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Luke Gregor

Bio: Luke Gregor is an academic researcher from ETH Zurich. The author has contributed to research in topics: Environmental science & Sink (geography). The author has an hindex of 10, co-authored 20 publications receiving 727 citations. Previous affiliations of Luke Gregor include University of Cape Town & Council of Scientific and Industrial Research.

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Journal ArticleDOI
Pierre Friedlingstein1, Pierre Friedlingstein2, Michael O'Sullivan1, Matthew W. Jones3, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters4, Wouter Peters5, Julia Pongratz6, Julia Pongratz7, Stephen Sitch2, Corinne Le Quéré3, Josep G. Canadell8, Philippe Ciais9, Robert B. Jackson10, Simone R. Alin11, Luiz E. O. C. Aragão12, Luiz E. O. C. Aragão2, Almut Arneth, Vivek K. Arora, Nicholas R. Bates13, Nicholas R. Bates14, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp15, Selma Bultan7, Naveen Chandra16, Naveen Chandra17, Frédéric Chevallier9, Louise Chini18, Wiley Evans, Liesbeth Florentie5, Piers M. Forster19, Thomas Gasser20, Marion Gehlen9, Dennis Gilfillan, Thanos Gkritzalis21, Luke Gregor22, Nicolas Gruber22, Ian Harris23, Kerstin Hartung7, Kerstin Hartung24, Vanessa Haverd8, Richard A. Houghton25, Tatiana Ilyina6, Atul K. Jain26, Emilie Joetzjer27, Koji Kadono28, Etsushi Kato, Vassilis Kitidis29, Jan Ivar Korsbakken, Peter Landschützer6, Nathalie Lefèvre30, Andrew Lenton31, Sebastian Lienert32, Zhu Liu33, Danica Lombardozzi34, Gregg Marland35, Nicolas Metzl30, David R. Munro11, David R. Munro36, Julia E. M. S. Nabel6, S. Nakaoka17, Yosuke Niwa17, Kevin D. O'Brien37, Kevin D. O'Brien11, Tsuneo Ono, Paul I. Palmer, Denis Pierrot38, Benjamin Poulter, Laure Resplandy39, Eddy Robertson40, Christian Rödenbeck6, Jörg Schwinger, Roland Séférian27, Ingunn Skjelvan, Adam J. P. Smith3, Adrienne J. Sutton11, Toste Tanhua41, Pieter P. Tans11, Hanqin Tian42, Bronte Tilbrook31, Bronte Tilbrook43, Guido R. van der Werf44, N. Vuichard9, Anthony P. Walker45, Rik Wanninkhof38, Andrew J. Watson2, David R. Willis23, Andy Wiltshire40, Wenping Yuan46, Xu Yue47, Sönke Zaehle6 
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, University of Groningen4, Wageningen University and Research Centre5, Max Planck Society6, Ludwig Maximilian University of Munich7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, Japan Agency for Marine-Earth Science and Technology16, National Institute for Environmental Studies17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: 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 Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

