The representation of stratocumulus with eddy diffusivity closure models Stephan de Roode KNMI.

Slides:



Advertisements
Similar presentations
Parametrization of PBL outer layer Martin Köhler Overview of models Bulk models local K-closure K-profile closure TKE closure.
Advertisements

Parametrization of PBL outer layer Irina Sandu and Martin Kohler
Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.
BBOS meeting on Boundary Layers and Turbulence, 7 November 2008 De Roode, S. R. and A. Los, QJRMS, Corresponding paper available from
Veldhoven, Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity Stephan de Roode (1,2) & Alexander Los (2)
A new unified boundary-layer scheme for RACMO: The DualM-TKE scheme Stephan de Roode with contributions from Geert Lenderink Roel Neggers Reinder Ronda.
Training course: boundary layer IV Parametrization above the surface layer (layout) Overview of models Slab (integral) models K-closure model K-profile.
Modeling Stratocumulus Clouds: From Cloud Droplet to the Meso-scales Stephan de Roode Clouds, Climate & Air Quality Multi-Scale Physics (MSP), Faculty.
Low clouds in the atmosphere: Never a dull moment Stephan de Roode (GRS) stratocumulus cumulus.
What controls the climatological PBL depth? Brian Medeiros Alex Hall Bjorn Stevens UCLA Department of Atmospheric & Oceanic Sciences 16th Symposium on.
© Crown copyright Met Office Cloud parametrization: current issues relating to microphysics Adrian Lock.
The role of the mean relative humidity on the dynamics of shallow cumuli - LES Sensitivity experiments Stephan de Roode (KNMI/IMAU) Simon Axelsen (IMAU)
Mass Flux Concepts:  Pier Siebesma KNMI, De Bilt The Netherlands Mass flux approximations Entraining Plume model Mixing.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,
GCSS BLCWG update Chris Bretherton, BLCWG Chair Thanks to Andy Ackerman (LES case leader) Margreet vanZanten/Bjorn Stevens SCM and LES case participants.
IACETH Institute for Atmospheric and Climate Science Boundary Layer parametrisation in the climate model ECHAM5-HAM Colombe Siegenthaler - Le Drian, Peter.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California 1 Joao Teixeira, Brian.
Page 1© Crown copyright 2005 RF01/RF02: LES sensitivity studies Adrian Lock and Eoin Whelan.
Relationships between wind speed, humidity and precipitating shallow cumulus convection Louise Nuijens and Bjorn Stevens* UCLA - Department of Atmospheric.
Observational study of the transition from unbroken marine boundary layer stratocumulus to the shallow cumulus regime Irina Sandu and Bjorn Stevens Max-Planck-Institut.
GFS Deep and Shallow Cumulus Convection Schemes
The scheme: A short intro Some relevant case results Why a negative feedback? EDMF-DualM results for the CFMIP-GCSS intercomparison case: Impacts of a.
Large-Eddy Simulation of a stratocumulus to cumulus transition as observed during the First Lagrangian of ASTEX Stephan de Roode and Johan van der Dussen.
Scientific Advisory Committee Meeting, November 25-26, 2002 Large-Eddy Simulation Andreas Chlond Department Climate Processes.
Can we use a statistical cloud scheme coupled to convection and moist turbulence parameterisations to simulate all cloud types? Colin Jones CRCM/UQAM
A dual mass flux framework for boundary layer convection Explicit representation of cloud base coupling mechanisms Roel Neggers, Martin Köhler, Anton Beljaars.
Radiative Properties of Eastern Pacific Stratocumulus Clouds Zack Pecenak Evan Greer Changfu Li.
Stephan de Roode (KNMI) Entrainment in stratocumulus clouds.
1 Indirect evidence of vertical humidity transport during very stable conditions at Cabauw Stephan de Roode
Modeling the Atmospheric Boundary Layer (2). Review of last lecture Reynolds averaging: Separation of mean and turbulent components u = U + u’, = 0 Intensity.
Yanjun Jiao and Colin Jones University of Quebec at Montreal September 20, 2006 The Performance of the Canadian Regional Climate Model in the Pacific Ocean.
The ASTEX Lagrangian model intercomparison case Stephan de Roode and Johan van der Dussen TU Delft, Netherlands.
Large-Eddy Simulations of the Nocturnal Low-Level Jet M.A. Jiménez Universitat de les Illes Balears 4th Meso-NH user’s meeting, Toulouse April 2007.
Forecast simulations of Southeast Pacific Stratocumulus with CAM3 and CAM3-UW. Cécile Hannay (1), Jeffrey Kiehl (1), Dave Williamson (1), Jerry Olson (1),
Lecture 15, Slide 1 Physical processes affecting stratocumulus Siems et al
Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observations Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted.
Atmospheric dry and shallow moist convection
Toulouse IHOP meeting 15 June 2004 Water vapour variability within the growing convective boundary layer of 14 June 2002 with large eddy simulations and.
Large Eddy Simulation of Low Cloud Feedback to a 2-K SST Increase Anning Cheng 1, and Kuan-Man Xu 2 1. AS&M, Inc., 2. NASA Langley Research Center, Hampton,
April Hansen et al. [1997] proposed that absorbing aerosol may reduce cloudiness by modifying the heating rate profiles of the atmosphere. Absorbing.
1. Introduction Boundary-layer clouds are parameterized in general circulation model (GCM), but simulated in Multi-scale Modeling Framework (MMF) and.
Boundary Layer Clouds.
1 Large Eddy Simulation of Stable Boundary Layers with a prognostic subgrid TKE equation 8 th Annual Meeting of the EMS, Amsterdam, 2008 Stephan R. de.
Sensitivity to the PBL and convective schemes in forecasts with CAM along the Pacific Cross-section Cécile Hannay, Jeff Kiehl, Dave Williamson, Jerry Olson,
1 Making upgrades to an operational model : An example Jongil Han and Hua-Lu Pan NCEP/EMC GRAPES-WRF Joint Workshop.
Simulating Stratocumulus clouds sensitivity to representation of: Drizzle Cloud top entrainment Cloud-Radiation interaction Large scale subsidence Vertical.
A dual mass flux scheme for shallow cumulus convection Results for GCSS RICO Roel Neggers, Martin Köhler, Anton Beljaars.
Continuous treatment of convection: from dry thermals to deep precipitating convection J.F. Guérémy CNRM/GMGEC.
A Thermal Plume Model for the Boundary Layer Convection: Representation of Cumulus Clouds C. RIO, F. HOURDIN Laboratoire de Météorologie Dynamique, CNRS,
Development and testing of the moist second-order turbulence-convection model including transport equations for the scalar variances Ekaterina Machulskaya.
Stratocumulus-topped Boundary Layer
Evaluation of cloudy convective boundary layer forecast by ARPEGE and IFS Comparisons with observations from Cabauw, Chilbolton, and Palaiseau  Comparisons.
Stephan de Roode The art of modeling stratocumulus clouds.
A Case Study of Decoupling in Stratocumulus Xue Zheng MPO, RSMAS 03/26/2008.
Shallow Moist Convection Basic Moist Thermodynamics Remarkable Features of Moist Convection Shallow Cumulus (Stratocumulus) Courtesy: Dave Stevens.
© Crown copyright Met Office CFMIP-GCSS Intercomparison of SCM/LES (CGILS) Results for the HadGEM2 SCM Mark Webb and Adrian Lock (Met Office) EUCLIPSE/GCSS.
A simple parameterization for detrainment in shallow cumulus Hirlam results for RICO Wim de Rooy & Pier Siebesma Royal Netherlands Meteorological Institute.
Relaminarisation of turbulent stratified flow Bas van de Wiel Moene, Steeneveld, Holtslag.
Forecasts of Southeast Pacific Stratocumulus with the NCAR, GFDL and ECMWF models. Cécile Hannay (1), Dave Williamson (1), Jim Hack (1), Jeff Kiehl (1),
Pier Siebesma Today: “Dry” Atmospheric Convection
Stratocumulus cloud thickening beneath layers of absorbing smoke aerosol – Wilcox, 2010 The semi-direct aerosol effect: Impact of absorbing aerosols.
Vertical resolution of numerical models
Shallow Cumulus Growth, (an idealized view)
Theories of Mixing in Cumulus Convection
Analysis of Parameterization in Single-Column Model
thanks to Pier Siebesma, Adrian Tompkins, Anton Beljaars
Entrainment rates in stratocumulus computed from a 1D TKE model
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model Cécile Hannay, Dave Williamson,
Cloud-topped boundary layer response time scales in MLM and LES
transport equations for the scalar variances
Presentation transcript:

The representation of stratocumulus with eddy diffusivity closure models Stephan de Roode KNMI

Global stratocumulus presence

Motivation of the problem ECMWF underestimates stratocumulus

ECMWF underpredicts stratocumulus Liquid Water Path (LWP) Duynkerke and Teixeira (2001) RACMO uses ECMWF physics!

Entrainment of relatively warm and dry inversion air into the cloudy boundary layer Bjorn Stevens

Entrainment: the holy grail in stratocumulus research "Dogma": solve the stratocumulus problem by getting the entrainment rate right in a model Topics: 1. Dogma is not true 2. Entrainment rates from a TKE scheme compared to parameterizations

Tendency equation for the mean Computation of the flux Turbulent transport in closure models

Eddy diffusivities diagnosed from LES results - Results from the FIRE I stratocumulus intercomparison case LES result ● LES: Eddy diffusivities for heat and moisture differ

Eddy diffusivities in K-closure models - Results from the FIRE I stratocumulus intercomparison case LES result 6 single-column models (SCMs) ● LES: Eddy diffusivities for heat and moisture differ ● SCM: typically much smaller values than LES K heat = K moisture

Should we care about eddy diffusivity profiles in SCMs? Simple experiment 1. Prescribe surface latent and sensible heat flux 2. Prescribe entrainment fluxes 3. Consider quasi-steady state solutions  fluxes linear function of height Consider mean state solutions from

Use fixed flux profiles from LES results (so we know the entrainment fluxes)

Vary eddy diffusivity profiles with a constant factor c ● K ref is identical to K qt from LES ● Multiply K ref times constant factor c

Remember the dogma? Dogma: solve the stratocumulus problem by getting the entrainment rate right in a model If we prescribe the entrainment fluxes, do we get the right cloud structure?

Mean state solutions

Liquid Water Path and cloud transmissivity LWP and cloud transmission VERY sensitive to eddy-diffusivity profile

ECMWF low eddy diffusivities explain low specific humidity ECMWF eddy diffusivity from Troen and Mahrt (1986)

Computation of the flux Representation of entrainment rate w e ECMWF K-profile K = w e  z, w e from parametrization RACMO TKE modelK(z) = TKE(z) 1/2 l(z), w e implicit Question Does w e from a TKE model compare well to w e from parametrizations? Turbulent mixing and entrainment in simple closure models

Entrainment parameterizations designed from LES of stratocumulus (Stevens 2002) Nicholls and Turton (1986) Stage and Businger (1981) Lewellen and Lewellen (1998) VanZanten et al. (1999) Lilly (2002) Moeng (2000) Lock (1998) w e = fct (H, LE, z b, z i,  F l,  q t,  l, LWP or q l,max )

Simulation of ASTEX stratocumulus case ASTEX A209 boundary conditions _________________________________ cloud base height = 240 m cloud top height = 755 m sensible heat flux = 10 W/m 2 latent heat flux = 30 W/m 2 longwave flux jump= 70 W/m 2 max liq. water content= 0.5 g/kg LWP = 100 g/m 2  l = 5.5 K  q t = -1.1 g/kg fill in these values in parameterizations, but vary the inversion jumps  l and  q t

Entrainment rate [cm/s] sensitivity to inversion jumps - Boundary conditions as for ASTEX A Note differences 2. Buoyancy reversal 3. Moisture jump sensitivity

Entrainment rate [cm/s] sensitivity to inversion jumps - Boundary conditions as for ASTEX A Note differences 2. Buoyancy reversal 3. Moisture jump sensitivity

Entrainment rate [cm/s] sensitivity to inversion jumps - Boundary conditions as for ASTEX A Note differences 2. Buoyancy reversal 3. Moisture jump sensitivity

Entrainment rate [cm/s] sensitivity to inversion jumps - Boundary conditions as for ASTEX A Note differences 2. Buoyancy reversal 3. Moisture jump sensitivity vertical lines sensitivity to moisture jumps  q t

RACMO How does a TKE model represent entrainment? ● Some model details ● Run ASTEX stratocumulus, and check sensitivity to entrainment jumps

TKE equation Flux 'integral' length scale (Lenderink & Holtslag 2004) buoyancy flux weighed with cloud fraction ASTEX A209 forcing and initialization  t = 60 s,  z = 5 m Mass flux scheme turned off, no precipitation Some details of the TKE model simulation

Entrainment sensitivity to inversion jumps from a TKE model buoyancy reversal criterion ASTEX A209 entrainment rates decrease in this regime many parameterizations predict rates that go to infinity slightly larger values than Stage-Businger Entrainment rate depends on moisture jump

Conclusions Eddy diffusivity experiments ● stratocumulus may dissappear by incorrect BL internal structure, even for 'perfect' entrainment rate TKE model ● appears to be capable to represent realistic entrainment rates

FIRE I stratocumulus observations of the stratocumulus inversion structure de Roode and Wang : Do stratocumulus clouds detrain? FIRE I data revisited. BLM, in press

Similar K profiles for heat and moisture - Interpretation Gradient ratio: (K drops out) Typical flux values near the surface for marine stratocumulus: H=10 W/m 2 and LE = 100 W/m 2 Then a 0.1 K decrease in  l corresponds to a change of 0.4 g/kg in q t The larger the latent heat flux LE, the larger the vertical gradient in q t will be!

GCSS stratocumulus cases ASTEX A209 boundary conditions _________________________________ cloud base height = 240 m cloud top height = 755 m sensible heat flux = 10 W/m 2 latent heat flux = 30 W/m 2 longwave flux jump= 70 W/m 2 max liq. water content= 0.5 g/kg LWP = 100 g/m 2  l = 5.5 K  q t = -1.1 g/kg fill in these values in parameterizations, but vary the inversion jumps  l and  q t