The study presents (a) a 44-year wintertime climatology of resolved gravity wave (GW) fluxes and associated zonal forcing in the extratropical stratosphere using ERA5, and (b) their composite evolution around gradual (final warming) and abrupt (sudden warming) transitions in the wintertime circulation. The connection between transformed Eulerian mean (TEM) equations and the linear GW pseudomomentum is leveraged to provide a glimpse of the importance of GW lateral propagation toward driving the wintertime stratospheric circulation by analyzing the relative contribution of the vertical vs. meridional flux convergence. The relative contribution from lateral propagation is found to be notable, especially in the Austral winter stratosphere where lateral (vertical) momentum flux convergence provides a peak climatological forcing of up to -0.5 (-3.5) m/s/day around 60oS at 40-45 km altitude. Prominent lateral propagation in the wintertime midlatitudes also contributes to the formation of a belt of GW activity in both hemispheres.

We present estimates of gravity wave momentum fluxes calculated from Project Loon superpressure balloon data collected between 2013 and 2021. In total, we analyzed more than 5000 days of data from balloon flights in the lower stratosphere, flights often over regions or during times of the year without any previous in-situ observations of gravity waves. Maps of mean momentum fluxes show significant regional variability; we analyze that variability using the statistics of the momentum flux probability distributions for six regions: the Southern Ocean, the Indian Ocean, and the tropical and extratropical Pacific and Atlantic Oceans. The probability distributions are all approximately log-normal, and using only their geometric means and geometric standard deviations we explain the sign and magnitude of regional mean and 99th percentile zonal momentum fluxes, and regional momentum flux intermittencies. We study the dependence of the zonal momentum flux on the background zonal wind and argue that the increase of the momentum flux with the wind speed over the Southern Ocean is likely due to a varying combination of both wave sources and filtering. Finally, we show that as the magnitude of the momentum flux increases, the fractional contributions by high-frequency waves increases, waves which need to be parameterized in large-scale models of the atmosphere. In particular, the near-universality of the log-normal momentum flux probability distribution, and the relation of its statistical moments to the mean momentum flux and intermittency, offer useful checks when evaluating parameterized or resolved gravity waves in models.

Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization in weather and climate models. While NNs are powerful tools for learning complex nonlinear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are 1) data imbalance related to learning rare (often large-amplitude) samples; 2) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and 3) generalization to other climates, e.g., those with higher radiative forcing. Here, we examine the performance of methods for addressing these challenges using NN-based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics-based gravity wave (GW) parameterizations as the test case. WACCM has complex, state-of-the-art parameterizations for orography-, convection- and frontal-driven GWs. Convection- and orography-driven GWs have significant data imbalance due to the absence of convection or orography in many grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria on when a NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4XCO2). However, the generalization accuracy is significantly improved by applying transfer learning, e.g., re-training only one layer using ~1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data-driven parameterizations for various processes, including (but not limited) to GWs.

Convection-generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth’s atmosphere’s general circulation (e.g. the quasi-biennial oscillation, the pole-to-pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection-permitting or -resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full-physics convection-permitting Weather Research and Forecasting (WRF) simulations, is predicted from weather radar observations using neural networks and a previously developed look-up table. These heating rates are then used to force an idealized GW-resolving dynamical model. Simulated CGWs forced in this way did closely resemble those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single-column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning \textit{with confidence}.

Many chemical processes depend non-linearly on temperature. Gravity-wave-induced temperature perturbations have been previously shown to affect atmospheric chemistry, but accounting for this process in chemistry-climate models has been a challenge because many gravity waves have scales smaller than the typical model resolution. Here, we present a method to account for subgrid-scale orographic gravity-wave-induced temperature perturbations on the global scale for the Whole Atmosphere Community Climate Model (WACCM). The method consists of deriving the temperature perturbation amplitude $\hat{T}$ consistent with the model’s subgrid-scale gravity wave parametrization, and imposing $\hat{T}$ as a sinusiodal temperature perturbation in the model’s chemistry solver. Because of limitations in the gravity wave parameterization, scaling factors may be necessary to maintain a realistic wave amplitude. We explore scaling factors between 0.6 and 1 based on comparisons to altitude-dependent $\hat{T}$ distributions in two observational datasets. We probe the impact on the chemistry from the grid-point to global scales, and show that the parametrization is able to represent mountain wave events as reported by previous literature. The gravity waves for example lead to increased surface area densities of stratospheric aerosols. This in turn increases chlorine activation, with impacts on the associated chemical composition. We obtain large local changes in some chemical species (e.g., active chlorine, NOx, N2O5) which are likely to be important for comparisons to airborne or satellite observations, but find that the changes to ozone loss are more modest. This approach enables the chemistry-climate modeling community to account for subgrid-scale gravity wave temperature perturbations in a consistent way.

