ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-8667-2016A semi-empirical model for mesospheric and stratospheric NOy produced by energetic particle precipitationFunkeBerndbernd@iaa.eshttps://orcid.org/0000-0003-0462-4702López-PuertasManuelhttps://orcid.org/0000-0003-2941-7734StillerGabriele P.https://orcid.org/0000-0003-2883-6873VersickStefanvon ClarmannThomasInstituto de Astrofísica de Andalucía, CSIC, Apdo. 3004, 18008 Granada, SpainKarlsruhe Institute of Technology (KIT), Institute for Meteorology and Climate Research (IMK-ASF), P.O. Box 3640, 76021 Karlsruhe, GermanyKarlsruhe Institute of Technology (KIT), Steinbuch Centre for Computing (SCC), P.O. Box 3640, 76021 Karlsruhe, GermanyBernd Funke (bernd@iaa.es)15July20161613866786937March201629March20166June201628June2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/8667/2016/acp-16-8667-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/8667/2016/acp-16-8667-2016.pdf
The MIPAS Fourier transform spectrometer on board Envisat has measured global
distributions of the six principal reactive nitrogen (NOy) compounds
(HNO3, NO2, NO, N2O5, ClONO2, and HNO4) during
2002–2012. These observations were used previously to detect regular polar
winter descent of reactive nitrogen produced by energetic particle
precipitation (EPP) down to the lower stratosphere, often called the EPP
indirect effect. It has further been shown that the observed fraction of
NOy produced by EPP (EPP-NOy) has a nearly linear relationship with
the geomagnetic Ap index when taking into account the time lag
introduced by transport. Here we exploit these results in a semi-empirical
model for computation of EPP-modulated NOy densities and wintertime
downward fluxes through stratospheric and mesospheric pressure levels. Since
the Ap dependence of EPP-NOy is distorted during episodes of strong
descent in Arctic winters associated with elevated stratopause events, a
specific parameterization has been developed for these episodes. This model
accurately reproduces the observations from MIPAS and is also consistent with
estimates from other satellite instruments. Since stratospheric EPP-NOy
depositions lead to changes in stratospheric ozone with possible implications
for climate, the model presented here can be utilized in climate simulations
without the need to incorporate many thermospheric and upper mesospheric
processes. By employing historical geomagnetic indices, the model also allows
for reconstruction of the EPP indirect effect since 1850. We found secular
variations of solar cycle-averaged stratospheric EPP-NOy depositions on
the order of 1 GM. In particular, we model a reduction of the EPP-NOy
deposition rate during the last 3 decades, related to the coincident decline
of geomagnetic activity that corresponds to 1.8 % of the NOy
production rate by N2O oxidation. As the decline of the geomagnetic
activity level is expected to continue in the coming decades, this is likely
to affect the long-term NOy trend by counteracting the expected increase
caused by growing N2O emissions.
Introduction
Both solar protons and energetic magnetospheric electrons affect the
chemistry in the stratosphere and mesosphere. These energetic particles can
alter atmospheric composition either via in situ production of reactive
nitrogen and hydrogen species, or by subsidence of air rich in odd nitrogen
from its source region, the upper mesosphere and lower thermosphere. In the
stratosphere these reactive species gain importance by participating in the
catalytic ozone destruction cycles. The in situ production is also
called the “direct” effect of energetic particle precipitation (EPP). It is
particularly important in the context of solar protons, because only these
have been shown to penetrate deep enough into the stratosphere and
references therein.
Subsidence of air rich in reactive nitrogen from above is called the EPP
indirect effect (EPP IE) ; it is particularly important in
the context of auroral electrons. The indirect effect is limited to polar
night regions, first, because this strong subsidence can only take place in
the downwelling branch of the overturning circulation, i.e., in the polar
winter mesosphere, and second, because odd nitrogen can survive its transport
through the mesosphere only in the absence of sunlight.
Based on measurements with the Michelson Interferometer for Passive
Atmospheric Sounding (MIPAS), estimated the total amount
of NOx ([NOx]=[NO]+[NO2]) descending from
the EPP source region into the stratosphere at 2.4 Gigamole (GM) for the
southern polar winter 2003. However, since below 50 km NOx is partly
converted into its reservoirs , the assessment of the
indirect EPP effect requires consideration of the entire NOy family
([NOy]=[NO]+[NO2]+[HNO3]+2[N2O5]+[ClONO2]).
In a recent paper, provide quantitative estimates of the
total amount of EPP-NOy for the years 2002–2012, also inferred from
MIPAS measurements. In a subsequent paper, showed that the
EPP IE, i.e., the descended EPP-NOy, is highly correlated with
geomagnetic activity, as indicated by the Ap index, in Southern
Hemisphere (SH) winters and dynamically unperturbed Northern Hemisphere (NH)
winters. This suggests that the indirect effect during those winters is
driven by the EPP source strength rather than by variations of subsidence.
Similar tight correlations with the Ap index have been found in
seasonally averaged upper stratospheric polar winter NO2 column density
in both hemispheres observed by the Global Ozone Monitoring By Stars (GOMOS)
instrument taken during 2002–2006 and in estimates of SH
EPP-NOx depositions from Halogen Occultation Experiment (HALOE)
observations during 1992–2005 .
However, in NH winters with perturbed dynamics, characterized by episodes of
sudden stratospheric warmings (SSW) and associated elevated stratopause (ES)
events, accelerated descent in the reformed polar vortex leads to much
stronger odd nitrogen descent than in quiescent winters with a similar
geomagnetic activity level. investigated the influence of
SSW/ES events on the transport of odd nitrogen produced by EPP from the
mesosphere–lower thermosphere to the stratosphere using the Whole Atmosphere
Community Climate Model (WACCM). They found that the NOx amount that
descends to the stratosphere is strongly affected by the timing of the event,
resulting in higher amounts for mid-winter SSW/ES events compared to those
occurring in late winter. This behavior was attributed by these authors to
the pronounced seasonal dependence of the strength of the vertical winds
following an event.
In recent years, the potential impact of particle precipitation on regional
climate has been gaining the attention of the climate modeling community.
Solar forcing recommendations for the recently launched Climate Model
Intercomparison Project Phase 6 (CMIP6) include, for the
first time, the consideration of energetic particle effects
. EPP is strongly linked to solar activity and hence to
the solar cycle, either directly by coronal mass ejections producing solar
energetic particles or indirectly by the impact of the solar wind on the
Earth's magnetosphere. EPP-induced ozone changes are thought to modify the
thermal structure and winds in the stratosphere that, in turn, modulate the
strength of the polar vortex. The introduced signal could then propagate down
to the surface, introducing significant solar-like variations of regional
climate .
Today, there is an increasing number of chemistry climate models capable of
dealing with EPP effects; however, not all of them extend up into the upper
mesosphere/lower thermosphere, where a large fraction of EPP-induced odd
nitrogen production occurs. Those models with their upper lid in the
mesosphere, i.e., which do not represent the entire EPP source region,
require an odd nitrogen upper boundary condition, accounting for EPP
productions higher up, in order to allow for simulation of the introduced EPP
IE in the model domain .
In this paper, we provide a detailed semi-empirical model for
retrodiction/prediction of the indirect EPP-NOy as a function of the
geomagnetic Ap index that has been adjusted to the decadal MIPAS
EPP-NOy record. In order to account for the pronounced EPP-NOy
increases during ES events, a specific parameterization has been included for
these episodes. The aim of this model is to provide the stratospheric and
lower mesospheric NOy to chemistry climate models that do not explicitly
model upper mesospheric and thermospheric EPP effects. A further application
of this model is the reconstruction of the EPP indirect effect on secular
timescales by employing historical geomagnetic indices.
This paper is organized as follows: Sect. describes
the MIPAS EPP-NOy data used for adjusting the semi-empirical model.
Section provides a detailed description of the
semi-empirical model for hemispheric EPP-NOy amounts and fluxes during
polar winters, excluding ES episodes in the NH. The extension of the latter
model with respect to the consideration of ES events is provided in
Sect. , and the detection of such events is discussed in
Sect. . The modeled EPP indirect effect is compared to
available observational estimates in Sect. . The
application of the model as an odd nitrogen upper boundary condition for
chemistry climate models with their upper lid in the mesosphere is discussed
in Sect. , and Sect. deals with the
reconstruction of the EPP indirect effect during 1850–2015.
Observations
The MIPAS instrument on the polar-orbiting Envisat
satellite provided global stratospheric and mesospheric measurements of
temperature , NOx, NOy and numerous other trace species
e.g., during 2002–2012. From these data, the
contribution of NOy produced by EPP has been distinguished from that
produced by N2O oxidation using a tracer correlation method that is
based on coincident CH4 and CO observations . The
latter tracer is used to restrict the EPP-NOy detection to observations
containing mesospheric air. The EPP-NOy uncertainty is dominated by the
multiplicative component of the NOy systematic retrieval error that is
about 10 %. The scatter in the tracer correlation results in a precision
of inferred EEP-NOy of 0.5 ppbv, which can be considered to be the
1-σ detection limit, particularly at lower altitudes. Other
uncertainties act systematically upon the estimated EEP-NOy and lead to
a possible underestimation. This is particularly true for the end of the
winter and during stratospheric warming episodes. For further details on the
error analysis of this method, see . The altitude resolution
is given by that of the MIPAS NOy data used to derive the EPP-NOy
record and ranges from 4–6 km in the stratosphere to 6–9 km in the
mesosphere. EPP-NOy VMR profiles have been converted to number density
profiles using temperature and pressure information inferred from the same
MIPAS spectra from which the NOy data were also retrieved.
Here, we use the daily zonal mean climatology of EPP-NOy densities,
available on latitudinal bins of 10∘ with global coverage. From this
EPP-NOy record, we determine the hemispheric EPP-NOy total amounts
Nt(z,t) by first vertically integrating the NOy densities from
z0= 40 hPa to pressure level z. The amount in GM within each latitude
bin ϕ is then calculated as the product of the respective zonal mean
column density and the area A(ϕ) covered by the bin. In a second step
these individual contributions are summed up for each hemisphere, i.e.,
Nt(z,t)=∑ϕ∫z0z[EPP-NOy](ϕ,z,t)A(ϕ)dz,
where [EPP-NOy] is the density of the EPP-related NOy
contribution. In a similar way, the differential EPP-NOy amount Nd(z,t) in units of
GM km-1, i.e., the vertical differential of Nt(z,t),
is calculated by
Nd(z,t)=∑ϕ[EPP-NOy](ϕ,z,t)A(ϕ).
This quantity is proportional to the hemispherically averaged mean density of
EPP-NOy. Finally, we derive the hemispherically integrated EPP-NOy
flux F(z,t) through z from
F(z,t)=ddtNtobs(z,t)+L(z,t),
where L(z,t) is the hemispheric photochemical loss rate of EPP-NOy
below z (in units of GM day-1). The latter was obtained from box
model calculations that have been constrained by observed fields of
temperature, O3, and NOxseefor more details.
