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Geosci. Model Dev., 16, 621–657, 2023
https://doi.org/10.5194/gmd-16-621-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model experiment description paper
A modern-day Mars climate in the Met Office Unified Model:
dry simulations
Danny McCulloch1, Denis E. Sergeev1, Nathan Mayne1, Matthew Bate1, James Manners2, Ian Boutle2,1,
Benjamin Drummond2, and Kristzian Kohary1
1Department of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK
2Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Correspondence: Danny McCulloch (dm575@exeter.ac.uk)
Received: 29 July 2022 – Discussion started: 11 August 2022
Revised: 21 December 2022 – Accepted: 23 December 2022 – Published: 27 January 2023
Abstract. We present results from the Met Office Unified
Model (UM), a world-leading climate and weather model,
adapted to simulate a dry Martian climate. We detail the
adaptation of the basic parameterisations and analyse results
from two simulations, one with radiatively active mineral
dust and one with radiatively inactive dust. These simula-
tions demonstrate how the radiative effects of dust act to ac-
celerate the winds and create a mid-altitude isothermal layer
during the dusty season. We validate our model through com-
parison with an established Mars model, the Laboratoire de
Météorologie Dynamique planetary climate model (PCM),
finding good agreement in the seasonal wind and tempera-
ture profiles but with discrepancies in the predicted dust mass
mixing ratio and conditions at the poles. This study validates
the use of the UM for a Martian atmosphere, highlights how
the adaptation of an Earth general circulation model (GCM)
can be beneficial for existing Mars GCMs and provides in-
sight into the next steps in our development of a new Mars
climate model.
1 Introduction
Understanding Mars’ climate has been the motivation for
many missions and numerical models for decades, and
through these, many of the mechanisms driving the Martian
climate have been unveiled. With our expanding comprehen-
sion of Mars’ climate, studies have been able to build, re-
fine and apply three-dimensional general circulation models
(GCMs). Such models include, but are not limited to, the
NASA AMES model (Pollack et al., 1993; Haberle et al.,
2019) and the Laboratoire de Météorologie Dynamique plan-
etary climate model (PCM; see Forget et al., 1999; Millour
et al., 2018). Through the use of numerical models we are
able to characterise Mars with limited observational data,
allowing us to simulate areas where observational data are
limited (Read et al., 2015; Martínez et al., 2017). Through
these efforts our understanding of many atmospheric pro-
cesses has been refined, including the annual CO2cycle (For-
get et al., 1998; Malin et al., 2001; Aharonson et al., 2004;
Hayne et al., 2012; Holmes et al., 2018; Banfield et al., 2020),
CO2availability in the interest of terraforming (Jakosky and
Edwards, 2018), its hydrological cycle (Houben et al., 1997;
Haberle et al., 2001; Brown et al., 2014; Shaposhnikov et al.,
2016, 2018; Singh et al., 2018; Pál et al., 2019), its sur-
face topography (Smith et al., 1999; Richardson and Wilson,
2002; Zalucha et al., 2010) and the effects of the climati-
cally dominant dust cycle (Kahre and Haberle, 2010; Wang
and Richardson, 2015; Forget and Montabone, 2017; Wang
et al., 2018; Gebhardt et al., 2020; Ball et al., 2021; Chaf-
fin et al., 2021). Simulations have been performed ranging in
scale from global (Navarro et al., 2014; Streeter et al., 2020;
Kass et al., 2020) to mesoscale levels (Montabone et al.,
2006; Spiga and Forget, 2009; Newman et al., 2021).
There are, however, still processes which are difficult to
capture in climate models. One of these is dynamically mod-
elling annular shifts in CO2freezing and thawing and the
subsequent change in surface pressure this causes (Paige and
Wood, 1992; Forget et al., 1999; Haberle et al., 2008; Kahre
and Haberle, 2010). Simulating this in a self-consistent way
using a GCM is difficult, and efforts so far have relied on pa-
rameterisations (described by Forget et al., 1998, 1999; Spiga
Published by Copernicus Publications on behalf of the European Geosciences Union.
622 D. McCulloch et al.: Modern Mars in the Unified Model
et al., 2017; Singh et al., 2018; Gary-Bicas et al., 2020). Re-
cent model developments by Way et al. (2017) have included
the CO2effects dynamically, providing a promising avenue
for the development of existing Martian GCMs. This is ben-
eficial because it captures the secondary effects of CO2pre-
cipitation between the atmosphere and surface as it descends.
Another major challenge these GCMs face is accurately un-
derpinning the cause of inter-annual dust storms and charac-
terising dust-uplifting rates prior to and following the dust
season (Mulholland et al., 2013; Spiga et al., 2013; Forget
and Montabone, 2017). Parameterising the methods for dust
uplifting has been essential for simulating the climate and
weather of Mars but has still required periodic manual ad-
justments in order to match observations (Montabone et al.,
2020). This limits the efficacy and self-sufficiency of Mar-
tian climate models, leading to difficulty in simulating global
dust storms that should occur without forcing across multiple
years and difficulty in predicting more local dust storms. As
Way et al. (2017) have highlighted by dynamically solving
for pressure variation as a consequence of CO2precipitation,
a more complete physical model capable of capturing dust
processes should also have beneficial feedback for the sys-
tem.
To work towards rectifying these gaps in our modelling
capabilities, we have adapted the Unified Model (hereafter
UM) to the study of the Martian climate as a foundation step
in building a comprehensive Martian climate model comple-
mentary to existing modelling efforts. The UM is used rou-
tinely for Earth weather and climate modelling. It has also
been used for other planetary climates, including Earth-like
exoplanets (Boutle et al., 2017; Sergeev et al., 2020; Eager-
Nash et al., 2020), hot Jupiters (Mayne et al., 2014; Lines
et al., 2018; Drummond et al., 2018) and mini Neptunes
(Mayne et al., 2019). By adapting the existing and well-
tested Earth parameterisations to Martian conditions, we can
model climate processes in comparative ways to existing
Mars GCMs (e.g. the quantity of available dust and size of
the particles in the atmosphere). Such steps are key if GCMs
are to progress to characterising Mars’ climate with less
manual prescription of parameters (Forget and Montabone,
2017; Montabone et al., 2020). In this aspect, the UM is
self-consistent, dynamically solving for dust availability and
using that prognostically to simulate Martian dust content
throughout the varying seasons. In this first study, we focus
on a dry climate, including orography and dust, and highlight
the importance of capturing dust accurately and its influences
on the Martian climate. We also show that our adapted UM
simulations capture key large-scale features of the Martian
atmospheric circulation, including a periodic dust cycle with-
out the prescription of fixed dust parameters.
In this paper, we present a description of the UM and the
adaptations made for the Martian climate (Sect. 2). That is,
we describe the key model adaptations to simulate a dry Mar-
tian climate, such as planetary variables (atmospheric com-
position and orbital parameters, Sect. 2.1), radiative transfer
(Sect. 2.2), orography (Sect. 2.3), dust (Sect. 2.4) and atmo-
spheric surface pressure (Sect. 4.1). In Sect. 3 we describe
how we configure the UM output for analysis, including a
configuration for two scenarios where one features radia-
tively active Martian dust (RA dust) and one has radiatively
inactive dust (RI dust). In Sect. 4 we present results from the
two configurations of the UM for Mars and validate the UM
with RA dust against the PCM. We highlight how differences
in dust parameterisation create differences between outputs,
both within the UM scenarios and between the RA UM and
PCM, and then discuss the reasons for these and their im-
plications. Finally, in Sect. 5, we discuss how the model can
be further developed to improve its accuracy, mainly via the
inclusion of schemes capturing the effects of CO2ice and
water vapour planned in future work.
2 Model description
For this study, we take the Global Atmosphere 7.0 science
configuration of the UM (Walters et al., 2019) and adapt it
to Martian conditions. The UM dynamical core (ENDGame,
described by Wood et al., 2014) simulates the atmosphere
as a non-hydrostatic fully compressible fluid and its nu-
merical formulation uses a semi-implicit timestep and semi-
Lagrangian advection scheme (Benacchio and Wood, 2016).
A full description of the model’s dynamical core is given by
Staniforth and Wood (2003, 2008) and Wood et al. (2014),
with a global climate configuration further detailed by Wal-
ters et al. (2019). The benefit of using existing schemes in a
GCM instead of creating new ones is that they capture essen-
tial atmospheric physics and are verified under a variety of
conditions (e.g. dust on exoplanets; Boutle et al., 2020). Fig-
ure 1 shows an illustrative example of output from the UM
configuration used to simulate the Martian climate.
The UM grid configuration used in the present study has
90 by 144 grid points, corresponding to a resolution of 2◦
in latitude and 2.5◦in longitude (i.e. a grid spacing of 118
and 147.5 km at the equator, respectively). This resolution
allows us to accurately capture climate trends at a relatively
high spatial resolution (Forget et al., 1999; Way et al., 2017),
with even higher spatial resolutions available at the cost of in-
creased computational power. This resolution is suitable for
observing seasonal patterns within the Martian year (Navarro
et al., 2014; Madeleine et al., 2011; Pottier et al., 2017), but
higher spatial resolutions could readily be used to investigate
selective regional climates (see for example Sergeev et al.,
2020) – a promising prospect for future applications.
In the vertical, we adopt 50 hybrid-height atmospheric lev-
els up to a model top of 80 km above the areoid level. We use
a quadratically stretched grid to enhance resolution near the
surface. Levels nearer the surface follow the terrain but are
smoothed out gradually as the height increases, reaching a
constant level height towards the highest altitudes (for our
study this happens at ∼52 km, Wood et al., 2014). The level
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 623
Figure 1. Overview three-dimensional plot of example output dur-
ing Southern Hemisphere summer (Ls=260◦). Included is a seg-
ment of the extracted regional dust layer as an isosurface, wind vec-
tors at 1 km height (arrows) and orography. Grid cells are cropped
to 30 km. Higher-resolution GIF and code are available at https:
//github.com/dannymcculloch/3d_Mars_gif (last access: 16 Jan-
uary 2023). Made with PyVista (Sullivan and Kaszynski, 2019).
heights can be seen in Table A2, where values are shown for
a point with 0 m surface height.
For this study we use planetary parameters (Sect. 2.1),
radiative transfer effects (Sect. 2.2), orography (Sect. 2.3),
prognostic dust (Sect. 2.4) and pressure (Sect. 4.1) set to
Martian values, with more description of each process in the
respective section.
2.1 Planetary parameters
Mars has an eccentric orbit (e=0.0934 compared to Earth’s
e=0.0167) leading to an annual oscillation in the received
irradiation. To capture this in the UM, we configure the
model to run with this orbit starting from 0◦Ls(Lsis the
ecliptic longitude of the Sun), and 0◦Lscorresponds to
Northern Hemisphere (NH) spring equinox. We use the stel-
lar output for the present-day Sun, but our simulated planet
is placed at the Martian distance. Table 1 shows the values
we implement for Mars’ planetary values.