1,764 citations

Journal ArticleDOI
Dorothee C. E. Bakker1, Benjamin Pfeil2, Benjamin Pfeil3, Camilla S. Landa3, Camilla S. Landa2, Nicolas Metzl4, K. O'Brien, Are Olsen3, Are Olsen2, K. Smith, Catherine E Cosca, S. Harasawa, Stephen D. Jones3, Stephen D. Jones2, Shin-Ichiro Nakaoka, Yukihiro Nojiri, Ute Schuster5, Tobias Steinhoff6, Colm Sweeney7, Colm Sweeney8, Taro Takahashi9, Bronte Tilbrook10, Bronte Tilbrook11, Chisato Wada, Rik Wanninkhof12, Simone R. Alin, Carlos F. Balestrini, Leticia Barbero12, Leticia Barbero13, Nicholas R. Bates14, Alejandro A. Bianchi, Frédéric Bonou15, Jacqueline Boutin4, Yann Bozec4, Eugene Burger, Wei-Jun Cai, R. D. Castle12, Liqi Chen16, Melissa Chierici17, Kim I. Currie, Wiley Evans18, Charles Featherstone12, Richard A. Feely, Agneta Fransson19, Catherine Goyet20, Naomi Greenwood, Luke Gregor21, S. Hankin, Nick J. Hardman-Mountford22, Jérôme Harlay23, Judith Hauck24, Mario Hoppema24, Matthew P. Humphreys14, Christopher W. Hunt25, Betty Huss12, J. Severino P. Ibánhez26, J. Severino P. Ibánhez15, Truls Johannessen2, Truls Johannessen3, Ralph F. Keeling, Vassilis Kitidis27, Arne Körtzinger6, Alex Kozyr28, Evangelia Krasakopoulou29, Akira Kuwata, Peter Landschützer30, Siv K. Lauvset2, Nathalie Lefèvre4, Claire Lo Monaco4, Ansley Manke, Jeremy T. Mathis, Liliane Merlivat4, Frank J. Millero13, Pedro M. S. Monteiro21, David R. Munro8, Akihiko Murata31, Timothy Newberger7, Timothy Newberger8, Abdirahman M Omar2, Tsuneo Ono, K. Paterson10, David A. Pearce, Denis Pierrot13, Denis Pierrot12, Lisa L. Robbins32, S. Saito33, Joe Salisbury25, Reiner Schlitzer24, Bernd Schneider34, Roland Schweitzer, Rainer Sieger24, Ingunn Skjelvan2, Kevin F. Sullivan13, Kevin F. Sullivan12, Stewart C Sutherland9, Adrienne J. Sutton, Kazuaki Tadokoro, Maciej Telszewski, Matthias Tuma35, Steven van Heuven, Doug Vandemark25, Brian Ward36, Andrew J. Watson5, Suqing Xu16 
TL;DR: This ESSD "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection.
Abstract: . The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770 . The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID .

408 citations

Journal ArticleDOI
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Atlantic Oceanographic and Meteorological Laboratory4, Bjerknes Centre for Climate Research5, University of Maryland, College Park6, CICERO Center for International Climate Research7, Leibniz Institute for Baltic Sea Research8, University of Reading9, Leibniz Institute of Marine Sciences10, Goddard Space Flight Center11, Flanders Marine Institute12, Food and Agriculture Organization13, Alfred Wegener Institute for Polar and Marine Research14, National Oceanic and Atmospheric Administration15, University of East Anglia16, Japan Meteorological Agency17, ETH Zurich18, National Institute for Environmental Studies19, Karlsruhe Institute of Technology20, Laboratoire des Sciences du Climat et de l'Environnement21, Tula Foundation22, Hertie Institute for Clinical Brain Research23, Nanjing University of Information Science and Technology24, Wageningen University and Research Centre25, Tsinghua University26, University of Western Sydney27, Cooperative Institute for Research in Environmental Sciences28, University of Florida29, Center for Neuroscience and Regenerative Medicine30, Woods Hole Research Center31, Michigan State University32, Tianjin University33, Auburn University34, Jilin Medical University35, Max Planck Institute for Meteorology36, Imperial College London37, Centre National de Recherches Météorologiques38, University of Groningen39, Tohoku University40, Ludwig Maximilian University of Munich41, Bank for International Settlements42, Institut Pierre-Simon Laplace43, Environment Canada44, North West Agriculture and Forestry University45, Northwest A&F University46, Pacific Marine Environmental Laboratory47, Stanford University48, Utrecht University49
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: 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 datasets 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 data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation 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 understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (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 the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