Equation () is only valid if there are no local EPP-NOy
productions below z. For this reason, we exclude episodes of solar proton
forcing from the calculated EPP-NOy flux data. In principle,
precipitating electrons from the radiation belts, depositing their energy
primarily in the middle and upper mesosphere, could also induce local
productions in the altitude range of interest, although recent studies
e.g., have indicated that their contributions are on
average negligibly small in the polar winter upper stratosphere and lower
mesosphere when comparing to the EPP indirect effect. It should be noted,
however, that for isolated cases (e.g., large geomagnetic storms) the
productions from radiation belt electrons can be important even in the lower
mesosphere .
Semi-empirical model for EPP-NOy in SH and NH winters (excluding ES episodes)
In this section, we develop an empirical model of hemispheric EPP-NOy
differential amounts and fluxes in SH and NH winters (excluding ES episodes)
as a function of the geomagnetic Ap index, altitude, and time, based on
the EPP-NOy distributions inferred from MIPAS during 2002–2012. Note
that the model is developed for hemispherically integrated quantities, where
the total EPP-NOy is conserved. These quantities, however, can be
converted into zonal mean densities and fluxes by imposing the observed
latitudinal distribution of EPP-NOy (see Sect. ).
Our model is based on the linear dependence of the observed stratospheric and
mesospheric EPP-NOy on the Ap index as demonstrated in
. They performed a multi-linear regression of monthly
averaged EPP-NOy amounts to the average Ap indices of the current
and 3 preceding months in order to empirically account for time lags
introduced by transport and its dispersion. In a more theoretical approach,
the EPP-NOy differential amount Nd(z,t) at pressure level
z and time t can be described by
Nd(z,t)=∫-∞tAp(t′)G(t′,Γ(z),Δ(z),l(z))dt′,
with the Green function G depending on the mean transport time Γ,
its dispersion Δ, and the photochemical loss rates l experienced
during the descent from the source region, the latter depending on altitude
and time. We further assume that the temporal variation of the photochemical
modulation during this descent is slow compared to the dispersion of
transport times such that
Nd(z,t)=Ñd(z,t)Ap‾(z,t),
where Ñd(z,t) is the spatio-temporal distribution of the
EPP-NOy amounts for a constant Ap index of unity, and
Ap‾(z,t) is the Ap propagation function that can be
described by
Ap‾(z,t)=∫-∞tAp(t′)G̃(t′,Γ(z),Δ(z))dt′.
Here, G̃(t′,Γ(z),Δ(z)) is the normalized Green
function, i.e.,
∫-∞tG̃(t′,Γ(z),Δ(z))dt′=1.
It describes the propagation of the Ap modulation from the source
region down to the stratosphere. This Green function has the same
mathematical structure as that describing the transport of a passive tracer
and can be approximated by an inverse Gaussian function,
i.e.,
G̃(t′,Γ,Δ)=Γ3/t′34πΔ2exp-Γ(t-Γ)24Δ2t′,
with the mean transport time Γ(z) from the source region to the
pressure level z and the width of the distribution Δ(z).
For Ñd(z,t) we use the following empirical function:
Ñd(z,t)=4Nm(z)exp-wN(z)(t-tmN(z))[1+exp-wN(z)(t-tmN(z))]2,
where t is the number of days passed since 1 July for the NH and since
1 January for the SH. Nm(z) is the maximum EPP-NOy
differential amount encountered at the pressure level z during the course
of the winter, tmN(z) the occurrence time of this maximum, and
wN(z) a parameter determining the temporal width of the distribution. We
have chosen this function among several candidate analytical functions
because it describes the observed distribution very closely and it allows us
to express the temporal evolution by a few, physically meaningful parameters.
The parameters Γ, Δ, Nm, tmN, and
wmN have been adjusted by performing a nonlinear least squares
fit of Eq. () to the observed daily vertical distributions of
EPP-NOy amounts for each pressure level z, excluding periods of SPE
events (orange-shaded areas in Fig. a and b). Also, episodes of
SSW/ES events in NH winters (grey-shaded areas in Fig. a) are
excluded. These events require a specific parameterization that is discussed
in Sect. .
Since, due to the complex temporal structure of the Ap evolution,
multiple maxima of the objective function of this optimization problem are
expected for the fit parameters Γ and Δ, we use a quasi-global
rather than local minimization strategy. This is, we scan, within reasonable
bounds, the Γ–Δ space and adjust for each pair of Γ and
Δ the corresponding quantities Nm, tmN, and
wmN. An additional constraint for Δ has been introduced
by assuming Δ2∼Γ, which would be the case for a linear
relationship between vertical advection and diffusion. have
reported a dependence of Δ2=0.7Γ for stratospheric
transport. We obtain the smallest χ2 values for
Δ(z)=0.35Γ(z)+4.24.
A smaller scaling factor of 0.35, compared to the value of 0.7 derived by
for tracer transport from the tropical transition layer
into the stratosphere, is reasonable since less eddy diffusion is expected
for vertical transport within the polar vortex. The empirically determined
“offset” of 4.24 days indicates that dispersion is more pronounced for
short transport times (i.e., in the mesosphere) compared to longer transport
times (i.e., in the stratosphere). As a consequence, the maximum of the
distribution function G̃(t′,Γ,Δ) is shifted to shorter
transport times compared to its mean value in the mesosphere. Physically,
this could be caused by the predominantly diffusive entry of EPP-NOy
from the auroral source region in the upper mesosphere and lower
thermosphere, where the mean circulation is upward . On the
other hand, local odd nitrogen productions by radiation belt electron
precipitation in the mesosphere would cause a similarly dispersed spectrum of
transport times.
Parameters Nm, tmN, and wN of the
empirical model for the vertical and temporal distributions of EPP-NOy
differential amounts (see Eq. ).
PressureNm (NH)Nm (SH)tmN (NH)tmN (SH)wN (NH)wN (SH)levelhPa10-3 GM km-110-3 GM km-1Days since 1 JulDays since 1 JanDays-1Days-130.0.511.20245.1303.40.09910.117420.0.624.74241.7280.00.06990.106215.0.676.50236.8267.40.06030.094810.0.807.46226.2252.80.05710.08087.0.977.40214.5241.70.06010.07265.1.157.04203.1232.10.06440.06853.1.376.26188.5218.50.06860.06662.1.445.51181.4208.90.06740.06621.51.424.95179.2202.70.06390.06541.01.284.13179.8195.30.05580.06310.71.103.46183.1190.00.04690.05980.50.912.88187.4186.10.03830.05620.30.672.16193.6182.10.02800.05080.20.581.74196.1180.20.02420.04800.150.571.50195.9179.40.02440.04700.100.661.28192.7178.50.02840.04690.070.791.15187.5177.80.03450.04770.050.921.11182.1176.90.04090.04830.031.021.14175.3175.00.04790.04790.021.061.22174.2173.00.04750.04730.011.151.35173.8172.80.04740.0472
Table lists the derived parameters Nm(z),
tmN(z), and wmN(z) for pressure levels between 30
and 0.01 hPa for both hemispheres. The best fitting values for Γ(z)
are shown in Fig. (diamonds). Although transport times may vary
over the winter season in dependence on the strength of the vertical winds,
our adjusted values of Γ are time-independent and thus represent
seasonal averages, implicitly weighted with the actual EPP-NOy amount by
the fitting algorithm. Therefore, the obtained values are most representative
of the period of the EPP-NOy maximum occurrence. Note that the fit of
Γ(z) becomes unstable below 0.5 hPa for NH winters due to small
signal-to-noise ratios, caused by the low EPP-NOy amounts together with
the large dynamical variability (not shown). In the SH, Γ increases
steadily towards lower pressure levels (higher pressures), as expected. The
fitted mean transport times are in very good agreement with those derived
from the SH mid-winter descent velocities estimated in
(indicated by dashed lines).
Mean transport times from the EPP source region to the indicated
pressure level in the SH (blue diamonds) and NH (red diamonds, only above
0.5 hPa) as derived from the best-fit mean value Γ(z) of the inverse
Gaussian used as an Ap weighting function. Transport times derived from
the mid-winter vertical velocities of are shown by dashed
lines. Solid lines correspond to the transport times expressed as a function
of tmN(z), used in the semi-empirical model.
The increase in Γ towards lower pressure levels (higher pressures) is
even more pronounced in the NH, above 0.3 hPa, where the fitted values
exceed the transport times derived from the mid-winter descent velocities.
Longer mesospheric transport times at the time of the EPP-NOy maximum
occurrence are expected in the NH due to the deceleration of mesospheric
descent in the second half of the winter . Below, the
fitted NH mean transport times become shorter again (and closer to those
derived from the mid-winter descent velocities) since the EPP-NOy
reaching those pressure levels has been transported through the mesosphere
primarily during the first half of the winter.
Temporal evolution of the vertical distribution of EPP-NOy
differential amounts Nd(z) during September–May in the NH (left)
and March–November in the SH (right) as a result of the empirical model for
a constant Ap index of 10 (2002–2012 average). Note the different color
scales for the NH and SH.
Mean transport times Γ and occurrence times of the observed
EPP-NOy maximum tmN(z) are closely linked in the SH;
however, the latter is shorter than the former. Such a time lag is likely
related to the seasonal dependence of mesospheric downward velocities (being
larger around solstice), introducing a distortion of the temporal evolution
of the differential EPP-NOy amounts. Γ(z) can be reasonably well
expressed by
ΓSH(z)=1.33tmN,SH(z)-165,
as indicated by the solid blue line in Fig. .
In the NH, the corresponding parameterization in terms of
tmN,NH(z) reproduces Γ(z) above 0.3 hPa.
Below, however, the resulting Γ(z) would be significantly
underestimated. This is expected because only EPP-NOy descending during
the first part of the winter reaches the stratosphere due to the deceleration
of mesospheric descent around mid-winter. As a consequence, the stratospheric
NH EPP-NOy maximum occurs much earlier than in the SH despite the longer
transport times as derived from the NH mid-winter descent velocities, the
latter providing an estimate of Γ in the vertical range where no
fitted values are available. On the other hand, NH mid-winter descent
velocities below 0.3 hPa can be expressed reasonably well as a function of
tmN,SH(z), and we obtain
ΓNH(z)=1.33tmN,NH(z)-165 above 0.3 hPa, and ΓNH(z)=1.33tmN,SH(z)-155 below 0.3 hPa,
as indicated by the solid red line in Fig. .
Figure shows the seasonal evolution of EPP-NOy differential
amounts for a constant Ap index of 10 corresponding to the average
Ap during the Envisat mission lifetime in both hemispheres, i.e., 10×Ñd(z,t). As expected, stratospheric NH differential
amounts are considerably smaller than those in the SH, the latter exceeding
the former by a factor of 8 around 10 hPa. Both distributions reflect the
decrease in descent rates from the mesosphere to the stratosphere, leading to
a change in the vertical gradient of the EPP-NOy tongue with a “knee”
at around 1 hPa. The deceleration of vertical transport below the
stratopause is also responsible for the increased amounts, there, due to
compression of EPP-NOy. The latter occurs because the temporal increase
in NOy is proportional to the vertical gradient of the descent rate,
which follows from mass conservation.
