Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 7 (2015): 349–396, doi:10.5194/essd-7-349-2015.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005–2014), EFF was 9.0 ± 0.5 GtC yr−1, ELUC was 0.9 ± 0.5 GtC yr−1, GATM was 4.4 ± 0.1 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 3.0 ± 0.8 GtC yr−1. For the year 2014 alone, EFF grew to 9.8 ± 0.5 GtC yr−1, 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr−1 that took place during 2005–2014. Also, for 2014, ELUC was 1.1 ± 0.5 GtC yr−1, GATM was 3.9 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and SLAND was 4.1 ± 0.9 GtC yr−1. GATM was lower in 2014 compared to the past decade (2005–2014), reflecting a larger SLAND for that year. The global atmospheric CO2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in EFF will be near or slightly below zero, with a projection of −0.6 [range of −1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of EFF and assumed constant ELUC for 2015, cumulative emissions of CO2 will reach about 555 ± 55 GtC (2035 ± 205 GtCO2) for 1870–2015, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2015).

Description: NERC provided funding to C. Le Quéré, R. Moriarty, and the GCP through their International Opportunities Fund specifically to support this publication (NE/103002X/1). G. P. Peters and R. M. Andrew were supported by the Norwegian Research Council (236296). J. G. Canadell was supported by the Australian Climate Change Science Programme. S. Sitch was supported by EU FP7 for funding through projects LUC4C (GA603542). R. J. Andres was supported by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programmes under US Department of Energy contract DE-AC05- 00OR22725. T. A. Boden was supported by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programmes under US Department of Energy contract DE-AC05-00OR22725. J. I. House was supported by the Leverhulme foundation and the EU FP7 through project LUC4C (GA603542). P. Friedlingstein was supported by the EU FP7 for funding through projects LUC4C (GA603542) and EMBRACE (GA282672). A. Arneth was supported by the EU FP7 for funding through LUC4C (603542), and the Helmholtz foundation and its ATMO programme. D. C. E. Bakker was supported by the EU FP7 for funding through project CARBOCHANGE (284879), the UK Ocean Acidification Research Programme (NE/H017046/1; funded by the Natural Environment Research Council, the Department for Energy and Climate Change and the Department for Environment, Food and Rural Affairs). L. Barbero was supported by NOAA’s Ocean Acidification Program and acknowledges support for this work from the National Aeronautics and Space Administration (NASA) ROSES Carbon Cycle Science under NASA grant 13-CARBON13_2-0080. P. Ciais acknowledges support from the European Research Council through Synergy grant ERC-2013-SyG-610028 “IMBALANCE-P”. M. Fader was supported by the EU FP7 for funding through project LUC4C (GA603542). J. Hauck was supported by the Helmholtz Postdoc Programme (Initiative and Networking Fund of the Helmholtz Association). R. A. Feely and A. J. Sutton were supported by the Climate Observation Division, Climate Program Office, NOAA, US Department of Commerce. A. K. Jain was supported by the US National Science Foundation (NSF AGS 12-43071) the US Department of Energy, Office of Science and BER programmes (DOE DE-SC0006706) and NASA LCLUC programme (NASA NNX14AD94G). E. Kato was supported by the ERTDF (S-10) from the Ministry of Environment, Japan. K. Klein Goldewijk was supported by the Dutch NWO VENI grant no. 863.14.022. S. K. Lauvset was supported by the project “Monitoring ocean acidification in Norwegian waters” from the Norwegian Ministry of Climate and Environment. V. Kitidis was supported by the EU FP7 for funding through project CARBOCHANGE (264879). C. Koven was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 as part of their Regional and Global Climate Modeling Program. P. Landschützer was supported by GEOCarbon. I. T. van der Lann-Luijkx received financial support from OCW/NWO for ICOS-NL and computing time from NWO (SH-060-13). I. D. Lima was supported by the US National Science Foundation (NSF AGS-1048827). N. Metzl was supported by Institut National des Sciences de l’Univers (INSU) and Institut Paul Emile Victor (IPEV) for OISO cruises. D. R. Munro was supported by the US National Science Foundation (NSF PLR-1341647 and NSF AOAS-0944761). J. E. M. S. Nabel was supported by the German Research Foundation’s Emmy Noether Programme (PO1751/1-1) and acknowledges Julia Pongratz and Kim Naudts for their contributions. Y. Nojiri and S. Nakaoka were supported by the Global Environment Research Account for National Institutes (1432) by the Ministry of Environment of Japan. A. Olsen appreciates support from the Norwegian Research Council (SNACS, 229752). F. F. Pérez were supported by BOCATS (CTM2013-41048-P) project co-founded by the Spanish government and the Fondo Europeo de Desarrollo Regional (FEDER). B. Pfeil was supported through the European Union’s Horizon 2020 research and innovation programme AtlantOS under grant agreement no. 633211. D. Pierrot was supported by NOAA through the Climate Observation Division of the Climate Program Office. B. Poulter was supported by the EU FP7 for funding through GEOCarbon. G. Rehder was supported by BMBF (Bundesministerium für Bildung und Forschung) through project ICOS, grant no. 01LK1224D. U. Schuster was supported by NERC UKOARP (NE/H017046/1), NERC RAGANRoCC (NE/K002473/1), the European Space Agency (ESA) OceanFlux Evolution project, and EU FP7 CARBOCHANGE (264879). T. Steinhoff was supported by ICOS-D (BMBF FK 01LK1101C) and EU FP7 for funding through project CARBOCHANGE (264879). J. Schwinger was supported by the Research Council of Norway through project EVA (229771), and acknowledges the Norwegian Metacenter for Computational Science (NOTUR, project nn2980k), and the Norwegian Storage Infrastructure (NorStore, project ns2980k) for supercomputer time and storage resources. T. Takahashi was supported by grants from NOAA and the Comer Education and Science Foundation. B. Tilbrook was supported by the Australian Department of Environment and the Integrated Marine Observing System. B. D. Stocker was supported by the Swiss National Science Foundation and FP7 funding through project EMBRACE (282672). S. van Heuven was supported by the EU FP7 for funding through project CARBOCHANGE (264879). G. R. van der Werf was supported by the European Research Council (280061). A. Wiltshire was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101) and EU FP7 Funding through project LUC4C (603542). S. Zaehle was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (QUINCY; grant agreement no. 647204). ISAM (PI: Atul K. Jain) simulations were carried out at the National Energy Research Scientific Computing Center (NERSC), which is supported by the US DOE under contract DE-AC02-05CH11231.

