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Comparing model with observations: methods, tools and results. Mélanie JUZA, Thierry Penduff, Bernard Barnier LEGI-MEOM, Grenoble. DRAKKAR meeting, Grenoble, France, 11-12-13 February 2009. Objectives / Activities.  Global Drakkar simulations: G70 (DFS3 forcing): ¼°, ½°, 1°, 2°.

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comparing model with observations methods tools and results

Comparing model with observations:methods, tools and results

Mélanie JUZA, Thierry Penduff, Bernard Barnier

LEGI-MEOM, Grenoble

DRAKKAR meeting, Grenoble, France, 11-12-13 February 2009

slide2

Objectives / Activities

 Global Drakkar simulations: G70 (DFS3 forcing): ¼°, ½°, 1°, 2°

  • Observations: T/S profiles (ENACT-ENSEMBLES), SLA (AVISO), SST (Reynolds)
  • Assessment of DRAKKAR simulations
  • - Quantitative and systematic comparisons model/observations
  • - Intercomparison of simulations
  • (impact of resolution, forcing, numerical scheme, parametrizations)
  • Observability of the ocean dynamics (OSSE)
  • - Accuracy of ARGO array
  • Distribution of data and tools to the scientific community
  • Development of tools: collocation model/observations, statistics, vizualization
  • Scientific studies. Papers in preparation…
slide3

Hydrography: collocation

ENACT/ENSEMBLES

(ARGO, XBT, CTD, buoys)

T,S(x,y,z,t) profiles (~8.106)

Global. 1956-2006

MODEL

T,S(x,y,z,t)

Global. 1958-2007

  • Keep good data only
  • Quadrilinear collocation (obs. space)

SAMPLING ERROR

VALIDATION

COLLOCATED

OBSERVED and MODEL

T,S(x,y,z,t) profiles

Dispersed in time and space

Temporal, spatial, vertical (mixed layer) integrations

Statistical analysis

slide4

Hydrography: collocation

ENACT/ENSEMBLES

(ARGO, XBT, CTD, buoys)

T,S(x,y,z,t) profiles (~8.106)

Global. 1956-2006

MODEL

T,S(x,y,z,t)

Global. 1958-2007

  • Keep good data only
  • Quadrilinear collocation (obs. space)

SAMPLING ERROR

VALIDATION

COLLOCATED

OBSERVED and MODEL

T,S(x,y,z,t) profiles

Dispersed in time and space

Temporal, spatial, vertical (mixed layer) integrations

Statistical analysis

ARGO 1998-2004

slide5

Hydrography: simulated and observed MLD

August 1998-2004

February 1998-2004

Mixed layer depths (MLD) (m)

ARGO

ORCA025-G70

 Realism of simulated and observed MLD

slide6

Hydrography: method for the analysis of mixed layer quantities

83%

Median

17%

  • Distribution of Mixed Layer Depth / Temperature / Salinity / Heat and Salt Contents
  • Medians and percentiles 17% and 83%

Exemple: MLD in North Atlantic

September 1998-2004

SAMPLING ERROR

MODEL BIAS

-- full model -- subsampled model (like ARGO) -- ARGO

slide7

Hydrography: sampling errors

Monthly cycles of MLD (1998-2004): zone MNW-ATL

-- subsampled model (ARGO)

-- full model

MLD

Solid lines = medians

Dashed lines = percentiles 17%, 83%

Sampling error  well observed monthly cycle. Sampling error in winter.

slide8

Hydrography: sampling errors at global scale

MLD

too shallow

too deep

ARGO sampling errors on the monthly MLD (1998-2004)

Sampling error = <subsampled model > – <full model>

Bins = 30° x 30° x 1 month (1998-2004)

  • ARGO sampling errors maximum in winter (extreme values ~100m)
  • Especially in inhomogene (Southern Ocean, North Atl.) and coastal regions
slide9

Hydrography: sampling errors at global scale

MLD

too shallow

too deep

ARGO sampling errors on the monthly MLD (1998-2004)

Sampling error = <subsampled model > – <full model>

Bins = 30° x 30° x 1 month (1998-2004)

  • ARGO sampling errors maximum in winter (extreme values ~100m)
  • Especially in inhomogene (Southern Ocean, North Atl.) and coastal regions
slide10

Hydrography: sampling errors at global scale

MLD

too shallow

too deep

ARGO sampling errors on the monthly MLD (1998-2004)

Sampling error = <subsampled model > – <full model>

Bins = 30° x 30° x 1 month (1998-2004)

