1. LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model O. Geoffroy J.L. Brenguier CNRM/GMEI/MNPCA

2. Why studying precipitation in BLSC (Boundary Layer Stratocumulus Clouds ) ? Parameterization of drizzle formation and precipitation in BLSC is a key step in numerical modeling of the aerosol impact on climate Why studying Stratocumulus clouds ? - Radiative properties : ALB strato ~10*ALB sea - Large occurrence : ~ 20-30 % of the ocean’s surface. Negative global radiative forcing Hydrological point of view : Precipitation flux in BLSC ~mm d -1 against ~mm h -1 in deep convection clouds  BLSC are considered as non precipitating clouds Energetic point of view : 1mm d -1 ~ -30 W m -2 Significant impact on the energy balance of STBL and on their life cycle Aerosol impact on climate NaNa rvrv NcNc precipitations

3. The problem of modeling precipitation formation in GCM Presently in GCM : parameterisation schemes of precipitation directly transposed from CRM bulk parameterization. Example : Problem - no physically based parameterisations - Numerical instability due to step function Are such parameterisations, with tuned coefficients, still valid to study the AIE? 2 nd solution A parameterisation of the precipitation flux averaged over an ensemble of cells is more relevant for the GCM resolution scale Underestimation of precipitation 1 st solution This bias is corrected by using tuning coefficients In Manton-Cotton parameterisation : rv crit =10 µm In GCM : rv crit reduced down to 5 µm. Problem : Inhomogeneity of microphysical variables. Formation of precipitation = non linear process local value have to be explicitely resolved LES resolution: ~100m horizontally, ~10 m vertically 3D view of LWC = 0.1 g kg -1 isocontour, from the side and above. LES domainCorresponding cloud in GCM grid point ~100m in BL ~100km Homogeneous cloud Cloud fraction F, (m -3 ) In GCM : variables are mean values over 10 to 100 km scales smoothing effect on local peak values.

4. Super bulk parameterisation At the scale of an ensemble of cloud cells : quasi stationnary state Is it feasible to express the mean precipitation flux at cloud base as a function of macrophysical variables that characterise the cloud layer as a whole ? (Pawlowska & Brenguier, 2003) Pawlowska & Brenguier (2003, ACE-2): Comstock & al. (2004, EPIC) : Van Zanten & al. (2005, DYCOMS-II) : Which variables drive at the cloud system scale ? Adiabatic model : LWP = ½C w H 2 (kg m -2 s -1 or mm d -1 ) H (m) or (kg m -2 ) N (m -3 ) In GCMs, H (or LWP) and N can be predicted at the scale of the cloud system - The LWC sink term due to precipitation, averaged over numerous cloud cells, can then be expressed as a function of these two variabless : (kg m -3 s -1 )

5. Objectives & Methodology Methodology: 3D LES simulations of BLSC fields with various H (LWP) and N values Objectives : - use LES to establish the relationship between, LWP and N, and empirically determine the coefficients. H or, N a = ? α = ? β = ? LES domainGCM grid point averaged LWP, N, and over the simulation domain 10 km

6. LES microphysical scheme - Implementation in MESONH of a modified version of the Khairoutdinov & Kogan (2000) LES bulk microphysical scheme (available in MASDEV4_7 version). Specificities : - 2 moments -> predict N for studies of the aerosol impact -- specifically designed for BLC = low precipitating clouds - coefficients tuned using an explicit microphysical model as data source -> using realistic distributions. - LES scheme -> valid only for CRM. - Modifications : Cohard and Pinty (1998) activation scheme and add of droplet sedimentation process. Condensation & Evaporation : Langlois (1973) Autoconversion : K&K (2000) Accretion :K&K (2000) Sedimentation of drizzle : K&K (2000) Activation : Cohard et al (1998) Evaporation : K&K (2000) Aerosol : N CCN (m -3 ) (Constant parameter) + Vertical velocity : W N act (m -3 ) Cloud : q cloud (kg/kg) N cloud (m -3 ) Drizzle: q drizzle (kg/kg) N drizzle (m -3 ) Sedimentation of cloud droplets Stokes law + gamma Vapour: q vapour (kg/kg) Microphysical processes & microphysical variables.

7. (H) : Stokes regime: Parameterisation of cloud droplets sedimentation Calculation of the cloud droplet sedimentation process requires an idealized droplet size distribution. Objective : Which distribution to select? With which parameter ? Generalized gamma law :Lognormal law : Methodology. By comparing with ACE-2 measured spectra (resolution = 100 m), find the idealized distribution which best represents the : - diameter of the 2 nd moment, - diameter of the 5 th moment, - effective diameter. The cloud sedimentation flux depends on the 2 nd and 5 th moments Radiatives flux in LW depends on the effective radius.