343 citations

Journal ArticleDOI
Pierre Friedlingstein1, Sönke Zaehle2, Corinne Le Quéré3, Christian Rödenbeck2, Bronte Tilbrook, Henry C. Bittig4, Denis Pierrot5, Louise Chini6, Jan Ivar Korsbakken7, Nicolas Bellouin8, Toste Tanhua9, Benjamin Poulter10, Peter Landschützer11, Francesco N. Tubiello12, Judith Hauck13, Are Olsen14, Vivek K. Arora15, Colm Sweeney16, Almut Arneth17, Marion Gehlen18, Hiroyuki Tsujino19, Daniel P. Kennedy20, Yosuke Iida19, Luke Gregor21, Jiye Zeng22, George C. Hurtt6, Nicolas Mayot23, Giacomo Grassi24, Shin-Ichiro Nakaoka22, Frédéric Chevallier18, Clemens Schwingshackl7, Wiley Evans25, Meike Becker26, Thomas Gasser27, Xu Yue28, Katie Pocock25, Stephanie Falk29, Thanos Gkritzalis11, Naiqing Pan30, Ingrid T. van der Laan-Luijkx31, Fraser Holding32, Carlos Gustavo Halaburda, Guanghong Zhou33, Peter Angele34, Jianling Chen1, e6gehqc68135, Carlos Muñoz Pérez23, Hiroshi Niinami36, Zongwe Binesikwe Crystal Hardy, Samuel Bourne37, Ralf Wüsthofen38, Paulo Brito, Christian Liguori39, Juan A. Martin-Ramos, Rattan Lal, kensetyrdhhtml2mdcom40, Staffan Furusten, Luca Miceli41, Eric Horster16, V. Miranda Chase, Field Palaeobiology Lab30, Living Tree Cbd Gummies, Lifeng Qin34, Yong Tang42, Annie Phillips43, Nathalie Fenouil26, mark, Karina Querne de Carvalho44, Satya Wydya Yenny, Maja Bak Herrie, Silvia Ravelli45, Andreas Gerster46, Denise Hottmann47, Wui-Lee Chang, Andreas Lutz48, Olga D. Vorob'eva49, Pallavi Banerjee1, Verónica Undurraga50, Jovan Babić, Michele D. Wallace9, Mònica Ginés-Blasi, 에볼루션카지노51, James Kelvin29, Christos Kontzinos1, Охунова Дилафруз Муминовна, Isabell Diekmann, Emily Burgoyne16, Vilemina Čenić52, Naomi Gikonyo26, CHAO LUAN21, Benjamin Pfluger53, Benjamin Pfluger54, A. J. Shields, Kobzos, Laszlo55, Adrian Langer56, Stuart L. Weinstein55, Abdullah ÖZÇELİK57, Yi Chen58, Anzhelika Solodka59, Valery Vasil'evich Kozlov60, Н.С. Рыжук, Roshan Vasant Shinde, Dr Sandeep Haribhau Wankhade, Dr Nitin Gajanan Shekapure, Mr Sachin Shrikant …61, Mylene Charon7, David Seibt62, Kobi Peled, None Rahmi52 
University of Exeter1, Max Planck Institute for Biogeochemistry2, Tyndall Centre3, Leibniz Institute for Baltic Sea Research4, Atlantic Oceanographic and Meteorological Laboratory5, University of Maryland, College Park6, CICERO Center for International Climate Research7, University of Reading8, Leibniz Institute of Marine Sciences9, Goddard Space Flight Center10, Flanders Marine Institute11, Food and Agriculture Organization12, Alfred Wegener Institute for Polar and Marine Research13, Geophysical Institute14, University of Victoria15, National Oceanic and Atmospheric Administration16, Karlsruhe Institute of Technology17, Laboratoire des Sciences du Climat et de l'Environnement18, Japan Meteorological Agency19, Indiana University20, ETH Zurich21, National Institute for Environmental Studies22, University of East Anglia23, European Commission24, Tula Foundation25, Bjerknes Centre for Climate Research26, Hertie Institute for Clinical Brain Research27, Nanjing University of Information Science and Technology28, Ludwig Maximilian University of Munich29, Auburn University30, Wageningen University and Research Centre31, University of Western Sydney32, Cooperative Institute for Research in Environmental Sciences33, Tsinghua University34, University of Florida35, Center for Neuroscience and Regenerative Medicine36, Woods Hole Research Center37, University of Alaska Fairbanks38, Princeton University39, Michigan State University40, University of Washington41, Appalachian State University42, Sun Yat-sen University43, Imperial College London44, University of Groningen45, University of Tennessee46, Washington University in St. Louis47, Jilin Medical University48, Tohoku University49, Rutgers University50, Centre for Research on Ecology and Forestry Applications51, Institut Pierre-Simon Laplace52, North West Agriculture and Forestry University53, Northwest A&F University54, Pacific Marine Environmental Laboratory55, Xi'an Jiaotong University56, Stanford University57, National Center for Atmospheric Research58, University of Edinburgh59, Max Planck Institute for Meteorology60, Utrecht University61, Oak Ridge National Laboratory62
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Abstract: 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 methodologies 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 data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation 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 understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, 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 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. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

