Introduction
The principle source of carbon monoxide (CO) in the middle
atmosphere is the photolysis of carbon dioxide (CO2) in the
thermosphere and its subsequent vertical transport, and its only sink is
through reaction with the hydroxyl radical (⚫OH) (Solomon et
al., 1985). The loss rates in the thermosphere are low and this leads to a
strong vertical gradient in CO concentrations (volume mixing ratio (VMR) is
the form of gas concentration used throughout this work, and the two terms
are considered to be synonymous here). As the production and loss mechanisms
for atmospheric CO require the presence of sunlight, the lifetime of CO
during polar winter is on the order of months (Solomon et al., 1985; Allen et
al., 1999), making it an excellent tracer for atmospheric dynamics. In spring
the lifetime in the upper stratosphere can be 15–20 days poleward of
60∘ latitude (Minschwaner et al., 2010). Due to the longer CO
lifetimes within the polar vortex during winter, there exists also a strong
horizontal concentration gradient across the vortex boundary.
While satellite measurements of CO profiles have been used regularly to study
atmospheric transport processes, particularly during northern winters (e.g.
Damiani et al., 2014; Lee et al., 2011; Manney et al., 2009; McLandress at
al., 2013), ground-based CO profile data sets for the poles are few and far
between. The ground-based millimetre-wave spectrometer (GBMS) installed in
Thule Air Base, Greenland (76.5∘ N, 68.7∘ W), was used to
study the composition of the Arctic
winter atmosphere in 2001/02 (Muscari et al., 2007) and the sudden
stratospheric warming (SSW) of 2009 (Di Biagio, et al., 2010). The Onsala
Space Observatory instrument (57∘ N, 12∘ E) measured CO in
2002–2008 (Forkman et al., 2012) and from 2014. The British Antarctic Survey
(BAS) radiometer data set for Troll Station (72∘ S, 2.5∘ E)
covers February 2008 to January 2010 (Straub et al., 2013). Each of these
three ground-based instruments are microwave radiometers that measure
emissions from molecules undergoing rotational transitions in the atmosphere,
offering the advantage of providing measurements during polar night, compared
to instruments that rely on the sun.
This paper presents a CO profile data set from 2008 to 2015 from measurements
made by the Kiruna Microwave Radiometer (KIMRA) at the Swedish Institute for
Space Physics, Kiruna (67.84∘ N, 20.41∘ E). KIMRA
measurements during the winters 2008/09 and 2009/10 have previously been used
(Hoffmann et al., 2011) to retrieve CO profiles that have been compared to
satellite data from the Microwave Limb Sounder (MLS) aboard Aura, the
Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS)
aboard SCISAT-1, and the Michelson Interferometer for Passive Atmospheric
Sounding (MIPAS) aboard Envisat.
The comparisons showed good agreement below 60 km but at higher
altitudes the profiles significantly diverged leading to a positive bias in
KIMRA CO of > 5 ppmv at 80 km. The shape of the bias in
the profile was consistent between satellite data sets, and its origin is
unclear.
This paper presents a CO data set from KIMRA, using an extended set of
measurements, beginning in 2008 and retrieved using a new inversion setup.
The structure of the paper is as follows: Sect. 2 provides details on the
KIMRA observation system, i.e. the measurements and the inversion technique.
Section 2 also offers an estimation of the errors in CO profiles and a word
on the interpretation of the data. To establish validity of the observation
system, Sect. 3 presents a comparison of the KIMRA data with MLS data, using
temperature information from the Special Sensor Microwave Imager/Sounder
(SSMIS) (Kunkee et al., 2008; Swadely et al., 2008) aboard the US Air Force's
Defense Meteorological Satellite Program F-18 satellite as input for the
KIMRA CO inversion. Section 4 investigates changes in the retrieved KIMRA CO
data when using temperature information from the European Centre for
Medium-Range Weather Forecasting (ECMWF) and presents this extended data set,
currently spanning 2008–2015. Section 5 concerns the availability and use of
the data, and Sect. 6 offers some concluding remarks.
Satellite data are exchanged for ECMWF analysis in the KIMRA inversion in
order to have a consistent temperature input for the entire KIMRA data set.
The time span of continuing KIMRA measurements will generally surpass the
lifetime of any satellite instrument. Many satellite instruments have well
exceeded their mission lifetime, the three instruments mentioned in this
section being good examples: MLS (2004–present), ACE-FTS (2003–present),
and MIPAS (2002–2012). Ground-based instruments, however, have the potential
to produce much longer data sets, albeit at one location, due to the much
smaller cost of the ground-based projects and the ability to maintain the
instruments. The Ozone Radiometer for Atmospheric
Measurements, OZORAM, has been
measuring ozone in the Arctic (79.9∘ N, 11.9∘ E) since 1994
(Palm et al., 2010), and Nedoluha et al. (2016) recently showed 20 years of
chlorine monoxide measurements in the Antarctic (77.85∘ S,
166.77∘ E) with the Chlorine Oxide Experiment (ChlOE1). The Fourier
transform infrared spectrometer at Kitt Peak (31.9∘ N,
111.6∘ W) (Brault, 1978) produces data sets beginning in the 1970s.
