Southern Ocean Fronts and eddies

Rosemary Morrow

LEGOS, Toulouse

Plan :

1) Monitoring frontal movements in the Southern Ocean with altimetry

(JB Sallee, K. Speer)

2) Eddy diffusivity in the Southern Ocean from surface drifters and altimetry (JB Sallee, K. Speer, R. Lumpkin)

3) Impact of sub-mesoscale processes (F. D’Ovidio)

4) Constraining coastal models with altimetry (P. DeMey)

1. FrontsFronts are generally described by horizontal property gradients (T, S, O2, N2, Dyn ht, etc), either at the surface or subsurface

Sokolov and Rintoul, 2004

Tasmania Antarctica

Fronts detected in September 1996 – SR3 – between Tasmania and Antarctica

Neutral Density – SR3 Section

Dyn height ( altimetry) – SR3 Section

Detecting Southern ocean frontsMonitor front movements using absolute SL

Jets merge and split, strengthen and weaken.

(Sokolov and Rintoul, JMS, 2002; JPO, 2006).

Strong SLA gradients often peak at absolute sea level contours ASL = (SLA + mean sea surface)

=>Gradient of sea surface height for January 1995 SR3 (color), with selected height contours corresponding toparticular fronts

=> Southern Ocean fronts are deep-reaching – can monitor their movements using altimetric absolute sea level

3. Variabilité frontale

Sallée et al. - Response of the ACC to atmospheric variability – J. Clim (2008)

Variability of Southern Ocean frontsi) Topographic influence along the circumpolar belt

Variabilité du SAF

SAF Variability:

Red = low variability

Fronts remain ~fixed

Blue = strong variability :

Fronts move over a large area

Frequency of SAF occurrence

Mean position +/- 1 STD

Intensity (dh/dy)

Does atmospheric variability impact on the frontal positions in deep basins ?

3. Variabilité frontaleVariability of the frontsii) Response to the climate variability : SAM and ENSO

Sallée et al. - Response of the ACC to atmospheric variability - – J. Clim (2008)

SLP/SAM SLA/SAM

SLP/ENSO SLA/ENSO

Atmosphere Ocean

High frequency :

(<3 month) SAM dominates

Covariance of PF and SAM or ENSO

Variability of the frontsii) Response to the climate variability : SAM and ENSO

Indian SE Pac

Low frequency :

(>1 year) ENSO dominates

Variability of the frontsiii) Mechanisms controlling the variability

Sallée et al. - Response of the ACC to atmospheric variability -

V ekman

Meridional Ekman transport anomaly regressed onto SAM :

Indian Ocean : Max Ekman transport SOUTH of the fronts

Response to a positive SAM event (timescale ~2 weeks)

Sallée et al. - Response of the ACC to atmospheric variability -

SE Pacific basin : Max Ekman transport NORTH of fronts

Response to a positive SAM event (timescale ~2 weeks)

V ekman

Variability of the frontsiii) Mechanisms controlling the variability

Fronts – future with SWOT

- Altimetry is important for monitoring subsurface frontal movements

- Hydrographic Fronts will have geostrophic adjustment at surface

- Currently using gridded AVISO maps (limited by the optimal interpolation scales : spatial : 70-100 km at mid to high latitude, temporal : 15-20 days)

- Need to resolve the Rossby radius :

- 10-20 km at high latitude

- > 3-5 days

-=> Measurements every 3-5 km

Eddy Diffusion and the Southern Oceanmixed layer heat budget2.

J.B. Sallee, K. Speer, R. Morrow, R. Lumpkin (JMR submitted, 2008)

Frequently studied in theory. In practice, we need lots of lagrangian particules.