98 citations

Journal ArticleDOI
TL;DR: In this paper, an ensemble average of six machine learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research -Machine Learning ensemble with Six members) is proposed to fill the gaps in sparse surface ocean CO2 measurements.
Abstract: . Over the last decade, advanced statistical inference and machine learning have been used to fill the gaps in sparse surface ocean CO2 measurements (Rodenbeck et al., 2015). The estimates from these methods have been used to constrain seasonal, interannual and decadal variability in sea–air CO2 fluxes and the drivers of these changes (Landschutzer et al., 2015, 2016; Gregor et al., 2018). However, it is also becoming clear that these methods are converging towards a common bias and root mean square error (RMSE) boundary: “the wall”, which suggests that pCO2 estimates are now limited by both data gaps and scale-sensitive observations. Here, we analyse this problem by introducing a new gap-filling method, an ensemble average of six machine-learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research – Machine Learning ensemble with Six members), where each model is constructed with a two-step clustering-regression approach. The ensemble average is then statistically compared to well-established methods. The ensemble average, CSIR-ML6, has an RMSE of 17.16 µ atm and bias of 0.89 µ atm when compared to a test dataset kept separate from training procedures. However, when validating our estimates with independent datasets, we find that our method improves only incrementally on other gap-filling methods. We investigate the differences between the methods to understand the extent of the limitations of gap-filling estimates of pCO2 . We show that disagreement between methods in the South Atlantic, southeastern Pacific and parts of the Southern Ocean is too large to interpret the interannual variability with confidence. We conclude that improvements in surface ocean pCO2 estimates will likely be incremental with the optimisation of gap-filling methods by (1) the inclusion of additional clustering and regression variables (e.g. eddy kinetic energy), (2) increasing the sampling resolution and (3) successfully incorporating pCO2 estimates from alternate platforms (e.g. floats, gliders) into existing machine-learning approaches.

72 citations


Cited by
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Pierre Friedlingstein1, Pierre Friedlingstein2, Michael O'Sullivan1, Matthew W. Jones3, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters4, Wouter Peters5, Julia Pongratz6, Julia Pongratz7, Stephen Sitch2, Corinne Le Quéré3, Josep G. Canadell8, Philippe Ciais9, Robert B. Jackson10, Simone R. Alin11, Luiz E. O. C. Aragão12, Luiz E. O. C. Aragão2, Almut Arneth, Vivek K. Arora, Nicholas R. Bates13, Nicholas R. Bates14, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp15, Selma Bultan7, Naveen Chandra16, Naveen Chandra17, Frédéric Chevallier9, Louise Chini18, Wiley Evans, Liesbeth Florentie5, Piers M. Forster19, Thomas Gasser20, Marion Gehlen9, Dennis Gilfillan, Thanos Gkritzalis21, Luke Gregor22, Nicolas Gruber22, Ian Harris23, Kerstin Hartung7, Kerstin Hartung24, Vanessa Haverd8, Richard A. Houghton25, Tatiana Ilyina6, Atul K. Jain26, Emilie Joetzjer27, Koji Kadono28, Etsushi Kato, Vassilis Kitidis29, Jan Ivar Korsbakken, Peter Landschützer6, Nathalie Lefèvre30, Andrew Lenton31, Sebastian Lienert32, Zhu Liu33, Danica Lombardozzi34, Gregg Marland35, Nicolas Metzl30, David R. Munro36, David R. Munro11, Julia E. M. S. Nabel6, S. Nakaoka17, Yosuke Niwa17, Kevin D. O'Brien37, Kevin D. O'Brien11, Tsuneo Ono, Paul I. Palmer, Denis Pierrot38, Benjamin Poulter, Laure Resplandy39, Eddy Robertson40, Christian Rödenbeck6, Jörg Schwinger, Roland Séférian27, Ingunn Skjelvan, Adam J. P. Smith3, Adrienne J. Sutton11, Toste Tanhua41, Pieter P. Tans11, Hanqin Tian42, Bronte Tilbrook31, Bronte Tilbrook43, Guido R. van der Werf44, N. Vuichard9, Anthony P. Walker45, Rik Wanninkhof38, Andrew J. Watson2, David R. Willis23, Andy Wiltshire40, Wenping Yuan46, Xu Yue47, Sönke Zaehle6 
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, University of Groningen4, Wageningen University and Research Centre5, Max Planck Society6, Ludwig Maximilian University of Munich7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, Japan Agency for Marine-Earth Science and Technology16, National Institute for Environmental Studies17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: 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 Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

