Long‐Term Trend of Topside Ionospheric Electron Density Derived From DMSP Data During 1995–2017. Issue 12 (28th December 2019)

• Record Type: Journal Article
• Authors: Cai, Yihui; Yue, Xinan; Wang, Wenbin; Zhang, Shunrong; Liu, Libo; Liu, Huixin; Wan, Weixing
• Is Part Of: Journal of geophysical research. Volume 124:Issue 12(2019)
• Page Start: 10708
• Page End: 10727
• Publication Date: 2019-12-28
• Subjects: long‐term trend -- topside ionosphere -- electron density -- DMSP -- TIEGCM; Magnetospheric physics -- Periodicals; Space environment -- Periodicals; Cosmic physics -- Periodicals; Planets -- Atmospheres -- Periodicals; Heliosphere (Astrophysics) -- Periodicals; Geophysics -- Periodicals; 523.01
• DOI: 10.1029/2019JA027522
• Languages: English
• ISSNs: 2169-9380

Abstract

Abstract: In recent decades, significant efforts have been made to characterize and understand the global pattern of ionospheric long‐term trend. However, little attention has been paid to the topside ionosphere trend. In this study, the unique in situ data measured by series Defense Meteorological Satellite Program (DMSP) satellites were utilized to derive the long‐term trend of the topside ionosphere for the first time. We checked carefully data quality, gap, and consistency between different satellites for both electron density and ion temperature, and compared the techniques of artificial neuron network (ANN) and multiple linear regression methods for deriving the trend. The electron density ( N e ) trend in the middle and low latitudes at ~860 km around 18 MLT was derived using the ANN method from 1995–2017. The trend from DMSP observations has a mean magnitude ranging from ~ − 2% to ~2% per decade, with clear seasonal, latitude and longitude variations. The derived trend was evaluated by directly comparing with the simulated trend at 500 km from the NCAR‐TIEGCM driven by realistic changes of CO2 level and geomagnetic field. The observed and simulated trends have similar geographic distribution patterns at 18 MLT. The good agreement between the observed trend around 860 km and the simulated trend near 500 km implies that the physical processes controlling the N e trends above the peak height might be identical. Further control simulations show that the geomagnetic field secular variation is the dominant factor of the electron density trend at around 500 km, rather than the CO2 long‐term enhancement. Plain Language Summary: The long‐term trends in the topside ionosphere have been rarely concerned mainly due to less data availability in comparison with the lower ionosphere. The Defense Meteorological Satellite Program spacecraft with orbital height of ~860 km provide a unique opportunity to explore the distribution pattern of long‐term trends in the topside ionosphere. Based on the DMSP data after critical data quality check, we used the artificial neuron network method to derive the long‐term trend of the topside (~860 km) ionospheric electron density ( N e ) around 18 MLT. We obtained, for the first time, the global, long‐term trend map of N e . The mean magnitude of the trend ranges from ~ − 2% to ~2% per decade, which shows clear temporal and spatial variations. Moreover, in order to understand the physical mechanism of the trend, we performed a series of numerical simulations based on a first principles model of TIEGCM. The simulated results of the trend at 500 km are compared with the observed ones near 860 km, and a good agreement is found between the two, especially around the sunset time. The good agreement enables us to study the physical mechanisms of the topside ionospheric trend through control simulations. Eventually, we found that the secular variation of geomagnetic field is the dominant factor of the trends in the topside ionosphere, rather than the increased CO2 . Key Points: The long‐term trend of topside ionospheric electron density is derived from DMSP measurements for the first time The trend at 18 MLT has a mean value ranging from ~ − 2% to ~2% per decade, with clear seasonal, latitude and longitude variations There is good agreement between the observed and simulated trends, especially near the sunset time