PSCs initiated by mountain waves in a global chemistry-climate model: A missing piece in fully modelling polar stratospheric ozone depletion

An important source of polar stratospheric clouds (PSCs), which play a crucial role in controlling polar stratospheric ozone depletion, is from the temperature fluctuations induced by mountain waves. These enable stratospheric temperatures to fall below the threshold value for PSC formation in regions of negative temperature perturbations or cooling-phases induced by the waves even if the synoptic-scale temperatures are too high. However, this formation mechanism is usually missing in global chemistry–climate models because these temperature fluctuations are neither resolved nor parameterised. Here, we 20 investigate in detail the episodic and localised wintertime stratospheric cooling events produced over the Antarctic Peninsula by a parameterisation of mountain-wave-induced temperature fluctuations inserted into a 30-year run of the global chemistryclimate configuration of the UM-UKCA (Unified Model United Kingdom Chemistry and Aerosol) model. Comparison of the probability distribution of the parameterised cooling-phases with those derived from climatologies of satellite-derived AIRS brightness temperature measurements and high-resolution radiosonde temperature soundings from Rothera Research 25 Station on the Antarctic Peninsula shows that they broadly agree with the AIRS-observations and agree well with the radiosonde-observations, particularly in both cases for the “cold tails” of the distributions. It is further shown that adding the parameterised cooling-phase to the resolved/synoptic-scale temperatures in the UM-UKCA model results in a considerable increase in the number of instances when minimum temperatures fall below the formation temperature for PSCs made from ice water during late austral autumn / early austral winter and early austral spring, and without the additional cooling-phase 30 the ice frost point is rarely exceeded above the Antarctic Peninsula in the model. Similarly, it was found that the formation potential for PSCs made from ice water was many times larger if the additional cooling is included. For PSCs made from NAT particles it was only during October that the additional cooling is required for the NAT temperature threshold to be exceeded https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c © Author(s) 2020. CC BY 4.0 License.

However, mountain-wave-induced PSC formation (and associated ozone depletion) is missing from current global chemistryclimate models. This is because they are unable to explicitly resolve localised mountain-wave dynamics and their associated temperature perturbations due to their coarse spatial resolution, which is on the order of a few hundred kilometres, while mountain waves typically have a wavelength of around 100 km or smaller. This failure was addressed in previous work described by Orr et al. (2015), which inserted a parameterisation scheme describing stratospheric mountain-wave-induced  (Hewitt et al., 2009), coupled to the United Kingdom Chemistry and Aerosol (UKCA) module (Morgenstern et al., 2009). This work showed that the parameterised temperature fluctuations over the Antarctic Peninsula were broadly in agreement with detailed results using a high-resolution regional climate model, and also that the amount of PSCs simulated over the Antarctic Peninsula 75 by the chemistry-climate model increased considerable following the inclusion of the cooling-phase of the parameterised temperature fluctuations. Novel developments such as this that make global chemistry-climate models more physically based / comprehensive are needed to improve our ability to make accurate predictions of stratospheric ozone, especially related to the expected recovery of the Antarctic ozone hole by approximately mid-century (and its role in offsetting the effects of increasing greenhouse gases), which requires the use of interactive stratospheric ozone chemistry for projections (Chiodo and 80 Polvani, 2017;Pope et al., 2020). The recovery of stratospheric ozone levels (together with greenhouse gas increases) is expected to result in profound changes to the high-latitude Southern Hemisphere climate system, primarily by affecting both the strength and latitude of the westerly polar jet (Eyring et al., 2013;Previdi and Polvani, 2014;Iglesias-Suarez et al. 2016;Chiodo and Polvani, 2017).
