This data package contains 1x1 degree coastal climatologies (mean fields of oceanographic variables on a regular geographic grid at specific depths) of pH on the Total Scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, total dissolved inorganic carbon content, and total alkalinity content from surface to 500 meters water depth on North American ocean margins. These climatologies were developed with the World Ocean Atlas (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, https://doi.org/10.5194/essd-13-2777-2021), along with the Global Ocean Data Analysis Product version 2 (GLODAPv2, version 2021, https://doi.org/10.5194/essd-13-5565-2021). The relevant variables were adjusted to the year of 2010 based on the algorithms as developed by Carter et al. (2021, https://doi.org/10.1002/lom3.10461) before the gridding. This data package contains a total of 8 NetCDF files, one for each of the variable. It is recommended to use the objectively analyzed mean fields (with "_an" postfix) for each variable.

Scientists of the ACCRETE (Acidification, Climate, and Coral Reef Ecosystems Team) Lab of AOML’s Ocean Chemistry and Ecosystems Division (OCED) constructed a tool to monitor ocean acidification over the eastern US coast. This tool utilizes satellite data and a data-assimilative hybrid model to map the components of the carbonate system of surface water. The variables provided in this dataset include partial pressure of carbon dioxide for seawater (pCO2sw), total alkalinity (TA), pH, aragonite saturation state, and calcite saturation state. This dataset represents an update to the experimental Ocean Acidification Product Suite (OAPS) developed by NOAA's Coral Reef Watch.

Increasing amounts of atmospheric carbon dioxide from human industrial activities are causing changes in global ocean carbon chemistry resulting in a reduction in pH, a process termed ocean acidification. In support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP), the South Florida Project Cruises (SFP) are utilized to collect water samples to measure surface water inorganic carbon and hydrographic parameters including nutrients. Samples are collected from 34 stations on a bi-monthly basis to monitor the outflow of the Shark River Slough (SRS) and red tide in the southwestern Gulf of Mexico. Water samples are sent to and analyzed by scientists at the Atlantic Oceanographic & Meteorological Laboratory (AOML) for dissolved inorganic carbon, pH, total alkalinity and nutrient concentrations. These data are used to observe the effects of the SRS on acidification in the coastal ocean.

Increasing amounts of atmospheric carbon dioxide from human industrial activities are causing changes in global ocean carbon chemistry resulting in a reduction in pH, a process termed ocean acidification. In support of the coastal monitoring and research objectives of the NOAA Ocean Acidification Program (OAP), the South Florida Project Cruises (SFP) are utilized to collect water samples to measure surface water inorganic carbon and hydrographic parameters including nutrients. Samples are collected from 34 stations on a bi-monthly basis to monitor the outflow of the Shark River Slough (SRS) and red tide in the southwestern Gulf of Mexico. Water samples are sent to and analyzed by scientists at the Atlantic Oceanographic & Meteorological Laboratory (AOML) for dissolved inorganic carbon, pH, total alkalinity and nutrient concentrations. These data are used to observe the effects of the SRS on acidification in the coastal ocean.

Scientists of the ACCRETE (Acidification, Climate, and Coral Reef Ecosystems Team) Lab of AOML’s Ocean Chemistry and Ecosystems Division (OCED) constructed a tool to monitor ocean acidification over the wider Caribbean and Gulf of Mexico. This tool utilizes satellite data and a data-assimilative hybrid model to map the components of the carbonate system of surface water. The variables provided in this dataset include partial pressure of carbon dioxide for seawater (pCO2sw), total alkalinity (TA), pH, aragonite saturation state, and calcite saturation state. This dataset represents an update to the experimental Ocean Acidification Product Suite (OAPS) developed by NOAA's Coral Reef Watch.

This data package contains a hybrid surface OA data product that is produced based on three recent observational data products: (a) the Surface Ocean CO2 Atlas (SOCAT, version 2022), (b) the Global Ocean Data Analysis Product version 2 (GLODAPv2, version 2022), and (c) the Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021), and 14 Earth System Models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The trajectories of ten OA indicators, including fugacity of carbon dioxide, pH on Total Scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content are provided under preindustrial conditions, historical conditions, and future Shared Socioeconomic Pathways: SSP1-19, SSP1-26, SSP2-45, SSP3-70, and SSP5-85 from 1750 to 2100 on a global surface ocean grid. These OA trajectories are improved relative to previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs over the 21st century.

This dataset contains discrete measurements of water temperature, salinity, total dissolved inorganic carbon (DIC), total titration alkalinity (TA), pH on total scale, and calcium concentration. These data were collected between April 2014 and April 2018 in Lavaca-Matagorda, Guadalupe-San Antonio, Nueces-Corpus Christi estuaries along the northwestern Gulf of Mexico.

Dataset Description: Gridded monthly data products of surface ocean acidification indicators from 1998 to 2024 and on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. Large Marine Ecosystems (LMEs) using a machine learning algorithm called random forest regression (RFR). The data products are called RFR-LMEs, and were constructed using observations from the Surface Ocean CO2 Atlas — co-located with surface ocean properties from various satellite, reanalysis, and observational products — with an approach that utilized two types of machine learning algorithms: (1) Gaussian mixture models to cluster the data into subregions with similar environmental variability and (2) RFRs that were trained and applied separately in each cluster to interpolate the observational data in space and time. RFR-LMEs also rely on previously published seawater property estimation routines to obtain the full suite of ocean acidification indicators. The products show a domain-wide carbo n dioxide partial pressure increase of 1.6 ± 0.4 μatm yr−1 and pH decrease of 0.0015 ± 0.0004 yr−1. More information on the creation and validation of RFR-LMEs is available in the following publication: Sharp, J.D., Jiang, L., Carter, B.R., Lavin, P.D., Yoo, H., Cross, S.L., 2024. A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States. Scientific Data, 11, 715, 10.1038/s41597-024- 03530-7.