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 dataset includes high-frequency (hourly to sub-hourly) coastal acidification time-series data collected during nine deployments in the aforementioned seven estuaries along the US East Coast, US West Coast and Gulf of Mexico from 2015-04-23 to 2020-07-29. These data include water temperature, salinity, partial pressure of carbon dioxide (pCO2) in water, dissolved oxygen (DO) in water, and pH on the total scale. The instruments used to collected these data include Sunburst SAMI-CO2, Pro-Oceanus CO2-Pro CV and a LiCOr LI-820 CO2 gas analyzers for autonomous pCO2 measurements, Sea-Bird SeapHOx and SeaFET instruments for pH measurements, Sea-Bird SeapHOx and Aanderaa Oxygen Optode instruments for DO measurements, and YSI water sensing instrument packages for measurements of conductivity (salinity), temperature and depth. Beginning in 2015, the U.S. Environmental Protection Agency’s (EPA) National Estuary Program (NEP) started a collaboration with partners in seven estuaries along the East Coast (Barnegat Bay; Casco Bay), West Coast (Santa Monica Bay; San Francisco Bay; Tillamook Bay), and the Gulf of Mexico (GOM) Coast (Tampa Bay; Mission-Aransas Estuary) of the United States to expand the use of autonomous monitoring partial pressure of carbon dioxide (pCO2) and pH sensors to evaluate carbonate chemistry in the estuarine environment.

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.

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.

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 consists of time-resolved reconstructions of ocean interior acidification from 1800 through 1994, 2004, and 2014. The basis of these reconstructions are observation-based estimates of the accumulation of anthropogenic carbon, combined with climatologies of hydrographic and biogeochemical properties in the ocean interior. Acidification trends are determined for several parameters of the marine CO2 system, namely the saturation state of aragonite (Ωarag), the carbonate ion concentration ([CO32-]), the free proton concentration ([H+]), and pH on the total scale (pHT). The underlying anthropogenic carbon concentration (ΔCant), the computed sensitivities of the four marine CO2 system parameters and their absolute state estimates are provided as well. The datasets contain in addition to the standard estimate also 14 sensitivity cases, which are intended to assess the robustness of our acidification estimates to changes in the estimation procedure of ΔCant as well as the climatological distributions of other hydrographic properties. All estimates are provided on a horizontal grid with 1° x 1° resolution and for 28 depth layers from 0 - 3000m. These data provide strong constraints on ocean interior acidification over the industrial era, unravelling in particular its progression since 1994.

This dataset contains a global gridded dataset of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification (v2023). The full marine carbonate system is calculated from machine learning estimates of Total Alkalinity (TA) and the fugacity of carbon dioxide (fCO2). The surface-ocean fCO2 presented here is the ensemble mean of 16 two-step clustering-regression machine learning estimates. The ensemble is a combination of eight clustering instances and two regression methods. For the clustering, we use K-means clustering (21 clusters) repeated with different initiations, resulting in slightly different clusters. Two machine learning regression methods are applied to each of these clustering instances. These machine learning methods are feed-forward neural-network (FFNN), and gradient boosted machine using decision trees (GBDT). The average of the ensemble members is used as the final estimate. Further, the standard deviation of the ensemble members is an analog of the uncertainty. The same two-step clustering-regression approach is used to estimate TA. The final estimate is the mean of 16 ensemble members. Eight of the ensemble members estimate standard TA while the other half estimate salinity normalized TA (S0 ≈ 34.0). Each ensemble member has 12 clusters. Support vector regression (SVR) is used as the regression method. Again, the standard deviation of the ensemble members is an analog of the uncertainty. Total alkalinity and pCO2 are then used to solve for the remaining parameters of the marine carbonate system using the PyCO2SYS software. The temperature and salinity products used in this calculation are provided in the file. Phosphate and silicate from the interpolated World Ocean Atlas (2018) product were used. We use the following total scale for pH. The product extends from the start of 1982 to the end of 2022.

This dataset contains monthly-averaged surface fields of temperature, salinity, total alkalinity (TA), and dissolved inorganic carbon (DIC) derived from the GoMBio model for 1981-2014. GoMBio is a ROMS-based 8-km resolution ocean-biogeochemical model encompassing the entire Gulf of Mexico. Model details can be found in Gomez et al. (2018; https://doi.org/10.5194/bg-15-3561-2018) and Gomez et al. (2020; https://doi.org/10.5194/bg-17-1685-2020). We include the results of four model experiments: (1) model hindcast, (2) climatological Mississippi-Atchafalaya chemistry experiment (CLM_MC), (3) climatological river experiment (CLM_RIV), and (4) climatological forcing experiment (CLM_FORC). The model hindcast experiment was forced with surface fluxes of momentum, heat, and freshwater from the European Center for Medium Range Weather Forecast reanalysis product (ERA-Interim). We prescribed a time-evolving monthly series of freshwater discharge for 28 river sources in the U.S., and a climatological discharge for 10 rivers in the U.S. and 11 rivers in Mexico. We also prescribed time evolving concentration of nutrients, TA, and DIC for the Mississippi and Atchafalaya River System (MARS), and long-term climatological values for the other river sources. The CLM_MC and CLM_RIV experiments are like the model hindcast, but the river inputs were modified to evaluate the model sensitivity to changes in river runoff. In the CLM_MC experiment we used a monthly climatology for the MARS. In the CLM_RIV experiment we used climatological river discharge values for all rivers, as well as the climatological chemistry for the MARS. Finally, in the CLM_FORC experiment, we prescribed climatological patterns for rivers, surface fluxes and the open boundary conditions. The only exceptions were the atmospheric CO2 and the open boundary conditions for DIC, which varied as in the model hindcast. This last experiment was conducted to examine the influence of climate variability in ocean acidification patterns.