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  1. 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. 
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    Free, publicly-accessible full text available August 23, 2024
  2. 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 
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  3. 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? 
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  4. 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. 
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  5. Abstract

    As a companion study of the Part 1 (J. C. Wang et al., 2022,https://doi.org/10.1029/2022JA030948), the impact of the lower‐thermospheric circulation on atomic oxygen (O) in the mesosphere and lower thermosphere (MLT) region is investigated in this Part 2 using Specified Dynamics Configuration Runs of the Whole Atmosphere Community Climate Model eXtended (SD‐WACCMX) output. The asymmetry of the O profile in the summer and winter MLT region is mainly driven by local vertical advection, which is associated with the lower‐thermospheric winter‐to‐summer circulation and middle‐to‐upper thermospheric summer‐to‐winter circulation. It is found that meridional transport and eddy diffusion only weakly modulate the O budget within this altitude range. The globally and annually averaged transport effect due to the vertical advection is quantitatively estimated. It is shown that the vertical advection is the dominant mechanism in redistributing O at altitudes between 84 and 103 km, suggesting the vertical wind can efficiently transport O between its source and sink region within the vertical column. This study demonstrates that whole atmosphere coupling on seasonal time scales is a complex interaction involving multiple underlying mechanisms within the space‐atmosphere interaction region.

     
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  6. Abstract

    The satellite‐based Cloud Imaging and Particle Size (CIPS) instrument and Atmospheric Infrared Sounder (AIRS) observed concentric gravity waves (GWs) generated by Typhoon Yutu in late October 2018. This work compares CIPS and AIRS nadir viewing observations of GWs at altitudes of 50–55 and 30–40 km, respectively, to simulations from the high‐resolution European Centre for Medium‐Range Weather Forecasting Integrated Forecasting System (ECMWF‐IFS) and ECMWF reanalysis v5 (ERA5). Both ECMWF‐IFS with 9 km and ERA5 with 31 km horizontal resolution show concentric GWs at similar locations and timing as the AIRS and CIPS observations. The GW wavelengths are ∼225–236 km in ECMWF‐IFS simulations, which compares well with the wavelength inferred from the observations. After validation of ECMWF GWs, five category five typhoon events during 2018 are analyzed using ECMWF to obtain characteristics of concentric GWs in the Western Pacific regions. The amplitudes of GWs in the stratosphere are not strongly correlated with the strength of typhoons, but are controlled by background wind conditions. Our results confirm that amplitudes and shapes of concentric GWs observed in the stratosphere and lowermost mesosphere are heavily influenced by the background wind conditions.

     
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  7. Abstract

    The cloud imaging and particle size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere satellite provides images of gravity waves (GWs) near the stratopause and lowermost mesosphere (altitudes of 50–55 km). GW identification is based on Rayleigh Albedo Anomaly (RAA) variances, which are derived from GW‐induced fluctuations in Rayleigh scattering at 265 nm. Based on 3 years of CIPS RAA variance data from 2019 to 2022, we report for the first time the seasonal distribution of GWs entering the mesosphere with high (7.5 km) horizontal resolution on a near‐global scale. Seasonally averaged GW variances clearly show spatial and temporal patterns of GW activity, mainly due to the seasonal variation of primary GW sources such as convection, the polar vortices and flow over mountains. Measurements of stratospheric GWs derived from Atmospheric InfraRed Sounder (AIRS) observations of 4.3 μm brightness temperature perturbations within the same 3‐year time range are compared to the CIPS results. The comparisons show that locations of GW hotspots are similar in the CIPS and AIRS observations. Variability in GW variances and the monthly changes in background zonal wind suggest a strong GW‐wind correlation. This study demonstrates the utility of the CIPS GW variance data set for statistical investigations of GWs in the lowermost mesosphere, as well as provides a reference for location/time selection for GW case studies.

     
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  8. The mesospheric polar vortex (MPV) plays a critical role in coupling the atmosphere-ionosphere system, so its accurate simulation is imperative for robust predictions of the thermosphere and ionosphere. While the stratospheric polar vortex is widely understood and characterized, the mesospheric polar vortex is much less well-known and observed, a short-coming that must be addressed to improve predictability of the ionosphere. The winter MPV facilitates top-down coupling via the communication of high energy particle precipitation effects from the thermosphere down to the stratosphere, though the details of this mechanism are poorly understood. Coupling from the bottom-up involves gravity waves (GWs), planetary waves (PWs), and tidal interactions that are distinctly different and important during weak vs. strong vortex states, and yet remain poorly understood as well. Moreover, generation and modulation of GWs by the large wind shears at the vortex edge contribute to the generation of traveling atmospheric disturbances and traveling ionospheric disturbances. Unfortunately, representation of the MPV is generally not accurate in state-of-the-art general circulation models, even when compared to the limited observational data available. Models substantially underestimate eastward momentum at the top of the MPV, which limits the ability to predict upward effects in the thermosphere. The zonal wind bias responsible for this missing momentum in models has been attributed to deficiencies in the treatment of GWs and to an inaccurate representation of the high-latitude dynamics. In the coming decade, simulations of the MPV must be improved. 
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