Christian J. Lao

During substorms, magnetospheric convection and magnetotail current sheet disturbances are two critical processes, both driving ionospheric electrojets. Dusk‐side SMU and dawn‐side SML (SuperMAG Electrojet indices) enhancements reflect two‐cell convection. Using this relation, we categorize substorms with different convection properties near onset. When primary SML enhancements occur near midnight, the presence or absence of dusk‐side SMU/dawn‐side SML activity distinguishes strong from weak convection events. Additionally, we identify “dawn‐dominant” events characterized by primary SML enhancements in the dawn sector as a separate category. “Strong convection” events feature stronger and longer southward IMF and faster solar wind speeds than “weak convection” events. “Dawn‐dominant” events share similar conditions with the “strong convection” category plus a tendency of negative B _y . “Strong convection” events statistically have lower onset magnetic latitudes (MLAT), indicating current sheet instabilities closer to the Earth. Field‐aligned current (FAC) evolution reveals that the two‐cell convection pattern extends from dayside to nightside before onset, possibly contributing to flux depletion and current sheet thinning. An event with strong convection exhibits continued pressure loading, with unloading occurring only after onset. This indicates a significant contribution of open flux accumulation to current sheet thinning. The example dawn‐dominant event has strong global convection dominating the SMU/L profile, and aurora are brightened in a wide MLT range. On top of this, localized bursts in SML, aurora, dipolarizations, and flux ropes suggest that meso‐scale current sheet disturbances occur. These classifications and analyses help clarify how convection and current sheet disturbances develop and interact, potentially valuable for elucidating the descriptions of substorm processes.

During substorms, magnetospheric convection and magnetotail current sheet disturbances are two critical processes, both driving ionosphere electrojets. Dusk-side SMU and dawn-side SML enhancements may reflect convection, linked to field-aligned currents (FAC) and aurora. We partly demonstrate this relation by showing that substorms with greater SMU enhancements have stronger ion two-cell convection flows and auroral precipitations, and categorized substorms based on this relationship. When primary SML enhancements occur near midnight, the presence or absence of dusk-side SMU/dawn-side SML activity distinguishes strong from weak convection events. Additionally, we identify ‘dawn-dominant’ events characterized by primary SML enhancements in the dawn sector as a separate category. ‘Strong convection’ events feature stronger southward IMF and faster solar wind speeds, while ‘dawn-dominant’ events share similar conditions plus a favor of negative By. ‘Strong convection’ events statistically have lower onset MLAT, indicating current sheet instabilities closer to the Earth. FAC evolution reveals that the two-cell convection pattern already extends from dayside to nightside before onset, possibly contributing to flux depletion and the resulting current sheet thinning. One case shows pressure loading and unloading, indicating additional contribution of open flux accumulation to current sheet thinning. The example dawn-dominant event has strong global convection dominate the SMU/L profile, while localized bursts in SML, aurora, FAC, dipolarizations and flux ropes suggest that wedgelets-type current sheet disturbances occur. These classifications and analyses help clarify how convection and current sheet disturbances develop and interact, potentially valuable for elucidating the descriptions of substorm processes.

A systems science approach based on canonical correlation analysis (CCA) is applied as a new, behavioral way to validate global geospace models. The biggest novelty of the technique is that it validates models at a system level, whereby a side‐by‐side comparison is performed of CCA applied to a 30‐day observational and the corresponding simulation data sets comprising quiet, moderate and active times. The simulation used the Multiscale Atmosphere‐Geospace Environment (MAGE) model. It is shown that (a) CCA must be combined with sensitivity analysis to be effective, (b) the MAGE model generally reproduces the observed behavior (more so for quieter time intervals), quantified by the intercorrelations between different variables and (c) the technique identifies the SuperMAG SML index as a quantity for which refinements of the model are needed.

Auroral Kilometric Radiation (AKR) is known to present emission at lower frequencies near substorm onset, corresponding to activation of higher altitude sources along auroral magnetic field lines and acting as a proxy of the vertical distribution of acceleration processes during these events. While remote observations are more difficult to relate to ionospheric activity than those made in situ due to the illumination of the instrument by multiple AKR sources, there is a question of how the association of a particular observation with substorm activity can change with observer local time (LT) and more generally whether AKR observations can track geomagnetic activity in the same way as other proxies. This study addresses these questions by computing binary contingency tables and classification statistics including the Matthews Correlation Coefficient (MCC), using automatically selected AKR observations from Wind/WAVES and substorm event lists. AKR observations made on the dusk and dawn flanks present the greatest balance between true positive and negative associations with substorm activity, where active acceleration regions due to the enhanced substorm current wedge are distinct from typical nightside activity. Observations from the dayside and L1 can also be a reliable indicator, with AKR rarely observed from this region outside of substorm activity. Our results show that observations of AKR below 70 kHz (corresponding to active source regions above 12,000 km at an L‐shell of 7) can act as a good discriminator of substorm activity, particularly for observations from LTs near midnight.

