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Olivier Geoffroy

LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model. CNRM/GMEI/MNPCA. Olivier Geoffroy. Jean-Louis Brenguier, Frédéric Burnet, Irina Sandu, Odile Thouron. Parameterisations in GCM = CRM bulk parameterisation. Ex :. AUTO.

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Olivier Geoffroy

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

  2. Parameterisations in GCM = CRM bulk parameterisation. Ex : AUTO - Formation of precipitation = non linear process : Nc=cste LWC Underestimation of precipitation in GCM Biais correctedby tuning coefficientsagainst observations • Problem • nophysically based parameterisations, numerical instability due to step function • Are such parameterisations, with tuned coefficients, still valid to study the AIE? The problem of modeling precipitation formation in GCM • Variables in GCM =mean valuesover a large area in GCM. A parameterisation of the precipitation flux averaged over an ensemble of cells is more relevant for the GCM resolution scale

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

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

  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). • Specificities : • 2 moments • low precipitating clouds : local qc < 1,1 g kg-1 • - coefficients tuned using an explicit microphysical model as data source -> using realistic distributions. • valid only for CRM. microphysical Processes and variables Evaporation : K&K (2000) Air : qv (kg/kg) θ (K) Autoconversion : K&K (2000) Cloud : qc(kg/kg) Nc (m-3) Drizzle : qr(kg/kg) Nr(m-3) Condensation & Evaporation : Langlois (1973) Accretion : K&K (2000) Air: Aerosols : C (m-3), k, µ, ß (= constant parameters) W (m s-1) θ (K) Na(m-3) Activation : Cohard and al (1998) Sedimentation of cloud droplets : Stokes law + generalized gamma law Sedimentation of drizzle drops : K&K (2000)

  7. Which distribution to select? With which parameter ? Generalized gamma : Lognormal : (H) : Stokes regime: • Methodology. • By comparing with ACE-2 measured droplet 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. E(d5) (%) E(d2) (%) Results for gamma law, α=3, υ=2 d2 d5 100 % Color = number of spectra in each pixel in % of nb_max 50 % 0 % E(deff) (%) E(deff) (%) deff only spectra at cloud top deff • - Generalized gamma law: best results for α=3, υ=2 • Lognormal law, similar results with σg=1,2-1,3 • ~ DYCOMS-II results • (Van Zanten personnal communication).

  9. E(d5) (%) E(d2) (%) Results for lognormal law, σg=1.5 d2 d5 100 % Color = number of spectra in each pixel in % of nb_max 50 % 0 % E(deff) (%) E(deff) (%) only spectra at cloud top deff deff Lognormal law, with σg=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 σg = 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. 6H 6H 6H 3H 3H 3H Median value of the ensemble of models observation Results, LWP, precipitation flux LWP (g m-2) = f(t) KK00, sed BR74, sed Central half of the simulation ensemble KK00, no sed • LWP too low Ensemble range BR74, no sed 6H 3H Rsurface (mm d-1) = f(t) • KK00 : underestimation of precipitation flux • by a factor 10 at surface • - BR74 : good agreement at surface ~0.35 mm d-1 6H 3H Rbase (mm d-1) = f(t) • KK00 : underestimation of precipitation flux by only a factor 2 at cloud base • BR74 : underestimation at cloud base by a factor 2, Rsurface = Rbase no evaporation NO DATA ~1.29 mm d-1

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

  14. Simulation of 2 ACE-II cases

  15. In situ measurements : Fast-FSSP 256 bins OAP-200X : 14 bins 35 µm 3,5 µm 315 µm 20 µm <0,25 µm Simulations : drizzle cloud drizzle cloud KK00 BR74 50 µm d 84 µm d Simulation of 2 ACE-II cases Objective : comparison of mean profiles of qr , Nr , dvr for 1 polluted and 1 marine case. • Domain : 10 km × 10 km, • resolution : horizontaly : 100 m, verticaly : 10 m in/above the cloud • initialisation : corresponding profile of thermodynamical variables. Comparison of macrophysical variables Macrophysical variables for measurements (Pawlowska and Brenguier, 2003) and simulations after 2H20

  16. KK00 / measurements hbase Results 26 june (pristine) BR74 / measurements hbase 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

  17. KK00 / measurements Results 9 july (polluted) BR74 / measurements BR74 : values < 10-2 l-1 BR74 : values < 10-5 g kg-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 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)

  18. Results, super bulk parameterisation

  19. Results, super bulk parameterisation • 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 Nact : 40 cm-3 < Nact < 260 cm-3 • Domain : 10 km * 10 km. • horizontal resolution : 100 m, • vertical resolution : 10 m near surface, in and above cloud Rbase (kg m-2 s-1) (= 1,7 mm d-1)

  20. Summary • 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 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 : Rbase is a function of LWP and Nact

  21. BR74, no sed KK00, no sed KK00, sed BR74, sed RF02 0–800 m

  22. BR74, no sed KK00, no sed KK00, sed BR74, sed RF02 > 450 m

  23. Profils ACE-2 9july 26 june

  24. Observations Simulations Ndrizzle (l-1) hsurf (m) Results, What about microphysics ? Nc (cm-3), Ndrizzle(l-1) CT leg CB leg CT BR74 KK00 CB Øvdrizzle (µm) hsurf (m) CT leg Øgc, Øgdrizzle (µm) CB leg CT BR74 KK00 CB 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 Ndrizzle, Øvdrizzle in each 30 m layer after 3 hours of simulation.

  25. 9 juillet

  26. 26 juin

  27. hsurf hbase hbase sigma dv hsurface

  28. cloud rain r=20 µm Paramétrisations « bulk » Modèle bulk On prédit les moments de la distribution qui représentent des propriétés d’ensemble (bulk) de la distribution. ex : M0=Ni , M3=qi Modèle explicite ou bin On prédit la distribution elle même. ~ 200 classes. Modèle bulk moins de variables

  29. Parametrisations bulk valides dans les GCM? collection accrétion autoconversion • Processus microphysiques(~10 m, ~1 s) dépendent non linéairement des variables locales (qc, qr, Nc, Nr …). • Distribution temporelle et spatiale des variables non uniforme. • le modèle doit résoudreexplicitement les variables locales pour que paramétrisations bulk soient valides. • utiliser paramétrisations bulk dans les GCM (~ 50 km, ~ 10 min) peut être remis en question.

  30. simulations • On veut plusieurs champs avec différentes valeurs de <LWP>, <N>,, <R>. • 7 simulation MESONH avec différentes valeurs de Na = 25, 50, 75, 100, 200, 400, 800 cm-3. • Fichier initial : champ de donnée à 12H de la simulation de cycle diurne d’Irina 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. • Schémamicrophysique : schéma modifié du schéma Khairoutdinov-Kogan (2000) Fig. Profil moyen du rapport de mélange en eau nuageuse qc en fonction du temps Début des simulations avec schéma microphysique

  31. 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 : • Nact, Nc en variables pronostiques (on veut différentes valeurs de N). • schéma développé spécialement pour les stratocumulus (particularité : pluie très faible)

  32. 7 simulations de 24 H. 1 sortie toutes les heures. 7*24 = 168 champs avec des valeurs différentes de H, <LWP>, N, <R>

  33. Profil moyen du rapport de mélange en eau de pluie en fonction du temps NCCN =25 cm-3 NCCN =50 cm-3 NCCN =400 cm-3 NCCN =100 cm-3

  34. 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 qadiab • 0,4H < h <0,6 H • Nr < 0,1 cm-3 • Calcul de R • R = < qr * (Vqr-w) >, R = < qr * Vqr > • Sur fraction nuageuse, à la base.

  35. 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 Vqr) • Flux calculé au niveau de la base du nuage.

  36. R = f(H3/N)

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

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

  39. ~100m in BL Homogeneous cloud Cloud fraction F <qc>, <Nc> (m-3) In GCM : variables are mean values smoothing effect on local peak values. ~100km Corresponding cloud in GCM grid point Underestimation of precipitation 1st solution coefficients tuned against observations • Problem • nophysically based parameterisations • 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 Problem : Inhomogeneity of microphysical variables. Formation of precipitation = non linear process local value have to be explicitely resolved LES domain 3D view of LWC = 0.1 g kg-1 isocontour, from the side and above.

  40. 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

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