Energetic particle precipitation (EPP) affects the
chemistry of the polar middle atmosphere by producing reactive nitrogen
(NOy) and hydrogen (HOx) species, which then catalytically destroy
ozone. Recently, there have been major advances in constraining these
particle impacts through a parametrization of NOy based on high-quality
observations. Here we investigate the effects of low (auroral) and middle
(radiation belt) energy range electrons, separately and in combination, on
reactive nitrogen and hydrogen species as well as on ozone during Southern
Hemisphere winters from 2002 to 2010 using the SOCOL3-MPIOM chemistry-climate model. Our results show that, in the absence of solar proton events, low-energy electrons produce the majority of NOy in the polar mesosphere
and stratosphere. In the polar vortex, NOy subsides and affects ozone
at lower altitudes, down to 10 hPa. Comparing a year with high electron
precipitation with a quiescent period, we found large ozone depletion in the
mesosphere; as the anomaly propagates downward, 15 % less ozone is found
in the stratosphere during winter, which is confirmed by satellite
observations. Only with both low- and middle-energy electrons does our model
reproduce the observed stratospheric ozone anomaly.
Introduction
Energetic particles originating from the Sun, the magnetosphere, or
outside the solar system continuously precipitate into the Earth's
atmosphere and can influence atmospheric processes. They ionize neutral air
molecules, especially in the middle and upper polar atmosphere, and create odd
nitrogen and hydrogen species, NOx ([N]+[NO]+[NO2]) and HOx ([H] + [OH] + [HO2]). NOx and HOx radicals can
catalytically deplete ozone. The in situ destruction of ozone in the mesosphere is
characteristic of HOx due to its fast reaction rates
(Bates and Nicolet, 1950). On the other hand, NOx, in the
absence of sunlight, subsides within the downwelling branch of the
overturning circulation, affecting ozone concentrations at lower altitudes
(Solomon et al., 1982).
High-energy particles, i.e. solar protons
(Jackman et al., 2008) and radiation belt electrons (Arsenovic
et al., 2016; Semeniuk et al., 2011), can penetrate directly into the mesosphere and
stratosphere. Radiation belt electrons (energies >30 keV) impact
chemistry below 90 km in the atmosphere (Turunen et al., 2009). Electrons of
lower energies (<30 keV, auroral) originate from the magnetosphere
as well as the radiation belt electrons
(Mironova et al., 2015), but they get
accelerated in the magnetotail and precipitate into the lower thermosphere
in the auroral ovals (55–70∘ geomagnetic latitude) (Baker
et al., 2001; Barth et al., 2003). Their peak impact is above 90 km in the thermosphere (Turunen et al., 2009).
There have been numerous attempts to include low-energy electrons (LEE) in
climate models. Chemistry-climate or chemistry-transport models with top in
the thermosphere, e.g. HAMMONIA (Schmidt et al., 2006), KASIMA
(Reddmann et al., 2010), and WACCM (Andersson
et al., 2018; Marsh et al., 2007), have included effects of LEE directly because they
deposit their energy within the model domain. For climate models that have
an upper lid below the thermosphere, a prescription of LEE either as
NOy influx through the model top or as concentrations (number density)
in the upper model boxes is recommended (Matthes et al., 2017). Baumgaertner et al. (2009) developed a
parameterization of this flux based on the geomagnetic activity Ap
index, a daily worldwide measure of the effects of solar wind on the Earth's
magnetic field. When incorporated into a chemistry-climate model, results
showed significant ozone depletion in the mesosphere and stratosphere
(Baumgaertner et al., 2011). For the SOCOL chemistry-climate model, Rozanov et al. (2012) also found significant ozone decreases in the mesosphere and
stratosphere, with peak values around 10 % in September around 36 km
altitude over the Antarctic.
