argo workshop buongiornonardelli · High%resolu,on%mapping%of%3D%semi4geostrophic%dynamics%from%a combinaon%of%ARGO%measurements%and%satellite%observaons% % %Bruno Buongiorno&Nardelli&

High resolu,on mapping of 3D semi-‐geostrophic dynamics from a combina,on of ARGO measurements and satellite observa,ons Bruno Buongiorno Nardelli Consiglio Nazionale delle Ricerche, Italy

High resolu,on mapping of 3D semi-‐geostrophic dynamics from a combina,on of ARGO measurements and satellite observa,ons àanalyze mesoscale dynamics from observa6ons àver6cal exchanges àmechanisms with poten6al impact on biology

Single observa,ons give us informa,on on some of the variables used to describe the ocean state (u,v,w,T,S,…)àlimited view

ARGO CTD/XCTD

ALTIMETRY

SeaSurfaceTemperature

Analyzing 3D Mesoscale dynamics: background

The degrees of freedom of the system are reduced by dynamical constraints, leading to autocorrela6on/correla6ons among ocean state variables

ARGO CTD/XCTD

ALTIMETRY

Analyzing 3D Mesoscale dynamics: background


Take advantage of correla,ons between the variables describing the ocean state to retrieve mesoscale dynamics from observa6ons

ARGO CTD/XCTD

ALTIMETRY

Analyzing 3D Mesoscale dynamics: strategy


High resolu6on 3D tracer fields Temperature Salinity Density

àbuild high resolu,on surface fields àver,cal extrapola,on

Analyzing 3D Mesoscale dynamics: approach and methodologies










Test areaà North Atlan6c/Gulf Stream

àGeostrophic (only horizontal component) àQuasi Geostrophic (QG Omega equa,onàw) àSemi Geostrophic (SG Omega equa,onàw)


High resolu6on 3D velocity fields








Study areaà Agulhas Current




Lagrangian trajectories 3D advec,on




àrelevant for biology àmay help interpreta,on à…




Lagrangian trajectories 3D advec,on


àrelevant for biology àmay help interpreta,on à…



àHR SSS needed by 3D reconstruc,on method ànew product poten,ally useful in combina,on with SMOS data Hypothesis: high correla6on between sea surface temperature (SST) and sea surface salinity (SSS) varia,ons can be expected (in the open ocean) at scales significantly smaller than the ones domina6ng atmospheric variabilityàboth T and S basically modified through advec,on and diffusion Proposed technique: op,mal interpola,on (Bretherton-‐like) algorithm that includes satellite (spa,ally high-‐pass filtered) SST differences in the covariance es,ma,on

)()( . backgroundobsbackgroundanalysis xyCRCxx −++= −1

222

⎟⎟⎠

⎞⎜⎜⎝

⎛ Δ−⎟

⎠⎞

⎜⎝⎛ Δ−⎟

⎠⎞

⎜⎝⎛ Δ−

=ΔΔΔ TSST

Lrt filtered

eeeSSTtr τ),,(CCovariance func6on parameters (i.e. spa,al (L), temporal (τ) and thermal (T) decorrela,on scales and spa,al filtering) determined empirically minimizing errors vs independent surface observa6ons

Mul6-‐parameter HR interpola6on of surface salinity data: method

Mul6-‐parameter HR interpola6on of surface salinity data: method

àHR SSS needed by 3D reconstruc,on method ànew product poten,ally useful in combina,on with SMOS data Hypothesis: high correla6on between sea surface temperature (SST) and sea surface salinity (SSS) varia,ons can be expected (in the open ocean) at scales significantly smaller than the ones domina6ng atmospheric variabilityàboth T and S basically modified through advec,on and diffusion Proposed technique: op,mal interpola,on (Bretherton-‐like) algorithm that includes satellite (spa,ally high-‐pass filtered) SST differences in the covariance es,ma,on

)()( . backgroundobsbackgroundanalysis xyCRCxx −++= −1

222

⎟⎟⎠

⎞⎜⎜⎝

⎛ Δ−⎟

⎠⎞

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⎠⎞

⎜⎝⎛ Δ−

=ΔΔΔ TSST

Lrt filtered

eeeSSTtr τ),,(CCovariance func6on parameters (i.e. spa,al (L), temporal (τ) and thermal (T) decorrela,on scales and spa,al filtering) determined empirically minimizing errors vs independent surface observa6ons

Same test performed on simulated observa6ons taking MERCATOR model output as ‘true’ SSS field

in situ SSS Red dots (input) 30 days window, centered on interpola,on dayà MyOcean INSITU-‐TAC ARGO, CTD and XCTD, referenced to 5 m depth Blue dots (valida6on) (only for interpola,on day) GOSUD and LEGOS TSG data

Mul6-‐parameter HR interpola6on of surface salinity data: test datasets

Background SSS (1/2°) MyOcean CORIOLIS SSS objec,vely analyzed maps (ISAS)



Background SSS MyOcean CORIOLIS SSS objec,vely analyzed maps


Background

ODYSSEA L4 SST (1/10°, daily)


Backgrou

nd

MESCLA

MESCLA high resolu6on SSS field and derived SSS gradient reveal more realis,c and smaller scale structures than those visible in CORIOLIS-‐SSS product.

