For decades, polar stratospheric clouds (PSCs) have been known to play a key role in explaining the stratospheric ozone hole . Reactions on their surfaces lead to the activation of chlorine reservoirs, known as chlorine activation. In addition, sedimentation of PSCs results in dehydration and denitrification of the lower stratosphere. Three types of PSCs with different composition and roles have been found to be important: (1) liquid supercooled ternary solution (STS) droplets that are major contributors to chlorine activation e.g.,; (2) ice PSCs that lead to dehydration of the stratosphere e.g.,; and (3) nitric acid trihydrate (NAT) particles that dominate the irreversible removal of nitric acid (HNO3) from the lower stratosphere via sedimentation, known as denitrification, thereby potentially reducing the reformation of chlorine reservoir species which can affect ozone loss e.g.,.

The existence of NAT particles in the stratosphere has been verified by a variety of airborne measurements . As the polar vortex in the Southern Hemisphere (SH) is generally more stable than in the Northern Hemisphere (NH) e.g.,, the time period when denitrification can occur is much longer in the SH polar vortex than in the NH. On the other hand, denitrification in the NH has been found to occur locally, and the role of low-number-density large-size NAT particles, so-called NAT “rocks”, has been discussed and verified by measurements . These large particles can lead to significant sedimentation, even on the shorter timescales needed to explain the occurrence of low HNO3 volume mixing ratios (VMRs) in the NH spring.

Satellite instruments are able to provide measurements of PSCs and gaseous HNO3 with daily near-global coverage . found the first evidence of a NAT belt around the Antarctic continent using the Michelson Interferometer for Passive Atmospheric Sounding onboard the Environmental Satellite (MIPAS/Envisat). Therefore, satellite measurements provide a unique opportunity for comparisons of HNO3 and denitrification with current chemistry–climate models in both hemispheres. They also the allow study of the range of the probability distributions in the data for different years and conditions.

Accounting for denitrification is an important process in chemistry–climate and in chemistry–transport models, which is why many parameterizations with different levels of detail have been developed to account for the microphysics and sedimentation of NAT particles in these models e.g.,. Some models account for the comprehensive microphysics of NAT particles e.g., to determine radii and concentrations which then redistribute gaseous HNO3. Others use an intermediate approach, not considering the full microphysics but accounting for the amount of NAT with a measured NAT distribution e.g.,. In addition, there are models with diagnostic approaches, as suggested by studies such as , where NAT is formed based on the available gaseous HNO3, sedimentation is computed and the HNO3 contained in NAT particles is released back to the gaseous phase within the same model time step.

The standard version of the Whole Atmosphere component of the Community Earth System Model includes a diagnostic parameterization of NAT with a NAT number density set to a global value of 10-2 cm-3 . However, found that this value leads to an overestimation of gaseous HNO3 in the NH for the extraordinarily cold NH winter of 2019/2020 in comparison to measurements of the Microwave Limb Sounder. They found a better agreement with the measurements using an adopted NAT number density of NNAT=10-5 cm-3. Here, we investigate this further by applying an adapted version of the method by to compare the distributions of various gaseous species within the polar vortex with MIPAS, which provided measurements during 2002–2012. MIPAS data are well suited to this task, as this instrument provides high signal-to-noise local measurements over multiple years in both hemispheres, with good dynamic range; other datasets may also be appropriate, but we use MIPAS here for this initial test. Further, using MIPAS and varying the NAT number density of the model, we can also investigate its associated impact on HNO3, ClONO2 and O3 for many spring seasons in both hemispheres.

Details about the model and the observation as well as the methods to compare them can be found in Sect. . Section  shows the comparison between the datasets. Finally, some concluding remarks are given in Sect. .

In this section, we will analyze the influence of changing denitrification in WACCM on the trace gases HNO3, ClONO2 and O3 during early spring in both hemispheres. Generally, these species are expected to be influenced by a changed NAT number density because it leads to a redistribution of HNO3 (lower HNO3 at high altitudes and higher HNO3 at lower altitudes). During early spring, HNO3 is photolyzed and forms NO2 which combines with ClO to form ClONO2. ClO is responsible for part of the catalytic ozone depletion and deactivated by the reaction into ClONO2.

