Southern Ocean Fronts: Where They Really Are ✎

Original: "New Perspectives on Frontal Variability in the Southern Ocean Using a Local Identification Scheme"
· Christopher C. Chapman
arXiv:1609.09616 · 2016-09-30 · arXiv non-exclusive · independent analysis · 2 min
A new method reveals that Antarctic Circumpolar Current fronts do not migrate; they split and merge.

Context

Studying Southern Ocean fronts is key to understanding global circulation and climate. Fronts separate water masses and influence heat and carbon transport. Traditionally, fronts were identified by contours of sea surface height, but this approach yields conflicting results about frontal migration. This work aligns with studies using satellite altimetry and local gradient methods, but offers a more rigorous identification approach.

Methods

The WHOSE (Wavelet/Higher Order Statistics Enhancement) method is applied to daily absolute dynamic topography maps spanning 1993–2015. It uses wavelet decomposition and higher-order statistics to extract step-like signals characteristic of fronts from noise. Then, frontal occurrence frequency maps are constructed and fitted with sums of skewed normal functions. The number of functions is chosen using the Akaike information criterion, allowing the number of fronts at each longitude to be estimated.

Results

The number of fronts varies greatly along longitude, from 2 to 15. Increases in frontal count (splitting) occur downstream of major topographic obstacles, such as the Kerguelen Plateau, while decreases (merging) occur where bottom topography steers the current. Frontal meandering remains relatively constant, with a standard deviation of about 0.5° of latitude. Over 1993–2014, no significant southward shift of fronts was detected, and sensitivity to the Southern Annular Mode and El Niño is weak and local.

Implications

This work shows that local detection methods yield a more realistic picture of frontal variability than contour-based methods. Unlike neighboring studies that use sea surface height contours, here fronts are treated as transient structures that can split and merge. This changes our understanding of Antarctic Circumpolar Current dynamics and calls for revisiting conclusions about frontal migration.

Future development

Future research could focus on the consequences of frontal reorganization for large-scale circulation. It is also important to better characterize frontal regimes and their connection to potential vorticity. The method could be applied to other regions and data types, such as sea surface temperature.

Impact

The results will influence oceanography, climatology, and modeling of Southern Ocean circulation. They will help improve interpretation of satellite data and predictions of climate change.

Next steps

A next step could be analyzing the link between frontal splitting and tracer transport. It would also be worthwhile to compare WHOSE with other local methods on independent data.

Key open problems

This work addresses the challenge of defining ocean fronts and their role in water mass mixing. It raises the question of how frontal reorganization affects deep water ventilation and the exchange of properties across latitudes.

Southern Ocean fronts can split into multiple branches, much like a river dividing into channels in a delta.

K(x) = \frac{E[(x-\bar{x})^4]}{(E[(x-\bar{x})^2])^2} - 3
A measure of the peakedness of a distribution; it is zero for a normal distribution and nonzero for step-like signals.

Key numbers

  • Data period: 1993–2015 (21 years)
  • Number of fronts: from 2 to 15 depending on longitude
  • Standard deviation of meandering: about 0.5° of latitude
  • Maximum frontal shift over the period: less than 1° of latitude
  • Data grid resolution: 1/4° (effectively lower)
Concepts
OISST v2.1 sea surface temperature Ocean heat content Southern Oscillation sea surface temperature anomaly Niño 4 Pacific decadal oscillation ENSO Oceanic Niño index marine heatwave Warm water volume relative ONI ocean current wind stress empirical orthogonal function ERSST equatorial wave propagation Hovmöller diagram natural variability wavelet transform