Comparing model with observations:Comparing model with observations:methods, tools and resultsmethods, tools and results

Mélanie JUZA, Thierry Penduff, Bernard Barnier Mélanie JUZA, Thierry Penduff, Bernard Barnier

LEGI-MEOM, GrenobleLEGI-MEOM, Grenoble

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

Objectives / Activities

• 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

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

Observations: T/S profiles (ENACT-ENSEMBLES), SLA (AVISO), SST (Reynolds)

Development of tools: collocation model/observations, statistics, vizualization

Scientific studies. Papers in preparation…

Hydrography: collocation

VALIDATIONSAMPLING

ERROR

ENACT/ENSEMBLES(ARGO, XBT, CTD, buoys)T,S(x,y,z,t) profiles (~8.106)Global. 1956-2006

MODELT,S(x,y,z,t)Global. 1958-2007

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

COLLOCATEDOBSERVED and MODEL

T,S(x,y,z,t) profiles Dispersed in time and space

Statistical analysis

Temporal, spatial, vertical (mixed layer) integrations

Hydrography: collocation

VALIDATIONSAMPLING

ERROR

ENACT/ENSEMBLES(ARGO, XBT, CTD, buoys)T,S(x,y,z,t) profiles (~8.106)Global. 1956-2006

MODELT,S(x,y,z,t)Global. 1958-2007

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

COLLOCATEDOBSERVED and MODEL

T,S(x,y,z,t) profiles Dispersed in time and space

Statistical analysis

Temporal, spatial, vertical (mixed layer) integrations

ARGO 1998-2004

Hydrography: simulated and observed MLD

ARGO

August 1998-2004 February 1998-2004

Mixed layer depths (MLD) (m)

ORCA025-G70

Realism of simulated and observed MLD

Hydrography: method for the analysis of mixed layer quantities

Exemple: MLD in North Atlantic

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

MODEL BIASSAMPLING ERRORSeptember 1998-2004

Median

17%

83%

Distribution of Mixed Layer Depth / Temperature / Salinity / Heat and Salt Contents

Medians and percentiles 17% and 83%

Hydrography: sampling errors

-- subsampled model (ARGO)

-- full model

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

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

MLD

Solid lines = medians

Dashed lines = percentiles 17%, 83%

Hydrography: sampling errors at global scale

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

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

ARGO sampling errors maximum in winter (extreme values ~100m)

Especially in inhomogene (Southern Ocean, North Atl.) and coastal regions

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

too shallow

too deep

MLD

Hydrography: sampling errors at global scale

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

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

ARGO sampling errors maximum in winter (extreme values ~100m)

Especially in inhomogene (Southern Ocean, North Atl.) and coastal regions

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

too shallow

too deep

MLD

Hydrography: sampling errors at global scale

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

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

ARGO sampling errors maximum in winter (extreme values ~100m)

Especially in high variable (Southern Ocean, North Atl.) and coastal regions

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

too shallow

too deep

MLD

Hydrography: sampling errors at global scale

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

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

ARGO sampling errors maximum in winter (extreme values ~100m)

Especially in high variable (Southern Ocean, North Atl.) and coastal regions

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

too shallow

too deep

MLD

Hydrography: conclusion

Assessment of the simulations

- Mixed layer monthly cycles

- Impact of resolution

Assessment of ARGO sampling errors

- More dependence on spatial distribution of floats rather than number of floats

- MLT, MLS, MLHC, MLSC

Perspectives

Extension to: - recent years (maximum ARGO coverage)

- the last 50 years (interannual cycles)

- all instruments (ARGO floats + CTD, XBT, moored buoys…)

COLLOCATEDMODEL and AVISO

SLA(x,y,t)

AVISO altimeterSLA(x,y,t) database

Quasiglobal. 1993-2004

MODELSSH(x,y,t)

Global. 1958-2007

• 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

Quantitative AssessmentVariances, Correlations,

EOFs, etc

Space-Time Lanczos Filtering

Time

Space

5months

18months

6°

Large-scale

Regional & mesoscale

Hi-

freq

An

nu

al In

ter

ann

ua

l

FILTEREDMODEL and AVISO

SLA(x,y,t)1993-2004

Altimetry: collocation

Altimetry: interannual SLA (statistics)

AVISO

¼°: ORCA025-G70 1°: ORCA1-R70 2°: ORCA246-G70

(1993-2004)

SLA standard deviation (cm)

½°: ORCA05-G70.113

Impact of resolution on low-frequency variability

Global increase of interannual variability with resolution

=> Forced vs intrinsic variability in the Southern Ocean

Model/obs SLA correlation

SLA standard deviation

Interannual variability increases in eddy-active regions Correlation decreases with resolution in S.O.

Altimetry: interannual variability (EOFS)

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

Associated obs. amplitude and mod. projections

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

Mode 1 – Observed SLA – %var=17 Lag with NAO (weeks)

Intergyre gyre of Marshall

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

Resolution improves space-time variability

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

obs ¼° ½° 1° 2°

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

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

Response to ENSO Resolution does not change variance projected on observations

Conclusion

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

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.

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