This study further investigates the parameterised mountain-wave-induced cooling-phase computed by the UM-UKCA model 85 described in Orr et al. (2015), focusing particularly on its rigorous validation to better constrain the scheme and an assessment of its impact on the formation potential (FP) of PSCs (Dörnbrack and Leutbecher, 2001), which is necessary before any assessment of the global impact on polar ozone chemistry. The investigation will again primarily focus on the Antarctic Peninsula due to it being a hot-spot for mountain-wave-induced PSCs in Antarctica and thus a highly suitable test-case, and will examine locally: i) a comparison of the distribution of observed and parameterised mountain-wave-induced stratospheric 90 cooling-phase, ii) the impact of the parameterisation scheme on minimum temperatures and the FP of PSCs, iii) an investigation into the conditions that produce mountain-wave-induced stratospheric cooling in the parameterisation scheme, and iv) the impact of the scheme on local PSC formation and heterogeneous chemistry. The investigation will finish by investigating the non-local impacts of the scheme by examining changes to ozone as well as temperature and pressure over the high-latitude 2 Materials and methods

Description of parameterisation scheme and inclusion in global chemistry-climate model
The mountain wave scheme is described by Dean et al. (2007) and computes the maximum negative ∆ − and positive ∆ + temperature fluctuations associated with the positive and negative vertical parcel displacement of gravity waves generated by flow passing over subgrid-scale orography (SSO) in a climate or general circulation model. The approach assumes that the 100 vertical propagation is described by linear hydrostatic mountain waves, generated by steady-state stratified flow over an isolated two-dimensional ridge, i.e. in the absence of wave dissipation mechanisms the change in wave amplitude/displacement with height is controlled by variations in the air density, the horizontal wind speed U (resolved in the direction of the wave vector), and the buoyancy frequency N. The scheme includes critical-level absorption and wave breaking to prevent the wave amplitude from exceeding the local "saturation amplitude", defined as ⁄ (where is the critical Froude number for 105 saturation). The initial wave amplitude is set equal to the "effective" mountain height ℎ of the SSO (i.e. ℎ − ℎ , where ℎ = is the height of the SSO and ℎ = ℎ − 0 0 ⁄ is the height of the blocked layer, is the standard deviation of the SSO, is a constant, is the critical Froude number at which flow blocking is deemed to first occur, and the subscript "0" refers to quantities averaged between the ground and ℎ).
As mentioned above, the scheme was previously inserted into the UM-UKCA global chemistry-climate model. UM-UKCA 110 uses a quasi-equilibrium PSC scheme which models two types of PSC particles: NAT and mixed NAT/ice, both calculated assuming thermodynamic equilibrium with gas-phase HNO3 and H2O (following Chipperfield, 1999). For NAT particles, the saturation vapour pressure of HNO3, calculated following Hanson and Mauersberger (1988), is used to calculate the mass of HNO3 in the solid phase, while for mixed NAT/ice the saturation vapour pressure of water vapour over ice is calculated following Goff (1957). Surface area density for both PSC types are calculated assuming spherical particles with fixed density 115 and radii. For NAT particles these are 1.35 g cm -3 and 1 µm, and for mixed NAT/ice particles 0.928 g cm -3 and 10 µm, respectively. As a result, in this scheme each individual NAT or mixed NAT/ice particle is assumed to be the same size, while the number density, and so surface area density, changes with the availability of HNO3 and H2O, as well as temperature and pressure.
Only the cooling-phase ∆ − computed by the mountain wave scheme is coupled / passed to the PSC scheme, i.e. the PSC 120 scheme uses as input a "total" temperature = − + ∆ − , where − is the temperature explicitly resolved by the UM-UKCA model. The cooling-phase only is used because, in the simple quasi-equilibrium PSC scheme, an instantaneous temperature rise will evaporate particles immediately if the temperature increases above the PSC formation threshold -when in reality this would take some time. This configuration -referred to from now on as the "perturbation" simulation -was run for 30 years (following a spin-up period of 30 years) for a perpetual year 2000 at a horizontal resolution of N48 (equivalent to 125 a grid spacing of 2.5º × 3.5º) and 60 vertical levels (up to 84 km), using prescribed sea-ice fraction and sea surface temperature.
Note that values of the constants/parameters used by the scheme were set to = 3, = 2, and = 4, which were selected following initial analysis to optimize its performance over the Antarctic Peninsula by best matching the magnitude of https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License.
the parameterised stratospheric temperature fluctuations with those explicitly resolved by a high-resolution regional configuration of the UM (see Orr et al. (2015) for further details). A control experiment -referred to from now on as the 130 "control" simulation -was also run, which is identical to the perturbation run but with the exception that the mountain wave scheme is switched off. Orr et al. (2015) provides more details of both experiments. Output from both the model runs are at 6hourly intervals (including values of ∆ − from the perturbation run) and are used as the basis for all subsequent analysis.
