Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity

Physics@FOM, Veldhoven, 2009.

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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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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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Atmospheric boundary-layer clouds simulated with large-eddy models: shallow cumulus and stratocumulus

shallow cumulus stratocumulusdeep convection


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Longwave radiative cooling drives turbulence at the stratocumulus cloud top

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∂θl

∂t= −

1ρcp

∂FL,net

∂z≈ −8K /hr Cloud top cooling


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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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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

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=32

LWPρ liqreff

, LWP = ρ air

zbase

ztop

∫ q ldz


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Real clouds are inhomogeneous

Stratocumulus albedo from satellite


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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


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

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effective = χτ mean


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)

The diurnal cycle of stratocumulus during FIRE I -Observations () and LES results (lines)

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LWP = ρ air

zbase

ztop

∫ q ldz


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


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]

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q liq = q tot −qsat T( )


Positive temperature (T) and total water (qt) correlation:more moisture -> warmer

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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


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


Model proposal based on LES results:Compare computed to reconstructed liquid water path

PDF


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

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'2 =3ρ 0 Hβ2ρ liqreff

⎛

⎝ ⎜

⎞

⎠ ⎟

2

q t '2

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∂q t '2

∂t= −2w' q t '

∂q t

∂z−

∂w' q t ' q t '∂z

−2εqt

LES thermodynamic fields

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q liq = q tot −qsat T( )

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LWP = ρ air

zbase

ztop

∫ q ldz

Is temperature important for liquid water fluctuations?

Aim: model cloud liquid water path variance

RACMO

Factor depends on the optical depth variance ()

Analytical results for the inhomogeneity factor Assumption: Gaussian optical depth distribution

not smaller than ~ 0.8

isolines

Vertical structure of fluctuations

In a cloudy subcolumn the mean liquid water fluctuation can be approximated to be constant with height

Effect of domain size

Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity

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