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Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.

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Presentation on theme: "Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University."— Presentation transcript:

1 Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University of Reading

2 Overview Part 1: Why cant models simulate mixed-phase altocumulus clouds? –These clouds are potentially a key negative feedback for climate –Getting these clouds right requires the correct specification of turbulent mixing, radiation, microphysics and sub-grid distribution –We use a 1D model and long-term cloud radar and lidar observations Part 2: Can models simulate boundary-layer type, and hence the associated mixing and clouds? –Important for pollution transport and evolution of weather systems –We use long-term Doppler lidar observations to evaluate the scheme in the Met Office model

3 Mixed-phase altocumulus clouds Small supercooled liquid cloud droplets –Low fall speed –Highly reflective to sunlight –Often in layers only m thick Large ice particles –High fall speed –Much less reflective for a given water content

4 Mixed-phase cloud radiative feedback Decrease in subtropical stratocumulus –Lower albedo -> positive feedback on climate Change to cloud mixing ratio on doubling of CO2 –Tsushima et al. (2006) Increase in polar boundary-layer and mid-latitude mid-level clouds –Clouds more likely to be liquid phase: lower fall speed and more persistent –Higher albedo -> negative feedback –But this result depends on questionable model physics!

5 Important processes in altocumulus Longwave cloud-top cooling Supercooled droplets form Cooling induces upside- down convective mixing Some droplets freeze Ice particles grow at expense of liquid by Bergeron-Findeisen mechanism Ice particles fall out of layer Many models have prognostic cloud water content, and temperature- dependent ice/liquid split, with less liquid at colder temperatures –Impossible to represent altocumulus clouds properly! Newer models have separate prognostic ice and liquid mixing ratios –Are they better at mixed-phase clouds?

6 How well do models get mixed-phase clouds? Ground-based radar and lidar (Illingworth, Hogan et al. 2007) CloudSat and Calipso (Hogan, Stein, Garcon and Delanoe, in preparation) Models typically miss a third of mid-level clouds This is cloud fraction – what about cloud water content?

7 Observations of long-lived liquid layer Radar reflectivity (large particles) Lidar backscatter (small particles) Radar Doppler velocity Liquid at –20 C

8 Cloudnet processing Use radar, lidar and microwave radiometer to estimate ice and liquid water content on model grid Illingworth, Hogan et al. (BAMS 2007)

9 21 altocumulus days at Chilbolton Met Office models (mesoscale and global) have most sophisticated cloud scheme Separate prognostic liquid and ice But these models have the worst supercooled liquid water content and liquid cloud fraction What are we doing wrong in these schemes?

10 1D EMPIRE model Single column model High vertical resolution –Default: z = 50m Five prognostic variables –u, v, θ l, q t and q i Default: follows Met Office model –Wilson & Ballard microphysics –Local and non-local mixing –Explicit cloud-top entrainment Frequent radiation updates (Edwards & Slingo scheme) Advective forcing using ERA-Interim Flexible: very easy to try different parameterization schemes –Coded in matlab Each configuration compared to set of 21 Chilbolton altocumulus days Variables conserved under moist adiabatic processes: Total water (vapour plus liquid): Liquid water potential temperature

11 EMPIRE model simulations

12 Evaluation of EMPIRE control model More supercooled liquid than Met Office but still seriously underestimated

13 Effect of turbulent mixing scheme Quite a small effect!

14 Effect of vertical resolution Take EMPIRE and change physical processes within bounds of parameterized uncertainty –Assess change in simulated mixed-phase clouds Significantly less liquid at 500-m resolution Explains poorer performance of Met Office model Thin liquid layers cannot be resolved

15 Effect of ice growth rate Liquid water distribution improves in response to any change that reduces the ice growth rate in the cloud Change could be: reduced ice number concentration, increased ice fall speed, reduced ice capacitance But which change is physically justifiable?

16 Summary of sensitivity tests Main model sensitivities appear to be: Ice cloud fraction –In most models this is a function of ice mixing ratio and temperature –We have found from Cloudnet observations that the temperature dependence is unnecessary, and that this significantly improves the ice cloud fraction in clouds warmer than –30 C (not shown) Vertical resolution –Can we parameterize the sub-grid vertical distribution to get the same result in the high and low resolution models? Ice growth rate –Is there something wrong with the size distribution assumed in models that causes too high an ice growth rate when the ice water content is small?

