First VarSITI General Symposium, June 6-10, 2016, Albena, Bulgaria

1. Seasonal and year-to-year patterns of atmospheric and ionospheric variabilities over Eastern SiberiaIrina Medvedeva and Konstantin RatovskyInstitute of Solar-Terrestrial Physics (ISTP), Siberian Branch, Russian Academy of Sciences, Irkutsk, Russia First VarSITI General Symposium, June 6-10, 2016, Albena, Bulgaria

2. Meteorological processes in the lower atmosphere may affect not only the neutral upper atmosphere, but also its ionized part. Waves can propagate over significant distances, and transport energy from the lower atmospheric levels to large heights, thus providing the «coupling» of the atmospheric layers. The ionospheric variability is believed to be caused by geomagnetic activity (effects of geomagnetic storms and geomagnetically disturbed conditions), atmospheric activity (planetary waves, tides, and internal gravity waves), and short-term solar activity variations. The geomagnetic contribution to the ionospheric variability is comparable with the meteorological contribution, and it is much larger than the effects of the short-term solar activity. Comparison between atmospheric and ionospheric variabilities allow us to quantitativelyestimate manifestation of the wave activity with different time scales for variations in the neutral upper atmosphere and ionosphere.

3. Objective: • to analyze the seasonal and year-to-year patterns of the atmospheric variability in different period ranges; • to analyze the seasonal and year-to-year patterns of the ionospheric variability in the same period ranges; • to compare the seasonal and year-to-year patterns of the ionospheric and atmospheric variability.

4. AnalyzedData • As a characteristic for the atmospheric variability, we used variability of the atmosphere temperature at the mesopause region (Tm) obtained from spectrometric observations of OH ((6-2), 834 nm, ~87 km) emission (51.8°N, 103.1°E, Tory). OH rotational temperature corresponds to the atmospheric temperature at the mesopauseregion. • As a characteristic for the ionospheric variability, we used the data of the F2 peak electron density (NmF2) from Irkutsk DPS-4 Digisonde (52.3º N, 104.3º E). The period under analysis is 2008-2015. The patterns of the NmF2 and Tm variability in different period ranges were analyzed and compared. The period range included day-to-day (periods T > 24 hrs) and tidal (8 hrs ≤ T ≤ 24 hrs) variations, as well as variations in the internal gravity wave period range (T < 8 hrs).

5. Method for the atmospheric variability estimation Temperature of the mesopause region is determined by using the OH(6-2) emission spectrum. The measurements are performed at nighttime, exposition time is 10 min. As a parameter of the temperature variability, we used standard deviations in the annual and night temperature variations. Using that method, one can analyze manifestations of the various-timescale wave process activity. Day-to-day mesopause temperature variability is mainly caused by the planetary wave propagation in the atmosphere. Diurnal temperature variability is mainly caused by tides and internal gravity waves (IGWs). Fig. 1. Month distribution for analyzed nights of observations. Total amount is 1400 nights.

6. Day-to-day variability of the mesopause temperature To analyze the manifestations of the planetary wave activity in the atmosphere, monthly mean temperature deviation from its annual behavior were used. To determine the temperature day-to-day variability (σdd) , seasonal variations were excluded from a set of its nightly values, and then the residual temperature deviations were analyzed. The seasonal variation harmonics were determined by fitting (through the least square method) the average night temperature series with the function Fig. 2. Nightly average values (dots) and the average seasonal variations in the temperatures (solid lines), obtained by least square fitting.

7. Night variability of the mesopause temperature The OH temperature standard deviation from the average night temperature (σ) has been accepted as its night variability characteristics. The method presented in [Bittner et al., 2002; Medvedeva and Ratovsky, 2015; Offermann et al., 2009; Reisin and Scheer, 2004; Perminov et al., 2014] was used. According to this method, one may present the square of the temperature standard deviation (i.e. a variability of night temperature) as: σ2=σ2td+σ2gw+σ2n characterizing the activity of tides (σtd2), internal gravity waves (σgw2), and fluctuations in the dark current of the CCD-matrix of the spectrograph recording camera (σn2) that are determined with the instrument's closed entrance slit. The values σgw and σtd may be sequentially determined upon selecting (through the least square fitting) the harmonics corresponding to the diurnal tide's 24-, 12-, and 8-hr components from the night temperature series.

8. Night variability of the mesopause temperature To estimate the contribution of tides to the temperature diurnal variations, we determined regular night trend for each night of observations through the least square fitting by sum of the first three diurnal tide harmonics. After that procedure, the trend calculated for each night was subtracted from nightly temperature sets. Fig. 3. Top panel: Temperature variations (dots, 10 min measurements) during the 2015 December 18-19 night. Bottom panel: The temperature series of the residual variations after subtracting the tidal harmonics with of 24-, 12-, and 8-h periods The time was counted off from 00:00 am on 2015 December 18.

