stephan de roode (1,2) & alexander los (2)
DESCRIPTION
A parameterization for the liquid water path variance to improve albedo bias calculations in large-scale models. Stephan de Roode (1,2) & Alexander Los (2) (1) Clouds, Climate and Air Quality, Department of Applied Sciences, TU Delft, Netherlands (2) KNMI, Netherlands. - PowerPoint PPT PresentationTRANSCRIPT
BBOS meeting on Boundary Layers and Turbulence, 7 November 2008De Roode, S. R. and A. Los, QJRMS, 2008.Corresponding paper available from http://www.srderoode.nl/publications.html
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A parameterization for the liquid water path variance to improve albedo bias calculations in large-scale models
Stephan de Roode(1,2)
&Alexander Los(2)
(1)Clouds, Climate and Air Quality, Department of Applied Sciences, TU Delft, Netherlands
(2)KNMI, Netherlands
Outline
What is the albedo bias effect
How is it modeled in large-scale models, e.g. for weather and climate
Albedo bias results from a Large-Eddy Simulation of stratocumulus
Parameterization of liquid water path variance
Conclusion
Albedo for a homogeneous cloud layer
cloud layer depth = 400 mcloud droplet size = 10 m optical depth = 25 albedo = 0.79
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
Albedo for a inhomogeneous cloud layer
cloud layer depth = 400 mcloud droplet size = 10 m optical depth = 5 and 45, mean = 25
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60
Cloud albedo
Cloud optical depth
in homogeneous stratocumuluscloud layer
mean albedo
mean albedo = 0.65 < 0.79
€
=32
LWPρ liqreff
, LWP = ρ air
zbase
ztop
∫ q ldz
Albedo bias effect
observed spatial variability in stratocumulus albedo
Albedo for a inhomogeneous cloud layer
inhomogeneous stratocumuluscloud layer
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60
Cloud albedo
Cloud optical depth
effective mean
mean albedo
homogeneous albedo
Simple parameterization of the inhomogeneity effect:
Inhomogeneity constant: = 0.7 (Cahalan et al. 1994)
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effective = χτ mean
The diurnal cycle of stratocumulus during FIRE I (Cahalan case)LES results
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LWP = ρ air
zbase
ztop
∫ q ldz
Factor diagnosed from all hourly 3D cloud fieldsfor fixed solar zenith angle =530
factor > 0.7
Factor depends on the optical depth variance ()
Analytical results for the inhomogeneity factor Assumption: Gaussian optical depth distribution
not smaller than ~ 0.8
isolines
Aim: model cloud liquid water path variance
RACMO
LES 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?
total humidity-liquid water 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( )
Temperature-humidity correlations
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l ' = T' −L v
cp
q l ' = 0 ⇒ T' =L v
cp
1+L v
cp
dqs
dT
⎛
⎝ ⎜
⎞
⎠ ⎟q t ' ≈ 1000q t '
Vertical structure of fluctuations
In a cloudy subcolumn the mean liquid water fluctuation can be approximated to be constant with height
Model: from qt' to LWP'
LWP'ρ0
=Hβqt'+12H'βqt'
l' ≈ 0 = 0.4
' ≈ 0 = 1
PDF reconstruction from total humidity fluctuations in the middle of the cloud layer
Effect of domain size
Conclusion
1. Why did Cahalan et al. (1994) found much lower values for the inhomogeneity factor
- They used time series of LWP
2. In stratocumulus l fluctuations are typicall small
- ql' = qt' , ≈ 0.4
3. Parameterizations for the variance of LWP and
- compute total water variance according to Tompkins (2002)
4. Current ECMWF weather forecast model uses LWP variance for McICA approach
LWP'2= ρ0Hβ( )2qt'
2
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'2 =3ρ 0 Hβ2ρ liqreff
⎛
⎝ ⎜
⎞
⎠ ⎟
2
q t '2