Norwegian Meteorological Institute met.noValidation of a 4km model ensemble for 1986

Norwegian Meteorological Institute met.noValidation of a 4km model ensemble for 1986


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!


Model set-up

20 km model4 km model

(10 member ensemble)

air/sea momentum &

heat fluxes from

ECMWF products ice concentrations by

met.no’s ice service climatology as the

20km model OBC 8 tidal constituents

added to the 4km BC climatological runoff


Data for validation

Vardø

Rørvik

Gimsøy NW

Bear Isl. W

Fugløya-

Bear Isl.

Vardø N

Hydrography

(IMR, WOD)

Sea levels


Vardø

Rørvik

Station σobs σmod-obs

Vardø 0.61 0.21

Rørvik 0.76 0.25

Sea level

raw data/results

Station σobs σmod-obs

Vardø 0.19 0.19

Rørvik 0.16 0.23

de-tided data/results

mean SSHcorr(ΔSLVardø, ΔSLRørvik) = 0.67


IMR hydrographic transects

Transect Cruises/year

Stations/cruise

Gimsøy NW 2 14

Bear Isl. W 1 13

Fugløya – Bear Isl.

6 20

Vardø N. 4 22


Transect/source bias σ

Gimsøy NW -0.40 1.73

Bear Isl. W 0.26 1.79

Fugløya – Bear Isl. 0.71 1.40

Vardø N. 1.00 1.28

World Ocean Datab. 0.65 1.53

Transect/source bias σ

Gimsøy NW -0.13 0.35

Bear Isl. W -0.45 0.30

Fugløya – Bear Isl. -0.24 0.25

Vardø N. -0.21 0.17

World Ocean Datab. -0.26 0.50

Salinity (0, 10, 20, 30m)

Temperature (0, 10, 20, 30m)


Transect #obs(1986)

0 m 20 m 50 m

Gimsøy NW 4 3.61 3.56 5.97

Bear Isl. W 14 20.22 7.96 6.17

Fugløya – Bear Isl. 99 1.82 1.92 2.95

Fugløya – Bear Isl.8-10 39 1.44 1.45 1.92

Vardø N. 39 1.31 1.42 3.01

Salinity


Smod-Sclim; Sclim>34.8,

surface

…the model is

salt-deprived;

Why? nesting problem?

No!

…problem

orginates

from 20km

model bias


Probability

density

functions

(Aug-Nov)

salinity(0, 10, 20, 30m)


temperature(0, 10, 20, 30m)


Ensemble variability & flow instabilities

Let ym be a result

from ensemble

member m;

split:

ym = y + y + ym′

Then,

(ym-y)2 = y

2

+ σy ′

2

and let

r = σy ′

2 / (ym-y )

2

^~

~ ^

~


Sea ice and ensemble variability


Results from rankingassume 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

34.8 34.9 35.0 35.1

1 2 3 4 5

…this observation has rank 2

Transect/source

Gimsøy NW 28

Bear Isl. W 56

Fugløya – Bear Isl. 432

Vardø N. 220

World Ocean Datab. 11836

#observations


Transect/source f<mnm mnm< f<max

max<f

Gimsøy NW 0.43 0.00 0.57

Bear Isl. W 0.45 0.20 0.36

Fugløya – Bear Isl. 0.28 0.16 0.56

Vardø N. 0.34 0.23 0.44

World Ocean Datab. 0.39 0.17 0.44

Temperature

Transect/source f<mnm mnm< f<max

max<f

Gimsøy NW 0.25 0.14 0.61

Bear Isl. W 0.57 0.21 0.21

Fugløya – Bear Isl. 0.30 0.08 0.62

Vardø N. 0.31 0.19 0.50

World Ocean Datab. 0.41 0.19 0.40

Salinity


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



timeaxis

‘+12 forcing’:

timeaxis

‘+24 forcing’:

Construction of ensemble

From atmospheric forecasts models:

an. +12 +24 +36 +48 +60 +72 +84 +96 timeaxis

an. +12 +24 +36 +48 +60 +72 +84 +96 12hr later:

an. +12 +24 +36 +48 +60 +72 +84 +96 12+12hr later:

Documents

Norwegian Meteorological Institute met.noValidation of a 4km model ensemble for 1986