NAT nucleation and denitrification i the Arctic stratosphere

Introduction Conclusions References

It has been known for more than a decade that large nitric acid containing particles of over 10 µm diameter can be present in the cold polar stratosphere (Fahey et al., 2001).the stratosphere, namely denitrification above altitudes of about 18 km and nitrification below.The process of denitrification is important for ozone depletion as it reduces springtime HNO 3 and thus slows down the chlorine deactivation which occurs through the reaction ClO + NO 2 at the end of the polar winter (e.g.Müller et al., 1994).Simulations using Lagrangian particle tracking have shown that it is possible to explain the observed denitrification by the vertical HNO 3 transport caused by the sedimentation of these large particles, which are assumed to consist of nitric acid trihydrate (NAT) (Carslaw et al., 2002;Grooß et al., 2005).For these Lagrangian particle trajectories a constant, air-volume based NAT nucleation rate was applied (i.e., constant number of nucleations per time unit per volume of air) whenever temperatures were below T NAT .Besides this on/off dependence for T < T NAT or T > T NAT no other temperature dependence was taken into account because of lacking experimental or observational information (Carslaw et al., 2002;Grooß et al., 2005).For the winter 2002/2003, a simulation with the nucleation rate derived from a single observation, where the NAT nucleation rate in the absence of ice could be constrained (Voigt et al., 2005), reproduced the basic features of the observed denitrification and nitrification (Grooß et al., 2005).However, for other winters a nucleation rate with a three times lower value needed to be chosen to successfully simulate the observed denitrification (Davies et al., 2005).
Recently, studies based on CALIOP data confirmed previous speculations, that heterogeneous nucleation on foreign particles, probably of meteoritic origin, contributes to the NAT nucleation in the polar stratosphere (Hoyle et al., 2013;Engel et al., 2013).Using micro-physical model calculations of the Zurich Optical and Microphysical box Model (ZOMM) along back-trajectories, the saturation-dependence of NAT particle nucleation could be determined (Hoyle et al., 2013).This information is used here to construct a new NAT nucleation parametrisation for the Chemical Lagrangian Model of the Stratosphere (CLaMS).
To this end, we present a comparison between CLaMS simulations and observations for the Arctic winter 2009/2010, in which the extensive measurement campaign of the project RECONCILE (von Hobe et al., 2013)  aim of this work is to constrain the value of the NAT nucleation rate by means of PSC observations, especially those by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the CALIPSO satellite (Pitts et al., 2009) and in this way improve the simulated denitrification.
Even though the vortex in winter 2009/2010 experienced a vortex split event in early December and was one of the warmest winters within the last two decades, when averaging December-March, the period between mid-December 2009 and end of January 2010 was exceptionally cold (Dörnbrack et al., 2012).During this period, spaceborne LIDAR observations by CALIOP indicated more PSCs than in previous Arctic seasons, with significantly more observations of ice clouds and higher number densities of NAT particles than hitherto observed by CALIOP (Pitts et al., 2011).
In addition to the present study, CLaMS has been used in a very similar configuration, however with a constant NAT nucleation rate, in several other recent works examining the Arctic winter of 2009/2010.For example, Hösen (2013) investigated in-situ tracer observations, while Woiwode (2013) and Kalicinsky et al. (2013) used CLaMS to interpret the remote sensing observations of the aircraft instruments MIPAS-ENVISAT and CRISTA-NF, respectively.Further, Wohltmann et al. (2013) performed simulations with a focus on the sensitivity of polar chlorine chemistry and ozone loss on heterogeneous reactions, also using a constant NAT nucleation rate.

CLaMS
The Chemical Lagrangian Model of the Stratosphere (CLaMS) is a Chemistry Transport Model that is based upon the Lagrangian principle (McKenna et al., 2002a, b;Konopka et al., 2004).The chemical composition of the atmosphere is simulated for multiple air parcels that are moving due to advection.Interaction between the air parcels is realised by an anisotropic mixing scheme (McKenna et al., 2002a;Konopka et al., 2005).The Introduction

