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
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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
1
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
![Page 2: Stephan de Roode (1,2) & Alexander Los (2)](https://reader035.vdocuments.us/reader035/viewer/2022070412/56814bdc550346895db8b474/html5/thumbnails/2.jpg)
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
![Page 3: Stephan de Roode (1,2) & Alexander Los (2)](https://reader035.vdocuments.us/reader035/viewer/2022070412/56814bdc550346895db8b474/html5/thumbnails/3.jpg)
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
€
=32
LWPρ liqreff
, LWP = ρ air
zbase
ztop
∫ q ldz
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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
![Page 5: Stephan de Roode (1,2) & Alexander Los (2)](https://reader035.vdocuments.us/reader035/viewer/2022070412/56814bdc550346895db8b474/html5/thumbnails/5.jpg)
Albedo bias effect
observed spatial variability in stratocumulus albedo
![Page 6: Stephan de Roode (1,2) & Alexander Los (2)](https://reader035.vdocuments.us/reader035/viewer/2022070412/56814bdc550346895db8b474/html5/thumbnails/6.jpg)
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)
€
effective = χτ mean
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The diurnal cycle of stratocumulus during FIRE I (Cahalan case)LES results
€
LWP = ρ air
zbase
ztop
∫ q ldz
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Factor diagnosed from all hourly 3D cloud fieldsfor fixed solar zenith angle =530
factor > 0.7
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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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Aim: model cloud liquid water path variance
RACMO
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LES 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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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]
€
q liq = q tot −qsat T( )
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Temperature-humidity correlations
€
l ' = T' −L v
cp
q l ' = 0 ⇒ T' =L v
cp
1+L v
cp
dqs
dT
⎛
⎝ ⎜
⎞
⎠ ⎟q t ' ≈ 1000q t '
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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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Model: from qt' to LWP'
LWP'ρ0
=Hβqt'+12H'βqt'
l' ≈ 0 = 0.4
' ≈ 0 = 1
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PDF reconstruction from total humidity fluctuations in the middle of the cloud layer
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Effect of domain size
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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
€
'2 =3ρ 0 Hβ2ρ liqreff
⎛
⎝ ⎜
⎞
⎠ ⎟
2
q t '2