Icarus 366 (2021) 114432

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Research Paper

Venus upper atmosphere revealed by a GCM: II. Model validation withtemperature and density measurements
Gabriella Gilli a, c, *, Thomas Navarro e, f, Sebastien Lebonnois b, Diogo Quirino a, c, Vasco Silva a, c,
Aurelien Stolzenbach d, Franck Lefèvre d, Gerald Schubert eaInstituto de Astrofisica e Ciências do Espaço (IA), OAL, Tapada da Ajuda, PT1349-018 Lisboa, Portugal
Laboratoire de Meteorologie Dynamique (LMD) Paris, France
Faculdade de Ciências, Campo Grande, PT1749-016 Lisboa, Portugald
LATMOS, CNRS, Sorbonne Université, Paris, FranceeUniversity of California, Los Angeles, CA, USAf
McGill University, Montreal, Canadabc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Venus GCM
Upper atmosphere
Variability
Transition region

An improved high resolution (96 longitude by 96 latitude points) ground-to-thermosphere version of the Institut
Pierre-Simon Laplace (IPSL) Venus General Circulation Model (VGCM), including non-orographic gravity waves(GW) parameterization and fine-tuned non-LTE parameters, is presented here. We focus on the validation of themodel built from a collection of data mostly from Venus Express (2006–2014) experiments and coordinatedground-based telescope campaigns, in the upper mesosphere/lower thermosphere of Venus (80–150 km). Thesesimulations result in an overall better agreement with temperature observations above 90 km, compared withprevious versions of the VGCM. Density of CO2 and light species, such as CO and O, are also comparable withobservations in terms of trend and order of magnitude. Systematic biases in the temperature structure are foundbetween 80 and 100 km approximately (e.g. GCM is 20 to 40 K warmer than measurements) and above 130 km at
the terminator (e.g. GCM is up to 50 K colder than observed). Possible candidates for those discrepancies are theuncertainties on the collisional rate coefficients used in the non-LTE parameterization (above 130 km), andassumptions on the CO2 mixing ratio made for stellar/solar occultation retrievals. Diurnal and latitudinal dis­tribution of dynamical tracers (i.e. CO and O) are also analyzed, in a region poorly constrained by wind mea­
surements and characterized by high variability over daily to weekly timescale. Overall, our simulations indicatethat a weak westward retrograde wind is present in the mesosphere, up to about 120 km, producing the CO bulge
displacement toward 2 h–3 h in the morning, instead of piling up at the anti-solar point, as for an idealized subsolar to anti-solar circulation. This retrograde imbalance is suggested to be produced by perturbations of a ~ 5days Kelvin wave impacting the mesosphere up to 110 km (described in a companion paper Navarro et al., 2021),
combined with GW westward acceleration in the lower thermosphere, mostly above 110 km. On the whole, thesemodel developments point to the importance of the inclusion of the lower atmosphere, higher resolution andfinely tuned parameterizations in GCM of the Venusian upper atmosphere, in order to shed light on existingobservations.

1. Introduction
Our understanding of the Venusian climate has noticeably increased
with progress with General Circulation Models (GCM), powerful 3D
tools to investigate the amount of data acquired by space missions,particularly in recent time by Venus Express (VEx) (e.g Drossart et al.,2007; Piccioni et al., 2007) and Akatsuki (e.g Satoh et al., 2017;

Fukuhara et al., 2017) missions, as well as on-going ground-based
telescope campaigns. For instance, some model experiments agree on
the role of thermal tides in the vertical transport of angular momentum
in the equatorial region and in maintaining the superrotation (Yama­
moto and Takahashi, 2006; Takagi and Matsuda, 2007; Lebonnois et al.,2010; Mendonça and Read, 2016; Yamamoto et al., 2021). Others suc­ceeded to reproduce the polar atmospheric structure and the “cold* Corresponding author at: Instituto de Astrofisica e Ciências do Espaço (IA), OAL, Tapada da Ajuda, PT1349-018 Lisboa, Portugal.
E-mail address: ggilli@oal.ul.pt (G. Gilli).
https://doi.org/10.1016/j.icarus.2021.114432
Received 27 November 2020; Received in revised form 4 February 2021; Accepted 10 March 2021
Available online 26 March 2021
0019-1035/© 2021 Elsevier Inc. All rights reserved.

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Icarus 366 (2021) 114432

collar” (Ando et al., 2016; Garate-Lopez and Lebonnois, 2018), in close
agreement with the observations, in particular when the latitudinalvariations of the cloud structure are taken into account.
Despite numerous and increasing observations, our view of the upper
mesosphere and lower thermosphere (UMLT) of Venus (i.e. 80 km–140
km approximately) still remains incomplete. Therefore, GCMs are also
used to understand the atmospheric conditions of high altitude regions
where some key observations are lacking, especially on the dayside(Brecht and Bougher, 2012; Gilli et al., 2017). The UMLT is character­ized by strong diurnal variations in atmospheric temperatures which
drive Subsolar-to-Antisolar (SS-AS) transport of photolyzed products
such as CO, O, NO, H, O2 singlet delta, toward strong nighttime en­hancements in the thermosphere (Clancy et al., 2012).
VEx observations (2006–2014) revealed an even more variable at­mosphere than expected: in particular, the so-called “transition” region(~ 80–120 km), between the retrograde superrotating zonal (RSZ) flow
and the day-to-night circulation, showed latitude and day-to-day vari­ations of temperature up to 80 K above 100 km at the terminator, andapparent zonal wind velocities measured around 96 km on the Venus
nighttime highly changing in space and time. Current 3D models do not
fully explain those variations, and specific processes (e.g. gravity wave
propagation, thermal tides, large scale planetary waves) responsible fordriving them are still under investigation.
However, it is difficult to simulate all individual wave sources of
temporal and spatial variability observed within the Venus atmosphere
due to resolution constraints. It is useful for a modeling approach to havea more extensive collection of measurements to provide statisticalmeans. An example of a comprehensive data inter-comparison is thework presented in Limaye et al. (2017) (hereinafter Limaye17), showing
the state-of-the-art temperature and total density measurements taken
during VEx lifetime by instruments onboard and by ground-based tele­scopes. The study by Limaye17 put in evidence that, although there is a
generally good agreement between the various experiments, differences
between individual experiments are more significant than measurement
uncertainties, especially above 100 km, revealing a considerable tem­
perature variability. These experiments observed the Venus atmo­
sphere’s vertical and latitudinal temperature structure, unveilingfeatures that appear to be systematically present, such as a succession ofwarm and cold layers.
Regarding abundances of tracers in the UMLT, Vandaele et al. (2016)
reported retrievals of CO spanning the 65–150 km altitude range during
the whole VEx mission with the instrument SOIR (Solar Occultation in
the InfraRed) together with an updated comparison of CO measurementsfrom the literature. Within the high variability observed in the shortterm, reaching one order of magnitude on less than one month period,
they also found a systematic latitudinal trend: CO abundances in theequatorial region were larger than high-latitude (60◦ -80◦ ) and polar(80◦ -90◦ ) abundances by a factor 2 and 3 respectively at an altitude of
130 km. However, below 100 km, the trend is reversed (i.e., equator-topole increasing), as reported by previous observations (Irwin et al.,2008; Marcq et al., 2015). This trend reversion supports the existence ofa Hadley cell circulation type (Taylor, 1995; Taylor and Grinspoon,
2009; Tsang et al., 2008; Marcq et al., 2015). Interestingly, the density
profiles measured by SOIR on board VEx showed a distinctive change ofslope with altitude occurring in the region usually lying between 100
and 110 km altitude, also present in the Venus International Reference
Atmosphere (VIRA) model, but at a lower altitude (Vandaele et al.,2016). In VIRA, this may be caused by different observations feeding themodel for CO: above 100 km, VIRA data is based on measurements re­ported by Keating et al. (1985), below 100 km the values are those re­ported by von Zahn et al. (1983) and later updated by de Bergh et al.
(2006) and Zasova et al. (2006). Simultaneous CO and temperature
measurements in the lower thermosphere (Clancy et al., 2012) sug­gested instead that a distinctive circulation pattern would force largescale air masses downward, creating dynamic increases in temperature
and CO mixing ratio. SOIR evening abundances are also systematicallyhigher than morning values above 105 km altitude, but the reverse isobserved at lower altitudes.
Regarding the wind, the most relevant constraints in the meso­
sphere/thermosphere are provided by the diurnal/meridional distribu­
tion of CO and of the O2 1.27 μm IR nightglow, whose peak emission
occurs at 96 ± 1 km in limb viewing (Drossart et al., 2007b). Both CO
and O are produced by CO2 photo-dissociation during daytime and thentransported to the nightside by the SS-AS circulation. Light species like
CO are expected to pile up at the converging stagnation point of the windfield (i.e., the point where the horizontal velocity converges to zero).
This stagnation point is at the anti-solar point for idealized SS-AS flow
and displaced toward the morning terminator when a westward retro­
grade zonal flow is added. The position of the maximum and its
magnitude depend on the relative values of equatorial velocity and amaximum cross-terminator velocity. Lellouch et al. (2008) observations
showed a moderate (factor 2) CO enhancement near the morningterminator. To first order, this picture should be valid for the O2 airglow.
However, the situation is somewhat more complex in this case because
the brightness of the O2 emission also depends on chemistry and verticaltransport (see companion paper by Navarro et al. (2021), hereinafter
Navarro21).
In addition, a combination of nightside and terminator Doppler CO
wind measurements took advantage of a rare solar transit in June 2012
to address several important questions regarding Venus’s upper atmo­
spheric dynamics. Moullet et al. (2012) found a substantial nightside
retrograde zonal wind field near the equator, also detected in othernightside distribution studies and less pronounced in the dayside lower
thermospheric circulation (Clancy and Muhleman, 1991; Gurwell et al.,
1995; Lellouch et al., 2008; Clancy et al., 2012). This was interpreted as
the presence of weaker morning versus evening cross-terminator winds,
likely to be associated with morning-evening asymmetric momentum
drag due to enhanced gravity wave (GW) absorption over the morningthermospheric region (Alexander et al., 1992; Bougher et al., 2006;
Clancy et al., 2015). Model simulations in Hoshino et al. (2013) showedthat gravity wave forcing, coupled with a 40 m/s westward retrograde
zonal wind field at 80 km altitude, is sufficient to impart a strong
morning-evening asymmetry, as observed apart from the observed
extreme temporal variability. Clancy et al. (2015) also measured sub­stantially weaker morning terminator winds compared with evening
terminator, consistent with Hoshino et al. (2013) results. However, thetheoretical basis for forcing this asymmetry and associated retrograde
zonal flow is not provided yet. Overall, wind distributions during
nightside show much larger changes (50–100 m/s) over timescales ofdays to weeks than dayside winds (Clancy et al., 2015).
Despite observational and modeling efforts in the last decades,
Venusian upper atmosphere’s temperature structure and dynamical
behavior still raise questions: what are the sources of the variability
observed in the airglow emissions on the nightside? Solar flux variation
is expected to play only a minor role in the rapid changes observed in thenightglow morphological distribution (Gérard et al., 2014, 2017).
Instead, gravity waves have been suggested as a source of variability but
have not been demonstrated to provide the required amplitude. In thecompanion article Navarro21, the state-of-the-art Venus GCM developed
at the Institut Pierre-Simon Laplace (IPSL-VGCM) is used as well toinvestigate the variability of the upper atmosphere caused by a Kelvinwave generated in the cloud deck reaching the UMLT, and a supersonicshock-like structure wave in the thermosphere. In this paper, this sameversion of the IPSL-VGCM is validated with the most recent literature on
observations of Venus UMLT atmospheric region, focusing on temper­
ature, CO2, CO and O abundances, and the effect of propagating nonorographic GW on those abundances is discussed.
Section 2 contains the description of the state-of-the-art of the IPSLVGCM. The details on the formalism adopted for the non-orographic GW
parameterization, with the GW parameters setup, and the improvementof non-LTE parameterization are in Appendices A and B. In this work,
the outputs of the simulations obtained with the current high-resolution
2

