Introduction
Total column ozone (O3) has decreased by approximately 2.5 % over most
of the planet during the 1980s and 1990s due to increased emissions of
chlorofluorocarbons (CFCs) (WMO, 2014). Thanks to the Montreal Protocol, and
the ban on CFC emissions, total ozone columns have remained relatively
unchanged since then, with recent indications of recovery (UNEP, 2015).
Models suggest that these concentrations will recover to pre-1980 values by
approximately 2060, but the projections are strongly dependant on future
emissions of carbon dioxide (CO2), nitrous oxide (N2O), and methane
(CH4) (WMO, 2014). Long-term (seasonal, yearly, and decadal)
measurements of stratospheric O3 are an essential part of understanding
how compositional changes in the atmosphere are linked to the future
radiative balance of the planet. Measurements of the Arctic atmosphere are
particularly important as the Arctic is known to respond relatively rapidly
to compositional changes that affect the radiative budget of the planet
(IPCC, 2013). With the decrease in observations from profiling satellite
instruments, ground-based instruments will represent an increasingly
important source of measurements needed to maintain a long-term stratospheric
O3 profile record. Calibrations with respect to ground-based instruments
are also needed in order to combine data from current and future satellites.
Millimetre-wave radiometry is a well-established technique for atmospheric
remote sensing. The technique offers the advantage of measuring radiation
emitted from the atmosphere, allowing year-round measurements at high
latitudes as there is no reliance on the sun as a source. High frequency
resolution in measured spectra, and the relatively low Doppler broadening
compared to pressure broadening in the atmosphere, allows the retrieval of
altitude profiles of atmospheric composition with ground-based instruments.
Millimetre-wave datasets of Arctic O3 are sparse in time and in
location. The Ozone Radiometer for Atmospheric Measurements (OZORAM) (Palm
et al., 2010) has operated in Ny Ålesund, Spitsbergen (79∘ N,
12∘ E), since November 1994 and has been used for studies such as
Palm et al. (2005) and Langer et al. (1999). The Radiometer for Atmospheric
Measurements At Summit (RAMAS) operated in Summit, Greenland (72∘ N, 38∘ W), briefly in 2003 (Golchert et al., 2005). The Kiruna
Microwave Radiometer (KIMRA) has operated in Kiruna, Sweden, since 2002, and
the O3 measurements have been used to investigate the 2002/2003 winter
(Raffalski et al., 2005) and for validation of the satellite instruments:
Global Ozone Monitoring by Occultation of Stars (GOMOS) (Meijer et al.,
2004) and Michelson Interferometer for Passive Remote Sounding (MIPAS)
(Steck et al., 2007) on board the Envisat satellite. The Millimeter wave
Radiometer 2 (MIRA 2) has been housed at Kiruna on previous occasions and
has been used to study the evolution of O3 during the SOLVE/THESEO 2000
campaign (Kopp et al., 2003). Between 2004 and 2010, MIRA 2 was stationed at
Pico Espejo, Mérida, Venezuela (4800 m a.s.l.), and used for validation of
the Sub-Millimetre Radiometer (SMR) aboard the Odin satellite (Kopp et al.,
2007). MIRA 2 has been installed indefinitely in Kiruna since November 2012.
The aim of this work is to develop and deploy operational inversion schemes
that use the atmospheric spectra provided by KIMRA and MIRA 2 to retrieve
O3 concentrations above Kiruna, to assess the quality of these gas
profiles through comparison with other O3 measurements, and to examine
the KIMRA data through an assessment of the wintertime variability of
O3 above Kiruna. The inversion set-up that was developed for this
purpose can also be used for future measurements, as well as for older KIMRA
data that have yet to be analysed.
Section 2 describes the instruments and datasets used in the study. Section 3 outlines
the inversion set-ups used for KIMRA and MIRA 2. Section 4 shows
the comparison of the retrieved O3 profiles from KIMRA and MIRA 2. In
Sect. 5, the KIMRA and MIRA 2 profiles are compared to measurements from
ozonesonde instruments launched from Sodankylä, and from the
satellite-borne instrument, Microwave Limb Sounder (MLS), on Aura. Section 6
examines the KIMRA data by looking at the variability of wintertime O3
concentrations above Kiruna from 2008 to 2013, and Sect. 7 offers some
concluding remarks.
Ground-based instruments and datasets
KIMRA
KIMRA was partly designed by the Institute for Meteorology and Climate
Research (IMK) at the Karlsruhe Institute of Technology (KIT) (Raffalski et
al., 2002) and built at the Swedish Institute of Space Physics (IRF) in
Kiruna, Sweden. The instrument has been operated at IRF since 2002. KIMRA
operates in the frequency range between 195 and 233 GHz. The instrument
has the capability to measure many species by tuning within this frequency
range but, due to baseline issues, has only been used to measure O3
and, since 2007, carbon monoxide (CO).
The detector in KIMRA is a Schottky diode mixer cooled to ∼ 25 K within a cryostat.
It has a single sideband (SSB) noise temperature of
∼ 1800 K. The sideband filter is a Martin–Pupplett
interferometer. A path length modulator (PLM) lies in the beam path to
suppress standing waves between optical elements, which contribute to waves
in the baseline of the spectra, or baseline waves. There are two black bodies
for calibration: at ∼ 125 and ∼ 293 K for the
cold and hot targets, respectively. The cold target is inside the cryostat.
