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This study investigates the centennial-scale (i.e., since 1926) variability of observed near-surface wind speed across Sweden. Results show that wind speed underwent various phases of change during 1926–2019: (a) a clear slowdown during 1926–1960; (b) a stabilization from 1960 to 1990; (c) another clear slowdown during 1990–2003; (d) a slight recovery/stabilization period for 2003–2014, which may continue with a possible new slowdown. Furthermore, the performance of three reanalysis products in representing past wind variations is evaluated. The observed low-frequency variability is properly simulated by the selected reanalyses and is linked to the variations of different large-scale atmospheric circulation patterns (e.g., the North Atlantic Oscillation). However, the evident periods of decreasing trend during 1926–1960 and 1990–2003, which drive most of the stilling in the last century, are missing in the reanalyses and cannot be realistically modeled through multiple linear regression by only using indexes of atmospheric circulation. Therefore, this study reveals that changes in large-scale atmospheric circulation mainly drives the low-frequency variability of observed near-surface wind speed, while other factors (e.g., changes in surface roughness) are crucial for explaining the periods of strong terrestrial stilling across Sweden.

Trabajo presentado en EGU General Assembly, celebrado en Viena (Austria) del 23 al 27 de mayo de 2022., The demand for high spatially distributed wind speed data is substantially increasing during the last decades. This increase stems from its crucial role for climate change studies and many socioeconomic and environmental issues, e.g., wind power generation. However, observed wind speed records from weather stations do not cover this demand due to their coarse spatial resolution and inhomogeneous time scales, limiting the possibility of developing accurate gridded wind speed products using traditional geostatistical gridding methods. Moreover, wind speed from reanalyses and climate simulations does not accurately reproduce observed wind speed and gusts at regional scales. For instance, it lacks capturing the multidecadal variability of wind speed, e.g., the stilling (decline in winds) vs. the reversal (increase in winds) phenomena. Artificial Intelligence is a powerful tool that can overcome these data availability and quality limitations of wind observations. In this study, we apply artificial intelligence to reconstruct wind speed data from in situ weather observations in the Eastern Iberian Peninsula, focusing on the Valencia region. The generated time series are then implemented to develop a high spatial resolution gridded wind speed observations at a regional scale. This new gridded wind speed dataset will allow computing, e.g., wind indices as a climate service for multiple socioeconomic and environmental sectors.

Great attention has been paid to the long-term decline in terrestrial near-surface wind speed (SWS) in China. However, how the SWS varies with regions and seasons and what modulates these changes remain unclear. Based on quality-controlled and homogenized terrestrial SWS data from 596 stations, the covarying SWS patterns during the Asian Summer Monsoon (ASM) and the Asian Winter Monsoon (AWM) seasons are defined for China using empirical orthogonal function (EOF) analysis for 1961–2016. The dominant SWS features represented by EOF1 patterns in both seasons show a clear decline over most regions of China. The interannual variability of the EOF1 patterns is closely related to the Northeast Asia Low Pressure (NEALP) and the Arctic Oscillation (AO), respectively. The EOF2 and EOF3 patterns during ASM (AWM) season describe a dipole mode of SWS between East Tibetan Plateau and East China Plain (between East Tibetan Plateau and Northeast China), and between Southeast and Northeast China (between Northeast China and the coastal areas of Southeast China), respectively. These dipole structures of SWS changes are closely linked with the oceanic-atmospheric oscillations on interannual scale., We acknowledge supports from the National Science Foundation of China (41822101, 41888101, 41971022 and 41772180), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB26020000), fellowship for the National Youth Talent Support Program of China (Ten Thousand People Plan), fellowship for Youth Talent Support Program of Fujian Province and the Innovation Team Project (IRTL1705). This research was also funded by the Swedish Formas (2017-01408). Cesar Azorin-Molina was supported by the IBER-STILLING project RTI2018-095749-A-100 (MCIU/AEI/FEDER,UE), the VENTS project AICO/2021/023 (GVA) and the CSIC Interdisciplinary Thematic Platform PTI-CLIMA.

Trabajo presentado en EGU General Assembly, celebrada en modalidad virtual del 19 al 30 de abril de 2021., Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden. During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.

Wind gusts represent one of the main natural hazards due to their increasing socioeconomic and environmental impacts on, as examples: human safety; maritime-terrestrial-aviation activities; engineering and insurance applications; and energy production. However, the existing scientific studies focused on observed wind gusts are relatively few compared to those on mean wind speed. In Australia, previous studies found a slowdown of near-surface mean wind speed, termed “stilling”, but a lack of knowledge on the multi-decadal variability and trends in the magnitude (wind speed maxima) and frequency (exceeding the 90th percentile) of wind gusts exists. A new homogenized daily peak wind gusts (DPWG) dataset containing 548 time series across Australia for the period 1941-2016 is analyzed to determine long-term trends in wind gusts. Here we show that both the magnitude and frequency of DPWG declined across much of the continent, with a distinct seasonality: negative trends in summer-spring-autumn and weak negative or non-trending (even positive) trends in winter. We demonstrate that ocean-atmosphere oscillations such as the Indian Ocean Dipole and the Southern Annular Mode partly modulate decadal-scale variations of DPWG. The long-term declining trend of DPWG is consistent with the “stilling” phenomenon, suggesting that global warming may have reduced Australian wind gusts., C.A.M. was supported by Ramon y Cajal fellowship (RYC-2017-22830) and the grants no. VR-2017-03780 and RTI2018-095749-A-I00 (MCIU/AEI/FEDER, UE). G.F.Z was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (No. 2019QZKK0606) and National Natural Science Foundation of China (Grant No. 41621061). S.W.S was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF2018R1A5A1024958).

