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Veldhoven, 2009. 1 Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity Stephan de Roode (1,2) & Alexander Los (2)

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Presentation on theme: "Veldhoven, 2009. 1 Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity Stephan de Roode (1,2) & Alexander Los (2)"— Presentation transcript:

1 Veldhoven, Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity Stephan de Roode (1,2) & Alexander Los (2) (1) Clouds, Climate and Air Quality, Multi-Scale Physics, Department of Applied Sciences, TU Delft (2) KNMI, De Bilt

2 Veldhoven, Outline  Introduction - Physical processes in stratocumulus  Research question - The stratocumulus "albedo bias" effect  Large-eddy simulation of stratocumulus as observed during the FIRE I experiment - Stratocumulus cloud albedo - Thermodynamic cloud structure  Synthesis of LES results - Parameterization of cloud liquid water variability  Summary

3 Veldhoven, Atmospheric boundary-layer clouds simulated with large-eddy models: shallow cumulus and stratocumulus shallow cumulusstratocumulusdeep convection

4 Veldhoven, Longwave radiative cooling drives turbulence at the stratocumulus cloud top Cloud top cooling

5 Veldhoven, Turbulence: Entrainment of warm and dry air at the stratocumulus cloud top entrainment: turbulent mixing of free atmosphere air into the boundary layer

6 Veldhoven, Stratocumulus cloud albedo: example cloud layer depth = 400 m effective cloud droplet radius= 10  m optical depth  = 25 homogeneous stratocumulus cloud layer

7 Veldhoven, Real clouds are inhomogeneous Stratocumulus albedo from satellite

8 Veldhoven, Albedo for an inhomogeneous cloud layer Redistribute liquid water: optical depths  = 5 and 45 inhomogeneous stratocumulus cloud layer mean albedo = 0.65 < 0.79

9 Veldhoven, Decrease optical thickness: Cahalan et al (1994):  = 0.7 (FIRE I observations) 9 Cloud albedo in a weather forecast or climate model  effective  mean inhomogeneous albedo homogeneous albedo

10 Veldhoven, DALES: Dutch Atmospheric Large-Eddy Simulation Model Dry LES code (prognostic subgrid TKE, stability dependent length scale) Frans Nieuwstadt (KNMI) and R. A Brost (NOAA/NCAR, USA) Radiation and moist thermodynamics (  l =  -L v /c p q liq, equivalent to s l =c p T+gz-L v /c p q liq ) Hans Cuijpers and Peter Duynkerke (KNMI/TU Delft, Utrecht University) Parallellisation Matthieu Pourquie (TU Delft) Drizzle Margreet Van Zanten and Pier Siebesma (UCLA/KNMI) Atmospheric Chemistry Jordi Vila (Wageningen University) Land-surface interaction, advection schemes Chiel van Heerwaarden (Wageningen University) Particle dispersion, numerics Thijs Heus and Harm Jonker (TU Delft)

11 The diurnal cycle of stratocumulus during FIRE I - Observations (  ) and LES results (lines)

12 Veldhoven, Inhomogeneity factor  computed from all hourly 3D cloud fields for fixed solar zenith angle  =53 0   > 0.7 (value used in some weather and climate models)   depends on the (optical depth) liquid water path variance

13 Veldhoven, Total water (q t ) and liquid water (q l ) PDFs Differences in PDFs: temperature effect (Clausius-Clapeyron) liquid water total water

14 Veldhoven, Positive temperature (T) and total water (q t ) correlation: more moisture -> warmer Physical explanation for  l '≈0: Approximate balance entrainment warming and longwave radiative cooling

15 Veldhoven, Model proposal based on LES results: From total water fluctuations to liquid water path fluctuations   l ' ≈ 0   = 0.4   ' ≈ 0   = 1

16 Veldhoven, Model proposal based on LES results: Compare computed to reconstructed liquid water path PDF

17 Veldhoven, Summary 1. LES results: - q l ' =  q t ',  ≈ 0.4 (and not  =1) 2. Parameterization of the variance of LWP and  : 3. Outlook: Weather and climate models will use liquid water path variance rather than prescribing a constant correction factor for the cloud albedo

18 LES thermodynamic fields Is temperature important for liquid water fluctuations?

19 Aim: model cloud liquid water path variance RACMO

20 Factor  depends on the optical depth variance (   )

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

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

23 Effect of domain size


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