Calculating Eddy Diffusion

.2urmsxx TuEffective Diffusivity (Taylor, 1921) :

In well-sampled homogeneous turbulence, k ~ constant after several integral time-scales

T is the Lagrangian timescale, related to the velocity autocorrelation function, R :

Calculating Lagrangian timescale (T), and velocity ACF, (R)from Global Drifter Program (GDP) 1995-2005

and virtual drifters from altimetric currents

Observed cross stream dispersion around the ACC from lagrangian GDP drifters

Linear dispersion regime

10 years of lagrangian drifter data Snapshot of altimetric currents overlaid on SLA

Cross stream eddy scales from GDP drifters

Eddy diffusion and the Upper Cell of the Southern Ocean

Lagrangian eddy time-scales first zero crossing (days)

Lagrangian eddy space-scales (km)

Cross stream eddy diffusion(Sallee et al., 2008)

Statistics calculated from GDP surface drifters in Southern Ocean.

higher around ACC and in western boundary currents

1-2 x 104 – order of magnitude larger than applied in climate models

Alongstream averages : Contribution and Error

Higher values than previous Southern Ocean studies.

- Consistent values with Gulf Stream or Kurushio calculations from GDP drifters. – Sallee and Speer - jbsallee@gmail.com

Ekman

0.5 deg grid particles

Particles on drifter “grid”

Real Drifters

Without WBCs

PF SAF SAF-N

Mesoscale geostrophic eddy contribution dominates. Ekman contribution is weak.

Application : Eddy heat diffusion

Eddy diffusion and the Upper Cell of the Southern Ocean

Here, eddy heat diffusion in the mixed layer is :

Temperature gradient derived from TMI/AMSR satellite SST data

Impact on the formation of deep winter mixed layers in the Southern Ocean

(Sallee et al; GRL, 2007)

Eddy heat diffusion in W.m-2 Winter mixed layer depths (m)

Eddy heat fluxes around KerguelenEddy heat fluxes around Kerguelen

Eddy diffusion coefficient estimated from GDP drifters (Davis, 1991), and meridional SST gradient is from satellite TMI/AMSR

W.m-30 10-10

SAZ

SAZ

T-S Diagram of a composite of ARGO floats in the SAZ

Strong « interleaving » near Kerguelen – cooling and freshening from eddy mixingSallee et al. 2006, Ocean

Dynamics

SAF

STF

Annual mean eddy heat diffusion

Summary – Eddy Diffusion

1) Surface Drifters and altimetry -> similar estimation in situ and satellite of eddy diffusion

2) Linear dispersion regime dominated by geostrophic mesoscale eddies

3) Values order 104 m2.s-1 in the western boundary current regions

4) Need to resolve Rossby radius

Submesoscale Eddies3.

R. Morrow, F. D’Ovidio, A. Koch-Larrouy, J.B. Sallee

(Jason-II CNES/NASA Proposal)

Estimating sub-mesoscale circulation from ¼° AVISO velocity maps

ALTIMETRIC EULERIAN FIELD

• Simple, instantaneous description

• Mesoscale structures O(100 km)

• 2D maps of horizontal currents used to estimate lagrangian evolution of filaments O (10 km)

LAGRANGIAN MANIFOLDS (FSLE)• Time-integrated structures

• Precise localization of transport barriers and filaments

• Strong mixing in submesoscale structures

With F. D’Ovidio, LMD

Submesoscale Eddies

Traditional analysis : altimetric EKEMesoscale eddiesResolution 30 km

Lagrangian analysis (Lyap. Exp)Sub-mesoscale FilamentsResolution 1-10 km

5 dec. 2000

5 dec. 2000

DIMES campaign:

=> release Lagrangian floats close to altimetry-detected hyperbolic points, to :

(1) "compute" in-situ the Lyapunov exponents

(2) follow the unstable manifolds, that for short times (a week or so) can be approximated by nearby lagrangian trajectories.

The length of the unstable manifold can be related to eddy diffusion, within the formalism of the effective diffusivity.

Emily Shuckburg (BAS), Francesco d’Ovidio (LMD)

WATER-HM/SWOT meetingCNES HQ, Paris, February 2008

Constraining coastal ocean models

with altimetry

Pierre De Mey, LEGOS/POC

EOF-179.8 %

EOF-211.0 %

EOF-34.9 %

SLA Depth-averaged velocity

Surface salinity

Temperature

Non-local, structured errors in coastal current

(Jordà et al., 2006)

What is this? Ensemble multivariate EOFs in the

Catalan Sea coastal current in response to coastal current inflow

perturbations (mimicking downscaling errors).

