ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-1457-2018Assessing the ability to derive rates of polar middle-atmospheric descent
using trace gas measurements from remote sensorsDerive rates of polar middle-atmospheric descentRyanNiall J.n_yan@iup.physik.uni-bremen.deKinnisonDouglas E.GarciaRolando R.https://orcid.org/0000-0002-6963-4592HoffmannChristoph G.https://orcid.org/0000-0003-2712-8648PalmMathiashttps://orcid.org/0000-0001-7191-6911RaffalskiUweNotholtJustusInstitute of Environmental Physics, University of Bremen, Bremen,
28359, GermanyAtmospheric Chemistry Observations and Modeling Laboratory, National
Center for Atmospheric Research, Boulder, Colorado, USAInstitute of Physics, University of Greifswald,
Felix-Hausdorff-Str. 6, 17489, Greifswald, GermanySwedish Institute of Space Physics, Box 812, 981 28 Kiruna, SwedenNiall J. Ryan (n_yan@iup.physik.uni-bremen.de)2February20181831457147415June201721June201727November201716December2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/1457/2018/acp-18-1457-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/1457/2018/acp-18-1457-2018.pdf
We investigate the reliability of using trace gas
measurements from remote sensing instruments to infer polar atmospheric
descent rates during winter within 46–86 km altitude. Using output from
the Specified Dynamics Whole Atmosphere Community Climate Model (SD-WACCM)
between 2008 and 2014, tendencies of carbon monoxide (CO) volume mixing
ratios (VMRs) are used to assess a common assumption of dominant vertical
advection of tracers during polar winter. The results show that dynamical
processes other than vertical advection are not negligible, meaning that the
transport rates derived from trace gas measurements do not represent the
mean descent of the atmosphere. The relative importance of vertical
advection is lessened, and exceeded by other processes, during periods
directly before and after a sudden stratospheric warming, mainly due to an
increase in eddy transport. It was also found that CO chemistry cannot be
ignored in the mesosphere due to the night-time layer of OH at approximately
80 km altitude. CO VMR profiles from the Kiruna Microwave Radiometer and the
Microwave Limb Sounder were compared to SD-WACCM output, and show good
agreement on daily and seasonal timescales. SD-WACCM CO profiles are
combined with the CO tendencies to estimate errors involved in calculating
the mean descent of the atmosphere from remote sensing measurements. The
results indicate errors on the same scale as the calculated descent rates,
and that the method is prone to a misinterpretation of the direction of air
motion. The “true” rate of atmospheric descent is seen to be masked by
processes, other than vertical advection, that affect CO. We suggest an
alternative definition of the rate calculated using remote sensing
measurements: not as the mean descent of the atmosphere, but as an effective
rate of vertical transport for the trace gas under observation.
Introduction
The rate of the descent of air above the poles during winter has been an
area of interest for some time because it influences the transport of trace
gases from the thermosphere to the middle atmosphere (mesosphere and
stratosphere; e.g. Plumb et al., 2002; Engel et al., 2006; Smith et al.,
2011; Manney et al., 2009). This vertical branch of the meridional
circulation can transport gases with high thermospheric volume mixing ratios
(VMRs), and change the composition of the middle atmosphere (e.g. Solomon
et al., 1985; Allen et al., 2000; Hauchecorne et al., 2007; Smith et al.,
2011; Garcia et al., 2014; Manney et al., 2009; 2015; Funke et al., 2014a, b, 2017). The downward transport of nitrogen oxide (NO), produced at around
110 km through energetic particle precipitation (EPP) events (e.g. Barth and
Bailey, 2004; Randall et al., 2005, 2007, 2015), garners particular
attention because NOx (NO + NO2) catalytically destroys ozone in
the stratosphere. Periods of strong atmospheric descent are known to
coincide with the timing of sudden stratospheric warmings (SSWs; e.g.
Manney et al., 2003, 2009; Jackman et al., 2009; Siskind et al., 2010; Holt
et al., 2013). Elevated VMRs of carbon monoxide (CO) and NOx have been
observed to linger in the middle atmosphere when there is an exceptionally
strong polar vortex after a strong SSW, helping to confine descending air at
the pole (Randall et al., 2006). Randall et al. (2009) suggest that, in
these cases, VMRs of EPP-produced NOx are controlled more by mesosphere
and lower thermosphere (MLT) descent rates than by the structure of the
vortex. Siskind et al. (2016) found that due to downward transport of
methane (CH4) in years with strong, uninterrupted mesospheric descent,
summertime upper stratospheric chlorine monoxide (ClO) is about 50 %
greater than in years with strong horizontal transport.
A partial list of studies that used atmospheric tracers to derive
rates of vertical air motion in the atmosphere.
ReferenceTracerLocationInstrumentRate of vertical motionApproximateTime of yearTerminology(m day-1)altitude(s)Allen at al. (2000)COAntarcticasatellite-250 at 60∘ S30–50 kmApr/May average,atmospheric descent-300 at 80∘ Saverage1992Forkman et al. (2005)CONH mid-latitudesground-based+250 to +45060–95 kmSpring, 2002mesospheric circulationForkman et al. (2005)CONH mid-latitudesground-based0 to -30060–95 kmAutumn, 2002mesospheric circulationNassar et al. (2005)CH4, H2OArcticsatellite-150upper stratosphereFeb/Mar, 2004atmospheric descentNassar et al. (2005)CH4, H2OArcticsatellite-175lower mesosphereOct 2003 to Feb 2004atmospheric descentaverageHauchercorne et al. (2007)NO2Arcticsatellitefrom -600 in Jan,45–70 km20 Jan to 10 Mar, 2004descent of NO2 layerto -200 in MarFunke et al. (2009)COArcticsatellite-350 to 40050–70 kmSep/Oct, 2003polar descentFunke et al. (2009)COArcticsatellite-200 to -30040–70 kmNov/Dec, 2003polar descentFunke et al. (2009)COArcticsatellite-1200mesosphereafter SSW, 2004polar descentDi Bagio et al. (2010)COArcticground-based-200 to -300descent fromafter SSW, 2009descent of air58–62 kmDi Bagio et al. (2010),H2OArcticground-based-200 to -300descent fromafter SSW, 2009descent of airfrom Orsolini et al. (2010)59 and 62 kmStraub et al. (2012)H2OArcticground-based-35052–68 km average5 Feb to 5 Marpolar descentaverage, 2010Straub et al. (2012)H2OArcticsatellite-36052–68 km averageJan/Feb/Marpolar descentaverage, 2010Bailey et al. (2014)NO, H2O, CH4Arcticsatelliteup to -100040–90 kmafter SSW, 2013atmospheric descent
A direct measurement of the mean vertical motion of air in the
middle atmosphere is currently not possible because of the small velocities,
and so an analysis of changes in measured tracer (relatively long-lived
trace gas) VMRs offers a means to indirectly observe the vertical motion.
This technique has often been used in combination with ozone measurements in
the stratosphere to separate chemical and dynamical influences when
determining ozone depletion (e.g. Proffit et al., 1990, 1993; Müller et
al., 1996, 2003; Salawitch et al., 2002; Rösevall et al., 2007). Another
indirect determination can be made using the diabatic circulation approach
(e.g. Dunkerton, 1978; Solomon et al., 1986; Medvedev and Fomichev, 1994)
but this method is not discussed further here. The chemical lifetime of CO
during polar night and the strong vertical gradient in its VMR make it a
good tracer (Solomon et al., 1985; Allen et al., 1999; Lee et al., 2011).
Water vapour (H2O) is used to infer vertical motion (e.g. Straub et
al., 2012) but a varying vertical gradient limits the altitudes at which it
can be used (Lee et al., 2011). Nassar et al. (2005) used nitrous oxide
(N2O), CH4, and H2O to infer rates of vertical motion in the
upper stratosphere and lower mesosphere, and Bailey et al. (2014) used a
combination of NO, H2O, and CH4 to derive profiles of vertical
motion in the middle atmosphere. A partial list of studies that have used
tracers to calculate rates of vertical motion is given in Table 1, similar
to Hoffmann (2012) for studies using CO. The tracers used and the
determined rates are also listed, along with the terminology used to
describe the motion. The descent rates range from -100 to -1200 m day-1
and show much variability, which can be expected as the studies were
performed for different years and/or different times of the year, and at
different locations (with the minus sign indicating descent rather than
ascent). The altitude range over which the rates were determined, and
whether averaging was performed, is also shown in Table 1. It is important
to note that an average over altitude can mask the higher descent rates that
are found in the mesosphere. For example, Straub et al. (2012) show a
descent rate of -325 m day-1 from averaged modelled wind profiles, between
0.6 (∼ 52 km) and 0.06 hPa (∼ 68 km),
whereas the individual wind profiles often show descent rates larger than
-1000 m day-1 at 0.06 hPa.
A general assumption made in the derivations of the rates is that the
observed change in a tracer VMR over time is caused by vertical advection.
The assumption draws from the fact that the polar vortex edge acts as a
barrier to lower-latitude air and hinders horizontal mixing of air masses
between the inside and outside of the vortex (Schoeberl et al., 1992; Manney
et al., 1994), but limited mixing still occurs and defining the edges of the
polar vortex is not straightforward (Manney et al., 1994, 1997; Harvey et
al., 2009, 2015). During the formation/breakdown of the polar vortex at the
beginning/end of winter, the transport barrier is weaker and the location of
the vortex edge becomes much less well-defined (Manney et al., 1997), making
it more difficult to identify inner-vortex air masses. This is also true for
SSW events where the vortex is weakened, or breaks down and reforms,
allowing increased mixing (Manney et al., 2009, 2015). When the vortex is
well established, the edge definitions that rely on wind fields (such as
scaled potential vorticity (e.g. Manney et al., 1994, 2007, 2011; Jin et
al., 2006) become less reliable in the mesosphere where wind observations
are uncommon and reanalysis winds are known to be fallible (Manney et al.,
2008a, b; Rienecker et al., 2011).
