IPRC Lunch Time Seminar, 12. March 2002

Hans von StorchInst. Coastal Research

GKSS Research CenterGeesthacht Germany

Issues in regional atmospheric modelling: large scale

control and divergence in phase space

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

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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

ControlT = 4.0 days

Denis and Laprise: BBESpecific humidity at 700 hPa

“J6”- Experiment

ControlT = 8.0 days

Denis and Laprise: BBESpecific humidity at 700 hPa

“J6”- Experiment

BB J6

Temporal standard deviation : precipitation rateContour intervals :

5 mm day-1

C = 88%

Denis and Laprise: BBE

BB J6

Contour intervals :

5 mm day-1

C = 90%

Temporal standard devation of fine-scale features : precipitation rate

= 98%

Denis and Laprise: BBE

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.

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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

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global model

Well resolved

Insufficiently resolved

Spatial scales

„Ene

rgy“

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Well resolved

Insufficiently resolved

Spatial scales

„Ene

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regional model

Added value

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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

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The regional atmospheric model REMO is forced with 6-hourly NCEP re-analyses of global weather.

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standard formulation large-scale nudging

Similarity of zonal wind at 850 hPa between simulations and NCEP re-analyses

large scales

medium scales

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I f KCorrelation between gridded precip analysis (MAP) and REMO (left) and NCEP estimates (right) (N. Groll, 2001, unpublished)

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Conclusions

1. Regional atmospheric modelling is not a boundary value problem but a problem of efficiently combining empirical 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.

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The problem of regional noise – indeterminacyThe problem of regional noise – indeterminacy

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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

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]

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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.

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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.

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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.

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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.)

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HIRLAM computation domain, covering the North Atlantic storm track, where wind-wave interaction is maximum. In

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1 year simulation (January – December 1993), SLP

Area average of rms difference between control (Charnock) and experiment (interactive WAM model)

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control (Charnock) experiment (WAM) difference

January episode with large differences Inst

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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).

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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

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Differences between members of the „control ensemble“

13. Jan

14. Jan

15. Jan

#3 - #1 #6 - #1 #6-#3

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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.

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AB

A is a situation with an insignificant difference, B a situation with a significant difference.

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

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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

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Effect of spectral nudging to suppress divergence

Standard ensemble Spectral nudging ensemble

SLP

standard

obs

Spectral nudging wind speed

Weisse and F

eser, unpublished

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.

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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

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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)