Wandi Yu

and 10 more

The Hunga Tonga Hunga-Ha’apai (HTHH) volcanic eruption on 15 January 2022 injected water vapor and SO2 into the stratosphere. Several months after the eruption, significantly stronger westerlies, and a weaker Brewer-Dobson circulation developed in the stratosphere of the Southern Hemisphere and were accompanied by unprecedented temperature anomalies in the stratosphere and mesosphere. In August 2022 the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite instrument observed record-breaking temperature anomalies in the stratosphere and mesosphere that alternate signs with altitude. Ensemble simulations carried out with the Whole Atmosphere Community Climate Model (WACCM6) indicate that the strengthening of the stratospheric westerlies explains the mesospheric temperature changes. The stronger westerlies cause stronger westward gravity wave drag in the mesosphere, accelerating the mesospheric mean meridional circulation. The stronger mesospheric circulation, in turn, plays a dominant role in driving the changes in mesospheric temperatures. This study highlights the impact of large volcanic eruptions on middle atmospheric dynamics and provides insight into their long-term effects in the mesosphere. On the other hand, we could not discern a clear mechanism for the observed changes in stratospheric circulation. In fact, an examination of the WACCM ensemble reveals that not every member reproduces the large changes observed by SABER. We conclude that there is a stochastic component to the stratospheric response to the HTHH eruption.

Min-Yang Chou

and 8 more

It is well-known that equatorial plasma bubbles (EPBs) are highly correlated to the post-sunset rise of the ionosphere on a climatological basis. However, when proceeding to the daily EPB development, what controls the day-to-day/longitudinal variability of EPBs remains a puzzle. In this study, we investigate the underlying physics responsible for the day-to-day/longitudinal variability of EPBs using the Sami3 is A Model of the Ionosphere (SAMI3) and the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X). Simulation results on October 20, 22, and 24, 2020 were presented. SAMI3/WACCM-X self-consistently generated midnight EPBs on October 20 and 24, displaying irregular and regular spatial distributions, respectively. However, EPBs are absent on October 22. We investigate the role of gravity waves on upwelling growth and EPB development and discuss how gravity waves contribute to the distributions of EPBs. Of particular significance is that we found the westward wind associated with solar terminator waves and gravity waves causes midnight vertical drift enhancement and collisional shear instability, which provides conditions favorable for upwelling growth and EPB development. The converging and diverging winds associated with solar terminator waves and midnight temperature maximum also affect the longitudinal distribution of EPBs. The absence of EPBs on October 22 is related to the weak upward drift induced by weak westward wind associated with solar terminator waves.

Dayakrishna Nailwal

and 3 more

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER, along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies.

Ja Soon Shim

and 16 more

Assessing space weather modeling capability is a key element in improving existing models and developing new ones. In order to track improvement of the models and investigate impacts of forcing, from the lower atmosphere below and from the magnetosphere above, on the performance of ionosphere-thermosphere models, we expand our previous assessment for 2013 March storm event [Shim et al., 2018]. In this study, we evaluate new simulations from upgraded models (Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model version 4.1 and Global Ionosphere Thermosphere Model (GITM) version 21.11) and from NCAR Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) version 2.2 including 8 simulations in the previous study. A simulation of NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model version 2 (TIE-GCM 2) is also included for comparison with WACCM-X. TEC and foF2 changes from quiet-time background are considered to evaluate the model performance on the storm impacts. For evaluation, we employ 4 skill scores: Correlation coefficient (CC), root-mean square error (RMSE), ratio of the modeled to observed maximum percentage changes (Yield), and timing error(TE). It is found that the models tend to underestimate the storm-time enhancements of foF2 (F2-layer critical frequency) and TEC (Total Electron Content) and to predict foF2 and/or TEC better in the North America but worse in the Southern Hemisphere. The ensemble simulation for TEC is comparable to results from a data assimilation model (Utah State University-Global Assimilation of Ionospheric Measurement (USU-GAIM)) with differences in skill score less than 3% and 6% for CC and RMSE, respectively.

Corwin Wright

and 13 more

The January 2022 Hunga Tonga–Hunga Haʻapai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for >12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

Adam C Kellerman

and 11 more

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.