Stratocumulus

1. Stratocumulus Adrian Lock

2. Overview • Stratocumulus climatology and recent changes in its representation in the Met Office climate model • Summary of ‘important’ aspects of the boundary layer parametrization • Other issues • Cloud scheme complexity • Resolution • GCM dynamics and grid staggering

3. Tropopause Inversion Cloud base Sub-tropics tropics Subsidence Inversion Entrainment Fq,T Fq,T Increasing SST Borrowed from Pier Siebesma

4. Stratocumulus climatology • Extensive cloud cover in the subtropics • Significant SW radiative impact (cooling the planet) • Errors lead to • SST errors in coupled climate models • surface temperature errors over land • Latest IPCC round includes an almost completely new Met Office model, HadGEM1

5. Climate model JJA total cloud amount HadGAM1(Released 2004) HadGAM1 - HadAM3(Released 1996) ISCCP HadGAM1 - ISCCP

6. Climate model improvement in low level cloud amount ISSCP HADAM3 (~1996) HadGAM1(~2004) • HadAM3 had excessive thick low cloud and too little thin • HadGAM1 very much better Thick (scale 0-20%) Intermediate (scale 0-40%)

7. GCM cross-section intercomparison (Siebesma et al 2004 and now GCSS) • GCMs, data from JJA 1998 • Cross-section from California to central Pacific • General underestimate of stratocumulus? • HadGAM1 not too bad • HadAM3 wouldn’t look too bad either HadGAM1 HadGAM1 total cloud cover California Central Pacific SSMI (all clouds) California HadGAM1 (low clouds) x x x x x x x x

8. Met Office GCM cloud fractionsEUROCS cross-section – 1998 JJA mean • HadGAM1 has more realistic boundary layer structure HadGAM1 (Released 2004) HadAM3 (Released 1996) Layer cloudfraction Convective cloud fraction ITCZ California ITCZ California

9. GPCI: SW radiation • More spread between models in radiation fluxes than in the clouds themselves • HadGAM1 has too much cloud in the Sc-Cu transition region HadGAM1 HadGAM1

10. LWP Diurnal cycle validation against satellite LWP kgm-2 • Wood et al (2002) analysed mean LWP diurnal cycle from satellite microwave measurements ( ) • Lock (2004) compared with HadGAM1 for the data from the EUROCS intercomparison (JJA in NE Pacific) HadGAM1: JJA mean LWP (kgm-2) x x Wood et al area x x x X EUROCS points

11. Validation against EPIC profiles • Comparison of HadGAM1 October mean profiles with mean profiles from EPIC for 16-21 October 2001 at (85W, 20S) • But HadGAM1 monthly mean LWP ~ 70gm-2 compared to the observed 150gm-2 HadGAM EPIC

12. Why the improvement from HadCM3 to HadGEM1? • Virtually everything is changed! • Doubled resolution (vertical and horizontal) • New dynamics • New Physics: • Microphysics • Cloud scheme (Smith + parametrized RHcrit) • Convection scheme (revised closure, triggering, CMT) • Radiation ~ the same?! • Plus new boundary layer scheme…

13. The new boundary layer parametrization in HadGEM1 • Old scheme (HadAM3) • Local Ri-dependent scheme • Kept for stable boundary layers in HadGEM1 • Non-local parametrization for unstable boundary layers • Specified (robust) profiles of turbulent diffusivities, for turbulence driven from the surface and/or cloud-top • Explicit BL top entrainment parametrization • Mass-flux convection scheme trigger based on boundary layer structure rather than local instability • BL mixes in subcloud layer, mass-flux in cloud layer • PBL scheme ~ a mixed layer model on a finite-difference grid

14. Mixed layer framework - motivation • Marine stratocumulus is often an equilibrium state arising from complicated interactions between many processes that are parametrized in GCMs • Need to replicate this balance within GCM parametrizations, often using schemes that currently operate independently • Simple conceptual framework: mixed layer model • turbulent mixing generally ensures that variables conserved under moist adiabatic ascent are close to uniform in the vertical. For example:

15. Observed profiles from stratocumulus Stevens et al (QJ,2003) Price (QJ, 1999)

16. Mixed layer model • Integrating over the boundary layer and assuming a discontinuous inversion gives the mixed layer model equations: where is the jump across the inversion and is the entrainment rate. • Mixed layer model is simple but effective • highlights the importance of entrainment

17. Entrainment efficiency (Stevens, 2002) • Cloud-top radiative cooling both cools the ML but also drives entrainment and so warms (and dries) it • Net effect of radiative cooling on ML evolution is therefore a strong function of the entrainment efficiency • Eg. for ‘minimal’ model: • Uncertain – parametrizations in the literature give widely different entrainment efficiencies (Stevens, 2002):

18. Entrainment efficiencies (Stevens, 2002) 0.5 0.4 • Differences in variability of entrainment efficiency as well as its magnitude • AL is noticeably weaker 0.9 0.8 0.7 0.7