These are just two examples of a large number of ground-based instruments.
Long-term (decades) ground-based data sets are important as they reveal
changes in the atmosphere on the timescale of changes in Earth's climate, and
the data can be used to validate satellite instruments, fill gaps in time
between satellite missions, and help combine satellite data sets that do not
overlap in time.
More general details of KIMRA at a glance. Also see Sect. 2.1.
System noise temperature
∼ 1800 K (single sideband)
Detector
Schottky diode at ∼ 25 K
Sideband filter
Martin–Pupplett interferometer
Standing wave suppression
Path length modulator
Hot–cold calibration
Blackbodies at ∼ 195 K/∼ 293 K
Spectrometer
FFTS, bandwidth/resolution:
110 MHz/107 kHz
Instrument and data set
KIMRA
KIMRA is housed at the Swedish Institute for Space Physics (IRF), Kiruna, and
was partly designed by the Institute for Meteorology and Climate Research
(IMK) at the Karlsruhe Institute of Technology (KIT) (Raffalski et
al., 2002). KIMRA utilises the frequency range of 195–233 GHz and
has been measuring, among others, atmospheric spectra that correspond to the
J=2→1 rotational transition (230.54 GHz) of CO. KIMRA has
operated in Kiruna since 2002 and has been making measurements of CO
emissions since 2008. The more general aspects of the instrument are given in
Table 1, and more specific details can be found in Raffalski et al. (2002)
and Hoffmann et al. (2011). The spectrometer used for CO measurements is a
fast Fourier transform spectrometer (FFTS) made by Omnisys Instruments, with
1024 channels and used with a bandwidth of 110 MHz to give a
resolution of ∼ 107 kHz per channel. For a given measurement
cycle, which includes multiple hot–cold calibrations and produces one
time-averaged atmospheric spectrum, KIMRA points to the atmosphere at an
elevation angle, between 5 and 90∘, that is chosen to provide the
best signal-to-noise ratio (SNR) at that time. This angle is governed by the
atmospheric conditions and so can change from one measurement cycle to
another. Because they are produced using different elevation angles, the
individual spectra are not averaged to reduce the SNR. Spectral averaging has
been used for similar measurements from other instruments that vary the
elevation angle, e.g. over timescales of 1 week in Nedoluha et al. (2013) and
1 h in Ohyama et al. (2016). In the case of KIMRA, each spectrum is used to
retrieve a CO profile, which may then be used in an average. The azimuth angle of a KIMRA measurement can change
from one measurement cycle to another. The azimuth and elevation angle are kept constant during each
measurement cycle. Figure 1a shows the distribution of the duration times of
each measurement.
Inversion setup
CO profiles are retrieved from the spectra using an optimal estimation method
(OEM) inversion (Rodgers, 2000). This is a Bayesian statistical approach that
constrains the retrieved CO profile according to some a priori atmospheric
information. The inversion was carried out using the Qpack 2 (Eriksson et
al., 2005) package, which employs the Atmospheric Radiative Transfer
Simulator (ARTS 2; Eriksson et al., 2011) to model radiative transfer through
the atmosphere, i.e. the forward model. All of the following information that
is input into the inversion is done so using Qpack 2. The a priori CO
information used here is the average of one winter period (September through
April) of output from the Whole Atmosphere Community Climate Model, version 4
(WACCM4) (Garcia et al., 2007) provided by Douglas Kinnison at the National
Center for Atmospheric Research (NCAR), with a standard deviation of
100 % at all altitudes. This combination was found to give enough freedom
to the inversion to fit expected changes in CO above Kiruna throughout a
given winter period (here September through May) while providing enough
regularisation of the retrieved solution so that no oscillations are readily
observed in the CO profiles. The WACCM data are on a 132-layer grid between
approximately the ground and 130 km. Ozone (O3) is also
retrieved simultaneously with CO, as there is an O3 spectral line
located at 231.28 GHz, and attenuation of the CO spectral line due to
water vapour is accounted for by including the water vapour continuum
described by Rosenkranz (1998) in the forward model and inversion. The
spectroscopic information used is from the HITRAN (HIgh-resolution TRANsmission molecular absorption database) 2008 catalogue (Rothmann et al., 2009). Continua of
molecular oxygen (O2) and nitrogen (N2) (Rosenkranz, 1993)
and nitric acid (HNO3) lines are also included in the inversion but
are not retrieved and are considered model parameters. A priori profiles of
O3, water vapour, and O2 are from the same WACCM run as the
CO a priori, and N2 and HNO3 a priori profiles are from the
FASCOD (Fast Atmospheric Signature Code) subarctic winter scenario
(Anderson et al., 1986). A priori profiles of CO, O3, water vapour,
O2, and N2 are unchanged from those described in Hoffman et
al. (2011, 2012).