The aim of this study is to assess the limits of the above assumption when
using tracer measurements from remote sounders to derive rates of vertical
motion in the middle atmosphere. Measurements alone do not provide enough
information to enable separation of the contributions to changes in tracer
VMRs, and so an atmospheric model must be employed. The specified dynamics
version of the Whole Atmosphere Community Climate Model (SD-WACCM) is used
to determine the relative contributions to changes in CO VMRs during polar
winter. The results are combined with daily average modelled CO to estimate
the error associated with descent rates calculated assuming pure vertical
advection of the tracer. Three commonly used representations of the data are
assessed: a local area above a specific location (Kiruna, 67.8∘ N, 20.4∘ E, in this case), a zonal mean at a certain latitude
(80∘ N is used as an example), and a polar mean (60–90∘ N). The winters of 2008/2009 and 2010/2011 are used in the
study as an example of a winter with a strong SSW and a winter with a
relatively stable vortex, respectively. The rate calculations were also
performed using CO measurements from the Kiruna Microwave Radiometer (KIMRA)
and the Microwave Limb Sounder (MLS) (not shown), and the results lead to
the same conclusion. This was expected due to the level of agreement found
in a comparison of the modelled and measured CO (Sect. 2.4).
Section 2 outlines the instruments and the model used in this study as well
as their datasets. A brief comparison of the three CO datasets is provided
to assess how well the model represents observations of the atmosphere.
Section 3 investigates the trajectories of air parcels arriving in the
Arctic during winter, and the evolution of the tendencies of CO VMRs in the
Arctic middle atmosphere due to each component of the continuity equation.
Section 4 shows the rates of vertical motion calculated using CO
measurements and the above assumption, and estimates how these rates change
when accounting for all parts of the continuity equation. Section 5 uses CO
VMR tendencies from 2008 to 2014 to assess their relative importance during
different months. Section 6 contains a discussion of the results and the
limitations of the study, and suggests a different interpretation of the
rates derived from tracer measurements. Section 7 provides concluding
remarks.
Instruments, model, and dataKIMRA
KIMRA is a ground-based microwave remote sensor located at the Swedish
Institute for Space Physics, Kiruna, Sweden (67.8∘ N,
20.4∘ E). The instrument is a passive remote sensor and radiances
from atmospheric CO are measured at a frequency 230.54 GHz. Specific details
on the instrument can be found in Raffalski et al. (2002) and Hoffmann et al. (2011). The current CO dataset from KIMRA covers Arctic winters from
2008 to 2015 with gaps corresponding to instrument non-operation and summer
periods when CO VMRs in the middle atmosphere are too low to be accurately
measured. The average altitude range of the data is 46–86 km and the
vertical resolution is 15–18.5 km, depending on the altitude. Details on
the measurement technique and CO inversion scheme are given in Ryan et al. (2017a). The average precision (values can vary from one profile to another)
of wintertime KIMRA CO VMRs range from 0.06 ppm at 46 km altitude to 2.7 ppm
at 86 km. The average time resolution of a CO measurement is around 2 h.
KIMRA CO data presented in this work have been averaged to give daily
profiles.
MLS
MLS is a microwave remote sensor aboard the Aura satellite, launched in July
2004, and is part of NASA's Earth Observing System. Atmospheric CO is
retrieved from radiance measurement made in two bands of the 240 GHz
radiometer. A description of the MLS instrument can be found in Waters et al. (2006) and details on the retrieval can be found
in Pumphrey et al. (2007) and Livesey et al. (2008). The data used here are version 4.2
(Schwartz et al., 2015), which is described in Livesey et al. (2015). These
CO profiles cover a pressure range of 215–0.0046 hPa (approximately 11–86 km) and have a maximum (largest) precision of 11 ppm at the highest (in
altitude) pressure level. For the data used here, the vertical resolution is
between 3.8 and 6.2 km, and the horizontal resolution is between 200 and
250 km. MLS data presented here as above Kiruna are within ±2∘ latitude
and ±10∘ longitude of Kiruna, and have been averaged to
produce daily profiles.
SD-WACCM
The Community Earth System Model version 1 (CESM1), Whole Atmosphere
Community Climate Model (WACCM), is a coupled chemistry climate model from
the Earth's surface to the lower thermosphere (Marsh et al., 2013 and
references therein). WACCM is a superset of the Community Atmosphere Model,
version 4 (CAM4), and includes all of the physical parameterizations of CAM4
(Neale et al., 2013) and a finite volume dynamical core (Lin, 2004) for the
tracer advection. The simulation of WACCM4 used in this study is run with
specified dynamics (SD) fields, using meteorological analyses from the
National Aeronautics and Space Administration (NASA) Global Modeling and
Assimilation Office (GMAO) Modern-Era Retrospective Analysis for Research
and Applications (MERRA; Rienecker et al., 2011). The chemical component is
based on version 3 of the Model for Ozone and Related Chemical Tracers
(Kinnison et al., 2007). Garcia et al. (2014) discuss an update to the
absorption cross section for O2, which increased modelled CO mixing
ratios in the MLT, bringing them closer to observations.
SD-WACCM constrains the atmosphere towards observations below 50 km through
nudging with data from the above-mentioned analysed meteorological fields.
Above 60 km the atmosphere is fully interactive and between 50 and 60 km the
nudging linearly decreases to zero. Details on the nudging of the
temperature and wind fields in the model can be found in Lamarque et al. (2012) and references therein. The model also tends towards observations at
altitudes above 60 km because the zonal-mean mesospheric winds and
temperatures at higher altitudes have been shown to be strongly constrained
by the stratosphere (Liu et al., 2010; McLandress, 2013), but precise
agreement cannot be expected.
For the model runs used in this work, the pressure grid consists of 88
layers from the ground to the thermosphere (∼ 133 km). The
altitude resolution of the grid increases from ∼ 0.1 km near
the surface to ∼ 3.5 km in thermosphere. The horizontal
resolution is 1.9∘× 2.5∘ in latitude and longitude.
Model output of daily averages from 2008 to 2014 are used for this study.
“Correlation coefficient, regression coefficient ± error”
for comparisons of daily CO VMRs from KIMRA, MLS, and SD-WACCM above Kiruna
for 2008 through 2014. The abscissa variable is the first-named of each
instrument pairing. A regression coefficient > 1 (< 1)
indicates a larger (smaller) range in the abscissa variable. Figure 1 shows
the time series of each CO VMR dataset. See Sect. 2.4 for details.
Comparisons of daily CO VMRs from KIMRA, MLS, and SD-WACCM above
Kiruna for 2008 through 2014. Values are displayed at 46, 56, 66, 76, and
86 km altitude. Correlation and regression coefficients for the datasets are
given in Table 2. See Sect. 2.4 for details.
CO VMR comparison
Figure 1 plots the CO VMRs from KIMRA, MLS, and SD-WACCM above Kiruna for
2008 through 2014. The aim of a comparison is to see whether the datasets
capture the same variability in middle-atmospheric CO. The VMRs are plotted
at five altitudes between 46 and 86 km. This is the average altitude range
of KIMRA CO data and reaches the upper altitude limit of MLS CO data. The
MLS and SD-WACCM VMRs are also plotted after the profiles have been smoothed
with the KIMRA averaging kernels (Rodgers and Connor, 2003). This method
allows a meaningful comparison of datasets that have significantly different
vertical resolutions. A more comprehensive comparison of the current KIMRA
and MLS data is given in Ryan et al. (2017a). SD-WACCM data are here
bilinearly interpolated to the location of Kiruna, but a significant change
in the results is not found when simply using the model coordinate grid point
closest to Kiruna. The three CO datasets show agreement on seasonal and
daily timescales. Agreement between the model and instruments on such timescales highlights the power of SD-WACCM as a tool for investigating trends
as well as temporally short events in the atmosphere. A systematic
difference is evident between MLS and SD-WACCM during the times of year when
CO VMRs are rapidly increasing or decreasing, with SD-WACCM showing larger
values of CO. The difference is most pronounced at higher altitudes and is
predominantly during August/September and April/May. Table 2 lists the
correlation and regression coefficients calculated for each data pairing.
Regression coefficients are calculated accounting for errors in the abscissa
and ordinate variables (York et al., 2004), with a 15 % error assumed for
SD-WACCM CO VMRs. Correlations between KIMRA and smoothed MLS, and KIMRA and
smoothed SD-WACCM, are ≥ 0.86 at all altitudes, and MLS and SD-WACCM
correlations are ≥ 0.88. MLS and SD-WACCM were compared at other polar
locations (not shown here) and display similar agreement. The values are
similar to those found for earlier versions of the model and data (Hoffmann
et al., 2012), with differences mainly due to updates to the modelled CO
(Garcia et al., 2014) and the data products (Livesey et al., 2015; Ryan et
al., 2017a).
