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Oceanic dissolved inorganic carbon (TC) is the largest pool of carbon that substantially interacts with the atmosphere on human timescales. Oceanic TC is increasing through uptake of anthropogenic carbon dioxide (CO2), and seawater pH is decreasing as a consequence. Both the exchange of CO2 between the ocean and atmosphere and the pH response are governed by a set of parameters that interact through chemical equilibria, collectively known as the marine carbonate system. To investigate these processes, at least two of the marine carbonate system's parameters are typically measured – most commonly, two from TC, total alkalinity (AT), pH, and seawater CO2 fugacity (fCO2; or its partial pressure, pCO2, or its dry-air mole fraction, xCO2) – from which the remaining parameters can be calculated and the equilibrium state of seawater solved. Several software tools exist to carry out these calculations, but no fully functional and rigorously validated tool written in Python, a popular scientific programming language, was previously available. Here, we present PyCO2SYS, a Python package intended to fill this capability gap. We describe the elements of PyCO2SYS that have been inherited from the existing CO2SYS family of software and explain subsequent adjustments and improvements. For example, PyCO2SYS uses automatic differentiation to solve the marine carbonate system and calculate chemical buffer factors, ensuring that the effect of every modelled solute and reaction is accurately included in all its results. We validate PyCO2SYS with internal consistency tests and comparisons against other software, showing that PyCO2SYS produces results that are either virtually identical or different for known reasons, with the differences negligible for all practical purposes. We discuss insights that guided the development of PyCO2SYS: for example, the fact that the marine carbonate system cannot be unambiguously solved from certain pairs of parameters. Finally, we consider potential future developments to PyCO2SYS and discuss the outlook for this and other software for solving the marine carbonate system. The code for PyCO2SYS is distributed via GitHub (https://github.com/mvdh7/PyCO2SYS, last access: 23 December 2021) under the GNU General Public License v3, archived on Zenodo , and documented online (https://pyco2sys.readthedocs.io/en/latest/, last access: 23 December 2021).

A common strategy for calculating the direction and rate of carbon dioxide gas (CO2) exchange between the ocean and atmosphere relies on knowledge of the partial pressure of CO2 in surface seawater (pCO2(sw)), a quantity that is frequently observed by autonomous sensors on ships and moored buoys, albeit with significant spatial and temporal gaps. Here we present a monthly gridded data product of pCO2(sw) at 0.25∘ latitude by 0.25∘ longitude resolution in the northeastern Pacific Ocean, centered on the California Current System (CCS) and spanning all months from January 1998 to December 2020. The data product (RFR-CCS; Sharp et al., 2022; 10.5281/zenodo.5523389) was created using observations from the most recent (2021) version of the Surface Ocean CO2 Atlas (Bakker et al., 2016). These observations were fit against a variety of collocated and contemporaneous satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from sensors on moored buoys, and find that the data product effectively captures seasonal pCO2(sw) cycles at nearshore sites. This result is notable because global gridded pCO2(sw) products do not capture local variability effectively in this region, suggesting that RFR-CCS is a better option than regional extractions from global products to representpCO2(sw) in the CCS over the last 2 decades. Lessons learned from the construction of RFR-CCS provide insight into how global pCO2(sw) products could effectively characterize seasonal variability in nearshore coastal environments. We briefly review the physical and biological processes – acting across a variety of spatial and temporal scales – that are responsible for the latitudinal and nearshore-to-offshorepCO2(sw) gradients seen in the RFR-CCS reconstruction ofpCO2(sw). RFR-CCS will be valuable for the validation of high-resolution models, the attribution of spatiotemporal carbonate system variability to physical and biological drivers, and the quantification of multiyear trends and interannual variability of ocean acidification.

