Earth Syst. Sci. Data, 18, 1405–1462, 2026
https://doi.org/10.5194/essd-18-1405-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Synthesis of data products for
ocean carbonate chemistry
Li-Qing Jiang1,2,59, Amanda Fay3, Jens Daniel Müller4,5, Luke Gregor4,6, Alizée Roobaert7,
Lydia Keppler8, Dustin Carroll9,10, Siv K. Lauvset11, Tim DeVries12, Judith Hauck13,14,
Christian Rödenbeck15, Nicolas Metzl16, Andrea J. Fassbender17, Jean-Pierre Gattuso18,19,
Peter Landschützer7, Rik Wanninkhof20, Christopher Sabine21, Simone R. Alin17, Mario Hoppema13,
Are Olsen22, Matthew P. Humphreys23, Kunal Chakraborty24, Ana C. Franco25,26,
Kumiko Azetsu-Scott27, Dorothee C. E. Bakker28, Leticia Barbero20,29, Nicholas R. Bates30,31,
Nicole Besemer20, Henry C. Bittig32, Albert E. Boyd20,29, Daniel Broullón33, Wei-Jun Cai34,
Brendan R. Carter35,17, Thi-Tuyet-Trang Chau36,37, Chen-Tung Arthur Chen38, Frédéric Cyr39,40,
John E. Dore41 , Ian Enochs20, Richard A. Feely17, Hernan E. Garcia2, Marion Gehlen36,
Prasanna Kanti Ghoshal24, Lucas Gloege3, Melchor González-Dávila42, Nicolas Gruber4,
Debby Ianson43, Yosuke Iida44, Masao Ishii45, Apurva Padamnabh Joshi24, Esther Kennedy46, Alex Kozyr1,2, Nico Lange11, Claire Lo Monaco16, Derek P. Manzello47, Galen A. McKinley3, Natalie M. Monacci48, Xose A. Padin33, Ana M. Palacio-Castro20,29, Fiz F. Pérez33, J. Magdalena Santana-Casiano42, Jonathan Sharp35,17, Adrienne Sutton17, Jim Swift49, Toste Tanhua50, Maciej Telszewski51 , Jens Terhaar52,53, Ruben van Hooidonk54, Anton Velo33, Andrew J. Watson55, Angelicque E. White21, Zelun Wu34, Liang Xue56, Hyelim Yoo1,2, Jiye Zeng57, and Guorong Zhong58
1Cooperative Institute for Satellite Earth System Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 20740, United States
2NOAA/NESDIS National Centers for Environmental Information,
Silver Spring, Maryland 20910, United States
3Columbia University and Lamont-Doherty Earth Observatory, Palisades, New York 10964, United States 4Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics,
ETH Zürich, Zurich, Switzerland
5Carbon to Sea Initiative, Washington, D.C., United States
6Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
7Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400 Ostend, Belgium
8Vycarb Inc., Brooklyn, New York, United States
9Moss Landing Marine Laboratories, San José State University, Moss Landing, California, United States 10Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, United States 11NORCE Research AS, Bjerknes Centre for Climate Research, Bergen, Norway 12Department of Geography and Earth Research Institute, University of California,
Santa Barbara, California, United States
13Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
14Department 02 Biology/Chemistry, University of Bremen, Bremen, Germany
15Max Planck Institute for Biogeochemistry, Jena, Germany
16Laboratoire LOCEAN/IPSL, Sorbonne Université – CNRS-IRD-MNHN, 75005 Paris, France 17NOAA/OAR Pacific Marine Environmental Laboratory, Seattle, Washington 98115, United States 18Sorbonne Université, CNRS, Laboratoire d’Océanographie de Villefranche,
06230 Villefranche-sur-Mer, France
19Institute for Sustainable Development and International Relations, Paris, France
20NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida 33149, United States
Published by Copernicus Publications.
21Department of Oceanography, University of Hawaii at Ma¯noa,
1000 Pope Rd., Honolulu, Hawaii 96822, United States
22Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway
23Department of Ocean Systems, NIOZ Royal Netherlands Institute for Sea Research, Texel, the Netherlands
24Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India
25Department of Earth, Ocean and Atmospheric Sciences,
University of British Columbia, Vancouver, BC, Canada
26Current affiliation: Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
27Fisheries and Oceans, Canada, Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada
28Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences,
University of East Anglia, Norwich, NR4 7TJ, United Kingdom
29Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine
and Atmospheric Science, University of Miami, Miami, Florida 33149, United States
30Bermuda Institute of Ocean Sciences, ASU-BIOS, 17 Biological Lane, St. Georges, GEO1, Bermuda
31School of Ocean Futures, Arizona State University, Tempe, Arizona 85281, United States
32Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Rostock, Germany
33Instituto de Investigacións Mariñas, IIM – CSIC, Vigo, Spain
34School of Marine Science and Policy, University of Delaware, Newark, Delaware 19716, United States
35Cooperative Institute for Climate, Ocean, and Ecosystem Studies,
University of Washington, Seattle, Washington, United States
36Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL, CEA-CNRS-UVSQ,
Université Paris-Saclay, 91191 Gif-sur-Yvette, France
37Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Centre de recherches INRIA,
Université Paris–Saclay, René Thom, 91190 Gif-sur-Yvette, France
38Department of Oceanography, National Sun Yat-Sen University, Kaohsiung, Taiwan
39Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada,
St. John’s, Newfoundland and Labrador, Canada
40Current affiliation: Centre for Fisheries Ecosystems Research, Fisheries and
Marine Institute of Memorial University of Newfoundland, St. John’s, Canada
41Department of Land Resources and Environmental Sciences,
Montana State University, Bozeman, Montana, United States
42Instituto de Oceanografía Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria,
35017 Las Palmas de Gran Canaria, Spain
43Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, British Columbia, Canada
44Atmosphere and Ocean Department, Japan Meteorological Agency,
3-6-9 Toranomon, Minato City, Tokyo, Japan
45Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
46Bodea Marine Laboratory, University of California Davis, Davis, California, United States
47Coral Reef Watch, US National Oceanic and Atmospheric Administration,
College Park, Maryland, United States
48College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, Alaska, United States
49Scripps Institution of Oceanography, La Jolla, California, United States
50GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany
51International Ocean Carbon Coordination Project, Institute of Oceanology of Polish Academy of Sciences,
Sopot, Poland
52Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
53Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
54Science for Climate Change Resilience, Boulder, Colorado 80302, United States
55Global Systems Institute, University of Exeter, Exeter, United Kingdom
56First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling,
Ministry of Natural Resources, Qingdao, China
57National Institute for Environmental Studies, Tsukuba, Japan
58Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
59Geosciences Research Division, Scripps Institution of Oceanography, University of California at San Diego,
La Jolla, CA 92037, United States
Correspondence: Li-Qing Jiang (liqing.jiang@noaa.gov)
Received: 1 May 2025 – Discussion started: 15 May 2025
Revised: 19 November 2025 – Accepted: 25 November 2025 – Published: 24 February 2026
Abstract. As the largest active carbon reservoir on Earth, the ocean is a cornerstone of the global carbon cycle, playing a pivotal role in modulating ocean health and the Earth’s climate system. Understanding these crucial roles requires access to a broad array of data products documenting the changing chemistry of the global ocean as a vast and interconnected system. This review article provides an overview of 68 existing ocean carbonate chemistry data products and data product sets, encompassing compilations of cruise datasets, derived gap-filled data products, model simulations, and compilations thereof. It is intended to help researchers identify and access data products that best align with their research objectives, thereby advancing our understanding of the ocean’s evolving carbonate chemistry. The list will be updated periodically to incorporate new data products. The most up-to-date list is available at https://oceanco2.github.io/co2-products/ (Gregor and Jiang, 2026).

1		Introduction
Since the onset of the Industrial Revolution in 1750, human activities, such as the burning of fossil fuels, cement production, and land-use change, have emitted ∼ 2600 Gt carbon dioxide (CO2) (1 Gt = 1015 g, 1 Gt CO2 = 0.273 Gt C, or Gt Carbon) into the atmosphere, causing the atmospheric CO2 levels to increase by ∼ 50 % (DeVries, 2022a; Friedlingstein et al., 2025; Tans and Keeling, 2026). The global carbon cycle, encompassing the exchange of CO2 among the atmosphere, oceans, terrestrial ecosystems, and geosphere, plays a critical role in regulating atmospheric CO2 levels (Archer, 2010; DeVries, 2022a; Friedlingstein et al., 2025; Lee et al., 2026). As the largest dynamic CO2 reservoir, the ocean holds approximately 45 times the amount of carbon found in the atmosphere currently and actively exchanges it with the air above and sediments below. On timescales from decades to millennia, the ocean imposes a dominant control over atmospheric CO2 levels (Revelle and Suess, 1957; Broecker, 1982; Archer et al., 2009; DeVries, 2022a).
The ocean currently absorbs about a quarter of human- caused CO2 emissions (Sabine et al., 2004; Gruber et al., 2019a, 2023; Carroll et al., 2022; Crisp et al., 2022; Terhaar et al., 2022a; DeVries et al., 2023; Müller et al., 2023a; Schimel and Carroll, 2024; Terhaar, 2025). The chemistry of the ocean has been shifting as a result of anthropogenic CO2 increase in the ocean (Feely et al., 2023; Ma et al., 2023; Müller et al., 2023a; Fassbender et al., 2023; Keppler et al., 2023a; Jiang et al., 2023; Müller and Gruber, 2024a; Terhaar et al., 2020, 2021a, 2024a). Since the beginning of the Industrial Revolution, the total amount of dissolved inorganic carbon (DIC) in the layer from 0 to 200 m has risen from 1690 to 1730 Gt of Carbon, and from 35 400 to 35560GtC below 200 m (Sabine et al., 2004; Müller et al., 2023a). The seemingly small increase of 0.5 % results in a substantial drop of the oceans’ buffer capacity (DeVries, 2022a). Buffer capacity refers to the ocean’s ability to resist changes in pH, and thus also the partial pressure of CO2 (pCO2), when CO2 or any other acid or base is added or removed.
As anthropogenic CO2 enters seawater, it reacts with water to form carbonic acid. This is the first in a series of rapid acid-base reactions that release protons (H+) and decrease the availability of carbonate ions, which are building materials that many marine organisms, such as mollusks, crustaceans, and corals, use to construct their shells and skeletons (Gattuso and Hansson, 2011). This process, referred to as “ocean acidification (OA)”, has already decreased surface ocean pH by roughly 0.11 (∼ 30 % increase in acidity) since 1750 (Orr et al., 2005; Jiang et al., 2019a, 2023; Kwiatkowski et al., 2020; IPCC, 2023). In some parts of the subsurface ocean, the trends of some acidification variables, e.g., pH, and total hydrogen ion content ([H+]total), can be even greater due to the increasing sensitivity of [H+] to DIC changes at depth (Chen et al., 2017; Pérez et al., 2021; Fassbender et al., 2023; Müller and Gruber, 2024a). This ongoing acidification threatens critical ocean ecosystem services, including food security, fisheries, aquaculture, and the broader Blue Economy, for billions of people globally (Cooley and Doney, 2009; Pérez et al., 2018; Doney et al., 2020).
In some parts of the ocean, OA is driven not only by the uptake of carbon but also by other processes (Delaigue et al., 2024), for example via alkalinity changes driven by freshening of the Arctic Ocean (Terhaar et al., 2021a) or changes in the carbon and alkalinity export from the Pacific Ocean and Arctic rivers (Terhaar et al., 2019; Qi et al., 2017, 2022; Bertin et al., 2023). Local anthropogenic inputs through rivers or from air pollution also contribute to OA (e.g. Sarma et al., 2015; Sridevi and Sarma, 2021). Furthermore, eutrophication and hypoxia in coastal regions may exacerbate OA in oxygen-deficient bottom waters, as biologically produced CO2 weakens the natural buffering capacity of seawater (Cai et al., 2011). If anthropogenic CO2 emissions continue without mitigation, as per the shared socioeconomic pathway (SSP5-8.5) scenario, surface ocean pH could decrease by a further 0.3 to 0.4 by 2100, equivalent to a 100 %–150 % increase in acidity (Kwiatkowski et al., 2020; Jiang et al., 2023). If society, however, succeeds in reducing emissions, the future acidity level becomes highly uncertain as it sensitively depends on the transient response of the Earth system and the amount of reductions of non-CO2 radiative agents (Terhaar et al., 2023).
In summary, monitoring ocean carbonate chemistry is essential for (a) tracking the evolving ocean carbon sink, and (b) understanding OA and its ecological impacts. Additionally, monitoring ocean carbonate chemistry is crucial when considering marine carbon dioxide removal (mCDR) strategies such as ocean alkalinity enhancement (OAE), artificial upwelling, ocean fertilization, and electrochemical ocean CO2 removal (Kheshgi, 1995; Bach et al., 2019; Schimel and Carroll, 2024; Oschlies et al., 2025). The ocean’s vast and interconnected nature necessitates that data from individual oceanographic cruises be meticulously preserved, subject to rigorous quality-control, and uniformly formatted to promote their usability (Brett et al., 2020; Schoderer et al., 2024). Following Lange et al. (2023), we curate an exhaustive catalogue of synthesis products pertaining to ocean carbonate chemistry, including cruise data compilations, gridded gap-filled data products, and other derived data products. This compilation spans both global and regional scales, providing a holistic view of the current state of ocean biogeochemistry data aggregation.
2		Methods
In this paper, data products are defined as outputs that quality-control, aggregate, and transform individual datasets from multiple sources into a unified, structured format to support research, decision-making, or operational needs for specific end users. The data products included in this study were identified through a literature review and discussions with researchers via the Ocean Acidification Information Exchange (OAIE) platform.
The products are organized into six categories based on end-user needs and listed within each class with no particular order (Fig. 1):
1.		Cruise data compilations (no interpolation or gapfilling).
2.		Time-series data products (no interpolation or gapfilling).
3.		Derived gap-filled (e.g., interpolated) products for the surface ocean, starting with products offering a climatological snapshot of the ocean, followed by those showing temporal changes.
4.		Derived gap-filled (e.g., interpolated) products for the interior ocean, also starting with products offering a climatological snapshot of the ocean, followed by those showing temporal changes.
5.		Multi-product analyses of 3 and 4. These compilations also include hindcast model simulations of the ocean carbon cycle and biogeochemistry.
6.		Model and hybrid data products projecting ocean carbonate system variables into the future [Note: Here the term ‘model’ refers to ocean biogeochemical models (Fennel et al., 2022). If a statistical model or machine learning model is used for gap-filling, the product is not categorized as a model output product in this compilation.]
Each category includes numbered descriptions of each data product in that class, as well as a summary table of the data products with corresponding IDs so the user can easily jump to the associated product description. For each data product, the description is followed by its access links. Persistent identifiers (e.g., digital object identifiers, or DOIs) and links to all data products are also summarized in the table in Sect. 4 “Data availability”.
Although some data products, such as Surface Ocean CO2 Atlas (SOCAT) and Lamont-Doherty Earth Observatory (LDEO) surface pCO2 Database report only one ocean carbonate system variable, i.e., fugacity of carbon dioxide (f CO2) or pCO2, they provide a foundation from which additional variables can be derived using empirical algorithms. For instance, total alkalinity content (TA) can be estimated from salinity and temperature and other factors (Lee et al., 2006) and by neural network approaches such as those developed by Velo et al. (2013) and Broullón et al. (2019). Beyond TA, neural network algorithms have been extended to estimate DIC as demonstrated by Broullón et al. (2020a), and even the full marine carbonate system (MCS) through frameworks like CANYON-B/CONTENT (Bittig et al., 2018) and Empirical Seawater Property Estimation Routines (ESPERs) (Carter et al., 2021). While these methods primarily employ neural networks, both Velo et al. (2013) and Carter et al. (2021) provide alternative estimation approaches based on local interpolation, through their 3dimensional moving window multilinear regression algorithm (3DwMLR) and locally interpolated regression (LIR) methods, respectively. Utilizing such derived data, the complete suite of ocean carbonate system variables can then be calculated using computer software, such as CO2SYS (Lewis and Wallace, 1998; Orr et al., 2018; Sharp et al., 2023) or its Python implementation PyCO2SYS (Humphreys et al., 2022). An in-depth explanation of the methods employed for these calculations can be found in the Supplement of Jiang et al. (2022a).

Figure 1. An overview diagram outlining the paper’s structure and flow.

