Simulating Stratocumulus cloudssensitivity to representation of:
DrizzleCloud top entrainment
Cloud-Radiation interactionLarge scale subsidence
Vertical Resolution
Colin G Jones.SMHI
Norrköping S601 76Sweden
Email: [email protected]
Stratocumulus Clouds are an importantComponent of the Climate System (esp. Sub-tropical oceanic Stratocumulus)
Also an important forecast parameterSc impact strongly on:
Downwelling Solar RadiationDownwelling Long wave Radiation (esp winter)Often (but not always) produce Drizzle
Major controls on the above are:
Cloud AmountCloud water content (LWP)Droplet size distribution & effective radiusCloud top entrainment.
Persistent Sc Clouds aften have a strong DiurnalCycle (LWP minimum~in mid-afternoon). Failureto capture this leads to large errrors in surface solarradiation flux
Stratocumulus Clouds are ubiquitous & complex
Solar radiation heats cloud & surface
Longwave radiation at cloud top/baseinduces turbulence
Cloud top entrainment of warm/dry airdilutes cloud water
Large scale subsidence counters cloudThickening and warms & dries cloud top.
Turbulent transport of heat & moisturesource term for cloud
Drizzle depletes cloud water
EUROCS: Diurnal Cycle of StratocumulusBased of July 17-18th 1987 FIRE caseOver San Nicolas Island off California.
Single Column & Large Eddy SimulationIntercomparsion.
LES models assumed case was non-precipitatingThis may NOT be a valid assumption e.g. DYCOMS2 (Stevens etal 2003) observed frequent drizzle rates ~0.5-1mm/day fromCalifornian Sc clouds.
LES may 1. Underestimate LWP 2. Get right LWP due to excess entrainment 3. N.B. Observed LWP will be for non-precipitating clouds. Total LWP will be higher and increased LWP within precipitating clouds WILL increase cloud albedo.
LWP sensitive to drizzlei.e. Auotconversion & subsequent collection
of cloud droplets to rain dropsRasch & Kristjansson J.Climate 1999
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Khairoutdinov & Kogan (MWR 2000)Multiple regression to explicit collection models:
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Slingo QJRMS 1987
Xu & Randall MWR 1996
Cloud Fraction Parameterisation
Both schemes only activate if RH(k)>RH(k)crit
This is the link of cloud onset to subgrid scalerelative humidity variablity (probably resolutiondependent)
K-1
K
Pin(k-1)
Microphsyics (ITER=1)
Pin(k-1) + Microphysics (ITER=1)
Microphsyics (ITER=2)
Pin(k-1) + Microphysics (ITER=1) + (ITER2)
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Autoconversion
Autoconversion + Collection
Parameterisation developed for coarse vertical resolution GCMsand includes a subgrid scale vertical parameterisation allowinginitial rain water production in a vertical layer (K) to influence further production of rain water in the same vertical layer (K) i.e. two iterations of the condensate to precipitation termis performed per vertical layer. All newly formed precipitation in 1st iteration is seen as an input to layer K in the 2nd iteration.
As vertical resolution increases the subgrid scale vertical schemeis not needed and the effect should be reduced.
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Vertical resolution sensitivty in cloud microphysics
Small changes in drizzle rates can greatly effectLWP which lies in sensitive range for cloud albedo changes. Solar flux at surface increased by ~50Wm-2
LWP sensitivity to drizzle reduced at higher verticalResolution due to increased cloud top mixing andDilution of cloud water. Precip becomes noisy withIncreased vertical resolution.
