Icarus 314 (2018) 1–11

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Latitudinal variation of clouds’ structure responsible for Venus’ cold
collarItziar Garate-Lopez∗, Sébastien Lebonnois
Laboratoire de Météorologie Dynamique (LMD/IPSL), Sorbonne Université, ENS, PSL Research University, Ecole Polytechnique, Université Paris Saclay, CNRS,Paris, Francea r t i c l e

i n f oa b s t r a c t

Article history:
Received 8 August 2017
Revised 27 March 2018
Accepted 14 May 2018
Available online 15 May 2018

Global Climate Models (GCM) are very useful tools to study theoretically the general dynamics and speciﬁc phenomena in planetary atmospheres. In the case of Venus, several GCMs succeeded in reproducing
the atmosphere’s superrotation and the global temperature ﬁeld. However, the highly variable polar temperature and the permanent cold collar present at 60◦ − 80◦ latitude have not been reproduced satisfactorily yet.

Keywords:
Venus atmosphere
Cold collar
Modelling

Here we improve the radiative transfer scheme of the Institut Pierre Simon Laplace Venus GCM in orderto numerically simulate the polar thermal features in Venus atmosphere. The main difference with theprevious model is that we now take into account the latitudinal variation of the cloud structure. Bothsolar heating rates and infrared cooling rates have been modiﬁed to consider the cloud top’s altitudedecrease toward the poles and the variation in latitude of the different particle modes’ abundances.
A new structure that closely resembles the observed cold collar appears in the average temperature ﬁeld
at 2 × 104 − 4 × 103 Pa (∼ 62 − 66 km) altitude range and 60◦ − 90◦ latitude band. It is not isolated fromthe pole as in the observation-based maps, but the obtained temperature values (220 K) are in good
agreement with observed values. Temperature polar maps across this region show an inner warm region
where the polar vortex is observed, but the obtained 230 K average value is colder than the observed
mean value and the simulated horizontal structure does not show the ﬁne-scale features present within
the vortex.
The comparison with a simulation that does not take into account the latitudinal variation of the cloud
structure in the infrared cooling computation, shows that the cloud structure is essential in the cold collar
formation. Although our analysis focuses on the improvement of the radiative forcing and the variations
it causes in the thermal structure, polar dynamics is deﬁnitely affected by this modiﬁed environment anda noteworthy upwelling motion is found in the cold collar area.
© 2018 Elsevier Inc. All rights reserved.

1. Introduction
In the last two decades, Global Climate Models (GCMs) haveturned out to be very useful tools to study the general atmospheric circulation on Venus and the role of different phenomenain the angular momentum budget. Several numerical simulations
by different GCMs have already reproduced the main characteristic of the Venus atmosphere: its superrotation (e.g. Yamamoto and
Takahashi, 2003; Lee et al., 2007; Sugimoto et al., 2014a; Lebonnois et al., 2016). At about 70 km altitude clouds circle the solid
planet at ∼100 m/s, sixty times faster than the rotation of the∗Corresponding author.
E-mail addresses: itziar.garate-lopez@lmd.jussieu.fr, itziar.garate@lmd.jussieu.fr(I. Garate-Lopez), sebastien.lebonnois@lmd.jussieu.fr (S. Lebonnois).
https://doi.org/10.1016/j.icarus.2018.05.011
0019-1035/© 2018 Elsevier Inc. All rights reserved.

planet. These simulations support the Gierasch–Rossow–Williams
mechanism (Gierasch, 1975; Rossow and Williams, 1979) to explain
the maintenance of the superrotation, which holds the latitudinal transport of angular momentum by horizontal planetary-scale
waves and by the meridional circulation responsible for the velocity ﬁeld in the atmosphere. GCMs also conﬁrmed that in the caseof Venus, the thermal tides play a key role in transporting the angular momentum vertically (Takagi and Matsuda, 2007; Lebonnois
et al., 2010). On the other hand, it is well known that there is awide variety of waves in the main cloud deck (Belton et al., 1976;
Rossow et al., 1980; Del Genio and Rossow, 1990; Peralta et al.,2008; Piccialli et al., 2014) that may contribute to the transport ofangular momentum and, therefore, recent GCMs studies have focused on the analysis of the wave activity present in the Venusian
atmosphere (Sugimoto et al., 2014b; Lebonnois et al., 2016).

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I. Garate-Lopez, S. Lebonnois / Icarus 314 (2018) 1–11

