Spectrophotometric Determination of Carbonate Ion Concentrations: Elimination of Instrument-Dependent Offsets and Calculation of In Situ Saturation States.

This work describes an improved algorithm for spectrophotometric determinations of seawater carbonate ion concentrations ([CO32-]spec) derived from observations of ultraviolet absorbance spectra in lead-enriched seawater. Quality-control assessments of [CO32-]spec data obtained on two NOAA research cruises (2012 and 2016) revealed a substantial intercruise difference in average Δ[CO32-] (the difference between a sample's [CO32-]spec value and the corresponding [CO32-] value calculated from paired measurements of pH and dissolved inorganic carbon). Follow-up investigation determined that this discordance was due to the use of two different spectrophotometers, even though both had been properly calibrated. Here we present an essential methodological refinement to correct [CO32-]spec absorbance data for small but significant instrumental differences. After applying the correction (which, notably, is not necessary for pH determinations from sulfonephthalein dye absorbances) to the shipboard absorbance data, we fit the combined-cruise data set to produce empirically updated parameters for use in processing future (and historical) [CO32-]spec absorbance measurements. With the new procedure, the average Δ[CO32-] offset between the two aforementioned cruises was reduced from 3.7 µmol kg-1 to 0.7 µmol kg-1, which is well within the standard deviation of the measurements (1.9 µmol kg-1). We also introduce an empirical model to calculate in situ carbonate ion concentrations from [CO32-]spec. We demonstrate that these in situ values can be used to determine calcium carbonate saturation states that are in good agreement with those determined by more laborious and expensive conventional methods.

A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States.

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 CO2 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.