Stability of solar correction for calculating ionospheric trends

Abstract

Abstract. Global climate change affects the whole atmosphere, including the thermosphere and ionosphere. Calculations of long-term trends in the ionosphere are critically dependent on solar activity (solar cycle) correction of ionospheric input data. The standard technique is to establish an experimental model via calculating the dependence of ionospheric parameter on solar activity from the whole analysed data set, subtract these model data from observed data and analyse the trend of residuals. However, if the solar activity dependence changes with time, the solar correction calculated from the whole data set may result in miscalculating the ionospheric trends. To test this, data from two European ionospheric stations -Juliusruh and Slough/Chilton -which provide long-term reliable data, have been used for the period 1975-2014. The main result of this study is the finding that the solar activity correction used in calculating ionospheric long-term trends need not be stable, as was assumed in all previous investigations of ionospheric trends. During the previous solar cycle 23 and the current solar cycle 24, the solar activity correction appears to be different from that for the previous period and the Sun seems to behave in a different way than throughout the whole previous era of ionospheric measurements. In future ionospheric trend investigations the non-stability of solar activity correction has to be very seriously taken into account, because it can substantially affect calculated long-term trends of ionospheric parameters.

Is the Relation Between Ionospheric Parameters and Solar Proxies Stable?

“…This means that for fo F 2 we may use equation that the solar activity quite dominantly controls the yearly values of fo F 2 and that the dependence of yearly values of fo F 2 on solar activity is highly linear. Table also shows that 99% of the total variance of fo E is described by F 10.7 for the first period (1976–1995) and it indicates some data problems for the second period (1996–2014) as discussed later, among others an outlier‐like value of fo E for Juliusruh in 2011 as already mentioned by Laštovička et al (). Nevertheless, Table indicates usability of equation also for fo E .…”

“…Mostly more than 95% of the total variance of yearly values, particularly for fo F 2, is described by equation and can be attributed to solar activity (mainly solar EUV flux variability). Star (*) indicates data problems (see discussion related to Figure and Laštovička et al, ).…”

“…Elias et al () studied long‐term trends in fo F 2 and found some changes of trends during the solar cycle 23 including the deep minimum 23/24, which they tentatively attributed to changes in the solar EUV‐ F 10.7 relationship. Laštovička et al () investigated the behavior of long‐term trends in fo E . They obtained results are consistent with the results of older papers only after dividing the whole analyzed interval into parts with different solar activity dependences of fo E .…”

A mechanism of midlatitude noontime foE long‐term variations inferred from European observations

“…It has been repeatedly stressed that only smoothed (normally 11 year smoothing is applied) observations could be used for long‐term trend analyses. As an example, it may be considered the recent paper by Laštovička et al []. The main result of their study is “the finding that the solar activity correction used in calculating ionospheric long‐term trends need not be stable, as was assumed in all previous investigations of ionospheric trends.” This means that the ionosphere reacts differently to solar activity during various historical periods.…”

The Use of Monthly Mean Average for Investigating the Presence of Hysteresis and Long‐Term Trends in Ionospheric NmF2

“…Many insights have been gained based on this kind of methods on the topics of both hysteresis (e.g., Elias, 2014;Kane, 1992;Ozguc et al, 2008;Perna & Pezzopane, 2016) and long-term trends (e.g., Laštovička et al, 2016;Pham Thi Thu et al, 2016). In previous studies, yearly mean (or running) average methods are usually employed to smooth out the short-term, cyclic variations.…”