KIMRA has an acousto-optical spectrometer (AOS) with a practical bandwidth
of 1.27 GHz and 1801 channels giving a resolution of ∼ 0.7 MHz, as well as two Fast Fourier Transform Spectrometers (FFTSs). The
narrowband FFTS, installed in 2007, is often centred on a nearby CO line
and has been used in retrieving CO between 40 and 80 km (Hoffmann et al.,
2011), and the broadband FFTS, installed in 2012, has been used to measure
atmospheric spectra in the region of 230 GHz. The data from the AOS are
presented here as its time series extends back to 2002 and the spectrometer
is the same model as the MIRA 2 spectrometer. A periscope-like mirror
system, with the sky mirror located in a dome on the roof of IRF, allows
KIMRA to view in any direction on the sky. The elevation angle for each
measurement is chosen automatically between 7 and 55∘ to give the highest signal-to-noise ratio (SNR) according to the
tropospheric transmissivity. O3 measurement durations range from 15 min
to 360 min, depending on the atmospheric conditions. First technical
descriptions of the instrument are given in Raffalski et al. (2002).
The KIMRA dataset presented here spans the time from 2008 to 2013, with some
gaps in operation. The data used for intercomparison of the retrieved
O3 profiles are from 20 November 2012 to 31 May 2013, and consist of
1152 retrieved profiles. The data used in examining the wintertime O3
variability above Kiruna are from January to March over the years 2008,
2009, 2010, 2011, and 2013. Data from January–May 2012 were not
available. While measurement data from IRF exist, with interruptions, as far
back as 2002, the KIMRA data used here from winter/spring 2012/2013 were
selected to overlap with MIRA 2. The January–March data for the other
years were selected because O3 above Kiruna is expected to have the
most variation over this time due to chemistry and dynamics: this makes it
an interesting dataset to study.
KIMRA looks only directly north or south for all of these measurements. The
elevation angle changes for each measurement, which prevents the averaging
of spectra, so each measured spectrum is inverted and any averaging of the
retrieved profiles can be applied afterwards. As a result, the
signal-to-noise ratio (SNR) changes for each spectrum due to atmospheric
conditions and the measurement duration. The system's local oscillator (LO)
is adjusted frequently (often every other measurement) in KIMRA, which gives
two differing spectral regions to be inverted. In one case, the LO is such
that the CO line at 230.535 GHz is in the spectrum (KIMRA_O3CO measurement). In the other case, the centre frequency is shifted up by
34 MHz. The spectrum for this case is centred on the O3 line and does
not contain the CO line (KIMRA_O3O3 measurement). The reason
for changing the LO was to see if incorporating more of the O3 line in
the spectrum would cause the inversion to improve/differ (however, this
result is not tested here). Slightly different inversion set-ups were needed
to account for these different cases.
MIRA 2
MIRA 2 was developed at the Forschungszentrum Karlsruhe to measure O3,
ClO, HNO3, and N2O between 268 and 280 GHz (Berg et al., 1998). The
detector is a Schottky diode mixer cooled to ∼ 25 K within a
cryostat. The SSB noise temperature is ∼ 800 K. The cold and
hot targets are at ∼ 47 and ∼ 300 K respectively. The cold target is located inside the cryostat. The sideband
filter is a Martin–Pupplett interferometer. The sky mirror is contained
within a removable periscope, which sticks out through a north-facing window.
The MIRA 2 AOS has a bandwidth of 1.4 GHz, and 2048 channels giving a
resolution of approximately 0.7 MHz. The spectra are centred on the O3
line at 273.051 GHz. A PLM lies in the beam path to reduce the effect of
standing waves. A more detailed description of the instrument can be found in
Berg et al. (1998).
The dataset from MIRA 2 presented here spans 1 December 2012 to 25 April
2013, and consists of 979 retrieved profiles. Inversions of measurements
from May were not included because of instrumental problems. MIRA 2
continuously points north for all of the measurements presented here. As
with KIMRA, the elevation angle of each measurement is automatically chosen
to give the best SNR according to the atmospheric conditions.
Inversion inputs and characteristics
Forward and inverse model parameters
The forward model used for these inversions is the second release of the
Atmospheric Radiative Transfer Simulator: ARTS 2 (Eriksson et al., 2011).
The inversion is done using the package Qpack 2 (Eriksson et al., 2005),
which uses the optimal estimation method (OEM) (Rodgers, 2000). Qpack 2 is
designed specifically to work with ARTS, so one can perform forward
modelling and retrieval work. Qpack 2 allows modelling of the instrument
characteristics through sensor response matrices. The a priori volume mixing
ratio (VMR) profiles used for the inversion of the KIMRA and MIRA 2 data are
the Fast Atmospheric Signature Code (FASCOD) subarctic winter scenario
profiles (Anderson et al., 1986). The temperature and pressure information
(zpTs) up to 50 km is from daily National Centers for Environmental
Prediction (NCEP) profiles, and above that is from the US Standard Atmosphere.
The forward model pressure grid is 200 layers that are evenly spaced in
altitude between ground level and approximately 100 km. The retrieval
pressure grid is 45 layers that are evenly spaced in altitude between ground
level and approximately 90 km. The retrieved quantity is the fractional VMR,
the VMR of the target gas as a fraction of the a priori VMR for that gas. A
polynomial of order 3 is included in the inversion and is fitted to each
spectrum to account for some of the baseline waves. The inversions are
non-linear and a Marquardt–Levenberg iterative root-finding method
(Marquardt, 1963) is used with Qpack 2.
Example spectra and fits for a KIMRA_O3CO
measurement and a MIRA 2 measurement. Note: the visible oscillatory pattern
in the baseline of the MIRA 2 spectra has recently been eliminated after
servicing of the AOS spectrometer.
