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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.

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

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.

This study examines the boundary layer wind profile and turbulence variables during the landfalls of Hurricanes Ida (2021) and Zeta (2020) using ground‐based Doppler radar observations and a nearby anemometer's wind measurements. While the radar sampled different parts of the hurricane circulation of the two cases, the observed maximum near‐surface wind and frictional velocity were similar. Radar‐retrieved wind profiles in both hurricanes revealed a boundary‐layer jet generally >1 km AGL, descending toward smaller radii as the hurricanes moved inland. A “knee‐like” structure in most wind profiles below the jet suggests an internal boundary layer (IBL) below 200 m and a log layer above it. Among the three methods for estimating near‐surface sustained winds from radar‐retrieved winds, leveraging low‐level IBL winds improves estimation accuracy and reduces the uncertainty to the selection of upstream surface roughness length. These findings offer valuable guidance for developing future probabilistic near‐surface wind products.

Launched on 5 June 2019, the BuFeng-1 A/B twin satellites were part of the first Chinese global navigation satellite system reflectometry (GNSS-R) satellite mission. In this letter, a brief introduction of the BF-1 mission and its preliminary results of sea surface wind retrieval are presented. Empirical fully developed sea (FDS) geophysical model functions (GMFs) relating the normalized bistatic radar cross-section to the sea surface wind speed are proposed for the BF-1 GNSS-R instruments. The FDS GMFs are derived from the collocated BF-1 observations, the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, and the advanced scatterometer (ASCAT) satellite observations. The preliminary tests reveal that the root-mean-square error (RMSE) between the derived wind speed and the reanalysis is 2.63 m/s for wind speeds in the range of 0.5–40.5 m/s. Further comparisons with the ASCAT observations and mooring buoys show that the RMSEs are 2.04 m/s and 1.77 m/s, respectively, at low-to-moderate wind speeds. This study demonstrates the effectiveness of BF-1 and provides a basis for the future GMF development of the BF-1 A/B mission.

Monthly wind data from 506 meteorological stations and ERA-Interim reanalysis during 1991–2015, are used to examine the surface wind trend over East China. Furthermore, combining the urbanization information derived from the DMSP/OLS nighttime light data during 1992–2013, the effects of urbanization on surface wind change are investigated by applying the observation minus reanalysis (OMR) method. The results show that the observed surface wind speed over East China is distinctly weakening with a rate of −0.16 m s−1 deca−1 during 1991–2015, while ERA-Interim wind speed does not have significant decreasing or increasing trend in the same period. The observed surface wind declining is mainly attributed to underlying surface changes of stations observational areas that were mostly induced by the urbanization in East China. Moreover, the wind declining intensity is closely related to the urbanization rhythms. The OMR annual surface wind speeds of Rhythm-VS, Rhythm-S, Rhythm-M, Rhythm-F and Rhythm-VF, have decreasing trends with the rates of −0.02 to −0.09, −0.16 to −0.26, −0.22 to −0.30, −0.26 to −0.36 and −0.33 to −0.51 m s−1 deca−1, respectively. The faster urbanization rhythm is, the stronger wind speed weakening presents. Additionally urban expansion is another factor resulted in the observed surface wind declining.

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (<25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co‐located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long‐term wind energy trends.