Contributions to the CO continuity equationThe TEM continuity equation
The mass transport of the residual mean meridional circulation is well
represented by the transformed Eulerian mean (TEM) formulation (Andrews and
McIntyre, 1976), and is covered in detail in Andrews et al. (1987). The TEM
circulation in the solstice seasons is dominated by flow from the summer to
the winter pole, accompanied by downward and upward transport above the
winter and summer poles, respectively. The TEM zonal-mean tracer continuity
equation has been described in various works (e.g. Garcia and Solomon,
1983; Andrews et al., 1987; Brasseur and Solomon, 2005; Monier and Weare,
2005; Smith et al., 2011):
∂χ‾∂t=-w∗‾∂χ‾∂z-v∗‾∂χ‾∂y+S‾+Xmol‾+1ρ0∇⋅M+1ρ0∂∂zρ0Kzz∂χ∂z‾,
where χ is the CO VMR, w∗‾ is the vertical
component of the residual mean meridional circulation, and v∗‾,
is the horizontal component. S‾ is the zonal-mean net chemical
production of CO and Xmol‾ is the zonal-mean molecular diffusion
of CO. ∇⋅M is the divergence of the resolved eddy flux vector
and describes the eddy transport of CO, with ρ0 as the basic
density. Kzz is the diffusion coefficient due to unresolved small-scale
gravity wave breaking, and the last component of Eq. (1) represents the
transport of CO due to parameterized eddy flux divergence (from gravity
waves). The right-hand-side (RHS) terms of Eq. (1) are calculated using
daily averaged output from SD-WACCM and details on the exact equations can
be found in Andrews et al. (1987). The value of Kzz calculated with
SD-WACCM depends, among other things, upon the Prandtl number (or more
properly, the “turbulent Prandtl number”), which describes the ratio of
momentum flux to heat flux. The Prandtl number is a property of the process
whereby gravity waves dissipate when they “break” (see e.g. Fritts and
Dunkerton, 1985, for more details). The Prandtl number is 2 for the model
runs in this work (see Sect. 6) and is used in SD-WACCM to parameterize
gravity wave breaking (Garcia et al., 2007). Finite differences in Eq. (1)
are calculated as centred differences except at the boundaries of grids,
where forward and backward differences are used.
The terms of Eq. (1) are renamed here as
∂χ‾∂t=adv_w∗+adv_v∗+chem+Xmol+Xedd+Xkzz
to simply express the tendencies of CO in the continuity equation. The
change in zonal-mean CO VMR with time is a sum of the contributions from
(following the RHS of Eq. 2): vertical advection, horizontal advection,
net chemical production, molecular diffusion, eddy transport, and unresolved
eddy transport, which, for present purposes, is due to gravity waves.
Trajectories during Arctic winter
As a first step to examine the assumption of purely vertical tracer
advection, the back-trajectories of air parcels during two Arctic winters
are plotted in Fig. 2. The parcels arrive at five altitudes between 46 and
86 km and at two locations, 67 and 80∘ N. They are
advected, from these locations, backwards in time over 60 days, in 4 h
steps, using w∗‾ and v∗‾ from SD-WACCM. The starting
date for the trajectory calculations (arrival date of the air parcels) is
28 February, for 2009 and 2011. The winter of 2009 had a major SSW
(during which the 10 hPa zonal circulation becomes easterly at 60∘ N) beginning on 24 January, and 2011 had a relatively stable
vortex throughout the winter. The results in Fig. 2 are consistent with
similar calculations in Smith et al. (2011) and Straub et al. (2012), for
the general shape of the trajectory and in that the parcels do not originate
above approximately 100 km. The parcels at 80∘ N arrive from
higher altitudes due to a stronger vertical component of the circulation
compared to 67∘ N. Conversely, the horizontal component of the
circulation is stronger at 67∘ N and the parcels arriving there
originate from lower latitudes compared to those arriving at 80∘ N. The magnitude of the TEM wind is larger for the higher altitudes, as is
also shown in Smith et al. (2011), and the air parcels that arrive above
66 km altitude originate in the summer hemisphere. The parcels that arrive
below this altitude, which could be considered as part of the Brewer–Dobson
circulation (Brewer, 1949), originate at latitudes closer to the equator. A
clear reversal of the trajectory around the time of the SSW can be seen for
the air parcel arriving at 56 km altitude, 80∘ N, because of
temporary changes in the direction of w∗‾ and v∗‾.
These changes cause the air parcel to reverse direction before starting to
descend again. It is evident from Fig. 2 that the circulations at the pole
have varying degrees of vertical and horizontal components.
Trajectories of air parcels advected backwards 60 days in time
from 28 February 2009 and 2011, by the TEM circulation using
SD-WACCM. Trajectories are calculated for air parcels arriving at
67 and 80∘ N, at 46, 56, 66, 76, and 86 km altitude
and locations are plotted at midnight each day. Parcel positions on 28 January
2009 (start date of an SSW) are indicated with a black asterisk. See
Sect. 3 for more details.
11-day running mean tendencies of CO (in ppmv day-1), calculated
using daily averaged SD-WACCM output. Tendencies shown are for
67.8∘ N for the winters of 2008/2009 and 2010/2011. See Sect. 3.1 for a description of the tendencies, which are represented in the TEM
continuity equation. Note that the labelled contours may be differently
spaced in each panel, to match the range of values for individual
tendencies.
Tendencies of CO during Arctic winter
Figures 3, 4, and 5 plot the wintertime tendencies of CO (RHS of Eq. 2) for
2008/2009 and 2010/2011, for the three scenarios of 67,
80∘ N, and a north polar average (60–90∘ N), respectively. The zonal mean tendencies are plotted as an 11-day
running mean. Note that the labelled contours may be differently spaced in
each panel, to match the range of values for individual tendencies. In the
context of a point measurement at Kiruna, a full rotation of the vortex is
of the order of 10 days (assuming a zonal wind speed of 20 m s-1 at
67∘ N). Relevant comments on the results are provided here but an
in-depth analysis is not made as it is not the focus of the study. Molecular
diffusion (Xmol) generally causes negligible changes in CO, compared to
other process, below approximately 83 km, and shows little variation between
different scenarios and winters. Above that, the magnitudes increase
quickly, with tendencies < -0.1 ppm day-1 in the altitude range shown
here. Unresolved eddy transport (Xkzz) is also negligible below
approximately 75 km, but can show tendencies < -0.2 ppm day-1 above
that altitude for short times (order of a week). Significant variation is
seen for the different winters. Both processes tend to cause a decrease in
CO VMRs throughout the winter in the upper mesosphere, agreeing with results
of Smith et al. (2011). For comparison, vertical advection
(adv_w∗) at these altitudes shows positive tendencies
ranging from < 0.2 to > 1.6 ppm day-1. Changes in CO due
to chemistry (chem) are small below approximately 70 km, but each scenario
and year shows a sustained sink for CO during the winter in a layer at
around 80 km altitude. The layer coincides with the location of a night-time
layer of hydroxyl (OH) around 82 km altitude (Brinksma et al., 1998; Pickett
et al., 2006; Damiani et al., 2010). OH is known as the dominant chemical
sink for middle-atmospheric CO (Solomon et al, 1985). chem tendencies are
stronger at 80∘ N compared to 67∘ N, with magnitudes
reaching more than 0.3 ppm day-1 in November and December 2010, ranging from
approximately 10 to 50 % of adv_w∗ over that
time. The results suggest that CO chemistry cannot be ignored in the
mesosphere during winter. Tendencies due to resolved eddy diffusion (Xedd)
show the most variation between positive and negative values, mainly at
67∘ N because of proximity to the edge of the polar vortex. The
north polar average shows that Xedd generally reduces CO VMRs during the
winter and, above ∼ 70 km, has magnitudes greater than
25 % of adv_w∗ for timescales of a week. The
largest tendency in CO is from adv_w∗, and causes an
almost constant increase in CO VMRs throughout the winter, before reversing
when the TEM vertical wind changes direction in spring (visible in all
adv_w∗ plots). The increase is due to the downward
motion of air and the positive gradient of CO VMR with altitude. The
tendency is stronger at 80∘ N compared to 67∘ N due to
a stronger vertical component of the residual circulation at the higher
latitude (Smith et al., 2011, and see Fig. 2). A signature of the major
SSW in 2009 can be seen in the adv_w∗ tendency for that
year, with a decrease and eventual change to a negative tendency. A negative
tendency generally indicates ascent of air at this time. For some time
directly afterwards, the tendency has a stronger positive magnitude than
before. This agrees with observations of stronger vertical motion above the
pole after a SSW (see references in Table 1). There is also a brief change
to a negative adv_w∗ at 80∘ N, around 80 km altitude, in
early January 2011. This coincides with a relatively strong positive value
for Xedd at the same time and location, indicating strong wave activity.
The CO tendency from horizontal advection (adv_v∗) is
negative almost everywhere. This is expected, considering the direction of
v∗‾, toward the winter pole, and the low-to-high gradient of CO
from lower to higher latitudes in the winter hemisphere. The magnitude of
the tendency decreases in spring in each scenario and year. but a change of
sign is not obvious by the end of April. The advection tendencies show
maximum values around 70–80 km for two main reasons. The first is the
larger magnitude of the TEM circulation, compared to lower altitudes, before
there is a turnaround in the direction of the circulation at higher
altitudes, at which point the circulation changes from poleward and downward
to poleward and upward (e.g. Lieberman et al., 2000; Smith et al., 2011).
The turnaround point is at approximately 95 km in WACCM (Smith et al.,
2011). The second is the generally increasing vertical gradient of CO with
altitude (see Eq. 1). At 67∘ N, the magnitudes of
adv_v∗ are roughly half that of adv_w∗, and at 80∘ N they are roughly one-fifth. Considering
this observation alone, changes in CO VMRs cannot be attributed solely to
vertical advection.
