Time-Varying Empirical Probability Densities of Southern Ocean Surface Winds: Linking the Leading Mode to SAM and QuantifyingWind Product Differences

Abstract

Southern Ocean (SO) surface winds are essential for ventilating the upper ocean by bringing heat and CO2 to the ocean interior. The relationships between mixed-layer ventilation, the Southern Annular Mode (SAM), and the storm tracks remain unclear because processes can be governed by short-term wind events as well as long-term means.In this study, observed time-varying 5-day probability density functions (PDFs) of ERA5 surface winds and stresses over the SO are used in a singular value decomposition to derive a linearly independent set of empirical basis functions. The first modes of wind (72% of the total wind variance) and stress (74% of the total stress variance) are highly correlated with a standard SAM index (r = 0.82) and reflect SAM’s role in driving cyclone intensity and, in turn, extreme westerly winds. This Joint PDFs of zonal and meridional wind show that southerly and less westerly winds associated with strong mixed-layer ventilation are more frequent during short and distinct negative SAM phases. The probability of these short-term events might be related to mid-latitude atmospheric circulation. The second mode describes seasonal changes in the wind variance (16% of the total variance) that are uncorrelated with the first mode.The analysis produces similar results when repeated using 5-day PDFs from a suite of scatterometer products. Differences between wind product PDFs resemble the first mode of the PDFs. Together, these results show a strong correlation between surface stress PDFs and the leading modes of atmospheric variability, suggesting that empirical modes can serve as a novel pathway for understanding differences and variability of surface stress PDFs.

High‐Frequency Fluctuations in Antarctic Bottom Water Transport Driven by Southern Ocean Winds

“…The major finding of this study is that fluctuations in Southern Ocean winds drive variability in meridional transport of AABW over time scales shorter than approximately 2 years (Figures 3a and 4). Fluctuations on these time scales account for almost all of the variability in the winds (Hell et al, 2021) and the export of AABW (Figure 2e). Furthermore, the transport fluctuations are comparable to the mean meridional transport of AABW (Figure 3c).…”

Sub‐Seasonal Forcing Drives Year‐To‐Year Variations of Southern Ocean Primary Productivity

“…Therefore, the annual mean SChl is higher in years with more or larger pulses, which is confirmed by high correlations between annual mean SChl and annual variance of the subseasonal component of SChl (Figure 8a). The inverse cascade toward low frequencies could result from ecological fluctuations or from changes in the prevalence of extreme wind events or eddy activity (Cravatte et al, 2021), which in turn may be connected to climate variability (Busecke & Abernathey, 2019;Hell et al, 2021), although sub-seasonal SChl variations were only weakly correlated with the SAM index (Figure 4d). The link between annual mean SChl and high-frequency events is important because sub-seasonal SChl variability occurs at spatial scales of ∼50-150 km (Figure S1c), which leads to a similarly small spatial autocorrelation for variations in annual mean SChl (Figure 8b).…”

“…Therefore, year‐to‐year changes in annual mean SChl reflect episodic forcing, such as storms and eddies, rather than multi‐annual climate variability. Although future work should investigate the role of climate modes in modulating the prevalence and magnitude of synoptic events and (sub‐)mesoscale mixing (Busecke & Abernathey, 2019; Hell et al., 2021). One implication of these results is that annual mean SChl only varies consistently over small spatial scales.…”

Swell Generation Under Extra‐Tropical Storms

“…Reanalysis products have biases in their representation of wind extremes (Gille, 2005; Hell et al., 2021). These wind extremes are represented in the Gaussian model as the peak wind speed.…”