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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 Seawater carbonate chemistry observations are increasingly necessary to study a broad array of oceanographic challenges such as ocean acidification, carbon inventory tracking, and assessment of marine carbon dioxide removal strategies. The uncertainty in a seawater carbonate chemistry observation comes from unknown random variations and systematic offsets. Here, we estimate the magnitudes of these random and systematic components of uncertainty for the discrete open‐ocean carbonate chemistry measurements in the Global Ocean Data Analysis Project 2022 update (GLODAPv2.2022). We use both an uncertainty propagation approach and a carbonate chemistry measurement “inter‐consistency” approach that quantifies the disagreement between measured carbonate chemistry variables and calculations of the same variables from other carbonate chemistry measurements. Our inter‐consistency analysis reveals that the seawater carbonate chemistry measurement community has collected and released data with a random uncertainty that averages about 1.7 times the uncertainty estimated by propagating the desired “climate‐quality” random uncertainties. However, we obtain differing random uncertainty estimates for subsets of the available data, with some subsets seemingly meeting the climate‐quality criteria. We find that seawater pH measurements on the total scale do not meet the climate‐quality criteria, though the inter‐consistency of these measurements improves (by 38%) when limited to the subset of measurements made using purified indicator dyes. We show that GLODAPv2 adjustments improve inter‐consistency for some subsets of the measurements while worsening it for others. Finally, we provide general guidance for quantifying the random uncertainty that applies for common combinations of measured and calculated values. 

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. 

Abstract The ocean carbonate system is critical to monitor because it plays a major role in regulating Earth's climate and marine ecosystems. It is monitored using a variety of measurements, and it is commonly understood that all components of seawater carbonate chemistry can be calculated when at least two carbonate system variables are measured. However, several recent studies have highlighted systematic discrepancies between calculated and directly measured carbonate chemistry variables and these discrepancies have large implications for efforts to measure and quantify the changing ocean carbon cycle. Given this, the Ocean Carbonate System Intercomparison Forum (OCSIF) was formed as a working group through the Ocean Carbon and Biogeochemistry program to coordinate and recommend research to quantify and/or reduce uncertainties and disagreements in measurable seawater carbonate system measurements and calculations, identify unknown or overlooked sources of these uncertainties, and provide recommendations for making progress on community efforts despite these uncertainties. With this paper we aim to (1) summarize recent progress toward quantifying and reducing carbonate system uncertainties; (2) advocate for research to further reduce and better quantify carbonate system measurement uncertainties; (3) present a small amount of new data, metadata, and analysis related to uncertainties in carbonate system measurements; and (4) restate and explain the rationales behind several OCSIF recommendations. We focus on open ocean carbonate chemistry, and caution that the considerations we discuss become further complicated in coastal, estuarine, and sedimentary environments. 

Abstract. As the largest active carbon reservoir on Earth, the ocean is a cornerstone of the global carbon cycle, playing a pivotal role in modulating ocean health and regulating climate. Understanding these crucial roles requires access to a broad array of data products documenting the changing chemistry of the global ocean as a vast and interconnected system. This review article provides a comprehensive overview of 60 existing ocean carbonate chemistry data products, encompassing compilations of cruise datasets, derived gap-filled data products, model simulations, and compilations thereof. It is intended to help researchers identify and access data products that best align with their research objectives, thereby advancing our understanding of the ocean's evolving carbonate chemistry.