Attenuation of the signal due to water vapour, mainly in the troposphere, is
accounted for with the Millimeter wave Propagation Model MPM93 H2O
continuum (Liebe et al., 1993), which can be included in the forward
modelling with ARTS. The spectroscopic parameters are taken from the HITRAN
2008 catalogue (Rothman et al., 2009). Estimates for the thermal measurement
noise on each spectrum are obtained by fitting a second-order polynomial to a
relatively flat part of the spectrum (covering 400 channels), and
calculating the standard deviation of the residual for the fit. This value
is used to calculate the measurement error covariance matrix (Rodgers,
1990). Error contributions from other instrumental and model parameters have
previously been estimated for KIMRA and MIRA 2 using a modified OEM and so
they have not been repeated here. For KIMRA, Raffalski et al. (2005)
estimated that the uncertainty in the retrieved profiles due to baseline
waves and systematic errors amounts to at least 1 ppmv. For MIRA 2, Kopp (2000) estimated an uncertainty of at least 1 ppmv is caused by errors due
to baseline waves, systematic errors, and thermal noise. These error
estimates are based on results of sensitivity tests and do not provide
information about the structure of errors caused by “actual” baseline
waves in the spectra.
Example fits and properties of retrieved states
Examples of fits to the data for a KIMRA_O3CO measurement and
a MIRA 2 measurement are shown in Fig. 1. There are substantial baseline
wave features in the spectra and the residua, often caused by internal
reflections within an instrument. The clearly visible structure in the
baseline of the MIRA 2 spectra (seen in Fig. 1) has been eliminated during
recent servicing of the AOS. This structure was likely an artefact produced
by a laser diode at the end of its life. There are some relatively
short-scale baseline wave features in both the KIMRA and MIRA 2 data that
would require the inclusion of a large-order polynomial in the baseline of the spectrum.
Polynomial orders up to 9 were tried but no appreciable difference was
found in the results. Since some oscillations were apparent in the retrieved
profiles, it was decided to increase the estimate of the noise on the
spectrum (Sect. 3.1) by a fixed amount, different for each instrument.
This offset was estimated based on the amplitude of the baseline waves, but
the final value was found by adjusting the offset until oscillations were
not clearly visible in the retrieved O3 profiles. These oscillations
are assumed to be due to the inversion fitting some fraction of the baseline
wave features in the spectra, and while not clearly visible in a single
profile, the results of the following sections show that the effect of the
baseline waves is still present in the retrieved profiles.
Left and middle: mean averaging kernels for KIMRA and MIRA 2
O3 retrieved profiles used for the comparison. The measurement response
divided by 2 is also shown as the solid black line. The vertical black line
indicates a measurement response of 0.8. Right: the altitude resolution of
the profiles, given by the full width at half maximum (FWHM) of the
averaging kernels.
The mean averaging kernels, measurement response, and altitude resolution
for the retrieved profiles for each instrument are shown in Fig. 2. The
actual averaging kernels, and quantities derived from them, will vary for
each measurement depending mainly on the SNR of the spectrum. The retrieval
altitude range is chosen using altitudes that have a measurement response
higher than 0.8. The choice of measurement response cut-off is somewhat
arbitrary. A value of 0.8 is used here as it limits the contribution of a priori
information in the retrieved profile and has been used for several similar
ground-based instruments (e.g. Hoffmann et al., 2011; Straub et al., 2010).
This cut-off gives a range of approximately 16–54 km. The mean
measurement response for KIMRA dips just below 0.8 at 35 km due to some
negative values in the corresponding averaging kernel but the inversion is
still defined as useable here. The altitude resolution is, at best, 8 km, and
begins to degrade quickly above 40 km altitude. The values found here for
MIRA 2 are very similar to previously shown values (Kopp, 2003). The degrees
of freedom for signal (DOFS) over the retrievable altitude range for the
retrieved states are approximately 4 for each instrument.
Comparison of KIMRA and MIRA 2
Coincidence criteria
Since MIRA 2 always points north, only north-facing KIMRA measurements were
used for this comparison. The measurement time and duration were used as
follows to determine which profiles to compare to each other. For a given
KIMRA measurement, it was determined whether there are any MIRA 2
measurements whose midpoint in measurement time lies within the duration of
KIMRA's measurement. If so, it was determined which measurement has a longer
duration (say it was MIRA 2). Then it was checked whether there were any
more KIMRA measurements that also had a midpoint that lays within the
duration of the MIRA 2 measurement. If so, the KIMRA profiles from all of
these measurements were averaged to produce a single profile that was
considered coincident with the corresponding MIRA 2 profile. If not, the two
single profiles were considered coincident. This method compares profiles
from measurements that overlap in time, and avoids using any measurement
twice. There were 177 coincident sets that were identified for the following comparison. For the
majority of the time, differences between coincident measurements are less
than 1 h. Measurement durations for each of the instruments range from 15 min to 4 h, with a mean time of approximately 1 h for the
coincident measurements.
Results of KIMRA and MIRA 2 comparison
Figure 3 shows the comparison of coincident KIMRA and MIRA 2 O3
profiles for December 2012 to April 2013. Both average VMR profiles in
Fig. 3 (left) have lower values than the a priori VMR except for below
about 18 km. The mean difference (KIMRA–MIRA 2) in the profiles shows an
oscillatory structure. KIMRA is generally low-biased with respect to MIRA 2
except for a peak at 30 km and the largest negative value is a peak of
-1.1 ppmv at 22.5 km. The standard deviation of the differences is largest
between 26 and 34 km. The standard error of the mean difference is also
shown but is small due to the sample size. There is a strong correlation of
the VMR values (∼ 0.95) above 35 km. This decreases to about
0.85 at 30 km and then drops rapidly to a minimum below 0.50 at 26 km.
Within 17–24 km the correlation is above 0.70 before decreasing to
∼ 0.50 at the lower retrieval limit.