One of the pending questions for the Venus GCMs is the simulation of the atmospheric thermal structure. Although the average temperature ﬁeld has been reproduced adequately, no model
has satisfactorily simulated one of its most puzzling properties: thepolar temperature distribution, formed by highly variable vortices
and cold air areas that surround the vortices permanently (Zasova
et al., 2007; Tellmann et al., 2009).
The temperature in the deep atmosphere is almost constantfrom equator to around 50°, and slowly decreases polewards(Tellmann et al., 2009). However, above ∼70 km altitude temperatures increase from equator to pole, in contrast to what is expected from radiative-convective equilibrium, since solar heating
is higher at equator than at the poles. This is known as the warm
polar mesosphere. At 60–70 km altitude the temperature ﬁeld in
the 60◦ − 80◦ region has a striking feature in both hemispheres,a strong temperature inversion called the cold collar (Taylor et al.,1980). It is a collar of cold air circling each pole of Venus. The latitudinally averaged temperature between 60° and 80° is about 20 K
colder than at the equator and 15 K colder than at the poles, i.e.
between 80° and 90° (Haus et al., 2014). This temperature difference would cause it to dissipate rapidly, however the cold collar
is a permanent structure at the sub-polar latitudes of Venus, implying that it is forced by some unknown mechanism. Enclosed by
the cold collar there is a warm polar vortex in each pole of Venus.
These are rapidly rotating cloud and temperature structures thatshow highly variable and complex morphologies with warm smallscale ﬁlaments (Piccioni et al., 2007; Luz et al., 2011; Garate-Lopezet al., 2013). This region features the greatest horizontal temperature gradient at the cloud top level. Neither the cold collar nor
the warm vortex are completely uniform along the latitude circles(Grassi et al., 2010; Garate-Lopez et al., 2015); therefore, the latitudinally averaged temperature ﬁeld hides the real thermal contrast
between these two features. The mean temperature difference between the coldest area of the cold collar and the warmest nucleusof the vortex is ∼30 K, but the temperature gradient can be as
high as dT/dr = 0.1 K/km (δ T = 50 K over 500 km) in this region(Garate-Lopez et al., 2015).
Although these polar structures have been observed repeatedly, their origin and formation mechanism are still unknown and,
therefore, numerical studies of the Venusian polar region wererecently conducted (Yamamoto and Takahashi, 2015; Ando et al.,2016; 2017; Lebonnois et al., 2016). Using GCMs that force thetemperature structure with Newtonian cooling and previously prepared heating rate proﬁles, Yamamoto and Takahashi (2015) andAndo et al. (2016) concluded that thermal tides play an important
dynamical role in the formation of the cold collar and warm vortex. The former stated that the baroclinic waves are also part of
the formation mechanism while the latter attributed the origin of
the polar structures to the combination of the thermal tides andthe residual meridional circulation. Ando et al. (2017) investigatedthe vertical structure of the temperature ﬂuctuation in the Venusian polar atmosphere by comparing radio occultation measurements and GCM results, and concluded that the thermal disturbance associated to the polar vortex could be a neutral barotropic
Rossby wave related to barotropic instability. These three works
obtained a temporally evolving polar region and some horizontal structure within the vortex. However, the average latitudealtitude distributions of temperature do not exactly reproduce theobserved cold collar structure. In observation-based maps (Zasova
et al., 2007; Tellmann et al., 2009; Haus et al., 2014) the cold collar is an isolated region and is located slightly lower than in themaps of Yamamoto and Takahashi (2015) and Ando et al. (2016,2017). In addition, the average temperature values obtained byAndo et al. (2016) are not warm enough compared to observations.
On the other hand, Lebonnois et al. (2016) used the IPSL (Institut Pierre Simon Laplace) Venus GCM, that has a full radiativetransfer module, to model the Venus atmosphere paying a special attention to polar regions. Their zonally and temporally averaged latitudinal proﬁles showed a small cold collar signature at
the cloud top ( ∼70 km), located slightly higher than observed. Dueto its axi-asymmetric distribution, the obtained cold collar looked
subtle when zonally averaged but it was more evident in the polar
temperature ﬁelds. They related the shape of the polar temperature
distribution (cold collar and warm vortex) to the combination ofthermal tide and high-frequency wave activity but the temperature
contrasts obtained inside the polar regions ( ∼10 K) were weaker
than those presented by Ando et al. (2016, 2017) ( ∼20-25 K) andwere far from the observed ∼30 K on average (Garate-Lopez et al.,2015).
In order to perform an in-depth study of the Venus polar atmosphere and try to better understand the origin and nature of
the cold collar and warm vortex, we have adapted the IPSL Venus
GCM by taking into account the latitudinal variation of the cloud
structure (not considered in any GCM mentioned above). The details about the improvements on the radiative transfer scheme aredescribed in Section 2. Then, Section 3 shows the average temperature and zonal wind ﬁelds obtained by the current model, which
are in better agreement with observations and reproduce the characteristic cold collar feature in a more realistic way than previously. We discuss the formation of the cold collar in terms of radiative transfer effects in Section 4 and we sum up the conclusions
in Section 5.
2. Simulations
2.1. IPSL Venus GCM
The general characteristics of the IPSL Venus GCM havebeen described in detail in Lebonnois et al. (2010) and
Lebonnois et al. (2016). Therefore, only a short summary is given
here. The model is based on the LMDZ latitude-longitude gridﬁnite-difference dynamical core (Hourdin et al., 2006). Since thegoal of the current study is the polar regions, it must be notedthat this dynamical core includes a longitudinal polar ﬁlter: thisFourier ﬁlter is applied at latitudes higher than 60°, removing highfrequencies and limiting the effective resolution to that at 60° latitude (Jablonowski and Williamson, 2011). Some of the most important features of the model are: the boundary layer scheme used,based on Mellor and Yamada (1982); the implementation of topography, with hybrid vertical coordinates (50 vertical levels, fromsurface to roughly 95 km altitude); the temperature dependence ofthe speciﬁc heat Cp (T), which affects the deﬁnition of the potential
temperature (as detailed in Lebonnois et al., 2010); and the horizontal resolution currently used, 96 longitudes × 96 latitudes, i.e.
3.75° × 1.875°.
The previous version of the model (Lebonnois et al., 2016) reproduced consistently the average zonal wind and temperatureﬁelds, but the obtained equatorial jet was too intense compared
to mid-latitude jets and the winds below the clouds were too
slow compared to measurements made by Venera and Pioneer
Venus probes (Schubert, 1983; Gierasch et al., 1997). On the other
hand, the modeled average temperature structure, although generally consistent with observations, resulted in colder temperatures in the deep atmosphere and at surface compared to observed proﬁles (Seiff et al., 1985), and in slightly higher temperatures above the clouds (Tellmann et al., 2009; Migliorini et al.,2012; Grassi et al., 2014). The latitudinal proﬁles showed a small
cold collar signature at ∼70 km and the polar temperature distribution at ∼67 km displayed some inner structure. However, in
the average altitude-latitude temperature map the cold collar was
not an isolated feature as in observation-based maps (Tellmann
et al., 2009; Haus et al., 2014) and the contrast between cold col-

