GLOBAL TROPOSPHERE-TO-MESOSPHERE MODELLING OF MARTIAN
CO2 ICE CLOUDS.
A. Määttänen, C. Mathé, LATMOS/IPSL, Sorbonne université, UVSQ Université Paris-Saclay, CNRS, Paris,
France (anni.maattanen@latmos.ipsl.fr, C. Mathé now at LMD), J. Audouard, LATMOS/IPSL, Sorbonne université,
UVSQ Université Paris-Saclay, CNRS, Paris, France. Now at: WPO, Paris, France, C. Listowski, LATMOS/IPSL,
UVSQ Université Paris-Saclay, Sorbonne université, CNRS, Paris, France. Now at: CEA/DAM/DIF, F-91297 Arpajon,
France, E. Millour, F. Forget, LMD/IPSL, Sorbonne Université, Centre National de la Recherche Scientifique (CNRS),
École Polytechnique, École Normale Supérieure (ENS), Paris, France, F. González-Galindo, Instituto de Astrofı́sica
de Andalucı́a-CSIC, Granada, Spain, L. Falletti, LATMOS/IPSL, Sorbonne université, UVSQ Université ParisSaclay, CNRS, Paris, France, D. Bardet, School of Physics and Astronomy, University of Leicester, Leicester, UK, L.
Teinturier, LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Univ. Paris Diderot, Sorbonne
Paris Cité, Meudon, France, M. Vals, LATMOS/IPSL, UVSQ Université Paris-Saclay, Sorbonne université, CNRS,
Paris, France, A. Spiga, LMD/IPSL, Sorbonne Université, Centre National de la Recherche Scientifique (CNRS),
École Polytechnique, École Normale Supérieure (ENS), Paris, France. Institut Universitaire de France (IUF), Paris,
France, F. Montmessin, LATMOS/IPSL, UVSQ Université Paris-Saclay, Sorbonne université, CNRS, Paris, France.

Introduction
Previous CO2 ice cloud modeling studies [Colaprete
et al., 2003, Tobie et al., 2003, Forget et al., 1998, Kuroda
et al., 2013, Colaprete et al., 2008] allowed to develop
and test ideas on CO2 ice cloud formation; however,
the studies were often made in idealized settings and included simplified physics and/or limited model boundaries such as a low model top. Listowski et al. [2014]
developed refined CO2 cloud microphysics adapted for
the Martian conditions of a near-pure vapor condensing in highly supersaturated conditions [Listowski et al.,
2013]. They showed with a one-dimensional model and
a temperature structure including tidal and gravity wave
effects that an additional source of condensation nuclei
(CN) was required in the mesosphere for the formation
of observed-like clouds.
The model of Listowski et al. [2014] has now been
coupled with the LMD Mars GCM (MGCM), jointly
developed by several laboratories, including LATMOS,
LMD, and IAA. The MGCM is now able to simulate
the formation of CO2 ice clouds in the Martian atmosphere taking into account mineral dust particles and
water ice crystals as potential condensation nuclei (CN).
The first simulations are used for testing the new microphysics and comparing its results to the previous simplified CO2 condensation parameterization. The simulations are compared to the published CO2 ice cloud
observations.

Model description
We are using the most recent version of the MGCM
and most of the included processes have been described
in Navarro et al. [2014], including water ice cloud microphysics and their radiative effect. The microphysics
use a subtimestep: CO2 cloud processes are calculated

50 times within a physics timestep (the microphysical
timestep is 18 s).
The new CO2 ice cloud microphysics follow closely
the implementation of water ice cloud microphysics
in the MGCM, performed via the so-called modal approach, frequently used in GCMs. This means that the
particle size distribution is described assuming a lognormal form for the distribution and its evolution is described through the integral properties (moments) of the
distribution. Our model uses two variables for describing each particle type: the total number concentration
of the particles and the total mass.
We do however discretize the CN size distribution
for calculating the heterogeneous nucleation rate and
probability. This is necessary for ensuring that a realistic
fraction of the available CN is activated for nucleation,
since nucleation is very dependent on the CN size. We
use the Classical Nucleation Theory applied to Mars
[Määttänen and Douspis, 2014] and a contact parameter
of 0.952 [Glandorf et al., 2002]. Nucleation is calculated
separately for the three potential CN types (water ice
crystals, dust particles and meteoric particles).
The growth rate of the CO2 ice crystals is calculated
as in Listowski et al. [2014], but the iterative solution
they proposed has been replaced by an analytical solution that is much faster and more efficient especially for
use in a GCM.
Sedimentation of CO2 ice crystals is calculated within
the microphysics timestep as well and the code sediments all CN that have been captured by the CO2 ice
crystals. This means that the CN are scavenged by
the CO2 crystals. During snowfall to the ground, the
ices and dust are incorporated into the surface reservoirs. The fall speed is calculated with the classical
Stokes-Cunningham relation with the correction taking
into account the flow regime.
We have performed three simulations, one with the
previous, simple parametrization of CO2 condensation,

Latitude (°N)

90 Zonal and diurnal mean of column density of CO2 ice
kg/m2101
75
60
45
30
10 1
15
0
-15
10 3
-30
-45
-60
-75
10 5
-90 0
90
180
270
360
Solar longitude (°)