Description: 〈jats:p〉Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical 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) is estimated with global ocean biogeochemistry models and observation-based fCO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. 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 incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr−1 (10.2±0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr−1, and SLAND was 3.8±0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023). 〈/jats:p〉

Description: Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research-Oceans 124(5), (2019): 2943-2968, doi:10.1029/2019JC015071.

Description: In the Southern Ocean, polynyas exhibit enhanced rates of primary productivity and represent large seasonal sinks for atmospheric CO2. Three contrasting east Antarctic polynyas were visited in late December to early January 2017: the Dalton, Mertz, and Ninnis polynyas. In the Mertz and Ninnis polynyas, phytoplankton biomass (average of 322 and 354 mg chlorophyll a (Chl a)/m2, respectively) and net community production (5.3 and 4.6 mol C/m2, respectively) were approximately 3 times those measured in the Dalton polynya (average of 122 mg Chl a/m2 and 1.8 mol C/m2). Phytoplankton communities also differed between the polynyas. Diatoms were thriving in the Mertz and Ninnis polynyas but not in the Dalton polynya, where Phaeocystis antarctica dominated. These strong regional differences were explored using physiological, biological, and physical parameters. The most likely drivers of the observed higher productivity in the Mertz and Ninnis were the relatively shallow inflow of iron‐rich modified Circumpolar Deep Water onto the shelf as well as a very large sea ice meltwater contribution. The productivity contrast between the three polynyas could not be explained by (1) the input of glacial meltwater, (2) the presence of Ice Shelf Water, or (3) stratification of the mixed layer. Our results show that physical drivers regulate the productivity of polynyas, suggesting that the response of biological productivity and carbon export to future change will vary among polynyas.

