Institut für Küstenforschung
description
Transcript of Institut für Küstenforschung
![Page 1: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/1.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f KCGD/CU-PAOS Seminar Boulder, Colorado, 3 Dec. 2001
Hans von StorchInst. Coastal Research
GKSS Research CenterGeesthacht, Germany
Issues in regional atmospheric modelling: large scale control
and divergence in phase space
![Page 2: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/2.jpg)
1. Validation – the „Big Brother“ experiment of Denis and Laprise
2. Boundary value problem or information recovery problem? – spectral nudging
3. The problem of regional noise – indeterminacy
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 3: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/3.jpg)
RCM
GCMGCM
Validation – the „Big Brother“ experiment of Denis and Laprise
Denis and Laprise: BBE
Coarse resolution Recovering regional scale detail with a RCM.
Denis, B., R. Laprise, D. Caya and J. Cote, 2001: Downscaling ability of one-way nested regional climate models: The Big Brother Experiment. Climate Dyn. (in press)
Jump in resolution at the lateral boundary: 1:6
![Page 4: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/4.jpg)
ControlT = 4.0 days
Denis and Laprise: BBESpecific humidity at 700 hPa
“J6”- Experiment
![Page 5: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/5.jpg)
ControlT = 8.0 days
Denis and Laprise: BBESpecific humidity at 700 hPa
“J6”- Experiment
![Page 6: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/6.jpg)
BB J6
Temporal standard deviation : precipitation rateContour intervals :
5 mm day-1
C = 88%
Denis and Laprise: BBE
![Page 7: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/7.jpg)
BB J6
Contour intervals :
5 mm day-1
C = 90%
Temporal standard devation of fine-scale features : precipitation rate
= 98%
Denis and Laprise: BBE
![Page 8: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/8.jpg)
Big Brother Experiment …
demonstrates that• regional atmospheric model recovers small scale
structures as a response to internal dynamics and small scale physiographic details,
• jump up to 12:1 is acceptable (at least in the BBE set-up).
Thus, RCMs do what they are constructed for.
![Page 9: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/9.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
von Storch, H., H. Langenberg and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev. 128: 3664-3673
Feser, F., R. Weisse and H. von Storch, 2001: Multidecadal atmospheric modelling for Europe yields multi-purpose data. EOS 82, 305+310
Boundary value problem or information recovery problem? – spectral nudging
Boundary value problem or information recovery problem? – spectral nudging
![Page 10: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/10.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
global model
Well resolved
Insufficiently resolved
Spatial scales
„Ene
rgy“
![Page 11: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/11.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
Well resolved
Insufficiently resolved
Spatial scales
„Ene
rgy“
regional model
Added value
![Page 12: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/12.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
.operator suitable a
)(
:nintegratio Forward
modeln observatio
model dynamical
errorsn observatio and model , h wit
equation n Observatio
equation space State
1111
*1
*1
1
1
Kwith
)dK(dΨΨ
Gd
);ηF(ΨΨ
G
F
δ) G(Ψd
ε) ;ηF(ΨΨ
t*t
*t t
tt
tt*t
tt
ttt
tttt
Data driven modeling ...
![Page 13: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/13.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
Usually, a regional model is forced only in a „sponge zone“ along the lateral boundaries. („standard“)
We use „large-scale nudging“ instead, i.e., additionally to the lateral forcing the large-scale (spectrally filtered) analysed state is imposed in the interior as well.
d*t = (filtered) large-scale NCEP re-analysis
![Page 14: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/14.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
The regional atmospheric model REMO is forced with 6-hourly NCEP re-analyses of global weather.
