Icarus 314 (2018) 149–158

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Planetary boundary layer and slope winds on Venus
⁎,a

b

a

Sébastien Lebonnois , Gerald Schubert , François Forget , Aymeric Spiga

T
a,c

a

Laboratoire de Météorologie Dynamique (LMD/IPSL), Sorbonne Université, ENS, PSL Research University, Ecole Polytechnique, Université Paris Saclay, CNRS, Paris,
FranceDepartment of Earth, Planetary and Space Sciences, UCLA, CA, USAc
Institut Universitaire de France, Paris, Franceb

A R T I C LE I N FO

A B S T R A C T

Keywords:
Venus
Atmosphere
Planetary boundary layer
Slope winds

Few constraints are available to characterize the deep atmosphere of Venus, though this region is crucial tounderstand the interactions between surface and atmosphere on Venus. Based on simulations performed with the
IPSL Venus Global Climate Model, the possible structure and characteristics of Venus’ planetary boundary layer(PBL) are investigated. The vertical proﬁle of the potential temperature in the deepest 10 km above the surface
and its diurnal variations are controlled by radiative and dynamical processes. The model predicts a diurnal
cycle for the PBL activity, with a stable nocturnal PBL while convective activity develops during daytime. The
diurnal convective PBL is strongly correlated with surface solar ﬂux and is maximum around noon and in low
latitude regions. It typically reaches less than 2 km above the surface, but its vertical extension is much higher
over high elevations, and more precisely over the western ﬂanks of elevated terrains. This correlation is explained by the impact of surface winds, which undergo a diurnal cycle with downward katabatic winds at nightand upward anabatic winds during the day along the slopes of high-elevation terrains. The convergence of these
daytime anabatic winds induces upward vertical winds, that are responsible for the correlation between heightof the convective boundary layer and topography.

1. Introduction
The interaction between the surface and the atmosphere is a key tounderstanding the processes driving the dynamics of both the atmosphere and the solid planet. The exchanges of heat and angular momentum drive the temperature and wind structure in the deepest layersof Venus’s atmosphere, and aﬀect Venus’s atmospheric superrotationand the rotation of the planet itself. Yet, the deep atmosphere of Venus(below 10 km altitude above the surface) remains largely unexploredbecause of the diﬃculty in obtaining data below the dense and planetwide cloud cover. Only a small number of probes have reached thesurface: the Russian Venera series and VeGa missions, and the American
Pioneer Venus mission. Among these probes, only one (VeGa-2) wasable to measure a complete reliable temperature proﬁle down to thesurface (Linkin et al., 1986; Lebonnois and Schubert, 2017).
On Earth, the study of the planetary boundary layer (PBL) must take
into account the variety of properties of the surface, and the water cycle
and associated latent heat release. Its knowledge is important for a goodunderstanding of the dispersion of trace atmospheric compounds, local
circulations, clouds and water cycle or energy balance of the surface(e.g., Garratt, 1994). Arid areas are more relevant for comparative⁎

Corresponding author.
E-mail address: sebastien.lebonnois@lmd.jussieu.fr (S. Lebonnois).

https://doi.org/10.1016/j.icarus.2018.06.006
Received 22 December 2017; Received in revised form 1 June 2018; Accepted 4 June 2018
Available online 05 June 2018
0019-1035/ © 2018 Elsevier Inc. All rights reserved.

planetology since the water latent heat contribution can be neglected(e.g., Wang et al., 2016). The Martian PBL has also been studied extensively since the Viking landers (Hess et al., 1977), in particular withradio-occultation datasets and turbulence-resolving simulations (e.g.,
Hinson et al., 2008; Spiga et al., 2010). The surface temperature diurnal
cycle is large on Mars, and the typical thickness of the diurnal convective PBL is much larger than on Earth. This PBL convective activity
plays a crucial role in the dust cycle. The surface slope winds are alsostrong on Mars, both during daytime and nighttime. They were shownto have a signiﬁcant impact on the near-surface temperature distributions (Spiga et al., 2011).
On Titan, the PBL may play a role in the near-surface methane cycle,
but very few observational data are available to characterize it. The insitu temperature proﬁle obtained during the Huygens probe descentprovided the best information to date to characterize the behavior of
Titan’s PBL. An interpretation of this proﬁle and of the associated PBL
characteristics was proposed by Charnay and Lebonnois (2012), basedon simulations with a General Circulation Model: signatures are presentof the height of the PBL at the time of the descent (300 m at 10:00 LT in
the morning), of the remnant of the previous diurnal cycle, with amaximum vertical extent of the PBL of roughly 800 m, and of the heightIcarus 314 (2018) 149–158

