previous years, uncertainties related
to fire emissions estimates, and the lack of data before the end of the year
that would allow deforested areas to be quantified accurately. Also, an
incomplete coverage of degradation by fire data makes our estimates
conservative, considering that degradation rates in the Amazon increased
from 2019 to 2020 (INPE, 2020).
Growth rate in atmospheric CO2 concentration
(GATM)Global growth rate in atmospheric CO2 concentration
The rate of growth of the atmospheric CO2 concentration is provided
by the US National Oceanic and Atmospheric Administration Earth System
Research Laboratory (NOAA/ESRL; Dlugokencky and Tans, 2020), which is
updated from Ballantyne et al. (2012). For the 1959–1979 period, the global
growth rate is based on measurements of atmospheric CO2 concentration
averaged from the Mauna Loa and South Pole stations, as observed by the
CO2 Program at Scripps Institution of Oceanography (Keeling et al.,
1976). For the 1980–2019 time period, the global growth rate is based on the
average of multiple stations selected from the marine boundary layer sites
with well-mixed background air (Ballantyne et al., 2012), after fitting each
station with a smoothed curve as a function of time, and averaging by
latitude band (Masarie and Tans, 1995). The annual growth rate is estimated
by Dlugokencky and Tans (2020) from atmospheric CO2 concentration by
taking the average of the most recent December–January months corrected for
the average seasonal cycle and subtracting this same average 1 year
earlier. The growth rate in units of ppm yr-1 is converted to units
of GtC yr-1 by multiplying by a factor of 2.124 GtC ppm-1 (Ballantyne
et al., 2012).
The uncertainty around the atmospheric growth rate is due to four main
factors: first, the long-term reproducibility of reference gas standards
(around 0.03 ppm for 1σ from the 1980s; Dlugokencky and Tans, 2020);
second, small unexplained systematic analytical errors that may have a
duration of several months to 2 years come and go – they have been
simulated by randomizing both the duration and the magnitude (determined
from the existing evidence) in a Monte Carlo procedure; third, the network
composition of the marine boundary layer with some sites coming or going,
gaps in the time series at each site, etc. (Dlugokencky and Tans, 2020) – this uncertainty was estimated by NOAA/ESRL with a Monte Carlo method by
constructing 100 “alternative” networks (Masarie and Tans, 1995; NOAA/ESRL,
2020) and added up to 0.085 ppm when summed in quadrature with the second uncertainty (Dlugokencky and Tans, 2020); fourth, the uncertainty
associated with using the average CO2 concentration from a surface
network to approximate the true atmospheric average CO2 concentration
(mass-weighted, in three dimensions) is needed to assess the total atmospheric
CO2 burden. In reality, CO2 variations measured at the stations
will not exactly track changes in total atmospheric burden, with offsets in
magnitude and phasing due to vertical and horizontal mixing. This effect
must be very small on decadal and longer timescales, when the atmosphere
can be considered well mixed. Preliminary estimates suggest this effect
would increase the annual uncertainty, but a full analysis is not yet
available. We therefore maintain an uncertainty around the annual growth
rate based on the multiple stations' data set ranges between 0.11 and 0.72 GtC yr-1, with a mean of 0.61 GtC yr-1 for 1959–1979 and 0.17 GtC yr-1 for 1980–2019, when a larger set of stations were available as
provided by Dlugokencky and Tans (2020), but recognize further exploration
of this uncertainty is required. At this time, we estimate the uncertainty
of the decadal averaged growth rate after 1980 at 0.02 GtC yr-1 based
on the calibration and the annual growth rate uncertainty, but stretched
over a 10-year interval. For years prior to 1980, we estimate the decadal
averaged uncertainty to be 0.07 GtC yr-1 based on a factor proportional
to the annual uncertainty prior and after 1980 (0.02×[0.61/0.17] GtC yr-1).
We assign a high confidence to the annual estimates of GATM because
they are based on direct measurements from multiple and consistent
instruments and stations distributed around the world (Ballantyne et al.,
2012).
In order to estimate the total carbon accumulated in the atmosphere since
1750 or 1850, we use an atmospheric CO2 concentration of 277 ± 3 ppm or 286 ± 3 ppm, respectively, based on a cubic spline fit to ice
core data (Joos and Spahni, 2008). The uncertainty of ±3 ppm
(converted to ±1σ) is taken directly from the IPCC's
assessment (Ciais et al., 2013). Typical uncertainties in the growth rate in
atmospheric CO2 concentration from ice core data are equivalent to
±0.1–0.15 GtC yr-1 as evaluated from the Law Dome data
(Etheridge et al., 1996) for individual 20-year intervals over the period
from 1850 to 1960 (Bruno and Joos, 1997).
Atmospheric growth rate projection
We provide an assessment of GATM for 2020 based on the monthly
calculated global atmospheric CO2 concentration (GLO) through August
(Dlugokencky and Tans, 2020), and bias-adjusted Holt–Winters exponential
smoothing with additive seasonality (Chatfield, 1978) to project to January
2021. Additional analysis suggests that the first half of the year shows
more interannual variability than the second half of the year, so that the
exact projection method applied to the second half of the year has a
relatively smaller impact on the projection of the full year. Uncertainty is
estimated from past variability using the standard deviation of the last 5 years' monthly growth rates.
Ocean CO2 sink
Estimates of the global ocean CO2 sink SOCEAN are from an ensemble
of global ocean biogeochemistry models (GOBMs, Table A2) that meet
observational constraints over the 1990s (see below). The GOBMs constrain
the air–sea CO2 flux by the transport of carbon into the ocean
interior, which is also the controlling factor of ocean carbon uptake in the
real world. They cover the full globe and all seasons and were recently
evaluated against surface ocean pCO2 observations, suggesting they are
suitable to estimate the annual ocean carbon sink (Hauck et al., 2020). We
use observation-based estimates of SOCEAN to provide a qualitative
assessment of confidence in the reported results, and two diagnostic ocean
models to estimate SOCEAN over the industrial era (see below).
Observation-based estimates
We primarily use the observational constraints assessed by IPCC of a mean
ocean CO2 sink of 2.2 ± 0.7 GtC yr-1 for the 1990s (90 %
confidence interval; Ciais et al., 2013) to verify that the GOBMs provide a
realistic assessment of SOCEAN. We further test that GOBMs and
data products fall within the IPCC estimates for the 2000s (2.3 ± 0.7 GtC yr-1) and the period 2002–2011 (2.4 ± 0.7 GtC yr-1; Ciais et al., 2013). The IPCC estimates are based on the observational
constraint of the mean 1990s sink and trends derived mainly from models and
one data product (Ciais et al., 2013). This is based on indirect
observations with seven different methodologies and their uncertainties,
using the methods that are deemed most reliable for the assessment of this
quantity (Denman et al., 2007; Ciais et al., 2013). The observation-based
estimates use the ocean–land CO2 sink partitioning from observed
atmospheric CO2 and O2/N2 concentration trends (Manning and
Keeling, 2006; Keeling and Manning, 2014), an oceanic inversion method
constrained by ocean biogeochemistry data (Mikaloff Fletcher et al., 2006),
and a method based on penetration timescales for chlorofluorocarbons (McNeil
et al., 2003). The IPCC estimate of 2.2 GtC yr-1 for the 1990s is
consistent with a range of methods (Wanninkhof et al., 2013).
We also use four estimates of the ocean CO2 sink and its variability
based on surface ocean pCO2 maps obtained by the interpolation of
measurements of surface ocean fugacity of CO2 (fCO2, which equals
pCO2 corrected for the non-ideal behaviour of the gas; Pfeil et al.,
2013). These estimates differ in many respects: they use different maps of
surface pCO2, different atmospheric CO2 concentrations, wind
products, and different gas-exchange formulations as specified in Table A3.
We refer to them as pCO2-based flux estimates. The measurements
underlying the surface pCO2 maps are from the Surface Ocean CO2
Atlas version 2020 (SOCATv2020; Bakker et al., 2020), which is an update of
version 3 (Bakker et al., 2016) and contains quality-controlled data through
2019 (see data attribution Table A5). Each of the estimates uses a different
method to then map the SOCAT v2020 data to the global ocean. The methods
include a data-driven diagnostic method (Rödenbeck et al., 2013;
referred to here as Jena-MLS), a combined self-organizing map and
feed-forward neural network (Landschützer et al., 2014; referred to here
as MPI-SOMFFN), an artificial neural network model (Denvil-Sommer et al.,
2019; Copernicus Marine Environment Monitoring Service, referred to here as
CMEMS), and an ensemble average of six machine learning estimates of
pCO2 using a cluster regression approach (Gregor et al., 2019; referred
to here as CSIR). The ensemble mean of the pCO2-based flux estimates is
calculated from these four mapping methods. Further, we show the flux
estimate of Watson et al. (2020) whose uptake is substantially larger, owing
to a number of adjustments they applied to the surface ocean fCO2 data
and the gas-exchange parameterization. Concretely, these authors adjusted
the SOCAT fCO2 downward to account for differences in temperature
between the depth of the ship intake and the relevant depth right near the
surface and also included a further adjustment to account for the cool
surface skin temperature effect. They then used the MPI-SOMFFN method to map
the adjusted fCO2 data to the globe. The Watson et al. (2020) flux estimate
hence differs from the others by their choice of adjusting the flux to a
cool, salty ocean surface skin. Watson et al. (2020) showed that this
temperature adjustment leads to an upward correction of the ocean carbon
sink, up to 0.9 GtC yr-1, which, if correct, should be applied to all
pCO2-based flux estimates. So far this adjustment is based on a single
line of evidence and hence associated with low confidence until further
evidence is available. The Watson et al. (2020) flux estimate presented here is
therefore not included in the ensemble mean of the pCO2-based flux
estimates. This choice will be re-evaluated in upcoming budgets based on
further lines of evidence.
The global pCO2-based flux estimates were adjusted to remove the
pre-industrial ocean source of CO2 to the atmosphere of 0.61 GtC yr-1 from river input to the ocean (the average of 0.45 ± 0.18 GtC yr-1 by Jacobson et al. ,2007, and 0.78 ± 0.41 GtC yr-1 by
Resplandy et al., 2018), to satisfy our definition of SOCEAN (Hauck
et al., 2020). The river flux adjustment was distributed over the
latitudinal bands using the regional distribution of Aumont et al. (2001;
north: 0.16 GtC yr-1, tropics: 0.15 GtC yr-1, south: 0.30 GtC yr-1). The CO2 flux from each pCO2-based product is scaled by
the ratio of the total ocean area covered by the respective product to the
total ocean area (361.9×106 km2) from ETOPO1 (Amante and Eakins, 2009;
Eakins and Sharman, 2010). In products where the covered area varies with
time (MPI-SOMFFN, CMEMS) we use the maximum area coverage. The data products
cover 88 % (MPI-SOMFFN, CMEMS) to 101 % (Jena-MLS) of the observed total
ocean area, so two products are effectively corrected upwards by a factor of
1.13 (Table A3, Hauck et al., 2020).
We further use results from two diagnostic ocean models, Khatiwala et al. (2013) and DeVries (2014), to estimate the anthropogenic carbon accumulated
in the ocean prior to 1959. The two approaches assume constant ocean
circulation and biological fluxes, with SOCEAN estimated as a response
in the change in atmospheric CO2 concentration calibrated to
observations. The uncertainty in cumulative uptake of ±20 GtC
(converted to ±1σ) is taken directly from the IPCC's review
of the literature (Rhein et al., 2013), or about ±30 % for the
annual values (Khatiwala et al., 2009).
Global ocean biogeochemistry models (GOBMs)
The ocean CO2 sink for 1959–2019 is estimated using nine GOBMs (Table A2). The GOBMs represent the physical, chemical, and biological processes
that influence the surface ocean concentration of CO2 and thus the
air–sea CO2 flux. The GOBMs are forced by meteorological reanalysis and
atmospheric CO2 concentration data available for the entire time
period. They mostly differ in the source of the atmospheric forcing data
(meteorological reanalysis), spin-up strategies, and in their horizontal and
vertical resolutions (Table A2). All GOBMs except one (CESM-ETHZ) do not
include the effects of anthropogenic changes in nutrient supply (Duce et
al., 2008). They also do not include the perturbation associated with
changes in riverine organic carbon (see Sect. 2.7.3).
Two sets of simulations were performed with each of the GOBMs. Simulation A
applied historical changes in climate and atmospheric CO2
concentration. Simulation B is a control simulation with constant
atmospheric forcing (normal year or repeated-year forcing) and constant
pre-industrial atmospheric CO2 concentration. In order to derive
SOCEAN from the model simulations, we subtracted the annual time series
of the control simulation B from the annual time series of simulation A.
Assuming that drift and bias are the same in simulations A and B, we thereby
correct for any model drift. Further, this difference also removes the
natural steady state flux (assumed to be 0 GtC yr-1 globally) which is
often a major source of biases. Simulation B of IPSL had to be treated
differently as it was forced with constant atmospheric CO2 but
observed historical changes in climate. For IPSL, we fitted a linear trend
to the simulation B and subtracted this linear trend from simulation A. This
approach ensures that the interannual variability is not removed from IPSL
simulation A.
The absolute correction for bias and drift per model in the 1990s varied
between < 0.01 and 0.35 GtC yr-1, with six models
having positive and three models having negative biases. This correction
reduces the model mean ocean carbon sink by 0.07 GtC yr-1 in the 1990s.
The CO2 flux from each model is scaled by the ratio of the total ocean
area covered by the respective GOBM to the total ocean area (361.9×106 km2) from ETOPO1 (Amante and Eakins, 2009; Eakins and Sharman, 2010).
The ocean models cover 99 % to 101 % of the total ocean area, so the
effect of this correction is small.
GOBM evaluation and uncertainty assessment for SOCEAN
The mean ocean CO2 sink for all GOBMs and the ensemble mean falls
within 90 % confidence of the observed range, or 1.5 to 2.9 GtC yr-1
for the 1990s (Ciais et al., 2013) and within the derived constraints for
the 2000s and 2002–2011 (see Sect. 2.4.1) before and after applying
corrections. The GOBMs and flux products have been further evaluated using
the fugacity of sea surface CO2 (fCO2) from the SOCAT v2020
database (Bakker et al., 2016, 2020). The fugacity of CO2 is
3 ‰–4 ‰ smaller than the partial pressure of CO2
(Zeebe and Wolf-Gladrow, 2001). We focused this evaluation on the root mean
squared error (RMSE) between observed fCO2 and modelled pCO2 and
on a measure of the amplitude of the interannual variability of the flux
(modified after Rödenbeck et al., 2015). The RMSE is calculated from
annually and regionally averaged time series calculated from GOBM and
data product pCO2 subsampled to open ocean (water depth > 400 m) SOCAT sampling points to measure the misfit between large-scale
signals (Hauck et al., 2020) as opposed to the RMSE calculated from binned
monthly data as in the previous year. The amplitude of the SOCEAN
interannual variability (A-IAV) is calculated as the temporal standard
deviation of the detrended CO2 flux time series (Rödenbeck et al.,
2015; Hauck et al., 2020). These metrics are chosen because RMSE is the most
direct measure of data–model mismatch and the A-IAV is a direct measure of
the variability of SOCEAN on interannual timescales. We apply these
metrics globally and by latitude bands (Fig. B1). Results are shown in Fig. B1 and discussed in Sect. 3.1.3.
The 1σ uncertainty around the mean ocean sink of anthropogenic
CO2 was quantified by Denman et al. (2007) for the 1990s to be ±0.5 GtC yr-1. Here we scale the uncertainty of ±0.5 GtC yr-1 to the mean estimate of 2.2 GtC yr-1 in the 1990s to
obtain a relative uncertainty of ± 18 %, which is then applied to
the full time series. To quantify the uncertainty around annual values, we
examine the standard deviation of the GOBM ensemble, which varies between
0.2 and 0.4 GtC yr-1 and averages to 0.30 GtC yr-1 during
1959–2019. We estimate that the uncertainty in the annual ocean CO2
sink increases from ±0.3 GtC yr-1 in the 1960s to ±0.6 GtC yr-1 in the decade 2010–2019 from the combined uncertainty of the
mean flux based on observations of ±18 % (Denman et al., 2007) and
the standard deviation across GOBMs of up to ±0.4 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 the uncertainty in annual
estimates from the standard deviation across the GOBM ensemble.
We examine the consistency between the variability of the model-based and
the pCO2-based flux products to assess confidence in SOCEAN. The
interannual variability of the ocean fluxes (quantified as A-IAV, the
standard deviation after detrending; Fig. B1) of the four pCO2-based
flux products plus the Watson et al. (2020) product for 1992–2019 ranges from 0.16
to 0.25 GtC yr-1 with the lower estimates by the two ensemble methods
(CSIR, CMEMS). The inter-annual variability in the GOBMs ranges between 0.11
and 0.17 GtC yr-1; hence there is overlap with the lower A-IAV
estimates of two data products.
Individual estimates (both GOBM and flux products) generally produce a
higher ocean CO2 sink during strong El Niño events. There is
emerging agreement between GOBMs and data products on the patterns of
decadal variability of SOCEAN with a global stagnation in the 1990s and
an extra-tropical strengthening in the 2000s (McKinley et al., 2020; Hauck
et al., 2020).
The annual pCO2-based flux products correlate with the ocean CO2
sink estimated here with a correlation coefficient r ranging from 0.80 to
0.97 (1985–2019). The central estimates of the annual flux from the GOBMs
and the pCO2-based flux products have a correlation r of 0.97
(1985–2019). The agreement between the models and the flux products reflects
some consistency in their representation of underlying variability since
there is little overlap in their methodology or use of observations. We
assess a medium confidence level to the annual ocean CO2 sink and its
uncertainty because it is based on multiple lines of evidence, it is
consistent with ocean interior carbon estimates (Gruber et al., 2019; see
Sect. 3.1.2), and the results are consistent in that the interannual
variability in the GOBMs and data-based estimates are all generally small
compared to the variability in the growth rate of atmospheric CO2
concentration.
Terrestrial CO2 sinkDGVM simulations
The terrestrial land sink (SLAND) is thought to be due to the combined
effects of fertilization by rising atmospheric CO2 and N inputs on
plant growth, as well as the effects of climate change such as the
lengthening of the growing season in northern temperate and boreal areas.
SLAND does not include land sinks directly resulting from land use and
land-use change (e.g. regrowth of vegetation) as these are part of the
land-use flux (ELUC), although system boundaries make it difficult to
attribute CO2 fluxes on land exactly between SLAND and ELUC (Erb et al., 2013).
SLAND is estimated from the multi-model mean of 17 DGVMs (Table 4). As
described in Sect. 2.2.2, DGVM simulations include all climate variability
and CO2 effects over land, with 12 DGVMs also including the effect of N
inputs. The DGVMs estimate of SLAND does not include the export of
carbon to aquatic systems or its historical perturbation, which is discussed
in Sect. 2.7.3.
