Surface Ocean CO2 Atlas (SOCAT) V4

Bakker, Dorothee C E; Pfeil, Benjamin; O'Brien, Kevin M; Currie, Kim I; Jones, Steve D; Landa, Camilla S; Lauvset, Siv K; Metzl, Nicolas; Munro, David R; Nakaoka, Shin-Ichiro; Olsen, Are; Pierrot, Denis; Saito, Shu; Smith, Karl; Sweeney, Colm; Takahashi, Taro; Wada, Chisato; Wanninkhof, Rik; Alin, Simone R; Becker, Meike; Bellerby, Richard G J; Borges, Alberto Vieira; Boutin, Jacqueline; Bozec, Yann; Burger, Eugene; Cai, Wei-Jun; Castle, Robert D; Cosca, Catherine E; DeGrandpre, Michael D; Donnelly, Matthew; Eischeid, Greg; Feely, Richard A; Gkritzalis, Thanos; González-Dávila, Melchor; Goyet, Catherine; Guillot, Antoine; Hardman-Mountford, Nicolas J; Hauck, Judith; Hoppema, Mario; Humphreys, Matthew P; Hunt, Christopher W; Ibánhez, J Severino P; Ichikawa, Tadafumi; Ishii, Masao; Juranek, Lauren W; Kitidis, Vassilis; Körtzinger, Arne; Koffi, Urbain K; Kozyr, Alexander; Kuwata, Akira; Lefèvre, Nathalie; Lo Monaco, Claire; Manke, Ansley; Marrec, Pierre; Mathis, Jeremy T; Millero, Frank J; Monacci, Natalie; Monteiro, Pedro M S; Murata, Akihiko; Newberger, Timothy; Nojiri, Yukihiro; Nonaka, Isao; Omar, Abdirahman M; Ono, Tsuneo; Padín, Xose Antonio; Rehder, Gregor; Rutgersson, Anna; Sabine, Christopher L; Salisbury, Joe; Santana-Casiano, Juana Magdalena; Sasano, Daisuke; Schuster, Ute; Sieger, Rainer; Skjelvan, Ingunn; Steinhoff, Tobias; Sullivan, Kevin; Sutherland, Stewart C; Sutton, Adrienne; Tadokoro, Kazuaki; Telszewski, Maciej; Thomas, Helmuth; Tilbrook, Bronte; van Heuven, Steven; Vandemark, Doug; Wallace, Douglas WR; Woosley, Ryan J

doi:10.1594/pangaea.866856

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…In a first step, the SST and surface salinity fields from EN4.2.1 were used to derive total surface alkalinity (A T ) using the LIARv2 total alkalinity regression algorithm 38 . In a second step, a monthly [H + ] field at 1°h orizontal grid resolution was derived using the CO2SYS carbonate chemistry package 77 from the monthly, gridded surface salinity, SST, and A T and from the interpolated global surface pCO 2 product MPI-SOMFNN 32 that covers the period 1982-2019 with monthly-mean temporal resolution and is based on data from the Surface Ocean CO 2 Atlas version 4 78 .…”

Section: Observation-based Datamentioning

confidence: 99%

Compound marine heatwaves and ocean acidity extremes

2022

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Compound MHW-OAX events, during which marine heatwaves (MHWs) co-occur with ocean acidity extreme (OAX) events, can have larger impacts on marine ecosystems than the individual extremes. Using monthly open-ocean observations over the period 1982–2019, we show that globally 1.8 in 100 months (or about one out of five present-day MHW months) are compound MHW-OAX event months under a present-day baseline, almost twice as many as expected for 90th percentile extreme event exceedances if MHWs and OAX events were statistically independent. Compound MHW-OAX events are most likely in the subtropics (2.7 in 100 months; 10°−40° latitude) and less likely in the equatorial Pacific and the mid-to-high latitudes (0.7 in 100 months; >40° latitude). The likelihood pattern results from opposing effects of temperature and dissolved inorganic carbon on [H+]. The likelihood is higher where the positive effect on [H+] from increased temperatures during MHWs outweighs the negative effect on [H+] from co-occurring decreases in dissolved inorganic carbon. Daily model output from a large-ensemble simulation of an Earth system model is analyzed to assess changes in the MHW-OAX likelihood under climate change. The projected long-term mean warming and acidification trends have the largest effect on the number of MHW-OAX days per year, increasing it from 12 to 265 days per year at 2 °C global warming relative to a fixed pre-industrial baseline. Even when long-term trends are removed, an increase in [H+] variability leads to a 60% increase in the number of MHW-OAX days under 2 °C global warming. These projected increases may cause severe impacts on marine ecosystems.

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Section: Observation-based Datamentioning

confidence: 99%

Compound marine heatwaves and ocean acidity extremes

2022

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“…Now, thanks to global networking programs, observations have increased widely and consistently, due to ship surveys, the ARGOS project, and mooring and autonomous platforms; furthermore, due to the availability of ever more complex biogeochemical models, the understanding of ocean global and regional carbonate system has advanced considerably. These activities provide accurate, long-term time series f CO 2 datasets, such as those found in the Surface Ocean CO 2 Atlas-SOCAT- [2,3] and the Global Ocean Data Analysis Project (GLODAPv2.2022), consisting of data products of biogeochemical data collected through the chemical analysis of water samples, including TA, DIC, and many others [4]. This information now shows that surface ocean waters show around a 26% increase in concentration of hydrogen ions since 1860, which is equivalent to a drop in pH from 8.2 to 8.1 [5].…”

Section: Introductionmentioning

confidence: 99%

High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning

Galdies

Guerra

2023

Water

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This study combines measurements of dissolved inorganic carbon (DIC), total alkalinity (TA), pH, earth observation (EO), and ocean model products with deep learning to provide a good step forward in detecting changes in the ocean carbonate system parameters at a high spatial and temporal resolution in the North Atlantic region (Long. −61.00° to −50.04° W; Lat. 24.99° to 34.96° N). The in situ reference dataset that was used for this study provided discrete underway measurements of DIC, TA, and pH collected by M/V Equinox in the North Atlantic Ocean. A unique list of co-temporal and co-located global daily environmental drivers derived from independent sources (using satellite remote sensing, model reanalyses, empirical algorithms, and depth soundings) were collected for this study at the highest possible spatial resolution (0.04° × 0.04°). The resulting ANN-estimated DIC, TA, and pH obtained by deep learning shows a high correspondence when verified against observations. This study demonstrates how a select number of geophysical information derived from EO and model reanalysis data can be used to estimate and understand the spatiotemporal variability of the oceanic carbonate system at a high spatiotemporal resolution. Further methodological improvements are being suggested.

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SOCAT V4 metadata list with links to data in original SOCAT format and cruise documentation files

Bakker,

Pfeil

2016

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Surface Ocean CO2 Atlas (SOCAT) V4

Cited by 6 publications

References 2 publications

Compound marine heatwaves and ocean acidity extremes

Compound marine heatwaves and ocean acidity extremes

High Resolution Estimation of Ocean Dissolved Inorganic Carbon, Total Alkalinity and pH Based on Deep Learning

SOCAT V4 metadata list with links to data in original SOCAT format and cruise documentation files

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