Many studies that discuss observed trends in wind speed focus primarily on regions of the Northern Hemisphere, so there is little research directed to the Southern Hemisphere. This paper pre- sents a preliminary inves...Many studies that discuss observed trends in wind speed focus primarily on regions of the Northern Hemisphere, so there is little research directed to the Southern Hemisphere. This paper pre- sents a preliminary investigation of possible statistically significant trends in wind speed over the Southern Hemisphere, with a detailing on the South American continent, between 1961 and 2008. Thus, data from the 20th Century Reanalysis V2 were examined with statistical tests of Mann- Kendall and Sen’s Bend in order to establish the significance and the magnitude of detected trends. The previous results indicate statistically significant trends of increase in average wind speedover the equatorial region of the planet, as well as in the eastern sector of the South Pacific and South Atlantic Oceans. In South America, the most significant trends of decrease in wind speed were noted in some areas of the southern sector of the continent, even as in the adjacent Atlantic Ocean to Argentina. Further studies should be performed to physically support the occurrence of these trends in wind speed. In addition, other observed and reanalysis data sets should be explored to update and corroborate these primary analyzes.展开更多
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 s...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 ChinaeNorth China substantial declining zone,EasteCentral China declining zone,Southeast China slightly declining zone,Southwest China very slightly declining zone,Northwest China declining zone,and QinghaieTibetan 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.展开更多
It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed duri...It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed during past 50 years (1961-2010). It shows peculiar results taking Karachi (24?53'N, 67?00'E), a coastal mega-city of Pakistan, as a case study. Mann-Kendall trend test shows that March, April and October and both summer and winter seasons show positive trends for the average monthly wind speed during the whole study period (1961-2010). For the earlier 25 years data, it has been found that January, March, May, August, November and December and annual wind speed data have shown the negative trends. Only summer season has shown the positive trend for the wind speed. Similarly, for the most recent 25 years data it has been found that January, February, March, April, May, June, October, November and December and annual and both summer and winter wind speed data have shown the positive trends showing some degree of change in wind speed pattern. Probabilistic analysis reveals that average monthly wind speed data sets follow lognormal, logistic, largest extreme value, and Weibull (two-and three-parameters) probability distributions. Change point analysis has also confirmed the change in the pattern of observed average monthly wind speed data near 1992. The analysis performed reveals the effect of global warming on the local urban wind speed which appears to be temporal non-stationary.展开更多
This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution...This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.展开更多
基于台站观测资料,评估了欧洲中期天气预报中心(ECMWF)最高时空分辨率的第五代大气再分析资料(ERA5)对1979~2018年间中国大陆区域10 m高度风速的气候特征及其变化趋势的再现能力,并同步对比分析了ERA5资料100 m高度风速的特征和长期趋...基于台站观测资料,评估了欧洲中期天气预报中心(ECMWF)最高时空分辨率的第五代大气再分析资料(ERA5)对1979~2018年间中国大陆区域10 m高度风速的气候特征及其变化趋势的再现能力,并同步对比分析了ERA5资料100 m高度风速的特征和长期趋势。结果表明,ERA5资料10 m和100 m风速在空间分布、年—季节—月尺度演变的气候特征方面与台站观测非常一致,10 m风速气候态空间相关系数达到0.66。观测和再分析资料均显示,中国近地层风速呈现出显著的区域性特征,风速大值区主要分布在内蒙古、东北地区西部、新疆北部以及青藏高原西部地区,上述区域的风速季节差异也相对明显,春季风速最大。台站观测、ERA5资料10 m和100 m全国平均风速在4月达到最大值,分别为2.6、3.0、4.5 m s^(-1),8月为最小值,分别为2.0、2.4、3.5 m s^(-1)。从月平均序列来看,ERA5资料的10 m风速较台站观测偏高0.3~0.5 m s^(-1),而100 m的风速较10 m风速整体偏高1.2~1.4 m s^(-1)。在风速变化方面,台站观测风速在中国陆地区域整体呈下降趋势–0.4 m s^(-1)(39 a)–1,春季下降趋势最显著[–0.5 m s^(-1)(39 a)–1],且1979~1992年冬季风速降幅最大[–0.7 m s^(-1)(14 a)–1],2013年以后风速逐渐增强。ERA5资料两个高度层的风速在整个中国区域均没有明显的长期变化趋势,4个季节风速变化趋势的空间分布与观测也存在差异,100m风速的长期变化趋势与10 m一致但变化幅度大于10 m风速。总之,ERA5资料对中国大陆区域气候平均风速具有较好的再现能力,但无法呈现台站观测风速的长期变化趋势。展开更多
文摘Many studies that discuss observed trends in wind speed focus primarily on regions of the Northern Hemisphere, so there is little research directed to the Southern Hemisphere. This paper pre- sents a preliminary investigation of possible statistically significant trends in wind speed over the Southern Hemisphere, with a detailing on the South American continent, between 1961 and 2008. Thus, data from the 20th Century Reanalysis V2 were examined with statistical tests of Mann- Kendall and Sen’s Bend in order to establish the significance and the magnitude of detected trends. The previous results indicate statistically significant trends of increase in average wind speedover the equatorial region of the planet, as well as in the eastern sector of the South Pacific and South Atlantic Oceans. In South America, the most significant trends of decrease in wind speed were noted in some areas of the southern sector of the continent, even as in the adjacent Atlantic Ocean to Argentina. Further studies should be performed to physically support the occurrence of these trends in wind speed. In addition, other observed and reanalysis data sets should be explored to update and corroborate these primary analyzes.
