Convection Permitting Simulation of Extreme Precipitation Events Lessons Learned

Transcription

1 Convection Permitting Simulation of Extreme Precipitation Events Lessons Learned A. F. Prein, M. Suklitsch, H. Truhetz, and A. Gobiet Wegener Center for Climate and Global Change (WEGC) and Institute for Geophysics, Astrophysics, and Meteorology (IGAM), Institude of Physics, University of Graz, Graz, Austria CLM-Community Assembly September, 2012, Leuven, Belgium

2 Research Question Do Convection Permitting Climate Simulations (CPCSs) improve the simulation of historical extreme precipitation events? Focus Region: Rhone & Aosta Valley NASA Prein et al., CLM-Community Assembly /25

3 Basics & Motivation Deep Convection: essential for extreme precipitation Complex Orography: demands for high resolution Convection permitting simulations (<4 km grid spacing) have benefits: more realistic precipitation patterns (Roberts and Lean 2008, Prein et al. 2012) precipitation maxima are better localized (Hohenegger et al. 2008) timing of the summertime precipitation diurnal cycle is improved (Hohenegger et al. 2008, Prein et al. 2012) intensity of precipitation extreme events is improved (Prein et al. 2012) more realistic (smaller and more peaked) precipitation objects (Prein et al. 2012) Prein et al., CLM-Community Assembly /12

4 Domain sensitivity studies Driving Data: ERA-Interim Small sensitivity of precipitation amounts to domain settings Large domains show slight advantages Prein et al., CLM-Community Assembly /25

5 Model and Namelist COSMO 4.8-CLM 16 Namelist: similar to CCLM setup for EURO-CORDEX 50 km Simulation: 3rd order two time-level Runge-Kutta split-explicit scheme; dt=360 s Tiedtke convection (parameterization) (Tiedtke 1989) cloud ice scheme with prognostic cloud water and cloud ice prognostic rain and snow with transport of rain/snow TKE-based turbulence scheme including subgrid scale effects of condensation/evaporation lateral boundary conditions (LBCs) and soil moisture initial conditions (SMICs) from ERA-Interim (Dee et al. 2011) 40 vertical levels; domain D km Simulation: dt=100 s LBCs from 50 km run Domain D km Simulation: dt=25 s LBCs from 12 km run shallow convection (no parameterized deep convection) Graupel as additional prognostic variable Domain D0304 Prein et al., CLM-Community Assembly /25

6 Model and Namelist 1 km Simulation: Crashed with Courant Friedrichs Lewy (CFL) condition error by using RK-solver Reason Solution NOTE: hd_corr[u,t,q] and nrdtau are time step dependent modify with grid spacing! At ~1 km horizontal grid spacing numeric gets instable at slopes >20 Reduction of dt from 10 s to 6 s Smoothing slopes <20 [rxso_mask=300] Nr. of special filter passes [numfilt_xso=3] Reduce the divergence damping [xkd=0.01] Prein et al., CLM-Community Assembly /25

7 Reference Data-Sets Reference Data-Sets which cover the Focus Region Name Time Spat. Res. Tem. Res. Param. Ref./Source: ETHZ-Frei ~16 km Daily pr ETH Zürich E-OBS ~25 km Daily t2m, pr, psl Haylock et al Stations Point Daily pr MetheoSwiss and ARPA 1 Stations Point Hourly pr MetheoSwiss ChDailyGrid ~2 km Daily t2m, pr, ssd MetheoSwiss ChHourlyGrid ~1 km Hourly pr MetheoSwiss Wüst et al km Hourly pr Wüst et al Agenzia Regionale per la Protezione dell'ambiente Pre 2000 no hourly data Difficulties comparing station data with gridded simulations Coars gridded data-sets Does not cover entire Focus Region Prein et al., CLM-Community Assembly /25

8 September 19 th -21 st 1999 Event Geopotential Sea Surface Pressure 3h Precipitation Prein et al., CLM-Community Assembly /25

9 September 19 th -21 st 1999 Event Finer grids more precipitation in Focus Region 12 km necessary to get location of maxima Finer grids more spatial variability +45 % +28 % +2 % +104 % +26 % +5 % Prein et al., CLM-Community Assembly /25

10 September 19 th -21 st 1999 Event Spatial Correlation Coefficients of Total Precipitation [Reference ChDailyGrid from MetheoSwiss] Improvement of spatial correlation and variability from 50 km to 12 km From 12 km to 3 km no correlation but variability improvement From 3 km to 1 km no big differences Prein et al., CLM-Community Assembly /25

11 June 1999 Event Geopotential Sea Surface Pressure 3h Precipitation Prein et al., CLM-Community Assembly /25

12 June 1999 Event Finer grids more precipitation in Focus Region 3 km necessary to get amplitude of maximum Finer grids more spatial variability +3 % +55 % +6 % +22 % +171 % -4 % Prein et al., CLM-Community Assembly /25