Note that earlier studies such as Orr et al. (2012) and Keeble et al. (2014) show that this model well represents the high-latitude Southern Hemisphere circulation and temperature structure. Nevertheless, of especial importance is an accurate representation 135 of circumpolar westerly flow at a height of say 850 hPa because of its role in generating wave activity over the Antarctic Peninsula (Orr et al., 2008). To test this here, the 30-year mean wind at 850 hPa for austral winter (June-July-August) from the control experiment was compared to the climatological mean from the reanalysis-product ERA5 (i.e. the fifth-generation reanalysis product from ECMWF, Hersbach and Dee, 2016) over the 1979 to 2019 period, and shown to be in excellent agreement (not shown). 140

Data
We use estimates of the amplitude of mountain-wave-induced cooling (i.e. maximum cooling) from Atmospheric Infrared Sounder (AIRS) measurements of radiance perturbations for a 16-yr period from 2002 to 2017 (Hoffmann et al., 2016(Hoffmann et al., , 2017, as well as from radiosonde soundings for a 14-yr period from 2002 to 2015 (Moffat-Griffin et al., 2011). The nadir scanning AIRS instrument is on board NASA's Aqua satellite, which since 2002 has typically made four passes per day over the 145 Antarctic Peninsula, performing an across-track scan covering a distance of 1765 km on the ground. Each scan consists of 90 individual footprints that vary in size between 13.5 × 13.5 km at nadir and 41 × 21.4 km at the scan edges. Here we use the 666.5 cm -1 radiance channel of AIRS, which peaks in sensitivity to atmospheric temperatures at an altitude of around 22 km and has a full width at half maximum of 9 km, i.e. encompassing an altitude range that is particularly favourable for the formation of PSCs. See Figure 1 from Orr et al. (2015) for a plot showing the temperature weighting function for this channel. 150 The minimum radiance perturbation values (i.e. maximum cooling) are calculated for each single footprint. Note that the relatively coarse vertical resolution of AIRS limits the detection of waves with vertical wavelengths less than approximately 12 km, resulting in the attenuation of the measured wave amplitude, i.e. AIRS underestimates the true wave amplitude at short vertical wavelengths (Hoffman et al., 2017). Note also that AIRS observes temperature disturbances from both orographic and non-orographic source regions, which in the context of this study would include those generated by storms over the Drake 155 Passage to the north of the Antarctic Peninsula (Plougonven et al., 2012). The radiosonde soundings were launched around two to four times per week from Rothera Research Station, which is located along the western side of the Antarctic Peninsula.
See Moffat-Griffin et al. (2011) for more details of the soundings. Figure 1 shows a map of the Antarctic Peninsula, which includes the location of Rothera Research Station, as well as orography from the Bedmap2 dataset (Fretwell et al., 2013).

Methodology 160
To verify the parameterised mountain-wave-induced stratospheric cooling-phase, 6-hourly values of ∆ − for May-June-July-August-September-October over the Antarctic Peninsula from the perturbation run were compared against brightness temperature fluctuations measured by AIRS and temperature fluctuations measured by the radiosonde soundings. The brightness temperature fluctuations measured by AIRS are determined by removing a fourth-order polynomial function, representing the background atmosphere, from the original brightness temperatures (see Orr et al., 2015). To facilitate a 165 comparison with the AIRS-observed minimum brightness temperature fluctuations (∆ − ) over the Antarctic Peninsula, the values of ∆ − are converted into brightness temperature (∆ − ) by computing a weighted-sum of ∆ − over all vertical model levels from 15 to 45 km, i.e. by summing the value of ∆ − multiplied by the associated normalised weighting function for the 666.5 cm -1 radiance channel of AIRS over the range of vertical levels. Figure 1 shows the region over the Antarctic Peninsula that were used to compute ∆ − and ∆ − . Note that the weighting function of the 666.5 cm -1 170 radiance channel is largely insensitive to atmospheric temperatures at altitudes both above 45 km and below 15 km. For the radiosonde-based measurements, we focus on the temperature perturbations ∆ − observed at an altitude of between 20.2 and 20.6 km above sea level (chosen because this range is both in the lower stratosphere and includes the vertical level of the UM- and are sensitive to the temperature, pressure, HNO 3 , and water vapour mixing ratio (Hansen and Mauersberger, 1988;Marti and Mauersberger, 1993), which are taken from either the perturbation or control runs. We also compute for each run the FP of PSCs at an altitude of around 20.4 km, using a metric which depends on both the size of the temperature difference below either or , as well as the area of the region. For example, the FP for PSCs composed of NAT particles would be defined as: 185 where i is an integer, N is the total number of model grid-boxes within the region defined in Fig. 1, and is the spatial area of the model grid-box. An analogous equation exists for the FP for PSCs composed of water ice. Note that as the 190 latitude/longitude grid used by the UM-UKCA model has non-uniform spacing / grid-box area (due to varying longitude), the https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License. results from Eq. (1) are also scaled by the cosine of latitude. Note also that again the results are computed for the box situated over the Antarctic Peninsula shown in Fig. 1.