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Full model also includes the Lagrangian simulation of NAT particles (Grooß et al., 2005).Hemispheric simulations starting on 1 December 2009 were carried out using CLaMS with two different spatial resolutions.The reference simulation has a horizontal resolution of 70 km northward of 10 • N and 100 km between 0 and 10 • N. The vertical model range is between 320 and 900 K, divided into 50 levels, resulting in a vertical resolution of about 450-500 m between 20 and 25 km altitude.This simulation uses about 2.9 million air parcels.Besides this reference simulation, a lower resolution hemispheric configuration was used, with a horizontal resolution of 100 km northward of 40 • N, and 300 km southward of 40 • N, comparable to earlier simulations, e.g.Grooß and Müller (2007).This configuration uses about 380 000 air parcels in 32 vertical levels and a vertical resolution of about 700 m between 20 and 25 km altitude.It was used for various sensitivity simulations, as described below.Also some evaluations that require large data output were based on the lower resolution configuration.
The underlying wind and temperature fields are taken from the ERA-Interim analysis data provided by the European Centre of Medium-Range Weather Forecasts (ECMWF) (Dee et al., 2011).The CLaMS simulations use a hybrid vertical coordinate ζ , that is equal to potential temperature above the 300 hPa pressure level and transitions to a pressure-like coordinate below (Konopka et al., 2007;Riese et al., 2012).As most of the analysis of this study refers to altitudes above the 300 hPa level, the vertical coordinate ζ can be interpreted here as potential temperature.The vertical velocities are derived from ERA-Interim total diabatic heating rates (Ploeger et al., 2010).For the mixing parametrisation (McKenna et al., 2002a), a time step of 24 h and a critical Lyapunov coefficient of 1.5 day −1 were used (Riese et al., 2012).
As micro-physical properties of PSCs are highly temperature-dependent, it is very important to base the simulations on realistic temperature data.Engel et al. (2013) indicated that the ERA-Interim temperature data compared better with the radiosonde observations than other available meteorological analyses.Also, temperature fluctuations occurring on time scales below the 6 h resolution of the ERA-Interim data used here were shown to have a relatively small impact on the NAT nucleation rates (Hoyle Introduction

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Full  , 2013).Further, the effects of such unresolved temperature changes in the model meteorological fields are implicitly accounted for in the NAT nucleation parametrisation.The CLaMS simulations include dynamical tracers such as passive ozone and passive NO y , that are not influenced by chemistry.These were initialised at the beginning of the simulations with the ozone and NO y fields, respectively.The difference between these tracers and their chemically active counterparts can be used to quantify changes due to chemistry or particle sedimentation.
Stratospheric chemistry in CLaMS is an updated version of the scheme described in McKenna et al. (2002b), in which reactions of importance in the upper stratosphere are now included.The 143 reactions of 45 variable chemical species now explicitly contain reactions involving H radicals as well as N 2 O and CFCs.The detailed list of reactions included in CLaMS is given in Table A1 in the appendix.Chemical reaction rates and absorption cross sections are based on Sander et al. (2011) including ClOOCl photolysis, which is based on Papanastasiou et al. (2009).Exceptions are the reaction rate for ClOOCl formation which is based on Nickolaisen et al. (1994) and the ClOOCl equilibrium constant which is based on Plenge et al. (2005), respectively, as suggested by Sumińska-Ebersoldt et al. (2012).Heterogeneous reaction rates on liquid aerosol particles are based on the parametrisation of Shi et al. (2001), see also Wegner et al. (2012) for details.As the NAT particles are handled in the separate sedimentation module at locations not congruent with the air parcels, the heterogeneous reactions on the NAT particles could not be considered here, which is justified since it is mostly the liquid particles in the polar stratosphere which are responsible for the heterogeneous reactions leading to the activation of the halogens (Solomon, 1999;Drdla and Müller, 2012;Wegner et al., 2012).
The Lagrangian sedimentation scheme described by Grooß et al. (2005) was used to simulate the vertical redistribution of NO y .However, the nucleation rate of NAT particles was adapted to the new parametrisation as described below.Within this scheme, the advection of the NAT particles is calculated in a Lagrangian way along individual trajectories.Each particle parcel corresponds to a number of particles with identical Introduction