Measurement noise (statistical noise on the spectrum) was estimated by
fitting a second-order polynomial to a wing of the spectrum and calculating
the standard deviation of the fit. As there is no windowing applied in the
operation of the FFTS, the spectrometer channels are specified in the
inversion as having a sinc-squared response function (Harris, 1978). Three
sine wave functions are fitted to the baseline of each spectrum during an
inversion to account for errors in the baseline, which are most often
produced by standing waves in the instrument. A fitting of functions to the
baseline of the measurement (baseline fit) can be included in the optimal
estimation performed by Qpack 2 and forms part of the general fit to the
measurement (inversion fit). For sine waves, the period and estimated
amplitude uncertainty are provided as input, and the amplitude and phase of
the waves are retrieved. The periods of the sine waves in the KIMRA spectra
were found by first inverting all of the measurements without a fit to the
baseline and then evaluating a periodogram of the residuals. This procedure
was also applied to subsets of the data in case some changes in the estimated
periods with time became evident. Sine waves in the baseline are not
generally visible by eye on the CO spectra, but no changes over time were
found in the determined wave periods. The periods of the fitted waves are
27.5, 55, and 36.3 MHz, with an estimated uncertainty in the
amplitudes of 0.5, 0.3, and 0.5 K, respectively. These calculated
sinewave periods are almost identical to those found by Hoffman et al. (2011)
for the 2008/09 and 2009/10 winters (27.5, 55, and 36.6 MHz), and are
similarly large in comparison to the width of the CO spectral line so that
they are uniquely distinguishable from it. A second-order polynomial is also
fitted to the baseline during the optimal estimation to account for any
offsets or long-period sine wave signatures. The zeroth-, first-, and
second-order coefficients have estimated uncertainties of 1, 0.5, and
0.5 K, respectively.
The altitude, pressure, and temperature information (zpT) for the inversion
is constructed in two ways. For the first case, used in the comparison work
with MLS (January 2011 to May 2014), information up to 10 hPa
(∼ 30 km) is from daily National Centres for Environmental
Prediction (NCEP) profiles; information for 10–0.01 hPa
(∼ 30–80 km: recommended range for use) is from SSMIS; and
information above that is from the NRLMSISE-00 empirical model of the
atmosphere (called MSIS hereafter) (Picone et al., 2002). Temperature data
from SSMIS currently begin in January 2011 and end in June 2014. For the
second case, used in the temporal extension of the KIMRA CO data set
(2008–2015): the information up to 0.01 hPa is from ECMWF
operational analyses output, and information above that is from MSIS. The
SSMIS data set was used because it compares well with other satellite data
sets (R. Larsson and P. Sheese, University of Toronto, personal
communication, 2016) and has approximately four colocations (sets of
measurements within 66–68∘ N, 15–25∘ E) with Kiruna per
day. Around the altitudes at which the different temperature profiles are
merged for use in the KIMRA inversion, the data are smoothed to avoid
discontinuities in the final temperature profile. The inversions utilising
SSMIS data are considered to be those using the most suitable available data
for the CO inversion, as the sensitivity of KIMRA CO profiles to atmospheric
temperature information is strongest within the retrievable altitude range
(on average between 48 and 84 km), and the resulting CO data set is
considered to be a reference point for inversion setups using alternate input
temperature information.
The pressure grid used in the forward model is 250 layers, spaced
approximately equally in altitude, between the ground and 125 km. The
retrieval grid is a 62-layer subset of the forward model pressure grid with
approximately 2 km spacing between the ground and 124 km. Using a
subset of the forward model grid is recommended by Patrick Eriksson (first
author of Qpack 2) as giving the most accurate mapping of information from
the forward model grid to the retrieval grid during an inversion. A
Marquardt–Levenberg iterative minimisation method (Marquardt, 1963) is used
to perform a nonlinear inversion. The CO profile is retrieved in relative
units (as a fraction of the a priori) for numerical stability due to the
strong gradients in atmospheric CO.
Characteristics of the retrieved data set
This section discusses the profiles retrieved using the NCEP–SSMIS–MSIS
temperature information between 2011 and mid-2014. The inverted CO data set
is restricted to the months of September–May, as summertime CO
concentrations in the middle atmosphere are very low. The data are then
further filtered to those that satisfy the following: a converged inversion,
a degree of freedom for signal (DOFS) greater than 1 (DOFSs are calculated as
the trace of the averaging kernel matrix; Rogers, 2000), a standard deviation
of the fit residual no greater than 1.5 times the initial estimate of the
measurement noise (to avoid overfitting the measurement), a mean of the fit
residual that lies in the range (-1, 1 K), and a baseline
brightness temperature of < 230 K (an ad hoc indication of too
cloudy weather). No filtering for outlying or anomalous concentration values
was applied to the data. Overall, 28 % of the data was identified as unusable, with the DOFS
criteria being responsible for about half of that number. If one finds this a
high rejection rate, bear in mind that KIMRA operates regardless of weather
conditions, and CO concentrations may be very low in time periods near the
beginning and end of winter. The fact that measurements during very low
middle-atmospheric CO concentrations are more likely to be filtered out means
that the KIMRA CO data set may have an average CO value that is higher than
the true atmosphere value.