3		Results and discussion
3.1		Data products for ocean carbonate chemistry
3.1.1		Cruise data compilations (no interpolation or gap-filling)
The data compilations described in this section standardize datasets collected from individual research vessels, ships of opportunity, and uncrewed platforms, presenting them in a uniform format for easy access (Table 1). These datasets typically undergo both primary QC (identifying outliers and obvious errors within an individual cruise dataset) and secondary QC (when possible, to objectively compare data from one cruise against another or a previously synthesized dataset to quantify systematic differences in reported values). It is important to note that data providers are expected to carry out rigorous QC prior to data submission.
1.		SOCAT: the Surface Ocean CO2 Atlas features surface f CO2 measurements from both the open ocean and the coastal ocean, predominantly sourced from research vessels, ships of opportunity, and autonomous platforms including fixed moorings and uncrewed surface vehicles (USVs) (Bakker et al., 2016). It represents the most extensive collection of observational ocean CO2 data for the global surface ocean. Since 2013, SOCAT has been updated annually. Dataset flags indicate the estimated uncertainty and completeness of metadata in SO- CAT synthesis products. The SOCAT gridded product (monthly 1° × 1°) contains f CO2 values with an estimated uncertainty of less than 5 µatm. To access the latest version of the SOCAT data product (with 40 million data points), visit https://socat.info/ (last access: 7 January 2026) (Bakker et al., 2025).
2.		LDEO Surface pCO2 Database: Dr. Taro Takahashi at LDEO in Palisades, New York started synthesizing global surface ocean CO2 data in 1997, compiling three decades of observations (∼ 250 000 measurements) to create inaugural monthly global surface pCO2 maps (Takahashi et al., 1997, 2002). The most recent version (V2019) expanded this dataset to approximately 14.2 million surface water pCO2 measurements spanning 1957–2019. Distinct from the SOCAT database, the LDEO database reports pCO2 instead of f CO2, exclusively from equilibrator-CO2 analyzer systems, with an average estimated uncertainty of ±2.5 µatm. The database is also interpolated onto a global surface ocean 4° × 5° grid for a reference year 2000 (Takahashi et al., 2009) and 2010 (Fay et al., 2024a). Access to the LDEO Surface pCO2 database (Version 2019) is provided by the Ocean Carbon and Acidification Data System (OCADS, Jiang et al., 2023a) at https://www.ncei.noaa.gov/ data/oceans/ncei/ocads/metadata/0160492.html (last access: 7 January 2026) (Takahashi et al., 2017). Additionally, a dedicated webpage for the LDEO Database is available at https://www.ncei.noaa.gov/ access/ocean-carbon-acidification-data-system/oceans/ LDEO_Underway_Database/ (last access: 7 January 2026).
3.		GLODAPv2: the Global Ocean Data Analysis Project Version 2 (GLODAPv2) aggregates biogeochemical data collected from discrete bottle samples, offering extensive global coverage from the surface to depth (Key et al., 2015; Olsen et al., 2016; Lauvset et al., 2024). While GLODAP is primarily a product for basin-scale hydrographic data, it also includes coastal datasets and observations from a few time-series. The GLODAPv2 data product provides rigorously quality- controlled measurements for 14 essential oceanographic variables: temperature, salinity, dissolved oxygen (DO), nitrate, silicate, phosphate, DIC, TA, pH, chlorofluorocarbons (CFC-11, CFC-12, CFC-113), carbon tetrachloride (CCl4), and sulfur hexafluoride (SF6). These variables, excluding temperature, undergo both primary and secondary quality-control procedures to detect outliers and adjust for significant measurement biases. GLODAPv2 was first published in 2016 and was updated annually through a living data process in Earth System Science Data from 2019 through “v2023” which was published in 2024. For these updates, new data (including historical data not previously included in the data product) are quality-controlled and adjusted to the 2016 version (Olsen et al., 2019, 2020; Lauvset et al., 2021, 2022, 2024). Since the global repeat hydrography programs operate with decadal repetitions, the aim is to produce a completely new version of GLODAP, where all cruise datasets will be reevaluated, every decade. Release of the GLODAPv3 data product is planned for 2026, and is expected to evolve the secondary data quality-control practices relative to those used in GLO- DAPv2. For more information on the secondary qualitycontrol process, refer to Tanhua et al. (2010) and Lau- vset and Tanhua (2015). GLODAPv2 offers two kinds of products: the compilation of quality-controlled data from discrete bottle samples taken at sampling location (Key et al., 2015; Olsen et al., 2016, 2019, 2020; Lauvset et al., 2021, 2022, 2024), and a gridded product, interpolated to a 1° × 1° grid and the 33 standard depth levels of World Ocean Atlas (WOA) (Lauvset et al., 2016). All versions of the GLODAPv2 data product can be accessed at https://glodap.info/ (last access: 7 January 2026.
GLODAPv2 builds upon three foundational data products: the original GLODAP (Sabine et al., 2004), CARbon dioxide IN the Atlantic Ocean (CARINA, Key et al., 2010), and PACIFic ocean Interior Carbon (PACIFICA, Suzuki et al., 2013). These data products remain available at NCEI: https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0001644.html (last access: 7 January 2026) (GLODAP, Sabine et al., 2005), https://www.ncei.noaa.gov/data/oceans/ncei/ ocads/metadata/0113899.html (last access: 7 January 2026) (CARINA, Tanhua et al., 2013), and https://www.ncei.noaa.gov/data/oceans/ncei/ocads/ metadata/0110865.html (last access: 7 January 2026) (PACIFICA, Suzuki et al., 2013), respectively.
4.		Quality Edited Hydrographic Data: the Quality Edited Hydrographic Data product offers both a user-friendly application and a library of ocean profile data curated by Jim Swift (Scripps Institution of Oceanography, La Jolla, California, United States). Similar to GLO- DAPv2, this data product serves as a comprehensive repository of quality-controlled discrete bottle-based measurements (and limited CTD), spanning from the surface to the depths of the global ocean. Unlike GLO- DAPv2, this data product does not apply offset corrections. It encompasses a range of oceanographic variables including temperature, salinity, DO, DIC, TA, silicate, phosphate, nitrate, nitrite, CFC-11, CFC-12, and SF6. To access the application and data, visit: https: //joa.ucsd.edu/ (last access: 7 January 2026). Currently, there is not a peer-reviewed paper or public-accessible report for this data product. Cite the data product itself as: Swift and Osborne (2025).
5.		WOD: in addition to the GLODAPv2 (No. 3) and Quality Edited Hydrographic Data (No. 4), users can also access historical and recent original biogeochemical data collected from discrete bottle samples in a uniform format and units, along with their originator quality-control (QC) flags, through the World Ocean Database (WOD) (Mishonov et al., 2024). Like the Quality Edited Hydrographic Data, these measured data remain unaltered. The WOD allows users to filter and subset data by specific variables, platforms, institutions, projects, regions, or time periods (Garcia et al., 2024). Users can visualize sampling locations on a “distribution plot” and access a cruise list for all selected data and variables. Users also have the option of exporting data in NetCDF or Comma- Separated Value (CSV) formats. Additionally, all data in the WOD are reproducible and traceable to their original data sources archived at NOAA’s National Centers for Environmental Information (NCEI). The WOD is accessible at https://www.ncei.noaa.gov/products/ world-ocean-database (last access: 7 January 2026).
6.		SNAPO-CO2: Metzl et al. (2024) aggregated over 44 400 measurements of DIC and TA from a series of research cruises and ships of opportunity across various oceanic regions from 1993–2022, under several French research programs, to create a product called “Service National d’Analyse des Paramètres Océaniques du CO2 (SNAPO-CO2)”. The majority of the samples were analyzed by the Service National d’Analyse des Paramètres Océaniques du CO2 (SNAPO-CO2) at the LOCEAN laboratory in Paris, France. Sampling was performed either from CTD-rosette casts (Niskin bottles) or collected from the ship’s flow-through system (intake at roughly 5 m depth). DIC and TA determinations were conducted simultaneously through potentiometric titration in a closed-cell setup, calibrated with certified reference material to achieve an accuracy of ±4 µmol kg-1 for both variables, following Edmond (1970). This methodology was also applied for real-time measurements during OISO cruises, with data from the South Indian Ocean for 1998–2018 included in this compilation. The data are split into two sets: one for the global ocean and coastal zones, and another for the Mediterranean Sea, both accessible in the same format at https://doi.org/10.17882/95414 (Metzl et al., 2023). Additionally, this data product is available at OCADS: https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0285681.html (last access: 7 January 2026) (Metzl et al., 2023).
7.		CODAP-NA: Jiang et al. (2021) synthesized two decades of discrete measurements of carbonate system variables, DO, and nutrient data from the North American continental shelves to generate the first version of Coastal Ocean Data Analysis Data Product in North America (CODAP-NA). The 2021 release encompasses 3391 oceanographic profiles from 61 research cruises spanning the North American continental shelves from Alaska to Mexico in the west and from Canada to the Caribbean in the east. It includes 14 key variables, including temperature, salinity, DO, DIC, TA, pH, carbonate ion, f CO2, silicate, phosphate, nitrate, all of which have undergone rigorous quality-control. Note that certain datasets meeting the GLODAPv2 QC standards are also included in the GLODAPv2 since its 2022 release (No. 3 above). CODAP-NA is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0219960.html (last access: 7 January 2026) (Jiang et al., 2020).
8.		AZMP Carbon: Gibb et al. (2023) compiled ocean carbonate system variables data from the Canadian Atlantic Zone Monitoring Program (AZMP Carbon) since 2014. More than 100 seagoing missions are represented in this dataset. The sampling strategy generally corresponds to full-depth water samples mostly collected along standardized hydrographic sections. The majority of these data were collected as part of the Atlantic Zone Monitoring Program (AZMP) of Fisheries and Oceans Canada (DFO). Implemented in 1998, the AZMP aims to characterize and understand the causes of oceanic variability at the seasonal, interannual and decadal scales in support of, among other things, fisheries management in the Atlantic Zone (including the Gulf of St. Lawrence, the Scotian shelf and the Newfoundland and Labrador shelf). Since 2014, a minimum of two of the three following carbonate system variables, DIC, TA, and pH, are also acquired by the program at standardized hydrographic stations across the zone (sampled up to three times a year). Each measurement is completed with corresponding temperature, salinity and, when available, nutrients and DO concentration data. This dataset also includes samples collected as part of ships of opportunity, fishing and other scientific trips. The entire dataset comprises 19 531 discrete samples [last updated 21 August 2024]. Among this number, 18 085 have at least two of the three carbonate system variables (e.g., TA, DIC and pH), allowing the derivation of other variables such as the saturation state relative to aragonite and calcite (•arag and •calc) and pCO2 (in µatm). The full dataset of measured and derived variables is available from the Federated Research Data Repository: https://doi.org/10.20383/102.0673 (Cyr et al., 2022) and is updated annually.
9.		MOCHA: Kennedy et al. (2023) curated a comprehensive coastal ocean data product called “Multistressor Observations of Coastal Hypoxia and Acidification (MOCHA)”, encompassing temperature, salinity, DO, ocean carbonate system variables (DIC, TA, pH, pCO2, f CO2), nutrients, and chlorophyll measurements from the full water column along the US west coast. The synthesis integrates observations from 71 different sources, including high-resolution autonomous sensors, synoptic oceanographic cruises, and shoreline samples. The MOCHA synthesis spans from the shoreline to well beyond the continental shelf and incorporates observations from CODAP-NA (see No. 7 above), California Cooperative Oceanic Fisheries Investigations (CalCOFI), and other large-scale oceanographic cruises to facilitate linking nearshore, high-resolution observations to broader oceanographic conditions. As of 2025, MOCHA includes 15.9 million temperature readings, 5.0 million salinity measurements, 3.9 million DO records, and 2.3 million pH measurements, along with 8368 DIC, 10 144 TA, and 505 000 pCO2/f CO2 measurements, with limited additional chlorophyll and nutrient observations. To reduce the computational load from high-resolution sensors, the synthesis is also available as a “daily aggregated” dataset, with all data sources averaged by day, location, and depth. All data in the MOCHA synthesis product has been quality- controlled to a “plausible and reasonable” standard, but researchers requiring high-precision coastal data may need to apply additional QC tests. The data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0277984.html (last access: 7 January 2026) (Kennedy et al., 2023), while the methods and the product are described in Kennedy et al. (2024).
10.		ARIOS: the Acidification in the Rias and the Iberian Continental Shelf (ARIOS) project involved compiling and analyzing the historical record of ocean carbonate system measurements and associated variables conducted by the Instituto de Investigacións Mar- iñas (IIM-CSIC) in Vigo, Spain. This dataset comprises 3343 oceanographic stations and 17 653 discrete samples, combining measurements of pH, TA, and other physical (pressure, temperature, and salinity) and biogeochemical variables (DO, nitrate, phosphate, and silicate) off the northwestern Iberian Peninsula from June 1976 to September 2018 (Padin et al., 2020). The oceanography cruises funded by 24 projects were primarily carried out in the Ría de Vigo coastal inlet, but also in an area ranging from the Bay of Biscay to the Portuguese coast. Robust seasonal cycles and longterm trends were calculated along a longitudinal section, gathering data from the coastal and oceanic zones of the Iberian upwelling system. The data product is available at https://doi.org/10.20350/digitalCSIC/12498 (Pérez et al., 2020).
11.		Marine Inorganic Carbonate Chemistry in the Northern Gulf of Alaska: Monacci et al. (2023) compiled a data product of discrete seawater samples collected each May and September over a 10-year period from 2008 to 2017 along the long-term hydrographic line in the Gulf of Alaska (GAK Line). Samples were collected from a sampling rosette on a profiling CTD. Data variables include profiled seawater temperature, salinity, and DO. Discrete sample variables include DO (i.e., Winkler titrations), macronutrients (nitrate, nitrite, phosphate, silicic acid), DIC, and TA. This data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0277034.html (last access: 7 January 2026) (Monacci et al., 2023), and the synthesis paper can be accessed at https://doi.org/10.5194/essd-16- 647-2024 (Monacci et al., 2024).
12.		Coral Reef Carbonate Chemistry Off the Florida Keys: Palacio-Castro et al. (2023) compiled discrete seawater samples from 38 permanent stations located along 10 inshore-offshore transects at the Florida Coral Reef. These samples were collected as part of NOAA’s National Coral Reef Monitoring Program (NCRMP) and the South Florida Ecosystem Restoration Research (SFER) cruises. Sampling efforts commenced in 2010, with every two months collections initiated in 2015, resulting in a total of 47 sampling cruises and 1538 discrete seawater samples. For all samples, a minimum of two of the carbonate system variables (TA, DIC) were measured, in addition to salinity and temperature. The •arag, pCO2, and pH were derived from the measured variables using the R package seacarb (Gat- tuso et al., 2021a). The time-series analysis provides insight into the dynamic carbonate conditions spanning the inshore to offshore gradients, encompassing four distinct regions of the Florida Coral Reef: Biscayne Bay, the Upper Keys, Middle Keys, and Lower Keys. Data is available at https://www.ncei.noaa.gov/access/ metadata/landing-page/bin/iso?id=gov.noaa.nodc: NCRMP- CO3- Atlantic (last access: 7 January 2026) (Manzello et al., 2018).
13.		Salish Cruise Data Package and Multi-stressor Data Product: Alin et al. (2025a) compiled data from 61 individual cruise data sets that sampled marine waters of the southern Salish Sea and northern Washington coast (United States) from 2008 to 2024. Since 2014, ongoing seasonal sampling has occurred during April, July, and September for Puget Sound cruises and most frequently during May and October for Sound-to-Sea cruises, which sample from Puget Sound through the Strait of Juan de Fuca to the northern Washington coast. The Salish cruise data package contains observations from water column profiles, with CTD sensor measurements of temperature, salinity, and DO; as well as discrete measurements of DO, nutrients (nitrate, phosphate, silicate, ammonium, nitrite), DIC, and TA. A follow-on data product is also available, containing only samples with complete records for temperature, salinity, and DO from sensors, and DO, nutrients, DIC, and TA from discrete measurements, along with the most commonly used calculated carbonate system variables: pH (total scale), f CO2, pCO2, •arag, and •calc (Alin et al., 2025b). The data package is available at https://www.ncei.noaa. gov/access/ocean-carbon-acidification-data-system/ oceans/SalishCruise_DataPackage.html (last access: 7 January 2026). The multi-stressor data product is available at https://www.ncei.noaa.gov/access/ ocean-carbon-acidification-data-system/oceans/ SalishCruises_DataProduct.html (last access: 7 January 2026). Two synthesis papers describing the Salish cruises, as well as seasonality and extreme ocean acidification conditions observed during the 2008–2018 part of the time-series, can be found at https://essd.copernicus.org/articles/16/837/2024/ (last access: 7 January 2026) (Alin et al., 2024a) and https://bg.copernicus.org/articles/21/1639/2024/
(last access: 7 January 2026) (Alin et al., 2024b). A preliminary description of the 2019–2024 Salish cruises can be found at https://www.psp.wa.gov/psmarinewatersoverview.php (last access: 7 January 2026) (Alin et al., 2025c).
14.		Line P Marine Carbonate Chemistry Compilation: this dataset contains marine carbonate system measurements collected during 55 Line P cruises from 1990 to 2019 in the subarctic Northeast Pacific. The dataset contains discrete profiles of DIC, TA, seawater temperature, salinity, DO and nutrients. From a total of 27 hydrographic time-series stations, only the five major stations where DIC and TA are routinely sampled were included in this compilation. Among them is the outermost station P26, also known as Ocean Station Papa (Freeland, 2007). Cruises were conducted approximately three times per year, typically in February, May/June and August/September. Each vertical profile was individually inspected and contrasted with the whole pool of data (including historical data) relative to salinity, density, and oxygen to detect and flag poor quality data following the World Ocean Circulation Experiment (WOCE) quality-control convention (Jiang et al., 2022a). Additionally, the recommended cruise-specific adjustments from PACIFICA were applied (Suzuki et al., 2013). The Line P marine carbonate chemistry compilation is described and analyzed in (Franco et al., 2021a) and is publicly available as a single synthesis product at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0234342.html (last access: 7 January 2026) (Franco et al., 2021b). The Line P carbonate chemistry timeseries is maintained by Fisheries and Oceans Canada and continues to the present day. Data are available and continuously updated in the Line P repository, which can be publicly accessed after generating an account at https://waterproperties.ca (last access: 7 January 2026).
15.		Anthropogenic Carbon in the Arctic Ocean: this dataset includes anthropogenic carbon estimates in the Arctic Ocean based on measurements of transient tracers, such as CFC-12 and SF6 (Terhaar et al., 2020; Tanhua et al., 2009). Using the transient time distribution (TTD) method, anthropogenic carbon estimates were estimated at measurement locations across all basins of the Arctic Ocean between 1983 and 2005. In addition to these estimates, adjusted estimates of anthropogenic carbon at these locations are provided to account for differences in the saturation of transient tracers and anthropogenic carbon in Arctic Ocean surface waters that caused anthropogenic carbon estimates to be biased low (Terhaar et al., 2020). It is recommended to use the adjusted estimates. This dataset can be accessed at https://doi.org/10.17882/103920 (Terhaar et al., 2024a).
3.1.2		Time-series products (no interpolation or gap-filling)
The time-series products described in this section include observations collected at regular time intervals, over a sustained period, and at fixed locations (Table 2). The data often represent changes in a particular oceanographic variable over time, such as temperature, salinity, TA and DIC. The list below includes both climate-quality time-series data products compiled at selected stations, and data products compiling time-series measurements at multiple locations. Additionally, some hydrographic sections are measured frequently enough to constitute a time-series, e.g., Line P in the northeast Pacific (Franco et al., 2021b; Freeland, 2007), sections in the northwest Pacific (Ishii et al., 2011a), the Observatoire de la Variabilité Interannuelle à DÉcennale (OVIDE) lines (Mercier et al., 2024). Measurements from these sections are typically included in cruise data compilations (Sect. 3.1.1) and are not listed separately here.
16.		BATS: the Bermuda Atlantic Time-series Study (BATS) observations and data products extend over forty years of observations of DIC and TA and OA indicators, and constitute the longest continuous record of warming, salinification, ocean deoxygenation, and OA in the open ocean (Bates and Johnson, 2023). The sustained observations at the BATS site began in October 1988, approximately 80 km to the southeast of Bermuda (https://bios. asu.edu/bats, last access: 7 January 2026). The program comprises monthly cruises with CTD, water-column biogeochemical sampling and rate measurements (e.g., primary and export production) plus additional cruises in the spring period and annual transects between the Gulf Stream and Puerto Rico. CO2-carbonate chemistry sampling includes full-depth bottle DIC and TA data (including additional surface measurements going back to 1983 collected at the Hydrostation S site). Hydrostation S is located ∼ 25 km southeast of Bermuda (https: //bios.asu.edu/research/projects/hydrostation-s, last access: 7 January 2026) and began in 1954 with biweekly cruises each year. Underway f CO2/pCO2 data collected from the R/V Atlantic Explorer that supports the BATS and Hydrostation S sites constitutes part of the annual data submission to SOCAT. The BATS project page at the Biological and Chemical Oceanography Data Management Office (BCO-DMO) includes metadata and data streams (https://demo.bco-dmo.org/ project/2124, last access: 7 January 2026). Hydrostation S data and DOIs are also available at BCO-DMO (https://www.bco-dmo.org/project/859583, last access: 7 January 2026).
17.		HOT: the Hawaii Ocean Time-series (HOT) CO2 measurement program documents more than 35 years of inorganic carbon dynamics in the open waters of the central North Pacific. Since October 1988, full ocean depth profiles of DIC and TA have been analyzed, and direct measurements of pH have been made over most of this longest-running Pacific Ocean time-series study. The program is based on shipboard observations and experiments conducted on ∼ 10 expeditions per year to Station ALOHA (22.75° N, 158° W). HOT program background information and details of sampling
Table 1. Ocean carbonate chemistry data products out of cruise data compilations (no gridding or gap-filling).
No.	Name	Open ocean or coastal ocean	Surface or water column	Discrete bottle or continuous	Highlights	Reference	
1	SOCAT	Open ocean + Coastal ocean	Surface	Continuous	The largest collection of in situ surface ocean f CO2 measurements	Bakker et al. (2016)	
2	LDEO Surface pCO2 Database	Open ocean + Coastal ocean	Surface	Continuous	The LDEO database reports pCO2 exclusively from equilibrator-CO2 analyzer systems	Takahashi et al. (2017)	
3	GLODAPv2	Open ocean	Water column	Discrete bottle	Adjustments are applied by comparing data in the deep ocean (> 2000 m) using a crossover and inversion method as described by Johnson et al. (2001)	Lauvset et al. (2024)	
4	Quality Edited Hydrographic
Data	Open ocean	Water column	Discrete bottle	Similar to GLODAPv2, with no adjustments	Swift and
Osborne (2025)	
5	WOD	Open ocean	Water column	Discrete bottle	Similar to GLODAPv2, with no adjustments	Mishonov et al. (2024)	
6	SNAPO-CO2	Open ocean + Coastal ocean	Water column	Discrete bottle and semi- continuous	A compilation of cruises from multiple French initiatives	Metzl et al. (2024)	
7	CODAP-NA	Coastal ocean	Water column	Discrete bottle	Like GLODAPv2, but for the coastal ocean	Jiang et al. (2021)	
8	AZMP Carbon	Continental shelf and Slope	Water column	Discrete bottle	A compilation of cruises from the Atlantic Zone Monitoring Program (AZMP) since 2014	Gibb et al. (2023)	
9	MOCHA	Coastal ocean	Water column	Discrete bottle
+ Continuous	US West Coast	Kennedy et al. (2024)	
10	ARIOS	Coastal ocean	Water column	Discrete bottle	An OA Database for the Galician Upwelling Ecosystem off the NW Iberian Peninsula from 1976 to 2018	Padin et al. (2020)	
11	Marine
Inorganic
Carbonate
Chemistry in the Northern
Gulf of Alaska	Coastal ocean	Water column	Discrete bottle	A synthesis of twenty cruises from 2008 to 2017 on the Gulf of Alaska (GAK) Line	Monacci et al. (2023)	
12	Coral Reef Carbonate Chemistry Off the Florida Keys	Coastal/Regional	Water column	Discrete bottle	Temporal trends of DIC, TA, pCO2, pH, •arag, in different areas of the Florida Keys	Palacio-Castro et al. (2023)	
Table 1. Continued.
No.	Name	Open ocean or coastal ocean	Surface or water column	Discrete bottle or continuous	Highlights	Reference	
13	Salish Cruise Data Package and
Multi-stressor
Data Product	Coastal/Estuarine	Water column	Discrete bottle	A data compilation and multi-stressor (OA, hypoxia, temperature) data product based on cruises from 2008 to 2024 in the southern Salish Sea and Washington coast	Alin et al. (2025a, b)	
14	Line P Marine Carbonate Chemistry Compilation	Open ocean	Water column	Discrete bottle	A compilation of fifty-five Line P cruises containing discrete DIC and TA profiles at five stations in the Northeast Pacific Ocean. Sampled approximately three times per year from 1990 to 2019	Franco et al. (2021a, b)	
15	Anthropogenic carbon in the
Arctic Ocean	Open ocean	Water column	Discrete bottle (adjusted to 2005)	Observation-based estimates of anthropogenic carbon in the Arctic Ocean	Tanhua et al. (2009),
Terhaar et al. (2020)	