2.2 Radiative transfer
To calculate radiative transfer, we use the SOCRATES radi-
ation scheme (described by Edwards and Slingo, 1996; Wal-
ters et al., 2019). This scheme uses a two-stream correlated-k
method as described by Walters et al. (2019) and references
therein. This scheme has been used extensively for studies
of Earth (Spafford and MacDougall, 2021), in addition to
hot Jupiters (Mayne et al., 2014), sub-Neptunes (Drummond
et al., 2018) and rocky exoplanets (Boutle et al., 2020; Eager-
Nash et al., 2020).
For radiative properties, we use an adapted version of
the ROCKE-3D spectral files1, which are appropriate for
the CO2-rich Martian atmosphere. We then added dust op-
tical properties based on parameterisation from Walters
et al. (2019). Mars’ atmosphere primarily consists of CO2
(∼95 %), N2(1.89 %) and Ar (∼1.93 %) (Read et al., 2015;
Martínez et al., 2017); we simplify this to 95 % CO2and 5 %
N2in our simulations. Ar was omitted in this study as the ef-
fects would be minimal on seasonal averages. The prescribed
gas ratios throughout the atmosphere are assumed to be well
mixed (Walters et al., 2019).
The Martian atmosphere features small amounts of water
vapour which are generally increased during the colder aphe-
lion months (Nazari-Sharabian et al., 2020). This humid-
ity affects the radiative transfer in every layer through wa-
ter vapour molecules and cloud condensate (Shaposhnikov
et al., 2016; Steele et al., 2017; Shaposhnikov et al., 2018;
Fischer et al., 2019). Mars has water ice clouds which in-
fluence radiative transfer between the surface and the upper
atmosphere (e.g. Navarro et al., 2014). For our set-up, we
use a completely dry atmosphere and surface. This is done
to simplify the dust-uplifting processes and to be able to cor-
rectly capture Martian seasonal trends initially. This allows
for a benchmark comparison which can be expanded on in
future studies, with a similar approach being carried out by
Turbet et al. (2022).
In addition, we also use the terrain-shading scheme de-
scribed by Manners et al. (2012), which corrects the sur-
face insolation depending on the zenith angle and obstruct-
ing elevation. This allows for a better representation of the
effect that Martian orographical extremes have on their sur-
roundings, e.g. the lone peak from Elysium Mons (25.02◦N
147.21◦E) casting a large shadow on the Northern Lowlands
or the depths of the Valles Marineris canyon often being in a
shade.
2.3 Orography and surface
Orography affects various aspects of the Martian climate
such as dust deposition and global circulation (Smith et al.,
1999; Zalucha et al., 2010; Pottier et al., 2017). Dominant
orographic features include the Tharsis region and Hellas
Basin but also a general hemispheric dichotomy featuring a
higher southern hemisphere that gradually descends north-
ward (Richardson and Wilson, 2002). Mars’ hemispheric
asymmetry heavily influences the atmospheric circulation,
leading to large seasonal differences amplified by Mars’ or-
bital eccentricity (Richardson and Wilson, 2002; Zalucha
et al., 2010). Therefore, in order to better characterise Mars’
1Files “sp_sw_42_dsa_mars_sun” and “sp_lw_17_dsa_mars”,
available at https://portal.nccs.nasa.gov/GISS_modelE/
ROCKE-3D/spectral_files/ (last access: 14 November 2022).
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
624 D. McCulloch et al.: Modern Mars in the Unified Model
Table 1. Orbital, planetary and atmospheric parameters in our simulations.
Constant Value
Epoch (Julian date) 2451545.0
Eccentricity 0.0934
Obliquity (radian) ∼0.4397
Mean acceleration due to gravity (m s−2) 3.711
Solar irradiance at 1 AU (W m−2) 1361.0
Semi-major axis (AU) 1.52368
Angular speed of planet rotation (radian s−1)∼7.0882×10−5
Radius (km) 3389.5
climate and atmospheric processes, correctly capturing the
surface elevation hemispheric dichotomy in Martian climate
models is important (Zalucha et al., 2010).
For this study, we use the sub-grid orographic drag pa-
rameterisation already present and verified in the UM (as de-
scribed in detail by Lott and Miller, 1997; Webster et al.,
2003; Vosper, 2015; Walters et al., 2019) but for Martian
values. This parameterisation allows for inter-grid-cell shad-
ing caused by areas of higher elevation (e.g. the upper edges
of the Valles Marineris shading the crevice below). We ob-
tained the widely used, high-resolution MOLA elevation data
(Fig. 2a, described by Smith et al., 1999)2. We regrid the
MOLA dataset to the resolution used in the current study. We
choose to use the resolution of 90 ×144 as it allows for an
adequate global representation needed to simulate global cli-
mate patterns present on Mars. In regridding, the cells from
the original dataset that encompass a single grid cell are av-
eraged, which leads to some height loss at the highest peaks,
where sub-grid elevation is varied. The effects of the regrid-
ding can be seen in Fig. 2b, where the MOLA dataset is
compared to the regridded version. There is some inevitable
height smoothing with regridding: Olympus Mons changes
from 25 km height to 19 km and the lowest parts of the Hel-
las Basin from −7.5 to −7.3 km.
For surface albedo, we assume a uniform value of 0.3.
Although this is higher than the average albedo for Mars
(∼0.16 Kieffer et al., 1977), this was chosen as it compen-
sates for the lack of polar CO2ice (which has an albedo of
∼0.5). For surface thermal inertia, we use a constant value
of 368.646 J m−2K−1s−0.5across the surface (prescribed as
a thermal capacity of 1.359×10−6J m3K−1), which is repre-
sentative of the majority of the Martian surface (Kieffer et al.,
1977; Palluconi and Kieffer, 1981; Mellon et al., 2008).
2MOLA dataset available at https://astrogeology.usgs.gov/
search/map/Mars/Topography/HRSC_MOLA_Blend/Mars_
HRSC_MOLA_BlendDEM_Global_200mp_v2 (last access:
15 June 2022).
2.4 Dust and surface roughness
Dust is synonymous with Mars: it is a key driver in a wide
range of atmospheric phenomena, ranging from mesoscale
dust devils, effective at uplifting local surface dust (Neakrase
et al., 2016), to global dust storms affecting global tem-
peratures for long periods of time (Forget and Montabone,
2017; Wang et al., 2018; Streeter et al., 2020). Dust is a
crucial contributor to the greenhouse effect on Mars. Be-
cause of this, large fluctuations in atmospheric dust con-
tent can have serious effects on the lower-altitude tempera-
tures (below ∼25 km) during global dust storms and have
been well described by Streeter et al. (2020) and Wang and
Richardson (2015). Dust also affects the diurnal cycle of tem-
peratures, retaining thermal radiation during the night and
reflecting solar radiation during the day (Madeleine et al.,
2011). The dust quantities vary across the Martian year, with
months 6 to 12 (month timings shown in Table A1) hav-
ing much higher atmospheric dust than the other months
(Forget and Montabone, 2017). Months 1 to 6 are gener-
ally colder on Mars, leading to weaker wind speeds and
subsequently less dust uplifting during the colder months.
Intra-annual shifts from the dust storm season to a colder
less dusty season are difficult to self-consistently capture in
three-dimensional GCMs (Madeleine et al., 2011; Forget and
Montabone, 2017).
We adapt the dust scheme available in the UM, which han-
dles dust parameterisation using nine particle radial size bins
(0.03–1000 µm). A normalised distribution characterising the
surface dust is set, with the values in bins 1–6 (0.03–30µm)
prescribed and the remaining dust equally distributed across
bins 7–9 (30–1000 µm). This is described in detail by Marti-
corena and Bergametti (1995) and Woodward (2001, 2011),
and an example of its application in a non-Earth climate can
be seen in Boutle et al. (2020). Atmospheric dust is absent
upon initialisation and is calculated throughout the model
simulation. Dust particles are transported by atmospheric dy-
namics, turbulence (Lock et al., 2000), saltation (for uplifting
larger particles, Woodward, 2001; Woodward et al., 2022)
and dry deposition. Absorption and scattering of short-/long-
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D. McCulloch et al.: Modern Mars in the Unified Model 625
Figure 2. Original MOLA elevation data (a) compared to the regridded elevation data used in the UM (b). Colour scales are matching
between plots.
wave radiation is calculated using Mie theory with the as-
sumption that dust particles are spherical.
To determine the size distribution for the respective dust
bins in the UM for Mars, we applied the same formula
as used in the second scenario by Madeleine et al. (2011),
namely
n(r) =N
√2π σ0rexp"−1
2ln(r/r0)
σ02#,(1)
where ris the potential size of the dust particle, between 0.03
and 30 microns. σ0is the variance and r0is the mean, given
by Madeleine et al. (2011) as 0.3 and 1.5 µm, respectively.
Nis the normalised maximum (to unity) number of parti-
cles available and n(r) is the probability of dust radii being
present dependent on r. This provides the distribution pre-
sented in Fig. 3, giving the probable radial size of any given
particle. The dust bin size ranges used have been overlaid
with coloured bars for each bin. The majority of dust resides
in bin 4, but all bins are used in this study.
Dust production, uplifting and deposition are all charac-
terised dynamically throughout the simulation. For dust pro-
duction, upon initialisation, the surface is assumed to have
an infinite amount of available dust to be uplifted. Dust pro-
duction for a particle at rest is dictated by weight (primarily
driven by particle size and composition), interparticle cohe-
sion forces and wind shear stress along the surface (Marti-
corena and Bergametti, 1995). As the set-up is completely
dry in the current configuration and we use a dry sand com-
position, the main factor which will impact dust production
in this scenario is the particle size.
Dust uplifting is primarily driven by aeolian processes.
Dust that has been freed from the surface is then transported
via turbulent eddies (“suspension”), saltation and creeping.
The ability for particles of a given size to be uplifted is pro-
portional to their weight against the aerodynamic drag ex-
perienced (Marticorena and Bergametti, 1995; Woodward,
2001). Further dust uplifting as a consequence of saltation
and creeping is influenced by the aerodynamic roughness
length, which dictates how much dust is further uplifted via
the impact of larger particles. This threshold value thus dic-
tates how easy it is for smaller dust particles to be uplifted
following an impact via saltation or creeping from a larger
particle (<60 µm in an Earth atmosphere; Marticorena and
Bergametti, 1995). In the UM, horizontal dust flux is a tun-
able parameter. In this study, the horizontal dust flux was
tuned to 7.5 to match atmospheric dust levels in the PCM
for month 9. Initial testing of this parameter with different
values did not change the distribution of the uplifted dust but
solely changed the amount of uplifted dust in the localised
regions.
In the UM, we use the aerodynamic roughness length map
from Hébrard et al. (2012), shown in Fig. 4. This aerody-
namic surface roughness threshold value could be set to a
constant value in order to make sure that dust is being up-
lifted equally across the planet, but this is likely to over-
simplify the climate. By using these values, we are able to
simulate regional dust production dispersion, as opposed to a
globally uniform dust production rate (Hébrard et al., 2012).
For dust optical properties, we used Earth dust optical val-
ues as shown in Table A3 and described in Balkanski et al.
(2007). These values are primarily used for Earth dust, but
as these values are highly similar to those used by Madeleine
et al. (2011) and references therein, they were not changed.