Atmospheric gravity waves (GWs) span a broad range of length scales. As a result, the un-resolved and under-resolved GWs have to be represented using a sub-grid scale (SGS) parameterization in general circulation models (GCMs). In recent years, machine learning (ML) techniques have emerged as novel methods for SGS modeling of climate processes. In the widely-used approach of supervised (offline) learning, the true representation of the SGS terms have to be properly extracted from high-fidelity data (e.g., GW-resolving simulations). However, this is a non-trivial task, and the quality of the ML-based parameterization significantly hinges on the quality of these SGS terms. Here, we compare three methods to extract 3D GW fluxes and the resulting drag (GWD) from high-resolution simulations: Helmholtz decomposition, and spatial filtering to compute the Reynolds stress and the full SGS stress. In addition to previous studies that focused only on vertical fluxes by GWs, we also quantify the SGS GWD due to lateral momentum fluxes. We build and utilize a library of tropical high-resolution ($\Delta x =3~km$) simulations using weather research and forecasting model (WRF). Results show that the SGS lateral momentum fluxes could have a significant contribution to the total GWD. Moreover, when estimating GWD due to lateral effects, interactions between the SGS and the resolved large-scale flow need to be considered. The sensitivity of the results to different filter type and length scale (dependent on GCM resolution) is also explored to inform the scale-awareness in the development of data-driven parameterizations.

An international joint research project, entitled Interhemispheric Coupling Study by Observations and Modelling (ICSOM), is ongoing. In the late 2000s, an interesting form of interhemispheric coupling (IHC) was discovered: when warming occurs in the winter polar stratosphere, the upper mesosphere in the summer hemisphere also becomes warmer with a time lag of days. This IHC phenomenon is considered to be a coupling through processes in the middle atmosphere (i.e., stratosphere, mesosphere, and lower thermosphere). Several plausible mechanisms have been proposed so far, but they are still controversial. This is mainly because of the difficulty in observing and simulating gravity waves (GWs) at small scales, despite the important role they are known to play in middle atmosphere dynamics. In this project, by networking sparsely but globally distributed radars, mesospheric GWs have been simultaneously observed in seven boreal winters since 2015/16. We have succeeded in capturing five stratospheric sudden warming events and two polar vortex intensification events. This project also includes the development of a new data assimilation system to generate long-term reanalysis data for the whole middle atmosphere, and simulations by a state-of-art GW-permitting general circulation model using reanalysis data as initial values. By analyzing data from these observations, data assimilation, and model simulation, comprehensive studies to investigate the mechanism of IHC are planned. This paper provides an overview of ICSOM, but even initial results suggest that not only gravity waves but also large-scale waves are important for the mechanism of the IHC.

The quasi-biennial oscillation (QBO), a ubiquitous feature of the zonal mean zonal winds in the equatorial lower stratosphere, is forced by selective dissipation of atmospheric waves that range in periods from days to hours. However, QBO circulations in numerical models tend to be weak compared with observations, probably because of limited vertical resolution that cannot adequately resolve gravity waves and the height range over which they dissipate. Observations are required to help quantify wave effects. The passage of a superpressure balloon (SPB) near a radiosonde launch site in the equatorial Western Pacific during the transition from the eastward to westward phase of the QBO at 20 km permits a coordinated study of the intrinsic frequencies and vertical structures of two inertia-gravity wave packets with periods near 1-day and 3 days, respectively. Both waves have large horizontal wavelengths of about 970 and 5500 km. The complementary nature of the observations provided information on their momentum fluxes and the evolution of the waves in the vertical. The near 1-day westward propagating wave has a critical level near 20 km, while the eastward propagating 3-day wave is able to propagate through to heights near 30 km before dissipation. Estimates of the forcing provided by the momentum flux convergence, taking into account the duration and scale of the forcing, suggests zonal force of about 0.3-0.4 m/s/day for the 1-day wave and about 0.4-0.6 m/s/day for the 3-day wave, which acts for several days.

