A multi-decade record of high-quality fCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; data in version 3 of the Surface Ocean CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; Atlas (SOCAT)

Bakker, D. C. E.; Pfeil, Benjamin; Landa, Camilla S.; Metzl, Nicolas; O’Brien, Kevin; Olsen, Are; Smith, Karl M.; Cosca, Catherine E; Harasawa, S.; Jones, S. D. M.; Nakaoka, Shin‐Ichiro; Nojiri, Yukihiro; Schuster, Ute; Steinhoff, Tobias; Sweeney, Colm; Takahashi, Taro; Tilbrook, Bronte; Wada, Chisato; Wanninkhof, Rik; Alin, Simone R.; Balestrini, Carlos F.; Barbero, Leticia; Bates, Nicholas R.; Bianchi, Alejandro A.; Bonou, Frédéric; Boutin, Jacqueline; Bozec, Yann; Burger, E. F.; Cai, Wei‐Jun; Castle, R. D.; Chen, Liqi; Chierici, Melissa; Currie, Kim; Evans, Wiley; Featherstone, Charles M.; Feely, Richard A.; Fransson, Agneta; Goyet, Catherine; Greenwood, Naomi; Gregor, Luke; Hankin, S.; Hardman-Mountford, Nick J.; Harlay, Jérôme; Hauck, Judith; Hoppema, Mario; Humphreys, Matthew; Hunt, Christopher W.; Huss, B.; Ibánhez, J. Severino P.; Johannessen, Truls; Kitidis, Vassilis; Körtzinger, Arne; Kozyr, Alex; Krasakopoulou, Εvangelia; Kuwata, Akira; Landschützer, Peter; Lauvset, Siv K.; Lefèvre, Nathalie; Monaco, Claire Lo; Manke, A.; Mathis, Jeremy T.; Merlivat, Liliane; Millero, Frank J.; Monteiro, Pedro M. S.; Munro, David R.; Murata, Akihiko; Newberger, Timothy; Omar, Abdirahman M; Ono, Tsuneo; Paterson, K.; Pearce, David J.; Pierrot, Denis; Robbins, Lisa L.; Saito, Shu; Salisbury, Joseph E.; Schlitzer, Reiner; Schneider, Bernd; Schweitzer, R.; Sieger, Rainer; Skjelvan, Ingunn; Sullivan, Kevin F.; Sutherland, Stewart C; Sutton, Adrienne J.; Telszewski, Maciej; Tuma, Matthias; Heuven, Steven M. A. C. van; Vandemark, Doug; Ward, Brian; Watson, Andrew; Xu, Suqing

doi:10.5194/essd-2016-15

Cited by 50 publications

(80 citation statements)

References 79 publications

(178 reference statements)

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“…In addition, both ocean climate (Fröb et al, ; Le Quéré et al, ; Pérez et al, ) and atmospheric and oceanic pCO 2 (Keenan et al, ; Schuster et al, ) are changing. Therefore, the actual changes in air‐sea CO 2 disequilibrium need to be quantified to reduce the uncertainty of the IG‐TTD method with more observational data, e.g., directly calculate it with the observed pCO 2 data (Bakker et al, ; Takahashi et al, ), derived empirically with hydrographic and nutrients data (Vázquez‐Rodríguez et al, ; Pardo et al, ; Vázquez‐Rodríguez et al, ), or assume that the change of disequilibrium at any point is, to a good approximation, proportional to the (known) anthropogenic perturbation in atmospheric pCO 2 (Khatiwala et al, ) with information from multiple (ensemble) models output.…”

Section: Discussionmentioning

confidence: 99%

A Model‐Based Evaluation of the Inverse Gaussian Transit‐Time Distribution Method for Inferring Anthropogenic Carbon Storage in the Ocean

Tjiputra

Langehaug

et al. 2018

JGR Oceans

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The Inverse Gaussian approximation of transit time distribution method (IG‐TTD) is widely used to infer the anthropogenic carbon (Cant) concentration in the ocean from measurements of transient tracers such as chlorofluorocarbons (CFCs) and sulfur hexafluoride (SF6). Its accuracy relies on the validity of several assumptions, notably (i) a steady state ocean circulation, (ii) a prescribed age tracer saturation history, e.g., a constant 100% saturation, (iii) a prescribed constant degree of mixing in the ocean, (iv) a constant surface ocean air‐sea CO2 disequilibrium with time, and (v) that preformed alkalinity can be sufficiently estimated by salinity or salinity and temperature. Here, these assumptions are evaluated using simulated “model‐truth” of Cant. The results give the IG‐TTD method a range of uncertainty from 7.8% to 13.6% (11.4 Pg C to 19.8 Pg C) due to above assumptions, which is about half of the uncertainty derived in previous model studies. Assumptions (ii), (iv) and (iii) are the three largest sources of uncertainties, accounting for 5.5%, 3.8% and 3.0%, respectively, while assumptions (i) and (v) only contribute about 0.6% and 0.7%. Regionally, the Southern Ocean contributes the largest uncertainty, of 7.8%, while the North Atlantic contributes about 1.3%. Our findings demonstrate that spatial‐dependency of Δ/Γ, and temporal changes in tracer saturation and air‐sea CO2 disequilibrium have strong compensating effect on the estimated Cant. The values of these parameters should be quantified to reduce the uncertainty of IG‐TTD; this is increasingly important under a changing ocean climate.

