1 / 16

O. Geoffroy

LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model. CNRM/GMEI/MNPCA. O. Geoffroy. J.L. Brenguier. Why studying Stratocumulus clouds ? - Radiative properties : ALB strato ~10*ALB sea - Large occurrence : ~ 20-30 % of the ocean’s surface.

eman
Download Presentation

O. Geoffroy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Why studying Stratocumulus clouds ? - Radiative properties : ALBstrato ~10*ALBsea - 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 • BLSCare considered as non precipitatingclouds Aerosol impact on climate Energetic point of view : 1mm d-1 ~ -30 W m-2 Significant impact on the energy balance of STBL and on their life cycle Nc rv Na precipitations 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

  3. 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 ~100m in BL Homogeneous cloud Cloud fraction F <qc>, <Nc> (m-3) In GCM : variables are mean values over 10 to 100 km scales smoothing effect on local peak values. ~100km LES domain Corresponding cloud in GCM grid point 3D view of LWC = 0.1 g kg-1 isocontour, from the side and above. Underestimation of precipitation 1st solution This bias is corrected by using tuning coefficients In Manton-Cotton parameterisation : rvcrit=10 µm In GCM : rvcrit reduced down to 5 µm. • Problem • nophysically based parameterisations • Numerical instability due to step function • Are such parameterisations, with tuned coefficients, still valid to study the AIE? 2nd solution A parameterisation of the precipitation flux averaged over an ensemble of cells is more relevant for the GCM resolution scale Presently in GCM : parameterisation schemes of precipitation directly transposed from CRM bulk parameterization. Example : The problem of modeling precipitation formation in GCM

  4. Which variables drive <Fprec> at the cloud system scale ? H (m) or <LWP> (kg m-2) N (m-3) Adiabatic model : LWP = ½CwH2 <Fprec> (kg m-2 s-1 or mm d-1) Pawlowska & Brenguier (2003, ACE-2): Van Zanten & al. (2005, DYCOMS-II) : Comstock & al. (2004, EPIC) : 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 : 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 <Fprec> as a function of macrophysical variables that characterise the cloud layer as a whole ? (Pawlowska & Brenguier, 2003) (kg m-3 s-1)

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

  6. Microphysical processes & microphysical variables. Evaporation : K&K (2000) Autoconversion : K&K (2000) Cloud : qcloud(kg/kg) Ncloud(m-3) Vapour: qvapour (kg/kg) Drizzle: qdrizzle (kg/kg) Ndrizzle(m-3) Condensation & Evaporation : Langlois (1973) Accretion :K&K (2000) Aerosol : NCCN(m-3) (Constant parameter) + Vertical velocity : W Nact(m-3) Sedimentation of drizzle : K&K (2000) Activation : Cohard et al (1998) Sedimentation of cloud droplets Stokes law + gamma • 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. LES microphysical scheme

  7. 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 : (H) : Stokes regime: The cloud sedimentation flux depends on the2nd and 5th moments Radiatives flux in LW depends on the effective radius. • Methodology. • By comparing with ACE-2 measured spectra (resolution = 100 m), • find the idealized distribution which best represents the : • - diameter of the 2nd moment , • diameter of the 5th moment , • effective diameter . Parameterisation of cloud droplets sedimentation

  8. Ø2 Ø5 σ Results for gamma law, α=3, υ=2 Number of spectra in % of max_pts Øe Øe 100 % 50 % only spectra at cloud top 0 % • - Generalized gamma law : best results for α=3, υ=2 • Lognormal law, similar results with σg=1.2 ~ DYCOMS-II results (M.C. Van Zanten personnal communication).

  9. % of max_pts 100 % 50 % 0 % Ø2 Ø5 σ Results for lognormal law, σg=1.5 Øe Øe only spectra at cloud top Lognormal law, with σg=1.5, overestimate sedimentation flux of cloud droplets.

  10. GCSS intercomparison exercise Case coordinator : A. Ackermann (2005) • Case studied : 2nd 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 σg = 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. observations LWP (g m-2) = f(t) Results, LWP, precipitation flux K&K, sed : ON Ensemble range C2R2, sed ON Central half of the simulation ensemble K&K, sed : OFF C2R2, sed OFF Median value of the ensemble of models 3H 3H 6H 6H Precipitation flux at surface (mm d-1) = f(t) ~0.35 mm d-1 3H 6H 3H 6H Precipitation flux at cloud base (mm d-1) = f(t) ~1.24 mm d-1 NO DATA - LWP a little too low - Underestimation of precipitation flux 3H 6H

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

  13. Observations Simulations Results, What about microphysics ? Nc (cm-3), Ndrizzle(l-1) Øgcø, Øgdrizzle (µm) qdrizzle(g kg-1) Ndrizzle(l-1) Cloud Top leg <top height> < base height> C2R2 K&K Øvdrizzle(µm) Øvcloud(µm) Cloud base leg C2R2 K&K 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 : Ndrizzle, qdrizzle, Øvdrizzle, Øvcloud • Underestimation of precipitation flux at the base for K&K scheme and C2R2 scheme. • Nc is too large in simulation? LWP is too low? • K&K scheme reproduce with good agreement microphysical variables. C2R2 scheme : large and few drops.

  14. Results, super bulk parameterization • 7 simulations with different values of N : Na = 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. <Fprec> = (LWP/N) <Fprec> : averaged precipitation flux at cloud base (kg m-2 s-1)

  15. Conclusion & Perspectives • Cloud droplet sedimentation : • Best fit with α = 3 , υ = 2 for generalized gamma law, • σg = 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 Nc 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 : <Fprec> 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

More Related