1. Wanninkhof, R., D. Pierrot, K. Sullivan, L. Barbero, and J. Trinanes. A 17-year dataset of surface water fugacity of CO2 along with calculated pH, aragonite saturation state, and air-sea CO2 fluxes in the northern Caribbean Sea. Earth System Science Data, 12(3):1489-1509, https://doi.org/10.5194/essd-12-1489-2020 2020

    Abstract:

    A high-quality dataset of surface water fugacity of CO2 (fCO2w), consisting of over a million observations, and derived products are presented for the northern Caribbean Sea, covering the time span from 2002 through 2018. Prior to installation of automated pCO2 systems on cruise ships of Royal Caribbean International and subsidiaries, very limited surface water carbon data were available in this region. With this observational program, the northern Caribbean Sea has now become one of the best-sampled regions for pCO2 of the world ocean. The dataset and derived quantities are binned and averaged on a 1° monthly grid and are available at http://accession.nodc.noaa.gov/0207749 (last access: 30 June 2020) (https://doi.org/10.25921/2swk-9w56; Wanninkhof et al., 2019a). The derived quantities include total alkalinity (TA), acidity (pH), aragonite saturation state (ΩAr), and air-sea CO2 flux and cover the region from 15–28°N and 88–62°W. The gridded data and products are used for determination of status and trends of ocean acidification, for quantifying air-sea CO2 fluxes, and for ground-truthing models. Methodologies to derive the TA, pH, and ΩAr and to calculate the fluxes from fCO2w temperature and salinity are described.

  2. Chen, S., C. Hu, B.B. Barnes, R. Wanninkhof, W.-J. Cai, L. Barbero, and D. Pierrot. A machine learning approach to estimate surface ocean pCO2 from satellite measurements. Remote Sensing of Environment, 228:203-226, doi:10.1016/j.rse.2019-04.019 2019

    Abstract:

    Surface seawater partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. Yet, the remote estimation of pCO2 in coastal waters (under influences of multiple processes) has been difficult due to complex relationships between environmental variables and surface pCO2. To date there is no unified model to remotely estimate surface pCO2 in oceanic regions that are dominated by different oceanic processes. In our study area, the Gulf of Mexico (GOM), this challenge is addressed through the evaluation of different approaches, including multi-linear regression (MLR), multi-nonlinear regression (MNR), principle component regression (PCR), decision tree, supporting vector machines (SVMs), multilayer perceptron neural network (MPNN), and random forest based regression ensemble (RFRE). After modeling, validation, and extensive tests using independent cruise datasets, the RFRE model proved to be the best approach. The RFRE model was trained using data comprised of extensive pCO2 datasets (collected over 16 years by many groups) and MODIS (Moderate Resolution Imaging Spectroradiometer) estimated sea surface temperature (SST), sea surface salinity (SSS), surface chlorophyll concentration (Chl), and diffuse attenuation of downwelling irradiance (Kd). This RFRE-based pCO2 model allows for the estimation of surface pCO2 from satellites with a spatial resolution of ~1 km. It showed an overall performance of a root mean square difference (RMSD) of 9.1 μatm, with a coefficient of determination (R2) of 0.95, a mean bias (MB) of −0.03 μatm, a mean ratio (MR) of 1.00, an unbiased percentage difference (UPD) of 0.07%, and a mean ratio difference (MRD) of 0.12% for pCO2 ranging between 145 and 550 μatm. The model, with its original parameterization, has been tested with independent datasets collected over the entire GOM, with satisfactory performance in each case (RMSD of ≤~10 μatm for open GOM waters and RMSD of ≤~25 μatm for coastal and river-dominated waters). The sensitivity of the RFRE-based pCO2 model to uncertainties of each input environmental variable was also thoroughly examined. The results showed that all induced uncertainties were close to, or within, the uncertainty of the model itself with higher sensitivity to uncertainties in SST and SSS than to uncertainties in Chl and Kd. The extensive validation, evaluation, and sensitivity analysis indicate the robustness of the RFRE model in estimating surface pCO2 for the range of 145–550 μatm in most GOM waters. The RFRE model approach was applied to the Gulf of Maine (a contrasting oceanic region to GOM), with local model training. The results showed significant improvement over other models suggesting that the RFRE may serve as a robust approach for other regions once sufficient field-measured pCO2 data are available for model training.

  3. Friedlingstein, P., M.W. Jones, M. O’Sullivan, R.M. Andrew, J. Hauck, G.P. Peters, W. Peters, J. Pongratz, S. Sitch, C. Le Quéré, D.C.E. Bakker, J.G. Canadell, P. Ciais, R.B. Jackson, P. Anthoni, L. Barbero, A. Bastos, V. Bastrikov, M. Becker, L. Bopp, E. Buitenhuis, N. Chandra, F. Chevallier, L.P. Chini, K.I. Currie, R.A. Feely, M. Gehlen, D. Gilfillan, T. Gkritzalis, D.S. Goll, N. Gruber, S. Gutekunst, I. Harris, V. Haverd, R.A. Houghton, G. Hurtt, T. Ilyina, A.K. Jain, E. Joetzjer, J.O. Kaplan, E. Kato, K.K. Goldewijk, J.I. Korsbakken, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, S. Lienert, D. Lombardozzi, G. Marland, P.C. McGuire, J.R. Melton, N. Metzl, D.R. Munro, J.E.M.S. Nabel, S.-I. Nakaoka, C. Neill, A.M. Omar, T. Ono, A. Peregon, D. Pierrot, B. Poulter, G. Rehder, L. Resplandy, E. Robertson, C. Rödenbeck, R. Séférian, J. Schwinger, N. Smith, P.P. Tans, H. Tian, B. Tilbrook, F.N. Tubiello, G.R. van der Werf, A.J. Wiltshire, and S. Zaehle. Global carbon budget 2019. Earth System Science Data, 11(4):1783-1838, https://doi.org/10.5194/essd-11-1783-2019 2019

    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 Quéré 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).