As a starting point, Fig.  shows vortex-mean profiles of the HNO3 VMR during the local spring months for both hemispheres and the years 2003 and 2011 for MIPAS and all WACCM simulations of this study. Analysis of the other years of the MIPAS period showed similar results, which is why we focus on the two example years 2003 and 2011 here. The signature of denitrification is indicated in the MIPAS measurements (first row of Fig. , panels a, f, k and p) by the HNO3 VMR decreasing over time at around 30 to 60 hPa, with corresponding increases due to renitrification at the end of the shown time series at lower levels, like in 2011 in the NH (Fig. f). In the simulation with the current operational NAT number density of 10-2 cm-3, HetAll.1e-2 in the second row of Fig.  (panels b, g, l and q), the NAT particles do not get large enough for the simulated HNO3 to compare well with the measured denitrification of the shown years. This means that larger HNO3 VMRs than observed remain at higher altitudes at the end of the time series, whereas the VMR is underestimated at lower altitudes. In contrast to this, with the smallest tested number density of 10-5 cm-3, HetAll.1e-5 in the last row of Fig.  (panels e, j, o and t), the HNO3 VMRs become too low, which indicates that the NAT particles become too large. By using a number density of 5×10-4 cm-3 (HetAll.5e-4 in the third row of Fig. , panels d, i, n and s), the differences compared to MIPAS HNO3 are smallest and the altitude regions of denitrification and renitrification are comparable to the measurements.

In order to generalize this comparison, Fig.  shows correlation plots of the HetAll simulations using different NAT number densities compared with the MIPAS measurements for the whole pressure range (30–150 hPa) and all MIPAS years in the NH and SH. The HetAll.5e-4 simulation (green) has a relatively compact correlation with MIPAS in both hemispheres, whereas the other simulations show a larger scatter. The HetAll.1e-2 simulation shows HNO3 VMRs that are too large compared with the observations because of NAT particles that are too small. As already suggested by Fig. , the HNO3 VMRs in HetAll.1e-5 are too small compared with MIPAS as a result of overly large NAT particles which lead to sedimentation below the shown pressure range. As this figure includes the data of all MIPAS years and the whole pressure range, we will investigate the PDFs of this pressure range in the following, using the approach based on .

PDFs of HNO3 for the whole MIPAS period from 2002 to 2012 and for the years 2003 and 2011 are shown in Fig.  within the polar vortex in both hemispheres. All individual years can be found in the Supplement (Fig. S1). The blue line corresponds to the simulation with the largest NAT density, the red line shows the simulation with smallest NAT density, and the green and yellow lines show the simulations using NNAT=5×10-4 cm-3, with the latter probing turning off all heterogeneous chemistry apart from N2O5 + H2O. The black line corresponds to the MIPAS HNO3 PDF.

Figure  demonstrates that the NAT density has a large impact on the PDFs in the model between 30 and 150 hPa. In HetAll.1e-2, larger HNO3 values are more frequent compared with the other simulations in all of the panels. In contrast to this, HetAll.1e-5 underestimates HNO3, as manifested by a higher frequency at smaller values. The simulation with peak values closest to the MIPAS observations in all panels of Fig.  is HetAll.5e-4, indicating that heterogeneous chemistry is relevant and that NNAT=5×10-4 cm-3 leads to PDFs comparable to MIPAS for all years (see also Fig. S1). However, the NAT parameterization in WACCM is not able to capture the minimum and maximum values; this is especially notable in the SH where VMRs as high as 12.5 ppbv have been measured by MIPAS, whereas the largest values in HetAll.5e-4 are around 10 ppbv (e.g., in Fig. d).

The overall agreement is also shown by the mean values illustrated by the dashed vertical lines in the figure. Moreover, the mean HNO3 value for HetAll.5e-4 is closest to the MIPAS mean value in all of the panels. In addition, the maximum difference, max(d), of the HetAll.5e-4 simulations is smallest in the shown years compared with the other simulations.