Funke et al. (2016) recently developed a
semi-empirical model that calculates concentrations and fluxes of
mesospheric and stratospheric NOy compounds ([NO]+[NO2]+2×[N2O5]+[HNO3]+[ClONO2]) based on the
Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)
observations. The model exploits the nearly linear relationship in the
mesosphere between the Ap index with observed NOy produced by EPP. This
advance in the representation of LEE in climate models motivates us to
investigate whether LEE can have a larger impact on atmospheric chemistry than
previously thought (Rozanov et al., 2012). Moreover,
this LEE parameterization is a part of the recommended solar forcing dataset
for climate models within the upcoming Coupled Model Intercomparison Project
Phase 6
(CMIP-6,
Matthes et al., 2017).
It is crucial to have a realistic representation of EPP in models as the
introduced signal impacts atmospheric chemistry and potentially regional
climate (Baumgaertner
et al., 2011; Maliniemi et al., 2014; Rozanov et al., 2012; Seppälä et al., 2013). Sinnhuber et al. (2018) showed the impact of one
possible implementation of the new Funke et al. (2016) LEE NOy parameterization in their EMAC model on NOy and
ozone; however, they did not explicitly consider MEE. Here we present
results from our state-of-the-art chemistry-climate model, employing a
different implementation of the same parameterization of LEE together with
the previous representations of other energetic particles. This paper
focuses on evaluating NOx and ozone response to LEE and MEE
precipitation, separately and in combination, in Antarctic winters (JJA:
June, July, and August), in order to avoid the more complicated Arctic polar
vortex with its high variability and strong dependence on meteorological
conditions (Hitchcock et al., 2013). We compare
our results with the satellite observations.
Methods
We used the SOCOL3-MPIOM coupled chemistry-climate model (Muthers
et al., 2014; Stenke et al., 2013). The atmospheric dynamic component of the model is
ECHAM5.4 (Roeckner and Bäuml, 2003), coupled to the MEZON air
chemistry module (Egorova et al., 2003; Rozanov et al., 1999) and the MPIOM interactive ocean module (Jungclaus
et al., 2006; Marsland et al., 2002). We carried out the experiments with T31 spectral
resolution on 39 vertical levels from the surface up to 0.01 hPa
(∼80 km).
The model boundary conditions and parameterizations are identical to those
described in Arsenovic et al. (2016), except for the LEE parameterization.
Following Calisto et al. (2011), galactic cosmic rays (GCR) are parameterized as a function of
geomagnetic latitude, pressure, and solar modulation potential. Ionization by
solar protons (SP) is treated according to Jackman et al. (2008) and ionization by
middle-energy electrons (MEE) with energies between 30 and 300 keV is taken
from the Atmospheric Ionization Module Osnabrück (AIMOS) v1.6
(Arsenovic et al., 2016; Wissing and
Kallenrode, 2009). Electrons of energies higher than 300 keV are not
included in the model due to a lack of adequate parameterization.
For LEE, we are using the semi-empirical model for NOy influx by Funke
et al. (2016) through the model top at 0.01 hPa (75–80 km in polar conditions).
Although MIPAS scans the atmosphere up to 68 km altitude, the applicability
of this parameterization above 70 km has been validated by comparison with
MIPAS middle- and upper-atmosphere observations (scanning up to 100 and 170 km, respectively). As more than 99 % of the NOy at this altitude is
in the form of nitrogen monoxide (nitric oxide), NO
(Brasseur and Solomon, 2005), we approximate the
NOy influx calculated by the semi-empirical model as NO influx at this
level in SOCOL3-MPIOM. As mentioned before, LEE precipitate above 90 km and
MEE precipitate between 70 and 90 km altitude
(Turunen et al., 2009). However, because
of our model top at 80 km, here we consider electrons that precipitate below
80 km to be MEE and electrons that precipitate above the model top to be LEE.
Matthes et al. (2017) and Sinnhuber et al. (2018)
also implemented the parameterization by Funke et al. (2016) in the EMAC model.