DGVM evaluation and uncertainty assessment for SLAND
We apply three criteria for minimum DGVM realism by including only those
DGVMs with (1) steady state after spin-up; (2) global net land flux
(SLAND – ELUC) that is an atmosphere-to-land carbon flux over the
1990s ranging between -0.3 and 2.3 GtC yr-1, within 90 % confidence
of constraints by global atmospheric and oceanic observations (Keeling and
Manning, 2014; Wanninkhof et al., 2013); and (3) global ELUC that is a
carbon source to the atmosphere over the 1990s, as already mentioned in
Sect. 2.2.2. All 17 DGVMs meet these three criteria.
In addition, the DGVM results are also evaluated using the International
Land Model Benchmarking system (ILAMB; Collier et al., 2018). This
evaluation is provided here to document, encourage, and support model
improvements through time. ILAMB variables cover key processes that are
relevant for the quantification of SLAND and resulting aggregated
outcomes. The selected variables are vegetation biomass, gross primary
productivity, leaf area index, net ecosystem exchange, ecosystem
respiration, evapotranspiration, soil carbon, and runoff (see Fig. B2 for
the results and for the list of observed databases). Results are shown in
Fig. B2 and discussed in Sect. 3.1.3.
For the uncertainty for SLAND, we use the standard deviation of the
annual CO2 sink across the DGVMs, averaging to about ±0.6 GtC yr-1 for the period 1959 to 2019. We attach a medium confidence level
to the annual land CO2 sink and its uncertainty because the estimates
from the residual budget and averaged DGVMs match well within their
respective uncertainties (Table 5).
The atmospheric inversion perspective
The worldwide network of in situ atmospheric measurements and satellite-derived atmospheric CO2 column (xCO2) observations 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 EFOS 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 fossil fuel
emissions.
Six atmospheric inversions (Table A4) used atmospheric CO2 data to the
end of 2019 (including preliminary values in some cases) to infer the
spatio-temporal distribution of the CO2 flux exchanged between the
atmosphere and the land or oceans. We focus here on the total land and ocean
CO2 fluxes and their partitioning among the northern extra-tropics
(30–90∘ N), the tropics (30∘ S–30∘ N), and the southern extra-tropics (30–90∘ S). We also break down those estimates for the land and
ocean regions separately. We use these estimates to comment on the
consistency across various data streams and process-based estimates.
The six inversion systems used in this release are described in Table A4.
The inversions are based on Bayesian inversion principles with prior
information on fluxes and their uncertainties. The inversion systems are
based on near-identical observations of surface measurements of CO2
time series (or subsets thereof) from various flask and in situ networks.
Two inversion systems (University of Edinburgh, UoE, and CAMS) were also applied using only satellite
xCO2 measurements from GOSAT or OCO-2, but their results at the larger
scales discussed in this work did not deviate substantially from their
in situ counterparts and are therefore not separately included (Palmer et
al., 2019). Each inversion system uses different methodologies and input
data but is rooted in Bayesian inversion principles (Table A4). These
differences mainly concern the selection of atmospheric CO2 data and
prior fluxes, as well as the spatial resolution, assumed correlation
structures, and mathematical approach of the models. The details of each
model's approach are documented extensively in the references provided in
Table A4. Each system uses a different transport model, which was
demonstrated to be a driving factor behind differences in atmospheric
inversion-based flux estimates, and specifically their distribution across
latitudinal bands (Gaubert et al., 2019; Schuh et al., 2019).
The inversion systems prescribe global fossil fuel emissions. For the first
time in this year's budget, most (five of the six) inversion systems
prescribed the same estimate for EFOS, specifically the GCP's Gridded
Fossil Emissions Dataset version 2020.1 (GCP-GridFEDv2020.1), which is an
update to 2019 of the first version of GCP-GridFED presented by Jones et al. (2020). GCP-GridFEDv2020.1 scales gridded estimates of CO2 emissions
from EDGARv4.3.2 (Janssens-Maenhout et al., 2019) within national
territories to match national emissions estimates provided by the GCP for
the years 1959–2019, which were compiled following the methodology described
in Sect. 2.1 with all data sets available on 31 July 2020 (Robbie Andrew, personal communication, 2020).
A new feature in this edition of the global carbon budget is the use of a
consistent prior emissions data set for EFOS across almost all inversion
models, which avoids the need to correct the estimated land sink (by up to
0.5 GtC in the northern extra-tropics) for most models. Only the UoE
inversion used an alternative data set and required a post-processing
correction (see Table A4). Further, the use of GCP-GridFEDv2020.1 for
EFOS ensures a close alignment with the estimate of EFOS used in
this budget assessment, enhancing the comparability of the inversion-based
estimate with the flux estimates deriving from DGVMs, GOBMs, and
pCO2-based methods.
The land and ocean CO2 fluxes from atmospheric inversions contain
anthropogenic perturbation and natural pre-industrial CO2 fluxes. On
annual timescales, natural pre-industrial fluxes are primarily land
CO2 sinks and ocean CO2 sources corresponding to carbon taken up
on land, transported by rivers from land to ocean, and outgassed by the
ocean. These pre-industrial land CO2 sinks are thus compensated over
the globe by ocean CO2 sources corresponding to the outgassing of
riverine carbon inputs to the ocean. We apply the distribution of
land-to-ocean C fluxes from rivers in three latitude bands using estimates
from Resplandy et al. (2018), which are constrained by ocean heat transport
to a total land-to-ocean carbon transfer of 0.61 GtC yr-1. The latitude
distribution of river-induced ocean CO2 sources (north: 0.16 GtC yr-1, tropics: 0.15 GtC yr-1, south: 0.30 GtC yr-1) from
carbon originating from land (north: 0.29 GtC yr-1, tropics: 0.32 GtC yr-1, south: < 0.01 GtC yr-1) is derived by scaling the
outgassing per latitude band from Aumont et al. (2001) to the global
estimate of 0.61 GtC yr-1. To facilitate the comparison, we adjusted
the inverse estimates of the land and ocean fluxes per latitude band with
these numbers to produce historical perturbation CO2 fluxes from
inversions.
The atmospheric inversions are also evaluated using vertical profiles of
atmospheric CO2 concentrations (Fig. B3). More than 30 aircraft
programs over the globe, either regular programs or repeated surveys over at
least 9 months, have been used in order to draw a robust picture of the
model performance (with space–time data coverage irregular and denser in the
0–45∘ N latitude band; Table A6). The six models are compared to
the independent aircraft CO2 measurements between 2 and 7 km above sea
level between 2001 and 2018. Results are shown in Fig. B3 and discussed in
Sect. 3.1.3.
Processes not included in the global carbon budget
The contribution of anthropogenic CO and CH4 to the global carbon
budget is not fully accounted for in Eq. (1) and is described in Sect. 2.7.1. The contribution of other carbonates to CO2 emissions is
described in Sect. 2.7.2. The contribution of anthropogenic changes in
river fluxes is conceptually included in Eq. (1) in SOCEAN and in
SLAND, but it is not represented in the process models used to quantify
these fluxes. This effect is discussed in Sect. 2.7.3. Similarly, the loss
of additional sink capacity from reduced forest cover is missing in the
combination of approaches used here to estimate both land fluxes (ELUC
and SLAND), and its potential effect is discussed and quantified in
Sect. 2.7.4.
Contribution of anthropogenic CO and CH4 to the global carbon
budget
Equation (1) only partly includes the net input of CO2 to the
atmosphere from the chemical oxidation of reactive carbon-containing gases
from sources other than the combustion of fossil fuels, such as (1) cement
process emissions, since these do not come from combustion of fossil fuels;
(2) the oxidation of fossil fuels; and (3) the assumption of immediate oxidation
of vented methane in oil production. It omits, however, any other
anthropogenic carbon-containing gases that are eventually oxidized in the
atmosphere, such as anthropogenic emissions of CO and CH4. An attempt
is made in this section to estimate their magnitude and identify the
sources of uncertainty. Anthropogenic CO emissions are from incomplete
fossil fuel and biofuel burning and deforestation fires. The main
anthropogenic emissions of fossil CH4 that matter for the global
(anthropogenic) carbon budget are the fugitive emissions of coal, oil, and
gas sectors (see below). These emissions of CO and CH4 contribute a net
addition of fossil carbon to the atmosphere.
In our estimate of EFOS we assumed (Sect. 2.1.1) that all the fuel
burned is emitted as CO2, and thus CO anthropogenic emissions associated
with incomplete fossil fuel combustion and its atmospheric oxidation into
CO2 within a few months are already counted implicitly in EFOS and
should not be counted twice (same for ELUC and anthropogenic CO
emissions by deforestation fires). Anthropogenic emissions of fossil
CH4 are, however, not included in EFOS, because these fugitive
emissions are not included in the fuel inventories. Yet they contribute to
the annual CO2 growth rate after CH4 gets oxidized into
CO2. Emissions of fossil CH4 represent 30 % of total
anthropogenic CH4 emissions (Saunois et al., 2020; their top-down
estimate is used because it is consistent with the observed CH4 growth
rate), which is 0.083 GtC yr-1 for the decade 2008–2017. Assuming a steady
state, an amount equal to this fossil CH4 emission is all converted to
CO2 by OH oxidation and thus explains 0.083 GtC yr-1 of the global
CO2 growth rate with an uncertainty range of 0.061 to 0.098 GtC yr-1 taken from the minimum and maximum of top-down estimates in Saunois et al. (2020). If this minimum–maximum range is assumed to be 2σ because Saunois
et al. (2020) did not account for the internal uncertainty of their min and
max top-down estimates, it translates into a 1σ uncertainty of
0.019 GtC yr-1.
Other anthropogenic changes in the sources of CO and CH4 from
wildfires, vegetation biomass, wetlands, ruminants, or permafrost changes are
similarly assumed to have a small effect on the CO2 growth rate. The
CH4 and CO emissions and sinks are published and analysed separately in
the Global Methane Budget and Global Carbon Monoxide Budget publications,
which follow a similar approach to that presented here (Saunois et al.,
2020; Zheng et al., 2019).
Contribution of other carbonates to CO2 emissions
This year we account for cement carbonation (a carbon sink) for the first
time. The contribution of emissions of fossil carbonates (carbon sources)
other than cement production is not systematically included in estimates of
EFOS, except at the national level where they are accounted for in the
UNFCCC national inventories. The missing processes include CO2
emissions associated with the calcination of lime and limestone outside
cement production. Carbonates are also used in various industries, including
in iron and steel manufacture and in agriculture. They are found naturally
in some coals. CO2 emissions from fossil carbonates other than cement
are estimated to amount to about 1 % of EFOS (Crippa et al., 2019),
though some of these carbonate emissions are included in our estimates
(e.g. via UNFCCC inventories).
Anthropogenic carbon fluxes in the land-to-ocean aquatic continuum
The approach used to determine the global carbon budget refers to the mean,
variations, and trends in the perturbation of CO2 in the atmosphere,
referenced to the pre-industrial era. Carbon is continuously displaced from
the land to the ocean through the land–ocean aquatic continuum (LOAC)
comprising freshwaters, estuaries, and coastal areas (Bauer et al., 2013;
Regnier et al., 2013). A significant fraction of this lateral carbon flux is
entirely “natural” and is thus a steady state component of the
pre-industrial carbon cycle. We account for this pre-industrial flux where
appropriate in our study. However, changes in environmental conditions and
land-use change have caused an increase in the lateral transport of carbon
into the LOAC – a perturbation that is relevant for the global carbon
budget presented here.
The results of the analysis of Regnier et al. (2013) can be summarized in
two points of relevance for the anthropogenic CO2 budget. First, the
anthropogenic perturbation of the LOAC has increased the organic carbon export from terrestrial ecosystems to the hydrosphere by as much as 1.0 ± 0.5 GtC yr-1 since pre-industrial times, mainly owing to enhanced
carbon export from soils. Second, this exported anthropogenic carbon is
partly respired through the LOAC, partly sequestered in sediments along the
LOAC, and to a lesser extent transferred to the open ocean where it may
accumulate or be outgassed. The increase in storage of land-derived organic
carbon in the LOAC carbon reservoirs (burial) and in the open ocean combined
is estimated by Regnier et al. (2013) to be 0.65 ± 0.35 GtC yr-1. The
inclusion of LOAC-related anthropogenic CO2 fluxes should affect
estimates of SLAND and SOCEAN in Eq. (1) but does not affect the
other terms. Representation of the anthropogenic perturbation of LOAC
CO2 fluxes is, however, not included in the GOBMs and DGVMs used in our
global carbon budget analysis presented here.
Loss of additional sink capacity
Historical land-cover change was dominated by transitions from vegetation
types that can provide a large carbon sink per area unit (typically,
forests) to others less efficient in removing CO2 from the atmosphere
(typically, croplands). The resultant decrease in land sink, called the
“loss of additional sink capacity”, can be calculated as the difference
between the actual land sink under changing land cover and the
counterfactual land sink under pre-industrial land cover. This term is not
accounted for in our global carbon budget estimate. Here, we provide a
quantitative estimate of this term to be used in the discussion. Seven of
the DGVMs used in Friedlingstein et al. (2019) performed additional
simulations with and without land-use change under cycled pre-industrial
environmental conditions. The resulting loss of additional sink capacity
amounts to 0.9 ± 0.3 GtC yr-1 on average over 2009–2018 and 42 ± 16 GtC accumulated between 1850 and 2018. OSCAR, emulating the
behaviour of 11 DGVMs, finds values of the loss of additional sink capacity
of 0.7 ± 0.6 GtC yr-1 and 31 ± 23 GtC for the same time
period (Gasser et al., 2020). Since the DGVM-based ELUC estimates are only
used to quantify the uncertainty around the bookkeeping models' ELUC we do
not add the loss of additional sink capacity to the bookkeeping estimate.
ResultsGlobal carbon budget mean and variability for 1959–2019
The global carbon budget averaged over the historical period (1850–2019) is
shown in Fig. 3. For the more recent 1959–2019 period where direct
atmospheric CO2 measurements are available, 81 % of the total
emissions (EFOS+ELUC) were caused by fossil CO2
emissions, and 19 % by land-use change. The total emissions were
partitioned among the atmosphere (45 %), ocean (24 %), and land (32 %),
with a near-zero unattributed budget imbalance (0 %). All components
except land-use change emissions have significantly grown since 1959, with
important interannual variability in the growth rate in atmospheric CO2
concentration and in the land CO2 sink (Fig. 4), and some decadal
variability in all terms (Table 6). Differences with previous budget
releases are documented in Fig. B4.
Combined components of the global carbon budget
illustrated in Fig. 2 as a function of time, for fossil CO2 emissions
(EFOS, including a small sink from cement carbonation; grey) and
emissions from land-use change (ELUC; brown), as well as their
partitioning among the atmosphere (GATM; blue), ocean (SOCEAN;
turquoise), and land (SLAND; green). The partitioning is based on
nearly independent estimates from observations (for GATM) and from
process model ensembles constrained by data (for SOCEAN and
SLAND) and does not exactly add up to the sum of the emissions,
resulting in a budget imbalance which is represented by the difference
between the bottom pink line (reflecting total emissions) and the sum of the
ocean, land, and atmosphere. All time series are in GtC yr-1. GATM
and SOCEAN prior to 1959 are based on different methods. EFOS values are
primarily from Gilfillan et al. (2020), with uncertainty of about ±5 % (±1σ). ELUC values are from two bookkeeping models (Table 2) with uncertainties of about ± 50 %. GATM prior to 1959 is
from Joos and Spahni (2008) with uncertainties equivalent to about ±0.1–0.15 GtC yr-1, and from Dlugokencky and Tans (2020) from 1959 with
uncertainties of about ±0.2 GtC yr-1. SOCEAN prior to 1959
is averaged from Khatiwala et al. (2013) and DeVries (2014) with uncertainty
of about ±30 %, and from a multi-model mean (Table 4) from 1959
with uncertainties of about ±0.5 GtC yr-1. SLAND is a
multi-model mean (Table 4) with uncertainties of about ±0.9 GtC yr-1. See the text for more details of each component and their
uncertainties.
Decadal mean in the five components of the anthropogenic CO2 budget for different periods, and last year available. All values are in GtC yr-1, and uncertainties are reported as ±1σ. The table also shows the budget imbalance (BIM), which provides a measure of the discrepancies among the nearly independent estimates and has an uncertainty exceeding ±1 GtC yr-1. A positive imbalance means the emissions are overestimated and/or the sinks are too small. All values are rounded to the nearest 0.1 GtC and therefore columns do not necessarily add to zero.
∗ Fossil emissions excluding the cement carbonation sink amount to 3.0 ± 0.2 GtCyr−1, 4.7 ± 0.2 GtCyr−1, 5.5 ± 0.3 GtCyr−1, 6.4 ± 0.3 GtCyr−1, 7.8 ± 0.4 GtCyr−1, and 9.6 ± 0.5 GtCyr−1 for the decades 1960s to 2010s respectively and to 9.9 ± 0.5 GtCyr−1 for 2019.
CO2 emissions
Global fossil CO2 emissions have increased every decade from an average
of 3.0 ± 0.2 GtC yr-1 for the decade of the 1960s to an average
of 9.4 ± 0.5 GtC yr-1 during 2010–2019 (Table 6, Figs. 2 and 5). The growth rate in these emissions decreased between the 1960s and the
1990s, from 4.3 % yr-1 in the 1960s (1960–1969), 3.1 % yr-1 in
the 1970s (1970–1979), 1.6 % yr-1 in the 1980s (1980–1989), to
0.9 % yr-1 in the 1990s (1990–1999). After this period, the growth
rate began increasing again in the 2000s at an average growth rate of
3.0 % yr-1, decreasing to 1.2 % yr-1 for the last decade
(2010–2019).
Components of the global carbon budget and their
uncertainties as a function of time, presented individually for (a) fossil CO2 emissions including cement carbonation sink (EFOS), (b) emissions from land-use
change (ELUC), (c) the budget imbalance that is not accounted
for by the other terms,(d) growth rate in atmospheric CO2
concentration (GATM), (e) the land CO2 sink
(SLAND, positive indicates a flux from the atmosphere to the land), and
(f) the ocean CO2 sink (SOCEAN, positive indicates a flux
from the atmosphere to the ocean). All time series are in GtC yr-1 with
the uncertainty bounds representing ±1σ in shaded colour.
Data sources are as in Fig. 3. The black dots in (a) show values
for 2018–2019 that originate from a different data set to the remainder of
the data (see text). The dashed line in (b) identifies the
pre-satellite period before the inclusion of emissions from peatland
burning.