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3

Table 1Chosen inputs for the simulations.

Initial state
0–200 Vd
20 0–30 0 Vd:- unifcld- varcld

Solar heating

Correlated-k set

Cloud model (IR)

C86a

H15b

L16c

Z07d

×

×
×

×

×
×

updated

H13eContinuum (3-7μm)
H14f

×updatedg

×
×

×
×

L16c

×

×
×

×
×

a

Crisp (1986).
Haus et al. (2015), with additional tuning in lower haze area (see text).
c
Lebonnois et al. (2016).
d
Zasova et al. (2007).
e
Haus et al. (2013), clouds taken as uniform with latitude.
fHaus et al. (2014), clouds taken as variable with latitude.
g
see text.
b

lar and warm vortex was weaker than observed, about 10 K in themodel vs ∼30 K in temperature retrievals from VIRTIS instrument(Garate-Lopez et al., 2015). The latitudinal structure of the clouds
was not taken into account in this previous IPSL model, though
Lebonnois et al. (2016) noted that it could play a role in the shapeand strength of the cold collar.
Due to the development of different tools necessary to implement the latitudinal variation of the cloud structure in the solar
heating rates and in the infrared cooling rates, the improvementsin the radiative transfer were done in two stages; (1) implementation of the cloud structure in the solar heating rates, plus sometuning, and (2) implementation of the cloud structure in the IR
cooling rates (these improvements and tuning are described in thenext section). The simulation started from an already superrotating
atmosphere obtained from previous simulations (Lebonnois et al.,2016) and it was then run for 200 Venus days (Vd, 117 Earth days)
with the partially-improved radiative transfer, the one obtained after the ﬁrst stage of the improvements. Two simulations were thenpursued for another 100 Vd: one with latitudinally uniform clouds
in the IR cooling computation (referred to as unifcld hereon) andanother one with latitudinal variations of the cloud structure forstudying the cold collar (varcld hereon). The former case, unifcld
simulation, is used for control by comparing it to the varcld simulation in the discussion of the formation of the cold collar, but
this simulation and its results have no physical sense and should
not be considered alone. In the latter case (varcld simulation) a
stable state of the atmosphere was obtained after roughly 60 Vdof simulation, so it is the simulation that represents the current
IPSL Venus GCM model. The different inputs used in each stage
and simulations are summarized in Table 1.
2.2. Improved radiative transfer
Previously, the radiative transfer included solar heating rate
proﬁles as a function of solar zenith angle taken from look-up tables based on Crisp (1986), and used the infrared net-exchange
rate matrix formulation (Eymet et al., 2009) with horizontally uniform opacity sources (gas and clouds).
For the current study, the main modiﬁcation in the radiativetransfer is related to the cloud model. Our computations now take
into account the more recent cloud model described by Haus et al.
(2014, 2015), which is based on the most recent observations performed by the Venus-Express spacecraft. In particular, this model
describes latitudinal variations of the cloud structure. According toHaus et al. (2014), the cloud top altitude (deﬁned as the altitude
where the optical depth equals the unity at 1 μm) decreases slowly
from about 71 km at the equator to about 70 km at 50°, but thenit quickly drops poleward reaching about 61 km over both poles.
Authors also found a strong latitude dependence of cloud opacity,which is transposed into the latitudinal behavior of cloud mode
factors, scaling the abundance of the different modes compared tothe equatorial vertical distribution. Retrieved mode 1 and 2 factors
gradually decrease poleward of 30° showing a small local minimum at 55°. The mode 3 factor is nearly constant from equatorto ∼15°, decreases between 15° and 30°, remains almost constant
up to 55°, but then strongly increases poleward (Haus et al., 2014).
This new cloud model and some tuning (see details below) were
implemented both in the solar heating and infrared cooling rates’
computations.
2.2.1. Solar heating
For the short-wavelength part of the radiative transfer scheme,
the principle used is basically the same as previously. Look-up tables are used, that contain the vertical proﬁle of the solar heating
rate as a function of solar zenith angle. The model described in
Haus et al. (2015) is now used to produce these tables. In addition,
since the latitudinal structure of the cloud is now taken into account, the tables vary with latitude, in bins of 5°. It can be notedthat in this cloud model, the unknown ultraviolet absorber is modeled independently from the cloud particle modes, regardless of
the absorber’s chemical composition.
The vertical proﬁle of the solar heating plays a crucial role in
the temperature proﬁle, as shown e.g. by Lebonnois et al. (2015).
However, it is poorly constrained by available data. A series of tests
were performed with the new solar heating rates (Lebonnois andSchubert, 2017, Methods and Supplementary Materials). Below approximately the cloud base, these solar heating rates are smaller
than the ones that were used previously (Crisp, 1986), and thisresulted in deep atmospheric temperature proﬁles signiﬁcantly
colder than observed. The composition of the lower haze particles, located between the cloud base (48 km) and 30 km andobserved by in-situ probe instruments (Knollenberg et al., 1980),
is not established, so their optical properties are not well constrained. This sub-cloud haze is not taken into account in the
Haus et al. (2015) model, except for the tail of the sulfuric
acid cloud mode vertical distributions. The absorption of the solar ﬂux in this region is therefore subject to uncertainty. To
better reproduce the temperature structure in the middle cloudand below, we decided to increase the solar heating rates inthis region, multiplying the solar heating rates provided by theHaus et al. (2015) model by a factor of 3. This tuning brings thevalues in the same range as other models (Crisp, 1986; Lee andRichardson, 2011), and allows the temperature proﬁle to reach observed values.
2.2.2. Infrared cooling
The long-wavelength part of the radiative transfer scheme isbased on the net-exchange rate (NER) formalism (Eymet et al.,4

