Long-term trends in the ionospheric response to solar extreme-ultraviolet variations
Long-term trends in the ionospheric response to solar extreme-ultraviolet variations
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DOI：
10.5194/angeo-37-1141-2019
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发表时间：
2019-12
影响因子：
1.9
通讯作者：
Rajesh Vaishnav;C. Jacobi;J. Berdermann
中科院分区：
文献类型：
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作者：
Rajesh Vaishnav;C. Jacobi;J. Berdermann
抽象的。由于热层-电离层系统与不断变化的太阳辐射通量的相互作用，它表现出高度的复杂性。我们使用国际全球导航卫星系统服务提供的 18 年（1999-2017 年）总电子含量（TEC）图和 12 个太阳代理（F10.7、F1.8、F3.2、F8、F15、F30、He II、Mg II 指数、Ly-α、Ca II K、每日太阳黑子面积（SSA）和太阳黑子数量，研究电离层对太阳活动的时间和空间响应（SSN））。交叉小波和 Lomb-Scargle 周期图 (LSP) 分析用于评估不同太阳代理对全球平均 TEC (GTEC) 的影响，这对于改进电离层建模和预报非常重要。所有太阳代理和 GTEC 的周期均已确定为 16 至 32 天。在这个时间尺度上，He II、Mg II 和 F30 指数与 GTEC 之间观察到最大相关性，有效时间延迟约为 1 d。 LSP 分析表明最主要的周期是 27 d ，这是由于平均太阳自转造成的，其次是 45 d 的周期性。此外，GTEC还存在半年和年度变化，其中赤道附近相关性最强，存在约1~2 d的时间延迟。小波方差估计方法用于计算太阳周期 SC 23 和 SC 24 最大值期间 GTEC 和 F10.7 的方差。小波方差估计表明，GTEC 方差在季节时间尺度（32 至 64 d 时段）最高，其次是 16 至 32 d 时段，与 F10.7 指数类似。 SC 23 期间的方差大于 SC 24 期间的方差。代表 16 至 32 d 和 32 至 64 d 时间尺度的太阳活动的最合适代理是 He II。 Mg II 指数、Ly-α 和 F30 可能排在第二位，因为这些指数与 GTEC 的相关性最强，但在太阳极大年和太阳最小年的相关性存在一些差异，因为代理的行为并不总是相同。指数 F1.8 和每日 SSA 在表示太阳对 GTEC 的影响方面作用有限。 TEC 数据的经验正交函数 (EOF) 分析表明，第一个 EOF 分量捕获了超过 86% 的方差，前三个 EOF 分量解释了 99% 的总方差。 EOF 分析表明，第一个分量与太阳通量相关，第三个 EOF 分量捕获地磁活动以及 EOF1 的其余部分。 EOF2 捕获了总变异性的 11%，并展示了半球不对称性。
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.