Université du Québec à Montréal!

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1 Université du Québec à Montréal! PhD candidate: Alejandro Di Luca! Director: René Laprise! Co director: Ramon de Elia! May 28th 2009!

2 !! Wide range of atmospheric phenomena...!! Important dependence between temporal and spatial scales! Atmospheric scale definitions: characteristic time and horizontal scales (adapted from Orlanski (1975)).!

3 !! RCM has higher spatial and temporal resolution but resolved a limited domain.! ! 10 4! GCMs! 300! RCMs! 50! Range of atmospheric temporal and spatial scales represented by GCMs and RCMs.!

4 !! Added value will be primarily associated with those scales which are only represented by the RCM.! Potential added value! Temporal and spatial scales only represented by the RCM.!

5 "! A necessary condition for added value is that processes must contain information of spatial scales between 50 and 300 km.! (15 min;10 4 km)! Potential added value! Temporal and spatial scales only represented by the RCM.!

6 "! Added value dominated by scales in the intersection between the only RCM resolved scales and atmospheric processes.! Temporal and spatial scales which are only resolved by the RCM and with important atmospheric processes.!

7 Example: simulated precipitation by the CRCM in the AMNO domain.! CRCM grid points (blue) and specification of the British Columbia region (red; ~350 x 350 km)! 4.0! Computational domain used in the CRCM (201 x 193 grid points).! 4.0!!! Upscale the high resolution RCM-simulated precipitation into several lower temporal-spatial resolution grids by averaging in space and time.!

8 !! Upscaling in several horizontal grids (constants in degrees )! Pr 0,0 : horizontal grid spacing = 0.5!!temporal spacing = 3 hours (*)! 40km! Pr 0,0! 3h! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

9 !! Upscaling in several horizontal grids (constants in degrees )! Pr 0,0 : horizontal grid spacing = 0.5!!temporal spacing = 3 hours (*)! 0.5! 0.5! 40km! Pr 0,0! 3h! CRCM grid points (blue) and the 0.5 horizontal grid (red, ~40 km).! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

10 !! Upscaling in several horizontal grids (constants in degrees )! Pr 1,0 :!horizontal grid spacing = 1.0!!temporal spacing = 3 hours (*)! 1.0! 1.0! 80km! Pr 1,0! 3h! CRCM grid points (blue) and the 1.0 horizontal grid (red, ~80 km).! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

11 !! Upscaling in several horizontal grids (constants in degrees )! Pr 2,0 :!horizontal grid spacing = 2.0!!temporal spacing = 3 hours (*)! 2.0! 2.0! 160km! Pr 2,0! 3h! CRCM grid points (blue) and the 2.0 horizontal grid (red, ~160 km).! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

12 !! Upscaling in the temporal scale! Pr 0,1 :!horizontal grid spacing = 0.5!!temporal spacing = 6 hours (*)! 40! Pr 0,1! 6h! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

13 !! Upscaling in the temporal scale! Pr 0,1 :!horizontal grid spacing = 0.5!!temporal spacing = 6 hours (*)! 3! 6! 9! 12! 15!18!21!24!3! 6! 9! 12!18! 40! Pr 0,1! 6! 12! 18! 24! 6! 12! 6h! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

14 !! Upscaling in the temporal scale! Pr 0,2 :!horizontal grid spacing = 0.5!!temporal spacing = 12 hours (*)! 3! 6! 9! 12! 15!18!21!24!3! 6! 9! 12!18! 40! Pr 0,2! 12! 24! 12! 12h! (*) The minimum interval of cumulative precipitation available in the NARCCAP project is 3 hours.!

15 !! Upscaling in the temporal and spatial scales!!! 7 temporal scales: 3, 6, 12, 24,, 192 h (8 days)!!! 4 spatial scales: 0.5, 1.0, 2.0, 4.0 (~350 km)! Pr 3,6! Pr 0,0!!! Calculate mean distributions of precipitation in the 4 x 4 regions by using the new datasets.!!! Estimation of 99 th percentiles from the distributions.!

16 Topography:! 1 km resolution! Vancouver! Edmonton! Lakes! Calgary! Winnipeg! Montreal!

17 !! Data: CRCM ( )!!! Region: EDMONTON!!! Variable: 99th PERCENTILE! RCMs! GCMs! RCMs! GCMs! "! Differences between seasons could be very important: fine spatial scales are more important in summer.! "! In both seasons, fine spatial scales are almost filtered out for time scales greater than ~48 hs.!

18 !! Data: CRCM ( )!!! Season: WINTER!!! Variable: 99th PERCENTILE! "! Influence of surface forcings could also be very important.! "! In winter, Vancouver shows much more fine spatial scale variability than Edmonton.!

19 !! Time scale: 24 hours!!! Season: SUMMER!!! Percentile: 99th! EDMONTON! "! The representation of precipitation in RCMs could be very different.! "! Not only because of absolute values of percentiles but also its variation through spatial scales!

20 !! The methodology allows to estimate the potential added value of RCMs as a function of regions and seasons.!!! The methodology permits to evaluate if RCMs simulate the proper physical structure of spatiotemporal variability of precipitation! Right for the right reasons?!!! The performance of models should be assessed in terms of observed data! US radar data (12 yr; 2 km; 15 min)!!! High resolution observed data can be used to estimate the potential added value that we can expect from future higher resolution RCMs.!