1,764 citations

Journal ArticleDOI
Corinne Le Quéré1, Robbie M. Andrew, Pierre Friedlingstein2, Stephen Sitch2, Judith Hauck3, Julia Pongratz4, Julia Pongratz5, Penelope A. Pickers1, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell6, Almut Arneth7, Vivek K. Arora, Leticia Barbero8, Leticia Barbero9, Ana Bastos5, Laurent Bopp10, Frédéric Chevallier11, Louise Chini12, Philippe Ciais11, Scott C. Doney13, Thanos Gkritzalis14, Daniel S. Goll11, Ian Harris1, Vanessa Haverd6, Forrest M. Hoffman15, Mario Hoppema3, Richard A. Houghton16, George C. Hurtt12, Tatiana Ilyina4, Atul K. Jain17, Truls Johannessen18, Chris D. Jones19, Etsushi Kato, Ralph F. Keeling20, Kees Klein Goldewijk21, Kees Klein Goldewijk22, Peter Landschützer4, Nathalie Lefèvre23, Sebastian Lienert24, Zhu Liu1, Zhu Liu25, Danica Lombardozzi26, Nicolas Metzl23, David R. Munro27, Julia E. M. S. Nabel4, Shin-Ichiro Nakaoka28, Craig Neill29, Craig Neill30, Are Olsen18, T. Ono, Prabir K. Patra31, Anna Peregon11, Wouter Peters32, Wouter Peters33, Philippe Peylin11, Benjamin Pfeil34, Benjamin Pfeil18, Denis Pierrot8, Denis Pierrot9, Benjamin Poulter35, Gregor Rehder36, Laure Resplandy37, Eddy Robertson19, Matthias Rocher11, Christian Rödenbeck4, Ute Schuster2, Jörg Schwinger34, Roland Séférian11, Ingunn Skjelvan34, Tobias Steinhoff38, Adrienne J. Sutton39, Pieter P. Tans39, Hanqin Tian40, Bronte Tilbrook30, Bronte Tilbrook29, Francesco N. Tubiello41, Ingrid T. van der Laan-Luijkx33, Guido R. van der Werf42, Nicolas Viovy11, Anthony P. Walker15, Andy Wiltshire19, Rebecca Wright1, Sönke Zaehle4, Bo Zheng11 
University of East Anglia1, University of Exeter2, Alfred Wegener Institute for Polar and Marine Research3, Max Planck Society4, Ludwig Maximilian University of Munich5, Commonwealth Scientific and Industrial Research Organisation6, Karlsruhe Institute of Technology7, Cooperative Institute for Marine and Atmospheric Studies8, Atlantic Oceanographic and Meteorological Laboratory9, École Normale Supérieure10, Centre national de la recherche scientifique11, University of Maryland, College Park12, University of Virginia13, Flanders Marine Institute14, Oak Ridge National Laboratory15, Woods Hole Research Center16, University of Illinois at Urbana–Champaign17, Geophysical Institute, University of Bergen18, Met Office19, University of California, San Diego20, Netherlands Environmental Assessment Agency21, Utrecht University22, University of Paris23, Oeschger Centre for Climate Change Research24, Tsinghua University25, National Center for Atmospheric Research26, Institute of Arctic and Alpine Research27, National Institute for Environmental Studies28, Hobart Corporation29, Cooperative Research Centre30, Japan Agency for Marine-Earth Science and Technology31, University of Groningen32, Wageningen University and Research Centre33, Bjerknes Centre for Climate Research34, Goddard Space Flight Center35, Leibniz Institute for Baltic Sea Research36, Princeton University37, Leibniz Institute of Marine Sciences38, National Oceanic and Atmospheric Administration39, Auburn University40, Food and Agriculture Organization41, VU University Amsterdam42
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: . 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 the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions ( EFF ) are based on energy statistics and cement production data, while emissions from land use and 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 (2008–2017), EFF was 9.4±0.5 GtC yr −1 , ELUC 1.5±0.7 GtC yr −1 , GATM 4.7±0.02 GtC yr −1 , SOCEAN 2.4±0.5 GtC yr −1 , and SLAND 3.2±0.8 GtC yr −1 , with a budget imbalance BIM of 0.5 GtC yr −1 indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in EFF was about 1.6 % and emissions increased to 9.9±0.5 GtC yr −1 . Also for 2017, ELUC was 1.4±0.7 GtC yr −1 , GATM was 4.6±0.2 GtC yr −1 , SOCEAN was 2.5±0.5 GtC yr −1 , and SLAND was 3.8±0.8 GtC yr −1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0±0.1 ppm averaged over 2017. For 2018, preliminary data for the first 6–9 months indicate a renewed growth in EFF of + 2.7 % (range of 1.8 % to 3.7 %) based on national emission projections for China, the US, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. The analysis presented here shows that the mean and trend in the five components of the global carbon budget are consistently estimated over the period of 1959–2017, but discrepancies of up to 1 GtC yr −1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations show (1) no consensus in the mean and trend in land-use change emissions, (2) a persistent low agreement among the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models, originating outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding the global carbon cycle compared with previous publications of this data set (Le Quere et al., 2018, 2016, 2015a, b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018 .