A decline in mean near-surface (10 m) wind speed has been widely reported for many land regions over recent decades, yet the underlying cause(s) remains uncertain. This study investigates changes in near-surface wind speed over northern China from 1961 to 2016, and analyzes the associated physical mechanisms using station observations, reanalysis products and model simulations from the Community Atmosphere Model version 5.1 (CAM5). The homogenized near-surface wind speed shows a significantly (p < 0.05) decline trend of −0.103 m s−1 decade−1, which stabilized from the 1990s onwards. Similar negative trends are observed for all seasons, with the strongest trends occurring in the central and eastern parts of northern China. Fast warming has occurred at high-latitudes (i.e., >50°N) in recent decades, which has weakened the annual and seasonal meridional air temperature gradient (−0.33°C to −0.12°C dec−1, p < 0.05, except autumn) between these regions (50°–60°N, 75°–135°E) and the northern China zone (35°–45°N, 75°–135°E). This caused a significant (p < 0.05) decrease in annual and seasonal pressure gradient (−0.43 to −0.20 hPa dec−1) between the two zones, which contributed to the slowdown of winds. CAM5 simulations demonstrate that spatially uneven air temperature increases and near-surface wind speed decreases over northern China can be realistically reproduced using the so-called “all forcing” simulation, while the “natural only forcing” simulation fails to realistically simulate the uneven warming patterns and declines in near-surface wind speed over most of northern China, except for summer., This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, Grant No. 2019QZKK0606), the National Natural Science Foundation of China (Grant No. 41621061), and by the National Key Research and Development Program—Global Change and Mitigation Project (Grant No. 2016YFA0602404). This work was also supported by a Swedish Research Council (2017-03780) and a Swedish Research Council for Sustainable Development (2019-00509) grant, and by the IBER-STILLING project, funded by the Spanish Ministry of Science, Innovation and Universities (RTI2018-095749-A-I00; MCIU/AEI/FEDER, UE).

Trabajo presentado en EGU General Assembly, celebrada en modalidad virtual del 4 al 8 de mayo de 2020., In a context of global climate change, the scientific community has evidenced a significant decrease in wind speed, a phenomenon known as «stilling». This climate trend has mainly been observed over mid-latitude continental surfaces since the 1980s. On the contrary, other studies have detected an increase in wind speed over ocean surfaces; and there is little conclusive scientific evidence on trends in wind speed across the troposphere. Furthermore, a reversal in global terrestrial stilling has recently been documented in few regional and global studies since the 2010s. The causes associated with the climate variability of wind speed have not yet been resolved and there are many uncertainties behind the «stilling» and «recovery» phenomenon because neither the quantity nor the quality of wind speed observations is adequate. This contribution shows an overview of the IBER-STILLING project (RTI2018-095749-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities. This project aims to move forward on the assessment of wind speed and wind gusts variability and underlying causes globally, with emphasis on the Spanish territory and surrounding ocean (Atlantic) and sea (Mediterranean) surfaces. The IBER-STILLING project will collect and generate climate information of wind speed from different data sources; climate data will be subject to a comprehensive protocol for quality control and homogenization. The statistical analysis of these climate databases will allow characterizing trends and climatic cycles of wind speed, allowing a pioneering global analysis of wind speed over continental and ocean surfaces, and across the boundary layer and the entire troposphere. The project will also conduct wind-tunnel experiments to quantify biases introduced by anemometers devices.

Assessing change in daily maximum wind speed and its likely causes is crucial for many applications such as wind power generation and wind disaster risk governance. Multidecadal variability of observed near-surface daily maximum wind speed (DMWS) from 778 stations over China is analyzed for 1975-2016. A robust homogenization protocol using the R package Climatol was applied to the DMWS observations. The homogenized dataset displayed a significant (p, 0.05) declining trend of 20.038 m s decade for all China annually, with decreases in winter (20.355 m s decade, p, 0.05) and autumn (20.108 m s decade; p, 0.05) and increases in summer (10.272 m s decade, p, 0.05) along with a weak recovery in spring (10.032 m s decade; p. 0.10); that is, DMWS declined during the cold semester (October-March) and increased during the warm semester (April-September). Correlation analysis of the Arctic Oscillation, the Southern Oscillation, and the west Pacific modes exhibited significant correlation with DMWS variability, unveiling their complementarity in modulating DMWS. Further, we explored potential physical processes relating to the atmospheric circulation changes and their impacts on DMWS and found that 1) overall weakened horizontal airflow [large-scale mean horizontal pressure gradient (from 20.24 to 10.02 hPa decade) and geostrophic wind speed (from 20.6 to 10.6 m s decade)], 2) widely decreased atmospheric vertical momentum transport [atmospheric stratification thermal instability (from 23 to 11.5 decade) and vertical wind shear (from 20.4 to 10.2 m s decade)], and 3) decreased extratropical cyclones frequency (from 20.3 to 0 month decade) are likely causes of DMWS change.

