“Wave turbopause” is defined as the mesospheric altitude level where the temperature fluctuation field indicates a substantial increase in wave amplitude in the vertical direction. It is similar to turbopause in seasonal and latitudinal variations, providing an almost global analysis of turbopause on the basis of satellite measurements rather than localized detection. Previous studies of “wave turbopause” were based on standard deviation of temperature, which is an integrated measure of wave activity. In this study, we distinguish different atmospheric waves and investigate their influences on “wave turbopause”. By comparing the altitude of “wave turbopause” with peak height of amplitude (PHA) for gravity waves and 6.5 days waves (6.5 DWs), whose period is approximately 6.5 days in the mesosphere and lower thermosphere, we find that the seasonal variation in altitude of “wave turbopause” is higher at the winter pole and lower at the summer pole, correlated with PHA for 6.5 DWs but anti-correlated with PHA for gravity waves. We infer that gravity waves reach saturation and break at lower altitudes in the winter when Brunt–Vaisala frequency is also lower between 80 and 100 km altitudes. Finally, the results may imply that seasonal variations of the “wave turbopause” are driven mainly by 6.5 DWs.

When vortex electromagnetic waves propagate through a turbulent atmosphere, the amplitude and phase of the electromagnetic waves are disturbed, creating the scintillation effect. According to the scintillation index of vortex waves, a new method of retrieving the turbulent refraction structure parameter was proposed using a genetic algorithm, and the feasibility of this method was verified by simulated experiments. The numerical results showed that the inversed value obtained by the genetic algorithm was close to the real parameter when the turbulent inner scale and outer scale were fixed. However, there was a gap between the inversed value and the real parameter when only the turbulent outer scale was fixed. These results suggest that vortex wave data can be used for turbulent refraction structure parameter inversion, and they provide new research directions for atmospheric remote sensing.

Thorpe analysis is a commonly used method to study turbulence, an important research object in the field of atmospheric science. However, using Thorpe analysis to retrieve turbulence parameters from radiosonde data can reduce inversion accuracy due to instrument noise. Therefore, this paper presents a new wavelet denoising method to determine the optimal decomposition layer based on adaptative Monte Carlo simulation, and the denoising effect of this method is tested by simulation data and real detection data. Numerical experiments shows that the stability and accuracy of the proposed new method are better than those of the previous methods.

The high-precision prediction of near-space atmospheric temperature holds significant importance for aerospace, national defense security, and climate change research. To address the deficiencies of extracting features in conventional convolutional neural networks, this paper designs a ConvLSTM hybrid model that combines the spatiotemporal feature extraction capability of 3D convolution with a residual attention mechanism, effectively capturing the dynamic evolution patterns of the near-space temperature field. The comparative analysis with various models, including GRU, shows that the proposed model demonstrates superior performance, achieving an RMSE of 2.433 K, a correlation coefficient R of 0.993, and an MRE of 0.76% on the test set. Seasonal error analysis reveals that the prediction stability is better in winter than in summer, with errors in the mesosphere primarily stemming from the complexity of atmospheric processes and limitations in data resolution. Compared to traditional CNNs and single time-series models, the proposed method significantly enhances prediction accuracy, providing a new technical approach for near-space environmental modeling.

Using multiple reanalysis datasets, this study investigates the decadal variability in the relationship between Northeast Pacific Sea surface temperature (SST) and Arctic stratospheric ozone (ASO), with a focus on the role of atmospheric dynamics in mediating this connection. A significant decadal shift is identified around the year 2000, characterized by a weakening of the previously strong negative correlation between January–February SST anomalies and February–March ASO. Prior to 2000 (1980–2000), warm SST in the northeastern Pacific suppressed upward planetary wave propagation, resulting in decreased stratospheric wave activity and a weakened Brewer–Dobson circulation. The weakened BD circulation reduced poleward transport of tropical ozone and heat, yielding a colder, ozone-poor polar vortex. The strong relationship enabled skillful seasonal predictability of ASO using SST precursors in a linear regression model. However, post-2000 (2001–2022), the weakened planetary wave response to SST anomalies resulted in a breakdown of this relationship, yielding non-significant predictive skill. The findings highlight the non-stationary nature of ocean-stratosphere coupling and underscore the importance of accounting for such decadal shifts in climate models to improve projections of Arctic ozone recovery and its surface climate impacts.

Understanding and estimating the impact of atmospheric turbulence on the growth of precipitation particles in freezing weather can help improve the prediction of freezing rain precipitation. In this study, the turbulent kinetic energy dissipation rate (ε) during a freezing weather event was retrieved using the power spectral analysis method, based on observational data from a Ka-band millimeter-wave cloud radar, radiosondes, and ground automatic weather stations, and the influence of atmospheric turbulence on the growth of freezing rain precipitation particles was investigated using nonlinear causal methods such as transfer entropy (TE), convergent cross-mapping (CCM), the phase-randomized surrogate test, and partial CCM. Causality tests confirmed a significant bidirectional causal relationship between ε and Z_e, with the information transfer from ε to Z_e systematically dominating throughout the entire height range of precipitation particles. The bidirectional original ρ was far higher than the 99% quantile of the surrogate distribution, and the coupling was enhanced after removing the influence of temperature and humidity, indicating that it was independent of the thermodynamic covariant background. The causal intensity in the ice crystal growth zone above 6 km was approximately 3 times the average in the liquid zone, indicating that atmospheric turbulence is one of the key factors driving the growth of precipitation particles in freezing weather and that the effect is concentrated in the dominant layer of ice crystals. This finding provides a practical basis for improving the parameterization scheme and numerical prediction of the precipitation particle microphysical processes in freezing weather.