strategy may be found in Karl and Lukas (1996) and Karl et al. (2001). Results from the HOT CO2 measurement program can be found in Winn et al. (1994, 1998), Dore et al. (2003, 2009, 2014), and Knor et al. (2023, 2025). The HOT project page, metadata, data streams and data identifiers are listed at https: //www.bco-dmo.org/project/2101 (last access: 7 January 2026). A MAPCO2 system on the Woods Hole Oceanographic Institution Hawaii Ocean Time-series Site mooring (WHOTS; https://www.soest.hawaii.edu/ whots/, last access: 7 January 2026) has provided a near-continuous record of surface pCO2 since 2004, and is anchored by the longer high-accuracy HOT ship-based program (see Sutton et al., 2019 and Knor et al., 2023). A surface ocean data product that includes CO2SYS-calculated values of pCO2, carbonate mineral saturation states and other derived quantities may be found at https://hahana.soest.hawaii.edu/ hot/hotco2/hotco2.html (last access: 7 January 2026) and https://doi.org/10.5281/zenodo.15060931 (Dore et al., 2025).
18.		ESTOC: the European Station for Time-series in the Ocean (ESTOC) began carbon dioxide monitoring in October 1995, providing a 30-year record on DIC, TA, and pH. This dataset represents the longest continuous monthly record of warming, rising carbon dioxide levels, and acidification in the eastern North Atlantic (González-Dávila and Santana- Casiano, 2023a). ESTOC is located 100 km north of the Canary Islands archipelago (https://plocan.eu/ en/installations/ocean-observatory, last access: 7 January 2026). The program includes a ship-based observation system, measuring physical, chemical, and biological variables throughout the 3670 m water column. It also features a moored platform for surface meteorological and oceanic observations as well as subsurface measurements, maintained by the Canary Island Oceanic Platform (PLOCAN, https://plocan.eu/ en, last access: 7 January 2026) and the University of Las Palmas de Gran Canaria (https://iocag.ulpgc. es/research/research-units/quima, last access: 7 January 2026). Carbonate system measurements include full-depth bottle sampling for photometric pH, TA, and DIC, conducted monthly from 1995 to 2008, every two months until 2018, and semiannually in recent years due to limited ship time, timed to coincide with moored structure maintenance. ESTOC is also visited every two weeks by a volunteer observing ship, ES-SOOP-CanOA (https://meta.icos-cp. eu/resources/stations/OS_687B, last access: 7 January 2026), part of the European Research Infrastructure ICOS (https://www.icos-cp.eu/observations/ocean/ stations, last access: 7 January 2026), which provides real-time surface data on carbon dioxide fluxes and OA. The program also includes the CO2-ESTOC oceanographic buoy (https://meta.icos-cp.eu/labeling/, last access: 7 January 2026). The full dataset with DOIs is accessible on Pangaea (González-Dávila and Santana- Casiano, 2023a).
19.		Point B Time-series: the Point B Time-series documents the carbonate chemistry at a coastal site in the Bay of Villefranche (43.686200°N 7.314800° E) in Villefranche-sur-mer, France, northwestern Mediterranean Sea. Since January 2007, seawater is sampled weekly at 1 and 50 m, and analyzed for DIC and TA (Kapsenberg et al., 2017). Salinity and temperature are extracted from CTD profiles. Variables of the carbonate system such as pH (total scale) are calculated using the R package seacarb (Gat- tuso et al., 2021a). Data are available at Pangaea: https://doi.org/10.1594/PANGAEA.727120 (Gattuso et al., 2021b).
20.		Ny-Ålesund Time-series: the Ny-Ålesund Time-series documents the carbonate chemistry at a coastal site of Kongsfjorden, Spitsbergen (78.930660° N 11.920030° E) during the period 2015–2021. It is the first high-frequency (1 h), multi-year (6 years) dataset of salinity, temperature, pCO2, pH, as well as calculated DIC and TA in the High-Arctic Ocean (Gattuso et al., 2023a). Data are available at Pangaea: https://doi.org/10.1594/PANGAEA.957028 (Gattuso et al., 2023b).
21.		SPOTS: the Synthesis Product for Ocean TimeSeries (SPOTS) is a ship-based biogeochemical pilot, aiming at regularly providing high quality data from fixed time-series stations with consistent format and semantics (Lange et al., 2024a). The pilot includes data from 12 fixed ship-based time-series programs with a focus on the Global Ocean Observing System’s biogeochemical essential ocean variables. These stations represent unique marine environments across a variety of spatiotemporal resolutions and ranges, with data from 1983 to 2021. While implementing the FAIR principles (Wilkinson et al., 2016) and promoting open data, the metadata of the time-series stations were enhanced to interoperate with the IOC-UNESCO Ocean Data and Information System (ODIS). Additionally, an extensive quality assessment resulted in enhanced intra- and inter-station comparability. Data are available at https: //www.bco-dmo.org/dataset/896862 (last access: 7 January 2026) (Lange et al., 2024b).
22.		pCO2 and pH Time-series from 40 Surface Buoys: Sutton et al. (2019) established a living dataset comprising 40 individual autonomous moored surface ocean pCO2 time-series established between 2004 and 2013, 17 of which also include autonomous pH measurements. These time-series characterize a wide range of surface ocean carbonate system conditions, across a variety of environments, including 17 oceanic and 13 coastal locations, as well as 10 coral reefs. Data are available at https://www.ncei.noaa.gov/data/oceans/ncei/ocads/ metadata/0173932.html (last access: 7 January 2026) (Sutton et al., 2018). Additionally, a dedicated webpage for this project is available at https://www.ncei.noaa. gov/access/ocean-carbon-acidification-data-system/ oceans/Moorings/ndp097.html (last access: 7 January 2026).
3.1.3		Gridded and derived products – surface ocean
Although cruise data compilations are valuable for making data available in a uniform format, they often are constrained by their sampling strategies and can have significant gaps in space and time. Gridded and derived data products address this limitation by making some variables available at all grid points on a standardized spatial grid and at standardized depth levels through processes such as interpolation and gap-filling. This section describes gridded data products that have been derived from observations through interpolation and other gap-filling procedures, depicting the surface ocean. Note that this compilation focuses primarily on data products with global coverage, acknowledging that many regional gap-filled products became available in recent years and shall be include in future updates.
23.		Takahashi Delta f CO2 and Flux Climatology: following on previous climatologies published by the late Taro Takahashi in 1997 and 2009, Fay et al. (2024a) created a legacy climatology using his methodology and the updated SOCAT database of observations. This product provides 12 months of delta f CO2 values and corresponding fluxes for a reference year of 2010 at 4° × 5° resolution, and subsequently regridded to 1° × 1° resolution and near-global coverage. This climatology represents the mean of ocean conditions over the last four decades and is distinctive relative to many other mechanistic machine learning approaches in that it interpolates in time and space using only the available f CO2 data and a surface water advection scheme rather than using proxy variables for gap-filling. It uses the median of observations to determine a reference year of 2010 and fluxes are provided using air– sea partial pressure differences and inputs from the SeaFlux product (Fay et al., 2021). The climatology product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0282251.html (last access: 7 January 2026). The related manuscript is available at ESSD: https://doi.org/10.5194/essd-16-2123-2024 (Fay et al., 2024a).
Table 2. Time-series based ocean carbonate chemistry data synthesis products.
No. Name	Open ocean or	Surface or	Discrete bottle or	Highlights	Reference
coastal ocean	water column	continuous	
16 BATS	Open ocean	Top 4600 m	CTD profiles, discrete One of the longest	Bates and
bottle data from	continuous records of	Johnson (2020,
hydrocast, rate	warming, salinification, 2023)
measurements	ocean deoxygenation,
(primary, export and	and OA in the open
bacterial production),	ocean
biomass and other biological measurements, underway measurements	
17 HOT	Open ocean	Top 4500 m	Discrete bottle for	35+ years of inorganic Dore et al.
shipboard	carbon dynamics in the (2009)
measurements; sensor	open waters of the
measurements on	central North Pacific
WHOTS mooring	
18 ESTOC	Open ocean	Top 3570 m	Discrete bottle for	The longest continuous González-
shipboard	monthly record of	Dávila and
measurements; sensor warming, rising carbon	Santana-
measurements on	dioxide levels, and OA	Casiano
ESTOC mooring	in the eastern North	(2023a)
Atlantic	
19 Point B Time-series	Coastal ocean	5 and 50 m	CTD profiles, discrete Carbonate chemistry at Kapsenberg et
bottle data	a coastal site of the Bay al. (2017)
of Villefranche, France	
20 Ny-Ålesund
Time-series	Coastal ocean	12 m	CTD, discrete bottle	Carbonate chemistry at Gattuso et al.
data, sensor	a coastal site of	(2023a, b)
measurements at the	Kongsfjorden,
COSYNA/MOSES-	Spitsbergen
AWIPEV underwater observatory	
21 SPOTS	Open ocean + Water column Discrete	The pilot includes	Lange et al.
Coastal ocean	biogeochemical data	(2024a, b)
from 12 fixed ship-based time-series programs	
22	pCO2 and pH
Time-series from 40 Surface Buoys	Open ocean + Surface	Continuous	Based on 40 moored	Sutton et al.
Coastal ocean	surface pCO2	(2019)
time-series, with 17 of
them containing pH	