The same as Fig. 3, but for 80∘ N.
The same as Fig. 3, but for the north polar average (60–90∘ N).
Rates of vertical motion, in km day-1, calculated by tracking CO
VMRs over time. wCO is calculated using daily
averaged SD-WACCM CO profiles. wCO_corrected is calculated using a combination of SD-WACCM CO profiles and
TEM tendencies (see Sect. 4 for details). The difference between the two
rates of descent is also shown. The results plotted are for above Kiruna for
the winters of 2008/2009 and 2010/2011. Contour lines are spaced by
0.2 km day-1. Areas with tightly packed contours (black areas) occur when
there are very low CO VMRs and the calculation method is unreliable. White
areas are where a CO VMR could not be tracked within the shown altitude
range. The start date of the SSW on 28 January 2009 is shown with a
vertical green dashed line. Rates calculated using KIMRA CO data are also
included in the upper two panels, titled
[wCO]KIMRA.
The same as Fig. 6, but for a zonal mean at 80∘ N, and
including rates calculated using MLS CO data in the upper two panels, titled
[wCO]MLS.
Rates of vertical motion with SD-WACCM CO
In this section, the rates of vertical motion are calculated, using CO
profiles from daily averaged SD-WACCM output, by two methods. The first
method assumes that observed changes in CO VMRs are due to vertical
advection alone. This is a commonly used method (see Table 1) and involves
tracking the altitude of a chosen VMR of CO over time and then performing a
linear regression on the data of altitude (z) vs. time (t). The rate for a
given date is calculated here by performing a regression on the CO data
within ±5 days of the date. This gives an 11-day running mean of the
rate of vertical motion. The calculation can be done for multiple VMRs, to
retrieve a vertical profile of the rates of vertical motion, as in Bailey et al. (2014). The term used here to denote the rate of vertical motion
calculated using CO VMR is wCO, following Hoffmann (2012). The second
method used here includes information on changes in CO VMRs due to all terms
of the continuity equation in Sect. 2. Before tracking a CO VMR from one
time step to the next, the VMR is adjusted using the tendencies of the
continuity equation, except for adv_w∗, for the
corresponding time frame. This accounts for changes in CO VMR from other
processes as one tracks its movement due to vertical advection. The
resulting rate is called wCO_corrected. This could be considered a
crude approach, combining daily averaged CO output with CO tendencies
calculated using the TEM formalism, but the aim here is to provide an
estimate of the errors that may be incurred by neglecting influences on CO
other than vertical advection. In any case, the results involving wCO_corrected are discussed in a qualitative manner, instead of for
quantitative error analysis. To illustrate the difference between wCO
and wCO_corrected, example algebraic expressions for the rates
between a time step n and n+1 are given: Eqs. (3), (4), and (5). In practice,
wCO and wCO_corrected were calculated by performing a linear
regression on the 11-day altitude vs. time data, including errors in the
estimated altitude of the measured CO VMRs. Equations (3), (4), and (5) refer
to scalar (values at a given altitude/latitude/longitude) VMRs and
tendencies, in contrast to Eqs. (1) and (2), which refer to three-dimensional
variables:
wCO(z,t)=zn+1-zntn+1-tnχ‾,wCO_corrected(z,t)=zχ‾n+1-zχ‾ntn+1-tn,
where
χ‾n+1=χ‾n+12∑i=nn+1adv_v∗+chem+Xmol+Xedd+Xkzztn+1-tn.
The results for winters 2008/2009 and 2010/2011 are shown. For each winter,
wCO and wCO_corrected are calculated as a local value above
Kiruna, a zonal mean at 80∘ N, and a polar mean (60–90∘ N), and plotted in Figs. 6, 7, and 8, respectively. The
methods above were found to be unreliable when there are very low CO VMRs,
and gave unrealistic rates of motion. This was more likely to occur at lower
altitudes where CO VMRs are relatively low. Sometimes a CO VMR could not be
followed within the specified altitude range (no extrapolation was used) and
so there are a few locations with no rate information. The differences
between the two rates, wCO_corrected-wCO, are also shown in each
panel. wCO was also calculated using the data from KIMRA, for
67∘ N, and from MLS, for the zonal mean at 80∘ N and the
north polar mean. The results are included in Figs. 6–8 for comparison.
The wCO values from SD-WACCM and from the instruments show good
agreement, with some differences that would be expected due to the levels of
agreement of the CO VMRs (see Sect. 2). Considering the calculations using
SD-WACCM, there are three main qualitative points, common to each scenario
and year, that are evident from the results. The same conclusions are
reached when using KIMRA and MLS in place of SD-WACCM.
The same as Fig. 6, but for the north polar average (60–90∘ N), and including rates calculated using MLS CO data in
the upper panel, titled
[wCO]MLS.
First, the values of wCO are generally of a smaller magnitude than
wCO_corrected during winter, meaning the calculated rates of descent
are stronger if one accounts for CO tendencies other than vertical
advection. This makes sense because, as seen in Figs. 3–5, the other
transport terms of the continuity equation (and the chemical loss term) tend
to oppose the vertical advection term. In other words, the results indicate
that the “true” rate of atmospheric descent is masked by sinks of CO, and
by transport processes that oppose the tendency due to vertical advection.
Second, the differences between the two rates are often of the same order as
wCO. Third, the signs of wCO and wCO_corrected are often
opposite, meaning the calculated direction of air motion is prone to change
when accounting for CO tendencies other than vertical advection. In each
example for 2008/2009, the magnitude of the positive (upward) motion around
the time of SSW is decreased for wCO_corrected compared to wCO.
After the SSW, and into March, the strongest descent values are seen around
70–80 km in wCO_corrected, compared to values of ascent seen in
wCO at the same location.
Relative strengths of CO tendencies, by month
To give an idea of the relative influence each tendency has on
middle-atmospheric CO VMRs each month, daily CO tendencies from 2008 to 2014
are used to provide relative values of their monthly mean. For a given
tendency, the daily values are separated by calendar month and averaged, to
give a monthly mean tendency. The daily sums of the absolute values of all
tendencies are also separated by month and averaged, to give a monthly mean
total absolute tendency. The monthly mean tendencies are then normalized by
the monthly mean total absolute tendency, and will be referred to here as
relative strengths. Using absolute values for normalization retains the sign
of the individual tendencies and avoids a large spread in the results when
there is a small denominator (i.e. when the tendencies cancel each other
and their sum is near zero). The relative strength of the sum of all
tendencies, excluding adv_w∗, is also included as a
variable. This variable will be referred to here as other processes and can be directly
compared to the relative strength of adv_w∗, to judge
the influence of vertical advection compared to all other processes. The
results are shown for the north polar average (60–90∘ N) in Fig. 9, and the south polar average (60–90∘ S)
in Fig. 10. For the north, tendencies corresponding to the 10
days directly before and after a SSW (starting on 22, 24, and 26 January
2008, 2009, 2010, respectively, and 6 February
2013) are excluded from the calculation, as well as the SSW
start date. A block of 10 days was chosen to remove effects directly before and after
a SSW, but signatures of the events remain in the data. The information from
before and after an SSW is used to separately calculate the relative
strengths for these times and is shown in Fig. 11.
North polar average (60–90∘ N) monthly
mean CO tendencies (see Eq. 2) around winter time, calculated using
SD-WACCM output from January 2008 to April 2014. Model output corresponding
to 10 days directly before and after a SSW, as well as the start date, is
excluded. The values are expressed relative to the monthly means of the sum
of the absolute values of all tendencies at a given altitude, and are
referred to as relative strengths (see Sect. 5). A relative strength of the
sum of the tendencies, excluding adv_w∗, is
also plotted.
For the north polar average in Fig. 9, the relative strength of other processes is
everywhere negative from October to March and the relative strength of
adv_w∗ is positive. Both adv_w∗
and other processes show a change of sign in April. The relative strength of
adv_w∗ reaches a maximum value of 0.8 at lower
altitudes in October and November, and decreases with altitude to
approximately 0.5 at 86 km for these months. The relative strength of other processes shows
an opposite trend: negative values strengthening with altitude to
approximately -0.4. At the lower limit of the altitude range in January,
adv_w∗ shows a lower relative strength than other processes due to a
strong Xedd influence. This is likely the “left-over” influence of the
SSWs in the data. By March, the relative strength of chem is prominent below
65 km, and the magnitude of adv_w∗ is matched by other processes. By
April, the residual mean circulation has reversed direction and
adv_w∗ has changed sign at most altitudes. The negative
value of chem is then dominant at lower altitudes, and there is a stronger
positive tendency above ∼ 80 km (from photolysis of carbon
dioxide; CO2).
There are no months where the relative strength of other processes can be considered
negligible compared to the relative strength of adv_w∗.
The closest approximations of this situation are at 50 km altitude in
October and at 46 km altitude in November, when other processes contributes 13.7 and
9.6 % of adv_w∗, respectively. These percentages
then vary significantly with altitude. For October, the value increases to
18.6 % at 46 km, 22.5 % at 60 km, and is 61.13 % at 80 km. For
November, the value increases to 34.4 % at 54 km, and is 70.8 % at
80 km.