17 Resolution dependence: idealised simulation Liquid Ice

18 Resolution dependence Typical NWP resolution Best NWP resolution

19 Effect 1: thin clouds can be missed Consider a 500-m model level at the top of an altocumulus cloud Consider prognostic variables l and q t that lead to q l = 0 θlθl qtqt qlql T P1P1 P2P2 –But layer is well mixed which means that even though prognostic variables are constant with height, T decreases significantly in layer –Therefore a liquid cloud may still be present at the top of the layer Gridbox-mean liquid can be parameterized

20 Diffusional growth: q i = ice mixing ratio, ice diameter RH i = relative humidity with respect to ice RH i qiqi dq i Effect 2: Ice growth too high at cloud top dt 100% 0 0 –q i zero at cloud top: growth too high Assume linear q i profile to enable gridbox-mean growth rate to be estimated: significantly lower than before P1P1 P2P2

21 Liquid Ice Parameterization at work Liquid Ice

22 Parameterization at work New parameterization works well over full range of model resolutions Typically applied only at cloud top, which can be identified objectively

23 Standard ice particle size distribution log(N) D N 0 = 2x10 6 Inverse exponential fit used in all situations Simply adjust slope to match ice water content –Wilson and Ballard scheme used by Met Office –Similar schemes in many other models Increasing ice water content But how does calculated growth rate versus ice water content compare to calculations from aircraft spectra?

24 Parameterized growth rates log(N) D N 0 = constant Ratio of parameterization to aircraft spectra Ice water content Fall speed Ice growth rate Ice clouds with low water content: –Ice growth rate too high –Fall speed too low Liquid clouds depleted too quickly!

25 Adjusted growth rates N 0 ~ IWC 3/4 log(N) Ratio of parameterization to aircraft spectra Ice water content Fall speed Ice growth rate D Delanoe and Hogan (2008) result suggests N 0 smaller for low water content –Much better agreement for growth rate and fall speed New ice size distribution leads to better agreement in liquid water content

26 Mixed-phase clouds: summary Mixed-phase clouds drastically underestimated in climate models, particularly those that have the most sophisticated physics! –Very difficult to simulate persistent supercooled layers Experiments with a 1D model evaluated against observations show: –Strong resolution dependence near cloud top; can be parameterized to allow liquid layers that only partially fill the layer vertically –More realistic ice size distribution has fewer, larger crystals at cloud top: lower ice growth and faster fall speeds so liquid depleted more slowly –Many other experiments have examined importance of radiation, turbulence, fall speed etc. Next step: apply new parameterizations in a climate model –What is the new estimate of the cloud radiative feedback?

27 Part 2 Boundary layer type from Doppler lidar Turbulent mixing in the boundary layer transports: –Pollutants away from surface: important for health –Water: important for cloud formation, and hence climate and weather forecasting – Heat and momentum: important for evolution of weather systems Mixing represented in four ways in models: –Local mixing (shear-driven mixing) –Non-local mixing (buoyancy-driven with strong capping inversion) –Convection (buoyancy-driven without strong capping inversion) –Entrainment (exchange across tops of stratocumulus clouds) Models must diagnose boundary-layer type to decide scheme to use –Getting the clouds right is a key part of this diagnosis Doppler lidar can measure many important boundary layer properties –Can we objectively diagnose boundary-layer type?

28 How is the boundary layer modelled? Met Office model has explicit boundary-layer types (Lock et al. 2000) Stable profile: Local mixing scheme Shear-driven mixing only: diffusivity K is a function of local Richardson number Ri Unstable profile: Non-local scheme Buoyancy-driven mixing: diffusivity profile determined by parcel ascents/descents Entrainment scheme If stratocumulus is present, entrainment velocity is parameterized explicitly Shallow cumulus scheme If cumulus present, mixing determined by mass-flux scheme