Typhoon and windstorm induced extreme winds (e.g., daily maximum wind speed, DMWS) cause enormous economic losses and deaths in China every year, and rapid urbanization increased surface roughness might play a key role in extreme wind speed variability. Here, observed near-surface (at 10 m height) DMWS from 115 meteorological stations and combined DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite) nighttime light data from 1992-2016 in Yangtze River Delta, a rapidly urbanized area of China, were used to analyze the impact of urbanization on DMWS variability. Raw wind speed observations were subject to a robust quality control and homogenization protocol using the Climatol package. The stations were firstly classified into six urbanized groups by the difference of nighttime light indices of each station between 1992 and 2016. The results show that DMWS in Yangtze River Delta has significantly (p < 0.05) declined by -0.209m s-1 decade-1 annually, with negative trends in most seasons, particularly in winter (-0.470 m s-1 decade-1, p < 0.05) and autumn (-0.300 m s-1 decade-1, p < 0.05), followed by spring (-0.178 m s-1 decade-1, p > 0.10), while a weak increase in summer DMWS was found (+0.002 m s-1 decade-1, p > 0.10). The stations in the highly urbanized group show a higher magnitude in the decline of annual DMWS, indicating the key role of urbanization in weakening DMWS. Further, this is confirmed by the regional climate model (RegCM4) sensitive experiments conducted with different land use and cover data, that is, DMWS in 1992 was higher in the experiment using the real land use and cover data than in the experiment using the land use and cover data in 2016.

Trabajo presentado en EGU General Assembly, celebrada en modalidad virtual del 4 al 8 de mayo de 2020., This contribution presents the first work package (WP1) of the project ¿Assessing centennial wind speed variability from a historical weather data rescue project in Sweden¿, funded by FORMAS ¿ A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously reported in EGU2019-17792-1. Under a warming climate, one of the major uncertainties on the causes driving the climate variability of winds over land (i.e., the ¿stilling¿ phenomenon and the recent ¿recovery¿ since the 2010s) is mainly due to short availability (i.e. since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). In this study we present the first steps of a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden. The aim of the WP1 is to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. 13 stations with daily wind speed data (in meters per second) during the period 1925-1938 have been selected for digitization; i.e., spanning back our records 2 decades more. To get wind observations from paper to screen we will follow the ¿Guidelines on Best Practices for Climate Data Rescue¿ of the World Meteorological Organization. Our protocol will consist on (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. WP2 will ensure the quality and homogeneity of wind speed series rescued.

Wind erosion in arid and semi-arid areas is an important global environmental issue, and changes in wind speed trends over time play a key role in wind erosion dynamics. In a warming climate, scientists have recently observed a widespread decline in wind speed, termed “stilling”. Here, we apply the Revised Wind Erosion Equation Model (RWEQ) to simulate the variability of wind erosion and quantify the impact of wind speed changes on soil degradation dynamics over the eastern agro-pastoral transitional zone of Northern China from 1982 to 2016. Our results show that a significant (i.e., p, This study is supported by the National Natural Science Foundation of China (Grant Nos. 41621061, 41271286), the National Key Research and Development Program–Global Change and Mitigation Project (Grant No. 2016YFA0602404), and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 703733 (STILLING project). This work has been also supported by the Swedish Research Council (VR 2017-03780) and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT CH2015-6226).

This work has been supported by the project “Detection and attribution of changes in extreme wind gusts over land” (2017-03780) funded by the Swedish Research Council.

This study analyzes the variability of winter haze days and visibility in the Beijing-Tianjin-Hebei (BTH) region, in relation to the regional average wind speed changes in the lower troposphere and emissions for 1961–2014. Winter mean surface meteorological data, NCEP/NCAR atmospheric reanalysis data, and fossil fuel emission data are used. The results reveal a significant increase in the haze days of +0.8 days decade-1 (p

This research quantitatively recognized the wind speed change using wind speed trend and trend of wind speed variability from 1961 to 2012 and regionalized the wind speed change on a county-level basis. The mean wind speed observation data and linear fitting method were used. The findings suggested that level-I regionalization includes six zones according to wind speed trend value in different regions, viz . Northeast China–North China substantial declining zone, East–Central China declining zone, Southeast China slightly declining zone, Southwest China very slightly declining zone, Northwest China declining zone, and Qinghai–Tibetan Plateau slightly declining zone. Level-II regionalization divides China into twelve regions based on trend of wind speed variability and the level-I regionalization results.