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

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 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

2. 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

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

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

5. 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?

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

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

8. 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…

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

10. 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

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

12. 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)

13. Scale separation • 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 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 XL are large scales (L > 800 km) XS are small scales (L < 800 km) (here using Discrete Cosine Transform)

16. Vertically integrated atmospheric water budget Winter Climatology (CRCM simulation) 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 mm/j Total fields 1) Balance between P, E and Div Q 2) Climate tendency is small (note scale of 100) Large scales L > 800 km Small scales L < 800 km

17. Transient-Eddy Variability Vertically integrated atmospheric water budget Winter Climatology (CRCM simulation) mm2/j2 Time variability is equally important in small and large scales Total fields 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) Large scales L > 800 km Small scales L < 800 km <- Special scale for E

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; 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).

20. Aggregating data to different spatio-temporal resolution • 5 spatial scales: 0.375, 0.75, 1.5, 3.0, 6.0° • (≈ virtual GCM) • 8 temporal scales: 3, 6, 12, 24, …, 16 days Time series in each “grid point”: Percentiles in each “grid point”:

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

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

23. Conclusions • The 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: • 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: • 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. 1-20 • Di Luca, A., R. de Elía and R. Laprise: Assessment of the potential added value in multi-RCM simulated precipitation (in preparation)