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Full characteristics distributed over a volume equal to the size of the air parcel.To this end, a number concentration is assigned to each particle parcel that is assumed to be constant over that volume.Multiple particle parcels per air parcel are common.The locations for possible NAT nucleation per day were homogeneously distributed in space with a 4 times higher density than that of the air parcels.For a sensitivity study, this ratio between daily nucleating NAT particle parcels and air parcels was increased from 4 to 64 with a decreasing corresponding assigned number density.Growth and evaporation of the individual particles was calculated depending on temperature and gas-phase HNO 3 and H 2 O of the neighbouring air parcels.Vertical descent due to sedimentation of the particles is included.The NAT particles are assumed to have the shape of compact spheres.
The dehydration of the stratospheric air masses that typically occurs at the tropopause level is implemented using a temperature-dependent parametrisation for heterogeneous freezing (Krämer et al., 2009;von Hobe et al., 2011).This parametrisation also allows for dehydration in the stratosphere to be simulated similar to that which was observed during January 2010 (Khaykin et al., 2013).
The chemical initialisation of the simulation for 1 December 2009, 12:00 UTC was based on satellite data, on MIPAS-ENVISAT (updated version 5 data from von Clarmann et al. (2009) and ACE-FTS (version 3.0) (Bernath et al., 2005) and observed tracer correlations.Additionally, we used data from a multi-annual CLaMS simulation with simplified chemistry (Pommrich et al., 2011;Ploeger et al., 2013).The details for the individual species are given below.
N 2 O was initialised from the multi-annual CLaMS simulation below 400 K and from MIPAS-ENVISAT above 500 K with a linear transition in between.O 3 was initialised above 400 K from MIPAS-ENVISAT data within ±2 days.The observation locations were transformed to the synoptic initialisation time using CLaMS trajectories and gridded to a 2 • × 6 • grid.Below 350 K, O 3 was taken from the multi-annual simulation with a linear transition in between.Similarly, H 2 O was gridded from MIPAS-ENVISAT data above 600 K and taken from the CLaMS multi-annual simulation with a linear transition Introduction

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Full Cl y and Br y were derived from CH 4 data correlation after Grooß et al. (2002), where Br y is increased by 10 % to account for the increase in bromine-containing source gases since the year 2000.Total inorganic nitrogen NO y could also be initialised from a correlation with N 2 O from ACE-FTS data, since ACE-FTS observed all major compounds of NO y (Jones et al., 2011).For the species CO, CFC-12, CH 3 Cl, CFC-22, CCl 4 , and CFC-113 a correlation was also derived from ACE-FTS version 3.0 data in November 2009.HNO 3 was derived similarly to O 3 from gridded MIPAS-ENVISAT data, whereas the remaining NO y species were scaled linearly to fit to the derived total sum of NO y .CFC-11 mixing ratios were derived from N 2 O using data correlation from ACE-FTS version 3.0 for November 2009 and different equivalent latitude ranges (10 • N, and > 60 • N).The polynomial fits of all these correlations are listed in Table A2 in the appendix.The remaining minor species as well as the partitioning within the various chemical families are taken from Mainz 2-D model (Grooß, 1996).The sulfate aerosol vertical distribution was initialised consistent with an aerosol surface area density climatology for the months November and December 1998/99, a period with low aerosol particle content (similar to the conditions for the 2009/2010 Arctic winter) from a climatology of D. Considine (Eyring et al., 2006), that is based on SAGE II data for these years.
The boundary conditions at the upper boundary at 900 K were derived from observations similar to the initialisation for two times per month.The mixing ratios of N 2 O, H 2 O, and HNO 3 were taken from MIPAS-ENVISAT and averaged to equivalent latitude bins.At the lower boundary of the simulations in the free troposphere (ζ = 320 K, corresponding to θ = 320 K within ±5 K at polar latitudes), no modification was made such that the chemical composition an the lower boundary is determined only from the initialisation and transport, predominantly the vertical descent inside the polar vortex.Introduction

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Full  2013) demonstrate that heterogeneous nucleation is needed to explain the occurrence of NAT PSCs observed in the Arctic during the second half of December 2009.A potential source of heterogeneous nuclei is meteoritic dust immersed in stratospheric aerosol particles (Curtius et al., 2005).Hoyle et al. (2013) showed that a constant nucleation rate cannot explain the observed CALIOP PSC data.They assumed that heterogeneous nucleation is triggered by active sites of the individual dust particles and that the nucleation efficiency of these active sites can be characterised by a distribution of contact angles.The occurrence probability distribution of the contact angles is shown in Fig. 2 of Hoyle et al. (2013).For the implementation of the heterogeneous NAT nucleation rates derived in such a way into CLaMS some modifications needed to be made.While in the study of Hoyle et al. (2013), the ZOMM simulations were performed on 10 day back-trajectories that exclusively started at temperatures above T NAT , the nucleation in a chemical transport model must be handled differently.
Here we use the 24 h forward air mass trajectories of the CLaMS simulation of which some may start at temperatures below T NAT .The supersaturations required to nucleate a NAT particle in a specific contact angle bin within a 1 h time interval were derived from the parametrisation of Hoyle et al. (2013), which implicitly accounts for the effect of unresolved temperature fluctuations.These values were tabulated to a 2-dimensional array as a function of temperature and 0.1 • contact angle bin, as shown in Fig. 1.The right ordinate shows the corresponding dust particle concentrations per contact angle bin taken from Hoyle et al. (2013).
To implement this model into the CLaMS simulation, the history of NAT supersaturation of the air parcels is traced from the time when the temperature falls below T NAT .For that, two additional tracers were introduced to the model that represent the maximum supersaturation of HNO 3 over NAT of an air parcel, S max NAT , and the corresponding temperature T min .The purpose is to trace the information about dust particles in the different contact angle bins that already have been used for NAT particle nucleation.For Introduction