1,458 citations

Journal ArticleDOI
University of East Anglia1, University of Oslo2, Commonwealth Scientific and Industrial Research Organisation3, University of Exeter4, Oak Ridge National Laboratory5, National Oceanic and Atmospheric Administration6, Woods Hole Research Center7, University of California, San Diego8, Karlsruhe Institute of Technology9, Cooperative Institute for Marine and Atmospheric Studies10, Centre national de la recherche scientifique11, University of Maryland, College Park12, National Institute of Water and Atmospheric Research13, Woods Hole Oceanographic Institution14, Flanders Marine Institute15, Alfred Wegener Institute for Polar and Marine Research16, Netherlands Environmental Assessment Agency17, University of Illinois at Urbana–Champaign18, Leibniz Institute of Marine Sciences19, Max Planck Society20, University of Paris21, Hobart Corporation22, Oeschger Centre for Climate Change Research23, University of Bern24, National Center for Atmospheric Research25, University of Miami26, Council of Scientific and Industrial Research27, University of Colorado Boulder28, National Institute for Environmental Studies29, Joint Institute for the Study of the Atmosphere and Ocean30, Geophysical Institute, University of Bergen31, Goddard Space Flight Center32, Montana State University33, University of New Hampshire34, Bjerknes Centre for Climate Research35, Imperial College London36, Lamont–Doherty Earth Observatory37, Auburn University38, Wageningen University and Research Centre39, VU University Amsterdam40, Met Office41
TL;DR: In this article, the authors 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.
Abstract: . 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 Nino 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 Quere 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 ).