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version (as Navarro21) are compared with a selection of data in Section
3, while an analysis of predicted diurnal and latitudinal variations of O
and CO is presented in Section 4. The results are further discussed in
Section 5 and the possible effects of propagating GW to the predicted
abundances analyzed. Section 6 is focused on conclusive remarks andrecommendations for future studies.

Table 1“Best-fit” values from the improved non-LTE parameterization used in this work,
compared with previous version in Gilli17. See Appendix B for details.

2. Model description and recent improvements
The IPSL Venus GCM (IPSL-VGCM) has been used to investigate all
regions of the Venusian atmosphere, as it covers the surface up to thethermosphere (150 km) (Lebonnois et al., 2016; Gilli et al., 2017;
Garate-Lopez and Lebonnois, 2018; Navarro et al., 2018). The verticalmodel resolution is approximately ~ 2–3 km between 100 and 150 km,
slightly smaller below 100 km. It includes a non-orographic GW
parameterization as described in details in the Appendix A, and aphotochemical module with a simplified cloud scheme, with fixed bimodal log-normal distribution of the cloud varying with altitude. The
aerosol number density is calculated for each mode and an explicit
description of the H2O and H2SO4 mixing ratios in the liquid phase isincluded. The mass sedimentation flux is added to tracers evolution, but
the microphysical scheme is not coupled with dynamics. The evaluatedmass loading in the cloud is not used by the radiative module whichinstead use a scheme recently updated in Garate-Lopez and Lebonnois(2018). That takes into account the latitudinal variation of the cloud
structure based on Venus Express observations, and lower haze heating
rate yielding to a better agreement with in-situ values of wind below thecloud deck (around 45 km altitude) by the Pioneer Venus probes(Schubert, 1983). Chemical abundances play a crucial role in non-LTE
effects (e.g., the impact of variable atomic O in the CO2 cooling rates)
and EUV heating processes, which are key processes in the upper at­mosphere of terrestrial planets (Bougher et al., 1999). Our model iscurrently the only GCM coupled with photochemistry, which enables thestudy of the upper atmosphere and its composition self-consistently(Stolzenbach, 2016; Stolzenbach et al. in preparation). Here we fol­
lowed the same approach as in Gilli17: the molecular weigh is calculated
up to 150 km according to the chemical composition of the atmosphere,
and the homopause level is evaluated by the GCM. The potential impactof a variable composition below 100 km, as revealed by N2 concentra­tion measurements between 60 and 100 km of 40% higher than thenominal value (Peplowski et al., 2020) was not taken into account in thisstudy, since we estimate that it would have a negligible impact on our
results. Note that the IPSL-VGCM considers only neutral species so far,but future developments will be addressed to extend the model up to theexobase of Venus (~ 250 km altitude) and take into account the iono­
spheric chemistry. Moreover, the inclusion of a stochastic nonorographic gravity wave parameterization is a plus compared to otherexistent thermospheric models (Brecht and Bougher, 2012; Bougher
et al., 2015) which accomplished deceleration of supersonic crossterminator winds artificially by Rayleigh friction.

Parameter description

Best-Fit

Gilli17

Solar Heating per Venusian day, Vd [Kday− 1]
Cloud top pressure level, p0 [Pa]Pressure below which non-LTE are significant, p1 [Pa]Central Pressure for LTE non-LTE transition, ptrans [Pa]

