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The ocean’s chemistry is changing due to the uptake of anthropogenic carbon dioxide (CO 2 ). This phenomenon, commonly referred to as “Ocean Acidification”, is endangering coral reefs and the broader marine ecosystems. In this study, we combine a recent observational seawater CO 2 data product, i.e., the 6 th version of the Surface Ocean CO 2 Atlas (1991–2018, ~23 million observations), with temporal trends at individual locations of the global ocean from a robust Earth System Model to provide a high-resolution regionally varying view of global surface ocean pH and the Revelle Factor. The climatology extends from the pre-Industrial era (1750 C.E.) to the end of this century under historical atmospheric CO 2 concentrations (pre-2005) and the Representative Concentrations Pathways (post-2005) of the Intergovernmental Panel on Climate Change (IPCC)’s 5 th Assessment Report. 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. We show that air-sea CO 2 disequilibrium is the dominant mode of spatial variability for surface pH, and discuss why pH and calcium carbonate mineral saturation states, two important metrics for OA, show contrasting spatial variability.

Ocean alkalinity enhancement (OAE) is a promising approach to marine carbon dioxide removal (mCDR) that leverages the large surface area and carbon storage capacity of the oceans to sequester atmospheric CO2 as dissolved bicarbonate (HCO3-). One OAE method involves the conversion of salt in seawater into aqueous alkalinity (NaOH), which is returned to the ocean. The resulting increase in seawater pH and alkalinity causes a shift in dissolved inorganic carbon (DIC) speciation toward carbonate and a decrease in the surface ocean pCO2. The shift in the pCO2 results in enhanced uptake of atmospheric CO2 by the seawater due to gas exchange. In this study, we systematically test the efficiency of CO2 uptake in seawater treated with NaOH at aquarium (15 L) and tank (6000 L) scales to establish operational boundaries for safety and efficiency in advance of scaling up to field experiments. CO2 equilibration occurred on the order of weeks to months, depending on circulation, air forcing, and air bubbling conditions within the test tanks. An increase of ∼0.7–0.9 mol DIC per mol added alkalinity (in the form of NaOH) was observed through analysis of seawater bottle samples and pH sensor data, consistent with the value expected given the values of the carbonate system equilibrium calculations for the range of salinities and temperatures tested. Mineral precipitation occurred when the bulk seawater pH exceeded 10.0 and Ωaragonite exceeded 30.0. This precipitation was dominated by Mg(OH)2 over hours to 1 d before shifting to CaCO3,aragonite precipitation. These data, combined with models of the dilution and advection of alkaline plumes, will allow the estimation of the amount of carbon dioxide removal expected from OAE pilot studies. Future experiments should better approximate field conditions including sediment interactions, biological activity, ocean circulation, air–sea gas exchange rates, and mixing zone dynamics.

This project produced a Python language implementation of locally interpolated regression (LIR) and neural network (NN) algorithms from empirical seawater property estimation routines (PyESPERv1.0.0). These routines estimate total alkalinity, dissolved inorganic carbon, total pH, nitrate, phosphate, silicate, and oxygen from geographic coordinates, depth, salinity, and 16 combinations of zero to four additional predictors (temperature and biogeochemical information) and were previously available only in the MATLAB programming language. Here, we document modifications to reduce discrepancies between the implementations, calculate the disagreements between the methods, and quantify Global Ocean Data Analysis Project (GLODAPv2.2022) reconstruction errors with PyESPER. While the PyESPER routine based on neural networks (PyESPER_NN) faithfully reproduces the corresponding MATLAB routine estimates of properties that do not require anthropogenic carbon change information, PyESPER_LIR and – to a lesser extent – PyESPER_NN estimates for total pH and dissolved inorganic carbon do not exactly reproduce the MATLAB routine estimates due to differences in interpolation and extrapolation methods between the programming languages. While the MATLAB and Python LIR-based estimates are not identical, we show that they are similarly skilled at reproducing the GLODAPv2.2022 data product and are thus comparable. This project increases the accessibility of ESPERv1.01.01 algorithms by providing users with code in the freely available Python language and enables future ESPER updates to be released in multiple coding languages.

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 2021 summer upwelling season off the United States Pacific Northwest coast was unusually strong leading to widespread near-bottom, low-oxygen waters. During summer 2021, an unprecedented number of ship- and underwater glider-based measurements of dissolved oxygen were made in this region. Near-bottom hypoxia, that is dissolved oxygen less than 61 µmol kg −1 and harmful to marine animals, was observed over nearly half of the continental shelf inshore of the 200-m isobath, covering 15,500 square kilometers. A mid-shelf ribbon with near-bottom, dissolved oxygen less than 50 µmol kg −1 extended for 450 km off north-central Oregon and Washington. Spatial patterns in near-bottom oxygen are related to the continental shelf width and other features of the region. Maps of near-bottom oxygen since 1950 show a consistent trend toward lower oxygen levels over time. The fraction of near-bottom water inshore of the 200-m isobath that is hypoxic on average during the summer upwelling season increases over time from nearly absent (2%) in 1950–1980, to 24% in 2009–2018, compared with 56% during the anomalously strong upwelling conditions in 2021. Widespread and increasing near-bottom hypoxia is consistent with increased upwelling-favorable wind forcing under climate change.

The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019. The major changes are data from 106 new cruises added, extension of time coverage to 2019, and the inclusion of available (also for historical cruises) discrete fugacity of CO2 (fCO2) values in the merged product files. GLODAPv2.2020 now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. These adjustments were derived by comparing the data from the 106 new cruises with the data from the 840 quality-controlled cruises of the GLODAPv2.2019 data product using crossover analysis. Comparisons to empirical algorithm estimates provided additional context for adjustment decisions; this is new to this version. The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data-handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete fCO2, were not subjected to bias comparison or adjustments. The original data and their documentation and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, last access: 20 June 2020). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). These bias-adjusted product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2020 methods and provides a broad overview of the secondary quality control procedures and results.

Ocean alkalinity enhancement (OAE) has the potential to mitigate ocean acidification (OA) and induce atmospheric carbon dioxide (CO2) removal (CDR). We evaluate the CDR and OA mitigation impacts of a sustained point‐source OAE of 1.67 × 1010 mol total alkalinity (TA) yr−1 (equivalent to 667,950 metric tons NaOH yr−1) in Unimak Pass, Alaska. We find the alkalinity elevation initially mitigates OA by decreasing pCO2 and increasing aragonite saturation state and pH. Then, enhanced air‐to‐sea CO2 exchange follows with an approximate e‐folding time scale of 5 weeks. Meaningful modeled OA mitigation with reductions of >10 μatm pCO2 (or just under 0.02 pH units) extends 100–100,000 km2 around the TA addition site. The CDR efficiency (i.e., the experimental seawater dissolved inorganic carbon (DIC) increase divided by the maximum DIC increase expected from the added TA) after the first 3 years is 0.96 ± 0.01, reflecting essentially complete air‐sea CO2 adjustment to the additional TA. This high efficiency is potentially a unique feature of the Bering Sea related to the shallow depths and mixed layer depths. The ratio of DIC increase to the TA added is also high (≥0.85) due to the high dissolved carbon content of seawater in the Bering Sea. The air‐sea gas exchange adjustment requires 3.6 months to become (>95%) complete, so the signal in dissolved carbon concentrations will likely be undetectable amid natural variability after dilution by ocean mixing. We therefore argue that modeling, on a range of scales, will need to play a major role in assessing the impacts of OAE interventions. Plain Language Summary The Intergovernmental Panel on Climate Change suggests that carbon dioxide (CO2) removal (CDR) approaches will be required to stabilize the global temperature increase at 1.5–2°C. In this study, we simulated the climate mitigation impacts of adding alkalinity (equivalent to 667,950 metric ton NaOH yr−1) in Unimak Pass on the southern boundary of the Bering Sea. We found that adding alkalinity can accelerate the ocean CO2 uptake and storage and mitigate ocean acidification near the alkalinity addition. It takes about 3.6 months for the Ocean alkalinity enhancement impacted area to take up the extra CO2. The naturally cold and carbon rich water in the Bering Sea and the tendency of Bering Sea surface waters to linger near the ocean surface without mixing into the subsurface ocean both lead to high CDR efficiencies (>96%) from alkalinity additions in the Bering Sea. However, even with high efficiency, it would take >8,000 alkalinity additions of the kind we simulated to be operating by the year 2100 to meet the target to stabilize global temperatures within the targeted range. Key Points We used regional ocean model to simulate single point‐source ocean alkalinity enhancement in the Bering Sea The steady state carbon dioxide removal efficiency was near one in years 3+ of the simulation The meaningful modeled ocean acidification mitigation is confined to the region near the alkalinity addition

Marine CO2 removal (CDR) using enhanced-alkalinity seawater discharge was simulated in the estuarine waters of the Salish Sea, Washington, US. The high-alkalinity seawater would be generated using bipolar membrane electrodialysis technology to remove acid and the alkaline stream returned to the sea. Response of the receiving waters was evaluated using a shoreline resolving hydrodynamic model with biogeochemistry, and carbonate chemistry. Two sites, and two deployment scales, each with enhanced TA of 2997 mmol m−3 and a pH of 9 were simulated. The effects on air-sea CO2 flux and pH in the near-field as well as over the larger estuary wide domain were assessed. The large-scale deployment (addition of 164 Mmoles TA yr−1) in a small embayment (Sequim Bay, 12.5 km2) resulted in removal of 2066 T of CO2 (45% of total simulated) at rate of 3756 mmol m−2 yr−1, higher than the 63 mmol m−2 yr−1 required globally to remove 1.0 GT CO2 yr−1. It also reduced acidity in the bay, ΔpH ≈ +0.1 pH units, an amount comparable to the historic impacts of anthropogenic acidification in the Salish Sea. The mixing and dilution of added TA with distance from the source results in reduced CDR rates such that comparable amount 2176 T CO2 yr−1 was removed over >1000 fold larger area of the rest of the model domain. There is the potential for more removal occurring beyond the region modeled. The CDR from reduction of outgassing between October and May accounts for as much as 90% of total CDR simulated. Of the total, only 375 T CO2 yr−1 (8%) was from the open shelf portion of the model domain. With shallow depths limiting vertical mixing, nearshore estuarine waters may provide a more rapid removal of CO2 using alkalinity enhancement relative to deeper oceanic sites.

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.