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.

The dataset contains two data products: sea-surface pCO2 and the air-sea CO2 flux for the Bay of Bengal region (Longitude: 76E-100E, Latitude: 5N-24N). This is climatological data, with 12 months of the climatological year. Each of these data product has a spatial resolution of 1/12°. The positive value of CO2 flux indicates the outgassing of CO2, and the negative value shows the uptake of atmospheric CO2.

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 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 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.

As part of a multi-year effort to quantify the flux of CO2 between the ocean and atmosphere, the Ocean Climate Observation Program of the National Oceanic and Atmospheric Administration (NOAA) supports the deployment of underway CO2 systems on NOAA research ships and volunteer observing ships (VOS) in the Atlantic and Pacific Oceans. The CO2 group of NOAA's Pacific Marine Environmental Laboratory (PMEL) has been monitoring sea surface CO2 concentrations in the equatorial Pacific since 1982. This is a particularly dynamic area exhibiting significant variation of CO2 concentrations, both interannually due to the effect of periodic El Niño events, and seasonally due to the changes in wind strength and upwelling patterns. By measuring the pCO2 in both the sea surface and atmosphere, along with sea surface temperature, pressure, and wind speed, the flux of CO2 can be calculated, affording a broader understanding of key processes and changes in these processes in the ocean on decadal time scales. A strong upwelled-driven inverse pCO2-SST relationship exists across the equatorial Pacific, allowing the development of an empirical approach to obtain highly resolved pCO2 distributions and CO2 fluxes. This data package represents pCO2 gathered by PMEL onboard VOS container ships from 2004 through 2018. The cruises during the 15-year period included 63 transects of the Pacific Ocean between the North Pacific West Coast and New Zealand/Australia.

As part of a multi-year effort to quantify the flux of CO2 between the ocean and atmosphere, the Ocean Climate Observation Program of the National Oceanic and Atmospheric Administration (NOAA) supports the deployment of underway CO2 systems on NOAA research ships and volunteer observing ships (VOS) in the Atlantic and Pacific Oceans. The CO2 group of NOAA's Pacific Marine Environmental Laboratory (PMEL) has been monitoring sea surface CO2 concentrations in the equatorial Pacific since 1982. This is a particularly dynamic area exhibiting significant variation of CO2 concentrations, both interannually due to the effect of periodic El Niño events, and seasonally due to the changes in wind strength and upwelling patterns. By measuring the pCO2 in both the sea surface and atmosphere, along with sea surface temperature, pressure, and wind speed, the flux of CO2 can be calculated, affording a broader understanding of key processes and changes in these processes in the ocean on decadal time scales. A strong upwelled-driven inverse pCO2-SST relationship exists across the equatorial Pacific, allowing the development of an empirical approach to obtain highly resolved pCO2 distributions and CO2 fluxes. This data package represents pCO2 gathered by PMEL onboard the NOAA Ka'imimoana from 1996 through 2010 as a companion project to the biannual servicing of the Tropical Atmosphere Ocean (TAO) Array moorings. The cruises during the 15-year period included 77 transects of the equatorial Pacific between 95°W and 165°E, and spanned several El Niño events.

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 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 chemical, meteorological, physical and time series data collected from Mooring CRIMP1_158W_21N in the North Pacific Ocean from 2005-12-01 to 2008-05-30. These data include air-sea difference - partial pressure of carbon dioxide (pCO2), barometric pressure, oxygen, pCO2 - atmosphere, pCO2 - water, pH on the total scale, sea surface salinity and sea surface temperature. The Moored Autonomous pCO2 (MAPCO2®) instruments used to collect these data include Bubble type equilibrator for autonomous carbon dioxide (CO2) measurement, Carbon dioxide (CO2) gas analyzer, Humidity Sensor, and oxygen meter. The Global CO2 Time-series and Moorings Project involves international groups from 18 countries who have mounted sensors on moored buoys to provide high resolution time-series measurements of atmospheric boundary layer and surface ocean CO2 partial pressure (pCO2). The CO2 Time-series and Moorings Project is coordinated by UNESCO International Ocean Carbon Coordination Project (IOCCP).