19. Verification of entrainment parametrization against LES and observations LES Cloud free: = surface heated Smoke clouds: = radiatively cooled = surf heat + rad cool Water clouds: X = rad cool (no b.r.) + = rad cool + buoy rev = buoyancy reversal only Observations = Nicholls & Leighton,1986; Price, 1999; Stevens et al 2003 Actual entrainment rate (m/s) Parametrized entrainment rate (m/s)

20. Entrainmentparametrization in GCMs • Hard to parametrize on (coarse) GCM grids using traditional down-gradient diffusion because gradients at inversions are large and • K(Ri): local stability dependence is not relevant • K(z/zi): very sensitive to the definition of zi • K(TKE): accurate TKE evolution hard to resolve • Explicit parametrization requires a formulation for we • Uncertain • But you know what you are (or should be) getting

21. Numerical handling of inversions • All model processes (turbulence, radiation, LS advection) should be coupled to preserve mixed layer budgets • i.e., no spurious numerical transport across inversion(Stevens et al 1999, Lenderink and Holtslag 2000, Lock 2001, Grenier and Bretherton 2001, Chlond et al 2004) • Explicit BL top entrainment parametrization with flux-coupling • A subgrid inversion diagnosis takes tendencies from subsidence, radiation and turbulence and realistically distributes them between the mixed layer and the inversion grid-level – ‘inversion flux-coupling’ Free atmosphere ql profile (idealised mixed layer) x Entrainment x Real inversion (rises/falls) x GCM inversion grid-level (cools/warms) Subsidence x Mixed layer x

22. Diagnosis of subgrid mixed-layer model profile GCM cell-averages Subgrid profile • Identify ‘inversion grid-level’ • Extrapolate GCM profiles into the inversion grid-level • Assume a discontinous subgrid inversion • Calculate subgrid inversion height and strength X X X Inversion grid-level X Flux grid-levels X Parcel ascent vl = l( 1+rmqt )

23. Example calculation of grid-level entrainment fluxes (ignoring subsidence) X = GCM fluxes Radiative flux = mixed layer model fluxes • Subsidence fluxes across the inversion must be coupled similarly X X X Inversion grid-level X X X X X X X Flux grid-levels X Heat Fluxes

24. Impact of inversion treatment • Errors from not coupling fluxes amount to spurious additional entrainment • GCM: JJA mean liquid water path • SCM: Time-height contour plots of liquid water from simulations with subsidence > entrainment but ML budget (so cloud depth) stationary With inversion flux-coupling: With inversion flux-coupling Inversion height Impact of removing inversion flux-coupling: Without inversion flux-coupling

25. Equilibrium mixed layers – Stevens 2002 • Minimal model with entrainment efficiency of 0.5 • For this fixed entrainment efficiency, heading towards California (increasing divergence, reducing SST): • Strong response: zi decreases, wTsurf increases (contours) • Weaker response: LWP decreases, wqsurf decreases (grey-scales) • Minimal model not too far from reality Towards California

26. Mixed layer model sensitivity to entrainment efficiency • Minimal model with entrainment efficiency of 0.5 • Efficiency of 1: lower LWP, much reduced wTsurf Towards California

27. GCM cloud sensitivity to entrainment Std Met Office • Test GCM sensitivity to cloud-top entrainment • More active entrainment (either explicit or numerical) gives an equilibrium state with smaller surface heat fluxes and less stratocumulus (as in Stevens, 2002) • Removing inversion flux-coupling is more severe than doubling we 2 x we No flux coupling

28. Surface heat flux sensitivity to entrainment Standard Met Office • Expect spurious numerical entrainment to be • so increases as you approach the coast • Implies wT surf would reduce towards the coast • As it does if flux-coupling removed in Met Office GCM • As it does in other GCMs from EUROCS • Spurious or deliberate increase in parametrized entrainment efficiency? 2 x we No flux coupling

29. Entrainment efficiencies (Stevens, 2002) 0.5 0.4 • None of these proposed parametrizations show entrainment efficiency increasing ‘towards California’ 0.9 1.0 0.8 1.0 0.8 0.7 0.8 0.7

30. What about precipitation? • But AL is still a ‘weak’ entrainment parametrization • Is this being compensated for in HadGAM1 by a spuriously high drizzle rate? • Requires a drizzle climatology – satellite borne radar? HadGAM1 No obs!

