1. BBOS meeting on Boundary Layers and Turbulence, 7 November 2008 De 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

2. 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

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

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

5. Albedo bias effect observed spatial variability in stratocumulus albedo

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

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

8. Factor diagnosed from all hourly 3D cloud fields for fixed solar zenith angle =53 0 factor > 0.7

9. Factor depends on the optical depth variance ( )

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

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

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 q t ' to LWP' l ' 0 = 0.4 ' 0 = 1

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

18. Effect of domain size

19. 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 - q l ' = q t ', 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