Post on 31-Jan-2016
description
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