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 includes Surface underway, chemical, meteorological and physical data collected in global oceans from 1957-10-22 to 2013-03-21. These data include BAROMETRIC PRESSURE, Partial pressure (or fugacity) of carbon dioxide - water, SALINITY, SEA SURFACE TEMPERATURE and WATER TEMPERATURE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer. These data were collected by Alex Kozyr of Carbon Dioxide Information Analysis Center (CDIAC) and Stewart C. Sutherland and Taro Takahashi of Lamont-Doherty Earth Observatory (LDEO) as part of the LDEO Database (Version 2012) dataset. CDIAC associated the following cruise ID with this dataset: LDEO20120101

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).

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)

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 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.