Left: the average of the 177 coincident O3 profiles for KIMRA
and MIRA 2 and the a priori profile used for the inversions. Middle: the mean of the
difference (KIMRA–MIRA 2) for coincident profiles. The solid error bars
are the standard error of the mean, and the dotted error bars are the
standard deviation of the differences in the profiles at each altitude.
Right: the correlation of the coincident pairs at each altitude.
Since there are approximately 4 DOFS for each measurement, the O3
profiles were split into four altitude regions, and the total O3
concentration (in molecules cm-2) was calculated for each region,
corresponding to four O3 partial columns. The column densities were
calculated using the temperature profiles from the zpTs and the ideal gas
law. The altitude ranges of the four partial columns are 16–26, 26–36, 36–46, and 46–56 km (the numbers are the centres of the
retrieval grid layers), and each region corresponds to approximately 1
DOFS. The correlations of the O3 partial column concentrations were
calculated, and a line of best fit was determined for each column. The fit
was determined using a linear regression for data with errors in both the X
and Y variables, following York et al. (2004). The regression coefficients
(slope and intercept) are calculated for two cases of KIMRA/MIRA 2 partial
column error estimates. The first case includes only the measurement error
on the profile: the error due to the statistical noise on the spectrum
(Rodgers, 1990), to which an offset has been added to account for short
scale waves in the spectral baseline (see Sect. 3.2). The second case is
the sum of 130 % measurement error and the mean of the measurement errors
on each partial column; the former increase is to try to account for other
errors that vary statistically (such as errors in the temperature profile),
and the latter is to include an error that does not change in magnitude over
time or depend on an individual observation. The idea here is that this will
help to capture some of the variation in the measurements that is neither
truly random nor systematic in nature, such as a baseline error. While not
based on it, the larger error estimate appears justified when one considers
the bias shown in Fig. 3. The limits of these slopes and their standard
errors define a range that should contain the value of 1 if the measurements
agree. A similar approach was used by Nedoluha et al. (1997), in which case
the standard deviation of the satellite measurements being compared was
added to the errors of the ground-based measurements.
Left: same as left plot in Fig. 3 (in ppmv) but with markers
showing the altitude separation of the partial columns. Right: scatter plot
of the partial columns of coincident KIMRA and MIRA 2 data. The error bars
show the two cases of error estimation for the ground-based instruments (see
Sect. 4.2). Lines of best fit are shown for each case, with the dashed
line indicating the case of the higher error estimate. The slope (m) and its
standard error, and the intercept (c) for each line are shown. The
correlation for each of the sets of partial columns is also shown.
The results for KIMRA and MIRA 2 are plotted in Fig. 4, showing the
correlations and the slopes and intercepts of the lines of best fit in each
case. The correlations between the partial columns are high, even for layers
containing the altitudes with poor profile correlation (Fig. 3; right),
with 36–46 km having the highest value of 0.97. A value of 1 lies within
the range of the slopes calculated for the two lowermost columns, albeit
just barely for the 16–26 km column. The 26–36 km columns agree for
both cases of error estimation. A value of 1 does not lie in the range of
slopes for the two higher partial columns, with MIRA 2 showing a larger
range of O3 values in both cases. A value of 1 for the slope does lie
in twice the standard error range of the higher error estimates, but for
the 46–56 km partial column this error is likely an overestimation as the
error bars are larger than the variation of the points from the line of best
fit.
With just the data from these two instruments, it is difficult to diagnose
the reason for the bias in the profiles; however, the oscillatory structure
in the mean difference of KIMRA and MIRA 2 profiles (Fig. 3; middle) is
present in all of the individual difference profiles. This suggests a
systematic error. It is reasonable to assume that this error arises from
the clear non-spectral line structures present in the spectra (see Fig. 1), i.e. baseline waves. These structures are often due to standing waves
in the instruments. Baseline waves that have a scale similar to that of the
spectral lineshape are the most difficult to separate from the sky signal,
especially if a baseline wave is symmetric about the line centre. For this
reason, it is assumed that the baseline waves in the KIMRA measurements will
have more of an impact on the retrieved O3 state than the those in the
MIRA 2 spectra, which have a shorter scale in frequency space. Part of the
variation in the differences between individual profiles would also be
explained with baseline waves as the cause, for the following reason: the
retrieved ozone state is affected by the opacity of the atmosphere. If some
of the baseline waves in the spectrum are incorrectly attributed to a
concentration of ozone at some altitude in the atmosphere, then that
contribution to the retrieved state will also vary depending on the
atmospheric opacity. A higher opacity will mean that the inversion
attributes a greater atmospheric concentration to the baseline wave in the
spectrum.
Comparison with ozonesondes and MLS
KIMRA and MIRA 2 O3 profiles were compared to profiles from ozonesondes
launched at Sodankylä, Finland (67.37∘ N, 26.63∘ E).
The ozonesondes are launched by the Finnish Meteorological Institute at
Sodankylä. The location of the launches is a good site for a comparison
as it has a similar latitude to IRF (67.84∘ N), and the meridional
gradient of O3 tends to be greater than the zonal gradient. Thirty-one
ozonesonde measurements were provided for this study. The data are from
between 31 October 2012 and 29 May 2013, and the sondes were launched
approximately once per week. The instruments are electrochemical
concentration cell (ECC) sondes, using a potassium iodide solution. The
partial pressure of O3 is calculated according to an electrical current
produced by the reaction between O3 and iodide (Kivi et al., 2007; Smit
et al., 2011). Overall uncertainty of the ozone measurements by ECC sondes
in the stratosphere is about 5 % (Deshler et al., 2008; Hassler et al.,
2014). In Sodankylä the sonde preparation procedures have followed the
generally accepted recommendations (Smit et al., 2011). The sounding system
is DigiCORA III from Vaisala. The radiosondes are RS92-SGP (Dirksen et al.,
2014). The radiosondes measure pressure, temperature, humidity, and wind
profiles during the balloon ascent and descent. Ozone data are transmitted
using the Vaisala Digital Interface OIF92. The sondes used in this work have
maximum measurement altitudes ranging from 18 to 34 km, with an effective
altitude resolution of the order of 100–150 m. The vertical resolution
depends on the balloon ascent rate and the sensor response time. The ascent
rate is typically 5 m s-1 and the response time of the ozone sensor is 20–30 s.