Fossil CO2 emissions for (a) the globe,
including an uncertainty of ±5 % (grey shading), and the emissions
extrapolated using BP energy statistics (black dots); (b) global
emissions by categories, including coal (salmon), oil (olive), gas
(turquoise), cement production (purple), and cement production minus carbonation sink (dotted purple), and
excluding gas flaring which is small (0.6 % in 2013); (c)
territorial (solid lines) and consumption (dashed lines) emissions for the
top three emitter countries (USA – olive; China – salmon; India – purple) and
for the European Union (EU; turquoise for the 27 member states of the EU as
of 2020); and (d) per capita emissions for the top three
emitter countries and the EU (all colours as in panel c) and the world
(black). In (b)–(c), the dots show the data that were extrapolated
from BP energy statistics for 2018–2019. All time series are in GtC yr-1 except the per capita emissions (d), which are in tonnes
of carbon per person per year (tC per person per year). Territorial
emissions are primarily from Gilfillan et al. (2020) except national data
for the USA and EU27 (the 27 member states of the EU) for 1990–2018, which
are reported by the countries to the UNFCCC as detailed in the text;
consumption-based emissions are updated from Peters et al. (2011a). See
Sect. 2.1.1 for details of the calculations and data sources.
In contrast, CO2 emissions from land use, land-use change, and forestry
have remained relatively constant, at around 1.4 ± 0.7 GtC yr-1
over the past half-century (Table 6) but with large spread across estimates
(Table 5, Fig. 6). These emissions are also relatively constant in the DGVM
ensemble of models, except during the last decade when they increase to 2.1 ± 0.5 GtC yr-1. However, there is no agreement on this recent
increase between the bookkeeping estimates, with HandN2017 suggesting a
downward trend as compared to a weak and strong upward trend in OSCAR and
the BLUE estimates respectively (Fig. 6).
CO2 exchanges between the atmosphere and the
terrestrial biosphere as used in the global carbon budget (black with
±1σ uncertainty in grey shading), for (a) CO2
emissions from land-use change (ELUC). Estimates from the three
bookkeeping models (brown lines) and the DGVM models (green) are shown
individually, as is the multi-model mean of DGVM models (dark green). The
dashed line identifies the pre-satellite period before the inclusion of
peatland burning. (b) CO2 gross sinks (from regrowth after agricultural
abandonment and wood harvesting) and gross sources (decaying material left
dead on site and from products after clearing of natural vegetation for
agricultural purposes, wood harvesting, and, for BLUE, degradation from
primary to secondary land through usage of natural vegetation as rangeland,
and emissions from peat drainage and peat burning). The sum of the gross
sinks and sources is ELUC. Estimates from the three bookkeeping models
(brown lines) are shown individually. (c) Land CO2 sink
(SLAND) with individual DGVMs (green). (d) Total land CO2
fluxes (c minus a) with individual DGVMs (green) and their
multi-model mean (dark green).
ELUC is a net term of various gross fluxes, which comprise emissions
and removals (see Sect. 2.2.1). Gross emissions are on average 2–3 times
larger than the net ELUC emissions, increasing from an average of 3.5 ± 1.2 GtC yr-1 for the decade of the 1960s to an average of 4.4 ± 1.6 GtC yr-1 during 2010–2019 (Fig. 6, Table 5), showing the
relevance of land management such as harvesting or rotational agriculture.
They differ more across the three bookkeeping estimates than net fluxes,
which is expected due to different process representation; in particular
explicit inclusion of shifting cultivation (BLUE, OSCAR) increases both
gross emissions and removals.
The uptake of CO2 by cement via carbonation has increased with
increasing stocks of cement products, from an average of 20 MtC yr-1 in
the 1960s to an average of 190 MtC yr-1 during 2010–2019 (Fig. 5). The
growth rate declined from 6.7 % yr-1 in the 1960s to 3.3 % yr-1 in the 1980s, rising again to 6.2 % yr-1 in the 2000s,
before declining again to 3.5 % yr-1 in the 2010s.
Partitioning among the atmosphere, ocean, and land
The growth rate in atmospheric CO2 level increased from 1.8 ± 0.07 GtC yr-1 in the 1960s to 5.1 ± 0.02 GtC yr-1 during
2010–2019 with important decadal variations (Table 6 and Fig. 3). Both ocean
and land CO2 sinks have increased roughly in line with the atmospheric
increase, but with significant decadal variability on land (Table 6 and Fig. 6), and possibly in the ocean (Fig. 7).
Comparison of the anthropogenic atmosphere–ocean CO2
flux showing the budget values of SOCEAN (black; with ±1σ uncertainty in grey shading), individual ocean models (teal), and the
ocean pCO2-based flux products (ensemble mean in dark blue, with
±1σ uncertainty in light blue shading – see Table 4; individual
products in cyan; Watson et al. (2020) as a dashed–dotted line not used for ensemble
mean). The pCO2-based flux products were adjusted for the
pre-industrial ocean source of CO2 from river input to the ocean, which
is not present in the ocean models, by adding a sink of 0.61 GtC yr-1
to make them comparable to SOCEAN (see Sect. 2.7.3).
The ocean CO2 sink increased from 1.0 ± 0.3 GtC yr-1 in the
1960s to 2.5 ± 0.6 GtC yr-1 during 2010–2019, with interannual
variations of the order of a few tenths of GtC yr-1 generally showing
an increased ocean sink during large El Niño events (i.e. 1997–1998)
(Fig. 7; Rödenbeck et al., 2014; Hauck et al., 2020). The GOBMs show the
same patterns of decadal variability as the mean of the pCO2-based flux
products, but of weaker magnitude (Sect. 2.4.3 and Fig. 7; DeVries et al.,
2019; Hauck et al., 2020). The pCO2-based flux products and the ocean
inverse model highlight different regions as the main origin of this decadal
variability, with the pCO2-based flux products placing more of the
weakening trend in the Southern Ocean and the ocean inverse model suggesting
that more of the weakening trend occurred in the North Atlantic and North
Pacific (DeVries et al., 2019). Both approaches also show decadal trends in
the low-latitude oceans (DeVries et al., 2019).
Although all individual GOBMs and data products fall within the
observational constraint, the ensemble means of GOBMs and data products
adjusted for the riverine flux diverge over time with a mean offset of 0.15 GtC yr-1 in the 1990s to 0.55 GtC yr-1 in the decade 2010–2019 and
≥ 0.70 GtC yr-1 since 2017. The GOBMs' best estimate of
SOCEAN over the period 1994–2007 is 2.1 ± 0.5 GtC yr-1 and
is in agreement with the ocean interior estimate of 2.2 ± 0.4 GtC yr-1 when taking into account the interior ocean carbon changes of 2.6 ± 0.3 GtC yr-1 due to the increase in atmospheric CO2 and
-0.4 ± 0.24 GtC yr-1 due to anthropogenic climate change and
variability effects on the natural CO2 flux (Gruber et al., 2019) to
match the definition of SOCEAN used here (Hauck et al., 2020). The
discrepancy between GOBMs and data products stems from the southern and
northern extra-tropics prior to 2005, and mostly from the Southern Ocean
since the mid-2000s. Possible explanations for the discrepancy in the
Southern Ocean could be missing winter observations or uncertainties in the
regional river flux adjustment (see Sect. “Regionality”, Hauck et al., 2020).
The terrestrial CO2 sink increased from 1.3 ± 0.4 GtC yr-1
in the 1960s to 3.4 ± 0.9 GtC yr-1 during 2010–2019, with
important interannual variations of up to 2 GtC yr-1 generally showing
a decreased land sink during El Niño events (Fig. 6), responsible for
the corresponding enhanced growth rate in atmospheric CO2
concentration. The larger land CO2 sink during 2010–2019 compared to
the 1960s is reproduced by all the DGVMs in response to the combined
atmospheric CO2 increase and the changes in climate and is consistent
with constraints from the other budget terms (Table 5).
The total atmosphere-to-land fluxes (SLAND – ELUC), calculated
here as the difference between SLAND from the DGVMs and ELUC from
the bookkeeping models, increased from a 0.2 ± 0.9 GtC yr-1
source in the 1960s to a 1.9 ± 1.1 GtC yr-1 sink during 2010–2019
(Table 5). Estimates of total atmosphere-to-land fluxes (SLAND –
ELUC) from the DGVMs alone are consistent with our estimate and also
with the global carbon budget constraint (EFOS-GATM-SOCEAN,
Table 5). Over the last decade, the land use emission estimate from the
DGVMs is significantly larger than the bookkeeping estimate, mainly
explaining why the DGVMs total atmosphere-to-land flux estimate is lower
than the other estimates.
Model evaluation
The evaluation of the ocean estimates (Fig. B1) shows an RMSE from annually
detrended data of 0.5 to 1.6 µatm for the five pCO2-based flux
products over the globe, relative to the fCO2 observations from the
SOCAT v2020 database for the period 1985–2019. The GOBM RMSEs are larger and
range from 3.5 to 6.9 µatm. The RMSEs are generally larger at high
latitudes compared to the tropics, for both the flux products and the GOBMs.
The five flux products have RMSEs of 0.4 to 1.9 µatm in the tropics,
0.6 to 1.9 µatm in the north, and 1.5 to 2.8 µatm in the
south. Note that the flux products are based on the SOCAT v2020 database, and
hence the latter is no independent data set for the evaluation of the flux
products. The GOBM RMSEs are more spread across regions, ranging from 2.7 to
4.0 µatm in the tropics, 3.1 to 7.3 µatm in the north, and 6.6
to 11.4 µatm in the south. The higher RMSEs occur in regions with
stronger climate variability, such as the northern and southern high
latitudes (poleward of the subtropical gyres).
The evaluation of the DGVMs (Fig. B2) generally shows high skill scores
across models for runoff, and to a lesser extent for vegetation biomass,
GPP, and ecosystem respiration (Fig. B2a). Skill score was lowest
for leaf area index and net ecosystem exchange, with a widest disparity
among models for soil carbon. Further analysis of the results will be
provided separately, focusing on the strengths and weaknesses in the DGVM
ensemble and its validity for use in the global carbon budget.
The evaluation of the atmospheric inversions (Fig. B3) shows long-term mean
biases in the free troposphere lower than 0.4 ppm in absolute values for
each product. These biases show some dependency on latitude and are
different for each inverse model, which may reveal biases in the surface
fluxes (Gaubert et al., 2019, Houweling et al., 2015). Despite tracking
surface and in situ CO2 observations, the systems reproduce NOAA's
global annual CO2 growth rate (Sect. 2.3.1) with mixed skill: where
decadal biases are typically small for all systems (< 0.08 ppm yr-1),
interannual differences are larger (1σ: 0.10–0.25 ppm yr-1, N=19 years) but can be as large as 0.6 ppm yr-1 for the model or year with the worst
performance on this metric.
Budget imbalance
The carbon budget imbalance (BIM; Eq. 1) quantifies the mismatch
between the estimated total emissions and the estimated changes in the
atmosphere, land, and ocean reservoirs. The mean budget imbalance from 1959
to 2019 is small (average of -0.03 GtC yr-1) and shows no trend over
the full time series. The process models (GOBMs and DGVMs) have been
selected to match observational constraints in the 1990s and derived
constraints for the 2000s and 2002–2011, but no further constraints have
been applied to their representation of trend and variability. Therefore,
the near-zero mean and trend in the budget imbalance is indirect evidence
of a coherent community understanding of the emissions and their
partitioning on those timescales (Fig. 4). However, the budget imbalance
shows substantial variability of the order of ±1 GtC yr-1,
particularly over semi-decadal timescales, although most of the variability
is within the uncertainty of the estimates. The positive carbon imbalance
during the 1960s, and early 1990s, suggests that either the emissions were
overestimated or the sinks were underestimated during these periods. The
reverse is true for the 1980s and late 1990s (Fig. 4).
We cannot attribute the cause of the variability in the budget imbalance
with our analysis; we only note that the budget imbalance is unlikely to be
explained by errors or biases in the emissions alone because of its large
semi-decadal variability component, a variability that is untypical of
emissions and has not changed in the past 50 years in spite of a near
tripling in emissions (Fig. 4). Errors in SLAND and SOCEAN are
more likely to be the main cause for the budget imbalance. For example,
underestimation of the SLAND by DGVMs was reported following the
eruption of Mount Pinatubo in 1991 possibly due to missing responses to
changes in diffuse radiation (Mercado et al., 2009) or other yet unknown
factors, and DGVMs are suspected to overestimate the land sink in response
to the wet decade of the 1970s (Sitch et al., 2008). Quasi-decadal
variability in the ocean sink has also been reported recently (DeVries et
al., 2019, 2017; Landschützer et al., 2015), with all methods agreeing
on a smaller than expected ocean CO2 sink in the 1990s and a larger
than expected sink in the 2000s (Fig. 7; DeVries et al., 2019; McKinley et
al., 2020). The decadal variability is possibly caused by changes in ocean
circulation (DeVries et al., 2017) not captured in coarse-resolution GOBMs
used here (Dufour et al., 2013), but also by external forcing from decadally
varying atmospheric CO2 growth rates and cooling effects through the
eruption of Mount Pinatubo in 1991 which is captured by GOBMs (McKinley et
al., 2020).
The decadal variability is thought to be largest in the high-latitude ocean
regions (poleward of the subtropical gyres) and the equatorial Pacific (Li
and Ilyina, 2018; McKinley et al., 2016, 2020). Some of
these errors could be driven by errors in the climatic forcing data,
particularly precipitation (for SLAND) and wind (for SOCEAN)
rather than in the models.
CO2 fluxes between the atmosphere and the surface,
SOCEAN and (SLAND – ELUC) by latitude bands for the (top row)
globe, (second row) north (north of 30∘ N), (third row)
tropics (30∘ S–30∘ N), and (bottom) south (south of
30∘ S), and over (left) total (SOCEAN+SLAND-ELUC), (middle) land only (SLAND-ELUC), and (right) ocean
only (SOCEAN). Positive values indicate a flux from the atmosphere to
the land and/or ocean. Mean estimates from the combination of the process
models for the land and oceans are shown (black line) with ±1σ of the model ensemble (grey shading). For total uncertainty, the land and
ocean uncertainties are summed in quadrature. Mean estimates from the
atmospheric inversions are shown (pink lines) with their ±1σ
spread (pink shading). Mean estimates from the pCO2-based flux products
are shown for the ocean domain (dark blue lines) with their ±1σ spread (light blue shading). The global SOCEAN (upper right) and the
sum of SOCEAN in all three regions represents the anthropogenic
atmosphere-to-ocean flux based on the assumption that the pre-industrial
ocean sink was 0 GtC yr-1 when riverine fluxes are not considered. This
assumption does not hold on the regional level, where pre-industrial fluxes
can be significantly different from zero. Hence, the regional panels for
SOCEAN represent a combination of natural and anthropogenic fluxes.
Bias-correction and area-weighting were only applied to global SOCEAN;
hence the sum of the regions is slightly different from the global estimate
(< 0.08 GtC yr-1).
Global carbon budget for the last decade (2010–2019)
The global carbon budget averaged over the last decade (2010–2019) is shown
in Figs. 2 and 9 (right panel). For this time period, 86 % of the
total emissions (EFOS+ELUC) were from fossil CO2
emissions (EFOS), and 14 % from land-use change (ELUC). The
total emissions were partitioned among the atmosphere (46 %), ocean
(23 %), and land (31 %), with an unattributed budget imbalance (-1 %).
Cumulative changes during 1850–2019 and mean fluxes
during 2010–2019 for the anthropogenic perturbation as defined in the
legend. Cement carbonation sink is included in EFOS.
CO2 emissions
Global fossil CO2 emissions grew at a rate of 1.2 % yr-1 for the
last decade (2010–2019), with a decadal average of 9.6 ± 0.5 GtC yr-1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr-1 when the cement carbonation sink is included) (Fig. 5, Table 6). China's emissions increased by +1.2 % yr-1 on average (increasing by +0.046 GtC yr-1 during the
10-year period), dominating the global trend, followed by India's emissions
increase by +5.1 % yr-1 (increasing by +0.025 GtC yr-1),
while emissions decreased in EU27 by -1.4 % yr-1 (decreasing by
-0.014 GtC yr-1) and in the USA by -0.7 % yr-1 (decreasing by
-0.01 GtC yr-1). In the past decade, fossil CO2 emissions
decreased significantly (at the 95 % level) in 24 growing economies:
Barbados, Belgium, Croatia, Czech Republic, Denmark, Finland, France,
Germany, Israel, Italy, Japan, Luxembourg, Malta, Mexico, the Netherlands,
Norway, Romania, Slovakia, Slovenia, the Solomon Islands, Sweden, Switzerland,
the United Kingdom, and the USA. The drivers of recent decarbonization are
examined in Le Quéré et al. (2019).
In contrast, there is no clear trend in CO2 emissions from land-use
change over the last decade (Fig. 6, Table 6), though the data are very
uncertain, with partly diverging trends over the last decade (Sect. 3.1.1).
Larger emissions are expected increasingly over time for DGVM-based
estimates as they include the loss of additional sink capacity, while the
bookkeeping estimates do not. The LUH2-GCB2020 data set also features large
dynamics in land use in particular in the tropics in recent years, causing
higher emissions in DGVMs, BLUE, and the OSCAR best-guess, which includes
simulations based on LUH2-GCB2020, than in HandN2017.
Partitioning among the atmosphere, ocean, and land
The growth rate in atmospheric CO2 concentration increased during
2010–2019, with a decadal average of 5.1 ± 0.02 GtC yr-1, albeit
with large interannual variability (Fig. 4). Averaged over that decade, the
ocean and land sinks amount to 2.5 ± 0.6 GtC yr-1 and 3.4 ± 0.9 GtC yr-1 respectively. During 2010–2017, the ocean CO2 sink
appears to have intensified in line with the expected increase from
atmospheric CO2 (McKinley et al., 2020). This effect is stronger in the
pCO2-based flux products (Fig. 7, McKinley et al., 2020). The reduction
of -0.16 GtC yr-1 (range: -0.43 to +0.03 GtC yr-1) in the ocean
CO2 sink in 2017 is consistent with the return to normal conditions
after the El Niño in 2015–2016, which caused an enhanced sink in previous
years.
The budget imbalance (Table 6) and the residual sink from global budget
(Table 5) include an error term due to the inconsistency that arises from
using ELUC from bookkeeping models, and SLAND from DGVMs. This
error term includes the fundamental differences between bookkeeping models
and DGVMs, most notably the loss of additional sink capacity. Other
differences include an incomplete accounting of LUC practices and processes
in DGVMs, while they are all accounted for in bookkeeping models by using
observed carbon densities, and bookkeeping error of keeping present-day
carbon densities fixed in the past. That the budget imbalance shows no clear
trend towards larger values over time is an indication that the loss of
additional sink capacity plays a minor role compared to other errors in
SLAND or SOCEAN (discussed in Sect. 3.1.4).