They used a different approach, prescribing NO concentrations (instead of
fluxes through the model top) in the model within the 0.09–0.01 hPa layer
and performed the simulations with specified dynamics. Prescribing
concentrations requires overwriting NO simulated values. It is inconsistent
with the treatment of the physical and chemical processes in our model
leading to accumulation of NOy. This is not the case for the influx
approach and therefore we prescribe the NO influx instead of NO
concentrations; however, prescribed NO concentrations can be used for models
with different treatments of the chemical and transport processes.
Figure 1 shows the monthly mean geomagnetic Ap index that covers our
simulated period. Period 2002–2005 was characterized by a rather high
Ap index and the 2006–2010 period by low values. For our simulations,
we have used daily NO fluxes calculated from daily Ap indices.
Four sets of six-member ensemble simulations were carried out, covering the
2002–2010 period: the “ALL” simulation, which includes all energetic
particles (GCR, SP, MEE, and LEE), the “LEE” simulation (GCR, SP, and LEE),
the “MEE” simulation (GCR, SP, and MEE), and the reference “REF”
simulation (GCR and SP). All these simulations have the same model boundary
conditions and differ only in the inclusion of the low-/middle-energy
electron precipitation.
The monthly mean geomagnetic Ap index during the simulated
period: years 2002–2005 were rather active, while the period 2006–2010
was geomagnetically quiescent (CMIP-6 dataset;
Matthes et al., 2017).
We used two satellite datasets to evaluate our model results: MIPAS for
nitrogen species and the Microwave Limb Sounder (MLS) for ozone. MIPAS was a
Fourier transform spectrometer aboard the ENVISAT satellite
(Fischer et al., 2008). The
quality of MIPAS NOy and individual NOy species has been
extensively assessed in SPARC (2017), as well as specific
validation studies (e.g.
Bender et al., 2015; Sheese et al., 2016). The top altitude of the MIPAS nominal limb
scans is 68 km, but it also contains information on the NOy above,
though with low vertical resolution. Since it provides the entire NOy
budget in the upper atmosphere (with a vertical resolution of 3–5 km), we used
this dataset to validate simulated NOy.
The MLS aboard the Aura satellite (Waters
et al., 2006) has provided daily measurements of ozone profiles
(Froidevaux
et al., 2008) in the middle and upper atmosphere since August 2004. We used MLS
observations to evaluate modelled ozone. The vertical resolution of MLS
O3 (v4.2) is about 3 km in the stratosphere, increasing up to about 5 km in the mesosphere (Livesey et al., 2018).
Monthly mean NOy volume mixing ratio anomaly in ppbv for the
Southern Hemisphere (>70∘ S average) calculated as
the difference of the year 2005 and the average of 2006–2010. (a) MIPAS
observations; (b) ensemble mean of ALL simulations; (c) ensemble mean of LEE
simulations; (d) ensemble mean of MEE simulations; (e) ensemble mean of REF
simulations. Colour levels are 1, 2.5, 5, 7.5, 10, 25, 50, 75, 100, 250, 500,
750, and 1000 ppbv and the black contour lines highlight 1, 10, 100, and 1000 ppbv. Coloured regions are significant at the 99 % confidence level
(calculated using a Student's t test).
ResultsNOy enhancement propagation
Figure 2 shows the difference in NOy concentration between the
geomagnetically active year 2005 and the mean over the geomagnetically
quiescent period 2006–2010 averaged over 70–90∘ S. Even
though year 2003 on average has higher Ap, here we choose year 2005 as the
geomagnetically active year. This allows us to compare modelled NOy and ozone
using two different satellite datasets, MIPAS and MLS (which have been available
only since 2005). MIPAS data are unavailable from September 2005 to the end
of the year, but our main period of interest is JJA, which is well covered
by the observations.
The MIPAS observations (Fig. 2a) show a NOy enhancement throughout
the mesosphere and upper stratosphere. In terms of mixing ratio, the highest
increase of 500–600 ppbv is found in the upper mesosphere around 0.01 hPa
(∼80 km). There, the highest monthly values are observed in
June. In the following months, this anomaly descends and reaches lower
levels. In July, the NOy enhancement of around 10 ppbv reaches the
upper stratosphere around 2 hPa, and the increase, although smaller, is
visible all the way down to 10 hPa. In the following months, the MIPAS
nominal data were unavailable due to special observation mode campaigns.