I. Garate-Lopez, S. Lebonnois / Icarus 314 (2018) 1–11

2009). The cloud model (density proﬁle of each mode, ni (z)) usedin the previous computation of the net-exchange rate matrices wasfrom Zasova et al. (2007). In this work, we now use the cloudmodel described in Haus et al. (2014). In order to take into accountthe latitudinal variations of this model, matrices are computed for
ﬁve latitudinal bins: 0° - 50°, 50° - 60°, 60° - 70°, 70° - 80° and
80° - 90°. When running the GCM, the matrices are interpolated
between the central latitudes of each bin.
The gas opacities are computed ﬁrst for high-resolution spectra,
taking into account updated spectral dataset, Voigt line shape proﬁles, and a truncation at 200 cm−1 . Then correlated-k coeﬃcientsare computed and used in the NER matrix calculation. In addition,
CO2 and H2 O collision-induced absorption are taken into account,as detailed in Lebonnois et al. (2015). However, as mentioned in
Lebonnois et al. (2015) and Lebonnois and Schubert (2017), it isdiﬃcult to have consistent temperature proﬁles in the middle andlower cloud and in the atmosphere below without including somesmall additional continuum to close the windows located in the [3- 7] μm range, through which energy is exchanged between thecloud base and the layers just below. Here, an additional continuum of 1.3 × 10−6 cm−1 amagat−2 is taken into account in the 30- 48 km region, and 4 × 10−7 cm−1 amagat−2 in the 16 - 30 km
region, for a best ﬁt of the VIRA (Seiff et al., 1985) and VeGa-2(Linkin et al., 1987; Zasova et al., 2006) temperature proﬁles (seeLebonnois and Schubert, 2017, Methods and Supplementary Information).
3. Results

Fig. 1. Horizontally and temporally averaged temperature proﬁle for the previous(red) and current (green) IPSL Venus GCM models at the end of simulation (300 Vd)
compared to VIRA reference proﬁle (black). Data is globally averaged over all longitudes and latitudes in each altitude, and over 2 Vd in time. (For interpretationof the references to colour in this ﬁgure legend, the reader is referred to the web
version of this article.)

3.1. Average temperature ﬁeld
As a direct consequence of the above described modiﬁcations
done in the radiative transfer (considering both stages), the average temperature ﬁeld has signiﬁcantly improved. The horizontally and temporally averaged temperature proﬁle is now (varcld simulation mentioned above) closer to the VIRA proﬁle (seeFig. 1) with warmer modeled temperatures below the clouds (andcolder above) than in the previous model (Lebonnois et al., 2016).
In the 3-dimensional simulations presented here, and comparedto the 1-dimensional tests performed in Lebonnois and Schubert (2017) (Methods and Supplementary Information), the averagetemperature ﬁeld is still approximately 10 K colder than observed
below roughly 70 km. However, due to the sensitivity to the solar
heating rates below the clouds and the associated uncertainties, noadditional tuning was performed.
The altitude-latitude distribution of the zonally and temporally
averaged temperature (Fig. 2) shows two clearly differentiated behaviors. Above 4 × 103 Pa the temperature increases from equator
to pole, but below 2 × 104 Pa it smoothly decreases towards thepole, just as seen in observation-based maps (e.g. Tellmann et al.,2009; Haus et al., 2014). Moreover, a new structure that resembles clearly the cold collar appears for the ﬁrst time in the IPSL
Venus GCM, at 2 × 104 − 4 × 103 Pa (∼ 62 − 66 km) altitude andbetween 60° and 90° in both hemispheres. This feature is not isolated from the pole as in the observation-based maps, but it is themost similar feature reproduced so far. The temperature values obtained with our model ( ∼220 K) are in good agreement with observations. Therefore, the latitudinal variation of the clouds’ structure plays a key role in the formation of the cold collar. The development of the cold collar related to the radiative transfer in thepolar atmosphere of Venus is discussed in detail below (Section 4).
3.2. Cold collar
Fig. 3 shows an example of the temperature ﬁeld at the northern pole at 7 × 103 Pa ( ∼64 km) altitude. The atmosphere between

