# The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars

M. A. J. Brown, M. R. Patel, S. R. Lewis, J. A. Holmes, Franck Lefèvre, J. P.

Mason, M. Crismani

► To cite this version:

M. A. J. Brown, M. R. Patel, S. R. Lewis, J. A. Holmes, Franck Lefèvre, et al. The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars. Journal of Geophysical Research. Planets, 2024, 129 (7), pp.e2024JE008302. 10.1029/2024JE008302. insu-04726438

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# JGR Planets

## RESEARCH ARTICLE

10.1029/2024JE008302

### Key Points:

* Heterogeneous reactions can have a major impact on martian ozone abundance at locations where water ice clouds persist for several sols

* Lifetime of adsorbed hydroxyl radicals depends on water ice sublimation and longer lifetimes result in a greater abundance of ozone

* A combination of longer lifetimes and dissociation of adsorbed HOₓ suppresses gaseous HOₓ and improves simulated ozone total column abundance

### Supporting Information:

Supporting Information may be found in the online version of this article.

### Correspondence to:

M. A. J. Brown,
majb4@cam.ac.uk

### Citation:

Brown, M. A. J., Patel, M. R., Lewis, S. R., Holmes, J. A., Lefèvre, F., Mason, J. P., & Crismani, M. (2024). The role and lifetime of dissociative heterogeneous processes in improving simulated ozone on Mars.

Journal of Geophysical Research: Planets, 129, e2024JE008302. https://doi.org/10.1029/2024JE008302

Received 17 JAN 2024

Accepted 15 JUN 2024

### Author Contributions:

**Conceptualization:** M. A. J. Brown

**Data curation:** J. P. Mason, M. Crismani

**Formal analysis:** M. A. J. Brown

**Investigation:** F. Lefèvre

**Methodology:** M. A. J. Brown

**Resources:** J. P. Mason, M. Crismani

**Supervision:** M. R. Patel, S. R. Lewis, J. A. Holmes

**Validation:** M. A. J. Brown

**Visualization:** M. A. J. Brown

**Writing – original draft:** M. A. J. Brown

**Writing – review & editing:**

M. A. J. Brown, M. R. Patel, S. R. Lewis, J. A. Holmes, F. Lefèvre, J. P. Mason, M. Crismani

© 2024. The Author(s).

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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# The Role and Lifetime of Dissociative Heterogeneous Processes in Improving Simulated Ozone on Mars

M. A. J. Brown<fnref n="1" />,² ID, M. R. Patel²,<fnref n="3" /> ID, S. R. Lewis² ID, J. A. Holmes² ID, F. Lefèvre⁴ ID, J. P. Mason² ID, and M. Crismani⁵ ID

## Abstract
Ozone simulated in Mars Global Climate Models (MGCMs) is used to assess the underlying chemistry occurring in the atmosphere. Currently, ozone total column abundance (TCA) is under-predicted in MGCMs by up to 120%, implying missing or inaccurate chemistry in models. Heterogeneous reactions of hydroxyl radicals (HOₓ) have been offered as an explanation for some of this bias, because they cause ozone to increase at locations where it's currently under-predicted. We use four simulations to compare modeled ozone TCA with observations from the UVIS spectrometer aboard the ExoMars Trace Gas Orbiter to improve the representation of heterogeneous processes and their impact on ozone. We use a gas-phase only run, a dissociative scheme, an adsorbed HOₓ retention scheme, and a hybrid scheme that combines the dissociative mechanism with the retention of HOₓ on water ice. We find retention of HOₓ is dependent on water ice sublimation, and ozone abundance increases when water ice persists for longer periods (1–20 sols). Over time, the loss of HOₓ causes a depletion in H₂O₂ concentration (HOₓ reservoir), and thus allows ozone concentration to increase. When adsorbed HOₓ are desorbed and dissociate into other by-products, HOₓ are not immediately available to destroy ozone. This results in larger ozone concentrations than if desorbed HOₓ are released directly back into their gaseous states. When using the hybrid scheme, ozone TCA is increased up to 50% where the ozone deficit is greatest, demonstrating the best agreement with observations, and implying that HOₓ radicals are both retained when adsorbed and dissociate.

## Plain Language Summary
Ozone is a trace gas in the martian atmosphere, sensitive to chemical and light-induced reactions. This makes it ideal for assessing the underlying reactions for chemical species called hydroxyl radicals, which destroy ozone and would otherwise be too reactive and shortlived to be measured directly. There has been an under-prediction of ozone in global climate models which implies missing or inaccurate reactions. Heterogeneous reactions (a reaction which includes two or more phases) have been suggested to fill the deficit in modeled ozone. This involves hydroxyl radicals adsorbing onto water ice, which causes ozone to increase. We look at different simulations to understand hydroxyl radical chemistry on the surface of water ice by comparing to observations from the ExoMars Trace Gas Orbiter. We run four climate model simulations to test different theories as to what is occurring at the surface. There are two theories (a) reactions happen at the surface which alter hydroxyl radicals into other chemicals, and (b) hydroxyl radicals stay adsorbed onto water ice for as long as the ice persists. When we combine these theories, we have a more accurate prediction of ozone, which we can then use to infer what happens at the surface.

## 1. Introduction

Ozone is a trace gas in the martian atmosphere, which is sensitive to chemical and photochemical reactions and can be measured by using the absorption wavelength band in ultraviolet region (known as the Hartley Band) (Hartley, 1881). This makes it an ideal candidate to assess the underlying chemistry in the atmosphere, which is too rapid and difficult to measure otherwise. A discrepancy between ozone observations and ozone modeled through global climate models (GCMs) implies missing or inaccurate chemistry in our knowledge of the martian atmosphere for example, Lefèvre et al. (2004); Clancy and Nair (1996).

The composition of Mars' atmosphere is governed by the chemical and photochemical processes occurring for example, Clancy and Nair (1996); Nair et al. (1994); Krasnopolsky (1993); Moreau et al. (1991); McElroy and

<footnote n="1">¹Centre for Atmospheric Science, University of Cambridge, Cambridge, UK, ²The Open University, Milton Keynes, UK,</footnote>

<footnote n="3">³Space Science and Technology Department, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Oxfordshire, UK, ⁴LATMOS, Sorbonne Université, UVSQ Université Paris-Saclay, CNRS, Paris, France, ⁵Department of Physics and Astronomy, California State University, San Bernardino, San Bernardino, CA, USA</footnote>
Donahue (1972). 95% of the atmosphere is carbon dioxide ($CO_2$), which photolyzes and breaks down into carbon monoxide (CO) and atomic oxygen (O):

$$CO_2 + h\nu \xrightarrow{\lambda < 200\text{nm}} CO + O \quad (1)$$

CO and O can recombine to produce $CO_2$;

$$CO + O \rightarrow CO_2 \quad (2)$$

However, this recombination is slow in comparison to the photolysis of $CO_2$ (McElroy & Donahue, 1972), and this reaction alone does not account for the large atmospheric composition of $CO_2$. Hydroxyl radicals (OH and $HO_2$, collectively known as $HO_X$) catalyze the recombination, and are responsible for maintaining the stability of $CO_2$ (Clancy & Nair, 1996; McElroy & Donahue, 1972). However, $HO_X$ are short-lived and highly reactive (lifetime $\approx$ second), making them difficult to track and measure.

In order to infer changes in these short-lived species and understand the underlying chemical processes in the atmosphere, ozone is used as a proxy due to its sensitivity to variations in $HO_X$. $HO_X$ are formed by the photolysis of water vapor in the atmosphere. An abundance of OH or $HO_2$ several magnitudes smaller than ozone can cause a series of chain reactions which continue to destroy ozone, while recycling $HO_X$ into the atmosphere (Clancy & Nair, 1996; Lefèvre et al., 2004). This leads to an established anti-correlation between ozone and water vapor for example, (Clancy & Nair, 1996; Lefèvre et al., 2021). $H_2O_2$ is a more stable species and, while not directly impacting ozone, acts as a reservoir for $HO_X$. By reacting with OH and O, it can replenish the $HO_X$ supply in the atmosphere (Lefèvre et al., 2004);

$$OH + H_2O_2 \rightarrow H_2O + HO_2 \quad (3)$$

and

$$O + H_2O_2 \rightarrow OH + HO_2, \quad (4)$$

as well as through photolysis;

