Search for: All records

Abstract The strong very high frequency (VHF) radiation from compact intra‐cloud discharges (CIDs) is attributed to streamers. An analytical model, taking altitude and applied electric field as input, is developed for effective representation of current for a double‐headed exponentially growing streamer. The decay of streamer current is attributed to two‐ and three‐body attachment of electrons to molecular oxygen. The model predicts streamers of growing strength and spatial scales at altitudes where electron losses due to three‐body attachment are suppressed with reducing air pressure. We show that CIDs at higher altitudes develop during a longer period such that the spectral content of recorded sferics shifts toward lower frequencies. The model is used to interpret the recorded sferics of two CIDs originating from km altitude in terms of radio signals emanating from an ensemble of streamers. The driving thundercloud electric fields are found to be , where is conventional breakdown threshold field. 

Abstract Global climate models parameterize a range of atmospheric‐oceanic processes, including gravity waves (GWs), clouds, moist convection, and turbulence, that cannot be sufficiently resolved. These subgrid‐scale closures for unresolved processes are a substantial source of model uncertainty. Here, we present a new approach to developing machine learning (ML) parameterizations of small‐scale climate processes by fine‐tuning a pre‐trained AI foundation model (FM). FMs are largely unexplored in climate research. A pre‐trained encoder‐decoder from a 2.3 billion parameter FM (NASA and IBM Research's Prithvi WxC)—which contains a latent probabilistic representation of atmospheric evolution—is fine‐tuned (or reused) to create a deep learning parameterization for atmospheric gravity waves (GWs); a process unseen during pre‐training. The parameterization captures GW effects for a coarse‐resolution climate model by learning the fluxes from an atmospheric reanalysis with 10 times finer resolution. A comparison of monthly averages and instantaneous evolution with a machine learning model baseline (an Attention U‐Net) reveals superior predictive performance of the FM parameterization throughout the atmosphere, even in regions excluded during pre‐training. This performance boost is quantified using the Hellinger distance, which is 0.11 for the baseline and 0.06 for the fine‐tuned model. Our findings emphasize the versatility and reusability of FMs, which could be used to accomplish a range of atmosphere‐ and climate‐related applications, leading the way for the creation of observations‐driven and physically accurate parameterizations for more earth system processes. 

Abstract We study the dynamical and thermal roles of internal gravity waves generated in the troposphere and above using the Coupled Middle Atmosphere Thermosphere‐2 General Circulation Model. This model incorporates the whole atmosphere nonlinear spectral gravity wave parameterization and its extension to include non‐tropospheric sources. We conducted model experiments for northern summer solstice conditions, first including only tropospheric sources, then including sources localized at 50 and 90 km, and uniformly distributed over all heights. The simulated differences in mean temperature and horizontal winds demonstrate that gravity waves produce the greatest dynamical and thermal changes in the latter case compared to the localized sources. While the gravity wave drag is longitudinally uniform in the lower thermosphere, it is more localized in the upper thermosphere in all the simulations. Waves from uniformly distributed sources increase the longitudinal variability of zonal winds in the thermosphere up to 150 km. Gravity wave effects exhibit different local time variations in the lower thermosphere (100–140 km) than in the upper thermosphere. In the upper thermosphere, gravity wave effects are stronger during the day than at night. In contrast, nighttime gravity wave effects are stronger than the daytime ones in the lower thermosphere. Finally, a comparison with ICON‐MIGHTI observations shows that the model reproduces the basic structure of thermospheric winds, performing better with zonal winds than with meridional winds. Adding non‐tropospheric wave sources modifies wind structures in wave‐breaking regions, but does not improve the global statistical comparison. 

Abstract We report Magnetospheric Multiscale observations of oxygen ions (O+) during a coronal mass ejection (CME) in April 2023 when the solar wind was sub‐Alfvénic and Alfvén wings formed. For the first time, O+ characteristics are studied at the contact region between the unshocked solar wind and the magnetosphere. The O+ ions show energies between 100s eV and ∼30 keV. The possible sources are the ring current, the warm plasma cloak, and the ionosphere. The O+ ions exhibit bi‐directional streaming along newly‐formed closed field lines (CFLs), and dominantly anti‐parallel on earlier‐formed CFLs. Escaping O+ ions in the unshocked solar wind are observed. During the recovery phase, the O+ pitch‐angle distribution associated with flux tubes shows dispersion, indicating potential loss to the solar wind. Our results show escaping as well as trapped O+ ions in the region where a magnetic cloud, an Alfvén wing, and magnetospheric field lines are mixed. 