31. Other issues • Cloud scheme • Prognostic vs diagnostic – does it matter? • Vertical resolution • Decoupling – structure within ‘mixed’ layers • New Dynamics

32. Other issues – cloud scheme complexity • Cloud-top height dependence on cloud scheme: HadGAM1 (Smith) PC2 (prognostic ql and CF)

33. Other issues – partial cloud cover Somewhere in between! CF~0.5 ? Near Hawaii CF~0.1 Convection scheme Near LA CF~1 BL scheme

34. AM2p12b ARPEGE Histograms of Cloud Cover JJA 1998 JJA 2003 all clouds low clouds (>700hPa) all clouds low clouds (>700hPa) CAM 3.0 number of events (%) GSM 0412 HadGAM RACMO2 Other issues – cloud scheme Cloud cover PDFs from GPCI • Medium cloud cover • Relatively low occurrence in HadGAM1 • Mixed layer model would break down • Possible cause of excessive cloud problems in Sc-Cu transition? Latitude HadGAM1: 0 Low cloud cover 1

35. Other issues • Cloud scheme • Vertical resolution • How much is sufficient? • Currently 38 levels ~280m at 1km ~ cloud thickness • Decoupling – structure within ‘mixed’ layers • New Dynamics

36. Lock (2001) Namibian Stratocumulus region31st March 2004 Model Level Striping in cloud fields not observed in albedo from GERB instrument (not shown)

37. Other issues • Cloud scheme • Vertical resolution • Decoupling – structure within ‘mixed’ layers • New Dynamics

38. Increasing the susceptibility to decoupling • EUROCS diurnal cycle of FIRE1 stratocumulus motivated changes to decoupling diagnosis (include subgrid cloudbase) • Operational testing (10 global forecast case studies) showed: • Reduced cloudiness, as expected • Significant improvement to tropical winds at 850hPa Change in total cloud cover Bias Control Revised scheme RMS speed error

39. Improved tropical winds hypothesis • Decoupling implies moister near surface implies reduced surface moisture fluxes in subtropical stratocumulus areas • Implies less moisture transported to tropics implies reduced tropical rainfall implies reduction in current model excessive tropical hydrological cycle implies beneficial reduction in trade wind strength • Implies mixed layer structure is important Change in total cloud cover Change in surface moisture flux

40. Other things • Cloud scheme • Vertical resolution • Decoupling – structure within ‘mixed’ layers • New dynamical formulation included: • Semi-implicit, semi-Lagrangian dynamics • Revised timestep (parallel calculation of ‘slow’ physics increments and BL diffusion coefficients) • Revised vertical grid staggering (better handling of normal modes and control of computational modes) • Showed a general improvement in low cloud • But no clean comparison (eg. with the same physics)

41. HadSM3 Cloud Response along GCSS Pacific Cross Section transect • Coupled atmosphere/slab-ocean models • Sc in HadSM4 (old dynamics) very different from HadGSM1 (new dynamics) • HadSM4 includes Lock et al (2000) scheme but not flux-coupling

42. Stratocumulus issues • Met Office model appears to have overcome the 1st order problem (it has stratocumulus-like clouds in some of the right places) but… • Entrainment • Still large spread in parametrizations (Stevens 2002) • Still large spread in LES (Duynkerke et al 2004 or Stevens et al 2005) • Is a special treatment of fluxes across inversions necessary in GCMs? • Need observed cloud-top height to validate NWP (eg. lidar or radar?) • Drizzle • Large term in mixed layer budget • How well is it represented in GCMs (lack of observed climatology)? • Role and parametrization of aerosols (cf land/sea split in NWP)? • Stabilising feedback on turbulent dynamics (Ackerman et al 2004)? • Mixed layer structure in subtropics is important for downwind deep convection in tropics • Beneficial impact from changes to decoupling • Testing revisions to non-local part of flux parametrization • How well-mixed are the wind profiles?

43. More parametrization issues • What level of cloud scheme complexity is necessary? • Smith scheme couples liquid water and cloud fraction too strongly • Is a prognostic scheme necessary for BL clouds? • Inhomogeneity – important for SW and drizzle • How to model partial cloud cover? • Massflux versus turbulent diffusion • Would unification help? • Communication would be easier (eg. Cu detraining into Sc) • Current massflux schemes seem incompatible with the top-down mixing predominant in stratocumulus • Debate is fuelled by a lack of understanding (limited case studies - need more extensive examination of parameter space) • If we knew (physically) how to parametrize BL clouds in general, wouldn’t the choice be made on numerical stability and accuracy?

44. Questions?

45. Stratocumulus forecasting • Important for forecasting surface temperatures • Case study of widespread low cloud over Europe in December 2004 (eg. 12Z on the 10th) Low cloud fraction in global UM T+48 x x Budapest Thanks to Martin Kohler

46. Stratocumulus forecasting 12Z 10th December Surface mixed layer depth (m) • Boundary layer depth evolution mimics the observed • General underestimate of boundary layer depth by ~100m (< grid size) • General cold moist bias • Analysis tends to ‘decouple’ Day of December 2004

47. Spin-up of cloud in forecast models • Significant underestimate of cloud cover in the analysis followed by spin-up, at all horizontal resolutions

48. + LW - CFMIP cloud feedback classes: Low positive class contributes most to uncertainty in sensitivity - SW +

49. Lidar versus IR retrieval – Wylie, from Internet!