1,224 citations

Journal ArticleDOI
Pierre Friedlingstein1, Pierre Friedlingstein2, Matthew W. Jones3, Michael O'Sullivan2, Robbie M. Andrew, Judith Hauck4, Glen P. Peters, Wouter Peters5, Wouter Peters6, Julia Pongratz7, Julia Pongratz8, Stephen Sitch2, Corinne Le Quéré3, Dorothee C. E. Bakker3, Josep G. Canadell9, Philippe Ciais10, Robert B. Jackson11, Peter Anthoni12, Leticia Barbero13, Leticia Barbero14, Ana Bastos8, Vladislav Bastrikov10, Meike Becker15, Meike Becker16, Laurent Bopp1, Erik T. Buitenhuis3, Naveen Chandra17, Frédéric Chevallier10, Louise Chini18, Kim I. Currie19, Richard A. Feely20, Marion Gehlen10, Dennis Gilfillan21, Thanos Gkritzalis22, Daniel S. Goll23, Nicolas Gruber24, Sören B. Gutekunst25, Ian Harris26, Vanessa Haverd9, Richard A. Houghton27, George C. Hurtt18, Tatiana Ilyina7, Atul K. Jain28, Emilie Joetzjer10, Jed O. Kaplan29, Etsushi Kato, Kees Klein Goldewijk30, Kees Klein Goldewijk31, Jan Ivar Korsbakken, Peter Landschützer7, Siv K. Lauvset15, Nathalie Lefèvre32, Andrew Lenton33, Andrew Lenton34, Sebastian Lienert35, Danica Lombardozzi36, Gregg Marland21, Patrick C. McGuire37, Joe R. Melton, Nicolas Metzl32, David R. Munro38, Julia E. M. S. Nabel7, Shin-Ichiro Nakaoka39, Craig Neill34, Abdirahman M Omar34, Abdirahman M Omar15, Tsuneo Ono, Anna Peregon40, Anna Peregon10, Denis Pierrot14, Denis Pierrot13, Benjamin Poulter41, Gregor Rehder42, Laure Resplandy43, Eddy Robertson44, Christian Rödenbeck7, Roland Séférian10, Jörg Schwinger15, Jörg Schwinger30, Naomi E. Smith45, Naomi E. Smith6, Pieter P. Tans20, Hanqin Tian46, Bronte Tilbrook33, Bronte Tilbrook34, Francesco N. Tubiello47, Guido R. van der Werf48, Andy Wiltshire44, Sönke Zaehle7 
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, Alfred Wegener Institute for Polar and Marine Research4, University of Groningen5, Wageningen University and Research Centre6, Max Planck Society7, Ludwig Maximilian University of Munich8, Commonwealth Scientific and Industrial Research Organisation9, Centre national de la recherche scientifique10, Stanford University11, Karlsruhe Institute of Technology12, Atlantic Oceanographic and Meteorological Laboratory13, Cooperative Institute for Marine and Atmospheric Studies14, Bjerknes Centre for Climate Research15, Geophysical Institute, University of Bergen16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, National Institute of Water and Atmospheric Research19, National Oceanic and Atmospheric Administration20, Appalachian State University21, Flanders Marine Institute22, Augsburg College23, ETH Zurich24, Leibniz Institute of Marine Sciences25, University of East Anglia26, Woods Hole Research Center27, University of Illinois at Urbana–Champaign28, University of Hong Kong29, Netherlands Environmental Assessment Agency30, Utrecht University31, University of Paris32, University of Tasmania33, Hobart Corporation34, University of Bern35, National Center for Atmospheric Research36, University of Reading37, Cooperative Institute for Research in Environmental Sciences38, National Institute for Environmental Studies39, Russian Academy of Sciences40, Goddard Space Flight Center41, Leibniz Institute for Baltic Sea Research42, Princeton University43, Met Office44, Lund University45, Auburn University46, Food and Agriculture Organization47, VU University Amsterdam48
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land use change, and show that 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.
Abstract: . 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 the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions ( EFF ) 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 (2009–2018), EFF was 9.5±0.5 GtC yr −1 , ELUC 1.5±0.7 GtC yr −1 , GATM 4.9±0.02 GtC yr −1 ( 2.3±0.01 ppm yr −1 ), SOCEAN 2.5±0.6 GtC yr −1 , and SLAND 3.2±0.6 GtC yr −1 , with a budget imbalance BIM of 0.4 GtC yr −1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr −1 , reaching 10 GtC yr −1 for the first time in history, ELUC was 1.5±0.7 GtC yr −1 , for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr −1 ( 42.5±3.3 GtCO2 ). Also for 2018, GATM was 5.1±0.2 GtC yr −1 ( 2.4±0.1 ppm yr −1 ), SOCEAN was 2.6±0.6 GtC yr −1 , and SLAND was 3.5±0.7 GtC yr −1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr −1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of 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 underestimation of the CO2 variability by ocean models outside the tropics. 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 (Le Quere et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019).