The Microwave Limb Sounder (MLS) is one of four instruments aboard the Aura
satellite. The satellite is part of the National Aeronautics and Space
Administration's (NASA) Earth Observing System. The MLS scans are
synchronised to the orbit and measurements are at approximately the same
time at the same latitude each day, spaced in distance by roughly 165 km on
the suborbital track. The O3 measurements used in this work are made
using the spectral lines in the 240 GHz band. More details on the instrument
and observation technique are found in Waters et al. (2006). The v3.3/v3.4
version of the level 2 data was used in this comparison (Livesey et al.,
2013). Gas concentrations are retrieved on a 55-layer pressure grid. The
ozone profiles have a vertical resolution of ∼ 3 km from 261–0.2 hPa,
and 4–5.5 km from 0.1 to 0.02 hPa. These levels cover the
“useful range” of the data quoted by the MLS team (Livesey et al., 2013).
The precision of the measurements is ∼ 0.04 ppmv from 215–46 hPa, 0.1–0.5 ppmv from 22–0.1 hPa, and 1.4 ppmv from 0.05–0.02 hPa. The validation of the previous v2.2 data has been documented in Jiang
et al. (2007), Froidevaux et al. (2008), and Livesey et al. (2008).
Coincidence criteria
Time, distance, and position relative to the polar vortex were the criteria
used in determining which individual profiles to compare, and are described
here for each instrument. The location of a measurement with respect to the
polar vortex was determined using scaled potential vorticity (sPV) values
(Manney et al., 2007). For the ground-based instruments the values were
calculated geometrically along the instrument's line of sight. An sPV value
of 1.4 × 10-4 s-1, or nearby values, have been used extensively in
previous works (e.g. Manney et al., 1994a, 2007, 2011; Jin et al., 2006) to
define the vortex edge centre. Values of 1.6 and 1.2 × 10-4 s-1
have been used in the cited works to define the inner and outer edges,
respectively, and the same values are used here. Both the north- and south-pointing measurements from KIMRA are used in the following comparisons.
The general procedure for ozonesondes vs. KIMRA/MIRA 2 and MLS vs.
KIMRA/MIRA 2 comparisons was as follows. For a given ozonesonde/MLS
measurement, a maximum limit on the difference in time between measurements
was used to choose a group of possible KIMRA/MIRA 2 measurements with which
to compare. This group was reduced to those measurements that were in the
same location as the ozonesonde/MLS measurement, relative to the polar
vortex (inside vortex/outside vortex/in vortex edge). From this new group,
the closest measurement in space, using distance along a great circle, was
chosen as the measurement for comparison. Each KIMRA/MIRA 2 measurement was
only used once in a comparison with another instrument.
For MLS, a maximum time difference of ± 4 h between measurements
was used. A small time limit was preferred, and it was decided that less
than 4 h produced too few coincidences, while more than 4 h did not
make a significant difference to the number of coincidences. Either way, the
choice of time criterion did not have a substantial effect on the presented
comparison results (there was a slight increase in standard deviation of the
differences for a looser time coincidence). For the distance criterion, the
closest measurement in space had to lie within a given latitude and
longitude box: ±2∘ latitude and ±10∘ longitude. A smaller longitude box of ±5∘ was also tried
but it halved the number of coincidences and made no significant difference
to the results. The altitude at which the distance between measurements was
calculated is 34 km. The reason for the choice is that this altitude
coincides approximately with the peak in the O3 VMR profile above
Kiruna, and it is also approximately the middle of the retrievable altitude
range for the ground-based instruments. The altitude used for the sPV
criterion for the MLS comparisons was 34 km, the same as that used for the
distance criterion, explained above.
For the ozonesondes, no criterion was placed on the measurements with
respect to their distance from KIMRA/MIRA 2 measurements. It is assumed that
the location of the sonde O3 profiles is above the launch station in
Sodankylä. For the time criterion, the closest KIMRA/MIRA 2 measurement
within 24 h was used to compare to the ozonesonde profile, although the
selected profile was almost always within a few hours of a ground-based
measurement. As the sonde profiles have differing maximum altitudes, the
resulting comparisons have different numbers of coincident points for
different altitudes. For the ozonesonde comparison, the criterion for sPV
was applied at an altitude of 18 km for the following reasons: 18 km is the
lowest maximum altitude of the used sondes, and it is the only altitude that
is common to every sonde.
Left: scatter plot of the KIMRA O3 partial columns against
Sodankylä ozonesonde partial columns from November 2012 through to May
2013. The line of best fit is shown with the accompanying correlation
coefficient. The slope (m) and its standard error, and the intercept (c) for
each line are shown. Error bars and lines of fit are shown for two cases of
error estimation for KIMRA, as in Fig. 4. Right: the same as left plot but
for MIRA 2 and Sodankylä ozonesonde measurements from December 2012
through to April 2013.
Both the ozonesonde and MLS profiles were smoothed (Rodgers and Connor,
2003) using the averaging kernels of the coincident KIMRA/MIRA 2
measurements before they were compared so that the profiles would have
similar resolution. The effect of excluding O3 measurements in the edge
of the vortex was examined for each comparison and discussed in the
following sections.
