This dataset contains monthly gridded pCO2 and air-sea CO2 fluxes from 1993-2018. The pCO2 data are gap-filled from the SOCAT database using the method of Landschützer (2013), but using additional winter pCO2 'pseudo observations' constructed using the method of Mackay and Watson (2021). The air-sea fluxes are constructed from the pCO2 product using a gas transfer parameterisation.

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.

ANN-NEPc is a gridded surface ocean pCO2 data product for the Northeast Pacific coastal ocean. It was created using non-linear functional relationships between pCO2 observations from the Surface Ocean CO2 Atlas v2021 as well as additional data from a Fisheries and Oceans Canada February 2019 Line P cruise, a West Coast Ocean Acidification cruise from July and August 2010, and La Perouse cruises from May 2007 and May 2010, and a variety of independent predictor variables (see supplemental information) using an artificial neural network self-organizing-map-feed-forward-network approach described and evaluated in Duke et al. (2024). This file contains monthly pCO2 and air-sea CO2 flux fields from January 1998 to December 2019 at 1/12 degree by 1/12 degree (approximately 9 km by 5km; latitude by longitude) spatial resolution within typically < 6 to 300 km of shore. The air-sea CO2 fluxes are computed from the air-sea CO2 partial pressure difference and a bulk gas transfer formulation following Duke et al. (2024).

This dataset contains pCO2 data measured during the period from 1981 to 1989. Each pCO2 data file is tab-separated text file which contains No., year, date, time (UTC) of measurements, position of measurements (latitude, N/S, longitude, E/W), atmospheric pressure (hPa), sea surface temperature(SST), SST flag, xCO2 (CO2 mole fraction in dry air, xCO2(a)) in the ambient air, xCO2(a) flag, xCO2 in the dry air equilibrated with surface seawater (xCO2(s)), xCO2(s) flag, and pH2O.

This dataset includes Surface underway, chemical, meteorological and physical data collected from JOHN P. TULLY, PARIZEAU, QUADRA and VANCOUVER in the Arctic Ocean, Beaufort Sea, Bering Sea, Coastal Waters of Southeast Alaska and British Columbia, Gulf of Alaska, Japan Sea, North Pacific Ocean, Olympic Coast National Marine Sanctuary and Sea of Okhotsk from 1973-08-12 to 2003-09-13. These data include ABSOLUTE HUMIDITY, AIR TEMPERATURE - DRY BULB, AIR TEMPERATURE - WET BULB, BAROMETRIC PRESSURE, Partial pressure (or fugacity) of carbon dioxide - atmosphere, Partial pressure (or fugacity) of carbon dioxide - water, SALINITY and SEA SURFACE TEMPERATURE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by C. S. Wong and Sophia C. Johannessen of Fisheries and Oceans Canada; Institute of Ocean Sciences as part of the Station P, Line P dataset. CDIAC associated the following cruise ID(s) with this dataset: Line P and Station P

This dataset includes Simulation and assimilation of global ocean pCO2 and air-sea CO2 fluxes using ship observations of surface ocean pCO2 in a simplified biogeochemical offline model from 1996-01-01 to 2004-12-31. These data were calculated by Vinu Valsala and Shamil Maksyutov of National Institute for Environmental Studies as part of the Gobal ocean pCO2 and air-sea CO2 fluxes data set. CDIAC associated the following cruise ID(s) with this data set: Gobal ocean pCO2 and air-sea CO2 fluxes.

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 the partial pressure of carbon dioxide (pCO2) climatology that was created by merging 2 published and publicly available pCO2 datasets covering the open ocean (Landschützer et. al 2016) and the coastal ocean (Laruelle et. al 2017). Both fields were initially created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT open and coastal ocean datasets (Bakker et. al 2016) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting driving variables, e.g., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships (see Landschützer et. al 2016 and Laruelle et. al 2017 for more detail). This results in monthly open ocean pCO2 fields at 1°x1° resolution and coastal ocean pCO2 fields at 0.25°x0.25° resolution. To merge the products, we divided each 1°x1° open ocean bin into 16 equal 0.25°x0.25° bins without any interpolation. The common overlap area of the products has been merged by scaling the respective products by their mismatch compared to observations from the SOCAT datasets (see Landschützer et. al 2020).