![Page 15: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/15.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
standard formulation large-scale nudging
Similarity of zonal wind at 850 hPa between simulations and NCEP re-analyses
large scales
medium scales
![Page 16: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/16.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 17: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/17.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f KCorrelation between gridded precip analysis (MAP) and REMO (left) and NCEP estimates (right) (N. Groll, 2001, unpublished)
![Page 18: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/18.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
Precip stats at 10 Spanish stations
Percentage of wet and dry days in one RCM grid box (~50 x 50 km²), four RCM grid box average (~200 x 200 km²), ECMWF operational analysis grid box (~200 x 200 km²)
wet dry
observed (point!) 21% 79% observed wet dry
RCM ~50x50 km² 44% 56% wet
dry
18%
3%
26%
53%
RCM ~200x200 km² 61% 39% wet
dry
20%
1%
41%
38%
ECMWF ~200x200 km² 67% 33% wet
dry
19%
2%
48%
31%
![Page 19: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/19.jpg)
List
Büsum
Cuxhaven
Bremerhaven
Norderney
Hallig Hooge
5.52 3.77
0.01 -0.31
4.49 4.38
-0.58 -0.43
5.52 6.94
1.55 1.10
3.89 3.40
-0.67 -0.77
5.57 6.75
0.75 0.29 5.44 6.08
-1.39 -0.64
Zonal component of the 10m-wind
Patterns of the first 2 EOFs
(winter 1969-1979)
Station data / Model data
Feser, 2001, (unpublished)
![Page 20: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/20.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 21: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/21.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 22: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/22.jpg)
Conclusions
1. Regional atmospheric modelling is not a boundary value problem but a problem of efficiently combining empirial knowledge and theoretical insight.
2. Regional atmospheric modelling aims at modelling regional scales while satisfying large-scale constraints.
3. Spectral nudging is one method to deal with the problem.
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 23: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/23.jpg)
The problem of regional noise – indeterminacyThe problem of regional noise – indeterminacy
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
Weisse, R., H. Heyen and H. von Storch, 2000: Sensitivity of a regional atmospheric model to a sea state dependent roughness and the need of ensemble calculations. Mon. Wea. Rev. 128: 3631-3642
![Page 24: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/24.jpg)
The Rinke & Dethloff study on regional modelling of the Arctic atmosphere
Rinke, A., and K. Dethloff, 2000: On the sensitivity of a regional Arctic climate model to initial and boundary conditions. Clim. Res. 14, 101-113.
Ensemble standard deviation 500 hPa height [m²/s²]
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 25: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/25.jpg)
Thus, the development in the interior of the limited domain is only partially controlled by the lateral boundary conditions.
Instead, the nonlinear chaotic processes acting on all spatial scales have a marked impact on the development. Small disturbances, be they in the initial conditions, lateral boundary conditions, or in the parameterizations introduce the potential of divergent evolution at any time.
The stronger the influence of the large-scale state, the smaller the potential for divergence.
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 26: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/26.jpg)
Not only in global GCMs but also in regional GCMs variations unrelated to external causes (noise) are formed.
The assessment of a paired model experiment, in which the effect of a treatment is studied, needs the discrimination between the effect of the treatment (signal) and noise.
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 27: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/27.jpg)
Example: The case of the relevance of the sea state on the atmospheric variability
Hypothesis: The dynamical state of the ocean waves (specifically the shape of the spectra, or age) affect in a physically significant way the state of the overlying atmosphere. Growing (young) waves suck momentum from the wind field, thereby damping the formation of storms.
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 28: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/28.jpg)
Experimental design: Regional atmospheric model (HIRLAM) covering the North Atlantic.
Control: roughness of sea surface parameterized by the Charnock formula.
Anomaly: roughness of sea surface determined from wave spectra simulated interactively with wave model WAM.
In each configuration one full year was simulated (conventional setup.)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 29: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/29.jpg)
HIRLAM computation domain, covering the North Atlantic storm track, where wind-wave interaction is maximum. In
stit
ut
für
Kü
sten
fors
chu
ng
I f K
![Page 30: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/30.jpg)
1 year simulation (January – December 1993), SLP
Area average of rms difference between control (Charnock) and experiment (interactive WAM model)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 31: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/31.jpg)
SL
P in
hP
a
15.
