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Seasonal variations in near‐surface wind speed (NSWS) significantly impact wind energy production, yet the role of the El Niño–Southern Oscillation (ENSO) in shaping these variations remains insufficiently explored. This study investigates ENSO‐driven seasonal NSWS variability across China and their implications for wind power density (WPD). We demonstrate that ENSO exerts strongly season‐dependent impacts on WPD in subregions, spanning the ENSO‐developing summer to the decaying summer. During these active ENSO phases, 15%–50% of stations within that regions exhibit WPD anomalies exceeding ±10% relative to their seasonal climatology in response to a 1°C sea surface temperature change in the central‐eastern Pacific during the ENSO peak winter. Furthermore, 850 hPa wind speeds show coherent variations with NSWS, indicating a strong dynamic connection between the lower troposphere and surface. These findings deepen our understanding of ENSO's influence in driving seasonal wind resources, providing actionable insights for regional wind energy management and strategic resource planning. Plain Language Summary Understanding variations in near‐surface wind speed (NSWS) is crucial for optimizing wind energy exploitation, particularly as China expands its renewable energy capacity to meet rising demand. This study examines the impact of the El Niño–Southern Oscillation (ENSO), a recurring climate phenomenon characterized by fluctuations in Pacific Ocean surface temperatures, on the seasonal variability of NSWS and wind power density (WPD) across China. Using station observations and reanalysis data sets, we show that ENSO significantly modulates NSWS and WPD in Northeast, North, Southwest, and South China. For instance, a typical El Niño event, with a 1°C increase in winter sea surface temperature in the central‐eastern Pacific, can cause WPD variations exceeding ±10% of the seasonal average at up to half of the monitoring stations in these regions. Additionally, our analysis reveals a strong link between ENSO‐driven wind changes in the lower troposphere and at the surface, highlighting a coupled response. These insights suggest ENSO's role in modulating wind energy potential, aiding seasonal wind resource forecasting. Such understanding is essential as China continues expanding its wind energy infrastructure and adapting to climate‐driven wind variations during ENSO events. Key Points El Niño–Southern Oscillation has significant influences seasonal variations in near‐surface wind speed (NSWS) across Northeast, North, Southwest, and South China A 1 K winter Niño 3.4 warming induces wind power density changes exceeding ±10% at 15%–50% of monitoring stations Coherent variations in 850 hPa wind speeds and NSWS indicate a tight dynamic coupling between the lower troposphere and the near‐surface levels

Previous research has extensively explored the “stilling” and “reversal” phenomena in annual near‐surface wind speed (NSWS). However, the variations in the strengths of these phenomena between different months remain unclear. Here the monthly changes in observed NSWS from 769 stations across China during 1979–2020 were analyzed. The analysis reveals a consistent decline in NSWS that ceased around 2011, followed by an increasing trend detected in all months except March, where a distinct hiatus is observed. The hiatus in March NSWS is primarily attributed to a significant reduction in NSWS over North and Northwest China. This reduction can be linked to the southward shift of the East Asian subtropical jet (EASJ), which resulted in a decreased meridional temperature gradient and weakened transient eddy activities across northern China. These findings emphasize the importance of considering changes in the EASJ to gain a comprehensive understanding of NSWS changes at regional scale. Plain Language Summary Understanding how near‐surface wind has changed and identifying the factors driving these changes are crucial. This can help in developing adaptation strategies to increase society's resilience to possible future climate, such as understanding the future revenues of electricity production from wind farms. By analyzing wind observations from 769 stations across China since 1979, we confirmed a general decrease (stilling) that ceased around 2011, followed by a general significant increasing tendency (reversal) in all months but March. Indeed, March's wind series after 2011 showed a pause (i.e., hiatus) from the 1979–2011 slowdown. This hiatus was mainly caused by the general wind reduction across northern China since 2011, which differs from the wind increase observed in other regions. The slowdown in March from 2011 to 2020 is related to the southward shift of East Asian subtropical jet streams, which are fast‐flowing, narrow, and meandering air currents in the upper atmosphere. Jet streams play an important role in shaping both upper and lower air circulation and influence surface wind by transporting high and low‐pressure systems. Key Points March near‐surface wind speed (NSWS) over China experienced a hiatus after 2011, distinct from other months The observed hiatus in March NSWS was primarily caused by a significant reduction in NSWS over North and Northwest China A southward shift of the East Asian subtropical jet may have contributed to the detected hiatus

