The research, led by Isabel H. Smith of the University of Reading, together with Paul D. Williams and Reinhard Schiemann, quantifies changes in moderate-or-greater mountain wave turbulence by 2050 under a high-end warming scenario. The team used a multi-model approach built on the high-resolution tier of the Coupled Model Intercomparison Project, known as HighResMIP, which was designed to close the gap between climate models and numerical weather prediction. After testing seven horizontal grid spacings across three global climate models, the authors settled on three of the finest: HadGEM3-GC3.1-HM at 25 kilometres, EC-Earth-3P-HR at 36 kilometres and MPI-ESM1.2-XR at 34 kilometres. The choice mattered. In their comparison of the 1950s with the 2040s, coarser models with grid spacings of 60 kilometres or more failed to resolve many known turbulence zones altogether, a consequence of smoothed topography and terrain gradient thresholds that erase the steep slopes where mountain waves are born.

Diagnosing turbulence in climate model output is an art of proxies. Because the models cannot simulate turbulent eddies directly at these scales, the researchers applied six established clear-air turbulence indices, including horizontal temperature gradient, frontogenesis function, wind divergence, wind speed, flow deformation and a combined flow deformation and wind speed measure. Each index was then multiplied by a terrain-dependent factor requiring surface heights above 200 metres, terrain gradients steeper than 0.5 kilometres per metre and sufficient low-level wind speed below one kilometre. This construction, validated in earlier work against pilot reports, effectively isolates turbulence generated by mountains. The team defined moderate-or-greater turbulence as the 98th percentile of each index at the 200 hectopascal pressure level, roughly the cruising altitude of jet aircraft, and only accepted trends that passed a 95 percent confidence test in a linear regression over the 101-year period from 1950 to 2050.

The headline result is a world divided. The largest increase in mountain wave turbulence is projected over the Antarctic, where the average trend reaches 0.52 percent per year, equivalent to roughly 52 percent more turbulence over the century, with the strongest seasonal rise of 0.31 percent per year in the Southern Hemisphere summer. Greenland follows closely, a finding with direct consequences for the busy transatlantic corridors that cross its southern slopes. Regions covering Georgia, Azerbaijan and eastern Turkey, along with parts of Chile and Argentina in the southern Andes, also show significant increases. In contrast, the Alps, the Atlas Mountains and the northern and central Andes are projected to become calmer, as are broad swaths of Africa. The study’s sub-continental analysis across 28 regions reveals that even within a single continent the picture can reverse: North America as a whole gains turbulence, yet the zone covering the Rocky Mountains loses it in every season, with a decline of more than 50 percent in spring, the greatest decrease found anywhere in the study.

Seasonality emerges as a critical dimension of the projections. Over North America, winter and autumn carry the strongest increases, consistent with the known climatology in which lowered tropopause heights and peak westerly winds crossing mountain ranges perpendicularly favour wave generation. The region encompassing Greenland, Iceland and northeastern Canada shows increases of 18.2 percent in summer, 16.2 percent in autumn, 11.1 percent in spring and 7.4 percent in winter by 2050. Over Asia, home to the Himalayas and the Tibetan Plateau, the models project more turbulence in winter but less in summer, with trends of 0.023 and minus 0.014 percent per year respectively. Australia displays a particularly sharp seasonal split, with significant declines in the southern summer and autumn but unanimous increases across all models and indices in winter and spring. The Antarctic is most turbulent in Southern Hemisphere winter and least so in summer, yet both seasons show rising trends.

The mechanism behind these shifts appears to lie substantially in the winds near the surface. Mountain waves draw their energy from low-level airflow, so any change in wind speed over terrain directly modulates wave amplitude and the likelihood of breaking. The study found a positive correlation between projected changes in turbulence and changes in 10-metre wind speed, examining both average and maximum winds to capture the general trend and the occurrence of extreme gusts. In the Antarctic, increases in turbulence in both summer and winter were linked to strengthening average and maximum low-level winds, a finding that echoes observational work by the British Antarctic Survey showing surface winds there have strengthened by about 15 percent since 1980. Over Canada and Alaska, rising turbulence tracked increasing winds, while the decline over the Rockies in autumn was associated with weakening maximum wind speeds, and the drop over India and Sri Lanka followed a decrease in average low-level winds.

These results complicate a simpler narrative from earlier research. Previous studies of clear-air turbulence, which is driven largely by shifting and strengthening jet streams, have consistently projected global increases under warming, and a 2023 analysis of mountain wave turbulence using coarser models found increases over roughly two-thirds of the globe, concentrated in the mid-to-high latitudes. The new high-resolution work partially confirms that latitudinal pattern but adds important exceptions, notably the declines over the Rockies and the northern and central Andes, which sit outside the tropical band where weakening was expected. The authors attribute some of the divergence to terrestrial stilling, the observed and projected slowing of surface winds across many Northern Hemisphere mid-latitude regions, which suppresses the raw energy available to generate mountain waves even as upper-level dynamics change.

