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Abstract Convective gravity waves are important for the forcing of the quasi biennial oscillation (QBO). There is a wave component that is stationary with respect to the convective cells that is triggered by convection acting like a barrier to the background flow (moving mountain mechanism). Waves from this mechanism have only been observed in a few case studies and are not parameterized in climate models. However, the representation of the whole spectrum of gravity waves is crucial for the simulation of the QBO, especially in the lowermost stratosphere (below 50 hPa) where the QBO amplitudes are under‐estimated in current global circulation models. In this study, we present analysis of convective gravity wave observations from superpressure balloons in boreal winter 2019 and 2021, retrieving phase speeds, momentum fluxes, and drag. We also identify waves generated by the moving mountain mechanism using the theory of the Beres scheme as a basis. These waves do not have a specific period, but are of smaller horizontal scale, on average around 300 km, which is similar to the scale of convective systems. Our results show that gravity waves contribute up to 2/3 to the QBO forcing below 50 hPa and waves from the moving mountain mechanism are responsible for up to 10% of this forcing. 

Abstract Gravity waves dispersing upward through the tropical stratosphere during opposing phases of the QBO are investigated using ERA5 data for 1979–2019. Log–log plots of two-sided zonal wavenumber–frequency spectra of vertical velocity, and cospectra representing the vertical flux of zonal momentum in the tropical lower stratosphere, exhibit distinctive gravity wave signatures across space and time scales ranging over two orders of magnitude. Spectra of the vertical flux of momentum are indicative of a strong dissipation of westward-propagating gravity waves during the easterly phase and vice versa. This selective “wind filtering” of the waves as they disperse upward imprints the vertical structure of the zonal flow on the resolved wave spectra, characteristic of (re)analysis and/or free-running models. The three-dimensional structures of the gravity waves are documented in composites of the vertical velocity field relative to grid-resolved tropospheric downwelling events at individual reference grid points along the equator. In the absence of a background zonal flow, the waves radiate outward and upward from their respective reference grid points in concentric rings. When a zonal flow is present, the rings are displaced downstream relative to the source and they are amplified upstream of the source and attenuated downstream of it, such that instead of rings, they assume the form of arcs. The log–log spectral representation of wind filtering of equatorial waves by the zonal flow in this paper can be used to diagnose the performance of high-resolution models designed to simulate the circulation of the tropical stratosphere. 

Abstract The study presents (a) a 44‐year wintertime climatology of resolved gravity wave (GW) fluxes and 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, focusing on lateral fluxes. The transformed Eulerian mean equations are leveraged to provide a glimpse of the importance of GW lateral propagation (i.e., horizontal propagation) toward driving the wintertime stratospheric circulation by analyzing the relative contribution of the vertical versus meridional flux dissipation. 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 60°S at 40–45 km altitude. Prominent lateral propagation in the wintertime midlatitudes also contributes to the formation of belts of GW activity in both hemispheres. 

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. 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 a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most 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 for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO₂). However, their performance is significantly improved by applying transfer learning, for example, 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. 

Abstract Tropical waves play an important role in driving the quasi‐biennial oscillation of zonal winds in the tropical stratosphere. In our study we analyze these waves based on temperature observations from the 2021–2022 Strateole‐2 campaign when the Reel‐down Atmospheric Temperature Sensor (RATS) was successfully deployed for the first time. RATS provides long‐duration, continuous and simultaneous high‐resolution temperature observations at two altitudes (balloon float level and 200 m below) allowing for an analysis of vertical wavelengths. This separation distance was chosen to focus on waves near the resolution limit of reanalyses. Here, we found tropical waves with periods between about 6 hr and 2 days, with vertical wavelengths between 1.5 and 5 km, respectively. Comparing our results to Fifth generation European Centre for Medium‐Range Weather Forecasts (ERA5) reanalyses we found good agreement for waves with a period longer than 1 day. However, the ERA5 amplitudes of higher‐frequency waves are under‐estimated, and the temporal evolution of most wave packets differs from the observations.