24.		MPI-ULB-SOM-FFN: Landschützer et al. (2020a) created a uniform pCO2 climatology combining open and coastal oceans. It is a monthly gridded global surface ocean pCO2 data product without adjusting for a specific reference year. Developed on a higher-resolution 0.25° × 0.25° global surface-ocean grid, this product is the result of combining two neural network-based pCO2 products: the open ocean product described below (i.e., Landschützer et al., 2016) and the coastal product created by Laruelle et al. (2017). Consequently, it represents coastal zones better. Data collected between 1998 and 2015 from the SOCAT database (Version 5) were used to create this data product. The merged climatology product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0209633.html (last access: 7 January 2026). Additionally, a dedicated web page for this project is available at https://www.ncei.noaa. gov/access/ocean-carbon-acidification-data-system/ oceans/MPI-ULB-SOM_FFN_clim.html (last access: 7 January 2026).
25.		VLIZ-SOM-FFN: Landschützer et al. (2016) employed the Self-Organizing-Map Feed-Forward Network (SOM-FFN) neural network method (Land- schützer et al., 2013) to map sea surface pCO2 from SOCAT (see No. 1 above) (Bakker et al., 2014) to generate monthly pCO2 fields on a 1° × 1° global surface ocean grid, covering the period from 1982 to near present. It is based on the gridded pCO2 measurements from SOCAT and is updated regularly. The creation of the pCO2 fields involves a two-step neural network approach, which has been extensively detailed and validated in previous works by Landschützer et al. (2013, 2014, 2016). In the initial step, the global ocean is clustered into biogeochemical provinces, and subsequently, the non-linear relationship between CO2 driver variables and gridded data from SOCAT (Bakker et al., 2016) is reconstructed. Air–sea CO2 fluxes are also computed based on the air–sea pCO2 difference, utilizing a bulk gas transfer formulation as described by Landschützer et al. (2013, 2014, 2016). The product is available at https://www.ncei.noaa.gov/ data/oceans/ncei/ocads/metadata/0160558.html (last access: 7 January 2026). Additionally, a dedicated page for this project is available at https://www.ncei.noaa. gov/access/ocean-carbon-acidification-data-system/ oceans/SPCO2_1982_present_ETH_SOM_FFN.html (last access: 7 January 2026).
26.		JMA-MLR: Iida et al. (2021) developed a monthly data product for inorganic carbonate variables on a 1° × 1° global surface ocean grid for the period 1993–2018. Variables include DIC, TA, pCO2, air– sea CO2 flux, pH, and •arag. They leveraged data products such as SOCAT.v2019 (Bakker et al., 2016) and GLODAPv2.2019 (Olsen et al., 2019), as well as satellite-based variables, including sea-surface dynamic height (SSDH), mixed layer depth (MLD), and chlorophyll a . The product is updated annually using the latest SOCAT and GLODAPv2 data. The data product can be accessed at https://www.data.jma.go. jp/kaiyou/english/co2_flux/co2_flux_data_en.html (last access: 7 January 2026).
27.		OceanSODA-ETHZ:
a.		OceanSODA-ETHZv1 is a monthly gridded global surface ocean data product for multiple ocean carbonate system variables, including DIC, TA, pCO2, pH (total scale), •arag, and •calc (Gregor and Gruber, 2020, 2021, 2023; Ma et al., 2023)	. This dataset is structured on a 1° × 1° global surface ocean grid with monthly resolution from 1982–2022, facilitating research on OA over seasonal to decadal scales. The OceanSODA-ETHZ data product was created by extrapolating in time and space the surface ocean observations of fCO2 from SOCATv2022 (Bakker et al., 2016) and TA from GLODAPv2.2022 using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (Gregor, 2021). TA and pCO2 were then used to calculate the remaining variables of the marine carbonate system with the PyCO2SYS software (Humphreys et al., 2022). Phosphate and silicate from WOA 2018 product was used (Boyer et al., 2018; Garcia et al., 2018a). The OceanSODA-ETHZ data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0220059.html (last access: 7 January 2026).
b.		OceanSODA-ETHZv2 is a surface f CO2 product with a 0.25° × 0.25° spatial resolution and an 8 d temporal resolution, providing estimates starting from 1982 (Gregor et al., 2024a, b). The high-resolution outputs are suitable for investigating the shorter- and finer-scale dynamics of surface f CO2. Despite sharing a name with its predecessor, OceanSODA-ETHZv2 does not provide TA estimates and employs a different methodology, as described in the following steps: (1) the atmospheric trend of CO2 is removed by subtracting marine boundary layer CO2 concentrations from SOCAT fCO2 producing a new target 1∗CO2 to reduce the biases at the start and end of the time-series. (2) An 8 d seasonal climatology of 1∗CO2 is estimated using Gradient Boosted Decision Trees (GBDT), which is later used as a predictor. (3) The non-seasonal thermal component is removed from 1∗CO2, resulting in a new target, 1∗ COn2onT. (4) The new target is estimated using a feed-forward neural network, with the GBDT as one of the forcing variables. (5) Steps 4 through to 1 are inverted to arrive at f CO2 . (6) Air–sea CO2 fluxes are computed using ERA5 winds. Data are available at https://doi.org/10.5281/zenodo.11206366 (Gregor et al., 2024b) and are updated annually.
28.		LDEO-HPD fCO2: the LDEO Hybrid Physics Data (LDEO-HPD) estimates the temporal evolution of surface ocean f CO2 and air–sea CO2 exchange, utilizing the strengths of observations and global ocean biogeochemical models (GOBMs) (Gloege et al., 2022). GOBMs are internally consistent, mechanistic representations of the ocean circulation and carbon cycle, and have long been the standard for making spatiotemporally resolved estimates of air–sea CO2 fluxes. However, there is often a bias between the modelled f CO2 and available surface ocean measurements (Fay and McKinley, 2021). The LDEO-HPD approach trains an eXtreme Gradient Boosting (XGB) algorithm to learn a non-linear relationship between model-data f CO2 mismatch and observed predictor variables: sea surface temperature (SST), sea surface salinity (SSS), chlorophyll concentration, mixed layer depth). The GOBM f CO2 is then corrected with the predicted model-data misfit to estimate real-world f CO2 for the observation period (Gloege et al., 2022). This results in reconstructed monthly surface ocean f CO2 and air–sea CO2 fluxes on a 1° × 1° grid in the open ocean beginning in 1982. Additional information can be found at oceancarbon.ldeo.columbia.edu. The data product is available at https://doi.org/10.5281/zenodo.4760205 (Gloege et al., 2021).
29.		LDEO-HPD with Extended Temporal Coverage: building on the work of Gloege et al. (2022), the LDEO- HPD product as mentioned above (No. 28) can be extended back in time to predict f CO2 for all available model years. Bennington et al. (2022a) find that the largest component of the GOBM corrections is climatological. The smaller corrections at other timescales suggest either that these are well captured by the GOBMs or the data are insufficient. The dominance of climatological corrections supports the extension of the LDEO- HPD f CO2 product backwards in time. A climatology of model-observation misfits for the best-observed period (2000–present) is applied to the GOBMs for 1959–1981, while an interannually varying correction is used for 1982 onward. (Bennington et al., 2022a). This results in reconstructed monthly surface ocean f CO2 and air–sea CO2 fluxes on a 1° × 1° grid covering the open ocean, beginning in 1959. Since 2022, the LDEO-HPD Back in Time product has been included in the annual release of the Global Carbon Budget (GCB). Additional information can be found at https://oceancarbon.ldeo.columbia.edu/ (last access: 7 January 2026). The data product can be accessed via Zenodo at https://doi.org/10.5281/zenodo.13891722 (Fay et al., 2024b).
30.		LDEO f CO2 – Residual Method: a frequently used approach for estimating full-coverage f CO2 is to train a machine learning algorithm on sparse in situ f CO2 data and associated physical and biogeochemical observations. While these associated variables have well- known relationships to f CO2, it is often unclear how they mechanistically drive fCO2 around the world. The LDEO f CO2 -Residual method takes the basic approach and enhances connections between physical understanding and reconstructed f CO2. The novel approach used here includes applying pre-processing to the f CO2 data to remove the direct effect of temperature – a relationship well-documented in literature and lab experiments (Takahashi et al., 2002). This enhances the biogeochemical/physical component of f CO2 in the target variable (now f CO2-Residual) and reduces the complexity that the machine learning must disentangle. The resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of f CO2 (Bennington et al., 2022b). This results in reconstructed monthly surface ocean f CO2 and air–sea CO2 fluxes on a 1° × 1° grid covering the open ocean, beginning in 1982 and extended to the most recent year of available data. Additional information can be found at oceancarbon.ldeo.columbia.edu. The data product can be accessed via Zenodo at https://doi.org/10.5281/zenodo.13941548 (Bennington et al., 2024).
31.		CMEMS-LSCE Surface Ocean Carbonate Data Products:
a.		CMEMS-LSCEv1: monthly surface ocean pCO2 and air–sea CO2 fluxes on a 1° × 1° grid in both the open ocean and coastal seas from 1985–2019 were reconstructed by Chau et al. (2022a). CMEMS- LSCE is short for Copernicus Marine Environment Monitoring Service – Laboratoire des Sciences du Climat et de l’Environnement. This product is generated from an ensemble-based reconstruction of pCO2 maps trained with gridded data from SOCATv2020 (Bakker et al., 2016). Sea-surface pCO2 values (converted from the original f CO2 values in SOCATv2020) were regressed against a set of predictors with non-linear functions, i.e., feed-forward neural network (FFNN) models. The predictors include: sea-surface height (SSH), SST, SSS, MLD, chlorophyll a (Chl a), atmospheric CO2 mole fraction (x CO2), and geographical coordinates (longitudes and latitudes). This data product is accessible at https://data.ipsl. fr/catalog/srv/eng/catalog.search#/metadata/ a2f0891b-763a-49e9-af1b-78ed78b16982 (last access: 7 January 2026).
b.		CMEMS-LSCEv2: CMEMS-LSCEv2 corresponds to the latest version of the CMEMS-LSCE FFNN. It uses the same ensemble-based reconstruction method for pCO2 maps as CMEMS-LSCEv1. Improvements include downscaling the spatial resolution to 0.25°×0.25° and reproducing additional surface ocean carbonate system variables on a global grid from 1985 onwards (Chau et al., 2024a). The additional surface ocean carbonate system variables are: pCO2, DIC, TA, pH, •arag, and •calc. Surface ocean pCO2 is reconstructed based on an ensemble of neural network models mapping gridded observation-based data provided by SOCATv2022 (Bakker et al., 2016). Surface ocean TA is estimated with a multiple linear regression approach (Carter et al., 2016, 2017). The remaining carbonate variables are calculated from pCO2 and TA using a MATLAB version of CO2SYS (Lewis and Wallace, 1998; Sharp et al., 2023). The CMEMS- LSCE product is updated yearly for surface ocean pCO2, air–sea fluxes, and the carbonate system variables. Updates are phased with release of the SOCAT database. For surface ocean pCO2 and air– sea fluxes the temporal coverage is extended to the present date with a latency of 1 month (Chau et al., 2024b). Both the multi-year reconstruction and the near-real time prediction can be accessed through the CMEMS portal: https://doi.org/10.48670/moi- 00047 (Chau et al., 2024c).
32.		CarboScope (Jena-MLS): the Jena Mixed-Layer Scheme (within the CarboScope family of data-based estimates of carbon-cycle variability) is based on observed sea surface pCO2 from SOCAT (see above No. 1) (Bakker et al., 2014). It provides daily global fields of pCO2 and air–sea CO2 fluxes from 1957 to the year before present, on a resolution of 2.5° × 2° degrees. In the original method (Rödenbeck et al., 2013), a diagnostic model of the carbon balance in the ocean mixed layer is being fitted to the pCO2 data, by adjusting the ocean-interior sources and sinks of carbon of the mixed layer. The multi-decadal trend is derived from the data-based Ocean Circulation Inverse Model (OCIM) estimate provided by DeVries (2022b). Since a later extension described in Rödenbeck et al. (2022), the variability in the oceaninterior sources and sinks is first regressed against variability in SST and wind speed. The regression step is followed by a correction step with explicit temporal variability, to also represent data variability not yet represented by the predictors of the regression. The CarboScope product is updated yearly. The results from current and previous releases can be downloaded from https://www.bgc-jena.mpg.de/CarboScope/ (last access: 7 January 2026).
33.		UOEx-Watson: this product is an estimate of the atmosphere-ocean flux of CO2 that takes into account near-surface temperature deviations (Watson et al., 2020). Most estimates use data on surface ocean pCO2 without considering corrections due to temperature gradients within the uppermost few millimeters of the sea surface (“Skin temperature effects”) or small effects due to changes in temperature that occur during sampling and measurement, especially when the measurement is from a commercial vessel rather than a research ship. This product takes these effects into account by recalculating pCO2 from the SOCAT data base (v2019) using co-located satellite observations of skin temperature. The result is a substantial increase in the calculated net global uptake of CO2. In other respects, the methodology for this data product follows the two-step neural network approach described by Landschützer et al. (2013, 2014). The gridded data set of sea surface f CO2 adjusted to satellite-derived subskin surface temperature, is available at https://doi.org/10.1594/PANGAEA.905316 (Holding et al., 2019). Ocean-atmosphere fluxes interpolated to monthly and 1° × 1° spatial resolution is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0301544.html (last access: 7 January 2026).
34.		NIES-ML3: the ensemble product of three machine learning methods (ML3) from the National Institute for Environmental Studies (NIES), Japan, includes monthly global surface ocean fCO2 in 1982–2024 on 1° × 1° grids. Using a leave-one-year-out (LOYO) validation method and three machine learning models, Zeng et al. (2022) found that the time variant trends of ocean CO2 could be estimated approximately by a harmonic function fitting of the annual atmospheric CO2. They removed the estimated trends from the ocean CO2 and applied the LOYO to the trend-removed data to obtain the trend that could not be approximated by the fitting for trend correction. The trend-removed data by the corrected trends were used to train the models. The gap- filled CO2 maps were constructed by adding the trends to model predictions. The product is available at NIES: https://doi.org/10.17595/20220311.001 (Zeng, 2022).
35.		CSIR-ML6: provides monthly 1° × 1° estimates of surface pCO2 (Gregor et al., 2019a). The approach uses the conceptual two-step approach of clustering and performing regressions for each cluster as Landschützer et al. (2016). CSIR-ML6 investigates the efficacy of various machine learning (ML) methods in estimating surface pCO2, namely, feed-forward neural networks (FFNN), extremely randomized trees (ERT), gradient boosting machines (GBM), and support vector regression (SVR). It is found that the ensemble of all but the ERT method resulted in the best estimate, highlighting the fact that various ML methods do not produce the same outcome, particularly when data is sparse. Further, the variance between ensemble members can inform us about regions where uncertainty may be large due to methodological differences. Despite this, all methods achieve roughly the same uncertainty – a barrier, or wall beyond which the community has yet to overcome. The data are available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0206205.html (last access: 7 January 2026) (Gregor et al., 2019b). The product is one of the six ensemble members of the SeaFlux dataset.
36.		Stepwise-FFNN: Zhong et al. (2022) constructed a monthly global 1° × 1° surface ocean pCO2 product from January 1992 to December 2024, by combining the stepwise regression algorithm and a feedforward neural network (FFNN) to select predictors of pCO2 based on the mean absolute error in each of the 11 biogeochemical provinces defined by the self-organizing map (SOM) method. The methodology for this data product used regionally optimal predictors to account for differences in pCO2 drivers, lowering local biases relative to a single global predictor set. The developed data product is available at https://doi.org/10.12157/IOCAS.20250814.001 (Zhong, 2025).
37.		AOML-ET: Wanninkhof et al. (2024, 2025) developed a monthly global ocean data product of seawater pCO2 and air–sea CO2 fluxes, referred to as AOML- ET, using an extremely randomized trees (ET) machine learning technique. These maps are created on 1° × 1° spatial grids, providing global surface ocean coverages from 1998 to 2023. AOML-ET incorporates several predictor variables, including time, location, SST, SSS, MLD, and chlorophyll a . The model was trained using the v2020 and v2023 releases of the SOCAT data product (No. 1). Air–sea CO2 fluxes were calculated using the air–sea CO2 partial pressure difference (1pCO2) and a bulk gas transfer formulation incorporating windspeed. The dataset contains monthly 1° × 1° NetCDF files of the AOML-ET outputs, along with the predictor variables. The data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0298989.html (last access: 7 January 2026) (Wanninkhof et al., 2024).
38.		ULB-SOM-FFN-Coastalv2.1: Roobaert et al. (2024) present high-resolution (0.25° × 0.25° grid) monthly maps showing the distribution of sea surface pCO2 across the global coastal ocean, spanning from 1982 to 2020. This product (ULB-SOM-FFN-coastalv2.1) builds upon the work by Laruelle et al. (2017), incorporating a two-step methodology that utilizes Self Organizing Maps (SOM) and Feed Forward Networks (FFN). This updated product now captures temporal variability, enabling the assessment of interannual variability and long-term trends in coastal air– sea CO2 exchange, unlike the product by Laruelle et al. (2017), which only offers a climatology for a short period (1998–2015). The enhancements include additional environmental predictors and an expanded dataset for training and validation, featuring approximately 18 million direct coastal observations from the SOCAT database, specifically the SO- CATv2022 release (Bakker et al., 2016). The product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0279118.html (last access: 7 January 2026) (Roobaert et al., 2023).
39.		RFR-LME: Sharp et al. (2024a) developed a data product delineating the temporal trends of OA indicators mapped on a 0.25° × 0.25° spatial grid, across eleven US Large Marine Ecosystems (LMEs), with monthly coverage from 1998–2023. These indicators, which include the pCO2, pH, •arag , DIC, TA, Revelle Factors, among others, were derived from SO- CATv2023, along with other oceanographic properties, e.g., SST, SSS, SSH, and MLD. The methodology combined Gaussian Mixture Models to categorize the data into environmentally similar subregions, Random Forest Regressions for the spatial and temporal extrapolation of observational f CO2 data, and regressions to estimate TA (Carter et al., 2021) to provide a second carbonate system constraint. The resulting maps are available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0287551.html (last access: 7 January 2026) (Sharp et al., 2024b), while an online portal at https://ecowatch.noaa.gov/thematic/ ocean-acidification (last access: 7 January 2026) presents regionally averaged time-series for three key indicators: pCO2, •arag, and pH.
40.		ReCAD-NAACOM-pCO2: Wu et al. (2025) developed a reconstructed pCO2 product for the North American Atlantic Coastal Ocean Margins (NAA- COM), spanning from the Gulf of Mexico/Gulf of America to the Grand Banks, called the Reconstructed Coastal Acidification	Database-pCO2 (ReCAD- NAACOM-pCO2). This product employed a two-step approach combining random forest regression and linear regression to generate monthly pCO2 data at 0.25° spatial resolution from 1993–2021. The model was trained using SOCAT v2023 observations as ground-truth values, incorporating various satellite-derived and reanalysis environmental variables known to influence sea surface pCO2. The ReCAD-NAACOM-pCO2 dataset is publicly accessible (https://doi.org/10.5281/zenodo.11500974, Wu et al., 2024) and will be updated regularly.
41.		Gridded Surface OA Indicators in the Northern Caribbean Sea: this dataset contains a high-quality dataset of derived products from over a million observations of surface water partial pressure/fugacity of carbon dioxide (pCO2w/f CO2w), for the Caribbean Sea, Gulf of Mexico/Gulf of America and North–West Atlantic Ocean covering the timespan from 1 January 2002 to 30 December 2019. The derived quantities include TA, acidity (pH), •arag and air–sea CO2 flux (Wanninkhof et al., 2020). This data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0207749.html (last access: 7 January 2026) (Wanninkhof et al., 2019).
42.		OA Data in the Gulf of Mexico/Gulf of America and Wider Caribbean: the Acidification, Climate, and Coral Reef Ecosystems Team (ACCRETE) Lab within AOML’s Ocean Chemistry and Ecosystems Division (OCED) developed a data product for tracking OA in the Caribbean and Gulf of Mexico/Gulf of America from 2014 to 2020 (van Hooidonk, 2022). Utilizing satellite imagery and a data-assimilative hybrid model, the tool maps key indicators of the water’s carbonate system, including pCO2, TA, pH, •arag, and •calc . This innovation builds upon an update to the experimental OA Product Suite (OAPS) developed by NOAA’s Coral Reef Watch. The data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0245950.html (last access: 7 January 2026) (van Hooidonk, 2022).
43.		pCO2 Climatology of the Baltic Sea: Bittig et al. (2024) used biogeochemical model output to inform the mapping of sea surface pCO2 observations in the Baltic Sea and to build a mean monthly climatology for the period 2003 to 2021, with spatial resolutions of 0.10° × 0.05° (approximately 3 nautical miles in both directions). In a first step, spatial patterns of variability were extracted from 20 years of model surface pCO2 data by an EOF analysis. These spatial patterns were then used to map surface pCO2 observations from SOCAT (see above No. 1) (Bakker et al., 2014) onto the Baltic Sea domain. By using an ensemble approach with varying number of EOF patterns, the spatial scales of the mapping were locally adjusted based on the observation’s data density. Mapped monthly fields of pCO2 from 2003–2021 were combined for the product into a mean monthly climatology and a spatially-resolved linear trend. The climatology product is available at PANGAEA: https://doi.org/10.1594/PANGAEA.961119 (Bittig et al., 2023).
44.		INCOIS-ReML: the Indian National Centre for Ocean Information Services-Regional Machine Learning model (INCOIS-ReML) pCO2 data product offers machine learning based monthly climatological sea surface pCO2 and the corresponding air–sea CO2 flux for the Bay of Bengal (Joshi et al., 2024). This data product integrates publicly available open-ocean observations with data from the Indian Exclusive Economic Zone. This high-resolution (0.083° × 0.083°) monthly climatological pCO2 data product is available from the INCOIS Portal: https://las.incois.gov.in (last access: 7 January 2026), and from OCADS: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/ metadata/0307627.html (last access: 7 January 2026) (Joshi et al., 2025a).
45.		INCOIS_TA: the Indian National Centre for Ocean Information Services-Total Alkalinity (INCOIS_TA) data product offers a machine learning based monthly interannual surface TA from 1993–2020 for the North Indian Ocean (Joshi et al., 2025b). This data product integrates publicly available open-ocean observations with data collected during Indian scientific expeditions and from the Indian Exclusive Economic Zone. This high- resolution (0.083° × 0.083°) long-term monthly TA data product is available from the INCOIS Portal: https: //las.incois.gov.in (last access: 7 January 2026, and from OCADS: https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0307789.html (last access: 7 January 2026) (Joshi et al., 2025c).
3.		1.4 Gridded and derived products – interior ocean
This section describes gridded data products derived from observations through interpolation and other gap-filling procedures, depicting the interior ocean (Table 4).
46.		GLODAPv2 Climatology (referenced to 2002): Lau- vset et al. (2016) generated a comprehensive set of global interior ocean climatologies, mapping key biogeochemical variables on a 1° × 1° grid for 33 depth levels from the surface to 5500 m. These climatologies cover temperature, salinity, DO, nitrate, phosphate, silicate, DIC, TA, pH, •arag, and •calc. This data product was created based on the quality-controlled and internally consistent GLODAPv2.2016 (Olsen et al., 2016) using the data-interpolating variational analysis (DIVA) method (Barth et al., 2014). The conceivably confounding temporal trends in DIC, pH, •arag and •calc due to anthropogenic influence were removed prior to mapping by normalizing their values to a reference year of 2002 using first-order calculations of anthropogenic carbon accumulation rates. For all variables, all data from the full 1972–2013 period were used, including data that did not receive full secondary quality-control. This data product is not updated each year along with the main GLODAPv2 data product. The mapped data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0286118.html (last access: 7 January 2026) (Lauvset et al., 2023a). It can also be accessed from the GLODAP website: https: //glodap.info/ (last access: 7 January 2026) For reference, the original GLODAP Climatology (Version 1.1) is accessible at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0001644.html (last access: 7 January 2026) (Sabine et al., 2005).
47.		Aragonite Saturation State Climatology: Jiang et al. (2015) developed a global interior-ocean •arag cli-