(a) A histogram of the KIMRA CO measurement durations from
January 2011 to May 2014 with n as the number of measurements (see
Sect. 2.2). (b) Upper: an example measurement from 5 November 2012
with the corresponding inversion fit (which includes the baseline fit; see
Sect. 2.2). Lower: the residual (measurement minus inversion fit) and the
baseline fit for comparison. (c) The mean averaging kernels for all
CO measurements, with the measurement response divided by 4 shown in black.
The dashed and dotted black lines indicate a measurement response of 0.8 and
1.0, respectively. (d) The corresponding mean altitude resolution of
the CO profiles, derived from the FWHM of the averaging kernels.
Figure 1b shows an example spectrum of a CO measurement with KIMRA and the
corresponding inversion fit. The inversion fit includes the baseline fit
described in Sect. 2.2 and the example baseline fit is plotted alongside the
residual in Fig. 1 for comparison. Considering the entire data set, the
standard deviation (averaged across all spectrometer channels) of the
amplitude of the fitted baseline is 0.21 K, and the average standard
deviation of the residual is 0.34 K. In other words, changes in the
retrieved amplitude of the baseline are, on average, lower than the
statistical measurement noise on the spectrum.
The mean of the averaging kernels for the CO data set is also shown in
Fig. 1c along with the measurement response (Fig. 1d) (sum of the rows of the
averaging kernel matrix) and the altitude resolution of the profiles (full
width at half maximum (FWHM) of the averaging kernels). Altitudes with a
measurement response greater than 0.8 are considered to represent the range
of useful profile information: the retrievable altitude range. This choice is
somewhat arbitrary, with 0.8 being a regularly used value (e.g. Forkman et
al., 2012; Hoffmann et al., 2011; Straub et al., 2013). The CO profiles here
have an average retrievable altitude range of 48–84 km and an
average vertical resolution of between 15 and 18.5 km depending on
the altitude. These compare to an average altitude range of 40–80 km
and average resolution of 16–22 km for the CO profiles shown in
Hoffmann et al. (2011) for the winters of 2008/09 and 2009/10. DOFSs for the
current data set have a mean of 2.0 and a standard deviation of 0.6. The
minimum and maximum of the lower and upper retrieval limit for the current
data set is 35 and 99 km, respectively, but the upper limit of any
profile (defined using measurement response) must be considered with the
following caveat.
The centres of the averaging kernels, when represented in VMR, are shifted
down in altitude compared to a representation using relative concentrations.
Hoffmann et al. (2011) provide a detailed discussion of the representation of
averaging kernels for ground-based CO measurements, and the major points are
worth repeating here. While here the lower limits of the retrievable altitude
ranges are set by the SNR of the measurement, the upper limit of the
measurements is set by the transition from a pressure-broadening regime to a
Doppler-broadening regime (not considering spectrometer channel resolution).
The result of this is that, above approximately 70 km in the VMR
representation, the altitude locations of the centres of the averaging
kernels do not increase anymore with the increase in respective retrieval
altitude. And while the retrieved CO values above 70 km do contain
information from the atmosphere corresponding to the retrieval altitude, the
VMR representation of the data above this altitude should be considered with
care.
Error estimates for the retrieved data set
Errors in the retrieved CO profiles arise from uncertainties in instrumental
parameters and in parameters that are used as input to the forward model. The
relative contributions from these uncertainties are calculated here using the
OEM error definitions and by introducing perturbations to the inputs. OEM
error definitions are described in detail in Rodgers (2000), among others,
and are not repeated here. Figure 2 shows the estimated contributions to the
CO profile error budget from the following uncertainties. There are, of
course, possible sources of error in any stage of the instrumentation, and
the parameters discussed here are considered to represent the dominant
uncertainties.
The statistical noise, ΔT, on a spectrum is governed by the so-called
radiometer equation ΔT=Tsys/ντ, with
Tsys as the system noise temperature of the instrument,
Δν as the channel bandwidth (Table 1), and τ as the integration
time for a measurement of the atmospheric signal. The uncertainty for the
temperature profile used in an inversion is the same uncertainty used in
Hoffman et al. (2011): 5 % below 80 km, 10 % above
100 km, and linearly interpolated in between. Uncertainties in the
spectroscopic parameters of the CO line are from the HITRAN 2008 catalogue
and are 1 % for the line intensity, 2 % for the air broadening
parameter, and 5 % for the temperature dependence of air broadening.
