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Climatological Distributions of pH, pCO2, Total CO2, Alkalinity, and CaCO3 Saturation in the Global Surface Ocean (NCEI Accession 0164568)
Climatological mean monthly distributions of pH in the total H+ scale, total CO2 concentration (TCO2), and the degree of CaCO3 saturation for the global surface ocean waters (excluding coastal areas) are calculated using a data set for pCO2, alkalinity and nutrient concentrations in surface waters (depths less than 50 m), which is built upon the GLODAP, CARINA and LDEO database. The mutual consistency among these measured parameters is demonstrated using the inorganic carbon chemistry model with the dissociation constants for carbonic acid by Lueker et al. (2000) and for boric acid by Dickson (1990). The global ocean is divided into 24 regions, and the linear potential alkalinity (total alkalinity + nitrate) versus salinity relationships are established for each region. The mean monthly distributions of pH and carbon chemistry parameters for the reference year 2005 are computed using the climatological mean monthly pCO2 data adjusted to a reference year 2005 and the alkalinity estimated from the potential alkalinity versus salinity relationships. The climatological monthly mean values of pCO2 over the global ocean are compiled for a 4° x 5° grid for the reference year 2005, and the gridded data for each of 12 months are included in this database. This is updated version of Takahashi et al. (2009) for the reference year 2000 representing non-El Niño years using a database of about 6.5 million pCO2 data (less coastal areas of North and South America) observed in 1957-2012 (Takahashi et al., 2013). The equatorial zone (4°N-4°S) of the Pacific is excluded from the analysis because of the large interannual changes associated with the El Niño-Southern Oscillation events. The pH thus calculated ranges from 7.9 to 8.2. Lower values are located in the upwelling regions in the tropical Pacific and in the Arabian and Bering Seas; and higher values are found in the subpolar and polar waters during the spring-summer months of intense photosynthetic production. The vast areas of subtropical oceans have seasonally varying pH values ranging from 8.05 during warmer months to 8.15 during colder months. The warm tropical and subtropical waters are supersaturated by a factor of as much as 4.2 with respect to aragonite and 6.3 for calcite, whereas the cold subpolar and polar waters are less supersaturated only by 1.2 for aragonite and 2 for calcite because of the lower pH values resulting from greater TCO2 concentrations. In the western Arctic Ocean, aragonite undersaturation is observed.
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Surface measurements of partial pressure of CO2 (pCO2), pH on total scale, water temperature, salinity, and other variables in the Casco Bay, Gulf of Maine from 2015-04-23 to 2020-06-12 (NCEI Accession 0229832)
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This dataset consists of surface measurements of partial pressure of CO2 (pCO2), pH on total scale, water temperature, salinity, and other variables in the Casco Bay, Gulf of Maine from 2015-04-23 to 2020-06-12. The central goal of this project was to install continuous monitoring sensors in Casco Bay to measure ocean and coastal acidification parameters at high temporal resolution. The system was installed at the Southern Maine Community College (SMCC) pier in South Portland, Maine. The pier is located in the Portland Channel, an important southern outlet of Casco Bay, and near outlets of the Fore and Presumpscot rivers in a relatively urban area of Casco Bay. This location was selected because it is nearshore, accessible, and has historic nutrient data collected by the Friends of Casco Bay. Sensors for temperature/conductivity/salinity, dissolved oxygen, pH and pCO2 were mounted to a ‘lander’ frame, which was lowered via davit to the bottom. Sensor data were thus collected approximately 1m from the bottom, and at varying depth depending on the tide.
Climatological distributions of sea-air DeltafCO2 and CO2 flux densities in the Global Surface Ocean (NCEI Accession 0282251)
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The late Taro Takahashi (LDEO/Columbia University) provided the first near-global monthly air-sea CO2 flux climatology in Takahashi et al. (1997), based on available surface water partial pressure of CO2 measurements. This product has been a benchmark for uptake of CO2 in the ocean. Several versions have been provided since, with improvements in procedures and large increases in observations, culminating in the authoritative assessment in Takahashi et al. (2009). Here we provide and document the last iteration using a greatly increased dataset (SOCATv2022) and determining fluxes using air-sea partial pressure differences as a climatological reference for the period 1980-2021. The resulting net flux for the open ocean region is estimated as -1.79 PgC yr-1 which compares well with other global mean flux estimates. While global flux results are consistent, differences in regional means and seasonal amplitudes are discussed. Consistent with other studies, we find the largest differences in the data-sparse southeast Pacific and Southern Ocean.
Continuous CO2 system data from the Carlsbad Aquafarm, Agua Hedionda Lagoon, Carlsbad, California from 2017-12-06 to 2018-12-06 (NCEI Accession 0284141)
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This dataset contains continuous pCO2, TCO2, pH, temperature and salinity data collected at a shore station instrument located at the Carlsbad Aquafarm in the Agua Hedionda Lagoon during from 2017-12-06 to 2018-12-06.