The Earth's inner magnetosphere contains multiple electron populations influenced by different factors. The cold electrons of the plasmasphere, warm plasma that contributes to the ring current, and the relativistic plasma of the radiation belts often seem to behave independently. Using omni‐directional flux and energy measurements from the HOPE and Magnetic Electron Ion Spectrometer instruments aboard the Van Allen Probes, we provide a detailed density and temperature description of the inner magnetosphere, offering a comprehensive statistical analysis of the entire Van Allen Probe era. While number density and temperature data at geosynchronous orbit are available, this study focuses on the warm plasma in the inner magnetosphere . Values of density and temperature are extracted by fitting energy and phase space density to obtain the distribution function. The fitted distributions are related to the zeroth and second moments to estimate the number density and temperature. Analysis has indicated that a two Maxwellian fit is sufficient over a wide range of and that there are two independent plasma populations. The more energetic population has a median number density of approximately and a temperature of around 130 keV, with a temperature peak observed between L* = 4 and L* = 4.5. This population is relatively uniform in magnetic local time (MLT). In contrast, the less energetic warm electron population has a median number density of about and a temperature of 7.4 keV. Strong statistical trends in density and temperature across both L* and MLT are presented, along with potential sources driving these variations.

Substorms have been identified from negative bays in the AL/SML index, which traces the minimum northward ground magnetic deflection at auroral latitudes, produced by enhancements of the westward electrojet. For substorms, negative bays are caused by the closure of the Substorm Current Wedge through the ionosphere, typically localized to the nightside and centered around 23‐00 magnetic local time (MLT). In this case, the equivalent current pattern that causes the magnetic deflections is given the name Disturbance Polar (DP) 1. However, negative bays may also form when the westward electrojet is enhanced by increased convection, driving Pedersen and Hall currents in the auroral zone. Convection enhancements also strengthen the eastward electrojet, monitored by AU/SMU index. In this case, the equivalent current pattern that produces the magnetic deflections is called DP2. Unlike other substorm identification methods, the Substorm Onsets and PHases from Indices of the Electrojets technique by Forsyth et al. (2015), https://doi.org/10.1002/2015ja021343 attempts to distinguish between the DP1 and DP2 enhancements that cause substorm‐like SML bays. Despite this, we find evidence that between 1997 and 2019 up to 59% of the 30,329 events originally identified as substorms come from enhancements of DP2 on top of the 2,627 convection enhancement events already identified. We explore ways to improve substorm identification using auroral indices to fully separate the DP1 and DP2 bays but conclude that there is insufficient information in the auroral indices to achieve this. In reality, any “substorm” list is a list of magnetic enhancements, auroral enhancements, etc., which may or may not correspond to substorm activity and should be treated that way.

A systems science approach based on canonical correlation analysis (CCA) is applied as a new, behavioral way to validate global geospace models. The biggest novelty of the technique is that it validates models at a system level, whereby a side-by-side comparison is performed of CCA applied to a 30-day observational and the corresponding simulation datasets comprising quiet, moderate and active times. The simulation used the Multiscale Atmosphere-Geospace Environment (MAGE) model. It is shown that (1) CCA must be combined with sensitivity analysis to be effective, (2) the MAGE model generally reproduces the observed behavior (more so for quieter time intervals), quantified by the intercorrelations between different variables and (3) the technique identifies the SuperMAG SML index as a quantity for which model refinements are needed.

Substorms have been identified from negative bays in the AL/SML index, which traces the minimum northward ground magnetic deflection at auroral latitudes, produced by enhancements of the westward electrojet. For substorms, negative bays are caused by the closure of the Substorm Current Wedge through the ionosphere, typically localised to the nightside and centred around 23-00 magnetic local time (MLT). In this case, the equivalent current pattern that causes the magnetic deflections is given the name Disturbance Polar (DP) 1. However, negative bays may also form when the westward electrojet is enhanced by increased convection, driving Pedersen and Hall currents in the auroral zone. Convection enhancements also strengthen the eastward electrojet, monitored by the maximum northward ground magnetic deflection as the AU/SMU index. In this case, the equivalent current pattern that produces the magnetic deflections is called DP2. Unlike other substorm identification methods, the SOPHIE technique by Forsyth et al. (2015) attempts to distinguish between the DP1 and DP2 enhancements that cause substorm-like SML bays by also examining the SMU index. Despite this, we find evidence that between January 1997 and December 2019 up to 59% of the 30329 events originally identified as substorms come from enhancements of DP2 on top of the 2627 convection enhancement events already identified. We explore ways to improve substorm identification using auroral indices to fully separate the DP1 and DP2 bays, but conclude that there is insufficient information in the auroral indices alone to achieve this.

Geomagnetically Induced Currents (GICs) are a severe space weather hazard, driven through coupling between the solar wind and magnetosphere. GICs are rarely measured directly, instead the ground magnetic field variability is often used as a proxy. Recently space weather models have been developed to forecast whether the magnetic field variability (R) will exceed specific, extreme thresholds. We test an example machine learning‐based model developed for the northern United Kingdom. We evaluate its performance (discriminative skill and calibration) as a function of magnetospheric state, solar wind input and magnetic local time. We find that the model's performance is highest during active conditions, for example, geomagnetic storms, and lowest during isolated substorms and “quiet” intervals, despite these conditions dominating the training data set. Correspondingly, the performance is high when the solar wind conditions are elevated (i.e., high velocity, large total magnetic field strength, and the interplanetary magnetic field oriented southward), and at a minimum when the north‐south component of the magnetic field is highly variable or around zero. Regarding magnetic local time, performance is highest within the dusk and night sectors, and lowest during the day. The model appears to capture multiple modes of magnetospheric activity, including substorms and viscous interactions, but poorly predicts impulsive phenomena (i.e., storm sudden commencements) and longer timescale coupling processes. Future models of mid‐latitude magnetic field variability will need to effectively use longer time intervals of unpropagated (i.e., observations from L1) solar wind to more completely describe the magnetospheric conditions and response.