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Journal ArticleDOI

Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme

TL;DR: A temporally and spatially resolved estimate of the global surface-ocean CO2 partial pressure field and the sea-air CO2 flux is presented, obtained by fitting a simple data-driven diagnostic model of ocean mixed-layer biogeochemistry to surface-of-the-atmosphere (CO2) partial pressure data from the SOCAT v15 database.
Abstract: A temporally and spatially resolved estimate of the global surface-ocean CO2 partial pressure field and the sea–air CO2 flux is presented, obtained by fitting a simple data-driven diagnostic model of ocean mixed-layer biogeochemistry to surface-ocean CO2 partial pressure data from the SOCAT v15 database Results include seasonal, interannual, and short-term (daily) variations In most regions, estimated seasonality is well constrained from the data, and compares well to the widely used monthly climatology by Takahashi et al (2009) Comparison to independent data tentatively supports the slightly higher seasonal variations in our estimates in some areas We also fitted the diagnostic model to atmospheric CO2 data The results of this are less robust, but in those areas where atmospheric signals are not strongly influenced by land flux variability, their seasonality is nevertheless consistent with the results based on surface-ocean data From a comparison with an independent seasonal climatology of surface-ocean nutrient concentration, the diagnostic model is shown to capture relevant surface-ocean biogeochemical processes reasonably well Estimated interannual variations will be presented and discussed in a companion paper

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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. 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


Cites methods from "Global surface-ocean pCO2 and sea–a..."

  • ...The SOCAT v5 data were mapped using a data-driven diagnostic method (Rödenbeck et al., 2013) and a combined selforganising map and feed-forward neural network (Landschützer et al., 2014)....

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References
More filters
Journal ArticleDOI
TL;DR: The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible, except that the horizontal resolution is T62 (about 210 km) as discussed by the authors.
Abstract: The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...

28,145 citations

Journal ArticleDOI
TL;DR: In this paper, the influence of variability in wind speed on the calculated gas transfer velocities and the possibility of chemical enhancement of CO2 exchange at low wind speeds over the ocean is illustrated using a quadratic dependence of gas exchange on wind speed.
Abstract: Relationships between wind speed and gas transfer, combined with knowledge of the partial pressure difference of CO2 across the air-sea interface are frequently used to determine the CO2 flux between the ocean and the atmosphere. Little attention has been paid to the influence of variability in wind speed on the calculated gas transfer velocities and the possibility of chemical enhancement of CO2 exchange at low wind speeds over the ocean. The effect of these parameters is illustrated using a quadratic dependence of gas exchange on wind speed which is fit through gas transfer velocities over the ocean determined by the natural-14C disequilibrium and the bomb-14C inventory methods. Some of the variability between different data sets can be accounted for by the suggested mechanisms, but much of the variation appears due to other causes. Possible causes for the large difference between two frequently used relationships between gas transfer and wind speed are discussed. To determine fluxes of gases other than CO2 across the air-water interface, the relevant expressions for gas transfer, and the temperature and salinity dependence of the Schmidt number and solubility of several gases of environmental interest are included in an appendix.

4,187 citations


"Global surface-ocean pCO2 and sea–a..." refers background or methods in this paper

  • ...Respective thick lines give a Gaussian of the same mean and standard deviation. See Supplementary materi al, Figure S5.2 for global maps of seasonal residuals. Fig. 8. Comparison between the estimated pCO2 field (runSFC, blue) and measurements (black dots) at test locations. Left: data contained in SOCAT v1.5 (larg r dots) at a pixel in the North Pacific, and the pixel around Station M in the North Atlantic. Right: two mooring sites in the North Atlantic (BTM, 32 N, 64 W) and South Pacific (Stratus, 19.70 ◦ S, 85.62 W) by Sabine et al. (2010) that are not part of SOCAT v1.5 (smaller dots). ThepCO2 field has been picked at the times where the measurements exist (connected by straight lines for clarity); the time axes have been limited to respective ye rs with data. The m nthly climatology by Takahashi et al. (2009) (regridded and with added trend parallel to the atmospheric CO 2 increase, violet) is also given....

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  • ...The (diagonal) matrixQc introduces a weighting among the concentration values, involving assumed measurement uncertainty, location-dependent model uncertainty, and a data density weighting, as described in Rödenbeck(2005). The modelled mixing ratios, taken at the same time and location as the observations, are simulated by a numerical transport model (here, the TM3 model, Heimann and K̈orner, 2003), which can formally be written as...

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  • ...We use the formulation according toWanninkhof(1992):...

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Journal ArticleDOI
TL;DR: In this paper, the solubility coefficients for carbon dioxide in water and seawater are calculated for the data of Murray and Riley, and are fitted to equations in temperature and salinity of the form used previously to fit solubilities of other gases.

2,788 citations

Journal ArticleDOI
TL;DR: In this paper, a 2° resolution global climatology of the mixed layer depth (MLD) based on individual profiles is constructed and a new global seasonal estimation of barrier layer thickness is also provided.
Abstract: [1] A new 2° resolution global climatology of the mixed layer depth (MLD) based on individual profiles is constructed. Previous global climatologies have been based on temperature or density-gridded climatologies. The criterion selected is a threshold value of temperature or density from a near-surface value at 10 m depth (ΔT = 0.2°C or Δσθ = 0.03 kg m−3). A validation of the temperature criterion on moored time series data shows that the method is successful at following the base of the mixed layer. In particular, the first spring restratification is better captured than with a more commonly used larger criteria. In addition, we show that for a given 0.2°C criterion, the MLD estimated from averaged profiles results in a shallow bias of 25% compared to the MLD estimated from individual profiles. A new global seasonal estimation of barrier layer thickness is also provided. An interesting result is the prevalence in mid- and high-latitude winter hemispheres of vertically density-compensated layers, creating an isopycnal but not mixed layer. Consequently, we propose an optimal estimate of MLD based on both temperature and density data. An independent validation of the maximum annual MLD with oxygen data shows that this oxygen estimate may be biased in regions of Ekman pumping or strong biological activity. Significant differences are shown compared to previous climatologies. The timing of the seasonal cycle of the mixed layer is shifted earlier in the year, and the maximum MLD captures finer structures and is shallower. These results are discussed in light of the different approaches and the choice of criterion.

2,345 citations


"Global surface-ocean pCO2 and sea–a..." refers background or methods in this paper

  • ...Mixed-layer depth is prescribed as a climatology by de Boyer Mont́egut et al.(2004) (downloaded from http://www.locean-ipsl.upmc.fr/∼cdblod/mld.htmlusing the MLD climatology mldT02sk.nc according to a temperature criterion; chosen because the MLD climatology based on a density criterion has incomplete coverage, and there is no MLD product directly based on data that also represents interannual variations)....

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  • ...h LOCEAN de Boyer Mont́ egut et al.(2004) Temperature criterion; All monthly climatology (interpolated)...

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  • ...Mixed-layer depth is prescribed as a climatology by de Boyer Mont́ egut et al.(2004) (downloaded from http://www....

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Journal ArticleDOI
TL;DR: In this paper, precise data on the solubilities of nitrogen, oxygen and argon in distilled water and seawater are fitted to thermodynamically consistent equations by the method of least squares.

1,826 citations

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