In contrast to the SH, minimum differential amounts are found around 0.2 hPa
in Arctic winters. The minimum is caused by the deceleration with time of the
vertical velocity occurring in the mid-winter at pressure levels above
0.2 hPa and the acceleration below that pressure level, causing a local
depletion of EPP-NOy. This sudden deceleration of mesospheric descent is
also responsible for the “splitting” of the EPP-NOy tongue into a
slowly descending mesospheric branch, reaching the 0.2 hPa level around
April, and a rapidly descending stratospheric branch, comparable to the
typical SH pattern.
Figure a and b show the observed and modeled temporal evolutions of
EPP-NOy differential amounts at pressure levels of 0.03, 0.3, 2, and
10 hPa in both hemispheres. There is generally good agreement, indicating
that most of the inter-annual variability encountered in the observed
EPP-NOy amounts can be reproduced by the semi-empirical model,
particularly in the SH. As expected, the agreement in NH winters is not as
good due to the more pronounced dynamical modulation. This is particularly
the case for winters with SSW and ES events, which are not accounted for in
the model for quiescent NH winters. During and after these events,
EPP-NOy amounts are underestimated by the model by up to an order of
magnitude, highlighting the need for specific parameterizations as presented
in Sect. . Also, the typically lower EPP-NOy amounts in
the NH, which in some winters are close to the detection limit, are
necessarily more dispersed relative to the model results. However, the
ability to reproduce singular features, such as the “peaky” evolution of
Nd(z,t) during January 2007 in the NH related to a short-term
increase in geomagnetic activity, provides confidence in the model.
(a) Observed (red diamonds) and fitted (solid black)
temporal evolution of hemispheric EPP-NOy amounts during 2002–2012 at
the pressure levels 0.03, 0.3, 2, and 10 hPa (top to bottom) in the NH.
Ap‾(z,t) of the empirical model for quiescent dynamical
conditions is shown with blue lines (dotted blue lines indicate Ap
levels with a spacing of 5 Ap units). Shaded areas have been excluded
from the fit due to perturbed dynamics (i.e., SSW and ES events, grey) and
large SPE events (orange). Note that the y axis range does not cover the
very high NOy amounts encountered in the NH 2003/04 winter.
(b) As for panel (a) but for the SH. Note the variable
y axis range.
The semi-empirical model for EPP-NOy fluxes through given pressure
levels has been constructed in a similar way to the model for the
EPP-NOy amounts. Since the flux F(z,t) can be expressed as the product
of the NOy differential amount Nd(z,t) at z and the
EPP-NOy descent rate , the latter being independent of
Ap, we can assume the same Ap dependence Ap‾(z,t) as
for the EPP-NOy differential amounts, i.e.,
F(z,t)=F̃(z,t)Ap‾(z,t).
For F̃(z,t) we use the same type of function as for
Ãd(z,t) in Eq. (), i.e.,
F̃(z,t)=Fm(z)exp-wF(z)(t-tmF(z))[1+exp-wF(z)(t-tmF(z))]2.
This empirical function is then adjusted to the “observed” fluxes
Fobs(z,t) through the vertical level z, divided by the Ap
propagation function at z, i.e.,
F̃obs(z,t)=Fobs(z,t)Ap‾(z,t).
As discussed in Sect. , Fobs(z,t) is
derived from the temporal changes in the sum of the observed EPP-NOy
total amounts Nt(z,t) and the accumulated photochemical losses
L(z,t). We assume that any reduction of the total amount is caused by
vertical mixing due to vortex rupture and that under these conditions
vertical velocities tend to be zero, such that we can limit the “observed”
fluxes to non-negative values see. Again, we exclude
periods of SPE and SSW/ES events (shaded areas in Fig. a).
Table lists the derived parameters Fm(z),
tmF(z), and wmF(z) for pressure levels between 30
and 0.02 hPa for both hemispheres. It also provides the modeled seasonal
EPP-NOy deposition, T10, below z corresponding to the 2002–2012
average geomagnetic forcing (Ap=10). Below 0.02 hPa, the modeled
depositions are 0.48 GM in the NH and 1.21 GM in the SH, that is, nearly 3
times more in the former than in the latter.
Parameters Fm, tmF, and wF of the
empirical model for the vertical and temporal distributions of EPP-NOy
fluxes (see Eq. ) through a given pressure level in both
hemispheres. The seasonally accumulated EPP-NOy amounts T10 for a
constant Ap index of 10 (2002–2012 average) are also listed.
PressureFm (NH)Fm (SH)tmF (NH)tmF (SH)wF (NH)wF (SH)T10 (NH)T10 (SH)levelhPa10-3 GM day-110-3 GM day-1Days since 1 JulDays since 1 JanDays-1Days-1GMGM300.0590.042229.6284.60.41810.12540.0060.013200.1530.317217.4267.40.25300.11100.0240.114150.1720.509209.2256.10.18220.10290.0380.198100.1820.753198.7241.50.12640.09370.0580.32170.1990.937190.3229.90.10490.08770.0760.42850.2311.086183.1220.00.09840.08340.0940.52130.3071.272173.6206.80.10000.07900.1230.64320.3801.392167.3197.90.10330.07700.1470.7231.50.4321.465163.4192.40.10440.07610.1650.7701.00.4951.556158.7185.60.10320.07540.1920.8260.70.5371.628155.4180.60.09940.07510.2160.8670.50.5631.690152.8176.60.09390.07510.2400.9000.30.5801.783150.0171.80.08380.07520.2770.9480.20.5831.858148.5168.90.07590.07530.3070.9870.150.5841.914147.8167.20.07110.07540.3291.0160.100.5941.997147.1165.30.06600.07540.3601.0600.070.6132.073146.8164.00.06330.07530.3871.1010.050.6412.142146.6163.10.06220.07520.4121.1400.030.7002.231146.4161.50.06220.07510.4501.1890.020.7472.268146.0160.10.06250.07520.4791.207
Temporal evolution of EPP-NOy fluxes FEEP(z)
through the vertical level z indicated by the y axis during
September–May in the NH (left) and March–November in the SH (right) as a
result of the empirical model for the 2002–2012 average Ap index of
10.
Figure shows the seasonal evolution of the modeled EPP-NOy
fluxes in both hemispheres, again for Ap=10. Maximum fluxes of
0.07 GM day-1 in the NH and 0.22 GM day-1 in the SH are found
at the uppermost pressure levels during the winter solstice. Towards lower
altitudes, both NH and SH fluxes are decreasing. In the mesosphere, this
decrease is mainly related to photochemical losses. At lower altitudes,
dynamical loss due to mixing out of the polar vortex is responsible for the
flux decrease. Assuming that the fraction of EPP-NOy mixed out of the
vortex is not being transported further downwards, the vertical gradient of
the seasonally integrated fluxes (T10 of Table ) hence
represents the deposition profile of EPP-NOy at the end of the winter.
Parameterization for elevated stratopause events
The challenge of parameterizing EPP-NOy amounts and fluxes during ES
events resides mainly in the scarcity of observational data during these
events. MIPAS has recorded NOy data with sufficient temporal coverage
only during two events occurring in January 2004 and February 2009.
have shown, using WACCM simulations with constant
geomagnetic forcing, that, besides the geomagnetic activity level, the event
timing is a crucial driver of the strength of odd nitrogen descent because of
the seasonal dependence of residual vertical wind speeds. In addition, it is
also likely that the EPP-NOy amount in the source region is modulated by
photochemical losses, again resulting in smaller EPP-NOy depositions
during events occurring later in the winter. The two events observed by MIPAS
have rather different characteristics regarding the geomagnetic activity
level and timing and hence cover a large range of the expected variability.
Coefficients of the polynomial expansion ∑i=0nailn(p)i used for parameters of the EPP-NOy model for ES events.
Values in parentheses should be read as powers of 10.
Our approach to provide a general parameterization of odd nitrogen descent
during ES events is, first, to parameterize the EPP-NOy amounts and
fluxes individually for each of the two observed events, and then to exploit
dependencies of the obtained parameters on the event timing. The time
evolution of the differential EPP-NOy amounts Nd(z,t) during ES
winters is finally calculated by adding the EPP-NOy residual amounts
during the ES event to the “quiescent” differential amounts.
The modeling of the individual 2004 and 2009 ES events is performed in a very
similar way as for the quiescent winters (see Eqs. and
of Sect. ). First, we adjust the parameters of the Ap
propagation function and the spatio-temporal term to the EPP-NOy
residual amounts, that is, the difference between the observed differential
amounts and those modeled for quiescent conditions, after the onset of the
event at the pressure levels reached by the descending NOy tongue. The
onset time t0ES is defined as the time (days since 1 July)
when the ES-related differential amount increases at 0.02 hPa, resulting in
t02004=196 and t02009=221. We further define
tmES(z) as the time lag between t0ES and
the occurrence of the EPP-NOy maximum at pressure level z, and we
assume that ΓES(z)=tmES(z). In order
to account for the fact that there are no residual amounts before the event,
we apply the following correction to the spatio-temporal term,
βES(z)=maxmint-t0EStmES(z)-t0ES0.3,1,0,
resulting in a stretching of the temporal distribution before the occurrence
of the EPP-NOy maximum, while leaving it unchanged afterwards. The
adjusted values of tmES(z) do not differ significantly
between the two events in most of the vertical range where the EPP-NOy
maximum occurs. In the vicinity of the lowermost pressure level reached
around equinox, however, tmES(z) increases drastically
due to the deceleration of descent. We parameterize therefore
tmES(z) as a function of t0ES and z,
tmES(z)=t̃mES(z)+expt0ES+t̃mES(z)-279/4.,
with t̃mES(z) being a fourth-order polynomial of
ln(z) (see Table for coefficients). The parameter
wES(z), describing the width of the EPP-NOy peak after the
ES event, is in first order height-independent, and has been adjusted to the
common value of 0.15 for both events, which corresponds to a full width at
half maximum of 13 days. Finally, the adjusted values of the
Ap-normalized maximum amounts NmES(z) for both
events are shown in Fig. a (blue and red squares for 2004 and 2009,
respectively). The observed maximum amounts peak around the stratopause,
being a factor of 3 higher in 2004 compared to 2009. This difference becomes
smaller with height and decreases at the uppermost levels to a factor of 2.