3 Experimental set-up
We initialise the UM from a motionless atmospheric state
with a uniform surface temperature of 250 K and a sur-
face pressure of 610 Pa. Other variable schemes are also
initialised at this stage (orography, Sect. 2.3, and dust,
Sect. 2.4). The model is then integrated for 40 Martian years
to achieve a steady state, which is determined as there being
no inter-annual increase or decrease in the balance between
incoming and outgoing radiation at the top of the atmosphere
(TOA) between Martian years (with years averaged to omit
for differences caused by orbit eccentricity). This also al-
lows for localised prognostic dust reservoirs to form, with
the model developing from a uniform surface dust amount
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626 D. McCulloch et al.: Modern Mars in the Unified Model
Figure 3. Dust size probability distribution used for the UM following Madeleine et al. (2011). Dust bin ranges are shown by the coloured
bars.
Figure 4. Surface roughness map from Hébrard et al. (2012) (left) and how it is represented in the UM after regridding (right).
(upon initialisation) to an atmosphere and surface with dust
content as a consequence of the previous year’s dust cycle.
Thus, by the end of the 40-year period, the distribution of
surface dust is non-uniform and features localised areas of
high and low dust abundance. After this, the model is run for
another Martian year, and this final year’s data are what is
presented in this paper. The model output is provided with
every sol across a Martian year (668 sols/687d). A sol is de-
fined here as 24 h and 40 min (simplified from a Martian solar
day of 24 h, 39min and 35 s). For each model diagnostic, val-
ues are recorded every 5min (20 min for radiation variables,
e.g. TOA radiation flux) and are then averaged at the end of
each sol. The year starting at 0◦Lsis then run for 688 Earth
days, taking an average across the sol. These sol outputs are
then aggregated into Martian months (Table A1); this is done
to better understand seasonal trends across the year and to
match the data to time distributions present in other models
(Forget et al., 1999).
To discern the effects of dust in the UM, we perform two
separate simulations, one with radiatively active (RA) dust
and one with radiatively inactive (RI) dust. Both set-ups are
identical in every other way (e.g. spin-up time, orography,
orbital parameters). For RI dust, the dust sizes and quanti-
ties are still prescribed, but all radiative effects of dust are
switched off. This allows us to observe the effects of a dust
scheme in our Mars set-up vs. what would already occur
without the presence of atmospheric dust. This is useful for a
variety of reasons. It allows us to highlight spatial/temporal
regions of interest where dust might originate from, partic-
ularly where there are differences between scenarios. It also
allows us to begin to distinguish the exclusive influence of
dust, as any differences between scenarios can be attributed
to this one variable.
To ensure that the UM reproduces seasonal patterns with
sufficient accuracy, we compare our results to those of an es-
tablished Mars GCM. In this study, we use year average (an
average of all years where a dust storm did not occur) re-
sults from the Mars Climate Database3, which provides out-
put from the PCM (Forget et al., 1999; Millour et al., 2018).
This dataset has 49 points in latitude and 64 points in longi-
tude, with 30 layers in the vertical extending up to 108 km.
The output is also separated into the same Martian months as
prescribed in the UM (e.g. Martian month 1 =sols 0 to 61,
as per Table A1). It features dust (Madeleine et al., 2011),
a hydrological cycle (Navarro et al., 2014), a CO2ice cycle
(Forget et al., 1998, 1999), atmospheric ozone (Lefèvre et al.,
2008) and an upper atmosphere layer above 80 km (Colaïtis
et al., 2013; González-Galindo et al., 2015).
3Available at http://www-mars.lmd.jussieu.fr/ (last access:
6 March 2022).
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 627
The PCM uses a terrain-following pressure-based vertical
coordinate σ=p/ps, where psand pare the surface pres-
sure and atmospheric pressure respectively, which is different
to the UM’s height-based vertical coordinate (Forget et al.,
1999; Wood et al., 2014). This presents a difficulty in precise
comparison between model simulations, as results cannot be
compared straightforwardly without some form of interpo-
lation. Furthermore, the UM and PCM have different upper
boundaries, which require datasets to be cropped until the
height is matched. Therefore, to validate our model against
the PCM, we linearly interpolate the UM output to σlevels
at each output timestep, focusing on the levels where there is
sufficient data for both models at the same pressure (σrang-
ing from 1 at the surface to 0.01 in the upper atmosphere).
As we are concerned with the large-scale seasonal climate
we compare zonal, monthly averages of the UM outputs to
those of the PCM.
4 Results
In the following sections, we show Mars’ annual mean pres-
sure observations at the Viking lander sites compared to the
UM (Sect. 4.1). We then describe Mars’ climate seasonality
and how this is portrayed in simulations (Sect. 4.2). We then
highlight the key differences between the RA dust scenario
and PCM in more detail (Sect. 4.3). In particular, we focus
on dust differences in our results and discuss further the im-
plications of this (Sect. 4.3.3).
4.1 Atmospheric pressure
Mars undergoes annual fluctuations in surface pressure, de-
creasing during colder months and increasing during the dust
season. This is mainly due to the net freezing and thaw-
ing of polar ice caps, which extract and release atmospheric
CO2, respectively. The mean surface pressure on Mars is
much lower than on Earth, resulting in large diurnal temper-
ature fluctuations and limiting dust loading capacity (Read
et al., 2015; Martínez et al., 2017) due to less heat retention
from the atmosphere. To show how pressure fluctuates in the
UM, we show surface pressure across the Martian year at
the approximate Viking lander sites in the model compared
to observational data from the landers. The values from the
UM are not from the exact spatial location of the landers,
due to their positions being within grid cells, but instead are
the closest data points to where the landers would be in the
UM. Figure 5 shows that the UM-observed pressure near the
Viking lander 1 and 2 (VL1 and VL2) sites remains steady
during the colder months and increases during the dust sea-
son. As the UM does not currently have a CO2ice cycle,
this cold season pressure drop present in observations is ab-
sent. This leads to a high disparity of pressures between sols
130 and 410 between simulated and observed pressures. The
UM does capture a minor pressure increase during sols 410–
Figure 5. UM surface pressure at approximate Viking lander 1 and
Viking lander 2 sites compared to observational data across a Mar-
tian year. Viking lander data available from Tillman (1989).
530 despite the lack of a CO2ice scheme. This increase in
pressure is likely due to a temperature increase as a result
of higher solar radiation. The absence of a pressure decrease
during months 2–6 would suggest that this could potentially
be a secondary feedback effect caused by heating from at-
mospheric dust. Since atmospheric dust quantities are much
lower in the colder months, their effect is minimal, but as
dust abundance increases, the magnitude of the effect of it is
amplified, increasing pressure further.
Accurate surface pressure is important for characterising
the climate, as it affects processes such as the thermal capac-
ity of the atmosphere and transport of material (such as dust)
across hemispheres (Gierasch and Toon, 1973; Hourdin et al.,
1993, 1995; Read et al., 2015; Martínez et al., 2017). Figure 6
shows surface pressure across the year for the UM RA and
PCM outputs. It shows that interaction observed in Fig. 5 but
as it occurs across the planet. In general, the UM RA features
higher pressure than the PCM, especially in month 6 just af-
ter when atmospheric pressures are at their lowest (median
sol of month 6 is sol 345). Surface pressures are most simi-
lar during month 9, with the majority of the planet featuring
equal or slightly lower pressures than the PCM. There are,
however, regions of extreme pressure difference in month 9.
The UM possesses higher surface pressure in the depths of
the Valles Marineris and lower pressure at the peak of Olym-
pus Mons and at the NH pole. The causes of these localised
pressure differences are likely the differences in orography
used in the UM and PCM, with the UM having higher peaks
and deeper channels.
4.2 Overview of a year of Martian climate
To compare the results between RA dust, RI dust and the
PCM, we show outputs of four atmospheric variables for
months 3, 6, 9 and 12 (sol breakdown given in Table A1).
Output is meaned zonally and temporally across the sols of
the given month. The variables are zonal (eastward) winds
(Fig. 7), meridional (northward) winds (Fig. 8), temperature
(Fig. 9) and dust mass mixing ratio (MMR, Fig. 10). The
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Figure 6. Surface pressure (Pa) across four seasons within the Martian year. For each month, the time average is taken of all sols within that
month. The RA dust scenario is shown on the left, the PCM output is in the centre and the differences between the RA and PCM outputs
are on the right. Colour scales in the left-hand and centre plots are matched across all months and between models with contour intervals
of 150 Pa. The colour scales in the difference plots are also matched across all months. Contours are not shown due to the sharp changes in
pressure around craters creating steep differences within a small area.
3×4 format and month dates are consistent throughout the
figures4. We explain the development of Mars’ climate across
4Additional figures are provided in the Appendix that compares
only two models at once (i.e. RA–RI and RA–PCM); these plots
allow for better one-to-one comparison between the RA output and
RI/PCM outputs.
a typical year and how this is simulated in our results, and we
then compare these outputs across simulations.
4.2.1 Month 3: Ls60–90◦
During this period, Mars is close to its coldest. NH tem-
perature maxima are ∼220 K at the northerly latitudes for
both scenarios, zonal and meridional winds are slower and
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uplifted dust quantities are low. Temperatures drop due to
Mars’ orbit taking the planet away from the Sun, which leads
to a reduction in solar radiation and a net cooling for the at-
mosphere and surface. This leads to a variety of secondary
effects occurring on Mars. In reality, CO2begins to freeze
more quickly than it thaws on the opposite pole, leading to
a global pressure reduction as CO2is sequestered from the
atmosphere, though this is not currently simulated. Tempera-
tures vary throughout the Martian year, but averages are low-
est during aphelion months (Fig. 9, month 3) and highest
during the dust season (Fig. 9, month 9). Temperature os-
cillation caused by Mars’ eccentric orbit does not seemingly
have a direct impact on atmospheric pressure in the UM, as
surface pressure remains consistent across aphelion months
(sols ∼150 to ∼300). Instead, the only influence on atmo-
spheric pressure in the UM (as seen in Fig. 5) is caused by
the increase in dust abundance in the atmosphere during sols
∼360 to ∼660. This temperature difference leads to weaker
winds during month 3 and less dust uplifting, leading to less
dust MMR throughout the majority of aphelion compared to
months during perihelion (Read et al., 2015). Temperatures
are highest in the NH, where it is summer, and then gradually
decrease southward. Temperatures also decrease as height in-
creases, as is to be expected. In terms of atmospheric circula-
tion, Mars features strong zonal jets that alternate between
hemispheres throughout the year, occurring during winter
seasons to the respective hemisphere as the planet transitions
between seasons (Fig. 7). Meridional winds feature a weak
jet near the equator surface, with an opposing jet at the up-
per boundary layer at ∼0.4σ(Fig. 8). The UM RA dust and
RI dust simulations are quite similar in month 3, with zonal
wind differences of about ∼1–2 m s−1and temperature dif-
ferences of ∼1 K. This is likely due to the low levels of dust
abundance in both simulations, and thus its radiative impact
during this time period is minimised (Fig. 10, month 3).