Description: This work was cofunded by the Australian Antarctic Division research projects AAS 4131 and 4291. This project was also supported by the Australian Government Cooperative Research Centres Programme through the Antarctic Climate & Ecosystems (ACE CRC). S. Moreau and C. Genovese were supported by the Australian Research Council's Special Research Initiative for Antarctic Gateway Partnership (project ID SR140300001). V. Puigcorbé and M. Roca‐Martí are grateful for the support from Pere Masque and Edith Cowan University. M.C. Arroyo was supported by the Dickhut Fellowship, administered by the Virginia Institute of Marine Science. The authors would like to thank the officers and crew of the R/V Aurora Australis for their logistic support, the CSIRO hydrochemists for their analyses of nutrient concentrations, and E. J. Yang for her microscope analysis of phytoplankton species. We also want to thank two anonymous reviewers for their very good comments on this study. The data presented in this paper are available on the Australian Antarctic Division (AAD) Data Centre at https://data.aad.gov.au/aadc/metadata/metadata_by_parameter.cfm.

Description: 2019-09-28

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Carter, B. R., Feely, R. A., Wanninkhof, R., Kouketsu, S., Sonnerup, R. E., Pardo, P. C., Sabine, C. L., Johnson, G. C., Sloyan, B. M., Murata, A., Mecking, S., Tilbrook, B., Speer, K., Talley, L. D., Millero, F. J., Wijffels, S. E., Macdonald, A. M., Gruber, N., & Bullister, J. L. Pacific anthropogenic carbon between 1991 and 2017. Global Biogeochemical Cycles, 33(5), (2019):597-617, doi:10.1029/2018GB006154.

Description: We estimate anthropogenic carbon (Canth) accumulation rates in the Pacific Ocean between 1991 and 2017 from 14 hydrographic sections that have been occupied two to four times over the past few decades, with most sections having been recently measured as part of the Global Ocean Ship‐based Hydrographic Investigations Program. The rate of change of Canth is estimated using a new method that combines the extended multiple linear regression method with improvements to address the challenges of analyzing multiple occupations of sections spaced irregularly in time. The Canth accumulation rate over the top 1,500 m of the Pacific increased from 8.8 (±1.1, 1σ) Pg of carbon per decade between 1995 and 2005 to 11.7 (±1.1) PgC per decade between 2005 and 2015. For the entire Pacific, about half of this decadal increase in the accumulation rate is attributable to the increase in atmospheric CO2, while in the South Pacific subtropical gyre this fraction is closer to one fifth. This suggests a substantial enhancement of the accumulation of Canth in the South Pacific by circulation variability and implies that a meaningful portion of the reinvigoration of the global CO2 sink that occurred between ~2000 and ~2010 could be driven by enhanced ocean Canth uptake and advection into this gyre. Our assessment suggests that the accuracy of Canth accumulation rate reconstructions along survey lines is limited by the accuracy of the full suite of hydrographic data and that a continuation of repeated surveys is a critical component of future carbon cycle monitoring.

