APHELION EQUATORIAL MESOSPHERIC CLOUDS OBSERVED BY MCS
M. Slipski, A. Kleinböhl, D. M. Kass, Jet Propulsion Laboratory, California Institute of Technology, Pasadena,
CA, USA. (marek.j.slipski@jpl.nasa.gov).

Introduction and background
Clouds in the middle atmosphere (>50 km) of Mars
have been observed by many instruments in the past few
decades [1, 2, 3, 4] and are important for better understanding the vertical transport of volatiles, dynamical
phenomena, and coupling processes between the lower
and middle atmopshere.
One population has been consistently observed in the
equatorial region during aphelion season [5, 6]. While
they are fairly well-studied, certain characteristics, such
as a clear pause in cloud formation near northern summer
solstice, are not well understood. They are generally
clustered around 2-3 longitudes between about 20◦ S to
20◦ N and are most frequently observed from Ls=0-180◦ ,
with noticeably less formation from Ls=60-100◦ . Many
clouds in this population have been spectroscopically
confirmed to be CO2 ice [1, 4, 6].
The mesosphere from 20◦ S to 20◦ N during the aphelion season is one of the coldest regions seen in the
Martian atmosphere (Fig. 1, top). Although thermal
tides can further cool this region [7], measured and
modeled temperatures are rarely below the CO2 frost
point. Gravity waves are small-scale perturbations that
can propagate into the middle atmosphere and have been
recognized as the likely source of further cooling since
mesospheric CO2 ice clouds were first suggested by
Mars Pathfinder observations [8, 9]. Recent observations have shown that some mesospheric clouds do form
in the cold pockets of gravity waves [10]. Modeling
suggests waves are more likely to propagate in regions
where clouds have been observed and saturate in regions
where they do not [11]. Furthermore, gravity wave activity is higher in the aphelion season than elsewhere
[12, 13]. Increased wave amplitudes lead to higher temperature variance and the aphelion equatorial middle
atmosphere displays large temperature variance (Fig 1,
bottom).
In this work, we identify equatorial mesospheric
clouds throughout the MY33 aphelion season during the
day and at night using observations from the Mars Climate Sounder (MCS) on MRO. We investigate the spatial
(longitude-altitude) and seasonal variations in clouds as
well as the background temperature and its variance.
From those parameters, we calculate the probability of
the temperature dropping below the CO2 frost point,
P (T <Tc ), in a given region. We find that P (T <Tc ) represents the distribution of mesospheric clouds reasonably well, highlighting the importance of the interplay
of both tides and waves in enabling conditions favorable

Figure 1: Mean temperature (top) and standard deviation in
temperature (bottom) from Ls=15-30.

for cloud formation.

Data and methods
Clouds are identified using MCS limb radiances and temperatures retrieved from MCS limb radiances are used in
this study. MCS has nine spectral channels (5 mid-IR, 3
far IR, and one visible/near-IR) each with a linear array
of 21 detectors, and acquires limb views which measure
radiance profiles from the surface to about 80 km altitude at ∼5 km vertical resolution [14]. The instrument
typically observes in the forward limb (“in-track") direction of motion of the spacecraft. Aerosol layers are
evident in in-track radiance time series profiles as they
appear as arch-shaped or “loop” features [15, 4]. An
example is shown in Fig. 2.
An arch is produced as MCS observes a cloud in the
forward limb across many profiles due to the spacecraft
moving along its orbit. As shown in Fig. 3 [15], high
altitude clouds viewed in the forward limb initially have
an apparent altitude at the surface. But as spacecraft’s
motion continues, the cloud’s position in the viewing

Figure 2: Example of an arch observed by MCS in the A2
channel at Ls=0 during MY29.

Figure 3: Diagram showing the geometry that leads an aerosol
layer to manifest as an arch shape in MCS observations.
Credit: [15]

geometry changes and its apparent altitude rises. When
it is at the tangent point, the apparent altitude is the true
altitude of the cloud. Then, the apparent altitude begins
to fall again until it is out of view.
Though they do not manifest as arches, clouds are
still present in cross-track observations in which MCS
views the limb 90◦ to the left or right of the orbital
trajectory through the use of its azimuth actuator. In
2010, MCS began ∼30 sol cross-track campaigns with
30-40 sols between campaigns [16]. During MY33, the
cross-track pattern was executed almost continuously.
We identified clouds from Ls=0-195◦ in MY33 in
in-track and cross-track observations in MCS’s A2 and
A6 channels. We separated the identified clouds by
night and day for each look-direction resulting in six
local times: approximately 1:30 AM (right cross-track),
3 AM (in-track), 4:30 AM (left cross-track) at night,
and 1:30 PM (left cross-track), 3 PM (in-track) and 4:30
PM (right cross-track) during the day. Then, we binned
the data into 15◦ Ls, 15◦ longitude, and 5 km altitude
bins and divided the identifications by the total number
of observations to calculate the cloud occurrence rate
(“cloud fraction”) in each bin.
In each bin, we also computed the mean and variance of temperatures retrieved from MCS excluding any
profiles containing an arch. The variance in a bin represents the geophysical variability from the background