The UM and the PCM both feature strong zonal jets in the
upper atmosphere in the SH, but the UM’s winds are slower
at the upper boundary at the equator and in the NH zonal jet
(Fig. 7). The meridional jet in the UM is lower and slower
than in the PCM simulations (by ∼4 m s−1, Fig. 8). Air tem-
perature in the UM is generally lower, with especially strong
differences between model outputs at the poles, negative at
the south pole and positive at the north pole. The lower atmo-
spheric temperature is likely caused by less dust in the atmo-
sphere. Its radiative effects are minimal in the UM, whilst it is
relatively abundant in the PCM (average temperature differ-
ence omitting the poles of ≈ −15 K, Fig. 9). The temperature
differences at the poles (exceeding −30 and 30K at either
pole, Fig. 12), which are much greater than those closer to
the equator, are mainly due to the absence of polar ice in the
UM, with the latent heat transfer and surface optical proper-
ties of ice being simulated in the PCM (Fig. 9). Lastly, dust
differences between model outputs are at their highest rel-
ative to concentration comparisons between models in this
month. The UM simulates far less atmospheric dust than the
PCM. This is likely due to the absence of forced dust up-
lifting and a dust devil parameterisation in the UM, which
is present in the PCM (described by Madeleine et al., 2011;
Spiga et al., 2013; Montabone et al., 2020).
4.2.2 Month 6: Ls150–180◦
Here, Mars’ hemispheres are transitioning seasons; this can
be seen in the jet reversal in the zonal winds and by the
location of the temperature maxima at the lower latitudes.
Mars features a single Hadley cell which reverses twice a
year, with polar cells at each pole. Dust MMR is more than
month 3 due to rising temperatures increasing wind speeds,
with larger concentrations near the equator (Fig. 10). Despite
this, dust in the RA scenario is uplifted more compared to
RI by ∼10 %. The reason for this difference is likely up-
lifted RA dust scattering solar radiation close to the surface,
causing more near-surface warming than there would be with
RI dust, where the increased temperature causes faster near-
surface winds increasing dust-uplifting rates. Temperature
differences between the RA and RI scenarios (Fig. 9) are
between −5 and 5 K, which is more than in month 3 but
lower than in later months. There is a clear difference be-
tween polar regions in opposing directions (colder NH pole
and warmer SH pole), in addition to a mid-altitude band of
warmer air by up to 3 K in the RA dust scenario. These in-
dicate that dust is beginning to impact the atmosphere more
actively. Zonal winds are also reflecting the increasing dif-
ferences (Fig. 7), with an NH polar zonal jet difference of up
to 40 m s−1. Meridional wind differences are still minimal
(Fig. 8). Mars’ seasonal cycle and the associated reversal of
its Hadley cell during this month are clearly shown in the
meridional wind patterns, with counter-flowing jets present
in months 6 but more stable meridional winds during months
3, 9 and 12.
Differences between the UM RA dust scenario and PCM
here are varied in their magnitude but are present for each
variable. For air temperatures (Fig. 9), the UM is compara-
ble at the surface near the equator, but the temperature in the
UM decreases more quickly with height up to the upper at-
mosphere. The UM, however, features a region with higher
temperatures at ∼60◦S latitude, which reaches ∼24 K at the
surface but gradually decreases with height. There are still
differences between the models at the poles, but these dif-
ferences are less substantial than they were during month 3
(now down to a difference of the UM being −16 K compared
to the PCM at the surface). The UM RA features a band of
warmer air at ∼55◦S by ∼20 K (Fig. 12), but temperatures
near the SH pole are colder than in the PCM. Zonal winds
feature a variety of differences between models, with faster
and slower zonally averaged wind speeds distributed across
the atmosphere. Both models feature polar jets in both hemi-
spheres, but the UM zonal jets are slower than the PCMs
by up to 40 m s−1at the centre of the SH jet, which gradu-
ally becomes more comparable further from the centre of the
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jet towards the surface. There is an area of faster winds by
∼10 m s−1in the UM at the SH pole, which is at the mid-
dle of the atmosphere and lessens towards the surface where
differences again become 0. This is likely due to large po-
lar temperature differences, leading to a weaker temperature
gradient and causing the SH polar jet in the UM to be more
stretched than the PCM. The polar jet in the NH is also slower
in the UM by up to 30 ms−1. Dust differences between mod-
els are still considerable throughout the entire atmosphere as
in month 3, with the PCM having up to 1.2×10−5kg kg1
(∼700 %) more dust than the UM near the surface, while
differences lessen with height. Here, the UM dust quantities
are much less than the PCM, but uplifting is increasing as
Mars approaches the dust season. In all three scenarios dust
concentration is increasing, this is promising as it shows the
ability to dynamically simulate a substantial change in at-
mospheric dust abundance during a transition of seasons, as
occurs on Mars.
4.2.3 Month 9: Ls240–270◦
Month 9 is the peak of the dust season, and this is where
differences between the RA and RI scenarios are greatest,
as higher abundances of dust affect radiation fluxes more
severely. Firstly, for both UM scenarios, dust (Fig. 10, month
9) is mainly concentrated in the SH, forming a large “plume”
that extends vertically. Dust abundances have increased from
earlier months and are now 2 orders of magnitude more than
in month 3. The dust in the RA simulation is more concen-
trated at ∼30◦S latitude, while RI dust is distributed more
widely across latitudes but with a minor difference towards
∼30◦S latitude. This difference can clearly be seen with
higher dust abundance at the equator at all altitudes for RA
dust compared to less dust nearer both poles than RI dust.
During colder months the temperatures are similar between
UM scenarios, but during the dust season, the temperature
differences are much more pronounced (Fig. 9, month 9).
This change in magnitude of differences shows the effects
of RA dust on temperature, highlighting how dust influences
radiative transfer in Mars’ atmosphere – particularly at mid
altitudes, where dust can remain suspended, influencing in-
coming solar radiation and outgoing thermal radiation from
the surface. Temperatures (Fig. 9, month 9) near the surface
are similar between scenarios, both being ∼20 K warmer
than month 3, but at lower pressures (σbetween ∼0.2 and
0.1), the RA dust scenario is considerably warmer (exceed-
ing 30 K). The RA dust scenario also features a NH pole
which is ∼40 K colder than the PCM but warmer by up to
∼80 K at the SH pole. This highlights the radiative effects
of suspended atmospheric dust and how the climate might be
different in its absence. Suspended atmospheric dust causes
the upper atmosphere to be warmer than if there were no ef-
fects from dust (Fig. 10). In the scenario with RI dust, this
dust layer does not affect incoming radiation, and solar ra-
diation can reach the surface; however, in the RA dust sce-
nario, the suspended dust layer scatters incoming solar radi-
ation, transferring energy to the suspended dust layer instead
of the surface. This “band” of warmer air stretches from the
mid altitudes up to the top of the atmosphere, across almost
all latitudes (with an exception at the NH pole). Faster wind
speeds are a consequence of sharper temperature gradients
due to the thermal wind balance relationship and so are af-
fected by the disparity of temperature maxima between hemi-
spheres. This can be seen by the higher wind speeds present
in month 9 compared to month 3 (first columns in Figs. 7 and
8). Zonal winds (Fig. 7, month 9) feature more extreme dif-
ferences between RA and RI simulations, ranging from −40
to 40 m s−1in some places. These are mainly at the higher al-
titudes where temperature differences were at their greatest,
above the dust layer. The polar jet in the RA dust scenario is
considerably quicker but lacks an opposing jet near the top
of the atmosphere in the SH (σ=0.1, 50◦S latitude), though
this jet is small in the RI dust scenario (−20 m s−1). RI dust
does not feature an equatorial jet in the uppermost part of
the simulated atmosphere (σ≤0.1). Meridional wind differ-
ences for this month (Fig. 8, month 9) are at their highest
compared to other months, ranging from −3 to 3 m s−1in the
lower/middle atmosphere (σ=1 to 0.5, 15◦N latitude). A re-
gion near the top of the atmosphere is faster in the RA dust
scenario (up to 15 m s−1,σ=0.1, 15◦N latitude). The dust
content in the RA scenario is more centralised around the
equator, while dust content in the RI scenario is more spread
across the planet, leading to more dust at the poles (Figs. 10
and 11). This highlights the thermal feedback effects of at-
mospheric dust, as the dust content in the RA scenario causes
more localised warming, driving increased vertical uplifting.
This thermal influence is not present in the RI scenario, caus-
ing vertical uplifting to higher levels to be reduced, leading to
a lower, more latitudinally dispersed, atmospheric dust layer.
This shows the ability to simulate strong vertical wind-driven
dust uplifting in the UM.
Differences between the UM RA dust scenario and PCM
during month 9 are at their least compared to other months
(when compared at the relative ranges of the different
months), suggesting the importance of radiatively active dust
for reproducing the salient features of atmospheric dynam-
ics on Mars. Dust MMR in both models is now much more
comparable, with the UM having uplifted more dust than
the PCM during prior months. Spatially, dust in the UM is
concentrated in a large central plume at ∼30◦S latitude,
with dust in the PCM being more spread out across the at-
mosphere. The reasons for this are uncertain but could po-
tentially be the parameterisation of dust uplifting: the UM
dynamically calculates dust reservoirs and horizontal flux,
whereas PCM uses “forced” dust injection to more closely
match observations (Spiga et al., 2013; Montabone et al.,
2015, 2020). This high vertical uplifting in the UM is respon-
sible for a high input of dust into the atmosphere past the near
surface, a process described in detail by Spiga et al. (2013).
The UM features comparable near-surface dust to the PCM
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Figure 7. Zonal mean zonal winds (m s−1) across four seasons within the Martian year. For each month, the time average is taken of all sols
within that month. The RA dust scenario is shown on the left, the differences between the RA and RI dust scenarios are in the centre and
the PCM output is on the right. Colour scales in the left-hand and right-hand plots are matched across all months and between models, with
contour intervals of 20 m s−1. The contours in the difference plots are not matched due to the varying intensity of the difference between
months. Positive values indicate an eastward wind and negative values a westward wind. Appendix Figs. A1 and A6 show the same data but
solely for the RA vs. RI and PCM outputs, respectively.
across the ∼30◦S latitudinal band (Fig. 11), but near-surface
dust levels in the PCM are higher above and below this re-
gion (above 0◦N and below ∼50◦S). Particular regions of
higher near-surface dust in the UM are the Hellas Basin and
the Tharsis region. Temperature differences between mod-
els exist throughout the atmosphere, with the largest differ-
ences occurring close to the surface at the poles and reaching
−40 K in the NH pole and 40 K in the SH pole. These large
differences are due to the current absence of ice at the poles
in the UM, which will impact temperatures through emis-
sivity and latent heat effects. The rest of the atmosphere is
colder in the UM, with differences exceeding 20K above the
SH pole and above the equator at the upper edge of the sim-
ulated atmosphere. There is an agreement between models
at the surface across the equator as in months 3 and 6, with
another small area of agreement between models above the
surface at σ=0.9 to 0.2 and 50◦N latitude. Zonal mean dif-
ferences here are concentrated in the NH polar jet, with the
rest of the atmosphere in agreement. The zonal NH polar jet
in the UM is more spread out meridionally than the PCM, so
the difference of 40 m s−1at σ=0.2, ∼60◦N latitude is a
result of the jet expanding horizontally in the UM rather than
vertically as in the PCM. This is further highlighted by the
−20 m s−1difference at σ=0.3, ∼45◦N latitude, where the
wind speeds in the UM are slower as a result of being more
dispersed horizontally. Lastly, meridional wind differences
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632 D. McCulloch et al.: Modern Mars in the Unified Model
Figure 8. Zonal mean meridional winds (m s−1) across four seasons within the Martian year. For each month, the time average is taken of
all sols within that month. The RA dust scenario is shown on the left, the differences between the RA and RI dust scenarios are in the centre
and the PCM output is on the right. Colour scales in the left-hand and right-hand plots are matched across all months and between models,
with contour intervals of 2 m s−1. The contours in the difference plots are not matched due to the varying intensity of the difference between
months. Positive values indicate a northward wind and negative values a southward wind. Appendix Figs. A2 and A7 show the same data but
solely for the RA vs. RI and PCM outputs, respectively.
between the UM and PCM in month 9 are not concentrated in
a single jet or in a single direction but instead occur sporadi-
cally throughout the atmosphere. There are some differences
of −3 m s−1near the surface and below the top of the simu-
lated atmosphere, with 3 ms−1faster wind speeds around the
middle atmosphere (σ=0.4, 0 and 20◦S latitude) and top of
the simulated atmosphere (σ≤0.1, 0 to 40◦N latitude).
