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Inhomogeneous radiative properties of cirrus clouds
(Photo courtesy of Steven Dobbie, University of Leeds Reading, Dec 8
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Clouds in GCMs Diagnostic Prognostic ice/liquid water content
‘Statistical’ Prognostic schemes (eg Tompkins 2002). Plane parallel homogeneous (PPH) clouds introduce bias (Cahalan 1994, Barker et al 1998 and Larson et al 2001)
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Motivation Climate is very sensitive to cirrus
Cirrus are poorly understood (GCSS WG2) Inhomogeneity and radiative properties
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Motivation Observed cloud structure (1, 2-3, 5 km)
Smith and Jonas, ‘96, ‘97 Quante et al., ‘96 Demoz et al., ‘96 Gultepe and Starr, ‘95 Starr et al., ‘92 Starr and Cox, ‘85 GCM sub-grid inhomogeneity treatment
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The Large Eddy Model The LEM: 2D mode (100 km domain)
100 m horizontal resolution, 125 m vertical resolution 3 phase microphysics with dual moment ice/snow Rigid top and bottom Periodic lateral boundary conditions Fu-Liou radiation model
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Initial Profiles UK LES CRM
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IWC Time Evolution
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Cellular Development
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Length-scales of inhomogeneity
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Inhomogeneity and Cloud Depth
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Spectral Dependence
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Radiative Heating Profiles
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Cloud Lifetime
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Radiation and Latent Stability Numbers
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Instability (Rad. Influenced)
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Instability (No Rad.)
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Previous studies Wide range of results between cirrus models even for simple idealised case studies (Starr et al 2000). There are few comparisons of CRM results with observations for ice clouds. In existing comparisons modelled inhomogeneity can be much less than observed (eg Benedetti and Stephens 2001)
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Observed and LEM profiles
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Ice Water Contents (IWCs)
Radar LEM Approx km
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Effects of shear on sub-grid variability
9.25 km 8.25 km Orange is with shear, Black is without (all are over 50 km in the horizontal and 375 m in the vertical). Ice water content Total Water Content
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Effect of shear on vertical correlation of IWC
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Motivation Question: By neglecting 3D radiative transfer, are we missing an important influence on cloud evolution or can it be neglected?
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PPA and ICA/IPA Radiaton
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Monte Carlo Radiation
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Radiative smoothing scale
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Inhomogeneous cirrus layers
MC IPA
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Inhomogeneous cirrus layers
Rmc=0.219 R4s=0.225 Rnr=0.196
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Inhomogeneous cirrus layers
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Finite cirrus layer MC IPA
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Finite cirrus layer Rmc=0.423 R4s=0.396 Rnr=0.372
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Finite cirrus layer
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Summary/Conclusions Radiative effects are important for cirrus
Inhomogeneous stratiform layers: 2-3% Finite, inhomogeneous cirrus clouds: 6-7%
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Further Work Realistic clouds (Chilbolton,FIREII, Emerald1, Crystal Face) with various geometries Longer duration simulations Quantify effects on cloud microphysics Effect for PDFs? Understand 3D radiation in presence of other effects, like shear Other cloud types
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[G. Heymsfield] Tropical Anvils
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Inhomogeneous layer
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Finite cirrus layer
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16 July C-F Anvil Mission P. Lawson D. Baumgardner A. Heymsfield
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Observations:. Supersaturation Frequently Observed in the Upper
Observations: Supersaturation Frequently Observed in the Upper Troposphere [J. Smith, A. Anderson, P. Bui] We observe supersaturation both in clear air and in the presence of cirrus.
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Observed and LEM profiles
Reading, Dec 8
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Microphysics
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Effects of shear on sub-grid variability
9.25 km 8.25 km Orange is with shear, Black is without (all are over 50 km in the horizontal and 375 m in the vertical). Ice water content Total Water Content
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Effects of shear on sub-grid variability
6.5 km 4.7 km Orange is with shear, Black is without (all are over 50 km in the horizontal and 375 m in the vertical). Ice water content Total Water Content
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Effect of shear on vertical correlation of IWC
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Conclusions Further Work
Distributions of modelled IWCs and total water contents are well described by beta functions. Shear tends to decrease the variance in IWC and total water contents. The decorrelation of IWC with height is initially linear (as suggested in Hogan and Illingworth 2002). At larger vertical separations correlations tend to be zero for zero shear and are more complex for inhomogenous clouds with large wind-shears. Further Work Improve the initialisation of the LEM. Study a better observed case (with aircraft observations). (Acknowledgements: Robin Hogan, Reading University)
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