60° and 85° is clearly colder than the rest of the latitudes, being on
average ∼20 K colder than the equator and about 10 K colder thanthe pole. The coldest region seen close to the terminator shows
values about 220 K, in good agreement with values observed by
VIRTIS (e.g. Haus et al., 2014; Garate-Lopez et al., 2015). The inner
warm core shows an average value of 230 K (colder than the observed mean values). However, we ﬁnd values as high as 240 K in
about 3% of the time over the last simulated 2 Vd.
The temporal evolution of the polar temperature obtained byour current model does not always show an inner region surrounded by the cold collar and the structure simulated within
the inner core is smoother than obtained by Yamamoto and Takahashi (2015) and Ando et al. (2016, 2017) (these GCMs use spectral
dynamical cores that do not have singularities at the pole). The
altitude level where the cold collar is reproduced ( ∼64 km) andthe temperature values ( ∼220 K) obtained by the IPSL Venus GCM
are, however, more consistent with observations (e.g. ∼62 km and∼220 K by Tellmann et al. 2009).
The coldest region shows a rotation around the pole with a period of about 5.85 Earth days in the model, that agrees with the 5- 10 Earth days period found by Luz et al. (2011) for the drift of therotation center of the vortex around the pole. However, the modeled inner core does not show a clear rotation around the pole, so
it is diﬃcult to compare its motion to the one observed for thewarm vortex (Luz et al., 2011; Garate-Lopez et al., 2013).
Fig. 3 in Lebonnois et al. (2016) displayed four temperatureﬁelds at pressure 3 × 103 Pa ( ∼67 km) in the northern polar region that showed a cold polar region with an inner warmer core.
The altitude level where these features are obtained is slightly different comparing the previous and current models but the horizontal structure simulated in both cases is very similar in general,
probably due to the longitudinal polar ﬁlter of the model that does
not allow to reproduce ﬁne-scale features.

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5

Fig. 2. Zonally and temporally averaged temperature ﬁeld (K) for the current IPSL Venus GCM model at the end of simulation (300 Vd). Data is averaged over 360° in
longitude and 2 Vd in time.

Fig. 4. Zonally and temporally averaged zonal wind ﬁeld for the current IPSL Venus
GCM model at the end of simulation (300 Vd). Data is averaged over 360° in longitude and 2 Vd in time.

Fig. 3. Example of polar temperature ﬁeld at 7 × 103 Pa ( ∼64 km) for the current
IPSL Venus GCM model. Temperature is averaged over 1/24 Vd ∼4.88 Earth days.
Black line shows the terminator with dayside on front.

3.3. Average wind ﬁeld
Considering the latitudinal structure of the clouds has also improved the zonally and temporally averaged zonal wind ﬁeld. The
modeled equatorial jet is now less intense than the mid-latitude
jets (Fig. 4) as cloud-tracking measurements suggest (Hueso et al.,2015). The modeled mid-latitude jets are still located too close to
the poles (and, therefore, the latitudinal wind gradient is still highpoleward of 70°) but their altitude has gone down slightly, while
the equatorial jet has ascended a bit.
In the previous model (Lebonnois et al., 2016) winds at 40–
60 km were approximately half of the observed values, but thecurrently modeled winds at this altitude range are on average∼20 m/s (about 75%) faster and are now in good agreement with
the Venera and Pioneer Venus probes’ proﬁles (see Fig. 5). Accord-

ing to a new wave activity analysis that is still on-going work andwill be presented in a subsequent publication, this improvementof the averaged zonal wind could be due to a new baroclinic activity found between 40 and 60 km altitude and that transports heat
and angular momentum equatorwards. Nevertheless, the velocityof the zonal winds below 40 km altitude has decreased showing
winds signiﬁcantly slower than observed, which was not the casepreviously. The large-scale gravity waves present in the deep atmosphere in the previous simulation (Lebonnois et al., 2016) are
no longer obtained, which probably explains this worsening in thezonal wind below 40 km.
4. Cold collar analysis
To analyze in detail the inﬂuence of the latitudinal structure of
the clouds in the formation of the cold collar, the simulation withthe latitudinal variations of the cloud structure (varcld simulation)is compared to a control simulation in which a uniform cloud
structure is maintained in the IR cooling computation (unifcld simulation). Note that the simulations differ in the second stage of the6

I. Garate-Lopez, S. Lebonnois / Icarus 314 (2018) 1–11

Fig. 5. Vertical proﬁles of the zonally and temporally averaged zonal wind at different latitudes for the previous (red) and current (green) IPSL Venus GCM models
at the end of simulation (300 Vd). Data is averaged over 360° in longitude and 2 Vd
in time. Measurements from different Pioneer Venus probes are added in gray. (Forinterpretation of the references to colour in this ﬁgure legend, the reader is referredto the web version of this article.)