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Section: Discussionmentioning

confidence: 99%

A Model‐Based Evaluation of the Inverse Gaussian Transit‐Time Distribution Method for Inferring Anthropogenic Carbon Storage in the Ocean

Tjiputra

Langehaug

et al. 2018

JGR Oceans

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“…TA at the ocean boundary was directly estimated with the empirical equation (Lee et al, ) based upon salinity and temperature at the ocean boundary. Ocean boundary DIC was calculated with the available TA, f CO 2 from Surface Ocean CO 2 Atlas (Bakker et al, ), salinity, and temperature using CO2SYS. Atmospheric p CO 2 data were obtained from National Oceanic and Atmospheric Administration‐Earth System Research Laboratory (https://www.esrl.noaa.gov/gmd/ccgg/trends/) with approximately 1.8‐ppm increase each year (~400 ppm in year 2015).…”

Section: Methodsmentioning

confidence: 99%

Ecosystem Metabolism and Carbon Balance in Chesapeake Bay: A 30‐Year Analysis Using a Coupled Hydrodynamic‐Biogeochemical Model

Shen

Testa

et al. 2019

JGR Oceans

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The carbon cycle in estuarine environments is difficult to quantify because of substantial spatiotemporal heterogeneity in the sources, exchanges, and fates of carbon. We overcame these challenges with a multidecade numerical modeling analysis of seasonal, interannual, and decadal variability in net ecosystem metabolism (NEM) and associated carbon fluxes in Chesapeake Bay. Interannual variability in NEM along the estuarine axis indicated a clear spatial dependency of NEM on riverine discharge, with elevated flows causing increasing upper bay heterotrophy and increasing lower bay autotrophy during wet years. Our 30‐year simulation suggested the Chesapeake Bay is somewhat unique among estuaries in its tendency toward net autotrophy as a consequence of its extremely high nutrient to organic matter input ratio and large size. Budgets of three different carbon pools revealed that the entire Chesapeake Bay is a CO2 source to the atmosphere and organic carbon source to the open shelf, providing quantitative export estimates for interpretation of anthropogenic perturbations to the regional carbon flux.

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“…The Southern Ocean plays an essential role in ocean uptake of carbon dioxide [ Frölicher et al ., ]. The observations of pH allow the partial pressure of CO 2 (pCO 2 ) to be estimated over complete annual cycles [ Williams et al ., ] in a region that has few winter observations [ Bakker et al ., ]. This allows the often competing processes of the solubility and biological carbon pumps [ Takahashi et al ., ] to be disentangled and their roles to be more completely understood.…”

Section: Introductionmentioning

confidence: 99%

Biogeochemical sensor performance in the SOCCOM profiling float array

et al. 2017

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The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) program has begun deploying a large array of biogeochemical sensors on profiling floats in the Southern Ocean. As of February 2016, 86 floats have been deployed. Here the focus is on 56 floats with quality‐controlled and adjusted data that have been in the water at least 6 months. The floats carry oxygen, nitrate, pH, chlorophyll fluorescence, and optical backscatter sensors. The raw data generated by these sensors can suffer from inaccurate initial calibrations and from sensor drift over time. Procedures to correct the data are defined. The initial accuracy of the adjusted concentrations is assessed by comparing the corrected data to laboratory measurements made on samples collected by a hydrographic cast with a rosette sampler at the float deployment station. The long‐term accuracy of the corrected data is compared to the GLODAPv2 data set whenever a float made a profile within 20 km of a GLODAPv2 station. Based on these assessments, the fleet average oxygen data are accurate to 1 ± 1%, nitrate to within 0.5 ± 0.5 µmol kg−1, and pH to 0.005 ± 0.007, where the error limit is 1 standard deviation of the fleet data. The bio‐optical measurements of chlorophyll fluorescence and optical backscatter are used to estimate chlorophyll a and particulate organic carbon concentration. The particulate organic carbon concentrations inferred from optical backscatter appear accurate to with 35 mg C m−3 or 20%, whichever is larger. Factors affecting the accuracy of the estimated chlorophyll a concentrations are evaluated.

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A multi-decade record of high-quality fCO<sub>2</sub> data in version 3 of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT)

Abstract: International audienc

Cited by 50 publications

References 79 publications

A Model‐Based Evaluation of the Inverse Gaussian Transit‐Time Distribution Method for Inferring Anthropogenic Carbon Storage in the Ocean

A Model‐Based Evaluation of the Inverse Gaussian Transit‐Time Distribution Method for Inferring Anthropogenic Carbon Storage in the Ocean

Ecosystem Metabolism and Carbon Balance in Chesapeake Bay: A 30‐Year Analysis Using a Coupled Hydrodynamic‐Biogeochemical Model

Biogeochemical sensor performance in the SOCCOM profiling float array

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