Mul6-‐parameter HR interpola6on of surface salinity data: results


Simulated dataset from MERCATOR model L= 475km τ=10 days T=1.75 °C Noise-‐to-‐signal=0.3 HR SSS RMSE reduced to <50% of corresponding ISAS error

In Situ/ODYSSEA SST observa6ons L= 400 km τ=6 days T=2.75 °C signal-‐to-‐noise=0.01 HR SSS RMSE reduced to <75% of corresponding ISAS error


∑=

=n

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Ver6cal extrapola6on: Mul6variate EOF Reconstruc6on (mEOF-‐R)

à mul6variate Empirical Orthogonal Func6on (mEOF) decomposi,on of T,S,SH from ARGO profiles àhypothesis that few modes explain the major part of the variability and that surface values of the parameters considered are known àincluding Steric Heights profiles in state vector provides dynamical informa,on

∑=

=n

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mEOF

amplitudes

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),()()()()()()(

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332211Core of mEOF-‐R method

Buongiorno Nardelli B., Santoleri R., J. Atmos. Ocean. Tech. 2005 Buongiorno Nardelli B. et al., J. Geophys. Res.2006 Buongiorno Nardelli B. et al., Ocean Sci.2012 (accepted last week)

In situ profiles (used to tain the model): Quality Controlled ARGO/CTD profiles provided by Coriolis (used in their OA) through MyOcean catalogue àProfiles already QC à Profiles were interpolated at regular pressure bins (10 dbar) Surface input (daily): SH extracted from AVISO ADT maps (updated 1/3° product, daily, upsized to 1/10°) Odyssea SST L4 (1/10°, daily) MESCLA SSS L4 (1/10°, daily)

Ver6cal extrapola6on: study area and input observa6ons

Study area: Agulhas Current Focus on eddies Period: 1st September 2010-‐18th November 2010

SYNTHETIC PROFILES RECONSTRUCTION ERRORS

Different configura6ons tested: à mEOFs computed from ARGO profiles selected within a moving monthly window à no further geographical subseing (one set of mEOFs for whole domain) à First three modes generally explaining >99% of variance, third mode O(10-‐4) à Errors minimized using only first two modes (blue and green)

First three modes

Ver6cal extrapola6on: mul6variate EOFs (T-‐S-‐SH)

−0.15 −0.1 −0.05 0 0.05 0.1

0

100

200

300

400

500

600

700

800

900

1000

pres

sure

(dba

r)

SH mEOF modes

mode 1mode 2mode 3

−0.1 −0.05 0 0.05 0.1

0

100

200

300

400

500

600

700

800

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pres

sure

(dba

r)

T mEOF modes

mode 1mode 2mode 3

−0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2

0

100

200

300

400

500

600

700

800

900

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pres

sure

(dba

r)

S mEOF modes

mode 1mode 2mode 3

SYNTHETIC PROFILES RECONSTRUCTION ERRORS

hindcast on training dataset (i.e. using surface values as input to mEOF-‐reconstruc,on)

−0.5 0 0.5 1 1.5

0

100

200

300

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pres

sure

(dba

r)

temperature errors (°C)

HINDCAST mEOF−R

STDEMBE

−0.5 0 0.5 1 1.5

0

100

200

300

400

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pres

sure

(dba

r)

temperature errors (°C)

satellite surface input mEOF−R

STDEMBE

−0.05 0 0.05 0.1 0.15

0

100

200

300

400

500

600

700

800

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1000

pres

sure

(dba

r)

salinity errors (psu)

HINDCAST mEOF−R

STDEMBE

−0.05 0 0.05 0.1 0.15

0

100

200

300

400

500

600

700

800

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1000

pres

sure

(dba

r)

salinity errors (psu)

satellite surface input mEOF−R

STDEMBE

Ver6cal extrapola6on: errors on one snapshot

matchup between synthe,c profiles and ARGO profiles

Ver6cal extrapola6on: surface and 3D fields

à It allows to es,mate w from density field and geostrophic veloci,es à It retains ageostrophic advec,on in the equa,ons à Improved accuracy over an extended range of dynamical condi,ons (larger Rossby numbers)

( ) ( ) ( ) *****

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fVxX +=

fUyY −= zZ =

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The eq. is derived applying a change of coordinates (see above), but it can be solved in the original coordinates (amer a lot of algebra… see also Viudez and Dritschel, J. Phys. Ocean., 2004)

geostrophic coordinates geostrophic vorAcity

Semi-‐geostrophic Omega equa6on: formula6on

QG Omega eq. SG Omega eq. PE solu6on (MERCATOR Model)

Tested using MERCATOR model output as simulated observa,ons and applying the simplified QG and SG diagnos,c models:

100 m 100 m 100 m

though not all the features are reproduced by QG and SG, SG improves the ver6cal velocity es6mates (look at the scales)

Semi-‐geostrophic Omega equa6on: valida6on

Some slides here originally contained recent (s6ll) unpublished results Please contact [email protected] for further informa6on

UNPUBLISHED RESULTS: work in progress

Conclusions and perspec,ves

Mesoscale resolving 3D tracer fields can be obtained by combina6on of different/complementary observa6ons ànot going to work everywhere and every,me, but improvements can be expected with SWOT al,metry and comparing different techniques Semi-‐geostrophic omega equa6on can be used to retrieve 3D ver6cal veloci6es from synthe6c 3D tracer fields à beper diagnos,c (working also at high Rossby numbers) à  limi,ng factor mostly related to true resolu,on of input surface fields and mEOF

trunca,ons (only looking at dominant modes) Evidence (?) of Vortex Rossby Waves modula6ng the evolu6on of eddies is seen in SG ver6cal veloci6es àrarely observed in the oceans, widely studied in the atmosphere à3D Lagrangian trajectories seem to indicate they might be relevant mechanisms driving ver,cal exchanges of nutrients (at ,mescales comparable to biological ones)

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argo workshop buongiornonardelli · High%resolu,on%mapping%of%3D%semi4geostrophic%dynamics%from%a combinaon%of%ARGO%measurements%and%satellite%observaons% % %Bruno Buongiorno&Nardelli&