The ALL experiment (Fig. 2b) shows a very similar pattern of NOy to
the observations. The NOy increase of 500–600 ppbv in the upper
mesosphere around 0.01 hPa is similar to the MIPAS observations. However,
the wintertime NOy peak below is slightly overestimated in the model
compared to MIPAS. This is particularly visible in the lower mesosphere in
June, as the modelled 100 ppbv NOy enhancement reaches 0.1 hPa. The
mesospheric anomaly extends into the stratosphere, but remains confined to
the upper stratosphere, above 10 hPa, as in observations. The modelled
NOy overestimation suggests that downward transport is somewhat too
fast in the model, or that the photochemical lifetime of NOy is too long, or
that horizontal mixing with mid-latitudes is underestimated. The modelled NOy
enhancement in September stems from an SP event (NOAA). In
contradiction to our results, the EMAC model slightly underestimates
NOy even during polar summer, for two pressure levels, 0.1 and 1 hPa (Matthes et al., 2017). Sinnhuber et al. (2018) showed underestimation of NOy in the upper
mesosphere in the EMAC and KASIMA models and overestimation of NOy in the 3dCTM
model in the Southern Hemisphere compared to MIPAS observations.
The LEE simulation (Fig. 2c) shows very similar anomalies to ALL. The
largest differences are in the upper mesosphere, where LEE anomalies reach
around 400 ppbv, which is underestimated compared to the 500–600 ppbv found in
MIPAS and ALL. A second interesting difference compared to ALL is the SP
event in September. In the LEE simulation, it reaches around 60 ppbv, while in
ALL it exceeds 100 ppbv. This difference comes from increased MEE
precipitation that coincided with the SP event (see Arsenovic et al., 2016, Fig. 1a). During strong SP events protons can contaminate the highest electron
channel, so this channel is excluded from the AIMOS dataset
(Yando et al., 2011). Although some degree of contamination is
still possible in the lower channels, protons are not the sole cause of the
increased NOy in this SP event. That is, SP events are often associated
with large coronal mass ejections that form a shock in front of them. Once the
shock hits the Earth it often leads to a geomagnetic storm which leads to
acceleration of electrons of >30 keV energies. Therefore,
increased MEE precipitation often happens very shortly after an SP event
because the shock and the geomagnetic storm are related to the same coronal
mass ejection driver (Asikainen and Ruopsa, 2016).
The MEE simulation (Fig. 2d) is drastically different from MIPAS as well
as the ALL and LEE simulations. Although NOy enhancement in the modelled
geomagnetically active year exists, it is significantly decreased compared
with the previous results. The modelled NOy mesospheric anomaly peak is
absent and enhancement of 10 ppbv does not reach the stratosphere.
Nevertheless, although less intense, increased NOy is present
throughout the mesosphere and stratosphere, and the NOy increase in
September due to the SP event again exceeds 100 ppbv, as in the ALL
simulation.
The reference run in Fig. 2e shows NOy increase due to the SP events
in the year 2005. In this year, there were six observed SP events in the shown
time frame – 14 May, 16 June, 14 and 27 July, 22 August, and 8 September
(NOAA). In the geomagnetically inactive period, 2006–2010, there were
no observed SP events in the presented months. Therefore, by excluding
electron precipitation, the SP events alone cannot reproduce the observed
features.
From the presented months, we conclude that the inclusion of only LEE was
sufficient to reproduce most of the NOy enhancements. The MEE
contribution to NOy increases is minor and brings the model closer to
observations mainly in the upper mesosphere. As coronal mass ejections drive
SP events and they can have an impact on the precipitation from the outer
Van Allen belt (Asikainen and Ruopsa, 2016;
Pierrard and Lopez Rosson, 2016), MEE precipitation could significantly
contribute to NOy increases in such events.