Results of comparison with ozonesondes
Since the ozonesondes cover varying altitude ranges, it was decided that it was most
appropriate to compare partial columns (calculated from 15 km to the maximum
sonde altitude) with the ground-based data. The densities for KIMRA and MIRA 2 data were calculated as in Sect. 4.2 but the column heights were chosen
to match the maximum heights of the coincident ozonesondes. The densities
for the ozonesonde data were calculated using the smoothed O3 profiles
and the temperature measurements made by the sondes during their flight.
Because the ground-based measurements have some sensitivity to O3 at
altitudes higher than the reach of the sondes, the sonde profiles were
extended above their maximum altitudes prior to performing the smoothing
calculation. This was done using the a priori concentrations (Sect. 3.1)
scaled to match the sonde data at its highest altitude. Figure 5 shows the
partial column densities from the ozonesondes plotted against the respective
coincident measurements for KIMRA and MIRA 2. A linear regression was
performed assuming that the ozonesonde data are true, and using the two cases
of error estimation for KIMRA and MIRA 2 that are defined in Sect. 4.2.
The line of best fit to the scatter plot of the KIMRA and sonde data gives a
slope of 0.46, and a correlation coefficient of 0.82. For MIRA 2, the slope
of the line of best fit is 0.77 and there is a high correlation coefficient
of 0.98. While there is good correlation, the results suggest that both
KIMRA and MIRA 2 measurements of lower stratospheric O3 are likely
affected by baseline errors that are not fully characterised by the error
estimates here, KIMRA more so than MIRA 2.
The sPV value corresponding to the measurement locations of KIMRA
(blue X), MIRA 2 (green X), and Sodankylä ozonesondes (black dot), at
altitudes of 18, 24, and 30 km. Lines of sPV values of 1.2 and
1.6 × 10-4 s-1, respectively defining the outer and inner edge of the polar vortex, are
also shown.
Upper row: the same as for Fig. 3, but for KIMRA–MLS. Both
the average of the smoothed (used for comparison) and unsmoothed MLS
profiles are shown here. Lower left: histogram of the time difference
between coincident measurements. Lower right: map showing the locations of
the coincident MLS measurements (magenta circles), Kiruna (yellow triangle),
and Sodankylä (cyan triangle).
Figure 6 shows the sPV value for measurements made by each instrument at
altitudes of 18, 24, and 30 km. The similarity of the sPV for the
ground-based instruments and the ozonesondes confirms Sodankylä as a
reasonable comparison site for Kiruna. Most likely due to the location of
both sites, the variability of sPV between altitudes shows that the
measurements sometimes simultaneously detect O3 concentrations
from inside, outside, and within the edge of the polar vortex, depending on
the respective altitudes. This makes the task more difficult when comparing
measurements. Since the largest concentration gradients lie within the
vortex edge, the results were examined to see whether it made a difference
if all O3 profiles that lie in this region (1.2 × 10-4 s-1 < sPV < 1.6 × 10-4 s-1) were excluded. The number of
coincidences decreased from 25 to 17 for KIMRA. The partial column
correlation remained at 0.82, and the slope of the line of best fit changed
from 0.46 ± 0.02/0.04 to 0.47 ± 0.02/0.04 (standard error for
smaller/larger error bars). The change for MIRA 2 was more dramatic: the
number of coincidences decreased from 14 to 7. The partial column
correlation increased from 0.98 to 0.99, and the slopes of the line of best
fit changed from 0.77 ± 0.03/0.07, to 0.88 ± 0.04/0.10. Although
there are only seven coincidences, these numbers indicate a good agreement in
the MIRA 2 and ozonesonde partial columns.
The same as Fig. 7 but for MIRA 2–MLS.
Results of comparison with MLS
Results of the profile comparison with MLS are shown in Fig. 7 for KIMRA
and in Fig. 8 for MIRA 2 for the period of December 2012 through to April
2013. There are 507 coincident measurements for KIMRA and 394 coincident
measurements with MIRA 2. Above 35 km, KIMRA has a good agreement with MLS
with a consistent low bias of ∼ 0.3 ppmv. Below that, the
oscillatory bias seen in the comparison with MIRA 2 (Fig. 3; middle) is
present, with a maximum/minimum of approximately ±1 ppmv. The steep drop in
the correlation for KIMRA is also seen again (see Fig. 3; right) at
approximately 26 km. In Fig. 8 MIRA 2 shows better agreement with MLS.
There is a general high bias below 47 km, with an oscillatory structure
peaking at approximately 0.6 ppmv at the lowest retrievable altitude for
MIRA 2. The correlation is above 0.90 for altitudes above 35 km and
decreases to about 0.70 at the lowest retrieval altitude range of MIRA 2.
Standard error of the mean difference is again very small because of the
large sample size.
Upper: the O3 partial column densities from KIMRA
(blue filled squares) and coincident MLS (hollow magenta diamonds) measurements plotted
against time. Lower: scatter plots of the corresponding coincident data.
Error bars and lines of fit are shown for two cases of error estimation for
KIMRA, as in Fig. 4. The slope (m) and its standard error, and the
intercept (c) for each line and correlation coefficients are also shown.
Same as Fig. 9 but for MIRA 2 (green filled squares) and MLS
(hollow magenta diamonds).
