Clouds and their turbulent environment

1
Clouds and theirturbulent 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 100-200 m thick

4
Mixed-phase cloud radiative feedback

• Change to cloud mixing ratio on doubling of CO2
• Tsushima et al. (2006)

• Decrease in subtropical stratocumulus
• Lower albedo -gt 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)

• This is cloud fraction what about cloud water
  content?

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Observations of long-lived liquid layer

• Radar reflectivity (large particles)
• Lidar backscatter (small particles)
• Radar Doppler velocity

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

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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, ?l, 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

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EMPIRE model simulations
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Evaluation of EMPIRE control model
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Effect of turbulent mixing scheme

• Quite a small effect!

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Effect of vertical resolution

• Take EMPIRE and change physical processes within
  bounds of parameterized uncertainty
• Assess change in simulated mixed-phase clouds

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Effect of ice growth rate
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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?

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Resolution dependence idealised simulation

• Liquid Ice

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Resolution dependence
Typical NWP resolution
Best NWP resolution
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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

?l
qt
ql
T
P1
P2
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Effect 2 Ice growth too high at cloud top

• Diffusional growth
• qi ice mixing ratio, ice diameter
• RHi relative humidity with respect to ice

dqi
RHi
qi
dt
P1
P2
100
0
0
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Parameterization at work

• Liquid Ice

• Liquid Ice

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Parameterization at work

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

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Standard ice particle size distribution

• 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

log(N)
N0 2x106
Increasing ice water content
D

• But how does calculated growth rate versus ice
  water content compare to calculations from
  aircraft spectra?

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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
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Adjusted growth rates
log(N)
Ice growth rate
D
N0 IWC3/4
Ratio of parameterization to aircraft spectra

• 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
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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?

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Part 2Boundary 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?

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How is the boundary layer modelled?

• Met Office model has explicit boundary-layer
  types (Lock et al. 2000)

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Turbulence from Doppler lidar

• Hogan et al. (QJRMS 2009)

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Skewness
Stratocumulus cloud

• Can diagnose the source of turbulence

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Boundary-layer types from observations
Lock type I
qv
Lock type III
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Probabilistic decision tree
Stable cloudless
Clear well mixed
Forced Cu under Sc
Decoupled Sc
Decoupled Sc over Cu
Cumulus
Cloudy well mixed
Stable stratus
Decoupled Sc over stable
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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

Harvey, Hogan and Dacre (2012)
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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 gt400 m thick
• Use radar to apply this criterion to obs
• Harvey, Hogan and Dacre (2012)

Summer
Autumn
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Comparison with Met Officeversus season and time
of day
Obs
Winter
Spring
Summer
Autumn
Model
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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/(ac) and false alarm rate F
  b/(bd)

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Forecast skill stability
b
a

• Surface layer stable?
• Model very skilful (but basically predicting day
  versus night)
• Better than persistence (predicting yesterdays
  observations)

c
d
random
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Forecast skill cumulus
a
b

• Cumulus present (given the surface layer is
  unstable)?
• Much less skilful than in predicting stability
• Significantly better than persistence

c
d
random
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Forecast skill decoupled
b
a

• Decoupled (as opposed to well-mixed)?
• Not significantly more skilful than a persistence
  forecast

d
c
random
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Forecast skill multiple cloud layers?
b
a
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
  0.5-0.7)

random
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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
  0.5-0.7)

b
a
d
c
random
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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

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Model evaluation using CloudSat and Calipso

• Use DARDAR cloud occurrence
• Hogan, Stein, Garcon and Delanoe (in preparation)

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Radiative properties

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

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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)

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Ice cloud fraction parameterisation
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Ice particle size distribution

• Large ice crystals are more massive and grow
  faster than smaller crystals
• Small crystals have largest impact on growth rate

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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