ES

Fossil Fuel Emissions

Cite as: Friedlingstein et al. (2020).

Friedlingstein et al., 2020: The Global Carbon Budget 2020. Available at: Earth System Science Data.

Methods:

The 1959-2017 estimates for fossil fuels and industry are primarily from an update of Gilfillan et al. (2019).

We modify these data as follows:

• Where available (42 countries) the national estimates reported by Annex-1 parties to the UNFCCC (2020) during 1990-2018 are used instead of CDIAC data.
• Emissions for India during 2009-2019 are overwritten using data from Andrew (2020a).
• Emissions for Norway during 1973–1989 are overwritten using superior data derived from local sources.
• Cement emissions are updated from Andrew (2019).
• Emissions for Aruba before 1950 and for the countries of the former Soviet Union in 1942-43 are updated based on information in Andrew (2020b).
• Global total emissions are calculated as the sum of all countries’ emissions plus emissions from international bunker fuels.
• The 2018 and 2019 estimates (or just 2019 where UNFCCC data are available) are preliminary and based on energy statistics published by BP (BP, 2020).

Cement emissions

Consumption-based Fossil Fuel Emissions

Land-Use Change Emissions

Gross Domestic Product

Population

Terms of use

Atmospheric inversions

General description: Atmospheric CO₂ inversions estimate surface-to-atmosphere net carbon fluxes by utilizing atmospheric CO₂ concentration measurements and an atmospheric transport model (see more details). The contributing inversions are recent state of the art systems that have been used for the RECCAP synthesis and compared in Peylin et al., Biogeosciences (2013). The inversion results have been provided by individual scientists, as credited, who have made them available and encourage their use.

The original data have been reformatted onto a 1×1 degree grid and further processed to correct for differences in fossil fuel emissions between inversions. Only the natural land and ocean fluxes are displayed under this portal. If you plan to use any of these results, you need to contact each modeling group. Note that regional totals and reformatted 3-D fluxes can be accessed under the “Transcom” web site (http://transcom.lsce.ipsl.fr/) after contacting Philippe Peylin (Peylin@lsce.ipsl.fr).

The use of data is conditional on citing the original data sources. Full details on how to cite the data are given for each inversion in the references below and in the corresponding web links. The Global Carbon Project facilitates access to data to encourage its use and promote a good understanding of the carbon cycle. Respecting original data sources is key to help secure the support to enhance, maintain and update valuable data.

MACC-II:

Contacts: Frederic Chevallier (frederic.chevallier@lsce.ipsl.fr)

Description: MACC-II corresponds to version 11.2 of the CO₂ inversion product from the Monitoring Atmospheric Composition and Climate – Interim Implementation (MACC-II) service (http://www.gmes-atmosphere.eu/). It is based on a variational formulation. Fluxes are solved at the resolution of the transport model (LMDz-v4 : 2.5° x 3.75°) and a 8-day daytime/nighttime resolution. Prior land and ocean fluxes come from the ORCHIDEE land surface model climatology and Takahashi et al. (2009), respectively. Raw atmospheric CO₂ concentrations from 134 observing sites are used.

References:

Chevallier, F., et al.: CO₂ surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, J. Geophys. Res., 115, D21307, doi:10.1029/2010JD013887, 2010.

Data access: http://www.gmes-atmosphere.eu/d/services/gac/delayed/ 

JENA-MPI:

Contacts: Christian Roedenbeck (Christian.Roedenbeck@bgc-jena.mpg.de)

Description: The Jena inversion has been designed to estimate interannual variations of land and ocean fluxes, based on raw CO₂ concentration data from 50 observing sites. It uses a variational approach with the TM3 transport model (4° x 5°, using interannually-varying winds). Fluxes are optimized at the spatial resolution of the transport model and at roughly weekly to interannual time scales. Prior land fluxes come from fossil fuel emissions and a modelled mean biosphere pattern, and ocean fluxes from an inverse estimate based on ocean carbon data (Mikaloff-Fletcher et al. 2006).

