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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. 

Abstract An open question in marine carbon chemistry is if organic alkalinity (or some other unidentified species) is present in non‐negligible quantities in the open ocean. If organic alkalinity is indeed present, different methods for total alkalinity (TA) analysis with different titration endpoints could titrate different amounts depending on the dissociation constants (pK_a) of the acids present, resulting in meaningful differences or offsets between methods. Two commonly used methods, open‐cell titration with non‐linear least squares fitting and single‐step titration with spectrophotometric endpoint detection, might titrate different amounts of organic alkalinity, if present, depending on their pK_a. We test this hypothesis using paired samples collected on two cruises, one in the northwest Pacific and one in the western Arctic, and analyze the TA using both methods. We found the differences to be statistically indistinguishable (∆TA_{[Open‐Cell−Single‐Step]} = 0.5 ± 3.9 μmol kg⁻¹_swmean and standard deviationN = 206). Adjustment of the single‐step TA to certified reference material could be obscuring a difference in the methods. The good agreement between methods indicated that the analytical method is not the cause of offsets in Pacific TA identified by the Global Ocean Data Analysis Project version 2. From these results, the presence of organic alkalinity in open ocean waters remains inconclusive but suggests that if present, the concentration is either very low or both methods titrate similar amounts. 

This dataset consists of carbonate chemistry discrete profile data collected on two of the Northeast US Coast Shelf LTER cruises, EN617 and EN627 summer 2018 and winter 2019. The dataset includes in situ temperature, salinity, and dissolved oxygen as measured from a CTD, and also includes discrete samples for dissolved inorganic carbon and total alkalinity. 

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 Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We firstdefineeach of the major C pools and fluxes and providerationalefor their importance to wetland C dynamics. For each approach, we clarifywhatcomponent of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such aswhereandwhenan approach is typically used,whocan conduct the measurements (expertise, training requirements), andhowapproaches are conducted, including considerations on equipment complexity and costs. Finally, we reviewkey covariatesandancillary measurementsthat enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions. 

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.