Table 3. Gridded and derived ocean carbonate chemistry data synthesis products in the surface ocean.
No.	Name	Open ocean or coastal ocean	Spatial resolution	Temporal resolution	Methodology	Highlights	Reference	
23	Takahashi
Delta f CO2 and Flux
Climatology	Open ocean	1°×1°	12-month climatology referenced to
2010	Advection
Scheme	Does not use proxy variables for extrapolation.
Only produced as monthly climatology	Fay et al. (2024a)	
24	MPI-ULB- SOM-FFN	Open ocean + Coastal ocean	0.25° × 0.25°	12-Month climatology without a reference year	2-step machine learning: Merged product of the SOM-FFN approach applied to the open ocean (Landschützer et al., 2016) and the coastal ocean (Laruelle et al., 2017)	Monthly gridded pCO2 without adjusting for a specific reference year, high-resolution coastal ocean coverage	Landschützer et al. (2020a)	
25	VLIZ SOM-FFN	Open ocean	1°×1°	Monthly from
January 1982 onwards	2-step machine learning: Self organizing map clustering followed by a feed forward network (SOM-FFN)	Monthly gridded pCO2 from 1982 through near present	Landschützer et al. (2016)	
26	JMA-MLR	Open ocean	1°×1°	Monthly from
January 1990 onwards	Multiple linear regressions	Temporal trends of DIC, TA, pCO2, air–sea CO2 flux, pH, and •arag	Iida et al. (2021)	
27(a)	OceanSODA- ETHZv1	Open ocean	1°×1°	Monthly from 1982 to 2023	Ensemble of 2-step members:
K-means clustering with gradient boosting and SVR regression	Temporal trends of DIC,
TA, pCO2, pH, •arag , and •calc	Gregor and
Gruber (2021)	
27(b)	OceanSODA- ETHZv2	Open ocean + Coastal ocean	0.25° × 0.25°	8 d from 1982 to 2022	FFNN	Highlighting fine-scale and short-term variability of the ocean carbon sink	Gregor et al. (2024a, b)	

Table 3. Continued.
No.	Name	Open ocean or coastal ocean	Spatial resolution	Temporal resolution	Methodology	Highlights	Reference	
28	LDEO-HPD fCO2	Open ocean	1°×1°	Monthly from 1982	XGBoost algorithm	Temporal evolution of surface ocean fCO2 and air–sea CO2 exchange	Gloege et al. (2022)	
29	LDEO-HPD with Extended Temporal Coverage	Open ocean	1°×1°	Monthly from 1959	XGBoost algorithm	Uses model-data misfit climatology to extend estimate back in time to 1959	Fay et al. (2024b)	
30	LDEO f CO2 –
Residual
Method	Open ocean	1°×1°	Monthly from 1982	XGBoost algorithm	Removes the temperature component before ML	Bennington et al. (2022b)	
31(a)	CMEMS- LSCEv1	Open ocean + Coastal ocean	1°×1°	Monthly from 1985 to 2019	FFNN	Seamless reconstruction from coastal to open ocean	Chau et al. (2022a)	
31(b)	CMEMS- LSCEv2	Open ocean + Coastal ocean	0.25° × 0.25°	Monthly from 1985 to 2025	FFNN	Yearly extension of time-series and monthly reconstruction at low latency	Chau et al. (2024a, b)	
32	CarboScope (Jena-MLS)	Open ocean	2.5° × 2°	Daily from 1957	Multi-linear regression against long-term predictors, plus auto-regressive correction	Variability of pCO2 and air–sea CO2 fluxes since 1957, sensitivities to SST and wind speed variations	Rödenbeck et al. (2022)	
33	UOEx-Watson	Open ocean	1°×1°	Monthly from
January 1992	Two-step neural network approach described by Landschützer et al. (2013, 2014, 2016)	Air–sea fluxes of CO2 with adjusted skin temperature effect	Watson et al. (2020)	

Table 3. Continued.
No.	Name	Open ocean or coastal ocean	Spatial resolution	Temporal resolution	Methodology	Highlights	Reference	
34	NIES-ML3	Open ocean	1°×1°	Monthly from 1982 to 2023	FNN, GBM, RF	The prediction of a ML method was obtained from ten trainings with different seeds. The mean of the three methods was taken as the final prediction	Zeng (2022), Zeng et al. (2022)	
35	CSIR-ML6	Open ocean	1°×1°	Monthly from 1982 to 2016	Ensemble:
FFNN, SVR, ERT, Gradient
Boosted Trees	Various ML methods produce different results when data is sparse, but all still achieving roughly the same uncertainty	Gregor et al. (2019a)	
36	Stepwise- FFNN	Open ocean	1°×1°	Monthly from 1992 to 2024	SOM, FFNN, Stepwise regression	ML-based selection of predictors considering regional differences of pCO2 drivers	Zhong et al. (2022)	
37	AOML-ET	Open ocean	1°×1°	Monthly from 1998 to 2023	Extremely randomized trees (ET) machine learning technique	Monthly global air–sea CO2 flux maps in modern era	Wanninkhof et al. (2025)	
38	ULB-SOM-
FFN- Coastalv2.1	Coastal ocean	0.25° × 0.25°	Monthly from 1982 to 2020	2-step machine learning: Self organizing map clustering followed by a feed forward network (SOM-FFN)	Global temporal trends of coastal pCO2 and air–sea CO2 fluxes based on SOCATv2022 with data collected from 1982–2020	Roobaert et al. (2024)	

Table 3. Continued.
No.	Name	Open ocean or coastal ocean	Spatial resolution	Temporal resolution	Methodology	Highlights	Reference	
39	RFR-LME	Coastal ocean	0.25° × 0.25°	Monthly from 1998 to 2023	Gaussian mixture models and random forest regressions	Temporal trends of OA indicators and estimated uncertainties across 11
US Large Marine Ecosystems (LMEs), with monthly coverage from 1998–2023	Sharp et al. (2024a)	
40	ReCAD- NAACOM- pCO2	Coastal ocean	0.25° × 0.25°	Monthly from 1993 to 2021	2-step machine learning: random forest regression followed by linear regression	Sea surface pCO2 in the North American Atlantic Coastal Ocean Margins (NAACOM)	Wu et al. (2025)	
41	Gridded
Surface OA
Indicators in the Northern Caribbean Sea	Coastal ocean	1°×1°	Monthly from 2002 to 2019	Gridding of the observations of fCO2,SST and SSS was performed by binning and averaging the data in (1° × 1° by month) cells	A 17-year record of fCO2, TA, pH, •arag, and air–sea CO2 flux in the Caribbean Sea	Wanninkhof et al. (2020)	
42	OA data in the Gulf of Mexico/Gulf of America and Wider Caribbean	Regional	0.088° × 0.88°	Monthly from 2014 to 2020	Utilizing satellite imagery and a dataassimilative hybrid model, the tool maps key indicators of the water’s carbonate system, including pCO2, TA, pH, •arag , and •calc .	A new tool to monitor OA over the wider Caribbean and Gulf of Mexico/Gulf of America	van Hooidonk (2022)	
43	pCO2
Climatology of the Baltic Sea	Regional	0.10° × 0.05°	12-month climatology referenced to
2013; linear trend
2003–2021	Extrapolation using model-based patterns of variability	Does not use proxy variables for extrapolation. Spatial scales adjust locally to data density	Bittig et al. (2024)	

Table 3. Continued.
No.	Name	Open	Spatial	Temporal	Methodology	Highlights	Reference	
		ocean	resolution	resolution				
		or						
		coastal						
		ocean						
44	INCOIS-ReML	Regional	0.083° × 0.083°	Monthly	Xtreme	This data	Joshi et al.	
				climatology referenced to 2015.	Gradient Boosting (XGB) based Machine Learning Model	product integrates publicly available
open-ocean observations
with data from the Indian EEZ region in the Bay of Bengal to provide surface pCO2 and air–sea
CO2 flux estimates	(2024)	
45	INCOIS_TA	Regional	0.083° × 0.083°	Monthly from 1993 to 2020	Xtreme Gradient Boosting (XGB) based Machine Learning Model	Integrates publicly available open-ocean observations with data collected	Joshi et al. (2025b)	
during Indian scientific expeditions and from the Indian Exclusive Economic
Zone to provide surface TA estimates for the North Indian Ocean

matology (referenced to 2000), on a 1° × 1° grid at 9 standardized depth levels from the surface down to 4000 m. This was accomplished by integrating data from the first version of GLODAP (Key et al., 2004), CARINA (Key et al., 2010), and PACIFICA (Suzuki et al., 2013), along with additional recent cruise datasets up to 2012. Temporal adjustments were made to a reference year of 2000, accounting for an annual f CO2 increase of 1.6 µatm in the surface mixed layer (SML), with a rate that decreases linearly to 0 µatm yr-1 from the bottom of the SML to a depth of 1000 m (Sabine et al., 2008). The data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0139360.html (last access: 7 January 2026) (Jiang and Feely, 2015).
48.		Mapped Observation-Based Oceanic Dissolved Inorganic Carbon (MOBO-DIC):
a.		MOBO-DIC (Version 2020): Keppler et al. (2020a) produced a global interior ocean DIC monthly climatology (average climatological values for January through December) on a 1° × 1° grid at 33 standardized depth levels from the surface to 2000 m. The MOBO-DIC mapping method adapts and extends the SOM-FFN technique originally introduced by Landschützer et al. (2013). It starts by categorizing the ocean into clusters with comparable physical and biogeochemical characteristics using self-organizing maps (SOM). Subsequently, within each SOM-defined cluster, a feed-forward network (FFN) is employed to estimate and enforce the statistical correlation between the targeted DIC data and the predictor data available in globally mapped fields. The product uses data from January 2004 to December 2017, and is thus centered around the years 2010/2011. The data product is available at https://www.ncei.noaa.gov/ access/ocean-carbon-acidification-data-system/ oceans/ndp_104/ndp104.html (last access: 7 January 2026).
b.		MOBO-DIC (Version 2023): Keppler et al. (2023a) extended the temporal resolution of MOBO-DIC to resolve monthly fields from January 2004 to December 2019, as opposed to the average climatological values in Keppler et al. (2020a). This data product is on a 1° × 1° grid at 28 depth levels from the surface to 1500 m. The data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0277099.html (last access: 7 January 2026) (Keppler et al., 2023b).
49.		Monthly Interior Ocean TA Climatology: Broullón et al. (2019) developed a monthly global interior ocean TA climatology using a feed-forward neural network approach. This dataset offers a spatial resolution of 1° × 1° in the horizontal, spans 102 depth levels (ranging from 0–5500 m) in the vertical dimension, and features a temporal resolution that varies from monthly (0–1500 m) to annual (1550–5500 m). The development of this climatology was based on the analysis of TA in relation to several key predictor variables, including temperature, salinity, nutrients (phosphate, nitrate, and silicate), DO, and sampling position (coordinates and depth), as outlined in Velo et al. (2013). Both TA and these predictor variables were sourced from GLODAPv2 (version 2016) (Olsen et al., 2016). The global interior ocean TA climatology was constructed by leveraging the established relationships between TA and the predictor variables, as well as the monthly climatologies of temperature, salinity, and DO from the WOA 2013 (Locarnini et al., 2013; Zweng et al., 2013; Garcia et al., 2014), and nutrients data that were obtained through the CANYON-B neural network process, applied to the previously mentioned fields. The data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0222470.html (last access: 7 January 2026) (Broullón et al., 2020b).
50.		Monthly Interior Ocean DIC Climatology: Broullón et al. (2020a) employed a feed-forward neural network approach to create a monthly global interior ocean DIC climatology, centered around the year 1995. This dataset offers a 1° × 1° spatial resolution in the horizontal domain, encompassing 102 depth levels ranging from 0–5500 m vertically. The temporal resolution varies, ranging from monthly (0–1500 m) to annual (1550–5500 m). In contrast to their previous work on TA (Broullón et al., 2019), this analysis includes the variable “year” to account for anthropogenic DIC pool changes. It also incorporates data from the LDEO pCO2 database (Takahashi et al., 2017) alongside GLO- DAPv2.2019 (Olsen et al., 2019) to establish relationships between DIC and its input variables: temperature, salinity, DO, as well as location, pressure, and time. The DIC climatology was derived using these relationships, along with monthly climatological data for temperature, salinity, and DO from WOA 2013 (Lo- carnini et al., 2013; Zweng et al., 2013; Garcia et al., 2014), as well as phosphate, nitrate, and silicate values computed from the CANYON-B neural network fed with the aforementioned fields. The data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0222469.html (last access: 7 January 2026) (Broullón et al., 2020c).
51.		Acidification Metrics in the Ocean Interior: Fassbender et al. (2023) generated estimates of global interior ocean changes to pH, [H+], •arag, pCO2, and the Revelle sensitivity factor driven by the accumulation of anthropogenic carbon (Cant) from the preindustrial period to 2002, and quantified the component of these changes caused by carbonate system nonlinearities. For each OA metric, the dataset includes year 2002 values and quasi-preindustrial values, which were estimated by subtracting Cant from the year 2002 carbonate chemistry information and recomputing each OA metric without considering any warming, circulation, or biological changes that may have occurred since the preindustrial era. Data from the upper 2000 m of the GLODAPv2 Climatology (No. 46, Lauvset et al., 2016) and from the preformed properties product of Carter et al. (2021) were used to make these estimates on the 1° × 1° GLODAPv2 Climatology grid for 26 depth levels from the surface to 2000 m. The provided uncertainties were estimated using a 1000- iteration Monte Carlo simulation. Calculation details are described in Fassbender et al. (2023). Year 2002 •arag and pH values, and their uncertainties, are reproduced from the GLODAPv2 Climatology and are provided in this dataset for user convenience with the permission of the original data producer. This data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0290073.html (last access: 7 January 2026) (Fassbender, 2024).
52.		Ocean Interior Acidification Over the Industrial Era: building on the total anthropogenic carbon estimates for 1994 from Sabine et al. (2005) and the decadal changes between 1994 and 2014 reconstructed by Müller et al. (2023a), Müller and Gruber (2024a) quantified ocean interior acidification over the industrial era. To convert the increasing anthropogenic carbon concentrations into acidification estimates, their approach relied on time-invariant climatologies of ocean interior DIC, TA, temperature, salinity, and other relevant variables to determine the background state of the marine carbonate system. Hence, their estimates resolve exclusively the acidification driven by the anthropogenic carbon accumulation. In contrast to direct observations of acidification variables, such as those collected at time-series stations, this approach does not account for changes in the natural carbon cycle or the displacement of water masses. The approach by Müller and Gruber (2024a) is conceptually similar to that of Fassbender et al. (2023), but provides temporally resolved estimates, enabling the tracking of both the spatial distribution and temporal evolution of ocean interior acidification. The data product is available at https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0298993.html (last access: 7 January 2026) (Müller and Gruber, 2024b).
53.		Decadal changes of anthropogenic CO2:
a.		Anthropogenic CO2 from 1994 to 2007: Gruber et al. (2019a) estimated the decadal time-scale changes in the oceanic content of anthropogenic CO2 (1Cant) between 1994 to 2007. The results were derived from the GLODAPv2.2016 product (Olsen et al., 2016), utilizing the eMLR(C∗) methodology pioneered by Clement and Gruber (2018). The product is combined with the estimated amount of Cant for 1994 derived by Sabine et al. (2004) from GLODAPv1 to infer Cant for 2007. All estimates are geospatially distributed on a horizontal grid with a resolution of 1° × 1°. Two primary files are available: one providing the complete three-dimensional distribution of 1Cant, and the other containing vertically integrated values, i.e., the column inventories. This data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0186034.html (last access: 7 January 2026) (Gruber et al., 2019b).
b.		Decadal Trends in Anthropogenic CO2 from 1994 to 2014: Müller et al. (2023a) extended the analysis by Gruber et al. (2019a) to reconstruct decadal trends in the oceanic storage of 1Cant in the global ocean interior from mid-year 1994 to mid-year 2004, and further to mid-year 2014. They applied the extended multiple linear regression (eMLR) method (Clement and Gruber, 2018) to ship-borne observations of DIC and other biogeochemical variables from GLO- DAPv2.2021 (Lauvset et al., 2021). All estimates are provided on a 1° × 1° horizontal grid. Two principal data files are provided: one featuring the comprehensive three-dimensional distribution of 1Cant for the two time periods, and the other presenting the vertically integrated quantities, i.e., the column inventories. The data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0279447.html (last access: 7 January 2026) (Müller et al., 2023b).
54.		Tracer-based rapid anthropogenic carbon estimation from 1750 to 2500: Carter et al. (2025) developed a method for estimating Cant based on a machine learning translation of ocean circulation information inferred from transient tracer distributions. They applied it to the gridded GLODAPv2 climatology to obtain estimates of the past and projected Cant distribution between 1750 and 2500. Projections are made using a range of simple assumptions and shared socioeconomic pathway projections. Estimates are provided on 1° × 1° spatial grids at 33 standard depth levels in micromoles Cant per kg of seawater. This data product is available at https://doi.org/10.5281/zenodo.15003059 (Carter, 2025).
55.		Preformed TA and other biogeochemical properties: Carter et al. (2020) estimated preformed seawater TA, nitrate, silicate, phosphate, and oxygen using empirical seawater property estimation routines (Carter et al., 2017) with ocean circulation pathway information from ocean circulation transport matrices (John et al., 2020). Preformed properties are estimated property contents that seawater had when it last left contact with the atmosphere, and are used as an aid in interpretation of measured ocean property distributions. This data product is available at https://doi.org/10.5281/zenodo.3745002 (BRCScienceProducts, 2020).
56.		Monthly Interior Ocean pH Climatology: Zhong et al. (2025) developed a monthly 1° × 1° gridded global seawater pH (total scale) climatology from 1992 to 2020 at in situ temperature, derived using a machine learning algorithm trained on pH observations from GLO- DAPv2 (Lauvset et al., 2024). The product spans from 1992 to 2020 and covers depths from the surface to 2000 m across 41 vertical levels. Its development involved a three-step machine-learning approach: (1) regional division using a self-organizing map neural network, (2) predictor selection via stepwise regression, which iteratively adds or removes variables based on their impact on reconstruction error, and (3) nonlinear regression using feedforward neural networks (FFNNs). The developed data product is available at https://doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023).
57.		CODAP-NA Climatology: Jiang et al. (2024) developed a coastal OA indicators climatology on a 1° × 1° grid, covering North American ocean margins from the surface to 500 m at 14 standardized depth levels. This product includes 10 key oceanographic variables: f CO2, pH, [H+]total, free hydrogen ion content ([H+]free), carbonate ion content ([CO23-]), •arag, •calc, DIC, TA, and Revelle Factor (RF), as well as temperature and salinity. The climatology was produced with the WOA gridding technologies of the NOAA National Centers for Environmental Information (NCEI), based on the recently released Coastal Ocean Data Analysis Product in North America (CODAP-NA) (Jiang et al., 2021), along with GLODAPv2.2022 (Lauvset et al., 2022). The relevant variables were adjusted to the year of 2010 before the gridding. The first-guess fields for this analysis were calculated using ESPERs (Carter et al., 2021), based on the WOA (Version 2018) climatologies for salinity (Zweng et al., 2019), temperature (Locarnini et al., 2019) and DO (Garcia et al., 2018b). The data product is available in NetCDF at https://www.ncei.noaa.gov/data/oceans/ncei/ocads/ metadata/0270962.html (last access: 7 January 2026) (Jiang et al., 2022b). Additionally, maps of these indicators are available in jpeg at https://www.ncei.noaa. gov/access/ocean-carbon-acidification-data-system/ synthesis/nacoastal.html (last access: 7 January 2026) (Jiang et al., 2022b).
3.1.5 Multi-product analyses
This section includes data products that have been generated by community synthesis efforts designed to inform the GCB (Table 5).
58.		SeaFlux: harmonization of air–sea CO2 fluxes from surface pCO2 data products using a standardized approach (Gregor and Fay, 2021). This resource provides an ensemble of six pCO2 products with air–sea CO2 fluxes computed consistently. The six included products are: CMEMS-LSCEv1, CSIR-ML6, JENA-MLS, JMA- MLR, MPI-SOMFFN, and NIES-FNN. First, missing areas of pCO2 estimates (mostly high-latitude and marginal seas) are filled using a linear-regression approach, thus addressing differences in spatial coverage between the mapping products. Further, it also accounts for methodological inconsistencies in flux calculations. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55) along with the application of a scaled gas exchange coefficient for each of the wind products. Through these steps, SeaFlux presents an ensemble product of interpolated global surface ocean pCO2 and air–sea carbon flux estimates for the years 1990–2019. For more details, refer to Fay et al. (2021).
59.		RECCAP2: in the context of the second iteration of the project REgional Carbon Cycle Assessment and Processes (RECCAP2), the ocean carbon community compiled, quality-controlled, and harmonized (in the sense of providing output on the same regular grid at the same spatial and temporal resolution) 12 GOBMs simulations, 11 pCO2 products, one ocean interior DIC product, and three data assimilation models to constrain the ocean carbon sink between 1985 and 2018. The RECCAP2 synthesis effort stands as a distinct but complementary resource to the GCB project (Friedlingstein et al., 2025), which primarily focuses on anthropogenically perturbed surface CO2 fluxes from a global budgeting perspective. The individual chapters of RECCAP2 were published in this special issue of Global Biogeochemical Cycles: https://agupubs.onlinelibrary.wiley.com/ doi/toc/10.1002/(ISSN)2169-8961.RECCAP2 (last access: 7 January 2026). The data products of this assessment are available on a 1° × 1° horizontal grid, with monthly resolution for surface ocean variables such as air–sea CO2 fluxes, and annual resolution for interior ocean variables, such as DIC content. The data compilation, which is described in detail in DeVries et al. (2023) and evaluated in Terhaar et al. (2024b), is available at https://doi.org/10.5281/zenodo.7990823 (Müller, 2023).
60.		Global Carbon Budget: the GCB collects annually updated estimates of the ocean carbon sink from currently nine f CO2-products and ten GOBMs for the period 1959 to the past calendar year (https://globalcarbonbudget.org/gcb-2025, last access: 7 January 2026, Friedlingstein et al., 2025). In contrast to Earth System Models (ESMs), the GOBMs are here forced with atmospheric reanalysis that ingested atmosphere and ocean observations and are thus thought to be closer to the observed climate. Gridded fields are provided on a 1° × 1° horizontal grid and monthly resolution. In addition, globally and regionally integrated air–sea CO2 fluxes from the native model grids are provided. Globally integrated time-series are adjusted for full ocean coverage and model bias and drift and are available for each individual f CO2- product and GOBM (https://globalcarbonbudget.org/ download/1442/?tmstv=1731323337, last access: 7 January 2026). The model data goes well beyond surface fluxes and includes data to analyze drivers of carbon fluxes, including several 3D variables. The model data request has been updated since RECCAP2 and also provides, for example, monthly interior ocean data of DIC, TA, nutrients and DO. The GOBM data request was also updated to have all variables available that are needed to serve as a testbed for f CO2-products (e.g., sea surface height). Gridded

