Stratospheric influence on MLT over mid-latitudes in winter by Fabry-Perot interferometer data

. In this paper, we study the response of the mesosphere and lower thermosphere (MLT) to sudden stratospheric warmings (SSWs) and the activity of stationary planetary waves (SPWs). We observe the 557.7-nm optical emission for retrieve the MLT wind, temperature with the Fabry-Perot interferometer (FPI) that has no analogs in Russia. The FPI is located at the mid latitudes of Eastern Siberia within the Tory Observatory (TOR) at the Institute of Solar-Terrestrial Physics 10 of the Siberian Branch of the Russian Academy of Sciences (ISTP SB RAS, 51.8N, 103.1E). Regular interferometer monitoring started in Dec 2016. Here, we address the temporal variations in the 557.7-nm emission intensity, as well as the variations in wind, temperature, and their variability obtained by using the line parameters measurement during the 2016-2020 winters. Both SSWs and SPWs appear to have equally strong effects in the upper atmosphere. When the 557.7-nm emission decreases due to some influences from below (SSWs or SPWs), the temperature variation observed by using this 15 line and the temperature itself increase significantly. The zonal wind dispersion does not show significant SPW- and SSW-correlated variations, but the dominant zonal wind reverses during major SSW events the same as the averaged zonal wind at 60N in the stratosphere does without significant delays. wind to SSWs and SPWs, obtained during the data analysis. The zonal wind shows a pronounced change during warmings. Like in the stratosphere, the eastward wind reverses westward in the MLT. Moreover, the stronger the wind inversion in the MLT, the stronger the wind inversion in the stratosphere. To analyze the effect of the stratosphere on the MLT wind, it is important to consider not only the standard 10 hPa height for SSW, but also the 1-hPa dynamics. For example, two 2017 SSW cases were minor as per the WMO classification. However, 185 during these warmings at 1 hPa, the westward wind intensified significantly. This was the reason for the wind inversion at the MLT heights. The 2018-2019 warming was major, but at 1 hPa, there was no wind inversion. Also, we see that the zonal wind only weakened and did not reverse at the MLT altitudes.

Long-term (covering several years and more) observations of the MLT region temperature and wind are sparse, especially within the Siberian region close to the SSW emergence and evolution. Analysis of such observations is useful to understand and predict global circulation, and to forecast middle and upper atmospheric models. Our measurements using the Fabry-65 Perot interferometer enable to simultaneously evaluate the MLT temperature and wind speed. In this paper, we address four winter periods of observations of the upper atmosphere and compare the measurements with the stratosphere dynamics.

Data and method
We analyze the data from the ISTP SB RAS Fabry-Perot interferometer located at the TOR in the Republic of Buryatia. Fig.   1 shows the map with the instrument location. 70 The Fabry-Perot Interferometer (FPI) conducts regular spectrometric observations of the natural airglow lines in the night atmosphere. Precise spectral analysis enables to observe the Doppler shift of a separate line, which characterizes the 75 movement rate for the corresponding radiating component of the atmosphere along the facility's line-of-sight. The combination of the Doppler shifts obtained in different directions within the medium stationarity time interval enables to reconstruct the full vector of the wind horizontal velocity, whereas the line broadening provides us with the information on the temperature (Vasilyev et al., 2017). In this paper, we address the data on the behavior of the wind speed zonal component and the temperature obtained by using the 557.7-nm line emission originating at about 90-100 km over the Earth surface. 80 The FPI is an optical instrument, therefore, measurements are possible only in the dark, on moonless nights, when there are no clouds within the FPI field of view. Due to this, the data have periodic (daily, lunar) and aperiodic (cloudiness, technical failures) gaps. In this paper, we analyze the night-averaged values for the 557.7-nm emission intensity (I), temperature (T), and zonal wind speed (U), as well as the standard deviations of those values during each night. https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License.
To study the stratosphere dynamics, we used the ECMWF Era5 climate archive (Hersbach et al., 2020). As per the SSW 85 criteria established by the WMO, we address the parameters, such as the zonal average air temperature along 80N and zonal average values of the wind zonal component along 60N at the 10hPa height on a 2.5x2.5 deg grid. We also studied the dynamics of stationary planetary waves with zonal wavenumbers 1 (SPW1) and 2 (SPW2). We addressed all the characteristics at the 1 hPa and 10 hPa heights.