To identify the role of atmospheric conditions on controlling the parameterised stratospheric temperature fluctuations, the sensitivity of the amplitude of the cooling-phase ∆ − to the vertical wind shear α is examined, with: 195 Eq. (2) where 1 = 0.85 km, 2 = 21.0 km, and here U is the zonal wind velocity (applicable because the large-scale wind regime over the region containing the Antarctic Peninsula is predominately zonal (Thompson and Wallace, 2000)). Additionally, the 200 sensitivity of ∆ − to directional shear was also investigated by examining its relationship to a change in the direction of the wind with height, between 2 and 1 . These results are again computed for the box shown in Fig. 1.
Finally, we investigated the local impact of the scheme on ozone chemistry by examining changes in both the surface area density of PSCs composed of NAT particles and the ClONO 2 (chlorine nitrate) + HCl (hydrochloric acid) reaction. This heterogeneous reaction is crucial as in their gas phase HCl and ClONO 2 are very unreactive, and so any chlorine they contain 205 is unable to destroy ozone (Solomon, 1999). However, in the presence of a PSC surface (either solid or liquid) they can react with each other to produce Cl 2 (chorine gas), as well as the removal of nitric acid (HNO 3 ) from the atmosphere, resulting in the denitrification of the stratosphere, an effect which allows Cl 2 to build up during wintertime. In the spring, the presence of solar ultraviolet radiation splits Cl 2 into two chlorine atoms (so-called chlorine activation), which plays an important role in stratospheric ozone depletion (Solomon, 1999). Note that these results are calculated over the region 76°S-64°S and 75°W-210 55°W, which includes the Antarctic Peninsula but is not the box depicted in Fig. 1. Furthermore, to look at the non-local impacts we examined changes to ozone over the high-latitude Southern Hemisphere, as well as temperature and pressure changes in the lower stratosphere, i.e. the polar vortex. Keeble et al. (2014) previously showed that in the version of UM-UKCA used here that polar ozone depletion can have significant impacts on the polar vortex. showing that both distributions peak at similar values (around -0.5 K for ∆ − and -1 K for ∆ − ) but differ in terms of their shape, with ∆ − restricted to a relatively narrow range and a high peak compared to a broader range and lower peak for ∆ − . However, the agreement between the two distributions improves over the lower / large cooling part of the tail, 220 with both showing a lower bound of around -6 K, which is perhaps the region of the distribution that is critical for decreasing temperatures below the threshold for PSC formation. Note that a possible reason for the discrepancies between the two https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License. distributions could be that the parameterised results only represent mountain-wave-induced disturbances, while AIRS results include contributions from both orographic and non-orographic source regions. It is noteworthy therefore that there is a much better agreement between the distributions of ∆ − and ∆ − over Rothera Research Station at an altitude of around 20.4 km 225 (Fig. 3), with both distributions showing a relatively narrow range which peaks at a value of around -0.5 K, and the lower / cooling part of the tail extending to around -8 K. as well as a hot-spot of mountain wave activity (Hoffmann et al. 2013). Note that here there are some contributions/waves from regions over the sea, which is due to the smoothness of the UM-UKCA mean orography (due to its relatively coarse resolution), which results in non-zero values of mean orography and associated SSO values over sea points around the coastline. By contrast, the AIRS-observed values show the peak source region to be more over the northern section of the 235 Antarctic Peninsula, but also the presence of non-orographic source regions, particularly to the north of the Antarctic Peninsula (as discussed earlier as a possible reason for some of the disagreement between the distributions of parametrised and AIRSobserved cooling-phase in Fig. 2).