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useful scheme used, e.g., to study Titan’s planetary boundary layer(Charnay and Lebonnois, 2012). This scheme is detailed in Appendix Bof Hourdin et al. (2002) and based on level 2.5 model described by
Mellor and Yamada (1982).
The GCM physics also includes a convective adjustment scheme. In
the case of an unstable situation, both the PBL mixing scheme (based on
Mellor and Yamada’s formulation) and the convective adjustment can
act to stabilize the unstable layer. In our GCM, the PBL scheme is applied before applying the convective adjustment and therefore thisadjustment rarely occurs. The PBL scheme can compute a mixing
coeﬃcient in an unstable layer even away from the surface, so it acts tomix and stabilize the layer both in the PBL near the surface and in thehighly convective cloud layer, with tendencies due to convective adjustment scarcely occurring. This adjustment mostly occurs in the ﬁrst
two layers just above the surface, when the PBL scheme does notcompletely remove the unstable gradient.
The reference simulation used in this work is described in detail by
Garate-Lopez and Lebonnois (2018), in particular the tuning done in
the radiative transfer. The horizontal resolution is 96 longitudes by 96
latitudes, on 50 vertical levels (hybrid coordinates, from surface toroughly 95 km altitude). In the deepest 10 km, the altitudes (above
surface) of the layers are located around 10, 50, 150, 370, 750 m, 1.3,
2.1, 3.2, 4.7, 6.4 and 8.5 km. It was run for 300 Venus days from analready superrotating initial state.
As proposed by Lebonnois and Schubert (2017), it is possible thatthe deepest 7 km of the atmosphere may not be uniformly mixed, with avertical gradient of nitrogen, from 3.5% at 7 km altitude to almost 0%
at the surface. This would have an impact on the stability of the PBL
since the mean molecular mass gradient reduces the buoyancy. A simulation was performed with this gradient of mean molecular mass
taken into account in order to investigate its impact on the PBL structure. Since no physical process can yet be formalized in the model tohave the N2 abundance vary, the current implementation is simple: theproﬁle of the mean molecular mass as a function of pressure obtained
from VeGa-2 analysis is used at each grid point, and the potential
temperature is modiﬁed accordingly (Lebonnois and Schubert, 2017).
The initial state is the reference simulation, then the simulation is run
for 50 Venus days with the modiﬁed mean molecular mass proﬁle. The
impact of this hypothesis on the PBL structure is discussed in
Section 4.3.
To separate purely radiative eﬀects from dynamical ones, 1-dimensional simulations (single vertical column) were also performed atselected locations. Starting from the temperature proﬁle obtained at agiven location by the GCM simulation, the 1-dimensional runs take into
account only radiative heating and cooling and local vertical turbulent
mixing. At the equator, this results in a temperature proﬁle that tends toincrease slowly, since energy is not redistributed to higher latitudes by
the dynamics (mean meridional circulation and planetary-scale waves).
The diurnal cycles of the 1-dimensional proﬁle and of the convection
are considered after a couple of Venus days.
A caution related to surface winds needs to be mentioned. Due toangular momentum conservation problems in the GCM (as discussed in
Lebonnois et al., 2016), the zonal component of the zonally and temporally averaged surface winds could be biased, in particular in lowlatitude regions. Caution should therefore be taken when discussingthese winds. However, we ﬁnd that the diurnal variations of the surface
winds are signiﬁcant, with amplitudes usually larger than the averagevalues of the zonal component. Typically, at 10 m altitude, the temporally averaged zonal wind u ranges between ± 0.15 m/s, while thetemporally averaged horizontal wind U ranges roughly from 0.2 to
0.6 m/s, often a factor of 3 to 5 larger than u (except in polar regions).
Therefore, the slope winds eﬀects discussed in this work should be rather independent of the bias due to angular momentum conservation
problems.

of the dominant surface circulation, with the seasonally-varying ascending branch (the equivalent of Earth’s inter-tropical convergencezone) reaching 2 km altitude.
There have been a few attempts to investigate the surface temperatures, the PBL and the near surface winds on Venus. Surface temperatures were measured in-situ by several Venera landers (with anaccuracy of ± 5 K) (Avduevskii et al., 1983) and the VeGa-2 lander(with an accuracy of ± 1 K) (Linkin et al., 1986). In addition, informations about the near-surface atmospheric temperatures can beinferred remotely from the analysis of near-infrared images in spectroscopic windows around 1.0, 1.10 and 1.18 microns. Such an analysis
was
done
with
the
VIRTIS/Venus-Express
datasetsbyMueller et al. (2009). However, other unknown variables aﬀect thisanalysis, such as the surface emissivity and the atmospheric opacities inthe deep atmosphere. Disentangling all these variables is not easy, andassumptions are made that prevent a conclusive retrieval of the atmospheric lapse rate near the surface. Dobrovolskis (1983, 1993) modeled
the atmospheric thermal tides induced by heating near the ground anddetermined wind magnitudes and directions in the PBL and the resulting surface stresses. These predictions are limited by simplifying
assumptions, one of the most important being the neglect of the winds
in the global circulation of the atmosphere. The PBL is aﬀected by morethan just the tidally driven winds at the surface. Several papers(Saunders et al., 1990; Greeley et al., 1991; 1994; 1995) have studied
the patterns of wind streaks and aeolian transport of surface materials
visible in the Magellan radar images in eﬀorts to infer the nature of thecirculation in the PBL. Greeley et al. (1994) analyzed thousands of windstreaks in the Magellan images associated with sand dunes and windsculpted hills. On the assumption that the streaks serve as local ”windvanes” their orientations represent a global map of near-surface wind
patterns on Venus. Equatorward streaks were most dominant, consistent with a Hadley circulation of the lower atmosphere.
While waiting for additional observations, the analysis of the PBL
can be investigated with the help of models. Yamamoto (2011) used a
microscale model to investigate the mixing induced by convective adjustment in idealized simulations of the PBL. However, the initialconditions assumed in these numerical experiments, i.e., the potential
temperature proﬁle and the surface thermal ﬂux, are poorly constrainedby observations. In addition, the eﬀect of the background circulation
was not taken into account. Therefore, these simulations may not reﬂect
real Venus surface conditions. Here we take a diﬀerent approach, anduse the latest simulations from the Venus Global Climate Model (GCM)
developed at the Institute Pierre-Simon Laplace (IPSL) to investigate theconvective boundary layer, characterized by the depth of the surface
convective activity, and the near-surface circulation. The GCM and thesimulations used are described in Section 2. The vertical structure of thepotential temperature is detailed in Section 3, the temporal and spatial
variability of the convective activity is investigated in Section 4.
Though there are no available observations of surface winds, our simulations suggest the presence of slope winds, so they are discussed
together with their impact on the near-surface thermal structure in
Section 5. Conclusions are given in Section 6.
2. Model and simulations
The IPSL Venus Global Climate Model has been developed for about
a decade (Lebonnois et al., 2010; 2016). It includes a full description of
the radiative transfer based on the latest modeling of the solar ﬂux(Haus et al., 2015) and infrared net exchange rates (Eymet et al., 2009)
taking into account the latest latitude-dependent cloud model(Haus et al., 2014), slightly tuned to get a vertical structure of thetemperature close to the available observations. The latest simulations
reproduce quite well the atmospheric structure, including the cold
collar and the superrotation of the atmosphere (Garate-Lopez andLebonnois, 2018). In the deep atmosphere, the planetary boundary
layer scheme used is based on Mellor and Yamada (1982), a simple but
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Fig. 1. Vertical proﬁles of potential temperature computed from the VeGa-2
temperature measurements and from the global average temperature proﬁle of
the GCM simulations, without and with N2 abundance variations in the lowest
7 km.