Carbon-14 Measurements in Surface Water CO2 from the Atlantic, Indian and Pacific Oceans from 1965-01-01 to 1994-12-31 (NCEI Accession 0157055)
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This dataset includes Carbon-14 Measurements in Surface Water CO2 from the Arctic Ocean, Barents Sea, Bay of Biscay, Indian Ocean, Ionian Sea, Mediterranean Sea, Atlantic Ocean, Pacific Ocean, Norwegian Sea, Southern Oceans, and Tasman Sea from 1965-01-01 to 1994-12-31. These data include DELTA CARBON-13, DELTA CARBON-14, SALINITY, SEA SURFACE TEMPERATURE and SIGMA-T. The instruments used to collect these data include not applicable. These data were collected by Reidar Nydal of Norwegian University of Science and Technology as part of the Carbon 14: Surface Measurements dataset.
Carbon dioxide from surface underway survey in global oceans from 1968 to 2006 (Version 1.0) (NCEI Accession 0040205)
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More than 3 million measurements of surface water partial pressure of CO2 obtained over the global oceans during 1968 to 2006 are listed in the Lamont-Doherty Earth Observatory database, which includes open ocean and coastal water measurements. The data assembled include only those measured by equilibrator CO2 analyzer systems and have been quality-controlled based on the stability of the system performance, the reliability of calibrations for CO2 analysis, and the internal consistency of data. Versions up to 2007 are included in this dataset
Revised estimates of ocean-atmosphere CO2 flux accounting for near-surface temperature and salinity deviations from 1985-01-01 to 2019-12-31 (NCEI Accession 0301544)
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The ocean is a sink for ~25% of the atmospheric CO2 emitted by human activities, an amount in excess of 2 petagrams of carbon per year (PgC yr−1). Time-resolved estimates of global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget. However, previous estimates of this flux, derived from surface ocean CO2 concentrations, have not corrected the data for temperature gradients between the surface and sampling at a few meters depth, or for the effect of the cool ocean surface skin. Here we calculate a time history of ocean-atmosphere CO2 fluxes from 1992 to 2018, corrected for these effects. These increase the calculated net flux into the oceans significantly.
A novel sea surface partial pressure of carbon dioxide (pCO2) data product for the global coastal ocean resolving trends over the 1982-2020 period (NCEI Accession 0279118)
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This dataset contains continuous monthly maps of sea surface partial pressure of CO2 (pCO2) in the coastal ocean from 1982 to 2020. This product is an updated version of the coastal product of Laruelle et al. (2017) and has been created using a 2-step Self Organizing Maps (SOM) and Feed Forward Network (FFN) method and uses ~ 18 million direct observations from the latest release of the Surface Ocean CO2 database (SOCATv2022, Bakker et al., 2014, 2022). In a first step, the global coastal ocean is divided into 10 biogeochemical provinces using SOM, which group regions with similar environmental properties. Then, for each province, the FFN algorithm reconstructs nonlinear relationships between a set of environmental variables (e.g., sea surface temperature, salinity...) and the observed pCO2. These relationships are then used to perform the spatiotemporal pCO2 extrapolation in regions and time periods where data are lacking. The output consists of continuous monthly pCO2 maps for the coastal ocean, with a spatial resolution of 0.25°, covering the 1982-2020 period. Additionally, this new coastal pCO2 product is used to generate a new coastal air-sea CO2 exchange (FCO2) product for each grid cell at the monthly time scale from 1982 to 2020 using the following equation: FCO2=k∙K0∙∆pCO2∙(1-ice) where FCO2 represents the coastal air-sea CO2 exchange (in mol C m-2 yr-1). By convention a positive FCO2 value corresponds to a CO2 source for the atmosphere. ∆pCO2 represents the difference between the oceanic pCO2 and the atmospheric pCO2 (in atm). K0 (mol C m-3 atm-1) represents the CO2 solubility in sea water which is a function of SST and SSS following the equation of Weiss et al. (1974). k represents the gas exchange transfer velocity (m yr-1) which is a function of the second moment of the wind speed and is calculated using the equation of Ho et al. (2011) and the Schmidt number based on the equation of Wanninkhof et al. (2014). The sea-ice coverage is represented by the term ice and has no units.
Global surface-ocean partial pressure of carbon dioxide (pCO2) estimates from a machine learning ensemble: CSIR-ML6 v2019a (NCEI Accession 0206205)
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This dataset contains surface-ocean partial pressure of carbon dioxide (pCO2) that the ensemble mean of six two-step clustering-regression machine learning methods. The ensemble is a combination of two clustering approaches and three regression methods. For the clustering approaches, we use K-means clustering (21 clusters) and open ocean CO2 biomes as defined by Fay and McKinley (2014). Three machine learning regression methods are applied to each of these two clustering methods. These machine learning methods are feed-forward neural-network (FFN), support vector regression (SVR) and gradient boosted machine using decision trees (GBM). The final estimate of surface ocean pCO2 is the average of the six machine learning estimates resulting in a monthly by 1° ⨉ 1° resolution product that extends from the start of 1982 to the end of 2016. Sea-air fluxes (FCO2) calculated from pCO2 are also presented in the data. The discrete boundaries of the clustering approach result in semi-discrete discontinuities in pCO2 and fCO2 estimates. These are smoothed by applying a 3 ⨉ 3 ⨉ 3 convolution (moving average) to the dataset in time, latitude and longitude.