The results for the south polar average, in Fig. 10, are qualitatively
similar to those for the north. The relative strength of adv_w∗ shows a maximum of ∼ 0.8. Both hemispheres show a peak
in chem at 80 km for most of winter (see Sect. 3.3). The relative strength of
Xedd is not as prominent at the south as the north, likely due to the
higher stability of the southern polar vortex. The points at which the
relative strength of other processes is smallest compared to adv_w∗
are at 56 km in April (8.3 %) and at 46 km in May (6.8 %). For April,
the value increases to 22.5 % at 46 km and 21.5 % at 66 km, and is
56.9 % at 80 km. In May, the value increases to 16 % at 54 km, and is
69.1 % at 80 km.
For the 10 days directly before and after SSWs, in Fig. 11, the relative
strength of adv_w∗ is less than 0.5 at all altitudes.
Xedd is strong below 60 km, such that the relative strength of other processes has a
larger magnitude than that of adv_w∗ at many altitudes.
The relative strength of adv_w∗ shows a more
oscillatory structure with altitude, and there is a local minimum at about
70 km in the data for 10 days after SSWs. There is also a positive peak in
the relative strength of Xkzz after SSWs at this altitude.
Aside from considering what value would classify as negligible, the
significant variation in strength of other processes compared to adv_w∗, over altitude, adds complexity to the method of following a tracer over
an altitude range to determine the descent rate. One must also consider that
while this section discusses monthly averaged data, tracers are often
followed for several days to determine the changes in altitude over that
time, and that the magnitudes of each tendency can vary significantly over
this timescale (see Figs. 3, 4, and 5).
Same plots as for Fig. 9, but for the south polar average
(60–90∘ S), around the time of southern hemisphere
winter, and with no exclusion of model output for SSWs.
Same as Fig. 9 (60–90∘ N), but for the 10 days directly before
and after SSWs within Jan 2008 to April 2014. See Sect. 5 for details.
The north polar average (60–90∘ N) TEM
vertical wind, w∗‾, for the winters of 2008/2009, and 2010/2011.
The values are calculated using daily averaged output from SD-WACCM.
Discussion
The results of the previous sections, using SD-WACCM, are clear on one
indication – that the assumption of observed changes in CO VMRs being solely
due to vertical advection is not a valid one. What is not obvious, is what
the rates inferred from the behaviour of tracer isolines (wχ, under
the nomenclature used here) represent. When making observations on
timescales of weeks, the TEM offers a suitable representation of the
governing dynamics. The vertical velocities calculated by observing CO,
however, are smaller than the magnitudes of the TEM vertical wind,
w∗‾, found with SD-WACCM. Figure 12 shows the polar average
(60–90∘ N) w∗‾ for the winters of
2008/2009 and 2010/2011, calculated using daily averaged SD-WACCM output.
Comparing this to the values of wCO and wCO_corrected in Fig. 8,
it is clear that the rates derived from CO values are generally of lower
magnitude than w∗‾. This agrees with the results of Hoffmann (2012). wCO_corrected more closely matches the sign and general
pattern of w∗‾, but does not reach the semi-persistent descent
rates of -1.2 km day-1 between 60 and 70 km, or the magnitude of the enhanced
rate of descent (< -1.6 km day-1) after the SSW in 2009. Similar
results are observed when using KIMRA and MLS CO profiles to derive vertical
velocity, instead of SD-WACCM output (not shown here).
Some of the difference may be attributed to the time resolution, or assumed
parameters, of the SD-WACCM output used in this work. Meraner and Schmidt (2016) showed that the tendency of NO due to w∗‾ can be quite
different (by > 1 ppm day-1 at 90 km) when calculated using
6 h averaged output or daily averaged output from HAMMONIA (Hamburg
Model of the Neutral and Ionized Atmosphere). Meraner et al. (2016) showed
that NOx transport in the mesosphere is highly sensitive to the
strength of the gravity wave source. The amplitudes of gravity waves
influence the altitude at which the waves break and deposit their momentum,
which in turn affects the vertical profile of w∗‾. Garcia et al. (2014) found that using a Prandtl number of 4 for WACCM (see Sect. 3.1),
instead of 2 (as used here), gave better agreement with polar CO profiles
from satellite instruments (the Atmospheric Chemistry Experiment Fourier
Transform Spectrometer (ACE-FTS) on SCISAT-1, and the Michelson
Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat). The
opposite was found for tropical and mid-latitude CO profiles, and Garcia et al. (2014) consider that deficiencies in the modelling of diffusive eddy
transport may be due to problems in producing the correct latitudinal
profile of Kzz, which cannot be addressed by simply adjusting the
Prandtl number.
As the results here using SD-WACCM indicate that the commonly used
approximation of w∗‾ with wχ (using tracer observations)
is not valid, we suggest an alternative interpretation of wχ: as an
effective rate of vertical transport for the trace gas χ. The
interpretation still concerns the descent of a tracer through the middle
atmosphere, but allows for a chemical sink, or for processes other than
vertical advection to influence the descent rate, e.g. horizontal advection
or an increase in unresolved eddy transport after a SSW. Hoffmann (2012)
put forward a similar interpretation for wCO, but with an assumption
that the overall dynamic effects on CO are representative for mesospheric
air, and so wCO is representative of wχ for all tracers. The
results here do not confirm that assumption, as it would require tracers to
have the same horizontal and vertical VMR gradients (see Sect. 2.5 and
references therein). The consistent negative chemical tendency of CO, seen
here in a layer around approximately 80 km, also indicates the need to
account for the behaviour of chemical sinks, even during polar night.
Conclusions
The aim of this work was to assess how well polar atmospheric descent rates
can be derived from remote sensing measurements of atmospheric trace gases.
Tendencies of middle atmospheric CO were calculated using output from
SD-WACCM for the years 2008 to 2014, within 46–86 km altitude, and used
to evaluate the relative influence of processes involved in the TEM CO
continuity equation. The results show that dynamical processes other than
vertical advection cause non-negligible changes in CO VMRs during winter,
and particularly directly before and after sudden stratospheric warmings
when eddy transport can become dominant. Significant changes in CO
tendencies from SD-WACCM occur over timescales of the order of days. The results also show a
chemical sink for CO, present throughout polar night, due to the layer of
night-time OH at approximately 80 km. Modelled CO profiles were used in
combination with the tendency data to provide a qualitative estimate of the
errors that can be incurred when assuming pure vertical advection of CO.
Rates of atmospheric motion were calculated when assuming only vertical
advection, and corrected rates were calculated by including tendency
information for all processes. The differences between the two results are
of the same order as the calculated rates, and the rates are prone to
showing opposite directions for the mean vertical wind. The corrected rates
more closely match the TEM vertical wind velocity from SD-WACCM, but both
results using CO show smaller magnitudes relative to the TEM vertical wind,
in agreement with the work of Hofmann (2012). The “true” rate of
atmospheric descent appears to be masked by sinks of CO, and by transport
processes that oppose the tendency due to vertical advection. Monthly mean
relative tendencies for CO show that the summed magnitude of processes other
than vertical advection can constitute a large fraction of the changes in CO
VMR. For a given month, the summed magnitude of the other processes,
relative to vertical advection, changes by several tens of percent over the
altitude range under investigation. The results here suggest that there are
no months during polar winter when vertical mean advection dominates the
budget of CO to such an extent that vertical mean velocity can be accurately
derived within the altitude range.
An assessment using SD-WACCM indicates that a commonly used approximation of
the vertical mean velocity of the atmosphere, w∗‾ , using tracer
(CO in this case) isolines is not valid, and an alternative interpretation
of the rates derived from trace gas measurements is suggested: an effective
rate of vertical transport for the given trace gas. Such an interpretation
still concerns the descent of trace gases from the mesosphere and
thermosphere, but allows for chemical sinks, and changes in VMR from
dynamical processes other than vertical advection. Due to possible
differences in the behaviour of chemical sinks and VMR gradients, it is not
clear whether the rate of vertical transport for one tracer is
representative of the rate for another. Continuous ground-based and
satellite measurements of trace gases remain an essential tool in
understanding the short- and long-term evolution of the middle atmosphere,
as well as for the validation and parameterization of atmospheric models.
The KIMRA CO data set (between December 2008 and May 2015) can be accessed
publicly through PANGAEA Data Publisher for Earth and Environmental Science at 10.1594/PANGAEA.861730 (Ryan et al., 2017b).
Model results shown in this paper are available on request via Doug Kinnison (dkin@ucar.edu). The Aura MLS v4.2
data are available from the Goddard Earth Sciences Data and Information Center at https://disc.gsfc.nasa.gov.
MP and CGH outlined the project. NJR
designed the method. DEK performed the SD-WACCM runs and
provided the output. RRG provided scripts to calculate TEM
variables from WACCM output. UR maintains and operates KIMRA, and
provided the instrument spectra. NJR performed the study with
valuable insight and interpretation of results provided by DEK and RRG. JN oversaw project development.
NJR prepared the paper with contributions from co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
A 3-month research stay by Niall J. Ryan at the National Centre for
Atmospheric Research, Boulder, Colorado, was funded by the University of
Bremen, under the German Excellence Initiative (ABPZuK-03/2014). Instrument
data used in the project were developed and acquired through funding from the
German Federal Ministry of Education and Research (BMBF) through the
research project “Role Of the Middle atmosphere in Climate” (ROMIC),
sub-project ROMICCO, project number 01LG1213A. The paper creation and
editing was supported through ROMICCO. We would like to thank the National
Center for Atmospheric Research (NCAR) for being so accommodating. The
National Center for Atmospheric Research is sponsored by the US National
Science Foundation. We express our gratitude to the MLS team for making
their data available. A special thank you to Sophia McCue, for all the smiles.
