Near-surface Wind Research Articles

The Influence of Soil Moisture on the Sea Breeze Circulation and the Marine Atmospheric Boundary Layer in Idealized Simulations

Abstract Coastal circulations like the sea breeze are key drivers of variability within the marine atmospheric boundary layer (MABL) near the coast. In this study, we analyze 36 idealized, mesoscale simulations designed to better understand how coastal soil moisture influences the MABL through the sea breeze circulation. We find that decreasing coastal soil moisture increases the strength, extent, and depth of the sea breeze circulation over the ocean. The onshore segment of the sea breeze in the simulations with the driest soil moisture extends over 200 km seaward from the coast and has near-surface wind speeds exceeding 8 m s−1. The stronger onshore flow in the drier soil conditions increases humidity within the MABL by enhancing surface latent heat fluxes near the coast. Statistical analysis of all 36 simulations with different soil moisture, SST, and cloud settings reveals that the sea breeze characteristics over the ocean have the strongest relationship to the depth of the mixed layer over land and the weakest relationship to the lower-tropospheric static stability over the ocean. Afternoon clouds and convection limit the diurnal growth of the mixed layer over land by reducing surface heat fluxes and stabilizing the lower troposphere. As a result, the simulations with clouds have weaker, shallower, and more coastally confined sea breezes over the ocean than corresponding cloud-free simulations. These findings suggest that accurate representations of coastal soil moisture and cloud processes in numerical weather prediction models are crucial for forecasting basic characteristics of the MABL more than 200 km offshore.

The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3

Abstract. The third version of the Regional Atmosphere and Land (RAL3) science configuration is documented. Developed through international partnerships, RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator (JULES) when applied across timescales with kilometre and sub-kilometre-scale model grids. The RAL3 configuration represents a major advance compared to previous versions by delivering a common science definition suitable for application to tropical and mid-latitude regions. Developments within RAL3 include the introduction of a double-moment microphysics scheme and a bimodal cloud scheme, replacing use of a single-moment scheme and different cloud schemes for mid-latitudes and tropics in previous versions. Updates have been implemented to the boundary layer scheme and a consolidation of land model settings to be more consistent with global atmosphere and land (GAL) science configurations. Physics developments aimed to address priorities for model performance improvement identified by users. This paper documents the RAL3 science configuration, including a series of iterative revisions delivered since its first release, and their characteristics. Evidence is provided from the variety of assessments of RAL3, relative to the previous version (RAL2). Collaborative development and evaluation across organizations have enabled evaluation across a range of domains, grid spacing and timescales. The analysis indicates more realistic precipitation distributions, improved representation of clouds and of visibility, a continued trend to more realistic representation of convection, and reduced near-surface wind speeds but a persistent cold-temperature bias. Overall the convective-scale verification scores and climatological model distributions relative to observations improve for the majority of variables. Ensemble results show improvements to the spread–error relationship. User feedback from subjective assessment activities has also been positive. Differences between RAL3 revisions and RAL2 are further illustrated through a process-based analysis of a convective system over the UK. The latest RAL3 configuration (RAL3.3) is therefore recommended for research, operational numerical weather prediction, and climate production at kilometre and sub-kilometre scales.

Impacts of the Assimilation of Radar Radial Velocity Data Using the Ensemble Kalman Filter (EnKF) on the Analysis and Forecast of Typhoon Lekima (2019)

High-resolution radar observations are essential to improving the numerical predictions of high-impact weather systems with data assimilation techniques. The numerical simulations of the landfall of Typhoon Lekima (2019) are conducted in the framework of the WRF model, investigating the impact of assimilating radar radial velocity observations via the Ensemble Kalman Filter (EnKF) on the typhoon’s analysis and forecast performance. The results demonstrate that the EnKF method significantly improves forecast accuracy for Typhoon Lekima, including track, intensity and the 24 h cumulative precipitation. To be specific, the control experiment significantly underestimated typhoon intensity, while EnKF-based radar radial velocity assimilation markedly improved near-surface winds (>48 m/s) in the typhoon core, refined vortex structure and reduced track forecast errors by 50–60%. Compared with the control and 3DVAR experiments, EnKF assimilation better captured typhoon precipitation patterns, with the highest ETS scores, especially for moderate-to-high precipitation intensities. Moreover, the detailed analysis and diagnostics of Lekima show that the warm core structure is better captured in the assimilation experiment. The typhoon system is also improved, as reflected by enhanced potential temperature and a more robust wind field analysis.