  4. Pierrot, D., and T. Steinhoff. Installation of autonomous underway pCO2) instruments onboard ships of opportunity. NOAA Technical Report, OAR-AOML-50, 31 pp., doi:10.25923/ffz6-0x48 2019

    Abstract:

    The oceans are the largest sustained sink of anthropogenic carbon with a flux into the ocean of about 2.4 1015 grams, or 2.4 gigatons, of carbon annually, thereby partially mitigating the rapid increase of this climate-forcing gas into the atmosphere. To provide meaningful projections of future atmospheric CO2 levels and surface oceanic CO2 concentrations, we must constrain the flux of CO2 across the air-water interface. An important component of this effort is to obtain more systematic observations of CO2 in the ocean by installing autonomous systems—underway pCO2 analyzers—on ships of opportunity. The purpose of this technical report is to provide the necessary information required to perform such an installation. The information it contains pertains specifically to the installation of the system built by General Oceanics, Inc. in Miami, Florida. However, most of the ­instructions and issues discussed should apply to any type of autonomous system.

  5. Wanninkhof, R., P.A. Pickers, A.M. Omar, A. Sutton, A. Murata, A. Olsen, B.B. Stephens, B. Tilbrook, D. Munro, D. Pierrot, G. Rehder, J.M. Santana-Casiano, J.D. Muller, J. Trinanes, K. Tedesco, K. O’Brien, K. Currie, L. Barbero, M. Telszewski, M. Hoppema, M. Ishii, M. Gonzalez-Davila, N.R. Bates, N. Metzl, P. Suntharalingam, R.A. Feely, S.-I. Nakaoka, S.K. Lauvset, T. Takahashi, T. Steinhoff, and U. Schuster. A surface ocean CO2 reference network, SOCONET, and associated marine boundary layer CO2 measurements. Frontiers in Marine Science, 6:400, doi:10.3389/fmars.2019.00400 2019

    Abstract:

    The Surface Ocean CO2 NETwork (SOCONET) and atmospheric Marine Boundary Layer (MBL) CO2 measurements from ships and buoys focus on the operational aspects of measurements of CO2 in both the ocean surface and atmospheric MBLs. The goal is to provide accurate pCO2 data to within 2 micro atmosphere (μatm) for surface ocean and 0.2 parts per million (ppm) for MBL measurements following rigorous best practices, calibration and intercomparison procedures. Platforms and data will be tracked in near real-time and final quality-controlled data will be provided to the community within a year. The network, involving partners worldwide, will aid in production of important products such as maps of monthly resolved surface ocean CO2 and air-sea CO2 flux measurements. These products and other derivatives using surface ocean and MBL CO2 data, such as surface ocean pH maps and MBL CO2 maps, will be of high value for policy assessments and socio-economic decisions regarding the role of the ocean in sequestering anthropogenic CO2 and how this uptake is impacting ocean health by ocean acidification. SOCONET has an open ocean emphasis but will work with regional (coastal) networks. It will liaise with intergovernmental science organizations such as Global Atmosphere Watch (GAW), and the joint committee for and ocean and marine meteorology (JCOMM). Here we describe the details of this emerging network and its proposed operations and practices.

  6. Le Quere, C., R.M. Andrew, P. Friedlingstein, S. Sitch, J. Hauck, J. Pongratz, P.A. Pickers, J.I. Korsbakken, G.P. Peters, J.G. Canadell, A. Arneth, V.K. Arora, L. Barbero, A. Bastos, L. Bopp, F. Chevallier, L.P. Chini, P. Ciais, S.C. Doney, T. Gkritzalis, D.S. Goll, I. Harris, V. Haverd, F.M. Hoffman, M. Hoppema, R.A. Houghton, G. Hurtt, T. Ilyina, A.K. Jain, T. Johannessen, C.D. Jones, E. Kato, R.F. Keeling, K.K. Goldewijk, P. Landschutzer, N. Lefevre, S. Lienert, Z. Liu, D. Lombardozzi, N. Metzl, D.R. Munro, J.E.M.S. Nabel, S.-I. Nakaoka, C. Neill, A. Olsen, T. Ono, P. Patra, A. Peregon, W. Peters, P. Peylin, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, L. Resplandy, E. Robertson, M. Rocher, C. Rodenbeck, U. Schuster, J. Schwinger, R. Seferian, I. Skjelvan, T. Steinhoff, A. Sutton, P.P. Tans, H. Tian, B. Tilbrook, F.N. Tubiello, I.T. van der Laan-Luijkx, G.R. van der Werf, N. Viovy, A.P. Walker, A.J. Wiltshire, R. Wright, S. Zaehle, and B. Zheng. Global carbon budget 2018. Earth System Science Data, 10(4):2141-2194, doi:10.5194/essd-10-2141-2018 2018

    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 Quéré et al., 2018, 2016, 2015a,b, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2018.