O3 anomaly propagation
In the study of Matthes et al. (2017), ozone responses were evaluated by comparing high and low
geomagnetic activity years, and their estimate shows good agreement with
satellite observations (Fytterer et al., 2015). To evaluate
our simulated ozone responses, we follow a similar approach to that used in
Matthes
et al. (2017); that is, we compared our simulations with observations from MLS. We
analysed the 2005–2010 period when both simulation and MLS data are
available.
Ozone anomalies from MLS observations during the high geomagnetically active
year are depicted in Fig. 3a. They are calculated as the difference
averaged over 70–90∘ S between the active year (2005) and the
average of geomagnetically quiescent years (2006–2010) divided by the
ozone averaged over the whole period (2005–2010). Observations show
around 20 % less ozone in the upper mesosphere (<0.1 hPa)
occurring mostly in the JJA period. The exception is the SP event on
8 September 2005. It created an ozone anomaly of up to 80 % stretching
throughout the whole mesosphere. The mesosphere below 0.1 hPa does not show a
statistically significant difference between the geomagnetically active and
quiescent years in the absence of SP events. The observed negative ozone anomaly
appears again around the stratopause in late June and propagates downwards
to nearly 10 hPa in early September. The peak ozone anomaly occurs in August
around 3 hPa, reaching ∼15 %. Our results agree with the
results from previous modelling studies (Reddmann
et al., 2010; Rozanov et al., 2012; Sinnhuber et al., 2018) and observations (Damiani et al., 2016; Fytterer et al., 2015).
Monthly mean ozone anomaly poleward of 70∘ S calculated
as the difference of year 2005 and average of 2006–2010 relative to the 2005–2010 period. (a) MLS observations; (b) ensemble mean of ALL simulations; (c) ensemble mean of LEE simulations; (d) ensemble mean of MEE simulations; (e) ensemble mean of REF simulations. Black lines highlight -10 %, -15 %,
and -50 % and dark red lines mark -10 % from MLS observations on every
plot. Note that mesospheric ozone depletion reaches 80 %–90 % during some
strong solar proton events. Coloured regions are significant at the 99 %
confidence level (calculated using a Student's t test).
The ALL simulation (Fig. 3b) shows a negative ozone anomaly in the
mesosphere as well. However, the magnitude is generally higher (around 30 %), and it is present from May to September. The September 2005 SP event
is visible in the model simulations as well and descends from around 1 hPa
in late September, reaching 10 hPa in late October. A similar pattern, but
less obvious, is seen in the observations. Ozone anomalies in the lower
mesosphere (0.5–0.1 hPa) are more pronounced in the model than in MLS
observations. This is particularly evident in June when the modelled
upper-mesosphere anomaly appears to relate to the upper-stratospheric
anomaly, in contrast to the observations. This suggests that HOx
production by MEE might be overestimated. In the upper stratosphere model
simulations agree well with observations. The decrease propagates downwards,
reaching approximately 10 hPa in August, with a peak around 15 % in good
agreement with the observations.
Ozone anomalies in the LEE simulation are shown in Fig. 3c. Negative ozone
anomalies are present mostly in the upper mesosphere (above 0.3 hPa) and
have similar magnitude to ALL. Although in the LEE simulation the
mesospheric ozone anomaly is overestimated compared to MLS observations, the
stratospheric anomaly is almost completely absent. This is surprising, as
there are very similar NOy anomalies in the ALL and LEE simulations
(see Fig. 2).
Our MEE simulation shows similar ozone anomalies compared to LEE (Fig. 3d). The anomalies are confined to a region above 1 hPa and are somewhat
reduced compared to LEE and ALL. Similar to LEE, the stratospheric ozone
anomaly seen in the observations and ALL simulation is almost absent.
In REF simulation (Fig. 3e) most of the ozone anomaly features seen in
observations and ALL are missing. The only depletion of ozone in this
simulation is caused by SP events in the year 2005. Most of the observed
events (14 May, 16 June, 14 and 27 July, 22 August, and 8 September) are
clearly visible.