Inter-comparison of flux estimatesRegionality
Figure 8 shows the partitioning of the total atmosphere-to-surface fluxes
excluding fossil CO2 emissions (SOCEAN+SLAND-ELUC) according to the multi-model average estimates from process
models (GOBMs and DGVMs), atmospheric inversions and ocean pCO2-based
products. Figure 8 provides information on the regional distribution of those
fluxes by latitude bands. The global mean total atmosphere-to-surface
CO2 flux from process models for 2010–2019 is 3.8 ± 0.7 GtC yr-1, below the global mean atmosphere-to-surface flux of 4.3 ± 0.5 GtC yr-1 inferred by the carbon budget (EFOS – GATM in
Eq. 1; Table 6). The total atmosphere-to-surface CO2 flux from
the inversions (4.5 ± 0.1 GtC yr-1) almost matches the value
inferred by the carbon budget, which is expected due to the constraint on
GATM incorporated within the inversion approach and the adjustment of
the fossil emissions prior to a value consistent with the EFOS budget
term (Jones et al., 2020; see Sect. 2.6).
In the southern extra-tropics (south of 30∘ S), the atmospheric
inversions suggest a total atmosphere-to-surface sink
(SOCEAN+SLAND-ELUC) for 2010–2019 of 1.4 ± 0.3 GtC yr-1, similar to the process models' estimate of 1.4 ± 0.3 GtC yr-1 (Fig. 8). An approximately neutral total land flux
(SLAND-ELUC) for the southern extra-tropics is estimated by both
the DGVMs (0.0 ± 0.1 GtC yr-1) and the inversion models (sink of
0.1 ± 0.2 GtC yr-1). The GOBMs (1.4 ± 0.3 GtC yr-1)
produce a lower estimate for the ocean sink than the inversion models (1.6 ± 0.2 GtC yr-1) or pCO2-based flux products (1.7 ± 0.1 GtC yr-1; discussed further below).
In the tropics (30∘ S–30∘ N), both the atmospheric
inversions and process models suggest that the total carbon balance in this
region (SOCEAN+SLAND-ELUC) is close to neutral over the
past decade. The inversion models indicate a small tropical source to the
atmosphere of -0.2 ± 0.6 GtC yr-1, whereas the process models
indicate a small sink of 0.2 ± 0.7 GtC yr-1. The GOBMs (-0.1 ± 0.2 GtC yr-1 source), inversion models (-0.1 ± 0.2 GtC yr-1 source), and pCO2-based flux products (-0.05 ± 0.02 GtC yr-1 source) all indicate an approximately neutral tropical ocean flux,
meaning that the difference in sign of the total fluxes stems from the land
component. Indeed, the DGVMs indicate a total land sink
(SLAND-ELUC) of 0.2 ± 0.7 GtC yr-1, whereas the
inversion models indicate a small land source of -0.1 ± 0.7 GtC yr-1, though with high uncertainty in both cases. Overall, the GOBMs,
pCO2-based flux products, and inversion models suggest either a neutral
ocean flux or a small ocean source, while the DGVMs and inversion models
suggest either a small sink or source on land. The agreement between
inversions and process models is significantly better for the last decade
than for any previous decade (Fig. 8), although the reasons for this better
agreement are still unclear.
In the northern extra-tropics (north of 30∘ N) the atmospheric
inversions suggest an atmosphere-to-surface sink
(SOCEAN+SLAND-ELUC) for 2010–2019 of 2.9 ± 0.6 GtC yr-1, which is higher than the process models' estimate of 2.3 ± 0.6 GtC yr-1 (Fig. 8). The difference derives from the total land flux
(SLAND-ELUC) estimate, which is 1.1 ± 0.6 GtC yr-1 in
the DGVMs compared with 1.7 ± 0.8 GtC yr-1 in the inversion
models. The GOBMs (1.2 ± 0.2 GtC yr-1), inversion models (1.2 ± 0.2 GtC yr-1) and pCO2-based flux products (1.2 ± 0.2 GtC yr-1) produce consistent estimates of the ocean sink.
The noteworthy differences between the annual estimates produced by
different data sources are as follows:
The southern SOCEAN flux in the pCO2-based flux products and
inversion models is higher than in the GOBMs. This might be explained by the
data products potentially underestimating the winter CO2 outgassing
south of the polar front (Bushinsky et al., 2019), or by the uncertainty in
the regional distribution of the river flux adjustment (Aumont et al., 2001,
Lacroix et al., 2020) applied to pCO2-based flux products to isolate
the anthropogenic SOCEAN flux.
The magnitude of the northern net land flux
(SLAND-ELUC) is larger in inversion models than in the DGVMs. Discrepancies
in the northern and tropical land fluxes conform with persistent issues
surrounding the quantification of the drivers of the global net land
CO2 flux (Arneth et al., 2017; Huntzinger et al., 2017) and the
distribution of atmosphere-to-land fluxes between the tropics and high
northern latitudes (Baccini et al., 2017; Schimel et al., 2015; Stephens et
al., 2007; Ciais et al., 2019). These differences cannot be simply explained.
They could either reflect a bias in the inversions or missing processes or
biases in the process models, such as the lack of adequate parameterizations
for land management for the DGVMs. In fact, the six inversions shown in Fig. 8
form two categories, one with a large northern land sink and a tropical land
source and another with a moderate northern land sink and a small tropical
sink (Sect. “Atmospheric inversion models differences”). The estimated contribution of the north and its uncertainty
from process models is sensitive both to the ensemble of process models used,
e.g. the inclusion of northern forest management in DGVMs and possibly emissions that are too strong from LUC (Bastos et al., 2020), and to the specifics of each
inversion, e.g. zonal and latitudinal transport and its covariance with
seasonal fluxes (Denning et al., 1995).
Interannual variability
The interannual variability in the southern extra-tropics is low because of
the dominance of ocean area with low variability compared to land areas. The
split between land (SLAND-ELUC) and ocean (SOCEAN) shows a
small contribution to variability in the south coming from the land, with no
consistency between the DGVMs and the inversions or among inversions. This
is expected due to the difficulty of separating exactly the land and oceanic
fluxes when viewed from atmospheric observations alone. The interannual
variability, calculated as the standard deviation from detrended time series
around the mean, was found to be similar in the pCO2-based flux
products including Watson et al. (2020) (0.05 to 0.10 GtC yr-1) and GOBMs (0.06 to
0.17 GtC yr-1) in 2010–2019 (Fig. B1).
Both the process models and the inversions consistently allocate more
year-to-year variability of CO2 fluxes to the tropics compared to the
northern extra-tropics (Fig. 8). The land is the origin of most of the
tropical variability, consistently among the process models and inversions.
The interannual variability in the tropics is similar among the ocean flux
products (0.03 to 0.09 GtC yr-1) and the models (0.02 to 0.09 GtC yr-1;
Sect. 3.1.3, Fig. B1). The inversions indicate that atmosphere-to-land
CO2 fluxes are more variable than atmosphere-to-ocean CO2 fluxes
in the tropics and produce slightly higher IAV than the ocean flux products
or GOBMs. With a sparsity of tropical atmospheric measurements, an aliasing
of the large land flux variations onto the tropical ocean fluxes in the
inversions is one likely cause of this difference.
In the northern extra-tropics, the models, inversions, and pCO2-based
flux products consistently suggest that most of the variability stems from
the land (Fig. 8). Inversions, GOBMs, and pCO2-based flux products
agree on the mean of SOCEAN, but with a higher interannual variability
in the pCO2-based flux products (0.05 to 0.08 GtC yr-1) than in the
GOBMs (0.04 to 0.10 GtC yr-1; Fig. B1).
Atmospheric inversion models differences
The expanded ensemble of atmospheric inversions (from N=3 to N=6) allows
a more representative sample of model–model differences, e.g. in
latitudinal transport and other inversion settings (Table A4). When assessed
for their tropical or northern land+ocean fluxes we see a dipole arise, where
three models estimate a northern extra-tropical sink close to 2.5 GtC yr-1,
and the other three a sink of close to 3.5 GtC yr-1. The inversions
resulting in a large northern sink also estimate a tropical source. Both
groups of models perform equally well on the evaluation metric of the misfit
of optimized CO2 from inversions against independent aircraft data in
Fig. B3 though, and resolving this difference will require the consideration
and inclusion of larger volumes of semi-continuous observations of
concentrations, fluxes, and auxiliary variables collected from (tall)
towers close to the surface CO2 exchange. Improvements in model resolution
and atmospheric transport realism together with expansion of the
observational record (also in the data-sparse boreal Eurasian area) may help
anchor the mid-latitude NH fluxes per continent. In addition, new metrics
could potentially differentiate between the more and less realistic
realizations of the Northern Hemisphere land sink shown in Fig. 8.
In previous versions of this publication, another hypothesized explanation
was that differences in the prior data set used by the inversion models, and
related adjustments to posterior estimates, drove inter-model disparity.
However, separate analysis has shown that the influence of the chosen prior
land and ocean fluxes is minor compared to other aspects of each inversion,
and the majority (5 of 6) of the inversion models presented in this update
now use a consistent prior for fossil emissions (Jones et al., 2020; see
Sect. 2.6).
Finally, in the 2020 effort, two inverse systems (UoE and CAMS) used column
CO2 products derived from GoSAT and OCO-2, respectively. Their
estimated fluxes and performance on the metrics evaluated in this work were
similar to their counterparts driven by in situ and flask observations, and
hence these solutions were not included separately (as noted by Chevallier
et al., 2019). Nevertheless, this convergence of solutions is an important
prerequisite for the use of longer remote sensing CO2 time series in
the future and could help to further study differences driven by
observational coverage and/or sparseness of the current network. Also,
column-CO2 products are likely to be less sensitive to vertical
transport differences between models, believed to be a remaining source of
uncertainty (Basu et al., 2018).
Budget imbalance
The budget imbalance (BIM) was low, -0.1 GtC yr-1 on average over
2010–2019, although the BIM uncertainty is large (1.4 GtC yr-1 over the decade). Also, the BIM shows significant departure from
zero on yearly timescales (Fig. 4), highlighting unresolved variability of
the carbon cycle, likely in the land sink (SLAND), given its large year-to-year variability (Figs. 4e and 6b), while the decadal variability could
originate from both the land and ocean sinks, given unresolved discussions
on the strength of the ocean carbon sink (Bushinsky et al., 2019; Watson et
al., 2020) and its decadal variability (DeVries et al., 2019).
Although the budget imbalance is near zero for the recent decades, it could
be due to compensation of errors. We cannot exclude an overestimation of
CO2 emissions, in particular from land-use change, given their large
uncertainty, as has been suggested elsewhere (Piao et al., 2018), combined
with an underestimate of the sinks. A larger SLAND would reconcile
model results with inversion estimates for fluxes in the total land during
the past decade (Fig. 8; Table 5). Likewise, a larger SOCEAN is also
possible given the higher estimates from the data products (see Sect. 3.1.2, Figs. 7 and 8) and the recently suggested upward correction of the
ocean carbon sink (Watson et al., 2020; Fig. 7). If data products with the
Watson et al. (2020) adjustment were to be used instead of GOBMs to estimate
SOCEAN, this would result in a BIM of the order of -1 GtC yr-1 indicating that a closure of the budget could only be achieved
with either anthropogenic emissions being larger and/or the net land sink
being smaller than estimated here.
More integrated use of observations in the Global Carbon Budget, either on
their own or for further constraining model results, should help resolve
some of the budget imbalance (Peters et al., 2017; Sect. 4).
Global carbon budget for year 2019CO2 emissions
Preliminary estimates of global fossil CO2 emissions are for growth of
only 0.1 % between 2018 and 2019 to remain at 9.7 ± 0.5 GtC in 2019
(Fig. 5), distributed among coal (39 %), oil (34 %), natural gas
(21 %), cement (4 %), and others (1.5 %). Compared to the previous
year, emissions from coal decreased by 1.8 %, while emissions from oil,
natural gas, and cement increased by 0.8 %, 2.0 %, and 3.2 %,
respectively. All growth rates presented are adjusted for the leap year,
unless stated otherwise.
In 2019, the largest absolute contributions to global fossil CO2
emissions were from China (28 %), the USA (14 %), the EU (27 member
states; 8 %), and India (7 %). These four regions account for 57 % of
global CO2 emissions, while the rest of the world contributed 43 %,
which includes aviation and marine bunker fuels (3.5 % of the total).
Growth rates for these countries from 2018 to 2019 were +2.2 % (China),
-2.6 % (USA), -4.5 % (EU27), and +1.0 % (India), with +1.8 %
for the rest of the world. The per capita fossil CO2 emissions in 2019
were 1.3 tC per person per year for the globe and were 4.4 (USA), 1.9
(China), 1.8 (EU27), and 0.5 (India) tC per person per year for the four
highest-emitting countries (Fig. 5).
The growth in emissions of 0.1 % in 2019 is within the range of the
projected growth of 0.6 % (range of -0.2 % to 1.5 %) published in
Friedlingstein et al. (2019) based on national emissions projections for
China, the USA, the EU27, and India and projections of gross domestic
product corrected for IFOS trends for the rest of the world. The growth
in emissions in 2019 for China, the USA, EU27, India, and the rest of the
world were all within their previously projected range (Table 7).
Comparison of the projection with realized fossil CO2 emissions (excluding cement carbonation sink). The “actual” values are first the estimate available using actual data, and the “projected” values refer to estimates made before the end of the year for each publication. Projections based on a different method from that described here during 2008–2014 are available in Le Quéré et al. (2016). All values are adjusted for leap years.
World China USA EU28 India Rest of world ProjectedActualProjectedActualProjectedActualProjectedActualProjectedActualProjectedActual2015a-0.6 %0.06 %-3.9 %-0.7 %-1.5 %-2.5 %––––1.2 %1.2 %(-1.6 to 0.5)(-4.6 to -1.1)(-5.5 to 0.3)(-0.2 to 2.6)2016b-0.2 %0.20 %-0.5 %-0.3 %-1.7 %-2.1 %––––1.0 %1.3 %(-1.0 to +1.8)(-3.8 to +1.3)(-4.0 to +0.6)(-0.4 to +2.5)2017c2.0 %1.6 %3.5 %1.5 %-0.4 %-0.5 %––2.00 %3.9 %1.6 %1.9 %(+0.8 to +3.0)(+0.7 to +5.4)(-2.7 to +1.0)(+0.2 to +3.8)(0.0 to +3.2)2018d2.7 %2.1 %4.7 %2.3 %2.5 %2.8 %-0.7 %-2.1 %6.3 %8.0 %1.8 %1.7 %(+1.8 to +3.7)(+2.0 to +7.4)(+0.5 to +4.5)(-2.6 to +1.3)(+4.3 to +8.3)(+0.5 to +3.0)2019e0.5 %0.1 %2.6 %2.2 %-2.4 %-2.6 %-1.7 %-4.3 %1.8 %1.0 %0.5 %0.5 %(-0.3 to +1.4)(+0.7 to +4.4)(-4.7 to -0.1)(-5.1 % to +1.8 %)(-0.7 to +3.7)(-0.8 to +1.8)2020f-6.7 %-1.7 %-12.2 %-11.3 % (EU27)-9.1 %-7.4 %
a Jackson et al. (2016) and Le Quéré et al. (2015a). b Le Quéré et al. (2016). c Le Quéré et al. (2018a). d Le Quéré et al. (2018b). e Friedlingstein et al. (2019). f This study (median of four reported estimates, Sect. “2020 projections”).
The largest absolute contributions to global CO2 emissions from a
consumption perspective were China (25 %), the USA (16 %), the EU (10 %),
and India (6 %) for 2016, the last year with available data. The
difference between territorial and consumption emissions (the net emission
transfer via international trade) has generally increased from 1990 to
around 2005 and remained relatively stable afterwards until the last year
available (2016; Fig. 5).
The global CO2 emissions from land-use change are estimated to be 1.8 ± 0.7 GtC in 2019, slightly larger than the previous decade, which
results in particular from the high peat and tropical
deforestation/degradation fires. First, unusually dry conditions for a
non-El Niño year occurred in Indonesia in 2019, which led to fire
emissions from peat burning, deforestation, and degradation in equatorial
Asia to be about twice as large as the average over the previous decade
(GFED4.1s, van der Werf et al., 2017). Second, 2019 saw a surge in
deforestation fires in the Amazon, causing about 30 % higher emissions
from deforestation and degradation fires over the previous decade (GFED4.1s,
van der Werf et al., 2017). This development was evident also in
deforestation rates, where 2019 (August 2018–July 2019), with 10.1 km2 forest clear-cut, saw the highest rate since 2008 (INPE, 2020). However,
confidence in the annual change remains low. This brings the total CO2
emissions from fossil plus land-use change (EFOS+ELUC) to 11.5 ± 0.9 GtC (42.2 ± 3.3 GtCO2).
Partitioning among the atmosphere, ocean, and land
The growth rate in atmospheric CO2 concentration corresponded to 5.4 ± 0.2 GtC in 2019 (2.54 ± 0.08 ppm; Fig. 4; Dlugokencky and
Tans, 2020), slightly above the 2010–2019 average of 5.1 ± 0.02 GtC yr-1.
The estimated ocean CO2 sink was 2.6 ± 0.6 GtC in 2019. Although
there is a significant difference of SOCEAN between GOBMs (2.6 GtC) and
pCO2-based products (3.4 GtC), they both suggest an average increase of
0.06–0.07 GtC in 2019 compared to 2018. Six models and two flux products
show an increase in SOCEAN (GOBM up to +0.30 GtC, data product up to
+0.29 GtC), while three models and two flux products show no change or a
decrease in SOCEAN (GOBMs down to -0.03 GtC, data products down to
-0.17 GtC; Fig. 7).
The terrestrial CO2 sink from the DGVM model ensemble was 3.1 ± 1.2 GtC in 2019, slightly below the decadal average (Fig. 4) and consistent
with constraints from the rest of the budget (Table 5). Atmospheric
inversions confirm a lower-than-average land sink in 2019 and consistently
estimate this as an increased source from the tropical land (+0.3 GtC).
The budget imbalance was +0.3 GtC in 2019, which is above the average over
the last decade (Table 6). This imbalance is indicative only, given its
significant year-to-year variability and large uncertainty (1.4 GtC yr-1).
Global carbon budget projection for year 2020 Fossil CO2 emissions
We present the results from the four separate methods in Table A8, with
monthly results for each country, for each region, and globally shown in Fig. B5.
The restrictions implemented in response to COVID-19 led to dramatic and
unprecedented changes in society, and this caused large changes in CO2
emissions. All countries had significant deviations from their previous
emission trends.
Year to date (YTD)
The four methods presented here use a mix of direct emissions estimates from
energy consumption data to the use of proxies as indicators of changes in
activity levels. Annual historical CO2 emissions estimates (pre-2020)
are largely derived from reported energy data. For 2020, we do not have
sufficient information to say that the use of monthly energy data gives any
more accurate estimates than proxy approaches. Monthly energy consumption
data are subject to revisions and can be estimated or incomplete, and it is
not known if proxy data may perform better. A full evaluation of monthly and
proxy methods can only be made when full-year data become available. As noted
in Forster et al. (2020) the reductions in CO2 emissions may be about
20 % overestimated based on meteorologically adjusted NOx observations.