Self-Limiting Effects of Global-Scale Desert Solar Farms: Climatic Feedbacks and Constraints on Wind-Solar Energy Synergy.

This study investigates the self-limiting effects of large-scale solar farms deployed in global desert regions, focusing on their far-reaching climatic and energy system impacts. By integrating a novel surface energy balance model into a global climate model, we simulate the consequences of installing solar farms over 20% of desert areas across five continents. Our findings reveal significant global and regional climatic feedbacks, including a 6.95% reduction in near-surface wind speeds, which leads to a 5.5% decline in global wind power generation potential (312.47 TW h annually). Furthermore, solar farms induce local atmospheric changes, such as increased surface temperatures and cloud cover, resulting in a 22.44 TW h reduction in solar power output. Crucially, these climatic impacts on surface radiation extend beyond immediate locations, influencing renewable energy resources in distant regions through teleconnections. This self-limiting dynamic underscores the inherent trade-offs between solar and wind energy, highlighting the need for integrated global energy planning that accounts for cross-border climatic and energy interdependencies. Our results emphasize the importance of balancing renewable energy expansion with the resilience of global energy systems, offering critical insights for sustainable energy strategies in a decarbonized future.

Machine Learning Indicates Stronger Future Thunderstorm Downbursts Affecting Southeast Australian Airports

Thunderstorms downbursts can be hazardous during aircraft landing and take-off. A warming climate increases low- to mid-level troposphere water vapor, typically transported from high sea-surface temperature regions. Consequently, the future occurrence and intensity of destructive wind gusts from wet microburst thunderstorms are expected to increase. Wet microbursts are downdrafts from heavily precipitating thunderstorms and are several kilometers in diameter, often producing near-surface extreme wind gusts. Brisbane airport recorded a wet microburst wind gust of 157 km/h in November 2016. Numerous locations in eastern Australia experience warm season (October to March) wet microbursts. Here, eight machine learning techniques comprising forward and backward linear regression, radial basis forward and backward support vector regression, polynomial-based forward and backward support vector regression, and forward and backward random forest selection were employed. They identified primary attributes for increased atmospheric instability by warm moist air influx from regions of high sea-surface temperatures. The climate drivers detected here are indicative of increased future eastern Australian warm season thunderstorm downbursts, occurring as wet microbursts. They suggest a greater frequency and intensity of impacts on aircraft safety and operations affecting major east coast airports, such as Sydney and Brisbane, and smaller aircraft at inland regional airports in southeastern Australia.

Second-generation downscaled earth system model data using generative machine learning

Second-generation downscaled earth system model data using generative machine learning

Preliminary Wind Resource Assessment of Songkhla, Thailand Using WAsP Simulation

This study presents a preliminary wind resource assessment at specific sites in southwestern Thailand for the deployment of onshore wind power for the future deployment. The primary objective of this research is to estimate wind power potential by using the near-surface wind observations over a period of 10-year (2013-2022) acquired from four weather observation stations. WAsP simulations were conducted to analyse prevailing wind direction, wind speed, and power density. The Bonus 300kW Mk III wind turbine was selected to estimate energy production at each site at 30 meters above ground level (AGL). The findings identify four sites Sadao, Hat Yai, Kho Hong, and Songkhla that demonstrate strong potential for prospective wind farm development. The WAsP analysis indicates mean wind speeds of 2.40 m/s for Sadao, 5.83 m/s for Hat Yai, 3.84 m/s for Kho Hong, and 3.41 m/s for Songkhla. The estimated power densities for Sadao, Hat Yai, Kho Hong, and Songkhla are 86 W/m², 74 W/m², 259 W/m², and 74 W/m², respectively. The mean net annual energy production values calculated for Sadao, Hat Yai, Kho Hong, and Songkhla are 171.401 MWh, 629.079 MWh, 351.292 MWh, and 217.072 MWh, respectively.

Investigating ozone build-up in the east of England during the July 2015 heat wave.

Investigating ozone build-up in the east of England during the July 2015 heat wave.