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Full each air parcel the maximum NAT supersaturation S max NAT is evaluated for each hour of the following 24 h time interval.For the case that S max NAT is larger than it was at the beginning of the time interval, a positive NAT nucleation rate is determined for this location by summing up the particle concentrations per contact angle bin between the two corresponding contact angles before and after the 24 h interval.This number corresponds to the additional dust particles activated for NAT nucleation.Figure 2 is a schematic that illustrates the determination of S max NAT .The hourly calculated values of S NAT are indicated as a red line and the daily determined values of S max NAT as green dots.For this arbitrary example, no additional NAT particles would be nucleated on the last day.Due to the strong non-linearity of the nucleation rate with respect to temperature, we assume that the NAT nucleation rate over one day is dominated by the hour with highest S NAT during the day where the corresponding dust particles are activated and we neglect the nucleation during the rest of the day.As the location of the start of a particle trajectory in general does not coincide with the air parcel, the parametrised nucleation rate is then interpolated onto the locations of NAT particle nucleation in the model.The NAT nucleation rate is then reflected in the NAT number density that is assigned to each individual NAT particle trajectory.

Sensitivity simulations
To evaluate the dependence of the results on details of this parametrisation and to compare the results with previous simulations, sensitivity simulations were performed, by varying the model parameters.
The labels of these sensitivity simulations given in the discussion below are listed in Table 1.As indicated above, CLaMS simulations were performed either with a high resolution of 70 km north of 10 • N (HR) or with a lower resolution of 100 km north of 40 • N (LR).To test the sensitivity of the results to the number of NAT particle parcels in which NAT could be nucleated, a simulation was performed in which the density of NAT particle parcels nucleated each day was increased from 4 to 64 per air parcel (S64).
In turn, the corresponding density assigned to each particle parcel was decreased by 22117 Introduction

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Full a factor of 16.In a further sensitivity simulation, the NAT nucleation rate was increased by a factor 5 (X5).For comparison with earlier publications we included also a simulation with constant NAT nucleation rate of 8 × 10 −6 cm −3 h −1 (Jconst) as used by Grooß et al. (2005), as well as a simulation with the NAT nucleation rate increased by a factor of 10 (Jx10).To investigate the temperature sensitivity, the simulations was also repeated with the ERA-Interim temperatures decreased by 1 K (T-1K).

Comparison with H 2 O and HNO 3 observations
For the simulation of NAT particles it is important to have realistic H 2 O and HNO 3 mixing ratios in the simulation.As a rule of thumb for typical polar stratospheric conditions, an increase of 0.5 ppmv H 2 O or 3 ppbv HNO 3 introduces a comparable increase to S NAT as a 1 K temperature decrease.Thus, we compared the simulated H 2 O and HNO 3 mixing ratios with observations from the balloon based cryogenic frost point hygrometer (CFH), and the satellite based ACE-FTS.
Figure 3 shows four representative comparisons of the model results with balloon based CFH observations made throughout winter, from Sodankylä, Finland (Vömel et al., 2007;Khaykin et al., 2013).Although CLaMS lacks the vertical resolution to reproduce the very fine structure seen in the observations, the large scale features are very well reproduced.The rapid decrease in water vapour above about 300 K is represented well in the model, with only the modelled profile in panel c placing this decrease about 25 K too low.At higher potential temperatures, the model again simulates the increasing water vapour with altitude very well, and even the greater variability of the vertical profile on the 12 March 2010, as shown in panel d appears to be captured to some extent in the CLaMS simulations.range 8 January 2010 ± 7 days and the corresponding simulated mixing ratios.This is the first time period during the simulation where latitude coverage of the ACE-FTS satellite observations allows a sufficient number of observations in the vortex core.Again, CLaMS reproduces the observed water vapour values very well, with the only minor deviation from the measurements being the slightly too low altitude reduction in water vapour below 400 K.The upper part of the profile is, however, almost exactly reproduced by the model.The case is similar in the comparison with the ACE-FTS HNO 3 measurements.CLaMS captures the general shape and variability of the observed profile extremely well.The small deviations of modelled water vapour from the observations at low altitudes are in an altitude region which will not influence PSC formation.The ability of CLaMS to reproduce the observed H 2 O and HNO 3 fields over the higher regions of the profile suggest that model uncertainty in these fields is unlikely to contribute significantly to any deviations of modelled from observed PSC properties or denitrification.

