LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model Olivier Geoffroy Jean-Louis Brenguier, Frédéric.

Présentation au sujet: "LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model Olivier Geoffroy Jean-Louis Brenguier, Frédéric."— Transcription de la présentation:

1 LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model Olivier Geoffroy Jean-Louis Brenguier, Frédéric Burnet, Irina Sandu, Odile Thouron CNRM/GMEI/MNPCA

2 AUTO N c =cste - Formation of precipitation = non linear process : LWC The problem of modeling precipitation formation in GCM A parameterisation of the precipitation flux averaged over an ensemble of cells is more relevant for the GCM resolution scale Problem - no physically based parameterisations, numerical instability due to step function Are such parameterisations, with tuned coefficients, still valid to study the AIE? - Variables in GCM = mean values over a large area in GCM. Underestimation of precipitation in GCM Biais corrected by tuning coefficients against observations Parameterisations in GCM = CRM bulk parameterisation. Ex :

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

4 Objectives & Methodology Methodology: 3D LES simulations of BLSC fields with various LWP, N act and corresponding R base values Objectives : - To establish the relationship between R base, LWP and N act, and empirically determine the coefficients. a = ? α = ? β = ? Suppose power law relationship Regression analysis

5 Outline Presentation of the LES microphysical scheme Particular focus on cloud droplet sedimentation parameterisation Validation of the microphysical scheme Simulation of 2 cases of ACE-2 campaign and GCSS Boundary layer working group intercomparaison exercise Come back to the problematic : Results of the parameterisation of precipitation in BLSC

6 LES microphysical scheme - Modified version of the Khairoutdinov & Kogan (2000) LES bulk microphysical scheme (available in next version of MESONH). Condensation & Evaporation : Langlois (1973) Autoconversion : K&K (2000) Accretion : K&K (2000) Sedimentation of drizzle drops : K&K (2000) Activation : Cohard and al (1998) Evaporation : K&K (2000) Air: Aerosols : C (m -3 ), k, µ, ß (= constant parameters) W (m s -1 ) θ (K) N a (m -3 ) Cloud : q c (kg/kg) N c (m -3 ) Drizzle : q r (kg/kg) N r (m -3 ) Sedimentation of cloud droplets : Stokes law + generalized gamma law Air : q v (kg/kg) θ (K) microphysical Processes and variables Specificities : - 2 moments - low precipitating clouds : local q c < 1,1 g kg -1 - coefficients tuned using an explicit microphysical model as data source -> using realistic distributions. - valid only for CRM.

7 Parameterisation of cloud droplets sedimentation Which distribution to select? With which parameter ? Generalized gamma : Lognormal : Methodology. By comparing with ACE-2 measured droplet 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. (H) : Stokes regime:

8 Results for gamma law, α=3, υ=2 Color = number of spectra in each pixel in % of nb_max 100 % 50 % 0 % d2d2 d eff d5d5 only spectra at cloud top E(d 5 ) (%) E(d 2 ) (%) E(d eff ) (%) - Generalized gamma law: best results for α=3, υ=2 - Lognormal law, similar results with σ g =1,2-1,3 ~ DYCOMS-II results (Van Zanten personnal communication).

9 Results for lognormal law, σ g =1.5 Color = number of spectra in each pixel in % of nb_max 100 % 50 % 0 % d2d2 d eff d5d5 only spectra at cloud top E(d 5 ) (%) E(d 2 ) (%) E(d eff ) (%) Lognormal law, with σ g =1.5, overestimate sedimentation flux of cloud droplets.

10 Scheme validation

11 GCSS intercomparison exercise Case coordinator : A. Ackermann (2005) Case studied : DYCOMS-II RF02 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 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 2 Microphysical schemes tested : - KK00 scheme, - MESONH 2 moment scheme = Berry and Reinhardt scheme (1974). 4 simulations : KK00, no sed / sed BR74, no sed / sed

12 Results, LWP, precipitation flux Central half of the simulation ensemble Ensemble range Median value of the ensemble of models KK00, sed KK00, no sed NO DATA LWP (g m -2 ) = f(t) R surface (mm d -1 ) = f(t) R base (mm d -1 ) = f(t) BR74, sed BR74, no sed 6H 3H observation ~0.35 mm d -1 ~1.29 mm d -1 6H 3H 6H 3H 6H 3H 6H 3H - KK00 : underestimation of precipitation flux by only a factor 2 at cloud base - BR74 : underestimation at cloud base by a factor 2, R surface = R base no evaporation - LWP too low - KK00 : underestimation of precipitation flux by a factor 10 at surface - BR74 : good agreement at surface