Similarly, we fit the spatio-temporal term in Eq. () to the
observed EPP-NOy fluxes during both events. As a good approximation, we
use the same width parameter wES(z)=0.15 and the same lag time
tmES(z) as for the residual amounts. The adjusted
values of the maximum Ap-normalized fluxes FmES(z)
for both events are shown as blue and red squares in Fig. b. For
both events, the maximum fluxes decrease monotonically towards lower pressure
levels, indicating photochemical and/or dynamical losses. The ratio of the
2004 and 2009 fluxes is nearly constant, with a value of 4.2 in the
mesosphere. Since the downwelling flux at 0.01 hPa is in first order the
product of the EPP-NOy amount and vertical velocity during the ES event
in the source region, we expect this ratio to be influenced by the seasonal
variations of both quantities. Around the stratopause and below, fluxes
decrease faster in 2009 because the maximum occurrence time is closer to
equinox at these pressure levels. The observed Ap-normalized maximum
flux can be parameterized as a function of t0ES and z by
FmESz,t0ES=Φt0ESmaxF̃mES(z),01+exp(t0ES+tmES(z)-273.)/8.,
with F̃mES(z) being a fourth-order polynomial of
ln(z) (see Table for coefficients). Φ takes the values of
0.00567 and 0.00125 for the 2004 and 2009 events, respectively.
(a) Parameterized (solid) and adjusted (symbols) residual
maximum amounts NmES(z) for 2004 (blue) and 2009 (red).
(b) Parameterized and adjusted maximum fluxes
FmES(z). The dashed lines represent
Φ(t0ES)F̃mES(z) (i.e., without
correction in the vicinity of equinox). (c) Parameterized and
adjusted descent rates ωES(z). The dashed lines represent
Ω(t0ES)ω̃mES(z) (i.e.,
without correction in the vicinity of equinox).
We calculate descent velocities ωmES(z), dividing
the adjusted values of FmES(z) by
NmES(z). The obtained values are shown with blue and
red squares for 2004 and 2009, respectively, in Fig. c. Mesospheric
descent rates are a factor of 2 higher in 2009 (2 km per day) compared to
2009 (1 km per day), in qualitative agreement with the model results of
for events with similar timing. The adjusted descent rates
for the 2004 event are also in good agreement with previous estimates of the
vertical component of the meridional circulations using MIPAS temperatures
and diabatic heating rates . During both events, the
descent rates decrease rapidly towards the stratopause, where they take
values of around 200 m day-1. The velocity ratio between both events
is rather constant down to the stratopause. We thus use a similar
parameterization as given by Eq. () for the vertical velocities,
ωmESz,t0ES=Ωt0ESexpω̃mES(z)1+exp(t0ES+tmES(z)-280.)/9.,
with ω̃mES(z) being a sixth-order
polynomial of ln(z) (see Table for coefficients). Ω
takes the values of 0.99 and 0.50 for the 2004 and 2009 events, respectively.
By multiplying ωmES and
FmES, we obtain the parameterization of
NmES as shown in Fig. a by solid lines.
Dependence of the maximum EPP-NOy flux
FmES through the 0.4 hPa level (black) and the descent
velocity ωmES at 0.08 hPa (red) on the ES event
timing.
Φ(t0ES) and Ω(t0ES) depend solely on the event timing and
are related to the Ap-normalized flux and vertical velocity,
respectively, in the source region. Since Φ(t0ES) depends on
Ω(t0ES), we use
Θt0ES=Φt0ESΩt0ES
in order to obtain an event time-dependent quantity related to the
EPP-NOy amount in the source region. Both Θ(t0ES)
and Ω(t0ES) are expected to maximize at solstice
(day 173) and to reach the zero level close to equinox. We use therefore a
similar expression as provided in Eq. () to parameterize their
dependence on the event timing:
Temporal evolution of the vertical distribution of EPP-NOy
differential amounts Nd(z) during September–May in NH winter
with ES events occurring on 15 December (upper left), 15 January (upper
right), 10 February (bottom left), and 5 March (bottom right), resulting from
the empirical model for a constant Ap index of 10 (2002–2012
average).
Figure shows the resulting values of FmES
at 0.4 hPa and ωES at 0.08 hPa as a function of
t0ES. Both time dependencies can be qualitatively compared to
the total EPP-NOx amounts crossing the 0.41 hPa level (i.e., the
integral flux through this level) and maximum descent at the 0.08 hPa level
for a large number of ES events simulated by WACCM as presented by
(their Figs. 6a and 9c, respectively). In the
parameterization, as well as in the WACCM simulations, descent rates decay
almost linearly with t0ES, reaching minimum values around the
end of March (note that the day of the event is defined in
as the central date of the preceding SSW, typically 8 days before
t0ES). ES events starting around 1 February are characterized
by half of the maximum descent rate for solstice ES events. Both
parameterized and WACCM-simulated fluxes decrease with time more nonlinearly
and reach the background level in mid-February (50 % of the solstice flux
around mid-January). In our semi-empirical model, the more pronounced
nonlinearity of the flux decay compared to descent is introduced by
Eqs. () and (), in consonance with our hypothesis that the
EPP-NOy flux depends on the temporal evolutions of both the descent
rates and the EPP-NOy amounts in the source region.
As mentioned above, the time evolution of the differential EPP-NOy
amounts Nd(z,t) during ES winters is finally calculated by
adding the EPP-NOy residual amounts during the ES event (as a function
of ΓES(z), NmES(z),
tmES(z), and wmES(z)) to the
“quiescent” differential amounts (Eq. ). In order to illustrate
the impact of the ES event timing on the differential amounts,
Fig. shows the seasonal evolution of the resulting differential
amounts for a constant geomagnetic level Ap=10 in NH winters with ES
events occurring on 15 December, 15 January, 10 February and 5 March. A
maximum differential amount of 0.14 GM km-1 is predicted for a
December event, exceeding the maximum amounts in SH winters (encountered
around 10 hPa) by a factor of nearly 2. On the other hand, the EPP-NOy
amounts after March events are hardly distinguishable from the background.
Also, the lowermost pressure level reached by the EPP-NOy tongue differs
significantly between the events, being 7, 3, 1, and 0.2 hPa, respectively,
for these events.
Figure compares the observed and modeled temporal evolutions of
EPP-NOy differential amounts at pressure levels 0.03, 0.1, 0.3, and
1 hPa during the ES winters 2004 and 2009. The generally good agreement
demonstrates the capability of the extended semi-empirical model to reproduce
the observed EPP-NOy also in NH winters with ES events, in contrast to
the model for quiescent winters (compare to Fig. a).
Observed (red diamonds) and modeled (solid black) temporal evolution
of NH EPP-NOy amounts during ES winters 2004 (left) and 2009 (right) at
pressure levels 0.03, 0.1, 0.3, and 1 hPa (top to bottom). The orange-shaded
area indicates the period affected by the large SPE event of
October/November 2003.
Determination of ES onsets relevant for EPP-NOy
While the detection of ES events and the determination of their onset date,
t0ES, is straightforward for the winters observed by MIPAS, a
specific criterion is required in general in order to model the EPP-NOy
distribution in longer time periods. Further, if the semi-empirical model is
used in chemistry climate models to provide an odd nitrogen upper boundary
condition (see Sect. ), the detection of ES events needs to be
performed online. An obvious quantity for the detection would be the
stratopause height derived from zonally averaged polar temperatures as
suggested by and . However, the
unequivocal detection of ES events from the polar zonal mean temperature
profile suffers from short-term excursions of the stratopause height that
result from transient wave forcing. The temporal smoothing, needed to reduce
this effect, disables the online detection on a daily basis, as required to
account for the fast increase in EEP-NOy in the mesosphere at the
beginning of an event. Also, if the semi-empirical model is used for
reconstruction of the EPP indirect effect over historical time periods, and
reanalyzed meteorological data need to be employed for ES detection, the
latter would suffer from the poor representation of mesospheric temperatures
in the reanalysis data.
An alternative approach to detection of ES events and determination of
t0ES from reanalysis or model temperature fields takes
advantage of the tight anti-correlation of the upper stratospheric and
mesospheric meridional temperature gradients during ES events. In particular,
the difference between the zonal mean temperature averaged over
0–30∘ N and that averaged over 70–90∘ N at 1 hPa, in the
following referred to as ΔT30–70, from MIPAS observations
during 2002–2012 shows pronounced increases of up to 55 K during the 2004
and 2009 events, not reached during quiescent winters. We have tested this
criterion using 1 hPa temperatures from a transient model simulation with
the ECHAM/MESSy Atmospheric Chemistry (EMAC) model, covering the period
1979–2014. The EMAC model is a numerical chemistry and climate simulation
system that includes sub-models describing tropospheric and middle atmosphere
processes and their interaction with oceans, land and human influences
. It uses the second version of the Modular Earth Submodel
System (MESSy2) to link multi-institutional computer codes. The core
atmospheric model is the 5th generation European Centre Hamburg general
circulation model . For the present study we applied EMAC
(ECHAM5 version 5.3.02, MESSy version 2.50) at the T42L90MA resolution, i.e.,
with a spherical truncation of T42 (corresponding to a quadratic Gaussian
grid of approx. 2.8 by 2.8∘ in latitude and longitude) with
90 vertical hybrid pressure levels of up to 0.01 hPa. Vorticity, divergence,
and temperature fields have been relaxed to ERA-Interim reanalysis data
below 1 hPa, facilitating the simulation of dynamic events
that have occurred during 1979–2014.
Temporal evolution of ΔT30–70 (in K)
=T (0–30∘N) -T(70–90∘N) at 1 hPa (solid red) and
70–90∘ N zonal mean CO anomalies (with respect to the
climatological seasonal mean, in ppmv) at 0.5 hPa (solid blue). The
threshold of ΔT30–70=53 K for ES detection is indicated
by the dotted red line. Detected event onsets are marked by vertical dashed
lines.
Figure shows the temporal evolution of ΔT30–70
over the whole simulation period. The 53 K level (indicated by the dotted
red line) is exceeded during all reported ES events in the present and last
decades, namely 2004, 2006, 2009, and 2013 (indicated by vertical dashed
lines). Elevated stratopause events are also detected in 1985 and 1987.
Table lists the dates of the first day exceeding the 53 K
threshold during all events. These dates coincide very precisely (within
1 day) with t0ES, as determined from the MIPAS data for the
2004 and 2009 events. Also, the onset dates for the 2006 and 2013 events are
consistent with the analysis performed in previous works
. An inspection of the modeled stratopause
evolution in 1985 and 1987 confirms elevated stratopauses after SSWs in these
winters (not shown). Also, winters with ΔT30–70 below the
53 K threshold show no elevated stratopause, despite the occurrence of
several SSWs. Since EPP was not considered in the EMAC simulation, we look at
the simulated CO evolution in the lower mesosphere in order to prove whether
enhanced descent indeed occurred during the detected ES events. CO is an
adequate tracer of mesospheric air due to increasing concentrations towards
the upper mesosphere/lower thermosphere and relatively long photochemical
lifetimes in polar winter. The solid blue line in Fig. represents
the 70–90∘ N CO anomalies (with respect to the climatological
seasonal mean) at 1 hPa. Noticeable increases in CO are found after all ES
events, although the magnitude of the increase after the 1987 event is rather
small, most likely related to the late onset.
