Title: Evaluation of ECHAM5 General Circulation Model using ISCCP simulator
1Evaluation of ECHAM5 General Circulation Model
using ISCCP simulator
Swati Gehlot Johannes Quaas Max-Planck-Institut
für MeteorologieHamburg, Germany
2Outline
- 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
3Motivation
- 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
4 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
5 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
6 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
7 Sub-grid scale variability conventional approach
(cloud overlap)
500m
200km grid box
Typical model grid box
Outline Motivation Model Sub-column sampler
Results Conclusion
8 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
9 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
10Case study India Diurnal cycle
Diurnal cycle for India model, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
11Case 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
12Case study Africa Diurnal cycle
Diurnal cycle for Africa model, ISCCP data, and
MODIS data
Outline Motivation Model Sub-column sampler
Results Conclusion
13Case 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
14Global 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
15Conclusion
- 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
16Thank you
Swati Gehlot Swati.gehlot_at_zmaw.de Max-Planck-Insti
tut für MeteorologieHamburg, Germany
Outline Motivation Model Sub-column sampler
Results Conclusion
17Spare Sheets
Outline Motivation Model Sub-column sampler
Results Conclusion
18 Geographical distribution of convective clouds
Outline Motivation Model Sub-column sampler
Results Conclusion
19Sub-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
20Case study Amazon Diurnal cycle
Outline Motivation Model Sub-column sampler
Results Conclusion
21Case study Amazon ISCCP histograms
Outline Motivation Model Sub-column sampler
Results Conclusion
22Case study Indonesia Diurnal cycle
Outline Motivation Model Sub-column sampler
Results Conclusion
23Case study Indonesia ISCCP histograms
Outline Motivation Model Sub-column sampler
Results Conclusion
24 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
25 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