Description: The data we use can be accessed at CCHDO website (https://cchdo.ucsd.edu/) and GLODAP website (https://www.glodap.info/). This research would not be possible without the hard work of the scientists and crew aboard the many repeated hydrographic cruises coordinated by GO‐SHIP, which is funded by NSF OCE and NOAA OAR. We thank funding agencies and program managers as follows: U.S., Australian, Japanese national science funding agencies that support data collection, data QA/QC, and data centers. Contributions from B. R. C., R. A. F., and R. W. are supported by the National Oceanic and Atmospheric Administration Global Ocean Monitoring and Observing Program (Data Management and Synthesis Grant: N8R3CEA‐PDM managed by Kathy Tedesco and David Legler). G. C. J. is supported by the Climate Observation Division, Climate Program Office, National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce and NOAA Research (fund reference 100007298), grant (N8R1SE3‐PGC). B. M. S was supported by the Australian Government Department of the Environment and CSIRO through the Australian Climate Change Science Programme and by the National Environmental Science Program. N. G. acknowledges support by ETH Zurich. This is JISAO contribution 2018‐0149 and PMEL contribution 4786. We fondly remember John Bullister as a treasured friend, valued colleague, and dedicated mentor, and we thank him for sharing his days with us. He is and will be dearly missed.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 7 (2015): 47-85, doi:10.5194/essd-7-47-2015.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO2, and land-cover-change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2004–2013), EFF was 8.9 ± 0.4 GtC yr−1, ELUC 0.9 ± 0.5 GtC yr−1, GATM 4.3 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 2.9 ± 0.8 GtC yr−1. For year 2013 alone, EFF grew to 9.9 ± 0.5 GtC yr−1, 2.3% above 2012, continuing the growth trend in these emissions, ELUC was 0.9 ± 0.5 GtC yr−1, GATM was 5.4 ± 0.2 GtC yr−1, SOCEAN was 2.9 ± 0.5 GtC yr−1, and SLAND was 2.5 ± 0.9 GtC yr−1. GATM was high in 2013, reflecting a steady increase in EFF and smaller and opposite changes between SOCEAN and SLAND compared to the past decade (2004–2013). The global atmospheric CO2 concentration reached 395.31 ± 0.10 ppm averaged over 2013. We estimate that EFF will increase by 2.5% (1.3–3.5%) to 10.1 ± 0.6 GtC in 2014 (37.0 ± 2.2 GtCO2 yr−1), 65% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the global economy. From this projection of EFF and assumed constant ELUC for 2014, cumulative emissions of CO2 will reach about 545 ± 55 GtC (2000 ± 200 GtCO2) for 1870–2014, about 75% from EFF and 25% from ELUC. This paper documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this living data set (Le Quéré et al., 2013, 2014). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2014).

Description: NERC provided funding to C. Le Quéré, R. Moriarty, and the GCP though their International Opportunities Fund specifically to support this publication (NE/103002X/1), and to U. Schuster through UKOARP (NE/H017046/1). G. P. Peters and R. M. Andrews were supported by the Norwegian Research Council (236296). T. A. Boden was supported by US Department of Energy, Office of Science, Biological and Environmental Research (BER) programmes under US Department of Energy contract DEAC05- 00OR22725. Y. Bozec was supported by Region Bretagne, CG29, and INSU (LEFE/MERMEX) for CARBORHONE cruises. J. G. Canadell and M. R. Raupach were supported by the Australian Climate Change Science Programme. M. Hoppema received ICOSD funding through the German Federal Ministry of Education and Research (BMBF) to the AWI (01 LK 1224I). J. I. House was supported by a Leverhulme Early Career Fellowship. A. K. Jain was supported by the US National Science Foundation (NSF AGS 12-43071) the US Department of Energy, Office of Science, and BER programmes (DOE DE-SC0006706) and the NASA LCLUC programme (NASA NNX14AD94G). E. Kato was supported by the Environment Research and Technology Development Fund (S-10) of the Ministry of Environment of Japan. C. Koven was supported by the Director, Office of Science, Office of Biological and Environmental Research, of the US Department of Energy under contract no. DE-AC02-05CH11231 as part of their Regional and Global Climate Modeling Program. I. D. Lima was supported by the U.S. National Science Foundation (NSF AGS-1048827). N. Metzl was supported by Institut National des Sciences de l’Univers (INSU) and Institut Paul Emile Victor (IPEV) for OISO cruises. A. Olsen was supported by the Centre for Climate Dynamics at the Bjerknes Centre for Climate Research. J. E. Salisbury was supported by grants from NOAA/NASA. T. Steinhoff was supported by ICOS-D (BMBF FK 01LK1101C). B. D. Stocker was supported by the Swiss National Science Foundation and FP7 funding through project EMBRACE (282672). A. J. Sutton was supported by NOAA. T. Takahashi was supported by grants from NOAA and the Comer Education and Science Foundation. B. Tilbrook was supported by the Australian Department of the Environment and the Integrated Marine Observing System. A.Wiltshire was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). P. Ciais,W. Peters, C. Le Quére, P. Regnier, and U. Schuster were supported by the EU FP7 through project GEOCarbon (283080). A. Arneth, P. Ciais, S. Sitch, and A. Wiltshire were supported by COMBINE (226520). V. Kitidis, M. Hoppema, N. Metzl, C. Le Quéré, U. Schuster, J. Schwiger, J. Segschneider, and T. Steinhoff were supported by the EU FP7 through project CARBOCHANGE (264879). A. Arnet, P. Friedlingstein, B. Poulter, and S. Sitch were supported by the EU FP7 through projects LUC4C (GA603542). P. Friedlingstein was also supported by EMBRACE (GA282672). F. Chevallier and G. R. van der Werf were supported by the EU FP7 through project MACC-II (283576).

