Sensitivity Tests of the Surface Characteristics in the GABLS4 Experiment

Transcription

1 Sensitivity Tests of the Surface Characteristics in the GABLS4 Experiment P. Le Moigne, Eric Bazile, Fleur Couvreux, Guylaine Canut, William Maurel Météo-France / CNRM. GABLS4 LSM intercomparison 2. Sensitivity tests in LSM and SCM 3. Sensitivity tests in LES 4. Conclusion 5th EMS & 2th ECAM Sofia, September 205

2 2 GABLS4 Land Surface Model intercomparison Studies have compared energy or mass balance estimates from a small number of snow models : Essery et al., 999; Fierz et al., 2003; Gustafsson et al., 200; Jin et al., 999b; Koivusalo and Heikinheimo, 999; Pan et al., 2003; Pedersen and Winther, 2005; Yang et al., 999b; Zierl and Bugmann, 2005 Only a few large model intercomparisons have been undertaken that explicitly consider snowpack outputs: WMO initiative (986) : comparison of models of snowmelt runoff AMIP (Frei and Robinson, 998) and AMIP2 (Frei et al., 2005) : evaluation of continental-scale estimates of snow cover and mass in GCMs PILPS 2(d) (Slater et al., 200), PILPS 2(e) (Bowling et al., 2003) and Rhône-AGG (Boone et al., 2004) : focus on evaluation of Land-Surface Scheme simulations of snowpack and runoff in snow-dominated catchments SnowMIP (Etchevers et al., 2004) compared point simulations in non vegetated conditions SnowMIP2 (Essery et al., 2009) snow forest processes GABLS4 LSM intercomparison (205) : Modest comparison of snow schemes at DomeC Limited number of participants: 7 insitutes and 0 models Homogenous site: no vegetation, no runoff in snowpack, no melting. No problem? Not really because of the extremely cold and dry conditions : strong stability not really well caught by model parameterizations in general

3 3 GABLS4 Land Surface Model intercomparison The goals of the intercomparison were : () to evaluate the models ability to simulate surface temperature, surface fluxes (accounting for measurement issues), and for multi-layer models, the surface temperature in snowpack. (2) to evaluate the models sensitivity to key surface parameters, such as roughness lengths (3) to initialize SCM simulations on the th of December 2009/2/0 2009/2/ 2009/2/5 LSM Same snow scheme for LSM and SCM LSM Snow Temperatures 0.K 2 SCM physics tested: Arpege and Arome 2 initial conditions tested in Arome SCM OBS Snow Temperatures SCM K

4 4 5d atmospheric forcing: st to 5th of December 2009 In situ measurements: SWD LWD PS In situ measurements (3.3m) gapfilled with ERA-interim reanalyses TA QA WIND

5 5 Some results of LSM simulations Friction velocity: USTAR Sensible Heat Flux: SHF Snow Surface Temperature: TS K 2.5K 5K Large variability in model results for USTAR, SHF and TS Part of it due to variability in surface parameter settings New GABLS4 simulations with homogenized settings

6 6 Models settings Albedo Emissivity z0m z0m/z0h Snow levels Sensitivity tests Density GDPS Profile D ARP ISBAES Profile CROCUS Profile CHTESSEL Active depth 300 CLM albedo 250 LMDZ JULES Profile NOAH var - Large uncertainty in roughness lengths values. A value of z0m=7.0-4 m was derived by E. Vignon (LGGE) from Obs source of motivation for a sensitivity study on z0

7 7 Sensitivity Tests to Roughness Lengths LSM and SCM simulations with the same -layer snow scheme (D95 in SURFEX) z0 for momentum of 0-4 m, 0-3 m and 0-2 m and ratio or 0 for heat Focus on the th of December LSM (SURFEX) response to a change in z0 z0h=0-4 m. Friction velocity z0m=0-4 m. Sensible heat flux z0m=0-4 z0m=0-3 z0h=0-4 z0h= % +0% Increasing z0m lead to an increase of the momentum flux Increasing z0h lead to an increase of the diurnal amplitude of sensible heat flux

8 8 Turbulent Exchange Coefficients In the model, surface fluxes are proportional to tranfer coefficients, that depend on RI and are growing functions of z0

9 9 Impact on surface fluxes USTAR and SHF LSM: SURFEX SCM: AROME SCM: ARPEGE USTAR SHF z0m=0-4 z0h=0-5 z0m=0-4 z0h=0-4 z0m=0-3 z0h=0-4 z0m=0-3 z0h=0-3 z0m=0-2 z0h=0-3 z0m=0-2 z0h=0-2 Overestimation of USTAR for AROME and ARPEGE - Stronger mixing Less variability at night for SCMs (7 W/m 2 ) compared to LSM (5 W/m 2 ) Prescription of z0m for GABLS4 new simulations is z0m=0-3 m and ratio 0

10 0 Impact on surface temperature LSM: SURFEX z0m=0-4 z0h=0-5 z0m=0-4 z0h=0-4 z0m=0-3 z0h=0-4 z0m=0-3 z0h=0-3 z0m=0-2 z0h=0-3 z0m=0-2 z0h=0-2 SCM: AROME Increasing z0 lead to a decrease of Ts diurnal amplitude Due to more mixing with colder upper air SCM: ARPEGE TS

11 Impact on 3m temperature SCM: AROME Increasing z0 lead to a small increase of T3m diurnal amplitude SCM: ARPEGE Roughness length affects the first meters of the SBL

12 2 Sensitivity of z0 in LES USTAR z0m=0-4 z0h=0-5 z0m=0-2 z0h=0-3 SHF TKE

13 3 Sensitivity of z0 in LES DAY NIGHT

14 4 Summary LSM exhibits large variability in model results for USTAR, SHF and TS Roughness lengths play a key role in the first meters of the SBL at DomeC Increasing z0m lead to an increase of the momentum flux Increasing z0h lead to an increase of the diurnal amplitude of sensible heat flux LSM and SCM: Increasing z0 lead to a decrease of Ts diurnal amplitude due to more mixing Increasing z0 lead to a small increase of T3m diurnal amplitude Overestimation of USTAR for AROME and ARPEGE - Stronger mixing Less variability at night for SCMs (7 W/m 2 ) compared to LSM (5 W/m 2 ) A value of z0m=0-3 m looks reasonable LES behave like LSM and SCM as far as surface fluxes are concerned Increasing z0 will increase Ustar and SHF Leading to more mixing in BL during day A warmer and higher BL height Thanks for your attention!