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO 2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr −1 and pH decrease of 0.0014 ± 0.0004 yr −1 . RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

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. Plain Language Summary A new data product, based on the latest computer simulations and observational data, offers improved projections of ocean acidification (OA) conditions from the start of the Industrial Revolution in 1750 to the end of the 21st century. These projections will support OA research at regional and global scales, and provide essential information to guide OA mitigation and adaptation efforts for various sectors, including fisheries, aquaculture, tourism, marine resource decision‐makers, and the general public. 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

For about 2 decades, oceanographers have been installing oxygen sensors on Argo profiling floats to be deployed throughout the world ocean, with the stated objective of better constraining trends and variability in the ocean's inventory of oxygen. Until now, measurements from these Argo-float-mounted oxygen sensors have been mainly used for localized process studies on air–sea oxygen exchange, upper-ocean primary production, biological pump efficiency, and oxygen minimum zone dynamics. Here, we present a new four-dimensional gridded product of ocean interior oxygen, derived via machine learning algorithms trained on dissolved oxygen observations from Argo-float-mounted sensors and discrete measurements from ship-based surveys and applied to temperature and salinity fields constructed from the global Argo array. The data product is called GOBAI-O2, which stands for Gridded Ocean Biogeochemistry from Artificial Intelligence – Oxygen (Sharp et al., 2022; 10.25921/z72m-yz67); it covers 86 % of the global ocean area on a 1∘ × 1∘ (latitude × longitude) grid, spans the years 2004–2022 with a monthly resolution, and extends from the ocean surface to a depth of 2 km on 58 levels. Two types of machine learning algorithms – random forest regressions and feed-forward neural networks – are used in the development of GOBAI-O2, and the performance of those algorithms is assessed using real observations and simulated observations from Earth system model output. Machine learning represents a relatively new method for gap filling ocean interior biogeochemical observations and should be explored along with statistical and interpolation-based techniques. GOBAI-O2 is evaluated through comparisons to the oxygen climatology from the World Ocean Atlas, the mapped oxygen product from the Global Ocean Data Analysis Project and to direct observations from large-scale hydrographic research cruises. Finally, potential uses for GOBAI-O2 are demonstrated by presenting average oxygen fields on isobaric and isopycnal surfaces, average oxygen fields across vertical–meridional sections, climatological seasonal cycles of oxygen averaged over different pressure layers, and globally integrated time series of oxygen. GOBAI-O2 indicates a declining trend in the oxygen inventory in the upper 2 km of the global ocean of 0.79 ± 0.04 % per decade between 2004 and 2022.

The ocean is warming, acidifying, and losing oxygen. The Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) carries out repeat hydrographic surveys along specified transects throughout all ocean basins to allow accurate and precise quantification of changes in variables such as temperature, salinity, carbon, oxygen, nutrients, velocity, and anthropogenic tracers, and uses these observations to understand ventilation patterns, deoxygenation, heat uptake, ocean carbon content, and changes in circulation. GO-SHIP provides global, full-depth, gold-standard data for model validation and calibration of autonomous sensors, including Argo floats. The Pacific Marine Environmental Laboratory (PMEL), through sustained funding from NOAA, has developed methods to measure several of the variables routinely sampled through GO-SHIP and is a core contributor to these repeat hydrographic cruises.

The ocean is one of the largest sinks for anthropogenic carbon dioxide (Canth) and its removal of carbon dioxide (CO2) from the atmosphere has been valued at hundreds of billions to trillions of US dollars in climate mitigation annually. The ecosystem impacts caused by planet-wide shifts in ocean chemistry resulting from marine Canth accumulation are an active area of research. For these reasons, we need accessible tools to quantify ocean Canth inventories and distributions and to predict how they might evolve in response to future emissions and mitigation activities. Unfortunately, Canth estimation methods are typically only accessible to trained scientists and modelers with access to significant computational resources. Here, we make modifications to the transit time distribution approach for Canth estimation that render the method more accessible. We also release software (BRCScienceProducts, 2025) called “Tracer-based Rapid Anthropogenic Carbon Estimation version 1” (TRACEv1) that allows users – with one line of code – to obtain Canth and water mass age estimates throughout the global open ocean from user-supplied values of geographic location, pressure, salinity, temperature, and the estimate year. We use this code to generate a data product of global gridded open-ocean Canth distributions (TRACEv1_GGCanth; Carter, 2025) that ranges from the preindustrial era through 2500 under a range of Shared Socioeconomic Pathways (SSPs, or atmospheric CO2 concentration pathways). We estimated the skill of these estimates by reconstructing Canth in models with known distributions of Canth and transient tracers and by conducting perturbation tests. In the model-based reconstruction test, TRACEv1 reproduces the global ocean Canth inventory to within ±10 % in 1980 and 2014. We discuss implications and limitations of the projected Canth distributions and highlight ways that the estimation strategy might be improved. One finding is that the ocean will continue to increase its net Canth inventory at least through 2500 due to deep-ocean ventilation, even with the SSP in which intense mitigation successfully decreases atmospheric Canth by ∼60 % in 2500 relative to the 2024 concentration. A notable limitation of this and similar projections made with TRACEv1 is that ongoing and potential future warming and changing oceanic circulation patterns with climate change are not captured by the method. The data products generated by this research are available as MATLAB code (https://doi.org/10.5281/zenodo.15692788, BRCScienceProducts, 2025) and a spatially and temporally gridded data product (https://doi.org/10.5281/zenodo.15692788, BRCScienceProducts, 2025).

