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 can’t 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
Abstract: This talk will tackle two aspects of evaluating and improving the representation of clouds in weather and climate models using observational data, both of which involve the challenging problem of understanding the interaction between clouds and their turbulent environment. The first part will address the problem of why virtually all models grossly underestimate the occurrence of mid-level altocumulus clouds, which represent an important uncertainty in climate prediction. These clouds are difficult to simulate because are often much thinner than the vertical resolution of the model, and it is necessary to correctly represent the processes of longwave cloud-top cooling, turbulent mixing and the exchange of water between the ice, liquid and vapour phases.   We have developed a flexible 1D model that can address this problem by running it at a cloud radar and lidar site to see which parameterizations lead to the best agreement with observations.  Two key bits of physics are needed to improve the occurrence of these clouds: firstly, a new parameterization for the sub-grid vertical distribution of thermodynamic quantities enables thin liquid water layers to be represented correctly in models with a coarse vertical resolution. Secondly, we show that an improved representation of the ice particle size distribution leading on from the work of Delanoe and Hogan (2008) provides a much better prediction of the rate at which ice crystals grow and fall out of the cloud. The second part of the talk will demonstrate how ground-based Doppler lidar can be used to classify the boundary layer into nine different types (e.g. stratus-topped stable layer, decoupled stratocumulus, cumulus-capped etc). It is demonstrated that Doppler lidar diagnostics such as vertical velocity variance and skewness are able to diagnose whether boundary-layer turbulence is being generated from surface heating or cloud-top radiative cooling, which is key to distinguishing between types such as cumulus clouds and broken stratocumulus. A two-year dataset is then used to evaluate the predictions of the Met Office model, one of many models that diagnose a boundary-layer type in order to decide which mixing scheme to use, which is important for the transport of pollution and the evolution of weather systems.

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
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!
Decrease in subtropical stratocumulus
Lower albedo -> positive feedback on climate

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

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: Dz = 50m
Five prognostic variables
u, v, θl, qt and qi
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
Best NWP resolution
Typical 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 ql and qt that lead to ql = 0 θl qt ql T P1
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 P2

20. Effect 2: Ice growth too high at cloud top
Diffusional growth:
qi = ice mixing ratio, ice diameter
RHi = relative humidity with respect to ice
qi zero at cloud top: growth too high
dqi
RHi qi dt P1
Assume linear qi profile to enable gridbox-mean growth rate to be estimated: significantly lower than before P2
100%

21. Parameterization at work
Liquid Ice
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)
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
N0 = 2x106
Increasing ice water content D
But how does calculated growth rate versus ice water content compare to calculations from aircraft spectra?

24. Parameterized growth rates
log(N)
Ice growth rate D
Ratio of parameterization to aircraft spectra
N0 = constant
Ice clouds with low water content:
Ice growth rate too high
Fall speed too low
Liquid clouds depleted too quickly!
Fall speed
Ice water content

25. Adjusted growth rates log(N)
New ice size distribution leads to better agreement in liquid water content
Ice growth rate D
Ratio of parameterization to aircraft spectra
N0 ~ IWC3/4
Delanoe and Hogan (2008) result suggests N0 smaller for low water content
Much better agreement for growth rate and fall speed
Fall speed
Ice 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)
Unstable profile: Non-local scheme
Buoyancy-driven mixing: diffusivity profile determined by parcel ascents/descents
Stable profile: Local mixing scheme
Shear-driven mixing only: diffusivity K is a function of local Richardson number Ri
Shallow cumulus scheme
If cumulus present, mixing determined by mass-flux scheme
Entrainment scheme
If stratocumulus is present, entrainment velocity is parameterized explicitly

29. Turbulence from Doppler lidar
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
Hogan et al. (QJRMS 2009)

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

31. Boundary-layer types from observations
Lock type I qv
Lock type III

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

33. Example day: 18 October 2009
Most probable boundary-layer type
II: Stratocu over stable surface layer
IIIb: Stratocumulus-topped mixed layer
Ib: Stratus
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
Harvey, Hogan and Dacre (2012)

34. Comparison to Met Office model
Winter
Spring
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)
Summer
Autumn

35. Comparison with Met Office versus season and time of day
Obs
Winter
Spring
Summer
Autumn
Model

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 yesterday’s observations) d c
random

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

39. Forecast skill: decoupledb a
Decoupled (as opposed to well-mixed)?
Not significantly more skilful than a persistence forecast d c
random

40. Forecast skill: multiple cloud layers?b a
Forecast skill: multiple cloud layers? d c
Cumulus under statocumulus as opposed to cumulus alone?
Not significantly more skilful than a random forecast
Much poorer than cloud occurrence skill (SEDI )
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 d c
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) 1
-23 C
0 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