Impact on minimum temperatures and formation potential of PSCs
The distributions of temperature difference − and − from the perturbation and control runs are shown in Note however that during July and August that the cold side of the tail extends to − < 0 K in the control run using = − . For PSCs composed of NAT particles the impact of the parameterisation in the perturbation run is particularly important for October (and to a lesser degree September), as this is the only month that the additional cooling ∆ − is required for T to drop below , increasing the likelihood of PSC formation in early austral spring. However, it should be noted that https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License.
the impact on PSC formation is also dependent on the local mixing ratios of HNO 3 and H 2 O, which in part are affected by PSC 255 formation and sedimentation earlier in the winter. We explore the impacts of the parameterisation on PSC surface area density in section 3.4.
Using Eq. (1), Fig. 7 shows the FP for PSCs composed of both water ice and NAT particles at an altitude of 20.4 km for the individual months from May to October from both the perturbation and control runs. This shows that the FP of PSCs composed of NAT particles is around two orders of magnitude larger than that for PSCs composed of water ice particles due to them 260 having a higher threshold temperature for formation (i.e. roughly around 195 K for and 188 K for at this altitude) and hence much more likely that the threshold is exceeded (c.f. Figs. 5 and 6). The results show FP values for NAT particles peaking at around -4 × 10 6 K km 2 in June and July, but with little sensitivity in any of the months to the inclusion of the additional cooling ∆ − in the perturbation run. However, consistent with Fig. 6 is that the FP of PSCs composed of water ice particles is highly sensitive to the inclusion of the additional cooling ∆ − in the perturbation run, with FP values around 4-5 265 times larger in July and August if the additional cooling is included compared to it being neglected in the control run, as well as significant increases also occurring during June and September, which otherwise show a negligible FP for the control run.
For PSC composed of NAT particles, the FP values obtained from the perturbation and control run are much more similar (c.f.

Figs. 5 and 6), although the inclusion of the added cooling in the perturbation run does still result in increases.
To further understand this, Fig. 8 shows maps of the difference in FP between the perturbed and control run for the two types of PSCs 270 examined, revealing that the differences evident in Fig. 7 (i.e. due to the addition of ∆ − to the synoptic-scale temperature) are dominated by the contribution from mountain waves originating from the high-elevation base of the Antarctic Peninsula (which Hoffmann et al. (2013) showed was a hot-spot of mountain wave activity).

Conditions required for large localised negative temperature anomalies
Using Eq. (2), Fig. 9 compares the range of vertical wind shear α that was associated with the top 10% (i.e. most cold) and 275 bottom 10% (i.e. least cold) of the distribution of the cooling-phase ∆ − at an altitude of 20.4 km. This shows that the largest negative cooling-phases are associated with larger (positive) values of α, which is consistent with the understanding that waves with long vertical wavelengths in the stratosphere generate large temperature fluctuations and are associated with conditions where wind speed increases with height, i.e. causing wave refraction (Wu and Eckermann, 2008). Hoffman et al. (2017) also showed that such conditions were conducive for the propagation of gravity waves into the lower stratosphere with long vertical 280 wavelengths, which AIRS can best identify. Note that the top 10% and bottom 10% of the distribution was comparatively insensitive to the change in wind direction with height (not shown), which perhaps reflects that the wind regime is predominately unidirectional with height, i.e. a similar structure at many height levels in both the troposphere and lower stratosphere, consistent with an equivalent barotropic structure (Thompson and Wallace, 2000). https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License.