When discussing dust, month 9 is the most relevant of the
four selected months, as it features the height of the dust sea-
son when the dust is at its most abundant in both models.
Our results show that we are able to simulate a dust cycle
with intra-annual fluctuations in the UM without dust forc-
ing. This month highlights the key takeaway from this study,
that we have intra-annual dust quantity oscillation that is en-
tirely reproduced dynamically by the GCM, with dust quan-
tities rising during earlier months, peaking during the dust
storm season and subsiding in later months.
4.2.4 Month 12: Ls330–360◦
In month 12, the dust season ends and the atmosphere of
Mars cools. The winds are weaker and dust MMR subsides
while the polar jets transition between hemispheres. The up-
lifted dust quantities are considerably less than those of the
peak dust season, with RA scenarios still centred around the
equator and RI dust shifting northward. Dust at the equator is
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Figure 9. Zonal mean air temperature (K) across four seasons within the Martian year. For each month, the time average is taken of all sols
within that month. The RA dust scenario is shown on the left, the differences between the RA and RI dust scenarios are in the centre and
the PCM output is on the right. Colour scales in the left-hand and right-hand plots are matched across all months and between models, with
contour intervals of 10 K. The contours in the difference plots are not matched due to the varying intensity of the difference between months.
Appendix Figs. A3 and A8 show the same data but solely for the RA vs. RI and PCM outputs, respectively.
more abundant in the RA scenario by ∼4.0×10−7kg kg−1
(∼10 % more dust in the RA scenario).
Overall, there is more dust uplifted in the RI scenario
compared to the RA scenario, though this varies spatially
(Fig. 10, month 12). Dust content in the RA scenario at
the NH pole is less than that in the RI scenario by up to
1.6×10−6kg kg−1(∼187 %), with a similar but less extreme
difference at the SH pole. Temperatures begin to decrease in
this season, and the warmest region is again in the lower lat-
itudes in both scenarios (Fig. 9, month 12). The RI case is
warmer at the equator in the upper atmosphere and colder
near the poles in the lower atmosphere than that in the RA
dust case. Zonal wind patterns change between hemispheres,
with higher polar jet speeds in both hemispheres for RA dust
(Fig. 7, month 12). Differences in months 6 and 12 do switch
hemispheres, but the magnitude of these differences is larger
for month 12. This is likely caused by more residual dust
in the atmosphere from month 9, causing temperature dif-
ferences to be higher in month 12 than in month 6, in turn
affecting the thermal wind relationship. This hemispheric re-
versal in the zonal wind mirrors what occurs in month 6 but
is stronger in month 12 and stronger in the RA dust scenario,
with maximum differences reaching now ∼20 m s−1(com-
pared to ∼10 m s−1in month 6). Meridional winds for both
scenarios show the Hadley cell direction reversal with the
seasonal cycle (detailed by Read et al., 2015); wind speeds
in both scenarios are once again comparable with the largest
differences being between −3 and 1 m s−1.
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Figure 10. Zonal mean dust mass mixing ratio (kg kg−1) across four seasons within the Martian year; each month is the average taken of
all sols within that month (months according to Table A1). The RA dust scenario is shown on the left, the differences between the RA and
RI dust scenarios are in the centre and the PCM output is on the right. Contour lines denote the mass mixing ratio and units are in kg kg−1.
Note that, due to the wide range of values present between months, the colour-scale ranges differ between months and models for this figure.
Appendix Figs. A4 and A9 show the same data but solely for the RA vs. RI and PCM outputs, respectively.
Comparison between the UM RA dust scenario and PCM
shows similar trends to month 6 in each variable but inverted
with respect to latitude (i.e. warmer temperature plume oc-
curs in the NH instead of the SH, as in month 6). Temper-
atures are once again colder in the UM but are generally
colder than the differences in month 6 (i.e. month 6 differ-
ences ranged from −24 to 24 K, but month 12 ranges from
−30 to 18 K). The UM again features a patch of warmer air at
∼60◦N latitude up to ∼0.3σbut in the opposite hemisphere
to month 6. There is also a small patch of colder air at the NH
pole. These temperature discrepancies are likely to be the re-
sult of no polar ice in the UM which is present in the PCM
(emissivity and thermal effects of polar ice are shown by For-
get et al., 1998; Way et al., 2017). Throughout the rest of the
atmosphere, as in month 6, the UM features lower air temper-
atures than those in the PCM, becoming lower as the height
increases up to the top of the atmosphere (Fig. 9, month 12).
Surface temperatures are also more comparable than month
9, with the only differences in temperatures >20 K being
at the NH pole, peaking at >85◦N by ∼40 K colder in the
UM. Zonal wind differences between models are quite varied
during month 12, with varied wind speed differences across
the upper atmosphere in both hemispheres and a weaker and
wider polar jet in the NH (Fig. 7, month 12). The winds
near the surface are quite comparable between models, but
at the NH pole, the wind speeds are faster in the UM, with
this difference increasing with altitude up to ∼0.2σwhere
the difference is ∼30 m s−1. The entire NH polar jet in the
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Figure 11. Dust mass mixing ratio (kg kg−1) and horizontal wind speed (m s−1) at σ=0.99 across four seasons within the Martian year;
each month is the average taken of all sols within that month (months according to Table A1). The RA dust scenario is shown on the left,
the differences between the RA and RI dust scenarios are in the centre and the PCM output is on the right. Colour scales in the left-hand
and right-hand plots are matched across all months and between models, with contour intervals of 5 ×10−7kg kg−1. The contours in the
difference plots are not matched due to the varying intensity of the difference between months. The PCM output is shown in the centre of
this figure to allow easier visual comparison between the UM RA dust scenario and PCM. Appendix Figs. A5 and A10 show the same data
but solely for the RA vs. RI and PCM outputs, respectively.
UM is larger than the PCM, with the centre of the jet be-
ing spread out across more of the upper atmosphere, this is
in contrast to the PCM where the NH polar jet is smaller
but faster above ∼50◦N latitude. The SH polar jet is also
weaker in the UM, but this difference is much less than that
of the NH (up to −10 K). Meridional wind differences are
also similar to month 6 in locations, but differences, where
they do occur, are less than in month 3. At ∼15◦N latitude
at ∼0.9σ, there is now no difference between models, as op-
posed to during month 6, where wind speed predicted by the
UM was −2 m s−1slower.
Dust MMR fluctuates in both models throughout the year,
becoming more abundant during month 9 (peak dust storm
season, Fig. 10) then dissipating during the colder peri-
helion (Fig. 10, month 3). Both RA and RI outputs fea-
ture strong columns at the mid latitudes spanning up into
the mid altitudes during dust season, with dust MMR be-
ing on average 2 orders of magnitude higher compared to
that during colder months. Although there is an increase
in dust MMR between seasons in both model outputs, the
intensity of the change varies regionally, with poleward
regions increasing less severely than equatorial regions.
Despite similarities between the RA and RI scenarios, there
are still some differences. Dust MMR is concentrated around
the mid-latitudes for RA dust and is more dispersed with RI
dust. Dust abundances are higher northward of the equator in
the RI dust scenario during peak dust season (Fig. 10, month
9), but as the dust season subsides (approaching NH spring
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636 D. McCulloch et al.: Modern Mars in the Unified Model
Figure 12. Surface temperature (K) across four seasons within the Martian year; each month is the average taken of all sols within that month
(months according to Table A1). The RA dust scenario is shown on the left, the PCM output is in the centre and the differences between the
RA and PCM outputs are on the right. Colour scales in the left-hand and centre plots are matched across all months and between models with
contour intervals of 20 K. The colour scales in the difference plots are also matched across all months and contour intervals are also 20 K.
equinox) there are higher abundances of dust MMR in the
RI scenarios towards the poles (Fig. 10, month 12). Because
of this, months 6 and 12 are essential for monitoring dust-
uplifting rates.
4.3 Variable comparison to the PCM
In this section, we summarise the key differences between
variables in the UM’s RA simulation and the PCM output
across the Martian year. We discuss zonal and meridional
winds (Sect. 4.3.1), air temperature (Sect. 4.3.2) and dust
(Sect. 4.3.3). We finish the section by discussing the impli-
cations of dust differences between models and speculate as
to their cause.
4.3.1 Winds
As shown in Fig. 7, overall patterns are similar between mod-
els, with both RA UM and PCM simulating strong eastward
jets that alternate between hemispheres throughout the Mar-
tian seasons. Wind speed maxima in the PCM are generally
faster than the UM, as is clearly seen in the plot for month
6, where the jets are present in both hemispheres but are
∼30 m s−1slower in the UM. This is likely due to less atmo-
spheric dust around 0.6σin the UM, which in turn leads to
lower temperatures; this reduces pressure gradients, causing
slower winds (Madeleine et al., 2011). Despite these discrep-
ancies, our results are encouraging, as they demonstrate the
ability to model the major seasonal wind patterns with the
UM.
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As shown in Fig. 8, meridional wind patterns are simi-
lar between models but do feature some key differences. In
month 3, the jet at the upper boundary of the model is situ-
ated lower in the UM, relative to the PCM output, in addition
to stronger surface winds in the UM. Month 6 features the
largest differences, with more distinctive jets in the UM, that
are more fragmented and overall weaker in the PCM output.
Months 9 and 12 are highly similar between outputs, with
the largest difference being a slightly faster mid-latitude jet
in the UM during month 12.
