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Modeling Clouds and Climate: A computational challenge

Modeling Clouds and Climate: A computational challenge. Stephan de Roode Clouds, Climate & Air Quality Multi-Scale Physics (MSP), Faculty of Applied Sciences with contributions from Harm Jonker (MSP) and Pier Siebesma (KNMI,MSP). Large Eddy Simulation 10km. Landsat 60 km 65km.

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Modeling Clouds and Climate: A computational challenge

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  1. Modeling Clouds and Climate:A computational challenge Stephan de Roode Clouds, Climate & Air Quality Multi-Scale Physics (MSP), Faculty of Applied Sciences with contributions from Harm Jonker (MSP) and Pier Siebesma (KNMI,MSP)

  2. Large Eddy Simulation 10km Landsat 60 km 65km ~1mm-100mm ~mm ~100m Length scales in the atmosphere Earth 107 m Courtesy Harm Jonker

  3. The Zoo of Atmospheric Models Cloud dynamics mm 10 m 100 m 1 km 10 km 100 km 1000 km 10000 km Cloud microphysics  turbulence Cumulus clouds Cumulonimbus clouds Mesoscale Convective systems Extratropical Cyclones Planetary waves DNS Subgrid Large Eddy Simulation (LES) Model Cloud System Resolving Model (CSRM) Numerical Weather Prediction (NWP) Model Global Climate Model

  4. ~1mm-100mm ~mm Rain and Radiation Observed cloud droplet spectrum drizzle drops cloud water aircraft observations during ASTEX, Duynkerke et al., 1999

  5. 1 minute course on cloud thermodynamics Adiabatic plume (does not mix with its environment) Conservation of energy

  6. 1 minute course on cloud thermodynamics Adiabatic plume (does not mix with its environment) Conservation of energy height s temperature T Rising plume

  7. 1 minute course on cloud thermodynamics Adiabatic plume (does not mix with its environment) Conservation of energy Conservation of water height qsaturation s qtot temperature T Rising plume

  8. 1 minute course on cloud thermodynamics Adiabatic clouds (clouds that do not mix with their environment) Conservation of energy Conservation of water qsaturation height sliq qtot qliq temperature T

  9. Cloud droplet size (condensational growth only)  Condensation  too small droplet sizes for rain (Rrain > 100 mm)  Rain forms by droplet collisions  gravity and in-cloud turbulence  Collision efficiency  laboratory experiments and by Direct Numerical Simulation qliq

  10. More rain in the weekend? Mon-Friday Sat-Sunday

  11. More rain in the weekend? Mon-Friday Sat-Sunday "weekdays" "weekend" Sat-Sunday? Fewer but larger droplets lead to more a more efficient formation of rain. Some investigations suggests a weak correlation between day of the week and precipitation, other ones do not.

  12. Droplet concentration and Radiation:"Indirect" aerosol effect Cloud albedo (reflectivity) depends on cross sectional area A of cloud droplets having a concentration N

  13. Feedback effects in a changing climate Cloud feedback Surface albedo feedback Water vapor feedback Radiative effects only Dufresne & Bony, Journal of Climate 2008

  14. Ensemble forecast with the ECMWF model: 50 simulations with perturbed initial conditions Edward Lorenz (1917-2008) http://www.knmi.nl/exp/pluim/vijftiendaagse/index.html

  15. Assess uncertainty in global temperature change due to uncertainties in parameterization coefficients/switches Murphy et al. 2004, Nature

  16. Uncertainty in cloud lateral mixing is identified as a major contributor to the large spread in the PDF Siebesma & Holtslag ‘96 Murphy et al. 2004, Nature current PhD project: LES of deep convection (Steef Boing)

  17. The playground for cloud physicists: Hadley circulation deep convection shallow cumulus stratocumulus

  18. Atlantic Stratocumulus to cumulus Transition EXperiment (ASTEX) LES, 1995 LES, 1999 De Roode and Duynkerke, 1997 64x64x60 grid points simulation time: 3 hours runs were done on a CRAY supercomputer 2010: run full Lagrangian transition (40 hours) on 256x256x128 grid points

  19. Future Sea water temperature: T+DT  enhanced surface evaporation EU Cloud Intercomparison, Process Study and Evaluation Project (EUCLIPSE) Negative Feedback? Present Positive Feedback? Entrainment drying dominates moisture tendency Sea water temperature: T

  20. Entrainment in a water tank (Harm Jonker's laboratory) Convection driven by a salinity flux at the surface Finding: considerable less top entrainment than in LES models

  21. Why different entrainment rates? DEISA: Distributed European Infrastructure for Supercomputing Applications resource allocation: 1.9M cpu-hr

  22. (potential) Temperature animation Animation of the temperature (Harm Jonker)

  23. The importance of large computations (Harm Jonker) Prandtl-number: range LES and observations atmosphere Top entrainment efficiency A Re number must be really large before fluid-properties can be neglected

  24. Outlook Large Eddy Simulation of clouds + Large domains and fine grid resolution + Long simulations (diurnal cycle, equilibrium solutions) + Exploration of parameter space and its effect on cloud transitions (surface temperature, inversion strength, subsidence etc.) + Rate of turbulent mixing across cloud interfaces (entrainment/detrainment in shallow and deep convection) Postprocessing - giant data sets are produced

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