Changes in wind speed over China during 1956–2004

Jiang, Ying; Luo, Yiqi; Zhao, Zongci; Tao, S.

doi:10.1007/s00704-009-0152-7

Cited by 271 publications

(230 citation statements)

References 13 publications

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“…The statistics based on the adjusted data here were generally consistent with those from Li et al (2011), who evaluated the regional trends (Table 4). Both this study and the prior study indicate that the decreasing amplitudes of wind speed trend in spring and winter were much larger than those in summer and autumn; this is also reflected in studies on annual and seasonal wind speed change over China (Xu et al 2006;Jiang et al 2010;Guo et al 2011;Fu et al 2011;and Chen et al 2013). Accordingly, based on the adjusted data, characteristics of near-surface wind speed change in Tianjin are consistent with those of regional background climate in China.…”

Section: Trend Amplitudes Before and After Adjustmentsupporting

confidence: 81%

“…Here, urban-rural regional trend differences were likely due to the impact of urbanization; the average annual wind speed caused by urbanization in the Tianjin area decreased by −0.046 m s −1 /10a, consistent with the influence of urbanization on wind speed over Beijing (Li et al 2011; −0.050 m s −1 /10a). The reduction in both was larger than that for all of China, ranging from −0.020 m s −1 /10a (Xu et al 2006) to −0.010 m s −1 /10a (Jiang et al 2010), although this effect is not observed in raw wind speed data from urban-rural areas.…”

Section: Trend Amplitudes Before and After Adjustmentmentioning

confidence: 69%

“…For the processing of research data, the wind speed data have been homogenized systematically by Li et al (2011) (italicized marking); in the studies of Xu et al (2006), Guo et al (2011), andChen et al (2013), the authors deleted stations with significant breakpoints detected by metadata or mathematical statistics (asterisk marking) but did not homogenize data. Original data from the studies of Jiang et al (2010) and Fu et al (2011) involved no quality control possibly owing to their research needs. Therefore, the differences in results obtained in this study and previously may have been caused by inhomogeneities of the data (Yan et al 2016).…”

Section: Trend Amplitudes Before and After Adjustmentmentioning

confidence: 99%

“…However, most long-time climate records do not adequately represent climate change characteristics because of changes in instrumentation, observation methods, station relocation, and other non-climate factors; these changes result in a distortion in climate change monitoring (Aguilar et al 2003;Jiang et al 2010). Therefore, many studies have investigated climate data homogenization globally, as early as the beginning of the 1980s (Quayle Robert et al 1991;Chenoweth 1992;Peterson Thomas 2003;Wijngaard Janet et al 2003;Della-Marta Paul and Wanner 2006;Haimberger et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Luo

Liang

2017

Theor Appl Climatol

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Historical Chinese surface meteorological records provided by the special fund for basic meteorological data from the National Meteorological Information Center (NMIC) were processed to produce accurate wind speed data. Monthly 2-min near-surface wind speeds from 13 observation stations in Tianjin covering 1951-2014 were homogenized using RHtestV4 combined with their metadata. Results indicate that 10 stations had significant breakpoints-77% of the Tianjin stations-suggesting that inhomogeneity was common in the Tianjin wind speed series. Instrument change accounted for most changes, based on the metadata, including changes in type and height, especially for the instrument type. Average positive quantile matching (QM) adjustments were more than negative adjustments at 10 stations; positive biases with a probability density of 0.2 or more were mainly concentrates in the range 0.2 m s −1 to 1.2 m s −1 , while the corresponding negative biases were mainly in the range −0.1 to −1.2 m s −1 . Here, changes in variances and trends in the monthly mean surface wind speed series at 10 stations before and after adjustment were compared. Climate characteristics of wind speed in Tianjin were more reasonably reflected by the adjusted data; inhomogeneity in wind speed series was largely corrected. Moreover, error analysis reveals that there was a high consistency between the two datasets here and that from the NMIC, with the latter as the reference. The adjusted monthly near-surface wind speed series shows a certain reliability for the period 1951-2014 in Tianjin.