Particle size distributions
During the RECONCILE Geophysica campaign, in situ observations of PSCs were obtained during five flights in late January 2010.The size distributions of the PSCs were measured in situ on the Geophysica by the experiments FSSP-100 (1.1-38 µm diameter) and FSSP-300 (0.4-24 µm diameter) (Baumgardner et al., 1992;Borrmann et al., 2000a).Here, we compare the NAT particle simulations with the FSSP observations.particle size bins is divided by the total number of air parcels in the considered volume.The CLaMS results in Fig. 5 show the log-normal distributions assumed for the liquid ternary aerosol as well as the histogram of the NAT particle size distribution.Observations include the FSSP-100 (green symbols) and the FSSP-300 (blue symbols) channel.The log-normal peak of the modelled liquid aerosol corresponds well with the FSSP-300 data that have the better resolution than FSSP-100 towards the lower particle diameter, although the particle phase cannot be derived from FSSP.In the 5-10 µm range, the model somewhat under-estimates the size distribution.Although in the CLaMS simulations, larger NAT particles than during the time of the Geophysica observations do occur in other periods of the winter, reaching median diameters of up to 20 µm, none of these large particles observed on board the Geophysica were present in the model for the time and location of the observational data.The part of the observed size distribution with very large particles greater than about 15 µm diameter is not present in the simulations as well as not in any of the sensitivity runs.Here the particles are assumed to be compact spheres.One possible cause for this discrepancy could be that the particles are likely to be non-spherical, which could lead to an over-estimation of the observed size (Woiwode, 2013;Borrmann et al., 2000b).

CALIOP particle classification
A direct comparison of the particles simulated by CLaMS with the CALIOP observations was obtained by simulating the optical signals that would have been caused by the CLaMS particle size distributions.For that purpose, the CLaMS particle size distribution was determined as above for points along the CALIPSO orbit path in the following way: for CALIOP observation profiles with about 25 km spacing along track and 180 m vertical spacing, all CLaMS particle parcels within a reference volume were combined to include enough particles parcels within one air parcel.We assume this volume to be of the order of the average volume of an air parcel in the simulation.This distance from the point of observation for the high resolution and low resolution runs, respectively.From that volume, a particle size distribution with 15 size bins between 0.8 and 28 µm diameter was determined.In addition, the simulated liquid aerosol particles are characterised by a log-normal distribution.Note that the derived size distributions for neighbouring points are not independent as their reference volumes overlap.
From these particle size distributions, we calculated aerosol backscatter ratios and perpendicular backscatter signals using Mie and T-matrix calculations (Mishchenko et al., 2010).For NAT, we assumed prolate spheroids with aspect ratios of 0.9 (diameter-to-length ratio) and a refractive index of 1.48.Afterwards, the modelled PSCs were classified according to the PSC classification scheme of Pitts et al. (2011).Figures 6 and 7 show the CALIOP results for two selected orbits as well as the corresponding T-matrix calculation results on 21 December and 30 December, respectively.CLaMS results are shown for the simulations Jconst and HR.The observation on 21 December corresponds to a time when the temperatures were low enough to allow the existence of NAT particles for the first few days.It seems that the location of the PSCs and their composition are reproduced in general by the simulation HR, although differences are visible.On 21 December, the extent of the NAT cloud seems to be somewhat under-estimated for the simulation HR, but the simulation Jconst with the constant nucleation rate over-estimates the extent of the PSC.For the CALIOP observation on 30 December the simulation HR also over-estimates the extent of the PSC, however, the simulation Jconst overestimates the observed PSC extent to a far greater degree.

