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Validation of a 4km model ensemble for 1986 Arne Melsom, met.no

Validation of a 4km model ensemble for 1986 Arne Melsom, met.no. Conclusions taken from the presentation from the first OPNet-meeting:. A preliminary finding is that the present ensemble underestimates variability The presentation was sketchy and not very focussed => much work to do!.

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Validation of a 4km model ensemble for 1986 Arne Melsom, met.no

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  1. Validation of a 4km model ensemblefor 1986 Arne Melsom, met.no

  2. Conclusions taken from the presentationfrom the first OPNet-meeting: • A preliminary finding is that the present ensemble underestimates variability • The presentation was sketchy and not very focussed => much work to do!

  3. 4 km model(10 member ensemble) Model set-up • air/sea momentum & heat fluxes from ECMWF products • ice concentrations bymet.no’s ice service • climatology as the 20km model OBC • 8 tidal constituents added to the 4km BC • climatological runoff 20 km model

  4. Vardø Rørvik Data for validation Vardø N Bear Isl. W Fugløya-Bear Isl. • Hydrography(IMR, WOD) • Sea levels Gimsøy NW

  5. Vardø Rørvik Sea level raw data/results de-tided data/results mean SSH corr(ΔSLVardø, ΔSLRørvik) = 0.67

  6. IMR hydrographic transects

  7. Temperature (0, 10, 20, 30m) Salinity (0, 10, 20, 30m)

  8. Salinity

  9. Smod-Sclim; Sclim>34.8, surface …the model is salt-deprived; Why? • nesting problem? No! …problem orginates from 20km model bias

  10. Probability density functions (Aug-Nov) salinity (0, 10, 20, 30m)

  11. temperature (0, 10, 20, 30m)

  12. Ensemble variability & flow instabilities Let ym be a result from ensemble member m; split: ym = y + y + ym′ Then, (ym-y)2 = y2+σy′2 and let r = σy′2/ (ym-y)2 ~ ^ ~ ^ ~

  13. Sea ice and ensemble variability

  14. 12345 34.8 34.9 35.0 35.1 Results from ranking assume e.g. Sobs(x1,y1,z1,t1) = 34.85Se.1(x1,y1,z1,t1) = 34.81Se.2(x1,y1,z1,t1) = 35.05Se.3(x1,y1,z1,t1) = 34.95Se.4(x1,y1,z1,t1) = 34.87 …this observation has rank 2 #observations

  15. Temperature Salinity

  16. Summary • Sea level: major fronts above continental slope high-frequency variability OK low-frequency variability poor • Salinity: model values are too low model range is too low very high cost function in Atlantic Water • Temperature: warm bias in model (generally) moderate cost functions • Ensemble: low ensemble variability high flow instability impact off Lofoten

  17. timeaxis an. +12 +24 +36 +48 +60 +72 +84 +96 12hr later: an. +12 +24 +36 +48 +60 +72 +84 +96 12+12hr later: an. +12 +24 +36 +48 +60 +72 +84 +96 ‘+12 forcing’: timeaxis ‘+24 forcing’: timeaxis Construction of ensemble From atmospheric forecasts models: .

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