Latitude (°N)

one with the new CO2 cloud microphysics but only including dust particles as CN, and one using both dust and
water ice as CN, respectively called PARAM, MPCO2,
and MPCO2+H2OCN. The simulations have been run
for three Martian years and we take the results from the
last simulation year to insure convergence. We use the
climatological dust scenario that describes well an average Martian Year without a global dust storm. We do not
include photochemistry in these simulations due to its
computational cost. This may induce an overestimation
of the water vapour concentration in the middle atmosphere, which may in turn lead to and overestimation of
the water ice formation and influence CO2 ice cloud formation in the mesosphere. We use a spatial resolution
of 5.6258° longitude x 3.758° latitude, corresponding
to a 64 longitude x 48 latitude horizontal grid, and 32
vertical levels. The model top is defined at the pressure
level 3×10−3 Pa that corresponds approximately to an
altitude of 120 km (for a surface pressure of 610 Pa and
an atmospheric scale height of 10 km).

90
75
60
45
30
15
0
-15
-30
-45
-60
-75
-90 0

Results
The model produces CO2 ice clouds in the polar regions in the troposphere during the winter and in the
mesosphere at equatorial altitudes. Figure 1 shows
the CO2 ice column density as a function of latitude
and solar longitude for the two simulations: MPCO2
and MPCO2+H2OCN with observations from several
instruments [Clancy et al., 2007, Montmessin et al.,
2006, Määttänen et al., 2010, McConnochie et al., 2010,
Scholten et al., 2010, Vincendon et al., 2011, Aoki et al.,
2018, Clancy et al., 2019, Jiang et al., 2019, Liuzzi et al.,
2021]. The polar clouds are formed in a larger altitude
range than in the observations, reaching from the surface up to 40 km altitude. The polar atmosphere is cold
and supersaturated in a deeper layer than in the observations, causing the thicker cloud formation that forms
earlier and lasts longer. The cause of the colder polar
winter troposphere may be the prescribed dust column
depth coming from observations: as there are very few
observations in the polar night, the column depth has
been given a minimum value that might still be too high
compared to reality. This larger dust content in the polar
night causing a too large radiative cooling may be the
reason for the cold temperatures. The snowfall from
polar clouds contributes about 10% to the formation of
the polar ice caps. Substantial mesospheric clouds only
form when the model takes into account CO2 ice nucleation on both water ice crystals and dust grains. The
seasonal distribution of the clouds agrees well with observations during most of the mesospheric cloud season,
but the pause in cloud formation around the aphelion
is more clear-cut than in the observations where cloud
formation seems to continue during the whole cloud
season, although at a lower frequency during a certain

90

180
Solar longitude (°)

270

10

7

10

9

10

11

10

13

360

Figure 1: Zonal and diurnal mean of CO2 ice column density.
Top: MPCO2 simulation; bottom: MPCO2+H2OCN simulation. The black solid line limits the area where MCS observed
temperatures below CO2 condensation [Hu et al., 2012]. The
black dots show mesospheric CO2 ice cloud observations (see
text).

REFERENCES

period around the aphelion. The clear pause in cloud
formation in the model is due to the rise in mesospheric
temperatures that seems to be related to a change in the
circulation regime and winds in the mesosphere. As
cloud formation is a very strong function of temperatures, even a small change in temperature can cause
large changes in cloudiness. The reasons for the sudden
change in the mesospheric state has not been investigated
further, but it is planned in a future study dedicated to
mesospheric clouds. The optical thickness of the clouds
remains two orders of magnitude below observed values,
pointing to a need for an exogenous CN source (meteoritic dust: [Listowski et al., 2014, Plane et al., 2018]).
Such a source is being added to the model (see the paper
by Mathé et al. in this conference).
Conclusion
We present the results of simulations with a new coupled 3D global model - CO2 ice cloud microphysical
model. The cloud model accounts for a condensation
theory adapted for the highly supersaturated near-pure
vapor conditions of the Martian atmosphere and water
ice crystals as condensation nuclei.
The MGCM produces CO2 ice clouds both in the
polar winter troposphere and in the tropical summer
mesosphere. The mesospheric only form when water
ice crystals are considered as condensation nuclei by

the model. This confirms that mineral dust lofted from
the surface to the mesosphere is not sufficient as CN.
Despite the good agreement of the modeled spatial and
seasonal cloud distributions, the modeled CO2 ice cloud
optical depths are orders of magnitude below those observed, confirming the need of an additional CN source
in the mesosphere, which could be provided by particles formed after meteor ablation. The polar clouds are
formed of crystals ranging from some microns in radius to some tens of microns, agreeing with observed
estimates. Snowfall from polar clouds contributes up
to 10% of the ice flux to the polar cap ice. This is
within the surface ice contribution estimated from observations. The polar clouds extend to higher altitudes
and the cloud formation season is longer than the observed ones. Overall, the modeled polar atmosphere is
colder than what the observations show, revealed by the
very deep supersaturated polar atmosphere in the model,
but this might be due to a too high dust optical thickness
in the polar regions leading to a large cooling.

Acknowledgements
We thank the Agence National de la Recherche for
funding (projet MECCOM, ANR-18-CE31-0013), the
LabEx ESEP, CNES and PNP. This work was performed
using HPC computing resources from GENCI-CINES
(Grant 2021-A0100110391), and resources at the ESPRI mesocentre of the IPSL institute.

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