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Section: Trend Amplitudes Before and After Adjustmentsupporting

confidence: 81%

Section: Trend Amplitudes Before and After Adjustmentmentioning

confidence: 69%

Section: Trend Amplitudes Before and After Adjustmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Luo

Liang

2017

Theor Appl Climatol

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“…We also examine the degree to which variability in nearsurface wind speeds is attributable to variations in the atmospheric circulation as manifest in teleconnection indices. (Xu et al, 2006;Jiang et al, 2010). For every station, a further data quality constraint 2581 is applied: that more than 85% observations are valid in each climatological season of each year.…”

Section: Objectivesmentioning

confidence: 99%

Wind speed trends over China: quantifying the magnitude and assessing causality

Chen

Pryor

2012

Intl Journal of Climatology

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ABSTRACT:Temporal trends in 10-m wind speeds from homogeneous observational data sets from 540 weather stations and reanalysis data sets are quantified and compared. Also, possible physical cause of inconsistencies between the data sets and temporal trends and variability in wind speeds are investigated. Annual mean wind speeds from the observational data exhibit pronounced downward trends especially in the upper percentiles and during spring. The NCEP/NCAR reanalysis reproduces the observed wind speeds, seasonality and temporal trends better than the ERA-40 even though it shows larger interannual fluctuations. The warm and cold AO and ENSO phases have significant influence on probability distribution of wind speeds, thus internal climate variability is a major source of both interannual and long-term variability.

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Trends of daily peak wind gusts in Spain and Portugal, 1961–2014

et al. 2016

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Given the inconsistencies of wind gust trends under the widespread decline in near-surface wind speed (stilling), our study aimed to assess trends of observed daily peak wind gusts (DPWG) across Spain and Portugal for 1961-2014 by analyzing trends of (i) the frequency (90th percentile) and (ii) the magnitude (wind speed maxima) of DPWG. Wind gust series were homogenized on a daily basis, using MM5-simulated series as reference, resulting in 80 suitable station-based data sets. The average DPWG 90th percentile frequency declined by À1.49 d decade À1 (p < 0.05) annually. This showed marked seasonal differences: decreasing in winter (À0.75 d decade À1 ; p < 0.05) and increasing in summer (+0.18 d decade À1 ; p > 0.10). A negligible trend was calculated for the annual magnitude of DPWG (À0.005 m s À1 decade À1 ; p > 0.10), with distinct seasonality: declining in winter (À0.168 m s À1 decade À1 ; p < 0.10) and increasing in summer (+0.130 m s À1 decade À1 ; p < 0.05). Combined, these results reveal less frequent and declining DPWG during the cold semester (November-April) and more frequent and increasing DPWG during the warm semester (May-October). Large-scale atmospheric changes such as the North Atlantic Oscillation Index (negative correlations~À0.4-À0.6; p < 0.05) and the Jenkinson and Collison scheme (positive correlations mainly with Westerly regime:~+0.5-0.6; p < 0.05) partly account for the decadal fluctuations of both frequency and magnitude of DPWG, particularly in winter. However, the North Atlantic Oscillation index-DPWG relationships are smaller in spring, summer, and autumn (~À0.1-À0.2; p > 0.10), especially for the frequency, suggesting the role of local-to-mesoscale drivers.In view of (i) the minimal number of studies reporting long-term changes from observed DPWG over land; (ii) the overall inconclusive nature DPWG trends observed from anemometers; and (iii) the substantial societal and environmental impact of this natural hazard (e.g., for human safety, maritime and aviation activities, engineering and insurance applications, and energy production), additional trend assessments, and studies that assess these trends from a large-scale atmospheric circulation perspective are needed to increase our understanding of wind extremes [Vose et al., 2014]. The principal objective of this study is to determine, for AZORIN-MOLINA ET AL.

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Changes in wind speed over China during 1956–2004

Cited by 271 publications

References 13 publications

Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Wind speed trends over China: quantifying the magnitude and assessing causality

Trends of daily peak wind gusts in Spain and Portugal, 1961–2014

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