Near‐surface wind speed (NSWS) over China shows multiple time‐scale changes at a centennial scale, but the contributions of internal variability (IV), anthropogenic forcing (ANT), and natural forcing (NAT) to those changes remain unknown. This study investigated the contributions of IV, ANT, and NAT to NSWS changes at a centennial scale. Results show that the NSWS changes were attributed mainly to IV. IV not only modulated the interannual changes in NSWS but also determined the interdecadal transition in NSWS. The relative contributions of IV to the interannual and decadal NSWS exceeded 75.0%. ANT contributed particularly to the long‐term reduction in NSWS; especially, it has contributed 55.0% of the reduction in NSWS since 1957, serving as the major contributor to the reduction in NSWS. NAT had a small‐to‐negligible effect on China's NSWS throughout the study period. This study enhances our understanding of NSWS changes at different time scales. Plain Language Summary Near‐surface wind speed (NSWS) is crucial because it can influence energy, water, and air move between the Earth's surface and the atmosphere, which can also affect weather and climate systems like dust storms, evaporation rates, and the water cycle. In the past decades, interannual and interdecadal changes in NSWS, as well as the long‐term trend of NSWS have been analyzed; however, the causes behind these changes are not clear. Our research focuses on understanding these changes over nearly a century. We discover that internal variability (IV) is a primary factor driving these changes in NSWS, especially in terms of its fluctuations and shifts over decades. In addition to IV, anthropogenic forcing also plays a crucial role, particularly for the decrease of NSWS since 1957. On the other hand, natural forcing seem to have a minimal or almost no impact on NSWS changes in China during the study period. This study not only enhances our understanding of NSWS changes over multiple time scales but also provides essential information for policymakers to develop climate strategies and adaptation measures. Key Points Internal variability determines the interannual and interdecadal changes in near‐surface wind speed (NSWS) across China Anthropogenic forcing is responsible for the slowdown in NSWS since 1957, its contribution reaches 55.0% Natural forcing has a small‐to‐negligible influence on the changes in NSWS

We utilize ocean 10-m wind speed (U10m) from the microwave Multi-sensor Advanced Climatology data set to examine the coupling between convective cloud and precipitation processes, synoptic state, and U10m and to evaluate the representation of U10m in global climate models (GCMs). We find that midlatitude U10m is underestimated by GCMs relative to observations. We examine two potential mechanisms to explain this model behavior: cold pool formation in cold air outbreaks (CAOs) associated with downdrafts that enhance U10m and sea surface temperature (SST) gradients affecting U10m through thermally forced surface winds at regional scales. When the effects of the CAO index (M) and SST gradients on U10m are accounted for, a relationship between GCM horizontal resolution and U10m appears. The strongest correlation between resolution and U10m is over the western boundary currents characterized by frequent CAOs atop strong SST gradients which drives the strongest surface fluxes on Earth.

As near‐surface wind speed (NSWS) largely controls wind power generation, robust projection is vital to wind energy planning and broader sustainability goals. However, the predictive skill of climate models for NSWS remains uncertain. Analysis of Coupled Model Intercomparison Project Phase 6 simulations shows that the models reproduce the mean NSWS over China reasonably well but substantially underestimate the observed long‐term trend and variability. Given these large biases, bias correction is essential for obtaining more reliable NSWS projections. We correct this bias using an Empirical Orthogonal Function approach to isolate the dominant spatial modes of NSWS variability. The corrected projections exhibit amplified future trends and increased variability of NSWS over China compared with the original model output, with the strongest changes emerging under higher greenhouse gas emissions. Our results indicated that unadjusted models may understate the magnitude of future NSWS changes. This study provides a more reliable reference for future evolution of NSWS.