Extreme hourly 10m wind speed trends and reduced daily variability during 1940−2024

Abstract Near-surface wind speed (NSWS) is a critical atmospheric variable influencing Earth’s climate dynamics, weather patterns, ocean processes, ecological systems, and renewable energy resources. Utilizing the ERA5 global reanalysis dataset, this study systematically investigates long-term hourly NSWS trends from 1940 to 2024. Our findings highlight significant global increases in maximum, minimum, and daily mean hourly NSWS, with notably pronounced enhancements over oceanic regions. Crucially, minimum hourly NSWS has increased at a faster rate than maximum hourly NSWS, resulting in a widespread reduction of the daily NSWS range, particularly prominent across mid- and low-latitude oceans. This narrowing of wind speed variability potentially impacts marine ecosystems, coastal resilience, atmospheric boundary layer dynamics, and renewable energy production. The outcomes underscore the necessity and value of high-temporal-resolution analyses for capturing nuanced shifts in wind regimes, thereby providing essential insights into climate change’s complex, interrelated impacts on Earth’s environmental and socio-economic systems.

Optimizing Topographic Boundary Conditions for East Pacific Climate Simulation

Abstract Overly smooth topography in general circulation models (GCMs) underestimates the blocking effect of the steep mountain ranges flanking the eastern Pacific. We explore the impact of this bias on common biases in Pacific climate simulation [i.e., the unrealistic cross-equatorial symmetry of near-surface winds, sea surface temperatures (SSTs), and precipitation] through sensitivity experiments with modified Central and/or South American topography in an atmosphere–ocean coupled GCM. Quantifying orographic blocking potential via the Froude number, we determine that an envelope topographic interpolation scheme best captures observed blocking patterns. Implementing envelope topography only in Central America reduced model biases as greater blocking of the trade winds warmed SST and enhanced convergence in the northeastern Pacific. Doing so additionally over the Andes improved the simulation of South Pacific circulation and the South Pacific convergence zone as stronger deflection of the westerlies intensified the South Pacific anticyclone. This mitigated convection biases in the southeast Pacific by increasing subsidence and cooling SST. However, remote impacts of the Andes exacerbated the dry bias in the northeast tropical Pacific, resulting in negligible improvement in the East Pacific double-ITCZ. We find that, due to the significant role of large-scale convergence in driving precipitation patterns, other model biases, such as cloud-radiative biases, may modulate the impact of altering topography. Our results highlight the importance of considering alternate methods for calculating model topographic boundary conditions, though the optimal interpolation scheme will vary with model resolution and the impact of topography on GCM biases can be sensitive to choices made in formulating parameterizations. Significance Statement In this study, we explore how the mountain ranges spanning Central and South America shape the climate of the Pacific by blocking large-scale midlatitude and tropical winds. We show that the height of these mountains is typically too low in climate models and that elevating them can improve patterns of rainfall, surface ocean temperatures, and near-surface winds in the Pacific. This is important because model biases in the Pacific climate limit their utility for understanding current and future climate variability. Improving the representation of blocking by mountains can thus be a simple method for reducing uncertainties in future climate projections.

Wind-Driven Ocean Circulation Changes Can Amplify Future Cooling of the North Atlantic Warming Hole

Abstract The North Atlantic warming hole is an area of relative cooling in the North Atlantic subpolar gyre. Observations and models have suggested numerous causes of the warming hole, including a role for wind-driven ocean circulation changes. We investigate the role of wind-driven ocean circulation changes on the development and projected future of the North Atlantic warming hole by comparing two ensembles within the Community Earth System Model, version 2 (CESM2). One ensemble includes wind-driven ocean circulation changes, while the other does not. The difference between the ensemble means isolates the role of wind-driven ocean circulation changes on the externally forced North Atlantic warming hole. We find that wind-driven ocean circulation changes do not alter the timing of the formation of an externally forced warming hole. However, anthropogenic changes to the near-surface winds lead to enhanced upwelling near Greenland, and wind stress changes enable a positive feedback loop that relies on changes to mechanical stirring. These mechanisms amplify the cooling in the high latitude North Atlantic and lead to increased sea level pressure and reduced precipitation near the southern tip of Greenland. Thus, changes to wind-driven ocean circulation are a crucial component of future changes in North Atlantic climate. Improved understanding of ocean–atmosphere coupling in this region will improve projections of sea surface temperatures and associated atmospheric impacts. Significance Statement The purpose of this study is to quantify the role that changes to the wind-driven component of ocean circulation have on future sea surface temperatures in the North Atlantic subpolar gyre region. This region has warmed less than the global average, often referred to as a “warming hole.” We use a targeted climate model experiment to demonstrate that wind-driven ocean circulation changes do not cause the modeled North Atlantic warming hole. However, wind-driven ocean circulation changes alter the warming hole beginning in 2040. This demonstrates that monitoring and understanding changes to the surface winds and ocean currents in the North Atlantic is important for understanding future climate changes in the region.