4.3.2 Temperatures
As shown in Fig. 9, the differences in temperature between
the UM and PCM outputs are notable throughout the year, the
highest occurring in months 6 and 12. There is a consistent
difference at the poles: this is likely due to the UM not having
any form of polar ice and its effect on albedo and heat transfer
(Forget et al., 1998, 1999). These missing parameterisations
will have major impacts around the near surface, as shown
in Figs. 9 and 12 at the poles, with the effect weakening
with height. Month 6 and 12 differences both feature patches
where the UM simulations are warmer, at ∼60◦in the hemi-
sphere exiting winter (∼−60◦during month 3, ∼60◦during
month 12). Interestingly, the differences in temperature are
small in months 3 and 9 (except at the poles), despite the ab-
sence and presence of higher dust abundance, respectively.
This occurs despite the difference in dust MMR maximum
and minimum with month 3 being half the amount of month
9 (6 ×10−6and 1.2×10−5, respectively). This is likely due
to the effects of dust uplifting and deposition (highest rate
of change during months 6 and 12) having higher horizon-
tal flux rates in the UM, leading to non-uniform differences
across the atmosphere. This is also suggested by the fairly
consistent distribution of differences in temperatures during
months 3 and 9, where the dust is at its lowest and highest
dust MMR abundances, respectively, but dust-uplifting and
deposition rates are fairly homogeneous.
Across all months, the PCM output is generally warmer
than the UM, with the higher temperature differences corre-
lating with months of higher dust differences (Fig. 9, month
9). This highlights the importance of atmospheric dust in
thermal insulation in the Martian atmosphere, as tempera-
tures nearer the upper boundary are cooler in the UM. This is
likely caused by the lower amounts of suspended dust com-
pared to the PCM, allowing more solar radiation to the sur-
face, causing a cooling gradient as height increases above
the missing dust layer (Madeleine et al., 2011). Surface polar
temperatures in the RA dust scenario are consistently differ-
ent to the PCM throughout the Martian year (Fig. 12). This
is most likely due to the absence of polar CO2and H2O ice
in the UM, which are included in the PCM. During month
9, there is still a residual ice cap in the PCM (Forget et al.,
1998, 1999), but as there is no such cap in the UM, the radi-
ation is incident directly on the polar soil. This will change
the albedo and thermal inertia properties of these regions,
leading to increased shortwave absorption in the UM than in
the PCM. The subsequent effects of this potentially explain
the stronger meridional circulation in the UM. As the hemi-
spheric temperature difference is stronger, transport from the
SH to the NH is amplified (as can be seen in Fig. 8).
4.3.3 Dust content
Differences in dust distributions between the UM and PCM
results are the most significant of all the variables, both
throughout the year and regionally within monthly outputs.
Differences between the dust amount vary greatly between
models, with dust season total quantities being comparable
but spatially varied and the cold season retaining vastly more
atmospheric dust in the PCM output than that in the RA UM
scenario (Fig. 10). Whilst the PCM output during month 3
still has less uplifted dust than its dust season quantities, there
is still significantly more dust in this month than the UM by
∼1.5 orders of magnitude. This disparity changes as both
models approach the dust season (Fig. 10, month 6), how-
ever, with the dust-uplifting rate in the UM being higher than
the PCM. This means that there are initially large differences
between the two models, but the UM is beginning to rec-
tify the disparity between atmospheric dust amounts as it ap-
proaches the dust season. This becomes apparent in month 9
(Fig. 10, month 9): where the dust season is at its peak, both
scenarios have much more similar amounts of dust in their
outputs compared to previous months. Both outputs differ in
their vertical and spatial (Figs. 10 and 11) distributions, with
the UM featuring a large equatorial plume that extends into
the upper atmosphere, whilst the PCM features higher abun-
dances at the SH pole. Month 9 features the first instance
of a localised higher amount of near-surface dust content in
the UM compared to the PCM (mainly in the Hellas Basin).
Near-surface dust in the PCM, however, is distributed more
evenly across the rest of the planet. This locality of dust in
the UM is likely the origin of the vertical plumes present at
higher altitudes (Fig. 10). Dust is being transported south-
ward from the equator and NH (as shown by the vectors in
Fig. 11) and is redirected vertically at ∼30◦S. As the simu-
lations progress towards month 12 (Fig. 10, month 12), dust
abundances in the UM reduce more quickly than the PCM,
resulting in higher dust abundance in the PCM output. This is
a reversal of the observed pattern as seen in month 6, where
UM dust uplifting is greater than the PCM, but instead the
UM dust deposition is now stronger than the PCM output.
This presents an interesting dilemma in understanding how
these variables are represented in simulations, as the mod-
els do not have the same parameterisations for dust uplifting,
and as a result, there is a clear disparity in the amount of dust
that can be uplifted between models. The usage of a “free”
dust scheme has also been explored by Neary and Daerden
(2018) with the Global Environmental Multiscale model (fur-
ther described by Husain et al., 2019). They are able to char-
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638 D. McCulloch et al.: Modern Mars in the Unified Model
acterise the Martian atmosphere according to Mars year 27
using a “free” dust scheme. Their work alongside this study
emphasises the potential of a “free” dust scheme and also
acts as a demonstration of model capabilities which we aim
to explore further. Although the cause of the disparity in dust
distributions between those predicted by the UM and PCM
is uncertain, we can speculate on potential causes. These
might be caused by dust over-sensitivity to temperature in
the UM. As the average atmospheric temperature decreases
after the cold season, lower temperatures could potentially
indirectly affect dust-uplifting or deposition rates via slower
wind speeds more severely in the UM, causing uplifted dust
to decrease more quickly than anticipated. There are also
consistently colder air temperatures in the UM that vary in
intensity throughout the Martian months, which could be am-
plifying this effect. Another reason might be that both mod-
els assume an infinite availability of dust reservoirs, which
start uniformly distributed across the surface but are allowed
to develop and congregate in areas as the model progresses.
This is currently an issue in Mars modelling, as the dust de-
position in “free” GCMs does not always match observations
(Montabone et al., 2020). To rectify this, the PCM uses dust-
uplifting maps to dictate where dust is being forced into the
atmosphere, as described by Madeleine et al. (2011), Spiga
et al. (2013) and Montabone et al. (2020). In our set-up, the
UM does not prescribe dust uplifting in such a way but in-
stead relies on the surface scheme to calculate dust reser-
voirs. Therefore, direct comparison to PCM output cannot
be solely attributed to the difference in model parameterisa-
tion. Despite this, a comparison of averaged seasonal trends
does show that both GCMs are able to capture annual dust
storm seasons and non-dust seasons. Where dust is deposited
in the model dictates source reservoirs for its subsequent
uplifting. Therefore, understanding the deposition of dust
and the formation of dust reservoirs (particularly after dust
storms due to the amount of dust transported) is paramount
to be able to reproduce the Martian dust cycle accurately
(Montabone et al., 2015; Montabone and Forget, 2017; For-
get and Montabone, 2017; Montabone et al., 2020). If certain
regions are key contributors to dust uplifting, then misrepre-
senting the surface wind conditions over them would have a
particularly noticeable effect on the global rate of dust up-
lifting. The third likely cause of such differences could be
dust nucleation scavenging from CO2and H2O condensa-
tion being present in the PCM but absent in the UM. This
affects global dust abundances where dust is being extracted
from the atmosphere in the PCM during colder conditions.
In the present paper, we focus only on dry simulations of
the UM, so the dust abundance is not affected by scaveng-
ing from condensation, leading to potential overestimation
of dust abundance during the colder cloud season. The to-
tal dust abundance during month 9 is also impacted by dust
uplifting in prior months, so where pressures vary between
models (Fig. 6), the ability for increased/decreased rates of
dust suspension will be likely to vary between models.
Rapid vertical dust-uplifting “rocket dust storms”, as de-
scribed by Spiga et al. (2013), play a key role in dust injec-
tion into the atmosphere. The PCM currently factors for this,
but the UM dust scheme has not previously been required
to simulate such intense vertical uplifting (since it does not
feature on the same scale on Earth), and therefore additional
changes to the dust scheme are required in the UM. Observa-
tions and model comparison investigating this uplifting rate
are essential to fine-tune the UM and verify whether the UM
captures this correctly, alongside determining which devel-
opments or adjustments might be required for the UM dust
scheme (Madeleine et al., 2011).
Further differences may be caused by the PCM featuring
a varying inter-annual dust content between Martian years
(Montabone et al., 2015), while the UM inter-annual dust
content remains largely the same as that displayed in Figs. 10
and 11. This was mainly mitigated by using the average sce-
nario for the MCD (described in Sec. 3). This remains some-
thing to consider, however, were the UM output to be used
to investigate the Martian climate across multiple Martian
years.
Disparities between dust MMR in the RA and PCM out-
puts are also affected by the absence/presence of a dust devil
parameterisation. Dust devils play a large role in dust verti-
cal transport, particularly during the NH summer (Newman
et al., 2002; Kahre et al., 2006, 2017), enabling and sus-
taining dust suspension above the surface (but mainly be-
low ∼8 km) during NH spring and summer (Heavens et al.,
2011; Neakrase et al., 2016; Gronoff et al., 2020; Newman
et al., 2022). While the PCM includes parameterisation for
this (Newman et al., 2002), the UM does not currently fea-
ture any explicit parameterisation for rapid vertical uplift-
ing other than by aeolian-driven processes (Marticorena and
Bergametti, 1995; Woodward, 2011).
Both schemes omit some dust microphysics due to the
difficulty in accurately characterising them within the dust
scheme, namely surface crusting and surface re-entrainment
(Woodward, 2001; Wolff et al., 2009; Madeleine et al., 2011;
Woodward et al., 2022), which likely contributes to the dis-
parity between GCM outputs (without forcing) and obser-
vations. The differences in spatial distribution could also
be caused by the differences in microphysics parameterisa-
tion between models, and therefore refining this would un-
doubtedly improve the representation of dust on Mars. The
magnitude of this potential improvement, however, will re-
main uncertain until an inter-GCM comparison takes place
where the initial and boundary conditions are identical. Such
work would be able to identify differences which are solely
due to these differences in parameterisations. Studies have
been conducted on a mesoscale level in conjunction with the
Mars 2020 lander (Newman et al., 2022) but took place prior
to recent major improvements in parameterisations in cur-
rent Mars GCMs (Kass et al., 2003; Madeleine et al., 2011;
Spiga et al., 2013; Colaïtis et al., 2013; Navarro et al., 2014;
González-Galindo et al., 2015). Global comparisons have
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D. McCulloch et al.: Modern Mars in the Unified Model 639
been extensively applied to Earth GCMs through CMIP6
projects 5(Eyring et al., 2016). It has also been recently been
conducted for exoplanets as part of the THAI project (de-
scribed by Turbet et al., 2022; Sergeev et al., 2022; Fauchez
et al., 2022), and the results have already identified new av-
enues for model improvement. If such a comparison was ap-
plied to Mars GCMs, it would allow for the identification of
limitations of Mars modelling, potentially identifying limi-
tations in our parameterisations, in turn allowing us to then
improve comparisons to observations.