In almost all years, heterogeneous chemistry leads to about 0.5 ppbv smaller HNO3 mixing ratios in the NH (see also Fig. S1). In the SH, this effect is not as large as in the NH. In total, the effect of halogen heterogeneous chemistry on HNO3 is small. The mean and shape of the distributions are similar for noHetAll.5e-4 and HetAll.5e-4.

As the number of profiles differs from year to year depending on the development of the polar vortex, more weight in the distributions of the whole period from 2002 to 2012 (Fig. a and d) is given to years with a larger polar vortex. This is why the distributions of all individual years are shown in the Supplement (Figs. S1, S2, S3). As a summary, the max(d) values of the individual years are shown in Tables  and  for the NH and SH, respectively. The simulation with the minimum difference is highlighted in bold for each year. Apart from some exceptions in the NH, the minimum difference occurs for the simulations using NNAT=5×10-4 cm-3. This is another indication that this value improves denitrification and the distribution of HNO3 in the polar vortex in both hemispheres.

Exceptions are the years 2004 and 2012 in the NH, where the simulation with the smallest NAT density (HetAll.1e-5) shows the minimum differences compared with the MIPAS distribution (see Table ). In 2004, relatively warm temperatures in the NH stratosphere lead to low denitrification . As shown in Fig. a, this is reflected by the WACCM HNO3 distributions which do not show large differences when changing the NAT density during that year. However, there seems to be a systematic difference between WACCM and MIPAS in this year, maybe due to a lower accuracy of the instrument or, for instance, a mismatch in the location of the polar vortex in the reanalysis data used for the model and the temperature history during that winter. The 2011/2012 Arctic winter was characterized by an early breakdown of the polar vortex at the end of December , but there is some evidence of large NAT particles during the cold period during December 2011 . This is why the HNO3 distributions change in WACCM and large NAT particles probably play a larger role in determining the distributions for that year (see Fig. b).

Moderate changes due to the different NAT densities can also be found in other species. Figure  shows the distributions of chlorine nitrate (ClONO2) for the same years and simulations as in Fig. . Both HetAll.1e-2 and HetAll.5e-4 have similar distributions and max(d) values. Depending on the year, minimum max(d) values occur in both simulations. HetAll.1e-5 seems to slightly underestimate the ClONO2 mixing ratios in both hemispheres, illustrated by higher frequencies of low mixing ratios.

As a result of increased denitrification, impacts on the maximum values in the ClONO2 distributions can be seen in both hemispheres. While the maximum ClONO2 mixing ratios in the NH are closer to the MIPAS distributions using NNAT=10-5 cm-3, the HetAll.5e-4 simulation captures the maximum values in the SH distributions.

There is also a clear signature of heterogeneous chemistry in the ClONO2 distributions. The deactivation reaction forming ClONO2, R1ClO+NO2+M→ClONO2+M, is usually faster than the deactivation reaction forming HCl, R2Cl+CH4→HCl+CH3, as long as NO2 is available and the ozone VMRs are large enough e.g.,. At the edge regions of the polar vortex, some of the chlorine originally in HCl at the start of the winter shows up in ClONO2 at the end of the winter, known as the ClONO2 “collar” e.g.,. Therefore, while the simulation without heterogeneous chemistry, noHetAll.5e-4, generally shows ClONO2 VMRs that are too low compared with MIPAS, it is notable that the maximum values are increased by about 1 ppbv and are then comparable to MIPAS in all simulations with heterogeneous chemistry.

Similar impacts can be seen in the distributions of ozone (O3), shown in Fig. . Ozone VMRs are about 1 ppmv too large in the SH for noHetAll.5e-4 compared with MIPAS. In the NH, the difference is smaller, but ozone is still overestimated in the simulation without heterogeneous chemistry.

showed that the agreement of WACCM model calculations of Arctic spring ozone losses in 2020 with ozonesonde data was heavily dependent on the denitrification parameterization used in the model, providing an important check. Figure  displays comparisons between WACCM and ozonesonde data at Syowa and Eureka for their respective spring seasons for all years between 2002 and 2012. A figure showing the comparison to Syowa without noHetAll.5e-4 can be found in the Supplement (Fig. S4). Similar comparisons are provided for the South Pole in Fig. S5. Because local Antarctic ozone abundances can drop to values lower than 1 ppmv between about 15 and 20 km in years with large ozone losses (see, e.g., Fig. a), care must be taken in interpreting percentage differences; we also provide absolute differences for that reason. The Syowa sondes agree considerably better with the model when the NAT number density of 5×10-4 cm-3 or less is used, compared with larger values of this parameter. At Eureka, results are far less sensitive due to smaller ozone losses, except in very cold years like the year 2020, as shown in.