$$H_2O_2 + h\nu \rightarrow OH + OH. \quad (5)$$

Ozone concentration (vertical profiles) and total column abundance (TCA) are currently under-predicted at high latitudes where water ice clouds form (Brown et al., 2022; Clancy et al., 2016; Khayat et al., 2021; Lefèvre et al., 2021; Määttänen et al., 2022; Patel et al., 2021). Even when temperature, dust, and water vapor have been assimilated (the latter of which is a key driver of ozone abundance), this discrepancy is still seen between observations and simulations. This implies that chemistry may have an important role, and atmospheric dynamics may not be wholly responsible for the ozone deficit. One proposed explanation for this ozone deficit is the heterogeneous uptake of $HO_X$ on water ice particles (Atreya & Blamont, 1990; Brown et al., 2022; Krasnopolsky, 1993; Lefèvre et al., 2008, 2021). Heterogeneous reactions adsorb $HO_X$ onto the surface of water ice, lowering the $HO_X$ abundance and, consequently, causing ozone abundances to increase (Krasnopolsky, 2006; Lefèvre et al., 2008);

$$OH + \text{ice} \rightarrow \text{products} \quad (6)$$

and

$$HO_2 + \text{ice} \rightarrow \text{products}. \quad (7)$$

where “products” in models are either a combination of $O_2$ and $H_2O$ (Lefèvre et al., 2021), or adsorbed $HO_X$ attached to the water ice surface, $ice_{HO_X}$ (Brown et al., 2022). The heterogeneous uptake coefficients used are from Cooper and Abbatt (1996), and are given as $\gamma_{OH} = 0.03 \pm 0.02$ and $\gamma_{HO_2} = 0.025 \pm 0.005$ for OH and $HO_2$.
respectively. Cooper and Abbatt (1996) note that the OH uptake could be significantly higher in the presence of nitrogen and chlorine species. In the model both reactions are treated as one way reactions. However, the nature of desorbed HOₓ from heterogeneous reactions is ambiguous and difficult to measure from laboratory experiments for example, (Cooper & Abbatt, 1996; Liu et al., 2009; Smith, 1943). Thus, the release or dissociation of adsorbed HOₓ is unknown.

Daerden et al. (2023) discounted the reaction rate for HO₂ on water ice, due to limitations with the experimental rate produced in Cooper and Abbatt (1996). The reaction rate for HO₂ on water ice was given no recommendation from the IUPAC (International Union of Pure and Applied Chemistry Crowley et al., 2010), due to only a single datapoint being available for the calculation of the reaction rate. In light of this, and as the concentration of HO₂ in the experiment far exceeded the concentration of HO₂ found in the martian atmosphere, Daerden et al. (2023) concluded that the reaction rate for HO₂ could be too high, and thus should be used with caution in a martian atmospheric chemical model. While they found that the inclusion of alternative heterogeneous reactions on dust caused ozone to increase in the polar regions, it caused the ozone abundance in the equatorial region to be over-predicted. Daerden et al. (2023) acknowledged that due to the unknown mineral type and terrestrial tropospheric conditions the reactions were conducted in, there was a high uncertainty on the reaction rates for uptake on martian dust.

While the HO₂ reaction rate from Cooper and Abbatt (1996) is limited, it is the only experiment to suggest a reaction rate for this species and was conducted under conditions suitable for Mars' atmosphere. Furthermore, given the uncertainty for dust uptake coefficients, it is difficult to quantify the errors introduced from adding in these reactions, compared to the single reaction rate of HO₂ onto water ice.

This work models different desorption mechanisms of heterogeneous reactions on water ice to understand the variation and distribution of HOₓ, and how the increase in ozone TCA varies when adsorbed HOₓ are released in different circumstances. Four simulations (one control, three with different heterogeneous schemes) are used to simulate ozone and study its sensitivity to the change in HOₓ. The impact of these different schemes on ozone abundance is then analyzed to see whether it reduces the discrepancy between modeled and observed ozone. One scheme releases gaseous HOₓ when water ice sublimates (based on Brown et al. (2022)), while another assumes all adsorbed HOₓ are immediately irreversibly lost (based on Lefèvre et al. (2021)). The third combines traits from both of the previous schemes. Section 2 introduces the setup of the GCM used, the different heterogeneous chemistry schemes, and the water vapor climatology. Section 3 shows the changes in water ice between heterogeneous simulations, as well as the comparison of simulated ozone values to observe. In Section 4, the different underlying chemistry between simulations is analyzed to explain how this impacts ozone concentration, while Section 5 summarizes and concludes these findings.

## 2. Methodology

The Mars Planetary Climate model (UK spectral version) (Forget et al., 1999; Holmes et al., 2020), henceforth called the Mars Global Climate Model (MGCM), is used to conduct simulations of the atmosphere used in this work. The MGCM was compiled with a T-31 resolution, with the chemistry and products evaluated and stored on a 5° × 5° latitude-longitude grid. It uses the updated chemical scheme from Cariolle et al. (2017) as described in Lefèvre et al. (2021). The model was compiled with 70 vertical layers and run with 1,440 dynamical timesteps per sol, or 60 timesteps per martian hour. Physical processes are evaluated 144 timesteps per sol, while chemical timesteps are based on the rate of change of chemical species. The gas-phase chemical reactions use the adaptive semi-implicit scheme from Cariolle et al. (2017), and the timestep ranges between once per 10 s, up to the standard physics timestep (one timestep per 615 s). In order to allow for the heterogeneous chemistry to equilibrate, simulations were started at Ls = 325°, MY 34, and run for 70 sols before data were analyzed from Ls = 0°, MY 35. The length of the spin up is only needed for the water vapor to settle, as other chemical species have a much shorter lifetime. The duration of this spin-up for water vapor (and water ice) is approximately 30 sols from an assimilated restart file used in model runs. For consistency, the control (gas-phase only) simulation was also run for the same length of time, with the same starting conditions.

A total of four simulations were run: three using heterogeneous schemes, and one with gas-phase chemistry only (control scheme). Data considered are between Ls = 0°–180° MY 35 as this is a chemically active season when both water ice and ozone are present in significant quantities. The model does not include the heterogeneous reactions of chemical species onto martian dust, and instead analyses the desorption of HOₓ on water ice. It uses
<table><caption>**Table 1**
Name and Description of the Four Simulations Used in This Analysis</caption><thead><tr><th>Name of scheme</th><th>Release of HO<sub>X</sub>/%</th><th>Irreversible loss of HO<sub>X</sub>/%</th></tr></thead><tbody><tr><td>Control (gas-phase only)</td><td>0</td><td>0</td></tr><tr><td>100% released (Brown et al., 2022)</td><td>100</td><td>0</td></tr><tr><td>50% released - Hybrid</td><td>50</td><td>50</td></tr><tr><td>0% released (Lefèvre et al., 2021)</td><td>0</td><td>100</td></tr></tbody></table>

*Note.* “Release of HO<sub>X</sub>” describes the proportion released as gaseous HO<sub>X</sub> when ice<sub>HO<sub>X</sub></sub> are desorbed. “Irreversible loss of HO<sub>X</sub>” is the proportion of ice<sub>HO<sub>X</sub></sub> converted to H<sub>2</sub>O and O<sub>2</sub> when HO<sub>X</sub> are desorbed. The 0% release scheme has no intermediate state and adsorbed HO<sub>X</sub> are immediately converted into the long-lived species H<sub>2</sub>O and O<sub>2</sub>.

three heterogeneous reactions on the surface of water ice; OH, HO<sub>2</sub>, and H<sub>2</sub>O<sub>2</sub>, as given in Lefèvre et al. (2021). The former two of which are irreversible while the latter is in equilibrium.

The three heterogeneous schemes have different release mechanisms and products for adsorbed HO<sub>X</sub>. The first uses the method described in Brown et al. (2022), and is henceforth referred to as the “100% release” scheme. All adsorbed OH and HO<sub>2</sub> are stored as separate tracers before being released back into their gaseous form when water ice sublimates;

$$OH + ice \rightarrow ice_{OH} \qquad (8)$$

and

$$HO_2 + ice \rightarrow ice_{HO_2}, \qquad (9)$$

where ice<sub>HO<sub>X</sub></sub> (ice<sub>HO<sub>X</sub></sub> = ice<sub>OH</sub> + ice<sub>HO<sub>2</sub></sub>) represents the “sink” for the OH or HO<sub>2</sub> which is adsorbed onto the water ice particle.

The second uses the scheme from Lefèvre et al. (2021), referred to as the “0% release” scheme, in which all adsorbed HO<sub>X</sub> are immediately converted to water vapor and oxygen (Equations 10 and 11 for HO<sub>2</sub>) (Lefèvre et al., 2021). There is no adsorbed HO<sub>X</sub> intermediate state in this scheme and OH and HO<sub>2</sub> are immediately converted into the long-lived species H<sub>2</sub>O and O<sub>2</sub>;

$$OH \rightarrow \frac{1}{2}H_2O + \frac{1}{4}O_2 \qquad (10)$$

and

$$HO_2 \rightarrow \frac{1}{2}H_2O + \frac{3}{4}O_2. \qquad (11)$$

The third, hybrid scheme combines the previous two, and is referred to as the “50% release” scheme (more information is given in Section 2.1). These three schemes and the control are summarized in Table 1.

