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Abstract On 3 February 2022, at 18:13 UTC, SpaceX launched and a short time later deployed 49 Starlink satellites at an orbit altitude between 210 and 320 km. The satellites were meant to be further raised to 550 km. However, the deployment took place during the main phase of a moderate geomagnetic storm, and another moderate storm occurred on the next day. The resulting increase in atmospheric drag led to 38 out of the 49 satellites reentering the atmosphere in the following days. In this work, we use both observations and simulations to perform a detailed investigation of the thermospheric conditions during this storm. Observations at higher altitudes, by Swarm‐A (∼438 km, 09/21 Local Time [LT]) and the Gravity Recovery and Climate Experiment Follow‐On (∼505 km, 06/18 LT) missions show that during the main phase of the storms the neutral mass density increased by 110% and 120%, respectively. The storm‐time enhancement extended to middle and low latitudes and was stronger in the northern hemisphere. To further investigate the thermospheric variations, we used six empirical and first‐principle numerical models. We found the models captured the upper and lower thermosphere changes, however, their simulated density enhancements differ by up to 70%. Further, the models showed that at the low orbital altitudes of the Starlink satellites (i.e., 200–300 km) the global averaged storm‐time density enhancement reached up to ∼35%–60%. Although such storm effects are far from the largest, they seem to be responsible for the reentry of the 38 satellites. 

Climate change is characterized by global surface warming associated with the increase of greenhouse gas population since the start of the industrial era. Growing evidence shows that the upper atmosphere is experiencing appreciable cooling over the last several decades. The seminal modeling study by Roble and Dickinson (1989) suggested potential effects of increased greenhouse gases on the ionosphere and thermosphere cooling which appear consistent with some observations. However, several outstanding issues remain regarding the role of CO 2 , other important contributors, and impacts of the cooling trend in the ionosphere and thermosphere: for example, (1) what is the regional variability of the trends? (2) the very strong ionospheric cooling observed by multiple incoherent scatter radars that does not fit with the prevailing theory based on the argument of anthropogenic greenhouse gas increases, why? (3) what is the effect of secular changes in Earth’s main magnetic field? Is it visible now in the ionospheric data and can it explain some of the regional variability in the observed ionospheric trends? (4) what is the impact of long-term cooling in the thermosphere on operational systems? (5) what are the appropriate strategic plans to ensure the long-term monitoring of the critical space climate? 

Abstract The upper boundary height of the traditional community general circulation model of the ionosphere‐thermosphere system is too low to be applied to the topside ionosphere/thermosphere study. In this study, the National Center for Atmospheric Research Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (NCAR‐TIEGCM) was successfully extended upward by four scale heights from 400–600 km to 700–1,200 km depending on solar activity, named TIEGCM‐X. The topside ionosphere and thermosphere simulated by TIEGCM‐X agree well with the observations derived from a topside sounder and satellite drag data. In addition, the neutral density, temperature, and electron density simulated by TIEGCM‐X are morphologically consistent with the NCAR‐TIEGCM simulations before extension. The latitude‐altitude distribution of the equatorial ionization anomaly derived from TIEGCM‐X is more reasonable. During geomagnetic storm events, the thermospheric responses of TIEGCM‐X are similar to NCAR‐TIEGCM. However, the ionospheric storm effects in TIEGCM‐X are stronger than those in NCAR‐TIEGCM and are even opposites at some middle and low latitudes due to the presence of more closed magnetic field lines. Defense Meteorological Satellite Program observations prove that the ionospheric storm effect of TIEGCM‐X is more reasonable. The well‐validated TIEGCM‐X has significant potential applications in ionospheric/thermospheric studies, such as the responses to storms, low‐latitude dynamics, and data assimilation. 

Abstract Ionospheric F‐region electron density is anomalously higher in the evening than during the daytime on many occasions in the summer in geomagnetic mid‐latitude regions. This unexpected ionospheric diurnal variation has been studied for several decades. The underlying processes have been suggested to be related to meridional winds, topside influx arising from sunset ionospheric collapse, and other factors. However, substantial controversies remain unresolved. Using a numerical model driven by the statistical topsideO⁺diffusive flux from the Millstone Hill incoherent scatter radar data, we provide new insight into the competing roles of topside diffusive flux, neutral winds, and electric fields in forming the evening density peak. Simulations indicate that while meridional winds, which turn equatorward before sunset, are essential to sustain the daytime ionization near dusk, the topside diffusive flux is critically important for the formation and timing of the summer evening density peak. 

Abstract Ionospheric day‐to‐day variability is essential for understanding the space environment, while it is still challenging to properly quantify and forecast. In the present work, the day‐to‐day variability of F2 layer peak electron densities (NmF2) is examined from both observational and modeling perspectives. Ionosonde data over Wuhan station (30.5°N, 114.5°E; 19.3°N magnetic latitude) are compared with simulations from the specific dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD‐WACCM‐X) and the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM) in 2009 and 2012. Both SD‐WACCM‐X and TIEGCM are driven by the realistic 3 h geomagnetic index and daily solar input, and the former includes self‐consistently solved physics and chemistry in the lower atmosphere. The correlation coefficient between observations and SD‐WACCM‐X simulations is much larger than that of the TIEGCM simulations, especially during dusk in 2009 and nighttime in 2012. Both the observed and SD‐WACCM‐X simulated day‐to‐day variability of NmF2 reveal a similar day‐night dependence in 2012 that increases large during the nighttime and decreases during the daytime, and shows favorable consistency of daytime variability in 2009. Both the observations and SD‐WACCM‐X simulations also display semiannual variations in nighttime NmF2 variability, although the month with maximum variability is slightly different. However, TIEGCM does not reproduce the day‐night dependence or the semiannual variations well. The results emphasize the necessity for realistic lower atmospheric perturbations to characterize ionospheric day‐to‐day variability. This work also provides a validation of the SD‐WACCM‐X in terms of ionospheric day‐to‐day variability. 

Abstract Ionospheric day‐to‐day variability is ubiquitous, even under undisturbed geomagnetic and solar conditions. In this paper, quiet‐time day‐to‐day variability of equatorial vertical E × B drift is investigated using observations from ROCSAT‐1 satellite and the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (WACCM‐X) v2.1 simulations. Both observations and model simulations illustrate that the day‐to‐day variability reaches the maximum at dawn, and the variability of dawn drift is largest around June solstice at ~90–180°W. However, there are significant challenges to reproduce the observed magnitude of the variability and the longitude distributions at other seasons. Using a standalone electro‐dynamo model, we find that the day‐to‐day variability of neutral winds in the E‐region (≤~130 km) is the primary driver of the day‐to‐day variability of dawn drift. Ionospheric conductivity modulates the drift variability responses to the E‐region wind variability, thereby determining its strength as well as its seasonal and longitudinal variations. Further, the day‐to‐day variability of dawn drift induced by individual tidal components of winds in June are examined: DW1, SW2, D0, and SW1 are the most important contributors.