Evaluation of ECHAM5 General Circulation Model using ISCCP simulator

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Evaluation of ECHAM5 General Circulation Model
using ISCCP simulator
Swati Gehlot Johannes Quaas Max-Planck-Institut
für MeteorologieHamburg, Germany
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Outline

• Background and motivation
• Model and data used
• Inclusion of sub-column sampler within the ISCCP
  simulator
• Results and discussion
• Conclusion

Outline Motivation Sub-column sampler
Results Conclusion
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Motivation

• Global climate models work at a much coarser
  scale to resolve cloud processes and hence they
  are parameterized, leading to uncertainty
• In order to have confidence in the cloud
  parameterizations, the evaluation studies for GCM
  clouds are very essential
• ISCCP satellite data provides an adequately long
  time series for cloud climatology and
  microphysics
• This study focuses on application of ISCCP
  satellite simulator for detailed cloud
  diagnostics from ECHAM5 atmospheric GCM and model
  evaluation with comparison to observations

Outline Motivation Model Sub-column sampler
Results Conclusion
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Model and data used

• Model evaluation studies for ECHAM5 GCM
• Simulations with T63 spectral resolution
• Additional module of ISCCP satellite simulator
• Analysis of diurnal cycle of convection using
  ECHAM5 data
• Comparison of ISCCP-type cloud cover diagnosed in
  the model with satellite data
• Focus on high and convective clouds
• Analysis of ISCCP histograms model Vs
  observations
• Model verification studies using satellite data
• International Satellite Cloud Climatology Project
  (ISCCP)
• MODerate Resolution Imaging Spectroradiometer
  (MODIS)

Outline Motivation Model Sub-column sampler
Results Conclusion
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ISCCP simulator

• Additional module of ISCCP simulator is coupled
  with ECHAM5 to create ISCCP like cloud types in
  the model output

ISCCP cloud fraction histogram distribution
ISCCP cloud types classification
Outline Motivation Model Sub-column sampler
Results Conclusion
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Need of a sub-column sampler
Figure from A Tompkins, ECMWF, 2005
Typical grid box in a GCM with inhomogeneous
distribution of clouds within it
Outline Motivation Model Sub-column sampler
Results Conclusion
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Sub-grid scale variability conventional approach
(cloud overlap)

500m
200km grid box
Typical model grid box
Outline Motivation Model Sub-column sampler
Results Conclusion
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Sub-column sampler
(Stochastically generated independent sub columns)

500m
200km grid box
Dealing with horizontal cloud inhomogeneity and
vertical overlap of clouds in the model grid box
using stochastic cloud generator (based on
Räisänen et al, QJRMS 2004)
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study over four tropical regions
Area 1 Africa 00oS to 30oS and 00oE
to 30oE Area 2 Amazon 00oS to 30oS and
25oW to 55oW Area 3 India 00oN to
30oN and 60oE to 90oE Area 4 Indonesia 10oN
to 20oS and 90oE to 120oE
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study India Diurnal cycle
Diurnal cycle for India model, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study India ISCCP histograms
Diurnal average ISCCP cloud fraction histograms
comparison of the model output, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study Africa Diurnal cycle
Diurnal cycle for Africa model, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study Africa ISCCP histograms
Diurnal average ISCCP cloud fraction histograms
comparison of the model output, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
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Global JJA averaged histograms (land and sea)
Diurnal average ISCCP cloud fraction histograms
comparison of the model output, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
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Conclusion

• ECHAM5 model is evaluated using ISCCP simulator
  containing sub-grid variability information
• The model simulates the total cloud cover quite
  reasonably for the land and sea areas
• An overestimation of high and deep convective
  clouds is seen on comparison with ISCCP
  observations
• The model as well as ISCCP data miss a large
  amount of mid-cloud cover compared to MODIS
  observations
• Underestimation of low clouds in the model when
  compared to observations
• ISCCP and MODIS data show large discrepancies,
  particularly for land areas

Outline Motivation Model Sub-column sampler
Results Conclusion
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Thank you
Swati Gehlot Swati.gehlot_at_zmaw.de Max-Planck-Insti
tut für MeteorologieHamburg, Germany
Outline Motivation Model Sub-column sampler
Results Conclusion
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Spare Sheets
Outline Motivation Model Sub-column sampler
Results Conclusion
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Geographical distribution of convective clouds
Outline Motivation Model Sub-column sampler
Results Conclusion
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Sub-grid cloud generator
Stochastic cloud generator for generating random
sub-columns in a model grid cell Initialized
with GCM grid mean values of cloud fraction,
liquid water and ice Vertical variance by
maximum-random cloud overlap assumption for cloud
fraction and cloud condensate Horizontal
variance of total cloud water, using the Tompkins
cloud scheme with beta distribution PDF The
generated sub-columns consist at each level of
entirely clear sky, or entirely cloudy sky with
constant cloud condensate Tested with 100 sub
columns, found reasonable global distributions of
ISCCP variables.
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study Amazon Diurnal cycle
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study Amazon ISCCP histograms
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study Indonesia Diurnal cycle
Outline Motivation Model Sub-column sampler
Results Conclusion
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Case study Indonesia ISCCP histograms
Outline Motivation Model Sub-column sampler
Results Conclusion
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Summary Diurnal cycle
Four tropical regions comprising of land and sea
areas are analyzed for evaluation of diurnal
cycle of ISCCP clouds (JJA04) The amplitudes of
diurnal cycle for model TCC varies between 5-17
(land) and 3-12 (sea) compared to ISCCP data TCC
which lies between 10-13 (land) and 3-13 (sea).
The land areas are slightly overestimated where
as the sea areas are relatively well simulated by
the model For all the regions (land/sea), the
high cloud cover (HCC) is overestimated in the
model (8 in Africa to 43 in Indonesia) The low
cloud cover (LCC) is underestimated in the model
in the range of 7 (in Amazon, fig 3) to 19 (in
Indonesia, fig 5) compared to the ISCCP satellite
observations. The model underestimates the
mid-cloud cover (MCC) with a range of 2 (in
Africa, fig 3) to 10 (in Indonesia, fig 5)
compared to the ISCCP satellite
observations The reasonable computation of ISCCP
TCC is due to the cancellation of errors by the
overestimation of HCC and the underestimation of
LCC and MCC
Outline Motivation Model Sub-column sampler
Results Conclusion
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Summary ISCCP histograms
For all the test areas, the model simulated ISCCP
histograms were computed using diurnal average
cloud amount for JJA 2004 The model diagnoses
larger amount of high clouds (for eg. India), and
the histograms reveal that these are optically
very thin The model misses much of the low cloud
cover (LCC) and the mid-cloud cover (MCC), when
histograms are compared with ISCCP
observations The globally averaged histograms
show that the sea area is relatively well
simulated in the model compared to the land
area The ISCCP simulator shows a decent
agreement with the COSP simulator in terms of
distribution of clouds in the ECHAM5 model
Outline Motivation Model Sub-column sampler
Results Conclusion