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.

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 contains observation-based pCO2 data and a derived monthly climatology. The observation-based pCO2 fields were created using a 2-step neural network method extensively described and validated in Landschützer et al. 2013, 2014, 2016. The method first clusters the global ocean into biogeochemical provinces and in a second step reconstructs the non-linear relationship between CO2 driver variables and observations from the v2022 release of the Surface Ocean CO2 Atlas (SOCAT, Bakker et al. 2016). This file contains the resulting monthly pCO2 fields at 1°x1° resolution covering the global ocean for the first time including the Arctic Ocean and few marginal seas (see Landschützer et al 2020). The air-sea CO2 fluxes are computed from the air-sea CO2 partial pressure difference and a bulk gas transfer formulation following Landschützer et al. 2013, 2014, 2016. Furthermore, the monthly climatology is created from the monthly average of the period 1985-present.

This dataset includes Surface underway, chemical, meteorological and physical data collected from PRINCE OF SEAS in the Caribbean Sea, English Channel, North Atlantic Ocean and North Sea from 1994-06-03 to 1995-08-04. These data include BAROMETRIC PRESSURE, Partial pressure (or fugacity) of carbon dioxide - atmosphere, Partial pressure (or fugacity) of carbon dioxide - water and SEA SURFACE TEMPERATURE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by Andrew J. Watson of Plymouth Marine Laboratory as part of the Prince of Seas (UK to Jamaica) dataset. CDIAC associated the following cruise ID(s) with this dataset: Prince of Seas (UK to Jamaica)

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.

This dataset includes surface underway data collected from R/V Ryofu Maru III cruises in the Pacific Ocean from 2020-01-10 to 2020-11-03. These data include partial pressure of carbon dioxide in the water, barometric pressure, sea surface salinity and sea surface temperature. The instruments used to collect these data include carbon dioxide (CO2) gas analyzer, shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement.

This dataset consists of the GLODAPv2.2023 data product composed of data from 1108 scientific cruises covering the global ocean between 1972 and 2021. It includes full depth discrete bottle measurements of salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon (TCO2), total alkalinity (TAlk), CO2 fugacity (fCO2), pH, chlorofluorocarbons (CFC-11, CFC-12, CFC-113, and CCl4), SF6, and various isotopes and organic compounds. It was created by appending data from 23 cruises to GLODAPv2.2022 (Lauvset et al., 2022, NCEI Accession 0257247). The data for salinity, oxygen, nitrate, silicate, phosphate, TCO2, TAlk, pH, CFC-11, CFC-12, CFC-113, CCl4, and SF6 were subjected to primary and secondary quality control. Severe biases in these data have been corrected for, and outliers removed. However, differences in data related to any known or likely time trends or variations have not been corrected for. These data are believed to be accurate to 0.005 in salinity, 1% in oxygen, 2% in nitrate, 2% in silicate, 2% in phosphate, 4 µmol kg-1 in TCO2, 4 µmol kg-1 in TAlk, and for the halogenated transient tracers and SF6: 5%.