Where and when should one hope to find added value from dynamical

downscaling of GCM data?

René LapriseDirector, Centre ESCER

(Étude et Simulation du Climat à l’Échelle Régionale)

Professor, UQAM(Université du Québec à Montréal)

WCRP Regional Climate Workshop: Facilitating the production of climate information and its use in impact and adaptation work

Lille (France), 14-16 June 2010

Potential added value of RCM• A resolution increase by about 10x…

– CGCM coarse mesh: • T30 (6o, 675 km) – T90 (2o, 225 km)

– RCM fine mesh: • 60 km – 10 km

• Higher resolution allows to…– Resolve some finer scale features, processes,

interactions– Reduce numerical truncation:

• Mesoscale Eddy resolving Vs Eddy permitting

In a T32-CGCM simulation Simulated by 45-km CRCM

Instantaneous field of 900-hPa Specific Humidity, on a winter day…

700-hPa Relative Humidity (Summer)NCEP reanalyses driving 45-km CRCM

Potential added value of RCM:

Resolution increase permits to resolve some finer scale features

– Clear to the naked eye in the time evolution of RCM-simulated fields

– But what about climatological (time-mean) fields?

Winter precipitation [mm/da]T32-CGCM 45km-CRCM Obs. (Willmott and Matsuura)

Mean Sea level pressure (black) and 500-hPa Geopotential (red dotted)[Summer]

T32-CGCM 45km-CRCM

Potential added value of RCM:

Resolution increase permits to resolve some finer scale features

– Clear to the naked eye in the time evolution of RCM-simulated fields

– But what about climatological (time-mean) fields?• Yes for fields strongly affected by local,

stationary forcings, such as mountains, land-sea contrast, etc.

• Usually not for other fields• But there are exceptions…

Winter precipitation [mm/da]T32-CGCM 45km-CRCM Obs. (Willmott and Matsuura)

Shadow effect downstream of the Rocky Mountains

Potential added value of RCM:

Resolution increase permits to resolve some finer scale features

– Clear to the naked eye in the time evolution of RCM-simulated fields

– But what about climatological (time-mean) fields?• Yes for fields strongly affected by local, stationary

forcings, such as mountains, land-sea contrast, etc.• Usually not for other fields• But on occasion there are detectable “large-scale”

effects resulting from “fine-scale” forcing: A sort of indirect effect of reduced truncation

Transient-eddy and time-mean (stationary) Kinetic Energy spectra (for January)

(taken from O’Kane et al. 2009, Atmos-Ocean)

Transient

Stationary(time-mean)

5,000 km

Typical scale range of RCM

2 x

100xTransient

Stationary(time-mean)

100x

Large scales Fine scales

Spectral decay rates differ with variables:• Pressure & temperature decay faster than winds;• Winds decay faster than moisture

Potential added value of RCM:

Resolution increase permits to resolve some finer scale features

– Clear to the naked eye in the time evolution of RCM-simulated fields

– But what about climatological (time-mean) fields?• Yes for fields strongly affected by local, stationary

forcings, such as mountains, land-sea contrast, etc.• Usually not for other fields:

– Time-averaged (stationary-eddy) fields variance mostly contained in large-scale part of the spectrum; well resolved by coarse-mesh GCM

– The small-scale part of the spectrum (added by hi-res RCM) is dominated by transient eddies (not seen in time-mean fields)

Scale separation

• For most atmospheric fields, the variance For most atmospheric fields, the variance spectrum of time-averaged (climatological) spectrum of time-averaged (climatological) fields is dominated by large scales:fields is dominated by large scales:

– This hides the potential added value of This hides the potential added value of increased resolution contained in fine scalesincreased resolution contained in fine scales

• Scale separation is a useful (sometimes Scale separation is a useful (sometimes necessary) tool to identify RCM potential necessary) tool to identify RCM potential added valueadded value

GCM and RCM resolved scales

RCM added scales

Spatial scale decomposition

• Fields can be decomposed in terms of spatial scales as follows

where XL are large scales (L > 800 km)

XS are small scales (L < 800 km) (here using Discrete Cosine Transform)

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X = XL + XS

mm/j

Vertically integrated atmospheric water budget

Winter Climatology (CRCM simulation)

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P

€

E

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∇.Q

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∂tq

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X = XL + XS

Total fields

Large scalesL > 800 km

Small scalesL < 800 km

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∂q ∂t

= −∇.Q + E − P

1) Balance between P, E and Div Q2) Climate tendency is small (note scale of 100)