The study is candid about its limitations. Climate model topography remains smoother than reality, so some known turbulence hotspots in the western United States are not fully captured, and mountain wave turbulence often develops downstream of ranges in ways that fixed terrain-gradient thresholds may miss. All projections assume the high-end SSP8.5 emissions scenario with little mitigation, so the changes described represent an upper bound rather than a central expectation. Index choice also matters: over South America, one index placed turbulence changes in a range of 0 to 50 percent while another suggested 180 to 250 percent, a spread the authors view as complementary rather than contradictory, since different diagnostics capture different generation mechanisms. Averaging across models and indices can also mask disagreement, as occurred over the Himalayas, where one model’s significant increase outweighed two smaller declines.

For the aviation industry, the practical message is one of targeted vigilance rather than uniform worsening. Turbulence already costs airlines millions of dollars a year in delays, injuries and structural damage, and pilot reports, the traditional observational record, carry median position uncertainties of around 50 kilometres. The new projections suggest airlines serving polar and subpolar routes, particularly those crossing Greenland or the few routes over the Antarctic, should anticipate a measurably rougher atmosphere by mid-century, while carriers over the Alps, the Atlas and the northern Andes may face somewhat reduced exposure. As high-resolution climate models continue to close the gap with weather forecasting systems, studies of this kind are turning an invisible, radar-proof hazard into something that can at least be mapped in advance, giving flight planners and meteorologists a decades-long head start on the turbulence of tomorrow.

Subject of Research: Projected global changes in mountain wave aviation turbulence under climate warming

Article Title: Global shifts in mountain wave turbulence within high resolution climate models

Article References: Smith, I. H., Williams, P. D., & Schiemann, R. (2026). Global shifts in mountain wave turbulence within high resolution climate models. Weather and Climate Dynamics, 7(3), 1733-1758. https://doi.org/10.5194/wcd-7-1733-2026

Image Credits: AI Generated

DOI: 10.5194/wcd-7-1733-2026

Keywords: mountain wave turbulence, aviation safety, climate change, HighResMIP, CMIP6, clear-air turbulence, gravity waves, Greenland, Antarctic, Rocky Mountains, terrestrial stilling, climate models

The team focused on the Yellow River Basin (YRB), a vast and topographically diverse region stretching from the rugged Tibetan Plateau margins in the upper reaches, through the Loess Plateau, down to the North China Plain. Wind behavior in this basin matters for practical reasons: it influences wind farm siting, soil erosion on the Loess Plateau, and evaporation rates that feed back into the basin’s already stressed water resources. To test how well regional models can capture these winds, the researchers used the fifth-generation Regional Climate Model, RegCM5, developed at the International Centre for Theoretical Physics (ICTP), and ran three parallel 50-year simulations spanning 1971 to 2020, each driven at its lateral boundaries by the ERA5 global reanalysis from the European Centre for Medium-Range Weather Forecasts.

The three configurations differed only in their dynamical cores, the numerical engines that solve the equations of atmospheric motion. Model 1 employed the traditional hydrostatic core, the long-standing workhorse of climate modeling, which assumes that vertical pressure forces balance gravity at every point. This hydrostatic approximation is valid for large-scale atmospheric motions but breaks down at fine horizontal scales where vigorous vertical accelerations occur. Model 2 used the MM5-like non-hydrostatic core, which solves the full equations of motion including vertical acceleration, a capability inherited from the Penn State–NCAR Mesoscale Model tradition. Model 3, the newest addition to RegCM5, employed the Moloch non-hydrostatic core, a dynamical framework originally developed in Italy and known for its efficient numerical formulation.

All three simulations used a 25-kilometer grid spacing, a resolution typical of regional climate downscaling experiments. The researchers validated their outputs against the CN05.1 observational dataset, a high-resolution gridded product built from station observations across China. The results revealed a striking pattern of shared strengths and shared weaknesses. All three configurations successfully reproduced the broad spatial climatology of wind speed across the basin, capturing the general geography of windy and calm zones. However, every configuration systematically overestimated near-surface wind speed, with mean biases ranging from +1.3 to +2.8 meters per second. The overestimation was most severe in the rugged Upper Reaches, where the authors point to sub-grid topographic smoothing and insufficient effective surface drag as likely culprits.

This bias mechanism deserves a closer look. When a model represents terrain on a 25-kilometer grid, individual peaks and valleys are averaged into smoother, lower-relief surfaces. Real winds near the ground are slowed by the friction of vegetation, buildings, and fine-scale topographic obstacles that the smoothed model surface simply cannot see. The result is a model atmosphere that flows too freely, producing winds that are too strong. This problem is well documented in the boundary-layer meteorology literature, and the new study confirms it persists across all three dynamical cores, suggesting the bias originates in the representation of the surface rather than in the core dynamics themselves.

