Multidecadal variability of observed near-surface wind speed from 24 stations across Sweden has been analyzed for 1956–2013, with a focus on 1979–2008 (incorporating an additional 9 stations) for comparison with previous studies. Wind speed data have been subjected to a robust data processing protocol, consisting of quality control, reconstruction, and homogenization, by using geostrophic wind speed series as reference. The homogenized dataset displays a significant (at p < 0.05) downward trend for 1956–2013 (−0.06 m s−1 decade−1) and an even larger decreasing trend for 1979–2008 (−0.14 m s−1 decade−1). However, differences have been observed seasonally, with significant decreasing values in spring, summer, and autumn and a small downward trend in winter for 1956–2013. Most interestingly, a nonsignificant wind speed increase has been detected in winter for 1979–2008, which contrasts with the marked “stilling” reported for this season in much of midlatitude regions. The decreasing rate in wind speed is larger for coastal stations and in the southern part of Sweden. Decreasing trends were found at 91.7% of the stations during summer, whereas 58.3% of the stations displayed decreasing trends in winter. On the contrary, increasing trends occurred in 41.7% of the stations for winter and in only 8.3% for summer. The possible impact of the North Atlantic Oscillation (NAO) index has also been investigated, showing evidence that the small increasing trend in winter for 1979–2008 is hypothetically associated with the positive tendency of the NAO index during the last decades. These results reveal the influence of large-scale atmospheric circulation on wind speed variability across Sweden.

The ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013-2017. ERA5 shows closer agreement than the previous ERA-Interim reanalysis with regard to both mean wind speed and gust measurements, although significant discrepancies are still found for inland and mountainous regions. Therefore, attempts have been made to improve formulations of the gust parametrization used in ERA5 by adding an elevationdependency and by adjusting the convective gust contribution. Major improvements, especially over mountain regions, are achieved when the elevation differences among the stations are considered. Closer agreement between the observed and parametrized gusts is reached when the convective gust contribution is also tuned. The newly designed gust parametrization was also tested for Norway, which is characterized by more complex topography. Wind gusts from the selected Norwegian stations are more realistically simulated when both the elevation-dependency and the tuned convective contribution are implemented, although the parametrized gusts are still negatively biased. Such biases are not explained by the different in gust duration in recorded wind gusts between Sweden and Norway.

ABSTRACT:We analyse recent trends and variability of observed near-surface wind speed from 19 stations across Saudi Arabia (SA) for . The raw wind speed data set was subject to a robust homogenization protocol, and the stations were then classified under three categories: (1) coast, (2) inland and (3) mountain stations. The results reveal a statistically significant (p < 0.05) reduction of wind speed of −0.058 m s −1 dec −1 at annual scale across SA, with decreases in winter (−0.100 m s −1 dec −1 ) and spring (−0.066 m s −1 dec −1 ) also detected, being non-significant in summer and autumn. The coast, inland and mountain series showed similar magnitude and significance of the declining trends across all SA series, except for summer when a decoupled variability and opposite trends of wind speed between the coast and inland series (significant declines: −0.101 m s −1 dec −1 and −0.065 m s −1 dec −1 , respectively) and the high-elevation mountain series (significant increase: +0.041 m s −1 dec −1 ) were observed. Even though wind speed declines dominated across much of the country throughout the year, only a small number of stations showed statistically significant negative trends in summer and autumn. Most interestingly, a break in the stilling was observed in the last 12-year (2002-2013) period (+0.057 m s −1 dec −1 ; not significant) compared to the significant slowdown detected in the previous 24-year period (−0.089 m s −1 dec −1 ). This break in the slowdown of winds, even followed by a non-significant recovery trend, occurred in all seasons (and months) except for some winter months. Atmospheric circulation plays a key role in explaining the variability of winds, with the North Atlantic Oscillation positively affecting the annual wind speed, the Southern Oscillation displaying a significant negative relationship with winds in winter, spring and autumn, and the Eastern Atlantic negatively modulating winds in summer.

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A B S T R A C TRecent studies on observed wind variability have revealed a decline (termed "stilling") of near-surface wind speed during the last 30-50 years over many mid-latitude terrestrial regions, particularly in the Northern Hemisphere. The well-known impact of cup anemometer drift (i.e., wear on the bearings) on the observed weakening of wind speed has been mentioned as a potential contributor to the declining trend. However, to date, no research has quantified its contribution to stilling based on measurements, which is most likely due to lack of quantification of the ageing effect. In this study, a 3-year field experiment (2014-2016) with 10-minute paired wind speed measurements from one new and one malfunctioned (i.e., old bearings) SEAC SV5 cup anemometer which has been used by the Spanish Meteorological Agency in automatic weather stations since mid-1980s, was developed for assessing for the first time the role of anemometer drift on wind speed measurement. The results showed a statistical significant impact of anemometer drift on wind speed measurements, with the old anemometer measuring lower wind speeds than the new one. Biases show a marked temporal pattern and clear dependency on wind speed, with both weak and strong winds causing significant biases. This pioneering quantification of biases has allowed us to define two regression models that correct up to 37% of the artificial bias in wind speed due to measurement with an old anemometer.