We conducted simulations with a 4-km resolution for Hurricane Joaquin in 2015 using the Weather Research and Forecast (WRF) model. The model data are used to study stratospheric gravity waves (GWs) generated by the hurricane and how they correlate with hurricane intensity. The simulation results show spiral GWs propagating upward and anticlockwise away from the hurricane center. GWs with vertical wavelengths up to 14 km are generated. We find that GW activity is more frequent and intense during hurricane intensification than during weakening, particularly for the most intense GW activity. There are significant correlations between the change of stratospheric GW intensity and hurricane intensity. Therefore, the emergence of intensive stratospheric GW activity may be considered a useful proxy for identifying hurricane intensification.

The Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) mission is a 6U CubeSat that has been making ionospheric measurements at 420 km altitude since February 19, 2020. The SORTIE sensor suite includes an Ion Velocity Meter (IVM), which is used in the present study to detect and characterize Traveling Ionospheric Disturbances (TIDs). On July 11, 2020 the SORTIE orbit passed over near-concentric TIDs that were seen in the Total Electron Content (TEC) data from ground-based Global Positioning System receivers distributed across the COntiguous United States (CONUS). The TID wave characteristics estimated from the IVM data agree well with those determined from the ground-based TEC data. The wave periods derived from the SORTIE data are shorter than the TID periods in the TEC data but are anticipated and explained in terms of the classical Doppler effect. A numerical simulation was performed using the Weather Research and Forecasting (WRF) model that shows excitation of atmospheric gravity waves (AGWs) from a deep convective storm over Texas preceding TID observations by SORTIE. We show that these AGWs were observed at stratospheric heights in close proximity to the convective storm by the Atmospheric Infrared Sounder onboard the NASA Aqua satellite, and in the lowermost mesosphere by the Cloud Imaging and Particle Size instrument onboard the NASA Aeronomy of Ice in the Mesosphere satellite. These storm-generated AGWs, or the associated higher-order AGWs, are the likely sources of the TIDs observed in the ground-based TEC and SORTIE IVM data.

We conduct a mesoscale simulation with a 4-km resolution for Hurricane Joaquin in 2015 using the Weather Research and Forecast (WRF) model. The model data are used to study stratospheric gravity waves (GWs) generated by the hurricane and how they correlate with hurricane intensity. The simulation results show spiral GWs propagating upward and anticlockwise away from the hurricane center. GWs with vertical wavelengths up to 14 km are generated. We find that GW activity is more frequent during hurricane intensification than during weakening, particularly for the most intense GW activity. There is a significant time offset between the appearance of intense stratospheric GWs and hurricane intensification. Therefore, the emergence of intensive stratospheric GW activity may be considered a useful proxy for identifying hurricane intensification.

This study exploits the high-density zonal sampling at the turnaround latitude of the High Resolution Dynamics Limb Sounder (HIRDLS) in the Southern Hemisphere to investigate the missing drag in chemistry-climate models near 60 S. Gravity wave (GW) properties including amplitude, zonal wavenumber, vertical wavelength, and momentum flux are estimated with a wavelet analysis method based on the S-transform. Monthly means of GW properties compare well with estimates from previous studies. We further investigate the contribution to GW momentum flux above orographic and nonorographic regions and find that while fluxes are much larger locally over orographic regions, the contribution to the zonal mean is roughly 3 times smaller than the contribution over nonorographic regions. We also investigate the relationship with the zonal wind and find that GW momentum flux is highly correlated with the near surface winds over orographic regions. In addition to momentum flux, we also provide estimates of the zonal drag and use these estimates to evaluate the current GW parameterizations and resolved wave forcing in models participating in phase 1 of the Chemistry-Climate Model Initiative (CCMI-1). The HIRDLS zonal drag estimates suggest that the CCMI-1 models have insufficient zonal drag, especially in June, July, and August, and that the majority of the missing drag is over nonorographic regions. Our discussion includes implications for the Brewer-Dobson Circulation and ozone hole.