In summary, the results in this section showed that the diagnostic parameterization of NAT particles in WACCM is able to reflect the main features in the three key gas-phase species, HNO3, ClONO2 and ozone. When using a reduced NAT particle number density of NNAT=5×10-4 cm-3 compared with the standard WACCM setup, the difference in the CDFs is minimized for almost all spring months of the MIPAS period from 2002 to 2012, and the bias between 30 and 150 hPa in comparison to the ozonesondes is reduced. This indicates that NNAT=5×10-4 cm-3 improves the distribution of these species in the polar regions and that it should be used in future WACCM simulations.

In this study, we investigated the impact of the NAT number density (NNAT) on the distributions of key gaseous species in the WACCM model. We compared PDFs within the polar vortex with the MIPAS satellite instrument which operated between 2002 and 2012. As a measure of the difference between the distributions of WACCM and MIPAS, we used the maximum difference, max(d), between the CDFs. To identify the impact of NAT on the distributions of the species, we performed sensitivity simulations varying NNAT between 10-2 and 10-5 cm-3. In the method applied here to compare the satellite measurements with the model, negative values in the satellite retrievals are cut off, which is why the method cannot be used for noisy data (such as N2O5 and ClO in MIPAS) and we restricted the analysis to the gaseous species HNO3, ClONO2 and O3.

The diagnostic parameterization of NAT in the WACCM model was shown to be able to reproduce the general shape of the MIPAS distributions for almost all years of the MIPAS period in the polar vortices of both hemispheres. However, there was a general overestimation of gaseous HNO3 when using the standard NNAT of 10-2 cm-3. Reducing this number concentration to 5×10-4 cm-3 was shown to also reduce the differences in the HNO3 distributions between MIPAS and WACCM for almost all spring seasons in both hemispheres during the MIPAS period. Changes in the distributions of ClONO2 and O3 due to the new value of NNAT were either negligible or the differences were reduced. Mean differences between the ozonesonde profiles and the WACCM simulations at the grid point closest to the Syowa and Eureka sonde stations were also shown to be decreased when reducing NNAT. Therefore, this study suggests the use of NNAT=5×10-4 cm-3 for future simulations. This new value is within the range of measured NAT number densities.

A simplified parameterization as applied in WACCM cannot capture all features of the distributions of all years. This was demonstrated by the years 2004 and 2012 where differences between the MIPAS and WACCM distributions were increased. Further, there are many uncertainties in the detailed chemistry from one year to another that may also be important for denitrification and ozone loss, including, for example, volcanic particle inputs, gravity and planetary wave amplitudes, and changes in circulation, to name only a few. It can also be seen that the smallest number density of 10-5 cm-3 is comparable to measurements during the exceptionally cold northern winter of 2019/2020 . In addition, although overall differences in the PDFs are reduced with NNAT=5×10-4 cm-3, the Kolmogorov–Smirnov test, used by studies such as with a similar approach to that used in this study, will fail if it does not employ a significance level α that is set to a very small value (O(10-70)). Therefore, although the differences are decreased, the distributions of MIPAS and WACCM are still different in a statistical sense, probably due to the simplifications in the NAT parameterization of WACCM.

Future studies could apply this methodology to other long-term satellite measurements to evaluate the usage of this new NAT number density for further years, although their precision has to be low enough to allow for comparisons with individual profiles. Nevertheless, as a follow-up of the publication by , we recommend using NNAT=5×10-4 cm-3 for future simulations with WACCM, indicating that NAT rocks play an important role, especially in the NH where we saw the largest impact on changing the NAT number density.