Uncertainty in the line position is ignored as the frequency grid can be
adjusted to match the line centre in a measurement, and the parameters
associated with self-broadening are considered as having negligible impact
due to the relatively low concentration of the observed gas (Ryan and Walker,
2015). For the blackbody targets used in the calibration of the atmospheric
measurement, an uncertainty of 2 K is assumed in the temperature of
the hot and cold target: a conservative estimate that accounts for
fluctuations and drifts in temperature. The uncertainty in the pointing of
the instrument to the sky is estimated as 1∘. This is 1 order of
magnitude higher than the precision of the motor that controls pointing, to
allow for a possible offset in the orientation of KIMRA.
The resulting error estimates as well as a sum, in quadrature, of the errors
are plotted in Fig. 2 and show that the predominant error in the CO profiles
comes from the statistical measurement noise on the spectrum. This peaks at
30 % of the mean KIMRA CO profile, at 53 km altitude. The error from
uncertainties in the temperature profile also shows a significant
contribution to the total error in the retrievable altitude range above
approximately 60 km. The error due to noise on the spectrum is
calculated during each inversion and is provided in the supplemental data
with the corresponding CO profiles. The error profile in Fig. 2 is an average
over all measurements. The other plotted errors are calculated about the a
priori CO profile and serve as an estimate of the respective error
contributions to each measurement. To calculate these errors for individual
measurements is computationally expensive, and so the values plotted in
Fig. 2 are provided with the available KIMRA CO data (see
Sect. 5).
Estimated profiles of error for the KIMRA CO profiles. Section 2.4
describes the sources of uncertainties used to estimate the errors. The error
due to spectrum noise is an average value over all measurements, and the
other errors are calculated about the KIMRA CO a priori profile.
Smoothing error and interpretation of the KIMRA profiles
The smoothing error in the profiles arises from the limited vertical
resolution of the retrieved profile and can be calculated with the OEM using
the averaging kernels and a priori information for a profile. The smoothing
error can be large for ground-based profiling instruments due the small
altitude spacing between retrieval grid points, chosen here for numerical
stability in the inversion (Eriksson, 1999), compared to the actual vertical
resolution of the retrieved profiles (see Fig. 1c and d). Smoothing error
should be assessed when one wishes to use or interpret the CO profiles
without consideration of the accompanying averaging kernels. As this is not a
recommended use of the data, the smoothing error is not assessed here. If
using KIMRA profiles to say something of the absolute value of CO at a given
retrieval altitude, one must be aware that a CO value at a retrieval grid
point contains information from a range of altitudes with a sensitivity
governed by the shape of the corresponding averaging kernel.
The smoothing error can be accounted for when comparing KIMRA CO profiles to
those of an instrument/model that has a significantly different vertical
resolution by using the KIMRA averaging kernel matrices and a priori to
smooth (Rodgers and Connor, 2013) the other profiles so that they have a
similar vertical resolution. KIMRA profiles can be used to observe changes in
CO concentrations over time, provided there is no significant difference in
the averaging kernels (and thus measurement response) of the profiles over
that time. In particular, care should be taken with data near the edges of
the retrievable altitude range (where the measurement response is decreasing
towards 0.8), as the measurement response in this region can change quickly
when there are sharp changes in atmospheric CO.
Comparison with MLS
This section presents a comparison of CO profiles from KIMRA and MLS that are
colocated in space and time. A description of the MLS instrument is given in
Waters et al. (2006). Version 4.2 of the CO data (Schwartz et al., 2015) is
used here, a description of which can be found in Livesey et al. (2015).
Version 4.2 CO data cover the pressure range of 215–0.0046 hPa. The
precision of the CO profile reaches a maximum (largest) value of
1.1 ppmv at the highest retrieval layer (0.0046 hPa). In the
middle atmosphere the data set has a positive bias of approximately 20 %
compared with the ACE-FTS satellite instrument. This estimate is given by
Livesey et al. (2015) using a validation of the MLS version 2.2 CO data
(Pumphrey et al., 2007), which showed a positive bias of approximately
30 %: later versions than 2.2 show a slight lowering of the MLS values,
bringing them closer to the ACE-FTS data.
Colocation of KIMRA and MLS measurements
For a given KIMRA measurement, a coincident MLS measurement was defined as
follows. MLS measurements that were made within 4 h, ±2∘
latitude, and ±10∘ longitude of the KIMRA measurement and lie in
the same position relative to the vortex edge as the KIMRA measurement
(inside, outside, or within the edge of the polar vortex) were identified.
The MLS measurement from this group that was closest, along a great circle,
to the KIMRA measurement was chosen as coincident. A given KIMRA–MLS
measurement could only be considered coincident with one MLS–KIMRA
measurement. The location of a measurement with respect to the polar vortex
was determined using scaled potential vorticity (sPV) values from NASA's
Global Modeling and Assimilation Office's (GMAO's) MERRA (Modern Era
Retrospective analysis for Research and Applications) (Rienecker et
al., 2011). The sPV values for KIMRA were calculated geometrically along the
instrument's line of sight. sPV values of 1.6 and 1.2×10-4 s-1 have been used extensively in previous works (e.g.