The article processing charges for this open-access publication were covered by the University of Bremen.
Edited by: William Ward
Reviewed by: four anonymous referees
References
Allen, D. R., Stanford, J. L., López-Valverde, M. A., Nakamura, N.,
Lary, D. J., Douglass, A. R., Cerniglia, M. C., Remedios, J. J., and Taylor,
F. W.: Observations of Middle Atmosphere CO from the UARS ISAMS during the
Early Northern Winter 1991/92, J. Atmos. Sci., 56, 563–583, 1999.
Allen, D. R., Stanford, J. L., Nakamura, N., López-Valverde, M. A., López-Puertas, M., Taylor, F. W., and
Remedios, J. J.: Antarctic polar descent and planetary wave activity observed in ISAMS CO from April to July 1992, Geophys. Res. Lett., 27, 665–668, 2000.
Andrews, D., Holton, J., and Leovy, C.: Middle Atmosphere Dynamics, Academic
Press, 489 pp., 1987.
Andrews, D. G. and McIntyre, M. E.: Planetary waves in horizontal and
vertical shear: The generalized Eliassen-Palm relation and mean zonal
acceleration, J. Atmos. Sci., 33, 2031–2048, 1976.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 III,
J. M.: A multi tracer analysis of thermo- sphere to stratosphere descent
triggered by the 2013 Stratospheric Sudden Warming, Geophys. Res. Lett., 41,
5216–5222, 10.1002/2014GL059860, 2014.Barth, C. A. and Bailey S. M.: Comparison of a thermospheric
photochemicalmodel with SNOE observations of nitric oxide, J. Geophys. Res.,
109, A03304, 10.1029/2003JA010227, 2004.
Brasseur, G. and Solomon, S.: Aeronomy of the Middle Atmosphere: Chemistry
and Physics of the Stratosphere and Mesosphere, Springer, 644 pp., 2005.Brewer, A. W.: Evidence for a world circulation provided by the measurements
of helium and water vapour distribution in the stratosphere, Q. J. Roy. Meteor. Soc., 75, 351–363, 10.1002/qj.49707532603, 1949.
Brinksma, E. J., Meijer, Y. J., McDermid, I. S., Cageao, R. P., Bergwerff,
J. B., Swart, D. P. J., Ubachs, W., Matthews, W. A., Hogervorst, W., and
Hovenier, J. W.: First lidar observations of mesospheric hydroxyl, Geophys.
Res. Lett., 25, 51–54, 1998.Damiani, A., Storini, M., Santee, M. L., and Wang, S.: Variability of the nighttime OH layer and mesospheric
ozone at high latitudes during northern winter: influence of meteorology, Atmos. Chem. Phys., 10, 10291–10303,
10.5194/acp-10-10291-2010, 2010.Di Biagio, C. D., Muscari, G., di Sarra, A., de Zafra, R. L., Eriksen, P.,
Fiocco, G., Fiorucci, I., and Fuà, D.: Evolution of temperature, O3,
CO, and N2O profiles during the exceptional 2009 Arctic major
stratospheric warming observed by lidar and mm-wave spectroscopy at Thule
(76.5∘ N, 68.8∘ W), Greenland, J. Geophys. Res., 115,
D24315, 10.1029/2010JD014070, 2010.
Dunkerton, T.: On the mean meridional mass motions of the stratosphere and
mesosphere, J. Atmos. Sci., 25, 2325–2333, 1978.Engel, A., Möbius, T., Haase, H.-P., Bönisch, H., Wetter, T., Schmidt, U., Levin, I.,
Reddmann, T., Oelhaf, H., Wetzel, G., Grunow, K., Huret, N., and Pirre, M.: Observation of mesospheric
air inside the arctic stratospheric polar vortex in early 2003, Atmos. Chem. Phys., 6, 267–282, 10.5194/acp-6-267-2006, 2006.Forkman, P., Eriksson, P., and Murtagh, D.: Observing the vertical branch of
the mesospheric circulation at lat N60∘ using ground based
measurements of CO and H2O, J. Geophys. Res., 110, D05107,
10.1029/2004JD004916, 2005.
Fritts, C. D. and Dunkerton, T. J.: Fluxes of Heat and Constituents Due to
Convectively Unstable Gravity Waves, J. Atmos. Sci., 42, 549–556, 1985.Funke, B., López-Puertas, M., García-Comas, M., Stiller, G. P., von Clarmann, T., Höpfner, M., Glatthor, N.,
Grabowski, U., Kellmann, S., and Linden, A.: Carbon monoxide distributions from the upper troposphere to the mesosphere inferred
from 4.7 µm non-local thermal equilibrium emissions measured by MIPAS on Envisat,
Atmos. Chem. Phys., 9, 2387–2411, 10.5194/acp-9-2387-2009, 2009.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.Funke, B., Ball, W., Bender, S., Gardini, A., Harvey, V. L., Lambert, A., López-Puertas, M., Marsh, D. R., Meraner, K.,
Nieder, H., Päivärinta, S.-M., Péot, K., Randall, C. E., Reddmann, T., Rozanov, E., Schmidt, H., Seppälä, A.,
Sinnhuber, M., Sukhodolov, T., Stiller, G. P., Tsvetkova, N. D., Verronen, P. T., Versick, S., von Clarmann, T., Walker, K. A., and
Yushkov, V.: HEPPA-II model-measurement intercomparison project: EPP indirect effects during the dynamically perturbed NH
winter 2008–2009, Atmos. Chem. Phys., 17, 3573–3604, 10.5194/acp-17-3573-2017, 2017.Garcia, R., Marsh, D., Kinnison, D., Boville, B., and Sassi, F.: Simulation
of secular trends in the middle atmosphere, 1950–2003, J. Geophys. Res.,
112, D09301, 10.1029/2006JD007485, 2007.Garcia, R. R. and Solomon, S.: A Numerical Model of the Zonally Averaged
Dynamical and Chemical Structure of the Middle Atmosphere, J. Geophys. Res.,
88, 1379–1400, 10.1029/JC088iC02p01379, 1983.Garcia, R. R., López-Puertas, M., Funke, D., Marsh, D. R., Kinnison, D. E.,
Smith, A. K., and González-Galindo, F.: On the distribution of CO2 and CO
in the mesosphere and lower thermosphere, J. Geophys. Res., 119, 5700–5718,
10.1002/2013JD021208, 2014.Harvey, V. L., Randall, C. E., and Hitchman, M. H.: Breakdown of potential
vorticity-based equivalent latitude as a vortex-centered coordinate in the
polar winter mesosphere, J. Geophys. Res., 114, D22105,
10.1029/2009JD012681, 2009.Harvey, V. L., Randall, C. E., and Collins, R. L.: Chemical definition of
the mesospheric polar vortex, J. Geophys. Res.-Atmos., 120, 10166–10179,
10.1002/2015JD023488, 2015.Hauchecorne, A., Bertaux, J.-L., Dalaudier, F., Russell III, J. M.,
Mlynczak, M. G., Kyrölä, E., and Fussen, D.: Large increase of
NO2 in the north polar mesosphere in January–February 2004: Evidence
of a dynamical origin from GOMOS/ENVISAT and SABER/TIMED data, Geophys. Res.
Lett., 34, L03810, 10.1029/2006GL027628, 2007.
Hoffmann, C. G.: Application of CO as a tracer for dynamics in the polar
winter middle atmosphere, PhD thesis, Institut für Umweltphysik,
Universität Bremen, Germany, 142 pp., 2012.Hoffmann, C. G., Raffalski, U., Palm, M., Funke, B., Golchert, S. H. W., Hochschild, G., and Notholt, J.:
Observation of strato-mesospheric CO above Kiruna with ground-based microwave radiometry – retrieval and satellite comparison,
Atmos. Meas. Tech., 4, 2389–2408, 10.5194/amt-4-2389-2011, 2011.Hoffmann, C. G., Kinnison, D. E., Garcia, R. R., Palm, M., Notholt, J., Raffalski, U., and Hochschild, G.: CO at 40–80 km
above Kiruna observed by the ground-based microwave radiometer KIMRA and simulated by the Whole Atmosphere Community Climate Model,
Atmos. Chem. Phys., 12, 3261–3271, 10.5194/acp-12-3261-2012, 2012.
Holt, L. A., Randall, C. E., Peck, E. D., Marsh, D. R., Smith, A. K., and
Harvey, V. L.: The influence of major sudden stratospheric warming and
elevated stratopause events on the effects of energetic particle
precipitation in WACCM, J. Geophys. Res., 118, 636–646, 2013.Jackman, C. H., Marsh, D. R., Vitt, F. M., Garcia, R. R., Randall, C. E.,
Fleming, E. L., and Frith, S. M.: Long-term middle atmospheric influence of
very large solar proton events, J. Geophys. Res., 114, D11304,
10.1029/2008JD011415, 2009.Jin, J. J., Semeniuk, K., Manney, G. L., Jonsson, A. I., Beagley, S. R.,
McConnell, J. C., Dufour, G., Nassar, R., Boone, C. D., Walker, K. A.,
Bernath, P. F., and Rinsland, C. P.: Severe Arctic ozone loss in the winter
2004/2005: observations from ACE-FTS, Geophys. Res. Lett., 33, L15801, 10.1029/2006GL026752, 2006.Kinnison, D. E., Brasseur, G. P., Walters, S., Garcia, R. R., Marsh, D. R.,
Sassi, F., Harvey, V. L., Randall, C. E., Emmons,
L., Lamarque, J. F., Hess, P., Orlando, J. J., Tie, X. X., Randel, W., Pan,
L. L., Gettelman, A., Granier, C., Diehl, T.,
Niemeier, U., and Simmons, A. J.: Sensitivity of chemical tracers to
meteorological parameters in the MOZART-3
chemical transport model, J. Geophys. Res., 112, D20302,
10.1029/2006JD007879, 2007.Lamarque, J.-F., Emmons, L. K., Hess, P. G., Kinnison, D. E., Tilmes, S., Vitt, F., Heald, C. L., Holland, E. A.,
Lauritzen, P. H., Neu, J., Orlando, J. J., Rasch, P. J., and Tyndall, G. K.: CAM-chem: description and evaluation of
interactive atmospheric chemistry in the Community Earth System Model, Geosci. Model Dev., 5, 369–411, 10.5194/gmd-5-369-2012, 2012.Lee, J. N., Wu, D. L., Manney, G. L., Schwartz, M. J., Lambert, A., Livesey,
N. J., Minschwaner, K. R., Pumphrey, H. C., and Read, W. G.: Aura Microwave
Limb Sounder observations of the polar middle atmosphere: Dynamics and
transport of CO and H2O, J. Geophys. Res., 116, D05110,
10.1029/2010JD014608, 2011.
