Long-term trends in the ionospheric response to solar extreme-ultraviolet variations

Abstract

Abstract. The thermosphere–ionosphere system shows high complexity due to its interaction with the continuously varying solar radiation flux. We investigate the temporal and spatial response of the ionosphere to solar activity using 18 years (1999–2017) of total electron content (TEC) maps provided by the international global navigation satellite systems service and 12 solar proxies (F10.7, F1.8, F3.2, F8, F15, F30, He II, Mg II index, Ly-α, Ca II K, daily sunspot area (SSA), and sunspot number (SSN)). Cross-wavelet and Lomb–Scargle periodogram (LSP) analyses are used to evaluate the different solar proxies with respect to their impact on the global mean TEC (GTEC), which is important for improved ionosphere modeling and forecasts. A 16 to 32 d periodicity in all the solar proxies and GTEC has been identified. The maximum correlation at this timescale is observed between the He II, Mg II, and F30 indices and GTEC, with an effective time delay of about 1 d. The LSP analysis shows that the most dominant period is 27 d, which is owing to the mean solar rotation, followed by a 45 d periodicity. In addition, a semi-annual and an annual variation were observed in GTEC, with the strongest correlation near the equatorial region where a time delay of about 1–2 d exists. The wavelet variance estimation method is used to find the variance of GTEC and F10.7 during the maxima of the solar cycles SC 23 and SC 24. Wavelet variance estimation suggests that the GTEC variance is highest for the seasonal timescale (32 to 64 d period) followed by the 16 to 32 d period, similar to the F10.7 index. The variance during SC 23 is larger than during SC 24. The most suitable proxy to represent solar activity at the timescales of 16 to 32 d and 32 to 64 d is He II. The Mg II index, Ly-α, and F30 may be placed second as these indices show the strongest correlation with GTEC, but there are some differences in correlation during solar maximum and minimum years, as the behavior of proxies is not always the same. The indices F1.8 and daily SSA are of limited use to represent the solar impact on GTEC. The empirical orthogonal function (EOF) analysis of the TEC data shows that the first EOF component captures more than 86 % of the variance, and the first three EOF components explain 99 % of the total variance. EOF analysis suggests that the first component is associated with the solar flux and the third EOF component captures the geomagnetic activity as well as the remaining part of EOF1. The EOF2 captures 11 % of the total variability and demonstrates the hemispheric asymmetry.

Ionospheric Response to Solar EUV Radiation Variations: Comparison based on CTIPe Model Simulations and Satellite Measurements

“…Solar activity varies at different time scales from minutes to years or even centuries. The periodic behavior in the solar proxies has been studied by various authors to explore the response of the terrestrial atmosphere and especially the T/I region, and to investigate the connection between solar variability and ionospheric parameters (Jacobi et al, 2016;Vaishnav et al, 2019). A widely used method to analyse periodicities in time series is the continuous wavelet transform (CWT).…”

“…The possible relations between solar activity, geomagnetic activity, and ionospheric parameters have been studied by several authors (e.g., Abdu, 2016;Fang et al, 2018;Vaishnav et al, 2019). However, in the past studies, due to the unavailability of high-resolution data sets, several studies used only daily resolution.…”

“…However, in the past studies, due to the unavailability of high-resolution data sets, several studies used only daily resolution. To estimate the ionospheric delay, different ionospheric parameters have been considered using daily resolution data, an ionospheric delay of about 1-2 d against solar proxies has been reported (Jakowski et al, 1991;Jacobi et al, 2016;Vaishnav et al, 2019). Only recently, Schmölter et al (2020) used SDO EVE and GOES EUV fluxes to calculate the ionospheric delay of about 17 h as a mean value based on hourly time resolution data.…”

Ionospheric anomalies associated with the <i>M</i><sub>w</sub> 7.3 Iran–Iraq border earthquake and a moderate magnetic storm

“…The earthquake was felt in Iraq, Iran, and as far away as Israel, the Arabian Peninsula, and Turkey. The focal mechanism of the earthquake was identified as a thrust fault dipping at a shallow angle to the northeast (Wang et al, 2018). The earthquake occurred on the continental collision between Eurasian and Arabian plates, which is located within the Zagros fold and thrust belt.…”

“…To the first order, the degree of effect is a function of the number of free electrons. The Sun is the primary determiner of the number of electrons and causes permanent and regular ionospheric trends, such as daily, 27 d, seasonal, semiannual, annual, and 11-year trends (Vaishnav et al, 2019). The number of electrons also increases/decreases due to disturbed space weather (Bagiya et al, 2009), earthquakes (Liu et al, 2004;Şentürk et al, 2019), tsunamis (Occhipinti et al, 2013), volcanic eruptions (Dautermann et al, 2009), hurricanes (Chou et al, 2017), and anthropogenic events (Lin et al, 2017).…”