5 Discussion and conclusions
By using multiple GCMs to simulate Mars’ climate in dif-
ferent ways, we are able to understand areas of inaccuracy
within these models (González-Galindo et al., 2010; Hinson
et al., 2014; Newman et al., 2021), as has been the case for
Earth (e.g. Eyring et al., 2016), other solar system bodies
(e.g. Lora et al., 2019) and exoplanets (Turbet et al., 2022;
Sergeev et al., 2022; Fauchez et al., 2022). With the UM we
are able to simulate the same Martian climate with a new
modelling framework. These differences are present down to
the core structure of the GCMs, with the UM being a non-
hydrostatic model, different parameterisations for dust cal-
culations and a height-based vertical structure. Using a non-
hydrostatic model is especially relevant to Mars, as it fea-
tures periodic pressure fluctuations which affect the entire
climate in multiple ways (e.g. varying wind speeds and dust-
uplifting rates due to less atmospheric mass, and a summary
of key differences between the models is further highlighted
by Turbet et al., 2022). Modelling comparable climates in
different GCMs is crucial for identifying differences between
them, potentially like those caused by parameterisation or as
a result of the different methods used to calculate variables.
This work and the developments with other Mars GCMs will
undoubtedly allow us to eventually expand our capabilities
in areas that currently elude us, such as being able to forecast
when global dust storms will occur.
The UM is capable of reproducing salient features of the
large-scale circulation but lacks two key physical processes
which have a considerable impact on Mars’ climate: water
and CO2cycle. Including parameterisations for these pro-
cesses is expected to further reduce the disparities between
the UM and PCM. Our goal, however, is not to make our
model identical to the PCM but to offer a new modelling
framework that can complement the PCM (and other GCMs)
while aiming for improvements in the aspects of Mars cli-
mate where current models struggle.
Firstly, by adding water vapour and radiatively active
clouds, which would affect temperatures in a variety of ways
5Full list of projects available at https://www.
wcrp-climate.org/modelling-wgcm-mip-catalogue/
modelling-wgcm-cmip6-endorsed-mips (last access: 16 Jan-
uary 2023).
(Navarro et al., 2014; Steele et al., 2017; Pál et al., 2019), in
addition to dust-uplifting rates (due to water acting as con-
densation nuclei for dust particles). The inclusion of these
parameterisations would undoubtedly alter how the UM sim-
ulates Mars’ dust and subsequently the planet’s surface, es-
pecially as dust deposition changes surface properties such as
thermal inertia or albedo (Bonev et al., 2008; Schmidt et al.,
2009; Kahre and Haberle, 2010; Forget and Montabone,
2017). Although H2O content is relatively low in the Mar-
tian atmosphere compared to other atmospheric compounds
(even lower than Earth when accounting for the difference
in atmospheric mass), it still is shown to have a large ef-
fect on Mars’ climate. Radiatively active clouds can affect
temperatures by up to 20 K (Madeleine et al., 2012; Navarro
et al., 2014; Cooper et al., 2021), and moisture also affects
dust nucleation and deposition (Walters et al., 2019). In the
present study, we use the UM with a completely dry atmo-
sphere similar to Turbet et al. (2022). However, as the UM
has been originally developed for Earth, it already has two
sophisticated cloud schemes which are routinely used for
climate and weather prediction (described by Wilson et al.,
2008b, a). Therefore, adding clouds to our set-up would be
a matter of adaptation of an existing scheme rather than cre-
ating one from the ground up. Adding the hydrological cy-
cle would affect dust deposition rates, especially during the
colder months, when Mars’ relative humidity is at its high-
est. Temperature profiles would be different across the atmo-
sphere (which will cause secondary effects on winds and dust
MMR) as clouds influence radiation transfer. An example of
this can be seen in Navarro et al. (2014) and the scenarios in
the PCM.
Secondly, our model needs to include a CO2cycle which
substantially affects Mars’ atmospheric pressure and the air–
surface interaction at the poles in particular (as shown by For-
get et al., 1999; Way et al., 2017). CO2condensation and sub-
limation lead to pressure fluctuations throughout the Martian
year (Fig. 5), so they have to be accounted for by the model
parameterisations to correctly reproduce horizontal pressure
gradients, and thus the wind patterns (Haberle et al., 2008;
Read et al., 2015; Martínez et al., 2017). Improving the at-
mospheric pressure characterisation will also likely improve
the accuracy of dust-uplifting rates during months 6 and 12,
when the surface pressure is the most different between mod-
els (Fig. 6). In some GCMs, this has been tackled by fixing
the available mass of atmospheric CO2to match the amount
for the given pressure amount, which has allowed models to
characterise an incredibly complicated process, enabling an
idealised representation of Mars (Forget et al., 1999). More
recently, work by Way et al. (2017) has been able to alter
pressure levels throughout the simulation without such ad
hoc prescription. In follow-up work, we are planning to im-
plement this or a similar parameterisation, and we expect this
would improve year-round simulation as all prognostic vari-
ables shown in this paper would be affected by pressure vari-
ations, particularly during the colder months, where pressure
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640 D. McCulloch et al.: Modern Mars in the Unified Model
difference between Viking lander observations and the UM
are highest. Once these processes have been added, further
refinement of the albedo and surface inertia can be imple-
mented. That is, an albedo that varies spatially across the
Martian surface and is affected by CO2ice (Kieffer et al.,
1977; Schmidt et al., 2009), and a thermal inertia map that
also varies spatially (Kieffer et al., 1977; Palluconi and Ki-
effer, 1981; Mellon et al., 2008) Despite the absence of the
aforementioned parameterisations, including a dust devil pa-
rameterisation and prescribed dust quantities in the UM RA,
our model still produces a high-altitude dust layer using a
free dust scheme. This offers a promising development in
Martian climate modelling (Montabone and Forget, 2017;
Montabone et al., 2020). While the dust quantities and their
seasonality in the UM RA are not entirely similar to those in
PCM (e.g. the UM RA features a single dust storm season,
while Mars features two seasons in reality; Madeleine et al.,
2011; Read et al., 2015; Martínez et al., 2017), the ability
to simulate seasonal dust levels with distributions character-
istic of the PCM without forcing emphasises the scientific
relevance of the UM. Once more parameterisations are im-
plemented (as mentioned in the previous two paragraphs),
results may be better matched across diurnal and monthly cy-
cles, allowing further work investigating these temporal pe-
riods. For this reason, we hope the UM will prove a vital tool
in the further research of the Martian climate using GCMs.
In this paper, we have shown the first application of the
UM to a modern-day Mars climate, using a dry set-up. We
have demonstrated how we can adapt a highly sophisti-
cated Earth climate model to simulate a climate on another
planet. The UM demonstrates comparable wind patterns and
temperature profiles to outputs from an established three-
dimensional Mars GCM, the PCM. We have shown how the
UM is able to simulate seasonal temperature variations and
their subsequent effects on winds. We have shown how the
UM can simulate uplifted dust and identified areas of dis-
parity during colder months where the absence of a CO2ice
and hydrological scheme likely play a role. Future work will
seek to use the existing moist physics in the UM as well as
to implement a CO2condensation scheme, allowing for the
interaction of these processes with dust – thus bringing more
realism into our Mars simulations. Once these additional pro-
cesses are implemented, the UM could be used to conduct
simulations of specific Martian years (as done in Montabone
et al., 2015, 2020), investigate diurnal tides (Hinson and Wil-
son, 2004; Chapman et al., 2017; Atri et al., 2023) or provide
an additional tool in the refinement of our characterisation of
the Martian climate in simulations.
Appendix A
In this section, we include a reference table for matching
Martian months, a reference table for vertical level height and
supplementary plots for Figs. 6–9 that compare two outputs
at a time (as opposed to the three in the main section of this
work). In Table A1, months can be matched with the respec-
tive solar longitude and number of sols in that month. Mars
features months that vary in their number of sols due to its
orbital eccentricity, with fewer sols per month nearer perihe-
lion and more sols per month closer to aphelion. Key months
used in this study are months 3, 6, 9 and 12. In Table A2, ver-
tical levels used in this configuration are given. Vertical lev-
els are compressed/expanded depending on orography at any
given point. In Figs. A1 through A4 we show the RA and RI
scenario outputs in the left and centre columns, respectively,
with the difference between the two in the right column. In
Figs. A5 through A10 we show the UM RA and PCM model
outputs in the left and centre columns, respectively, with the
difference between the two in the right column.
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Table A1. Martian months, corresponding solar longitude (Ls) and
number of sols within that month. 0◦Lscorresponds to Northern
Hemisphere spring equinox.
Month LsSols Sols of month
1 0–30 61 0–61
2 30–60 66 61–127
3 60–90 66 127–193
4 90–120 65 193–258
5 120–150 60 258–318
6 150–180 54 318–372
7 180–210 50 327–422
8 210–240 46 422–468
9 240–270 47 468–515
10 270–300 47 515–562
11 300–330 51 562–613
12 330–360 56 613–669
Table A2. Vertical level heights used in our Mars set-up. Values
are given for a point at areoid height. This format allows for higher
resolution at the surface.
θlevels – m height
25.432 101.72 228.864 406.872 635.744
915.464 1246.056 1627.496 2059.8 2542.968
3076.992 3661.872 4297.616 4984.216 5721.672
6509.992 7349.176 8239.208 9180.112 10171.864
11 214.48 12 307.96 13 452.288 14 647.488 15 893.536
17 190.456 18 538.224 19 936.856 21 386.344 22 886.696
24 387.088 25 888.032 27 391.424 28 901.376 30 425.168
31 974.128 33 564.536 35 218.528 36 964.968 38 840.408
40 889.92 43 168.056 45 739.696 48 680.976 52 080.208
56 038.736 60 671.856 66 109.72 72 498.248 80 000
ρlevels – m height
12.712 63.576 165.296 317.872 521.312
775.608 1080.76 1436.776 1843.648 2301.384
2809.976 3369.432 3979.744 4640.912 5352.944
6115.832 6929.584 7794.192 8709.656 9675.984
10 693.176 11 761.216 12 880.128 14049.888 15270.512
16 541.992 17 864.336 19 237.544 20 661.6 22 136.52
23 636.888 25 137.56 26 639.728 28 146.4 29 663.272
31 199.648 32 769.336 34 391.528 36 091.744 37 902.688
39 865.168 42 028.992 44 453.872 47 210.336 50 380.592
54 059.472 58 355.296 63 390.784 69 303.984 76 249.128
Table A3. Dust refractive index used, as described in Balkanski
et al. (2007).