15.92
1985
0.1
0.2

18.13
1320
0.008
0.5

was also higher than observed, and systematically warmer, indicating an
overestimation of daytime heating rates produced by absorption of IR
solar radiation by CO2 molecules. Among several sources of discrep­
ancies, we identified one possible candidate. First, we discarded theeffect of the uncertainty in the collisional rate coefficient Kv–t used in the
non-LTE parameterization because, as shown in Fig. 12 in Gilli17, thevariation associated with this coefficient only modified the temperature
profiles in the thermosphere above 10− 2 Pa (about 120 km), not below.
We focused instead on the heating rate used as “reference” in Gilli17,
extracted from non-LTE radiative transfer line-by-line model results in(Roldán et al., 2000). In that model, the kinetic temperature and density
input profiles were taken from VIRA (Keating et al., 1985; Hedin et al.,1983). Nevertheless, VEx observations revealed that VIRA is not repre­
sentative of the atmosphere of Venus above 90 km (Limaye et al., 2017).
VIRA is based on Pioneer Venus measurements below 100 km and above
140 km, leaving a gap where the empirical models perform poorly(Bougher et al., 2015). New measurements of the thermal structure of
the Venusian atmosphere offer the opportunity to improve theseempirical models. Therefore, we decided to perform here a semiempirical fine-tuning of the non-LTE parameterization implemented in
Gilli17, based on temperature profiles by SOIR and SPICAV, as describedin the next session. Note that we first performed baseline simulations
with a less computationally intensive version (i.e. low horizontal reso­
lution version 7.5◦ x 5.625◦ as in Gilli17). Successively, we increased thehorizontal resolution to 3.75◦ x 1.875◦ , without significant changes in
the heating/cooling rates above 90 km. The combination of this highhorizontal resolution and a more realistic representation of zonal winds(Garate-Lopez and Lebonnois, 2018) and temperature gave rise to a very
unique GCM, able to describe the circulation of the upper atmosphere ofVenus with unprecedented details, as described in the companion paper
Navarro21.
2.2. Fine-tuning of non-LTE parameters
In order to fine-tune and select the set of “best-fit” parameters listed
in Table 1 we performed about 100 GCM test simulations with the goalof reducing as best as possible the discrepancies found between the
averaged temperature profiles of the IPSL-VGCM and VEx dataset, inparticular with SOIR/VEx (Mahieux et al., 2012) and SPICAV/VEx(Piccialli et al., 2014) profiles at the terminator and at night time,
respectively. The major difficulty of this fine-tuning exercise was to finda combination of parameters described in Appendix B which also fulfills
model numerical stability requirements (e.g. that allowed to run themodel for several Venus days). The range of parameters that have beentested in this fine-tuning exercise is: 15.576 K to 24.60 K/day for the
Solar heating per Venusian day, 1200 to 4350 Pa for the cloud-top
pressure level, 6×10− 3 to 2.4 Pa for the pressure below which nonLTE effects are significant, and from 0.10 to 5 Pa for the center of thetransition between non-LTE and LTE regime. The collisional rate coef­
ficient was not changed compared to Gilli17. It is fixed at 3×10− 12, thatis a “median” value commonly used in terrestrial atmosphere models.
Fig. 1 shows an example of this improvement. Predicted upper
mesospheric temperature (above 110 km approximately) are noticeably
reduced by 20 K compared with the previous IPSL-VGCM version in
Gilli17, and both the intensity and the altitude of the warm layer around
100 km are in better agreement with SOIR profiles. At nighttime the2.1. Recent improvements
Compared with the previous ground-to-thermosphere VGCM
described in Gilli et al. (2017) (hereinafter Gilli17), we include here
several improvements in both radiative transfer code and non-LTE
parameterization. The details of the improved non-LTE parameteriza­tion are provided in Appendix B. Also, we increased the horizontal
resolution from 7.5◦ × 5.625◦ to 3.75◦ × 1.875◦ resulting in qualitativechanges of the UMLT nighttime circulation dynamics, as explained inNavarro21. The predicted thermal structure presented in Gilli17 alreadycaptured a succession of warm and cold layers, as observed by VEx, but
localized data-model discrepancies suggested model improvements. At
the terminator and at nighttime thermospheric temperatures were about
40–50 K colder and up to 30 K warmer, respectively. The altitude layerof the predicted mesospheric local maximum (between 100 and 120 km)
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Fig. 1. Averaged temperature vertical profiles as function of altitude predicted by the IPSL-VGCM before (solid black line) and after (solid red line) improving thenon-LTE parameterization, together with observational data from Venus Express. Top panel: temperature retrieval results at the terminator (green for morning andblue for evening terminator) at the latitude bin 30◦ N-60◦ N extracted from the Venus Atmosphere from SOIR data at the Terminator (VAST) database (Mahieux et al.,2015). SOIR standard deviation is also plotted as shaded green and blue areas. Bottom panel: Temperature retrieval results from SPICAV/VEx for the Northern
Hemisphere (SPICAV - NH), 30◦ N-50◦ N in dark blue, and for the Southern Hemisphere (SPICAV - SH), 30◦ S-50◦ S in green (after Piccialli et al. (2015)). Standard
deviation of the predicted temperature mean, in the same local time and latitude range as observed, is represented by dashed lines. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version of this article.)agreement is improved above 100 km with colder simulated tempera­ture by 20 K, closer to observations. Above approximately 115 km, GCM
results are within SPICAV error bars, though there are still data-model
differences of about 20 K in the region between 100 and 115 km. The
peak observed around 90–95 km is still higher in the model (around 100km), despite a significant improvement. A detailed comparison of IPSLVGCM results with a number of temperature measurements by VEx andground-based experiments is provided in the next section. Temperaturecross section (local time versus altitude) before and after the improve­
ment of non-LTE parameterization is shown in Fig. 2. The peak oftemperature around midday and midnight is reduced by about 40 K and
20 K, respectively, and it is located about 10 km lower than predicted in
Gilli17 (e.g. around 105 km and 100 km at midday and midnight,
respectively). The temperature structure predicted by the IPSL-VGCM
used in this work is in better agreement not only with VEx observa­
tions (see next section), but also with the National Center for Atmo­
spheric Research (NCAR) Venus Thermospheric General Circulation
Model presented in Brecht and Bougher (2012) (see their Fig. 1).

3. IPSL-VGCM high resolution simulations: model-data
validation above 90 km
The results discussed from this section onwards were performed
using high horizontal resolution (96 × 96 × 78) simulations of the stateof-the-art IPSL-VGCM described in section 2. This is the highest reso­
lution ever used for a Venus upper atmosphere model, corresponding togrid cells of approximately 200 km in the meridional direction and 400
km in the zonal direction, at the equator. This increased resolution al­lows to resolve smaller-scale waves compared to those in Gilli et al.
(2017) and to shed a light on the observed variability in the nightside ofVenus (see Navarro21). We aim to present an exhaustive data-modelcomparison with the state-of-the-art IPSL-VGCM, focusing on the at­mospheric layers above 85–90 km, using the most complete compilationof Venus temperature and CO2 density dataset so far, obtained both fromspace mission and ground based observations as in Limaye2017.
Moreover, a selection of CO retrieved measurements by SOIR/VEx at theterminator (Vandaele et al., 2016), by VIRTIS/VEx during daytime (Gilli
4

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Fig. 2. Temperature field (local time versus altitude) obtained with IPSL-VGCM before (left panel) and after (right panel) the non-LTE parameterization im­provements, listed in Table 1.

et al., 2015) and from Earth-based telescopes (Clancy et al., 2012) areconsidered here. For the atomic oxygen, only indirect O nighttime
densities retrieved from O2 nightglows as in Soret et al. (2012) areavailable for this comparison exercise. The instruments and measure­ments used in this validation exercise are listed in Table 2.

orbiter observations (PVO), and from many ground based and VEx ob­servations (e.g. Clancy et al., 2008, 2012; Migliorini et al., 2012; Grassi
et al., 2010; Mahieux et al., 2010; Gilli et al., 2015; Piccialli et al., 2015;
Krause et al., 2018). A comprehensive compilation of temperature re­trievals since the publication of VIRA to Venus Express era was given in
Limaye17, which provided for the first time a consistent picture of thetemperature and density structure in the 40–180 km altitude range. Thisdata intercomparison also aimed to make the first steps toward gener­
ating an updated VIRA model.
Beside the high variability observed even on short timescales, a
succession of warm and cool layers above 80 km is one of remarkable

3.1. Temperature and total density
Our current knowledge of the Venus temperature structure comesfrom empirical models such as VIRA (Keating et al., 1985; Seiff et al.,1985) and VTS3 (Hedin et al., 1983), mostly based upon Pioneer Venus
5

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Table 2
Observations of Temperature, CO2, CO and O densities used in this paper (see Figs. 3-6). Adapted from Table 1 in Limaye et al. (2017).
Experiment/Instrument

MethodLat Coverage

LT Coverage

Alt
Coverage

Retrieved variable

References

Remote sensing observations
from spacecraft
MagellanRadio occultationnight/day side

38–100 km

Temperature

Jenkins et al. (1994)

Venera 15

Radio occultationnight/day side

38–100 km

Temperature

Pioneer Venus

Radio occultationnight/day side

38–100 km

Temperature

Gubenko et al. (2008); Haus et al.
(2013)
Kliore (1985); Seiff et al. (1985)

VeRa/VEx

Radio occultationnight/day side

38–100 km

Temperature, CO2

Tellmann et al. (2009)

SPICAV/VEx

Stellar occultationnight side

90–140 km

Temperature, CO2

Piccialli et al. (2010)

SOIR/VEx

Solar occultation

terminators

70–170 km

Temperature, CO2, COVIRTIS-H/VEx Limb

day side

Temperature, COnight side

100–150km
65–80 km

Mahieux et al. (2010); Vandaele et al.
(2016)
Gilli et al. (2015)

VIRTIS-H/VEx

non-LTE 4.7 μm CO
band
4.3 μm CO2 band

N/Shemisphere
N/Shemisphere
N/Shemisphere
N/Shemisphere
N/Shemisphere
N/Shemisphere
N hemisphere

Temperature

Migliorini et al. (2012)

VIRTIS-M/VEx Nadir

4.3 μm CO2 bandnight side

65–80 km

Temperature

Haus et al. (2013); Grassi et al. (2014)

night side

~ 96 kmO

Soret et al. (2012)

morning
terminator
78–98◦ SZA

CO2

Müller-Wodarg et al. (2016)

CO2

Persson (2015)

Temperature

Tanga et al. (2012); Pere et al. (2016)

VExADE-AER/VEX

Aerobraking

N/Shemisphere
N/Shemisphere
N/Shemisphere70◦ N-80◦ N

VExADE-TRQ/VEXTorque measurements

70◦ N-80◦ N

HMI/SDO

Venus transit

Terminator

Ground-based observations
THIS/HIPWAC

N/S
hemisphere130–140km
165–200km
70–110 km

non-LTE CO2 emission

N/Shemisphere
N/Shemisphere
N/Shemisphere
N/Shemisphere

day side

110 kmTemperaturenight/day side

75–120 km

Temperature, CO

Sornig et al. (2008); Krause et al.
(2018)
Clancy et al. (2012)

night/day side

75–110 km

Temperature

Rengel et al. (2008)

night side

85–110 km(mean) rotationaltemperature

Crisp et al. (1996); Bailey et al. (2008);
Ohtsuki et al. (2008)