Fig. 3. Diurnally averaged surface potential temperature (K) as a function of
surface elevation for every model grid point, shifted by +11 K to compensate
the bias in the GCM temperature structure (blue). Surface potential temperatures computed from measurements by the Venera 9 to 12 probes(Avduevskii et al., 1983) are plotted in red and by the VeGa-2 probe(Linkin et al., 1986) in yellow. (For interpretation of the references to colour inthis ﬁgure legend, the reader is referred to the web version of this article.)

3. Vertical structure of potential temperature
The deep atmosphere of Venus is structured in alternating layers of
stable and well-mixed regions. Stable regions are characterized by potential temperature increasing with altitude, while well-mixed (neutral)regions have a roughly constant potential temperature. Despite recent
adjustments done in the radiative transfer (Lebonnois and Schubert,
2017; Garate-Lopez and Lebonnois, 2018), the modeled temperaturesare still below observed values (5 to 15 K). However, the structure
obtained with the IPSL Venus GCM (Fig. 1) is in agreement with thepotential temperature proﬁles deduced from in-situ observations by the
Pioneer Venus and VeGa-2 probes (Lebonnois and Schubert, 2017):
below the cloud, a stable region is present down to roughly 30 km altitude. Below this level the atmosphere is well mixed down to roughly
20 km altitude, mostly due to large-scale circulation and to very shortdistance infrared radiative exchanges, according to the model thatpredicts little convective activity in this region. The atmosphere isstable again between 18–20 km and 8–10 km altitude. In the deepest
10 km, the atmosphere is again near neutral stability, with no convective activity predicted except in the PBL, as discussed in Section 4.
The modeled surface temperatures shown in Fig. 2 are quite

constant at a given location, with diurnal variations limited to at most
3 K. Because the temperature proﬁle below 30 km altitude is mostly
determined by the temperature at the base of the clouds and by neutral
stability (Lebonnois et al., 2015), the surface temperature is highly
correlated with surface topography (vertical gradient close to adiabatic). The relation between surface potential temperature and surface
elevation is shown in Fig. 3. If the surface temperature were controlled
only by the adiabatic lapse rate, the potential temperature would behomogeneous on the planet. However, in our GCM simulations, thevertical proﬁle of temperature along the slopes is weakly stable, especially in the deeper plains. This behavior may be in agreement with insitu surface measurements, though uncertainties on these measurements are too large to get a good constraint. The oﬀset between themodeled surface temperatures and observed values is around 11 K
(taken into account for the comparison in Fig. 3).
In the atmospheres of Earth, Mars and even Titan, the diurnal cycleof insolation drives the cycle of the PBL dynamics. The discussion islimited here to the dry atmosphere, without consideration of any latent
heat exchange. At night, the cooling of the surface yields a stable PBL,
with potential temperature increasing with altitude. During daytime
hours, solar heating of the surface induces convective activity thatgenerates a well-mixed PBL wherein potential temperature is nearly
constant with altitude, up to a stable layer with increasing potential
temperature. On Venus, the diurnal cycle of the potential temperature
proﬁle is also present. However, this proﬁle is rather diﬀerent since thedeepest region is already close to neutrality, with the potential temperature increasing only very slightly up to 10 km altitude. Stablenocturnal PBL and diurnal near-surface convective activity develop at
the bottom of this quasi-neutral region.
The diurnal evolution of the vertical distribution of potential temperature for two diﬀerent locations on the equator (diﬀerent longitudes
and elevations) is shown in Fig. 4. During the night, a stable PBL ispresent, due to surface temperature decreasing through infrared coolingof the surface to the atmosphere. During the day, the potential temperature becomes vertically mixed over a layer that depends on local
time. This mixing is the result of convective activity.
To explore the roles of radiative forcing and regional dynamical
processes in shaping the potential temperature proﬁle below 10 km, 1-

Fig. 2. Diurnally averaged surface temperature (K) with surface pressure contours (bar).
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Fig. 4. Potential temperature variations with local time at (left) 92E/equator (surface pressure 74.1 bars) and (right) 0E/equator (surface pressure 94.6 bars).

