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.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).

Automatic dynamic mask extraction for PIV images containing an unsteady interface, bubbles, and a moving structure

When performing Particle Image Velocimetry (PIV) measurements in complex fluid flows with moving interfaces and a two-phase flow, it is necessary to develop a mask to remove non-physical measurements. This is the case when studying, for example, the complex bubble sweep-down phenomenon observed in oceanographic research vessels. Indeed, in such a configuration, the presence of an unsteady free surface, of a solid–liquid interface and of bubbles in the PIV frame, leads to generate numerous laser reflections and therefore spurious velocity vectors. In this note, an image masking process is developed to successively identify the boundaries of the ship and the free surface interface. As the presence of the solid hull surface induces laser reflections, the hull edge contours are simply detected in the first PIV frame and dynamically estimated for consecutive ones. As for the unsteady surface determination, a specific process is implemented like the following: i) the edge detection of the gradient magnitude in the PIV frame, ii) the extraction of the particles by filtering high-intensity large areas related to the bubbles and/or hull reflections, iii) the extraction of the rough region containing these particles and their reflections, iv) the removal of these reflections. The unsteady surface is finally obtained with a fifth-order polynomial interpolation. The resulted free surface is successfully validated from the Fourier analysis and by visualizing selected PIV images containing numerous spurious high intensity areas. This paper demonstrates how this data analysis process leads to PIV images database without reflections and an automatic detection of both the free surface and the rigid body. An application of this new mask is finally detailed, allowing a preliminary analysis of the hydrodynamic flow.