Jan
uary
1
4. J
anua
ry
1
3. J
anua
ry
control (Charnock) experiment (WAM) difference
January episode with large differences Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 32: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/32.jpg)
Additionally, another 20 months were simulated with HIRLAM.
For each configuration, control (Charnock) and anomaly (WAM model coupled), 5 Januaries and 5 Junes were simulated. They differed only with respect to the initial state, which was taken from the year-long simulation one day apart (e.g. 2, 3, 4, 5 and 6 January).
Thus for the basic experiment, two ensembles of 6 „control“ and „anomaly“ members each were available to assess the internal variability (noise) and the systematic difference (signal).
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 33: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/33.jpg)
Area averaged rms of the six control simulations, relative to their joint spatial average (solid)and of the six anomaly simulations relative to their joint spatial average (dashed).
Note that the rms is calculated for each time separately – the noise is not stationary but time dependent.
SLP
January
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 34: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/34.jpg)
Differences between members of the „control ensemble“
13. Jan
14. Jan
15. Jan
#3 - #1 #6 - #1 #6-#3
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 35: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/35.jpg)
Rms of members of the anomaly ensemble (interactive WAM model) compared to control ensemble variations.For both ensembles, the rms is calculated relative to the control average.
The blue band is the estimated 95% „confidence“ interval of rms of the control ensemble. 95% of all states consistent with the control should be within the band.
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
AB
A is a situation with an insignificant difference, B a situation with a significant difference.
![Page 36: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/36.jpg)
A: Large differences and large noise, thus inconclusive result.
Ensemble mean differences in SLP [hPa]
Points with significant t-statistics are in blue.
Six anomaly (interactive WAM; solid) and six control
simulations (Charnock; dashed) of 500 hPa height [gpm]
15. Jan, 0 UTC
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 37: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/37.jpg)
B: Small differences but statistically significant. Evidence for physically insignificant treatment.
Ensemble mean differences in SLP [hPa]
Points with significant t-statistics are in blue.
Six anomaly (interactive WAM; solid) and six control simulations
(Charnock; dashed) of 500 hPa height [gpm]
29. Jan, 0 UTC
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 38: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/38.jpg)
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
Effect of spectral nudging to suppress divergence
Standard ensemble Spectral nudging ensemble
SLP
standard
obs
Spectral nudging wind speed
Weisse and F
eser, unpublished
![Page 39: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/39.jpg)
Conclusions
(1) Also in regional climate models internal variability is formed; only part of the variability is related to varying boundary forcing.
(2) Numerical experiments with RCMs need to discriminate between noise and signal, like in global GCM experiments.
(3) The noise in RCMs is not stationary so that its statistics can hardly be extracted from extended simulations; instead sufficiently large ensembles are needed.
Inst
itu
t fü
r K
üst
enfo
rsch
un
g
I f K
![Page 40: Institut für Küstenforschung](https://reader035.fdocuments.in/reader035/viewer/2022070418/568159a1550346895dc6f04d/html5/thumbnails/40.jpg)
Recommendations
1. Obviously, all models suffer from various defects. In fact, trivially, numerical models are a reduced image of a considerably more complex reality. In this sense, all models are wrong and can be made more realistic in very many different ways. Therefore the process of improving models should be guided by the needs of the specific applications.
2. The reduction of errors in the driving GCMs should remain a priority for climate modellers.
3. The assessment of RCM climate simulations continues to be hampered by the lack of high-resolution observed gridded climate data over many regions of the globe. Regional data re-analysis projects using observations from national archives should be encouraged. In
stit
ut
für
Kü
sten
fors
chu
ng
I f K
Report of the "Joint WGCM/WGNE ad hoc Panel on Regional Climate Modelling“:
Atmospheric regional climate models (RCMs): A multiple purpose tool?Richard Jones (Hadley Centre, England), Ben Kirtman (Center for Ocean-Land Studies - COLA, USA), René Laprise, (Convenor; Université du Québec à Montréal, Canada), Hans von Storch (GKSS Research Centre, Germany), Werner Wergen (Deutscher Wetterdienst - DWD, Germany)