A recent study based on CCM WACCM (Andersson et al., 2018) showed
ozone anomaly propagation differences between high-Ap and low-Ap
winters in the Southern Hemisphere. Their results are comparable with our
ALL and LEE simulations. Compared with our ALL simulation, their ozone
anomaly in the case of all EEP of around 7 % is lower and occurs later (in
October as opposed to August). However, their LEE simulation does not show
a significant ozone anomaly in the stratosphere, which is also the case in our
results. In the study of Sinnhuber et al. (2018) the three analysed models (3dCTM, KASIMA, and EMAC) generally show
good agreement with the satellite observations.
EEP effect on NOy, HOx, and O3
To estimate the total effect of energetic electron precipitation on
NOy, HOx, and ozone, we calculated the differences of experiment
simulations (ALL, LEE, and MEE) and REF simulation for the geomagnetically
active period (2002–2005) using the simulated monthly mean values. Note
that this is an idealized comparison and it is not directly comparable with
observations, as there is always some amount of particle precipitation in
the atmosphere (Funke et al., 2014), unlike in the LEE, MEE, and REF simulations.
The zonal mean of austral winter (JJA) average NOy differences between
ALL and REF is shown in Fig. 4a. In polar night, NOy is transported
to lower altitudes by descending air motion. Significant modelled NOy
enhancements are present in the whole mesosphere and upper stratosphere
above 10 hPa. Around 0.01 hPa, EPP produced NOy increases from 50 ppbv
at around 60∘ S, where NOy lifetime is decreased due to the
sunlight, to more than 500 ppbv at the pole, in the polar night. The
differences in HOx between those two experiments are shown in Fig. 4b. Increases are mostly confined to the upper mesosphere and they reach the
maximum of around 5 ppbv. However, smaller (<1 ppbv) but
statistically significant HOx increase appears in the lower mesosphere and
upper stratosphere around 60∘ S. Increases in NOy and
HOx impact the ozone chemistry. Figure 4c shows changes in ozone
concentrations due to electron precipitation. Ozone is significantly reduced
throughout the whole polar region above 10 hPa. There are two peaks of ozone
anomaly. The maximum decrease of up to 65 % (350–400 ppbv) is located
in the upper mesosphere. This decrease is more severe than in previous
modelling studies (Rozanov et al., 2012), but this is because we focus on the
geomagnetically active winters, when EPP effects are much more pronounced.
The magnitude of ozone depletion is gradually decreasing with height,
reaching ∼15 % (>200 ppbv) at the stratopause.
The second ozone depletion peak is located between 10 and 1 hPa, reaching 15 % (>400 ppbv). A similar ozone response to ALL has been
shown by Semeniuk et al. (2011).
Summary of zonally averaged results. Columns: NOy(a, d, g);
HOx(b, e, h); O3(c, f, i). Rows: including ALL energetic particles (a–c); only with LEE (d–f); only with MEE (g–i). All panels show
results for the geomagnetically active period (2002–2005) for austral
winter (JJA) from the respective simulations minus the REF simulation.
Colours show absolute differences in ppbv for NOy (colour levels are 1,
2.5, 5, 7.5, 10, 25, 50, 75, 100, 250, 500, 750, and 1000 ppbv) and HOx
plots and difference in percent for O3 plots. Isolines show difference
in absolute values in ppbv. Coloured regions are significant at the 99 %
confidence level (calculated using a Student's t test).
Figure 4d shows the difference between modelled NOy in LEE and REF
simulation. Similarly, as in Fig. 2, modelled NOy in LEE simulation is
very similar to ALL, confirming the fact that most of the NOy is
coming from LEE. Slight reduction to ALL still exists, visible mostly at 0.1 hPa at 90∘ S. Here, the value of NOy is 100 ppbv, while it is
somewhat more in Fig. 4a. A second difference is the absence of the
enhancement equatorward of 30∘ S which is present in Fig. 4a.