Table 4. Gridded and derived ocean carbonate chemistry data synthesis products in the subsurface ocean.
No.	Name	Open ocean or coastal ocean	Resolution	Temporal resolution	Methodology	Highlights	Reference	
46	GLODAPv2 Climatology	Open ocean	1°×1°	Referenced to
2002	Data Interpolating Variational Analysis (DIVA)	Ocean interior climatology for multiple variables from the surface to the bottom of the ocean (referenced to 2002)	Lauvset et al. (2016)	
47	Aragonite Saturation State Climatology	Open ocean	1°×1°	Referenced to
2000	Data Interpolating Variational Analysis (DIVA)	Ocean interior climatology for •arag from the surface to 4000 m (referenced to 2000)	Jiang et al. (2015)	
48(a)	MOBO-DIC
(Version 2020)	Open ocean	1°×1°	Monthly climatology referenced to
January 2011	Machine learning	Seasonal variability of DIC in the interior ocean from the surface to 2000 m	Keppler et al. (2020a)	
48(b)	MOBO-DIC
(Version 2023)	Open ocean	1°×1°	Monthly from
January 2004 to December 2019	Machine learning	Temporal trends and interannual variability of DIC in the interior ocean from the surface to 1500 m	Keppler et al. (2023a, b)	
49	Monthly
Interior Ocean TA Climatology	Open ocean	1°×1°	Monthly climatology	Machine learning	Ocean interior climatology for TA from the surface to the bottom	Broullón et al. (2019, 2020b)	
50	Monthly
Interior Ocean DIC Climatology	Open ocean	1°×1°	Monthly from 1957 to 2018	Machine learning	Ocean interior climatology for DIC from the surface to the bottom (referenced to 1995)	Broullón et al. (2020a, c)	

Table 4. Continued.
No.	Name	Open ocean or coastal ocean	Resolution	Temporal resolution	Methodology	Highlights	Reference	
51	Acidification Metrics in the Ocean Interior	Open ocean	1°×1°	2002 and preindustrial	Reproduced from
GLODAPv2 Climatology (Lauvset et al., 2016) and the preformed properties of Carter et al. (2021)	Metrics of acidification in the ocean interior (to 2000 m) and the component of those changes caused by carbonate system nonlinearities	Fassbender et al. (2023)	
52	Ocean Interior Acidification over the Industrial Era	Open ocean	1°×1°	1800,1994,
2004, 2014	Conversion of anthropogenic carbon accumulation into acidification rates	Temporal trends in the progression of acidification in the interior ocean are resolved	Müller and
Gruber (2024a)	
53(a)	Anthropogenic
CO2 from 1994 to 2007	Open ocean	1°×1°	From 1994 to 2007	eMLR(C∗) extended
Multiple Linear
Regression applied to the tracer C∗	The oceanic sink for anthropogenic
CO2 over the period 1994 to 2007	Gruber et al. (2019a)	
53(b)	Decadal Trends in Anthropogenic
CO2 from 1994 to 2014	Open ocean	1°×1°	Decadal from 1994 to 2014	eMLR(C∗) extended
Multiple Linear
Regression applied to the tracer C∗	Temporal trends in the accumulation of anthropogenic CO2 in the interior ocean are resolved	Müller et al. (2023a)	
54	Anthropogenic carbon from 1750 to 2500 (TRACE)	Open ocean	1°×1°	Referenced to 20 years ranging from ∼ 1750 to 2500	Estimated from atmospheric transients and ocean tracer distributions	Estimates over time based on assumptions of steady state ocean circulation and CO2 exchange	Carter et al. (2025)	
55	Preformed TA and other biogeochemical properties	Open ocean	1°×1°	Not referenced in time	Empirical algorithms with inversions to find source outcrops	Estimates of the properties of seawater at the time of last contact with the atmosphere	Carter et al. (2021)	
Table 4. Continued.
No.	Name	Open ocean or coastal ocean	Resolution	Temporal resolution	Methodology	Highlights	Reference	
56	Monthly
Interior Ocean pH Climatology	Open ocean	1°×1°	Monthly from
January 1992 to December 2020	Machine learning	Temporal variability of pH in the interior ocean from the surface to 2000 m over 3 decades	Zhong et al. (2025)	
57	CODAP-NA Climatology	Coastal ocean	1°×1°	Referenced to
2010	Objective analysis approach of the WOA	The first discrete bottle-based climatology in the North American ocean margins	Jiang et al. (2024)	

surface data of sea surface fugacity and air–sea CO2 flux of all f CO2-products and GOBMs as used in the latest release of GCB (Version 2024) are published on Zenodo (https://doi.org/10.5281/zenodo.14639761, Hauck et al., 2025). All other GOBM output is available via https://globalcarbonbudgetdata.org/ closed-access-requests.html (last access: 7 January 2026).
3.1.6 Model-based and hybrid products and analyses
Model-based projections of biogeochemical variables are often available from global and regional models, such as those in the Seventh Coupled Model Intercomparison Project (CMIP7) (Dunne et al., 2025; Durack et al., 2025). This section further includes hybrid data products, which adjust model estimates towards observation-based constraints (Table 6).
61.		Decadal Trends in the Ocean Carbon Sink: The DeVries et al. (2019) analysis examines decadal trends in global and regional air–sea CO2 fluxes from a variety of ocean biogeochemical models that contributed to the GCB (see No. 60). Three sets of model simulations were performed. Simulation A uses variable climate forcing (e.g., variable wind stress, heat and freshwater fluxes) and observed atmospheric CO2 forcing, simulation B uses constant (repeated) climate forcing and observed atmospheric CO2, and simulation C uses both constant climate forcing and constant atmospheric CO2 concentrations. With these simulations, the
authors partitioned decadal trends in ocean CO2 uptake into those driven by climate variability and those driven by atmospheric CO2. They found that climate variability drove a weakening trend of the ocean carbon sink during the 1990s, and a strengthening trend during the first decade of the 2000s. The magnitude of these trends agreed with those of an OCIM that was trained to replicate tracer data from the 1990s and 2000s (DeVries et al., 2017), indicating that the decadal trends may be driven by variability in ocean circulation. The data from this analysis are accessible at https://doi.org/10.6084/m9.figshare.8091161.v1 (DeVries, 2019).
62.		ECCO-Darwin: Carroll et al. (2022) used the Estimating the Circulation and Climate of the OceanDarwin (ECCO-Darwin) global-ocean biogeochemistry state estimate to generate a data-constrained DIC budget and investigate how spatiotemporal variability in advection and mixing, air–sea CO2 flux, and the biological pump have modulated the ocean sink for 1995–2018. ECCO-Darwin assimilates ocean circulation and physical tracers, including temperature, salinity, and sea ice, derived from the Estimating the Circulation and Climate of the Ocean (ECCO) LLC270 global-ocean and sea-ice data synthesis (Zhang et al., 2018). Additionally, it assimilates biogeochemical observations encompassing the cycling of carbon, nitrogen, phosphorus (PO4), iron (Fe), silica (SiO2), DO, and TA. This inclusive approach enhances the model’s fidelity by aligning it with a diverse array of observations. All ECCO-Darwin model output is available on the ECCO Data Por-

Table 5. Ocean carbonate chemistry data product synthesis and harmonizations.
No.	Name	Open ocean or coastal ocean	Surface or water column	Spatial resolution	Temporal resolution	Methodology	Highlights	Reference	
58	SeaFlux	Open ocean + Coastal ocean	Surface only	1°×1°	Monthly from 1990 to 2022	Consistent flux calculations for 6 pCO2 products to produce an ensemble estimate	Careful consideration of flux calculation provides a resource and code to the community for independent flux calculations	Gregor and Fay (2021)	
59	RECCAP2	Open ocean + Coastal ocean	Surface + Water	1°×1° (open)
0.25° ×	Monthly from 1985 to 2018	Harmonized compilation of surface f CO2	Quality- controlled data compilation	Müller (2023), DeVries et al. (2023),	
			column	0.25° (coastal)		products, model simulations and ocean interior
products	with a harmonized horizontal grid and temporal resolution	Resplandy et al. (2024)	
60	Global	Open ocean +	Surface	1°×1°	Monthly from	Harmonized	Annually	Friedlingstein	
	Carbon	Coastal ocean	+		1959 to 2023	compilation of	updated and	et al. (2025)	
	Budget		Water column			surface f CO2 products, and GOBM simulations	quality- controlled datasets.
Availability of monthly 4D ocean model output.		

