By Maria-Theresia Walach & Elliot Day (Lancaster University)
Joule heating is a major energy sink in the solar wind-magnetosphere-ionosphere system and modeling it is key to understanding the impact of space weather on the neutral atmosphere. Ion drifts and neutral wind velocities are key parameters when modeling Joule heating, however there is limited validation of the modeled ion and neutral velocities at mid-latitudes. We use the Blackstone Super Dual Auroral Radar Network radar and the Michigan North American Thermosphere Ionosphere Observing Network Fabry-Perot interferometer to obtain the local nightside ion and neutral velocities at ∼40° geographic latitude during the nighttime of 16 July 2014. Despite being a geomagnetically quiet period, we observe significant sub-auroral ion flows in excess of 200 ms−1. We calculate an enhancement to the local Joule heating rate due to these ion flows and find that the neutrals impart a significant increase or decrease to the total Joule heating rate of >75% depending on their direction. As is shown in the figure. Different patches in the ionosphere move at different speeds and therefore create varying Joule heating rates. We compare our observations to outputs from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM). At such a low geomagnetic activity however, TIEGCM was not able to model significant sub-auroral ion flows and any resulting Joule heating enhancements equivalent to our observations. We found that the neutral winds were the primary contributor to the Joule heating rates modeled by TIEGCM rather than the ions as suggested by our observations. This presents a gap in TIEGCM's capabilities and care has to be taken when estimating Joule heating rates from models, which make assumptions about ionospheric flows at mid-latitudes.
Panels a, b, c, d and e shows the estimated Joule heating components and total heating for each identified patch of
ionospheric flow, the panel labels correspond to the different patch velocities in the paper.
Each component is plotted according to the legend in panel a.
Panel f shows the Joule heating components and total heating modeled by TIEGCM.
Panel g shows the total Joule heating rate calculated as the average heating rate of all patches in the common area,
while re-plotting TIEGCM's total Joule heating in orange for comparison.
See publication for details:
Day, E. K., Grocott, A., Walach, M.-T., Wild, J. A., Lu, G., Ruohoniemi, J. M., & Coster, A. J. (2024). Observation of quiet-time mid-latitude Joule heating and comparisons with the TIEGCM simulation. Journal of Geophysical Research: Space Physics, 129, e2024JA032578. https://doi.org/10.1029/2024JA032578
By Christian Lao (UCL, Mullard Space Science Laboratory)
Substorms are a major energy unloading process in the solar wind-magnetosphere-ionosphere system that are capable of processing approximately 10^15J of stored solar wind energy through a complete cycle (Tanskanen et al., 2002). In broad terms, energy is built up and stored in the magnetosphere during substorm growth phases by enhanced reconnection between the solar wind and the magnetosphere on the dayside. Energy is released during substorm expansion and recovery phases by reconnection and other plasma instabilities in the magnetotail, with the start of the expansion phase known as substorm onset. Over the years, numerous signatures and techniques have been used to identify substorm onsets, but these are typically developed or used in isolation. In this study, we quantify the association between the different signatures used for identification of substorms (more specifically, substorm onset). Generally, we found that methods developed and applied to ground magnetometer data achieved the best associations, with events identified at mid-latitude (Chu et al., 2015) and at auroral latitudes (Newell & Gjerloev, 2011) showing the highest levels of coincidence with other substorm indicators. On the other hand, we found that events identified by particle injections (Borovsky & Yakymenko, 2017) showed much poorer associations with other substorm lists, in particular with no improvement over chance agreement with events identified from the aurora. Remarkably, we found less than 50% agreement on the timing and occurrence of substorms between any of the lists studied. As such, any studies dependent upon such lists may come to premature conclusions about substorm dynamics or impacts. Furthermore, a significant number of events in each list may not share multiple substorm signatures, calling into question the legitimacy of each identification. This highlights the need to further cross calibrate our methods or use multiple signatures to evaluate a more robust set of events.
True positive rate “heatmap” showing the percentage of events from the Reference (Horizontally labelled)
substorm list a Comparison substorm list (Vertically labelled) also observes. These are the best
performing methods on the datasets they are leveraging. A higher score/lighter colour indicates better association.
References:
Tanskanen et al. 2002: https://doi.org/10.1029/2001JA900153
Chu et al., 2015: https://doi.org/10.1002/2015JA021104
Newell & Gjerloev, 2011: https://doi.org/10.1029/2011JA016779
Borovsky & Yakymenko, 2017: https://doi.org/10.1002/2016JA023625
See publication for details:
Lao, C. J., Forsyth, C., Freeman, M. P., Smith, A. W., & Mooney, M. K. (2024). On the association of substorm identification methods. Journal of Geophysical Research: Space Physics, 129, e2024JA032762. https://doi.org/10.1029/2024JA032762