  7. Le Quéré, C., R.M. Andrew, P. Friedlingstein, S. Sitch, J. Pongratz, A.C. Manning, J.I. Korsbakken, G.P. Peters, J.G. Canadell, R.B. Jackson, T.A. Boden, P.P. Tans, O.D. Andrews, V.K. Arora, D.C.E. Bakker, L. Barbero, M. Becker, R.A. Betts, L. Bopp, F. Chevallier, L.P. Chini, P. Ciais, C.E. Cosca, J. Cross, K. Currie, T. Gasser, I. Harris, J. Hauck, V. Haverd, R.A. Houghton, C.W. Hunt, G. Hurtt, T. Ilyina, A.K. Jain, E. Kato, M. Kautz, R.F. Keeling, K. Klein Goldewijk, A. Körtzinger, P. Landschützer, N. Lefèvre, A. Lenton, S. Lienert, I. Lima, D. Lombardozzi, N. Metzl, F. Millero, P.M.S. Monteiro, D.R. Munro, J.E.M.S. Nabel, S. Nakaoka, Y. Nojiri, X.A. Padin, A. Peregon, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, J. Reimer, C. Rödenbeck, J. Schwinger, R. Séférian, I. Skjelvan, B.D. Stocker, H. Tian, B. Tilbrook, F.N. Tubiello, I.T. van der Laan-Luijkx, G.R. van der Werf, S. van Heuven, N. Viovy, N. Vuichard, A.P. Walker, A.J. Watson, A.J. Wiltshire, S. Zaehle, and D. Zhu. Global carbon budget 2017. Earth System Science Data, 10(1):405-448, doi:10.5194/essd-10-405-2018 2018

    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 Niño 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 Quéré et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

  8. Reverdin, G., N. Metzl, S. Olafsdotir, V. Racape, T. Takahashi, M. Benetti, H. Valdimarsson, A. Benoit-Cattin, M. Danielsen, J. Fin, A. Naamar, D. Pierrot, K. Sullivan, F. Bringas, and G. Goni. SURATLANT: A 1993-2017 surface sampling in the central part of the North Atlantic subpolar gyre. Earth System Science Data, 10(4):1901-1924, doi:10.5194/essd-10-1901-2018 2018

    Abstract:

    This paper presents the SURATLANT data set (SURveillance ATLANTique). It consists of individual data of temperature, salinity, parameters of the carbonate system, nutrients, and water stable isotopes (δ18O and δD) collected mostly from ships of opportunity since 1993 along transects between Iceland and Newfoundland (https://doi.org/10.17882/54517). We discuss how the data are validated and qualified, their accuracy, and the overall characteristics of the data set. The data are used to reconstruct seasonal cycles and interannual anomalies, in particular of sea surface salinity (SSS); inorganic nutrients; dissolved inorganic carbon (DIC); and its isotopic composition δ13CDIC, total alkalinity (At), and water isotope concentrations. Derived parameters such as fCO2 and pH are also estimated. The relation between salinity and At is estimated from these data to investigate the possibility to replace missing At when estimating other parameters of the carbonate system. When examining the average seasonal cycle in the deep ocean, in both these data with other climatologies, we find a period of small seasonal change between January and late April. On the Newfoundland shelf and continental slope, changes related with spring stratification and blooms occur earlier. The data were collected in a period of multi-decennial variability associated with the Atlantic multi-decadal variability with warming between 1994 and 2004–2007, and with the recent cooling having peaked in 2014–2016. We also observe strong salinification in 2004–2009 and fresher waters in 1994–1995 as well as since 2010 south of 54°N and in 2016–2017 north of 54°N. Indication of multi-decadal variability is also suggested by other variables, such as phosphate or DIC, but cannot be well resolved seasonally with the discrete sampling and in the presence of interannual variability. As a whole, over the 24 years, the ocean fCO2 trend (+1.9µatmyr−1) is close to the atmospheric trend and associated with an increase in DIC (+0.77µmolkg−1yr−1). The data also revealed a canonical pH decrease of −0.0021yr−1. There is also a decrease in δ13CDIC between 2005 and 2017 (in winter, −0.014‰yr−1, but larger in summer, −0.042‰yr−1), suggesting a significant anthropogenic carbon signal at play together with other processes (mixing, biological activity).

  9. Cai, W.-J., W.-J. Huang, G.W. Luther, D. Pierrot, M. Li, J. Testa, M. Xue, A. Joesoef, R. Mann, J. Brodeur, Y.-Y. Xu, B. Chen, N. Hussain, G.G. Waldbusser, J. Cornwell, and W.M. Kemp. Redox reactions and weak buffering capacity lead to acidification in the Chesapeake Bay. Nature Communications, 8(1):369, doi:10.1038/s41467-017-00417-7 2017

    Abstract:

    The combined effects of anthropogenic and biological CO2 inputs may lead to more rapid acidification in coastal waters compared to the open ocean. It is less clear, however, how redox reactions would contribute to acidification. Here we report estuarine acidification dynamics based on oxygen, hydrogen sulfide (H2S), pH, dissolved inorganic carbon, and total alkalinity data from the Chesapeake Bay, where anthropogenic nutrient inputs have led to eutrophication, hypoxia and anoxia, and low pH. We show that a pH minimum occurs in mid-depths where acids are generated as a result of H2S oxidation in waters mixed upward from the anoxic depths. Our analyses also suggest a large synergistic effect from river–ocean mixing, global and local atmospheric CO2 uptake, and CO2 and acid production from respiration and other redox reactions. Together they lead to a poor acid buffering capacity, severe acidification, and increased carbonate mineral dissolution in the USA’s largest estuary.

  10. Pierrot, D., and F.J. Millero. The speciation of metals in natural waters. Aquatic Geochemistry, 23(1):1-20, doi:10.1007/s10498-016-9292-4 2017

    Abstract:

    The equilibria and rates of reactions of trace metals in natural waters are affected by their speciation or the form of the metal in the solution phase. Many workers have shown, for example, that biological uptake (Anderson and Morel, 1982), toxicity (Sunda and Ferguson, 1983), as well as solubility (Millero et al., 1995; Liu and Millero, 1999) are affected by the speciation. For example, Fe(II) and Mn(II) are biologically available for marine organisms, while Fe(III) and Mn(IV) are normally not available. The speciation of metals also affects the rates of oxidation (Millero, 1985, 1994; Sharma and Millero, 1989; Vazquez et al., 1989) and reduction (Millero et al., 1991) of metals in natural waters. The ionic interactions of metals are controlled by interactions with inorganic (Cl, OH, CO32−, etc.) and organic ligands (e.g., fulvic and humic acids). The speciation of metals is also affected by the oxidation potential (Eh) and the pH in the solution. In this paper, we have developed a Pitzer Model (Pitzer, 1973, 1991) that can be used to determine the speciation of trace metals in seawater and other natural waters. It is based upon the Miami Pitzer Model (Millero and Pierrot, 1998) that has been shown to predict reliable activity coefficients for the major components of seawater. The computer code (Pierrot, 2002) for these calculations is described in detail in this paper. It has been used in an earlier paper (Millero and Pierrot, 2002) and more recently used to examine the effect of pH on the speciation of metals in seawater (Millero et al., 2009).