Fig. 5. Same as Fig. 4, but obtained with a 1-dimensional simulations, after 2 Venus days: potential temperature variations with local time at (left) 92E/equator(surface pressure 74.1 bars) and (right) 0E/equator (surface pressure 94.6 bars).

Fig. 6. Atmospheric dynamical heating rates at 4 diﬀerent local times, at (left) 92E/equator (surface pressure 74.1 bars) and 0E/equator (surface pressure 94.6 bars).

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Fig. 7. Sensible heat ﬂux in W/m2 (positive for upward direction): (left) at noon everywhere, with surface pressure contours (bar); (right) as a function of local timeat 60E longitude.

Fig. 8. Surface heat ﬂuxes as a function of local time at (left) 60E/equator and (right) 60E/80N, in W/m2 (positive for upward direction).

dimensional simulations were run at several locations at the equator.
Fig. 5 shows the potential temperature evolution in the 1-dimensionalruns at the same locations as in Fig. 4, obtained after 2 Venus days
(initial state is the GCM proﬁle). These proﬁles are slightly warmer thanin the GCM; large-scale dynamics therefore has a role in slightly coolingthe lowest 5 km above the surface. This is conﬁrmed in Fig. 6: dynamics
tends to cool the deeper atmosphere during daytime hours. This aﬀects
the vertical proﬁle of potential temperature, with regional variations: it
tends to be more stable in plains than at high elevations. In Fig. 4, thetwo cases that are presented show diﬀerent vertical variations of thepotential temperature (mainly the depth of the daytime mixed region).
But the same two cases are very similar in Fig. 5, i.e., in the 1-dimensional simulations. This illustrates another impact of large-scale dynamics related to topography, the depth of the convective activity, thatis discussed in the next sections.

4. Variability of convective activity
Fig. 9. Turbulent mixing coeﬃcient (in m2/s) at 2 km above surface at noon
everywhere, with surface pressure contours (bars).

The temporal and spatial variability of the convective activity in the
PBL is investigated through an analysis of the sensible heat ﬂux at thesurface and of the convective ﬂux.

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mixing coeﬃcient computed by the PBL parameterization scheme isstudied. This coeﬃcient is related to the turbulent kinetic energy, as
detailed in Hourdin et al. (2002), Appendix B. Fig. 9 shows the map of
the maximum value (obtained at noon, LT=12h) of this mixing coefﬁcient 2 km above the surface. A correlation between the strength ofthe convective activity and the topography appears at low to mid latitudes, where the sensible heat ﬂux is most active.
This variation of the thickness of the noon convective layer with
topography is further illustrated in Fig. 10, showing the maximum
mixing coeﬃcient along the equator (at LT=12h everywhere), with the
topographic proﬁle also plotted. The diurnal variations of the convective activity are illustrated for two diﬀerent locations along thisproﬁle in Fig. 11.
Such a correlation with topography is not observed on Earth, but isknown on Mars (Spiga et al., 2010). The evolution of the potential
temperature in the PBL is driven by the second law of thermodynamics.
On Mars, taking into account several approximations, such as neglecting heat conduction, as well as large-scale vertical advection terms,
the variation of the mixed-layer potential temperature θ may be written
as (Spiga et al., 2010):
Fig. 10. Turbulent mixing coeﬃcient (in m2/s) as a function of longitude along
the equator, at noon everywhere. Surface elevation is indicated by the blue line(with respect to a radius of 6048 km, to oﬀset it in the ﬁgure). (For interpretation of the references to colour in this ﬁgure legend, the reader is referredto the web version of this article.)δθδ < w′θ′>=−+ (JSW + JLW )/Π,
δt
δz

(1)

