1. The effect of indiscriminate nudging time in regional climate modeling of the Mediterranean basin Tamara Salameh, Philippe Drobinski, Thomas Dubos and Hiba Omrani Laboratoire de Météorologie Dynamique/Ecole Polytechnique,

2. Tamara Salameh General context Global climate change Global climate models agree on: -A decrease in precipitation rate (mm/j) for scenario A1B (21st century) -Increase in extreme cold temperature -Increase in extreme precipitation (Goubanova and Li 2006) Need to know as precise as possible future climate conditions at regional scales (city, valley,…) IPSL -0,4 -0,8 0 0,4 0,8 1,2 CNRM By J.L. Dufresne 1/13

3. Tamara Salameh General context –Important needs in water and energy –Understanding the Mediterranean regional climate has environmental, economical and societal implications –Coupled system (ocean-atmosphere-hydrology) –Strong topographic component that induces extreme events Strong scales interaction 2/13

4. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin Brief state of the art Consists on forcing a regional model (limited area model, RCM) by a GCM, generally to ameliorate the realism of the modeled fields (Mass et al., 2002) Sensitivity studies on initial and boundary conditions of the GCM (forcing resolution, frequency of update of boundary conditions), physical parameterization of the RCM (Bhaskaran et al., 1996; Seth et Giorgi, 1998; Noguer et al., 1998; Denis et al., 2002, 2003) impact on the RCM results High-cost calculation method but transferrable from region to another For long regional runs, periodic reinitilization gives better results (scores) than the continuous regional run (Qian et al. 2003; Lo and Yang, 2008) affect the temporel variability alternative solution: nudging the RCM fields to the GCM fields (relatively less considered in the literature): 2 nudging types: temporal (Salameh et al., en révision) and spectral (Von Storch et al., 2000), both need adjustment of constants 3/13

5. Tamara Salameh Example: equation to resolve for a linear approach 1D (e.g. q = PV) The GCM problem Small-scale source (e.g. convection, relief) Stationary answer to the large-scale forcing General solution 1 Simplified approach, one dimension, linear 4/13 Challenge: evaluation of the impact of nudging on the physical processes at all scales and evaluation of the existence of an optimal nudging time that minimizes the total error committed on the large and small scales Problem: relaxing the fields of the RCM to those of the GCM by adding to the conservation equations a relaxation term to the large-scale forcing variables Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin

6. Tamara Salameh Example: equation to resolve for a linear approach 1D (e.g. q = PV) The GCM problem Small-scale source (e.g. convection, relief) Stationary answer to the large-scale forcing General solution 1 Simplified approach, one dimension, linear 4/13 Challenge: evaluation of the impact of nudging on the physical processes at all scales and evaluation of the existence of an optimal nudging time that minimizes the total error committed on the large and small scales Problem: relaxing the fields of the RCM to those of the GCM by adding to the conservation equations a relaxation term to the large-scale forcing variables Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin Approached equation resolved by the RCM In the Fourier space Numerical diffusion induced by the discretization of conservation equations Relaxation time to the large-scale q ls 3 2

7. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin Objective: determination of an optimal nudging time that minimizes the total error committed on the large and small scales (if it exists) Optimal nudging time minimizing the total error Total error Exact solution Small-scale error Large-scale error The real variability of the large-scale flow Exact solution: and K num 0 The regional solution: Contribution of the large-scale Contribution of the small-scale 5/13

8. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin =1h =3h =5h =12h =2h =4h =6h = Mean wind (MM5: Nov-Dec. 1998 * ) * Year rich in Mistral events and dense water formation MM5 simulations all indentic except of the nudging time Real RCMs approach Weak impact on mean wind field at 10 m but strong impact on the wind variability 6/13

9. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin =1h =3h =5h =12h =2h =4h =6h = Stability of wind direction =1h =3h =5h =12h =2h =4h =6h = Variability of wind speed This variability has very strong impact on precipitation and extreme wind 7/13

10. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin q r =potential vorticity (PV) ||q ls || is the variance for = 0 ; ||q ss || is the variance for = The cutoff frequency K c = π/Δx (resolution of ERA-40) 9/13 =1h =3h =5h =12h =2h =4h =6h =

11. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin Domination of the diffusion: Linear model ~ OK Diffusion+contribution of non linear processes: Linear model not OK Evolution of the variance dominated by the development of fine-scale structures : linear model ~ OK 10/13

12. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin Domination of the diffusion: Linear model ~ OK Diffusion+contribution of non linear processes: Linear model not OK Evolution of the variance dominated by the development of fine-scale structures : linear model ~ OK 10/13 num = 10h ss = 1h Robust estimation

13. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin Conclusions: 1.Strong impact of nudging on dynamical regionalization: variability of surface wind precipitation and wind extremes 2.Simplified linear one dimension model 1.Existence of an optimal nudging time 2.Existence of two characteristic time scales : et 3.Impact of nudging on the small-scale in MM5 ~ linear model 13/13

14. Tamara Salameh Application to regional climate simulations conducted with WRF 11/13 WRF forced by IPSL CM4 (LMDZ outputs) used for IPCC AR4 Wintertime periods (November to March) 1861-1871 and 1990-2000 60 km spatial resolution

15. Tamara Salameh Application to regional climate simulations conducted with WRF 12/13 The impact of nudging is not uniform over the domain Nudging produces better results The difference in averaged temperature between 1990-2000 and 1861-1871 is positive over all the domain Increase in the precipitation rate over mountains and decreases everywhere else WRF tends to over-estimate precipitation over the domain Nudging produces more precipitation over the eastern basin and less over mountains Nudging time = 6 h Sim-obs CRU 0.5° (1990_2000) Nudged simulation – no nudging simulation 1990_2000 -1861_1871 No nudging 1990-2000

16. Tamara Salameh The end!!!! Thank you for your attention!!

17. Tamara Salameh Regional dynamical modeling of the atmospheric circulation over the Mediterranean basin =1h =3h =5h =12h =2h =4h =6h = Précipitations Extreme wind (>15 m s -1 ) =1h =3h =5h =12h =2h =4h =6h = Coherent result with Lo et al. 2008 on the necessity of nudging to ameliorate precipitation 8/13