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Comparing model with observations: methods, tools and results Mélanie JUZA, Thierry Penduff, Bernard Barnier LEGI-MEOM, Grenoble DRAKKAR meeting, Grenoble,

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Presentation on theme: "Comparing model with observations: methods, tools and results Mélanie JUZA, Thierry Penduff, Bernard Barnier LEGI-MEOM, Grenoble DRAKKAR meeting, Grenoble,"— Presentation transcript:

1 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

2 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…

3 Hydrography: collocation VALIDATION SAMPLING ERROR ENACT/ENSEMBLES (ARGO, XBT, CTD, buoys) T,S(x,y,z,t) profiles (~8.10 6 ) Global. 1956-2006 MODEL T,S(x,y,z,t) Global. 1958-2007 Keep good data only Quadrilinear collocation (obs. space) COLLOCATED OBSERVED and MODEL T,S(x,y,z,t) profiles Dispersed in time and space Statistical analysis Temporal, spatial, vertical (mixed layer) integrations

4 Hydrography: collocation VALIDATION SAMPLING ERROR ENACT/ENSEMBLES (ARGO, XBT, CTD, buoys) T,S(x,y,z,t) profiles (~8.10 6 ) Global. 1956-2006 MODEL T,S(x,y,z,t) Global. 1958-2007 Keep good data only Quadrilinear collocation (obs. space) COLLOCATED OBSERVED 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

5 Hydrography: simulated and observed MLD ARGO August 1998-2004February 1998-2004 Mixed layer depths (MLD) (m) ORCA025 -G70  Realism of simulated and observed MLD

6 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%

7 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%

8 Hydrography: sampling errors at global scale Bins = 30° x 30° x 1 month (1998-2004) Sampling error = –  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

9 Hydrography: sampling errors at global scale Bins = 30° x 30° x 1 month (1998-2004) Sampling error = –  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

10 Hydrography: sampling errors at global scale Bins = 30° x 30° x 1 month (1998-2004) Sampling error = –  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

11 Hydrography: sampling errors at global scale Bins = 30° x 30° x 1 month (1998-2004) Sampling error = –  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

12 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…)

13 COLLOCATED MODEL and AVISO SLA(x,y,t) AVISO altimeter SLA(x,y,t) database Quasiglobal. 1993-2004 MODEL SSH(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 Assessment Variances, Correlations, EOFs, etc Space-Time Lanczos Filtering Time Space 5 months 18 months 6° Large- scale Regional & mesoscale Hi-freqAnnualInterannual FILTERED MODEL and AVISO SLA(x,y,t) 1993-2004 Altimetry: collocation

14 Altimetry: interannual SLA (statistics) AVISO ¼°: ORCA025-G701°: ORCA1-R702°: 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 correlationSLA standard deviation Interannual variability increases in eddy-active regionsCorrelation decreases with resolution in S.O.

15 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=17Lag 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°

16 Altimetry: interannual variability (EOFS) Exemple: large-scale (>6°) and interannual SLA in Southern Ocean (1993-2004) Mode 1 – Observed SLA – %var=18Associated 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 ENSOResolution does not change variance projected on observations

17 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/

18 Hydrography: model bias at global scale MLD too shallow too deep  Model biaises: seasonal, regional, too deep MLD in winter (max=50m)  The increase of resolution improves the representation of MLD Model bias of the monthly MLD (1998-2004) = Bins = 30° x 30° x 1 month (1998-2004) run ORCA025-G70 run ORCA246-G70

19 Hydrography: model bias at global scale MLD too shallow too deep Model bias of the monthly MLD (1998-2004) = run ORCA025-G70 run ORCA246-G70 Conclusion: - ORCA025-G70, ORCA05-G710.113, ORCA1-R70, ORCA246-G70 - MLT, MLS, MLHC, MLSC - Resolution improves mixed layer monthly cycles - Use of all instruments from 1956 to present (interannual cycles)

20 Altimetry: SLA standard deviation AVISO ORCA025-G70 ORCA1-R70 ORCA05-G70.113 ORCA246-G70 (1993-2004) HF (T<5months)MF (5<T<18months)LF (T>18months)

21 Altimetry: SLA zonal variance and correlation SLA standard deviation (cm) Model/obs SLA correlation AVISO ORCA025-G70 ORCA05-G70.113 ORCA1-R70 ORCA246-G70  Zonal variability increases with resolution  Zonal correlation decreases with resolution in S.O. => Forced vs intrinsic variability in the Southern Ocean HF (high freq.) MF (annual) LF ( interannual) (1993-2004)

22 Biais global T/S – modèle global ¼° Structure verticale: moyennes temporelles Pdf de Structure horizontale: intégration sur les couches de surface (1998-2004) Ecart = modèle ¼° (sous-échantillonné comme ARGO) - observations (ARGO)

23 Courant Nord AtlantiqueKuroshio Biais régional T/S – modèle global ¼° -3°C 300-500m -0.6 à -0.25 0-600m +2°C 100-400m +0.2 100-400m

24 Conclusion - Perspectives  In the future… Continue to investigate the impact of the resolution on the realism of the model (2°,1°,1/2°,1/4°) Systematize the assessment of simulations (forcing, parametrization,…) Regional simulations (NATL4, NATL12), with assimilation (HYCOM), … Scientific studies and collaborations Others datasets: current meters (G. Holloway), ice field thickness (A. Worby), gravimetry, maregraph, SSS, …  Altimetry Resolution improve space-time variability (lag NAO) Increased resolution yields: stronger local variances (depend on latitude), similar or smaller correlations (increased intrinsic variability), improved basin-scale space-time variability Interannual variability: impact resolution: correlation ~, variance increase In general, enhanced variance projects on observations (except in Southern Ocean) perspective: Impact of mesoscale on low-frequency variability. Forced vs intrinsic variability.

25 Altimetry: interannual SLA (statistics) AVISO ORCA025-G70ORCA1-R70ORCA246-G70 (1993-2004)  Global increase of std(SLA) with resolution SLA standard deviation (cm) => Forced vs intrinsic variability in the Southern Ocean  Zonal variability increases with resolution  Zonal correlation decreases with resolution in S.O. Zonal model/obs SLA correlationZonal SLA standard deviation ORCA05-G70.113

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