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Stephan de Roode (1,2) & Alexander Los (2)

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.

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Stephan de Roode (1,2) & Alexander Los (2)

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  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

  2. 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 Outline

  3. Albedo for a homogeneous cloud layer homogeneous stratocumulus cloud layer cloud layer depth = 400 m cloud droplet size = 10 mm optical depth t = 25 albedo = 0.79

  4. Albedo for a inhomogeneous cloud layer mean albedo in homogeneous stratocumulus cloud layer cloud layer depth = 400 m cloud droplet size = 10 mm optical depth t = 5 and 45, mean = 25 mean albedo = 0.65 < 0.79

  5. Albedo bias effect observed spatial variability in stratocumulus albedo

  6. homogeneous albedo Albedo for a inhomogeneous cloud layer mean albedo inhomogeneous stratocumulus cloud layer teffective tmean Simple parameterization of the inhomogeneity effect: Inhomogeneity constant: c = 0.7 (Cahalan et al. 1994)

  7. The diurnal cycle of stratocumulus during FIRE I (Cahalan case)LES results

  8. Factor c diagnosed from all hourly 3D cloud fieldsfor fixed solar zenith angle q=530 factor c > 0.7

  9. Factor c depends on the optical depth variance (st)

  10. Analytical results for the inhomogeneity factor cAssumption: Gaussian optical depth distribution c isolines c not smaller than ~ 0.8

  11. Aim: model cloud liquid water path variance RACMO

  12. LES fields Is temperature important for liquid water fluctuations?

  13. total humidity-liquid water PDFs liquid water total water Differences in PDFs: temperature effect (Clausius-Clapeyron)

  14. Temperature-humidity correlations

  15. Vertical structure of fluctuations In a cloudy subcolumn the mean liquid water fluctuation can be approximated to be constant with height

  16. Model: from qt' to LWP' ql' ≈ 0  b = 0.4 T' ≈ 0  b = 1

  17. PDF reconstruction from total humidity fluctuations in the middle of the cloud layer

  18. Effect of domain size

  19. 1. Why did Cahalan et al. (1994) found much lower values for the inhomogeneity factor c? - They used time series of LWP 2. In stratocumulus ql fluctuations are typicall small - ql' = bqt' , b ≈ 0.4 3. Parameterizations for the variance of LWP and t - compute total water variance according to Tompkins (2002) 4. Current ECMWF weather forecast model uses LWP variance for McICA approach Conclusion

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