In the time series comparisons in Figs. 9 and 10, the profiles are
split into four partial columns corresponding to the same altitude ranges as
in Fig. 4. The partial columns for MLS were calculated using the MLS
temperature measurements that are coincident with the MLS O3
measurements. There is a clear increase in the column densities at the
beginning of January that is shown by both KIMRA and MLS columns, and there
is unfortunately a data gap here for MIRA 2. The ground-based time series
appear to follow MLS well in monthly and sub-daily variation, with fewer
outlying values for MIRA 2 than for KIMRA. There are some likely unphysical
outliers in the KIMRA data that can be seen in both plots in Fig. 9, but
there has been no reason yet to eliminate them.
sPV values at 34 km at the location and time of all KIMRA
measurements (blue X), and coincident MLS measurements (magenta square). The
same is shown for all MIRA 2 measurements (green X) and coincident MLS
measurements (magenta triangle). The black lines indicate the edges of the
polar vortex (see Fig. 6).
Time series of the daily average O3 profiles (in ppmv) for
KIMRA, MIRA 2, and unsmoothed MLS measurements.
Retrieved daily averaged O3 concentrations (in ppmv) above
Kiruna from KIMRA, for January to March for the years 2008, 2009, 2010,
2011, and 2013.
Lines of best fit were calculated, accounting for errors in X and Y. The
correlations between KIMRA and MLS (Fig. 9; lower) vary between 0.66 and
0.80, and slopes of best fit for the partial columns vary between 0.81 and
0.96, for the case of the lower error estimate. Only for the lowermost
column does a value of 1 lie in twice the standard error range of the
calculated slopes, but it should be noted that the slopes for the lower error
estimate all lie within 19 % of 1. The correlations between MIRA 2 and MLS
(Fig. 10; lower) are high, between 0.88 and 0.94, and a value of 1 lies
in the range of calculated slopes for the two lowermost columns. It can be
seen from the two 46–56 km panels in Fig. 10 that MIRA 2 is low-biased
in the case of high O3 columns at these altitudes.
In most instances for the comparisons with MLS, the higher error estimate
has a small change (< 0.03) on the value of the calculated slopes,
but a large change is seen for the two highest columns in the KIMRA and MLS
comparison in Fig. 9 (lower). This is likely due to the smaller natural
variation in O3 at these altitudes and the presence of outliers in the
KIMRA data. MIRA 2 in general shows better agreement with MLS, compared to
KIMRA.
Figure 11 shows the sPV values at 34 km for all KIMRA and MIRA 2
measurements, and all coincident MLS data. One can see that no MIRA 2
measurements lie within the edge of the polar vortex during the time range.
When excluding the vortex edge data from the KIMRA comparison, the absolute
change in the calculated slopes is < 0.04 and the change in
correlations is < 0.04, except for the lowermost column, which
showed an increase in correlation from 0.66 to 0.73.
The O3 profiles from all the KIMRA, MIRA 2, and unsmoothed MLS
measurements from December 2012 through April 2013 were averaged by day. The
daily averages are plotted against time in Fig. 12. Each dataset shows a
similar evolution of O3 over the winter: the generally low
stratospheric O3 concentration in December, a rapid increase in early
January, and the descent of high O3 concentration air, by about 10 km
lower in altitude, through March and April. The differences in the profiles,
found in Sect. 4.2, can also be seen: the low bias in KIMRA O3 at
∼ 22 km is apparent, and also the high bias at ∼ 28 km, most easily noticeable in December and January. The higher MIRA 2
profile values around 40 km are attributed to a high bias of ∼ 0.5 ppmv in the MIRA 2 data at this altitude; this conclusion is reached by
comparing the differences in the KIMRA and MIRA 2 data (Fig. 3) as well as
their respective differences with MLS (Figs. 7 and 8).
It is important for the following section to see that the apparent “dip”
in the O3 profile, that one can see between approximately 35 km
(January) and 28 km (March), is present in each dataset and is not a result
of the oscillatory structure in the KIMRA dataset. The high bias in KIMRA
O3 at ∼ 28 km may make this dip more pronounced, but the
low bias in KIMRA O3 occurs at a lower altitude: ∼ 22 km.
As the location in altitude of this local minimum can change throughout the
winter, due to descent of air for instance, the oscillatory bias in the
KIMRA profile can either enhance or obscure its presence. Thus, it will be
challenging to accurately identify a local minimum feature with KIMRA data
alone. It is assumed from this point on that the KIMRA O3 data have a
low bias of 1 ppmv at ∼ 22 km and a high bias of 1 ppmv at
∼ 28 km, each maximum with a half width of ∼ 5 km.
The KIMRA dataset: daily variability in wintertime ozone above
Kiruna
This section moves on from the comparisons between datasets into an
examination of the KIMRA dataset over 5 years between 2008 and 2013, by
looking at the daily variability of wintertime O3 above Kiruna. Because
one cannot definitively explain the variations in O3 profiles at one
location without using other data and/or model output, and that is not the
aim of this work, only some general observations are made.
January to March, 5-year O3 time series
O3 profiles have been retrieved from KIMRA measurements for January,
February, and March, from 2008 to 2013. Data were not available from 2012.
Daily averages were made and the resulting time series for each year are
shown in Fig. 13. January predominantly shows the lowest O3
concentrations, except for in one region at around 30 km (particularly for
2009 and 2013), which has a sharp maximum. The sharp rise in O3
concentrations in January 2009 and 2013 coincides with strong “major”
sudden stratospheric warmings (SSWs) that started on 24 and 6 January in
those years, respectively, and broke down the vortex for about 1 month
(e.g. Manney et al., 2015). There was a similar SSW in 2010 that began on
26 January, and the KIMRA data show an increase in mid-stratospheric O3
concentrations a few days later. The increase in concentrations in late
February 2008 also coincides with a brief SSW that occurred at that time.
The most interesting feature is the aforementioned dip in the profile. The
O3 dip is present for some period of time each year, and disappears
in late February or March. It is persistent up to the end of March in 2009.