8. Results for gamma law, α=3, υ=2 Number of spectra in % of max_pts 100 % 50 % 0 % Ø2Ø2 σ ØeØe ØeØe Ø5Ø5 - Generalized gamma law : best results for α=3, υ=2 - Lognormal law, similar results with σ g =1.2 ~ DYCOMS-II results (M.C. Van Zanten personnal communication). only spectra at cloud top

9. Results for lognormal law, σ g =1.5 % of max_pts 100 % 50 % 0 % Ø2Ø2 σ ØeØe ØeØe Ø5Ø5 Lognormal law, with σ g =1.5, overestimate sedimentation flux of cloud droplets. only spectra at cloud top

10. GCSS intercomparison exercise Case coordinator : A. Ackermann (2005) Case studied : 2 nd research flight (RF02) of DYCOMS-II experiment (Stevens et al., 2003) Domain : 6.4 km × 6.4 km × 1.5 km horizontal resolution : 50 m, vertical resolution : 5 m near the surface and the initial inversion at 795 m. fixed LW radiative fluxes, fixed surface fluxes, fixed cloud droplet concentration : Nc = 55 cm -3 2 simulations : - 1 without cloud droplet sedimentation. - 1 with cloud droplet sedimentation : lognormale law with σ g = 1.5 Microphysical schemes tested : - K&K scheme, - C2R2 scheme (= Berry and Reinhardt scheme (1974)). 4 simulations. K&K, sed ON / sed OFF C2R2, sed ON / sed OFF

11. Results, LWP, precipitation flux Central half of the simulation ensemble Ensemble range Median value of the ensemble of models K&K, sed : ON K&K, sed : OFF NO DATA LWP (g m -2 ) = f(t) Precipitation flux at surface (mm d -1 ) = f(t) Precipitation flux at cloud base (mm d -1 ) = f(t) C2R2, sed ON C2R2, sed OFF 6H 3H 6H 3H6H observations - LWP a little too low - Underestimation of precipitation flux ~0.35 mm d -1 ~1.24 mm d -1

12. Results,discussion Strong variability of N and F prec : Black : F prec > 5 mm d -1 Light grey : F prec < 1 mm d -1 N c (cm -3 ) Variation of N c along 1 cloud top leg Resolution : 1 km (Van Zanten et al, 20004) measures Nc < 55 cm -3 in heavily precipitating areas.

13. Results, What about microphysics ? Observations Variations of N, geometrical diameter for cloud and for drizzle, along 1 cloud top leg, 1 cloud base leg. (Van Zanten personnal communication). Averaged profils on precipitating grid points after 2 hours of simulation : N drizzle, q drizzle, Øv drizzle, Øv cloud C2R2 K&K N drizzle (l -1 ) q drizzle (g kg -1 ) Ø v drizzle (µm) Ø v cloud (µm) Simulations - Underestimation of precipitation flux at the base for K&K scheme and C2R2 scheme. N c is too large in simulation? LWP is too low? - K&K scheme reproduce with good agreement microphysical variables. C2R2 scheme : large and few drops. N c (cm -3 ), N drizzle (l -1 )Øg cø, Øg drizzle (µm) Cloud Top leg Cloud base leg K&K C2R2

14. Results, super bulk parameterization : averaged precipitation flux at cloud base (kg m -2 s -1 ) 7 simulations with different values of N : N a = 25, 50, 75, 100, 200, 400, 800 cm -3 -> different values of N Simulations of diurnal cycles -> variations of LWP Domain : 2,5 km * 2,5 km * 1220 m horizontal resolution : 50 m, vertical resolution : 10 m. = (LWP/N)

15. Conclusion & Perspectives - Cloud droplet sedimentation : Best fit with α = 3, υ = 2 for generalized gamma law, σ g = 1,2 for lognormal law. - Validation of the microphysical scheme : GCSS intercomparison exercise The K&K scheme shows a good agreement with observations for microphysical variables Underestimation of the precipitation flux with respect to observations. Nc too large ? -> Simulations with N c prognostic Simulation of 2 ACE-2 case -> Simulations of a clear and a polluted case of the ACE-2 experiment and comparison with observations - Parameterisation of the precipitation flux for GCM : corroborates experimental results : is a function of LWP and N -> 3D simulations over a larger domain in order to improve statistics -> 1D water budget simulations for explaining the dependence