## 2.1. Hybrid Heterogeneous Setup

A simple combination of these two approaches from Brown et al. (2022) and Lefèvre et al. (2021) is for a proportion of desorbed iceHO<sub>X</sub> to be released back into gaseous HO<sub>X</sub>, while the remaining fraction is converted to water vapor and oxygen.

The conversion of iceHO<sub>X</sub> to water vapor and oxygen is a simplified representation of HO<sub>X</sub> dissociation at the water ice surface. The dissociation of HO<sub>X</sub> from water ice is ambiguous; laboratory experiments are able to detect and measure the uptake of HO<sub>X</sub>, but are unable to measure the release of HO<sub>X</sub> or products formed from chemical reactions at the surface (Cooper & Abbatt, 1996; Smith, 1943). As this is not quantified, the 50% release scheme
therefore combines the 0% release and 100% release approach. It uses an equal split of the desorption of iceHOx between releasing gaseous HOx and converting to water vapor and oxygen when water ice sublimates;

$$ \begin{array}{l} \text{ice}_{\text{OH}} \xrightarrow{50\%} \text{OH} \\ \xrightarrow{50\%} \frac{1}{2}\text{H}_2\text{O} + \frac{1}{4}\text{O}_2 \end{array} \qquad (12) $$

$$ \begin{array}{l} \text{ice}_{\text{HO}_2} \xrightarrow{50\%} \text{HO}_2 \\ \xrightarrow{50\%} \frac{1}{2}\text{H}_2\text{O} + \frac{3}{4}\text{O}_2. \end{array} \qquad (13) $$

For this study, a 50%-50% release proportion is considered as a first approximation to determine the impact on ozone abundance, as there is no theoretical or empirical justification for other values.

## 2.2. H₂O₂ Chemistry

H₂O₂ also undergoes a heterogeneous reaction with water ice under Mars temperatures and pressure, which was first included in Lefèvre et al. (2021). Unlike OH and HO₂, the reaction with water ice is in equilibrium;

$$ H_2O_2 + \text{ice} \rightleftharpoons \text{ice}_{H_2O_2}. \qquad (14) $$

All heterogeneous simulations in this work use the Langmuir isotherm method as described in Brown et al. (2022) from the experimental results produced in Pouvesle et al. (2010). Daerden et al. (2023) later developed this reaction and used a BET (Brunauer-Emmett-Teller) isotherm model, allowing a multilayer adsorption of H₂O₂ onto water ice. However, as the H₂O₂ heterogeneous reaction has little impact on ozone (Brown et al., 2022; Daerden et al., 2023), the multi-surface version of this reaction was not included in this work.

## 2.3. Adaptations to Heterogeneous Chemical Scheme

The MGCM was run with the microphysics cloud scheme (MPCS) from Navarro et al. (2014). The MPCS uses dust particles as cloud condensation nuclei, onto which water ice can condense. The inclusion of the MPCS in the MGCM means the dust and water ice particle mass must be accounted for. In order to track the movement of these particles, additional tracers of mass and number are required. Thus, water ice mass in the MPCS is subject to sedimentation within the model. Adsorbed species from the heterogeneous reactions described in Brown et al. (2022) represent a form of water ice, and, therefore, must also mimic the same fall velocity to ensure they are transported similarly in the model. Thus, to adapt the heterogeneous scheme from Brown et al. (2022) to be suitable for the MGCM, the sedimentation scheme for this work has been modified to allow for adsorbed species to undergo the same sedimentation fall velocity as water ice. This adaptation was based on the sedimentation scheme described in Risi et al. (2010). The proportion of water ice sedimentation is calculated and applied to the adsorbed species, allowing the iceHOx species to follow water ice sedimentation.

## 2.4. Water Vapor Climatology

Observations of ozone and water vapor TCAs are filtered to remove data with large errors. Ozone data are taken from J. P. Mason et al. (2024), while water vapor data are taken from Crismani et al. (2021), which use data from the UVIS and Limb Nadir Occultation respectively, both aboard the ExoMars Trace Gas Orbiter. The details of this filtering both for ozone and water vapor observations are given Text S1 in Supporting Information S1.

Before analyzing the ozone observations and simulations, it is important to evaluate the water vapor climatology for the period of study, as water vapor is the primary source of HOx. As HOx are one of the main chemical sinks for ozone, ozone TCA is intimately coupled to water vapor TCA for example, Clancy and Nair (1996); Patel et al. (2021); Khayat et al. (2021). To ensure these differences in ozone TCA are not due to the under or over-prediction of water vapor TCA in the MGCM, simulated water vapor was compared to observed water vapor from Crismani et al. (2021), Limb Nadir Occultation/NOMAD. The MGCM simulation has starting conditions taken from the OpenMARS reanalysis data set from Holmes et al. (2022), of which water vapor TCA from
<figure><img src="image_3.png" /><figcaption>**Figure 1.** (a) Zonally averaged difference in water vapor TCA (observations-control) for $L_s = 0°-60°$, MY 35. (b) (points) Water vapor TCA observations showing the median of grouped 0.5° latitudes. Color indicates median $L_s$ for each grouped latitude shows whether the datapoint is representative of the full time period shown. (lines) Zonal median ozone TCA for (orange) control, (purple) 100% release, (blue) 0% release, (green) 50% release simulations averaged between $L_s = 0°-60°$.</figcaption></figure>

Crismani et al. (2021) was assimilated. Therefore, water vapor TCAs, by default, are a similar magnitude to the observations.

In general, simulated water vapor TCA from the control is in good agreement with observations from Crismani et al. (2021) (shown in Figure S3 in Supporting Information S1). The exception to this is at the beginning of the year, between $L_s = 0°-60°$, shown in Figure 1. In Figure 1b, the zonally and temporally averaged water vapor abundance is shown against latitude, with the $L_s$ colorbar showing the spread of the averaged data across the period $L_s = 0°-60°$. Most data points have a mean $L_s$ of 30°, indicating that in conjunction with Figure 1a, they are representative of the period of interest. Water vapor is slightly over-predicted by 2–3pr-µm (50% more) at low latitudes (30°S–30°N) and at high northern latitudes (>60°N) in the MGCM as seen in Figure 1b. Due to both the model and observations being averaged temporally and meridionally, the discrepancy is not as large that shown in
Figure 1a. Between 40° and 60°N, water vapor is under-predicted by up to 3pr-µm (Figure 1a), which implies that too much water is being transported from 40°N to 60°N. This disagreement is highlighted in Steele et al. (2014) who found that when running the model without assimilation, a surplus of water vapor was transported to high northern latitudes during this time of year. The latitudinal mismatch in water vapor would potentially result in a peak ozone abundance occurring at different latitudes between observed and simulated ozone. For example, simulated ozone abundances between 40° and 60°N would be lower than observed ozone abundances due to a greater amount water vapor in the model, and thus more HOₓ at these latitudes. The averaged overall abundance of water vapor is, however, similar between both the observations and simulations. Thus, while there may be a latitudinal difference in ozone through the differences in water vapor, there should not be a magnitudinal discrepancy, unless there are other underlying chemical mechanisms for which have not been accounted.

## 3. Results

### 3.1. Water Ice Perturbations

The inclusion of any heterogeneous chemistry scheme in the MGCM causes small, spatial fluctuations to occur in water ice optical depth. Products from heterogeneous reactions (either the release of HOₓ or production of water vapor and oxygen) alter the concentration and distribution of HOₓ. As HOₓ are highly reactive and often propagate through chain reactions, a change in HOₓ abundance impacts other species that are several orders of magnitudes greater, such as water vapor. Small alterations in water vapor then impact water ice. As the MGCM uses radiatively active water ice, small perturbations in the water ice profile, therefore, affect the incoming solar radiation and near surface temperature. Changes in the temperature profile (not shown) cause water ice clouds to form at different longitudes. This effect is most pronounced at the edge of the polar regions, where the water ice cloud wave structure is altered and water ice clouds are present at different local times. After several sols, the water ice distribution differs between simulations, as shown between the 100% release and control simulations in Figure 2.

After 10 and 20 sols, the water ice structure at northern latitudes (>45°N) differs by up to 0.3 optical depth, and the water ice wave structures are not synchronised between simulations. However, if water ice or any of the other varying fields (water vapor, temperature) are averaged across a larger time scale (e.g., 10–30 sols), there is little net difference in water vapor abundance, water ice abundance, and temperature between the heterogeneous simulations (0% release, 50% release, and 100% release schemes) and the control simulation. These short-term variations significantly affect the spatial distribution of species, and introduce the problem of direct comparison between simulations. In order to account for this, heterogeneous simulations are compared to the control simulation, and the effects can be compared to observations by temporal and spatial averaging.