Manney et al., 1994, 2007, 2011; Jin et al., 2006) to define the respective
inner and outer edge of the vortex, and the same values are used here.
The location of a measurement, its position relative to the vortex, and the
distance between measurements were calculated at 50 km altitude. The
position relative to the vortex changes with altitude, which means that a
single profile may simultaneously contain information from inside, outside,
and the edge of the polar vortex. The altitude chosen to define the three
positions relative to the vortex is 50 km because it divides the CO
measurements from KIMRA into the three most distinct populations of
concentration values. This was tested by using different altitudes to define
the position relative to the vortex, calculating partial (46–66 and
66–86 km) CO column concentrations and testing whether the column
concentrations were significantly different from each other when grouped by
vortex relative position. This is not to say that there should always be
three distinct air masses of CO, but it can be expected based on the strong
cross-vortex gradient of the gas (see Sect. 1). The strong CO gradient during
winter has recently been used in a chemical definition of the mesospheric
vortex (Harvey et al., 2015).
Figure 3 shows the results for the KIMRA CO columns with vortex relative
positions calculated using sPV values at 40, 50, and 60 km. The sPV
information is available up to approximately 62 km. Using
40 km, the partial columns defined as outside and in the edge of the
vortex are statistically indistinguishable at the 5 % significance level
using an unpaired two-sample t test, and using 60 km, the inside
and edge of vortex 46–66 km partial columns are indistinguishable.
With the same test and using 50 km, the three groups of partial
columns comprise three distinct populations of concentration values in both
the stratosphere and the mesosphere. Using other altitudes to define the
vortex relative position also produced distinguishable concentrations, and
50 km was chosen as it gives the most distinct vortex relative
concentration values in both the stratosphere and mesosphere and also because
the sPV-defined location of the vortex edge in the upper stratosphere becomes
much less well-defined as the winter progresses (Manney et al., 1997). Both
the sPV and CO concentration gradients will be less distinct before/after and
during the formation/breakdown of the polar vortex.
The distributions of KIMRA CO partial (46–66 and 66–86 km)
column concentrations divided into groups defined by their position relative
to the polar vortex edge using sPV (see Sect. 3.1). The relative positions
are calculated using sPV values at 40, 50, and 60 km, as indicted on
respective plots. Using sPV at 50 km gives the three most distinct
distributions, as calculated with an unpaired two-sample t test (see
Sect. 3.1). The asterisks indicate plots with three distinct distributions.
Comparison of colocated measurements
There are 916 coincident profiles found using the criteria in the previous
section. The MLS CO profiles have a vertical resolution more than twice as
fine as that of the KIMRA profiles: 3.5–5 km from the upper
troposphere to the lower mesosphere and 6–7 km in the upper
mesosphere (Livesey et al., 2015). Because of this, the MLS profiles were
smoothed with the averaging kernels of coincident KIMRA profiles to account
for the difference in vertical resolution. MLS CO profiles are retrieved up
to 0.001 hPa in the atmosphere and use a constant CO concentration
above this. Because there is some low sensitivity of the KIMRA CO profiles to
atmospheric concentrations above this (see the averaging kernels in Fig. 1),
the MLS profiles were extrapolated from 0.001 hPa before smoothing. A
linear extrapolation in pressure space was used to extend the MLS profiles
instead of using scaled KIMRA a priori information so as to avoid creating
artificial agreement between KIMRA and MLS. Figure 4 shows the mean of the
extrapolated and the original MLS profiles, as well as the KIMRA a priori for
comparison. The extrapolated profile is considered a more realistic
representation of the atmosphere than the constant value provided for MLS at
these altitudes. MLS values at 82 and 84 km are often defined as
unusable for scientific work due to an a priori contribution that is too high
(Livesey et al., 2015), and the precision is given as negative in this case.
The CO values are still considered here as useful for comparative purposes,
but quantities derived using the precision (e.g. the slope of a line) are not
meaningful at these altitudes. Figure 5a–e show the results of the
comparison of the 916 pairs of coincident CO profiles found between January
2011 and May 2014. The means of KIMRA and MLS CO profiles are shown (Fig. 5a)
as well as the mean of the differences (bias) in the coincident profiles
in ppmv (Fig. 5b) and relative to the mean of KIMRA and MLS profiles
(Fig. 5c), the sample Pearson correlation coefficient for the profiles at
each altitude (Fig. 5d), and the slope of a line of best fit to KIMRA vs. MLS
at each altitude layer (Fig. 5e), explained below. The times between
coincident measurements and the location of the coincident MLS profiles are
also shown (Fig. 5f). The statistics in Fig. 5 are calculated using all
colocated pairs of profiles and are also assumed to be representative of
subsets of the data as there appears to be no particular time of the year
during which the differences are more/less pronounced.
The mean of the coincident MLS CO profiles and the a priori CO
profile used for the KIMRA inversion (Sect. 2.2). “MLS orig.” shows the
mean of the supplied MLS profiles, which use a constant CO concentration
above 0.001 hPa (∼ 98 km). “MLS extrap.” shows the
mean of the profiles that have been linearly extrapolated in pressure space
above 0.001 hPa to provide more physical CO concentrations at these
altitudes.