Increase in HOx in the case of LEE is illustrated in Fig. 4e. Changes in
HOx are very small and statistically insignificant, except for a small
(<1 ppbv) increase in the polar upper mesosphere. This is expected
as LEE do not produce HOx. The small increase could be explained by an
increase in NOy which causes small increases in background HOx
through the Verronen and Lehmann (2015) mechanism, where enhanced NO coming
from EEP leads to HOx repartitioning increasing HOx
concentrations. Figure 4f shows ozone changes due to the LEE. A similar ozone
decrease pattern to Fig. 4c exists but with a reduced intensity. The
upper-mesospheric reduction reaches 35 % (∼200 ppbv) and
the upper-stratospheric anomaly is halved compared to ALL
(200 ppbv =∧10 %). HOx
increases and reduced ozone anomalies compared to ALL illustrate the
importance of MEE.
Figure 4g shows an increase in NOy due to the MEE. Although MEE cause
increases in NOy, modelled NOy is significantly reduced in the
whole area compared to LEE and ALL simulation. In the upper mesosphere, this
increase is around 50 ppbv, or a tenth of the total produced NOy in ALL
simulation. Between 30 and 35∘ S NOy enhancement is
present again, as in ALL simulation. This enhancement is coming from the
fact that MEE do not necessarily precipitate inside the polar vortex, as
they precipitate in the sub-auroral ovals, which are centered around the
geomagnetic pole. In contrast, NOy coming from LEE descends into the
mesosphere in the downwelling air motion inside of the polar vortex. The
sum of NOy increases (not shown) due to the LEE (Fig. 4d) and due to the
MEE (Fig. 4g) closely reassembles NOy increase as in the ALL case (Fig. 4a).
Increases in HOx due to MEE are presented in Fig. 4h. Enhancements
are present mostly in the upper mesosphere, reaching 4 ppbv. The position and
intensity of HOx are very similar to ALL, but are somewhat reduced. Because
MEE produce OH, neglecting MEE in climate models would lead to an
underestimation of HOx; neglecting LEE would also lead to an
underestimation of HOx through the changed HOx partitioning
(Verronen and Lehmann, 2015). Changes in ozone concentrations due to MEE are
shown in Fig. 4i. Negative ozone anomalies are present in the mesosphere
and in the upper stratosphere, albeit the stratospheric anomaly is
statistically not significant. The biggest reduction with 35 %
(∼200 ppbv) is visible in the upper mesosphere. The anomaly
in the upper stratosphere (10–1 hPa) does not exceed 100 ppbv.
Interestingly, summing stratospheric ozone anomaly from LEE (Fig. 3f) and
from MEE (Fig. 3i) does not reproduce ALL ozone anomaly (Fig. 3c). The sum of
the LEE and MEE ozone anomaly accounts for around 300 ppbv, while ALL shows
about 400 ppbv between 10 and 1 hPa. Since the sum of enhanced NOy due
to LEE and MEE corresponds to ALL NOy and HOx enhancements occur
in the mesosphere, this discrepancy in ozone anomaly cannot be chemically
explained. It could be caused by changes in dynamics (polar vortex strength)
and temperature (which affects reaction rates).
Our results indicate that LEE and MEE are equally responsible for the ozone
anomaly in the mesosphere. LEE deplete ozone through the production of large
amounts of NOy, while MEE contribute to the anomaly mostly through
production of HOx, which is the more efficient ozone destructor
(Brasseur and Solomon, 2005). Both LEE and MEE produce the stratospheric
anomaly; however, LEE, through the production of large amounts of NOy,
are more important.