Start and end dates of ES events detected by the ΔT30–70>53 K criterion.
Start dateEnd datet0ES23 Jan 198531 Jan 19852078 Feb 198723 Feb 198722311 Jan 200416 Feb 20041954 Feb 200624 Feb 20062195 Feb 20098 Mar 200922026 Jan 201322 Feb 2013210
investigated the occurrence of ES events, as detected
from polar stratopause jumps, in MERRA reanalysis data
covering 1979–2011. They identified the events detected by our ΔT30–70 criterion (see Table ), but also additional
events in winters 1980/81, 1983/84, 1989/90, 1994/95, and 2009/10. In these
additional ES events, the maximum values of ΔT30–70 of the
nudged EMAC simulation remained well below 53 K. Further, no significant CO
increases at 0.5 hPa were simulated with the EMAC model, except for 1983/84.
In this particular winter, however, the CO enhancements have occurred already
before the event. The EPP-NOy evolution in the 2009/10 NH winter, which
has been observed by MIPAS, does not show indications for ES-related odd
nitrogen intrusions. Most of the additional ES events detected by
were accompanied by minor stratospheric warmings, in
contrast to the events detected by the ΔT30–70 criterion
that were preceded by major SSWs.
We thus conclude that our criterion based on ΔT30–70
allows us to detect the ES events with strong descent of mesospheric air and
associated efficient deposition of EPP-NOy in the stratosphere. Also, we
found that the first crossing time of the ΔT30–70=53 K
threshold provides a reasonable estimate of the onset time,
t0ES.
(a) Inter-annual variation of seasonal EPP-NOy
depositions during 1978–2014 calculated with the semi-empirical model in the
SH below the pressure levels of 1 hPa (top) and 0.1 hPa (bottom).
EPP-NOy deposition estimates from MIPAS observations
are indicated by filled red diamonds. HALOE-derived estimates of
are shown by the grey-shaded area (limited by their
“average” and “maximum excess NOx” estimates) and are shifted by
0.5 GM (dashed blue line) in order to facilitate comparisons to the MIPAS
estimates. Open blue symbols represent the adjusted “maximum excess”
estimates (scaled by a factor of 0.7 to fit the MIPAS estimates).
(b) As for panel (a) but for the NH and for the 0.7 hPa
(top) and 0.1 hPa (bottom) levels. Modeled EPP-NOy depositions without
consideration of ES events are also shown by dashed lines. Observational
deposition estimates from satellite data are also shown: MIPAS
(filled red diamonds); MIPAS (2003–2004), ACE-FTS
(2005-2009), and LIMS (1979) (filled blue diamonds); SOFIE
(2009–2013) (filled green diamonds). Open blue and green
symbols represent the adjusted estimates of and
, respectively, after applying a constant offset (colored
dotted lines) and an scale factor in order to facilitate comparisons with the
MIPAS-derived depositions of (see text for more details).
Note that years indicated on the x axis correspond to the second year of
the season; e.g., “2003” means “winter 2002/2003”.
EPP indirect effect during 1978–2014 and comparison with previous estimates
Figure a and b show the semi-empirical model estimates of the
EPP-NOy depositions in the SH and NH winters during 1978–2014 together
with previous estimations. First, we observe a generally good agreement
between the results of the semi-empirical model and the estimates of the EPP
indirect effect provided by . This is not surprising, since
both are based on the same MIPAS observations, but this comparison gives us a
good measure of the quality of the fitting of the model to the actual
measurements from which it has been derived.
Figure a also shows the estimates on the EPP indirect effect
of for the SH winters 1992–2005 from HALOE NOx
solar occultation observations in the upper stratosphere (note that NOx
is nearly equivalent to NOy at these altitudes and hence comparable to
our results). Like , they also used a tracer correlation
method to extract the EPP-NOx contribution, but, in contrast to the
MIPAS-derived depositions, they derived it from the accumulated NOx flux
through the 45 km altitude level (∼1 hPa; see Fig. a).
The flux was calculated from the observed NOx density at that level
assuming a constant SH polar winter descent rate of 400 m day-1. Due
to the sparse sampling of HALOE they made important assumptions about the
latitudinal distribution of the EPP-NOx inside the vortex that led to
uncertainties as large as 100 % in their estimates (see the grey-shaded
area in Fig. a). Further, a rather conservative threshold was
used for discriminating the EPP-NOx from the background NOx, which
might have offset their resulting estimates. We are interested in evaluating
the consistency of the MIPAS and HALOE estimates, particularly in terms of
inter-annual variability. For that purpose, and in order to account for
possible biases related to the different measurements and estimation methods,
we adjusted an offset and a scale factor to the HALOE estimates. The
determined scale factor of 0.7 is well within the range encompassed by the
“average” and “maximum excess NOx” estimates of .
The offset of 0.5 GM is rather high but plausible, since comparable
EPP-NOy depositions are expected during SH winters with similarly low
geomagnetic activity levels such as 1996 (HALOE-derived estimate of 0.1) and
2007 (MIPAS-derived estimate of 0.6). Note also that a negative bias of
0.5 GM in the EPP-NOy depositions would be introduced by an
underestimation of about 2×108 cm-3 in the EPP contribution
to the NOx densities at 1 hPa, which is comparable to the conservative
threshold for EPP-NOx discrimination from background values used by
(see their Fig. 6). The inter-annual variations of our
modeled EPP-NOy depositions below 1 hPa are highly consistent with the
adjusted estimates from HALOE in the 1992–2005 period. Particularly during
1993–1998, the agreement is excellent.
provided observational EPP-NOx deposition estimates for
NH winters by employing the same method as but using
MERRA-derived vertical velocities instead of a fixed 400 m day-1
descent rate in the flux calculation. They used NOx observations from
the Limb Infrared Monitor of the Stratosphere (LIMS) for the winter 1978/79,
MIPAS for the winters 2002/03 and 2003/04, as well as Atmospheric Chemistry
Experiment Fourier transform spectrometer (ACE-FTS) data for the Arctic
winters in 2004–2009. They reported depositions below the 2000 and 3000 K
potential temperature surfaces, corresponding roughly to the 0.7 and 0.1 hPa
pressure levels, respectively. Again, we adjust these estimates to those
derived by using a time-independent offset and scale.
For both vertical levels, we determine an offset of 0.2 GM, being
considerably lower than for the SH estimates of . This
might be partly related to the latitude coverage of the employed instruments
(global sampling in the case of LIMS and MIPAS, and 60–85∘ N for
ACE-FTS in the NH), resulting in a better polar coverage compared to HALOE
(< 55∘ S during May–August). These sampling differences might
also explain why no scaling (derived scale factor of 0.995) needs to be
applied in order to fit the 3000 K deposition estimates to those of
for the 0.1 hPa level. The modeled seasonally integrated
fluxes through this pressure level during the NH winters 2004/05, 2005/06 and
2006/07, not available from MIPAS data, show very good agreement with the
estimates from ACE-FTS shifted by 0.2 GM.
However, the EPP-NOx depositions below 2000 K need to be scaled by a
factor of 0.77 in order to achieve consistency with those of
for the 0.7 hPa level. A possible explanation for this
mismatch is the use of MERRA-derived vertical velocities in
to convert the EPP-NOx densities in fluxes. These velocities might be
overestimated by up to 40 % at this pressure level .
However, differences might also be introduced by comparing depositions below
pressure levels and depositions below potential temperature surfaces, because
they are characterized by different latitudinal and temporal variations and
the EPP-NOy fluxes have strong gradients, particularly in this vertical
region. After applying the adjustment to the estimates of ,
the agreement of the inter-annual variations with those of the semi-empirical
model is reasonably good.
employed the same method as in SOFIE NO
observation. In this case, we apply an offset of 0.12 GM and a scale factor
of 0.77 to compare their estimates of the seasonally integrated EPP-NOy
fluxes through 0.7 hPa in the winters 2008/09 and 20012/13 to the
semi-empirical model, and again find reasonable agreement.
The modeled SH seasonal EPP-NOy depositions during the 1978–2014
period, covering three solar cycles, are on average 1.26 GM below the
0.1 hPa level and 0.99 GM below 1 hPa. The large EPP indirect effect in
2003 – the strongest during the MIPAS observation period – is only exceeded
by that of the Antarctic winter 1991 with about 30 % higher depositions.
The average NH depositions in 1978–2014 are 0.50 (0.25) GM below the 0.1
(1) hPa level. The EPP-NOy deposition of the extraordinary ES winter
2003/04 is at 3 GM below 0.1 hPa the strongest of the whole period,
followed by the 1984/85 ES winter with 1.9 GM. The average contribution of
the ES events to the EPP-NOy depositions in the 1978–2014 period is
only 4 % (0.02 GM at 0.1 hPa and 0.01 GM at 1 hPa). This indicates
that strong descent episodes related to ES events, while being of high
relevance for the EPP-NOy evolution during individual ES winters, seem
to play only a minor role on longer timescales. However, the average
EPP-NOy contributions due to ES events increase noticeably when
considering only the last decade. The question of whether the clustering of
ES events during the latter period is part of the natural variability or
indicative of a tendency, however, still remains open.
EPP-NOy upper boundary conditions for atmospheric models
A major purpose of this semi-empirical model is to provide an upper boundary
condition (UBC) for chemistry climate models with an upper lid in the
mesosphere. These models leave a large fraction of the EPP source region
(extending to the lower thermosphere) uncovered and hence do not allow for a
detailed simulation of the EPP indirect effect. However, EPP can still be
taken properly into account by prescribing NOy at the upper model lid.
This can either be done by specifying a flux of NOx into the top of the
model domain e.g., or by specifying a NOx
concentration at the uppermost model layer(s) e.g.,.
Taking into account that NOy≃ NOx at pressure levels higher
than approximately 1 hPa, our semi-empirical model allows for both types of
NOx UBCs, although the prescription of fluxes should be restricted to
model levels at 0.02 hPa and lower altitudes in order to minimize
contaminations by local productions related to radiation belt electrons (see
above).
Typically, the NOy (or NOx) flux or concentration is assumed to
have a zonally homogeneous distribution. also
assumed a homogeneously distributed flux within 55–90∘ latitude,
roughly coinciding with the mesospheric polar vortex. We have analyzed the
latitudinal distribution of the MIPAS-derived EPP-NOy averaged over the
2002–2012 period in order to come up with a more realistic distribution. The
derived meridional dependency is then used to distribute the differential
amounts and fluxes in the respective hemisphere. Figure shows
the fraction of the hemispheric differential amount polewards of a given
latitude separately for SH winters, quiescent NH winters, and ES episodes as
a function of pressure. SH and quiescent NH distributions are very similar,
with around 60 % of the EPP-NOy at latitudes > 65∘ and
90 % at > 50∘. The distributions tend to widen below 0.1 hPa
by about 5∘. No pronounced variation of the distributions along the
winter season has been encountered. During ES events, the distribution is
more confined over the pole, with 60 % of the EPP-NOy at latitudes
> 75∘ and 90 % at > 60∘. No significant variation
with height is found during ES episodes. Normalized latitudinal distributions
Ψ(ϕ,z) of EPP-NOy concentrations in the vertical range
1–0.01 hPa are provided in Tables , , and
for SH winters, NH winters, and ES episodes, respectively.