21 !! The methodology allows to estimate the potential added value of RCMs as a function of regions and seasons.!!! The methodology permits to evaluate if RCMs simulate the proper physical structure of spatiotemporal variability of precipitation! Right for the right reasons?!!! The performance of models should be assessed in terms of observed data! US radar data (12 yr; 2 km; 15 min)!!! High resolution observed data can be used to estimate the potential added value that we can expect from future higher resolution RCMs.! Thank you for your attention! Comments?! Questions?!

22 !! Data: CRCM ( )!!! Season: WINTER!!! Region: VANCOUVER! 99th PERCENTILE! "! Differences between seasons could be very important: fine spatial scales are more important in summer season.! "! In both seasons, fine spatial scales are filtered out for time scales greater than ~48 hs.!

23 !! Data: CRCM ( )!!! Region: EDMONTON!!! Variable: 99th PERCENTILE! "! Differences between seasons could be very important: fine spatial scales are more important in summer season.! "! In both seasons, fine spatial scales are filtered out for time scales greater than ~48 hs.!

24 Many things to do!! Use of other variables as Tmin, Tmax, etc.!! Also others statistics. For precipitation, was already done for 90th, 95th and 99th percentile and also for the frequency of dry days variable.!! Could be useful to define some normalized variables to avoid problems with the scale. For example, this is the advantage of frequency of dry days (always between 0 and 1).!! Spatial scale x-axis: Change size of region by number of grid points/stations!! I have to add MRCC5 to the comparison. The only reason that is not there is that it is not part of NARCCAP, so not netcdf file so a little more complicate to read data. A question of time..!! I already have radar data for US (U.S. composite 2 km 15 minute instantaneous reflectivity data set from National Weather Service Radars (NWS) data). Temporal resolution of 15 minutes for 12 years in almost all US. Because we are interested with differences (example, RCMAV definition) and not with absolute values, radar data could be very interesting. Is in netcdf format but files are huge so I need to work a little to read and include data in the comparison.!! I will try to find 6 hs stations, probably in US. So, we have to change regions but this is not a problem: all forcings are in US and the program is very easy to change.!!the more important work to do is to find physical arguments to explained results find (example, the maximum added value in SON). This should be the most difficult work and in which I invested the least amount of energy so far.

25 !! Data: CRCM!!! Region: EDMONTON!!! Variable: 99th PERCENTILE! "! Differences between seasons could be very important "! In Vancouver, the temporal scale choosing to analyze could influence P99 significantly in summer season

26 !! Data: CRCM!!! Season: WINTER!!! Variable: 99th PERCENTILE! "! Differences between seasons could be very important "! In Vancouver, the temporal scale choosing to analyze could influence P99 significantly in summer season

27 SOME OTHERS ISSUES "! The problem about the effective resolution of numerical climate models is still present. But maybe could be partially evaluated by comparing results with different spatial scales at given temporal ones. If results are not very different when going from 5*#x to 1*#x, it does not suggest that the model is working well at its finest scale? " Maybe could be thinking as an empirical measure of the effective resolution. I$m not sure of this and I must think more about it. " "! The experiment not compare models working at different resolutions because of two reasons:" 1.! Because of the last point: effective resolution. Some data do not have the same reliability (?)." 2.! When producing upscaling data we expect that some information of finer scales "

28 !! More general: Where, when and for which variable, the RCM could add some value to the GCM simulated precipitation?!!! More particular: Is it necessary a 50 km grid spacing climate model to simulate the daily winter-distribution of precipitation in central Canada?!!! Objective: study the representation of different spatial and temporal scales of precipitation as simulated by several RCMs.! "!Idea to study de problem:! From a model with horizontal grid interval "x and temporal interval "t, we produce time series {Pr s,t } with horizontal grid interval 2 n *"x and temporal interval 2 n *"t.!

29 !! Regional climate models (RCMs) are developed with the aim of representing fine scale structures that are absent in the atmospheric fields simulated by the coarse-resolution coupled general circulation models (CGCMs).!!! The RCM simulated small scales represent the main potential added value of the high-resolution RCM over the CGCM.!!! Magnitude of fine scale features depend on a variety of factors:! 1.! Variable and climate statistic! 2.! Type and intensity of surface forcings! 3.! Weather regime!

30 !! Data: CRCM!!! Season: WINTER!!! Region: VANCOUVER! 99th PERCENTILE! "! Differences between seasons could be very important "! In Vancouver, the temporal scale choosing to analyze could influence P99 significantly in summer season

31 ADDED VALUE AS A FUNCTION OF! TEMPORAL-SPATIAL SCALES! "! Added value as the intersection of RCM resolved scales and processes in the atmosphere.!

32 ADDED VALUE AS A FUNCTION OF! TEMPORAL-SPATIAL SCALES! "! Added value as the intersection of RCM resolved scales and processes in the atmosphere.!

33 DATA "! Variable: total precipitation rate for 20 years ( ).! "! NARCCAP models: 6 RCMs with similar resolution ~50 km and the same domain of integration.!! CRCM!! ECPC!! HRM3!! MM5I!! RCM3!! WRFP!