Long-term variations in the plasma sheet ion composition and substorm occurrence over 23 years

Abstract

The Geotail satellite has been operating for almost two solar cycles (~23 years) since its launch in July 1992. The satellite carries the energetic particle and ion composition (EPIC) instrument that measures the energetic ion flux (9.4-212 keV/e) and enables the investigation of long-term variations of the ion composition in the plasma sheet for solar cycles 22-24. From the statistical analysis of the EPIC data, we find that (1) the plasma ion mass (M) is approximately 1.1 amu during the solar minimum, whereas it increases to 1.5-2.7 amu during the solar maximum; (2) the increases in M seem to have two components: a raising of the baseline levels (~1.5 amu) and a large transient enhancement (~1.8-2.7 amu); (3) the baseline level change of M correlates well with the Mg II index, which is a good proxy for the solar extreme ultraviolet (EUV) or far ultraviolet (FUV) irradiance; and (4) the large transient enhancement of M is caused by strong magnetic storms. We also study the long-term variations of substorm occurrences in 1992-2015 that are evaluated with the number of Pi2 pulsations detected at the Kakioka observatory. The results suggest no clear correlation between the substorm occurrence and the Mg II index. Instead, when the substorms are classified into externally triggered events and non-triggered events, the number of the non-triggered events and the Mg II index are negatively correlated. We interpret these results that the increase in the solar EUV/FUV radiation enhances the supply of ionospheric ions (He + and O + ions) into the plasma sheet to increase M, and the large M may suppress spontaneous plasma instabilities initiating substorms and decrease the number of the non-triggered substorms. The present analysis using the unprecedentedly long-term dataset covering ~23 years provides additional observational evidence that heavy ions work to prevent the occurrence of substorms.

Recent Advancements and Remaining Challenges Associated With Inner Magnetosphere Cross‐Energy/Population Interactions (IMCEPI)

“…The in situ and ground‐based data sets have tremendously grown in the past few years due to active spacecraft missions such as Cluster, Van Allen Probes, and Arase, providing substantial data samples for understanding the inner magnetosphere environment. The ample data sets help derive long‐term trends and evolutions of the physical processes within the inner magnetosphere in response to different driving conditions, by looking into various wave properties (e.g., occurrence rate, amplitudes, spatial sizes, obliquity, and propagation; Artemyev et al, ; Aryan et al, , ; Fu et al, ; Kersten et al, ; W. Li et al, ; W. Li et al, ; Malaspina et al, , ; Meredith et al, ; Nemec et al, ; Ni et al, ; Saikin et al, ; Santolík, Macúsová, et al, ; Spasojevic et al, ; Yue et al, ; Zhima et al, , ), inner magnetosphere plasma compositions (Claudepierre et al, ; Fernandes et al, ; Kistler & Mouikis, ; Kistler, Mouikis, Spence, et al, ; Sarno‐Smith et al, ; Yue et al, ), ion mass density along closed magnetic field lines as well as mass loading in response to geomagnetic activity levels (Sandhu et al, , ), plasma sheet composition outside the inner magnetosphere (e.g., Denton et al, ; Nosé, ), the spacecraft surface charging environment (Sarno‐Smith et al, ), plasmapause model (He et al, ; Liu et al, ; X.‐X. Zhang et al, ), electric field model (Califf et al, ), and global magnetospheric field model (Tsyganenko & Andreeva, ), which upgraded from a series of previous models (e.g., Tsyganenko, , , ; Tsyganenko & Sitnov, ).…”

Integration of RAM-SCB into the Space Weather Modeling Framework

“…The in situ and ground-based data sets have tremendously grown in the past few years due to active spacecraft missions such as Cluster, Van Allen Probes, and Arase, providing substantial data samples for understanding the inner magnetosphere environment. The ample data sets help derive long-term trends and evolutions of the physical processes within the inner magnetosphere in response to different driving conditions, by looking into various wave properties (e.g., occurrence rate, amplitudes, spatial sizes, obliquity, and propagation; Artemyev et al, Aryan et al, 2014Aryan et al, , 2016Fu et al, 2014;Kersten et al, 2014;Malaspina et al, 2016Malaspina et al, , 2017Meredith et al, 2014;Nemec et al, 2016;Ni et al, 2017;Saikin et al, 2015;Santolík, Macúsová, et al, 2014;Spasojevic et al, 2015;Yue et al, 2017;Zhima et al, 2014Zhima et al, , 2015, inner magnetosphere plasma compositions (Claudepierre et al, 2016;Fernandes et al, 2017;Kistler, Mouikis, Spence, et al, 2016;Sarno-Smith et al, 2015;Yue et al, 2018), ion mass density along closed magnetic field lines as well as mass loading in response to geomagnetic activity levels (Sandhu et al, 2016(Sandhu et al, , 2017, plasma sheet composition outside the inner magnetosphere (e.g., Denton et al, 2017;Nosé, 2016), the spacecraft surface charging environment (Sarno-Smith et al, 2016), plasmapause model (He et al, 2017;Liu et al, 2015;, electric field model (Califf et al, 2014), and global magnetospheric field model (Tsyganenko & Andreeva, 2017), which upgraded from a series of previous models (e.g., Tsyganenko, 1989Tsyganenko, , 1996Tsyganenko, , 2002Tsyganenko & Sitnov, 2007).…”