THE NASA/AMES MARS GENERAL CIRCULATION MODEL: MODEL
IMPROVEMENTS AND COMPARISON WITH OBSERVATIONS.
RM Haberle NASA/Ames Research Center (firstname.lastname@example.org), J.L Hollingsworth, NASA/Ames
Research Center OejJh@humbabe.arc.nasa.gov), A. Colaprete, NASA/ Ames Research Center
(email@example.com) A.F.C. Bridger, San Jose State University (firstname.lastname@example.org) C.P.
McKay, NASA/Ames Research Center (email@example.com), J.R. Murphy, New Mexico State Univer-
sity (firstname.lastname@example.org), J. Schaeffer, Raytheon Corporation (email@example.com), and R. Freed-
man, NASA/Ames Research Center (firstname.lastname@example.org).
Introduction: For many years, the NASA/Ames
Mars General Circulation Model (GCM) has been
built around the UCLA B-grid dynamical core. An
attached tracer transport scheme based on the aerosol
microphysical model of Toon et al. (1988) provided
a tool for studying dust storm transport and feed-
backs (Murphy et al., 1995). While we still use a B-
grid version of the model, the Ames group is now
transitioning to the ARIES/GEOS Goddard C-grid
dynamical core (Suarez and Takacs, 1995). The C-
grid produces smoother fields when the model top is
raised above 50 km, and has a built in transport
scheme for an arbitrary number of tracers. All of our
transport simulations are now carried out with the C-
We have also been updating our physics pack-
age. Several years ago we replaced our bulk boundary
layer scheme with a level 2 type diffusive scheme,
and added a multi-level soil model (Haberle et al.,
2000). More recently we replaced our radiation code
with a more generalized two-stream code that ac-
counts for aerosol multiple scattering and gaseous
absorption. This code gives us much more flexibility
in choosing aerosol optical properties and radiatively
active gases. Thus, we have several versions of our
GCM and these are listed in Table 1 .
also been coupled to a sophisticated cloud micro-
physics package (i.e., the Community Aerosol and
Radiation Model for Atmospheres - CARMA, see
Colaprete and Toon, 2000) to begin exploring water
and C0 2 ice cloud formation. However, the version
we are transitioning to is GCM 2.0, which is now
undergoing final testing, and check out.
Model Improvements: The C-grid transport
scheme advects tracers using the same numerical
algorithm developed for potential temperature. At
present, we use this scheme to transport water vapor,
and an arbitrary number of dust and cloud particles.
Dust can be lifted into the atmosphere through a
prescribed source, or a model-predicted parameteriza-
tion. Once into the lowest layer (nominally 10 m
thick) dust is vertically mixed by a stability depend-
ent diffusive scheme followed by a convective ad-
justment. Water vapor is treated similarly, though
we do not yet have a good evaporation parameteriza-
tion for surface ice. Dust is removed by gravitational
settling; water vapor by precipitation. The latter can
range in sophistication from simple successive satu-
ration removal, to a full up CARMA cloud micro-
The model-predicted dust lifting schemes are
based on the work of Murphy (1999) (with details
Table 1. Versions of the NASA/ Ames Mars General Circulation Model
t>BL / Soil
Bulk scheme /
Dust/C0 2 -
H 2 clouds
Dust/C0 2 -
Dust/C0 2 -
GCM 1 .0 was our original model but has been
retired (though it is still available for comparison
purposes). GCM 1 .5 was used to interpret Pathfinder
observations and was the first version of the model
used to assess the effects of MOLA topography on
the general circulation. GCM 1.7 is used to compare
given in Haberle et al, 2002) and Newman et al.