Table 2 shows the global water ice mass and the percentage change between different simulations and the control simulation for $L_S = 0°–180°$. Higher latitudes tend to have a lower mass as the grid points cover smaller areas, and thus have a lower weighting to the overall global mass. The 100% release and 50% release simulations have a small, positive increase in water ice mass, less than 3% of the control. The distribution of water ice in the 50% scheme favors the northern hemisphere, with an increase in water ice mass between 30° and 90°N and a decrease below 30°N. In contrast, the 0% release simulation has a greater water ice mass at southern latitudes (30°–90°S), and a decrease at latitudes above 30°S. The global water ice mass is slightly lower with a total mass decrease of –6.21% from the control.

These differences in water ice are due to radiative feedback of water ice clouds. A higher water ice optical depth results in more solar radiation being reflected back into space. This, in turn, leads to a reduction in near surface temperature, which causes more water vapor to be condensed to water ice and creates a feedback loop. This could be further investigated by using an isolated system to test the response of the radiation feedback of water ice, and how this changes when the HOₓ distribution also changes through heterogeneous reactions. The effect of different heterogeneous schemes on HOₓ and how this propagates to impact water ice could be investigated, although this is beyond the scope of this work.

In areas of low water vapor abundance (<5pr-µm), the concentration of HOₓ is very low (e.g., < 10 µg m⁻² at high northern latitudes), $L_S = 0°–60°$ (not shown), compared to the equatorial region (>60 µg m⁻²). When there is a low HOₓ concentration, the rate of heterogeneous reactions is less significant, as HOₓ must first be present for the reaction to occur. The limiting factor of the reaction is the HOₓ concentration, rather than the water ice surface
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Diurnally averaged water ice optical depth at sol 0, 5, 10, and 20, starting at $L_s = 0°$ for the (left) 100% release and (middle) control simulation. (right) Water ice optical depth difference (100% release-control).</figcaption></figure>

area (Lefèvre et al., 2021). The largest differences in water ice occur at high latitudes at times when there is a low $HO_x$ concentration. Therefore, simulations can still be compared against each other despite these differences in water ice abundance.

## 3.2. Ozone Control Comparison to Observations

Similarly to other GCMs (e.g., Patel et al. (2021); Daerden et al. (2019)), the control (gas-phase chemistry only) MGCM simulation in this work also under-predicts ozone TCA when compared to observations from UVIS/TGO

<caption>**Table 2**</caption>

Global Water Ice Mass (Tg) for $L_s = 0°–180°$ From Different Chemical Schemes and the Percentage Increase From the Control Simulation

<table><thead><tr><th>Scheme</th><th>Control</th><th>0% release</th><th>50% release</th><th>100% release</th></tr></thead><tbody><tr><td>Global water ice mass/Tg</td><td>4.47 × 10<sup>5</sup></td><td>4.20 × 10<sup>5</sup></td><td>4.58 × 10<sup>5</sup></td><td>4.52 × 10<sup>5</sup></td></tr><tr><td>Global % increase from Control</td><td>-</td><td>-6.21</td><td>2.42</td><td>0.94</td></tr><tr><td>% increase 90°–60°N</td><td>-</td><td>-9.05</td><td>16.87</td><td>-4.50</td></tr><tr><td>% increase 60°–30°N</td><td>-</td><td>-0.40</td><td>17.86</td><td>1.87</td></tr><tr><td>% increase 30°N–0°</td><td>-</td><td>-9.56</td><td>-1.68</td><td>-0.03</td></tr><tr><td>% increase 0°–30°S</td><td>-</td><td>-8.22</td><td>-2.14</td><td>-0.97</td></tr><tr><td>% increase 30°–60°S</td><td>-</td><td>3.92</td><td>-2.88</td><td>9.37</td></tr><tr><td>% increase 60°–90°S</td><td>-</td><td>11.04</td><td>-3.36</td><td>17.17</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Zonally averaged difference in ozone TCA (observations-control) for $L_s = 0°–180°$, MY 35 from the MGCM simulation and UVIS/TGO observations. The gray area is where no retrievals are available.</figcaption></figure>

J. P. Mason et al. (2024). Figure S5 in Supporting Information S1 shows the ozone observations from UVIS/TGO between $L_s = 0°–180°$, with the greatest ozone abundance at 20 µm-atm occurring poleward of 60°N between $L_s = 0°–20°$, and at 20 µm-atm poleward of 40°S, between $L_s = 140°–160°$. Figure 3 shows the ozone TCA difference between observations and the control simulation (observations-control) for the same period of time. Simulated data were matched to observations by the closest latitude, longitude, $L_s$, and local time. The two largest under-predictions in the control simulation occur at northern latitudes >45°N, $L_s = 0°–60°$ and $L_s = 160°–180°$, and southern latitudes, >40°S, $L_s = 150°–180°$.

At latitudes poleward of 30° S between $L_s = 0°–180°$, ozone TCA is under-predicted by up to 15 µm-atm. The largest deficit occurs between $L_s = 130°–180°$, when observations are able to capture the edge of the South Polar Hood (SPH). Similarly, a slight deficit can be seen between $L_s = 30°–60°$ when ozone concentration is under-predicted by up to 6 µm-atm. Unlike the northern hemisphere, ozone observations have a larger variation, ranging from 0 µm-atm to more than 30 µm-atm between 30° and 60°S. This large spread of ozone TCA is due to the Hellas Basin, which is between latitudes 30°–50°S (Figure S5 in Supporting Information S1). Ozone abundance is greater in the Hellas Basin because of its depth –7 km below the MOLA reference areoid. The pressure is higher within the Hellas Basin (average 1,100 Pa; Forget et al. (1999); Millour et al. (2018)), which results in a higher molecular density and larger ozone TCA (Clancy et al., 2016). This further increases ozone TCA, and is the reason for this wide range in ozone observations.

While ozone TCA is under-predicted in the MGCM at latitudes poleward of 30°N/S, there are locations at certain times of the year when it is over-predicted. Between $L_s = 30°–180°$, simulated ozone TCA is over-predicted by up to 3 µm-atm between latitudes 30°N and 30°S. This is during the occurrence of the Aphelion Cloud Belt (ACB) (Giuranna et al., 2021; Mateshvili et al., 2007). When zonally averaged, observed ozone TCAs are typically 2–3 µm-atm at these latitudes, which is 25% lower than simulated ozone TCA. This over-prediction in the model is likely due to inaccurate atmospheric conditions such as vertical profile temperature, as when temperature profiles are assimilated, ozone is not over-predicted at these latitudes (Holmes et al., 2020).

### 3.3. Intercomparison of Heterogeneous Simulations

Overall, the inclusion of heterogeneous chemistry causes a global increase in ozone abundance. This is summarized in Table 3, which shows the total mass of ozone in the atmosphere from $L_s = 0°–180°$.

The 100% release simulation has the smallest increase in ozone total mass, producing an ozone burden of 6,860 Tg in the year, 100 Tg (1.42%) more than the control simulation. In the 100% release scheme, all adsorbed $HO_x$ are released as gaseous $HO_x$ when water ice sublimates. This results in an overall small net increase in ozone mass, as the ozone produced within water ice clouds is then destroyed by the released $HO_x$ after water ice sublimates. During the occurrence of the ACB, there is a decrease in ozone abundance in 100% release scheme. This is due to the diurnal variation of the ACB; the complete sublimation of water ice clouds in the morning (Lefèvre
<caption>**Table 3**</caption>

Global Ozone Mass (Tg) for $L_S$ = 0°–180° From Different Chemical Schemes and the Percentage Increase From the Control Simulation

<table><thead><tr><th>Scheme</th><th>Control</th><th>100% release</th><th>0% release</th><th>50% release</th></tr></thead><tbody><tr><td>Global ozone mass/Tg</td><td>6,760</td><td>6,860</td><td>7,370</td><td>7,240</td></tr><tr><td>Global % increase from Control</td><td>-</td><td>1.42</td><td><strong>8.93</strong></td><td>7.03</td></tr><tr><td>% increase 90°–60°N</td><td>-</td><td>10.10</td><td>18.82</td><td><strong>23.38</strong></td></tr><tr><td>% increase 60°–30°N</td><td>-</td><td>0.84</td><td><strong>7.97</strong></td><td>5.18</td></tr><tr><td>% increase 30° N–0°</td><td>-</td><td>-0.38</td><td><strong>5.15</strong></td><td>3.90</td></tr><tr><td>% increase 0°–30°S</td><td>-</td><td>-1.18</td><td><strong>7.13</strong></td><td>4.34</td></tr><tr><td>% increase 30°–60°S</td><td>-</td><td>0.84</td><td><strong>9.17</strong></td><td>6.84</td></tr><tr><td>% increase 60°–90°S</td><td>-</td><td>3.86</td><td><strong>10.66</strong></td><td>8.16</td></tr></tbody></table>

Note. Numbers in **bold** highlight the largest increase in ozone mass relative to the other schemes.

et al., 2004) causes all adsorbed HOₓ to be released and for ozone to be destroyed, resulting in an overall decrease in ozone mass.

