Parameterization Cloud Scheme Validation

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ParameterizationCloud Scheme Validation
tompkins_at_ictp.it
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Cloud Validation The issues

• AIM To perfectly simulate one aspect of nature
  CLOUDS
• APPROACH Validate the model generated clouds
  against observations, and to use the information
  concerning apparent errors to improve the model
  physics, and subsequently the cloud simulation

sounds easy?
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Cloud Validation The problems

• How much of the error derives from
  observations?

Cloud observations error e1
parameterisation improvements
error
Cloud simulation error e2
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Cloud Validation The problems

• Which Physics is responsible for the error?

Cloud observations
parameterisation improvements
error
Cloud simulation
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The path to improved cloud parameterisation
parameterisation improvement
?
Composite studies
NWP validation
Case studies
cloud validation
Comparison to Satellite Products
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Model climate - Broadband radiative fluxes
Can compare Top of Atmosphere (TOA) radiative
fluxes with satellite observations TOA Shortwave
radiation (TSR)
JJA 87
TSR
CY18R6-ERBE
Stratocumulus regions bad - also North Africa
(old cycle!)
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Model climate - Cloud radiative forcing

• Problem Can we associate these errors with
  clouds?
• Another approach is to examine cloud radiative
  forcing

JJA 87
SWCRF
CY18R6-ERBE
Cloud Problems strato-cu YES, North Africa NO!
Note CRF sometimes defined as Fclr-F, also
differences in model calculation
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Model climate - Cloud fraction or Total cloud
cover
Can also compare other variables to derived
products CC
JJA 87
TCC
CY18R6-ISCCP
references ISCCP Rossow and Schiffer, Bull Am
Met Soc. 91, ERBE Ramanathan et al. Science 89
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Climatology The Problems
If more complicated cloud parameters are desired
(e.g. vertical structure) then retrieval can be
ambiguous
Channel 1
Channel 2
..
Assumptions about vertical structures
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Simulating Satellite Channels
Examples Morcrette MWR 1991 Chevallier et al, J
Clim. 2001
More certainty in the diagnosis of the existence
of a problem. Doesnt necessarily help identify
the origin of the problem
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A more complicated analysis is possible
Observations late afternoon peak in convection
Model morning peak (Common problem)
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Has this Improved? 29r1, 06 UTC
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29r1, 12 UTC
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29r1, 18UTC
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NWP forecast evaluation

• Differences in longer simulations may not be the
  direct result of the cloud scheme
• Interaction with radiation, dynamics etc.
• E.g poor stratocumulus regions
• Using short-term NWP or analysis restricts this
  and allows one to concentrate on the cloud scheme

Introduction of Tiedtke Scheme
cloud cover bias
Time
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Example over Europe
-11
13
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-3-1
-8-3

1. What are your conclusions concerning the cloud
   scheme?

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Example over Europe
-11
13
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-3-1
-8-3

• Who wants to be a Meteorologist?
• Which of the following is a drawback of SYNOP
  observations?

(a) They are only available over land
(b) They are only available during daytime
(c) They do not provide information concerning
vertical structure
(d) They are made by people
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Case Studies

• These were examples of general statistics
  globally or for specific regions
• Can look concentrate on a particular location in
  more details, for which more data is
  collectedCASE STUDY
• Examples
• GATE, CEPEX, TOGA-COARE, ARM...

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Evaluation of vertical cloud structure
Mace et al., 1998, GRL Examined the frequency of
occurrence of ice cloud Reasonable match to data
found
ARM Site - America Great Plains
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Evaluation of vertical cloud structure
Hogan et al., 2000, JAM
Analysis using the Chilbolton radar and Lidar
Reasonable Match
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Hogan et al. More details possible
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Hogan et al.
Found that comparison improved when snow was
taken into account
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Issues Raised

• WHAT ARE WE COMPARING?
• Is the model statistic really equivalent to what
  the instrument measures?
• e.g Radar sees snow, but the model may not
  include this is the definition of cloud fraction.
  Small ice amounts may be invisible to the
  instrument but included in the model statistic
• HOW STRINGENT IS OUR TEST?
• Perhaps the variable is easy to reproduce
• e.g Mid-latitude frontal clouds are strongly
  dynamically forced, cloud fraction is often zero
  or one. Perhaps cloud fraction statistics are
  easy to reproduce in short term forecasts

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Can also use to validate components of cloud
scheme
EXAMPLE Cloud Overlap Assumptions Hogan and
Illingworth, 00, QJRMS
Issues Raised HOW REPRESENTATIVE IS OUR CASE
STUDY LOCATION? e.g Wind shear and dynamics very
different between Southern England and the
tropics!!!
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Composites

• We want to look at a certain kind of model
  system
• Stratocumulus regions
• Extra tropical cyclones
• An individual case may not be conclusive Is it
  typical?
• On the other hand general statistics may swamp
  this kind of system
• Can use compositing technique

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Composites - a cloud survey
From Satellite attempt to derive cloud top
pressure and cloud optical thickness for each
pixel - Data is then divided into regimes
according to sea level pressure anomaly Use ISCCP
simulator
Data Model Modal-Data
-ve SLP
Tselioudis et al., 2000, JCL
Cloud top pressure
ve SLP

