Dynamic Topography and Sea Level Anomalies of the Southern Ocean: Variability and Teleconnections

Abstract

This study combines sea surface height (SSH) estimates of the ice‐covered Southern Ocean with conventional open‐ocean SSH estimates from CryoSat‐2 to produce monthly composites of dynamic ocean topography (DOT) and sea level anomaly (SLA) on a 50 km grid spanning 2011–2016. This data set reveals the full Southern Ocean SSH seasonal cycle for the first time; there is an antiphase relationship between sea level on the Antarctic continental shelf and the deeper basins, with coastal SSH highest in autumn and lowest in spring. As a result of this pattern of seasonal SSH variability, the barotropic component of the Antarctic Slope Current (ASC) has speeds that are regionally up to twice as fast in the autumn. Month‐to‐month circulation variability of the Ross and Weddell Gyres is strongly influenced by the local wind field, and is correlated with the local wind curl (Ross: −0.58; Weddell: −0.67). SSH variability is linked to both the Southern Oscillation and the Southern Annular Mode, dominant modes of southern hemisphere climate variability. In particular, during the strong 2015–2016 El Niño, a sustained negative coastal SLA of up to −6 cm, implying a weakening of the ASC, was observed in the Pacific sector of the Southern Ocean. The ability to examine sea level variability in the seasonally ice‐covered regions of the Southern Ocean—climatically important regions with an acute sparsity of data—makes this new merged sea level record of particular interest to the Southern Ocean oceanography and glaciology communities.

Oceanic Heat Delivery to the Antarctic Continental Shelf: Large‐Scale, Low‐Frequency Variability

“…In sections and , we used the conceptual model of Spence et al () to explain the seasonal and interannual variabilities of as a response to the cross‐shelf downwelling circulation forced by circumpolar Ekman transport convergence. The markedly circumpolarly symmetric pattern of negative correlation between the SAM index and the coastal sea level anomaly at zero lag, combined with the lack of correlation at larger lags (Armitage et al, , their Figure S7) supports this interpretation. We find the highest correlation between the circumpolar Ekman transport divergence ( ) and SAM at zero lag, while the highest correlation between and Niño 3.4 is at 10‐month lag (not shown).…”

“…Within individual segments, the along‐isobath averages of Φ are also significantly correlated with SAM at zero lag at the Byrd, Amundsen, and Bellingshausen segments in West Antarctica (Figure c) and across all of East Antarctica (segments W‐EA, C‐EA, and E‐EA; Figure e). These results suggest that the SAM, possibly via its modulation of the ASF structure (Armitage et al, ), is more important than ENSO as a climate driver of onshore heat transport at the circumpolar scale.…”

Variability of the Ross Gyre, Southern Ocean: Drivers and Responses Revealed by Satellite Altimetry

“…MCA1 represents the effect of Ekman pumping associated with wind-induced ocean stress on SSH variability, because OSC-driven convergence (divergence) of the local Ekman transport induces an increase (decrease) of SSH. This suggests that sea level variability in the West Antarctic sector, including the RG, is regulated by large-scale meridional Ekman transport forced by circumpolar zonal winds, which removes (adds) mass from (to) the coastal region (Armitage et al, 2018;Vivier et al, 2005). This suggests that sea level variability in the West Antarctic sector, including the RG, is regulated by large-scale meridional Ekman transport forced by circumpolar zonal winds, which removes (adds) mass from (to) the coastal region (Armitage et al, 2018;Vivier et al, 2005).…”

“…CryoSat-2 operates in different modes in the Southern Ocean: Low-Resolution Mode in the open ocean away from sea ice, Synthetic Aperture Radar (SAR) over sea ice, and SAR Interferometric in coastal regions. A seasonal offset between the lead and open-ocean data was identified, caused by the different retrackers used to fit the altimeter return echoes; this was added back to the lead data to correct the bias (Armitage et al, 2016(Armitage et al, , 2018Bulczak et al, 2015;Giles et al, 2012). Open-ocean SSH data were processed using standard techniques.…”

Arctic Sea Level and Surface Circulation Response to the Arctic Oscillation

“…The AO index is renormalized to have zero mean and standard deviation of one in the period 2003-2014, and the seasonal cycle is removed. SLA, SLP, u g , u 10 , and u i composites are constructed for positive and negative phases of the AO index by computing the AO index-weighted mean SLA, SLP, u g , u 10 , and u i for all positive months and all negative months (after Armitage et al, 2018). Given that the AO is an atmospheric mode of variability, we expect a fast (month-to-month) wind-driven barotropic sea level response, an expectation supported by results showing that the AO has a signature in GRACE ocean mass data (Peralta-Ferriz et al, 2014), but this response will be masked by larger steric sea level variations.…”