where Π = (p / pref ) κ with p the pressure, pref a reference surface pressureand κ = R/ cp (cp is the speciﬁc heat capacity at constant pressure),δ < w′θ′ >represents the divergence of the convective heat ﬂux (q′ denotesδz
a deviation from the averaged quantity < q > ), and JSW and JLW are
the solar heating and infrared cooling rates (in K/s). In the Martian
atmosphere, the radiative heating rates are dominant in the energy
budget of the convective layer, with surface temperatures that do notdepend on altitude. A pressure eﬀect is obtained for similar solar
heating and IR cooling rates due to Π in Eq. (1), as demonstrated in
Spiga et al. (2010). In the Earth’s deserts, the convective term in Eq. (1)is dominant over solar and infrared heating rates, so no eﬀect due tosurface pressure occurs.
In the case of Venus, as on Earth, the dominant term is the convective term, with negligible solar and IR heating rates. However, as it
will be shown below, the large-scale advection term is not negligible on
Venus. Therefore, Eq. (1) applies only to the 1-dimensional simulations.
In this case, a very small topographical eﬀect is obtained in the intensityof the convection (not shown), though there is a correlation between
surface solar ﬂux, sensible heat ﬂux expressed as Hs/(ρcp), and4.1. Sensible heat ﬂux
The surface sensible heat ﬂux Hs is the turbulent and conductive
heat exchanged between the surface and the bottom of the planetary
boundary layer. The variations of the sensible heat ﬂux in the GCM
simulation are shown in Fig. 7. The diurnal cycle is clear, with amaximum sensible heat ﬂux at noon (12LT) and at low latitudes. This
results from a correlation with the solar ﬂux, as also seen in Fig. 8. At
high latitudes, solar heating of the surface is mostly compensated byinfrared cooling and no convective activity is predicted.
4.2. Correlation with topography
In Fig. 4, the vertical extent of the region with almost uniform potential temperature at noon (correlated with vertical mixing) is different at the two locations. To explore the strength and vertical extent
of the modeled convective activity, the distribution of the turbulent

Fig. 11. Turbulent mixing coeﬃcient (in m2/s) as a function of LT at (left) 0E/equator (surface pressure 94.6 bars) and (right) 92E/equator (surface pressure 74.1
bars).
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Fig. 12. Same as Fig. 4, but with N2 variations: potential temperature variations with local time at (left) 92E/equator (surface pressure 74.1 bars) and (right) 0E/
equator (surface pressure 94.6 bars).

Fig. 13. Same as Fig. 10 and Fig. 11 (right), but with N2 variations: turbulent mixing coeﬃcient (in m2/s) (right) as a function of longitude along the equator, at noon
everywhere (surface elevation in blue); (left) as a function of LT at 92E/equator (surface pressure 74.1 bars). (For interpretation of the references to colour in thisﬁgure legend, the reader is referred to the web version of this article.)

5. Slope winds at the surface of Venus

elevation. On Venus, unlike on Mars, the correlation with topographyseen in Fig. 9 is therefore not related to topography itself (i.e., surfacepressure), but to the dynamics associated with topography. In fact, inFigs. 9 and 10, the maxima of convective activity appear to be locatedon the western ﬂanks of topographic highs. The reason for this correlation is to be found in the near-surface circulation, as demonstrated in
Section 5.

5.1. Properties
Very few data are available to constrain near-surface winds, mostly
measurements by the Venera (e.g. Avduevskii et al., 1977; Golitsyn,1978) and Pioneer-Venus (Counselman et al., 1980) probes, as well as
analyses of aeolian features in Magellan images (Greeley et al., 1994;
1995). However, Lorenz (2016) presented an analysis of these sparse
constraints to propose a probability distribution of the near-surface
winds, showing that the datasets were compatible with near-surface
winds less than 0.7 m/s half the time, and an upper limit of 1.8 m/s(with a probability that the wind is lower of 99%), or up to 2.2 m/s if
the upper limits of the measurements were taken into account. In theﬁrst layer of the GCM, 10 m above the surface, horizontal winds havean upper limit around 1.5 m/s, with temporally averaged values ranging roughly from 0.2 to 0.6 m/s depending on the location (values up
to 1 m/s in the vicinity of the ﬂanks of Ishtar Terra). This appears to be
fully compatible with the analysis provided by Lorenz (2016).

4.3. Impact of the possible gradient of nitrogen mixing ratio
When this gradient is taken into account in the GCM simulation, it
aﬀects the vertical extension of the convective layer above topographical features. The stability of the 0–10 km altitude layer is slightly
increased by the change in composition, as seen in Fig. 12 compared toFig. 4, though the overall dynamics and circulation are barely aﬀected.
The layer is then less easy to destabilize and for a similar energy balance and similar large-scale wind conditions, the convective activity
reaches lower altitudes (Fig. 13, compared to Figs. 10 and 11).
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surface at an altitude around a few hundred meters, depending on thelocation and local time. The distribution of these near-surface winds is
illustrated in Fig. 14, at 150 m altitude. A complete comparison of thesemodeled winds with constraints from Magellan data (Greeley et al.,
1994; 1995) is not straightforward, as it may depend on the local topography, on the inﬂuence of the diurnal cycle, and on the formation
mechanisms of the surface features. In the model, on the night side,strong katabatic winds are mostly ﬂowing down the slopes. On the dayside, the winds are reversing and anabatic winds are going up theslopes. A westward component is added to this up-slope tendency,
maybe due to westward winds from upper layers unblocked by topography. This yields some convergence of the wind on the westward
ﬂanks of the mountains, as illustrated for Beta Regio in Fig. 15. In aprevious GCM study with topography and diurnal cycle (Yamamoto and
Takahashi, 2009), a signiﬁcant increase in the eddy-component of thenear-surface winds was also obtained.
5.2. Relation to the near-surface thermal structure
The potential temperature proﬁles for two locations, one on top of
Beta Regio and one down its eastern ﬂank, at noon and midnight local
times are shown in Fig. 16, on the same altitude scale. During the night,
the potential temperature is signiﬁcantly colder at the surface on themountain slopes than at the same pressure level in the atmosphereaway from the surface. This slope-induced baroclinicity drives thenighttime katabatic winds. During daytime, the reverse is true: thesurface on the slopes is slightly warmer than the atmosphere at thesame pressure level, inducing anabatic winds.
The tendencies on temperature due to the circulation are illustrated
in Fig. 6. At night on highlands, the circulation tends to bring relatively
warm air to replace the near-surface cold air transported downward by
the katabatic winds, inducing heating. The near-surface adiabatic
heating associated with these downward katabatic winds is stronger
near the bottom of slopes. However, some cooling is present in thelowest elevation plains below 1 km altitude (e.g., Fig. 6, right). During
daytime, cooling in the lowlands is also visible, explaining the correlation between topography and surface potential temperature, as discussed in Section 3.
Comparison between Earth and Martian katabatic winds was discussed by Spiga (2011). The slope-buoyancy term (Eq. 2 of Spiga, 2011)leads to stronger katabatic winds for higher surface temperature inversions and higher gravity, though on Earth these conditions are not
suﬃcient and the presence of katabatic winds is observed only in very
stable conditions with weak ambient ﬂow perturbations, such as over
the Antarctic continent. On Mars, winds are larger because of the strong
near-surface inversion caused by the radiative timescales. On Venus,
nighttime stability becomes large near the surface, with weak ambient

