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Simulation of Cloud Droplets in Parameterized Shallow Cumulus During RICO and ICARTT

Simulation of Cloud Droplets in Parameterized Shallow Cumulus During RICO and ICARTT Knut von Salzen 1 , Richard Leaitch 2 , Nicole Shantz 3 , Jonathan Abbatt 3 , Frederic Burnet 4 1 Canadian Centre for Climate Modelling and Analysis (CCCma) ‏ , EC, Victoria, Canada

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Simulation of Cloud Droplets in Parameterized Shallow Cumulus During RICO and ICARTT

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  1. Simulation of Cloud Droplets in Parameterized Shallow Cumulus During RICO and ICARTT Knut von Salzen1, Richard Leaitch2, Nicole Shantz3, Jonathan Abbatt3, Frederic Burnet4 1Canadian Centre for Climate Modelling and Analysis (CCCma)‏, EC, Victoria, Canada 2Climate Chemistry Measurements and Research, EC, Toronto, Canada 3Department of Chemistry, University of Toronto, Toronto, Canada 4CNRM/MGEI, Météo France, Toulouse, France Cloud-Aerosol Feedbacks and Climate (CAFC) research network

  2. ICARTT Experiment Goal: Study air quality, intercontinental transport, and radiation over North America and Europe. Location of Canadian experiment: Near Cleveland, Ohio Time: 2 flights available, August 3 & 16, 2004 Measurements: Canadian Convair 580, R. Leaitch et al. 1Hz Cloud & aerosol microphysics and chemistry

  3. Modelling Approach for Shallow Cumulus - Fundamental Components • Parameterizations for mixing ofthermodynamic properties. • Parameterizations for mixing of cloud droplets. • Microphysical model for aerosol and droplet growth (by condensation) for cloud core.

  4. Parameterization for Mixing of Thermodynamic Properties for Shallow Cumulus • Entraining plume model, based on continuity equations for mass, total water, energy, and momentum. • Idealized cumulus lifecycle: Variable cloud top heights and final detrainment. • Lateral and cloud-top mixing processes. • Non-homogenous clouds: Statistical distributions of thermodynamic properties consistent with mixing line. • Cloud-base closure based on simplified mixed layer TKE budget. • Recent improvements: Mixing probability and vertical velocity. von Salzen and McFarlane (2002)‏ von Salzen et al. (2005)‏ - see also talk by Francesco Isotta -

  5. Evidence for Mixing Line from Observations RICO RF06 ICARTT Ft12 ICARTT Ft21 Linear mixing for total water (rt) and moist static energy (h): Cloud core Cloud environment Composites of observations from different levels in the clouds. Dark colours refer to low, light colours to high levels. Crosses: dry samples; bullets: cloudy samples

  6. Simulated range Bullets: Mean Total Water Mixing Ratio Probability Distributions RICO RF06 ICARTT Ft12 ICARTT Ft21 Simulated Observed (cloud) Observed (clear-sky)

  7. Modelling Approach for Shallow Cumulus - Fundamental Components • Parameterizations for mixing ofthermodynamic properties. • Parameterizations for mixing of cloud droplets. • Microphysical model for aerosol and droplet growth (by condensation) for cloud core.

  8. Microphysical Aspects of Turbulent Mixing cloudy clear homogeneous ~ conserved thermodynamic tracer cloud droplet concentration inhomogeneous ~ liquid water intermediate fraction of environmental air

  9. Mixing line Independent columns Microphysical Aspects of Turbulent Mixing inhomogeneous intermediate cloud droplet volume homogeneous fraction of environmental air

  10. Microphysical Aspects of Turbulent Mixing RICO RF06 ICARTT Ft12 ICARTT Ft21 Composites of observations from different levels in the clouds. Dark colours refer to low, light colours to high levels in clouds. Lines refer to parameterizations. Bullets: FSSP96 Open circles: FSSP124

  11. Modelling Approach for Shallow Cumulus - Fundamental Components • Parameterizations for mixing ofthermodynamic properties. • Parameterizations for mixing of cloud droplets. • Microphysical model for aerosol and droplet growth (by condensation) for cloud core.

  12. 25 cm/s 50 cm/s 100 cm/s 200 cm/s Open circles: New model Bullets: Detailed parcel model updraft wind speed New Model for Nucleation and Growth of Droplets for Cloud‏ Core • Fully prognostic numerical solution of droplet growth equation(for condensation). • Efficient: Quasi-steady state approximation for supersaturation ► look-up tables. Few iterations for water and energy budgets. • Multi-component aerosol size distributions based on PLA method (von Salzen, 2005). • Vertical velocity, total water, and moist static energy from shallow cumulus scheme (cloud core conditions). Water-soluble organics in aerosol Water-insoluble organics in aerosol height (m) supersaturation (%) supersaturation (%)

  13. Modelling Approach for Shallow Cumulus - Fundamental Components • Parameterizations for mixing ofthermodynamic properties. • Parameterizations for mixing of cloud droplets. • Microphysical model for aerosol and droplet growth (by condensation) for cloud core.

  14. Simulated range Bullets: Mean Droplet Effective Radius – Intermediate Mixing RICO RF06 ICARTT Ft12 ICARTT Ft21 FSSP96 FSSP124 Simulated 500 cm-3 1000 cm-3 FFSSP adiabatic obs.

  15. Conclusions • Realistic representation of thermodynamic cloud properties for 3 flights from RICO and ICARTT. • Relatively simple convective plume model for cloud droplets, including model for prognostic droplet growth for cloud core and new mixing-line based parameterizations for mixing processes. • Broadening of droplet size probability distribution towards smaller sizes owing to increasing probability of diluted air away from cloud base for homogeneous and intermediate mixing. • Free parameter in parameterization for intermediate mixing based on fitting without accounting for turbulent mixing time scales yet. • No collision/coalescence yet. • Future research with focus on effects on climate effects in GCM.

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