implementation (see Section 2.1), i.e. in the last 100 Vd, so the solar heating rates take into account the latitudinal variations in both
simulations (same look-up table). The unifcld simulation has no
physical sense since the cloud model is different between the solar
heating and IR cooling treatments. But its results are very similar
to the previous simulation presented by Lebonnois et al. (2016),
so we think that the comparison between unifcld and varcld simulations is useful to identify which are the key elements in theformation of the cold collar.
The cold collar is formed in the varcld simulation but not in
the unifcld simulation, which indicates that the generation of thischaracteristic feature is not affected by latitudinal variations of theclouds in the solar heating rates. It is the inﬂuence that the cloud
structure has in the IR cooling rates that forms the cold collar.
In the unifcld simulation (Fig. 6a), there is a cold region around
103 Pa and 65° that extends downward and poleward, creating a
warmer region close to the pole and above that altitude level, but
this cold feature is not exactly as the one observed (e.g. Tellmann
et al., 2009; Haus et al., 2014), it is not completely surrounded bywarmer air. This structure, as well as the global thermal structurein the unifcld simulation, is very similar to the structure obtainedin previous works and presented there as the cold collar (Andoet al., 2016; 2017; Lebonnois et al., 2016). The relation between
this structure and the thermal tides in the polar atmosphere wasdemonstrated by Ando et al. (2016) and it should be similar in theunifcld simulation presented here.
On the other hand, the cold feature seen in the average temperature map that corresponds to the varcld simulation resembles signiﬁcantly more to the observed cold collar, being more contrasted
from the surrounding warmer atmosphere and located at the correct altitude and latitude. It is not completely isolated which isa characteristic different from observations. It is connected to thepole at an altitude of about 104 Pa ( ∼62 km) and then extends
upward and equatorward. However, the connection to the pole ismost likely related to the grid used currently in the model which
is based on a longitude-latitude scheme with a longitudinal polarﬁlter that reduces the latitudinal resolution and removes the highfrequency variations in the polar regions. So the results at latitudes
higher than 80° should be taken with caution. A new icosahedral
dynamical core (Dubos et al., 2015) will be used in the near future that should improve the robustness of the computation of thepolar circulation. This should affect the ﬁne structure of the cold
collar and the inner warm core (at about 4 × 103 Pa and 80° – 90°).
To conﬁrm the dominant inﬂuence of the cloud structure on
the IR cooling and on the temperature ﬁeld, the IR cooling rates ofboth unifcld and varcld simulations are compared. Fig. 7 shows thedifference between varcld and unifcld IR cooling (IRvarcld - IRunifcld )at the ﬁrst timestep of the simulations after the new cloud modelis implemented in the IR cooling computation (0.01 Vd), so thatthe background temperature ﬁeld has not changed and is the samefor both simulations, and after 100 Vd, at the end of simulations.

Fig. 6. Zonally and temporally averaged temperature ﬁeld (K) obtained at the end of simulation (300 Vd) when considering uniform (left) and variable (right) clouds in the
IR cooling computation during the last 100 Vd. Data is averaged over 360° in longitude and 2 Vd in time. Dashed line shows the cloud top altitude at 1 μm corresponding
to the Haus et al. (2014) cloud model.

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7

Fig. 7. Difference of the IR cooling rates (K/s) between the varcld and unifcld simulations as soon as the latitudinal cloud structure is implemented in the IR coolingcomputation (a, 0.01 Vd, instantaneous ﬁeld) and 100 Vd after (b, ﬁeld averaged over 2 Vd). Data is averaged over 360° in longitude. Solid lines show 205K, 225K, and 240K
temperature contours of the varcld simulation (unifcld simulation’s temperature ﬁeld at 0.01 Vd is the same as the varcld simulation and 100 Vd after is very similar to thatat 0.01 Vd). Dotted lines show the cloud top at 1 μm for the uniform and variable cloud models. Crosses show the cloud upper altitude boundary (see text).

Fig. 8. IR energy exchanges between each layer and space at 75° latitude at the endof simulation (300 Vd, proﬁles averaged over 360° in longitude and 2 Vd in time)
when considering uniform (red, unifcld) and variable (green, varcld) clouds in the
IR cooling computation during the last 100 Vd. Red dashed line indicates the 1 μm
cloud-top level in the unifcld simulation, while the green dashed line indicates thecloud upper altitude boundary in the varcld simulation. (For interpretation of thereferences to colour in this ﬁgure legend, the reader is referred to the web versionof this article.)

Note that the negative values in Fig. 7 mean IR cooling is stronger
in the varcld simulation while positive values mean it is stronger
in the unifcld simulation. The unit used in the Figure is K/s, sothat 1 × 10−4 K/s corresponds to ∼8.64 K/Earth day. This is in good
agreement with Lee and Richardson (2011) who states that the IR
cooling exceeds 10 K/Ed only above 100 Pa.
From the very ﬁrst moment when the cloud’s latitudinal structure is taken into account in the IR radiation computation, unifcld

and varcld simulations evolve in a completely different way withno possible convergence (see Supplementary Material). The atmosphere at the varcld simulation develops a stronger cooling region
around 75° latitude and 104 Pa altitude. It also develops a weakercooling region around 85° latitude and 4 × 102 Pa altitude. Duringtens of Venusian days the polar atmosphere evolves in such a waythat after 60 Vd it reaches a new equilibrium, although the averagetemperature at the cold collar oscillates slightly afterwards. Fig. 7bshows that the stronger and weaker cooling regions in the varcld
simulation are still present 40 Vd after reaching the equilibrium, at
100 Vd. This is important because it means that the atmosphere at
the newly formed cold collar will be constantly cooling by meansof IR radiation in a quite stronger way than the atmosphere aboveit (referred to as the warm mesospheric pole hereon). As long as
this new equilibrium endures, the cold collar will remain colder
than its surroundings due to this stronger IR cooling.
Fig. 8 displays the vertical proﬁle of the IR energy exchange between different atmospheric levels and space for both unifcld andvarcld simulations at the end of simulation (100 Vd) and at 75° latitude (cold collar region). The energy loss to space from the upper
clouds ( ∼50 – 70 km altitude) is more localized in altitude in thevarcld simulation than in the unifcld simulation. This, as well as the
decay rate of the cooling with altitude above the clouds, is inherited from the cloud model by Haus et al. (2014). At high-latitude(higher than 60°) the retrieval of the cloud top altitude required
the estimation of another quantity, the cloud upper altitude boundary, above which no cloud particles are considered. This cloud upper altitude boundary (located slightly above the cloud top deﬁned
at 1 μm) strongly affects the highest peak in the cooling rates, intensifying it. This peak corresponds mainly to the cooling rates in
the 6–12 μm range and it is associated to the uppermost layer ofcloud particles in the model. The lowest peak in the vertical proﬁles of Fig. 8 corresponds mostly to the cooling at 20–30 μm andis associated to the base of the upper clouds, while the coolingabove the clouds occurs mainly in the aisles of the 15 μm band
(Lebonnois et al., 2015).
The position of the cold collar with respect to the cloud top(deﬁned at 1 μm) and the cloud upper boundary is better seen in
Fig. 7. It is located just in between, coinciding with the highest
peak of the cooling rates in the vertical proﬁle. This means that