Simulating Near-Surface Winds in Europe with the WRF Model: Assessing Parameterization Sensitivity Under Extreme Wind Conditions

Accurately simulating near-surface wind speeds is indispensable for wind energy development, particularly under extreme weather conditions. This study utilizes the Weather Research and Forecasting (WRF) model with a 6 km resolution to evaluate 80 m wind speed simulations over Europe, using the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis version 5 (ERA5) as initial and lateral boundary conditions. Two cases were analyzed: a normal case with relatively weak winds, and an extreme case with intense cyclonic activity over 7 days, focusing on offshore wind farm regions and validated against Forschungsplattformen in Nord- und Ostsee (FINO) observational data. Sensitivity experiments were conducted by modifying key physical parameterizations associated with wind simulation to assess their impact on accuracy. Results reveal that while the model realistically captured temporal wind speed variations, errors were significantly amplified in extreme cases, with overestimation in weak wind regimes and underestimation in strong winds (approximately 1–3 m/s). The Asymmetrical Convective Model 2 (ACM2) planetary boundary layer (PBL) scheme demonstrated superior performance in extreme cases, while there were no significant differences among experiments under normal cases. These findings emphasize the critical role of physical parameterizations and the need for improved modeling approaches under extreme wind conditions. This research contributes to developing reliable wind speed simulations, supporting the operational stability of wind energy systems.

Large-scale forcing of principal near-surface wind speed variability patterns in the Arctic since 1979

Abstract The ERA5 reanalysis dataset is used to characterize the principal patterns of annual and seasonal near-surface wind speeds in the Arctic from 1979 to 2023 by the empirical orthogonal function (EOF) method, together with their relationship with large-scale atmospheric circulation. The leading EOF (EOF1) accounts for about 14%–22% of annual and seasonal wind speed variability, and is composed of widely positive anomalies across most of the Arctic Ocean largely influenced by the positive Arctic Oscillation. Seasonally, the positive anomalies shift from the central Arctic Ocean in summer toward the Greenland and Norwegian Seas in winter, driven by the North Atlantic Oscillation (NAO). The second EOF (EOF2) explains 9.5% of annual wind speed changes and consists of a meridional dipole structure, i.e. positive anomalies over the Russian Arctic seas and negative anomalies over the Greenland and Norway Seas, mainly driven by the Arctic dipole and NAO. The time series of EOF2 shows a decadal switch from negative to positive anomalies at the beginning of the 21st century, related to the phase transition of the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation.

Radiosonde Measurements and Polar WRF Simulations of Low-Level Wind Jets in the Amundsen Sea Embayment, West Antarctica

Abstract We show that low-level jets (LLJs) occurred in 11 out of 22 radiosonde profiles in late austral summer over the coastal region of the Amundsen Sea Embayment, with ten of the LLJs directed offshore. The LLJs had core speeds from 9 to 32 m s−1, jet core heights from 80 to 800 m, and were associated with strong, low-level temperature inversions. Seven of the observed offshore LLJs were reasonably simulated by the polar-optimized Weather Research and Forecasting (Polar WRF) model, with output from the model subsequently used to elucidate their generation mechanisms. This study shows that one of the offshore LLJs simulated by the Polar WRF was caused by katabatic winds, while the remaining six were caused by the enhancement of katabatic winds by synoptic forcing in response to a low-pressure system over the Bellingshausen Sea, i.e., the offshore wind component associated with this system plays a crucial role in the enhancement of the katabatic LLJ. Examination of the Polar WRF output further shows that the LLJs extended over large areas of the Amundsen Sea Embayment, resulting in substantially enhanced near-surface wind speeds over both the Thwaites and Pine Island ice shelves, as well as the open ocean over the continental shelf. The wind-driven forcing associated with the LLJs could perhaps have important impacts on the redistribution of snow over the ice shelves significantly, as well as to affecting sea-ice and ocean circulation variability, including the transport of relatively warm water over the continental shelf to the ice shelf cavities and extension basal melting.