Relevance to WATER-HM? SLA errors are small-

scale (O(40km)) and strongly correlated to fine-

scale (u,v,T,S) 3-dimensional errors which

we can then expect to correct if SLA is observed at sufficiently fine scales.

What is this? Ensemble multivariate EOFs in the

Catalan Sea coastal current in response to coastal current inflow

perturbations (mimicking downscaling errors).

Relevance to WATER-HM? SLA errors are small-

scale (O(40km)) and strongly correlated to fine-

scale (u,v,T,S) 3-dimensional errors which

we can then expect to correct if SLA is observed at sufficiently fine scales.

EOF-179.8 %

EOF-211.0 %

EOF-34.9 %

SLA Depth-averaged velocity

Surface salinity

Temperature

Non-local, structured errors in coastal current

(Jordà et al., 2006)

Activation of coherent error features by storms

Ensemble EOF-3 SLA, 3D BoB model

July 1 August 312004

What is this? The SLA component of a particular

ensemble EOF in response to atmospheric forcing errors. It is a proxy of the actual model

errors. As the time series shows, it is activated during

the July 7-8 storm and is characterized by a shelf-wide response, a surge response, and a mesoscale response with O(1day) time scale.

Relevance to WATER-HM? Questions 1 (mesoscale), 2

(coastal) and 3 (storm-related). We expect a wide-

swath altimeter to consistently constrain the fine-scale,

multivariate ocean response to those fast events, and

hopefully help better predict the associated phenomena.

What is this? The SLA component of a particular

ensemble EOF in response to atmospheric forcing errors. It is a proxy of the actual model

errors. As the time series shows, it is activated during

the July 7-8 storm and is characterized by a shelf-wide response, a surge response, and a mesoscale response with O(1day) time scale.

Relevance to WATER-HM? Questions 1 (mesoscale), 2

(coastal) and 3 (storm-related). We expect a wide-

swath altimeter to consistently constrain the fine-scale,

multivariate ocean response to those fast events, and

hopefully help better predict the associated phenomena.

A

B

Time variations of ensemble variance

Point A: EC Point B: BoB

SLA, Ub errors linked to local wind errorsKelvin waves propagation in error subspace

SLA errors attributable to pressure errors

Wide-swath vs. nadir in Bay of BiscayStochastic modelling with atm. forcing perturbations in 3D BoB

Top: Wide Swath (4 dof’s)Mid: WS over deep ocean (2 dof’s)

Bottom: JASON (1 dof)

Scaled RM spectra

Array Modes -- SLA

“Slosh” Meso1 Meso2

(after Le Hénaff & De Mey, 2008)

(left panel)What is this? The RM spectra plot

on the left shows the number of degrees of freedom of model (forecast) error which can be detected by a particular array

amidst observational noise. This is done by counting eigenvalues

above 1. This is shown for three arrays (legend). Representer

matrices are calculated by stochastic modelling with

atmospheric forcing errors.Relevance to WATER-HM?

Questions 1 (mesoscale) and 2 (coastal). One Wide-Swath

altimeter on a JASON orbit detects 4 degrees of freedom, while one nadir instrument (JASON) detects only one. The more d.o.f.’s are

detected, the more critical ocean processes will be constrained.

(left panel)What is this? The RM spectra plot

on the left shows the number of degrees of freedom of model (forecast) error which can be detected by a particular array

amidst observational noise. This is done by counting eigenvalues

above 1. This is shown for three arrays (legend). Representer

matrices are calculated by stochastic modelling with

atmospheric forcing errors.Relevance to WATER-HM?

Questions 1 (mesoscale) and 2 (coastal). One Wide-Swath

altimeter on a JASON orbit detects 4 degrees of freedom, while one nadir instrument (JASON) detects only one. The more d.o.f.’s are

detected, the more critical ocean processes will be constrained.