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I. Garate-Lopez, S. Lebonnois / Icarus 314 (2018) 1–11

Fig. 9. (a) Dynamical heating in the varcld simulation, (b) difference of the dynamical heating between varcld and unifcld simulations, (c) horizontally and (d) verticallytransported dynamical heating in the varcld simulation. All ﬁelds are plotted after 100 Vd with the latitudinal cloud structure implemented (varcld) or not (unifcld) in the IR
cooling computation and are averaged over 360° in longitude and 2 Vd in time. Solid lines, dotted lines and crosses: same as in Fig. 7. Units: K/s.

the opacity at 1 μm equals unity within the upper clouds, but that
there is a region with cloud particles above it where the cold collar
is formed. The cold collar is, therefore, located at the uppermost
layer of the clouds. Its position is, however, cloud model dependent
since it is strongly linked to the cloud upper boundary deﬁned byHaus et al. (2014).
The induced modiﬁcations in the temperature structure affects
the circulation and the thermal energy transport, both horizontallyand vertically. A detailed analysis of the new dynamical state of
the polar atmosphere and of the transport of heat and angular momentum is beyond the scope of the present paper, but the tendencies in the temperature ﬁelds due to dynamical heat transport are
analyzed here to better understand the new equilibrium in which
the cold collar is formed. This quantity (referred to as dynamical
heating hereon) considers the temperature variations due to theadvection by the mean meridional circulation and due to transient
waves. The dynamical heating/cooling rates in the varcld simula-tion can be seen in Fig. 9a. Dynamics is globally cooling the lowlatitudes and heating the polar regions, as in the simulation withuniform cloud model and previous simulations (not shown here).
Fig. 9b shows the dynamical heating difference between unifcld
and varcld simulations with positive values meaning stronger heating in the varcld simulation. Not surprisingly, it is the inverse of
Fig. 7b since dynamical heating should compensate the radiative
cooling in order to have an atmosphere in thermal equilibrium.
Actually, in the cold collar area this heating due to dynamics isslightly stronger in the varcld simulation than in the unifcld simulation to compensate the excess of IR cooling. And in the warm pole,
including the warm mesospheric pole (85° latitude and 4 × 102 Pa)
and the inner warm core (85° latitude and 4 × 104 Pa), the dynamical heating is weaker due to the weaker IR cooling there.
In Figs. 9c and 9 d dynamical heating in the varcld simulationis separated between horizontally and vertically transported heating. In both panels heating is represented by red colors (positive

I. Garate-Lopez, S. Lebonnois / Icarus 314 (2018) 1–11

9Fig. 10. Zonally and temporally averaged vertical wind velocity (Pa/s) at the end of simulation (300 Vd), when considering uniform (a) and variable (b) clouds in the IR
cooling computation during the last 100 Vd. Data is averaged over 360° in longitude and 2 Vd in time. Solid lines, dotted lines and crosses: same as in Fig. 7.

values) and the cooling due to dynamics by blue colors (negativevalues). The atmosphere shows a patchy structure with dynamical heating and cooling regions interspersed with each other inboth horizontal and vertical terms. These two terms are inversely
related; regions where the horizontal dynamics heats the atmosphere are cooled by vertical dynamics, and vice versa. However,recall that the effects of taking into account the latitudinal variations of the clouds take place in the cold collar and warm poleareas (Fig. 9b), where dynamics heats the atmosphere (Fig. 9a).
Above the clouds, in the warm mesospheric pole, the polar heating
due to dynamics occurs by means of vertical transport (Fig. 9d),but it is mitigated by horizontal transport (Fig. 9c). On the other
hand, in the cold collar and inner warm core areas, horizontaltransport mostly heats the area (although a region of dynamicalcooling is appreciated between the main two heating regions). This
heating is mitigated by vertical heating transport.
Studying the variation of the circulation, particularly of thezonal and meridional winds, is not trivial due to the high variability present in the polar regions. This study of the horizontal distribution of the dynamical polar atmosphere in the varcld simulation
will be presented in a subsequent paper together with the newwave activity. However, the variation of vertical wind is interestingand noteworthy. Fig. 10 shows the vertical wind velocity ﬁelds inboth unifcld and varcld simulations (in Pa/s units, so negative values mean upwelling motion). Although the main differences takeplace at the clouds and levels below, an altered subsidence structure that extends down to 50° latitude is seen in the varcld simulation, responsible of the vertical heating seen in Fig. 9d. Ascending
air is found in the cold collar between 60° and 70° latitudes and at
about 104 Pa altitude for the varcld simulation which is associatedwith the dynamical cooling seen in Fig. 9d. A reversal of the vertical circulation in the 70° – 90° polar region from unifcld to varcld
simulations is also clearly seen.
Hence, according to the results of the new IPSL Venus GCM, thecold collar feature observed in Venus’ polar atmosphere is formedby the infrared cooling region generated by the decrease of thecloud top altitude poleward and the latitudinal variation of theparticles modes. Since the radiative forcing cools the area while
the dynamics heats it, resulting in a region characteristically cold,
it seems that the driver of the cold collar is the radiative trans-

