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Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity

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. Outline.  Introduction

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Large-eddy simulation of stratocumulus – cloud albedo and cloud inhomogeneity

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  1. 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. 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 - Parameterizationof cloud liquid water variability Summary

  3. Atmospheric boundary-layer clouds simulated with large-eddy models: shallow cumulus and stratocumulus deep convection shallow cumulus stratocumulus

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

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

  6. Stratocumulus cloud albedo: example homogeneous stratocumulus cloud layer cloud layer depth = 400 m effective cloud droplet radius = 10 mm optical depth t = 25

  7. Real clouds are inhomogeneous Stratocumulus albedo from satellite

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

  9. Cloud albedo in a weather forecast or climate model inhomogeneous albedo homogeneous albedo Decrease optical thickness: Cahalan et al (1994): c = 0.7 (FIRE I observations) teffective tmean

  10. 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 (ql=q-Lv/cp qliq , equivalent to sl=cpT+gz-Lv/cp qliq ) 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. Inhomogeneity factor c computed from all hourly 3D cloud fieldsfor fixed solar zenith angle q=530 c > 0.7 (value used in some weather and climate models) c depends on the (optical depth) liquid water path variance

  13. Total water (qt) and liquid water (ql) PDFs liquid water total water Differences in PDFs: temperature effect (Clausius-Clapeyron)

  14. Positive temperature (T) and total water (qt) correlation:more moisture -> warmer Physical explanation for ql'≈0: Approximate balance entrainment warming and longwave radiative cooling

  15. Model proposal based on LES results:From total water fluctuations to liquid water path fluctuations ql' ≈ 0  b = 0.4 T' ≈ 0  b = 1

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

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

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

  19. Aim: model cloud liquid water path variance RACMO

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

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

  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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