The observational record is striking. Drawing on measurements from 150 meteorological stations operated by the National Meteorological Information Center of the China Meteorological Administration, the team found that annual mean near-surface wind speed over the Tibetan Plateau declined at a rate of 0.159 meters per second per decade between 1979 and 2014. The decline was not spread evenly through the year. Spring showed the steepest weakening, with wind speeds falling by 0.235 meters per second per decade, a seasonal signal that matters because spring winds play a central role in the plateau’s exchange of heat and moisture with the surrounding atmosphere and in shaping the Asian monsoon system downstream.

To test whether climate models could reproduce this stilling, the researchers turned to the Coupled Model Intercomparison Project Phase 6, specifically its High-Resolution Model Intercomparison Project, known as HighResMIP. This experiment was designed in part to answer a long-standing question in climate science: would simply cranking up the horizontal resolution of global models improve their simulations? The team assembled 22 historical simulations from 11 model pairs, each pair consisting of a higher-resolution and a lower-resolution version of the same model, and compared them against the station observations over the 1979 to 2014 period.

The verdict on resolution was sobering. Both the higher-resolution and lower-resolution ensemble means captured the broad spatial pattern of wind speed across the plateau, but both badly underestimated the magnitude of the decline. The higher-resolution ensemble produced an annual trend of only 0.024 meters per second per decade, while the lower-resolution ensemble managed just 0.020 meters per second per decade. In other words, the models reproduced only about one-seventh of the observed stilling. Perhaps more telling, the difference between the two resolution tiers was small, suggesting that increasing horizontal resolution alone offers limited improvement in capturing the historical wind decline over this complex, high-altitude terrain.

This widespread stilling bias is not merely an academic curiosity. Near-surface wind speed governs a host of processes with real-world consequences: the evaporation of water from land surfaces, the dispersal of air pollutants, the mixing of the atmospheric boundary layer, and the output of wind turbines. The Tibetan Plateau is also the source region of major Asian rivers and a critical driver of monsoon circulation, so systematic errors in simulating its winds can propagate into broader failures in regional climate prediction. Previous studies have linked wind declines across China to factors ranging from urbanization and increased surface roughness to weakening pressure gradients and changes in large-scale circulation, but representing these processes faithfully in global models has remained elusive.

To dig into why the models fall short, the team applied a random forest machine learning analysis, a technique that can rank the relative importance of many candidate variables in explaining an outcome. The analysis pointed to biases in key dynamical and thermodynamical processes, with snow variability and surface sensible heat flux emerging as particularly influential factors. This makes physical sense. Snow cover on the plateau alters surface albedo and the partitioning of energy between sensible and latent heat, which in turn modulates the temperature contrasts that drive near-surface winds. If a model misrepresents how snow accumulates and melts across the plateau’s rugged topography, the resulting errors in surface heating can distort the very circulation features that generate wind.

Having identified which simulations performed best against observations using multiple statistical metrics, the researchers selected seven optimal models and constructed an optimal ensemble mean for future projections. Under the high-emission Shared Socioeconomic Pathway 5-8.5, the projections indicate that the stilling of the Tibetan Plateau will continue through the coming decades. Between 2015 and 2049, the ensemble projects an annual wind speed decline of approximately 0.02 meters per second per decade, unfolding alongside continued regional warming. While the projected rate is gentler than the historical observed trend, the direction of change is consistent, lending robustness to the conclusion that winds over the plateau will keep weakening in a warming world.

The juxtaposition of a robust future decline with a poorly simulated past decline creates something of a paradox for climate scientists. If models underestimate how much wind has already slowed, should we trust their projections of future slowing? The authors argue that the consistency of the projected decline across the selected optimal models, combined with the physical plausibility of continued warming-driven circulation changes, supports confidence in the sign of the projection even if the magnitude remains uncertain. At the same time, the study makes clear that closing the gap between simulated and observed trends requires more than computational horsepower.

That message may be the study’s most consequential takeaway for the modeling community. HighResMIP was conceived on the premise that finer grids would better resolve steep orography, coastlines, and mesoscale processes, and for some variables that promise has held. But for near-surface wind over the Tibetan Plateau, the near-identical performance of high- and low-resolution model pairs indicates that the bottleneck lies elsewhere, in the representation of physical processes such as snow-atmosphere interactions, surface energy budgets, and boundary-layer dynamics. Improving these process representations, the study suggests, may matter more than simply increasing spatial resolution for reducing persistent model biases in wind simulation.

For the people and ecosystems of the plateau, the implications extend well beyond model evaluation. Weakening winds influence glacier mass balance through altered turbulent heat exchange, affect the timing and intensity of the monsoon onset that billions of people depend on, and reshape the viability of wind energy development in one of the world’s most promising but fragile high-altitude environments. As the Third Pole continues to warm faster than the global average, the slow, steady quieting of its winds serves as another reminder that climate change reshapes even the most fundamental features of the atmosphere, and that our models, for all their sophistication, are still learning to listen.

Subject of Research: Evaluation and projection of near-surface wind speed trends over the Tibetan Plateau using CMIP6 HighResMIP simulations and station observations

Article Title: Near-surface wind speed over the Tibetan Plateau: evaluation, trend and projection

Article References: You, Q., Wu, F., Wu, T., Cai, Z., Sun, G., Jin, Z., Jiang, Z., Ullah, S., & Li, M. (2026). Near-surface wind speed over the Tibetan Plateau: evaluation, trend and projection. Climate Dynamics, 64(10), Article 411. https://doi.org/10.1007/s00382-026-08342-6

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08342-6

Keywords: Tibetan Plateau, near-surface wind speed, global stilling, CMIP6, HighResMIP, climate modeling, Climate Dynamics, SSP5-8.5, random forest, snow cover, sensible heat flux, monsoon

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