TEOS-10 and EOS-80 yield measurable differences in calculated seawater salinity.
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Peer-reviewed literature demonstrates that TEOS-10 and EOS-80 employ different equations, state variables, and salinity definitions (such as Absolute Salinity versus Practical Salinity), leading to measurable differences in calculated seawater properties like density and salinity.
Abstract. The determination of salinity by means of electrical conductivity relies on stable salt proportions in the North Atlantic Ocean, because standard seawater, which is required for salinometer calibration, is produced from water of the North Atlantic. To verify the long-term stability of the standard seawater composition, it was proposed to perform measurements of the standard seawater density. Since the density is sensitive to all salt components, a density measurement can detect any change in the composition. A conversion of the density values to salinity can be performed by means of a density–salinity relation. To use such a relation with a target uncertainty in salinity comparable to that in salinity obtained from conductivity measurements, a density measurement with an uncertainty of 2 g m−3 is mandatory. We present a new density–salinity relation based on such accurate density measurements. The substitution measurement method used is described and density corrections for uniform isotopic and chemical compositions are reported. The comparison of densities calculated using the new relation with those calculated using the present reference equations of state TEOS-10 suggests that the density accuracy of TEOS-10 (as well as that of EOS-80) has been overestimated, as the accuracy of some of its underlying density measurements had been overestimated. The new density–salinity relation may be used to verify the stable composition of standard seawater by means of routine density measurements.
Water in its three ambient phases plays the central thermodynamic role in the terrestrial climate system. Clouds control Earth's radiation balance, atmospheric water vapour is the strongest "greenhouse" gas, and non-equilibrium relative humidity at the air-sea interface drives evaporation and latent heat export from the ocean. On climatic time scales, melting ice caps and regional deviations of the hydrological cycle result in changes of seawater salinity, which in turn may modify the global circulation of the oceans and their ability to store heat and to buffer anthropogenically produced carbon dioxide. In this paper, together with three companion articles, we examine the climatologically relevant quantities ocean salinity, seawater pH and atmospheric relative humidity, noting fundamental deficiencies in the definitions of those key observables, and their lack of secure foundation on the International System of Units, the SI. The metrological histories of those three quantities are reviewed, problems with their current definitions and measurement practices are analysed, and options for future improvements are discussed in conjunction with the recent seawater standard TEOS-10. It is concluded that the International Bureau of Weights and Measures, BIPM, in cooperation with the International Association for the Properties of Water and Steam, IAPWS, along with other international organisations and institutions, can make significant contributions by developing and recommending state-of-the-art solutions for these long standing metrological problems in climatology.
Abstract. In June 2009, the Intergovernmental Oceanographic Commission of UNESCO released The international thermodynamic equation of seawater – 2010 (TEOS-10 for short; IOC et al., 2010) to define, describe and calculate the thermodynamic properties of seawater. Compared to the Equation of State-1980 (EOS-80 for short), the most obvious change with TEOS-10 is the use of Absolute Salinity as salinity argument, replacing the Practical Salinity used in the oceanographic community for 30 years. Due to the lack of observational data, the applicability of the potentially increased accuracy in Absolute Salinity algorithms for coastal and semi-enclosed seas is not very clear to date. Here, we discuss the magnitude, distribution characteristics, and formation mechanism of Absolute Salinity and Absolute Salinity Anomaly in Chinese shelf waters, based on the Marine Integrated Investigation and Evaluation Project of the China Sea and other relevant data. The Absolute Salinity SA ranges from 0.1 to 34.66 g kg−1. Instead of silicate, the main composition anomaly in the open sea, CaCO3 originating from terrestrial input and re-dissolution of shelf sediment is most likely the main composition anomaly relative to SSW and the primary contributor to the Absolute Salinity Anomaly δSA. Finally, relevant suggestions are proposed for the accurate measurement and expression of Absolute Salinity of the China offshore waters.
Ji, F., Pawlowicz, R., and Xiong, X.: Estimating the Absolute Salinity of Chinese offshore waters using nutrients and inorganic carbon data, Ocean Sci., 17, 909–918, https://doi.org/10.5194/os-17-909-2021, 2021. Received: 07 Feb 2021 – Discussion started: 18 Feb 2021 – Revised: 18 Apr 2021 – Accepted: 28 Apr 2021 – Published: 09 Jul 2021 1 Introduction Absolute Salinity, which is traditionally defined as the mass fraction of
At present, the TEOS-10 Absolute Salinity of a seawater sample is obtained by adding the Absolute Salinity Anomaly δ S A to Reference Salinity S R , in which S R is the mass fraction of dissolved material in a stoichiometric composition model (the Reference Composition or RC) of seawater, defined by Millero (2008), for which the reference material known as International Association for the Physical Sciences of the Ocean (IAPSO) Standard Seawater (SSW for short), is a good approximation and of the same conductivity as that of the sample. δ S A is the mass fraction change caused by composition variations relative to RC.