References:

C. Rödenbeck et al.: CO₂ flux history 1982-2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys. 3, 1919-1964 (2003) ; Updated version s96_v3.51

Data access: Results can be downloaded from “http://www.bgc-jena.mpg.de/~christian.roedenbeck/download-CO2/”

LSCE-ana:

Contacts: Philippe Peylin (Peylin@lsce.ipsl.fr)

Description: LSCE-ana inversion is based on a Bayesian synthesis method described in Peylin et al. (2005) and corresponds to the results described in Piao et al., 2009. Fluxes are optimized at the resolution of the transport model (LMDz-v3 : 2.5° x 3.75°) on a monthly base. Prior land and ocean fluxes come from ORCHIDEE land surface model climatology and Takahashi et al. (2002), respectively. Monthly mean CO₂ concentrations from 74 observing sites are used.

References:

Peylin, P., et al.: Daily CO₂ flux estimates over Europe from continuous atmospheric mea- surements: 1, inverse methodology, Atmos. Chem. Phys., 5, 3173–3186, doi:10.5194/acp-5-3173-2005, 2005.

Piao, S. L., Fang, J. Y., Ciais, P. Peylin, P., Huang, Y., Sitch, S., and Wang, T.: The carbon balance of terrestrial ecosystems in China, Nature, 458, 1009–1014, 2009.

Data access: contact Philippe Peylin

CCAM:

Contacts:  Rachel Law (rachel.law@csiro.au)

Description: CCAM inversion uses a Bayesian synthesis method, described in Rayner et al. 2008. It is based on CCAM transport model but with not interannual-varying winds. Fluxes are solved for 94-land and 52-ocean regions on a monthly base. Prior land and ocean fluxes come from CASA land surface model climatology and Takahashi et al. (1999), respectively. Monthly mean observations from 73 atmospheric CO₂ records and 7 d13C records are used.

References:

Rayner, P. J., Law, et al.: Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO2 and d 13 CO₂ measurements, Global Biogeochem. Cy., 22, GB3008, doi:10.1029/2007GB003068, 2008.

Data access: contact Rachel Law

MATCH:

Contacts:  Peter Rayner (prayner@unimelb.edu.au)

Description: MATCH inversion uses a Bayesian synthesis method, described in Rayner et al. 2008. It is based on MATCH transport model but with not interannual-varying winds. Fluxes are optimized for 67-land and 49-ocean regions on a monthly base. Prior land and ocean fluxes come from CASA land surface model climatology and Takahashi et al. (1999), respectively. Monthly mean observations from 73 atmospheric CO₂ records and 7 d13C records are used.

References:

Rayner, P. J., Law, et al.: Interannual variability of the global carbon cycle (1992–2005) inferred by inversion of atmospheric CO₂ and d 13 CO₂ measurements, Global Biogeochem. Cy., 22, GB3008, doi:10.1029/2007GB003068, 2008.

Data access: contact Peter Rayner

CTE2013:

Contacts: Wouter Peters (wouter.peters@wur.nl)

Description: CTE2013 inversion corresponds to the CarbonTracker system, described in Peters et al. (2007) with a specific zoom over Europe and North America. It is based on TM5 transport model with two nested grids (1°x1° resolution over Europe/North America and 3° x 2° elsewhere) and with interannually-varying winds. It uses an ensemble Kalman filter to optimized weekly fluxes over 138-land and 30-ocean regions. Prior land and ocean fluxes come CASA biosphere model (including interannual flux variations) and from the ocean inversions reported in Jacobson et al. (2007), respectively. Raw CO₂ concentrations from 117 observing sites are used.

References:

Peters et al. An atmospheric perspective on North American carbon dioxide ex- change: CarbonTracker, P. Natl. Acad. Sci. USA, 104, 18925– 18930, doi:10.1073/pnas.0708986104, 2007.

Jacobson, A. R., et al.: A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide: I. Methods and global-scale fluxes, Global Biogeochem. Cy., 21, GB1019, doi:10.1029/2005GB002556, 2007.

Data access: contact Wouter Peters and access via http://www.carbontracker.eu/.

RIGC:

Contacts: Prabir Patra (prabir@jamstec.go.jp)

Description: RIGC inversion results are based on the TransCom Level 3 Bayesian synthesis method, using the NEIS/FRCGC transport model with interannually-varying winds. They are described in details in Patra et al. (2005). Fluxes are optimized for 42-land and 22-ocean regions on a monthly base. Prior land and ocean fluxes come from CASA biosphere model (climatology) and Takahashi et al. (1999), respectively. Monthly mean CO₂ concentrations from 74 observing sites are used.