1) Balance is dominated by large scales2) Small scales play a negligible role in time-mean budget, except locally near mountains and coast lines

mm2/j2

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P

€

E

€

∇.Q

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∂tq

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σc2 X( ) = σ c

2 XL( ) +σ c2 XS( ) + cov XL ,XS( )

Transient-Eddy Variability Vertically integrated atmospheric water budget

Winter Climatology (CRCM simulation)

Total fields

Large scalesL > 800 km

Small scalesL < 800 km

<- Special scale for E

Time variability is equally important

in small and large scales

1) Time variability is dominated by Div Q and water vapour tendency, followed by P.2) Variability in E is negligible

(note special scale below)

Influence of space and time scales on distributions and extremes

Idealised “upscaling” experiment:• Use CRCM data as reference• Aggregate it in space (and time) as a “virtual” GCM• Analyse the “lost value” with low resolution

RCM AND REANALYSIS DATARCM AND REANALYSIS DATA

6 RCMs from NARCCAP (North American Regional Climate Change Assessment Program; Mearns, 2005; http://www.narccap.ucar.edu/about/index.html ).All RCMs are driven by NCEP-DOE reanalysis for the period 1979 - 2004.

NARR (North American Regional Reanalysis; Mesinger, 2005).

Iowa University MM5I

Scripps, U. of California at San Diego ECPC/RSM

Pacific North West National Lab, WA WRFP/WRF

U. of California at Santa Cruz RCM3/RegCM3

Hadley Center, Exeter, UK PRECIS/HADRM3

Ouranos, Montréal CRCM (version 4.2.0)

5 spatial scales: 0.375, 0.75, 1.5, 3.0, 6.0°

(≈ virtual GCM) 8 temporal scales: 3, 6, 12, 24, …, 16 days

Aggregating data to different spatio-temporal resolutionAggregating data to different spatio-temporal resolution

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Prin , jntm( )

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qin , jn

m

Time series in each “grid point”:

Percentiles in each “grid point”:

RCMsRCMs

• Variable: 3-hrs MEAN 95th PERCENTILE

INFLUENCE OF SPATIAL SCALEINFLUENCE OF SPATIAL SCALEon precipitationon precipitation

WARM SEASONCOLD SEASON

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PAV = P950.5° − P953.0°o Potential added value measure:

Virtual GCMs

Virtual GCMs

WARM SEASONCOLD SEASON

o Warm season rPAV larger than cold season rPAVo Some datasets indicate more/less rPAV…

Influence of surface forcing:Influence of surface forcing:Cross-section through the continentCross-section through the continent

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rPAV = P950.5° − P953.0°

P950.5°

Conclusions• The main potential added value (PAV) of high-resolution RCM is The main potential added value (PAV) of high-resolution RCM is

contained in the fine scalescontained in the fine scales– Although some large-scale effects may be felt as a result of small-scale Although some large-scale effects may be felt as a result of small-scale

processes affecting large scalesprocesses affecting large scales• Do not look for PAV in time-averaged, climatological quantities:Do not look for PAV in time-averaged, climatological quantities:

– Except where there is strong local stationary forcing (e.g. mountains, Except where there is strong local stationary forcing (e.g. mountains, land-sea contrast), time averaging tends to remove small scalesland-sea contrast), time averaging tends to remove small scales

– Scale separation is a useful, sometimes necessary, tool to identify PAVScale separation is a useful, sometimes necessary, tool to identify PAV• Look for PAV in variability statistics:Look for PAV in variability statistics:

– Transient-eddy variabilityTransient-eddy variability– Extremes in distributionsExtremes in distributions

References:References:• Laprise, R., R. de Elía, D. Caya, S. Biner, Ph. Lucas-Picher, E. P. Diaconescu, M. Leduc, A. Alexandru and L. Separovic, 2008: Challenging some tenets of Regional Climate Modelling. Meteor. Atmos. Phys. 100 • Bresson, R., and R. Laprise, 2009: Scale-decomposed atmospheric water budget over North America as simulated by the Canadian Regional Climate Model for current and future climates. Clim. Dyn. 1-20 • Di Luca, A., R. de Elía and R. Laprise: Assessment of the potential added value in multi-RCM simulated precipitation (in preparation)