Fig. 14. Maps of the winds at 150 m altitude above the surface (vectors, in m/s)
at two diﬀerent time steps. Contours represent the solar ﬂux received at thesurface (in W/m2). Colors show the elevation of the surface (in km, relative to aradius of 6051 km).

Everywhere, the vertical proﬁle of the horizontal wind is globally
increasing with altitude, with westward winds around 3 m/s near
10 km altitude. However, a secondary maximum is present near the

Fig. 15. Same as Fig. 14, but zoomed on Beta Regio. Local time over the area is roughly (left) noon (LT=12) and (right) midnight (LT=0).
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5.3. Impact on the daytime convective PBL
The convergence of the daytime anabatic winds is shown around
150 m altitude in Fig. 17, with local time ﬁxed to noon everywhere.
This convergence of horizontal winds in the near-surface layers induces
upwelling of air near topographical features, and more speciﬁcallyalong the western slopes of these elevated regions, as illustrated inFig. 18, that shows the vertical winds at 2 km altitude. A strong correlation is clearly seen between these upward motions and the regions
where convective activity reaches higher altitudes (Fig. 9), though
limited to the mid-latitudes because of the dependence of the convective activity and sensible heat ﬂux on solar ﬂux, as discussed previously.
The impact of the vertical wind in the environment on the verticalextension of the PBL is described, e.g., by Garratt (1992). The temporal
evolution z˙i of the PBL depth zi is to ﬁrst order the sum of the verticalentrainment velocity we (related to buoyancy and convective turbulence) and the background vertical velocity wb (related to regional andlarge-scale ascending or subsiding motions). The latter term explains
how, even in the Martian environment where turbulent plumes in the
PBL are very powerful, regional circulations can dramatically impact
the depth of the convective PBL. This is exempliﬁed by Tyler andBarnes (2015) in the case of Gale Crater, the Curiosity rover’s landingsite. There, the daytime subsidence over crater ﬂoors (as a result ofslope circulation) produces adiabatic warming, which inhibits thegrowth of the convective boundary layer by forming a capping inversion. Similar phenomena are at play on Venus, where regional upwelling associated with anabatic winds during noon hours causes the PBL
to be deeper above the western slopes of elevated regions: as an example, upwelling vertical winds of the order of 5 mm/s over 1 Venusianhour (1Vh=4.2 × 105s) can increase the vertical depth of the PBL by
2 km, which is consistent with the value of z˙i seen in Fig. 11 (rightpanel).

Fig. 16. Vertical proﬁles of the potential temperature at two diﬀerent locations,one on top of Beta Regio (78W, 30N, blue lines) and one in the plain down theeastern ﬂank of Beta Regio (68W, 30N, red lines), and at two diﬀerent local
times (noon: LT12, solid lines; midnight: LT0, dashed lines). The surface potential temperatures between these two locations are plotted as dots (red for
midnight, blue for noon). Altitude is indicated here with respect to the elevationof the lower location. (For interpretation of the references to colour in thisﬁgure legend, the reader is referred to the web version of this article.)

6. Conclusion
The possible structure and characteristics of Venus’ planetary
boundary layer have been investigated with general circulation simulations based on the IPSL Venus GCM. Though the PBL is modeled basedon a parameterization in this large-scale climate model, it allows us todescribe the dominant features of the dynamics of the near-surface
regions.
The model predicts a diurnal cycle for the PBL activity, with a stablenocturnal PBL while convective activity develops during daytime. The
diurnal convective PBL is strongly correlated with surface solar ﬂux andis maximum around noon and in low latitude regions. It typicallyreaches less than 2 km above the surface, but vertical extension is muchhigher over high elevations, and more precisely over the western ﬂanks
of elevated terrains. The surface winds undergo a diurnal cycle, withkatabatic winds at night and anabatic winds during the day.
Convergence of these daytime anabatic winds induces upward winds,that are responsible for the correlation between topographical featuresand larger vertical extension of the PBL.
Caution must be kept, as models are always subject to uncertainties
and assumptions. In particular, these simulations are sensitive to theradiative heating rates in the deep atmosphere, which are poorly constrained. LES simulations could help validate the behavior of the PBLparameterization. However, large-scale dynamics has to be taken into
account in these models, since the GCM shows that its thermal impact isa crucial term in the potential temperature vertical proﬁle, and thebackground vertical wind is strongly aﬀecting the development of theconvection.
Additional data are crucially needed from the deepest 10 km above
the surface, better to understand this region, the interface between
surface and atmosphere. Accurate temperature and radiative ﬂuxes
proﬁles are essentials to validate the predictions of models.

