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.
Highlights
The interaction of solar radiation with the ionosphere is complicated due to several mechanisms with the potential to modulate the thermosphere–ionosphere (T-I) system at different timescales ranging from the 11-year solar cycle down to minutes (e.g., Liu et al, 2003; Afraimovich et al, 2008; Liu and Chen, 2009; Chen et al, 2012)
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))
In SC 23, the TEC values at low latitudes reach up to 80 TECU, while during SC 24 TEC was considerably smaller, which confirms that the zonal mean TEC behavior is strongly dependent on the solar activity, as the solar activity was very low during SC 24 compared to SC 23
Summary
The interaction of solar radiation with the ionosphere is complicated due to several mechanisms with the potential to modulate the thermosphere–ionosphere (T-I) system at different timescales ranging from the 11-year solar cycle down to minutes (e.g., Liu et al, 2003; Afraimovich et al, 2008; Liu and Chen, 2009; Chen et al, 2012). The ionosphere plasma response to solar EUV and UV variations has been widely studied using ground- and space-based observations (e.g., Jakowski et al, 1991; Jacobi et al, 2016; Schmölter et al, 2018; Jakowski et al, 1999), as well as by numerical and empirical modeling (e.g., Ren et al, 2018; Vaishnav et al, 2018a, b). R. Vaishnav et al.: Long-term trends in the ionospheric response to solar EUV variations
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