SearcharxivSearch

arXiv · 2503.19366

A Revisit of Large-Scale Patterns in Middle Stratospheric Circulation Variations

Abstract

Variations in stratospheric atmospheric circulation significantly influence tropospheric weather and climate, and understanding these variations can guide stratospheric aircraft development and operations. Despite a century of progress, large-scale patterns in stratospheric circulation remain poorly understood due to the stratosphere's complex nature. To address this, we applied the eigen microstate approach (EMA) to analyze zonal wind from 70-10 hPa using ERA5 reanalysis data from 1980-2022. We focused on the three leading modes, corresponding to the quasi-biennial oscillation (QBO) and stratospheric circulation in the Arctic and Antarctic. After removing high-frequency components, we observed a significant 11-year cycle in the Antarctic stratospheric circulation mode, possibly linked to the solar cycle. In contrast, the Arctic mode showed a 5-6-year cycle without 11-year periodicity. This difference likely arises from the seasonal timing of polar vortex breakdowns: the Antarctic vortex persists into late spring and summer, making it more sensitive to solar radiation, while the Arctic vortex peaks in winter and early spring. The fourth mode showed features of a Southern Hemisphere dipole and was significantly correlated with the Antarctic mode, leading it by about two months. Finally, we developed a linear prediction model that demonstrated predictive skill for the Antarctic polar vortex.

Explore related subjects

Keep this discovery

BibTeXRIS

Ningning Tao, Xiaosong Chen, Fei Xie, Yongwen Zhang, Yan Xia, Xuan Ma, Han Huang, Hongyu Wang. 2025-03-25. A Revisit of Large-Scale Patterns in Middle Stratospheric Circulation Variations. https://doi.org/10.3390/e27040327

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph