large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity
DESCRIPTION
Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity. Stephan de Roode (1,2) & Alexander Los (2) (1) Clouds, Climate and Air Quality, Multi-Scale Physics, Department of Applied Sciences, TU Delft (2) KNMI, De Bilt. Outline. Introduction - PowerPoint PPT PresentationTRANSCRIPT
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Physics@FOM, Veldhoven, 2009.
1
Large-eddy simulation of stratocumulus –cloud albedo and cloud inhomogeneity
Stephan de Roode(1,2)
&Alexander Los(2)
(1)Clouds, Climate and Air Quality, Multi-Scale Physics, Department of Applied Sciences, TU Delft
(2)KNMI, De Bilt
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Physics@FOM, Veldhoven, 2009.
2
Outline
Introduction- Physical processes in stratocumulus
Research question - The stratocumulus "albedo bias" effect
Large-eddy simulation of stratocumulus as observed during the FIRE I experiment- Stratocumulus cloud albedo- Thermodynamic cloud structure
Synthesis of LES results- Parameterization of cloud liquid water variability
Summary
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Physics@FOM, Veldhoven, 2009.
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Atmospheric boundary-layer clouds simulated with large-eddy models: shallow cumulus and stratocumulus
shallow cumulus stratocumulusdeep convection
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Physics@FOM, Veldhoven, 2009.
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Longwave radiative cooling drives turbulence at the stratocumulus cloud top
€
∂θl
∂t= −
1ρcp
∂FL,net
∂z≈ −8K /hr Cloud top cooling
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Physics@FOM, Veldhoven, 2009.
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Turbulence: Entrainment of warm and dry air at the stratocumulus cloud top
entrainment: turbulent mixing of free atmosphere air into the boundary layer
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Physics@FOM, Veldhoven, 2009.
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Stratocumulus cloud albedo: example
cloud layer depth = 400 m
effective cloud droplet radius = 10 m
optical depth = 25
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60
Cloud albedo
Cloud optical depth
homogeneous stratocumuluscloud layer
€
=32
LWPρ liqreff
, LWP = ρ air
zbase
ztop
∫ q ldz
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Physics@FOM, Veldhoven, 2009.
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Real clouds are inhomogeneous
Stratocumulus albedo from satellite
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Physics@FOM, Veldhoven, 2009.
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Albedo for an inhomogeneous cloud layer
Redistribute liquid water:
optical depths = 5 and 45
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60
Cloud albedo
Cloud optical depth
inhomogeneous stratocumuluscloud layer
mean albedo = 0.65 < 0.79
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Physics@FOM, Veldhoven, 2009.
Decrease optical thickness:
Cahalan et al (1994): = 0.7 (FIRE I observations)
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Cloud albedo in a weather forecast or climate model
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60
Cloud albedo
Cloud optical depth
effective mean
inhomogeneous albedo homogeneous
albedo
€
effective = χτ mean
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Physics@FOM, Veldhoven, 2009.
DALES: Dutch Atmospheric Large-Eddy Simulation Model
Dry LES code (prognostic subgrid TKE, stability dependent length scale)Frans Nieuwstadt (KNMI) and R. A Brost (NOAA/NCAR, USA)
Radiation and moist thermodynamics (θl=θ-Lv/cp qliq , equivalent to sl=cpT+gz-Lv/cp qliq )Hans Cuijpers and Peter Duynkerke (KNMI/TU Delft, Utrecht University)
ParallellisationMatthieu Pourquie (TU Delft)
Drizzle Margreet Van Zanten and Pier Siebesma (UCLA/KNMI)
Atmospheric ChemistryJordi Vila (Wageningen University)
Land-surface interaction, advection schemesChiel van Heerwaarden (Wageningen University)
Particle dispersion, numericsThijs Heus and Harm Jonker (TU Delft)
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The diurnal cycle of stratocumulus during FIRE I -Observations () and LES results (lines)
€
LWP = ρ air
zbase
ztop
∫ q ldz
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Physics@FOM, Veldhoven, 2009.
Inhomogeneity factor computed from all hourly 3D cloud fields
for fixed solar zenith angle θ=530
> 0.7 (value used in some weather and climate models) depends on the (optical depth) liquid water path variance
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Physics@FOM, Veldhoven, 2009.
Total water (qt) and liquid water (ql) PDFs
Differences in PDFs: temperature effect (Clausius-Clapeyron)
liquid water
total water
0
10
20
30
40
50
-20 -10 0 10 20 30 40 50
qsaturation
[g/kg]
temperature [0C]
€
q liq = q tot −qsat T( )
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Physics@FOM, Veldhoven, 2009.
Positive temperature (T) and total water (qt) correlation:more moisture -> warmer
€
θl ' = T' −L v
cp
q l ' = 0 ⇒ T' =L v
cp
1+L v
cp
dqs
dT
⎛
⎝ ⎜
⎞
⎠ ⎟q t ' ≈ 1000q t '
Physical explanation for θl'≈0: Approximate balance entrainment warming and longwave radiative cooling
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Physics@FOM, Veldhoven, 2009.
Model proposal based on LES results:From total water fluctuations to liquid water path
fluctuations
LWP'ρ0
=Hβqt'+12H'βqt'
θl' ≈ 0 = 0.4
' ≈ 0 = 1
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Physics@FOM, Veldhoven, 2009.
Model proposal based on LES results:Compare computed to reconstructed liquid water path
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Physics@FOM, Veldhoven, 2009.
Summary
1. LES results:
- ql' = qt' , ≈ 0.4 (and not =1)
2. Parameterization of the variance of LWP and :
3. Outlook: Weather and climate models will use liquid water path variance rather
than prescribing a constant correction factor for the cloud albedo
LWP'2= ρ0Hβ( )2qt'
2
€
'2 =3ρ 0 Hβ2ρ liqreff
⎛
⎝ ⎜
⎞
⎠ ⎟
2
q t '2
€
∂q t '2
∂t= −2w' q t '
∂q t
∂z−
∂w' q t ' q t '∂z
−2εqt
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LES thermodynamic fields
€
q liq = q tot −qsat T( )
€
LWP = ρ air
zbase
ztop
∫ q ldz
Is temperature important for liquid water fluctuations?
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Aim: model cloud liquid water path variance
RACMO
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Factor depends on the optical depth variance ()
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Analytical results for the inhomogeneity factor Assumption: Gaussian optical depth distribution
not smaller than ~ 0.8
isolines
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Vertical structure of fluctuations
In a cloudy subcolumn the mean liquid water fluctuation can be approximated to be constant with height
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Effect of domain size