VIRTIS-M/VEX

JCMT
HHSMT
AAT, IRIS2, CFHT, CSHELL

O2 nightglow at 1.27μm

sub/mm CO line
absorption
sub/mm CO line
absorption
O2 nightglow at 1.27
μm

feature that has been systematically detected. This was also correctly
captured by model simulations (Gilli et al., 2017; Brecht and Bougher,
2012; Bougher et al., 2015) and interpreted as a combination of radia­
tive and dynamical effects: the local maximum at daytime is produced
by solar absorption in the non-LTE CO2 IR bands, and then advected tothe terminator, while a nighttime warm layer around 90 km is producedby subsidence of day-to-night air at the AS point. Above, the cold layer
around 125–130 km is suggested to be caused by CO2 15 μm cooling.
Nevertheless, especially above 100 km, temperature variation is large
and the difference between individual experiments seems to be higher.
Figs. 3-5 are adapted from Figs. 16–19 in Limaye17 and show a selectionof observed temperature profiles together with IPSL-VGCM simulations
on daytime (local time LT = 7 h–17 h), nighttime (LT = 19 h–7 h) and at
the terminator regions (LT ≈ 18 h and 6 h), averaged in latitude bins.
Here we show only equatorial region (0◦ -30◦ ) and middle/high (50◦ 70◦ ) latitudes, as examples. Figs. 3 and 4 include retrieved temperature
profiles from several instruments on board Venus Express binned in asimilar latitude/local time range. Both channels of the Visible andInfrared Imaging Spectrometer (VIRTIS) are used here: VIRTIS-M tem­
perature retrieved using CO2 4.3 μm band reported by (Grassi et al.,2014; Haus et al., 2015) and VIRTIS-H profiles as in Migliorini et al.
(2012). Data from solar and stellar occultation experiment on-board
VEx, i.e. SOIR Piccialli et al. (2015) and SPICAV Mahieux et al.
(2015), respectively, from Radio occultation measurements (Tellmann
et al., 2009) and from O2 nightglow observations at low latitudes (Pic­cioni and VIRTIS/Venus Express Team, 2009) are added. For daytime,
only temperatures retrieved from non-LTE CO2 emissions measured by
VIRTIS/VEx (Gilli et al., 2015) are available above 110 km, as shown in
Fig. 5. Mean temperatures derived from the sunlight refraction in themesosphere during the 2012 Venus transit (Tanga et al., 2012; Pereet al., 2016), and ground-based observations by the JCMT (Clancy et al.,2008, 2012), HHSTM (Rengel et al., 2008) and THIS/HIPWAC (Krause
et al., 2018) are also taken into account. Empirical models such as VIRA
(Seiff et al., 1985; Keating et al., 1985) and VTS3 (Hedin et al., 1983) arealso plotted as reference.
As mentioned previously in Sec. 2.2, nightside temperatures (Fig. 3)
predicted by the IPSL-VGCM are still overestimated in the 90–115 km
altitude region, by 20 to 30 K, with a peak temperature altitude (around
100 km) that is in agreement with Earth-based observations (e.g. JCMT
profiles), but about 5 km altitude higher than the peak of SPICAV
temperature retrievals. Below 90 km, predicted temperatures tend to beat the upper limit of the range of observed values. At terminators(Fig. 4), simulated temperature profiles are in good agreement withmost of the datasets, except for a cold bias of around 20 K above 130 km,
compared to SOIR data. The current uncertainty in the collisional rate
coefficients used in the non-LTE parameterization (see Section 2.1) is apossible candidate to explain this bias, since Kv–t variation has an impact
on the temperature above 120 km approximately.
Dayside simulated temperature profiles (Fig. 5) reproduce the warmand cold layers observed near 110 km and 125 km, respectively, but tend
to overestimate their amplitude. Note that the VIRA and VTS3 models,that were under-constrained in the 100–150 km altitude range, do notpredict these temperature inversions. Nevertheless, it is difficult to get
any conclusion from Fig. 5, given the high error bars of daytime tem­
perature measurements obtained with VIRTIS/VEx.
Retrieved vertical total density profiles from 80 km to 150 km areshown in Fig. 6 as measured by the different Venus Express experiments
mentioned above. In addition, density measurements from Venus
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Fig. 3. Compilation of available nighttime Venus temperature profiles above 80 km both from spacecrafts and ground based telescopes versus model predictions
averaged at equatorial latitudes 0–30◦ from Southern and Northern hemispheres (top panel) and 50◦ -70◦ N/S (bottom panel). Corresponding approximate values foraltitude is given on the right hand side of the panel. Panel a) and panel b) are adapted from Figs. 15 and 17 in Limaye et al. (2017), respectively. See text for details.
Uncertainties (one standard deviation) are either plotted as colored areas for averaged profiles in the same bin (Venus Express datasets, JCMT, HHSMT) or as error
bars (O2 nightglow). The IPSL-VGCM predicted profile is plotted with solid black line.

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Fig. 4. Same as Fig. 3 but at the terminators (morning and evening) and with the addition of the mean temperature values retrieved at the evening terminator during
the Venus transit in 2012. See text for details. Panel a) and panel b) are adapted from Figs. 15 and 17 in Limaye et al. (2017), respectively. The IPSL-VGCM predictedprofiles are in solid and dashed black lines for the ET and MT, respectively.

Express Atmospheric Drag Experiment (VExADE) are included for highlatitudes (panel d of Fig. 6): between 130 and 140 km from acceler­ometer readings during aerobreaking (Müller-Wodarg et al., 2016), andin the altitude range 165–200 km from spacecraft torque measurements(Persson, 2015). Nighttime densities are in very good agreement withSPICAV and VeRa, with the exception of the equatorial region, where
IPSL-VGCM densities are a factor of 2–3 greater than SPICAV observa­
tions between 120 and 130 km. Densities retrieved by SOIR at theterminator are also up to a factor 2–3 lower then our simulations in theequatorial region, and up to 1 order of magnitude lower in the altitude
between 100 and 140 km at high latitudes. However, in this region, it isworth noticing that our model predicted values fit very well with the
VExADE density profile. The discrepancy with SOIR may be linked to theassumption of spherical homogeneous layers made by the authors andon the a-priori CO2 mixing ratio. SOIR and SPICAV measured directly
the CO2 number density from its absorption structure, thus have to as­sume a CO2 volume mixing ratio, taken from VTS3 semi-empirical model(Hedin et al., 1983) above 100 km. VTS3 is mainly based on PV
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Fig. 5. Same as Fig. 3 but for daytime and with the addition of temperature retrieved from CO non-LTE emissions observed by VIRTIS/VEx. See text for details. Panela) and panel b) are adapted from Figs. 15 and 17 in Limaye et al. (2017), respectively.

measurements above 140 km and on model extrapolations assuminghydrostatic equilibrium between 100 and 140 km. This model revealed
not to be adapted to represent the atomic oxygen densities derived fromO2 night-glow observations (Soret et al., 2012), the measured value
being 3 times higher than predicted by VTS3. Moreover, the observedhigh variability of atmospheric quantities by VEx instruments was notreflected in previous empirical models (e.g. VIRA and VTS3) (Limaye
et al., 2017). Therefore, the uncertainties of SOIR profiles may reflect
uncertainties in the VTS3 densities and have to be taken into accountwhen comparing the observations with models.
3.2. CO densities
Figs. 7 to 10 show density of CO, compared with a selection of VEx
experiments and ground-based measurements at different local time andlatitudes. For daytime, retrieved CO density measurements are very
scarce. We used here VIRTIS/VEx measurement in the altitude range
100–150 km as in Gilli et al. (2015) retrieved from non-LTE dayside IR
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Fig. 6. Measured total density profiles (molecule/cm3) retrieved with different Venus Express instruments at nighttime (Panels a and b) and at the terminators(Panels c and d) for two latitude bins 0–30◦ and 70◦ -80◦ (North/South averages) as indicated. The colored areas mark the uncertainty of the respective averaged
profiles at one standard deviation. The figures are adapted from Figs. 20 and 23 in Limaye et al. (2017). Model predictions averaged at same latitudes and local time
as observations are plotted with solid and dashed black lines. Vertical axis is given in altitudes, and approximate pressure (in mbar) is shown as the right hand side
vertical axis.