13 June 1999 Event Spatial Correlation Coefficients of Total Precipitation [Reference ChDailyGrid from MetheoSwiss] From 50 km to 12 km: improvement of spatial correlation From 12 km to 3 km: constant correlation but ~threefold variability From 3 km to 1 km: slight improvements of correlation Prein et al., CLM-Community Assembly /25

14 Conclusions CCLM is able to simulate precipitation extremes in complex terrain with high quality Finer grids lead to more precipitation in extremes (reduce dry bias) Spatial correlation coefficients are highly improved in 12 km runs (compared to 50 km sim.) Spatial variability increases with smaller grid spacings Problems: Lack/accuracy of high resolution observations in focus region Prein et al., CLM-Community Assembly /25

15 Outlook Physic Sensitivity Studies Graupel (itype_gscp=4) 3D Turbulence (l3dturb=.true.; l3dturb_metr =.True. ) Deep Convection Parametr. in 3 km and 1 km runs [Suklitsch et al. 2012] consistent treatment of diffusion coefficients and fluxes (Lexpcor=.True.) [Langhans et al. 2011] explicit diffusion (imode_turb=3) [Langhans et al. 2011] Investigation of further historical extreme events Investigation of events with different synoptic situations and seasons Prein et al., CLM-Community Assembly /25

16 Thank You Anny Questions? Prein et al., CLM-Community Assembly /25

17 Prein et al., CLM-Community Assembly /25

18 Simulation Setup Name Model setting rd order two time-level Runge-Kutta split-explicit scheme with 360 s time step; Tiedtke convection (parameterization) (Tiedtke 1989); cloud ice scheme with prognostic cloud water and cloud ice; prognostic rain and snow with transport of rain/snow; TKE-based turbulence scheme including subgrid scale effects of condensation/evaporation; lateral boundary conditions (LBCs) and soil moisture initial conditions(smics) from ERA-Interim (Dee et al. 2011); 40 vertical levels; domain D CCLM in version COSMO 4.8-CLM 12; LBCs from ERA-40 (Uppala et al. 2006); artificial SIMCs; 32 vertical levels; Kain-Fritsch moist convection (Kain and Fritsch 1993; Kain 2003); domain D LBCs from ERA-40 (Uppala et al. 2006); artificial SIMCs; 32 vertical levels; Kain-Fritsch moist convection (Kain and Fritsch 1993; Kain 2003); domain D LBCs and SMICs from ERA-40 (Uppala et al. 2006); 32 vertical levels; Kain-Fritsch moist convection (Kain and Fritsch 1993; Kain 2003); domain D LBCs and SMICs from ERA-40 (Uppala et al. 2006); domain D LBCs and SMICs from 5002; domain D1201; 100 s time step 1202 LBCs and SMICs from 5002; domain D1202; 100 s time step 1203 LBCs and SMICs from 5002; domain D1203; 100 s time step 1204 LBCs and SMICs from 5005; domain D1201; 100 s time step 1205 LBCs and SMICs from 5005; domain D1202; 100 s time step 1206 LBCs and SMICs from 5005; domain D1203; 100 s time step 1207 LBCs and SMICs from ERA-Interim (Dee et al. 2011); domain D1201; 100 s time step 0301 LBCs and SMICs from 1204; shallow convection; graupel as additional prognostic variable; domain D0301; 25 s time step 0302 LBCs and SMICs from 1204; shallow convection; graupel as additional prognostic variable; domain D0302; 25 s time step 0303 LBCs and SMICs from 1204; shallow convection; graupel as additional prognostic variable; domain D0303; 25 s time step 0304 LBCs and SMICs from 1204; shallow convection; graupel as additional prognostic variable; domain D0304; 25 s time step 0101 LBCs and SMICs from 0304; shallow convection; graupel as additional prognostic variable; domain D0101; 6 s time step; extra smoothing of steep slopes > LBCs and SMICs from 0304; shallow convection; graupel as additional prognostic variable; domain D0102; 6 s time step; extra smoothing of steep slopes > LBCs and SMICs from 0304; shallow convection; graupel as additional prognostic variable; domain D0103; 6 s time step; extra smoothing of steep slopes > LBCs and SMICs from 0304; shallow convection; graupel as additional prognostic variable; domain D0104; 6 s time step; extra smoothing of steep slopes >20 Prein et al., CLM-Community Assembly /25

19 Domain sensitivity studies Spatial differentes between annual and seasonsl average precipitation in the Focus Region (ref. ETHZ data-set) Finer grids lead to: 1. More precip. 2. Reduction of error ranges 1207 Simulation: ERA-Interim driven draying out problem Small sensitivste to domain settings Prein et al., CLM-Community Assembly /25

20 Domain sensitivity studies Prein et al., CLM-Community Assembly /25

21 Domain sensitivity studies DJF MAM JJA SON Prein et al., CLM-Community Assembly /25