Abstract We expand the assessment study of modeling capabilities in the prediction of foF2 and hmF2 for the ionospheric climatology (Tsagouri et al., 2018,https://doi.org/10.1029/2018sw002035) by using updated empirical (IRI and MIT Empirical model) and physics‐based models (CTIPe, WACCM‐X, and TIE‐GCM) as well as the additional observations in the southern hemisphere. Monthly medians of foF2 and hmF2 are considered to evaluate the model performance for the entire year of 2012. For quantitative evaluation, we employ several metrics including the correlation coefficient (R), coefficient of determination (R²), root‐mean square error (RMSE), mean error (ME), and mean relative error (MRE). The linear regression analysis shows that the empirical models perform much better than physics‐based models for foF2 but to a lesser degree for hmF2. There are negligible hemispheric differences in the predictions from empirical models. All the physics‐based models show relatively good correlations with the observations for foF2 in the northern hemisphere compared to the southern hemisphere, but the hemispheric differences are small for hmF2. The results of the study indicate that recent versions of empirical models tend to perform better than old versions of the models, but this is not always true for physics‐based models. 

Abstract Gravity waves (GWs) make crucial contributions to the middle atmospheric circulation. Yet, their climate model representation remains inaccurate, leading to key circulation biases. This study introduces a set of three neural networks (NNs) that learn to predict GW fluxes (GWFs) from multiple years of high‐resolution ERA5 reanalysis. The three NNs: a ANN, a ANN‐CNN, and an Attention UNet embed different levels of horizontal nonlocality in their architecture and are capable of representing nonlocal GW effects that are missing from current operational GW parameterizations. The NNs are evaluated offline on both time‐averaged statistics and time‐evolving flux variability. All NNs, especially the Attention UNet, accurately recreate the global GWF distribution in both the troposphere and the stratosphere. Moreover, the Attention UNet most skillfully predicts the transient evolution of GWFs over prominent orographic and nonorographic hotspots, with the model being a close second. Since even ERA5 does not resolve a substantial portion of GWFs, this deficiency is compensated by subsequently applying transfer learning on the ERA5‐trained ML models for GWFs from a 1.4 km global climate model. It is found that the re‐trained models both (a) preserve their learning from ERA5, and (b) learn to appropriately scale the predicted fluxes to account for ERA5's limited resolution. Our results highlight the importance of embedding nonlocal information for a more accurate GWF prediction and establish strategies to complement abundant reanalysis data with limited high‐resolution data to develop machine learning‐driven parameterizations for missing mesoscale processes in climate models. 

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 Magnetic reconnection changes the magnetic field topology and facilitates the energy and particle exchange at magnetospheric boundaries such as the Earth's magnetopause. The flow shear perpendicular to the reconnecting plane prevails at the flank magnetopause under southward interplanetary magnetic field conditions. However, the effect of the out‐of‐plane flow shear on asymmetric reconnection is an open question. In this study, we utilize kinetic simulations to investigate the impact of the out‐of‐plane flow shear on asymmetric reconnection. By systematically varying the flow shear strength, we analyze the flow shear effects on the reconnection rate, the diffusion region structure, and the energy conversion rate. We find that the reconnection rate increases with the upstream out‐of‐plane flow shear, and for the same upstream conditions, it is higher at the dusk side than at the dawn side. The diffusion region is squeezed in the outflow direction due to magnetic pressure which is proportional to the square of the Alfvén Mach number of the shear flow. The out‐of‐plane flow shear increases the energy conversion rate , and for the same upstream conditions, the magnitude of is larger at the dusk side than at the dawn side. This study reveals that out‐of‐plane flow shear not only enhances the reconnection rate but also significantly boosts energy conversion, with more pronounced effects on the dusk‐side flank than on the dawn‐side flank. These insights pave the way for better understanding the solar wind‐magnetosphere interactions. 

Abstract Both ground based magnetometers and ionospheric radars at Earth have frequently detected Ultra Low Frequency (ULF) fluctuations at discrete frequencies extending below one mHz‐range. Many dayside solar wind drivers have been convincingly demonstrated as driver mechanisms. In this paper we investigate and propose an additional, nightside generation mechanism of a low frequency magnetic field fluctuation. We propose that the Moon may excite a magnetic field perturbation of the order of 1 nT at discrete frequencies when it travels through the Earth's magnetotail 4–5 days every month. Our theoretical prediction is supported by a case study of ARTEMIS magnetic field measurements at the lunar orbit in the Earth's magnetotail. ARTEMIS detects statistically significant peaks in magnetic field fluctuation power at frequencies of 0.37–0.47 mHz that are not present in the solar wind.