(2002). Murphy's lifting scheme is parameterized in
terms of surface stress, whereas the Newman et al.
scheme is based on a saltation flux calculated from
the friction velocity. Both schemes are threshold
THE NASA/AMES GCM: R.M. Haberle et al.
hard-wired to specific dust and water ice optical
properties, with C0 2 being the only radiatively active
gas. Furthermore, the range of surface pressures this
code can accommodate is limited to < 100 hPa. Our
new radiation code is based on a generalized two-
stream solution to the radiative transfer equation
with gaseous opacities calculated using correlated-
k's. The two-stream solutions can accommodate Ed-
dington, Quadrature, Hemispheric Mean, and Delta
function approximations. We are presently using the
Quadrature approximation for solar radiation and the
energy conserving Hemispheric Mean approximation
in the thermal infrared.
At present we run with 34 spectral intervals
from 0.3 to 250 microns. The correlated-k's for these
intervals are generated from a line-by-line code using
the HITEMP data base from HITRAN for C0 2 , and a
version of the Schwenke data base (to include lines
too weak to appear in HITRAN) for H 2 0. In both
cases line widths are adjusted to represent C0 2
broadening. A Voigt profile is used at low pres-
sures, and a Lorentz profile at high pressures. The
line widths are extended at high pressures so as to
include all significant absorption. The abundance of
the deuterated species for H 2 was adjusted for Mars
conditions. The line-by-line calculations were then
windowed, and sorted to produce the k coefficients.
We use a gauss scheme of 8 & 8 points in each spec-
tral interval with the dividing point at .95 to extract
the actual coefficients from the sorted probability
distribution. The k coefficients have been computed
for a range of pressures, temperatures, and relative
humidities that allows us to simulate past as well as
present Martian climates. A example of how this
new code compares with Dave Crisp's DART code
for a pure C0 2 atmosphere is shown in Fig. 1 .
- A — Y
• •— ft
1 ■ 1
-M -« -X -N -10 10 20 X « »
Fig. 1. Solar and IR heating rates computed from the
new radiation code (black) and Crisp's DART code.
For aerosols, we calculate the wavelength de-
Dendent scatterine DroDerties (di~. e. and 0„<) off
line using Mie theory. The dust scattering properties
we are presently using are taken from the Ockert-Bell
et al. (1997) work in the visible and Forget (1998) in
the infrared. The visible (.67 microns) to infrared
opacities are scaled to produce a value of 2 at 9 mi-
During the past several years we have coupled
the CARMA cloud microphysics package into the C-
grid (GCM version 1 .7) and have begun exploring
the behavior of H 2 and C0 2 ice clouds in the pre-
sent climate system (e.g., Colaprete and Haberle,
2001). The microphysics model accounts for the
particle-size dependent processes of nucleation, con-
densation, sedimentation, and evaporation. The ex-
pressions used for these processes and the rationale
for them are given in Colaprete (2000) and references
therein. The model keeps track of three particle types
in an arbitrary number of size bins: dust, ice, and
ice-coated dust. The ice can be water ice or C0 2 ice.
The code is general enough to handle both.
Comparison with Observations: We have been
comparing GCM 1.5, 1.7, and 2.0 with Viking and
MGS observations. With GCM 1.5 we were able to
pin down the annual global mean surface pressure on
Mars. We tuned the polar cap properties until the
model-predicted surface pressures gave a good fit to
the Viking Lander 1 and 2 data (Fig. 2). The result-
ing global mean annual surface pressure was 6. 1 hPa,
coincidentally (?) indistinguishable from the triple
point pressure of water.
Fig. 2. GCM fit to daily averaged Viking Lander
MOLX.D2 1 7 VL-TFH
M MD SO 1?0 1»0 TK" 210 W P6 1» M» JM
MOLXJ>2 1 7- VL-2 Frt
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Hwrtm m «i *.-!
