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 monthly interpolated sea surface fugacity of carbon dioxide (fCO2) from 1982 through 2015 is estimated from monthly atmospheric CO2, temperature, and salinity. Firstly, the disequilibrium between the observed seawater fCO2 and fCO2air (i.e., ΔfCO2; ΔfCO2 = fCO2 – fCO2air) is estimated using a bayesian-neural-network approach for each 1°×1° grid box in the MAB and the SAB. The input parameters are latitude, longitude, SST, and SSS. The output parameter is ΔfCO2. The feedforward backpropagation network is constructed by two hidden layers with tanh activation functions. The neural network is trained with the Levenberg-Marquardt algorithm (Hagan and Menhaj, 1994). Then fCO2 is calculated using ΔfCO2 and fCO2air. Next, continuous monthly SST and SSS data from 1982 to 2015 are used to calculate output with the trained network to fill in SOCAT data gaps. Monthly pH, DIC, and Ωarag are calculated from fCO2 and salinity-derived TA using CO2SYS (Van Heuven et al., 2009) with the first and second dissociation constants of carbonic acid in seawater (K1 and K2) from Lueker et al. (2000) and borate-to-salinity ratio determined by Uppström (1974). TA is derived from salinity using their linear relationships in the MAB and the SAB (Cai et al., 2010).

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 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).

The ocean is a sink for ~25% of the atmospheric CO2 emitted by human activities, an amount in excess of 2 petagrams of carbon per year (PgC yr−1). Time-resolved estimates of global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget. However, previous estimates of this flux, derived from surface ocean CO2 concentrations, have not corrected the data for temperature gradients between the surface and sampling at a few meters depth, or for the effect of the cool ocean surface skin. Here we calculate a time history of ocean-atmosphere CO2 fluxes from 1992 to 2018, corrected for these effects. These increase the calculated net flux into the oceans significantly.

This dataset contains full-depth carbonate chemistry profile data at the seasonal frequency of ship-based hydrographic surveys for the Florida Straits at 27°N from 2002 to 2018. We firstly developed an algorithm to estimate subsurface (~50 m) dissolved inorganic carbon (DIC) using measured data from carbonate chemistry-focused research cruise surveys across the Florida Straits at 27°N. This algorithm was then applied to the long-term non-carbonate chemistry time-series dataset to generate a DIC record. Total alkalinity of the full water column was estimated from a linear relationship with salinity based on measured data from carbonate chemistry focused research cruise surveys across the Florida Straits at 27°N. Furthermore, subsurface pH and aragonite saturation state (Ωarag) were calculated from DIC and TA using CO2SYS (Van Heuven et al., 2009) with the first and second carbonic acid dissociation constants K1 and K2 from Lueker et al. (2000), the acidity constant of the ion HSO4- from Dickson (1990), and borate-to-salinity ratio from Lee et al. (2010). For surface mixed layer (<50 m), DIC, pH, and (Ωarag) were calculated from TA and fCO2, where the latter was calculated using algorithms provided by Wanninkhof et al. (2020) based on the observational data from the Atlantic Oceanographic and Meteorological Laboratory (AOML) Ship of Opportunity-CO2 (SOOP-CO2) program from Royal Caribbean international cruise ships.

This dataset contains continuous monthly maps of sea surface partial pressure of CO2 (pCO2) in the coastal ocean from 1982 to 2020. This product is an updated version of the coastal product of Laruelle et al. (2017) and has been created using a 2-step Self Organizing Maps (SOM) and Feed Forward Network (FFN) method and uses ~ 18 million direct observations from the latest release of the Surface Ocean CO2 database (SOCATv2022, Bakker et al., 2014, 2022). In a first step, the global coastal ocean is divided into 10 biogeochemical provinces using SOM, which group regions with similar environmental properties. Then, for each province, the FFN algorithm reconstructs nonlinear relationships between a set of environmental variables (e.g., sea surface temperature, salinity...) and the observed pCO2. These relationships are then used to perform the spatiotemporal pCO2 extrapolation in regions and time periods where data are lacking. The output consists of continuous monthly pCO2 maps for the coastal ocean, with a spatial resolution of 0.25°, covering the 1982-2020 period. Additionally, this new coastal pCO2 product is used to generate a new coastal air-sea CO2 exchange (FCO2) product for each grid cell at the monthly time scale from 1982 to 2020 using the following equation: FCO2=k∙K0∙∆pCO2∙(1-ice) where FCO2 represents the coastal air-sea CO2 exchange (in mol C m-2 yr-1). By convention a positive FCO2 value corresponds to a CO2 source for the atmosphere. ∆pCO2 represents the difference between the oceanic pCO2 and the atmospheric pCO2 (in atm). K0 (mol C m-3 atm-1) represents the CO2 solubility in sea water which is a function of SST and SSS following the equation of Weiss et al. (1974). k represents the gas exchange transfer velocity (m yr-1) which is a function of the second moment of the wind speed and is calculated using the equation of Ho et al. (2011) and the Schmidt number based on the equation of Wanninkhof et al. (2014). The sea-ice coverage is represented by the term ice and has no units.