Research Article

Climatology and modeling of ionospheric irregularities over Greenland based on empirical orthogonal function method

¹ Department of Physics, University of Oslo, PO Box 1048, Blindern, 0316 Oslo, Norway
² University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering, ul. Oczapowskiego 2, 10-719 Olsztyn, Poland
³ German Aerospace Center (DLR), Institute for Solar-Terrestrial Physics, Kalkhostweg 53, 17235 Neustrelitz, Germany
⁴ GNSS Research Center, Wuhan University, Luoyu Road 129, Wuhan city, Hubei Province, PR China
⁵ Department of Mathematics, IonSAT, Universitat Politecnica de Catalunya, 08034 Barcelona, Spain

^* Corresponding author: yaqi.jin@fys.uio.no

Received: 15 November 2021
Accepted: 31 May 2022

Abstract

This paper addresses the long-term climatology (over two solar cycles) of total electron content (TEC) irregularities from a polar cap station (Thule) using the rate of change of the TEC index (ROTI). The climatology reveals variabilities over different time scales, i.e., solar cycle, seasonal, and diurnal variations. These variations in different time scales can be explained by different drivers/contributors. The solar activity (represented by the solar radiation index F10.7P) dominates the longest time scale variations. The seasonal variations are controlled by the interplay of the energy input into the polar cap ionosphere and the solar illumination that damps the amplitude of ionospheric irregularities. The diurnal variations (with respect to local time) are controlled by the relative location of the station with respect to the auroral oval. We further decompose the climatology of ionospheric irregularities using the empirical orthogonal function (EOF) method. The first four EOFs could reflect the majority (99.49%) of the total data variability. A climatological model of ionospheric irregularities is developed by fitting the EOF coefficients using three geophysical proxies (namely, F10.7P, Bt, and Dst). The data-model comparison shows satisfactory results with a high Pearson correlation coefficient and adequate errors. Additionally, we modeled the historical ROTI during the modern grand maximum dating back to 1965 and made the prediction during solar cycle 25. In such a way, we can directly compare the climatic variations of the ROTI activity across six solar cycles.