Dust refractive index
Number Wavelength (m) Real part Imaginary part
1 2.00 ×10−71.520 0.001560
2 2.50 ×10−71.520 0.001560
3 3.00 ×10−71.520 0.001560
4 3.37 ×10−71.520 0.001550
5 4.00 ×10−71.520 0.001620
6 4.88 ×10−71.520 0.001710
7 5.15 ×10−71.520 0.001540
8 5.50 ×10−71.520 0.001470
9 6.33 ×10−71.520 0.001540
10 6.94 ×10−71.520 0.001290
11 8.60 ×10−71.520 0.000940
12 1.06 ×10−61.520 0.000669
13 1.30 ×10−61.510 0.000601
14 1.54 ×10−61.510 0.000537
15 1.80 ×10−61.510 0.000471
16 2.00 ×10−61.510 0.000473
17 2.25 ×10−61.510 0.000495
18 2.50 ×10−61.510 0.000672
19 2.70 ×10−61.510 0.001100
20 3.00 ×10−61.500 0.001780
21 3.20 ×10−61.500 0.002250
22 3.39 ×10−61.500 0.002710
23 3.50 ×10−61.500 0.002970
24 3.75 ×10−61.490 0.003610
25 4.00 ×10−61.490 0.004270
26 4.50 ×10−61.450 0.005780
27 5.00 ×10−61.410 0.007700
28 5.50 ×10−61.360 0.009540
29 6.00 ×10−61.290 0.023400
30 6.20 ×10−61.240 0.037400
31 6.50 ×10−61.130 0.166000
32 7.20 ×10−61.390 0.080500
33 7.90 ×10−61.080 0.051200
34 8.20 ×10−60.792 0.255000
35 8.50 ×10−61.010 0.504000
36 8.70 ×10−61.090 0.552000
37 9.00 ×10−61.300 0.714000
38 9.20 ×10−61.380 0.758000
39 9.50 ×10−62.140 0.843000
40 9.80 ×10−62.540 0.631000
41 1.00 ×10−52.480 0.411000
42 1.06 ×10−51.950 0.126000
43 1.10 ×10−51.830 0.143000
44 1.15 ×10−51.810 0.135000
45 1.25 ×10−51.630 0.160000
46 1.30 ×10−51.720 0.115000
47 1.40 ×10−51.460 0.165000
48 1.48 ×10−51.500 0.124000
49 1.50 ×10−51.470 0.125000
50 1.64 ×10−51.250 0.258000
51 1.72 ×10−51.200 0.413000
52 1.80 ×10−51.170 0.593000
53 1.85 ×10−51.190 0.776000
54 2.00 ×10−51.420 0.950000
55 2.13 ×10−51.670 1.490000
56 2.25 ×10−52.840 0.874000
57 2.50 ×10−51.920 0.652000
58 2.79 ×10−52.070 0.393000
59 3.00 ×10−51.850 0.592000
60 3.50 ×10−51.670 0.538000
61 4.00 ×10−51.630 0.555000
62 1.00 ×10−21.630 0.554000
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
642 D. McCulloch et al.: Modern Mars in the Unified Model
Figure A1. Zonal mean zonal winds (m s−1) across four seasons within the Martian year. For each month, the time average is taken of all
sols within that month. The RA dust scenario is shown on the left, the RI dust scenario in the centre and the differences between scenarios on
the right. Colour scales in the left-hand and right-hand plots are matched across all months and between scenarios, with contour intervals of
20 m s−1. The contours in the difference plots are not matched due to the varying intensity of the difference between months. Positive values
indicate a northward wind and negative values a southward wind.
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 643
Figure A2. Zonal mean meridional winds (m s−1) across four seasons within the Martian year. For each month, the time average is taken of
all sols within that month. The RA dust scenario is shown on the left, the RI dust scenario in the centre and the differences between scenarios
on the right. Colour scales in the left-hand and centre plots are matched across all months and between scenarios, with contour intervals of
2 m s−1. The contours in the difference plots are not matched due to the varying intensity of the difference between months. Positive values
indicate an eastward wind and negative values a westward wind.
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
644 D. McCulloch et al.: Modern Mars in the Unified Model
Figure A3. Zonal mean air temperature (K) across four seasons within the Martian year. For each month, the time average is taken of all
sols within that month. The RA dust scenario is shown on the left, the RI dust scenario in the centre and the differences between scenarios
on the right. Colour scales in the left-hand and right-hand plots are matched across all months and between scenarios, with contour intervals
of 10 K. The contours in the difference plots are not matched due to the varying intensity of the difference between months.
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 645
Figure A4. Zonal mean dust mass mixing ratio (kg kg−1) across four seasons within the Martian year; each month is the average taken of all
sols within that month (months according to Table A1). The RA dust scenario is shown on the left, the RI dust scenario in the centre and the
differences between scenarios on the right. Contour lines denote the mass mixing ratio and units are in kg kg−1. Note that, due to the wide
range of values present between months, the colour-scale ranges differ between months and scenarios for this figure.
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
646 D. McCulloch et al.: Modern Mars in the Unified Model
Figure A5. Dust mass mixing ratio (kg kg−1) and horizontal wind speed (m s−1) at σ=0.99 across four seasons within the Martian year;
each month is the average taken of all sols within that month (months according to Table A1). The RA dust scenario is shown on the left, the
RI dust scenario in the centre and the differences between scenarios on the right. Colour scales in the left-hand and centre plots are matched
across all months and between scenarios. The contours in the difference plots are matched across months.
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 647
Figure A6. Zonal mean zonal winds (m s−1) across four seasons within the Martian year. For each month, the time average is taken of all
sols within that month. The RA dust scenario is shown on the left, the PCM output in the centre and the differences between models on
the right. Colour scales in the left-hand and right-hand plots are matched across all months and between models, with contour intervals of
20 m s−1. The contours in the difference plots are not matched due to the varying intensity of the difference between months. Positive values
indicate a northward wind and negative values a southward wind.
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
648 D. McCulloch et al.: Modern Mars in the Unified Model
Figure A7. Zonal mean meridional winds (m s−1) across four seasons within the Martian year. For each month, the time average is taken of
all sols within that month. The RA dust scenario is shown on the left, the PCM output in the centre and the differences between models on
the right. Colour scales in the left-hand and right-hand plots are matched across all months and between models, with contour intervals of
2 m s−1. The contours in the difference plots are not matched due to the varying intensity of the difference between months. Positive values
indicate an eastward wind and negative values a westward wind.
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 649
Figure A8. Zonal mean air temperature (K) across four seasons within the Martian year. For each month, the time average is taken of all sols
within that month. The RA dust scenario is shown on the left, the PCM output in the centre and the differences between models on the right.
Colour scales in the left-hand and right-hand plots are matched across all months and between models, with contour intervals of 10 K. The
contours in the difference plots are not matched due to the varying intensity of the difference between months.
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
650 D. McCulloch et al.: Modern Mars in the Unified Model
Figure A9. Zonal mean dust mass mixing ratio (kg kg−1) across four seasons within the Martian year; each month is the average taken of
all sols within that month (months according to Table A1). The RA dust scenario is shown on the left, the PCM output in the centre and the
differences between models on the right. Contour lines denote the mass mixing ratio and units are in kg kg−1. Note that, due to the wide
range of values present between months, the colour scale ranges differ between months and scenarios for this figure.
Geosci. Model Dev., 16, 621–657, 2023 https://doi.org/10.5194/gmd-16-621-2023
D. McCulloch et al.: Modern Mars in the Unified Model 651
Figure A10. Dust mass mixing ratio (kg kg−1) and horizontal wind speed (m s−1) at σ=0.99 across four seasons within the Martian year;
each month is the average taken of all sols within that month (months according to Table A1). The RA dust scenario is shown on the left, the
PCM output in the centre and the differences between models on the right. Colour scales in the left-hand and centre plots are matched across
all months and between models. The contours in the difference plots are matched across months.
Code availability. Scripts to process and visualise the post-
processed UM data used in this study, alongside package require-
ments and tutorials, are available as a Zenodo dataset: https://doi.
org/10.5281/zenodo.6974260 (McCulloch et al., 2022). If you do
use those data, then please cite this paper and add the following
statement: “UM data have been obtained from https://doi.org/10.
5281/zenodo.6974260”.
Due to intellectual property right restrictions, we cannot pro-
vide either the source code or documentation papers for the UM
or JULES. The Met Office Unified Model is available for use
under licence. A number of research organisations and national
meteorological services use the UM in collaboration with the
Met Office to undertake basic atmospheric process research, pro-
duce forecasts, develop the UM code, and build and evaluate
Earth system models. For further information on how to apply
for a licence, see https://www.metoffice.gov.uk/research/approach/
modelling-systems/unified-model (last access: 18 April 2022). Ob-
taining JULES: JULES is available under licence free of charge. For
further information on how to gain permission to use JULES for re-
search purposes, see http://jules-lsm.github.io/access_req/JULES_
access.html (last access: 3 April 2022). UM–JULES simulations
https://doi.org/10.5194/gmd-16-621-2023 Geosci. Model Dev., 16, 621–657, 2023
652 D. McCulloch et al.: Modern Mars in the Unified Model
are compiled and run in suites developed using the Rose suite en-
gine (http://metomi.github.io/rose/doc/html/index.html, last access:
16 January 2023) and scheduled using the cylc workflow engine
(https://cylc.github.io/, Oliver et al., 2019). Both Rose and cylc are
available under v3 of the GNU General Public License (GPL). In
this framework, the suite contains the information required to ex-
tract and build the code as well as configure and run the simulations.
Each suite is labelled with a unique identifier and is held in the same
revision-controlled repository service in which we hold and develop
the model code. This means that these suites are available to any li-
censed user of both the UM and JULES.
Data availability. A post-processed dataset is provided in McCul-
loch et al. (2022) (https://doi.org/10.5281/zenodo.6974260). This
dataset contains the zonally meaned outputs from the UM RA and
RI scenarios. For PCM data, please contact the MCD team (http:
//www-mars.lmd.jussieu.fr/mars/info_web/index.html, last access:
16 January 23).
Author contributions. DM led the writing and suite development
with supervision from DES, NM, and MB. JM, BD and IB provided
assistance in tuning the model and provided thorough descriptions
on how they work. KK provided technical support in IT infrastruc-
ture to access the model code and Monsoon2 system. The paper was
reviewed and contributed to by all the co-authors.
Competing interests. The contact author has declared that none of
the authors has any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. We thank two anonymous reviewers for their
comments that helped improve this paper. We would like to thank
the wider Exeter Exoplanet Theory Group for their feedback and
support in UM development, as well as Patrick McGuire from
the University of Reading for acting as a springboard for ideas.
The authors also acknowledge the MCD data team: François For-
get, Aymeric Spiga, Ehouarn Millour, for providing freely accessi-
ble Mars climate data at http://www-mars.lmd.jussieu.fr/mars/info_
web/index.html (last access: 6 March 2023). Material produced us-
ing Met Office Software. We acknowledge use of the Monsoon2
system, a collaborative facility supplied under the Joint Weather and
Climate Research Programme, a strategic partnership between the
Met Office and the Natural Environment Research Council. This
work was partly supported by a Science and Technology Facilities
Council Consolidated Grant (ST/R000395/1), a Leverhulme Trust
research project grant (RPG-2020-82) and a UKRI Future Leaders
Fellowship (grant no. MR/T040866/1). For the purpose of open ac-
cess, the authors have applied a Creative Commons Attribution (CC
BY) licence to any Author Accepted Manuscript version arising.
Financial support. This work was partly supported by a Science
and Technology Facilities Council Consolidated Grant (grant no.
ST/R000395/1), a Leverhulme Trust research project grant (grant
no. RPG-2020-82) and a UKRI Future Leaders Fellowship (grant
no. MR/T040866/1).
Review statement. This paper was edited by Jinkyu Hong and re-
viewed by two anonymous referees.
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