The YTD results (Fig. B5, Table A8) run to September for all regions and
methods, except the EU27 which is to July (limited by the Eurostat data used
by the GCB method). To September (July) 2020, the four methods indicate
fossil CO2 emissions were down in all regions and globally. However,
the background for these declines varies by country. The EU and the USA had
declining emission trends before COVID-19, so the pandemic effect is on top
of these existing emission reductions. In both the EU and the USA, reductions
in coal use have been accelerated by COVID-19. Similarly, India's emissions
were in decline through 2019, but this time because of economic troubles
(Andrew, 2020b), but COVID-19 is potentially superimposed on the longer-term
trend of increasing emissions in India. In contrast, China and the rest of
the world have the COVID-19 effect on the top of rising emissions. China has
lower reductions, but this may also indicate that the full impact of the
COVID-19 restrictions occurred earlier and the economy has had a longer time
to recover.
Based on the three studies providing sufficient data, from January to
September, global emissions may have declined by around 8 % (median, based on
model estimates of -7.6 %, UEA; -7.6 %, Carbon Monitor; -14.1 %,
Priestley Centre). This range between estimates does not include the
uncertainty inherent in each method, which would increase the spread.
2020 projections
The full-year projection for 2020 must necessarily be interpreted
cautiously. Only Le Quéré et al. (2020) include a formal projection,
by assuming confinement measures in place on 13 November remain in place
until the end of the year at current or lower levels in each country.
Forster et al. (2020) use a simple extrapolation, assuming the declines in
emissions from their baselines remain at 66 % of the level over the last
30 d with estimates. Liu et al. (2020) and the GCB method did not perform
a projection for 2020, and for purposes of comparison we use a simple
approach to extrapolating their observations by assuming the remaining
months of the year change by the same relative amount compared to 2019 as
the final month of observations.
Based on these assumptions, the countries and regions considered are all
expected to see a decline in annual total emissions, with the potential
exception of China, which may have a slight increase according to Carbon
Monitor and the GCB method (Fig. B5). The year 2020 is behaving in many ways
entirely differently to any year in history, and the confidence in the 2020
projection is therefore currently low, due to both the spread in results and
the uncertain developments of the disease itself, strength of future
societal and industrial restrictions, and stimulus packages throughout the
remainder of 2020. The largest source of uncertainty comes from the
emissions in China, because of the limited available information both on
monthly emissions and for proxy data, and emissions for the rest of the world, because it
represents around 40 % of the world's emissions in aggregate.
Based on the median value of the four methods considered, global emissions
may decline by about 7 % in 2020 (-5.8 %, GCB; -6.5 %, Carbon Monitor;
-6.9 % (range -2.7 to -10.8 %), UEA; -13.0 %, Priestley Centre), with
additional uncertainty from each method on top of this (Fig. B5, Table A8).
Using a purely GDP-based projection, based on the IMF GDP forecast as of
June 2020, and assuming the 10-year trend in CO2/GDP continues in 2020,
emissions would decline by 7.5 % – well within the range of other estimates.
In October 2020, the IEA forecasted a drop of 7 % in fossil energy
emissions (IEA, 2020). The decrease in emissions for the full year 2020
appears more pronounced in the USA, EU27, and India, partly due to
pre-existing trends. In contrast the decrease in emissions appears to be least pronounced in China, where restriction measures associated with COVID-19
occurred early in the year and lockdown measures were more limited in time.
Synthesis
Given a negative median growth rate of about -7 % across methods, global
fossil CO2 emissions (EFOS) would be around 9.0 GtC (33.2 GtCO2) in 2020, assuming a cement carbonation sink of 0.2 GtC yr-1 (Table A8). These figures do not include the uncertainty
from this method in projecting 2020 emissions.
Our preliminary estimates of fire emissions in deforestation zones and
Amazon deforestation rates indicate that emissions from land-use change
(ELUC) for 2020 are similar to the 2010–2019 average (Sect. 2.2.4). We
therefore expect ELUC emissions of around 1.6 GtC in 2020. The apparent
decrease in the mean value of ELUC emissions compared to 2019 is
largely related to the transition from an anomalously dry to a wet year in
Indonesia (see Sects. 2.2.4 and 3.2.1 for detail).
We hence project global total anthropogenic CO2 emissions from fossil
and land-use changes to be around 10.6 GtC (39 GtCO2) in 2020.
Partitioning among the atmosphere, ocean, and land
The 2020 growth in atmospheric CO2 concentration (GATM) is
projected to be about 5.3 GtC (2.5 ppm) based on GLO observations until the
end of August 2020, bringing the atmospheric CO2 concentration to an
expected level of 412 ppm averaged over the year. Combining projected
EFOS, ELUC and GATM suggests a combined land and ocean sink
(SLAND+SOCEAN) of about 5.3 GtC for 2020. Although each term
has large uncertainty, the oceanic sink SOCEAN has generally low
interannual variability and is likely to remain close to its 2019 value of
around 2.6 GtC, leaving a rough estimated land sink SLAND (including
any budget imbalance) of around 2.7 GtC, slightly below the 2019 estimate.
Cumulative sources and sinks
Cumulative historical sources and sinks are estimated as in Eq. (1) with
semi-independent estimates for each term and a global carbon budget
imbalance. Cumulative fossil CO2 emissions for 1850–2019 were 445 ± 20 GtC for EFOS and 210 ± 60 GtC for ELUC (Table 8;
Fig. 9), for a total of 650 ± 65 GtC. The cumulative emissions from
ELUC are particularly uncertain, with large spread among individual
estimates of 150 GtC (HandN2017), 275 GtC (BLUE), and 200 GtC (OSCAR) for
the three bookkeeping models and a similar wide estimate of 200 ± 60 GtC for the DGVMs. These estimates are consistent with indirect constraints
from vegetation biomass observations (Li et al., 2017), but given the large
spread a best estimate is difficult to ascertain.
Cumulative CO2 for different time periods in gigatonnes of carbon (GtC). All uncertainties are reported as ±1σ. The budget imbalance provides a measure of the discrepancies among the nearly independent estimates. Its uncertainty exceeds ±60 GtC. The method used here does not capture the loss of additional sink capacity from reduced forest cover, which is about 20 GtC and would exacerbate the budget imbalance (see Sect. 2.7.4). All values are rounded to the nearest 5 GtC and therefore columns do not necessarily add to zero. Cement carbonation sink is included in EFOS.
a Using projections for the year 2020 (Sect. 3.4). Uncertainties are the same as the 1850–2019 period.
b Cumulative ELUC 1750–1849 of 30 GtC based on multi-model mean of Pongratz et al. (2009), Shevliakova et al. (2009), Zaehle et al. (2011), and Van Minnen et al. (2009). 1850–2019 from mean of HandN2017 (Houghton and Nassikas, 2017) and BLUE (Hansis et al., 2015). 1750–2019 uncertainty is estimated from standard deviation of DGVMs over 1870–2019 scaled by 1750–2019 emissions.
c Cumulative ELUC based on HandN, BLUE, and OSCAR. Uncertainty is estimated from the standard deviation of DGVM estimates.
d Cumulative ELUC based on HandN, BLUE, and OSCAR. Uncertainty is formed from the uncertainty in annual ELUC over 1959–2019, which is 0.7 GtC yr-1 multiplied by length of the time series.
e Ocean sink uncertainty from IPCC (Denman et al., 2007).
Emissions during the period 1850–2019 were partitioned among the atmosphere
(265 ± 5 GtC; 40 %), ocean (160 ± 20 GtC; 25 %), and
land (210 ± 55 GtC; 32 %). This cumulative land sink is broadly
equal to the cumulative land-use emissions, making the global land nearly
neutral over the 1850–2019 period. The use of nearly independent estimates
for the individual terms shows a cumulative budget imbalance of 20 GtC
(3 %) during 1850–2019 (Fig. 2), which, if correct, suggests that
emissions are too high by the same proportion or that the land or ocean
sinks are underestimated. The bulk of the imbalance could originate from the
estimation of large ELUC between the mid-1920s and the mid-1960s, which
is unmatched by a growth in atmospheric CO2 concentration as recorded
in ice cores (Fig. 3). The known loss of an additional sink capacity of 30–40 GtC due to reduced forest cover has not been accounted for in our method and
would further exacerbate the budget imbalance (Sect. 2.7.4).
Cumulative emissions through to the year 2020 increase to 655 ± 65 GtC
(2340 ± 240 GtCO2), with about 70 % contribution from EFOS
and about 30 % contribution from ELUC. Cumulative emissions and their
partitioning for different periods are provided in Table 8.
Given the large and persistent uncertainties in historical cumulative
emissions, we suggest extreme caution is needed if using this estimate to
determine the remaining cumulative CO2 emissions consistent with an
ambition to stay below a given temperature limit (Millar et al., 2017;
Rogelj et al., 2016, 2019).
Discussion
Each year when the global carbon budget is published, each flux component is
updated for all previous years to consider corrections that are the result
of further scrutiny and verification of the underlying data in the primary
input data sets. Annual estimates may be updated with improvements in data
quality and timeliness (e.g. to eliminate the need for extrapolation of
forcing data such as land use). Of all terms in the global budget, only the
fossil CO2 emissions and the growth rate in atmospheric CO2
concentration are based primarily on empirical inputs supporting annual
estimates in this carbon budget. The carbon budget imbalance, yet an
imperfect measure, provides a strong indication of the limitations in
observations in understanding and representing processes in models, and/or
in the integration of the carbon budget components.
The persistent unexplained variability in the carbon budget imbalance limits
our ability to verify reported emissions (Peters et al., 2017) and suggests
we do not yet have a complete understanding of the underlying carbon cycle
dynamics. Resolving most of this unexplained variability should be possible
through different and complementary approaches. First, as intended with our
annual updates, the imbalance as an error term is reduced by improvements of
individual components of the global carbon budget that follow from improving
the underlying data and statistics and by improving the models through the
resolution of some of the key uncertainties detailed in Table 9. Second,
additional clues to the origin and processes responsible for the variability
in the budget imbalance could be obtained through a closer scrutiny of
carbon variability in light of other Earth system data (e.g. heat balance,
water balance), and the use of a wider range of biogeochemical observations
to better understand the land–ocean partitioning of the carbon imbalance
(e.g. oxygen, carbon isotopes). Finally, additional information could also
be obtained through higher resolution and process knowledge at the regional
level, and through the introduction of inferred fluxes such as those based
on satellite CO2 retrievals. The limit of the resolution of the carbon
budget imbalance is as yet unclear but most certainly not yet reached given
the possibilities for improvements that lie ahead.
Major known sources of uncertainties in each component of the Global Carbon Budget, defined as input data or processes that have a demonstrated effect of at least ±0.3 GtC yr-1.
Source of uncertaintyTimescale (years)LocationStatusEvidenceFossil CO2 emissions (EFOS; Sect. 2.1) Energy statisticsannual to decadalglobal, but mainly China and major developing countriessee Sect. 2.1Korsbakken et al. (2016), Guan et al. (2012)Carbon content of coalannual to decadalglobal, but mainly China and major developing countriessee Sect. 2.1Liu et al. (2015)System boundaryannual to decadalall countriessee Sect. 2.1Net land-use change flux (ELUC; Sect. 2.2) Land-cover and land-use change statisticscontinuousglobal; in particular tropicssee Sect. 2.2Houghton et al. (2012),Gasser et al. (2020)Sub-grid-scale transitionsannual to decadalglobalsee Table A1Wilkenskjeld et al. (2014)Vegetation biomassannual to decadalglobal; in particular tropicssee Table A1Houghton et al. (2012)Wood and crop harvestannual to decadalglobal; SE Asiasee Table A1Arneth et al. (2017),Erb et al. (2018)Peat burningamulti-decadal trendglobalsee Table A1van der Werf et al. (2010)Loss of additional sink capacitymulti-decadal trendglobalnot includedSect. 2.7.4Pongratz et al. (2014),Gasser et al. (2020)Atmospheric growth rate (GATM; Sect. 2.3) no demonstrated uncertainties larger than ±0.3 GtC yr-1bOcean sink (SOCEAN; Sect. 2.4) Variability in oceanic circulationcsemi-decadal to decadalglobalsee Sect. 2.4DeVries et al. (2017, 2019)Internal variabilityannual to decadalhigh latitudes;equatorial Pacificno ensembles/coarse resolutionMcKinley et al. (2016)Anthropogenic changes in nutrient supplymulti-decadal trendglobalnot includedDuce et al. (2008)Land sink (SLAND; Sect. 2.5) Strength of CO2 fertilizationmulti-decadal trendglobalsee Sect. 2.5Wenzel et al. (2016)Response to variability in temperature and rainfallannual to decadalglobal; in particular tropicssee Sect. 2.5Cox et al. (2013)Nutrient limitation and supplyResponse to diffuse radiationannualglobalsee Sect. 2.5Mercado et al. (2009)
a As result of interactions between land use and climate.
b The uncertainties in GATM have been estimated to be ±0.2 GtC yr-1, although the conversion of the growth rate into a global annual flux assuming instantaneous mixing throughout the atmosphere introduces additional errors that have not yet been quantified.
c Could in part be due to uncertainties in atmospheric forcing (Swart et al., 2014).
Estimates of global fossil CO2 emissions from different data sets are in
relatively good agreement when the different system boundaries of these
data sets are taken into account (Andrew, 2020a). But while estimates of
EFOS are derived from reported activity data requiring much less
complex transformations than some other components of the budget,
uncertainties remain, and one reason for the apparently low variation
between data sets is precisely the reliance on the same underlying reported
energy data. This year we have added cement carbonation, a carbon sink, to
EFOS.The budget excludes some sources of fossil CO2 emissions,
which available evidence suggests are relatively small (< 1 %). In
non-Annex I countries, and before 1990 in Annex I countries, we still omit
emissions from carbonate decomposition apart from those in cement
production, a focus of future updates. We have also included new estimates
for India, which are now for the calendar year instead of its fiscal year
and include the significant changes in coal stocks missing from other
data sets. Estimates for Japan and Australia, two other large emitters, are
still reported for fiscal years not aligned with the calendar year. Some
errors in pre-1950 emissions were uncovered by Andrew (2020a), and these
have been corrected this year.
Estimates of ELUC suffer from a range of intertwined issues, including
the poor quality of historical land-cover and land-use change maps, the
rudimentary representation of management processes in most models, and the
confusion in methodologies and boundary conditions used across methods (e.g.
Arneth et al., 2017; Pongratz et al., 2014; see also Sect. 2.7.4 on the
loss of sink capacity). Uncertainties in current and historical carbon
stocks in soils and vegetation also add uncertainty in the LUC flux
estimates. Unless a major effort to resolve these issues is made, little
progress is expected in the resolution of ELUC. This is particularly
concerning given the growing importance of ELUC for climate mitigation
strategies, and the large issues in the quantification of the cumulative
emissions over the historical period that arise from large uncertainties in
ELUC.
The assessment of the GOBMs used for SOCEAN with flux products based on
observations highlights a substantial discrepancy in the Southern Ocean
(Fig. 8, Hauck et al., 2020). The long-standing sparse data coverage of
pCO2 observations in the Southern compared to the Northern Hemisphere
(e.g. Takahashi et al., 2009) continues to exist (Bakker et al., 2016, 2020)
and to lead to substantially higher uncertainty in the SOCEAN estimate
for the Southern Hemisphere (Watson et al., 2020). This discrepancy points
to the need for increased high-quality pCO2 observations, especially in
the Southern Ocean. Further uncertainty stems from the regional distribution
of the river flux adjustment term being based on one model study yielding
the largest riverine outgassing flux south of 20∘ S (Aumont et
al., 2001), with a recent study questioning this distribution (Lacroix et
al., 2020). The data products suggest an underestimation of variability in
the GOBMs globally and, consequently, the variability in SOCEAN appears
to be underestimated. The size of the underestimation of the amplitude of
interannual variability (order of < 0.1 GtC yr-1, A-IAV; see
Fig. B1) could account for some of the budget imbalance, but not all of it.
The assessment of the net land–atmosphere exchange derived from land sinks
and net land-use change flux with atmospheric inversions also shows a
substantial discrepancy, particularly for the estimate of the total land
flux over the northern extra-tropics in the past decade. This discrepancy
highlights the difficulty to quantify complex processes (CO2
fertilization, nitrogen deposition, N fertilizers, climate change and
variability, land management, etc.) that collectively determine the net land
CO2 flux. Resolving the differences in the Northern Hemisphere land
sink will require the consideration and inclusion of larger volumes of
observations (Sect. 3.2.3).
As introduced in 2018, we provide metrics for the evaluation of the ocean
and land models and the atmospheric inversions. These metrics expand the use
of observations in the global carbon budget, helping (1) to support
improvements in the ocean and land carbon models that produce the sink
estimates, and (2) to constrain the representation of key underlying
processes in the models and to allocate the regional partitioning of the
CO2 fluxes. However, GOBMs have changed little since the introduction
of the ocean model evaluation. This is an initial step towards the
introduction of a broader range of observations that we hope will support
continued improvements in the annual estimates of the global carbon budget.
We assessed before that a sustained decrease of -1 % in global emissions
could be detected at the 66 % likelihood level after a decade only (Peters
et al., 2017). Similarly, a change in behaviour of the land and/or ocean
carbon sink would take as long to detect, and much longer if it emerges more
slowly. To continue reducing the carbon imbalance on annual to decadal timescales, regionalizing the carbon budget and integrating multiple variables
are powerful ways to shorten the detection limit and ensure the research
community can rapidly identify issues of concern in the evolution of the
global carbon cycle under the current rapid and unprecedented changing
environmental conditions.
Conclusions
The estimation of global CO2 emissions and sinks is a major effort by
the carbon cycle research community that requires a careful compilation and
synthesis of measurements, statistical estimates, and model results. The
delivery of an annual carbon budget serves two purposes. First, there is a
large demand for up-to-date information on the state of the anthropogenic
perturbation of the climate system and its underpinning causes. A broad
stakeholder community relies on the data sets associated with the annual
carbon budget including scientists, policy makers, businesses, journalists,
and non-governmental organizations engaged in adapting to and mitigating
human-driven climate change. Second, over the last decade we have seen
unprecedented changes in the human and biophysical environments (e.g.
changes in the growth of fossil fuel emissions, impact of the COVID-19 pandemic,
Earth's warming, and strength of the carbon sinks), which call for frequent
assessments of the state of the planet, a better quantification of the
causes of changes in the contemporary global carbon cycle, and an improved
capacity to anticipate its evolution in the future. Building this scientific
understanding to meet the extraordinary climate mitigation challenge
requires frequent, robust, transparent, and traceable data sets and methods
that can be scrutinized and replicated. This paper helps
to keep track of new budget updates via “living data”.
