Episodic Southern Ocean Heat Loss and Its Mixed Layer Impacts Revealed by the Farthest South Multiyear Surface Flux Mooring

Ogle, Sarah; Tamsitt, Veronica; Josey, Simon A.; Gille, Sarah T.; Cerovečki, Ivana; Talley, Lynne D.; Weller, Robert A.

doi:10.1029/2017gl076909

Cited by 34 publications

(65 citation statements)

References 33 publications

(52 reference statements)

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“…In contrast the central box receives enhanced warmer northerly winds off the subtropics, shoaling the mixed layers. These findings are backed up by the dominance of the turbulent heat flux terms in setting wintertime ocean heat loss and agree with the analysis of Ogle et al () based on in situ air‐sea flux mooring data. This pattern of wind stress driving a dipole over the eastern and central SAMW pools dominates both meridional wind stress and net heat flux variability, and their first EOF mode captures over 55% and 61% of their variance, respectively (Figure S2).…”

Section: Discussionsupporting

confidence: 90%

“…The dominant modes of atmospheric variability in the South Pacific are the SAM and ENSO. These exert a strong influence on regional ocean properties (Naveira Garabato et al, ), circulation (Sallee et al, ), sea ice extent (Haumann et al, ), regional winds (Turner et al, ), and air‐sea heat fluxes (Ogle et al, ). The map of regional regression of SAMW winter thickness onto the ENSO (Niño3.4) and SAM indexes is striking in its relationship to the central and eastern pools of variability (Figures a and b).…”

Section: Relationship To Atmospheric Modes Of Variabilitymentioning

confidence: 99%

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A See‐Saw in Pacific Subantarctic Mode Water Formation Driven by Atmospheric Modes

Meijers

Cerovečki

King

et al. 2019

Geophysical Research Letters

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Subantarctic Mode Water (SAMW) in the Pacific forms in two distinct pools in the south central and southeast Pacific, which subduct into the ocean interior and impact global storage of heat and carbon. Wintertime thickness of the central and eastern SAMW pools vary predominantly out of phase with each other, by up to ±150 m between years, resulting in an interannual thickness see‐saw. The thickness in the eastern (central) pool is found to be strongly positively (negatively) correlated with both the Southern Annular Mode (SAM) and El Niño–Southern Oscillation (ENSO). The relative phases of the SAM and ENSO set the SAMW thickness, with in phase reinforcing modes in 2005–2008 and 2012–2017 driving strong differences between the pools. Between 2008 and 2012 out of phase atmospheric modes result in less coherent SAMW patterns. SAMW thickness is dominated by local formation driven by SAM and ENSO modulated wind stress and turbulent heat fluxes.

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Section: Discussionsupporting

confidence: 90%

Section: Relationship To Atmospheric Modes Of Variabilitymentioning

confidence: 99%

A See‐Saw in Pacific Subantarctic Mode Water Formation Driven by Atmospheric Modes

Meijers

Cerovečki

King

et al. 2019

Geophysical Research Letters

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“…The subseasonal variability of the fluxes of buoyancy and momentum are not as well understood compared to the annual mean and seasonal cycles, but they have been studied, both in the context of the NCEP reanalysis (e.g., Gulev and Belyaev, ; Zolina and Gulev, ) and in observations (e.g., Gille, ; Konda et al, ; Monahan, ; Monahan, ; Ogle et al, ; Sathiyamoorthy and Moore, ). For some analysis comparing the NCEP reanalysis winds that underpin CORE, in situ buoy winds, and satellite winds, see Figure S8 (Atlas et al, ; Wentz et al, ), and see Figure 1 and section 3 of Alford () (for further evaluation and discussion of the high‐frequency winds at high latitudes, see also Alford, ).…”

