Ocean submesoscale dynamics are thought to play a key role in both the climate system and ocean productivity, however, subsurface observations at these scales remain rare. Seismic oceanography, an established acoustic imaging method, provides a unique tool for capturing oceanic structure throughout the water column with spatial resolutions of tens of meters. A drawback to the seismic method is that temperature and salinity are not measured directly, limiting the quantitative interpretation of imaged features. The Markov Chain Monte Carlo (MCMC) inversion approach has been used to invert for temperature and salinity from seismic data, with spatially quantified uncertainties. However, the requisite prior model used in previous studies relied upon highly continuous acoustic reflection horizons rarely present in real oceanic environments due to instabilities and turbulence. Here we adapt the MCMC inversion approach with an iteratively updated prior model based on hydrographic data, sidestepping the necessity of continuous reflection horizons. Furthermore, uncertainties introduced by the starting model thermohaline fields as well as those from the MCMC inversion itself are accounted for. The impact on uncertainties of varying the resolution of hydrographic data used to produce the inversion starting model is also investigated. The inversion is applied to a mid-depth Mediterranean water eddy (or meddy) captured with seismic imaging in the Gulf of Cadiz in 2007. The meddy boundary exhibits regions of disrupted seismic reflectivity and rapid horizontal changes of temperature and salinity. Inverted temperature and salinity values typically have uncertainties of 0.16°C and 0.055 psu, respectively, and agree well with direct measurements. Uncertainties of inverted results are found to be highly dependent on the resolution of the hydrographic data used to produce the prior model, particularly in regions where background temperature and salinity vary rapidly, such as at the edge of the meddy. This further advancement of inversion techniques to extract temperature and salinity from seismic data will help expand the use of ocean acoustics for understanding the mesoscale to finescale structure of the interior ocean.

Mediterranean eddies (meddies) play an essential role in transferring heat, salinity and momentum into the Atlantic Ocean. The rate of heat (and salt) flux from the meddy and its ultimate lifetime are key proxies to understanding how meddies impact the redistribution of heat and salt in the ocean system. A Mediterranean eddy was observed in the Gulf of Caidz in 2007 using seismic and hydrographic data. The spatial distribution of turbulent dissipation rates around the meddy is estimated from the seismically derived internal wave spectra subrange using fine-scale parameterization. Turbulent dissipated rates are lowest (10−11 W/kg) within the core of the meddy but rise by nearly two orders of magnitude at the upper and lower boundaries, where signs of double diffusive convection are observed. Along the left flank of the meddy, thermohaline intrusions and interleaving of water masses are found in inverted temperature and salinity profiles, transporting heat laterally from the warm core to the Atlantic water with a flux of around 470 Wm−2. The meddy presented in this study is shown to decay in 2 years, primarily due to the heat loss associated with thermohaline intrusions. For the first time, heat fluxes around the meddy and its lifetime are quantified using seismic oceanography data, and the methods proposed here can be applied to more seismic datasets in the global oceans.

<p>Seismic oceanography (SO) has been widely used on the inversion of physical oceanographic properties due to its higher lateral resolution up to 10m, compared to conventional oceanographic measurement methods. Normally, the inversion process requires seismic data and in-situ hydrographic data, and the latter is acquired by deploying XBTs/XCTDs. Recently, due to the advantage of providing quantifiable uncertainties of the inverted parameters, a Markov chain Monte Carlo (MCMC) algorithm has been used for the temperature and salinity inversion from SO data. Based on the MCMC inversion method, this study investigates the effect of the lateral density of XBT deployments on the resultant uncertainties of inverted temperature and salinity. We analysed the seismic data acquired in the Gulf of Cadiz (SW Iberia) in 2007 in the framework of the Geophysical Oceanography project. A nonlinear Temperature-Salinity relation is modelled using a Genetic Algorithm from CTD casts collected in the research area. Combining the temperature data from XBTs with the T-S relation, smoothed temperature and salinity prior distributions are derived. Then the posterior distributions of temperature and salinity are estimated using the prior information and the field reflectivity data. In this study, priors are changed by controlling the amount of XBTs used, after which the corresponding uncertainties of the inverted temperature and salinity are calculated. The result quantifies the impact of the prior models with different XBT deployment densities on the uncertainties of inverted results. It is proposed that the acquisition of a reasonable temperature starting model is the prior consideration when deciding the XBT deployment strategy along the seismic oceanography survey.</p>