29 Hogan et al. (QJRMS 2009) Input of sensible heat grows a new cumulus-capped boundary layer during the day (small amount of stratocumulus in early morning) Convection is switched off when sensible heat flux goes negative at 1800 Surface heating leads to convectively generated turbulence Turbulence from Doppler lidar

30 Height Potential temperature Longwave cooling Shortwave heating Cloud Height Negatively buoyant plumes generated at cloud top: upside- down convection and negative skewness Positively buoyant plumes generated at surface: normal convection and positive skewness Stratocumulus cloud Can diagnose the source of turbulence Skewness

31 Boundary-layer types from observations Lock type I Lock type III v

32 Probabilistic decision tree Stable cloudless Stable stratus Forced Cu under Sc Decoupled Sc over stable Clear well mixed Cloudy well mixed Decoupled Sc Decoupled Sc over Cu Cumulus Test surface sensible heat flux Test skewness Test skewness & velocity variance Use lidar backscatter

33 Example day: 18 October 2009 Usually the most probable type has a probability greater than 0.9 Now apply to two years of data and evaluate the type in the Met Office model Most probable boundary-layer type II: Stratocu over stable surface layer IIIb: Stratocumulus- topped mixed layer Ib: Stratus Harvey, Hogan and Dacre (2012)

34 Winter Summer Autumn Spring Comparison to Met Office model Model has: Too little stable Too little well-mixed Too much cumulus Note: Model cumulus needs to be >400 m thick Use radar to apply this criterion to obs Harvey, Hogan and Dacre (2012)

35 WinterSpringSummerAutumn Obs Model Comparison with Met Office versus season and time of day

36 Forecast skill 6x6 contingency table is difficult to analyse –Most skill scores operate on a 2x2 table: a (hits), b (false alarms), c (misses), d (correct negatives) –Instead consider each decision separately Use symmetric extremal dependence index (SEDI) of Ferro & Stephenson (2011): many desirable properties (equitable, robust for rare events etc) Where hit rate H = a/(a+c) and false alarm rate F = b/(b+d)

37 Forecast skill: stability Surface layer stable? –Model very skilful (but basically predicting day versus night) –Better than persistence (predicting yesterdays observations) b a d c random

38 Forecast skill: cumulus Cumulus present (given the surface layer is unstable)? –Much less skilful than in predicting stability –Significantly better than persistence b a d c random

39 Forecast skill: decoupled Decoupled (as opposed to well- mixed)? –Not significantly more skilful than a persistence forecast b a dc random

40 Forecast skill: multiple cloud layers? Cumulus under statocumulus as opposed to cumulus alone? –Not significantly more skilful than a random forecast –Much poorer than cloud occurrence skill (SEDI ) b a dc random

41 Forecast skill: Nocturnal stratocu Stratocumulus present (given a stable surface layer)? –Marginally more skilful than a persistence forecast –Much poorer than cloud occurrence skill (SEDI ) b a dc random

42 Summary and future work Doppler lidar opens a new possibility to evaluate boundary layer schemes –Model rather poor at predicting boundary layer type –In addition to boundary-layer type, can we evaluate the diagnosed diffusivity profile – this is what matters for evolution of weather? –How do models perform over oceans or urban areas? How can boundary layer schemes be improved? –Combination of radar-lidar retrievals and 1D modelling demonstrated that shortcomings of altocumulus models could be identified and fixed –The same strategy could be applied to the boundary layer


44 Model evaluation using CloudSat and Calipso Use DARDAR cloud occurrence Hogan, Stein, Garcon and Delanoe (in preparation)

45 Radiative properties Using Edwards and Slingo (1996) radiation code Water content in different phase can have different radiative impact

46 Modelling mixed-phase clouds - GCMs Until recently: most models diagnostic split More recently: improved computer power and desire for physicality prognostic ice (Met Office, ECMWF, DWD) C0 C Temperature Liquid fraction All liquid All ice Mixed phase

47 Ice cloud fraction parameterisation

48 Ice particle size distribution Large ice crystals are more massive and grow faster than smaller crystals Small crystals have largest impact on growth rate

49 Skewness Skewness defined as –Positive in convective daytime boundary layers –Agrees with aircraft observations of LeMone (1990) when plotted versus the fraction of distance into the boundary layer Useful for diagnosing source of turbulence

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