13 50 µm KK00 & measurements 84 µm BR74 d d cloud drizzle cloud drizzle Results, What about microphysics ? Averaged profils of N drizzle, dv drizzle in each 30 m layer after 3 hours of simulation and averaged value of measured N drizzle, dmean drizzle (resolution : 12 km) at cloud base and at cloud top (Van Zanten personnal communication) BR74 KK00 - KK00 scheme reproduce with good agreement microphysical variables at cloud top and cloud base - BR74 scheme : too few and too large drops. CB CT h surf (m) N drizzle (l -1 ) dv drizzle (µm) CB CT BR74 KK00

14 Simulation of 2 ACE-II cases

15 26 june, pristine case9 July, polluted case Macrophysical variablesH (m)N act (cm -3 )H (m)N act (cm -3 ) measurements20251167256 Simulations KK00, BR7419048-49170193 Macrophysical variables for measurements (Pawlowska and Brenguier, 2003) and simulations after 2H20 Domain : 10 km × 10 km, resolution : horizontaly : 100 m, verticaly : 10 m in/above the cloud initialisation : corresponding profile of thermodynamical variables. Objective : comparison of mean profiles of q r, N r, dv r for 1 polluted and 1 marine case. Comparison of macrophysical variables 50 µm KK00 84 µm BR74 d d cloud drizzle cloud drizzle Simulations : Fast-FSSP 256 bins In situ measurements : OAP-200X : 14 bins 35 µm 20 µm 315 µm 3,5 µm <0,25 µm

16 Results 26 june (pristine) KK00 / measurements BR74 / measurements Vertical profile of qr (g kg -1 )Vertical profile of Nr (g kg -1 )Vertical profile of dvr (g kg -1 ) Mean values in each 30 m layers h base

17 Results 9 july (polluted) KK00 / measurements BR74 / measurements Vertical profile of qr (g kg -1 )Vertical profile of Nr (g kg -1 ) Mean values in each 30 m layers Vertical profile of dvr (g kg -1 ) BR74 : values < 10 -5 g kg -1 BR74 : values < 10 -2 l -1 Pristine case : KK00 represents with good agreement precipitating variables Polluted case : KK00 underestimate precipitation. BR74 : underestimate precipitation by making too large drops but with very low concentration

18 Results, super bulk parameterisation

19 R base (kg m -2 s -1 ) Initial profiles : profiles (or modified profiles) of ACE-2 (26 june), EUROCS, DYCOMS-RF02 differents values of LWP : 20 g m -2 < LWP < 130 g m -2 different values of N act : 40 cm -3 < N act < 260 cm -3 Domain : 10 km * 10 km. horizontal resolution : 100 m, vertical resolution : 10 m near surface, in and above cloud (= 1,7 mm d -1 )

20 Summary - 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 KK00 scheme shows a good agreement with observations for microphysical variables Underestimation of the precipitation flux with respect to observations. LWP too low ? Simulation of 2 ACE-2 case Good agreement with observations for microphysical variables for KK00 Parameterisation of the precipitation flux for GCM : Corroborates experimental results : R base is a function of LWP and N act

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22 KK00, sedKK00, no sedBR74, sedBR74, no sed RF02 0–800 m

23 KK00, sedKK00, no sedBR74, sedBR74, no sed RF02 > 450 m

24 Profils ACE-2 9july 26 june

25 Results, What about microphysics ? Observations Variations of mean values of N and geometrical diameter for cloud and for drizzle, along 1 cloud top leg,, 1 cloud base leg. Mean values over 12 km. (Van Zanten personnal communication). Averaged profils of N drizzle, Øv drizzle in each 30 m layer after 3 hours of simulation. BR74 KK00 Simulations N c (cm -3 ), N drizzle (l -1 ) Øg c, Øg drizzle (µm) CT leg CB leg CB CT CT leg CB leg h surf (m) N drizzle (l -1 ) Øv drizzle (µm) CB CT BR74 KK00