Conclusions
We used the period 2005–2010 comprising intervals of high and low
geomagnetic activity, which is well characterized by stratospheric and
mesospheric measurements of NOy and O3, to investigate the
accuracy of representations of energetic particle forcing in a
chemistry-climate model. We assessed the impact of employing a new
parameterization of LEE (<30 keV) recommended for CMIP-6 in
combination with the AIMOS parameterization for MEE (30–300 keV) on the
simulated NOy, HOx, and ozone variability. We used the SOCOL3-MPIOM
climate model and focused on the Southern Hemispheric winter season. We
compared NOy with stratospheric and mesospheric MIPAS observations. The
model captures the main features very well, but shows some differences in
the winter maxima. LEE can reproduce most of the NOy features, without
including MEE. However, increased MEE precipitation coincident with SP
events may be a significant contribution to the observed NOy amounts.
Simulated ozone depletion has been compared to MLS satellite observations,
showing that patterns of ozone anomalies during the high EPP year 2005
compared to 2006–2010 match reasonably well. The model overestimates
mesospheric ozone anomalies, but in the stratosphere a good match is
accomplished. Ozone depletion of up to 15 % is found during July and
August and reaches into the lower stratosphere. In essence, without
including both LEE and MEE, the stratospheric anomaly cannot be accurately
modelled. Future work is required to address the roles of indirect changes in
temperature and dynamics in the EPP-induced stratospheric ozone variation.
Most of the NOy in the mesosphere and stratosphere is produced by LEE
in the upper mesosphere and lower thermosphere (<0.01 hPa) and
transported downwards. A smaller fraction, namely ∼10 %,
is generated in situ by ionization due to precipitating electrons of higher
energies. These electrons play an important role because they produce
HOx, which depletes ozone near the HOx source region in the
mesosphere. Although not producing HOx directly, LEE increase NOy
concentrations, which then causes repartitioning of HOx and an increase
in the HOx lifetime (Verronen and Lehmann, 2015).
In summary, LEE and MEE lead to a reduction of ozone throughout the
mesospheric and stratospheric polar region, with a maximum percentage ozone
depletion in the mesosphere (-65 %) and a second peak anomaly in the
upper stratosphere (-15 %) with respect to the simulation where they are
omitted. These chemical EPP signals can cause dynamical changes in the
stratosphere that propagate into the lower atmosphere, which eventually
affect regional climate (Rozanov et al., 2012).
Therefore, we recommend including both LEE and MEE in climate models.
Data availability
Due to the size limitations, SOCOL3-MPIOM model code, model boundary
conditions, and satellite data are only available upon request. The model
output analysed in this study can be found at https://data.mendeley.com/datasets/kgzwjgf4bk/1 (10.17632/kgzwjgf4bk.1, Arsenovic, 2019).
Author contributions
PA and ER proposed the idea and designed the experiments; PA carried out the
simulations and prepared the manuscript. AD analysed MLS data and made
Fig. 3. BF provided MIPAS data. TP formulated the general line of research
and supervised the project. All authors provided critical feedback and
helped shape the research, analysis, and manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is a part of ROSMIC WG1
activity within the SCOSTEP VarSITI programme and WG3 and 5 activities within
the SPARC SOLARIS-HEPPA project. The authors thank NASA Goddard Earth Science
Data and Information Services Center (GES DISC) for providing Aura/MLS data
(https://mls.jpl.nasa.gov/, last access: 4 July 2019), Timo Asikainen (University of
Oulu) for clarification of the MEE–SP events relationship, Marina Dütsch
(University of Washington) and Jelisaveta Arsenovic for assistance with
improving the graphics, and Amewu Mensah (ETH, Zürich) and William Ball
(PMOD/WRC, Davos, and ETH, Zürich) for correcting the language. We thank
our editor, Gabriele Stiller, Svenja Lange, and three anonymous reviewers
whose comments significantly improved the quality of this paper.
Financial support
This research has been supported by the Swiss National Science Foundation (grant no. CRSII2-147659), the FONDECYT (grant no. 1171690), the MCINN (grant no. ESP2014-54362-P), the JST/CREST/EMS/TEEDDA (grant JPMJCR15K4), the EC FEDER, and the Russian Science Foundation (grant no. 17-17-01060).
Review statement
This paper was edited by Gabriele Stiller and reviewed by three anonymous referees.
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