基金supported by the National Basic Research Program of China (2012CB955404,2012CB955402)the National Natural Science Foundation of China (41321001)
文摘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 ChinaeNorth China substantial declining zone,EasteCentral China declining zone,Southeast China slightly declining zone,Southwest China very slightly declining zone,Northwest China declining zone,and QinghaieTibetan 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.
文摘It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed during past 50 years (1961-2010). It shows peculiar results taking Karachi (24?53'N, 67?00'E), a coastal mega-city of Pakistan, as a case study. Mann-Kendall trend test shows that March, April and October and both summer and winter seasons show positive trends for the average monthly wind speed during the whole study period (1961-2010). For the earlier 25 years data, it has been found that January, March, May, August, November and December and annual wind speed data have shown the negative trends. Only summer season has shown the positive trend for the wind speed. Similarly, for the most recent 25 years data it has been found that January, February, March, April, May, June, October, November and December and annual and both summer and winter wind speed data have shown the positive trends showing some degree of change in wind speed pattern. Probabilistic analysis reveals that average monthly wind speed data sets follow lognormal, logistic, largest extreme value, and Weibull (two-and three-parameters) probability distributions. Change point analysis has also confirmed the change in the pattern of observed average monthly wind speed data near 1992. The analysis performed reveals the effect of global warming on the local urban wind speed which appears to be temporal non-stationary.
基金The National Key R&D Program of China under contract No.2016YFC1401905the National Natural Science Foundation of China under contract No.41776004the Fundamental Research Funds for the Central Universities under contract No.2016B12514
文摘This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.
文摘基于台站观测资料,评估了欧洲中期天气预报中心(ECMWF)最高时空分辨率的第五代大气再分析资料(ERA5)对1979~2018年间中国大陆区域10 m高度风速的气候特征及其变化趋势的再现能力,并同步对比分析了ERA5资料100 m高度风速的特征和长期趋势。结果表明,ERA5资料10 m和100 m风速在空间分布、年—季节—月尺度演变的气候特征方面与台站观测非常一致,10 m风速气候态空间相关系数达到0.66。观测和再分析资料均显示,中国近地层风速呈现出显著的区域性特征,风速大值区主要分布在内蒙古、东北地区西部、新疆北部以及青藏高原西部地区,上述区域的风速季节差异也相对明显,春季风速最大。台站观测、ERA5资料10 m和100 m全国平均风速在4月达到最大值,分别为2.6、3.0、4.5 m s^(-1),8月为最小值,分别为2.0、2.4、3.5 m s^(-1)。从月平均序列来看,ERA5资料的10 m风速较台站观测偏高0.3~0.5 m s^(-1),而100 m的风速较10 m风速整体偏高1.2~1.4 m s^(-1)。在风速变化方面,台站观测风速在中国陆地区域整体呈下降趋势–0.4 m s^(-1)(39 a)–1,春季下降趋势最显著[–0.5 m s^(-1)(39 a)–1],且1979~1992年冬季风速降幅最大[–0.7 m s^(-1)(14 a)–1],2013年以后风速逐渐增强。ERA5资料两个高度层的风速在整个中国区域均没有明显的长期变化趋势,4个季节风速变化趋势的空间分布与观测也存在差异,100m风速的长期变化趋势与10 m一致但变化幅度大于10 m风速。总之,ERA5资料对中国大陆区域气候平均风速具有较好的再现能力,但无法呈现台站观测风速的长期变化趋势。
