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Where and when should one hope to find added value from dynamical downscaling of GCM data? René Laprise Director, Centre ESCER (Étude et Simulation du.

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Presentation on theme: "Where and when should one hope to find added value from dynamical downscaling of GCM data? René Laprise Director, Centre ESCER (Étude et Simulation du."— Presentation transcript:

1 Where and when should one hope to find added value from dynamical downscaling of GCM data? René Laprise Director, Centre ESCER (Étude et Simulation du Climat à lÉchelle 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), June 2010

2 Potential added value of RCM A resolution increase by about 10x… – CGCM coarse mesh: T30 (6 o, 675 km) – T90 (2 o, 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

3 In a T32-CGCM simulation Simulated by 45-km CRCM Instantaneous field of 900-hPa Specific Humidity, on a winter day…

4 700-hPa Relative Humidity (Summer) NCEP reanalyses driving 45-km CRCM

5 Potential added value of RCM: R e s o l u t i o n i n c r e a s e p e r m i t s t o r e s o l v e s o m e f i n e r s c a l e f e a t u r e s – Clear to the naked eye in the time evolution of RCM-simulated fields – But what about climatological (time- mean) fields?

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

7 Mean Sea level pressure (black) and 500-hPa Geopotential (red dotted) [Summer] T32-CGCM 45km-CRCM

8 Potential added value of RCM: R e s o l u t i o n i n c r e a s e p e r m i t s t o r e s o l v e s o m e f i n e r s c a l e f e a t u r e s – 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…

9 Winter precipitation [mm/da] T32-CGCM 45km-CRCM Obs. (Willmott and Matsuura) Shadow effect downstream of the Rocky Mountains

10 Potential added value of RCM: R e s o l u t i o n i n c r e a s e p e r m i t s t o r e s o l v e s o m e f i n e r s c a l e f e a t u r e s – 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

11 Transient-eddy and time-mean (stationary) Kinetic Energy spectra (for January) (taken from OKane et al. 2009, Atmos-Ocean) Transient Stationary (time-mean) 5,000 km Typical scale range of RCM 2 x 100x Transient 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 Spectral decay rates differ with variables: Pressure & temperature decay faster than winds; Winds decay faster than moisture

12 Potential added value of RCM: R e s o l u t i o n i n c r e a s e p e r m i t s t o r e s o l v e s o m e f i n e r s c a l e f e a t u r e s – 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)

13 Scale separation For most atmospheric fields, the variance spectrum of time-averaged (climatological) fields is dominated by large scales:For most atmospheric fields, the variance spectrum of time-averaged (climatological) fields is dominated by large scales: –This hides the potential added value of increased resolution contained in fine scales Scale separation is a useful (sometimes necessary) tool to identify RCM potential added valueScale separation is a useful (sometimes necessary) tool to identify RCM potential added value

14 GCM and RCM resolved scales RCM added scales

15 Spatial scale decomposition Fields can be decomposed in terms of spatial scales as follows where X L are large scales (L > 800 km) X S are small scales (L < 800 km) (here using Discrete Cosine Transform)

16 mm/j Vertically integrated atmospheric water budget Winter Climatology (CRCM simulation) Total fields Large scales L > 800 km Small scales L < 800 km 1) Balance between P, E and Div Q 2) Climate tendency is small (note scale of 100) 1) Balance is dominated by large scales 2) Small scales play a negligible role in time-mean budget, except locally near mountains and coast lines

17 mm 2 /j 2 Transient-Eddy Variability Vertically integrated atmospheric water budget Winter Climatology (CRCM simulation) Total fields Large scales L > 800 km Small scales L < 800 km <- Special scale for E Time variability is equally important in small and large scales 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)

18 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

19 RCM AND REANALYSIS DATA 6 RCMs from NARCCAP (North American Regional Climate Change Assessment Program; Mearns, 2005; ). All RCMs are driven by NCEP-DOE reanalysis for the period NARR (North American Regional Reanalysis; Mesinger, 2005). Iowa UniversityMM5I Scripps, U. of California at San DiegoECPC/RSM Pacific North West National Lab, WAWRFP/WRF U. of California at Santa CruzRCM3/RegCM3 Hadley Center, Exeter, UKPRECIS/HADRM3 Ouranos, MontréalCRCM (version 4.2.0)

20 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 resolution Time series in each grid point: Percentiles in each grid point:

21 RCMs Variable: 3-hrs MEAN 95th PERCENTILE INFLUENCE OF SPATIAL SCALE on precipitation WARM SEASONCOLD SEASON o Potential added value measure: Virtual GCMs

22 WARM SEASONCOLD SEASON o Warm season rPAV larger than cold season rPAV o Some datasets indicate more/less rPAV… Influence of surface forcing: Cross-section through the continent

23 Conclusions The main potential added value (PAV) of high-resolution RCM is contained in the fine scalesThe main potential added value (PAV) of high-resolution RCM is contained in the fine scales –Although some large-scale effects may be felt as a result of small-scale processes 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, land-sea contrast), time averaging tends to remove small scales –Scale separation is a useful, sometimes necessary, tool to identify PAV Look for PAV in variability statistics:Look for PAV in variability statistics: –Transient-eddy variability –Extremes in distributions 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 Di Luca, A., R. de Elía and R. Laprise: Assessment of the potential added value in multi-RCM simulated precipitation (in preparation)

24

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

26 925-hPa Specific Humidity Variance (from Denis et al. 2002, MWR)

27 From: Nastrom and Gage 1985, JAS

28 ò Data: NARR ( ) ò Temporal scale: 3 hrs INTENSITY-FREQUENCY DISTRIBUTIONS WARM SEASONCOLD SEASON o Low- and high-precipitation events are more frequent in higher resolution datasets o High-precipitation events are more sensitive to changes in the horizontal resolution

29 ò NARR data WARM SEASONCOLD SEASON o PAV increases with the order percentile and for short time scales o WARM season PAV is larger than COLD season PAV INFLUENCE OF TEMPORAL SCALES AND PERCENTILES

30 WARM SEASONCOLD SEASON Relative Potential Added Value (PAV) o PAV increases with the order percentile and for short time scales o WARM season PAV is larger than COLD season PAV ò NARR data


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