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33 h surface h base h surf sigma dv

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35 Paramétrisations « bulk » cloud rain r=20 µm On prédit les moments de la distribution qui représentent des propriétés densemble (bulk) de la distribution. ex : M0=N i, M3=q i Modèle bulk moins de variables Modèle explicite ou bin On prédit la distribution elle même. ~ 200 classes. Modèle bulk

36 Parametrisations bulk valides dans les GCM? Processus microphysiques (~10 m, ~1 s) dépendent non linéairement des variables locales (q c, q r, N c, N r …). Distribution temporelle et spatiale des variables non uniforme. le modèle doit résoudre explicitement les variables locales pour que paramétrisations bulk soient valides. utiliser paramétrisations bulk dans les GCM (~ 50 km, ~ 10 min) peut être remis en question. collection autoconversion accrétion

37 simulations On veut plusieurs champs avec différentes valeurs de,,,. 7 simulation MESONH avec différentes valeurs de N a = 25, 50, 75, 100, 200, 400, 800 cm -3. Fichier initial : champ de donnée à 12H de la simulation de cycle diurne dIrina et al. sans schéma de précipitation. 24H de simulation pour chaque simulation -> LWP varie (cycle diurne du nuage). Domaine : 2,5 km * 2,5 km * 1220 m Resolution horizontale : 50 mailles, verticale : 122 niveaux. Pas de temps : 1 s. Schéma microphysique : schéma modifié du schéma Khairoutdinov-Kogan (2000) Début des simulations avec schéma microphysique Fig. Profil moyen du rapport de mélange en eau nuageuse qc en fonction du temps

38 Schéma K&K modifié K&K : schéma microphysique bulk pour les stratocumulus. Les coefficients ont été ajustés avec un modèle de microphysique explicite (bin). Intérêt : –N act, N c en variables pronostiques (on veut différentes valeurs de N). –schéma développé spécialement pour les stratocumulus (particularité : pluie très faible)

39 7 simulations de 24 H. 1 sortie toutes les heures. 7*24 = 168 champs avec des valeurs différentes de H,, N,

40 Profil moyen du rapport de mélange en eau de pluie en fonction du temps N CCN =25 cm-3 N CCN =400 cm-3 N CCN =100 cm-3 N CCN =50 cm-3

41 Calcul de H, LWP, N, R mailles nuageuses : mailles ou qc > 0,025 g kg-1 cumulus sous le nuage sont rejetés. Calcul de H –Définition de la base? Calcul de LWP Calcul de N –qc > 0,9 q adiab –0,4H < h <0,6 H –N r < 0,1 cm-3 Calcul de R –R =, R = –Sur fraction nuageuse, à la base.

42 Comparaison avec les données DYCOMS-II, ACE-2 ACE-2 –Mesures in-situ -> vitesse des ascendances w pas prise en compte dans le calcul du flux. –Flux calculé sur la fraction nuageuse (dans le nuage) DYCOMS-II –Mesures radar -> mesure du moment 6 de la distribution -> vitesse de chute réel. (vitesse ascendances w + vitesse terminal des gouttes V qr ) –Flux calculé au niveau de la base du nuage.

43 R = f(H 3 /N)

44 R = f(LWP/N) Observation dun hystérésis : Déclenchement de la pluie avec un temps de retard. -> Il faut prendre en compte la tendance des variables détat?

45 Conclusion On retrouve bien les résultats expérimentaux : dépendance de R en fonction des variables H ou LWP, N Hystérésis de + en + prononcé lorsque N CCN augmente (lorsque R augmente). => rajouter une variable pronostique supplémentaire (q r ) ? utiliser la tendance de LWP : dLWP/dt ? Expliquer cette dépendance en isolant une seule cellule et en regardant comment varient q c, q r …

46 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 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 coefficients tuned against observations Problem : Inhomogeneity of microphysical variables. Formation of precipitation = non linear process local value have to be explicitely resolved 3D view of LWC = 0.1 g kg -1 isocontour, from the side and above. LES domain Corresponding cloud in GCM grid point ~100m in BL ~100km Homogeneous cloud Cloud fraction F, (m -3 ) In GCM : variables are mean values smoothing effect on local peak values.

47 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 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

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