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

Chen, L.; Li, D.; Pryor, Sara C.

doi:10.1002/joc.3613

Cited by 92 publications

(94 citation statements)

References 48 publications

(105 reference statements)

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“…8, the wind speed series after adjustment at all 10 stations were coincident, with a reducing trend at the 95% significant level. This result is consistent with current researches on assessment of observed near-surface wind speed trends in China (Rong and Liang 2008;Cheng 2010;Zou et al 2010;Fu et al 2011;Guo et al 2011;Chen et al 2013;Zhang et al 2014;Xiong 2015), and also similar to trends reported for other parts of the world (Pryor et al 2009;Wan et al 2010;William and Martha 2014). Based on the adjusted data, the trends in the regional average annual and seasonal (spring, summer, autumn, and winter) wind speed series were −0.277, −0.325, −0.228, −0.223, and −0.336 m s −1 /10a, respectively (Fig.…”

Section: Trend Amplitudes Before and After Adjustmentsupporting

confidence: 91%

“…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%

“…The Tianjin region is located in North China, and has the same regional characteristics while also reflecting the territorial climate change. 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.…”

Section: Trend Amplitudes Before and After Adjustmentmentioning

confidence: 99%

“…Therefore, it is necessary to homogenize climate data before assessing interannual and multidecadal variabilities in wind speed (Pryor et al 2009;Fu et al 2011;Chen et al 2013;William and Martha 2014). Wind speed is a random variable easily influenced by the underlying surface, especially for monthly or daily observations, and time series tend to fluctuate due to local climate effects.…”

Section: Introductionmentioning

confidence: 99%

See 3 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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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: 91%

Section: Trend Amplitudes Before and After Adjustmentsupporting

confidence: 81%

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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“…Due to the uncertainty unavoidably associated with future projections, the nodes ProjectedRainfall and ProjectedWindSpeed are described by imprecise random variables. Climate data projections for the north-western region of China have been deduced from the available literature [5] as well as the baseline for the wind speed distribution [1]. Conversely, the probability distribution of rainfall has been defined on the basis of the data collected close to the station of reference.…”

Section: Climate Scenariosmentioning

confidence: 99%

Vulnerability of Hydropower Installations to Climate Change: Preliminary Study

Tolo

Patelli

Chen

2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. The climate trends observed worldwide over the past few decades appear to corroborate the concerns of the scientific community about the many threats posed by global warming. Future changes of the current climate are expected to occur on different scales all around the globe, hence modifying the environmental background on the basis of which technological installations have been designed and operated. This can potentially threat the safety of the installations as well as their. The development of suitable tools aiming to predict the impact of climate change on technological installations is then essential in the wider context of climate change mitigation. Hydropower installations play often a crucial role not only as a long-term renewable resource of energy but also for flood control and water supply in the case of droughts. All these aspects highlight the increasing importance of such installations as well as their growing vulnerability to natural hazards. It is hence essential to enlarge the current understanding of the interaction mechanisms between such installations and the changing surrounding environment in order to take adequate measures for climate change adaptation and ensure the future safety and productivity of hydropower production. The current study aims to provide a novel model for the evaluation of the impact of climate change on the safety of hydropower stations. The approach adopted allows to include in the model the uncertainty inevitably associated with the input variables and to propagate such uncertainty within the analysis. The model proposed is finally applied to a realistic case-study in order to highlight its potential and limitations.

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Changes in reference evapotranspiration over China during 1960–2012: Attributions and relationships with atmospheric circulation

Chai

Sun

Chen

et al. 2018

Hydrological Processes

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This study investigates reference evapotranspiration (ET0) trends in China from 1960 to 2012 based on the Penman–Monteith equation and gridded meteorological measurements. Under the combined impacts of factors influencing ET0 (i.e., net radiation [RN], mean temperature [TAVE], vapour pressure deficit [VPD], and wind speed [WND]), both seasonal and annual ET0 for the whole China and more than half of the grids decreased over the past 53 years. The attribution analyses suggest that for the whole China, the WND is responsible for annual and seasonal ET0 decreases (excluding summer, where RN is responsible). Across China, the annual cause of WND with the largest spatial extent (43.1% of grids) mainly derives from north of the Changjiang River Basin (CJRB), whereas VPD (RN) as a cause is dispersedly distributed (within and to the south of the CJRB). In summer, RN is dominant in more than half of the grids, but the dominance of VPD and WND accounts for approximately 90% of grids during the remaining seasons. Finally, the correlation coefficients between ET0 and the Atlantic Oscillation (AO), North AO, Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO) indices with different lead times are calculated. For the whole China, annual and seasonal ET0 always significantly correlate with these indices (excluding the IOD) but with varied lead times. Additionally, near half of the grids show significant and maximum (i.e., the largest one between ET0 and a certain index with a lead time of 0–3 seasons) correlation coefficients of ET0 with PDO in spring and summer, ENSO in autumn, and AO in winter. This study is not only significant for understanding ET0 changes, but it also provides preliminary and fundamental reference information for ET0 prediction.

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

Cited by 92 publications

References 48 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

Vulnerability of Hydropower Installations to Climate Change: Preliminary Study

Changes in reference evapotranspiration over China during 1960–2012: Attributions and relationships with atmospheric circulation

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