Results and discussion 90
3.1 Sudden stratospheric warmings Fig. 2 shows the daily zonal mean zonal wind at 60N (blue) and the temperature at 80N (red) obtained from the ERA5 reanalysis dataset for 1 Oct 2016 through 31 Mar 2017 at the 10 hPa height (solid) and at 1 hPa (dotted). We see that two stratospheric warmings were observed with a peak on Feb 1 (251 K) and Feb 27 (251 K), SSW1 and SSW2, respectively, marked by dotted vertical lines in the figure. The grey rectangles show the SSW duration. As a criterion for the warming 95 onset, we accepted a day with a sharp temperature increase (more than 10 deg per day). We accepted the sharp (about 2 deg per day) temperature decrease end as the SSW end. SSW1 started on Jan 20 and ended on Feb 12. SSW2 started on Feb 23 and ended on Mar 5. As per the World Meteorological Organization (WMO) standard criteria, observed were two minor warming events during the 2017 winter. Note that the warming at the 1 hPa height was significant, and the zonal circulation reversed. This may be important to analyze vertical interactions that we address below. 100 In the introduction, we discussed that waves, including planetary waves, are the cause for vertical interaction in the atmosphere. Periods of increase in the planetary wave amplitude in the stratosphere are not always accompanied by the SSW 105 https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License. evolution. Therefore, we address (and mark on the plots) the periods of planetary wave amplitude increase without SSW, and compare them with the MLT dynamics in the next section. In the figures, we mark the SPW1 amplitude increase above the average value for each winter with a light grey rectangle. Fig. 3 shows that, in early Nov 2016, there was a significant SPW1 amplitude increase that persisted for about a month. The SSW spatial structure may also be important for the upper atmosphere response. We analyzed temperature maps during warmings. As an example, we give a temperature map on the SSW maximum day (Fig. 4) at 10hPa. In the 2016-2017 winter, both SSW cases evolved in the Eastern Hemisphere, the warming center was located near the FPI location. 115 In the 2017-2018 winter, there was one SSW case that started on Feb 10 and ended on Mar 2. The maximal temperature was 120 246K by Feb 18. In Fig. 5, we can see that the warming was major, the zonal wind inversion was observed at 10 hPa and 1 hPa. The warming predominantly evolved in the Western Hemisphere over America, and the FPI was at the SSW periphery https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License. 6 ( Fig. 7). Note that the 1-hPa temperature decreased during the SSW. Before the SSW onset, two SPW1 increases were observed in the stratosphere. An SPW1 increase was noted on Dec 2017, as well as from mid-January to early February. Fig.   6 shows these periods with light gray rectangles. 125   In the 2018-2019 winter, one SSW case was observed. The SSW emerged on 22 Dec and lasted until 19 Jan. The maximal temperature during that warming was 248K on 29 Dec (Fig. 8). A temperature increase was observed throughout the stratosphere. During the warming, the wind changed its direction to the westward. The FPI was within the area of warming in that winter (Fig. 8). In Nov and Dec 2018, increased SPWs were observed; we marked those periods with light gray 135 rectangles in Fig. 10. The latter shows that the warming covered the entire polar region, with the interferometer site being influenced by warm air in the stratosphere. minor and lasted 30 Jan through 20 Feb with a maximal temperature of 239K on 5 Feb. SSW2 caused a significant temperature increase at 10 and 1 hPa; at 1 hPa, the zonal wind reversed. The SSW2 lasted 9 Mar through 28 Mar, the maximal temperature was 255K on 23 Mar. Two SPW increases preceded the SSW evolution in the stratosphere; we note that planetary waves were maximal for 2016-2020 (Fig. 12). During both SSW events, the TOR was within the warming area. (Fig. 13) 150 https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License. https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License.

FPI-measured average night values of the 557.7-nm emission and temperature
In this section, we address the FPI-measured mean night values for the 557.7-nm emission, temperature, and zonal wind. In Figs. 14-15, we noted the SSW periods (grey rectangle), the day of maximal SSW temperature (dotted white vertical line), and the periods of the planetary wave increased activity (light grey rectangle). The amplitudes of stationary planetary waves 165 were calculated along 60N with zonal wave numbers 1 and 2 (SPW1 and SPW2, respectively) from the Era5 data. Fig. 14 shows the 557.7-nm emission decrease during the SSW. We can also see that an intensity decrease was observed during the periods without stratospheric warming. However, we note that low emission is always observed during the increased activity of planetary waves, especially SPW1. The MLT temperature is inversely related to the 557-nm emission.
During SSW periods and increased activity of planetary waves, the temperature rises. The temperature rises may be 170 explained by different heights of the radiation formation. The green line can be radiated from the heights with higher temperatures and reach values of up to 250 K, because the temperature height gradient over the mesopause can be extremely high (up to 10K/km). Analyzing the temperature behavior obtained with the 557.7-nm line, we can conclude that the green line emission shifted to the beginning of the thermosphere and decreased due to the inverse temperature dependence of the Barth mechanism (Barth, 1961). Thus, in addition to SSW, the planetary wave activity impacts on the MLT dynamics. The 175 SPW activity most often precedes SSWs, but it may appear long before the warming onset and cause a response (often stronger than SSW) in the MLT temperature regime.
Preliminary analysis of the full vector of the wind velocity showed that obvious responses to the SSW and SPW events exist only for the zonal wind. However, this does not mean the absence of response from both the vertical and meridional wind on the lower atmosphere dynamics. We think that this should be investigated in a separate study. In this section, we address 180 only the strongest and most obvious response of the zonal wind to SSWs and SPWs, obtained during the data analysis. The zonal wind shows a pronounced change during warmings. Like in the stratosphere, the eastward wind reverses westward in the MLT. Moreover, the stronger the wind inversion in the MLT, the stronger the wind inversion in the stratosphere. To analyze the effect of the stratosphere on the MLT wind, it is important to consider not only the standard 10 hPa height for SSW, but also the 1-hPa dynamics. For example, two 2017 SSW cases were minor as per the WMO classification. However, 185 during these warmings at 1 hPa, the westward wind intensified significantly. This was the reason for the wind inversion at the MLT heights. The 2018-2019 warming was major, but at 1 hPa, there was no wind inversion. Also, we see that the zonal wind only weakened and did not reverse at the MLT altitudes.

Spectral analysis through the Lomb-Scargle method
Researchers also focused on diurnal variabilities of atmospheric characteristics during SSW impact (Merzlyakov et al., 2020;Pochotelov et al., 2018, Manson et al., 2002. They report the tide variability due to non-linear interactions with the planetary waves. The Lomb-Scargle (LS) periodogram method seems to be an appropriate technique for spectral analysis of the non-equidistant time rows, especially for FPI, because of gaps due to daytime, intense moonlight nights, and cloud 200 covers. The Fig. 16 upper panel presents the calculated LS periodograms for the zonal wind variations observed during 2017-2018 winter. The main spectral components with 24-, 12-, 8-hour periods dominate in the upper atmosphere. Namely, these spectral components are present on the spectral characteristic for the zonal wind data. 12-hr oscillations have the largest amplitude. To check the validity of the retrieved spectral data, we prepared a testing sample of the data on the regular non-interrupted grid with 8-, 12-, and 24-hr spectral components having the 0.3, 1, and 0.3 amplitudes, respectively. The 205 sample time step was 15 minutes, which corresponds to the FPI data minimal time step. We also added the normal noise to the data with zero mean and sigma equal to 3. The Fig. 16 bottom panel presents the LS periodogram for the artificial data sample. The Fig. 16 middle panel contains the LS periodogram of the described artificial data sample, but with the same gaps (daytime, moonlight, clouds) of the observed zonal wind during 2017-2018 winter. One can see a significant distortion in the spectral picture apparently due to the regular (daytime) 12-hr gaps that significantly increase the initial 8-hr and 24-hr 210 spectral components and also generate additional spectral components. Therefore, a detailed spectral analysis for such nonregular data as the FPI samples is apparently impossible without additional information or some special processing of the initial datasets, or without significand modification of the analysis method. Still, we can estimate the diurnal variability of all tides by calculating the standard deviation of the diurnal dataset.
https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License. In this section, we analyze the 557.7-nm emission standard deviation, temperature, and zonal wind measurements for each night. During warmings and increased activity of planetary waves, emission fluctuations decrease, but temperature fluctuations increase during night. Fig. 17 shows that temperature variations reach tens of degrees. Airglow and its night 220 variations in the MLT were minimal during the periods of active stratosphere. Figs. 14 and 17 show that variations in the mean values for the emission and temperature correlate directly with the behavior of their standard deviations during the entire winter. This correlation is especially clear during SPWs and SSWs.
Unfortunately, in the 2019-2020 winter, there were technical problems with the interferometer measurements. Therefore, some of the data are missing for that winter. However, during the 2019-2020 winter, we also see the opposite behavior of the 225 mean values of temperature and emission, and their standard deviations. The only peculiarity of that winter is that a sharp temperature increase occurred several days before the NNE onset. While we cannot answer exactly, why this occurred, it is possible that the influence was exerted by the stratosphere dynamics at 1 hPa, because there were higher temperatures https://doi.org/10.5194/angeo-2020-73 Preprint. Discussion started: 10 November 2020 c Author(s) 2020. CC BY 4.0 License. throughout March at that height. In our opinion, the increase in temperature standard deviations is due to an increase in the MLT tide amplitude, because the tides are the dominant mode in the MLT dynamics. 230 The variations in the wind standard deviation appear to be noisier, than those in the temperature standard deviation. Most often, we see that, during SSWs and SPWs, the zonal wind standard deviation increases. But this increase does not exceed the average background of wind variations even in the quiet stratosphere.