  11. Xu, Y.-Y., D. Pierrot, and W.-J. Cai. Ocean carbonate system computation for anoxic waters using an updated CO2SYS program. Marine Chemistry, 195:90-93, doi:10.1016/marchem.2017.07.002 2017

    Abstract:

    In anoxic/hypoxic waters, the presence of hydrogen sulfide (H2S) and ammonia (NH3) influences results of the computation of parameters in the ocean carbonate system. To evaluate their influences, H2S and NH3 contributions to total alkalinity are added to CO2SYS, which is a most often used publicly available software package that calculates oceanic carbonate parameters. We discuss how these two metabolites affect the carbonate parameters and compare the differences in total alkalinity, dissolved inorganic carbon, pH, fCO2, and aragonite saturation state between the CO2SYS packages with and without considering the acid-base systems of H2S and NH3. The results show that, without considering these two acid-base systems, even low to moderate concentrations (e.g., 2‑20 μmol kg−1) of these metabolites cause errors in the calculated carbonate parameters larger than the accuracies of the best measurements, and thus, it is important to include contributions from these metabolites. The outputs from this updated version of CO2SYS agree well with outputs from AquaEnv, which is the only other computation program for the ocean carbonate system that includes the acid-base systems of H2S and NH3. Users are encouraged to use the updated version of CO2SYS to calculate carbonate parameters in anoxic/hypoxic waters.

  12. Bakker, D.C.E., B. Pfeil, C.S. Landa, N. Metzl, K.M. O'Brien, A. Olsen, K. Smith, C. Cosca, S. Harasawa, S.D. Jones, S.-I. Nakaoka, Y. Nojiri, U. Schuster, T. Steinhoff, C. Sweeney, T. Takahashi, B. Tilbrook, C. Wada, R. Wanninkhof, S.R. Alin, C.F. Balestrini, L. Barbero, N.R. Bates, A.A. Bianchi, F. Bonou, J. Boutin, Y. Bozec, E.F. Burger, W.-J. Cai, R.D. Castle, L. Chen, M. Chierici, K. Currie, W. Evans, C. Featherstone, R.A. Feely, A. Fransson, C. Goyet, N. Greenwood, L. Gregor, S. Hankin, N.J. Hardman-Mountford, J. Harlay, J. Hauck, M. Hoppema, M.P. Humphreys, C.W. Hunt, B. Huss, J.S.P. Ibánhez, T. Johannessen, R. Keeling, V. Kitidis, A. Körtzinger, A. Kozyr, E. Krasakopoulou, A. Kuwata, P. Landschützer, S.K. Lauvset, N. Lefèvre, C. Lo Monaco, A. Manke, J.T. Mathis, L. Merlivat, F.J. Millero, P.M.S. Monteiro, D.R. Munro, A. Murata, T. Newberger, A.M. Omar, T. Ono, K. Paterson, D. Pearce, D. Pierrot, L.L. Robbins, S. Saito, J. Salisbury, R. Schlitzer, B. Schneider, R. Schweitzer, R. Sieger, I. Skjelvan, K.F. Sullivan, S.C. Sutherland, A.J. Sutton, K. Tadokoro, M. Telszewski, M. Tuma, S.M.A.C. Van Heuven, D. Vandemark, B. Ward, A.J. Watson, and S. Xu. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 8:383-413, doi:10.5194/essd-8-383-2016 2016

    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.

  13. Le Quéré, C., R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, G.P. Peters, A.C. Manning, T.A. Boden, P.P. Tans, R.A. Houghton, R.F. Keeling, S. Alin, O.D. Andrews, P. Anthoni, L. Barbero, L. Bopp, F. Chevallier, L.P. Chini, P. Ciais, K. Currie, C. Delire, S.C. Doney, P. Friedlingstein, T. Gkritzalis, I. Harris, J. Hauck, V. Haverd, M. Hoppema, K. Klein Goldewijk, A.K. Jain, E. Kato, A. Körtzinger, P. Landschützer, N. Lefèvre, A. Lenton, S. Lienert, D. Lombardozzi, J.R. Melton, N. Metzl, F. Millero, P.M.S. Monteiro, D.R. Munro, J.E.M.S. Nabel, S.I. Nakaoka, K. O'Brien, A. Olsen, A.M. Omar, T. Ono, D. Pierrot, B. Poulter, C. Rödenbeck, J. Salisbury, U. Schuster, J. Schwinger, R. Séférian, I. Skjelvan, B.D. Stocker, A.J. Sutton, T. Takahashi, H. Tian, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van der Werf, N. Viovy, A.P. Walker, A.J. Wiltshire, and S. Zaehle. Global carbon budget 2016. Earth System Science Data, 8(2):605-649, doi:10.5194/essd-8-605-2016 2016

    Abstract:

    The global carbon budget 2016 is the 11th annual update of emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land, and ocean. This data synthesis brings together measurements, statistical information, and analyses of model results to provide an assessment of the global carbon budget and their uncertainties for years 1959 to 2015, with a projection for year 2016.