The mean profiles show a maximum absolute bias of ∼ 0.65 ppmv
at 68 km and a maximum relative bias of 22 % (0.44 ppmv)
at 60 km, with bias being defined as KIMRA minus MLS. The standard
deviation of the differences in the profiles peaks at 21 % at
60 km. These standard deviation values are similar in magnitude to
the estimated uncertainties in the KIMRA CO profiles (Fig. 2). The standard
error on the bias is not shown in Fig. 5b and c because it is very small due
to the number of coincidences, but rather the standard deviation of the
differences is shown as the whiskers on the bias. The combination defines a
1 σ space in which a KIMRA profile lies with respect to an MLS
profile. The correlation of the profiles is high at all altitudes, only
dropping below 0.90 above 82 km. The correlation between KIMRA and
unsmoothed MLS profiles is also plotted, with values between 0.81 and 0.90.
Previous CO retrievals for winter 2008/09 and 2009/10 (Hoffmann et al., 2011)
showed a bias with respect to MLS, increasing with altitude, with a value of
> 5 ppm at 80 km. The structure of this bias was also
shown in comparisons of KIMRA with ACE-FTS and MIPAS, and it appears that
this attribute is not present in the retrievals presented here.
(a) The means of the coincident KIMRA and MLS CO profiles
from 2011 to 2014, the mean of the unsmoothed MLS profiles, and the a priori
profile used for the KIMRA inversion. (b) The mean of the difference
between the KIMRA and smoothed MLS profiles with the standard deviation of
the differences as the whiskers on the line. (c) The same
as (b) but in relative units, as percent of the mean of KIMRA and
MLS CO profiles. (d) The correlation coefficients of KIMRA and
smoothed (solid) and unsmoothed (dashed) MLS data. (e) The slope and
standard error of a line of best fit to KIMRA vs. smoothed MLS, calculated at
each level using given MLS error estimates and two estimations of KIMRA
error: the measurement error (black) and double the measurement error (grey)
(see Sect. 3.2). The slope values at 82 and 84 km are unreliable as
MLS precision is often quoted as negative at these altitudes.
(f) The location of the MLS measurements (magenta) with respect to
Kiruna (blue) and a histogram of the times between coincident measurements.
n is the number of pairs of colocated profiles. The temperature input for
the KIMRA inversions shown here includes SSMIS data (see Sect. 2.2).
The slope of a line of best fit to KIMRA vs. MLS measurements was calculated
individually for each altitude layer as follows: for a given retrieval grid
point the slope and intercept (or regression coefficients) for a line of best
fit to the KIMRA and MLS values was calculated, accounting for errors in the
abscissa (MLS) and ordinate (KIMRA) values according to York et al. (2004).
Two cases of KIMRA CO error estimates were used when calculating a line of
best fit: the first being the measurement error in the profile (the error due
to statistical noise on the spectrum; Sect. 2.4) and the second being twice
the measurement error. The former is an underestimation of the true error on
the profile as there are more error sources than measurement error alone, and
the latter is likely an overestimation of the true error as the measurement
error is a predominant source of error in the profile (Fig. 2). The results
(Fig. 5e) show that the slope is always greater than the ideal value of 1.0
for both KIMRA error estimates, meaning that KIMRA shows a greater range of
CO concentrations at all altitudes and the variation is not explained by the
estimated random errors in the profile. The reason for the difference could
be due to errors, e.g. spectroscopic information, calibration, and baseline
wave signatures, that can have a contribution that is neither truly
systematic nor random. The difference in calculated slopes for the two KIMRA
error estimates is insignificant within the standard error, likely due to the
large natural variation in CO concentrations. Despite the > 1.0 slope
values, KIMRA and MLS are considered to show agreement, according to the
level of difference and correlation between profiles.
Extension of the KIMRA data set
After establishing a reliable inversion scheme through comparison with MLS,
the KIMRA data set is extended in time by substituting ECMWF operational
analyses model output for the SSMIS temperature data in the inversion (see
Sect. 2.2).
The effect of temperature input on KIMRA CO profiles
ECMWF temperatures are available four times per day, in 6-hourly intervals
beginning at midnight. The same filtering procedure for the retrieved data is
employed as outlined in Sect. 2.3. To evaluate the effect of using a
different temperature data set as input to the inversion, the two KIMRA data
sets are compared where they overlap between January 2011 and May 2014, and
the results are shown in Figs. 6 and 7.
A comparison of the two sets of temperature profiles (ECMWF–MSIS minus/vs.
NCEP–SSMIS–MSIS) is shown in Fig. 6. Figure 6a and b show the mean and
standard deviation of the differences between temperature profiles in
absolute and relative units, Fig. 6c shows the correlation at each altitude,
and Fig. 6d shows the slope of the lines of best fit at each altitude. The
same is shown for the two respective sets of CO profiles in Fig. 7. No
smoothing with averaging kernels is applied to the data.
