Estimating Southern Ocean Storm Positions With Seismic Observations

Abstract

Surface winds from Southern Ocean cyclones generate large waves that travel over long distances (>1,000 km). Wave generation regions are often colocated with enhanced air‐sea fluxes and upper ocean mixing. Ocean wave spectra contain information about storm wind speed, fetch size, and intensity at their generation site. Two years of seismic observations on the Ross Ice shelf, combined with modern optimization (machine learning) techniques, are used to trace the origins of wave events in the Southern Ocean with an accuracy of ±110 km and ±2 hr from a hypothetical point source. The observed spectral energy attenuated within sea ice and in the ice shelf but retains characteristics that can be compared to parametric wave models. Comparison with the Modern‐Era Retrospective Analysis for Research and Applications, Version 2, and ERA5 reanalyses suggests that less than 45% of ocean swell events can be associated with individual Southern Ocean storms, while the majority of the observed wave events cannot be matched with Southern Ocean high wind events. Reanalysis cyclones and winds are often displaced by about 350 km or 10 hr in Modern‐Era Retrospective Analysis for Research and Applications, Version 2, and ERA5 compared to the most likely positions inferred from the seismic spectra. This high fraction of displaced storms in reanalysis products over the South Pacific can be explained by the limited availability of remote sensing observations, primarily caused by the presence of sea ice. Deviation of wave rays from their great circle path by wave‐current interaction plays a minor role.

Swell Generation Under Extra‐Tropical Storms

“…(2018). It is also found to be a sufficient minimal model to explain observed displacements of estimated swell source location compared to the highest wind forcing locations (Section 3.3; Figures 9b and 9c; Hell et al., 2020). The combination of a Lagrangian wave‐growth model with an optimized swell propagation model suggests three stages in the life cycle of swell wave energy.…”

“…(1966), as detailed in Hell et al. (2019, 2020). This method triangulates the spatio‐temporal coordinates of a single swell source which is simultaneously observed at five wave buoy stations.…”

“…Note that a successful optimization of the multi‐station cost function may not always be straightforward. Indeed, local wind swell and wave‐current interactions on the swell travel paths are able to distort the wave buoys observations (Gallet & Young, 2014; Villas Bôas et al., 2017), and possibly alter the optimization procedure (Hell et al., 2020). Figures 7b–7f compares instances of the parametric wave model (colored contours) for the most likely source location (green hexagon in panel a) to the respective observations (colored shading).…”

“…This match between the forward calculation of the wave growth model forced by reanalysis winds (Equation ) and the back triangulation of linear swell propagation (Figure 7) provides evidence that the conceptual idea of a Gaussian wind model (Section 2.2) is sufficient to capture the necessary dynamics of wave growth and swell generation by a moving storm. This is, to some extent, surprising given the nonlinear nature of Equation and potential biases in the surface winds (Allen et al., 2020; Gille, 2005; Hell et al., 2020; Ribal & Young, 2019; Trindade et al., 2020; Wentz et al., 2015).…”

“…However, these self‐similar relations, first established by Kitaigorodskii (1962), do not account for the spatial and temporal variability of the wind forcing. It is thus unclear how a continuously varying wind field leads to the generation of one dominant single wave event that seems to stem from a very small source region, at least an order of magnitude smaller than the storm (Barber & Ursell, 1948; Collard et al., 2009; Hell et al., 2020; Husson et al., 2012; Munk, 1947).…”