  14. Le Quéré, C., R. Moriarty, R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, P. Friedlingstein, G.P. Peters, R.J. Andres, T.A. Boden, R.A. Houghton, J.I. House, R.F. Keeling, P. Tans, A. Arneth, D.C.E. Bakker, L. Barbero, L. Bopp, J. Chang, F. Chevallier, L.P. Chini, P. Ciais, M. Fader, R.A. Feely, T. Gkritzalis, I. Harris, J. Hauck, T. Ilyina, A.K. Jain, E. Kato, V. Kitidis, K. Klein Goldewijk, C. Koven, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, I.D. Lima, N. Metzl, F. Millero, D.R. Munro, A. Murata, J.E.M.S. Nabel, S. Nakaoka, Y. Nojiri, K. O’Brien, A. Olsen, T. Ono, F.F. Pérez, B. Pfeil, D. Pierrot, B. Poulter, G. Rehder, C. Rödenbeck, S. Saito, U. Schuster, J. Schwinger, R. Séférian, T. Steinhoff, B.D. Stocker, A.J. Sutton, T. Takahashi, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van der Werf, S. van Heuven, D. Vandemark, N. Viovy, A. Wiltshire, S. Zaehle, and N. Zeng. Global carbon budget 2015. Earth System Science Data, 7(2):349-396, doi:10.5194/essd-7-349-2015 2015

    Abstract:

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

  15. Bakker, D.C.E., B. Pfeil, K. Smith, S. Hankin, A. Olsen, S.R. Alin, C. Cosca, S. Harasawa, A. Kozyr, Y. Nojiri, K.M. O’Brien, U. Schuster, M. Telszewski, B. Tilbrook, C. Wada, J. Akl, L. Barbero, N.R. Bates, J. Boutin, Y. Bozec, W.-J. Cai, R.D. Castle, F.P. Chavez, L. Chen, M. Chierici, K. Currie, H.J.W. de Baar, W. Evans, R.A. Feely, A. Fransson, Z. Gao, B. Hales, N.J. Hardman-Mountford, M. Hoppema, W.-J. Huang, C.W. Hunt, B. Huss, T. Ichikawa, T. Johannessen, E.M. Jones, S.D. Jones, S. Jutterstrom, V. Kitidis, A. Kortzinger, P. Llandschutzer, S.K. Lauvset, N. Lefevre, A.B. Manke, J.T. Mathis, L. Merlivat, N. Metzl, A. Murata, T. Newberger, A.M. Omar, T. Ono, G.-H. Park, K. Paterson, D. Pierrot, A.F. Rios, C.L. Sabine, S. Saito, J. Salisbury, V.V.S.S. Sarma, R. Schlitzer, R. Sieger, I. Skjelvan, T. Steinhoff, K.F. Sullivan, H. Sun, A.J. Sutton, T. Suzuki, C. Sweeney, T. Takahashi, J. Tjiputra, N. Tsurushima, S.M.A.C. van Heuven, D. Vandemark, P. Vlahos, D.W.R. Wallace, R. Wanninkhof, and A.J. Watson. An update to the surface CO2 atlas (SOCAT version 2). Earth System Science Data, 6(1):69-90, doi:10.5194/essd-6-69-2014 2014

    Abstract:

    The Surface Ocean CO2 Atlas (SOCAT), an activity of the international marine carbon research community, provides access to synthesis and gridded fCO2 (fugacity of carbon dioxide) products for the surface oceans. Version 2 of SOCAT is an update of the previous release (version 1) with more data (increased from 6.3 million to 10.1 million surface water fCO2 values) and extended data coverage (from 1968–2007 to 1968–2011). The quality control criteria, while identical in both versions, have been applied more strictly in version 2 than in version 1. The SOCAT website (http://www.socat.info/) has links to quality control comments, metadata, individual data set files, and synthesis and gridded data products. Interactive online tools allow visitors to explore the richness of the data. Applications of SOCAT include process studies, quantification of the ocean carbon sink, and its spatial, seasonal, year-to-year and longer term variation, as well as initialization or validation of ocean carbon models and coupled climate-carbon models.

  16. Pfeil, B., A. Olsen, D.C.E. Bakker, S. Hankin, H. Koyuk, A. Kozyr, J. Malczyk, A. Manke, N. Metzl, C.L. Sabine, J. Akl, S.R. Alin, N. Bates, R.G.J. Bellerby, A. Borges, J. Boutin, P.J. Brown, W.-J. Cai, F.P. Chavez, A. Chen, C. Cosca, A.J. Fassbender, R.A. Feely, M. González-Dávila, C. Goyet, B. Hales, N. Hardman-Mountford, C. Heinze, M. Hood, M. Hoppema, C.W. Hunt, D. Hydes, M. Ishii, T. Johannessen, S.D. Jones, R.M. Key, A. Körtzinger, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, A. Lourantou, L. Merlivat, T. Midorikawa, L. Mintrop, C. Miyazaki, A. Murata, A. Nakadate, Y. Nakano, S. Nakaoka, Y. Nojiri, A.M. Omar, X.A. Padin, G.-H. Park, K. Paterson, F.F. Perez, D. Pierrot, A. Poisson, A.F. Ríos, J.M. Santana-Casiano, J. Salisbury, V.V.S.S. Sarma, R. Schlitzer, B. Schneider, U. Schuster, R. Sieger, I. Skjelvan, T. Steinhoff, T. Suzuki, T. Takahashi, K. Tedesco, M. Telszewski, H. Thomas, B. Tilbrook, J. Tjiputra, D. Vandemark, T. Veness, R. Wanninkhof, A.J. Watson, R. Weiss, C.S. Wong, and H. Yoshikawa-Inoue. A uniform, quality controlled Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 5(1):125-143, doi:10.5194/essd-5-125-2013 2013

    Abstract:

    A well-documented, publicly available global data set of surface ocean carbon dioxide (CO2) parameters has been called for by international groups for nearly two decades. The Surface Ocean CO2 Atlas (SOCAT) project was initiated by the international marine carbon science community in 2007 with the aim of providing a comprehensive, publicly available, regularly updated, global data set of marine surface CO2, which had been subject to quality control (QC). Many additional CO2 data, not yet made public via the Carbon Dioxide Information Analysis Center (CDIAC), were retrieved from data originators, public websites, and other data centers. All data were put in a uniform format following a strict protocol. Quality control was carried out according to clearly-defined criteria. Regional specialists performed the quality control, using state-of-the-art web-based tools, specially developed for accomplishing this global team effort. SOCAT version 1.5 was made public in September 2011 and holds 6.3 million quality controlled surface CO2 data points from the global oceans and coastal seas, spanning four decades (1968-2007). Three types of data products are available: individual cruise files, a merged complete data set, and gridded products. With the rapid expansion of marine CO2 data collection and the importance of quantifying net global oceanic CO2 uptake and its changes, sustained data synthesis and data access are priorities.

  17. Sabine, C.L., S. Hankin, H. Koyuk, D.C.E. Bakker, B. Pfeil, A. Olsen, N. Metzl, A. Kozyr, A. Fassbender, A. Manke, J. Malczyk, J. Akl, S.R. Alin, R.G.J. Bellerby, A. Borges, J. Boutin, P.J. Brown, W.-J. Cai, F.P. Chavez, A. Chen, C. Cosca, R.A. Feely, M. González-Dávila, C. Goyet, N. Hardman-Mountford, C. Heinze, M. Hoppema, C.W. Hunt, D. Hydes, M. Ishii, T. Johannessen, R.M. Key, A. Körtzinger, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, A. Lourantou, L. Merlivat, T. Midorikawa, L. Mintrop, C. Miyazaki, A. Murata, A. Nakadate, Y. Nakano, S. Nakaoka, Y. Nojiri, A.M. Omar, X.A. Padin, G.-H. Park, K. Paterson, F.F. Perez, D. Pierrot, A. Poisson, A.F. Ríos, J. Salisbury, J.M. Santana-Casiano, V.V.S.S. Sarma, R. Schlitzer, B. Schneider, U. Schuster, R. Sieger, I. Skjelvan, T. Steinhoff, T. Suzuki, T. Takahashi, K. Tedesco, M. Telszewski, H. Thomas, B. Tilbrook, D. Vandemark, T. Veness, A.J. Watson, R. Weiss, C.S. Wong, and H. Yoshikawa-Inoue. Surface Ocean CO2 Atlas (SOCAT) gridded data products. Earth System Science Data, 5(1):145-153, doi:10.5194/essd-5-145-2013 2013

    Abstract:

    As a response to public demand for a well-documented, quality controlled, publicly available, global surface ocean carbon dioxide (CO2) data set, the international marine carbon science community developed the Surface Ocean CO2 Atlas (SOCAT). The first SOCAT product is a collection of 6.3 million quality controlled surface CO2 data from the global oceans and coastal seas, spanning four decades (1968-2007). The SOCAT gridded data presented here is the second data product to come from the SOCAT project. Recognizing that some groups may have trouble working with millions of measurements, the SOCAT gridded product was generated to provide a robust, regularly spaced CO2 fugacity (fCO2) product with minimal spatial and temporal interpolation, which should be easier to work with for many applications. Gridded SOCAT is rich with information that has not been fully explored yet (e.g., regional differences in the seasonal cycles), but also contains biases and limitations that the user needs to recognize and address (e.g., local influences on values in some coastal regions).

  18. Cai, W.-J., L. Chen, B. Chen, Z. Gao, S.-H. Lee, J. Chen, D. Pierrot, K. Sullivan, Y. Wang, X. Hu, W.-J. Huang, Y. Zhang, S. Xu, A. Murata, J.M. Grebmeier, E.P. Jones, and H. Zhang. Decrease in the CO2 uptake capacity in an ice-free Arctic Ocean basin. Science, 329(5991):556-559, doi:10.1126/science.1189338 2010

    Abstract: It has been predicted that the Arctic Ocean will sequester much greater amounts of carbon dioxide (CO2) from the atmosphere as a result of sea ice melt and increasing primary productivity. However, this prediction was made on the basis of observations from either highly productive ocean margins or ice-covered basins before the recent major ice retreat. We report here a high-resolution survey of sea-surface CO2 concentration across the Canada Basin, showing a great increase relative to earlier observations. Rapid CO2 invasion from the atmosphere and low biological CO2 drawdown are the main causes for the higher CO2, which also acts as a barrier to further CO2 invasion. Contrary to the current view, we predict that the Arctic Ocean basin will not become a large atmospheric CO2 sink under ice-free conditions.

  19. Metzl, N., A. Corbiere, G. Reverdin, A. Lenton, T. Takahashi, A. Olsen, T. Johannessen, D. Pierrot, R. Wanninkhof, S.R. Olafsdottir, J. Olafsson, and M. Ramonet. Recent acceleration of the sea surface fCO2 growth rate in the North Atlantic subpolar gyre (1993-2008) revealed by winter observations. Global Biogeochemical Cycles, 24:GB4004, 13 pp., doi:10.1029/2009GB003658 2010

    Abstract:

