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 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).

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 product is a result of the use of a decision tree-based advanced machine learning algorithm (XGB) combined with an ensemble approach to predict the surface total alkalinity (TA) of the North Indian Ocean (NIO) (Longitude: 50E-100E, Latitude: 0-26N) using ship-based observations collected from different sources for the period 1978-2019 and a reanalysis data product. The ensemble approach enables the development of the best high-resolution (1/12°) surface TA estimates (INCOIS_TA) with an uncertainty map. This data has a monthly frequency and ranges from 1993 to 2020.

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 spatial distribution of surface ocean pH (total hydrogen scale), acidity (or hydrogen ion activity, unit: nmol/kg, or 10^-9 mol/kg) and Revelle Factor (a measure of the ocean's buffer capacity, unitless) on a 1x1 degree global grid (Longitude: [20.5:1:379.5], Latitude: [-89.5:1:89.5]) in all 12 months of the years from 1770 to 2100 (1770, 1780, 1790, ..., 2100). This data product is produced by combining a recent observational seawater carbon dioxide (CO2) data product, i.e., the 6th version of the Surface Ocean CO2 Atlas (1991-2018, ~23 million observations), with temporal trends at individual locations of the global ocean from a robust Earth System Model (ESM2M), to provide a high-resolution regionally varying view of global surface ocean pH, acidity, and the Revelle Factor. The climatology extends from the pre-Industrial era (1770 C.E.) to the end of this century under historical atmospheric CO2 concentrations (pre-2005) and the Representative Concentrations Pathways (RCP2.6, RCP4.5, RCP6.0 and RCP8.5, post-2005) of the Intergovernmental Panel on Climate Change (IPCC)’s 5th Assessment Report (AR5). By linking the modeled pH trends to the observed modern pH distribution, the climatology benefits from recent improvements in both model design and observational data coverage, and is likely to provide improved regional OA trajectories than the model output could alone, therefore, will help guide the regional OA adaptation strategies. Revelle Factor is defined as the ratio between the fractional change in pCO2 to the fractional change in dissolved inorganic carbon (DIC). This dataset is available in netCDF format. Some plots and animation files are also available for your presentation purposes. For details of the calculation and gridding method, please refer to Jiang, L.-Q., B. R. Carter, R. A. Feely, S. Lauvset, and A. Olsen (2019), Surface ocean pH and buffer capacity: past, present and future, Nature Scientific Reports, 9:18624, https://doi.org/10.1038/s41598-019-55039-4.

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.

This dataset contains discrete measured and calculated surface seawater properties from samples taken on the first week of every month over the course of one or two days between 11/2021 and 07/2022 (9 months) at the Equatorial Pacific region off the coast of Colombia, around Gorgona Island National Natural Park. The dataset contains also measured monthly sea-surface temperature (SST), sea-surface salinity (SSS), pH in total scale (derived directly from in situ pH measurements) with depth of 1.8 metres along 7 sampling sites in a coast-ocean gradient (1 to 7) with the following geographical coverage: 1. Lat. -78.201138 Lon. 3.0163611; 2. Lat. -78.221416 Lon. 2.9705833; 3. Lat. -78.163861 Lon. 2.9960278; 4. Lat. -78.101555 Lon. 2.9473889; 5. Lat. -78.101555 Lon. 2.9322222; 6. Lat. -78.122865 Lon. 2.8756667; 7. Lat. -77.979865 Lon. 2.7204915. The measurements were taken with two CTD divers (SonTek CastAway-CTD and Eijkelkamp CTD-Diver 11.11.60) and one Niskin bottle (5L). The water samples where taken to a oceanography lab in the Pontifical Xavierian University to be tested for total alkalinity (TA), and to the Autonomous University of Baja California for Dissolved Inorganic Carbon (DIC) measurements. This was done in order to calculate the carbon fluxes on a monthly scale throughout nine months during the 2021-2022 La Niña year for the whole study site, allowing for observations in the spatiotemporal flux behavior variability (shown in dataset as fco2_measured).