CO emission at 4.7 μm. Below 112 km and for latitudes between 30◦ N50◦ N, we also included the CO density profile from the semi-empiricalmodel by Krasnopolsky (2012) which is representative of averaged
values under intermediate solar conditions. We found that our results
are very well comparable with observations in order of magnitude.
However, daytime retrieved CO density between 110 and 125 km areabout a factor 2 larger than our model profiles for most latitudes, except
for high latitude bins (50◦ N-70◦ N) where instead observed CO densities
are smaller than simulations, up to a factor 3. Those discrepancies could
be related to the biases in the predicted temperature structure that we
found above 90 km, the model being systematically warmer than thedata between 90 and 110 km (approximately 102—10− 1 Pa), and colder
between 120 and 150 km.
Regarding the terminators (Figs. 9-10), our predicted CO densities
are in overall agreement with SOIR dataset (Vandaele et al., 2016) inparticular at evening terminator, but with larger differences withobserved values below about 110 km on the morning side. The

discrepancies are up to one order of magnitude between 90 and 100 km,
and factor 2–3 elsewhere. Larger discrepancies are seen in the polar
region (80◦ N-90◦ N), where our model is up to one order of magnitudelarger than data for both MT and ET.
Daytime and nighttime disk averaged CO measurements were also
obtained in several ground-based campaigns (de Bergh et al., 1988;
Clancy and Muhleman, 1991; Lellouch et al., 1989; Clancy et al., 2008,2012) suggesting not only large variability over daily to weekly time­scales, but also a distinct dayside versus nightside circulation in terms ofzonal wind in particular (Clancy et al., 2012). Fig. 8 shows the com­
parison of model outputs and CO volume mixing ratio measurements
during ground-based campaigns described in Clancy et al. (2012). They
represent 2001–2009 inferior conjunction sub-mm CO line observations,covering most of the disk (e.g. 60◦ S-60◦ N latitudes). Daytime model
outputs are averaged for LT 10 h–14 h, and nighttime for LT 2 h–22 h, inthe observed latitudinal bin. Our results are in general good agreementwith observations for pressure ranges 103–10 Pa, but above that pressure
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Fig. 7. CO density retrieved by VIRTIS/VEx during daytime (LT 10 h–14 h) after Gilli et al. (2015) together with IPSL-VGCM averaged profiles binned at similar
latitude and local time, as indicated in the panels. In addition, semi-empirical CO profile from Krasnopolsky (2012) representative of mean conditions is shown forcomparison with mid-latitude (30◦ N-50◦ N) daytime averaged profiles.

the trends are diverging (i.e. increasing volume mixing ratio (vmr)
predicted by the model versus almost constant CO values suggested byobservations). Nighttime measurements of the 2007–2008 campaign arewithin model dispersion bars (calculated as the standard deviation in theselected latitude/local time bin), and show a better agreement than
2000–2002 campaigns. Part of the difference between the two cam­paigns is likely related to the much reduced beam coverage for thoseearly observations (Clancy, private communication).
Given the high variability of CO measurements over short timescales, it is difficult to find a general consensus among observations, anda clear defined trend. Our simulations indicate that the predicted
abundances are affected by both the asymmetry of the SS-AS zonal flowat the terminator produced by non-orographic GW propagation in thelower thermosphere and the variability of zonal winds induced by
Kelvin waves generated in the cloud deck, and propagating up to themesosphere, as discussed in Navarro21. A possible interpretation of CO
distribution is also provided in Section 5.

profile near the equator (24◦ S) and nighttime O density extracted from asemi-empirical model by Krasnopolsky (2012). In very good agreementwith Soret et al. (2012), modeled values peak at 2 × 1011 cm− 3 around
110 km. Brecht et al. (2011) also found a similar value at midnight intheir thermospheric GCM simulations at lower altitudes (3.4 × 1011
cm− 3 at 104 km). Below 95 km, O densities predicted by our model are
decreasing much faster with altitude than observed in Soret et al.
(2014), but this is still consistent with Krasnopolsky (2012). A more
complete description of the latitudinal and diurnal distribution of O
predicted by the IPSL-VGCM is presented in the next section.
4. Diurnal and latitudinal variations of CO and O above 80 km
by the IPSL-VGCM
The latitudinal and diurnal variations of CO and O predicted by the
IPSL-VGCM are analyzed here in greater detail. Atomic oxygen chemical
lifetime is shorter than carbon monoxide. Therefore the O mixing ratio
decreases more rapidly with increasing pressure than CO, and its lat­
itudinal variations are not significant in the GCM above ~10 Pa (95 km
approximately). This is clearly shown in Figs. 12-13: CO and O vmr
profiles by IPSL-VGCM as function of pressure are plotted for several
latitude bins, at daytime (LT: 10 h–14 h) and nighttime (LT: 22 h–2 h).
In the lower mesosphere CO variations are primarily dominated by
the global meridional dynamics, with CO vmr increasing from equator-

3.3. O densities
Only nighttime oxygen density measurements are available in the
UMLT of Venus and they are derived from O2 nightglow in the altitude
range 85–110 km observed with VIRTIS/VEX, as described in Soret et al.
(2012). Fig. 11 shows IPSL-VGCM results together with the observed
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Fig. 8. IPSL-VGCM CO volume mixing ratio profiles (green dots) as function of pressure, averaged in latitude bin 60◦ N-60◦ S, 10 h–14 h LT for daytime and 22 h–2 h
for nighttime, with variability indicated as standard deviation of the average. CO profiles retrieved from sub-mm CO absorption line observations as in Clancy et al.
(2012) for daytime (top panel) and nighttime (bottom panel) are also plotted. 2000–2002 disk average daytime/nighttime measurements are in dark red/blue solidlines, while 2007–2008 disk average daytime/nighttime measurements are in red/blue solid line. (For interpretation of the references to colour in this figure legend,
the reader is referred to the web version of this article.)

to-pole (Tsang et al., 2008; Marcq et al., 2008). At high latitudes,
downward fluxes from the Hadley-type cell circulation bring air
enriched in CO, whereas the ascending branch of these cells has the
opposite effect near the equator (Marcq et al., 2008). This equator-topole variation is predicted by our model below 1 Pa (~ 97–100 km),while oxygen vmr variation is negligible. In the upper mesosphere(above 1 Pa), CO as other light species is expected to pile up at theconverging stagnation point of the wind field (i.e. where the horizontal
velocity converges to zero). This point can be displaced from the antisolar point when the circulation is not simply the SS-AS flow. Our sim­
ulations show that the CO nighttime bulge is shifted toward the morning
between 85 and 100 km approximately, notably around the equator (seeFig. 14), suggesting that CO is preferentially transported westwards inthe transition region. Interestingly, Lellouch et al. (2008) observations
showed a moderate (factor 2) CO enhancement near the morningterminator, although there are no systematic observations supporting
this enhancement of CO on the morning side.
O chemical lifetime is shorter than the characteristic times of theatmospheric dynamics below 1 Pa approximately, where changes in vmr
are driven by chemistry. The opposite is expected above 100 km, where
the dynamical effects dominate (Brecht et al., 2011). The atomic oxygen

density peaks at midnight at the equator between 100 and 110 km, and it
is about 1 order of magnitude larger than density values at the same
altitude, at noon. The daytime peak is also located below 100 km. Thisenhancement from day to night side was also predicted in the Venus
GCM by Brecht et al. (2011) (e.g. a factor of 6 larger), and it is the resultof efficient transport of atomic oxygen atoms from their day sources totheir nightside chemical loss at and below about 110 km. In addition,
our model predicts a second peak around 120 km (Fig. 14), clearly
defined for local time 10 h–14 h and at terminator, but weaker at
midnight. Unfortunately, no observational data can be used to validate
those trends for daytime and terminator. Only O density retrieved
indirectly from O2 nightlow are available so far, and they are in very
good agreement with our prediction (see Fig. 11). The origin of thissecond peak is beyond the scope of this paper and deserves a dedicatedtheoretical study, supported by systematic measurements of O densities.
5. Discussion: dynamical implications
5.1. Impact of the Kelvin wave
Several studies (Hueso et al., 2008; Clancy et al., 2012; Gérard et al.,12

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Fig. 9. IPSL-VGCM simulated CO density profiles as function of altitude (green dashed lines) at the morning terminator (MT) (LT 6 h) averaged at four selected
latitude bins as indicated in the panels: 0◦ -30◦ N, 30◦ N-40◦ N, 60◦ N-80◦ N, 80◦ N-90◦ N. CO density profiles and relative error bars retrieved by SOIR/VEx at the MT in
the same latitude bins are plotted in blue. VAST stands for: “Venus Atmosphere from SOIR data at the Terminator”, after Vandaele et al. (2016). (For interpretation of
the references to colour in this figure legend, the reader is referred to the web version of this article.)

