1. Evaluation and improvement of mixed-phase cloud schemes using radar and lidar observations
Robin Hogan, Andrew Barrett
Department of Meteorology, University of Reading
Richard Forbes
ECMWF

2. Overview Why are mixed-phase clouds so poorly captured in GCMs?
These clouds are potentially a key negative feedback for climate
Getting these clouds right requires the correct specification of turbulent mixing, radiation, microphysics, fall speed, sub-grid structure etc.
Vertical resolution is a key issue for representing thin liquid layers
Can we devise a scale-independent parameterization?
Use a 1D model and long-term cloud radar and lidar observations
Easy to perform many sensitivity studies to changed physics
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 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 so more persistent
Higher albedo -> negative climate feedback (Mitchell et al. 1989)
Depends on questionable model physics!
Decrease in subtropical stratocumulus
Lower albedo -> positive feedback on climate

4. How well do models capture mid-level clouds?
CloudSat & Calipso (Hogan, Stein, Garcon, Delanoe, Forbes, Bodas-Salcedo, in prep.)
How well do models capture mid-level clouds?
Models miss at least a third of mid-level clouds
Ground-based radar and lidar (Illingworth, Hogan et al. 2007)
ISCCP and CERES (Zhang et al. 2005)
t<3.6
3.6<t<23

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
Ice particles fall out of layer H lE
Most previous studies (e.g. Xie et al. 2008) in Arctic: surface fluxes important
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. Observations of long-lived liquid layer
Liquid at –20C
Radar reflectivity (large particles)
Lidar backscatter (small particles)
Estimate ice water content from radar reflectivity factor and temperature
Estimate liquid water content from microwave radiometer using scaled adiabatic method

7. 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?

8. 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

9. EMPIRE model simulations

10. Evaluation of EMPIRE control model
More supercooled liquid than Met Office but still seriously underestimated
Note that ice & liquid cloud fraction is diagnosed so errors not so fundamental

11. Effect of turbulent mixing scheme
Quite a small effect!

12. 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

13. 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?

14. Summary of sensitivity tests
Main model sensitivities appear to be:
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?
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)

15. Resolution dependence: idealised simulation
Liquid Ice

16. Resolution dependence
Best NWP resolution
Typical NWP resolution

17. 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

18. 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%

19. Parameterization at work
Liquid Ice
Liquid Ice

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

21. Standard ice particle size distribution
log(N)
“Marshall-Palmer” inverse exponential 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?

22. 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

23. 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) suggest N0 smaller for low water content
Much better growth rate and fall speed
Need to account for ice shattering!
Fall speed
Ice water content

24. Conclusions Why are mixed-phase clouds so poorly captured in GCMs?
Two key effects that lead to ice growth too fast at cloud top
Sub-grid structure in the vertical
Strong resolution dependence near cloud top; can be parameterized to allow liquid layers that only partially fill the layer vertically
We have parameterized effect on liquid occurrence and ice growth
Error in assumed ice size distribution
More realistic size distribution has fewer, larger crystals at cloud top
Lower ice growth and faster fall speeds so liquid depleted more slowly
Implications for large scale models
NWP: Richard Forbes shown large surface temperature errors unless cloud-top ice growth scaled back: now has physical basis
Climate: urgent need to re-evaluate mixed-phase cloud contribution to climate sensitivity using models with better physics

26. Ice cloud fraction parameterisation

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

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

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

30. 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