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A new version of the US National Science Foundation National Center forAtmospheric Research (NSF NCAR) thermosphere-ionosphere-electrodynamicsgeneral circulation model (TIEGCM) has been developed and released. Thispaper describes the changes and improvements of the new version 3.0since its last major release (2.0) in 2016. These include: 1) increasingthe model resolution in both the horizontal and vertical dimensions, aswell as the ionospheric dynamo solver; 2) upward extension of the modelupper boundary to enable more accurate simulations of the topsideionosphere and neutral density in the lower exosphere; 3) improvedparameterization for thermal electron heating rate; 4) resolvingtransport of minor species N(2D); 5) treating helium as a major species;6) parameterization for additional physical processes, such as SAPS andelectrojet turbulent heating; 7) including parallel ion drag in theneutral momentum equation; 8) nudging of prognostic fields near thelower boundary from external data; 9) modification to the NO reactionrate and auroral heating rate; 10) outputs of diagnostic analysis termsof the equations; 11) new functionalities enabling model simulations ofcertain recurrent phenomena, such as solar flares and eclipse. Wepresent examples of the model validation during a moderate storm andcompare simulation results by turning on/off new functionalities todemonstrate the related new model capabilities. Furthermore, the modelis upgraded to comply with the new computer software environment at NSFNCAR for easy installation and run setup and with new visualizationtools. Finally, the model limitations and future development plans arediscussed. 

Abstract This study investigates the impact of the lower‐thermospheric winter‐to‐summer circulation on the thermosphere's thermal structure and meridional circulation. Using NCAR TIE‐GCM, we compare simulations with and without the lower‐thermospheric circulation, finding that its inclusion enhances summer‐to‐winter thermospheric circulation by 40% in the summer hemisphere but decelerates it in the winter thermosphere. Meanwhile, vertical wind exhibits stronger upward motion poleward of latitude above hPa (174 km) when lower‐thermospheric circulation is incorporated. This dynamic coupling functions as an atmospheric “gear mechanism,” accelerating momentum and energy transfer to higher altitudes. Including lower‐thermospheric circulation improves agreement between the nudged run and NRLMSIS 2.1 in intra‐annual variability (IAV) of mass density. This suggests lower‐thermospheric circulation is a key factor in modulating IAV in the coupled thermosphere‐ionosphere system. This study reveals a new coupling mechanism between the lower atmosphere, thermosphere, and ionosphere, with significant implications for understanding upper‐atmospheric dynamics and improving space weather models. 

Abstract The Gravity Field and Steady‐State Ocean Circulation Explorer (GOCE) and CHAllenging Minisatellite Payload (CHAMP) satellites measure in‐situ thermospheric density and cross‐track wind. When propagating obliquely to the satellite track in a horizontal plane (i.e., not purely along‐track or cross‐track), gravity waves (GWs) can be observed both in the density and cross‐track wind perturbations. We employ the Wavelet Analysis, red noise model, dissipative dispersion and polarization relations for thermospheric GWs, and specific criteria to determine whether a quiet‐time (Kp < 3) thermospheric traveling atmospheric disturbances (TADs) event is a GW or not. The first global morphology of thermospheric GWs instead of TADs is reported. The fast intrinsic horizontal phase speed (c_IH> 600 m/s) of most GWs suggests that they are not generated in the lower/middle atmosphere (wherec_IH < 300 m/s). A second population of GWs with slower speeds (c_IH = 50–250 m/s) in GOCE are likely from the lower/middle atmosphere, but they occur much less frequently in CHAMP. GW hotspots occur during the high‐latitude and the winter midlatitude regions. GW amplitudes exhibit semi‐annual and annual variations. These findings suggest that most GOCE and CHAMP GWs are higher‐order GWs from primary GW sources in the lower/middle atmosphere. Finally, the average propagation direction of the CHAMP GWs exhibits a clear diurnal cycle, with clockwise (counterclockwise) occurring in the northern (southern) hemisphere and equatorward propagation occurring at ∼13 LST. This suggests that the predominant GW propagation direction is opposite to the background wind direction. 

This study explores the meteorological source and vertical propagation of gravity waves (GWs) that drive daytime traveling ionospheric disturbances (TIDs), using the specified dynamics version of the SD-WACCM-X (Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension) and the SAMI3 (Sami3 is Also a Model of the Ionosphere) simulations driven by SD-WACCM-X neutral wind and composition. A cold weather front moved over the northern-central USA (90–100°W, 35–45°N) during the daytime of 20 October 2020, with strong upward airflow. GWs with ~500–700 km horizontal wavelengths propagated southward and northward in the thermosphere over the north-central USA. Also, the perturbations were coherent from the surface to the thermosphere; therefore, the GWs were likely generated by vertical acceleration associated with the cold front over Minnesota and South Dakota. The convectively generated GWs had almost infinite vertical wavelength below ~100 km due to being evanescent. This implies that the GWs tunneled through their evanescent region in the middle atmosphere (where a squared vertical wavenumber is equal to or smaller than 0) and became freely propagating in the thermosphere and ionosphere. Medium-scale TIDs (MSTIDs) also propagated southward with the GWs, suggesting that the convectively generated GWs created MSTIDs. 

Given the existential threat of climate change, we urge the heliophysics scientific community to consider ways in which we might further contribute to global efforts to address climate change. Whole atmosphere studies reveal that climate change processes impact even the uppermost regions of the atmosphere. The heliophysics research community now has models spanning the surface through the upper thermosphere and a diversity of observational datasets of the middle and upper atmosphere that span multiple decades. These studies indicate that the middle and upper atmosphere provide multiple vertical footprints for climate change and thus can contribute to an understanding of whole atmosphere climate change processes in the complex atmosphereland- ocean system. This white paper outlines recommendations for expansion of long-term data sets; simulations of climate with whole atmosphere models; engagement in collaborations with the tropospheric research community; and exploration of the possibility of heliophysics contributions to climate assessment efforts. Additionally, we recommend education and outreach efforts to help members of the wider community become more knowledgeable about climate change; support for efforts to increase the diversity of the heliophysics science community; support for international collaborations, and climate mitigation measures that our science community can implement to reduce greenhouse gas emissions from our research, education, and outreach activities. 

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? 

Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy. 

Key Points Validation of ionospheric total electron content (TEC) by the state‐of‐the‐art ionospheric models hosted by NASA Community Coordinated Modeling Center, National Oceanic and Atmospheric Administration Space Weather Prediction Center, and NASA Jet Propulsion Laboratory (JPL) Multiple metrics and skill scores are used to assess the performance of ionospheric models in capturing storm time TEC anomaly GLObal Total Electron Content and JPL Global Ionospheric Map perform best, and physics‐based models perform better than the empirical model in capturing storm TEC variations