For about 2 decades, oceanographers have been installing oxygen sensors on Argo profiling floats to be deployed throughout the world ocean, with the stated objective of better constraining trends and variability in the ocean's inventory of oxygen. Until now, measurements from these Argo-float-mounted oxygen sensors have been mainly used for localized process studies on air-sea oxygen exchange, upper-ocean primary production, biological pump efficiency, and oxygen minimum zone dynamics. Here, we present a new four-dimensional gridded product of ocean interior oxygen, derived via machine learning algorithms trained on dissolved oxygen observations from Argo-float-mounted sensors and discrete measurements from ship-based surveys and applied to temperature and salinity fields constructed from the global Argo array. The data product is called GOBAI-O.sub.2, which stands for Gridded Ocean Biogeochemistry from Artificial Intelligence - Oxygen (Sharp et al., 2022;

Internally consistent, quality-controlled (QC) data products play an important role in promoting regional-to-global research efforts to understand societal vulnerabilities to ocean acidification (OA). However, there are currently no such data products for the coastal ocean, where most of the OA-susceptible commercial and recreational fisheries and aquaculture industries are located. In this collaborative effort, we compiled, quality-controlled, and synthesized 2 decades of discrete measurements of inorganic carbon system parameters, oxygen, and nutrient chemistry data from the North American continental shelves to generate a data product called the Coastal Ocean Data Analysis Product in North America (CODAP-NA). There are few deep-water (> 1500 m) sampling locations in the current data product. As a result, crossover analyses, which rely on comparisons between measurements on different cruises in the stable deep ocean, could not form the basis for cruise-to-cruise adjustments. For this reason, care was taken in the selection of data sets to include in this initial release of CODAP-NA, and only data sets from laboratories with known quality assurance practices were included. New consistency checks and outlier detections were used to QC the data. Future releases of this CODAP-NA product will use this core data product as the basis for cruise-to-cruise comparisons. We worked closely with the investigators who collected and measured these data during the QC process. This version (v2021) of the CODAP-NA is comprised of 3391 oceanographic profiles from 61 research cruises covering all continental shelves of North America, from Alaska to Mexico in the west and from Canada to the Caribbean in the east. Data for 14 variables (temperature; salinity; dissolved oxygen content; dissolved inorganic carbon content; total alkalinity; pH on total scale; carbonate ion content; fugacity of carbon dioxide; and substance contents of silicate, phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium) have been subjected to extensive QC. CODAP-NA is available as a merged data product (Excel, CSV, MATLAB, and NetCDF; https://doi.org/10.25921/531n-c230, https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0219960.html, last access: 15 May 2021) (Jiang et al., 2021a). The original cruise data have also been updated with data providers' consent and summarized in a table with links to NOAA's National Centers for Environmental Information (NCEI) archives (https://www.ncei.noaa.gov/access/ocean-acidification-data-stewardship-oads/synthesis/NAcruises.html).

A common strategy for calculating the direction and rate of carbon dioxide gas (CO.sub.2) exchange between the ocean and atmosphere relies on knowledge of the partial pressure of CO.sub.2 in surface seawater (pCO.sub.2(sw) ), a quantity that is frequently observed by autonomous sensors on ships and moored buoys, albeit with significant spatial and temporal gaps. Here we present a monthly gridded data product of pCO.sub.2(sw) at 0.25.sup.\" latitude by 0.25.sup.\" longitude resolution in the northeastern Pacific Ocean, centered on the California Current System (CCS) and spanning all months from January 1998 to December 2020. The data product (RFR-CCS; Sharp et al., 2022;