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 8 (2016): 605-649, doi:10.5194/essd-8-605-2016.

Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr−1, ELUC 1.0 ± 0.5 GtC yr−1, GATM 4.5 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 3.1 ± 0.9 GtC yr−1. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr−1 that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr−1, GATM was 6.3 ± 0.2 GtC yr−1, SOCEAN was 3.0 ± 0.5 GtC yr−1, and SLAND was 1.9 ± 0.9 GtC yr−1. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2016).

Description: Australia, Integrated Marine Observing System and Antarctic Climate and Ecosystems CRC BT; European Commission (EC) Copernicus Atmosphere Monitoring Service, European Centre for Medium-Range Weather Forecasts (ECMWF) FC; EC H2020 (AtlantOS; grant no. 633211) NL, AO; EC H2020 (CRESCENDO; grant no. 641816) CD, RS, OA, PF; EC H2020 European Research Council (ERC) (QUINCY; grant no. 647204). SZ; EC H2020 ERC Synergy grant (IMBALANCE-P; grant no. ERC-2013-SyG-610028) PC; France, BNP Paribas Foundation grant to support the Global Carbon Atlas PC; French Institut National des Sciences de l’Univers (INSU) and Institut Paul Emile Victor (IPEV) for OISO cruises NM; French Institut de recherche pour le développement (IRD) NL; German Federal Ministry of Education and Research (grant no. 01LK1224I ICOS-D) MH; German Research Foundation’s Emmy Noether Programme (grant no. PO1751/1-1) JN; German Max Planck Society CR, SZ; Germany, Federal Ministry of Education and Research (BMBF) AK; Germany, Helmholtz Postdoc Programme (Initiative and Networking Fund of the Helmholtz Association) JH; Japan Ministry of Agriculture, Forestry and Fisheries (MAFF) OT; Japan Ministry of Environment SN; Japan Ministry of Environment (grant no. ERTDF S-10) EK; NASA LCLUC programme (grant no. NASA NNX14AD94G) AJ; New Zealand National Institute of Water and Atmospheric Research (NIWA) Core Funding KC; Norway Research Council (grant no. 229752) AMO; Norway Research Council (grant no. 569980) GPP, RMA, JIK; Norway Research Council (project EVA; grant no. 229771) JS; Norwegian Environment Agency (grant no. 16078007) IS; Research Fund – Flanders (FWO; formerly Hercules Foundation) TG; South Africa Council for Scientific and Industrial Research (CSIR) PMSM; UK Natural Environment Research Council (RAGANRAoCC; grant no. NE/K002473/1) US; UK Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil) AJW; US Department of Agriculture, National Institute of Food and Agriculture (grant no. 2015-67003- 23485) DL; US Department of Energy (grant no. DE-FC03-97ER62402/A010) DL; US Department of Energy, Biological and Environmental Research Program, Office of Science (grant no. DE-AC05-00OR22725) APW; US Department of Commerce, NOAA’s Climate Observation Division of the Climate Program Office SRA, AJS; US Department of Energy, Office of Science and BER programme (grant no. DOE DE-SC0016323) AJ; US National Science Foundation (grant no. AGS-1048827) SD; US National Science Foundation (grant no. AOAS-1543457) DRM; US National Science Foundation (grant no. AOAS-1341647) DRM; US NOAA’s Climate Observation Division of the Climate Program Office (grant no. N8R1SE3P00); US NOAA’s Ocean Acidification Program (grant no. N8R3CEAP00) DP, LB; US National Science Foundation (grant no. NSF AGS 12-43071)