Mechanisms of the Extreme Wind Speed Response to Climate Change in Variable-Resolution Climate Simulations of Western, Central, and Atlantic Canada

Abstract Confidence in climate change projections of midlatitude wind extremes is limited by poor model skill at representing near-surface winds and incomplete understanding of the physical mechanisms that may cause extreme winds to respond to climate change. This study addresses these issues by analyzing climate change projections of extreme winds with regional refinement at 7 km over Western, Central, and Atlantic Canada, using the variable resolution version of the National Center for Atmospheric Research’s Community Earth System Model (NCAR VR-CESM). This study extends previously reported results for Central Canada’s Southern Ontario region. VR-CESM consistently represents conditions linked with extreme winds in all regions more credibly than the global uniform (100 km) resolution version of CESM, which lends confidence to the projected climate response of the refined resolution simulations. VR-CESM also consistently projects a strengthening of extreme wind speeds over land in all regions, albeit with mixed statistical significance, while uniform resolution CESM projections exhibit weakening. The increased extreme wind speeds in VR-CESM are associated with downward mixing of high momentum air in the boundary layer, which is present in all regions. This association is poorly represented with coarse resolution. However, additional factors, including increasing regional extratropical cyclone intensity, also contribute to the extreme wind response in the Western and Atlantic regions. The changes to extratropical cyclone intensity exhibit resolution dependence that is harder to explain. Our results highlight the need for regionally-focused dynamical downscaling of global climate projections for climate change impact assessment.

Analysis of vegetation response to four climate factors based on the CTSS-RESTREND method

Abstract Climate change significantly influences vegetation growth, necessitating an in-depth understanding of the climate-driven dynamics of vegetation to formulate ecological and environmental policies. This study addresses the limitations of traditional correlation analysis methods by utilizing a combined approach of Residual Trend Analysis (RESTREND) and Time Series Segmentation Residual Trend Analysis (TSS-RESTREND), known as CTSS-RESTREND. By using this method, we examined the influence of near-surface air temperature, precipitation, humidity, and wind speed on vegetation growth in Guangdong Province from 2000 to 2020, using Normalized Difference Vegetation Index (NDVI) data and climatic variables. Using MOD13Q1 NDVI data and ERA5 downscaled climate reanalysis data, this research utilizes the CTSS-RESTREND algorithm to quantify the climate effects on vegetation. The analysis reveals that precipitation and humidity are the primary positive drivers of vegetation growth, temperature has a slightly higher positive than negative impact on vegetation, while wind speed generally has a negative impact on vegetation, but its effect is relatively slight. During the growing season, the growth of vegetation becomes more sensitive to the three climatic factors: temperature, precipitation, and humidity. This study provides a more accurate and detailed understanding of the spatiotemporal changes and climate driving factors affecting vegetation in Guangdong Province.

Application of adjoint method to the evaluation of temporal and spatial variations of the eddy viscosity coefficient

This study investigated the temporal and spatial variations of the eddy viscosity coefficient (EVC) in an Ekman model using an adjoint method. The time- and depth-dependent EVC was represented as a Fourier series, which consisted of four summations. The effectiveness of the model was significantly influenced by the number of terms included in each summation. In the first three groups of ideal experiments, a constant drag coefficient was used at low surface wind speeds. An analysis of the inversion results indicated that more terms should be added to each summation if the EVC varied over shorter periods, whether in time or depth. Additionally, the findings indicated that the model performed better when simulating the time- and depth-dependent EVC over longer periods. Two additional groups of ideal experiments were conducted to investigate the impact of near-surface wind speeds on the inversion of the K-Profile EVC. The drag coefficient associated with wind speeds over time was utilized in these experiments. An analysis of the inversion results indicated that the model effectively captured the temporal and spatial distribution of the EVC. Finally, the EVC was simulated during a super typhoon. The evaluation of the simulated EVC and ocean currents suggested that greater values of the simulated EVC appear at depths ranging from 50 to 65 m under strong wind conditions. Variations in wind directions could further enhance the inverted EVC within the Ekman layer.