Fig. 17. Horizontal wind convergence (colors, in 10 −6 s −1) taken at LT12 everywhere, at roughly 150 m above the surface, with wind direction as vectors,
and surface pressure contours (bars).

winds, which favors development of strong katabatic winds. An upper
limit for the acceleration of the winds along the slope due to this baroclinicity is taken from Eq. 2 in Spiga (2011): it involves the averageslope α, the temperature inversion ΔT and the averaged near-surface
temperature < T > . According to Fig. 16, with the two locations separated by roughly 1000 km, α is around 2 × 10−3, < T > is around726 K and ΔT is roughly 0.8 K for midnight and 0.05 K for noon. This
gives accelerations of up to nearly 2 × 10−5 m/s2 at midnight and1.2 × 10−6 m/s2 at noon. Given the duration of one Venusian hour inlocal time (which is 4.2 × 105s), this means that katabatic winds may
easily reach several m/s, as obtained in the simulation, while anabatic
winds are less intense and more aﬀected by vertical mixing associatedwith convective motions in the PBL.

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011.
Garratt, J.R., 1992. The Atmospheric Boundary Layer. Cambridge Univ. Press, New York.
Garratt, J.R., 1994. Review: the atmospheric boundary layer. Earth Sci. Rev. 37, 89–134.
Golitsyn, G.S., 1978. Estimates of the turbulent state of the atmosphere near the surface ofVenus from the data of Venera 9 and Venera 10. Cosmic Res. 16, 125–127.
Greeley, R., Bender, K., Thomas, P.E., Schubert, G., Limonadi, D., Weitz, C.M., 1995.
Wind-related features and processes on Venus: summary of magellan results. Icarus
115, 399–420.
Greeley, R., Marshall, J.R., Clemens, D., Dobrovolskis, A.R., Pollack, J.B., 1991. Venus concentrations of radar-reﬂective minerals by wind. Icarus 90, 123–128.
Greeley, R., Schubert, G., Limonadi, D., Bender, K.C., Newman, W.I., Thomas, P.E., Weitz,
C.M., Wall, S.D., 1994. Wind streaks on Venus: clues to atmospheric circulation.
Science 263, 358–361.
Haus, R., Kappel, D., Arnold, G., 2014. Atmospheric thermal structure and cloud featuresin the southern hemisphere of Venus as retrieved from VIRTIS/VEX radiation measurements. Icarus 232, 232–248.
Haus, R., Kappel, D., Arnold, G., 2015. Radiative heating and cooling in the middle andlower atmosphere of Venus and responses to atmospheric and spectroscopic parameter variations. Planet. Space Sci. 117, 262–294.
Hess, S.L., Henry, R.M., Leovy, C.B., Tillman, J.E., Ryan, J.A., 1977. Meteorological results from the surface of Mars - Viking 1 and 2. J. Geophys. Res. 82, 4559–4574.
Hinson, D.P., Pätzold, M., Tellmann, S., Häusler, B., Tyler, G.L., 2008. The depth of theconvective boundary layer on Mars. Icarus 198, 57–66.
Hourdin, F., Couvreux, F., Menut, L., 2002. Parameterization of the dry convective
boundary layer based on a mass ﬂux representation of thermals. J. Atmos. Sci. 59,
1105–1123.
Lebonnois, S., Eymet, V., Lee, C., Vatant d’Ollone, J., 2015. Analysis of the radiative
budget of Venus atmosphere based on infrared net exchange rate formalism. J.
Geophys. Res. Planets 120, 1186–1200.
Lebonnois, S., Hourdin, F., Eymet, V., Crespin, A., Fournier, R., Forget, F., 2010.
Superrotation of Venus’ atmosphere analysed with a full general circulation model. J.
Geophys. Res. 115. E06006
Lebonnois, S., Schubert, G., 2017. The deep atmosphere of Venus and the possible role ofdensity-driven separation of CO2 and N2. Nat. Geosci. 10, 473–477.
Lebonnois, S., Sugimoto, N., Gilli, G., 2016. Wave analysis in the atmosphere of Venus
below 100-km altitude, simulated by the LMD venus GCM. Icarus 278, 38–51.
Linkin, V.M., Blamont, J.E., Lipatov, A.N., Devyatkin, S.I., D’yachkov, A.V., Ignatova,
S.P., Kerzhanovich, V.V., Khlyustova, L.I., Malique, C., Sanotskil, Y.V., Shurupov,
A.A., Stadnyk, B.I., Stolyarchuk, P.G., Terterashvili, A.V., 1986. Vertical thermal
structure in the Venus atmosphere from provisional Vega 2 temperature and pressuredata. Sov. Astron. Lett. 12, 40–42.
Lorenz, R.D., 2016. Surface winds on Venus: probability distribution from in-situ measurements. Icarus 264, 311–315.
Mellor, G.L., Yamada, T., 1982. Development of a turbulent closure model for geophysicalﬂuid problems. Rev. Geophys. Space Phys. 20, 851–875.
Mueller, N., Helbert, J., Hashimoto, G.L., Tsang, C., Erard, S., Piccioni, G., Drossart, P.,
2009. Venus surface thermal emission at 1 µm in VIRTIS imaging observations: evidence for variation of crust and mantle diﬀerentiation conditions. J. Geophys. Res.
114. E00B39
Saunders, R.S., Wall, S.D., Dobrovolskis, A.R., Greeley, R., 1990. Large-scale patterns ofeolian sediment transport on Venus - predictions for magellan. Geophys. Res. Lett. 17,
1365–1368.
Spiga, A., 2011. Elements of comparison between martian and terrestrial mesoscalemeteorological phenomena: katabatic winds and boundary layer convection. Planet.
Space Sci. 59, 915–922.
Spiga, A., Forget, F., Lewis, S.R., Hinson, D.P., 2010. Structure and dynamics of theconvective boundary layer on Mars as inferred from large-eddy simulations and remote-sensing measurements. Q. J. R. Meteorol. Soc. 136, 414–428.
Spiga, A., Forget, F., Madeleine, J.B., Montabone, L., Lewis, S.R., Millour, E., 2011. The
impact of martian mesoscale winds on surface temperature and on the determinationof thermal inertia. Icarus 212, 504–519.
Tyler, D., Barnes, J.R., 2015. Convergent crater circulations on Mars: inﬂuence on thesurface pressure cycle and the depth of the convective boundary layer. Geophys. Res.
Lett. 42, 7343–7350.
Wang, M.Z., Lu, H., Ming, H., Zhang, J., 2016. Vertical structure of summer clear-sky
atmospheric boundary layer over the hinterland and southern margin of Taklamakan
desert. Meteorol. Appl. 23, 438–447.
Yamamoto, M., 2011. Microscale simulations of Venus’ convective adjustment and mixing
near the surface: thermal and material transport processes. Icarus 211, 993–1006.
Yamamoto, M., Takahashi, M., 2009. Inﬂuences of Venus’ topography on fully developed
superrotation and near-surface ﬂow. Earth Planets Space 61. E45–e48