(a) The absolute mean of the difference in the temperature
profiles (ECMWF–MSIS minus NCEP–SSMIS–MSIS) used in the two inversion
setups for KIMRA. The altitude ranges of the temperature information used in
the profiles (see Sect. 2.2) are shown here. (b) The mean of the
percentage difference in the profiles (difference divided by the average of
the two profiles). (c) The correlation between the two data sets.
(d) The slope of a line of best fit to the data sets at each level,
with ECMWF–MSIS as the dependant (Y) variable. The slope value at the
highest altitude shown has a relatively large standard error because of the
lower number of points at this altitude after conversion from a pressure to
an altitude grid.
(a–d) The same calculations as shown in Fig. 6 but for the
CO volume mixing ratios retrieved using the respective temperature data sets.
The slopes and their standard errors (d) are calculated with the
same two KIMRA error estimates as in Fig. 4 (see Sect. 3.2), with the larger
error bars corresponding to the larger error estimate. Note the different
altitude range compared to Fig. 6.
The bias for the temperature profiles is very low below 50 km,
showing good agreement between ECMWF and NCEP output, as well as the
lower-altitude SSMIS data, and then moves to a minimum of ∼ -4 %
at 68 km. The maximum in the bias is about 4 % at 118 km.
The correlation is high below 50 km but has minima of < 0.50 at
∼ 70 km and ∼ 0.80 at 100 km. It should be noted
that while MSIS is used in both temperature data sets, the time of the MSIS
output is governed by the times for the ECMWF output and the SSMIS
measurements, and so the high-altitude (> 0.01 hPa) temperature
values are not necessarily equal for the two inversion setups. The slopes of
the lines of best fit were calculated using the same temperature error
estimate, as described in Sect. 2.4, for each data set. The slope is within
11 % of 1.0 below 50 km altitude, above which it decreases to around
0.65 at 56 km before increasing to 1.4 at 66 km and then
varies about 1 with another peak of 1.3 at 102 km.
There is a general positive bias in the CO profiles that use ECMWF–MSIS,
seen in Fig. 7a and b. The bias is small, reaching a maximum of
∼ 5 % in the range of 68–78 km. The correlation of the
profiles is very high, greater than 0.98 at all altitudes below
82.5 km. The slopes of the lines of best fit were calculated with the
same error estimates described in Sect. 3.2. A value of 1.0 lies in the range
of standard error of the slope below 56 km and above 80 km
and between these altitudes reaches a maximum of 1.06. As the only difference
in the inversion setups is the temperature input, it follows that any
inequalities of the respective KIMRA CO profiles are ultimately due to this
difference. Overall, the CO profiles using the differing temperature inputs
shown here agree very well.
KIMRA CO data set from 2008 to 2015
Figure 8 shows the KIMRA CO data set between December 2008 and May 2015.
Daily averaged CO concentrations between 46 and 86 km are shown. Data
gaps in this time range are due to non-operation of the instrument or a lack
of CO spectral line measurements. Data from winter 2015/16 are unavailable
due to a failure of the KIMRA cooling system.
While it is impossible to fully characterise the concentrations shown without
inclusion of other instrument data and/or model output, some observations are
made here. The beginning of each winter (from about September through
November) shows a movement of CO to lower altitudes, which can in general be
expected as predominantly due to vertical advection (Allen et al., 1999;
Minschwaner et al., 2010; Solomon et al., 1985). The high CO concentrations
remain for most of winter before decreasing again from March onwards,
generally due to loss of CO because of increased ⚫OH and
movement of low-CO air from lower latitudes as the final warming of the pole
occurs. Signatures of “major” SSWs (during which the 10 hPa zonal
circulation becomes easterly at 60∘ N), beginning 24 January,
26 January, and 6 January, 2009, 2010, and 2013, respectively, can be seen by
the quickly (order of days) decreasing CO concentrations around these dates
and then the subsequent increases as the vortex recovered (see Sect. 1)
(Manney et al., 2009, 2015). The effects of a “minor” SSW (during which the
10 hPa zonal circulation remains westerly at 60∘ N) in early
January 2015 (Manney et al., 2015) can also be seen. Decreases in CO
concentrations during SSWs are mainly due to the influx of lower-latitude air
as the polar vortex destabilises. There are other visible fluctuations in the
presented KIMRA CO data over various timescales which, while not interpreted
here, can be used in the characterisation of winter-time dynamics above
Kiruna.
Daily averaged CO volume mixing ratios (in ppmv) above
Kiruna from December 2008 through May 2015. Blank areas within this time are
gaps in the data record. Data are plotted using the Isoluminant colour map
from Kindlmann et al. (2002), and non-uniformly spaced contours (black lines)
between 0.4 and 28 ppmv are added to guide the eye. The temperature
input for the KIMRA inversions shown here includes ECMWF analysis (see
Sect. 2.2).