  • ARGO sampling errors maximum in winter (extreme values ~100m)
  • Especially in high variable (Southern Ocean, North Atl.) and coastal regions
slide11

Hydrography: sampling errors at global scale

MLD

too shallow

too deep

ARGO sampling errors on the monthly MLD (1998-2004)

Sampling error = <subsampled model > – <full model>

Bins = 30° x 30° x 1 month (1998-2004)

  • ARGO sampling errors maximum in winter (extreme values ~100m)
  • Especially in high variable (Southern Ocean, North Atl.) and coastal regions
slide12

Hydrography: conclusion

  • Assessment of ARGO sampling errors
  • - More dependence on spatial distribution of floats rather than number of floats
  • - MLT, MLS, MLHC, MLSC
  • Assessment of the simulations
  • - Mixed layer monthly cycles
  • - Impact of resolution
  • Perspectives
  • Extension to: - recent years (maximum ARGO coverage)
  • - the last 50 years (interannual cycles)
  • - all instruments (ARGO floats + CTD, XBT, moored buoys…)
slide13

Altimetry: collocation

AVISO altimeter

SLA(x,y,t) database

Quasiglobal. 1993-2004

MODEL

SSH(x,y,t)

Global. 1958-2007

Quantitative Assessment

Variances, Correlations,

EOFs, etc

FILTERED

MODEL and AVISO

SLA(x,y,t)

1993-2004

  • Trilinear collocation on 1/3°x1/3°x7day AVISO Maps
  • Mask AVISO under MODEL Ice
  • Mask MODEL under AVISO Ice
  • Linear detrending
  • Remove 1993-1999 means
  • Remove spatial averages

Space-Time Lanczos Filtering

Annual

Hi-freq

Inter

annual

Space

COLLOCATED

MODEL and AVISO

SLA(x,y,t)

Large-

scale

Regional & mesoscale

18

months

5

months

Time

slide14

Altimetry: interannual SLA (statistics)

SLA standard deviation

Model/obs SLA correlation

=> Forced vs intrinsic variability in the Southern Ocean

Impact of resolution on low-frequency variability

SLA standard deviation (cm)

(1993-2004)

AVISO

¼°: ORCA025-G70

½°: ORCA05-G70.113

1°: ORCA1-R70

2°: ORCA246-G70

 Global increase of interannual variability with resolution

Interannual variability increases in eddy-active regions

Correlation decreases with resolution in S.O.

slide15

Altimetry: interannual variability (EOFS)

Assess the ability of models to reproduce the observed interannual variability in various regions

  • Data processing
  • - Observed SLA EOFs (decomposition: spatial mode + temporal amplitude-PC)
  • Projection of simulated SLA on observed SLA EOFS
  • Comparison PC(obs)/projections: % variance, correlation

Exemple: interannual SLA in North Atlantic (1993-2004)

Mode 1 – Observed SLA – %var=17

Associated obs. amplitude and mod. projections

Lag with NAO (weeks)

obs¼° ½° 1° 2°

Projections of simulated SLA reproduce main features of the obs. variability. More explained variance with 1/4°

Simulated lags more realistic with increase of resolution

Intergyre gyre of Marshall

 Resolution improves space-time variability

slide16

Altimetry: interannual variability (EOFS)

Exemple: large-scale (>6°) and interannual SLA in Southern Ocean (1993-2004)

Mode 1 – Observed SLA – %var=18

Associated obs. amplitude and mod. projections

Response to ENSO

Resolution does not change variance projected on observations

Conclusion: - Global and regional (North Atl., Gulf Stream, Equat. Pac., Indian, Southern Ocean)

- Resolution improves space-time variability, except in Southern Ocean (intrinsic variability?)

- Similar processing applied to SST analysis (Reynolds, NCEP)

- Response of ocean to atmospheric variability (NAO, ENSO, SAM, AAO…)

- Impact of mesoscale on low-frequency variability

slide17

Conclusion

  • Collocate and compare model & observations: T, S, SLA, SST
  • Assess simulations. Quantify model sensitivities
  • Evaluate the accuracy of observing systems (ARGO sampling errors, paper in preparation)
  • Tools are mature. Technical report & users manual. Fields are being distributed.
  • Perspectives

• Further assess the interannual variability in eddying models (paper in preparation)

  • Evaluate every new simulation (global, regional, reanalyses)
  • Extend to new datasets: current meters (G. Holloway), ice field thickness (A. Worby),
  • gravimetry, maregraph, SSS, …
  • Foster collaborations

http://www-meom.hmg.inpg.fr/Web/pages-perso/MelanieJuza/