The delayed ionospheric response to solar and geomagnetic activity

Abstract

Thermospheric and related ionospheric processes react delayed to the variations of solar UV and EUV radiation and are strongly influenced by solar wind changes. The coupling of processes in magnetosphere, thermosphere and ionosphere is also crucial for resulting trends of the ionospheric delay, which is driven by these complex interactions. Preceding studies estimated an ionospheric delay of 1 to 2 days by comparison of solar flux measurements or solar proxies with ionospheric parameters (most commonly the electron density). The physical and chemical processes causing the ionospheric delay are only partially understood and the delay variability has only been explored on broad scales (e.g. 11-year solar cycle). This study analyzes and discusses the ionospheric delay with calculations at hourly resolution based on solar EUV flux data and data of different ionospheric parameters. This approach introduces more possibilities for the analysis compared to preceding studies. The correlations between solar and geomagnetic activity with the ionospheric delay are presented with a detailed discussion of seasonal and spatial variations. Short- and long-term trends of the ionospheric delay are discussed to further analyze these variations and dependencies. An ionospheric delay between approximately 10 and 27 hours is retrieved throughout several analyses within this study. The results are also compared to calculations with the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) and similarities but also differences are discussed. Both, results based on observational data and model runs, contribute to the discussion of physical and chemical processes causing the ionospheric delay and confirm the imbalance of ion-production and loss as the major process behind the delayed ionospheric response. The findings of this study give more insight into ionospheric processes and their relationship to the upper atmosphere's response to solar variability.