Wide-swath vs. nadir in Bay of BiscayStochastic modelling with atm. forcing perturbations in 3D BoB

Top: Wide Swath (4 dof’s)Mid: WS on deep ocean (2 dof’s)

Bottom: JASON (1 dof)

Scaled RM spectra

Array Modes -- SLA

“Slosh” Meso1 Meso2

(after Le Hénaff & De Mey, 2008)

(right panel)What is this? The “array modes” of

model error corresponding to the spectra to the left. For each array,

mode 1 is mostly water sloshing around between shelf and deep-ocean domains; modes 2 and 3 are a mix of

mesoscale & submeso response, slope current variability and shelf

processes.Relevance to WATER-HM?

Questions 1 (mesoscale) and 2 (coastal). As was seen on the left panel, JASON can only detect (and constrain) the “slosh” mode. One needs a wide-swath instrument to

detect (constrain) all three modes + a 4th one not shown. In this way, one can objectively demonstrate that a wide-swath instrument is needed to

constrain the coastal ocean mesoscale and coastal current

variability. (A collaboration between LEGOS and OSU’s OST proposals has

been proposed on this topic)

(right panel)What is this? The “array modes” of

model error corresponding to the spectra to the left. For each array,

mode 1 is mostly water sloshing around between shelf and deep-ocean domains; modes 2 and 3 are a mix of

mesoscale & submeso response, slope current variability and shelf

processes.Relevance to WATER-HM?

Questions 1 (mesoscale) and 2 (coastal). As was seen on the left panel, JASON can only detect (and constrain) the “slosh” mode. One needs a wide-swath instrument to

detect (constrain) all three modes + a 4th one not shown. In this way, one can objectively demonstrate that a wide-swath instrument is needed to

constrain the coastal ocean mesoscale and coastal current

variability. (A collaboration between LEGOS and OSU’s OST proposals has

been proposed on this topic)

Summary – what is needed : SWOT

• Space : Resolving the Rossby radius in the Southern Ocean : 10-20 km means sea level observations at 2-5 km

• Time : Resolving geostrophic adjustion time-scales of 2-5 days

=>With this resolution, finer scale filaments can be determined (eg FSLEs)

• Precision : cms

Impact of internal tides

Regions of conversion of M2 barotropic tide into baroclinic internal waves

Parametrisations exist for this conversion :

- 1/3 energy dissipated locally with bottom drag

- 2/3 energy radiates away as internal tides

In small closed seas or basins, internal tides also dissipate locally

Le Provost et al. 1994, Lyard et al., 2006

Koch-Larrouy et al. 2007

I. ITF - 1) Parametrisation of tidal mixing in ¼° OGCM

Impact of the internal tides on coupled ocean-atmosphere models

SST

Koch-Larrouy et al. 2008

1) Analyse the role of eddy fluxes and tidal mixing in modifying SAMW and AAIW in key regions, using the DRAKKAR ¼° model

(Ariane Koch-Larrouy, Post-Doc, LEGOS, 2008)

Jason-II proposal : Submesoscale Eddies and Tidal mixing

Kerguelen Macquarie Ridge

Fracture Zone

Drake Passage

M2 internal tide generation sites,

(Le Provost et al. 1994, Carrère et Lyard 2003)

Winter ML density, ML >

300 m,

(Sallee et al. 2007)

Monitoring fronts with satellite data :Meridional gradients in SSH and SST

Surface position can be offset from subsurface position

Eg. Summer stratification + strong Ekman transport can shift surface fronts northward in summer

Grad SSH :

Grad SST :

Altimetry is important for monitoring subsurface frontal movements

SSS data

Winter ML heat budget terms

Air-sea Flux, Q

Ekman Heat Flux

Eddy heat flux

Total : 3 terms

Zones clés

Sallee et al. 2007, GRL

Total heat forcing : 3 terms Winter ML depth

Winter ML heat budget terms

Circumpolar evolution of winter ML

Sum of 3 forcing terms in winter – relative to the SAF-N axis

Winter ML Depth – relative to the SAF-N axis

Winter ML Density for ML > 200 m