References:

Patra, P. K., et al.: Role of biomass burning and climate anomalies for land-atmosphere carbon fluxes based on inverse modeling of atmospheric CO₂, Global Biogeochem. Cy., 19, GB3005, doi:10.1029/2004GB002258, 2005.

Data access: contact Prabir Patra

TRCOM-mean:

Contacts: Kevin Gurney (kevin.gurney@asu.edu)

Description: The TRCOM-mean results are based on the TransCom 3 Level 2 analysis found in Gurney et al. (2008), based on a Bayesian synthesis method. The individual posterior flux results from 11 transport models (using no interannually-varying winds) are averaged to generate the multi-model mean. Fluxes are optimized for 11-land and 11-ocean regions (sub-continental size) on a monthly base. Prior land and ocean fluxes come from CASA biosphere model (climatology) and Takahashi et al. (1999), respectively.  Monthly mean CO₂ concentrations from 103 observing sites are used.

References:

Gurney, K. R., et al., Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO₂ in- versions for the period 1980 to 2005, Global Biogeochem. Cy., 22, GB3025, doi:10.1029/2007GB003082, 2008.

Data access: contact Kevin Gurney

NICAM-TM:

Contacts: Yosuke Niwa (yniwa@mri-jma.go.jp)

Description: NICAM inversion is based upon the TransCom 3 Bayesian synthesis method, further described in Niwa et al. (2012). It relies on the NICAM-TM transport model with interannually-varying winds. Fluxes are optimized for 29-land and 11-ocean regions on a monthly base. Prior land and ocean fluxes are taken from the CASA biosphere model (climatology) and Takahashi et al. (2009), respectively. Monthly mean CO₂ concentrations from 59 surface sites and from 12 aircraft points of CONTRAIL are used.

References:

Niwa, Y., et al.: Imposing strong constraints on tropical terrestrial CO2 fluxes using passenger aircraft based measurements, J. Geophys. Res., 117, D11303, doi:10.1029/2012JD017474, 2012.

Data access: contact Yosuke Niwa

JMA:

Contacts: Takashi Maki (tmaki@mri-jma.go.jp)

Description: JMA inversion is based upon the TransCom 3 Bayesian synthesis method, further described in Maki et al.  (2010). It relies on the JMA-CDTM transport model with interannually-varying winds. Fluxes are optimized for 11-land and 11-ocean regions on a monthly base. Prior land and ocean fluxes are taken from the CASA biosphere model (climatology) and Takahashi et al. (2002), respectively. Monthly mean CO₂ concentrations from 146 sites are used.

References:

Maki et al. New technique to analyse global distributions of CO₂ concentra- tions and fluxes from non-processed observational data, Tellus B, 62, 797–809, doi: 10.1111/j.1600-0889.2010.00488.x, 2010.

Data access: contact Takashi Maki

Land models

General description: A terrestrial carbon model encapsulates natural and human induced processes through which land ecosystems absorb and emit CO₂, and carbon is stored by plants and soils. There is a large variety of such models, going from simple empirical to highly complex models describing water, energy and sometimes nutrients cycling, as well as carbon dynamics. The terrestrial carbon model results shown here are so called Dynamic Global Vegetation Models that were used in the TRENDY project (Trends in net land atmosphere carbon exchanges) coordinated by Stephen Sitch (S.A.Sitch@exeter.ac.uk). The model simulations that are reported here were performed with observed cropland and pasture distributions, with changing climate, and with changing atmospheric CO₂ concentrations (S2 scenario). Climate forcing corresponds to a merge between CRU data and NCEPv4 reanalysis, over the period 1901-2010. These data should be considered provisional, and subject to change.

For data access and data policy please refer to (http://dgvm.ceh.ac.uk/node/9).

The use of data is conditional on citing the original data sources. Full details on how to cite the data are given for each land model in the references below and in the corresponding web links. The Global Carbon Project facilitates access to data to encourage its use and promote a good understanding of the carbon cycle.  Respecting original data sources is key to help secure the support to enhance, maintain and update valuable data.

LPJ:

Contacts: Benjamin Poulter (Benjamin.poulter@lsce.ipsl.fr)

Description: LPJ computes the energy, water and carbon balances at daily time step; see reference.

References:

Sitch S, et al. : Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic vegetation model. Global Change Biology, 9, 161-185, 2003.