The 0% release simulation produces the largest global ozone mass (8.93% more than the control), with the greatest increase in mass occurring at mid-to-high latitudes (>30°N/S). This is to be expected, as these are the locations which water ice clouds form and, thus, heterogeneous reactions are most affective at removing HOₓ and increasing ozone abundance. HOₓ which are adsorbed are immediately converted to other more stable species (water vapor and oxygen) and HOₓ must be reformed through photolysis before destroying ozone.

The 50% release scenario has a 7.03% increase of global ozone total mass, which is constrained to high latitudes (>60°N/S). In general, the 50% release scheme has a lower ozone total mass than the 0% release simulation. This is demonstrated at lower latitudes where there is a lower ozone total mass than the 0% release simulations (<5.2% increase between 60°N and 30°S). The exception to this is at high northern latitudes (>60°N), when ozone total mass is 23.38% greater than the control, compared to 0% release (18.82% increase from control). These differences from the 0% release are due to the length of time that water ice clouds exist, and how the delay in desorption results in a build up of iceHOₓ, as well as the split of desorbed iceHOₓ between gaseous HOₓ and dissociation to water vapor and oxygen.

Ozone TCA from the 50% release simulation would be expected to be within the range of the two extreme ends of the heterogeneous schemes (100% release and 0% release), as the by-products of desorbed iceHOₓ are an equal split between gaseous HOₓ, and water vapor and oxygen. However, the largest ozone TCA is produced by the 50% release simulation, with 14 µm-atm at 75°N. This is shown in Figure 4, which shows ozone observations from NOMAD/TGO (gray) with the ozone abundances from the four simulations matched by $L_S$, location, and local time for MY 35, $L_S$ = 0°–180°. Both the 0% release and 100% release simulations produce similar ozone abundances, with the maximum of 13 µm-atm, despite having different heterogeneous mechanisms. Given that the 50% release simulation is a combination of the previous two heterogeneous schemes, and produces a greater ozone TCA than either the 100% release or 0% release simulations, this implies that the underlying chemistry occurring between the 100% release and 0% release schemes differ. The inclusion of heterogeneous reactions in the MGCM improves the overall ozone TCA deficit.

Ozone TCA between all simulations (three heterogeneous chemistry and the control) are similar to each other when compared to observed ozone TCA, and many values of averaged ozone TCA overlap between simulations (Figure 4). However, there are variations between each of the schemes, which are difficult to distinguish when comparing ozone TCA directly against observations at specific locations.

In Figure 4a, the MGCM fails to produce ozone TCA as large as those observed. Furthermore, the model does not capture the observed steep temporal decrease in ozone abundance between $L_S$ = 10°–90°, and instead reaches a minimum at $L_S$ = 50° (Note that the logarithmic scale on the y-axis distorts the gradient). Similarly, between $L_S$ = 160°–180° at latitudes above 30°N, simulated ozone TCA does not increase at the same rate, and is under-predicted by up to 5 µm-atm. These time periods are during the breakdown and formation of the North Polar Hood
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Median of ozone TCA binned $L_s = 1°$ for (a) >45°N, (b) 10°N–10°S, and (c) >30°S for (gray) observed, (purple) 100% release simulated, (orange) control simulated, (blue) 0% release, and (green) 50% release simulated ozone TCA from MGCM, $L_s = 0°–180°$ for MY 35. Observed data are from UVIS/TGO. Note the logarithmic scale on y-axis and different y-axis for panel (b).</figcaption></figure>

(NPH), which lead to a decrease and increase of ozone TCA respectively (Clancy et al., 2016; Holmes et al., 2017; Lefèvre et al., 2021).

4. Vertical Distribution of Ozone and HOₓ

The short-term variations in water vapor and ice significantly affect the spatial distribution of ozone, and introduce the problem of direct comparison of observations and simulations and between simulations. The effects of heterogeneous chemistry are most prominent in the presence of water ice. Therefore, a major drawback of a direct comparison to observations is that in order to directly compare each value, water ice and water vapor are required to have similar abundances between not only the observations, but between the simulations themselves at the same local time. In order to address this, the three heterogeneous simulations are compared to the control simulation, and the effects of heterogeneous reactions on ozone can be compared to observations by temporal and spatial averaging.

In order to quantify the underlying chemistry, the vertical profiles of HOₓ must first be investigated. Altitudes up to 25 km consist of 93% of the total mass of the atmosphere (assuming an average surface pressure 610 Pa), and TCA measurements are not as sensitive to variations in ozone concentration at higher altitudes. The impact that heterogeneous reactions has on ozone TCA is not, therefore, always visible through TCA measurements. Vertical profiles of ozone and HOₓ concentration are investigated between $L_s = 5°–10°$ as this is the location where the ozone deficit is greatest, as well as the heterogeneous schemes having the largest impact on ozone (Figure 3). By averaging ozone abundance zonally and temporally (across 10 sols), the differences in meteorologies are minimized, and the ozone vertical structure can be compared between simulations while reducing the impact of these water ice spatiotemporal structures and allowing the impact of heterogeneous reactions to be isolated. Indirect impacts of heterogeneous chemistry on ozone abundance, such as the radiative effects of water ice clouds can be smoothed by zonally and temporally averaging simulations.

Figure 5 shows the vertical profiles of ozone, HOₓ, and H₂O₂ for the four simulations, averaged between $L_s = 5°–10°$ across all longitudes. The first column shows the abundance of the respective chemicals in the control simulation, while the remaining three columns show the percentage difference of the heterogeneous simulations with respect to the control simulation.
<figure><img src="image_7.png" /><figcaption>Figure 5. First column shows the zonally averaged vertical profiles of (top) ozone, (middle) HOx (OH and HO2), and (bottom), H2O2. Other columns show the percentage difference between the control and (second column) 100% release scheme, (third column) 0% release scheme, and (fourth column) 50% release scheme of the respectively chemical species. Difference is calculated by heterogeneous divided by control, multiplied by 100. Contour lines show the abundance of that species from that simulation. Simulations are for Ls = 5°–10°, MY 35.</figcaption></figure>

4.1. 100% Release Simulation

The largest difference in ozone abundance in the 100% release scheme occurs at high northern latitudes (>60°N), where ozone concentration in the 100% release simulation is between 60 and 160 ppbv from areoid altitude −4–10 km. This is a 35–75% increase in ozone concentration, which is shown by the red in Figure 5b. Water ice concentration ranges between 40 and 100 ppmv at areoid altitudes −4–5 km (see Figure S4c in Supporting Information S1). Furthermore, because water ice is present at low altitudes over an extended period of time, the adsorbed HOx—iceHOx—are not desorbed, and remain locked in the adsorbed state. HOx concentration at high northern latitudes (>60°N) ranges between 0 and 600 pptv (Figure 5f), which is under half of the concentration that is adsorbed. This indicates that most of the HOx present in this area (25 – 75%) are adsorbed to iceHOx.

At these latitudes the northern polar vortex is still present, trapping cold air and preventing ozone from being distributed globally. This is seen by the rapid decrease in temperature above 60° N, which changes from 180K to below 150K (areoid altitudes −4–5 km) in Figure S4a in Supporting Information S1. HOx are adsorbed onto water ice, which is most affective at low altitudes when water ice abundance is greater, molecular density is higher, and the temperature is colder. Water vapor is also very low at these latitudes (<25ppmv; Figure S4b in Supporting Information S1) as well as there being limited sunlight, so the reformation of HOx from water vapor photolysis is limited. The effects of heterogeneous chemistry are amplified within the polar vortex as water ice is present and HOx cannot be easily reformed.

Similar to the increase in ozone concentration, HOx concentration decreases at altitudes above those where water ice forms. HOx are circulated within the polar vortex and, when in the presence of a water ice cloud, are adsorbed onto the surface of water ice particle. Between Ls = 5°–10° water ice at low altitudes is constantly present, and thus acts as a sink for HOx, with little to no adsorbed HOx being released.

Lefèvre et al. (2021) show that the main contributor of HOx production in dry polar vortices is;

$$H_2 + O(^1D) \rightarrow OH + H \quad (15)$$

H2O2 is a temporary reservoir for HOx and its main loss is through photolysis. Even without the photolysis of water vapor, HOx are still produced from H2O2 through the reactions;
$$OH + H_2O_2 \rightarrow H_2O + HO_2 \quad (16)$$

and

$$O + H_2O_2 \rightarrow OH + HO_2. \quad (17)$$

$HO_X$ are locked in the adsorbed state and are unreactive. As a result, there are no $HO_2$ and OH molecules to replenish and reform $H_2O_2$ through the reaction;

$$HO_2 + HO_2 \rightarrow H_2O_2 + O_2. \quad (18)$$

The retention of $HO_X$ on water ice results in a decrease in the formation of $H_2O_2$. Figure 5j shows the percentage difference in $H_2O_2$ concentration between the control and 100% release simulations. Above 60°N, there is a 25%–75% decrease in $H_2O_2$ concentration in the 100% release simulation. While there is only a small $H_2O_2$ concentration present at these latitudes (<1 ppbv; Figure 5i), this is a large decrease relative to the concentration of $HO_X$ (which are of the order of pptv). These changes in $H_2O_2$ are in line with those detected by Encrenaz, T. et al. (2019) before the northern Spring equinox, $L_s = 320°–350°$, where values at high northern latitudes range between 0 and 10 ppbv.