tal: https://data.nas.nasa.gov/ecco/ (last access: 7 January 2026). The model code and platform-independent instructions for running ECCO-Darwin simulations can be found at https://github.com/MITgcm-contrib/ecco_ darwin (last access: 7 January 2026).
63.		Global Surface Ocean Acidification Indicators:
a.		Surface pH and Revelle Factor: Jiang et al. (2019a) produced a high-resolution (1° × 1°) data product delineating a regionally varying view of global surface ocean pH, acidity, and Revelle Factor (RF) from 1770 to 2100 by amalgamating recent observational seawater CO2 data from the SOCAT database (Version 6) (Bakker et al., 2016) and temporal trends at individual locations of the global surface ocean from an Earth System Model, i.e., GFDL-ESM2M (Dunne et al., 2013). The calculations were conducted under historical atmospheric CO2 levels (pre-2005) and four Representative Concentrations Pathways (post-2005) correspond-
ing to the Intergovernmental Panel on Climate Change (IPCC)’s 5th Assessment Report, specifically RCP2.6, RCP4.5, RCP6.0, and RCP8.5. Surface ocean TA was calculated from SSS and SST using the updated locally interpolated alkalinity regression (LIARv2) method (Carter et al., 2017). Surface ocean pH, acidity, and RF were then calculated using a MATLAB version of the CO2SYS program (Orr et al., 2015). The data product is available at https://www.ncei.noaa.gov/data/ oceans/ncei/ocads/metadata/0206289.html (last access: 7 January 2026) (Jiang et al., 2019b).
b.		Surface OA Indicators: Jiang et al. (2023b) developed a comprehensive model-data fusion product that delineates the trajectory of 10 OA indicators: fCO2, pH, [H+]total, [H+]free, [CO23-], •arag, •calc, DIC, TA, and RF, as well as temperature and salinity at all locations of the global surface ocean from 1750 to 2100. This product marks a significant improvement in OA forecasting by refining
temporal trends with data from 14 ESMs within CMIP6, and by applying bias and drift corrections using three updated observational ocean carbonate system data products: SOCAT (Version 2022) (Bakker et al., 2016), GLODAPv2.2022 (Lauvset et al., 2022), and CODAP-NA (Jiang et al., 2021). This dataset offers 10-year averages on a 1° × 1° global surface ocean grid, capturing trends from preindustrial times (1750) through historical conditions (1850–2010), and projects future conditions to 2100 across five Shared Socioeconomic Pathways: SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3- 7.0, and SSP5-8.5. The gridded data product is available in NetCDF at https://www.ncei.noaa.gov/ data/oceans/ncei/ocads/metadata/0259391.html (last access: 7 January 2026) (Jiang et al., 2022c), and global maps of these indicators are available in JPEG at https://www.ncei.noaa.gov/ access/ocean-carbon-acidification-data-system/ synthesis/surface- oa- indicators.html (last access: 7 January 2026) (Jiang et al., 2022c).
64.		Simulated and Constrained Global and Southern Ocean Carbon Sink: these two datasets include spatially- integrated and annually averaged values for the ocean carbon sink from 1850 to 2100 for different scenarios over the 21st century for the global ocean (Terhaar et al., 2022a, b) and the Southern Ocean (Terhaar et al., 2021b, c). All results are based on CMIP5 and CMIP6 models. For the global ocean carbon sink, values are available for SSP1-2.6, SSP2-4.5, and SSP5- 8.5. For the Southern Ocean, values are also available for SSP1-2.6, SSP2-4.5, and SSP5-8.5 and additionally for RCP2.6, RCP4.5, and RCP8.5. In addition, to the raw simulated values, constrained estimates of the annually averaged ocean carbon sink estimates are available. These constrained estimates adjusted the simulated carbon sink estimates for biases on the ocean’s circulation and surface carbonate chemistry (see Terhaar et al., 2021b, 2022a for details). It is recommended to use the constrained estimates. The datasets are available at https://doi.org/10.17882/103934 (Terhaar et al., 2022b) and https://doi.org/10.17882/103938 (Terhaar et al., 2021c).
65.		Composite model-based estimate of the ocean carbon sink from 1959 to 2022: this data product, developed by Terhaar (2025), presents an estimate of the global ocean carbon sink by combining forced hindcast simulations and simulations made by coupled ESMs. Hindcast models manage to adequately simulate the shortterm variability of the ocean, but struggle to simulate the long-term climate change trend (Huguenin et al., 2022; Takano et al., 2023; Hollitzer et al., 2024). ESMs cannot simulate the observed short-term variability by definition, but accurately simulate long-term trends (Takano et al., 2023; Hollitzer et al., 2024). The composite model-based estimate combines the simulated shortterm variability from hindcast simulations and the longterm trend from ESMs. The output is supplied with the associated study (https://doi.org/10.5194/bg-22-1631- 2025) (Terhaar, 2025).
66.		pCIBR_Clim and pCIBR_Int: a machine learning (ML) model is employed to correct biases in surface pCO2 simulations generated by the INCOIS-BIO-ROMS model (pCO2model) over the period 1980–2019. The ML model is trained using the differences between observed (pCO2obs) and modeled pCO2 to estimate the spatio-temporal deviations (pCO2obs-pCO2model). These interannually and climatologically varying deviations are then added back to the original model output, resulting in two improved data products: pCIBR_Int and pCIBR_Clim (Ghoshal et al., 2025a). Evaluation against independent datasets, including moored observations (BOBOA), the gridded SOCAT product, and other ML-based pCO2 products (such as CMEMS- LSCEv2 and OceanSODA), demonstrates a significant improvement of approximately 40 % ± 3.31 % in RMSE compared to the original model. This high- resolution (0.083° × 0.083°), long-term monthly pCO2 data product is available from the INCOIS Portal (https: //las.incois.gov.in, last access; 7 January 2026) and from OCADS: https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0307788.html (last access: 7 January 2026) (Ghoshal et al., 2025b).
67.		INCOIS-BIO-ROMS Simulated Surface pCO2 and pH for the Indian Ocean: this data product presents a comprehensive assessment of OA trends across the Indian Ocean and its sub-regions from 1980 to 2019, leveraging outputs from a regional, high- resolution coupled ocean-ecosystem model (INCOIS- BIO-ROMS), an offline biogeochemical (BGC) model, and two machine learning-based products (Chakraborty et al., 2024). INCOIS-BIO-ROMS, configured at 1/12° resolution for the Indian Ocean, was developed in accordance with the “RECCAP-2: Ocean Modeling Protocol” for regional oceans. The INCOIS-BIO- ROMS simulated surface pCO2 and pH data product is available from the INCOIS Portal (https:// las.incois.gov.in, last access: 7 January 2026) and from OCADS: https://www.ncei.noaa.gov/data/oceans/ ncei/ocads/metadata/0307663.html (last access: 7 January 2026) (Chakraborty et al., 2025).
68.		Ocean Circulation Inverse Model (OCIM): DeVries (2022b) utilized a two-step procedure to estimate anthropogenic carbon in the ocean interior using an ocean inverse model. First, a steady-state ocean circulation inverse model (OCIM) was fit to observations of physical circulation tracers such as temperature, salinity, radiocarbon, and CFCs (Holzer et al., 2021). Then, the circulation model was coupled to an abiotic carbon cycle model, spun up to equilibrium in 1780 and then forced by observed atmospheric CO2 time history from 1781–2020. Simulations were run with and without historical changes in sea surface temperatures. The difference between preindustrial and transient simulations represents the anthropogenic carbon accumulation in the ocean. Results are provided on a regular grid with a nominal resolution of 2° in the horizontal with 48 depth levels. The output is available at https://doi.org/10.6084/m9.figshare.19341974.v2 (DeVries, 2022c).
3.2 Overlaps and history
Many of the data products described above exhibit significant overlap in various forms. In some cases, one or more products are used to generate new ones, while in others, the same collection-level cruise datasets underpin multiple products. There are a few foundational data products, such as GLODAPv2 and SOCAT, which are widely utilized to develop other data products, including their respective gridded products (e.g., Lauvset et al., 2016). For instance, SOCAT forms the backbone of nearly all derived products listed in Table 3, serving as a key resource for product development or validation. Some derived products, such as the JMA-MLR (No. 26) and OceanSODA-ETHZv1 (No. 27a), incorporate both SOCAT and GLODAPv2 during development. Having overlaps in data and derived products has provided opportunities for data quality-control and intercomparison of different approaches to gap-filling that would not have been available otherwise. Additional overlaps between these data products are provided below:
3.2.1		SOCAT and LDEO
The quality-control and synthesis of global surface ocean CO2 data began in 1997 with Dr. Taro Takahashi and his colleagues at LDEO in Palisades, New York. His pioneering work led to the creation of the LDEO Surface pCO2 Database (No. 2), which focused on high-quality data collected by his team and from various US and international expeditions. Over time, this data set expanded to include contributions from other laboratories, resulting in a highly influential collection of pCO2 data and several seminal papers on global surface ocean CO2 variations and air–sea CO2 fluxes (Takahashi et al., 1997, 2002, 2009). The last update to the LDEO database was in 2019, following Dr. Takahashi’s passing, and no further updates are anticipated (Takahashi et al., 2017).
The SOCAT project was developed to address questions around the current and future drivers of CO2 fluxes raised at the 2007 Surface Ocean CO2 Variability and Vulnerability (SOCOVV) workshop in Paris, France (Metzl et al., 2007)	. SOCAT was developed to synthesize all of the publicly available, discoverable, and citable surface CO2 data. Following the GLODAP model, there was a strong emphasis on an open and transparent secondary quality-control process to ensure the highest data quality. The first data release came in 2011 (Pfeil et al., 2013; Sabine et al., 2013) and included contributions from numerous laboratories, as well as the freely available CO2 data from the LDEO database. As of 2024, SOCAT contains ∼ 40 million data points, with new observations added annually. All data are rigorously standardized, and recalculated as f CO2. SOCAT represents an ongoing global community effort, with participants from all continents contributing data and participating in the qualitycontrol process. Initially new versions were released every other year, however automation allowed annual public releases since version 4.
3.2.2		GLODAPv2 and quality edited hydrographic data
Starting in the late 1980s, the WOCE, Joint Global Ocean Flux Study (JGOFS), and the NOAA Ocean-Atmosphere Exchange Study (OACES) collaborated in a multinational effort to conduct a decadal global hydrographic survey of unparalleled quality and quantity. At the conclusion of the survey at the end of the 1990s, GLODAP combined and publicly released all of the available hydrographic data with high-quality ocean carbonate system measurements as a single database (Key et al., 2004; Sabine et al., 2005). The data were subjected to extensive secondary quality-control checks where cruise tracks intersected one another, making it the most comprehensive and highest-quality ocean inorganic carbon dataset ever generated. A gridded, full-depth global ocean carbon climatology was also created and released as part of the project. These data and associated climatology have been extensively used to evaluate carbon distributions as well as the accumulation of anthropogenic CO2 in the ocean. Other regional datasets, like the CARINA data synthesis project, an international collaborative effort of the European Union CARBOOCEAN program (Key et al., 2010; Tanhua et al., 2010), and PACIFICA, an international synthesis of Pacific Ocean data organized through the North Pacific Marine Science Organization (PICES) (Ishii et al., 2011b; Suzuki et al., 2013), were combined with GLODAP after its initial release. The GLODAP database is continuing to grow with new data collected as part of the Global Ocean ShipBased Hydrographic Investigations Program (GO-SHIP).
For discrete bottle measurements spanning the entire oceanic water column, GLODAPv2 (No. 3) and the Quality Edited Hydrographic Data (No. 4) are the primary data products. Most cruise datasets contributing to these two data products overlap, but the key difference lies in their approach to data adjustment. The former applies crossover and inversion analysis for bias correction, while the latter presents the data without such adjustments. GLODAPv2 achieves consistency by applying adjustments based on deep-ocean offsets,

Table 6. Model based data synthesis products and analyses for ocean carbonate chemistry.
No.	Name	Open ocean or coastal ocean	Surface or water column	Spatial resolution	Temporal resolution	Highlights	Reference	
61	Decadal Trends in the Ocean
Carbon Sink	Open ocean	Surface	Variable	Decadal	Climate variability drove weakened ocean CO2 uptake in the 1990s, and strengthened CO2 uptake in the 2000s	DeVries et al. (2019)	
62	ECCO-Darwin	Open ocean + Coastal ocean	Water column	1/3° × 1/3°	3-hourly, daily, and month fields available	Model-data synthesis product based on the Estimating the Circulation and Climate of the Ocean (ECCO) ocean state estimate. Fully-closed, physically- consistent 3D biogeochemical budgets	Carroll et al. (2020, 2022, 2024)	
63(a)	Surface pH and
Revelle Factor	Open ocean	Surface	1°×1°	Decadal from 1770 to 2100	A modelobservation fusion product for pH, acidity and Revelle Factor, leveraging GFDL- ESM2M and SOCATv6	Jiang et al. (2019a)	
63(b)	Surface OA Indicators	Open ocean	Surface	1°×1°	Decadal from 1750 to 2100	A modelobservation fusion product for all major OA indicators, leveraging a consortium of 14 ESMs and 3 observational data products	Jiang et al. (2023b)	

No. Name	Open ocean or	Surface	Spatial	Temporal	Highlights	Reference
coastal ocean	or	resolution	resolution
water
column	
64 Simulated and Constrained
Global and Southern
Ocean Carbon
Sink	Open ocean	Surface Spatially	Annual from	A constrained	Terhaar et al.
integrated	1850 to 2100	estimate of the	(2021b, 2022a)
ocean carbon sink based on the simulated carbon sink from CMIP5 and CMIP6 models and constrained with observations of the ocean physics and carbonate chemistry	
65 Composite model-based estimate of the ocean carbon sink from 1959 to 2022	Open ocean	Surface Spatially	Annual from	A model-based Terhaar (2025)
integrated	1959 to 2022	estimate of the
ocean carbon sink combining the respective strengths of hindcast simulations and simulations by coupled ESMs	
66 pCIBR_Clim
and pCIBR_Int	Open ocean + Surface 0.083° × 0.083° Monthly	This data	Ghoshal et al.
Coastal ocean	product has	(2025a)
been developed
by employing
an innovative
hybrid
approach, where a
machine
learning
algorithm was
used to correct
high-resolution
(1/12°) coupled
ocean
ecosystem
model outputs
using
observational
data from
SOCAT
(1984–2019)
and SAS
(1991–2019)
for the Indian
Ocean	

No. Name	Open ocean or	Surface	Spatial	Temporal	Highlights	Reference
coastal ocean	or	resolution	resolution
water
column	
67 INCOIS-BIO-
ROMS
Simulated
Surface pCO2 and pH for the Indian Ocean	Open ocean + Surface	0.083° × 0.083°	Monthly from INCOIS-BIO- Chakraborty et
Coastal ocean	1980 to 2019	ROMS was	al. (2024)
developed in
accordance
with the
“RECCAP-2:
Ocean
Modeling
Protocol” for
regional
oceans. By
integrating
model
simulations
with available
field
observations
and
reconstructed
data products,
this study
advances the
current
understanding
of OA in the
Indian Ocean	
68 Ocean
Circulation
Inverse Model (OCIM)	Open ocean	Surface 2° × 2°	Annual from	Data-	DeVries
and	horizontal,	1780 to 2020	constrained	(2022b)
Water	48 vertical	estimate of
column	layers	anthropogenic
CO2
accumulation
in the ocean
from inverting
physical ocean circulation
tracers	

whereas Quality Edited Hydrographic Data provides the data in its original form. While there is substantial overlap between the two, data from a specific expedition might differ slightly due to GLODAPv2’s secondary quality-control adjustments. Both GLODAPv2 and Quality Edited Hydrographic Data offer global coverage, but several independent regional data products are also available, such as SNAPO- CO2 (No. 6), CODAP-NA (No. 7), AZMP Carbon (No. 8), MOCHA (No. 9), and ARIOS (No. 10). Data from these regional products often partially or fully overlap with GLO- DAPv2 and Quality Edited Hydrographic Data.
(a)		GLODAPv2 and CODAP-NA
All cruise datasets contributing to CODAP-NA were forwarded to the GLODAPv2 quality-control team in 2022. Data from select cruises with deep-water sampling (> 1500 m), enabling crossover analysis, were subsequently incorporated into the GLODAPv2.2022 data product update (Lauvset et al., 2022).
(b)		GLODAPv2 and SPOTS
Some time-series data are included in both GLODAPv2 and the Synthesis Product for Ocean Time-Series (SPOTS). Usu
ally, data present in both products were not measured on dedicated time-series cruises but rather were collected as part of a larger cruise passing by a time-series location. As the quality-control of SPOTS is restricted to assigning method flags, adjustments that are applied as a result of the QC of GLODAP are not present in SPOTS. Additional crossover analyses between SPOTS and GLODAP have revealed good consistency (Lange et al., 2024a).
3.2.3		RECCAP2 and GCB
RECCAP2 and GCB are not data products themselves, but analyses and syntheses of data-based and model-based products. Users should be aware that there is a large degree of overlap between the f CO2 products and GOBMs that contributed to both RECCAP2 and GCB, and of the resulting datasets. However, the RECCAP2 and GCB analyses serve different purposes. GCB is updated annually to the latest complete calendar year and its main purpose is to present and estimate the magnitude (and uncertainty) of the ocean CO2 sink and the role of CO2 and climate drivers since 1959 with a focus on the last year, while RECCAP2 presents a deeper analysis of the magnitude, trends, and variability of the global and regional ocean CO2 sink over the period 1985– 2018.
3.2.4		Jiang et al. (2019a, 2023)
Both products contain the projection of surface ocean pH, [H+ ]total, and buffer capacity from 1750 to 2100. However, the former is based on one GFDL model ESM2M, while the latter is based on a consortium of 14 ESMs, and additional observational data. The latter also contains the projection of seven other OA variables, including carbonate ions, •arag, •calc, f CO2, DIC, TA, and [H+]free.
4		Data availability
Access links for all data products mentioned in this paper are provided in their respective paragraphs. Additionally, access links for all products are available in Table 7.
5		Summary
The synthesis and gridded data products presented here reflect significant community-based efforts that have been made to advance understanding of the ocean’s role in global carbon cycling. This synthesis provides an overview of key data compilations and gridded data products essential for coastal and global ocean carbonate chemistry research. It highlights the key features of each product, serving as a resource for researchers seeking the necessary data for their work. The list will be updated periodically to incorporate new data products. The most up-to-date list is available at https://oceanco2.github.io/co2-products/ (Gregor and Jiang, 2026)	. A submission interface is also available on the data product page. After submitting a new data product, please send a notification to noaa.ocads@noaa.gov to ensure the submission is reviewed and added to the webpage.
Author contributions. LQJ prepared the initial draft. LG designed and implemented the GitHub webpage and supporting scripts to present the most current list of products. AR prepared Fig. 1. All authors contributed to the writing of the manuscript. The first 23 authors are listed based on their contributions, while the remaining authors are listed alphabetically by their last names.
Competing interests. At least one of the (co-)authors is a member of the editorial board of Earth System Science Data. The peerreview process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Acknowledgements. We extend our gratitude to all scientists who collected and measured the original data and those who compiled and quality-controlled these data products. We thank Pierre Friedlingstein (University of Exeter, Exeter, UK), Bronte Tilbrook (CSIRO Oceans and Atmosphere and Australian Antarctic Program Partnership, University of Tasmania, Australia), and Dwight Gledhill (NOAA/OAR Ocean Acidification Program, United States) for their valuable insights and discussions, which helped shape the vision of this paper. Additionally, we thank Xinping Hu (University of Texas at Austin, United States), Tessa Hill (University of California, Davis, United States), and Patrick Duke (University of Victoria, Canada) for recommending numerous data products incorporated into this compilation. This is PMEL contribution 5728. This is IN- COIS contribution no. 596.
Financial support. Funding for Li-Qing Jiang was from NOAA Ocean Acidification Program (https://ror.org/02bfn4816, last access: 7 January 2026) and NOAA grant NA24NESX432C0001 (Cooperative Institute for Satellite Earth System Studies – CISESS) at the Earth System Science Interdisciplinary Center, University of Maryland. Jens Daniel Müller acknowledges funding by Carbon to Sea through the Windward Fund and support from Google for OAEMIP. Maciej Telszewski acknowledge funding from the United States National Science Foundation (grant no. OCE- 2513154) to the Scientific Committee on Oceanic Research (SCOR, United States) for the International Ocean Carbon Coordination Project (IOCCP) and from the United Nations Educational, Scientific and Cultural Organization (UNESCO) to the Institute of
Table 7. Access links for the compiled ocean carbonate chemistry data products. n/a is short for not applicable. Last access date for all URLs mentioned in this table: 7 January 2026.
No.	Name	Data access link	DOI	Reference	
1	SOCAT	https://socat.info/	https://doi.org/
10.25921/648f-fv35	Bakker et al. (2016, 2025)	
2	LDEO Surface pCO2 Database	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0160492.html	https://doi.org/
10.3334/cdiac/otg.ndp088(v2015)	Takahashi et al. (2017)	
3	GLODAPv2	https://glodap.info/	https://doi.org/
10.25921/zyrq-ht66	Lauvset et al. (2023b, 2024)	
4	Quality Edited Hydrographic
Data	https://joa.ucsd.edu/	n/a	Swift and
Osborne (2025)	
5	WOD	https://www.ncei.noaa.gov/products/ world-ocean-database	https://doi.org/
10.25923/z885-h264	Mishonov et al. (2024)	
6	SNAPO-CO2	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0285681.html	https://doi.org/
10.17882/95414	Metzl et al.
(2023, 2024)	
7	CODAP-NA	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0219960.html	https://doi.org/
10.25921/531n-c230	Jiang et al.
(2020, 2021)	
8	AZMP Carbon	n/a	https://doi.org/
10.20383/102.0673	Cyr et al. (2022)	
9	MOCHA	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0277984.html	https://doi.org/
10.25921/2vve-fh39	Kennedy et al. (2023)	
10	ARIOS	https:
//digital.csic.es/handle/10261/205135	https://doi.org/
10.20350/digitalCSIC/12498	Pérez et al. (2020)	
11	Marine Inorganic Carbonate
Chemistry in the Northern
Gulf of Alaska	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0277034.html	https://doi.org/
10.25921/x9sg-9b08	Monacci et al. (2023, 2024)	
12	Coral Reef Carbonate Chemistry Off the Florida Keys	https://www.ncei.noaa.gov/access/ metadata/landing-page/bin/iso?id=gov. noaa.nodc:NCRMP-CO3-Atlantic	https://doi.org/
10.25921/vfz0-dg77	Manzello et al. (2018)	
13	Salish Cruise Data Package and
Multi-stressor Data Product	https://www.ncei.noaa.gov/access/ ocean- carbon- acidification- data- system/ oceans/SalishCruise_DataPackage. html, https://www.ncei.noaa.gov/access/ ocean- carbon- acidification- data- system/ oceans/SalishCruises_DataProduct.
html	https://doi.org/
10.25921/jgrz-v584,
https://doi.org/10.25921/4y18-rw26	Alin et al. (2024a, b, 2025a, b, c)	
14	Line P Marine Carbonate Chemistry Compilation	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0234342.html	https://doi.org/
10.25921/zrw8-kn24	Franco et al. (2021a, b)	