1. High Clouds too thin
2. Low clouds too thick

Optical depth
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Composites Extra-tropical cyclones
Overlay about 1000 cyclones, defined about a
location of maximum optical thickness
Plot predominant cloud types by looking at
anomalies from 5-day average

• High Clouds too thin
• Low clouds too thick

High topsRed, Mid topsYellow, Low topsBlue
Klein and Jakob, 1999, MWR
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A strategy for cloud parametrization evaluation
Jakob, Thesis
Where are the difficulties?
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Recap The problems

• All Observations
• Are we comparing like with like? What
  assumptions are contained in retrievals/variationa
  l approaches?
• Long term climatologies
• Which physics is responsible for the errors?
• Dynamical regimes can diverge
• NWP, Reanalysis, Column Models
• Doesnt allow the interaction between physics to
  be represented
• Case studies
• Are they representative? Do changes translate
  into global skill?
• Composites As case studies.
• And one more problem specific to NWP

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NWP cloud scheme development

• Timescale of validation exercise
• Many of the above validation exercises are
  complex and involved
• Often the results are available O(years) after
  the project starts for a single version of the
  model
• NWP operational models are updated 2 to 4 times a
  year roughly, so often the validation results are
  no longer relevant, once they become available.
• Requirement A quick and easy test bench

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Example LWP ERA-40 and recent cycles
model
23r4 June 2001
SSMI
Diff
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Example LWP ERA-40 and recent cycles
model
23r4 June 2001
26r3 Oct 2003
SSMI
Diff
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Example LWP ERA-40 and recent cycles
model
23r4 June 2001
28r1 Mar 2004
SSMI
Diff
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Example LWP ERA-40 and recent cycles
model
23r4 June 2001
28r3 Sept 2004
SSMI
Diff
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Example LWP ERA-40 and recent cycles
model
23r4 June 2001
29r1 Apr 2005
SSMI
Diff
Do ERA-40 cloud studies still have relevance for
the operational model?
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So what is used at ECMWF?

• T799-L91
• Standard Scores (rms, anom corr of U, T, Z)
• operational validation of clouds against SYNOP
  observations
• Simulated radiances against Meteosat 7

• T159-L91 climate runs
• 3 ensemble members of 13 months
• Automatically produces comparisons to
• ERBE, NOAA-x, CERES TOA fluxes
• Quikscat SSM/I, 10m winds
• ISCCP MODIS cloud cover
• SSM/I, TRMM liquid water path
• (soon MLS ice water content)
• GPCP, TRMM, SSM/I, Xie Arkin, Precip
• Dasilva climatology of surface fluxes
• ERA-40 analysis winds

All datasets treated as truth
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OLR
-150
Model T95 L91
-300
-150
CERES
Conclusions?
-300
too high
Difference
too low
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SW
350
Model T95 L91
100
350
CERES
Conclusions?
100
albedo high
Difference
albedo low
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TCC
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Model T95 L91
5
80
ISCCP
Conclusions?
5
TCC high
Difference
TCC low
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First Ice Validation microwave limb sounders
316 hPa
215 hPa
Color MLS White bars EC IWC sampled with MLS
tracks
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TCLW LWP
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Model T95 L91
5
80
SSMI
Conclusions?
5
high
Difference
low
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Map of MLS ice error
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Daily Report 11th April 2005Lets see what you
think? ?
Going more into details of the cyclone, it can
be seen that the model was able to reproduce the
very peculiar spiral structure in the clouds
bands. However large differences can be noticed
further east, in the warm sector of the frontal
system attached to the cyclone, were the model
largely underpredicts the typical high-cloud
shield. Look for example in the two maps below
where a clear deficiency of clod cover is evident
in the model generated satellite images north of
the Black Sea. In this case this was systematic
over different forecasts. Quote from ECMWF
daily report 11th April 2005
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Same Case, water vapour channels
Blue moist Red Dry
30 hr forecast too dry in front region Is not a
FC-drift, does this mean the cloud scheme is at
fault?
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Future Long term ground-based validation,
CLOUDNET

• Network of stations processed for multi-year
  period using identical algorithms, first Europe,
  now also ARM sites
• Some European provide operational forecasts so
  that direct comparisons are made quasi-realtime
• Direct involvement of Met services to provide
  up-to-date information on model cycles

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Cloudnet Example

• In addition to standard quicklooks, longer-term
  statistics are available
• This example is for ECMWF cloud cover during June
  2005
• Includes preprocessing to account for radar
  attenuation and snow
• See www.cloud-net.org for more details and
  examples!

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Future Improved remote sensing capabilities,
CLOUDSAT

• CloudSat is an experimental satellite that will
  use radar to study clouds and precipitation from
  space. CloudSat will fly in orbital formation as
  part of the A-Train constellation of satellites
  (Aqua, CloudSat, CALIPSO, PARASOL, and Aura)
• Launched 28th April

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Cloudsat
transect
Zonal mean cloud cover
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In summary Many methods for examining clouds
but all too often...
parameterisation improvement