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)

Length scales in the atmosphere Landsat 60 km 65km Large Eddy Simulation 10km ~mm~100m ~1  m-100  m Earth 10 7 m Courtesy Harm Jonker

Cloud dynamics 10 m100 m1 km10 km100 km1000 km10000 km turbulence  Cumulus clouds Cumulonimbus clouds Mesoscale Convective systems Extratropical Cyclones Planetary waves Large Eddy Simulation (LES) Model Cloud System Resolving Model (CSRM) Numerical Weather Prediction (NWP) Model Global Climate Model The Zoo of Atmospheric Models DNS mm Cloud microphysics

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

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

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

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

1 minute course on cloud thermodynamics Adiabatic clouds (clouds that do not mix with their environment) Conservation of energy Conservation of water s liq height q tot q saturation q liq temperature T

Cloud droplet size (condensational growth only) q liq  Condensation  too small droplet sizes for rain (R rain > 100  m)  Rain forms by droplet collisions  gravity and in-cloud turbulence  Collision efficiency  laboratory experiments and by Direct Numerical Simulation

More rain in the weekend? Mon-FridaySat-Sunday

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

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

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

Ensemble forecast with the ECMWF model: 50 simulations with perturbed initial conditions Edward Lorenz ( )

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

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

The playground for cloud physicists: Hadley circulation deep convectionshallow cumulusstratocumulus

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

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

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

atmospheretank (heat)tank (salt) Reynolds numberRe=10 8 Re=10 3 Prandtl number Pr=1Pr=10Pr=1000 computationally expensive Why different entrainment rates? SiteArchitectureMax nr cores usedGrid SARAIBM Power x 1024 x 768 CINECAIBM BCX/ x 2048 x 1024 LRZSGI Altix x 1536 x 768 JuelichBluegene32, x 3072 x 1536 DEISA: Distributed European Infrastructure for Supercomputing Applications resource allocation: 1.9M cpu-hr

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

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

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