Following Eq. (), the UBC for prescribing EPP-NOy
concentrations (in units of cm-3) is then given by
EPP-NOySH(ϕ,z,t)=NA106NdSH(z,t)ΨSH(ϕ,z)∑ϕSHΨSH(ϕ,z)A(ϕ),EPP-NOyNH(ϕ,z,t)=NA106NdNH(z,t)ΨNH(ϕ,z)∑ϕNHΨNH(ϕ,z)A(ϕ)+NdES(z,t)ΨES(ϕ,z)∑ϕNHΨES(ϕ,z)A(ϕ),
where NA is the Avogadro constant and A(ϕ) is the area in
km2 enclosed by the model latitude bin corresponding to ϕ. Assuming
the same latitudinal dependence, the UBC for specifying an EPP-NOy flux
into the top of the model domain (in units of cm-2 s-1) is given by
fSH(ϕ,z,t)=10-124×3600NAFSH(z,t)ΨSH(ϕ,z)∑ϕSHΨSH(ϕ,z)A(ϕ),fNH(ϕ,z,t)=10-124×3600NAFNH(z,t)ΨNH(ϕ,z)∑ϕNHΨNH(ϕ,z)A(ϕ)+FES(z,t)ΨES(ϕ,z)∑ϕNHΨES(ϕ,z)A(ϕ).
Latitudinal distribution of EPP-NOy from MIPAS averaged over
2002–2012, shown as the fraction of the hemispheric differential amount
polewards of the indicated latitude, for SH winters (top), NH winters
(middle), and ES winters (bottom). Averages for individual months are shown
by colored lines. The amount-weighted seasonal average is indicated by the
thick black line.
Background NOy concentrations, i.e., the NOy contributions not
related to the EPP indirect effect, are not negligible in the lower/middle
mesosphere and need to be considered when prescribing concentrations at the
model's top layer(s). When specifying fluxes, this step is not required
because the NOy entering the model domain in polar winters is in good
approximation, exclusively originating from the EPP source. We model the
background NOy concentrations by fitting the following regression
function to the seasonal composite of the background
[NOybg]=[NOy]-[EPP-NOy]
obtained from the MIPAS observations in 2002–2012:
[NOybg](ϕ,z,t)=a0(ϕ,z)1+∑n=13an(ϕ,z)×sin2πnt365+bn(ϕ,z),
with t being here the day of the year. The regression coefficients
an(ϕ,z) and bn(ϕ,z) for pressure levels within 1–0.01 hPa
are listed in Tables –. Note that this
parameterization of NOybg does not provide a full description
of the observations since inter-annual variations (e.g., introduced by the
QBO) are not considered. For prescription of NOy in models with upper
lids above 1 hPa, the consideration of merely seasonal variations of the
background NOy is a good approximation. Figure compares the
resulting latitude–time NOy distribution of the semi-empirical model to
the MIPAS observations at 0.5 and 0.02 hPa. While the background
contribution at the latter pressure level is nearly 2 orders of magnitude
smaller than the EPP contribution, this is not the case at 0.5 hPa. Here,
the background is comparable to the EPP contribution in many NH and SH
winters. Overall, the modeled NOy densities reproduce very well the
observed latitude distribution and time evolution, except for episodes of
large solar proton events (e.g., October/November 2003 and
January/March 2012). This is expected since the semi-empirical model does not
account for the EPP direct effect.
Latitude–time sections of NOy densities observed by MIPAS
(left) and from the UBC model (right) at 0.02 hPa (top) and 0.5 hPa
(bottom).
The consideration of ES-related enhanced odd nitrogen descent in the UBC for
chemistry climate models is relatively straightforward in nudged model
simulations since the ES onset dates, needed to drive the semi-empirical
model, are known beforehand. However, its consideration in free-running
simulations would require one to diagnose ES events “online” in a
quasi-instantaneous manner, e.g., by analyzing the modeled temperature fields
averaged over a narrow time window covering the past model day. Then, in case
of the detection of an event onset, it would be required to update the UBC,
accounting for the ES event as described in Sect. , from the ES
onset date to at least the end of the actual NH winter season. The main task
is hence the implementation of the online ES detection scheme in the model
system. We have proposed in Sect. a detection criterion,
ΔT30–70>53 K, based on the difference of 0–30 and
70–90∘ N temperature averages at 1 hPa, which allowed for
quasi-instantaneous detection of ES event onsets (associated with enhanced
odd nitrogen descent) in EMAC simulations. Its application in other model
systems, however, might require an adjustment of the detection threshold
(which could be achieved by calculating ΔT30–70 from a
nudged simulation covering 1980–2014 and tuning the threshold such that it
is exceeded only for ES events listed in Table ).
Historical reconstruction of the EPP indirect effect
The semi-empirical model also allows for a historical reconstruction of
EPP-NOy depositions for the period covered by the Ap record (i.e.,
since 1932). This period can by extended by use of the aa index (available
since 1868) and the Helsinki Ak index (available for 1840–1912). Both
aa and Ak indices provide a similar proxy of geomagnetic activity as
Ap, however, based on observation from only one (or two) stations.
Therefore, both data sets can be combined, although biases have to be
accounted for. Such a combined, de-biased data set, expressed as a
homogenized Ap index, has been generated as part of the solar forcing
recommendations for CMIP6, available at
http://solarisheppa.geomar.de/solarisheppa/cmip6. A detailed
description of the methodology for homogenization of these three indices can
be found in . We use here the extended Ap index for
the EPP-NOy reconstruction in the period 1850–2014, corresponding to
the historical simulation as part of the CMIP6 DECK experiments
.
Reconstructed stratospheric EPP-NOy deposition below 0.5 hPa
(∼ 50 km) in the SH (red) and NH (without ES events, dark blue) during
1850–2015. NH depositions with consideration of ES events (only 1979–2014)
are shown with the light blue line. Solar cycle average depositions are
indicated with the dashed lines.
Figure shows the reconstruction of seasonal stratospheric
EPP-NOy depositions below 0.5 hPa (∼ 50 km) in both hemispheres
in the period 1850–2014 covering solar cycles 9–24. The temporal evolution
of the EPP indirect effect follows closely that of solar variability on
multi-decadal timescales, as expected due to the strong link of solar and
geomagnetic activity. On shorter timescales, EPP-NOy depositions also
show a solar cycle modulation, however, with maxima shifted in tendency
towards the declining phase of the cycle, as expected due to the correlation
of Ap with the solar wind. A long-term variation of EPP-NOy
depositions with highest amounts in cycles 19–22 (Modern maximum) is clearly
visible. On average, depositions have increased by a factor of 3 from the
Gleissberg minimum around 1900 to the recent Modern maximum. The highest
EPP-NOy amounts since 1850 were deposited into the stratosphere during
the 1991 SH winter, but the 2003 SH and 2004 NH winters are also among the
four strongest EPP winters since 1850. On the other hand, the prolonged solar
minimum around 2008 led to exceptionally small EPP-NOy depositions that
are as small as during the Gleissberg minimum. In this sense, solar cycle 23
had one of the largest amplitudes of EPP variability in this 164-year period.
Looking at the variability during the last three solar cycles covered by the
“satellite era”, we model a reduction of the average global EPP-NOy
deposition rate of 0.8 GM year-1 that corresponds to 1.7 % of the
global production rate by N2O oxidation. This is likely to affect the
long-term NOy trend by counteracting the expected increase caused by
growing N2O emissions (about 6 % in the same period), although the
impact is most probably limited to mid to high latitudes.
Conclusions
We have presented a semi-empirical model for computation of hemispheric
energetic particle precipitation (EPP)-NOy amounts transported to
stratospheric and mesospheric pressure levels, as well as the associated
vertical fluxes, during Antarctic and Arctic winters. The model has been
trained with the EPP-NOy record inferred from MIPAS observations during
2002–2012 . Inter-annual variations of the EPP indirect
effect at a given time of the winter are related to variations of the EPP
source strength, the latter being considered to depend linearly on the
geomagnetic Ap index. A finite impulse response approach is employed to
describe the impact of vertical transport on this modulation at given
pressure levels. The seasonal dependence of the EPP-NOy vertical
distribution, driven by variations of chemical losses and transport patterns,
is assumed to be independent of inter-annual dynamical variability. This
assumption is shown to be a reasonably good approximation for SH winter and
dynamically quiescent NH winters. For episodes of accelerated descent
associated with elevated stratopause (ES) events in Arctic winters, however,
this assumption does not hold and a dedicated parameterization of the
spatio-temporal EPP-NOy distribution needs to be employed. This
parameterization takes into account the dependence of the EPP-NOy
amounts and fluxes during ES-related descent episodes on the event timing in
accordance with results from the model study of .
In order to consider accelerated descent during ES events in the
semi-empirical model, a criterion for ES detection is required.
identified ES events by looking at abrupt increases in
the polar cap stratopause height, the latter defined as the altitude between
20 and 100 km where temperature maximizes. We have shown, by analyzing
temperature and CO from EMAC simulations nudged to ERA-Interim reanalysis
data, that the upper stratospheric meridional temperature gradient, expressed
as the difference of 0–30 and 70–90∘ N temperature averages at
1 hPa, provides a reliable alternative criterion for detection of strong
descent episodes. Further, the proposed criterion allows for a
quasi-instantaneous detection of a commencing ES event since no temporal
smoothing of the temperature time series is required. It is therefore well
suited to drive the semi-empirical model when used to prescribe NOy
concentrations or fluxes in transient simulations with models lacking a
detailed representation of the EPP source region. However, due to its
definition as a discrete threshold, our criterion may need to be adjusted
when applied to other models.
We have quantified the EPP indirect effect in both hemispheres during
1978–2014 with the semi-empirical model, considering the ES events as
detected from the EMAC simulations. The resulting wintertime EPP-NOy
depositions have been compared to observational estimates from satellite
instruments, including LIMS, HALOE, MIPAS, ACE-FTS, and SOFIE. In order to
account for multiplicative and additive biases between the different
estimates, related to instrumental uncertainties and/or differences in the
employed estimation methods, we have adjusted the other instrument's
estimates to the MIPAS data by applying an offset and a scale factor. The
resulting homogenized time series of observational EPP-NOy deposition
estimates is in very good agreement with the results of our semi-empirical
model. The simulated average EPP-NOy deposition per year into the
stratosphere during 1978–2014 was found to be 1.26 GM in the SH and 0.5 GM
in the NH. Strong descent associated with ES events in Arctic winters, while
being of high relevance during individual events, led to an increase of only
4 % in the average NH deposition during these 3 decades.