In the 100% released scheme, adsorbed $HO_X$ are released back to their gaseous form when water ice sublimates, as described in Brown et al. (2022). This assumes that adsorbed $HO_X$ do not undergo any reactions while on the water ice surface. As a result, $HO_X$ are preserved in the adsorbed state and redistributed as water ice is transported. This extends the lifetime of $HO_X$, which, under photochemical activity are on the order of seconds (Lefèvre et al., 2004), and $HO_X$ are removed from the atmosphere. The sublimation of water ice clouds and release of $HO_X$ causes a decrease in ozone TCA as seen in the 100% release simulation. The decrease in ozone abundance is particularly apparent when water ice clouds fluctuate diurnally, and all $HO_X$ are continuously adsorbed and released.

## 4.2. 0% Release Simulation

In the 0% release scheme from Lefèvre et al. (2021), water vapor and oxygen immediately form as products of heterogeneous reactions without the delay of water ice sublimation. This approach assumes all $HO_X$ are lost when adsorbed and, in order for $HO_X$ to be replenished, water vapor must be photolyzed and reaction 15 must occur. Heterogeneous reactions are more effective in areas of low water vapor abundance (Brown et al., 2022), and there is a larger relative decrease of $HO_X$. The reformation of $HO_X$ is restricted; there is little water vapor available to photolyze (with the exception of supersaturation), and during the winter there is little to no sunlight available to photolyze water vapor.

There is a 25%–75% decrease in relative $HO_X$ concentration at the areoid altitudes –4–5 km, where water ice forms at high northern latitudes (>60°N) (Figure 5g). While the 0% release and 100% release schemes increase in ozone concentration by different amounts and at different altitudes (e.g., 0% release simulation does not have an increase in ozone concentration between 5 and 12 km), the two simulations have a similar decrease in $HO_X$ concentration; 0% release simulation has <25% decrease in $HO_X$ concentration at similar altitudes to the 100% release simulation. The cause for these differences in the vertical structure of ozone concentration are due to the change in $H_2O_2$ concentration. In the 0% release simulation, $H_2O_2$ concentration only has a minor decrease between 25% and 50% between areoid altitudes –4–15 km (Figure 5k). The main loss of $H_2O_2$ is via photolysis, which does not occur during the polar night. $H_2O_2$ can only be destroyed by reacting with O, OH, or $HO_2$, or through surface condensation. The change in O concentration is minimal (<25% increase; not shown), implying that this difference in $H_2O_2$ concentration is a result of differing OH and $HO_2$ activity. One possible explanation could be due to differences in $H_2O_2$ condensation on the surface, which is governed by temperature. However, the zonally averaged surface temperature at >60°N is similar between all simulations (not shown). Another explanation for the larger $H_2O_2$ in the 0% release simulation could be due to production of $HO_X$ from water vapor. $H_2O_2$ does not undergo a large depletion, as seen in the 100% simulation, despite the $H_2O_2$ heterogeneous mechanisms being the same in both heterogeneous simulations. In Figure 5k, $H_2O_2$ only decreases by up to 50% at high latitudes (>60°), compared to the 50%–100% decrease in the 100% release simulation (Figure 5j).
<figure><img src="image_8.png" /><figcaption>**Figure 6.** (points) Ozone TCA observations showing the median of grouped 0.5° latitudes between $L_s = 5°–10°$. (lines) Zonal median ozone TCA for (orange) control, (purple) 100% release, (blue) 0% release, (green) 50% release simulations averaged between $L_s = 5°–10°$.</figcaption></figure>

At higher altitudes, converted water vapor can photolyze to form HOx;

$$H_2O + h\nu \xrightarrow{\lambda \approx 200\text{nm}} H + OH \quad (19)$$

or react with excited atomic oxygen to produce OH;

$$O(^1D) + H_2O \rightarrow OH + OH. \quad (20)$$

As water ice concentration is low at higher altitude (>5 km), the reformed HOx are not adsorbed. Water vapor converted from heterogeneous reactions reacts with O(^1D) or photolyzes to produce HOx, which can then replenish H₂O₂ concentration. Unlike the 100% release scheme, HOx are not removed from the system and locked in an unreactive state until water ice sublimates.

The largest ozone concentration occurs at latitudes above 60°N, between areoid altitudes -4-4 km, ranging between 120 and 200 ppbv (50%-100% increase from the control; Figure 5d), which coincides with large water ice concentrations (40 ppmv; Figure S4c in Supporting Information S1). The concentration of atmospheric water vapor is low, and the water vapor produced as a by-product of heterogeneous chemistry must first be photolyzed. Any water vapor produced is likely immediately condensed to water ice, as the saturation of water vapor within the polar vortex is reached. Thus HOx cannot be reformed at low altitudes, resulting in a reduction in ozone destruction.

Ozone concentration in the 0% release scheme also increases by 25%-75% between latitudes 30°-60°N at areoid altitudes -2-3 km, between 20 and 100 ppbv (Figure 5c). This occurs despite the zonally averaged water ice concentration being low (<15 ppmv; Figure S4c in Supporting Information S1), and is not present in the 100% release scheme. This is because no gaseous HOx are released, and the scheme is independent of water ice sublimation. Water ice clouds can extend down to latitudes 35°N, although between 35° and 60°N they follow the thermal tide and vary with local time (Clancy et al., 2016; Montmessin et al., 2004). Ozone in the 0% release simulation is, therefore, not destroyed by the released HOx when water ice sublimates, enabling ozone concentration to increase at lower latitudes, even when water ice fluctuates diurnally and allows it to be transported to other locations.

### 4.3. 50% Release Simulation

Ozone TCA in the 50% release simulation produces a similar magnitude of ozone TCA as the 0% and 100% release simulation. This is shown by the green line in Figure 6 in which the ozone TCA at northern latitudes
(>60°N) is up to 4.5 µm-atm greater than that of the control, and approximately 1 µm-atm greater than either the 0% or 100% release schemes.

The vertical profile of ozone in the 50% release simulation increases in similar concentrations as the 0% release simulation, while also increasing at similar altitudes as the 100% release simulation (Figure 5d). At high northern latitudes (>60°N), between areoid altitudes –4–10 km, ozone concentrations increase by up to 250%, between 40 and 200 ppbv. Moreover, the increase in ozone concentration spans for a greater altitude range than the 0% release simulation (–4–10 km compared to –4–5 km), which incorporates approximately 60% of the total atmospheric mass. The combination of a greater absolute increase in ozone concentration as well as a larger altitude range is the reason why ozone TCA is larger in the 50% release scheme than either of the other two schemes.

From investigating the vertical profiles of ozone, HOₓ, and H₂O₂ concentrations, the 100% release and 0% release schemes cause ozone TCA to increase with different underlying chemistry. The 50% release scheme combines both the chemical pathways by implementing the dissociation of adsorbed HOₓ to water vapor and oxygen, while also incorporating the adsorbed and unreactive state of HOₓ. The latter of which traps HOₓ as iceHOₓ until water ice sublimates. When water ice clouds exist over longer periods of time such as between Lₛ = 5°–10°, adsorbed HOₓ build up, and the heterogeneous reactions have a larger impact on ozone, for example, at >60°N.

Similar to the 0% release, there is a 25%–50% increase in ozone concentration in the 50% release simulation between latitudes 35°–60°N at low altitudes (–2 – 4 km; Figure 5d). Water ice clouds at these latitudes fluctuate diurnally, which causes HOₓ to be adsorbed and desorbed. Unlike the 100% release scheme where the adsorbed HOₓ immediately destroy ozone when released back into their gaseous form, the 50% release scheme equally splits the desorbed HOₓ into gaseous HOₓ and dissociative by-products (water vapor and oxygen). Therefore, a lower concentration of HOₓ is released than initially adsorbed. Fewer HOₓ are available to destroy ozone after being desorbed, which results in a net increase in ozone concentration when zonally averaged.