Multifactor Change in Western U.S. Nighttime Fire Weather

Abstract Reports from western U.S. firefighters that nighttime fire activity has been increasing during the spans of many of their careers have recently been confirmed by satellite measurements over the 2003–20 period. The hypothesis that increasing nighttime fire activity has been caused by increased nighttime vapor pressure deficit (VPD) is consistent with recent documentation of positive, 40-yr trends in nighttime VPD over the western United States. However, other meteorological conditions such as near-surface wind speed and planetary boundary layer depth also impact fire behavior and exhibit strong diurnal changes that should be expected to help quell nighttime fire activity. This study investigates the extent to which each of these factors has been changing over recent decades and, thereby, may have contributed to the perceived changes in nighttime fire activity. Results quantify the extent to which the summer nighttime distributions of equilibrium dead woody fuel moisture content, planetary boundary layer height, and near-surface wind speed have changed over the western United States based on hourly ERA5 data, considering changes between the most recent decade and the 1980s and 1990s, when many present firefighters began their careers. Changes in the likelihood of experiencing nighttime meteorological conditions in the recent period that would have registered as unusually conducive to fire previously are evaluated considering each variable on its own and in conjunction (simultaneously) with one another. The main objective of this work is to inform further study of the reasons for the observed increases in nighttime fire activity. Significance Statement Western U.S. firefighters have reported a problematic rise in nocturnal wildfire activity. Verifying their hypothesis that meteorological variability is responsible is a first step toward better understanding the predictability of the underlying processes. This study expands upon our previous investigation of multidecadal change in seasonally averaged nocturnal vapor pressure deficit by looking at changes in the frequency of dry-fuel nights over the western United States and their coincidence with other fire-conducive nocturnal meteorological conditions. Dry-fuel nights have become >10× more frequent in the 2010s compared to the 1980s and 1990s in some regions. Over 81% of the study area, increasing dry-fuel night frequency has been compounded by the double, or triple, threats of simultaneously windier and deeper planetary boundary layers.

Long-term global trends and influencing factors of surface urban cool and heat islands

ABSTRACT Global warming and urbanization have intensified temperature differences (ΔT) between urban and nonurban areas, creating distinct surface urban heat islands (SUHIs) and surface urban cool islands (SUCIs). This study analyzes SUHI and SUCI trends across 1,963 global cities from 2001 to 2018, exploring their spatiotemporal patterns and drivers. Pearson correlation analysis was used to examine the relationships between ΔT and key factors, including the enhanced vegetation index (EVI), proportion of impervious surface (PIS), and climatic variables such as precipitation (PR), surface downwelling shortwave radiation (RSDS), mean daily air temperature (TAS), near-surface wind speed (Sfc Wind), near-surface relative humidity (HURS), and potential evapotranspiration (PET). Key findings include: (1) SUCIs are more common in winter but most significant in summer, with daytime SUCIs transitioning to nighttime SUHIs; (2) SUHI cities show a ΔT increase of 0.153°C per decade (P < 0.01), while SUCI cities show a decrease of −0.102°C per decade (P < 0.01); (3) HURS is the dominant climatic driver, with PR and RSDS more influential in summer, and TAS being crucial in winter. Future research should explore the dynamic interactions between climate and human factors in shaping ΔT for adaptive urban planning.

Impact of Climate Change on Wind Power Generation Studied Using Multivariate Copula Downscaling: A Case Study in Northwestern China

Climate change can modify regional wind power generation ability, as it may affect wind speed. Here, we developed a multivariate copula downscaling (MvCD) approach to statistically downscale the near-surface wind speed of CMIP5 global climate models (GCMs) to the scale of wind farms in Urumqi, China. The low computational cost and high random analysis capability of this approach allowed the rapid assessment of projected changes and randomness from nine GCMs, spanning a range of potential futures under four scenarios. Simulation data from multiple GCMs and historical data of the study area were incorporated into the MvCD to generate a high dimensional multivariate copula. Thereafter, the high dimensional multivariate copula was further used to identify future wind speed patterns based on multiple GCMs under different CO2 emission scenarios. The estimated amount of wind power generation was obtained using future wind speed data. Results revealed the regional characteristics and periodicity of wind speed for Urumqi in the future. Wind power generation results revealed the impacts of climate changes on regional wind power generation and indicated that high wind speeds would occur from June to September and low wind speeds would occur from December to March in future scenarios. Wind speed would be more extreme under each scenario in the future than before. The highest and lowest wind speeds will increase and decrease, respectively. Sustained high winds would increase the potential of wind power generation in the future. Wind instability based on CO2 emission increases will lead to wind power being curtailed and low wind-power generation.