2014; Lellouch et al., 2008; Moullet et al., 2012) indicated that both
retrograde zonal and SS-AS flows affect the global distribution of CO, O
and other light species at mesospheric altitudes (70–120 km). Thosechemical tracers are transported by winds and are expected to pile up at
the converging stagnation point of the wind. This point is usually at
midnight (LT = 0 h) for pure SS-AS flow but it can be displaced toward
the morning terminator if westward retrograde zonal flow is added. The
position of the maximum and its magnitude depend on the relativevalues of equatorial velocity and a maximum cross-terminator velocity.
The majority of Doppler winds retrieved at mesospheric and lower
thermospheric altitudes (Clancy et al., 2008, 2012; Rengel et al., 2008;
Widemann et al., 2007; Gurwell et al., 1995) suggested local time
variation of the wind velocity and the presence of a substantial retro­
grade zonal flow at altitudes above 90 km. Lellouch et al. (2008) ob­
servations showed a moderate (factor of 2) CO enhancement near themorning terminator, and Gurwell et al. (1995) showed a CO maximum
centered at 3 h30 at 95 km, and at 2 h at 100 km, approaching 2 × 10− 3volume mixing ratio.
This retrograde imbalance of the flow toward the morning side is also
predicted by our model. CO nighttime bulge is not located at the ASpoint but shifted toward the morning (see Fig. 15 and Fig. 16) and invery good agreement with Gurwell et al. (1995). The companion paper
Navarro21 describes how the nightside mesospheric circulation isperturbed by a Kelvin wave with a period of approximately 5 Earth days
In our simulations the equatorial wave appears to be excited between
the middle and upper cloud (60–65 km altitude) and it propagates
downward as a Kelvin wave, faster than zonal wind but also upward. At
the cloud top, this wave propagates slower than the mean zonal wind,while recent Akatsuki UV observations suggest that the Kelvin wave
propagates faster (Imai et al., 2019). Kelvin waves with wavenumber-1
have also been predicted by other model experiments (e.g Yamamoto
and Takahashi, 2006; Yamamoto, 2019; Sugimoto et al., 2014) but
contrarily to our simulations they are slower near the cloud base (~ 50km), with a period of 5–6 days. On average, this perturbation shifts theconvergence of equatorial zonal wind between LT 0 h and 3 h, as shownwith the Venus-day averaged view of Figs. 15, or the instantaneous
views of Fig. 16. In the second column of Fig. 16 we show how CO, witha longer chemical lifetime than the Kelvin wave period, accumulates
preferentially toward the morning terminator, particularly at altitudes
90 to 110 km. On the contrary, atomic oxygen (not shown here), with ashorter life span, peaks near the AS point. To our knowledge, there is nosuch observation of CO matching this GCM result. Kelvin wave impact
on the nightside also enhances the poleward meridional circulation. As aconsequence, CO and O are also periodically transported to high lati­
tudes likewise, as explained in Navarro2021 and shown in the thirdcolumn of Fig. 16. The poleward episodes create the high-latitude events
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Fig. 10. Same as Fig. 9 but for evening terminator (ET), LT = 18 h.

of oxygen nightglow observed by VEx as high as 80◦ (Soret et al., 2014).
However, our GCM simulations did not produce such events at latitudes
higher than 60s◦ , probably due to SS-AS too strong in the simulations.
Therefore, we would expect that high-latitude nighttime CO is also
underestimated in these simulations. Figs. 9-10 instead show that the
GCM largely overestimate SOIR measurements at the terminator in thelatitude range 80◦ N-90◦ N. There are not CO nighttime measurements
available at high-latitudes to contrast this result so far. Anotherremarkable characteristic of the Venus upper atmosphere emerging fromthe IPSL-VGCM results is the quiet daytime upper mesosphere (above
100 km) compared with highly variable nighttime, in agreement withobservations.
5.2. Impact of non-orographic GW
Non-orographic GW breaking above 100 km also have a modest
impact on the CO concentration. Previous theoretical studies (e.g.
Hoshino et al. (2013) and Zalucha et al. (2013)) included a GW-dragparameterization in their model to investigate the effect of GW on thegeneral circulation in the Venusian mesosphere and thermosphere.
Hoshino et al. (2013) found the existence of weak wind layer at ~125km, not seen in previous simulations which used the Rayleigh friction
scheme. Instead, Zalucha et al. (2013) suggested that gravity waves did
not propagate above an altitude of nearly 115 km at the terminatorbecause of total internal reflection.
The non-orographic GW paramerization and the baseline parameterschosen here are described in Appendix A.2. An analysis of the horizontal

Fig. 11. Nigthtime O density retrieved by VIRTIS/VEx from O2 nightglow
observations at the equator (Lat: 24◦ S) as in Soret et al. (2012) (blue line) andfrom Krasnopolsky (2012) semi-empirical model (green line). IPSL-VGCM
simulated nighttime profile (LT: 22 h–2 h) for the same latitude is shownwith red dashed line. (For interpretation of the references to colour in thisfigure legend, the reader is referred to the web version of this article.)

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Fig. 12. Latitudinal variations of CO and O vmr during daytime (10 h–14 h) predicted by the IPSL-VGCM. The latitude bins are indicated in each panel. The pressurelevels correspond to an altitude range between 75 and 140 km, approximately. CO profiles show a clear transition of regime at around 1 Pa (100–110 km).

Fig. 13. Latitudinal variations of CO and O vmr during nighttime predicted by the IPSL-VGCM. The latitude bins are indicated in each panel.

distribution of the wind velocity driven by GW is beyond the scope of
this work, but the role of non-orographic GW forcing to the retrograde
zonal flow in the transition region may be identified in our simulations.
They show a clear asymmetry of the SS-AS zonal flow between themorning and the evening branches and at altitudes above 100 km (seeFig. 16). As just explained in section 5.1, this asymmetry is caused by
Kelvin wave impacting the mesosphere, but also by non-orographic GW.
Indeed, these waves deposit momentum where they break, above 10− 1

Pa (110 km approximately), decelerating wind speed (e.g. causing ac­
celeration of the SS-AS morning branch and deceleration of the SS-AS
evening branch). The GW drag predicted by our model is stronger at
the terminator, as in previous studies (e.g. Alexander et al., 1992;
Hoshino et al., 2013; Zalucha et al., 2013), but has similar times values
for the morning and evening terminator near the equator, as shown in
Fig. 17. The deceleration of the evening branch of the SS-AS flow isstronger between LT 16 h and LT 18 h, with a maximum of ~ 0.02 m s− 215

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Fig. 14. Local time variations of IPSL-VGCM CO (top panels) and O (bottom panels) density profiles at equatorial regions 10S–10N (left panels) and middle-highlatitudes 50 N–70 N (right panels). Daytime and nighttime profiles are averaged between LT = 10 h–14 h and LT = 22 h–2 h, respectively. Evening (18 h) andmorning (6 h) terminators are also plotted.

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Fig. 15. 3D maps of Venus nightside at 5 Pa (left) and 0.1 Pa (right) averaged for one Venus solar day, the anti-solar point is indicated with a star. Contour whitelines: CO vmr and colors are zonal wind fields. Yellow lines indicate where the horizontal wind converges to zero values. The location of the terminator is represented
by red dashed lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

at 3 × 10− 1 Pa. The acceleration instead occurs over larger local timeand altitude ranges, reaching the maximum around LT 5 h–8 h (e.g.
0.018 m s− 2). Therefore, the mean zonal forcing integrated over local
times (not shown here) is positive on average. All in all, the cause of the
westwards dominant nightside winds and CO bulge toward the morningterminator is twofold: a westwards propagating Kelvin wave impacting
the mesosphere, up to 110 km, and a weaker westwards acceleration by
GW in the lower thermosphere, above 110 km.

explain this bias is the uncertainty in the collisional rate coefficientsused in the non-LTE parameterization, that have an impact on theheating/cooling rate at thermosphere layers. Daytime simulated
temperature profiles reproduce the warm and cold layers observed
near 110 km and 125 km, respectively, but tend to overestimate theiramplitude.
• CO2 observed densities are also in overall agreement with IPSLVGCM results, with the exception of the equatorial region, wherepredicted values are 2–3 times larger than SPICAV between 120 and
130 and than SOIR everywhere. Our model predicts very well density
profiles measured during the VExADE aerobraking experiment(Persson, 2015). The tion on the a-priori of CO2 mixing ratio used toretrieve density and temperature in SOIR may partially explain thediscrepancies that we found, in addition to the temperature cold bias
found above 130 km at the terminator, mentioned above.
• Regarding the CO and O, model predictions are very well comparablewith observations in order of magnitude and trend. Discrepancies
found at daytime with VIRTIS data (Gilli et al., 2015) at low/middle
latitudes between 110 and 125 km (the model slightly un­derestimates the CO density) could be linked to the biases in thepredicted temperature, the model being systematically warmer thanthe data between 90 and 110 km and colder between 120 and 150
km. At the terminator, CO densities are in overall agreement withSOIR dataset, in particular at the ET, but with larger differences (upto one order of magnitude between 90 and 100 km and a factor 2–3
elsewhere) at the MT, especially at low latitudes. O densities are invery good agreement with nighttime indirect measurements from O2nightglow, peaking at 2 × 1011 cm− 3 around 110 km, as observed.
• The latitudinal and diurnal variation of CO and O has also been
investigated. We found that the CO nighttime bulge is shifted toward
the morning between 85 and 100 km, approximately, suggesting that
CO is preferentially transported westwards in the transition region.
This feature is supported by ground-based observations of CO atnighttime (Gurwell et al., 1995; Lellouch et al., 2008; Moullet et al.,2012) which suggested the presence of a substantial westward