981 citations

Journal ArticleDOI
Corinne Le Quéré1, Robbie M. Andrew, Pierre Friedlingstein2, Stephen Sitch2, Julia Pongratz3, Andrew C. Manning1, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell4, Robert B. Jackson5, Thomas A. Boden6, Pieter P. Tans7, Oliver Andrews1, Vivek K. Arora, Dorothee C. E. Bakker1, Leticia Barbero8, Leticia Barbero9, Meike Becker10, Meike Becker11, Richard Betts12, Richard Betts2, Laurent Bopp13, Frédéric Chevallier14, Louise Chini15, Philippe Ciais14, Catherine E Cosca7, Jessica N. Cross7, Kim I. Currie16, Thomas Gasser17, Ian Harris1, Judith Hauck18, Vanessa Haverd4, Richard A. Houghton19, Christopher W. Hunt20, George C. Hurtt15, Tatiana Ilyina3, Atul K. Jain21, Etsushi Kato, Markus Kautz22, Ralph F. Keeling23, Kees Klein Goldewijk24, Kees Klein Goldewijk25, Arne Körtzinger26, Peter Landschützer3, Nathalie Lefèvre27, Andrew Lenton28, Andrew Lenton29, Sebastian Lienert30, Sebastian Lienert31, Ivan D. Lima19, Danica Lombardozzi32, Nicolas Metzl27, Frank J. Millero33, Pedro M. S. Monteiro34, David R. Munro35, Julia E. M. S. Nabel3, Shin-Ichiro Nakaoka36, Yukihiro Nojiri36, X. Antonio Padin37, Anna Peregon14, Benjamin Pfeil11, Benjamin Pfeil10, Denis Pierrot9, Denis Pierrot8, Benjamin Poulter38, Benjamin Poulter39, Gregor Rehder40, Janet J. Reimer41, Christian Rödenbeck3, Jörg Schwinger10, Roland Séférian14, Ingunn Skjelvan10, Benjamin D. Stocker, Hanqin Tian42, Bronte Tilbrook28, Bronte Tilbrook29, Francesco N. Tubiello43, Ingrid T. van der Laan-Luijkx44, Guido R. van der Werf45, Steven van Heuven46, Nicolas Viovy14, Nicolas Vuichard14, Anthony P. Walker6, Andrew J. Watson2, Andy Wiltshire12, Sönke Zaehle3, Dan Zhu14 
University of East Anglia1, University of Exeter2, Max Planck Society3, Commonwealth Scientific and Industrial Research Organisation4, Stanford University5, Oak Ridge National Laboratory6, National Oceanic and Atmospheric Administration7, Cooperative Institute for Marine and Atmospheric Studies8, Atlantic Oceanographic and Meteorological Laboratory9, Bjerknes Centre for Climate Research10, Geophysical Institute, University of Bergen11, Met Office12, École Normale Supérieure13, Centre national de la recherche scientifique14, University of Maryland, College Park15, National Institute of Water and Atmospheric Research16, International Institute for Applied Systems Analysis17, Alfred Wegener Institute for Polar and Marine Research18, Woods Hole Oceanographic Institution19, University of New Hampshire20, University of Illinois at Urbana–Champaign21, Karlsruhe Institute of Technology22, University of California, San Diego23, Utrecht University24, Netherlands Environmental Assessment Agency25, Leibniz Institute of Marine Sciences26, University of Paris27, Hobart Corporation28, Cooperative Research Centre29, University of Bern30, Oeschger Centre for Climate Change Research31, National Center for Atmospheric Research32, University of Miami33, Council of Scientific and Industrial Research34, Institute of Arctic and Alpine Research35, National Institute for Environmental Studies36, Spanish National Research Council37, Goddard Space Flight Center38, Montana State University39, Leibniz Institute for Baltic Sea Research40, University of Delaware41, Auburn University42, Food and Agriculture Organization43, Wageningen University and Research Centre44, VU University Amsterdam45, University of Groningen46
TL;DR: In this paper, the authors quantify the five major components of the global carbon budget and their uncertainties, and the resulting carbon budget imbalance (BIM) is a measure of imperfect data and understanding of the contemporary carbon cycle.
Abstract: 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 the five major components of the global carbon budget and their uncertainties. 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 land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (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 (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the high fossil emissions and the small SLAND consistent with El Nino conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.0 % (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quere et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

884 citations