Fig. 18. Vertical winds (in mm/s) taken at LT12 everywhere, at roughly 2 km
above the surface, with surface pressure contours (bars). Positive winds are
upward.

Acknowledgments
S.L., F.F. and A.S. acknowledge the support of the Centre Nationald’Etudes Spatiales. G.S. acknowledges the support of the Keck Institutefor Space Studies under the project ”Techniques and technologies forinvestigating the interior structure of Venus”. GCM simulations weredone at CINES, France, under the project n°11167.
Supplementary material
Supplementary material associated with this article can be found, inthe online version, at doi:10.1016/j.icarus.2018.06.006
References
Avduevskii, V.S., Marov, M.Y., Kulikov, Y.N., Shari, V.P., Gorbachevskii, A.Y., Uspenskii,
G.R., Cheremukhina, Z.P., 1983. Structure and parameters of the Venus atmosphereaccording to Venera probe data. In: Hunten, D.M., Colin, L., Donahue, T.M., Moroz,
V.I. (Eds.), VenusUniv. of Arizona Press, pp. 280–298.
Avduevskii, V.S., Vishnevetskii, S.L., Golov, I.A., Karpeiskii, I.I., Lavrov, A.D., Likhushin,V.I., Marov, M.I., Melnikov, D.A., Pomogin, N.I., Pronina, N.N., 1977. Measurementof wind velocity on the surface of Venus during the operation of stations Venera 9 and
Venera 10. Cosmic Res. 14, 710–713.
Charnay, B., Lebonnois, S., 2012. Two boundary layers in Titan’s lower troposphere inferred from a climate model. Nat. Geosci. 5, 106–109.
Counselman, C.C., Gourevitch, S.A., King, R.W., Loriot, G.B., Ginsberg, E.S., 1980. Zonal
and meridional circulation of the lower atmosphere of Venus determined by radio
interferometry. J. Geophys. Res. 85, 8026–8030.
Dobrovolskis, A.R., 1983. Atmospheric tides on Venus. III - the planetary boundary layer.
Icarus 56, 165–175.
Dobrovolskis, A.R., 1993. Atmospheric tides on Venus. IV - topographic winds and sediment transport. Icarus 103, 276–289.
Eymet, V., Fournier, R., Dufresne, J.L., Lebonnois, S., Hourdin, F., Bullock, M.A., 2009.
Net-exchange parameterization of the thermal infrared radiative transfer in Venus’
atmosphere. J. Geophys. Res. 114. E11008
Garate-Lopez, I., Lebonnois, S., 2018. Latitudinal variation of clouds’ structure responsible for Venus cold collar. Icarus. http://dx.doi.org/10.1016/j.icarus.2018.05.

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