Random and systematic uncertainty in ship‐based seawater carbonate chemistry observations - Brendan R. Carter; Jonathan D. Sharp; Maribel I. García‐Ibáñez; Ryan J. Woosley; Michael B. Fong; Marta Álvarez; Leticia Barbero; Simon L. Clegg; Regina Easley; Andrea J. Fassbender; Xinyu Li; Katelyn M. Schockman; et al. (Limnology and Oceanography, 2024)
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.