Data availability
The data presented here are made available in the belief that their wide
dissemination will lead to greater understanding and new scientific insights
into how the carbon cycle works, how humans are altering it, and how we can
mitigate the resulting human-driven climate change. The free availability of
these data does not constitute permission for publication of the data. For
research projects, if the data are essential to the work, or if an important
result or conclusion depends on the data, co-authorship may need to be
considered for the relevant data providers. Full contact details and
information on how to cite the data shown here are given at the top of each
page in the accompanying database and summarized in Table 2.
The accompanying database includes two Excel files organized in the
following spreadsheets.
The file Global_Carbon_Budget_2020v1.0.xlsx includes the following:
summary;
the global carbon budget (1959–2019);
global CO2 emissions from fossil fuels and cement production by fuel type, and the per capita emissions (1959–2019);
CO2 emissions from land-use change from the individual methods and models (1959–2019);
ocean CO2 sink from the individual ocean models and pCO2-based products (1959–2019);
terrestrial CO2 sink from the DGVMs (1959–2019);
additional information on the historical global carbon budget prior to 1959 (1750–2019).
cement carbonation sink (1959–2019);
additional information on the historical global carbon budget prior to 1959 (1750–2019).
The file National_Carbon_Emissions_2020v1.0.xlsx includes the following:
summary;
territorial country CO2 emissions from fossil CO2 emissions (1959–2019) from CDIAC with UNFCCC data overwritten where available, extended to 2019 using BP data;
consumption country CO2 emissions from fossil CO2 emissions and emissions transfer from the international trade of goods and services (1990–2016) using CDIAC/UNFCCC data (worksheet 3 above) as reference;
emissions transfers (Consumption minus territorial emissions; 1990–2016);
country definitions;
details of disaggregated countries;
details of aggregated countries;
Both spreadsheets are published by the Integrated Carbon Observation System
(ICOS) Carbon Portal and are available at 10.18160/gcp-2020 (Friedlingstein et al., 2020). National
emissions data are also available from the Global Carbon Atlas
(http://www.globalcarbonatlas.org/, last access: 16 November 2020).
Supplementary tables
Comparison of the processes included in the bookkeeping method and DGVMs in their estimates of ELUC and SLAND. See Table 4 for model references. All models include deforestation and forest regrowth after abandonment of agriculture (or from afforestation activities on agricultural land). Processes relevant for ELUC are only described for the DGVMs used with land-cover change in this study (Fig. 6a and b).
Bookkeeping models DGVMs HandNBLUEOSCARCABLE-POPCLASSICCLM5.0DLEMIBISISAMISBA-CTRIP(h)JSBACHJULES-ESLPJ-GUESSLPJLPX-BernOCNv2ORCHIDEEv3SDGVMVISITYIBsProcesses relevant for ELUCWood harvest and forest degradationayesyesyesyesnoyesyesyesyesnoyesnoyesyesnodyesyesnoyesnoShifting cultivation/Subgrid-scale transitionsnobyesyesyesnoyesnonononoyesnoyesyesnodnononoyesnoCropland harvest (removed, R, or added to litter, L)yes (R)myes (R)myes (R)yes (R)yes (L)yes (R)yesyes (R)yesyes (R+L)yes (R+L)yes (R)yes (R)yes (L)yes (R)yes (R+L)yes (R)yes (R)yes (R)noPeat firesyesyesyesnonoyesnonononononononononononononoFire as amanagement toolyesmyesmyesjnononononononononononononononononoN fertilizationyesmyesmyesjnonoyesyesnoyesnonoyeskyesnoyesyesyesnononoTillageyesmyesmyesjyesyesgnononononononoyesnononoyesgnononoIrrigationyesmyesmyesjnonoyesyesnoyesnononoyesnononononononoWetland drainageyesmyesmyesjnononononononononononononononononoErosionyesmyesmyesjnononoyesnononononononononononoyesnoPeat drainageyesyesyesnononononononononononononononononoGrazing and mowing harvest (removed, r, oradded to litter, l)yes (r)myes (r)myes (r)yes (r)nonononoyes (l)noyes (l)noyes (r)yes (l)noyes (r+l)nonononoProcesses also relevant for SLANDFire simulationand/or suppressionfor USonlynoyeslnoyesyesyesyesnoyesyesnoyesyesyesnonoyesyesnoClimate and variabilitynonoyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesCO2 fertilizationnoinoiyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesyesCarbon-nitrogen interactions,including Ndepositionnomnomnojyesnofyesyesnoyesnoeyesyesyesnoyesyesyesyescnono
a Refers to the routine harvest of established managed forests rather than pools of harvested products.b No back and forth transitions between vegetation types at the country level, but if forest loss based on FRA exceeded agricultural expansion based on FAO, then this amount of area was cleared for cropland and the same amount of area of old croplands abandoned.
c 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.d Available but not active.e Simple parameterization of nitrogen limitation based on Yin (2002; assessed on FACE experiments).f Although C–N cycle interactions are not represented, the model includes a parameterization of down-regulation of photosynthesis as CO2 increases to emulate nutrient constraints (Arora et al., 2009).g Tillage is represented over croplands by increased soil carbon decomposition rate and reduced humification of litter to soil carbon.h ISBA-CTRIP corresponds to SURFEXv8 in GCB2018.i Bookkeeping models include the effect of CO2 fertilization as captured by present-day carbon densities, but not as an effect transient in time.j As far as the DGVMs that OSCAR is calibrated to include it.k Perfect fertilization assumed; i.e. crops are not nitrogen limited and the implied fertilizer diagnosed.l Fire intensity responds to climate and CO2, but no fire suppression.m Process captured implicitly by use of observed carbon densities.
Comparison of the processes and model set-up for the global ocean biogeochemistry models for their estimates of SOCEAN. See Table 4 for model references.
NEMO-PlankTOM5NEMO-PISCES (IPSL)MICOM-HAMOCC (NorESM1-OCv1.2)MPIOM-HAMOCC6CSIROFESOM-1.4-REcoM2NEMO3.6-PISCESv2-gas (CNRM)MOM6-COBALT (Princeton)CESM-ETHZSPIN-UP procedureInitialization of carbon chemistryGLODAPv1 corrected for anthropogenic carbon from Sabine et al. (2004)GLODAPv2GLODAP v1 (pre-industrial DIC)initialization fromprevious model simulationsGLODAPv1 pre-industrialGLODAPv2 alkalinity and pre-industrial DICGLODAPv2GLODAPv2 for Alkalinity and DIC. DIC is corrected to 1959 level for simulation A and corrected to pre-industrial level for simulation B using Khatiwala et al. (2009, 2013)GLODAPv2 pre-industrialPre-industrial spin-up prior to 1850spin-up 1750–1947spin-up starting in 1836 with 3 loops of JRA-551000 year spin upspin-up with ERA20C800 yearsnolong spin-up (> 1000 years)Other biogeochemical tracers are initialized from a GFDL-ESM2M spin-up (> 1000 years)spin-up 1655–1849Atmospheric forcing forpre-industrial spin-uplooping NCEP year1980JRA-55CORE-I (normal year) forcingERA20CCORE+JRA-55not applicableNCEP2 repeat year1948 perpetuallyGFDL-ESM2M internal forcingCOREv2 forcing until 1835, three cycles of conditions from 1949–2009. from 1835–1850: JRA forcingAtmospheric forcing for historical spin-up 1850–1958 for simulation A1750–1947: looping NCEP year 1980; 1948–2019: NCEP1836–1958: looping full JRA-55 reanalysisCORE-I (normal year) forcing; from 1948 onwards NCEP-R1 with CORE-II correctionsNCEP/NCEP+ERA20C (spin-up)JRA55-do cyclic 1958JRA55-do-v1.3.1 repeat year 1961NCEP2 repeat year 1948 perpetuallyJRA55-do-v1.4 repeat year 1959 (81 years)JRA-55 version 1.3, repeat cycle between 1958–2018.Atmospheric CO2 for historical spin-up 1850–1958 for simulation Aprovided by the GCP; converted to pCO2 temperature formulation (Sarmiento et al., JGR 1992), monthly resolutionxCO2 as provided by the GCB, global mean, annual resolution, converted to pCO2 with sea-level pressure and water vapour pressurexCO2 as provided by the GCB, converted to pCO2 assuming constant standard seal level pressure, no water vapour correctionxCO2 provided by the GCB, no conversionxCO2 provided by GCP converted to pCO2 with SLP, no water vapour correctionxCO2 as provided by the GCB, converted to pCO2 with sea-level pressure and water vapour pressure, global mean, monthly resolutionxCO2 as provided by the GCB, converted to pCO2 with constant sea-level pressure and water vapour pressure, global mean, yearly resolutionxCO2 at year 1959 level (315 ppm), converted to pCO2 with sea-level pressure and water vapour pressure, global mean, yearly resolutionxCO2 as provided by the GCB, converted to pCO2 with atmospheric pressure, and locally determined water vapour pressure from SST and SSS (100 % saturation)Atmospheric forcing forcontrol spin-up 1850–1958 for simulation B1750–2019: looping NCEP 1980not availableCORE-I (normal year) forcingspin-up initial restart file with cyclic 1957 NCEP; run 1957–2017JRA55-do cyclic 1958JRA55-do-v1.3.1 repeat year 1961NCEP2 repeat year 1948 perpetuallyJRA55-do-v1.4 repeat year 1959 (81 years)normal year forcing created from JRA-55 version 1.3, NYF = climatology with anomalies from the year 2001Atmospheric CO2 for control spin-up 1850–1958 for simulation B (ppm)constant 278 ppm;converted to pCO2 temperature formulation (Sarmiento et al., 1992), monthly resolutionn/axCO2 of 278 ppm, converted to pCO2 assuming constant standard seal level pressure278, no conversion, assuming constant standard sea level pressure280, converted to pCO2 with SLP, no water vapour correctionxCO2 of 278 ppm, converted to pCO2 with sea-level pressure and water vapour pressurexCO2 of 278 ppm, converted to pCO2 with constant sea-level pressure and water vapour pressurexCO2 of 278 ppm, converted to pCO2 with sea-level pressure and water vapour pressurexCO2 as provided by the GCB for 1850, converted to pCO2 with atmospheric pressure, and locally determined water vapour pressure from SST and SSS (100 % saturation)Simulation AAtmospheric forcing for simulation ANCEPJRA-55NCEP-R1 with CORE-II correctionsNCEP/NCEP+ERA-20C (spin-up)JRA55-doJRA55-do-v1.4.0 1958–2018 and JRA55-do-v1.4.0.1b for 2019NCEP with CORE-II correctionsJRA55-do-v1.4.0 1959–2018 and JRA55-do-v1.4.0.1b for 2019JRA-55 version 1.3
n/a: not applicable
Continued.
NEMO-PlankTOM5NEMO-PISCES (IPSL)MICOM-HAMOCC (NorESM1-OCv1.2)MPIOM-HAMOCC6CSIROFESOM-1.4-REcoM2NEMO3.6-PISCESv2-gas (CNRM)MOM6-COBALT (Princeton)CESM-ETHZSimulation BAtmospheric CO2 for simulation Aprovided by the GCP; converted to pCO2 temperature formulation (Sarmiento et al., 1992), monthly resolutionxCO2 as provided by the GCB, global mean, annual resolution, converted to pCO2 with sea-level pressure and water vapour pressuremonthly xCO2 as provided by the GCB, converted to pCO2 assuming constant standard seal level pressuremonthly xCO2 as provided by the GCB, no conversionxCO2 provided by GCP converted to pCO2 with SLP, no water vapour correctionxCO2 as provided by the GCB, converted to pCO2 with sea-level pressure and water vapour pressure, global mean, monthly resolutionxCO2 as provided by the GCB, converted to pCO2 with constant sea-level pressure and water vapour pressure, global mean, yearly resolutionxCO2 as provided by the GCB, converted to pCO2 with sea-level pressure and water vapour pressure, global mean, yearly resolutionxCO2 as provided by the GCB, converted to pCO2 with atmospheric pressure, and locally determined water vapour pressure from SST and SSS (100 % saturation)Atmospheric forcingfor simulationBNCEP 1980n/aCORE-I (normal year) forcingspin-up initial restart file (278) with cyclic 1957 NCEP; run 1957–2017 with 278JRA55-do cyclic 1958JRA55-do-v1.3.1 repeat year 1961NCEP with CORE-II corrections cycling over 1948–1957JRA55-do-v1.4.0 repeat year 1959normal year forcing created from JRA-55 version 1.3, NYF = climatology with anomalies from the year 2001Atmospheric CO2 for simulation Bconstant 278 ppm; converted to pCO2temperature formulation (Sarmiento et al., JGR 1992), monthly resolutionn/axCO2 of 278 ppm, converted to pCO2 assuming constant standard seal level pressure280xCO2 of 278 ppm, converted to pCO2 with sea-level pressure and water vapour pressurexCO2 of 278 ppm, converted to pCO2 with constant sea-level pressure and water vapour pressurexCO2 of 278 ppm, converted to pCO2 with sea-level pressure and water vapour pressurexCO2 as provided by the GCB for 1850, converted to pCO2 with atmospheric pressure, and locally determined water vapour pressure from SST and SSS (100 % saturation)Model specificsPhysical ocean modelNEMOv2.3-ORCA2NEMOv3.6-eORCA1L75MICOM (NorESM1-OCv1.2)MPIOMMOM5FESOM-1.4NEMOv3.6-GELATOv6-eORCA1L75MOM6-SIS2CESMv1.4 (ocean model based on POP2)Biogeochemistry modelPlankTOM5.3PISCESv2HAMOCC (NorESM1-OCv1.2)HAMOCC6WOMBATREcoM-2PISCESv2-gasCOBALTv2BEC (modified and extended)Horizontal resolution2∘ long, 0.3 to 1.5∘ lat1∘ long, 0.3 to 1∘ lat1∘ long, 0.17 to 0.25 lat (nominally 1∘)1.5∘1∘×1∘ with enhanced resolution at the tropics and in the high-lat Southern Oceanunstructured multi-resolution mesh. CORE-mesh, with 20–120 km resolution. Highest resolution north of 50∘ N, intermediate in the equatorial belt and Southern Ocean, lowest in the subtropical gyres1∘ long, 0.3 to 1∘ lat0.5∘ long, 0.25 to 0.5∘ latlong: 1.125∘, lat varying from 0.53∘ in the extra-tropics to 0.27∘ near the EquatorVertical resolution31 levels75 levels, 1 m at the surface51 isopycnic layers +2 layers representing a bulk mixed layer40 levels, layer thickness increase with depth50 levels, 20 in the200 m46 levels, 10 m spacing in the top 100 m75 levels, 1 m at surface75 levels hybrid coordinates, 2 m at surface60 levels(z coordinates)Total ocean area on native grid (km2)3.6080E+083.6270E+083.6006E+083.6598E+083.6134E+083.6475E+083.6270E+083.6110E+083.5926E+08
Continued.
NEMO-PlankTOM5NEMO-PISCES (IPSL)MICOM-HAMOCC (NorESM1-OCv1.2)MPIOM-HAMOCC6CSIROFESOM-1.4-REcoM2NEMO3.6-PISCESv2-gas (CNRM)MOM6-COBALT (Princeton)CESM-ETHZGas-exchange parameterizationquadratic exchange formulation (function of T+0.3×U2)×(Sc/660)-0.5); Wanninkhof et al. (1992) (Eq. 8)see Orr et al. (2017): kw parameterized from Wanninkhof (1992), with kw=a×(Sc/660)-0.5)×u2×(1-f_ice) with a from Wanninkhof et al. (2014)see Orr et al. (2017): kw parameterized from Wanninkhof (1992), with kw=a×(Sc/660)-0.5)×u2×(1-f_ice) with a=0.337 following the OCMIP2 protocolsGas transfer velocity formulation and parameter set-up of Wanninkhof (2014), including updated Schmidt number parameterizations for CO2 to comply with OMIP protocol (Orr et al., 2017)Quadratic exchange formulation (function of T+0.3timesU2)×(Sc/660)-0.5); Wanninkhof et al. (1992) (Eq. 8)see Orr et al. (2017): kw parameterized from Wanninkhof (1992), with kw=a×(Sc/660)-0.5)×u2×(1-f_ice) with a from Wanninkhof et al. (2014)see Orr et al. (2017): kw parameterized from Wanninkhof 1992, with kw=a×(Sc/660)-0.5)×u2×(1-f_ice) with a from Wanninkhof et al. (2014)see Orr et al. (2017): kw parameterized from Wanninkhof (1992), with kw=a×(Sc/660)-0.5)×u2×(1-f_ice) with a from Wanninkhof et al. (2014)gas exchange is parameterized using the Wanninkhof (1992) quadratic wind speed dependency formulation, but with the coefficient scaled down to reflect the recent 14C inventories; concretely, we used a coefficient a of 0.31 cm h-1 s2 m-2 to read kw= 0.31 u2(1-f_ice)(Sc/660){-1/2}Time step96 min45 min3200 s60 min15 min15 min15 min30 min3757 sOutput frequencyMonthlymonthlymonthly/dailymonthlymonthlymonthlymonthlymonthlymonthlyCO2 chemistry routinesfollowing Broeckeret al. (1982)mocsyfollowing Dickson et al. (2007)as in Ilyina et al. (2013) adapted to comply with OMIP protocol (Orr et al., 2017).OCMIP2 (Orr et al., 2017)mocsymocsymocsyOCMIP2 (Orr et al., 2017)River carbon input(GtC yr-1)60.24 Tmol yr-1; 0.723 GtC yr-10.61 GtC yr-10none00∼ 0.6 GtC yr-1∼ 0.11 GtC yr-10.33 GtC yr-1Burial/net fluxinto the sediment (GtC yr-1)0.723 GtC yr-10.59 GtC yr-10around 0.4 GtC yr-100∼ 0.7 GtC yr-1∼ 0.21 GtC yr-10.25 GtC yr-1
Description of ocean data products used for assessment of SOCEAN. See Table 4 for references.