A major purpose of the semi-empirical model is to provide an odd nitrogen
upper boundary condition (UBC) for chemistry climate models with their upper
lid in the mesosphere and, thus, missing the EPP-NOy production
occurring above. This is achieved by distributing the hemispheric
EPP-NOy amounts and fluxes at given pressure levels in latitude bands,
using the MIPAS average meridional distribution during 2002–2012, and
expressing them either as concentrations (in units of cm-3) or as
molecular fluxes (in units of cm-2 s-1). In order to avoid top
boundary artifacts in the models when specifying NOy concentrations at
latitudes not dominated by EPP, we also provide a background NOy
contribution (from N2O oxidation in the stratosphere) obtained from a
simple regression model adjusted to the MIPAS seasonal 2002–2012 composite.
The resulting UBC model hence provides global zonal mean NOy
concentrations and EPP-NOy molecular fluxes on an adaptable pressure
level and latitude grid as a function of time for upper model lids within
1–0.01 hPa.
Odd nitrogen UBCs have previously been used in chemistry climate models not
extending into the EPP source region for representation of the EPP indirect
effect. In some model studies, the UBC was taken directly from NOx
observations e.g.,, which, however,
implies the restriction to the relatively short time period spanned by the
observations. In other cases, a simple parameterization in dependence of the
seasonally averaged Ap index was employed
e.g.,, enabling extended simulations
over multi-decadal time periods. Our UBC model is designed for the latter
application and represents an improved parameterization due to its more
detailed representation of geomagnetic modulations, latitudinal distribution,
and seasonal evolution, as well as the ability to reproduce odd nitrogen
enhancements due to ES events in Arctic winters. It has been successfully
tested in simulations carried out with the EMAC model .
By employing historical geomagnetic indices, as provided with the CMIP6 solar
forcing, we also estimated the EPP indirect effect since 1850. We found
long-term changes in solar cycle-averaged stratospheric EPP-NOy
depositions on the order of 1 GM, which can be attributed to secular
variations of geomagnetic and solar activity. Inter-annual variations along
the solar cycle were particularly pronounced during solar cycles 16, 22, and
23, with cycle amplitudes of up to 2.5 GM. We also found a reduction in the
EPP-NOy deposition rate during the last 3 decades related to a decline
of geomagnetic activity that corresponds to 1.8 % of the NOy
production rate by N2O oxidation. The negative trend in the geomagnetic
activity level is closely related to the reduction of solar cycle amplitudes
encountered for cycles 23 and 24. As the decline of solar activity is
expected to continue in the coming decades , this is
likely to affect the long-term NOy trend by counteracting the expected
increase caused by growing N2O emissions .
A limitation of our semi-empirical model for reconstructions on multi-decadal
timescales, however, is related to potential secular variations of meridional
circulation patterns in the mesosphere . A deviation
from the dynamical mean state characteristic for the 2002–2012 period could
lead to modifications of the EPP indirect effect not considered in our model.
However, such dynamically induced variations are expected to be small
compared to the geomagnetically induced variations. In particular, it seems
unlikely that mesospheric circulation changes could outweigh the simulated
reduction of stratospheric EPP-NOy depositions in the last decades
related to the decline of solar variability.
Data availability
The semi-empirical UBC model is available as IDL and MATLAB routines at
http://solarisheppa.geomar.de/solarisheppa/cmip6 (Funke, 2016) for its
use with geomagnetic proxy data provided with the CMIP6 solar forcing
(available on the same webpage).
Latitudinal distribution of EPP-NOy densities and fluxes in the UBC model
Normalized latitudinal distribution
Ψ(ϕ,z) of EPP-NOy densities and fluxes in SH winters as a
function of pressure level.
Lat. bin ϕ1.000.700.500.300.200.150.100.070.050.030.020.01∘ ShPahPahPahPahPahPahPahPahPahPahPahPa90–800.2900.2850.2880.2990.3110.3190.3270.3400.3570.3740.3930.41280–700.2590.2480.2410.2400.2460.2600.2810.3020.3090.3060.2970.29470–600.2100.2010.1950.1930.1960.2020.2070.2080.2060.2020.1970.18760–500.1530.1600.1610.1550.1470.1360.1210.1040.0920.0860.0830.07850–400.0700.0840.0920.0910.0820.0690.0530.0380.0280.0240.0220.02140–300.0160.0200.0210.0200.0170.0130.0100.0080.0070.0060.0060.00630–200.0020.0020.0020.0010.0010.0000.0010.0010.0010.0010.0010.001
Normalized latitudinal distribution
Ψ(ϕ,z) of EPP-NOy densities and fluxes in NH winters as a
function of pressure level.
Lat. bin ϕ1.000.700.500.300.200.150.100.070.050.030.020.01∘ NhPahPahPahPahPahPahPahPahPahPahPahPa20–300.0030.0040.0040.0040.0030.0030.0030.0030.0020.0020.0020.00230–400.0110.0160.0210.0230.0230.0200.0160.0110.0080.0060.0050.00540–500.0360.0490.0600.0660.0670.0570.0440.0300.0230.0190.0180.01850–600.0960.1060.1200.1300.1390.1310.1150.0990.0880.0830.0810.08060–700.1850.1840.1910.2060.2290.2450.2500.2460.2380.2300.2280.22670–800.3070.2910.2730.2630.2610.2780.2990.3180.3250.3260.3250.32580–900.3620.3510.3320.3080.2780.2660.2730.2930.3160.3330.3420.344
Normalized latitudinal distribution
Ψ(ϕ,z) of EPP-NOy densities and fluxes during ES episodes as a
function of pressure level.
Lat. bin ϕ1.000.700.500.300.200.150.100.070.050.030.020.01∘ NhPahPahPahPahPahPahPahPahPahPahPahPa20–300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.00030–400.0010.0010.0020.0020.0020.0020.0020.0020.0010.0010.0010.00140–500.0090.0080.0080.0080.0080.0080.0080.0080.0070.0070.0060.00650–600.0440.0420.0390.0360.0340.0350.0370.0400.0420.0420.0420.04160–700.1460.1470.1480.1390.1350.1320.1370.1450.1500.1530.1530.15070–800.3330.3310.3300.3340.3390.3410.3440.3430.3410.3360.3320.33080–900.4670.4710.4720.4810.4810.4820.4710.4620.4590.4620.4670.473Coefficients of Eq. (24) describing NOy,bg(ϕ,z) in the UBC model
Coefficients a0(ϕ,z) in units of cm-3. Values in
parentheses should be read as powers of 10.
The IAA team was supported by the Spanish MCINN under grant ESP2014-54362-P and EC FEDER funds.
Edited by: W. Ward
Reviewed by: two anonymous referees
ReferencesAndersson, M. E., Verronen, P. T., Rodger, C. J., Clilverd, M. A., and
Seppälä, A.: Missing driver in the Sun–Earth connection from
energetic electron precipitation impacts mesospheric ozone, Nat. Commun., 5,
5197, 10.1038/ncomms6197, 2014.Andrews, A. E., Boering, K. A., Daube, B. C., Wofsy, S. C., Hintsa, E. J.,
Weinstock, E. M., and Bui, T. P.: Empirical age spectra for the lower
tropical stratosphere from in situ observations of CO2: Implications for
stratospheric transport, J. Geophys. Res., 104, 26581–26595, 1999.Arsenovic, P., Rozanov, E., Stenke, A., Funke, B., Wissing, J., Mursula, K.,
Tummon, F., and Peter, T.: The influence of Middle Range Energy Electrons on
atmospheric chemistry and regional climate, J. Atmos. Sol.-Terr. Phys.,
10.1016/j.jastp.2016.04.008, in press, 2016.Bailey, S. M., Thurairajah, B., Randall, C. E., Holt, L., Siskind, D. E.,
Harvey, V. L., Venkataramani, K., Hervig, M. E., Rong, P., and Russell,
J. M.: A multi tracer analysis of thermosphere to stratosphere descent
triggered by the 2013 Stratospheric Sudden Warming, Geophys. Res. Lett., 41,
5216–5222, 10.1002/2014GL059860, 2014.Baumgaertner, A. J. G., Jöckel, P., and Brühl, C.: Energetic particle
precipitation in ECHAM5/MESSy1 – Part 1: Downward transport of upper
atmospheric NOx produced by low energy electrons, Atmos. Chem. Phys., 9,
2729–2740, 10.5194/acp-9-2729-2009, 2009.Baumgaertner, A. J. G., Jöckel, P., Dameris, M., and Crutzen, P. J.: Will
climate change increase ozone depletion from low-energy-electron
precipitation?, Atmos. Chem. Phys., 10, 9647–9656,
10.5194/acp-10-9647-2010, 2010.Baumgaertner, A. J. G., Seppälä, A., Jöckel, P., and Clilverd, M.
A.: Geomagnetic activity related NOx enhancements and polar surface air
temperature variability in a chemistry climate model: modulation of the NAM
index, Atmos. Chem. Phys., 11, 4521–4531, 10.5194/acp-11-4521-2011,
2011.Chandran, A., Collins, R. L., Garcia, R. R., Marsh, D. R., Harvey, V. L.,
Yue, J., and de la Torre, L.: A climatology of elevated stratopause events in
the whole atmosphere community climate model, J. Geophys. Res., 118,
1234–1246, 10.1002/jgrd.50123, 2013.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy,
S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette,
J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut,
J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and
performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137,
553–597, 10.1002/qj.828, 2011.de la Torre, L., Garcia, R. R., Barriopedro, D., and Chandran, A.:
Climatology and characteristics of stratospheric sudden warmings in the Whole
Atmosphere Community Climate Model, J. Geophys. Res., 117, D04110,
10.1029/2011JD016840, 2012.Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R.
J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project
Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9,
1937–1958, 10.5194/gmd-9-1937-2016, 2016.Fischer, H., Birk, M., Blom, C., Carli, B., Carlotti, M., von Clarmann, T.,
Delbouille, L., Dudhia, A., Ehhalt, D., Endemann, M., Flaud, J. M., Gessner,
R., Kleinert, A., Koopman, R., Langen, J., López-Puertas, M., Mosner, P.,
Nett, H., Oelhaf, H., Perron, G., Remedios, J., Ridolfi, M., Stiller, G., and
Zander, R.: MIPAS: an instrument for atmospheric and climate research, Atmos.
