1. Simulating Stratocumulus clouds sensitivity to representation of: Drizzle Cloud top entrainment Cloud-Radiation interaction Large scale subsidence Vertical Resolution Colin G Jones. SMHI Norrköping S601 76 Sweden Email: Colin.Jones@smhi.seColin.Jones@smhi.se

2. Stratocumulus Clouds are an important Component of the Climate System (esp. Sub-tropical oceanic Stratocumulus) Also an important forecast parameter Sc impact strongly on: Downwelling Solar Radiation Downwelling Long wave Radiation (esp winter) Often (but not always) produce Drizzle Major controls on the above are: Cloud Amount Cloud water content (LWP) Droplet size distribution & effective radius Cloud top entrainment.

3. Persistent Sc Clouds aften have a strong Diurnal Cycle (LWP minimum~in mid-afternoon). Failure to capture this leads to large errrors in surface solar radiation flux

4. Stratocumulus Clouds are ubiquitous & complex

5. Solar radiation heats cloud & surface Longwave radiation at cloud top/base induces turbulence Cloud top entrainment of warm/dry air dilutes cloud water Large scale subsidence counters cloud Thickening and warms & dries cloud top. Turbulent transport of heat & moisture source term for cloud Drizzle depletes cloud water

6. EUROCS: Diurnal Cycle of Stratocumulus Based of July 17-18th 1987 FIRE case Over San Nicolas Island off California. Single Column & Large Eddy Simulation Intercomparsion. LES models assumed case was non-precipitating This may NOT be a valid assumption e.g. DYCOMS2 (Stevens etal 2003) observed frequent drizzle rates ~0.5-1mm/day from Californian 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.

7. LWP sensitive to drizzle i.e. Auotconversion & subsequent collection of cloud droplets to rain drops Rasch & Kristjansson J.Climate 1999 Q l is cloud liquid water content N is assumed droplet density of cloud droplets N=400 cm -3 (over land in PBL)…polluted air N=(40-150) (over sea & land above PBL)..clean air effc=collection efficiency of cloud droplets pptloc=local incloud precip rate(mm/day) critpr=1.0 mm/day Khairoutdinov & Kogan (MWR 2000) Multiple regression to explicit collection models:

8. 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 scale relative humidity variablity (probably resolution dependent)

9. K-1 K P in (k-1) Microphsyics (ITER=1) Pin(k-1) + Microphysics (ITER=1) Microphsyics (ITER=2) Pin(k-1) + Microphysics (ITER=1) + (ITER2) K+1 K-1 K Pin(k-1)=0 Autoconversion Autoconversion + Collection Parameterisation developed for coarse vertical resolution GCMs and includes a subgrid scale vertical parameterisation allowing initial 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 term is 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 scheme is not needed and the effect should be reduced. Vertical resolution sensitivty in cloud microphysics

10. Small changes in drizzle rates can greatly effect LWP which lies in sensitive range for cloud albedo changes. Solar flux at surface increased by ~50Wm -2

11. LWP sensitivity to drizzle reduced at higher vertical Resolution due to increased cloud top mixing and Dilution of cloud water. Precip becomes noisy with Increased vertical resolution.

12. Sensitivity of LWP and Solar flux to parameterised Cloud fraction and assumed number droplet concentration capnc=(150, 40) spread in Khairoutdinov & Kogan 2000) HIR40L with no parameterisation of cloud top entrainment

13. Statistical Cloud schemes and linkage to turbulence schemes using moist conserved variables Moist TKE schemes naturally lend themselves to use of  l and q t (conserved in non-precipitating moist adiabatic mixing). These schemes need a cloud fraction to determine the incloud buoyancy flux Statistical cloud schemes naturally lend themselves to such an approach. Cloud fraction & water content can be diagnosed directly from  l and q t Assume  l and q t have an some distirbution about the mean grid box value (Gaussian and/or skewed) Diagnose cloud fraction from normalised saturation Deficit (Q 1 ) and  s (variance of  l and q t about mean) Chaboureau & Bechtold (2002) derived cloud fraction And q l from CRM simulations as a function of Q 1 &  s

14. Q 1 is normalised saturation deficit  s is variance of (aq l -bT l ). These terms link cloud & cloud water terms to model grid box variables & variance of these variables derived through turbulence scheme L tke is a diagnosed length scale from the moist turbulence scheme (cloud buoyancy included). Hence subgrid scale aspect of 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

15. Single column model overestimates LWP compared to LES. But they (perhaps correctly) precipitate. Total LWP may therefore be more correct compared to total observed LWP (non-precipitating LWP from models Maximises at ~200 g/m2)

16. strong turbulent entrainment at cloud top Turbulence scheme represents subgrid mixing by: Replace at cloud top with parameterised entrainment velocity Cloud Top Entrainment E is evaporative enhancement factor  m b average buoyancy of cloudy & clear mixtures

17. Parameterised entrainment increases mixing of dry, warm air into cloud. Dilutes cloud and reduces LWP and drizzle rates.(ie drizzle sensitive to entrainment at cloud top)

18. Increased vertical resolution leads to more mixing at cloud top directly from turbulence scheme. Analagous impact to parameterised entrainment on LWP & drizzle. need for parameterised entrainment a function of vertical resolution. Increased mixing also reduced LWP by day and greatly impacts surface solar flux.

19. Reduced incloud absorption of solar radiation allows larger LWP by day and greatly improves surface solar flux. Solar flux VERY sensitive to LWP.

20. Source: Martin Koehler ECMWF

21. Large scale subsidence depresses cloud thickness and LWP and drizzle rates. Strong impact on surface solar radiation flux

22. 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 thickens by a model level.

23. HIR150 + 60s timestep. Respective cloud fraction is used in the moist turbulence scheme. In XUCLD & RHCLD cloud water prognosed by condensation routine. Timestep plot, noise related to cloud growth by a model level.

24. Timestep level nosie not visible in 3 hour means. but will affect model stability.

25. XUCLD & RHCLD run at 40L with 60s timestep Reduced evidence of noise. Microphysics is very sensitive to vertical resolution. Moist turbulence approach exhibits greatly reduced sensitivity

26. The overall representation of Stratocumulus cloud is sensitive to model vertical resolution. This is probably as important as horizontal resolution. As vertical resolution increases, cloud top mixing simulated by the turbulence scheme increases. This reduces cloud water and as a consequence drizzle 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 sensitive to LWP. This will be true to a lesser extent winter for longwave radiation via cloud emissivity LWP is very sensitive to the representation of drizzle. This sensitivity decreases as cloud top entrainment increases (i.e. As vertical resolution increases) Daytime LWP is sensitive to the fraction of solarf lux that is aborbed in the cloud layer. This both evaporates the cloud and decouples the cloud from the surface. Moist Turbulence coupled to a statistical cloud scheme that diagnoses cloud fraction & liquid/ice water seems a promising and numerically stable manner to simulate stratocumulus clouds at high (vertical)resolution. Conclusions

27. Diurnal Cycle of Cloud Water for FIRE Stratocumulus Case

28. Diurnal Cycle of TKE and cloud fraction (isolines) For FIRE Stratocumulus case

29. Diurnal Cycle of relative humidity for FIRE Stratocumulus case

30. Diurnal Cycle of cloud fraction for FIRE Stratocumulus case