Lieberman, R. S., Smith, A. K., Franke, S. J., Vincent, R. A., Isler, J. R.,
Manson, A. H., Meek, C. E., Fraser, G. J., Fahrutdinova, A., Thayaparan, T.,
Hocking, W., MacDougall, J., Nakamura, T., and Tsuda, T.: Comparison of
mesospheric and lower thermospheric residual wind with High Resolution
Doppler Imager, medium frequency, and meteor radar winds, J. Geophys. Res.,
105, 27023–27035, 2000.
Lin, S.-J.: A “vertically-Lagrangian” finite-volume dynamical core for
global atmospheric models, Mon. Weather Rev., 132, 2293–2307, 2004.Livesey, N. J., Livesey, Filipiak, M. J., Froidevaux, L., Read, W. G., Lambert, A., Santee, M. L., Jiang, J. H., Waters, J. W.,
Cofield, R. E., Cuddy, D. T., Daffer, W. H., Drouin, B. J., Fuller, R. A., Jarnot, R. F., Jiang, Y. B., Knosp, B. W., Li, Q. B.,
Perun, V. S., Schwartz, M. J., Snyder, W. V., Stek, P. C., Thurstans, R. P., Wagner, P. A., Pumphrey, H. C., Avery, M., Browell, E. V.,
Cammas, J.-P., Christensen, L. E., Edwards, D. P., Emmons, L. K., Gao, R.-S., Jost, H.-J., Loewenstein, M., Lopez, J. D., Nedelec, P.,
Osterman, G. B., Sachse, G. W., and Webster, C. R: Validation of Aura
Microwave Limb Sounder O3 and CO observations in the upper troposphere
and lower stratosphere, J. Geophys. Res., 113, D15S02,
10.1029/2007JD008805, 2008.
Livesey, N. J., Read, W. G., Wagner, P. A., Froidevaux, L., Lambert, A.,
Manney, G. L., Millán Valle, L. F., Pumphrey, H. C., Santee, M. L.,
Schwartz, M. J., Wang, S., Fuller, R. A., Jarnot, R. F., Knosp, B. W., and
Martinez, E.: Version 4.2x Level 2 data quality and description document,
Tech. rep., Jet Propulsion Laboratory, 2015.Liu, H., Foster, B. T., Hagan, M. E., McInerney, J. M., Maute, A., Qian, L.,
Richmond, A. D., Roble, R. G., Solomon, S. C., Garcia, R. R., Kinnison, D.,
Marsh, D. R., Smith, A. K., Richter, J., Sassi, F., and Oberheide, J.:
Thermosphere extension of the Whole Atmosphere Community Climate Model, J.
Geophys. Res., 115, A12302, 10.1029/2010JA015586, 2010.
Manney, G. L., Zurek, R. W., O'Neill, A., Swinbank, R., Kumer, J. B.,
Mergenthaer, J. L., and Roche A. E.: Stratospheric warmings during February
and March 1993, Geophys. Res. Lett., 21, 813–816, 1994.Manney, G. L., Froidevaux, L., Santee, M. L., Zurek, R. W., and Waters, J. W.: MLS Observations of Arctic Ozone Loss in 1996–97,
Geophys. Res. Lett., 24, 2697–2700, 10.1029/97GL52827, 1997.Manney, G. L., Froidevaux, L., Santee, M. L., Livesey, N. J., Sabutis, J. L., and Waters J. W.:
Variability of ozone loss during Arctic winter (1991–2000) estimated from UARS Microwave Limb Sounder measurements,
J. Geophys. Res., 108, 4149, 10.1029/2002JD002634, 2003.Manney, G. L., Daffer, W. H., Zawodny, J. M., Bernath, P. F., Hoppel, K. W.,
Walker, K. A., Knosp, B. W., Boone, C., Remsberg, E. E., Santee, M. L.,
Harvey, V. L., Pawson, S., Jackson, D. R., Deaver, L., McElroy, C. T.,
McLinden, C. A., Drummond, J. R., Pumphrey, H. C., Lambert, A., Schwartz, M.
J., Froidevaux, L., McLeod, S., Takacs, L. L., Suarez, M. J., Trepte, C. R.,
Cuddy, D. C., Livesey, N. J., Harwood, R. S., and Waters, J. W.: Solar
occultation satellite data and derived meteorological products: sampling
issues and comparisons with Aura Microwave Limb Sounder, J. Geophys. Res.,
112, D24S50, 10.1029/2007JD008709, 2007.Manney, G. L., Daffer, W. H., Strawbridge, K. B., Walker, K. A., Boone, C. D., Bernath, P. F., Kerzenmacher, T., Schwartz, M. J.,
Strong, K., Sica, R. J., Krüger, K., Pumphrey, H. C., Lambert, A., Santee, M. L., Livesey, N. J., Remsberg, E. E., Mlynczak, M. G.,
and Russell III, J. R.: The high Arctic in extreme winters: vortex, temperature, and MLS and ACE-FTS trace gas evolution,
Atmos. Chem. Phys., 8, 505–522, 10.5194/acp-8-505-2008,
2008a.Manney, G. L., Krüger, K., Pawson, S., Minschwaner, K., Schwartz, M. J.,
Daffer, W. H., Livesey, N. J., Mlynczak, M. G., Remsberg, E. 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, 2008b.Manney, G. L., Schwartz, M. J., Krüger, K., Santee, M. L., Pawson, S.,
Lee, J. N., Daffer, W. H., Fuller, R. A., and Livesey, N. J.: Aura Microwave
Limb Sounder observations of dynamics and transport during the
record-breaking 2009 Arctic stratospheric major warming, Geophys. Res.
Lett., 36, L12815, 10.1029/2009GL038586, 2009.Manney, G. L., Lawrence, Z. D., Santee, M. L., Read, W. G., Livesey, N. J.,
Lambert, A., Froidevaux, L., Pumphrey, H. C., and Schwartz, M. J.: A minor
sudden stratospheric warming with a major impact: Transport and polar
processing in the 2014/2015 Arctic winter, Geophys. Res. Lett., 42,
7808–7816, 10.1002/2015GL065864, 2015.Marsh, D. R., Mills, M. J., Kinnison, D. E., Lamarque, J.-F., Calvo, N., and Polvani, L. M.: Climate change from 1850 to
2005 simulated in CESM1 (WACCM), J. Climate, 26, 7372–7391, 10.1175/JCLI-D-12-00558.1,
2013.
McLandress, C., Scinocca, J. F., Shepherd, T. G., Reader, M. C., and Manney,
G. L.: Dynamical control of the mesosphere by orographic and non-orographic
gravity wave drag during the extended northern winters of 2006 and 2009, J.
Atmos. Sci., 70, 2152–2161, 2013.Medvedev, A. S. and Fomichev, V. I.: Net radiative heating and diagnostics
of the diabatic circulation in the 15–110 km height layer, 56,
1574–1581, 10.1016/0021-9169(94)90087-6, 1994.Meraner, K. and Schmidt, H.: Transport of nitrogen oxides through the winter
mesopause in HAMMONIA, J. Geophys. Res.-Atmos., 121, 2556–2570,
10.1002/2015JD024136, 2016.Meraner, K., Schmidt, H., Manzini, E., Funke, B., and Gardini A.:
Sensitivity of simulated mesospheric transport of nitrogen oxides to
parameterized gravity waves, J. Geophys. Res.-Atmos., 121, 12045–12061,
10.1002/2016JD025012, 2016.Monier, E. and Weare, B. C.: Climatology and trends in the forcing of the stratospheric ozone transport,
Atmos. Chem. Phys., 11, 6311–6323, 10.5194/acp-11-6311-2011, 2011.
Müller, R., Crutzen, P. J., Grooß, J.-U., Brühl, C., Russel III, J. M., and Tuck, A. F.: Chlorine activation and ozone depletion in the
Arctic vortex: Observations by the Halogen Occultation Experiment on the
Upper Atmosphere Research Satellite, J. Geophys. Res., 101, 12531–12554, 1996.Müller, R., Tilmes, S., Grooß, J.-U, McKenna, D. S., Müller,
M., Schmidt, U., Toon, G. C., Stachnik, R. A., Margitan, J. J., Elkins, J.