Impact on chlorine activation and PSCs over the Antarctic Peninsula 285
The impact of the additional cooling ∆ − in the perturbation run on PSCs composed of NAT particles and chlorine activation is shown in Fig. 10. In the control run, the maximum surface area density of PSCs composed of NAT particles is modelled in June at an altitude of around 20 km, and extending from around 10 to 30 km. Between June and September, the surface area of the NAT particles decreases due to both rising (synoptic scale) temperatures and the effects of denitrification and dehydration of the polar vortex by PSC sedimentation (Fahey et al., 1990;Teitelbaum et al., 2001). The result is that by August 290 and September, little PSC surface area remains for chlorine activation. However, in the perturbation run, the surface area density of the NAT particles is increased at higher altitudes throughout the winter and early spring and reduced at lower altitudes. Importantly for chlorine activation in the late winter and spring (August and September), surface area density is increased by up to 20%. Also shown in Fig. 10 is the flux through the ClONO 2 + HCl heterogeneous reaction, a key reaction for the activation of chlorine from the major chlorine reservoir species. Surface area density changes of the NAT particles have 295 only a modest impact on chlorine activation throughout the winter, but the small increases in surface area density in the late winter and early spring in the perturbation experiment result in large increases in chlorine activation throughout August and September, and thus enhancing ozone depletion (Solomon, 1999).
3.5 Impact on mean total column ozone, temperature, and pressure over the high-latitude Southern Hemisphere Figure 11 shows the impact of the additional cooling in the perturbation run on October monthly mean total column ozone. 300 While Fig. 10 highlights the local impacts of the parameterisation scheme on PSC formation and chlorine activation, it can be seen from Fig. 11 that the impacts of the parameterisation scheme extend far beyond the region of the Antarctic Peninsula. This is unsurprising, as not only is the Antarctic Peninsula responsible for differences both upstream and downstream of the region, but other hot-spots of mountain wave activity exist over Antarctica that can also play a role in PSC formation, such as the Transantarctic mountains (e.g. Noel et al., 2009;Hoffmann et al., 2013Hoffmann et al., , 2017Alexander et al., 2017) which would also be 305 sources of cooling via the parameterisation scheme. While perhaps it would be expected that October monthly mean total column ozone would be reduced above and downwind from the Antarctic Peninsula when the additional cooling ∆ − is included in the perturbation run, there is little change to column ozone values here. Instead, total column ozone is reduced between 30°E-130°E and increased between 120°W-180°W. This is indicative of a shift of the polar vortex away from the Pacific sector of the Southern Ocean, and towards the Indian Ocean sector. This result is supported by the 25 km pressure and 310 temperature differences between the two simulations, which both indicate a change in the position of the polar vortex (Fig.   11).

Conclusions
Mountain-wave-induced PSC formation, which is a significant influence on ozone chemistry, is missing from current coarseresolution global chemistry-climate models because the small-scale temperature fluctuations associated with mountain waves are neither resolved nor parameterised -limiting our ability to make accurate predictions of stratospheric ozone. This study examines in detail an attempt to make global chemistry-climate models more physically based / comprehensive by including a novel parameterisation of mountain-wave-induced temperature fluctuations inserted into a 30-year run of the global chemistry-climate configuration of the UM-UKCA global chemistry-climate model.
The study firstly examined the detailed representation of episodic and localised wintertime stratospheric cooling-phases over 320 the Antarctic Peninsula, secondly the subsequent impact of the cooling-phases on local chlorine activation and PSC formation, and thirdly the impacts of the scheme over the entire high-latitude Southern Hemisphere (i.e. the inclusion of mountain-waveinduced cooling-phases from many other orographic hot-spots, and not just the Antarctic Peninsula) on ozone and the stratospheric polar vortex. The main findings were: -The probability distribution of the parameterised cooling-phases are in reasonable agreement with values derived 325 from long-term AIRS brightness temperature measurements ∆ − (with a possible reason for the discrepancy being that AIRS also includes contributions from non-orographic source regions) and in excellent agreement with values derived from long-term radiosonde temperature soundings ∆ − from Rothera Research Station situated on the Antarctic Peninsula.
-In both cases the agreement with the AIRS and radiosonde values was particularly good for the lower / large cooling 330 part of the tail of the distributions, with a lower bound of up to -6 K for ∆ − and ∆ − and up to -8 K for ∆ − and ∆ − -which is perhaps the region of the distribution that is critical for decreasing temperatures below the threshold for PSC formation.
-The addition of ∆ − to the resolved/synoptic-scale temperatures in the UM-UKCA model (i.e. = − + ∆ − ) results in a considerable increase in the number of instances when minimum temperatures fall below 335 during late austral autumn / early austral winter and early austral spring by extending the lower bound of the − distribution from around − = 0 K to − = −10 K, i.e. without the additional cooling-phase the ice frost point is rarely exceeded in the model by more than a degree Kelvin or so during these periods.