The increase in ozone concentration in the 50% release simulation can be explained by the decrease in HOₓ and H₂O₂ concentrations (Figures 5h and 5l, respectively). HOₓ concentration decreases above 60°N by 25–75% between areoid altitudes –4–10 km. This decrease spans a similar altitude range to the 100% release simulation, and is a result of the continuous depletion of HOₓ by constant adsorption onto water ice clouds over the duration of Lₛ = 5°–10°. While H₂O₂ can photolyze or react with O or OH to produce both OH and HO₂, a depletion in HO₂ without the immediate by-products of water vapor and oxygen means H₂O₂ cannot be easily replenished. This results in a decrease in H₂O₂ concentration at similar altitudes HOₓ are depleted, as seen in Figure 5l.

**5. Conclusions**

Ozone TCA is currently under-predicted in Mars GCMs, indicating there is missing or inaccurate chemistry in models. Heterogeneous chemistry of HOₓ has previously been suggested as an explanation for part of this deficit. This study analyses three different approaches to heterogeneous chemistry to assess the sensitivity of ozone to HOₓ, as well as using the comparison to ozone observations to imply the underlying chemistry occurring at the surface of water ice particles. The three heterogeneous schemes assess the dissociation and lifetime of HOₓ when adsorbed onto water ice.

Overall, the increases in ozone TCA in the 100% release, 0% release, and 50% release simulations are not enough to account for the overall global ozone TCA deficit between observations and the control simulation (15 µm-atm at the times and places the observations are taken). The differences in ozone TCA between heterogeneous chemical schemes highlight the sensitivity of ozone and the importance of the chemical assumptions made in each of the heterogeneous schemes. All three heterogeneous simulations increase ozone TCA at similar locations and times ozone TCA is under-predicted in the control simulation, suggesting that heterogeneous chemistry needs to be considered in models and their representation in an MGCM is important for modeling ozone. By comparing different implementations of heterogeneous chemical schemes in the model, the underlying chemistry between HOₓ and H₂O₂ has been studied and used to improve simulated ozone TCA when compared to observations from NOMAD/TGO.

The 100% release scheme shows that when water ice clouds exist, the lifetime of adsorbed HOₓ is extended as HOₓ are retained on the surface, allowing for a build up of ozone concentration. When the release of adsorbed HOₓ species is dependent on sublimation of water ice, H₂O₂ is depleted at locations water ice persists. In contrast to the 100% release scheme, the 0% release scheme shows that it is the dissociation of adsorbed HOₓ which causes
ozone abundance to increase. Of the three heterogeneous schemes, it causes the largest increase in ozone globally, but not necessarily at all locations which are under-predicted. At low latitudes, such as during the ACB, ozone is already over-predicted in the control scheme due to the atmospheric conditions, and the addition of the dissociative heterogeneous chemistry causes a further over-prediction in ozone.

A third heterogeneous scheme, 50% release scheme, merges both the 100% and 0% release mechanisms together; when the dissociation is combined with the delayed release of HOₓ, this causes a larger increase in ozone abundance and can, in places, account for up to 50% of the ozone deficit between model and observations when compared directly against one another. Between Lₛ = 5°–10° at northern latitudes when water ice clouds persist, the 50% release scheme produces a greater ozone TCA, matching the magnitude of that of the observations at a latitudinal offset.

The investigation of retention and dissociation of adsorbed species causes an increase in ozone TCA while having a negligible impact on ozone in the equatorial region. The 50% release scheme provides the best match between simulated and observed ozone TCA, implying that both underlying chemistries which have been investigated— dissociation at the surface and retention of adsorbed species—significantly influences ozone climatology. Furthermore, the combination of heterogeneous reactions with different chemistries implies that (a) some HOₓ which are adsorbed onto water ice interact and breakdown into other components and, (b) the adsorbed HOₓ remain adsorbed for a period of time dependent on the existence of water ice clouds. The inclusion of heterogeneous reactions and the merging of both these approaches increases ozone abundance and brings modeled data toward better agreement with observations. However, it does not explain all of the ozone deficit and there are still areas where the model under-predicts ozone TCA. Other factors, such as water vapor transport, are likely responsible for the spatial misalignment in peak ozone abundances. Moreover, the merging of these chemistries (50% release scheme) is a first attempt at integrating the dissociation of adsorbed HOₓ with the retention of HOₓ which is dependent on the sublimation of water ice clouds.

Further improvements would be to evaluate the mechanism and the sensitivity of ozone by changing the proportions released in gaseous and dissociative products. A more realistic mechanism could be one which does not release a static proportion of adsorbed HOₓ to dissociative by-products (as in the 50% release scheme), but rather changes conditions depending on the density of adsorbed HOₓ. To improve the heterogeneous reactions in the model, the deposition of OH and HO₂ onto surface water ice could be implemented, which may bring ozone TCA more in-line with observations. A further study of the effects of H₂O₂ is required to fully analyze the impacts of heterogeneous reactions on H₂O₂ at varying spatial and temporal locations. Similarly, the impacts of heterogeneous reactions on water ice require a separate analysis to understand how HOₓ can potentially impact the global distribution of water in the atmosphere. Overall, in order to improve the ozone cycle represented in a GCM, further analysis in laboratory and simulated experiments is required, both for heterogeneous reactions and other potential chemistries which could increase ozone abundance, bringing simulations more in line with observations.

## Data Availability Statement

MGCM data for Lₛ = 5°–10°, and data comparison between observations can be found in the repository at Brown (2024). The full ozone observation data set can be found at J. Mason and Patel (2023). Colormaps are from Scientific Colour Maps Version 8.0 from Crameri (2021).

## Acknowledgments

MB was supported by the Science and Technology Facilities Council under Grant ST/P006760/1, the DISCnet Centre for Doctoral Training in Data-Intensive Science, and the National Environment Research Council through grants NE/ X010236/1. SRL thanks UKSA for support under Grants ST/W002949/1 and ST/ V005332/1, and STFC under grant ST/ X001180/1. JAH, MRP, and JPM were supported by UK Space Agency and STFC through Grants ST/V002295/1, ST/ V005332/1, ST/Y000234/1, ST/ W00268X/1, ST/X001180/1 and ST/ X006549/1.

## References

Atreya, S., & Blamont, J. (1990). Stability of the martian atmosphere: Possible role of heterogeneous chemistry. *Geophysical Research Letters*, *17*(3), 287–290. https://doi.org/10.1029/GL017i003p00287

Brown, M. A. J. (2024). The role and lifetime of dissociative heterogeneous processes in improving simulated ozone on mars [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10514425

Brown, M. A. J., Patel, M. R., Lewis, S. R., Holmes, J. A., Sellers, G. J., Streeter, P. M., et al. (2022). Impacts of heterogeneous chemistry on vertical profiles of martian ozone. *Journal of Geophysical Research: Planets*, 127(11), e2022JE007346. https://doi.org/10.1029/2022JE007346

Cariolle, D., Moinat, P., Teyssèdre, H., Giraud, L., Josse, B., & Lefèvre, F. (2017). Asis v1.0: An adaptive solver for the simulation of atmospheric chemistry. *Geoscientific Model Development*, 10(4), 1467–1485. https://doi.org/10.5194/gmd-10-1467-2017

Clancy, R. T., & Nair, H. (1996). Annual (perihelion-aphelion) cycles in the photochemical behavior of the global mars atmosphere. *Journal of Geophysical Research*, 101(E5), 12785–12790. https://doi.org/10.1029/96JE00836

Clancy, R. T., Wolff, M. J., Lefèvre, F., Cantor, B. A., Malin, M. C., & Smith, M. D. (2016). Daily global mapping of mars ozone column abundances with Marci UV band imaging. *Icarus*, 266, 112–133. https://doi.org/10.1016/j.icarus.2015.11.016

Cooper, P. L., & Abbatt, J. P. D. (1996). Heterogeneous interactions of oh and HO₂ radicals with surfaces characteristic of atmospheric particulate matter. *The Journal of Physical Chemistry*, 100(6), 2249–2254. https://doi.org/10.1021/jp952142z
Crameri, F. (2021). *Scientific colour maps*. Zenodo. https://doi.org/10.5281/zenodo.5501399

Crismani, M. M. J., Villanueva, G. L., Liuzzi, G., Smith, M. D., Knutsen, E. W., Daerden, F., et al. (2021). A global and seasonal perspective of martian water vapor from ExoMars/NOMAD. *Journal of Geophysical Research: Planets*, **126**(11), e2021JE006878. https://doi.org/10.1029/2021JE006878

Crowley, J. N., Ammann, M., Cox, R. A., Hynes, R. G., Jenkin, M. E., Mellouki, A., et al. (2010). Evaluated kinetic and photochemical data for atmospheric chemistry: Volume v – Heterogeneous reactions on solid substrates. *Atmospheric Chemistry and Physics*, **10**(18), 9059–9223. https://doi.org/10.5194/acp-10-9059-2010