Table 7. Continued.
No.	Name	Data access link	DOI	Reference	
15	Anthropogenic Carbon in the Arctic Ocean	https://www.seanoe.org/data/ 00927/103920/	https://doi.org/
10.17882/103920	Terhaar et al.
(2024a)	
16	BATS	https://demo.bco-dmo.org/ project/2124	https://doi.org/
10.26008/1912/bco-dmo.894099.6, https://doi.org/10.26008/1912/bco- dmo.893182.6,
https://doi.org/10.26008/1912/bco-
dmo.926534.4,
https://doi.org/10.26008/1912/bco-
dmo.893521.6,
https://doi.org/10.26008/1912/bco-
dmo.917255.5,
https://doi.org/10.26008/1912/bco-
dmo.939210.9,
https://doi.org/10.26008/1912/bco-
dmo.3782.8,
https://doi.org/10.26008/1912/bco-
dmo.3918.10,
https://doi.org/10.26008/1912/bco-
dmo.881861.6	Bates et al. (2024a, b, c, d, e, 2025), Johnson et al.
(2025a, b), Steinberg and Cope (2025)	
17	HOT	https://www.bco-dmo.org/ project/2101,
https://doi.org/10.5281/zenodo. 15060931	https://doi.org/
10.26008/1912/bco-dmo.3773.3,
https://doi.org/10.5281/zenodo.15060931	Winn et al.
(1994, 1998), Dore et al.
(2003, 2009, 2014, 2025), Knor et al.
(2023, 2025, White et al.
(2025)	
18	ESTOC	n/a	https://doi.org/
10.1594/PANGAEA.959856,
https://doi.org/10.1594/PANGAEA.856590,
https://doi.org/10.1594/PANGAEA.856615 ,
https://doi.org/10.1594/PANGAEA.856608,
https://doi.org/10.1594/PANGAEA.856616,
https://doi.org/10.1594/PANGAEA.856593,
https://doi.org/10.1594/PANGAEA.856612,
https://doi.org/10.1594/PANGAEA.856614,
https://doi.org/10.1594/PANGAEA.856607,
https://doi.org/10.1594/PANGAEA.956272	González- Dávila and Santana- Casiano (2023b), González- Dávila and Melchor (2016a, b, c, d, e, f, g, h), González- Dávila et al.
(2023)	
19	Point B Time-series	n/a	https://doi.org/
10.1594/PANGAEA.727120	Gattuso et al. (2021b)	
20	Ny-Ålesund Time-series	n/a	https://doi.org/
10.1594/PANGAEA.957028	Gattuso et al. (2023b)	

Table 7. Continued.
No.	Name	Data access link	DOI	Reference	
21	SPOTS	https:
//www.bco-dmo.org/dataset/896862	https://doi.org/
10.26008/1912/bco-dmo.896862.2	Lange et al. (2024a, b)	
22	pCO2 and pH Time-series from
40 Surface Buoys	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0173932.html	https://doi.org/
10.7289/v5db8043	Sutton et al. (2018)	
23	Takahashi delta f CO2 and flux climatology	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0282251.html	https://doi.org/
10.25921/295g-sn13	Fay et al. (2023)	
24	MPI-ULB- SOM-FFN	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0209633.html	https://doi.org/
10.25921/qb25-f418	Landschützer et al. (2020a, b)	
25	VLIZ SOM-FFN	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0160558.html	https://doi.org/
10.7289/v5z899n6	Landschützer et al. (2016), Jersild et al. (2017)	
26	JMA-MLR	https:
//www.data.jma.go.jp/kaiyou/english/
co2_flux/co2_flux_data_en.html	n/a	Iida et al. (2021)	
27(a)	OceanSODA-
ETHZv1	http_s:	___
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0220059.html	https://doi.org/
10.25921/m5wx-ja34	Gregor and
Gruber (2020)	
27(b)	OceanSODA- ETHZv2	https://doi.org/
10.5281/zenodo.11206366	https://doi.org/
10.5281/zenodo.11206366	Gregor et al. (2024b)	
28	LDEO-HPD fCO2	https://doi.org/
10.5281/zenodo.4760205	https://doi.org/
10.5281/zenodo.4760205	Gloege et al. (2021)	
29	LDEO-HPD with Extended Temporal Coverage	https://doi.org/
10.5281/zenodo.13891722	https://doi.org/
10.5281/zenodo.13891722	Fay et al. (2024b)	
30	LDEO fCO2 – Residual
Method	https://doi.org/
10.5281/zenodo.13941548	https://doi.org/
10.5281/zenodo.13941548	Bennington et al. (2024)	
31(a)	CMEMS-
LSCEv1	https://data.ipsl.fr/catalog/srv/eng/	https://doi.org/
catalog.search#/metadata/	10.14768/a2f0891b-763a-49e9-af1b-
a2f0891b-763a-49e9-af1b-78ed78b16982 78ed78b16982	Chau et al. (2022b)	
31(b)	CMEMS-
LSCEv2	https://data.marine.copernicus.eu/ product/MULTIOBS_GLO_BIO_ CARBON_SURFACE_MYNRT_015_ 008/services	https://doi.org/
10.48670/moi-00047	Chau et al.
(2024a, b, c)	
32	CarboScope (Jena-MLS)	https://www.bgc-jena.mpg.de/
CarboScope/?ID=oc	https://doi.org/
10.17871/CarboScope-oc_v2024E (or analogously for previous and upcoming releases)	Rödenbeck (2024), Rödenbeck et al. (2022)	
33	UOEx-Watson	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0301544.html	https://doi.org/
10.25921/2dp5-xm29	Watson et al. (2025)	

Table 7. Continued.	
No.	Name	Data access link	DOI	Reference	
34	NIES-ML3	https://db.cger.nies.go.jp/DL/10.17595/
20220311.001.html.en	https://doi.org/
10.17595/20220311.001	Zeng (2022), Zeng et al. (2022)	
35	CSIR-ML6	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0206205.html	https://doi.org/
10.25921/z682-mn47	Gregor et al. (2019b)	
36	Stepwise- FFNN	https://msdc.qdio.ac.cn/data/ metadata- special- detail?id= 1955061943609876482	https://doi.org/
10.12157/IOCAS.20250814.001	Zhong (2025), Zhong et al. (2022)	
37	AOML-ET	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0298989.html	https://doi.org/
10.25921/0s8y-q287	Wanninkhof et al. (2024, 2025)	
38	ULB-SOM-
FFN- coastalv2.1	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0279118.html	https://doi.org/
10.25921/4sde-p068	Roobaert et al.
(2023, 2024)	
39	RFR-LME	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0287551.html	https://doi.org/
10.25921/h8vw-e872	Sharp et al. (2024a, b)	
40	ReCAD- NAACOM- pCO2	https://doi.org/
10.5281/zenodo.11500974	https://doi.org/
10.5281/zenodo.11500974	Wu et al.
(2024, 2025)	
41	Gridded
Surface OA Indicators and Air–sea CO2 Fluxes in the Northern
Caribbean Sea	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0207749.html	https://doi.org/
10.25921/2swk-9w56	Wanninkhof et al. (2019)	
42	OA data in the Gulf of Mexico/Gulf of America and Wider
Caribbean	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0245950.html	https://doi.org/
10.25921/tt1c-dx53	van Hooidonk (2022)	
43	pCO2
Climatology of the Baltic Sea	n/a	https://doi.org/
10.1594/PANGAEA.961119	Bittig et al. (2023)	
44	INCOIS-ReML	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0307627.html	https://doi.org/
10.25921/2sjr-pg16	Joshi et al.
(2024, 2025a)	
45	INCOIS_TA	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0307789.html	https://doi.org/
10.25921/7as7-et15	Joshi et al. (2025b, c)	
46	GLODAPv2 Climatology	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0286118.html	https://doi.org/
10.3334/cdiac/otg.ndp093_glodapv2	Lauvset et al.
(2016, 2023a)	
47	Aragonite Saturation State Climatology	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0139360.html	https://doi.org/
10.7289/v5q81b4p	Jiang and Feely (2015), Jiang et al. (2015)	
Earth Syst. Sci. Data, 18,	1405–1462, 2026	https://doi.org/10.5194/essd-18-1405-2026	

Table 7. Continued.
No.	Name	Data access link	DOI	Reference	
48(a)	MOBO-DIC
(Version 2020)	https://www.ncei.noaa.gov/access/ ocean-carbon-acidification-data-system/
oceans/ndp_104/ndp104.html	https://doi.org/
10.25921/yvzj-zx46	Keppler et al. (2020b)	
48(b)	MOBO-DIC
(Version 2023)	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0277099.html	https://doi.org/
10.25921/z31n-3m26	Keppler et al. (2023a, b)	
49	Monthly
Interior Ocean TA Climatology	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0222470.html	https://doi.org/
10.25921/5p69-y471	Broullon et al. (2019, 2020b)	
50	Monthly
Interior Ocean DIC Climatology	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0222469.html	https://doi.org/
10.25921/ndgj-jp24	Broullón et al. (2020a, c)	
51	Acidification Metrics in the Ocean Interior	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0290073.html	https://doi.org/
10.25921/rdtr-9t74	Fassbender et al.
(2023), Fassbender
(2024)	
52	Ocean Interior Acidification over the Industrial Era	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0298993.html	https://doi.org/
10.25921/tefm-x802	Müller and Gruber (2024a, b)	
53(a)	Anthropogenic
CO2 from 1994 to 2007	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0186034.html	https://doi.org/
10.25921/wdn2-pt10	Gruber et al. (2019a, b)	
53(b)	Decadal Trends in Anthropogenic
CO2 From 1994 to 2014	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0279447.html	https://doi.org/
10.25921/ppcf-w020	Müller et al. (2023a, b)	
54	Anthropogenic carbon from 1750 to 2500 (TRACE)	https://github.com/
BRCScienceProducts/TRACEv1	https://doi.org/
10.5281/zenodo.15003059	Carter (2025),
Carter et al. (2025)	
55	Preformed TA and other biogeochemical properties	https:
//github.com/BRCScienceProducts/
PreformedPropertyEstimates	https://doi.org/
10.5281/zenodo.3745002	BRCScienceProducts (2020), Carter et al. (2020)	
56	Monthly
Interior Ocean pH Climatology	https://doi.org/
10.12157/IOCAS.20230720.001	https://doi.org/
10.12157/IOCAS.20230720.001	Zhong et al. (2023, 2025)	
57	CODAP-NA Climatology	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0270962.html	https://doi.org/
10.25921/g8pb-zy76	Jiang et al. (2022b, 2024)	
58	SeaFlux	https://doi.org/
10.5281/zenodo.5482547	https://doi.org/
10.5281/zenodo.5482547	Gregor and Fay (2021)	
59	RECCAP2	https://doi.org/
10.5281/zenodo.7990823	https://doi.org/
10.5281/zenodo.7990823	Müller (2023)	

Table 7. Continued.
No.	Name	Data access link	DOI	Reference	
60	Global Carbon Budget	https://doi.org/
10.5281/zenodo.14639761,
https://globalcarbonbudget.org/
download/1442/?tmstv=1731323337,
https://globalcarbonbudgetdata.org/
closed-access-requests.html	https://doi.org/
10.5281/zenodo.14639761	Hauck et al. (2025)	
61	Decadal Trends in the Ocean Carbon Sink	n/a	https://doi.org/
10.6084/m9.figshare.8091161.v1	DeVries (2019), DeVries et al. (2019)	
62	ECCO-Darwin	https://data.nas.nasa.gov/ecco/	n/a	Carroll et al. (2020)	
63(a)	Surface pH and
Revelle Factor	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0206289.html	https://doi.org/
10.25921/kgqr-9h49	Jiang et al. (2019a, b)	
63(b)	Surface OA Indicators	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0259391.html	https://doi.org/
10.25921/9ker-bc48	Jiang et al.
(2022c, 2023)	
64	Simulated and Constrained Global and Southern
Ocean Carbon Sink	https:
//www.seanoe.org/data/00927/103934/,
https:
//www.seanoe.org/data/00927/103938/	https://doi.org/
10.17882/103934,
https://doi.org/10.17882/103938	Terhaar et al.
(2021c, 2022b)	
65	Composite model-based estimate of the ocean carbon sink from 1959 to 2022	https://bg.copernicus.org/articles/22/
1631/2025/ (the data is in the annex)	https://doi.org/
10.5194/bg-22-1631-2025	Terhaar (2025)	
66	pCIBR_Clim and pCIBR_Int	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0307788.html	https://doi.org/
10.25921/r2q9-d197	Ghoshal et al. (2025a, b)	
67	INCOIS-BIO- ROMS Simulated
Surface pCO2 and pH for the Indian Ocean	https:
//www.ncei.noaa.gov/data/oceans/ncei/
ocads/metadata/0307663.html	https://doi.org/
10.25921/z2x4-vt48	Chakraborty et al. (2024, 2025)	
68	Ocean Circulation
Inverse Model
(OCIM)	https://figshare.com/articles/dataset/
OCIM2-48L_abiotic_ocean_carbon_
cycle_model_output/19341974	https://doi.org/
10.6084/m9.figshare.19341974.v2	DeVries (2022b, c)	

Oceanology of Polish Academy of Sciences for the GOOS Biogeochemistry Panel (4500540682). Siv K. Lauvset and Nico Lange acknowledge funding from OceanICU. OceanICU was funded by the European Union under grant agreement no. 101083922. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Funding for GLODAPv2 was provided by the EU FP7 project CarboChange (grant agreement 264879) and the Research Council of Norway project DECApH (grant agreement 214513), and the 2019–2023 updates were funded by the EU Horizon 2020 innovation action EuroSea (grant agreement 862626). Andrea J. Fassbender, Simone R. Alin, and Richard A. Feely were supported by the NOAA Pacific Marine Environmental Laboratory. Galen A. McKinley was funded by NOAA NA24OARX431G0151-T1-01. Jean- Pierre Gattuso was supported by the Service d’Observation Rade de Villefranche (SO-Rade), the Service d’Observation en Milieu Littoral (SOMLIT/CNRS-INSU), the Coastal Observing System for Northern and Arctic Seas (COSYNA), the two Helmholtz large- scale infrastructure projects: ACROSS and MOSES, the French Polar Institute (IPEV), and the European Commission, Horizon 2020 Framework Programme (grant nos. 871153, 951799, 727890 and 869154). Discrete samples reported by were analyzed for CT and AT by the Service National d’Analyse des Paramètres Océaniques du CO2. Anton Velo and Fiz F. Pérez were supported by BOCATS2 (PID2019-104279GB-C21) project funded by MI- CIU/AEI/10.13039/501100011033, by FICARAM+ (PID2023- 148924OB-100) and by EuroGO-SHIP project (Horizon Europe #101094690). Nicolas Metzl and Claire Lo Monaco acknowledge Institut National des Sciences de l’Univers du Centre National de la Recherche Scientifique (INSU/CNRS) and Observa- toire des Science de l’Univers (OSU ECCE-Terra) for supporting the SNAPO-CO2 facility housed by the LOCEAN laboratory in Paris/France. The AZMP Carbon dataset is a contribution to Fisheries and Oceans Canada Atlantic Zone Monitoring Program. Funding for the HOT program and WHOTS mooring maintenance were provided by the National Science Foundation (grant no. OCE-2241005). CMEMS-LSCE is supported by the European Copernicus Marine Environment Monitoring Service (CMEMS, grant no. 83-CMEMSTAC-MOB). The work of Luke Gregor was supported by the ESA OceanHealth-OA project (contract number 4000137603/22/I-DT), the European Space Agency (OceanSODA project, grant no. 4000112091/14/I-LG), the European Commission (COMFORT project, grant no. 820989), and the Horizon 2020 (4C project, grant no. 821003). Peter Landschützer received funding through the Horizon Europe research and innovation program under grant agreement No. 101137682 (AI4PEX), the Horizon2020 program (4C project, grant no. 821003) and through Schmidt Sciences (OBVI InMOS). Judith Hauck received funding from the Initiative and Networking Fund of the Helmholtz Association (Helmholtz Young Investigator Group Marine Carbon and Ecosystem Feedbacks in the Earth System [MarESys], Grant VH-NG-1301), and from the ERC-2022- STG OceanPeak (Grant 101077209). Alex Kozyr was funded by the NOAA Global Ocean Monitoring and Observing program (https://ror.org/037bamf06, last access: 7 January 2026). Jens Terhaar was funded by the Swiss National Science Foundation under grant # PZ00P2_209044 (ArcticECO). Henry C. Bittig acknowledges funding by the German Federal Ministry of Education and Research under grants no. 03F0877D (C-SCOPE project) and 03F0773A (BONUS INTEGRAL project). Tim DeVries acknowledges support from the US National Science Foundation through Grant # 1948955. Rik Wanninkhof, Leticia Barbero, Adrienne J. Sutton, Simone R. Alin, and Richard A. Feely acknowledge support from the Office of Oceanic and Atmospheric Research of NOAA, US Department of Commerce, including resources from the Global Ocean Monitoring and Observing Program and the Ocean Acidification Program (Open Funder Registry numbers 100018302 and 100018228, respectively). Simone R. Alin and Richard A. Feely acknowledge support from the Washington Ocean Acidification Center. Natalie M. Monacci acknowledges support from the Alaska Ocean Observing System (AOOS), the Exxon Valdez Oil Spill Trustee Council (EVOS), Gulf Watch Alaska, and the North Pacific Research Board (NPRB). Kunal Chakraborty acknowledges the support of the Development of Climate Change Advisory Services project, undertaken by the Indian National Centre for Ocean Information Services (INCOIS) under the Deep Ocean Mission programme of the Ministry of Earth Sciences (MoES), Government of India. Liang Xue was supported by the National Key R&D Program of China (2023YFE0113101 and 2023YFC3108102), the National Natural Science Foundation of China (42176051and 42376048), and the Taishan Scholar Project of Shandong Province (tsqn202306294). Kumiko Azetsu-Scott and Debby Ianson acknowledge funding from Fisheries and Oceans Canada, including, but not limited to, Aquatic Climate Change Adaptation Service Program. Debby Ianson and Ana Franco acknowledge funding from Canada’s Marine Environmental Observation Prediction and Response Network-OxyNet. Funding for the MOCHA synthesis was provided by California Ocean Protection Council (grant no. R/OPCOAH-04), the NOAA Ocean Acidification Program (grant no. 2906368), the National Park Service (grant no. P17AC01416), and the National Science Foundation (grant no. 1734999). Ian Enochs, Nicole Besemer and Ana M. Palacio- Castro acknowledge support from the NOAA Ocean Acidification Program (grant no. 25027), and the Coral Reef Conservation program (grant no. 743).
Review statement. This paper was edited by Sebastiaan van de Velde and reviewed by Meg Yoder and one anonymous referee.
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