Stratosphere–troposphere exchange (STE) is a fundamental process in which ozone-rich stratospheric air enters the troposphere, modulating tropospheric ozone distributions and, under favorable conditions, influencing surface air quality. This study investigates an atypical STE event over North America from 6 to 14 March 2016, which coincided with a severe surface ozone pollution episode in Mexico City. Analyses using ERA5 reanalysis, potential vorticity (PV) diagnostics, ozone tracers, Lagrangian trajectories, and isentropic methods attribute the event to anticyclonic Rossby wave breaking, a PV cut-off low, and a persistent tropopause fold. Deep intrusions of high-PV air penetrated the troposphere, with maximum downward transport occurring approximately 1 d before the peak in surface ozone (O3) mixing ratios. Equatorward wave amplification facilitated coherent isentropic transport, enabling efficient descent of ozone-rich stratospheric air over the elevated basins of central Mexico. Backward trajectory analysis confirmed stratospheric origins, while isentropic advection quantified quasi-horizontal transport along 320–340 K surfaces. Tropopause folding, intensified by the subtropical jet and the elevated terrain of central Mexico, resulted in enhanced O3 mixing ratios of 8 × 10−8 kg kg−1 at 750–700 hPa, preconditioning background ozone levels prior to surface exceedance rather than serving as a direct surface source. The analysis identifies recurrent tropopause folds preceding high-ozone episodes, highlighting the persistent role of STE as a dynamical preconditioning mechanism for regional air-quality extremes. These results indicate that the location of cities in elevated tropical basins, in combination with Rossby wave dynamics and quasi-horizontal isentropic transport pathways, determines the depth, persistence, and significance of STE.
Marine heatwaves and coral bleaching pose significant threats to the Great Barrier Reef (GBR), particularly during austral summer when atmospheric conditions can significantly enhance ocean surface warming. One such condition is the development of doldrums, characterised by weak winds and reduced cloud cover, which promote increased solar heating of the ocean surface. When these conditions persist, this buildup of heat can develop into marine heatwaves contributing to widespread coral bleaching. Despite these impacts, the formation and persistence of the doldrums remain poorly understood. Here, we identify doldrums events over the GBR through a K-means cluster analysis and investigate the tropical and extratropical interactions associated with their onset and maintenance. Expanding upon previous links between doldrums formation and Rossby waves, the cyclonic anomaly within the Rossby wave packet is found to disrupt the typical trade wind easterlies over the GBR through anomalous westerlies over eastern Australia. If the cyclonic anomaly propagates east quickly, the doldrums tend to cease; if the anomaly stalls, the doldrums tend to persist. These stalled Rossby wave packets are associated with repeated cyclonic Rossby wave breaking over Australia, which weakens the zonal flow and enhances blocking near New Zealand. Concurrently, favourable conditions are needed in the tropics to establish and maintain the doldrums. During longer doldrums, the slower-moving Madden–Julian Oscillation and Equatorial Rossby wave more frequently align their suppressed phases over the GBR, reinforcing anomalous westerlies to further counter the trade easterlies. Finally, Lagrangian trajectory analysis reveals that dry descending air within the doldrums typically originates in the mid-latitudes. Here, the positioning of the cyclonic anomaly or cut-off low forces the parcels to sweep across southern Australia over the dry interior before reaching the GBR. Together, these results highlight the tropical-extratropical interactions that promote the doldrums persistence over the GBR and favours the development of both marine and terrestrial heatwaves.
Using a multi-model approach, this paper quantifies global changes in moderate or greater mountain wave turbulence (MWT) within a high-end warming scenario by 2050. We first show that simulated MWT changes depend on model resolution and therefore select three high resolution global climate model experiments for further analysis; HadGEM3-GC3.1-HM (25 km), EC-Earth-3P-HR (36 km) and MPI-ESM1.2-XR (34 km). The wide model and index uncertainty across continental average projections lead to a sub-continental 28 area approach. Using this method, we determined MWT is projected to increase over North America as a whole but decrease over the Rocky Mountains. An increase in MWT is projected for other regions including the Antarctic, Greenland, Georgia, Azerbaijan and parts of Chile and Argentina. A decline in MWT is projected for the Alps, Atlas and northern and central Andes. We also explore the modelled relationship of 10 m wind speed and MWT production; there is a positive correlation between the projected changes of MWT and surface wind speed. The aviation sector should be aware of the future projections in MWT, particularly for regions where a large increase in MWT is projected, such as the Antarctic and Greenland.