6. Conclusions
This paper presents a comprehensive data-model comparison oftemperature, CO2, CO and O abundances in the Venus upper atmosphereusing the results of an improved high resolution ground-tothermosphere (0–150 km) version of the IPSL-VGCM. For this valida­tion exercise we selected a collection of data from VEx experiments andcoordinated ground-based campaigns available so far following
Limaye17.
• Improvements of the non-LTE parameterization previously imple­
mented in the IPSL-VGCM and described in Appendix B allow to
predict a thermal structure in better agreement with observations.
Both the intensity and the altitude of warm layer around 100 km iscloser to measurements. The peak of temperature around midday
and midnight is reduced by 40 K and 20 K, respectively and it islocated about 10 km lower than in Gilli17. The temperature inver­sion observed by SOIR (Vandaele et al., 2016) around 125 km, with aminimum of 125–130 K is also very well captured by our model.
Despite significant improvements, a number of discrepancies still
remain. Nighttime temperature are still overestimated in the 90–115
km altitude region, by 20 to 30 K, with a peak temperature altitude ~5 km higher than SPICAV (Piccialli et al., 2015) retrieved profiles.
Below 90 km, predicted temperatures tend to be at the upper limit of
the range of observed values. A cold bias of 20 K to 50 K above 130
km is systematically found, compared to SOIR data. A candidate to17

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Fig. 16. Four consecutive snapshots, from left to right, of: (1) 2D local time vs. altitude map of zonal wind averaged for latitudes 20◦ S-20◦ N, (2) 2D local time vs.
altitude map of CO vmr [mol/mol] averaged for latitudes 20◦ S-20◦ N (3) 3D map of CO vmr [mol/mol] at 100 km, the AS point is indicated by a black star.

retrograde zonal flow at altitudes above 90 km. Atomic oxygen
density instead peaks at midnight in the equatorial region between
100 and 110 km, and they are about one order of magnitude larger
than at noon, at the same altitude, as a result of efficient transport ofatomic oxygen atoms from their day sources to their nighttime
chemical loss at and below the peak.
• Our model includes a non-orographic GW parameterization
following a stochastic approach as in Lott et al. (2012); Lott and Guez
(2013). Both westward and eastward GW generated above typicalconvective cells propagate upwards. In our simulations they break
mostly above 10− 1 Pa (110 km altitude approximately) and accel­
erate/decelerate zonal wind by momentum deposition near theterminator. The mean zonal forcing is positive on average,

contributing to retrograde imbalance of the flow in the lower ther­mosphere, above 110 km. However, given the lack of systematic
observations of GW, which are necessary to constrain model pa­
rameters, our experience with different GCM configurations let usconclude that the total zonal wind (i.e. averaged for all local times)
value is very sensitive to many GCM quirks, and can be either
negative or positive. Therefore, it is not excluded that this weakacceleration in the lower thermosphere is linked to the sensitivity ofthe model to unconstrained parameters.
• The increased horizontal resolution used in this paper had a negli­
gible impact on the heating/cooling rate above 90 km compared to
the lower resolution version in Gilli17, therefore a small influence on
the validation of the temperature and densities. However, the shock18

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Fig. 17. Local time-pressure distribution of the GW zonal drag (contours) integrated over all latitudes (θ) and normalized by cos(θ) to account for conservation ofangular momentum. Zonal winds (colour) at equator regions (10◦ S-10◦ N) are also shown. Solid line indicates acceleration, dotted line deceleration. Positive zonal
wind values are westward and negative zonal wind values are eastward. Dashed-line indicates the region where the zonal wind converges to zero.

like feature above 110 km which reduces horizontal wind speeds andincreases the number of large-scale eddies, is obtained only in thehigh-resolution case (see Navarro21). Those eddies may be respon­sible of the high variation observed in O2 nightglow and not
explained by other models using a lower resolution in the thermo­
sphere (Hoshino et al., 2013; Brecht et al., 2011).
• A retrograde imbalance of the flow toward the morning side is pre­dicted by our model at equatorial regions, and interpreted as a
combination of non-orographic GW asymmetric drag on the zonal
wind and perturbation produced by a Kelvin wave generated at thecloud deck. The impact of this ~5 Earth day wave on the middle/
upper atmosphere of Venus is described in details in the companion
paper Navarro21.

Acknowledgements
GG was supported by the European Union’s Horizon 2020 researchand innovation programme under the Marie Sklodowska-Curie grant
agreement No. 796923 and by Fundação para a Ciência e a Tecnologia(FCT) through the research grants UIDB/04434/2020, UIDP/04434/
2020, P-TUGA PTDC/FIS-AST/29942/2017. The authors thank A.C.
Vandaele for providing VAST and CO database from SOIR, and T. Clancy
for sharing JCMT CO measurements. TN and GS acknowledge NASA
Akatsuki Participating Scientist Program under grant NNX16AC84G. SL
thanks the support from the Centre National d’Etudes Spatiales (CNES).
FL thanks the Programme National de Planétologie (PNP) for financial
support. This work used computational and storage services associatedwith the Hoffman2 Shared Cluster provided by UCLA Institute for Digital
Research and Education’s Research Technology Group, as well as theHigh-Performance Computing (HPC) resources of Centre Informatique
National de l’Enseignement Supérieur (CINES) under the allocations
A0060110391 and A0080110391 made by Grand Equipment Nationalde Calcul Intensif (GENCI).

Declaration of Competing Interest
None.

Appendix A. Non-orographic GW parameterization
A.1. Formalism
The scheme implemented here is based on a stochastic approach, as fully described in Lott et al. (2012) and Lott and Guez (2013), also successfully
implemented in the LMD Mars GCM (Gilli et al., 2020). In such a scheme, a finite number of waves (here M = 8), with characteristics chosen randomly,
are launched upwards at each physical time-step (δt = 1.5 min) from every horizontal grid point to simulate their global effect. The question of thelocation of the non-orographic GW sources is a difficult one and given the lack of information on the location of GW sources, launching the gravity
waves at each grid point, and at a fixed altitude, is probably the simplest assumption. The number of waves is given as M = NK × NO × NP, with NK = 2
values of GW horizontal wave-numbers, NO = 2 absolute values of phase speed, and NP = 2 directions (westward and eastward) of phase speed. Thisapproach allows the model to treat a large number of waves at a given time t by adding the effect of those M waves to that of the waves launched at
previous steps, with a remnant time constant Δt chosen to 1 Earth day (as for Earth and Mars).
The GW spectrum is then discretised in ~ 7700 stochastic harmonics (M ×Δt/δt) which contribute to the wave field each day and at a given
horizontal grid point. Δt is a characteristic time interval which contains the GW life cycle (i.e. from its generation to wave break) and which has toexceed significantly the GCM time step δt. On Earth, GW theory indicates that atmospheric disturbances induced by convection have life cycles with19

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Icarus 366 (2021) 114432

duration Δt around 1 day (Δt = 24 h)(Lott and Guez, 2013). Here, as a first approximation we adopted this value (Δt ≫ δt = 1.5 min). At each time t thevertical velocity field w′ of an upward propagating GW can be represented by the sum over harmonics:
′

∞
∑

w =

(A.1)

′

Cn wn
n=1

∑
2
′
where Cn are normalization coefficients such that ∞n=1Cn = 1. It is assumed that each of the wn can be treated independently from the others, and2′each Cn can be viewed as the probability that the wave field is represented by wn (see also the expression for Cn in Eq. A.6). This formalism is thenapplied to a very simple multi-wave parameterization, in which wn′ represents a monochromatic wave as follows{}′
̂ n (z)ez/2H ei(kn x+ln y− ωn t)
wn = ℜ w(A.2)
where the wavenumbers kn, ln and frequency ωn are chosen randomly. In Eq. A.2, H~4 km is a middle atmosphere characteristic vertical scale for
Venus (Lee et al., 2012; Mahieux et al., 2015) and z is the log-pressure altitude z = H ln(Pr/P), with Pr a reference pressure (Pr = 5 × 104 Pa, taken here
̂ n (z), it is randomly chosen at a given launchingat the height of the source (i.e. above typical convective cells, z~ 55 km). To evaluate the amplitude w
altitude z0, and then iterated from one model level z1 to the next z2 by a Wentzel-Kramers-Brillouin (WKB) approximation (see Eq. 4 of Lott et al.
̂(2012) for details). Using that expression plus the polarization relation between the amplitudes of large scale horizontal wind ̂u and vertical wind w(not shown here), we can deduce the Eliassen-Palm (EP) flux (vertical momentum flux of waves),
→
{ → }
k
→z
uw
̂ * = ρr ⃒ ⃒2 m(z)‖ w
F (k, l, ω) = ℜ ρr ̂
̂ (z) ‖2
⃒→⃒
k
⃒ ⃒