    Recent studies based on ocean and atmospheric carbon dioxide (CO2) observations, suggesting that the ocean carbon uptake has been reduced, may help explain the increase in the fraction of anthropogenic CO2 emissions that remain in the atmosphere. Is it a response to climate change or a signal of ocean natural variability or both? Regional process analyses are needed to follow the ocean carbon uptake and to enable better attributions of the observed changes. Here, we describe the evolution of the surface ocean CO2fugacity (fCO2oc) over the period 1993–2008 in the North Atlantic subpolar gyre (NASPG). This analysis is based primarily on observations of dissolved inorganic carbon (DIC) and total alkalinity (TA) conducted at different seasons in the NASPG between Iceland and Canada. The fCO2oc trends based on DIC and TA data are also compared with direct fCO2 measurements obtained between 2003 and 2007 in the same region. During winters 1993–2003, the fCO2oc growth rate was 3.7 (±0.6) μatm yr−1, higher than in the atmosphere, 1.8 (±0.1) μatm yr−1. This translates to a reduction of the ocean carbon uptake primarily explained by sea surface warming, up to 0.24 (±0.04) °C yr−1. This warming is a consequence of advection of warm water northward from the North Atlantic into the Irminger basin, which occurred as the North Atlantic Oscillation (NAO) index moved into a negative phase in winter 1995/1996. In winter 2001–2008, the fCO2oc rise was particularly fast, between 5.8 (±1.1) and 7.2 (±1.3) μatm yr−1 depending on the region, more than twice the atmospheric growth rate of 2.1 (±0.2) μatm yr−1, and in the winter of 2007–2008 the area was supersaturated with CO2. As opposed to the 1990s, this appears to be almost entirely due to changes in seawater carbonate chemistry, the combination of increasing DIC and decreasing of TA. The rapidfCO2oc increase was not only driven by regional uptake of anthropogenic CO2 but was also likely controlled by a recent increase in convective processes-vertical mixing in the NASPG and cannot be directly associated with NAO variability. The fCO2oc increase observed in 2001–2008 leads to a significant drop in pH of −0.069 (±0.007) decade−1.

  20. Pierrot, D., P. Brown, S. Van Heuven, T. Tanhua, U. Schuster, R. Wanninkhof, and R.M. Key. CARINA TCO2 data in the Atlantic Ocean. Earth System Science Data, 2(2):177-187, doi:10.5194/essd-2-177-2010 2010

    Abstract: Water column data of carbon and carbon-relevant hydrographic and hydrochemical parameters from 188 cruises in the Arctic Mediterranean Seas, Atlantic and Southern Ocean have been retrieved and merged in a new data base: the CARINA (CARbon IN the Atlantic) Project. These data have gone through rigorous quality control (QC) procedures so as to improve the quality and consistency of the data as much as possible. Secondary quality control, which involved objective study of data in order to quantify systematic differences in the reported values, was performed for the pertinent parameters in the CARINA data base. Systematic biases in the data have been tentatively corrected in the data products. The products are three merged data files with measured, adjusted and interpolated data of all cruises for each of the three CARINA regions (Arctic Mediterranean Seas, Atlantic and Southern Ocean). Ninety-eight cruises were conducted in the Atlantic defined as the region south of the Greenland-Iceland-Scotland Ridge and north of about 30°S. Here we report the details of the secondary QC which was done on the total dissolved inorganic carbon (TCO2) data and the adjustments that were applied to yield the final data product in the Atlantic. Procedures of quality control, including crossover analysis between stations and inversion analysis of all crossover data, are briefly described. Adjustments were applied to TCO2 measurements for 17 of the cruises in the Atlantic Ocean region. With these adjustments, the CARINA database is consistent both internally as well as with GLODAP data, an oceanographic data set based on the WOCE Hydrographic Program in the 1990s, and is now suitable for accurate assessments of, for example, regional oceanic carbon inventories, uptake rates, and model validation.

  21. Tanhua, T., R. Steinfeldt, R.M. Key, P. Brown, N. Gruber, R. Wanninkhof, F. Perez, A. Kortzinger, A. Velo, U. Schuster, S. van Heuven, J.L. Bullister, I. Stendardo, M. Hoppema, A. Olsen, A. Kozyr, D. Pierrot, C. Schirnick, and D.W.R. Wallace. Atlantic Ocean CARINA data: Overview and salinity adjustments. Earth System Science Data, 2(1):17-34, doi:10.5194/essd-2-17-2010 2010

    Abstract: Water column data of carbon and carbon-relevant hydrographic and hydrochemical parameters from 188 previously non-publicly available cruise data sets in the Arctic Mediterranean Seas, Atlantic and Southern Ocean have been retrieved and merged into a new database: CARINA (CARbon dioxide IN the Atlantic Ocean). The data have gone through rigorous quality control procedures to assure the highest possible quality and consistency. The data for the pertinent parameters in the CARINA database were objectively examined in order to quantify systematic differences in the reported values, i.e., secondary quality control. Systematic biases found in the data have been corrected in the three data products: merged data files with measured, calculated, and interpolated data for each of the three CARINA regions, i.e., the Arctic Mediterranean Seas, the Atlantic and the Southern Ocean. These products have been corrected to be internally consistent. Ninety-eight of the cruises in the CARINA database were conducted in the Atlantic Ocean, defined here as the region south of the Greenland-Iceland-Scotland Ridge and north of about 30°S. Here we present an overview of the Atlantic Ocean synthesis of the CARINA data and the adjustments that were applied to the data product. We also report the details of the secondary QC (Quality Control) for salinity for this data set. Procedures of quality control, including crossover analysis between stations and inversion analysis of all crossover data, are briefly described. Adjustments to salinity measurements were applied to the data from 10 cruises in the Atlantic Ocean region. Based on our analysis we estimate the internal consistency of the CARINA-ATL salinity data to be 4.1 ppm. With these adjustments the CARINA data products are consistent both internally as well as with GLODAP data, an oceanographic data set based on the World Hydrographic Program in the 1990s, and is now suitable for accurate assessments of, for example, oceanic carbon inventories and uptake rates and for model validation.