Data access: contact Benjamin Poulter

LPJ_GUESS:

Contacts: Anders Ahlström (anders.ahlstrom@nateko.lu.se) ; Benjamin Smith (ben.smith.lu@gmail.com)

Description: LPJ_GUESS computes the energy, water and carbon balances at daily time step; see reference.

References:

Smith, B, Prentice IC Sykes, MT : Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecology and Biogeography, 10(6), 621-637, 2001.

Data access: contact Anders Ahlström

ORCHIDEE:

Contacts: Nicolas Viovy (Nicolas.viovy@lsce.ipsl.fr)

Description: ORCHIDEE computes the energy, water and carbon balances at half-hourly time step; see reference.

References:

Krinner G, et al. :A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199, 2005.

Data access: contact Nicolas Viovy

CLM4CN:

Contacts: Sam Lewis (slevis@ucar.edu)

Description: CLM4CN computes the energy, water, carbon and nitrogen balances at half-hourly time step; see references.

References:

Bonan GB and Levis S : Quantifying carbon-nitrogen feedbacks in the Community Land Model  (CLM4). Geophysical Research Letters, 37, L07401, doi:10.1029/2010GL042430, 2010.

Lawrence DM, et al. : Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. Journal of Advances in Modeling Earth Systems, 3, doi: 10.1029/2011MS000045, 2011.

Data access: contact Sam Lewis

TRIFFID:

Contacts: Stephen Sitch (S.A.Sitch@exeter.ac.uk)

Description: TRIFFID computes the energy, water and carbon balances at half-hourly time step; see references.

References:

Cox PM : Description of the “TRIFFID” dynamic global vegetation model. Hadley Centre Technical Note 24, 2001.

Clark, D.B., et al. The Joint UK Land Environment Simulator (JULES), Model description, Part 2: Carbon fluxes and vegetation, Geosci. Model Dev. Discuss., 4, 641- 688, 2011.

Data access: contact Stephen Sitch

VEGAS:

Contacts: Ning Zen (zeng@umd.edu) ; Fang Zhao (fzhao@atmos.umd.edu)

Description: VEGAS computes the energy, water and carbon cycles at daily time step; see reference.

References:

Zeng, N. : Glacial-interglacial atmospheric CO2 change – The glacial burial hypothesis, Advances in Atmospheric Sciences, 20(5), 677-673, 2003.

Zeng, N., A. Mariotti, and P. Wetzel : Terrestrial mechanisms of interannual CO2 variability, Global Biogeochemical Cycles, 2005.

Data access: contact Ning Zen and access from http://www.atmos.umd.edu/~cabo/

SDGVM:

Contacts: Mark Lomas (m.r.lomas@sheffield.ac.uk)

Description: SDGVM computes the energy, water and carbon cycles at daily time step; see reference.

References:

Woodward FI, Smith TM, Emanuel WR : A global land primary productivity and phytogeography model. Global Biogeochemical Cycles, 9, 471-490, 1995.

Woodward FI, Lomas MR : Vegetation-dynamics- simulating responses to climate change. Biological Reviews, 79, 643-670, 2004.

Data access: contact Mark Lomas

Hyland:

Contacts: Peter Levy (plevy@ceh.ac.uk)

Description: Hyland computes the energy, water and carbon cycles at daily time step; see reference.

References:

Levy, P. E., Cannell, M. G. R., & Friend, A. D. (2004). Modelling the impact of future changes in climate, CO₂ concentration and land use on natural ecosystems and the terrestrial carbon sink. Global Environmental Change, 14(1), 21-30. https://doi.org/10.1016/j.gloenvcha.2003.10.005

Levy, P. E., Friend, A. D., White, A., & Cannell, M. G. R. (2004). The influence of land use change on global-scale fluxes of carbon from terrestrial ecosystems. Climatic Change, 67(2-3), 185-209.

Data access: contact Peter Levy

Ocean models

General description: The contributing models correspond to the ocean biogeochemistry models that were used in the RECCAP synthesis (see http://www.globalcarbonproject.org/reccap/products.htm). All simulations use a prognostic biogeochemistry model embedded in a physical ocean circulation model that is run online. The surface forcing for the dynamical models consists of using atmospheric flux fields derived from a combination of reanalysis and remotely sensed products. Surface buoyancy forcing is accomplished through the use of bulk formulas or other 20 methods for heat and freshwater fluxes, with a restoring of SSS towards climatological values being characteristic of most of the models. The models considered here are coarse resolution models that are neither eddy permitting nor eddy resolving.