Sensitivity of LWP and Solar flux to parameterisedCloud fraction and assumed number droplet concentrationcapnc=(150, 40) spread in Khairoutdinov & Kogan 2000)HIR40L with no parameterisation of cloud top entrainment
Statistical Cloud schemes and linkage to turbulenceschemes using moist conserved variables
Moist TKE schemes naturally lend themselvesto use of l and qt (conserved in non-precipitatingmoist adiabatic mixing). These schemes need a cloudfraction to determine the incloud buoyancy flux
Statistical cloud schemes naturally lend themselvesto such an approach. Cloud fraction & water contentcan be diagnosed directly from l and qt
Assume l and qt have an some distirbution aboutthe mean grid box value (Gaussian and/or skewed)Diagnose cloud fraction from normalised saturationDeficit (Q1) and s (variance of l and qt about mean)
Chaboureau & Bechtold (2002) derived cloud fractionAnd ql from CRM simulations as a function of Q1 & s
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Q1 is normalised saturation deficit s is varianceof (aql-bTl). These terms link cloud & cloud waterterms to model grid box variables & varianceof these variables derived through turbulence scheme
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Ltke is a diagnosed length scale from the moist turbulence scheme (cloud buoyancy included). Hence subgrid scale aspectof cloud & cloud water evolution linked directly to turbulence.Promising framework for high resolution models.**No arbitrary thresholds for cloud onset
s parameterised using turbulence length scale
Single column model overestimates LWP compared toLES. But they (perhaps correctly) precipitate. TotalLWP may therefore be more correct compared to totalobserved LWP (non-precipitating LWP from modelsMaximises at ~200 g/m2)
strong turbulententrainment at cloud top
zgKw mhmh
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Turbulence scheme represents subgrid mixing by:
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Replace at cloud top with parameterised entrainment velocity
Cloud Top Entrainment
E is evaporative enhancement factormb average buoyancy of cloudy & clear mixtures
Parameterised entrainment increases mixing of dry, warmair into cloud. Dilutes cloud and reduces LWP anddrizzle rates.(ie drizzle sensitive to entrainment at cloud top)
Increased vertical resolution leads to more mixing atcloud top directly from turbulence scheme. Analagous impact to parameterised entrainment on LWP & drizzle.need for parameterised entrainment a function ofvertical resolution. Increased mixing also reduced LWP by day and greatly impacts surface solar flux.
Reduced incloud absorption of solar radiation allowslarger LWP by day and greatly improves surface solarflux. Solar flux VERY sensitive to LWP.
Source: Martin Koehler ECMWF
Large scale subsidence depresses cloud thickness andLWP and drizzle rates. Strong impact on surface solarradiation flux
HIR150 level model (~25m in PBL) with moist turbulence & statistical cloud scheme and 60 second timestep. Results plotted every timestep. Small hops occur when cloud thickensby a model level.
HIR150 + 60s timestep. Respective cloud fraction isused in the moist turbulence scheme. In XUCLD &RHCLD cloud water prognosed by condensationroutine. Timestep plot, noise related to cloud growthby a model level.
Timestep level nosie not visible in 3 hour means.but will affect model stability.
XUCLD & RHCLD run at 40L with 60s timestepReduced evidence of noise. Microphysics is very sensitive to vertical resolution. Moist turbulence approach exhibits greatly reduced sensitivity
The overall representation of Stratocumulus cloudis sensitive to model vertical resolution. This isprobably as important as horizontal resolution.
As vertical resolution increases, cloud top mixingsimulated by the turbulence scheme increases.This reduces cloud water and as a consequencedrizzle rates decrease and surface SW flux increases
Stratocumulus LWP lies in the critical range~50-300 g/m2 where cloud albedo changes dramatically for a small LWP change. Hence simulated surface solar fluxes are very sensitiveto LWP. This will be true to a lesser extent winterfor longwave radiation via cloud emissivity
LWP is very sensitive to the representation of drizzle.This sensitivity decreases as cloud top entrainmentincreases (i.e. As vertical resolution increases)
Daytime LWP is sensitive to the fraction of solarf luxthat is aborbed in the cloud layer. This both evaporatesthe cloud and decouples the cloud from the surface.
Moist Turbulence coupled to a statistical cloud schemethat diagnoses cloud fraction & liquid/ice water seems a promising and numerically stable manner to simulate stratocumulus clouds at high (vertical)resolution.
Conclusions
Diurnal Cycle of Cloud Water for FIRE Stratocumulus Case
Diurnal Cycle of TKE and cloud fraction (isolines)For FIRE Stratocumulus case
Diurnal Cycle of relative humidity forFIRE Stratocumulus case
Diurnal Cycle of cloud fraction for FIRE Stratocumulus case