  22. Pierrot, D., C. Neil, K. Sullivan, R. Castle, R. Wanninkhof, H. Lueger, T. Johannessen, A. Olsen, R.A. Feely, and C.E. Cosca. Recommendations for autonomous underway pCO2 measuring systems and data reduction routines. Deep-Sea Research, Part II, 56(8-10):512-522, doi:10.1016/j.dsr2.2008.12.005 2009

    Abstract:

    In order to facilitate the collection of high quality and uniform surface water pCO2 data, an underway pCO2 instrument has been designed based on community input and is now commercially available. Along with instrumentation, agreements were reached on data reduction and quality control that can be easily applied to data from these systems by using custom-made freeware. This new automated underway pCO2 measuring system is designed to be accurate to within 0.1 μatm for atmospheric pCO2 measurements and to within 2 µatm for seawater pCO2, targeted by the scientific community to constrain the regional air-sea CO2 fluxes to 0.2 Pg C year-1. The procedure to properly reduce the underway pCO2 data and perform the steps necessary for calculation of the fugacity of CO2 from the measurements is described. This system is now widely used by the scientific community on many different types of ships. Combined with the recommended data-reduction procedures, it will facilitate producing data sets that will significantly decrease the uncertainty currently present in estimates of air-sea CO2 fluxes.

  23. Schuster, U., A.J. Watson, N.R. Bates, A. Corbiere, M. Gonzalez-Davila, M. Metzl, D. Pierrot, and M. Santana-Casiano. Trends in North Atlantic sea-surface fCO2 from 1990 to 2006. Deep-Sea Research, Part II, 56(8-10):620-629, doi:10.1016/j.dsr2.2008.12.011 2009

    Abstract:

    We examine observations from 1990 to 2006 from four voluntary observing ships and two time-series stations in the North Atlantic, fitting a sinusoidal annual cycle and linear year-on-year trend at all locations where there are sufficient data. Results show that in the subtropical regions, sea-surface fCO2 has closely followed the increasing trend in atmospheric fCO2. In contrast, farther north, sea-surface fCO2 has increased faster than fCO2 in the atmosphere. The resulting ΔfCO2, driving air-sea flux of CO2, has therefore decreased in the North Atlantic, particularly at higher latitudes, as has the annual mean air-sea flux. Several underlying causes may have led to the observed changes in sea-surface fCO2. Low-frequency modes, such as the North Atlantic Oscillation, lead to changes in the sea-surface temperature, in sea-surface circulation and in vertical mixing, affecting sea-surface fCO2 through biogeochemical processes. A comparison with measurements covering a longer time period shows that the sea-surface fCO2 rise has accelerated since 1990 in the northern North Atlantic.

  24. Millero, F.J., D. Pierrot, K. Lee, R. Wanninkhof, R.A. Feely, C.L. Sabine, R.M. Key, and T. Takahashi. Dissociation constants for carbonic acid determined from field measurements. Deep-Sea Research, Part I, 49(10):1705-1723, doi:10.1016/S0967-0637(02)00093-6 2002

    Abstract:

    A number of workers have recently shown that the thermodynamic constants for the dissociation of carbonic acid in seawater of Mehrbach et al. are more reliable than measurements made on artificial seawater. These studies have largely been confined to looking at the internal consistency of measurements of total alkalinity (TA), total inorganic carbon dioxide (TCO2) and the fugacity of carbon dioxide (fCO2). In this paper, we have examined the field measurements of pH, fCO2, TCO2, and TA on surface and deep waters from the Atlantic, Indian, Southern, and Pacific oceans to determine the pK1, pK2, and pK2-pK1. These calculations are possible due to the high precision and accuracy of the field measurements. The values of pK2 and pK2-pK1 over a wide range of temperatures (-1.6-38°C) are in good agreement (within ±0.005) with the results of Mehrbach et al. The measured values of pK1 at 4°C and 20°C are in reasonable agreement (within ±0.01) with all the constants determined in laboratory studies. These results indicate, as suggested by internal consistency tests, that the directly measured values of pK1+pK2 of Mehrbach et al. on real seawater are more reliable than the values determined for artificial seawater. It also indicates that the large differences of pK2-pK1 (0.05 at 20°C) in real and artificial seawater determined by different investigators are mainly due to differences in pK2. These differences may be related to the interactions of boric acid with the carbonate ion. The values of pK2-pK1 determined from the laboratory measurements of Lee et al. and Lueker et al. at low fCO2 agree with the field-derived data to ±0.016 from 5°C to 25°C. The values of pK2-pK1 decrease as the fCO2 or TCO2 increases. This effect is largely related to changes in the pK2 as a function of fCO2 or TCO2. The values of fCO2 calculated from an input of TA and TCO2, which require reliable values of pK2-pK1, also vary with fCO2. The field data at 20°C has been used to determine the effect of changes of TCO2 on pK2 giving an empirical relationship: pK2TCO2 = pK2-1.6 x 10-4 (TCO2-2050) which is valid at TCO2 > 2050 µmol kg-1. This assumes that the other dissociation constants such as KB for boric acid are not affected by changes in TCO2. The slope is in reasonable agreement with the laboratory studies of Lee et al. and Lueker et al. (-1.2 x 10-4 to -1.9 x 10-4). This equation eliminates the dependence of the calculated fCO2 on the level of fCO2 or TCO2 in ocean waters (σ = 29.7 µatm in fCO2). An input of pH and TCO2 yields values of fCO2 and TA that are in good agreement with the measured values (±22.3 µatm in fCO2 and ±4.3 µmol kg-1 in TA). The cause of the decrease in pK2 at high fCO2 is presently unknown. The observed inconsistencies between the measured and computed fCO2 values may be accounted for by adding the effect of organic acid (~8 µmol kg-1) to the interpretation of the TA. Further studies are needed to elucidate the chemical reactions responsible for this effect.