The model results are compared in “Ishii, M., et al. (2013): Air-sea CO2 flux in the Pacific Ocean for the period 1990–2009, Biogeosciences, 10, 12155-12216, doi:10.5194/bgd-10-12155-2013, 2013”.

The use of data is conditional on citing the original data sources. Full details on how to cite the data are given for each land model in the references below and in the corresponding web links. The Global Carbon Project facilitates access to data to encourage its use and promote a good understanding of the carbon cycle.  Respecting original data sources is key to help secure the support to enhance, maintain and update valuable data.

WHOI:

Contacts: Scott Doney (sdoney@whoi.edu)

Description:  see references

References :

Doney, S. C., et al.: Mechanisms governing interannual variability in upper-ocean inorganic carbon system and air–sea CO2 fluxes: Physical climate and atmospheric dust, Deep-Sea Research Part Ii-Topical Studies in Oceanography, 56, 640-655, 2009

Doney, S. C., et al.: Skill metrics for confronting global upper ocean ecosystem-biogeochemistry models against field and remote sensing data, Journal of Marine Systems, 76, 95-112, http://dx.doi.org/10.1016/j.jmarsys.2008.05.015, 2009.

Data access: contact Scott Doney

NEMO-PISCES:

Contacts: Laurent Bopp (Laurent.bopp@lsce.ipsl.fr)

Description: see reference

References :

Aumont, O., and Bopp, L.: Globalizing results from ocean in situ iron fertilization studies, Global Biogeochemical Cycles, 20, GB2017, 10.1029/2005GB002591, 2006.

Data access: contact Laurent Bopp

MICOM-HAMOCC:

Contacts: Jörg Schwinger (jorg.schwinger@gfi.uib.no)

Description: see reference

References:

Assmann, K. M., et al.: An isopycnic ocean carbon cycle model, Geoscientific Model Development, 3, 143-167, 2010.

Data access: contact Jörg Schwinger

MPI-MET:

Contacts:  Joachim Segschneider (Joachim.segschneider@mpimet.mpg.de)

Description: see reference

References:

Ilyina, T., et al.: The global ocean biogeochemistry model HAMOCC: Model architecture and performance as component of the MPI-Earth System Model in different CMIP5 experimental realizations, Journal of Advances in Modeling Earth Systems, doi: 10.1002/jame.20017, 2013.

Data access: contact Joachim Segschneider

NEMO-PlankTOM5:

Contacts: Corinne Le Quéré (c.lequere@uea.ac.uk) ; Erik T. Buitenhuis (e.buitenhuis@uea.ac.uk)

Description: see http://lgmacweb.env.uea.ac.uk/green_ocean/model/description.shtml?u11r1

References:

Buitenhuis, E. T., et al.: Biogeochemical fluxes through microzooplankton, Global Biogeochemical Cycles, GB4015, 10.1029/2009GB003601, 2010.

Data access: contact Corinne Le Quéré

CSIRO-BOGCM:

Contacts: Andrew Lenton (Andrew.Lenton@csiro.au)

Description: see references

References:

Lenton, A. and R.J. Matear (2007). Role of the Southern Annular Mode (SAM) in Southern Ocean CO2 uptake. Global Biogeochem. Cycles, 21, GB2016.

Matear, R. J. and A. Lenton (2008). Impact of Historical Climate Change on the Southern Ocean Carbon Cycle. J. Climate, 21, 5820–5834. doi: http://dx.doi.org/10.1175/2008JCLI2194.1

Data access: contact Andrew Lenton

Disclaimer:

This material is general in nature. It is made available on the understanding that the Global Carbon Project (GCP) is not thereby engaged in rendering professional advice. Before relying on the material in any important matter, users should carefully evaluate its accuracy, currency, completeness and relevance for their purposes, and should obtain any appropriate professional advice relevant to their particular circumstances.

In some cases the material may incorporate or summarise views, guidelines or recommendations of third parties. Such material is assembled in good faith, but does not necessarily reflect the considered views of the Global Carbon Project (GCP), or indicate a commitment to a particular course of action. Links to other websites are inserted for convenience and do not constitute endorsement of material at those sites, or any associated organisation, product or service.