Chem. Phys., 8, 2151–2188, 10.5194/acp-8-2151-2008, 2008.Funke, B.: Semi-empirical UBC model, translation to MATLAB by Stefan Versick,
SPARC/WCRP SOLARIS-HEPPA (webpage maintained by GEOMAR, Kiel, Germany),
available at: http://solarisheppa.geomar.de/solarisheppa/cmip6, last
access: 15 July 2016.Funke, B., López-Puertas, M., Gil-López, S., von Clarmann, T.,
Stiller, G. P., Fischer, H., and Kellmann, S.: Downward transport of upper
atmospheric NOx into the polar stratosphere and lower mesosphere during
the Antarctic 2003 and Arctic 2002/2003 winters, J. Geophys. Res., 110,
D24308, 10.1029/2005JD006463, 2005a.Funke, B., López-Puertas, M., von Clarmann, T., Stiller, G. P., Fischer,
H., Glatthor, N., Grabowski, U., Höpfner, M., Kellmann, S., Kiefer, M.,
Linden, A., Mengistu Tsidu, G., Milz, M., Steck, T., and Wang, D. Y.:
Retrieval of stratospheric NOx from 5.3 and 6.2 µm nonlocal
thermodynamic equilibrium emissions measured by Michelson Interferometer for
Passive Atmospheric Sounding (MIPAS) on Envisat, J. Geophys. Res., 110,
D09302, 10.1029/2004JD005225, 2005b.Funke, B., López-Puertas, M., Stiller, G. P., and von Clarmann, T.:
Mesospheric and stratospheric NOy produced by energetic particle
precipitation during 2002–2012, J. Geophys. Res., 119, 4429–4446,
10.1002/2013JD021404, 2014a.Funke, B., Puertas, M. L., Holt, L., Randall, C. E., Stiller, G. P., and von
Clarmann, T.: Hemispheric distributions and interannual variability of
NOy produced by energetic particle precipitation in 2002–2012, J.
Geophys. Res., 119, 13565–13582, 10.1002/2014JD022423, 2014b.Holt, L., Randall, C., Harvey, V., Remsberg, E., Stiller, G., Funke, B.,
Bernath, P., and Walker, K.: Atmospheric Effects of Energetic Particle
Precipitation in the Arctic Winter 1978–1979 Revisited, J. Geophys. Res.,
117, D05315, 10.1029/2011JD016663, 2012.Holt, L. A., Randall, C. E., Peck, E. D., Marsh, D. R., Smith, A. K., and
Lynn Harvey, V.: The influence of major sudden stratospheric warming and
elevated stratopause events on the effects of energetic particle
precipitation in WACCM, J. Geophys. Res., 118, 11636–11646,
10.1002/2013JD020294, 2013.Jackman, C. H., Marsh, D. R., Vitt, F. M., Garcia, R. R., Fleming, E. L.,
Labow, G. J., Randall, C. E., López-Puertas, M., Funke, B., von Clarmann,
T., and Stiller, G. P.: Short- and medium-term atmospheric constituent
effects of very large solar proton events, Atmos. Chem. Phys., 8, 765–785,
10.5194/acp-8-765-2008, 2008.Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H.,
Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the
Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717–752,
10.5194/gmd-3-717-2010, 2010.Maliniemi, V., Asikainen, T., and Mursula, K.: Spatial distribution of
Northern Hemisphere winter temperatures during different phases of the solar
cycle, J. Geophys. Res., 119, 9752–9764, 10.1002/2013JD021343, 2014.Manney, G. L., Krueger, K., Minschwaner, S. P. K., Schwartz, M. J., Daffer,
W., Livesey, N. J., Mlynczak, M. G., Remsberg, E., Russell, J. M., and
Waters, J. W.: The evolution of the stratopause during the 2006 major
warming: Satellite Data and Assimilated Meteorological Analyses, J. Geophys.
Res., 113, D11115, 10.1029/2007JD009097, 2008.Matthes, K., Funke, B., Anderson, M. E., Barnard, L., Beer, J., Charbonneau,
P., Clilverd, M. A., Dudok de Wit, T., Haberreiter, M., Hendry, A., Jackman,
C. H., Kretschmar, M., Kruschke, T., Kunze, M., Langematz, U., Marsh, D. R.,
Maycock, A., Misios, S., Rodger, C. J., Scaife, A. A., Seppälä, A.,
Shangguan, M., Sinnhuber, M., Tourpali, K., Usoskin, I., van de Kamp, M.,
Verronen, P. T., and Versick, S.: Solar Forcing for CMIP6 (v3.1), Geosci.
Model Dev. Discuss., 10.5194/gmd-2016-91, in review, 2016.Mengistu Tsidu, G., von Clarmann, T., Stiller, G. P., Höpfner, M.,
Fischer, H., Glatthor, N., Grabowski, U., Kellmann, S., Kiefer, M., Linden,
A., Milz, M., Steck, T., Wang, D.-Y., and Funke, B.: Stratospheric
N2O5 in the austral spring 2002 as retrieved from limb emission
spectra recorded by the Michelson Interferometer for Passive Atmospheric
Sounding (MIPAS), J. Geophys. Res., 109, D18301, 10.1029/2004JD004856,
2004.Päivärinta, S.-M., Seppälä, A., Andersson, M. E., Verronen,
P. T., Thölix, L., and Kyrölä, E.: Observed effects of solar
proton events and sudden stratospheric warmings on odd nitrogen and ozone in
the polar middle atmosphere, J. Geophys. Res., 118, 6837–6848,
10.1002/jgrd.50486, 2013.Pérot, K., Urban, J., and Murtagh, D. P.: Unusually strong nitric oxide
descent in the Arctic middle atmosphere in early 2013 as observed by
Odin/SMR, Atmos. Chem. Phys., 14, 8009–8015, 10.5194/acp-14-8009-2014,
2014.Randall, C. E., Harvey, V. L., Singleton, C. S., Bailey, S. M., Bernath,
P. F., Codrescu, M., Nakajima, H., and Russell III, J. M.: Energetic particle
precipitation effects on the Southern Hemisphere stratosphere in 1992–2005,
J. Geophys. Res., 112, D08308, 10.1029/2006JD007696, 2007.Randall, C. E., Harvey, V. L., Holt, L. A., Marsh, D. R., Kinnison, D.,
Funke, B., and Bernath, P. F.: Simulation of energetic particle precipitation
effects during the 2003–2004 Arctic winter, J. Geophys. Res., 120,
5035–5048, 10.1002/2015JA021196, 2015.Ravishankara, A. R., Daniel, J. S., and Portmann, R. W.: Nitrous Oxide
(N2O): The Dominant Ozone-Depleting Substance Emitted in the 21st
Century, Science, 326, 123–125, 10.1126/science.1176985, 2009.Reddmann, T., Ruhnke, R., Versick, S., and Kouker, W.: Modeling disturbed
stratospheric chemistry during solar-induced NOx enhancements observed
with MIPAS/ENVISAT, J. Geophys. Res., 115, D00I11,
10.1029/2009JD012569, 2010.Reddmann, T., Funke, B., Konopka, P., Stiller, G., Versick, S., and Vogel,
B.: The influence of energetic particles on the chemistry of the middle
atmosphere, in: Climate And Weather of the Sun-Earth System (CAWSES):
Highlights from a priority program, edited by: Lübken, F.-J., Springer
Atmospheric Sciences, Springer, Dordrecht, the Netherlands, 249–278,
10.1007/978-94-007-4348-9_15, 2012.Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom,
S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for
Research and Applications, J. Climate, 24, 3624–3648,
10.1175/jcli-d-11-00015.1, 2011.
Roeckner, E., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kornblueh,
L., Manzini, E., Schlese, U., and Schulzweida, U.: Sensitivity of Simulated
Climate to Horizontal and Vertical Resolution in the ECHAM5 Atmosphere Model,
J. Climate, 19, 3771–3791, 2006.Rozanov, E., Calisto, M., Egorova, T., Peter, T., and Schmutz, W.: Influence
of the Precipitating Energetic Particles on Atmospheric Chemistry and
Climate, Surv. Geophys., 33, 483–501, 10.1007/s10712-012-9192-0, 2012.Seppälä, A., Verronen, P. T., Clilverd, M. A., Randall, C. E.,
Tamminen, J., Sofieva, V., Backman, L., and Kyrölä, E.: Arctic and
Antarctic polar winter NOx and energetic particle precipitation in
2002–2006, Geophys. Res. Lett., 34, L12810, 10.1029/2007GL029733,
2007.Seppälä, A., Matthes, K., Randall, C. E., and Mironova, I. A.: What
is the solar influence on climate? Overview of activities during CAWSES-II,
Progress in Earth and Planetary Science, 1, 1–12,
10.1186/s40645-014-0024-3, 2014.Sinnhuber, M., Funke, B., von Clarmann, T., Lopez-Puertas, M., Stiller, G.
P., and Seppälä, A.: Variability of NOx in the polar middle
atmosphere from October 2003 to March 2004: vertical transport vs. local
production by energetic particles, Atmos. Chem. Phys., 14, 7681–7692,
10.5194/acp-14-7681-2014, 2014.Smith, A. K., Garcia, R. R., Marsh, D. R., and Richter, J. H.: WACCM
simulations of the mean circulation and trace species transport in the winter
mesosphere, J. Geophys. Res., 116, D20115, 10.1029/2011JD016083, 2011.Steinhilber, F. and Beer, J.: Prediction of solar activity for the next
500 years, J. Geophys. Res., 118, 1861–1867, 10.1002/jgra.50210, 2013.Stiller, G. P., Mengistu Tsidu, G., von Clarmann, T., Glatthor, N.,
Höpfner, M., Kellmann, S., Linden, A., Ruhnke, R., Fischer, H.,
López-Puertas, M., Funke, B., and Gil-López, S.: An enhanced
HNO3 second maximum in the Antarctic mid-winter upper stratosphere 2003,
J. Geophys. Res., 110, D20303, 10.1029/2005JD006011, 2005.von Clarmann, T., Glatthor, N., Grabowski, U., Höpfner, M., Kellmann, S.,
Kiefer, M., Linden, A., Mengistu Tsidu, G., Milz, M., Steck, T., Stiller,
G. P., Wang, D. Y., Fischer, H., Funke, B., Gil-López, S., and
López-Puertas, M.: Retrieval of temperature and tangent altitude pointing
from limb emission spectra recorded from space by the Michelson
Interferometer for Passive Atmospheric Sounding (MIPAS), J. Geophys. Res.,
108, 4736, 10.1029/2003JD003602, 2003.von Clarmann, T., Höpfner, M., Kellmann, S., Linden, A., Chauhan, S.,
Funke, B., Grabowski, U., Glatthor, N., Kiefer, M., Schieferdecker, T.,
Stiller, G. P., and Versick, S.: Retrieval of temperature, H2O, O3,
HNO3, CH4, N2O, ClONO2 and ClO from MIPAS reduced
resolution nominal mode limb emission measurements, Atmos. Meas. Tech., 2,
159–175, 10.5194/amt-2-159-2009, 2009.
Waugh, D. W. and Hall, T. M.: Age of stratospheric air: theory, observations,
and models, Rev. Geophys., 40, 1010, 10.1029/2000RG000101, 2002.