The dataset contains sea-surface temperature, salinity, dissolved inorganic carbon, total alkalinity, pH, and partial pressure of CO2 for the Indian Ocean region (Longitude: 30°E-120°E, Latitude: 30°S-30°N). The data is available from 1980 to 2019 on a monthly time scale. Each of these data variables has a spatial resolution of 1/12°.

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.

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 dataset consists of the estimated decadal changes in the oceanic content of anthropogenic CO2 (∆Cant) between 1994, 2004 and 2014 as described in detail in Müller et al. (2023, in press, AGU Advances). These estimates have been derived from the GLODAPv2.2021 product (Lauvset et al., 2021) using the eMLR(C*) method developed by Clement & Gruber (2018). The datasets contain in addition to the standard estimate also 10 sensitivity cases, which are intended to assess the robustness of the standard estimates to different changes in the estimation procedure. All estimates are provided on a horizontal grid with 1° x 1° resolution. Two primary files are provided: one containing the full three-dimensional distribution of ∆Cant and one containing the vertically integrated values, i.e., the column inventories.

This dataset consists of year 2002 and preindustrial (pi) OA metric values and their uncertainties (u), total OA metric changes (d) due to anthropogenic carbon accumulation to the year 2002 and the component of those changes caused by carbonate system nonlinearities (n), with associated uncertainties provided. Uncertainties were estimated using a 1000 iteration Monte Carlo simulation. Data from the upper 2000 m of the GLODAPv2.2016b mapped data product (https://doi.org/10.3334/cdiac/otg.ndp093_glodapv2), described in Lauvset et al., 2016 (https://doi.org/10.5194/essd-8-325-2016), and from the preformed properties product of Carter et al., 2021 (https://doi.org/10.1029/2020GB006623) were used to make these estimates. Calculation details are described in Fassbender et al., 2023 (https://doi.org/10.1029/2023GB007843). Year 2002 aragonite saturation state and pH values, and their uncertainties, are reproduced from the GLODAPv2.2016b mapped data product (https://doi.org/10.7289/v5kw5d97) and are provided here for user convenience with the permission of the original data producer. Version 1.0.

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.

This data package contains 10 global subsurface ocean acidification (OA) indicators at standardized depth levels of 50, 100, and 200 meters. The indicators include fugacity of carbon dioxide, pH on the 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. They are presented on a global ocean grid of 1° × 1°, as decadal averages spanning from preindustrial conditions (1750) through historical conditions (1850–2010) and projected into five future scenarios defined by Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from 2020 to 2100. These OA indicators were generated by following the same approach as described by Jiang et al. (2023) (https://doi.org/10.1029/2022MS003563), and utilized data from 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), as well as a gridded data product provided by Lauvset et al. (2016) (https://doi.org/10.5194/essd-8-325-2016).

This dataset consists of the estimated changes in the ocean content of anthropogenic CO2 (Cant) between 1994 and 2007 as described in detail in Gruber et al. [2019] (Science). These estimates have been derived from the GLODAPv2 product [Olsen et al., 2016] using the eMLR(C*) method developed by Clement and Gruber [2018]. This method is based on the eMLR method [Friis et al., 2005], which determines the change in Cant on the basis of linear regression fits to data from two different time periods (here the JGOFS–WOCE era (~1994) and the Repeat Hydrography–GO-SHIP era (~2007)). The dataset contains in addition to the standard estimate also the estimates of 13 sensitivity cases, where different elements of the estimation procedure were changed to assess the robustness of the estimates. All estimates are given on 1x1 degree resolution. Two files are provided, i.e., one containing the full three-dimensional distribution of the change in Cant between 1994 and 2007 and one containing the vertically integrated values, i.e., the column inventories. These data provide strong constraints on the role of the ocean as a sink for anthropogenic CO2, and given the global nature of our assessment also constraints on the global carbon budget, specifically the magnitude of the land carbon sink. The estimates will prove also useful to assess ocean acidification and evaluate ocean models with regard to their carbon uptake and storage.

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.