W., Arvelius, J., and Russel III, J. M.: Chlorine activation and chemical
ozone loss deduced from HALOE
and balloon measurements in the Arctic during the winter of 1999–2000, J.
Geophys. Res., 108, 8302, 10.1029/2001JD001423, 2003.Nassar, R., Bernath, P. F., Boone, C. D., Manney, G. L., McLeod, S. D.,
Rinsland, C. P., Skelton, R., and Walker, K. A.: ACE-FTS measurements across
the edge of the winter 2004 Arctic vortex, Geophys. Res. Lett., 32, L15S05,
10.1029/2005GL022671, 2005.
Neale, R., Richter, J., Park, S., Lauritzen, P., Vavrus, S., Rasch, P., and
Zhang, M.: The mean climate of the Community Atmosphere Model (CAM4) in
forced SST and fully coupled experiments, J. Climate, 26, 5150–5168, 2013.Orsolini, Y. J., Urban, J., Murtagh, D., Lossow, S., and Lympasuvan, V.: Descent from the polar mesosphere and anomalously
high stratopause observed in 8 years of water vapor and temperature satellite observations by the Odin submillimeter
radiometer, J. Geophys. Res., 115, D12305, 10.1029/2009JD013501, 2010.Pickett, H. M., Read, W. G., Lee, K. K., and Yung, Y. L.: Observation of
night OH in the mesosphere, Geophys. Res. Lett., 33, L19808,
10.1029/2006GL026910, 2006.Plumb, R. A., Heres W., Neu, J. L., Mahowald, N. M., del Corral, J., Toon,
G. C., Ray, E., Moore, F., and Andrews, A. E.: Global tracer modeling during
SOLVE: High-latitude descent and mixing, J. Geophys. Res., 107, 8309,
10.1029/2001JD001023, 2002.
Proffitt, M. H., Margitan, J. J., Kelly, K. K., Loewenstein, M., Podolske,
J. R., and Chan, K. R.: Ozone loss in the Arctic polar vortex inferred from
high altitude aircraft measurements, Nature, 347, 31–36, 1990.
Proffitt, M. H., Aikin, K., Margitan, J. J., Loewenstein, M., Podolske, J.
R., Weaver, A., Chan, K. R., Fast, H., and Elkins, J. W.: Ozone loss inside
the northern polar vortex during the 1991–1992 winter, Science, 261,
1150–1154, 1993.Pumphrey, H. C., Filipiak, M. J., Livesey, N. J., Schwartz, M. J., Boone,
C., Walker, K. A., Bernath, P., Ricaud, P., Barret,
B., Clerbaux, C., Jarnot, R. F., Manney, G. L., and Waters, J. W.:
Validation of middle-atmosphere carbon monoxide retrievals from MLS on Aura,
J. Geophys. Res., 112, D24S38, 10.1029/2007JD008723, 2007.
Raffalski, U., Berg, H., Hochschild, G., and Kopp, G.: Continuous ozone
measurements over Kiruna during winter/spring 2002: A new millimeter wave
radiometer operated at the Swedish Institute of Space Physics, Kiruna,
Sweden, Proceedings of the Sixth European Symposium on Stratospheric Ozone
Research, Gothenberg, Sweden, 369–372, 2002.Randall, C. E., Harvey, V. L., Manney, G. L., Orsolini, Y., Codrescu, M.,
Sioris, C., Brohede, S., Haley, C. S., Gordley, L. L., Zawodny, J. M., and
Russell, J. M.: Stratospheric effects of energetic particle precipitation in
2003–2004, Geophys. Res. Lett., 32, L05802, 10.1029/2004GL022003, 2005.Randall, C. E., Harvey, V. L., Singleton, C. S., Bernath, P. F., Boone, C.
D., and Kozyra, J. U.: Enhanced NOx in 2006 linked to strong upper
stratospheric Arctic vortex, Geophys. Res. Lett., 33, L18811,
10.1029/2006GL027160, 2006.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., Siskind, D. E., France, J., Bernath, P. F.,
Boone, C. D., and Walker, K. A.: NOx descent in the Arctic middle
atmosphere in early 2009, Geophys. Res. Lett., 36, L18811,
10.1029/2009GL039706, 2009.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.-Space, 120, 5035–5048,
10.1002/2015JA021196, 2015.
Rienecker, M. M, Suarez, M. J., Todling, R., Bacmeister, J., Takacs, L.,
Liu, H.-C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stajner, I.,
and Nielsen, J. E.: The GEOS-5 Data Assimilation System-Documentation of
Versions 5.0.1, 5.1.0, and 5.2.0, Tech. Rep. 104606 V27, NASA, Greenbelt,
MD, 2008.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.Rodgers, C. D. and Connor, B. J.: Intercomparison of remote sounding
instruments, J. Geophys. Res., 108, 4116, 10.1029/2002JD002299, 2003.Rösevall, J. D., Murtagh, D. P., and Urban, J.: Ozone depletion in the
2006/2007 Arctic winter, Geophys. Res. Lett., 34, L21809,
10.1029/2007GL030620, 2007.Ryan, N. J., Palm, M., Raffalski, U., Larsson, R., Manney, G., Millán, L., and Notholt, J.: Strato-mesospheric carbon monoxide
profiles above Kiruna, Sweden (67.8∘ N, 20.4∘ E), since 2008, Earth Syst. Sci. Data, 9, 77–89, 10.5194/essd-9-77-2017, 2017a.Ryan, N. J., Palm, M., Raffalski, U., Larsson, R., Manney, G., Millán, L., Notholt, J.: Middle atmospheric carbon monoxide above
Kiruna, Sweden (67.8∘ N, 20.4∘ E), 2008–2015, PANGAEA, 10.1594/PANGAEA.861730, 2017b.Salawitch, R. J., Margitan, J. J., Sen, B., Toon, G. C., Osterman, G. B.,
Rex. M., Elkins, J. W., Ray, E. A., Moore, F. L., Hurst, D. F., Romashkin,
P. A., Bevilacqua, R. M., Hoppel, K. W., Richard, E. C., and Bui, T. P.:
Chemical loss of ozone during the Arctic winter of 1999/2000: An analysis
based on balloon-borne observations, J. Geophys. Res., 107, 8269,
10.1029/2001JD000620, 2002.
Schoeberl, M. R., Lait, L. R., Newman, P. A., and Rosenfield, J. E.: The
structure of the polar vortex, J. Geophys. Res., 97, 7859–7882, 1992.Schwartz, M., Pumphrey, H., Livesey, N., and Read, W.: MLS/Aura Level 2
Carbon Monoxide (CO) Mixing Ratio V004, version 004, Greenbelt, MD, USA,
Goddard Earth Sciences Data and Information Services Center (GES DISC),
10.5067/AURA/MLS/DATA2005, 2015.Siskind, D. E., Eckermann, S. D., McCormack, J. P., Coy, L., Hoppel, K. W.,
and Baker, N. L.: Case studies of the mesospheric response to minor, major
and extended stratospheric warmings, J. Geophys. Res., 115, D00N03,
10.1029/2010JD014114, 2010.Siskind, D. E., Nedoluha, G. E., Sassi, F., Rong, P., Bailey, S. M., Hervig, M. E., and Randall, C. E.: Persistence of
upper stratospheric wintertime tracer variability into the Arctic spring and summer, Atmos. Chem. Phys., 16, 7957–7967,
10.5194/acp-16-7957-2016, 2016.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.
Solomon, S., Garcia, R. R., Olivero, J. G., Bevilacqua, R. M., Schwarzt, P.
R., Clancy, R. T., and Muhleman, D. O.: Photochemistry and Transport of
Carbon Monoxide in the Middle Atmosphere, J. Atmos. Sci., 42, 1072–1083,
1985.Solomon, S., Kiehl, J. T., Garcia, and Grose, W.: Tracer Transport by the
Diabatic Circulation Deduced from Satellite Observations, J. Atmos. Sci.,
43, 1603–1617, 10.1175/1520-0469(1986)043<1603:TTBTDC>2.0.CO;2, 1986.
Straub, C., Tschanz, B., Hocke, K., Kämpfer, N., and Smith, A. K.: Transport of mesospheric H2O during and after the
stratospheric sudden warming of January 2010: observation and simulation, Atmos. Chem. Phys., 12, 5413–5427, 10.5194/acp-12-5413-2012, 2012.
Waters, J., Froidevaux, L., Harwood, R., Jarno, R., Pickett, H., Read, W.,
Siegel, P., Cofield, R., Filipiak, M., Flower, D.,
Holden, J., Lau, G., Livesey, N., Manney, G., Pumphrey, H., Santee, M., Wu,
D., Cuddy, D., Lay, R., Loo, M., Perun, V.,
Schwartz, M., Stek, P., Thurstans, R., Boyles, M., Chandra, S., Chavez, M.,
Chen, G.-S., Chudasama, B., Dodge, R., Fuller, R., Girard, M., Jiang, J.,
Jiang, Y., Knosp, B., LaBelle, R., Lam, J., Lee, K., Miller, D., Oswald, J.,
Patel, N., Pukala, D., Quintero, O., Scaff, D., Snyder, W., Tope, M.,
Wagner, P., and Walch, M.: The Earth Observing System Microwave Limb Sounder
(EOSMLS) on the Aura satellite, IEEE T. Geosci. Remote, 44, 1075–1092,
2006.
York, D., Evensen, N. M., Martinez, M. L., and Delgado, J. D. B.: Unified
equations for the slope, intercept, and standard errors of the best straight
line, Am. J. Phys., 72, 367–375, 2004.