It is very unlikely that this feature is caused by chemical ozone depletion
as ozone loss resulting from heterogeneous reactions in the lower
stratosphere has never been seen extending to this altitude in the Arctic
(e.g. Manney et al., 2003, 2015; Kuttippurath et al., 2010; Livesey et al.,
2015). A strong O3 dip (most similar to 2010 presented here) has been
observed previously with KIMRA, in the winter of 2002/2003 (Raffalski et al.,
2005). This coincided with ozone mini-holes between 4 and 11 December 2002,
as reported by the European Ozone Research Coordinating Unit and discussed in Raffaslski et al. (2005), but
the KIMRA measurements presented for that winter still show the structure of
an O3 dip throughout most of December. The latitudinal extent of the
polar vortex has been shown to vary with altitude (e.g. Schoeberl et al.,
1992; Manney et al., 1995; Harvey et al., 2002), which could explain an
occurrence of a local minimum/maximum, but such a feature would not remain
stable long enough to account for the observations shown here. A possible
explanation for the observed shape is the combination of downward motion of
air within polar vortex, and transport of extra-vortex air into the middle
to upper stratosphere, due to wave activity, for instance (e.g. Manney et
al., 1994b, 1995, and 2015; Kuttippurath et al., 2012). The downward motion
of vortex air would increase O3 concentrations in the lower
stratosphere, and the transport of extra-vortex air would increase O3
concentrations in the middle to upper stratosphere. This could give a local
minimum in between, but the timing and extent of these processes can vary
strongly interannually (see references in this section), and so further study
is needed.
Left: monthly averages of daily Kiruna O3 profiles (in ppmv)
from KIMRA measurements for January, February, and March by year. A missing
year means that there were not enough measurements during the month to
produce a representative average. Right: mean and standard deviation of the
daily profiles for each month.
The monthly average of daily mean O3 profiles for each year is shown in
Fig. 14, as well as the overall multi-year mean and standard deviation.
For each month, O3 shows the most variability in the middle
stratosphere, between approximately 20 and 33 km. Some of the variation may
be due to the fact that KIMRA switches its observation direction between
north and south, but the daily averaging of the profiles should remove much
of it. Additionally, as discussed in Sect. 4.2, the baseline waves in the
O3 spectra will cause systematic biases that can vary as a function of
atmospheric opacity, and add to the natural variance in the profile. The
peak in O3 at about 35–40 km (can be seen in the a priori profile) is, on
average, lowest in January and increases through March. The lower altitude
peak at about 25 km decreases in March compared to January and February.
This general change in the altitude of the global maximum of O3 can be
expected if the polar vortex weakens and breaks up toward the end of winter,
but individual years show strong variation in vortex break-up and
reformation times related to SSWs and other variations in wave activity.
In the left panel of Fig. 14, for January and February, every year shows
an O3 dip to some degree (most pronounced in 2010 and least in 2008),
and at varying altitudes. These features tend to decrease in magnitude in
March, except for 2009, which maintains an O3 dip greater than 1 ppmv.
While it is emphasised here that this feature is not purely a result of the
described biases in KIMRA O3, it will be difficult to quantitatively
separate the two features.
Summary and conclusions
The aim of this work is to develop and deploy an inversion scheme for the
KIMRA and MIRA 2 instruments at IRF, Kiruna, and to characterize the
retrieved O3 profiles, through comparison with each other and with
O3 profiles from ozonesondes launched from Sodankylä and from Aura
MLS. KIMRA and MIRA 2 O3 profiles from November 2012 to May 2013 were
used in the comparison. The retrieval altitude range for the O3
profiles is approximately 16–54 km, with a resolution of, at best, 8 km,
derived from the full width at half maximum (FWHM) of the averaging kernels.
KIMRA and MIRA 2 profiles show generally good agreement with each other. The
mean of the difference in their profiles (bias) lies within
∼ ± 1 ppmv.
An oscillatory bias was identified in the KIMRA data: there is a low bias of
∼ 1 ppmv at 22 km, and a high bias of ∼ 1 ppmv at
28 km, both with a half width of ∼ 5 km. MIRA 2 shows an
oscillatory bias, but with smaller amplitude (< 0.5 ppmv) and finer
altitude structure, which covers the whole retrieval altitude range. Both
KIMRA and MIRA 2 otherwise show generally good agreement with MLS O3
profiles, and KIMRA shows a general low bias with respect to the
ozonesondes. MIRA 2 shows overall better agreement with MLS and the
ozonesondes, compared to KIMRA, but both ground-based datasets show some
differences that are not explained by random errors. The relatively stable
oscillatory structures present in the KIMRA profiles are assumed to be
caused by a mixture of baseline waves in the spectra, but their origin is
not known. A recent servicing of the MIRA 2 AOS has eliminated the visible
oscillation in the MIRA 2 spectra.
KIMRA O3 profiles from January to March, 2008, 2009, 2010, 2011, and
2013 were used to explore the local day-to-day variability of O3 above
Kiruna. The middle stratosphere between approximately 20 and 33 km shows the
most variability. The lowest O3 concentrations are found in January,
and tend to increase through March. The location of the maximum in the
O3 profile shifts from ∼ 30 km in January to
∼ 39 km (the location of the maximum in the a priori profile)
in March. The most interesting feature in the data is a local minimum in the
O3 profile, present to some extent in all years and can be persistent
for timescales larger than 2 months. The feature may be difficult to
quantitatively assess with KIMRA because it tends to partially overlap in
altitude with the oscillatory bias in the KIMRA data. Previous measurements
with KIMRA during winter 2002/2003 showed a similar local minimum in the
O3 profile throughout most of December 2002.