Description: Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model-data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include 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. The evolution of these OA indicators is presented on a global surface ocean 1° × 1° grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850–2010), and to five future Shared Socioeconomic Pathways (2020–2100): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These OA trajectories represent an improvement over 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. The generated data product offers a state-of-the-art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/surface-oa-indicators.html. Key Points: - This study presents the evolution of 10 ocean acidification (OA) indicators in the global surface ocean from 1750 to 2100 - By leveraging 14 Earth System Models (ESMs) and the latest observational data, it represents a significant advancement in OA projections - This inter-model comparison effort showcases the overall agreements among different ESMs in projecting surface ocean carbon variables

Description: Observation‐based quantification of ocean carbon dioxide (CO 2 ) uptake relies on synthesis data sets such as the Surface Ocean CO 2 ATlas (SOCAT). However, the data collection effort has dramatically declined and the number of annual data sets in SOCATv2023 decreased by ∼35% from 2017 to 2021. This decline has led to a 65% increase (from 0.15 to 0.25 Pg C yr −1 ) in the standard deviation of seven SOCAT‐based air‐sea CO 2 flux estimates. Reducing the availability of the annual data to that in the year 2000 creates substantial bias (50%) in the long‐term flux trend. The annual mean CO 2 flux is insensitive to the seasonal skew of the SOCAT data and to the addition of the lower accuracy data set available in SOCAT. Our study highlights the need for sustained data collection and synthesis, to inform the Global Carbon Budget assessment, the UN‐led climate negotiations, and measurement, reporting, and verification of ocean‐based CO 2 removal projects. Plain Language Summary The Surface Ocean CO 2 ATlas (SOCAT) data set plays a crucial role in estimating the ocean carbon sink component of the Global Carbon Budget. However, the number of data sets available in SOCAT each year has drastically decreased since 2017. This study shows that the uncertainty in the data‐based ocean CO 2 flux estimate has increased by 65% due to this decline in data availability. The estimated fluxes, especially the long‐term flux trend, are remarkably affected by the data availability in SOCAT, reducing the reliability of ocean CO 2 uptake estimates in years and regions with sparse observations. Key Points Lower surface ocean f CO 2 data availability leads to higher uncertainty in data‐based estimates of ocean CO 2 uptake The long‐term trend in the ocean CO 2 flux increases by 1.5 times for subsequent years if the data availability is reduced to that in 2000 The annual mean CO 2 flux is not sensitive to the seasonal skew in the data and to the addition of low accuracy data