Vertical redistribution of NO y
The vertical NO y redistribution is a direct consequence of the sedimentation of NAT particles.The vertical flux of HNO 3 associated with this sedimentation strongly depends on the size and number of the NAT particles and therefore on the parametrisation of the NAT nucleation rate.Figure 8  ulations.This flux is evaluated at the 463 K model level for the area where NAT particles were present in the model.The results for the simulations HR, LR and S64 are almost identical.This shows that the calculated mean HNO 3 flux does not significantly depend on model resolution or the chosen density of model NAT particle parcels.However, significant differences of HNO 3 flux to the other sensitivity simulation occur especially in the early phase (20-25 December) and the late phase (20-30 January).In the simulations with lower temperatures (T-1K) and with constant nucleation rate (Jx10 and Jconst) the onset of HNO 3 flux in late December is earlier and the HNO 3 flux is larger than in the reference simulation for the first 10 days.Differences are also larger towards the end of the cold period.For some sensitivity simulations (Jconst, T-1K) the HNO 3 flux is less than that of the reference run likely because less HNO 3 is available due to earlier denitrification.The resulting amount of denitrification or nitrification can be deduced by subtracting the passive tracer NO * y from the simulated NO y .The vortex core average of this difference is shown in Fig. 9 as a function of time and the vertical coordinate ζ for the reference run.Clearly visible is the development of a denitrification region above about 425 K and the nitrification peak below where the NAT particles have evaporated.The maximum simulated denitrification reaches 8.5 ppbv on 19 January near 500 K potential temperature.The maximum simulated nitrification was 6 ppbv on 21 January near 400 K potential temperature.
The differences in the vertical NO y redistribution throughout all simulations is shown in Fig. 10 for two exemplary days.The patterns of denitrification and nitrification are very similar, with the main differences between the model runs being in the magnitude of the denitrification and nitrification, and not in its altitude.In general, larger NAT nucleation rates cause larger amounts of denitrification and nitrification.Due to the lower temperatures in the simulation T-1K, the particles can sediment longer with the consequence that the nitrification peak is about 20 K lower.Around 425 K in mid February, the patterns of denitrification and nitrification at individual locations vary within the sensitivity simulations (not shown).But average profiles, e.g.within the vortex core are very Introduction

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Full similar.Especially later in the winter, the difference between the different sensitivity simulations becomes smaller.A very useful data set to investigate the vertical redistribution of NO y is the ACE-FTS satellite experiment (Bernath et al., 2005).All major components of NO y are observed so that the sum NO y can be determined with high accuracy (Jones et al., 2011).Figure 11 shows the comparison between ACE-FTS version 3.0 and CLaMS for NO y .Average vortex profiles are shown (equivalent latitudes greater than 70 • N) for time periods with a sufficient number of observations within the vortex core.NO y mixing ratios lower than the passive tracer NO * y mixing ratios (grey line) correspond to denitrification and NO y mixing ratios higher than NO * y to nitrification.Model results for the new nucleation parametrisation as well as for constant nucleation rates are shown, for four time periods in which ACE-FTS observation latitudes cover the Arctic region.In early January, the process of vertical NO y redistribution starts with less than about 1-2 ppbv denitrification and nitrification.The HR simulation closely reproduces the observed NO y , with a slight underestimation at around 475 K.The simulation Jconst produced similar results, however with a slight overestimation at around 575 K and a greater underestimation at 475 K than the simulation HR.Jx10 produces a too severe denitrification.During the end of January and early February, a severe denitrification of over 5 ppbv and a distinct nitrification layer is present in both observational and simulated data.For the end of January, the observed profile is best represented by the Jx10 simulation, while both HR and Jconst show too much NO y throughout most of the profile.For mid February however, HR again provides the best representation of the observations, with Jconst giving very similar results, and Jx10 again underestimating the NO y throughout most of the column.In early March, the signature of the nitrification layer decreased due to mixing with air unaffected by nitrification, whereby the denitrification signature is smoothed out but is still visible.Both the HR and Jconst runs reproduce the observed profile well, with Jx10 underestimating NO y throughout the profile.
The simulated NO y redistribution was also compared with gas-phase in situ NO y observations made from the Stratospheric Observation Unit for Nitrogen Oxide (SIOUX) Introduction