Figure 4: Distribution of day (red) and night (blue) average
cloud fractions from Ls=15-30◦ . Daytime cloud fractions are
from z = 65-70 km and nighttime cloud fractions from z =
55-60 km.

mean at that location. Wave activity causes variability in
temperatures, strongly influencing the temperature variance in any bin. Temperature variance can be used as
a proxy for wave amplitudes [17, 18], though it cannot
distinguish between various types of waves. For each
bin, given the mean temperature and variance, we determined the probability of the temperature in that bin
being below the CO2 frost point by simply calculating
the cumulative probability up to the CO2 frost point,
P (T <Tc ). We investigated seasonal, longitudinal, and
local time variability of temperature, its variance, and
P (T <Tc ). and compare with binned cloud fractions.

Results
We find that the both the daytime and nighttime clouds
have a wave-3 longitude structure with two primary clusters and a less prominent cluster east of 50◦ E (Fig 4).
The dayside distribution agrees well with previous observations of mesospheric equatorial clouds [6]. While
some mesospheric clouds have been observed at night,
we show for the first time that the nighttime longitude
distribution is offset in phase from the dayside; the nighttime clouds occur in the gaps of the daytime clouds. The
differences between the three night local times are small;
cloud occurrence rates are slightly lower for 4:30 AM
than 3 AM and 1:30 AM. On the dayside, far fewer
clouds are observed at 1:30 PM than at 3 PM and 4:30
PM (not shown).
The spatial distribution and the day-night differences
indicate of the role of thermal tides in cloud formation,
and indeed, the longitudes where the clouds correspond
to tidal minima in temperature. In addition, the temperature standard deviations are also higher in these regions than in others. Cold average temperatures and
high temperature variance lead to low P (T <Tc ) in the
regions where cloud occurrence rates are high. This is
demonstrated in Fig 5. The cloud occurrence rates are

REFERENCES

in the mesosphere, which may be due to changing circulation patterns that can lead to a increased wave dissipation. This suggests that the solsticial pause may be
related to seasonal changes in gravity wave activity.
Summary and conclusion

Figure 5: P (T <Tc ) from Ls=0-45◦ at 3 AM as a function of
longitude and altitude. Red contours correspond to observed
A2 cloud fraction (dotted=0.6%, dashed=2%, solid=6%).

We have identified mesospheric clouds in MCS radiance data during the aphelion season of MY33. Utilizing MCS cross-track observations, we find local time
variability of equatorial meospheric clouds, with more
clouds observed at 3 PM and 4:30 PM than 1:30 PM.
Comparing the occurrence rates of those clouds with the
temperature mean and standard deviation in a given bin,
we find that clouds form where both the mean temperature is low and the standard deviation is high. The
close agreement between the spatial and seasonal variation of the clouds—including the solsticial pause—and
P (T <Tc ) demonstrates that the complementary effects
of waves and tides not only enable individual clouds to
form, but can also lead directly to the observed climatology of mesospheric clouds.
Acknowledgements

Figure 6: P (T <Tc ) as a function of Ls for 120-135◦ W at
3 AM. Contours correspond to cloud fractions: dotted=0.5%,
dashed=2%, solid=4%.

shown in red contours (increasing from dotted to dashed
to solid) plotted over the P (T <Tc ). In this example (3
AM), P (T <Tc ) reproduces both the distribution and the
magnitude of the cloud fraction reasonably well.
Cloud occurrence rates are highest early in the season and drop off dramatically after Ls=45. As has
been observed previously, there is a lull in clouds from
Ls=45◦ to Ls∼90◦ both during the day and at night. The
cloud occurrence rates increase again, with a second
peak around Ls=105◦ on the dayside and Ls=140-180◦ at
night. The altitude of the peak occurrence rate decreases
from Ls=0-45◦ and increases during the second phase.
An example of this seasonal trend can be seen in Fig.
6, where the red contours illustrate the change in cloud
occurrence rates at 3 AM for the longitude bin.
The low values of P (T <Tc ) during the solsticial
pause in clouds is driven by a decrease in the temperature standard deviations at mesospheic altitudes (the
background temperature cools). At night, temperatures
rise slightly and the temperature standard deviation decreases significantly. The decrease in the standard deviation during equinox reflects smaller wave amplitudes

Marek Slipski’s research was supported by an appointment to the NASA Postdoctoral Program at the Jet
Propulsion Laboratory, administered by Universities Space
Research Association. This work was performed at the
Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics
and Space Administration. © 2022, California Institute of Technology. Government sponsorship acknowledged.
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