Section: Model Setup Rationale and Metricsmentioning

confidence: 99%

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

Whitt

Nicholson²,

Carranza

2019

JGR Oceans

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Subseasonal surface wind variability strongly impacts the annual mean and subseasonal turbulent atmospheric surface fluxes. However, the impacts of subseasonal wind variability on the ocean are not fully understood. Here, we quantify the sensitivity of the ocean surface stress ( ), buoyancy flux (B), and mixed layer depth (MLD) to subseasonal wind variability in both a one-dimensional (1-D) vertical column model and a three-dimensional (3-D) global mesoscale-resolving ocean/sea ice model. The winds are smoothed by time filtering the pseudo-stresses, so the mean stress is approximately unchanged, and some important surface flux feedbacks are retained. The 1-D results quantify the sensitivities to wind variability at different time scales from 120 days to 1 day at a few sites. The 3-D results quantify the sensitivities to wind variability shorter than 60 days at all locations, and comparisons between 1-D and 3-D results highlight the importance of 3-D ocean dynamics. Globally, subseasonal winds explain virtually all of subseasonal variance, about half of subseasonal B variance but only a quarter of subseasonal MLD variance. Subseasonal winds also explain about a fifth of the annual mean MLD and a similar and spatially correlated fraction of the mean friction velocity, u * = √ | |∕ sw where sw is the density of seawater. Hence, the subseasonal MLD variance is relatively insensitive to subseasonal winds despite their strong impact on local B and variability, but the mean MLD is relatively sensitive to subseasonal winds to the extent that they modify the mean u * , and both of these sensitivities are modified by 3-D ocean dynamics. Key Points: • We quantify the global impact of subseasonal winds on ocean surface fluxes and mixed-layer depth (MLD) in a mesoscale-resolving model • Globally, subseasonal winds are responsible for about a fifth of the annual average MLD and about a quarter of the subseasonal MLD variance • The increase in the mean MLD with subseasonal winds is caused by a higher friction velocity, but other factors modify the sensitivity Supporting Information: • Supporting Information S1A few previous studies have looked into the sensitivity of the climatological MLD to subseasonal atmospheric variability in 3-D global ocean circulation models by explicitly modifying the wind forcing in ocean models using grids that do not fully resolve mesoscales. For example, Lee and Liu (2005) explore the impact of diurnal winds on the MLD and sea surface temperature using an eddy-parameterizing (nominal 1 • resolution) global ocean model. Their model is forced with daily averaged fluxes, except for the wind stresses, which are averaged over 12 hr or subsampled every 24 hr. The annual and zonal mean MLD increases by up to a maximum of about 10 m with 12-hr winds compared to subsampled 24-hr winds, and the response of the MLD is largely attributable to enhanced vertical mixing by high-frequency winds at middle-to-high latitudes. More recently, Condron and Renfrew (2013) parameterize the effects of unresolved mesoscale a...

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“…The lack of in situ surface wind sampling can lead to errors in reanalysis products and wave models. For example, the addition of a single mooring at the Ocean Observatories Initiative site in the southeast Pacific had a significant impact on the regional weather forecast produced by the European Centre for Medium‐Range Weather Forecasts (Ogle et al, ). Anecdotally, this suggests that Southern Hemisphere reanalysis deficiencies in representing synoptic scale variability could stem from lack of observations.…”

Section: Introductionmentioning

confidence: 99%

Estimating Southern Ocean Storm Positions With Seismic Observations

et al. 2020

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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.

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Episodic Southern Ocean Heat Loss and Its Mixed Layer Impacts Revealed by the Farthest South Multiyear Surface Flux Mooring

Cited by 34 publications

References 33 publications

A See‐Saw in Pacific Subantarctic Mode Water Formation Driven by Atmospheric Modes

A See‐Saw in Pacific Subantarctic Mode Water Formation Driven by Atmospheric Modes

Global Impacts of Subseasonal (<60 Day) Wind Variability on Ocean Surface Stress, Buoyancy Flux, and Mixed Layer Depth

Estimating Southern Ocean Storm Positions With Seismic Observations

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