Global warming is expected to have far‐reaching impacts on the frequency and intensity of extreme events, but the effects of anthropogenic warming on the emergence seasons of year‐maximum near‐surface wind speed (NSWS) remain poorly understood. We provide a comprehensive map of the changing emergence seasons of year‐maximum NSWS using Coupled Model Intercomparison Project Phase 6 projections. Our analysis reveals a rapid response of synoptic‐scale extreme NSWS to global warming, with consistent spatial patterns observed across various periods and warming scenarios. The most significant increase (∼16%) in the emergence season is projected to occur in December‐January‐February (DJF) over Mid‐high‐latitude Asia by the end of the 21st century. The study also anticipates changes in the emergence seasons of year‐maximum NSWS at a regional scale. These results deepen our understanding of the complex and interconnected nature of global climate change and underscore the need for concerted efforts in addressing this pressing challenge. Plain Language Summary Global warming is indisputably triggering changes in the world's weather systems, leading to more frequent and intense extreme weather events. However, it is unclear how anthropogenic warming affects the timing of the annual strongest near‐surface wind speed (NSWS). In this study, we used state‐of‐the‐art global climate models to create a comprehensive map illustrating these NSWS patterns of response to global warming. We discovered that these changes are consistent across various time periods (near to long term) and warming scenarios (low to high warming), revealing a robust relationship between extreme NSWS and global warming. The most significant change is observed during December‐January‐February in Mid‐high‐latitude Asia, with an increase of about 16% by the end of the 21st century. Our findings suggest that we can expect more year‐maximum NSWS occurs in different regions during specific seasons: December‐January‐February in North America and Asia, March‐April‐May in Africa, June‐July‐August in Asia and West Africa, and September‐October‐November in South America and Australia. These results offer valuable insights for guiding adaptation efforts even if ambitious climate actions manage to limit global warming at a lower level. Key Points Changing emergence seasons of the land year‐maximum near‐surface wind speed (NSWS) map is created There is a rapid response of emergence seasons of year‐maximum NSWS to anthropogenic warming The strongest increase (16%) in emergence season is projected to occur in December‐January‐February over Mid‐high‐latitude Asia

Changes in terrestrial near-surface wind speed (SWS) are induced by a combination of anthropogenic activities and natural climate changes. Thus, the study of the long-term changes of SWS and their causes is very important for recognizing the effects of these processes. Although the slowdown in SWS has been analyzed in previous studies, to the best of knowledge, no overall comparison or detailed examination of this research has been performed. Similarly, the causes of the decreases in SWS and the best directions of future research have not been discussed in depth. Therefore, we analyzed a series of studies reporting SWS trends spanning the last 30 years from around the world. The changes in SWS differ among different regions. The most significant decreases have occurred in Central Asia and North America, with mean linear trends of − 0.11 m s−1 decade−1; the second most significant decreases have occurred in Europe, East Asia, and South Asia, with mean linear trends of − 0.08 m s−1 decade−1; and the weakest decrease has occurred in Australia. Although the SWS in Africa has decreased, this region lacks long-term observational data. Therefore, the uncertainties in the long-term SWS trend are higher in this region than in other regions. The changes in SWS, caused by a mixture of global-, regional-, and local-scale factors, are mainly due to changes in driving forces and drag forces. The changes in the driving forces are caused by changes in atmospheric circulation, and the changes in the drag forces are caused by changes in the external and internal friction in the atmosphere. Changes in surface friction are mainly caused by changes in the surface roughness due to land use and cover change (LUCC), including urbanization, and changes in internal friction are mainly induced by changes in the boundary layer characteristics. Future studies should compare the spatio-temporal differences in SWS between high and low altitudes and quantify the effects of different factors on the SWS. Additionally, in-depth analysis of extreme SWS events and prediction of future mean and extreme SWS values at global and regional scales are also necessary.

Surface and near‐surface wind speeds, critical factors for dust emission, are often overestimated in desert regions by models, leading to exaggerated predictions of sandstorm extent and dust concentration. This study implements a new turbulent orographic form drag (TOFD) parameterization scheme using 30‐m‐resolution terrain data in the WRF‐Chem model. Over a 1‐month simulation, this scheme reduced the overestimated surface wind speed by 45%. Compared with observations from 41 meteorological stations and one radio‐sounding station in the Taklimakan Desert, it also decreased the root mean square errors for surface and near‐surface winds by about 20% and 5%, respectively. Furthermore, the 30‐day simulation showed a 31% reduction in PM10 RMSE and a better‐matched aerosol optical depth distribution. The results demonstrate that the novel TOFD scheme, which utilizes high‐resolution terrain data, effectively resolves dunes and accurately accounts for the drag of small dunes on the near‐surface atmosphere in deserts.