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Full aboard the Geophysica aircraft (Voigt et al., 2005).Generally, the SIOUX observations during the RECONCILE campaign show both denitrification and the nitrification layer.An evaluation of the simulated NO y is however difficult since there are small-scale structures below the model resolution that cannot be reproduced.Figure 12 shows the comparison for four flight segments in which SIOUX NO y observations within the vortex have been made.The observations are compared with the sensitivity simulations with high and low resolution (HR, LR) and the simulation with 1 K temperatures decrease (T-1K).The flights on 30 January and 2 February are located at the transition between the denitrification and the nitrification region (compare Fig. 9).The flight on 30 January was the so-called self-match flight (Sumińska-Ebersoldt et al., 2012) that attempted to probe the same air masses twice resulting in mirroring data structures before and after about 8:20 UTC (dotted line Fig. 12a).The symmetry before and after the turnaround point is visible in the data and also in the model results.But the agreement between model versions and data here are not very good.For 30 January and 2 February, a very detailed small scale structure of NO y is visible both in the observations and in the model.However, the small-scale structure is not reproduced in detail and also the individual sensitivity runs show different structures among themselves.Especially significant differences can be seen between the runs with different spatial resolutions (HR, LR) that is not seen in the other comparisons.Even in the HR run, the smallest structures in this region cannot be resolved in every detail, probably because small differences in winds or temperatures below the accuracy of the ERA-INTERIM data cause relevant differences in the location of particle evaporation.It should be noted however, that the global results as zonal mean or vortex mean profiles of NO y or HNO 3 flux agree quite well for the two resolutions.On 2 March, a vortex remnant was observed in the vicinity of Spitsbergen between about 10:00 and 11:00 UTC, where all the performed simulations under-estimate the observed denitrification.On 10 March, when observations within the vortex were taken at somewhat higher potential temperature, the results of the different simulations are closer together and comparable with the observations.Between about 08:50 and 09:20 UTC the observations are located outside the vortex.Introduction

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Full A filament of extra-vortex air that was recently drawn into the vortex observed around 8:30 UTC (Hösen, 2013) marked by the dotted lines shows no denitrification which can be deduced from NO * y .The altitude dependence of the NO y redistribution is shown similarly in Fig. 13 for flights on 4 different days with altitude profiles containing the nitrification peak.The simulations reproduce the general features like the denitrification region and the nitrification peak.However, also the small-scale variability can not be reproduced in every detail as explained above.The evaluation of the simulated vertical NO y redistribution and the comparison with ACE-FTS and SIOUX data indicates that the sensitivity on the choice of nucleation rate parametrisation in early March is less pronounced than in January.

Conclusions
We have shown global model simulations of NAT particle nucleation, particle growth, sedimentation and resulting denitrification.For the first time, a temperature-dependent NAT nucleation rate, derived from CALIOP observations (Hoyle et al., 2013;Engel et al., 2013)  and temperature biases, non-sphericity of the NAT particles, model initialisation, and the nucleation rate related assumptions.Sensitivity studies with respect to model resolution and analysed temperature show areas where the actual structure of NO y redistribution cannot be exactly simulated, where the results in mid-March are less sensitive to the model uncertainties than the results for the beginning of the PSC period.Especially during the setup of the PSC period, the differences of the new method proposed here with respect to using a constant NAT nucleation rate are large.The numerical cost of the new parametrisation is similar to using a constant nucleation rate.As there is a slight improvement in the modelled denitrification with the new parametrisation, and also because a NAT saturation dependent parametrisation is more realistic than assuming a constant rate, we propose that the new parametrisation is used in future studies of PSC formation and denitrification.In future studies on the basis of more observational data, it may be possible to better attribute the remaining differences between observations and model results to specific processes in the model, such as sedimentation rates, or to specific uncertainties in the measurements.Introduction

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Full  Full  A1.Reactions included into the CLaMS chemistry.Only the additional reactions are listed, which were not described by McKenna et al. (2002b), from which the bimolecular reactions (B1-B64), trimolecular or thermal decomposition reactions (T1-T12), photolysis reactions (J1-J27) and heterogeneous reactions (H1-H11) are used.Carbon and Fluorine containing products from halocarbon decomposition are neglected.

Nr
Reaction

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Full i and degree n ≤ 4. The unit of CH 4 is ppmv, the units of N 2 O, Cl y , NO y , and CO are ppbv and the unit of Br y and halocarbons is pptv.For values of [x] outside of the valid range, the closest valid value was used.The correlations are based on ACE-FTS version 3.0 data from October and November 2009 for equivalent latitudes above 50 • N with the exception of Cl y and Br y that are taken from Grooß et al. (2005).The initialisation of CFCl 3 (CFC-11) is based on a wider range of equivalent latitudes (Φ e ) given in the third column (in degrees).
Discussion Paper | Discussion Paper | Discussion Paper | * now at: School of Earth Sciences, The University of Melbourne, Melbourne, Australia 1 Introduction These particles are thought to be responsible for the vertical redistribution of NO y in Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | in between.The initialisation of CH 4 was derived from N 2 O using the correlation from ACE-FTS N 2 O and CH 4 data from November 2009 for equivalent latitudes > 65 • N.
Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Figure 4 shows the satellite observations of H 2 O and HNO 3 obtained by ACE-FTS (average and standard deviation) for the equivalent latitudes above 70 • N and time Introduction Discussion Paper | Discussion Paper | Discussion Paper |