-The addition of ∆ − extends the lower bound of the − distribution from around − = −10 K to − = −20 K, however it is only during October (and to a lesser degree September) that the additional cooling 340 ∆ − is required for T to drop below , i.e. early austral spring (although it should be noted that the impact on PSC formation is also dependent on the local mixing ratios of HNO 3 and H 2 O, which in part is affected by PSC formation and sedimentation earlier in the winter).
-Values of the FP of PSCs composed of water ice particles are many times larger if the additional cooling ∆ − is included, while for PSCs consisting of NAT particles although the additional cooling resulted in an increase in FP, it 345 was small. https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License.
-The addition of ∆ − results in an increase in the surface area density of NAT particles throughout the winter and early spring, which is important for chlorine activation -evident in a large increases in the flux through the ClONO 2 + HCl reaction throughout August and September.
-Examination of the total column ozone during October shows that the addition of ∆ − results in a reduction between 350 30°E-130°E and an increase between 120°W-180°W, indicative of a shift of the polar vortex away from the Pacific sector of the Southern Ocean, and towards the Indian Ocean sector.
Note that Keeble et al. (2014) demonstrated that in the version of UM-UKCA used here that polar ozone depletion can have significant impacts on the polar vortex, affecting both the strength and latitude of the westerly polar jet, and this relationship has also been noted by other studies (e.g. McLandress et al., 2010;Son et al., 2010;Polvani et al., 2011). The study thus shows 355 that both the local and non-local impacts of including the scheme are substantial, and that inclusion of the scheme in a global chemistry-climate model is a step towards it becoming more consistent with our physically based understanding of the atmosphere. This we suggest is essential for understanding how models respond to changes to ozone-depleting substances and greenhouse gases, and hence for improving predictions of ozone and the high-latitude Southern Hemisphere climate system.
Note also that next generation models, such as the ICON-ART (ICOsahedral Nonhydrostatic model with Aerosols and Reactive 360 Trace gases) global modelling system (Schröter et al., 2018), may be able to employ variable spatial resolution with local grid refinement where the resolution increases locally over mountainous regions so that the mountain-wave-induced temperature fluctuations are resolved explicitly, negating the need for their parameterisation.
As one of the main aims of global chemistry-climate models is the prediction of ozone, which to determine accurately requires a realistic treatment of PSCs, further work will focus on assessing the representation of PSCs in this state-of-the-art 365 configuration of the UM-UKCA by comparing results in both hemispheres against a comprehensive climatology of PSC coverage based on MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations (Spang et al., 2018).
Moreover, although the UM-UKCA model (in common with many other global climate models) employs a rather simplistic PSC scheme which limits its ability to accurately predict ozone, the improved representation of PSC formation detailed in this study will also eventually be used to develop better projections of future polar ozone levels in response to climate change, such 370 as narrowing uncertainties in the rate and timing of the closure of the Antarctic ozone hole (Eyring et al., 2013).

Code/data availability
The AIRS measurements of brightness temperature perturbations used in this study are registered under https://www.re3data.org/repository/r3d100012430, with a DOI http://doi.org/10.17616/R34J42, and can be downloaded from https://datapub.fz-juelich.de/slcs/airs/gravity_wave. The high-resolution radiosonde data from Rothera Research Station can 375 be downloaded from https://catalogue.ceda.ac.uk/uuid/37f2bef57e28bcd780a5cbfe077f4bf8. Please contact the lead author if you would like access to the UM-UKCA output. Data analysis in this paper was conducted using the open source python libraries SciTools-Iris (https://scitools.org.uk/iris) and Pandas (https://pandas.pydata.org/). https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License.    Fig. 1 for location) and compared against parameterised values from the perturbation run, which are taken from the grid box that contains this location. Note that a minimum threshold of T < -0.1 K is used to reduce the inclusion of noise/spurious events. 570 575 580 https://doi.org/10.5194/acp-2020-560 Preprint. Discussion started: 7 July 2020 c Author(s) 2020. CC BY 4.0 License. Fig. 2. Note that the AIRS results also include some contribution from non-orographic wave sources. Note also that the maximum number is used to rescale/normalise the values from 0 to 1.