(A.3)

with k, l the horizontal wavenumber and ω the frequency of the vertical velocity field. This last one is included in the WKB non-rotating approximation
for the vertical wavenumber m =→N| k |
Ω , with Ω = ω −→→k u and N the Brunt-Vaisala frequency (see Lott et al. (2012) for details). In Eq. (A.3), ρr is thedensity at the reference pressure level Pr.
̂ n and the EP-flux at a given launching altitude z0 (see Section Appendix A.2) are randomly chosen. To move from one model
In this scheme, both w
level to the next model level above, the EP-flux is essentially conserved, but a small diffusivity is allowed, ν = μ/ρ0, which can be included by replacing
Ω with Ω + iνm2. This small diffusivity is here to guarantee that the waves are ultimately dissipated over the few last model levels, if they have not been
before (hence the division by the density ρ0). In addition, this new EP-flux amplitude is limited to that produced by a saturated monochromatic wave
̂ s following Lindzen (1981):
w
Ω2
k*
w
̂ s = Sc ⃒⃒→⃒⃒ e− z/2H ⃒⃒→⃒⃒⃒ k ⃒N⃒k⃒(A.4)

̂ = 0 when Ω changes sign, to treat critical levels. In Eq. A.4, Sc is a tunable parameter and k* a characteristic horizontal wavelengthor either w
corresponding to the longest wave being parameterized (see more details in Sec. Appendix A.2).
→z
Finally, the accelerations ρ− 1 δz F n′ (n′ = 1, M) produced by the GW drag on the winds are computed. Since wn′ s are independent realizations, themean acceleration they produce is the average of these M acceleration. Thus, the averaged acceleration is first redistributed over the longer time scale
Δt by re-scaling it by δt/Δt and second, the auto-regressive (AR-1) relation described in (Lott et al., 2012) is used as follows:( →)t( )t− δt→zMδuδt 1 ∑
1 δ F n′ Δt − δt δ→
u
=
(A.5)
+
δt GWs Δt M ′ ρ0 δzΔtδt GWsn =1This indicates that, at each time step, M new waves are promoted by giving them the largest probability to represent the GW field, and theprobabilities of all the others are degraded by the multiplicative factor (Δt − δt)/Δt. As explained in Lott et al. (2012), by expressing the cumulative
sum underneath the AR-1 relation in Eq. A.5, the formalism for infinite superposition of stochastic waves is recovered by taking:(Δt − δt)p δt
Cn2 =
(A.6)
Δt
MΔt
where p is the nearest integer that rounds (n − 1)/M (i.e. toward lower values).
A.1.1. GW parameters setup
Here we describe the main tunable parameters used in the non-orographic GW scheme implemented in the IPSL-VGCM. The characteristics of every
wave launched in the GCM are selected randomly with a prescribed box-shaped probability distribution, whose boundaries are key model parameters.
These are chosen on the basis of observational constraints (whenever available) and theoretical considerations: each parameter has a physicalmeaning, as described below.
Source height and duration First, considering that non-orographic GW are expected to be generated by deep cloud convective layer on Venus,
and mostly driven by longwave radiation, we assumed that the non-orographic GW source is placed above typical convective cells (i.e. at 5. × 104 Pa,
around 55 km of altitude), turned on all day, uniform and without latitudinal variation.
EP-flux amplitudeFz from Eq. A.3 gives the vertical rate of transfer of wave horizontal momentum per units of area. This value has never been
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Icarus 366 (2021) 114432

measured in the Venus atmosphere and it represents an important degree of freedom in the parameterization of gravity waves. Thus, in our scheme we
impose the maximum value of the probability distribution Fzmax at the launching altitude z0 (see previous point), at the beginning of the first run. In
order to define F0max we have explored typical values used in the literature to evaluate the order of magnitude of the EP-flux within the realm of what isrealistic. It should be stressed here that the stochastic approach implemented in our scheme has the advantage of allowing to treat a wide diversity ofemitted gravity waves, thereby a wide diversity of momentum fluxes. Our only setting is the maximum EP-flux amplitude at the launching altitude,that was fixed as 0.005 [kg m− 1 s− 2] (i.e. 5 mPa), following (Lefèvre et al. (2018) who simulated the gravity waves generated by the convective layer ina Large-Eddy-Simulation model describing realistically the Venusian convective activity.
Horizontal wavenumber In our scheme the horizontal wave number amplitude is defined as in Lott et al. (2012) k * < ∣ k ∣ < ks. The minimum√̅̅̅̅̅̅̅̅̅̅̅̅
value is k* = 1/ ΔxΔy, where Δx and Δy are comparable with the GCM horizontal grid. The maximum (saturated) value is ks < N2/u, N being theBrunt-Vaisala frequency associated to the mean flow, and u0 the mean zonal wind at the launching altitude. We kept the minimum and maximum
values for the GW horizontal wavelength (λh = 2π/k) 50 km and 500 km, respectively as in Gilli17. Those values are within the observed range of GW
wavelengths (Peralta et al., 2008; Piccialli et al., 2010; Tellmann et al., 2009; Altieri et al., 2014; Garcia et al., 2009) However, in order to correctly
implement this parameterization with the increased resolution (δx and δy at the equator ≈ 400 km and 200 km in this study), we adapted the minimal
horizontal wavenumber kmin of individual waves as it follows:
(
π)
*
kmin
= max kmin ,(A.7)
Awith A the surface area of the grid cell. This value corresponds to the longest waves that one parameterizes with the current GCM horizontal resolution,
larger wavelengths are explicitly resolved by the GCM.
Phase speed Another key parameter is the amplitude of absolute phase speed ∣c ∣ = ∣ ω/k∣. As for the other tunable parameters, we impose theminimum cmin and maximum cmax values of the probability distribution at the beginning of the runs, and the model chooses randomly ∣c∣ between cminand cmax. Here cmin is set to 1 m/s (i.e for non-stationary GW) and cmax is of the order of the zonal wind speed at the launching altitude (u ≈ 60 m/s).
Both eastward (c > 0) and westward (c < 0) moving GWs are considered.
Saturation parameterSc is a tunable parameter in our scheme, on the right hand side of Eq. A.4, which controls the breaking of the GW by limiting
the amplitude ws. In the baseline simulation we set Sc = 1.
Appendix B. Improved Non-LTE parameterization set-up
The model includes 1-parameter formula that mimics the heating rates calculated by line-by-line non-LTE simulations for each pressure level p andsolar zenith angle μ (see Gilli17 for details). It is given by the following expression:)− b√̅̅̅̅̅̅̅(∂T∂T
r 2 p0p̃μ 1+ 1(B.1)(p, r, μ) =(p0 , r0 , 0) × 02
∂t
∂t
r
p
p
where several orbital assumptions for Venus were considered: a circular planetary orbit with a mean solar distance of r = r0= 0.72 AU, no obliquity,
μ is corrected for atmospheric refraction using the following function: ̃μ =and no seasonal variation. The cosine of the solar zenith angle ̃[()/]11224μ2 + 1 1225 2 .
In this paper, the values of four non-LTE parameters used in Gilli17 are reviewed and adjusted: (i) cloud top pressure level in [Pa], p0; (ii) heating
rate per Venusian day (Vd) in [Kday− 1], δTδt (p0 , r0 , 0); (iii) pressure below which non-LTE effects become significant in [Pa], p1; and (iv) central pressure
for transition from LTE to non-LTE radiation tendencies in [Pa], ptrans.
First, we tuned the parameter p1 to 0.1 [Pa] and ptrans to 0.2 [Pa], which better represent the transition from LTE to non-LTE radiation. Second,
following a literature review on the observed altitude of cloud tops (Ignatiev et al., 2009; Haus et al., 2015) the mean cloud top altitude isapproximately located between 69 and 74 km. We tested several pressure levels, p0, in this range of altitudes with corresponding solar heating rates
from look-up tables in Crisp (1986). The selected combination of those four parameters is listed in Table 1 together with those used in Gilli17, forcomparison. Fig. B.18 shows the predicted net radiative rates in the two works, at different local time. The daytime heating/cooling peaking at 0.5 Pa(about 100 km) is reduced by almost a factor 2, while morning and evening terminator heating peaks around 0.1 Pa are about 3 times smaller,
compared with Gilli17 simulations. Interestingly, nighttime radiative tendencies are affected by the changes of non-LTE parameter values mostly inthe thermosphere, above 0.1 Pa (110 km approximately), suggesting that the region below is dominated by dynamical tendencies (e.g the adiabatic
heating produces the local warm layer at nighttime), consistent with GCM results by Brecht and Bougher (2012).

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Icarus 366 (2021) 114432

Fig. B.18. Net radiative and dynamical rates [K/s] obtained in this work for mid northern latitudes (30◦ N-50◦ N) after improving the Non-LTE parameterization for
midday, nighttime and terminators, as indicated in the panels. For comparison, the rates obtained in a previous version of the IPSL-VGCM (Gilli17), labeled as“reference”, are also shown. Approximate altitudes are indicated on the right side of the panels.

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