Transcript Evaluation and Improvements of Cloud Model Dynamics and Microphysics Jiun-Dar Chern

Evaluation and Improvements of Cloud Model Dynamics and Microphysics
in Multi-Scale Modeling System
Jiun-Dar Chern(1), Wei-Kuo Tao(1), Bo-Wen Shen(1),
R. Atlas (2),Steve Lang(1), Z. Johnny Luo(3), and Graeme L. Stephens(3)
(1) NASA/GSFC; (2) NOAA/AOML;
(3) Department of Atmospheric Science, Colorado State University
1. Multi-Scale Modeling Framework (MMF)
One of the major uncertainties in climate modeling
is the representation of sub-grid processes in the
General Circulations Models (GCMs). The ideal of
MMF or a super parameterization, which replaces
the conventional cloud parameterizations with a
Cloud Resolving Model (CRM) in each grid column
of a GCM, is a promising approach to break the
deadlock of conventional parameterizations in GCMs
(Grabowski 2001; Randall et al. 2003; Khairoutdinov
et al. 2005). The Goddard MMF (Tao et al. 2007)
includes the fvGCM running at 2.5o x 2o resolution
and a two-dimensional GCE embedded in each
GCM grid box. Globally, there are a total of 13,104
GCEs running at the same time and interact with the
host GCM through a “forcing-feeback” coupling
mechanism.
Moist physics tendencies (T and q)
Cloud and precipitation
3. Evaluate Cloud Microphysics in the MMF
3.1 A new Bulk Microphysic scheme (Lang et al.
2007)
During TRMM LBA experiment in Brazil, dualDoppler radar observations were collected on 26
January 1999 by the NASA TOGA and NCAR S-Pol
radars. The case is an example of an easterly
regime mesoscale convective system (MCS) that
propagated into the TRMM-LBA domain from the
northeast. The 3D GCE model was used to simulate
this squall line case with the modified Rutledge and
Hobbs (1984) three-class ice scheme. CFADs
(contoured frequency by altitude diagrams) analyses
from radar are used to evaluate the model simulation
and lead to improvements in cloud microphysical
processes.
Rain Radar Reflectivity and CFADS
OBS
CONTROL
Monthly Precipitation Rate in July 2006
TRMM
MMF
Radar Profile Classification
DIFF
Follow Stephens and Wood 2006:
RH84
New
Scheme
3.3 Evaluate MMF Results with TRMM Data
CFAD and ETH for 30S-30N (Jul-Aug 2006)
Zonal Mean Hydrometeor Profile
TRMM TMI
CloudSat
CONTROL
Control
.
NEW
Z => P
Z <= P
ELASTIC
NEW MICROPHYS
Elastic
New Microphys
Large-scale forcings
Background profiles (T, q, u, v, w)
2. Evaluate Cloud Dynamics in the MMF
Although most of CRMs use dynamics with
anelastic assumption, the host GCMs in MMFs are
usually constructed with elastic dynamics. To be
consistent with the dynamics of host fvGCM, an
elastic dynamical core has been implemented into
the 2D GCE. To study the impacts of elastic system
on the performance of MMF, one-month MMF
simulations with anelastic and elastic dynamics have
been carried out using observed NOAA weekly OI
SST in July 2006.
Mean Vertical Profile of Hydrometeors (40S-40N)
Vertical Profile of Hydrometeors from 3D GCE
Simulations of TRMM LBA Experiment
RH84
New Scheme
TRMM TMI
Control
Microphys
Elastic
Monthly Precipitation Rate in July 2006
TRMM
Anelastic
Elastic
MMF
DIFF
3.2 Impacts of the New Microphysical scheme in
MMF system
One of the advantages of the MMF approach is
it can provide global coverage and long-term
simulation. The new microphysics was implemented
into the embedded 2D GCE in the MMF to assess
their effects on large scale circulation and climate.
3.4 Evaluate MMF Results with CloudSat Data
To compare the first two-month (July and
August, 2006) observations from newly launched
CloudSat, QuickBeam radar simulator developed by
John Haynes at CSU is used to produces profiles of
cloud radar reflectivity from model outputs.
4. Summary and Conclusion
1) Both elastic dynamic and the new bulk microphysics improve the MMF simulations by reducing
the excessive precipitation over Asia summer
monsoon region and increasing cloud ice water
content in upper atmosphere.
2) Preliminary results show the usefulness of
cloudsat simulator and reflectivity CFAD statistical
analyses to understand and improve the cloud
microphysical processes in the model.
3) Comparisons with observations show some model
deficiencies. More works need to be done to use
observations from in-situ and remote sensing
platforms as model constrains.
5. References
Lang S., W.-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J.
Simpson, 2007: Improving simulations of convective systems from TRMM LBA:
Easterly and westerly regimes, J. Atmps. Sci. (in press).
Tao, W.-K. and coauthors, 2007: A multi-scale modeling: Developments,
applications and critical Issues. J. Geophys. Res. (submitted)