fer rather than dynamics. However, the presence of the cold collar
strongly inﬂuences the dynamics, thus affecting the polar circulation of Venus. Even though the cloud distribution, radiative transfer, and dynamics form a coupled system in reality and, therefore,
any change in one of the mentioned aspects will bring along achange in the other aspects, this coupling is not yet present in our
GCM and we can not characterize it in detail.
The temporal evolution observed in the modeled polar temperature maps at 7 × 103 Pa altitude (not shown here) presents a cold
collar that is displaced from the pole and rotates around it. The
obtained rotation period of 5.85 Earth days agrees with the period
found by Luz et al. (2011) for the drift of the rotation center ofthe vortex around the pole. This result, together with the structureof the cold feature in the average temperature ﬁeld, indicates thatthe current IPSL Venus GCM models satisfactorily the subpolar latitudes. However, the current polar latitudes’ treatment is not ideallysuited and, therefore, the modeled inner core does not show neither a clear rotation around the pole nor detailed horizontal structure within it, as the observed polar vortex does.
5. Conclusions
The previous IPSL Venus GCM (Lebonnois et al., 2016) has been
modiﬁed in order to improve the radiative transfer, update thecloud model, and take into account the latitudinal variation of theclouds (by means of cloud top altitude and different modes’ abundances) in the solar heating proﬁles and infrared cooling computation. The new cloud model used in the current version is described by Haus et al. (2014) and shows that the cloud top altitude decreases towards the pole in Venus’s atmosphere and thatthe different particle modes’ abundances change from equator topole.
The zonally and temporally averaged zonal wind and temperature ﬁelds have been improved showing now a better agreementwith the observations. Particularly, the equatorial jet’s intensity has
decreased compared to the mid-latitude jets’ intensity, the zonal
wind velocities between 40 and 60 km altitude are faster than previously, and the averaged temperature values below (resp. above)the clouds are now warmer (resp. colder), thus closer to the VIRA
reference proﬁle.

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I. Garate-Lopez, S. Lebonnois / Icarus 314 (2018) 1–11

The main result, however, is the appearance of a feature in
the averaged altitude-latitude temperature map that resembles the
subpolar cold collar observed in Venus’s atmosphere. The atmosphere between 60° and 85° shows a thermal minimum at 2 ×
104 − 4 × 103 Pa (∼ 62 − 66 km) altitude, being ∼20 K colder thanthe equator and about 10 K colder than the pole. The coldest region shows values about 220 K, in good agreement with observed
values, but the inner warm core shows an average value of 230 K
that is colder than the observed values. The structure simulated
within the inner core is smoother than that obtained by Yamamoto
and Takahashi (2015) and Ando et al. (2016, 2017), but the altitudelevel where the cold collar is reproduced and the modeled temperature values are more consistent with observations.
According to our analysis the cloud’s latitudinal structure is an
essential element in the formation of the cold collar via the IR
cooling rates. A strong cooling region at ∼75° and 104 Pa is developed as soon as the new cloud structure is implemented in the
IR computations and it remains cooling strongly after a new equilibrium is reached and the cold collar is formed. A region of weak
IR cooling is also developed above the polar clouds just where thewarm mesospheric pole is observed.
The position of the cold collar is cloud model dependent
since it is strongly linked to the cloud upper altitude boundary (level above which no particles are considered) deﬁned byHaus et al. (2014) at high latitudes. The IR cooling peak is locatedright below this cloud upper boundary, above the cloud top altitude. This means that the cold collar is located at the uppermost
layer of the clouds, in a region with cloud particles but above thelevel where the opacity equals one at 1 μm.
Once the atmosphere reaches a new thermal equilibrium, theradiative cooling is compensated by heating due to dynamics. Consequently, the dynamical heating is stronger at the cold collar area
than in the warm mesospheric pole (85° and 4 × 102 Pa). Our analysis points to vertical transport (associated to a subsidence motionthat extends down to 50°) as the main source of dynamical heating in the warm mesospheric pole and to horizontal transport at
the cold collar.
Since the radiative forcing cools the area while the dynamics
heats it, resulting in a region characteristically cold, it seems thatthe driver of the cold collar is the radiative transfer rather than
dynamics.
Acknowledgments
This work was supported by the Centre National d’Etudes Spatiales (CNES). IGL was supported by CNES postdoc grant. GCM simulations were done at CINES, France, under the project n°11167. We
also thank E. Millour for his technical support with the model.
Supplementary material
Supplementary material associated with this article can befound, in the online version, at doi:10.1016/j.icarus.2018.05.011.
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