Decadal variations in near‐surface wind speed (NSWS) and their causes are poorly understood. We found that the decadal transition of winter NSWS in northern China (NC) was 10 years earlier than in southern China (SC), which could be linked to the changes in intensities of the eastward wave‐activity flux and Siberian High (SH) induced by the Warm Arctic‐Cold Eurasia (WACE) dipole pattern. From 1973 to 1990, the WACE pattern from positive to negative phases confined the eastward wave trains to high latitudes with a decreasing SH, inducing an NSWS reduction. From 1991 to 2000, the WACE strengthened from negative to positive phases, causing a decadal transition in NSWS first in NC. After 2000, accompanied by the strengthening of the positive WACE, the eastward wave trains propagated downstream to lower latitudes, the SH and the meridional pressure gradient enhanced. Therefore, the transition of decadal NSWS occurred in SC until 2000. Plain Language Summary Near‐surface wind speed (NSWS) is critical in exchanging energy, water, and momentum between the Earth's surface and the lower atmosphere. Previous studies have reported that the slowdown in NSWS and its reversal could be a manifestation of decadal variations in the climate system. However, the regional non‐synchronization of decadal variations in NSWS and the corresponding cause are poorly understood. This study reported a non‐synchronization of the decadal transition in winter NSWS between northern China (NC) and southern China (SC). The significant turning point of winter NSWS across NC was 10 years earlier than those across SC, and it was caused mainly by the zonal wind. The non‐synchronization of decadal variations in winter NSWS between NC and SC was linked to the Warm Arctic‐Cold Eurasia (WACE) atmospheric circulation pattern, and the Siberian High (SH) could serve as a bridge through which the WACE atmospheric circulation pattern influences the asynchronous transition of decadal NSWS between NC and SC. This study improves the understanding of decadal variations in NSWS across China. Key Points The decadal near‐surface wind speed (NSWS) transition in winter over northern China (NC) was 10 years earlier than in southern China (SC) The non‐synchronization of the decadal transition in NSWS between NC and SC was linked with the Warm Arctic‐Cold Eurasia pattern Intensities of wave‐activity flux and Siberian High influenced the non‐synchronization of decadal transition in NSWS between NC and SC

Accurate estimation of coastal near‐surface winds during hurricane landfalls remains challenging, partly attributable to an insufficient understanding of the wind profiles within the internal boundary layer (IBL) induced by an abrupt surface roughness change. This study addresses this issue by performing three semi‐idealized large‐eddy simulations. Results indicate that a nascent log layer emerges within the IBL, and its depth gradually increases from ∼60 m near the coast to ∼400 m 12 km inland, where the boundary layer transition is nearly complete. This nascent log layer is superimposed by another log layer originating from the upstream marine boundary layer. While turbulence kinetic energy (TKE) is maximized near the surface over both water and land, peak TKE values over land are a factor of 2 greater due to the amplified near‐surface vertical wind shear. The capability and uncertainty of coastal radars and radiosondes to detect IBL and estimate 10‐m winds are discussed. Plain Language Summary Hurricane landfalls are typically associated with severe wind‐related compound hazards (infrastructure damage, blackout, subsequent heatwaves during power outages, etc.) in the coastal region. Knowing exactly how strong near‐surface winds will be during hurricane landfalls is crucial for risk communication, effective preparation of coastal communities, and post‐storm rescue and assistance (e.g., by FEMA). However, this intention is compromised by our limited understanding of the evolution of near‐surface winds during landfalls, partly attributable to the scarcity of the coordinated observations of low‐level winds over both water and land. Using specially configured turbulence‐resolving computer model simulations, this study provides insights into the effects of land surface types and distance inland on the near‐surface wind profile. This study also quantifies the uncertainty of the 10‐m wind estimate derived from different observation‐based approaches for the first time. These findings can guide future field campaigns and hurricane landfall studies. Key Points A novel large‐eddy simulation framework was further improved to study the internal boundary layer (IBL) during hurricane landfalls Twin log layers coexist within a narrow coastal transition zone Radar wind retrievals in principle can capture the IBL wind profile while estimating 10‐m winds using radiosonde data is highly uncertain