Structure, transports and transformations of the water masses in the Atlantic Subpolar Gyre

We discuss the distributions and transports of the main water masses in the North Atlantic Subpolar Gyre (NASPG) for the mean of the period 2002–2010 (OVIDE sections 2002–2010 every other year), as well as the inter-annual variability of the water mass structure from 1997 (4x and METEOR sections) to 2010. The water mass structure of the NASPG, quantitatively assessed by means of an Optimum MultiParameter analysis (with 14 water masses), was combined with the velocity fields resulting from previous studies using inverse models to obtain the water mass volume transports. We also evaluate the relative contribution to the Atlantic Meridional Overturning Circulation (AMOC) of the main water masses characterizing the NASPG, identifying the water masses that contribute to the AMOC variability. The reduction of the magnitude of the upper limb of the AMOC between 1997 and the 2000s is associated with the reduction in the northward transport of the Central Waters. This reduction of the northward flow of the AMOC is partially compensated by the reduction of the southward flow of the lower limb of the AMOC, associated with the decrease in the transports of Polar Intermediate Water and Subpolar Mode Water (SPMW) in the Irminger Basin. We also decompose the flow over the Reykjanes Ridge from the East North Atlantic Basin to the Irminger Basin (9.4 ± 4.7 Sv) into the contributions of the Central Waters (2.1 ± 1.8 Sv), Labrador Sea Water (LSW, 2.4 ± 2.0 Sv), Subarctic Intermediate Water (SAIW, 4.0 ± 0.5 Sv) and Iceland–Scotland Overflow Water (ISOW, 0.9 ± 0.9 Sv). Once LSW and ISOW cross over the Reykjanes Ridge, favoured by the strong mixing around it, they leave the Irminger Basin through the deep-to-bottom levels. The results also give insights into the water mass transformations within the NASPG, such as the contribution of the Central Waters and SAIW to the formation of the different varieties of SPMW due to air–sea interaction.

Diagnosis of vibriosis in the era of genomics: lessons from invertebrates

Global changes linked to increases in temperature and ocean acidification, but also to more direct anthropogenic influences such as aquaculture, have caused a worldwide increase in the reports of Vibrio-associated illnesses affecting humans and also animals such as shrimp and molluscs. Investigation of the emergence of Vibrio pathogenesis events requires the analysis of microbial evolution at the gene, genome and population levels, in order to identify genomic modifications linked to increased virulence, resistance and/or prevalence, or to recent host shift. From a more applied point of view, the elucidation of virulence mechanisms is a prerequisite to devising prophylactic methods to fight infectious agents. In comparison with human pathogens, fairly little is known about the requirements for virulence in vibrios pathogenic to animals. However, the advent of genome sequencing, especially next-generation technologies,the possibility of genetically manipulating most of the Vibrio strains, and the recent availability of standardised animals for experimental infections have now compensated for the considerable delay in advancement of the knowledge of non-model pathogens such as Vibrio and have led to new scientific questions.

Processing Bio-Argo nitrate concentration at the DAC Level

The only method used to date to measure dissolved nitrate concentration (NITRATE) with sensors mounted on profiling floats is based on the absorption of light at ultraviolet wavelengths by nitrate ion (Johnson and Coletti, 2002; Johnson et al., 2010; 2013; D’Ortenzio et al., 2012). Nitrate has a modest UV absorption band with a peak near 210 nm, which overlaps with the stronger absorption band of bromide, which has a peak near 200 nm. In addition, there is a much weaker absorption due to dissolved organic matter and light scattering by particles (Ogura and Hanya, 1966). The UV spectrum thus consists of three components, bromide, nitrate and a background due to organics and particles. The background also includes thermal effects on the instrument and slow drift. All of these latter effects (organics, particles, thermal effects and drift) tend to be smooth spectra that combine to form an absorption spectrum that is linear in wavelength over relatively short wavelength spans. If the light absorption spectrum is measured in the wavelength range around 217 to 240 nm (the exact range is a bit of a decision by the operator), then the nitrate concentration can be determined. Two different instruments based on the same optical principles are in use for this purpose. The In Situ Ultraviolet Spectrophotometer (ISUS) built at MBARI or at Satlantic has been mounted inside the pressure hull of a Teledyne/Webb Research APEX and NKE Provor profiling floats and the optics penetrate through the upper end cap into the water. The Satlantic Submersible Ultraviolet Nitrate Analyzer (SUNA) is placed on the outside of APEX, Provor, and Navis profiling floats in its own pressure housing and is connected to the float through an underwater cable that provides power and communications. Power, communications between the float controller and the sensor, and data processing requirements are essentially the same for both ISUS and SUNA. There are several possible algorithms that can be used for the deconvolution of nitrate concentration from the observed UV absorption spectrum (Johnson and Coletti, 2002; Arai et al., 2008; Sakamoto et al., 2009; Zielinski et al., 2011). In addition, the default algorithm that is available in Satlantic sensors is a proprietary approach, but this is not generally used on profiling floats. There are some tradeoffs in every approach. To date almost all nitrate sensors on profiling floats have used the Temperature Compensated Salinity Subtracted (TCSS) algorithm developed by Sakamoto et al. (2009), and this document focuses on that method. It is likely that there will be further algorithm development and it is necessary that the data systems clearly identify the algorithm that is used. It is also desirable that the data system allow for recalculation of prior data sets using new algorithms. To accomplish this, the float must report not just the computed nitrate, but the observed light intensity. Then, the rule to obtain only one NITRATE parameter is, if the spectrum is present then, the NITRATE should be recalculated from the spectrum while the computation of nitrate concentration can also generate useful diagnostics of data quality.