, j .*-. i . .— i i i i ■ ■ '
3D BO »0 170
1BO Z1D Z*Q r^C JM JW *"
These simulations have also been compared to
TES temperature data, where we find reasonable
agreement with zonal mean values, but significant
differences with the inferred amplitudes of the winter
stationary waves. TES data show winter wave 1 am-
2002), whereas our fixed dust and globally uniform
GCM simulations produce ~20K in the south and ~
4K in the north. We can beat down the wave 1 am-
plitudes in the south by either lowering the global
opacity to ~ 0.1, or by running with a relatively clear
polar atmosphere. The latter is more realistic. In the
north however, the 4K amplitudes are robust to
changes in the dust distribution. The northern ampli-
tudes increase somewhat when the center of the time
averaging window is moved a little earlier or later in
the season. But the amplitudes never reach the 8K
seen in the TES data. Figure 3 shows our GCM re-
sults when we run using the TES observed opacities.
bution of observed dust storms is more or less longi-
tudinally uniform in both hemispheres, which is
consistent with model predictions in the Northern
Hemisphere, but less so in the Southern Hemisphere.
Interestingly, very little lifting is predicted in the
tropics (between ±30°) where only a few dust storms
were observed. Overall, the model compares re-
markably well with observations considering the
assumptions of uniform surface roughness, threshold
stress, and atmospheric dust loading.
Deflotion Potentiol (cm), Prwent Obliquity: Ls-109-
WAVE 1 [K) lou vorloble (Jli) U: 090
Fig. 3. Stationary wave 1 temperature amplitudes for
southern winter (top) and northern winter (bottom).
To compare the model results with observed
dust storm activity we introduce the concept of a
deflation potential, which we define as the depth of
dust that could be removed from the surface during a
specified period of time. The deflation potential from
one of our fixed dust (tau=0.3) experiments based on
the Murphy (1999) lifting parameterization for the
period between L s =109°-274° is shown in Fig. 4.
Also shown are Cantor et al's (2001) observations of
local dust storms by the MOC wide angle camera
during the same period. Both model and observa-
tions show that dust lifting occurs mostly poleward
of 30° in either hemisphere. There is also a modest
correlation between the density of dust storms and
thf> marrnitiirlf. nf tVie Hoflatinn rvntontiul Tho Hictri.
-180 -150 -120 -90
120 150 180
Fig. 4. Deflation potential (contours) and dust
An example of our fully coupled GCM and
cloud microphysical model is shown in Fig. 5. In
this simulation we employ the full capability of
CAR.MA and carry 6 dust bins, 6 cloud bins (water
and C0 2 ), and 1 water vapor bin. The figure depicts
the zonally-averaged mass-weighted mean water ice
cloud particle sizes at Ls=103°. The tropical aphe-
lion cloud belt is readily simulated. Most of the
water for these clouds comes from the subliming
Zona* Awoqefl tfl«1i* Cloud Pwlkle Rortm imenrt)
l,» 130 1JC 110 Z« W 1M UO M° i90
Fig. 5. Zonally-averaged ice cloud particle sizes from
cimiilattrm ncirto P A T? \A A
THE NASA/AMES GCM: R.M. Haberle et al.
north polar residual ice cap. The water is transported
off the cap at low levels and is then swept up in the
ascending branch of the Hadley circulation where it
is quickly transported into the southern hemisphere.
Some of this water precipitates out as it moves
across the equator thereby moistening the lower at-
mosphere. Once into the southern hemisphere, the
remaining water is moved back toward the surface in
the descending branch of the Hadley circulation
where some of it condenses out onto the seasonal
C0 2 ice cap which extends to about 60°S in this
One aspect of the observations, which our model
does not compare well with, is the thermal tides.
The amplitudes of our diurnal and semidiurnal sur-
face pressure tides are significantly lower than ob-
served at either Viking lander site, or the Pathfinder
site. Though this is not necessarily a serious flaw,
the fact that other GCMs do find good agreement has
motivated us to better understand the reason for our
weaker tides. Given the sensitivity of the tides to
dust heating, this is the obvious thing to explore
first. So we have begun simulations with GCM 2.0
to determine the tidal response to different assump-
tions about the dust radiative properties. We hope to
report these results at the workshop.
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