Data productsJena-MLSMPI-SOMFFNCMEMSCSIRWatson et al. (2020)MethodSpatio-temporal interpolation (update of Rödenbeck et al., 2013, version oc_v2020). Specifically, the sea–air CO2 fluxes and the pCO2 field are numerically linked to each other and to the spatio-temporal field of ocean-internal carbon sources/sinks through process parameterizations, and the ocean-internal sources/sink field is then fit to the SOCATv2020 pCO2 data (Bakker et al., 2020). The fit includes a multi-linear regression against environmental drivers to bridge data gaps, and interannually explicit corrections to represent the data signals more completely.Two-step neural network method wherein a first step the global ocean is clustered into 16 biogeochemical provinces using a self-organizing map (SOM). In a second step, the non-linear relationship between available pCO2 measurements from the SOCAT database (Bakker et al., 2016) and environmental predictor data (SST, SSS, MLD, CHL a, atmospheric CO2 – see Landschützer et al., 2016) is established using a feed-forward neural network (FFN) for each province separately. The established relationship is then used to fill the existing data gaps (see Landschützer et al., 2013, 2016).An ensemble of neural network models trained on 100 subsampled data sets from the Surface Ocean CO2 Atlas (SOCAT, Bakker et al., 2016). Like the original data, subsamples are distributed after interpolation on 1×1 grid cells along ship tracks. Sea surface salinity, temperature, sea surface height, mixed layer depth, atmospheric CO2 mole fraction, chlorophyll, spco2 climatology, latitude, and longitude are used as predictors. The models are used to reconstruct sea surface pCO2 and then convert to air–sea CO2 fluxes.An ensemble average of six machine learning estimates of pCO2 using the approach described in Gregor et al. (2019) with the updated product using SOCAT v2020. All ensemble members use a cluster-regression approach. Two different cluster configurations are used based on (1) K-means clustering and (2) Fay and McKinley (2014)'s CO2 biomes. Three regression algorithms are used: (1) gradient boosted decision trees, (2) a feed-forward neural network, (3) support vector regression. The product of the cluster configurations and the regression algorithms results in an ensemble with six members.Derived from the SOCAT(v2020) pCO2 database but corrected to the subskin temperature of the ocean as measured by satellite, using the methodology described by Goddijn-Murphy et al. (2015). A correction to the flux calculation is also applied for the cool and salty surface skin. In other respects the product uses interpolation of the data using the two-step neural network based on MPI-SOMFFN: in the first step the ocean is divided into a monthly climatology of 16 biogeochemical provinces using a SOM, In the second step a feed-forward neural network establishes non-linear relationships between pCO2 and SST, SSS, mixed layer depth (MLD), and atmospheric xCO2 in each of the 16 provinces. Further description in Watson et al. (2020).Gas-exchange parameterizationQuadratic exchange formulation (k×U2×(Sc/660)-0.5) (Wanninkhof, 1992) with the transfer coefficient k scaled to match a global mean transfer rate of 16 cm h-1 by Naegler (2009)Quadratic exchange formulation (k×U2×(Sc/660)-0.5) (Wanninkhof, 1992) with the transfer coefficient k scaled to match a global mean transfer rate of 16 cm h-1 (calculated myself over the full period 1982–2019 – not following Naegler, 2009)Quadratic exchange formulation (k×U2×(Sc/660)-0.5) (Wanninkhof, 2014) with the transfer coefficient k scaled to match a global mean transfer rate of 16 cm h-1 by Naegler (2009)Quadratic exchange formulation (k×U2×(Sc/660)-0.5) (Wanninkhof, 1992) with the transfer coefficient k scaled to match a global mean transfer rate of 16 cm h-1 by Naegler (2009)Nightingale et al. (2000) formulation: K=((Sc/600)-0.5)×(0.333×U+0.222×U2)Wind productNCEP reanalysis (Kalnay et al., 1996)ERA 5ERA5ERA5CCMP wind product, 0.25∘×0.25∘× 6-hourly, from which we calculate mean and mean square winds over 1×1∘ and 1-month intervals.Spatial resolution2.5∘ longitude ×2∘ latitude1∘×1∘1∘×1∘1∘×1∘1∘×1∘TemporalresolutionDailyMonthlyMonthlyMonthlyMonthlyAtmospheric CO2Spatially and temporally varying field based on atmospheric CO2 data from 156 stations (Jena CarboScope atmospheric inversion sEXTALL_v2020)Atmospheric pCO2_wet calculated from the NOAA ESRL marine boundary layer xCO2 and the NCEP sea level pressure with the moisture correction by Dickson et al., 2007 (details and references can be obtained from Appendix A3 in Landschützer et al., 2013)Spatially and monthly varying fields of atmospheric pCO2 computed from CO2 mole fraction (Chevallier, 2013), and atmospheric dry-air pressure which is derived from monthly surface pressure (ERA5) and water vapour pressure fitted by Weiss and Price (1980)Mole fraction of CO2 from NOAA marine boundary layer product interpolated longitudinally onto ERA5 monthly mean sea level pressure (MSLP). A water vapour pressure correction is applied to MSLP using the equation from Dickson et al. (2007).Atmospheric pCO2 (wet) calculated from NOAA marine boundary layer XCO2 and NCEP sea level pressure, with pH2O calculated from Cooper et al. (1998). (2019 XCO2 marine boundary values were not available at submission so we used preliminary values, estimated from 2018 values and increase at Mauna Loa.)Total ocean area on native grid (km2)3.63E+083.21E+083.21E+083.35E+083.48E+08
Comparison of the inversion set-up and input fields for the atmospheric inversions. Atmospheric inversions see the full CO2 fluxes, including the anthropogenic and pre-industrial fluxes. Hence they need to be adjusted for the pre-industrial flux of CO2 from the land to the ocean that is part of the natural carbon cycle before they can be compared with SOCEAN and SLAND from process models. See Table 4 for references.
CarbonTracker Europe (CTE)Jena CarboScopeCopernicus AtmosphereMonitoring Service (CAMS)UoEMIROCNISMON-CO2Version numberCTE2020sEXTocNEET_v2020v19r1In situ4ObservationsAtmosphericobservationsHourly resolution (well-mixed conditions) obspack GLOBALVIEWplus v5.0 and NRT_v5.2aFlasks and hourly (outliers removed by 2σ criterion)Daily averages of well-mixed conditions – OBSPACK GLOBALVIEWplus v5.0 and NRT v5.2, WDCGG, RAMCES, and ICOS ATCHourly resolution (well-mixed conditions) obspack GLOBALVIEWplus v5.0 and NRT_v5.2a34 surface sites from obspack GLOBALVIEWplus v5.0 and NRT_v5.2aHourly resolution (well-mixed conditions) obspack GLOBALVIEWplus v5.0 and NRT_v5.2a+ NIES observationsPeriod covered2001–20191957–20191979–20192001–20191996–20191990–2019Prior fluxesBiosphere and firesSIBCASA biosphereb with 2019 climatological, GFAS firesNo priorORCHIDEE (climatological), GFEDv4.1 and GFAS after 2019CASA v1.0, climatology after 2016 and GFED4.0CASAVISIT and GFEDv4.1sOceanoc_v1.7 (Rodenbeck et al., 2014) with updates, 2019 climatology + anomalies from oc_v2020oc_v2020 (Rodenbeck et al., 2014) with updatesCMEMS Copernicus ocean fluxes (Denvil-Sommer et al., 2019), with updatesTakahashi climatologyTakahashi climatologyJMA global ocean mapping (Iida et al., 2015)Fossil fuelsGridFED v2020 (Jones et al., 2020)GridFED v2020 (Jones et al., 2020)GridFED v2020 (Jones et al., 2020)ODIAC v2016, after 2015 constantGridFED v2020 (Jones et al., 2020)GridFED v2020 (Jones et al., 2020)Transport andoptimizationTransport modelTM5TM3LMDZ v6GEOS-CHEMACTMNICAM-TMWeather forcingECMWFNCEPECMWFMERRA2JRA-55JRA-55Horizontal resolutionGlobal: 3∘× 2∘, Europe: 1∘× 1∘, North America: 1∘× 1∘Global: 4∘× 5∘Global: 3.75∘× 1.875∘Global: 4∘× 5∘Global: 2.8∘× 2.8∘Isocahedral gl5: ∼ 225 km × 225 kmOptimizationEnsemble Kalman filterConjugate gradient(re-ortho-normalization)cVariationalEnsemble Kalman filterMatrix inversion with 84big regionsVariational
a CGADIP (2020), Carbontracker Team (2020),
b van der Velde et al. (2014),
c ocean prior not optimized.
Attribution of fCO2 measurements for the year 2019 included in SOCATv2020 (Bakker et al., 2016, 2020) to inform ocean pCO2-based flux products.
PlatformRegionsNo. ofPrincipalNo. ofPlatformsamplesinvestigatorsdata setstypeAllure of the SeasTropical Atlantic110 103Wanninkhof, R.; Pierrot, D.46ShipAtlantic CondorNorth Atlantic5051Wallace, D.; Atamanchuk, D.1ShipAtlantic ExplorerNorth Atlantic24 534Bates, N. R.19ShipAurora AustralisSouthern Ocean24 269Tilbrook, B.2ShipBell M. ShimadaNorth Pacific20 176Alin, S.; Feely, R. A.6ShipBjarni SaemundssonNorth Atlantic17 364Benoit-Cattin, A.; Ólafsdóttir, S. R.3ShipBluefinNorth Pacific, tropical Pacific40 110Alin, S. R.; Feely, R. A.6ShipCap San LorenzoNorth Atlantic, tropical Atlantic17 496Lefèvre, N.4ShipCB-06_125W_43NNorth Pacific223Sutton, A.; Hales, B.1MooringColibriNorth Atlantic; tropical Atlantic27 823Lefèvre, N.5ShipColumbiaNorth Pacific76 458Evans, W.; Lebon, G. T.; Harrington, C. D.; Bidlack, A.1ShipDiscoveryNorth Atlantic1457Kitidis, V.1ShipEquinoxTropical Atlantic84 273Wanninkhof, R.; Pierrot, D.41ShipFinnmaidNorth Atlantic144 037Rehder, G.; Glockzin, M.; Bittig, H. C.3ShipFloraNorth Atlantic, tropical Atlantic,tropical Pacific58 550Wanninkhof, R.; Pierrot, D.21ShipG.O. SarsNorth Atlantic93 203Skjelvan, I.11ShipGordon GunterNorth Atlantic48 162Wanninkhof, R.; Pierrot, D.9ShipGulf ChallengerNorth Atlantic6072Salisbury, J.; Vandemark, D.; Hunt, C.6ShipHealyNorth Pacific, Arctic28 988Takahashi, T.; Sweeney, C.; Newberger, T.; Sutherland S. C.; Munro, D. R.2ShipHenry B. BigelowNorth Atlantic66 186Wanninkhof, R.; Pierrot, D.12ShipInvestigatorIndian Ocean, South Pacific, Southern Ocean126 943Tilbrook, B.7ShipJames Clark RossNorth Atlantic, Southern Ocean10 305Kitidis, V.3ShipKeifu Maru IINorth Pacific, Tropical Pacific8935Kadono, K.6ShipLaurence M. GouldSouthern Ocean38 380Sweeney, C.; Takahashi, T.; Newberger, T.; Sutherland, S. C.; Munro, D. R.4ShipMaliziaNorth Atlantic88 495Landschützer, P.; Tanhua, T.3ShipMarion DufresneIndian, Southern oceans9107Lo Monaco, C.; Metzl, N.; Tribollet, A.2ShipNew Century 2North Pacific, tropical Pacific, North Atlantic28 434Nakaoka, S.-I.13ShipNewrest – Art and FenetresNorth Atlantic, tropical Atlantic37 651Tanhua, T.; Landschützer, P.2ShipNuka ArcticaNorth Atlantic65 462Becker, M.; Olsen, A.20ShipOscar DysonNorth Pacific30 373Alin, S.; Feely, R. A.6ShipR/V SikuliaqNorth Pacific, Arctic68 540Takahashi, T.; Sweeney, C.; Newberger, T.; Sutherland, S. C.; Munro, D. R.11ShipRonald H. BrownNorth Atlantic, tropical Atlantic25 605Wanninkhof, R.; Pierrot, D.4ShipRVIB Nathaniel B. PalmerSouthern Ocean22 759Takahashi, T.; Sweeney, C.; Newberger, T.; Sutherland, S. C.; Munro D. R.2ShipRyofu Maru IIINorth Pacific, tropical Pacific9981Kadono, K.6ShipSimon StevinNorth Atlantic26 389Gkritzalis, T.6ShipTangaroaSouthern Ocean34Currie, K. I.2ShipTAO110W_0NTropical Pacific180Sutton, A.1MooringThomas G. ThompsonNorth Atlantic, tropical Atlantic, South Atlantic, Southern Ocean28 965Alin, S.; Feely, R. A.3ShipTrans CarrierNorth Atlantic10 767Omar, A.1ShipTrans Future 5North Pacific, tropical Pacific, South Pacific16 694Nakaoka, S.-I.; Nojiri, Y.16ShipWakataka MaruNorth Pacific69 661Tadokoro, K.; Ono, T.4ShipWaveglider1741South Pacific2287Sutton, A.1ASV
Aircraft measurement programs archived by Cooperative Global Atmospheric Data Integration Project (CGADIP, 2020) that contribute to the evaluation of the atmospheric inversions (Fig. B3).
Site codeMeasurement program name in ObspackSpecific doiData providersAAOAirborne Aerosol Observatory, Bondville, IllinoisSweeney, C.; Dlugokencky, E. J.ACGAlaska Coast GuardSweeney, C.; McKain, K.; Karion, A.; Dlugokencky, E. J.ALFAlta FlorestaGatti, L. V.; Gloor, E.; Miller, J. B.;AOAAircraft Observation of Atmospheric trace gases by JMAghg_obs@met.kishou.go.jpACTAtmospheric Carbon and Transport –AmericaSweeney, C.; Dlugokencky, E. J.; Baier, B; Montzka, S.; Davis, K.BNEBeaver Crossing, NebraskaSweeney, C.; Dlugokencky, E. J.BGIBradgate, IowaSweeney, C.; Dlugokencky, E. J.CARBriggsdale, ColoradoSweeney, C.; Dlugokencky, E.J.CMACape May, New JerseySweeney, C.; Dlugokencky, E. J.CONCONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner)10.17595/20180208.001Machida, T.; Matsueda, H.; Sawa, Y.; Niwa, Y.CRVCarbon in Arctic Reservoirs Vulnerability Experiment (CARVE)Sweeney, C.; Karion, A.; Miller, J. B.; Miller, C. E.; Dlugokencky, E. J.DNDDahlen, North DakotaSweeney, C.; Dlugokencky, E. J.ESPEstevan Point, British ColumbiaSweeney, C.; Dlugokencky, E.J .ETLEast Trout Lake, SaskatchewanSweeney, C.; Dlugokencky, E.J.FWIFairchild, WisconsinSweeney, C.; Dlugokencky, E. J.GSFCNASA Goddard Space Flight CenterAircraft CampaignKawa, S. R.; Abshire, J. B.; Riris, H.HAAMolokai Island, HawaiiSweeney, C.; Dlugokencky, E. J.HFMHarvard University Aircraft CampaignWofsy, S. C.HILHomer, IllinoisSweeney, C.; Dlugokencky, E. J.HIPHIPPO (HIAPER Pole-to-Pole Observations)10.3334/CDIAC/HIPPO_010Wofsy, S. C.; Stephens, B. B.; Elkins, J. W.; Hintsa, E. J.; Moore, F.INXINFLUX (Indianapolis Flux Experiment)Sweeney, C.; Dlugokencky, E. J.; Shepson, P. B.; Turnbull, J.LEFPark Falls, WisconsinSweeney, C.; Dlugokencky, E. J.NHAOffshore Portsmouth, New Hampshire(Isles of Shoals)Sweeney, C.; Dlugokencky, E. J.OILOglesby, IllinoisSweeney, C.; Dlugokencky, E. J.PFAPoker Flat, AlaskaSweeney, C.; Dlugokencky, E. J.RBA-BRio BrancoGatti, L. V.; Gloor, E.; Miller, J. B.RTARarotongaSweeney, C.; Dlugokencky, E. J.SCACharleston, South CarolinaSweeney, C.; Dlugokencky, E. J.SGPSouthern Great Plains, OklahomaSweeney, C.; Dlugokencky, E. J.; Biraud, S.TABTabatingaGatti, L. V.; Gloor, E.; Miller, J. B.THDTrinidad Head, CaliforniaSweeney, C.; Dlugokencky, E.J.TGCOffshore Corpus Christi, TexasSweeney, C.; Dlugokencky, E. J.WBIWest Branch, IowaSweeney, C.; Dlugokencky, E. J.
Main methodological changes in the global carbon budget since first publication. Methodological changes introduced in one year are kept for the following years unless noted. Empty cells mean there were no methodological changes introduced that year.
Publication yearFossil fuel emissions LUC emissionsReservoirs Uncertainty and otherchangesGlobalCountry (territorial)Country (consumption)AtmosphereOceanLand2006aSplit in regions2007bELUC based on FAO-FRA 2005; constant ELUC for 20061959–1979 datafrom Mauna Loa;data after 1980from global averageBased on one ocean model tuned to reproduced observed 1990s sink±1σ provided for all components2008cConstant ELUC for20072009dSplit between Annex B and non-Annex BResults from an independent study discussedFire-based emission anomalies used for2006–2008Based on four ocean models normalized to observations with constant deltaFirst use of five DGVMs to compare with budget residual2010eProjection forcurrent year based on GDPEmissions for topemittersELUC updated withFAO-FRA 20102011fSplit between Annex B and non-Annex B2012g129 countries from1959129 countries and regions from 1990–2010 based onGTAP8.0ELUC for 1997–2011 includes interannual anomalies from fire-based emissionsAll years from global averageBased on five ocean models normalized to observations with ratio10 DGVMs available for SLAND; first use of four models to compare with ELUC2013h250 countries134 countries and regions 1990–2011 based on GTAP8.1, with detailed estimates for years 1997, 2001, 2004, and 2007ELUC for 2012 estimated from 2001–2010 averageBased on six models compared with two data products to year2011Coordinated DGVM experiments for SLAND and ELUCConfidence levels; cumulative emissions; budget from 17502014i3 years of BP data3 years of BPdataExtended to 2012 with updated GDP dataELUC for 1997–2013 includes interannual anomalies from fire-based emissionsBased on seven modelsBased on 10 modelsInclusion of breakdown of the sinks in three latitude bands and comparison with three atmospheric inversions2015jProjection for current yearbased on January–August dataNational emissions from UNFCCC extended to 2014 also providedDetailed estimatesintroduced for 2011 based on GTAP9Based on eight modelsBased on 10 modelswith assessment of minimum realismThe decadal uncertainty for the DGVM ensemble mean now uses ±1σ of the decadal spread across models
a Raupach et al. (2007),
b Canadell et al. (2007),
c Online,
d Le Quéré et al. (2009),
e Friedlingstein et al. (2010),
f Peters et al. (2012b),
g Le Quéré et al. (2013), Peters et al. (2013),
h Le Quéré et al. (2014), i Le Quéré et al. (2015b), j Le Quéré et al. (2016).
Relative changes in fossil CO2 emissions (excluding cement carbonation sink) for the year 2020 to date and projections for the full year. Methods of the four approaches are described in Sect. 2.1.5 and Appendix C.
