Approximately 14.2 million measurements of surface water pCO2 made over the global oceans during 1957-2019 have been processed to make a uniform data file in this Version 2019. Measurements made in open oceans as well as in coastal waters are included. The data assembled include only those measured using equilibrator-CO2 analyzer systems, and have been quality-controlled based upon the stability of the system performance, the reliability of calibrations for CO2 analysis and the internal consistency of data. We have added 567,632 data points comprised of 158 leg/cruise segments in this version. All of these were collected on the 4 ships in our current field program. These 4 ships operate primarily in high latitudes in both hemispheres and have built decades long records in these areas. R/V Nathaniel B. Palmer’s system has been operating since 1995, R/V Laurence M. Gould’s system since 2001, USCGC Healy since 2011, R/V M. Langseth since 2010 (terminated in 2018), and R/V Sikuliaq since 2015. Our contribution to this database through many years of 3.31 million records is primarily for the polar and sub-polar seas. These underway data have been quality controlled and corrected for the time lag and temperature differences between the water intake and pCO2 measurements. In order to allow re-examination of the data in the future, a number of measured parameters relevant to pCO2 in seawater are listed. The overall uncertainty for the pCO2 values listed is estimated to be ± 2.5 uatm on the average. The names and institutional affiliations of the contributors are listed in Table 1. The documentation for the previous versions (V1.0, V2007, V2008, V2009, V2010, V2011, V2012, V2013, V2014, V2015, V2016, V2017, and V2018) of our database are available at NCEI via Ocean Carbon data System (OCADS) LDEO Database web page. The global pCO2 dataset is available free of charge as a numeric data package (NDP) from the OCADS: https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/LDEO_Underway_Database/. The NDP consists of the oceanographic data files and this printed documentation, which describes the procedures and methods used to obtain the data.

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.

This dataset includes Surface underway, chemical and physical data collected from ARMORIQUE in the English Channel from 2012-04-25 to 2013-01-03. These data include DISSOLVED INORGANIC CARBON (DIC), Partial pressure (or fugacity) of carbon dioxide - water, SALINITY and TOTAL ALKALINITY (TA). The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by Yann Bozec of Centre National de la Recherche Scientifique (CNRS) and Pierre Marrec of Roscoff Biological Station as part of the Coastal_FerryBox_Roscoff_Plymouth_35AQ20120425 dataset. CDIAC associated the following cruise ID(s) with this dataset: 35AQ20120425

This dataset includes chemical, physical and surface underway data collected from ARMORIQUE in the English Channel from 2014-03-18 to 2014-10-09. These data include DISSOLVED INORGANIC CARBON (DIC), Partial pressure (or fugacity) of carbon dioxide - water, SALINITY and TOTAL ALKALINITY (TA). The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by Yann Bozec of Centre National de la Recherche Scientifique (CNRS) and Pierre Marrec of Roscoff Biological Station as part of the Coastal_FerryBox_Roscoff_Plymouth_35AQ20140318 dataset. CDIAC associated the following cruise ID(s) with this dataset: 35AQ20140318

This dataset includes Surface underway, chemical and physical data collected from ARMORIQUE in the English Channel from 2013-03-15 to 2013-12-22. These data include DISSOLVED INORGANIC CARBON (DIC), Partial pressure (or fugacity) of carbon dioxide - water, SALINITY and TOTAL ALKALINITY (TA). The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by Yann Bozec of Centre National de la Recherche Scientifique (CNRS) and Pierre Marrec of Roscoff Biological Station as part of the Coastal_FerryBox_Roscoff_Plymouth_35AQ20130315 dataset. CDIAC associated the following cruise ID(s) with this dataset: 35AQ20130315

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.