Recent variability of the global ocean carbon sink
TL;DR: In this article, an observation-based estimate of the global oceanic carbon dioxide (CO2) sink and its temporal variation on a monthly basis from 1998 through 2011 and at a spatial resolution of 1×1.
Abstract: We present a new observation-based estimate of the global oceanic carbon dioxide (CO2) sink and its temporal variation on a monthly basis from 1998 through 2011 and at a spatial resolution of 1×1. This sink estimate rests upon a neural network-based mapping of global surface ocean observations of the partial pressure of CO2 (pCO2) from the Surface Ocean CO2 Atlas database. The resulting pCO2 has small biases when evaluated against independent observations in the different ocean basins, but larger randomly distributed differences exist particularly in high latitudes. The seasonal climatology of our neural network-based product agrees overall well with the Takahashi et al. (2009) climatology, although our product produces a stronger seasonal cycle at high latitudes. From our global pCO2 product, we compute a mean net global ocean (excluding the Arctic Ocean and coastal regions) CO2 uptake flux of −1.42 ± 0.53 Pg C yr−1, which is in good agreement with ocean inversion-based estimates. Our data indicate a moderate level of interannual variability in the ocean carbon sink (±0.12 Pg C yr−1, 1𝜎) from 1998 through 2011, mostly originating from the equatorial Pacific Ocean, and associated with the El Nino–Southern Oscillation. Accounting for steady state riverine and Arctic Ocean carbon fluxes our estimate further implies a mean anthropogenic CO2 uptake of −1.99 ± 0.59 Pg C yr−1 over the analysis period. From this estimate plus the most recent estimates for fossil fuel emissions and atmospheric CO2 accumulation, we infer a mean global land sink of −2.82 ± 0.85 Pg C yr−1 over the 1998 through 2011 period with strong interannual variation.
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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
Cites methods from "Recent variability of the global oc..."
...…diagnostic method (Rödenbeck et al., 2013; referred to here as Jena-MLS), a combined self-organizing map and feed-forward neural network (Landschützer et al., 2014; referred to here as MPI-SOMFFN), an artificial neural network model (Denvil-Sommer et al., 2019; Copernicus Marine…...
[...]
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
Cites background or methods from "Recent variability of the global oc..."
..., 2013) and a combined self-organising map and feed-forward neural network (Landschützer et al., 2014)....
[...]
...The SOCAT v6 data were mapped using a data-driven diagnostic method (Rödenbeck et al., 2013) and a combined self-organising map and feed-forward neural network (Landschützer et al., 2014)....
[...]
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
Cites methods from "Recent variability of the global oc..."
...The SOCAT v4 were mapped using a data-driven diagnostic method (Rödenbeck et al., 2013) and a combined self-organising map and feed-forward neural network (Landschützer et al., 2014)....
[...]
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
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 background or methods from "Recent variability of the global oc..."
..., 2013) and a combined selforganising map and feed-forward neural network (Landschützer et al., 2014)....
[...]
...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)....
[...]
References
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Abstract: Evidence gleaned from the instrumental record of climate data identifies a robust, recurring pattern of ocean–atmosphere climate variability centered over the midlatitude North Pacific basin. Over the past century, the amplitude of this climate pattern has varied irregularly at interannual-to-interdecadal timescales. There is evidence of reversals in the prevailing polarity of the oscillation occurring around 1925, 1947, and 1977; the last two reversals correspond to dramatic shifts in salmon production regimes in the North Pacific Ocean. This climate pattern also affects coastal sea and continental surface air temperatures, as well as streamflow in major west coast river systems, from Alaska to California.
6,719 citations
TL;DR: A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present as mentioned in this paper.
Abstract: A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly −0.03°C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 1°C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales wit...
4,346 citations
"Recent variability of the global oc..." refers methods in this paper
...For the SOM-based clustering, we used the same four variables as Landschützer et al. [2013], namely sea surface temperature (SST) [Reynolds et al., 2002], sea surface salinity (SSS) from Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) [Menemenlis et al., 2008], mixed layer…...
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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
"Recent variability of the global oc..." refers background in this paper
...These are a quadratic [Wanninkhof, 1992], a combination of a quadratic and a cubic [Nightingale et al., 2000] and a cubic relationship [Wanninkhof and McGillis, 1999]....
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