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Abstract Ultra‐low frequency (ULF) waves are a ubiquitous carrier of energy in geospace. However, their efficiency in transferring solar wind energy into the upper atmosphere remains a fundamental and not well‐understood question. This is due to their global presence, which cannot be fully quantified by spatially limited observations, and the need for self‐consistent global modeling to account for their dependence on dynamic, inhomogeneous magnetic fields and plasma densities. In this study we use a purely global magnetohydrodynamic model to investigate energy inputs to the ionosphere in the form of Poynting flux. Oscillations in solar wind dynamic pressure excite field line resonances in the magnetosphere. The total Alfvénic Poynting flux entering the ionosphere can be comparable to the total quasi‐steady Poynting flux under northward interplanetary magnetic field. The efficiency of this energy transfer via ULF waves depends on the driving frequency of the solar wind and the ionospheric Pedersen conductance. 

Abstract The Starlink satellites launched on 3 February 2022 were lost before they fully arrived in their designated orbits. The loss was attributed to two moderate geomagnetic storms that occurred consecutively on 3–4 February. We investigate the thermospheric neutral mass density variation during these storms with the Multiscale Atmosphere‐Geospace Environment (MAGE) model, a first‐principles, fully coupled geospace model. Simulated neutral density enhancements are validated by Swarm satellite measurements at the altitude of 400–500 km. Comparison with standalone TIEGCM and empirical NRLMSIS 2.0 and DTM‐2013 models suggests better performance by MAGE in predicting the maximum density enhancement and resolving the gradual recovery process. Along the Starlink satellite orbit in the middle thermosphere (∼200 km altitude), MAGE predicts up to 150% density enhancement near the second storm peak while standalone TIEGCM, NRLMSIS 2.0, and DTM‐2013 suggest only ∼50% increase. MAGE also suggests altitudinal, longitudinal, and latitudinal variability of storm‐time percentage density enhancement due to height dependent Joule heating deposition per unit mass, thermospheric circulation changes, and traveling atmospheric disturbances. This study demonstrates that a moderate storm can cause substantial density enhancement in the middle thermosphere. Thermospheric mass density strongly depends on the strength, timing, and location of high‐latitude energy input, which cannot be fully reproduced with empirical models. A physics‐based, fully coupled geospace model that can accurately resolve the high‐latitude energy input and its variability is critical to modeling the dynamic response of thermospheric neutral density during storm time. 

An important question that is being increasingly studied across subdisciplines of Heliophysics is “how do mesoscale phenomena contribute to the global response of the system?” This review paper focuses on this question within two specific but interlinked regions in Near-Earth space: the magnetotail’s transition region to the inner magnetosphere and the ionosphere. There is a concerted effort within the Geospace Environment Modeling (GEM) community to understand the degree to which mesoscale transport in the magnetotail contributes to the global dynamics of magnetic flux transport and dipolarization, particle transport and injections contributing to the storm-time ring current development, and the substorm current wedge. Because the magnetosphere-ionosphere is a tightly coupled system, it is also important to understand how mesoscale transport in the magnetotail impacts auroral precipitation and the global ionospheric system response. Groups within the Coupling, Energetics and Dynamics of Atmospheric Regions Program (CEDAR) community have also been studying how the ionosphere-thermosphere responds to these mesoscale drivers. These specific open questions are part of a larger need to better characterize and quantify mesoscale “messengers” or “conduits” of information—magnetic flux, particle flux, current, and energy—which are key to understanding the global system. After reviewing recent progress and open questions, we suggest datasets that, if developed in the future, will help answer these questions.