Transcript Document

Evaluating and improving
the representation of
clouds in climate models
using spaceborne radar
and lidar
Robin Hogan, Julien Delanoë, Nicky Chalmers,
Thorwald Stein, Anthony Illingworth
University of Reading
Clouds in climate models
Vertically integrated cloud water (kg m-2)
• Via their interaction with solar and terrestrial radiation, clouds are
one of the greatest sources of uncertainty in climate forecasts
• But cloud water content in models varies by a factor of 10
• Need instrument with high vertical resolution…
But all models
tuned to give
about the
same top-ofatmosphere
radiation
14 global
models
(AMIP)
0.25
0.20
0.15
0.10
0.05
90N
80
60
40
20
0
Latitude
-20
-40
-60
-80 90S
The properties
of ice clouds
are particularly
uncertain
Spaceborne radar, lidar and radiometers
EarthCare
The A-Train
– NASA
– 700-km orbit
– CloudSat 94-GHz radar (launch 2006)
– Calipso 532/1064-nm depol. lidar
– MODIS multi-wavelength radiometer
– CERES broad-band radiometer
– AMSR-E microwave radiometer
EarthCARE (launch 2013)
– ESA+JAXA
– 400-km orbit: more sensitive
– 94-GHz Doppler radar
– 355-nm HSRL/depol. lidar
– Multispectral imager
– Broad-band radiometer
– Heart-warming name
What do CloudSat and Calipso see?
Cloudsat radar
CALIPSO lidar
Target classification
•
Radar: ~D6,
detects whole
profile, surface
echo provides
integral constraint
•
Lidar: ~D2, more
sensitive to thin
cirrus and liquid
clouds but
attenuated
Insects
Aerosol
Rain
Supercooled liquid cloud
Warm liquid cloud
Ice and supercooled liquid
Ice
Clear
No ice/rain but possibly liquid
Ground
Delanoe and Hogan (2008, 2010)
Lidar observations
Example ice
cloud retrievals
Delanoe and Hogan (2010)
Lidar forward model
Visible extinction
Radar observations
Ice water content
Radar forward model
Effective radius
Evaluation using CERES TOA fluxes
• Radar-lidar retrieved profiles containing only ice used
with Edwards-Slingo radiation code to predict CERES fluxes
• Small biases but large random shortwave error: 3D effects?
Shortwave
Bias 4 W m-2, RMSE 71 W m-2
Longwave
Bias 0.3 W m-2, RMSE 14 W m-2
Nicky Chalmers
A-Train
versus
models
• Ice water
content
• 14 July 2006
• Half an orbit
• 150°
longitude at
equator
Delanoe et al.
(2010)
Evaluation of gridbox-mean ice water content
In-cloud mean ice water content
• Both models lack high thin cirrus
• Met Office has too narrow a distribution of in-cloud IWC
• ECMWF lacks high IWC values; using this work, ECMWF have
developed a new scheme that performs better
Cloud structures in particular locations
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How can we identify & cure errors in modelling African convection?
Unified Model simulations at a range of resolutions
Evaluate using A-Train retrievals
Also run “CloudSat simulator” to obtain radar reflectivity from model
Location of African easterly jet
Parker et al. (QJRMS 2005)
Mid-level outflow
African easterly jet
Moist monsoon flow
Saharan air layer
Met Office 40-km model versus CloudSat
• Frequency of occurrence of reflectivity greater than –30 dBZ
• Plot versus “dynamic latitude” (latitude relative to location of AEJ)
Unified Model
CloudSat (~01.30 LT) CloudSat (~13.30 LT)
• Anvil cirrus too low in model
• Little sign of mid-level outflow
Thorwald Stein
Met Office 4-km model versus CloudSat
• Note increase from 38 to 70 levels
Unified Model
CloudSat (~01.30 LT) CloudSat (~13.30 LT)
• Anvil cirrus now at around the right altitude
• Slightly more mid-level cloud
• Large overestimate of stratocumulus (and too low)
Thorwald Stein
Ongoing A-Train and EarthCARE activity
•
Preparation for EarthCARE
– Professor Anthony Illingworth is the European lead scientist
– Professor Robin Hogan is leading the European development of algorithms
exploiting the synergy of instruments on EarthCARE: novel variational retrieval
methods for clouds, precipitation and aerosol being developed for EarthCARE
and tested on A-Train data
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Past and future projects
– Radiative properties of clouds from the A-Train (NERC): Nicky Chalmers (PhD)
– Evaluation of models using CloudSat and Calipso (NERC): Julien Delanoe (finished)
– High-resolution model evaluation using CloudSat (NERC): Thorwald Stein
– Lidar retrievals of liquid clouds (NCEO): Nicola Pounder
– Synergy algorithms for EarthCARE (NCEO): Chris Westbrook
– Radiative Transfer for EarthCARE (ESA): Julien Delanoe then Chris Westbrook
– Variational Synergy algorithms for EarthCARE (ESA): not yet started
Future challenges
– Assimilate radar and lidar observations into ECMWF model using forward models
developed at University of Reading
– Retrieve global cloud fields that are consistent with the radiative measurements:
can diagnose not only what aspects of clouds are wrong in models, but the
radiative error associated with each