Figure 5
Figure5shows a comparison with the observations between 11:36 and 11:51 UTC on 22 January 2010.This was the period with the largest particle counts for particles larger than 1 µm diameter.The length of this flight path segment is 167 km at a potential temperature level of 425 K.For the determination of the particle size distribution from CLaMS, the particles within a certain volume need to be combined.This model volume over which the particles are gathered and included in the composition of the size distribution is defined here by up to 100 km distance within this flight path segment and within ±0.5 model level thickness.The sum of particle densities in the individual 22119 volume is given vertically by one model layer thickness and horizontally by 50 km and 70 km 22120 Discussion Paper | Discussion Paper | Discussion Paper | shows the vertical HNO 3 flux for the cold time period between late December 2009 and end of January 2010 for the different sensitivity sim-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | has been applied to a global chemical transport model.The processes leading to denitrification have been compared with observations including CALIOP particle classification, observed size distributions and observations of NO y and its compounds by various observational techniques and platforms.The comparisons show that the general behaviour of the observations is reproduced by the simulations.The locations and extent of the observed NAT PSCs as seen in the CALIOP data are generally better reproduced by the new nucleation scheme than by using a constant nucleation rate.The model configuration employing the new NAT nucleation parametrisation reproduces the vortex averaged NO y profiles observed by ACE-FTS slightly better than the version based on the constant rate based NAT nucleation parametrisation.However, deviations between observations and the simulation are still evident.These differences can be due to various reasons, including small-scale temperature fluctuations Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ACE-FTS team for providing the ACE-FTS data.The CLaMS simulations were performed on the supercomputer JUROPA in Jülich under the VSR project ID JICG11.The balloon-borne measurements of water vapour were obtained within the LAPBIAT2 atmospheric sounding campaign that was supported by EU under the IHP Access to Research Infrastructures and the Finnish Academy under grant number 140408.CRH was funded via Swiss National Science Foundation (SNSF) grant number 200021_140663.The service charges for this open access publication have been covered by a Research Centre of the Helmholtz AssociationDiscussion Paper | Discussion Paper | Discussion Paper | Woiwode, W.: Qualification of the airborne FTIR spectrometer MIPAS-STR and study on denitrification and chlorine deactivation in Arctic winter 2009/10, Dissertation, Karlsruhe Insitute for Technology, Faculty of Chemistry and Biosciences, Karlsruhe, Germany, 2013.22111Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Table Discussion Paper | Discussion Paper | Discussion Paper | Table A2.Tracer correlations used in the initialisation procedure.The tracer correlations are derived from the observations using a polynomial fit of the form [y] = n i=0 a i • [x]

Fig. 1 .
Fig. 1.Supersaturation needed for NAT nucleation for each 0.1• wide contact angle bin displayed as function of contact angle and temperature derived fromHoyle et al. (2013).The second vertical axis shows the corresponding number of dust particles per bin.

Fig. 2 .Fig. 3 .Fig. 4 .Fig. 5 .Fig. 6 .
Fig. 2. Schematic of the determination of S max NAT along an exemplary air mass trajectory to demonstrate the principle of the parametrisation (no absolute values given).The red line corresponds to the hourly calculated values of S NAT and the green dots indicate the derived value of S max NAT for each day of simulation.

Fig. 12 .
Fig. 12.Comparison of CLaMS NO y mixing ratios with SIOUX data measured aboard the Geophysica aircraft.Shown are four time sections with NO y observations inside the vortex for flights on 30 January, 2 February, 2 March and 10 March 2010.SIOUX data are displayed as green lines.CLaMS results are displayed for 3 different simulations, the high resolution (HR, black), the low resolution (LR, red), and the simulation with 1 K temperatures decrease (T-1K, pink).The passive NO * y from CLaMS is shown as grey line.The average potential temperature of the flight segments is 432 K, 433 K, 447 K, and 472 K, respectively.In panel a the turnaround time and in panel d the observation of a filament of extra-vortex air in the vortex are marked with dotted lines.

Table 1 .
Sensitivity simulations performed with CLaMS and corresponding labels.Different horizontal resolutions and assumptions for the NAT nucleation rate J are indicated.