Where the cores did differ was in the details of their statistical performance. The Moloch-based Model 3 achieved the lowest mean bias and the closest match to the observed probability distribution of wind speeds, meaning its simulated winds were not only closer on average but also better captured the frequency of calm and windy conditions. In probability density function skill scores, a standard diagnostic introduced by Perkins and colleagues for evaluating model distributions, Model 3 outperformed its rivals. Interestingly, the older hydrostatic Model 1 and the MM5-like Model 2 retained slightly higher correlations in some temporal diagnostics and Taylor-diagram comparisons, indicating that no single core dominates every metric. The authors conclude that the Moloch core improves mean-state and distributional fidelity under this experimental setup but does not by itself resolve errors in the timing of wind variations or in long-term trends.

Perhaps the most sobering finding concerns the phenomenon known as terrestrial stilling. Observations worldwide, synthesized in influential reviews by McVicar and colleagues and by Vautard and colleagues, show that near-surface wind speeds declined over much of the land surface during recent decades, a trend partly attributed to increases in surface roughness from vegetation growth and land-use change, though later work by Zeng and colleagues documented a reversal in some regions. The CN05.1 observations over the Yellow River Basin show this stilling signal clearly. All three model configurations captured part of the interannual variability in wind speed, but they substantially underestimated the observed multi-decadal declining trend. The authors attribute this shortfall to the use of static, time-invariant land-use and land-cover forcing: a model whose vegetation and surface roughness never change cannot simulate winds that slow because the landscape itself has changed. They call for updated or time-varying land-cover forcing and for independent assessment of uncertainties in both the ERA5 driving fields and the observational datasets.

Beyond accuracy, the study delivers a message that will resonate with any modeling group facing finite computing budgets: the choice of dynamical core dramatically changes computational cost. The MM5-like non-hydrostatic core proved expensive because its explicit treatment of sound-wave dynamics imposes a strict stability constraint known as the Courant-Friedrichs-Lewy condition, forcing a time step of only 25 seconds. The Moloch core, by contrast, remained numerically stable with a time step of 200 seconds, eight times larger. The payoff was substantial: total runtime dropped by 58 percent relative to the MM5-like core and 17 percent relative to the hydrostatic core under identical computing environments. For decadal or multi-decadal downscaling campaigns, ensemble experiments, or convection-permitting applications where every second of wall-clock time counts, this efficiency advantage is far from trivial.

The researchers did not stop at comparing cores. They designed a one-decade sensitivity experiment to test how much the land surface itself contributes to the wind speed bias. By refining the sub-grid land-surface representation, they reduced the whole-basin positive wind bias by approximately 39 percent, a substantial improvement, although spatial correlation decreased in some regions, a reminder that fixing one aspect of model performance can trade off against another. Combining this result with the core comparison, the authors conclude that the Moloch core, especially when paired with improved land-surface representation, is the most practical configuration for long-term RegCM5 wind downscaling over complex terrain within their experimental design.

The implications extend well beyond the Yellow River Basin. As wind energy expands globally, with the Global Wind Energy Council reporting continued rapid growth in installed capacity, accurate wind climatologies from regional climate models are increasingly used to assess future resource reliability under climate change. China’s onshore wind potential, in particular, is a subject of intense study in the context of carbon-neutrality goals. This study shows that the tools used for such assessments carry systematic biases that depend on both the dynamical core and the land surface, and that these biases can be quantified and partially corrected. It also demonstrates that efficiency and accuracy need not be opposing goals: the newest core in RegCM5 delivered both the best distributional fidelity and the fastest runtime. For scientists downscaling winds over mountains, plateaus, and river valleys worldwide, the message is clear: the engine under the hood matters, and so does the landscape you tell the model it is blowing over.

Subject of Research: Comparison of hydrostatic and non-hydrostatic dynamical cores in the RegCM5 regional climate model for simulating 50 years of near-surface wind speed over the Yellow River Basin

Article Title: Evaluating hydrostatic vs. non-hydrostatic dynamics in RegCM5: a 50-year simulation of near-surface wind speed over the Yellow River Basin

Article References: Jiao, Y., Deng, J., Li, Q., Zhao, N., Yue, T., Popovas, D., Zhou, C., Quan, C., Fang, Z., Si, P., & Song, X. (2026). Evaluating hydrostatic vs. non-hydrostatic dynamics in RegCM5: a 50-year simulation of near-surface wind speed over the Yellow River Basin. Climate Dynamics, 64(10), Article 439. https://doi.org/10.1007/s00382-026-08396-6

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08396-6

Keywords: RegCM5, dynamical cores, near-surface wind speed, Yellow River Basin, Moloch core, non-hydrostatic dynamics, hydrostatic approximation, terrestrial stilling, regional climate modeling, ERA5 reanalysis, computational efficiency, land-surface representation