9. Method of ionospheric variability estimation A NmF2 disturbance was considered as a deviation of the observed value (NmF2OBS) from the 27-day running median (NmF2MED): ΔNmF2 = NmF2OBS - NmF2MED , ΔRNmF2(%) = ΔNmF2/NmF2MED∙100%, where ΔNmF2 and ΔRNmF2 are the absolute and relative disturbances, respectively. The disturbance series were separated into three period bands: the long-period part with periods T > 24 hrs, mid-period part (8 hrs ≤ T ≤ 24 hrs), and short-period part (T < 8 hrs). Fig. 4. Steps of peak electron density NmF2 disturbance calculation: (a) observed NmF2 (NmF2OBS, black) and 27-day running median (NmF2MED, grey); (b) relative disturbance (ΔRNmF2); (c) ΔRNmF2 components: day-to-day (dash, T > 24 hrs), tidal (red, 8 ≤ T ≤ 24 hrs), and IGW (green, T < 8 hrs). The variability is considered as the root mean square of NmF2 disturbances:

10. Season variations. Day-to-day variability. A high day-to-day variability in winter months is probably caused by influence of sudden stratospheric warmings on the upper atmosphere. Maxima near spring and autumn equinoxes may be caused by the seasonal transitions in the atmospheric circulation. A similar character in the behavior of the studied parameters may indicate that the planetary waves propagating upward from the atmosphere lower layers impact the mesopause temperature regime and the NmF2 day-to-day variations. Fig.5. Seasonal pattern of atmospheric day-to-day variability σdd: a) absolute values, b) normalized on mean temperature. Fig.6. Seasonal pattern of ionospheric day-to-day variability: daytime (left), nighttime (center) and day+night (right).

11. Seasonal variations. Diurnal variability. The common feature for the atmospheric and ionospheric variability is that the winter variability is higher than the summer one. The main distinction is that the seasonal variations in the ionospheric tidal and IGW variability has monotonous character with maxima in December-January and minima in May-August. The seasonal pattern of the atmospheric variability is more complicated. Fig. 7. Seasonal patterns of atmospheric variability caused by tides (σtd, solid line) and gravity waves (σgw, dashed line). Fig. 8. Seasonal patterns of ionospheric tidal (solid line) and IGW (dashed line) variability for daytime (left), nighttime (center) and day+night (right). Fig. 9. Seasonal variations of monthly mean Ap index averaged over 2008-2015.

12. Year-to-year variations Fig.12. Year-to-year variations of geomagnetic (Ap, blue) and solar (F10.7, red) activities. Fig.10. Year-to-year pattern of day-to-day (a, b), tidal (c, d triangles) and IGW (c, d, white circles) mean annual ionospheric variability for daytime (left) and nighttime (right) conditions. Annual mean ionospheric variability agrees well with geomagnetic activity only for daytime day-to-day variations. There is no clear correlations between year-to-year patterns of ionospheric and atmospheric variabilities. Fig.11. Year-to-year pattern of day-to-day (left), tidal (right, black circled) and IGW (right, white circles) mean annual atmospheric variability.

13. Summary • Comparative analysis between atmospheric and ionospheric variabilities revealed manifestations of wave activity of various time scales over a wide height range in the upper atmosphere. There are significant seasonal changes of Tm and NmF2 variabilities within a year. • The comparison revealed both common features and distinctions in the seasonal patterns of the ionospheric and atmospheric variability. High winter day-to-day Tm and NmF2 variability may be caused by an impact of sudden stratospheric warmings on the upper neutral atmosphere and the ionosphere. Maxima around the equinoxes may be explained by seasonal (springtime/fall) transition of the atmospheric circulation. • A similar character of the atmospheric and ionospheric day-to-day variabilities may indicate that the planetary waves propagating from the atmosphere lower layers impact the mesopause temperature regime and the NmF2 day-to-day variations. • A common feature of the atmospheric and ionospheric variability within the tide and IGW periods range is that the winter variability exceeds the summer one. The main distinction is that the seasonal variations in the ionospheric tidal and IGW variability have monotonous character with maxima in December-January and minima in May-August. The season pattern of the atmospheric variability is more complicated. • Comparison of year-to-year patterns between Tm and NmF2 variabilities did not revealed correlations in behavior of these parameters.

14. References: • Bittner M., Offermann D., Graef H.-H., Donner M., Hamilton K., (2002), An 18-year time series of OH rotational temperatures and middle atmosphere decadal variations, J. Atmos. Sol. Terr. Phys., 64, 1147-1166. • Medvedeva, I., and K. Ratovsky (2015), Studying atmospheric and ionospheric variabilities from long-term spectrometric and radio sounding measurements, J. Geophys. Res. Space Physics, 120, Issue 6, J. Geophys. Res. Space Physics, 120, 5151–5159, doi:10.1002/2015JA021289. • Offermann, D., Gusev, O., Donner, M., Forbes, J.M., Hagan, M., Mlynczak, M.G., Oberheide, J., Preusse, P., Schmidt, H., Russell III, J.M., (2009), Relative intensities of middle atmosphere waves, J. Geophys. Res., 114, D06110, doi: 10.1029/2008JD010662. • Reisin, E.R., Scheer, J., (2004), Gravity wave activity in the mesopause region from airglow measurements at El Leoncito, J. Atmos. Solar-Terr. Phys., 66, 655–661. • Perminov V.I., Semenov A.I., Medvedeva I.V., ZheleznovYu.A., (2014b), Variability of mesopause temperature from the hydroxyl airglow observations over midlatitudinal sites, Zvenigorod and Tory, Russia, Adv. Space Res., .54, 2511–2517.

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