Three algorithms for calculating Absolute Salinity in the open ocean are provided in TEOS-10. The two that avoid a direct measurement either make assumptions about the dominant biogeochemical processes in the ocean that affect the Absolute Salinity Anomaly or rely on empirically determined correlations. However, the applicability and accuracy of the TEOS-10 algorithms are still not very clear for estuaries and semi-enclosed oceanic basins where the relative compositions of the seawater may be different from that of the open ocean.
2 Methods and data 2.1 Calculation of Absolute Salinity The TEOS-10 Solution Absolute Salinity of seawater is essentially based on adding up the mass of solute in a seawater sample: (1) S A soln = ∑ i = 1 N c M i c i , where c i is the molar concentration of component i in seawater kg −1 , M i is the molar mass of the component, and N c is the number of species of component in seawater. However, it is impractical to carry out a full chemical analysis for the seawater to get the S A soln regularly.
The primary and most demanding purpose of oceanographic salinity measurements is the calculation of seawater density to estimate significant ocean currents driven by sometimes tiny horizontal pressure gradients. In TEOS-10, Absolute Salinity is instead defined so that the density of seawater can be accurately calculated by the following equation: (2) ρ = f TEOS-10 ( S A , t , p ) , where f TEOS-10 is a specified function. Therefore, S A is also called a Density Salinity. Unfortunately, although for many purposes we can treat S A and S A soln interchangeably, at highest precisions S A ≠ S A soln due to small changes in the relative composition of sea salt.
China offshore seawater is a mixture of the Kuroshio water originating from the North Equatorial Current and the runoff into the sea. The Absolute Salinity Anomaly in Pacific surface waters in any case is generally small; it is the deeper waters that have (relatively) large Absolute Salinity Anomalies arising from remineralization in the subsurface branch of the ocean's overturning circulation. In this paper, we ignore the relative composition difference between the Kuroshio and SSW for now. Following Feistel et al.
3.5 Contrast to the δ S A calculated by GSW Using the GSW function library and the corresponding climatological silicate and Practical Salinity data, the calculated δ S A of China offshore waters ranges from 0 to 0.002 g kg −1 . This is 2 orders of magnitude less than the values calculated in Sect. 3.2. The spatial distribution characteristics are also significantly different. These differences mainly come from the following aspects: Instead of silicate, CaCO 3 is most likely the main relative composition anomaly of China offshore seawater and the primary contributor to the δ S A , where it is greater than 0.05 g kg −1 .
It can be indicated that the GSW climatological dataset basically reflects the distribution characteristics of silicate in these areas. Figure 6 S i (OH) 4 isoclines of sea surface in summer. 4 Conclusion and analysis The proposal and implementation of the concept of S A in TEOS-10 are meant to accurately quantify the total mass of inorganic substance dissolved in seawater, to ensure that the density and related quantities are accurately represented by the Gibbs function for seawater, and to correct errors caused by measuring the properties of seawater such as chloride and conductivity to get the salinity.
This article presents a review of current practice in estimating steric sea level change, focussed on the treatment of uncertainty. Steric sea level change is the contribution to the change in sea level arising from the dependence of density on temperature and salinity. It is a significant component of sea level rise and a reflection of changing ocean heat content. However, tracking these steric changes still remains a significant challenge for the scientific community. We review the importance of understanding the uncertainty in estimates of steric sea level change. Relevant concepts of uncertainty are discussed and illustrated with the example of observational uncertainty propagation from a single profile of temperature and salinity measurements to steric height. We summarise and discuss the recent literature on methodologies and techniques used to estimate steric sea level in the context of the treatment of uncertainty. Our conclusions are that progress in quantifying steric sea level uncertainty will benefit from: greater clarity and transparency in published discussions of uncertainty, including exploitation of international standards for quantifying and expressing uncertainty in measurement; and the development of community "recipes" for quantifying the error covariances in observations and from sparse sampling and for estimating and propagating uncertainty across spatio-temporal scales.