Where and when should one hope to find added value from dynamical downscaling of GCM data?

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

€

∂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

€

∂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)