Forecast busts – episodes of abnormally low forecast skill – remain a persistent challenge for numerical weather prediction despite steady improvements in forecasting skill. Previous studies have highlighted the roles of moist mesoscale processes and diabatically generated potential vorticity (PV) anomalies in triggering rapid error growth and downstream circulation misrepresentation, processes whose representation is highly sensitive to model grid spacing. In this study, we select three events that were classified as forecast busts in the Integrated Forecasting System (IFS) and investigate their sensitivity to horizontal grid spacing. The cases comprise (i) the explosive cyclogenesis of Storm Dennis (February 2020), (ii) a blocking event following the extratropical transition of Hurricane Franklin (September 2023), and (iii) a June 2020 event characterized by ridge amplification due to a warm conveyor belt (WCB) over the North Atlantic. For each case, we use IFS initial conditions and perform global ICON ( Icosahedral Non-hydrostatic model) ensemble forecasts with horizontal grid spacings ranging from 40 to 2.5 km. We evaluate forecast skill against ERA5 reanalysis using anomaly correlation coefficients (ACC) of 500 hPa geopotential height. Across all cases, forecast busts are consistently linked to diabatically generated upper-level negative PV anomalies originating from strong latent heat release in organized convection, including mesoscale convective systems (MCSs), extratropical cyclones, and WCB ascent. These PV anomalies modify the upper-level waveguide, perturb the jet stream, and amplify downstream Rossby wave packets, degrading medium-range predictability. Decreasing horizontal grid spacing systematically improves the representation of diabatic processes, enhances the spatial extent and intensity of upper-level negative PV anomalies, and reduces wave-amplitude error growth. We observe that kilometer-scale simulations with horizontal grid spacing ≤5 km consistently yield the highest forecast skill, with substantial ACC improvements relative to coarser-resolution simulations. All three case studies systematically show that mesoscale diabatic processes are a primary source of forecast error in bust situations, and that kilometer-scale global simulations significantly improve the representation of scale interactions governing Rossby wave amplification. These findings underscore the importance of kilometer-scale simulations for reliably representing scale interactions in strongly diabatic flow regimes. They are not only important for weather forecasting but also for climate simulations which feature long-standing deficiencies in capturing Rossby wave breaking and associate extreme weather events.
This research explores the predictability of seasonal storminess in the North Sea using machine learning methods and the weather model emulator ACE2, focusing on how the stratosphere and upper troposphere influence winter storms. Understanding the drivers of winter storminess is essential for improving sub-seasonal prediction skill in regions strongly affected by extratropical cyclones. Using ERA5 reanalysis data (1940–2024), we built a storminess index based on storm-event frequency, examined its relationship with large-scale atmospheric fields, and explored its predictability on seasonal timescales. We aim to predict North Sea storminess using two approaches: one based on the ACE2 climate emulator and another on the Random Forest machine learning algorithm. The ACE2 simulations show that by decreasing the stratospheric temperature and increasing the wind speed on 1 December, we can increase the emulated mean January surface wind speeds by about 0.5–3 m s−1 across much of the North Sea. Similar sensitivity emulations initialised at the start of other months, e.g. 1 November, failed to produce a meaningful response in the ensuing month. This suggests a dynamic link between early-winter stratospheric conditions and increased mid-winter surface storminess. For the Random Forest regression model, monthly means of air temperature, zonal wind at 70 hPa, and geopotential height at 200 hPa were used as predictors after dimensionality reduction using Principal Components Analysis (PCA). The highest skill was obtained when predicting January storminess from December fields, with a correlation of about 0.55–0.60, while predictive skill was substantially weaker and sometimes negative for other month-to-month predictions. This seasonal predictability pattern, derived from both the ACE2 emulations and the Random Forest model, follows the seasonal cycle of the polar vortex's average intensity. The circumpolar westerly jet strengthens from autumn and peaks in winter, when predictability is highest. This higher skill is likely linked to stronger stratosphere–troposphere coupling that peaks in late December and extends until mid-February, as polar vortex anomalies develop and begin to descend toward the surface. Both indicate that the seasonal predictability of storminess peaks in mid-winter, with a predictability lead time of about 4 to 6 weeks. It fades for the earlier and late winter periods. We found that North Sea storminess, measured as the monthly or seasonal number of storms, is not statistically correlated with storminess in any other region of the globe. This suggests that although individual seasons or months may show increased storminess over large areas, the statistical predictors of North Sea storminess must have a small-scale regional character specific to the North Sea. We also found that the North Sea storminess shows no statistical persistence from month to month. This suggests that although individual winters may display North Sea storminess across all winter months, predictors of North Sea monthly storminess should also have a short-term subseasonal temporal scale. Together, these two findings indicate that sea-surface temperatures are not an adequate statistical predictor of North Sea storminess, although they may play a role in individual winters. Overall, this research shows that stratospheric conditions play an essential role in shaping North Sea winter storminess and that machine learning methods can improve sub-seasonal predictions in this region.
