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Abstract 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 CO₂Atlas, 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⁻¹and 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. 

Abstract. Internally consistent, quality-controlled (QC) data products play animportant role in promoting regional-to-global research efforts tounderstand societal vulnerabilities to ocean acidification (OA). However,there are currently no such data products for the coastal ocean, where mostof the OA-susceptible commercial and recreational fisheries and aquacultureindustries are located. In this collaborative effort, we compiled, quality-controlled, and synthesized 2 decades of discrete measurements ofinorganic carbon system parameters, oxygen, and nutrient chemistry data fromthe North American continental shelves to generate a data product calledthe Coastal Ocean Data Analysis Product in North America (CODAP-NA). Thereare few deep-water (> 1500 m) sampling locations in the currentdata product. As a result, crossover analyses, which rely on comparisonsbetween measurements on different cruises in the stable deep ocean, couldnot form the basis for cruise-to-cruise adjustments. For this reason, carewas taken in the selection of data sets to include in this initial releaseof CODAP-NA, and only data sets from laboratories with known qualityassurance practices were included. New consistency checks and outlierdetections were used to QC the data. Future releases of this CODAP-NAproduct will use this core data product as the basis for cruise-to-cruisecomparisons. We worked closely with the investigators who collected andmeasured these data during the QC process. This version (v2021) of theCODAP-NA is comprised of 3391 oceanographic profiles from 61 researchcruises covering all continental shelves of North America, from Alaska toMexico in the west and from Canada to the Caribbean in the east. Data for 14variables (temperature; salinity; dissolved oxygen content; dissolvedinorganic carbon content; total alkalinity; pH on total scale; carbonateion content; fugacity of carbon dioxide; and substance contents of silicate,phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium) have beensubjected 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 withdata providers' consent and summarized in a table with links to NOAA'sNational Centers for Environmental Information (NCEI) archives(https://www.ncei.noaa.gov/access/ocean-acidification-data-stewardship-oads/synthesis/NAcruises.html). 

Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above. 

Abstract Anthropogenic carbon emissions and associated climate change are driving rapid warming, acidification, and deoxygenation in the ocean, which increasingly stress marine ecosystems. On top of long‐term trends, short term variability of marine stressors can have major implications for marine ecosystems and their management. As such, there is a growing need for predictions of marine ecosystem stressors on monthly, seasonal, and multi‐month timescales. Previous studies have demonstrated the ability to make reliable predictions of the surface ocean physical and biogeochemical state months to years in advance, but few studies have investigated forecast skill of multiple stressors simultaneously or assessed the forecast skill below the surface. Here, we use the Community Earth System Model (CESM) Seasonal to Multiyear Large Ensemble (SMYLE) along with novel observation‐based biogeochemical and physical products to quantify the predictive skill of dissolved inorganic carbon (DIC), dissolved oxygen, and temperature in the surface and subsurface ocean. CESM SMYLE demonstrates high physical and biogeochemical predictive skill multiple months in advance in key oceanic regions and frequently outperforms persistence forecasts. We find up to 10 months of skillful forecasts, with particularly high skill in the Northeast Pacific (Gulf of Alaska and California Current Large Marine Ecosystems) for temperature, surface DIC, and subsurface oxygen. Our findings suggest that dynamical marine ecosystem prediction could support actionable advice for decision making.