La présentation est en train de télécharger. S'il vous plaît, attendez

La présentation est en train de télécharger. S'il vous plaît, attendez

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

Présentations similaires


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 = 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 ) measurements Simulations KK00, BR 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 < g kg -1 BR74 : values < 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

21

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

26 9 juillet

27 26 juin

28

29

30

31

32

33 h surface h base h surf sigma dv

34

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

48


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

Présentations similaires


Annonces Google