2020 year-to-date fossil emissions UEAPriestleyCarbon MonitorGCBMedianAverageMinMaxRangeChina (September)-4.1-10.5-1.80.5-2.9-4.0-10.50.511.0USA (September)-11.1-17.0-13.4-12.1-12.8-13.4-17.0-11.15.9EU27 (July)-10.0-14.8-11.6-16.9-13.2-13.3-16.9-10.06.8India (September)-12.4-21.2-12.0-12.7-12.6-14.6-21.2-12.09.2Rest of the world (September)-7.6-14.2-8.4-8.4-10.1-14.2-7.66.6World (September)-7.6-14.1-7.6-7.6-9.8-14.1-7.66.62020 projection of fossil emissions UEAPriestleyCarbon MonitorGCBMedianAverageMinMaxRangeChina-3.1-9.4-0.30.4-1.7-3.1-9.40.49.8USA-10.5-16.3-13.7-10.6-12.2-12.8-16.3-10.55.8EU27-9.6-12.9-7.1-17.0-11.3-11.7-17.0-7.19.9India-9.7-19.2-8.5-8.1-9.1-11.4-19.2-8.111.1Rest of the world-7.1-13.0-7.7-6.4-7.4-8.6-13.0-6.46.5World-6.9-13.0-6.5-5.8-6.7-8.0-13.0-5.87.2
Funding supporting the production of the various components of the global carbon budget in addition to the authors' supporting institutions (see also acknowledgements).
Funder and grant number (where relevant)Author initialsAustralia, Integrated Marine Observing System (IMOS)BTAustralian Government as part of the Antarctic Science Collaboration Initiative programALAustralian Government National Environment Science Program (NESP)JGC, VHBelgium Research Foundation – Flanders (FWO) (grant number UA C130206-18)TGBNP Paribas Foundation through Climate and Biodiversity initiative, philanthropic grant for developments of the Global Carbon AtlasPCChina, National Natural Science Foundation (grant no. 41975155)XYChina, National Natural Science Foundation (grant no. 71874097 and 41921005) and Beijing Natural Science Foundation (JQ19032)ZLEC Copernicus Atmosphere Monitoring Service implemented by ECMWF on behalf of the European CommissionFCEC Copernicus Marine Environment Monitoring Service implemented by Mercator OceanMGEC H2020 (4C; grant no. 821003)PF, RMA, SS, GPP, MOS, JIK, SL, NG, PL, TIEC H2020 (CHE; grant no. 776186)LFEC H2020 (CRESCENDO; grant no. 641816)RS, EJ, AJPS, TIEC H2020 (CONSTRAIN; grant no. 820829)RS, PMFEC H2020 European Research Council (ERC) Synergy grant (IMBALANCE-P; grant no. ERC-2013-SyG-610028)TGEC H2020 (QUINCY; grant no. 647204)SZEC H2020 project (VERIFY; grant no. 776810)CLQ, GPP, JIK, RMA, MWJ, PC, NVEuropean Space Agency Climate Change Initiative ESA-CCI RECCAP2 project 655 (ESRIN/4000123002/18/I-NB)PF, PC, SS, MOSFrench Institut National des Sciences de l'Univers (INSU) and Institut Pau- Emile Victor (IPEV), Sorbonne Universités (OSU Ecce-Terra), TAAF (Terres Australes et Antarctique Françaises), Museum National d'Histoire Naturelle (MNHN)NMFrench Institut de Recherche pour le Développement (IRD)NL, NMGerman Integrated Carbon Observation System (ICOS), Federal Ministry for Education and Research (BMBF); BONUS INTEGRAL (BONUS Blue Ocean and Federal Ministry of Education and Research grant no. 03F0773A)HCBGerman Helmholtz Association in its ATMO programme and the state Baden-Württemberg, Germany, through bwHPCAAGerman Helmholtz Young Investigator Group Marine Carbon and Ecosystem Feedbacks in the Earth System (MarESys; grant number VH-NG-1301)JHGerman Research Foundation's Emmy Noether Programme (grant no. PO1751/1-1)JPGerman Stifterverband für die Deutsche Wissenschaft e.V. in collaboration with Volkswagen AGSBIcelandic Ministry for the Environment and Natural ResourcesABCJapan Global Environmental Research Coordination System, Ministry of the Environment (grant number E1751)SN, TOJapan Environment Research and Technology Development Fund of the Ministry of the Environment (JPMEERF20142001 and JPMEERF20172001)YN, NCJapan Meteorological Agency (JMA)KKKuehne + NagelTTMonaco Foundation Prince Albert II de Monaco (http://www.fpa2.org, last access: 16 November 2020)NM, TTMonaco, Yacht Club de MonacoTTNorwegian Research Council (grant no. 270061)JSNorwegian ICOS Norway and OTC Research Infrastructure Project, Research Council of Norway (grant number 245927)MB, IS, AOSwiss National Science Foundation (grant no. 200020_172476)SLUK Natural Environment Research Council (SONATA; grant no. NE/P021417/1)DRWUK Natural Environment Research Council (NE/R015953/1; NE/N018095/1)VKUK Natural Environmental Research Council (NE/R016518/1)PIPUK Newton Fund, Met Office Climate Science for Service Partnership Brazil (CSSP Brazil)AW, ERUK Royal Society: The European Space Agency OCEANFLUX projectsAJW
Continued.
Funder and grant number (where relevant)Author InitialsUSA Department of Agriculture, National Institute of Food and Agriculture (grant nos. 2015-67003-23489 and 2015-67003-23485)DLLUSA Department of Commerce, NOAA/OAR's Global Ocean Monitoring and Observation ProgramRW, AS, SA, DP, NRB, DRMUSA Department of Commerce, NOAA/OAR's Ocean Acidification ProgramRW, SA, AJS, DPUSA Department of Energy, Office of Science and BER program (grant no. DE-SC000 0016323)AKJUSA Department of Energy, SciDac award number is DESC0012972, IDS grant award number is 80NSSC17K0348LC, GHUSA NASA Interdisciplinary Research in Earth Science Program.BPUS National Science Foundation (grant number 1903722)HTUSA Princeton University Environmental Institute and the NASA OCO2 science team, grant number 80NSSC18K0893.LRORNL is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725.APWComputing resources Norway UNINETT Sigma2, National Infrastructure for High Performance Computing and Data Storage in Norway (NN2980K/NS2980K)JSThe supercomputer systems of NIES (SX-Aurora) and MRI (FUJITSU Server PRIMERGY CX2550M5)YNMIROC4-ACTM inversion is run from JAMSTEC Super Computer system in coordination with Prabir PatraNCJapan National Institute for Environmental Studies computational resourcesEKTGCC under allocation 2019-A0070102201 made by GENCIFCUEA High Performance Computing Cluster, UKDRW, CLQSupercomputing time was provided by the Météo-France/DSI supercomputing center.RS, EJCarbonTracker Europe was supported by the Netherlands Organization for Scientific Research (NWO; grant no. SH-312, 17616)WPDeutsches Klimarechenzentrum (allocation bm0891)JEMSN, JPThe Leibniz Supercomputing Centre provided computing time on its Linux-ClusterKHPRACE for awarding access to JOLIOT CURIE at GENCI@CEA, FranceLBThe CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR.DLLSupplementary figures
Evaluation of the GOBMs and flux products using the root
mean squared error (RMSE) for the period 1985 to 2019, between the
individual surface ocean pCO2 estimates and the SOCAT v2020 database.
The y axis shows the amplitude of the interannual variability (A-IAV, taken
as the standard deviation of a detrended time series calculated as a
12-month running mean over the monthly flux time series; Rödenbeck et
al., 2015). Results are presented for the globe, north (> 30∘ N), tropics (30∘ S–30∘ N), and south
(< 30∘ S) for the GOBMs (see legend circles) and for the
pCO2-based flux products (star symbols). The five pCO2-based flux
products use the SOCAT database and therefore are not fully independent from
the data (see Sect. 2.4.1).
Evaluation of the DGVM using the International Land
Model Benchmarking system (ILAMB; Collier et al., 2018) (a) absolute
skill scores and (b) skill scores relative to other models. The
benchmarking is done with observations for vegetation biomass (Saatchi et
al., 2011; GlobalCarbon unpublished data; Avitabile et al., 2016), GPP
(Jung et al., 2010; Lasslop et al., 2010), leaf area index (De Kauwe et al.,
2011; Myneni et al., 1997), net ecosystem exchange (Jung et al.,
2010; Lasslop et al., 2010), ecosystem respiration (Jung et al., 2010; Lasslop
et al., 2010), soil carbon (Hugelius et al., 2013; Todd-Brown et al., 2013),
evapotranspiration (De Kauwe et al., 2011), and runoff (Dai and Trenberth,
2002). For each model–observation comparison a series of error metrics are
calculated, scores are then calculated as an exponential function of each
error metric, and finally for each variable the multiple scores from different
metrics and observational data sets are combined to give the overall
variable scores shown in (a). Overall variable scores increase
from 0 to 1 with improvements in model performance. The set of error metrics
vary with data set and can include metrics based on the period mean, bias,
root mean squared error, spatial distribution, interannual variability and
seasonal cycle. The relative skill score shown in (b) is a
Z score, which indicates in units of standard deviation the model scores
relative to the multi-model mean score for a given variable. Grey boxes
represent missing model data.
Evaluation of the atmospheric inversion products. The
mean of the model minus observations is shown for four latitude bands in
three periods: (a) 2001–2010, (b) 2011–2018, (c) 2001–2018. The
four models are compared to independent CO2 measurements made on board
aircraft over many places of the world between 2 and 7 km above sea level.
Aircraft measurements archived in the Cooperative Global Atmospheric Data
Integration Project (CGADIP, 2020) from sites, campaigns or programs that
cover at least 9 months between 2001 and 2018, and that have not been
assimilated have been used to compute the biases of the differences in four
45∘ latitude bins. Land and ocean data are used without distinction.
Comparison of global carbon budget components released
annually by GCP since 2006. CO2 emissions from (a) fossil
CO2 emissions (EFOS) and (b) land-use change
(ELUC), as well as their partitioning among (c) the atmosphere
(GATM), (d) the land (SLAND), and (e) the ocean
(SOCEAN). See legend for the corresponding years, and Tables 3 and A7
for references. The budget year corresponds to the year when the budget was
first released. All values are in GtC yr-1. Grey shading shows the
uncertainty bounds representing ±1σ of the current global
carbon budget. Note that the 2020 estimate of EFOS includes the cement carbonation sink.
Monthly 2020 fossil CO2 emission based on
year-to-date data (solid lines) and projections (dashed lines) following
four available approaches for (a) the whole world, (b) China, (c) the USA, (d) the European Union, (e) India, and (f) the rest of the world. Methods of the
four approaches are described in Sect. 2.1.5 and Appendix C.
Supplementary informationDetails of the Global Carbon Budget projection methodChina
The method for the projection uses (1) the sum of monthly
domestic production of raw coal, crude oil, natural gas, and cement from the
National Bureau of Statistics (NBS, 2020a); (2) monthly net imports of coal,
coke, crude oil, refined petroleum products, and natural gas from the General
Administration of Customs of the People's Republic of China (2019); and (3) annual energy consumption data by fuel type and annual production data for
cement from the NBS, using data for 2000–2018 (NBS, 2019), with the growth
rates for 2019 taken from official preliminary statistics for 2019 (NBS,
2020a, b). We estimate the full-year growth rate for 2020 using a
Bayesian regression for the ratio between the annual energy consumption data
(3 above) from 2014 through 2019, and monthly production plus net imports
through August of each year (1+2 above). The uncertainty range uses the
standard deviations of the resulting posteriors. Sources of uncertainty and
deviations between the monthly and annual growth rates include lack of
monthly data on stock changes and energy density, variance in the trend
during the last 3 months of the year, and partially unexplained
discrepancies between supply-side and consumption data even in the final
annual data. The YTD estimate is made in the same way, but instead of
regressing the ratio between historical monthly data for August and
full-year annual data, monthly data for December are used instead, to produce
regression results that capture the systematic differences between the
monthly supply and annual consumption data, without the additional effect of
projecting forward from August to the end of the year.
Note that in recent years, the absolute value of the annual growth rate for
coal energy consumption, and hence total CO2 emissions, has been
consistently lower (closer to zero) than the growth or decline suggested by
the monthly, tonnage-based production and import data, and this is reflected
in the projection. This pattern is only partially explained by stock changes
and changes in energy content, and it is therefore not possible to be
certain that it will continue in any given year. For 2020 in particular, the
COVID-19-related lockdown and reopening in China, similar but delayed
restrictions in major export markets, and unusual amounts of flooding
and extreme weather during the summer months imply that seasonal patterns
and correlations between supply, stock changes, and consumption are likely to
be quite different this year than in the previous years that the regression
is based on. This adds a major but unquantified amount of uncertainty to the
estimate.
USA
We use emissions estimated by the US Energy Information
Administration (EIA) in their Short-Term Energy Outlook (STEO) for emissions
from fossil fuels to get both a YTD and full-year projection (EIA, 2020).
The STEO also includes a near-term forecast based on an energy forecasting
model which is updated monthly (last update with preliminary data through
August 2020) and takes into account expected temperatures, 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–June 2020, assuming changes
in cement production over the first part of the year apply throughout the
year.
India
We use monthly emissions estimates for India updated from
Andrew (2020b) through August. These estimates are derived from many official
monthly energy and other activity data sources to produce direct estimates
of national CO2 emissions, without the use of proxies. For purposes of
comparison with other methods, we use a simple approach to extrapolating
their observations by assuming the remaining months of the year change by
the same relative amount compared to 2019 in the final month of
observations.
EU
We use (1) monthly coal delivery data from Eurostat for January
through June 2020 (Eurostat, 2020); (2) monthly oil and gas demand data for
January through June from the Joint Organisations Data Initiative (JODI,
2020), with adjustments for deliveries to petrochemical industries using
data from Eurostat (2020); and (3) cement production, which is assumed stable. For
purposes of comparison with other methods, we use a simple approach to
extrapolating their observations by assuming the remaining months of the
year change by the same relative amount compared to 2019 in the final month
of observations.
Rest of the world
This method only provides a full-year
projection. We use the close relationship between the growth in GDP and the
growth in emissions (Raupach et al., 2007) to project emissions for the
current year. This is based on a simplified Kaya identity, whereby EFOS
(GtC yr-1) is decomposed by the product of GDP (USD yr-1) and the
fossil fuel carbon intensity of the economy (IFOS; GtC USD-1) as
follows:
EFOS=GDP×IFOS.
Taking a time derivative of Eq. (3) and rearranging gives
1EFOSdEFOSdt=1GDPdGDPdt+1IFOSdIFOSdt,
where the left-hand term is the relative growth rate of EFOS, and the
right-hand terms are the relative growth rates of GDP and IFOS,
respectively, which can simply be added linearly to give the overall growth
rate.
The IFOS is based on GDP in constant PPP (purchasing power parity) from
the International Energy Agency (IEA) up to 2017 (IEA/OECD, 2019) and
extended using the International Monetary Fund (IMF) growth rates through
2019 (IMF, 2020). Interannual variability in IFOS is the largest source
of uncertainty in the GDP-based emissions projections. We thus use the
standard deviation of the annual IFOS for the period 2009–2019 as a
measure of uncertainty, reflecting a ±1σ as in the rest of
the carbon budget.
World
This method only provides a full-year projection. The global
total is the sum of each of the countries and regions, but this year we
additionally apply a GDP approach to the world to provide an additional
consistency check (see “rest of world” description).
Author contributions
PF, MOS, MWJ, CLQ, RMA, JH, GPP, WP, JP, SS,
AO, JGC, PC, and RBJ designed the study, conducted the analysis, and wrote
the paper. RMA, GPP, and JIK produced the emissions, their uncertainties, and the GCB 2020 emission projections and analysed the emissions data. DG and
GM provided emission data. PPT provided key atmospheric CO2 data. WP,
PC, FC, CR, NC, YN, PIP, and LF provided an updated atmospheric inversion,
developed the protocol, and produced the evaluation. JP, KH, SB, TG, and RAH
provided updated bookkeeping land-use change emissions. LPC, LEOCA, and
GRvdW provided forcing data for land-use change. AA, VH, AKJ, EJ, EK, SL,
DLL, JRM, JEMSN, BP, HT, NV, APW, AJW, WY, XY, and SZ provided an update of a
DGVM. IH provided the climate forcing data for the DGVMs. ER provided the
evaluation of the DGVMs. JH, LB, NG, TI, AL, LR, JS, RS, and DW provided an
update of a GOBM. MG, LG, PL, CR, and AJW provided an update of an ocean
flux product. SA, NRB, MB, AB, HCB, WE, TG, KK, VK, NL, NM, DRM, SN, KO, AO,
TO, DP, IS, AJS, TT, BT, and RW provided ocean pCO2 measurements for
the year 2019, with synthesis by AO and KO. PF, MOS, and MWJ revised all
figures, tables, text, and/or numbers to ensure the update is clear from the
2019 edition and in phase with the globalcarbonatlas.org.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank all people and institutions who provided
the data used in this carbon budget and Ian G. C. Ashton, Matthew Chamberlain, Ed
Chan, Laique Djeutchouang, Christian Ethé, Liang Feng, Matthew Fortier, Lonneke
Goddijn-Murphy, Thomas Holding, George Hurtt, Joe Melton, Tristan Quaife, Marine
Remaud, Shijie Shu, Jamie Shutler, Anthony Walker, Ulrich Weber, and David K. Woolf for their involvement in the development, use, and analysis of the
models and data products used here. We thank Ed Dlugokencky for providing
atmospheric CO2 measurements; We thank Benjamin Pfeil, Steve Jones,
Rocío Castaño-Primo, and Maren Karlsen of the Ocean Thematic Centre
of the EU Integrated Carbon Observation System (ICOS) Research
Infrastructure for their contribution, as well as Karl Smith of NOAA's
Pacific Marine Environmental Laboratory and Kim Currie, Joe Salisbury, Doug
Vandermark, Chris Hunt, Douglas Wallace, and Dariia Atamanchuck, who
contributed to the provision of ocean pCO2 observations for the year 2019
(see Table A5). This is NOAA-PMEL contribution number 5167. We thank the
institutions and funding agencies responsible for the collection and quality
control of the data in SOCAT, and the International Ocean Carbon
Coordination Project (IOCCP) for its support. We thank the FAO and its member
countries for the collection and free dissemination of data relevant to this
work. We thank data providers ObsPack GLOBALVIEWplus v5.0 and NRT v5.2 for
atmospheric CO2 observations. We thank Trang Chau who produced the CMEMS
pCO2-based ocean flux data and designed the system together with Marion Gehlen, Anna
Denvil-Sommer, and Frédéric Chevallier1. We thank the individuals and institutions that
provided the databases used for the model evaluations introduced here, and
Nigel Hawtin for producing Figs. 2 and 9. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM2. We thank Fortunat Joos,
Samar Khatiwala, and Timothy DeVries for providing historical data. We thank
all people and institutions who provided the data used in this carbon budget
and the Global Carbon Project members for their input throughout the
development of this update. Finally, we thank all funders who have supported
the individual and joint contributions to this work (see Table A9), as well
as the reviewers of this paper and previous versions, and the many
researchers who have provided feedback.
Financial support
For a list of all funders that have supported this research, please refer to Table A9.
Review statement
This paper was edited by David Carlson and reviewed by Albertus J. (Han) Dolman, Tomohiro Oda and two anonymous referees.
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