Daerden, F., Crowley, J. N., Neary, L., Smith, M. D., Loeffler, M. J., Clancy, R. T., et al. (2023). Heterogeneous processes in the atmosphere of mars and impact on H₂O₂ and O₃ abundances. *Journal of Geophysical Research: Planets*, **128**(12), e2023JE008014. https://doi.org/10.1029/2023JE008014

Daerden, F., Neary, L., Viscardy, S., Muñoz, A. G., Clancy, R., Smith, M., et al. (2019). Mars atmospheric chemistry simulations with the gem- mars general circulation model. *Icarus*, **326**, 197–224. https://doi.org/10.1016/j.icarus.2019.02.030

Encrenaz, T., Greathouse, T. K., Aoki, S., Daerden, F., Giuranna, M., Forget, F., et al. (2019). Ground-based infrared mapping of H₂O₂ on mars near opposition. *A&A*, **627**, A60. https://doi.org/10.1051/0004-6361/201935300

Forget, F., Hourdin, F., Fournier, R., Hourdin, C., Talagrand, O., Collins, M., et al. (1999). Improved general circulation models of the martian atmosphere from the surface to above 80 km. *Journal of Geophysical Research*, **104**(E10), 24155–24175. https://doi.org/10.1029/1999JE001025

Giuranna, M., Wolkenberg, P., Grassi, D., Aronica, A., Aoki, S., Scaccabarozzi, D., et al. (2021). The current weather and climate of mars: 12 years of atmospheric monitoring by the planetary Fourier spectrometer on mars express. *Icarus*, **353**, 113406. https://doi.org/10.1016/j.icarus.2019.113406

Hartley, W. (1881). Xxi.—On the absorption of solar rays by atmospheric ozone. *Journal of the Chemical Society Transactions*, **39**(0), 111–128. https://doi.org/10.1039/ct8813900111

Holmes, J. A., Lewis, S. R., & Patel, M. R. (2017). On the link between martian total ozone and potential vorticity. *Icarus*, **282**, 104–117. https://doi.org/10.1016/j.icarus.2016.10.004

Holmes, J. A., Lewis, S. R., & Patel, M. R. (2020). Openmars: A global record of martian weather from 1999 to 2015. *Planetary and Space Science*, **188**, 104962. https://doi.org/10.1016/j.pss.2020.104962

Holmes, J. A., Lewis, S. R., Patel, M. R., Alday, J., Aoki, S., Liuzzi, G., et al. (2022). Global variations in water vapor and saturation state throughout the mars year 34 dusty season. *Journal of Geophysical Research: Planets*, **127**(10), e2022JE007203. https://doi.org/10.1029/2022JE007203

Khayat, A. S. J., Smith, M. D., Wolff, M., Daerden, F., Neary, L., Patel, M. R., et al. (2021). Exomars tgo/nomad-uvis vertical profiles of ozone: 2. The high-altitude layers of atmospheric ozone. *Journal of Geophysical Research: Planets*, **126**(11), e2021JE006834. https://doi.org/10.1029/2021JE006834

Krasnopolsky, V. A. (1993). Photochemistry of the martian atmosphere (mean conditions). *Icarus*, **101**(2), 313–332. https://doi.org/10.1006/icar.1993.1027

Krasnopolsky, V. A. (2006). Photochemistry of the martian atmosphere: Seasonal, latitudinal, and diurnal variations. *Icarus*, **185**(1), 153–170. https://doi.org/10.1016/j.icarus.2006.06.003

Lefèvre, F., Bertaux, J.-L., Clancy, R. T., Encrenaz, T., Fast, K., Forget, F., et al. (2008). Heterogeneous chemistry in the atmosphere of mars. *Nature*, **454**(7207), 971–975. https://doi.org/10.1038/nature07116

Lefèvre, F., Lebonnois, S., Montmessin, F., & Forget, F. (2004). Three-dimensional modeling of ozone on mars. *Journal of Geophysical Research*, **109**(E7), E07004. https://doi.org/10.1029/2004JE002268

Lefèvre, F., Trokhimovskiy, A., Fedorova, A., Baggio, L., Lacombe, G., Määttänen, A., et al. (2021). Relationship between the ozone and water vapor columns on mars as observed by SPICAM and calculated by a global climate model. *Journal of Geophysical Research: Planets*, **126**(4), e2021JE006838. https://doi.org/10.1029/2021JE006838

Liu, L.-M., Crawford, P., & Hu, P. (2009). The interaction between adsorbed oh and O₂ on TiO₂ surfaces. *Progress in Surface Science*, **84**(5), 155–176. https://doi.org/10.1016/j.progsurf.2009.01.002

Määttänen, A., Lefèvre, F., Verdier, L., Montmessin, F., Listowski, C., Guilbon, S., et al. (2022). Ozone vertical distribution in mars years 27–30 from SPICAM/MEX UV occultations. *Icarus*, **387**, 115162. https://doi.org/10.1016/j.icarus.2022.115162

Mason, J., & Patel, M. (2023). UVIS ozone abundances for 2.5 mars years. https://doi.org/10.21954/ou.rd.24877095.v1

Mason, J. P., Patel, M. R., Holmes, J. A., Wolff, M. J., Alday, J., Streeter, P., et al. (2024). Climatology and diurnal variation of ozone column abundances for 2.5 mars years as measured by the nomad-UVIS spectrometer. *Journal of Geophysical Research: Planets*, **129**(4), e2023JE008270. https://doi.org/10.1029/2023JE008270

Mateshvili, N., Fussen, D., Vanhellemont, F., Bingen, C., Dodion, J., Montmessin, F., et al. (2007). Martian ice cloud distribution obtained from SPICAM nadir UV measurements. *Journal of Geophysical Research*, **112**(E7), E07004. https://doi.org/10.1029/2006JE002827

McElroy, M. B., & Donahue, T. M. (1972). Stability of the martian atmosphere. *Science*, **177**(4053), 986–988. https://doi.org/10.1126/science.177.4053.986

Millour, E., Forget, F., Lopez-Valverde, M., Lefèvre, F., Gonzalez-Galindo, F., Lewis, S., et al. (2018). Exploring the interannual variability of the martian atmosphere with the mars climate database v5.3. In *42nd COSPAR scientific assembly* (Vol. 42, p. C4-3).

Montmessin, F., Forget, F., Rannou, P., Cabane, M., & Haberle, R. M. (2004). Origin and role of water ice clouds in the martian water cycle as inferred from a general circulation model. *Journal of Geophysical Research*, **109**(E10), E10004. https://doi.org/10.1029/2004JE002284

Moreau, D., Esposito, L. W., & Brasseur, G. (1991). The chemical composition of the dust-free martian atmosphere: Preliminary results of a two-dimensional model. *Journal of Geophysical Research*, **96**(B5), 7933–7945. https://doi.org/10.1029/90JB02544

Nair, H., Allen, M., Anbar, A. D., Yung, Y. L., & Clancy, R. (1994). A photochemical model of the martian atmosphere. *Icarus*, **111**(1), 124–150. https://doi.org/10.1006/icar.1994.1137

Navarro, T., Madeleine, J.-B., Forget, F., Spiga, A., Millour, E., Montmessin, F., & Määttänen, A. (2014). Global climate modeling of the martian water cycle with improved microphysics and radiatively active water ice clouds. *Journal of Geophysical Research: Planets*, **119**(7), 1479–1495. https://doi.org/10.1002/2013JE004550

Patel, M. R., Sellers, G., Mason, J. P., Holmes, J. A., Brown, M. A. J., Lewis, S. R., et al. (2021). ExoMars TGO/NOMAD-UVIS vertical profiles of ozone: 1. Seasonal variation and comparison to water. *Journal of Geophysical Research: Planets*, **126**(11), e2021JE006837. https://doi.org/10.1029/2021JE006837

Pouvesle, N., Kippenberger, M., Schuster, G., & Crowley, J. N. (2010). The interaction of H₂O₂ with ice surfaces between 203 and 233 K. *Physical Chemistry Chemical Physics*, **12**(47), 15544–15550. https://doi.org/10.1039/C0CP01656J
Risi, C., Bony, S., Vimeux, F., & Jouzel, J. (2010). Water-stable isotopes in the LMDZ4 general circulation model: Model evaluation for present-day and past climates and applications to climatic interpretations of tropical isotopic records. *Journal of Geophysical Research, 115*(D12), D12118. https://doi.org/10.1029/2009JD013255

Smith, W. (1943). The surface recombination of h atoms and oh radicals. *The Journal of Chemical Physics, 11*(3), 110-125. https://doi.org/10.1063/1.1723811

Steele, L. J., Lewis, S. R., Patel, M. R., Montmessin, F., Forget, F., & Smith, M. D. (2014). The seasonal cycle of water vapour on mars from assimilation of thermal emission spectrometer data. *Icarus, 237*, 97-115. https://doi.org/10.1016/j.icarus.2014.04.017