首页|Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models

Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models

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Wind energy development in Central Asia can help alleviate drought and fragile ecosystems.Nevertheless,current studies mainly used the global climate models(GCMs)to project wind speed and energy.The simulated biases in GCMs remain prominent,which induce a large uncertainty in the projected results.To reduce the uncertainties of projected near-surface wind speed(NSW)and better serve the wind energy development in Central Asia,the Weather Research and Forecasting(WRF)model with bias-corrected GCMs was employed.Compared with the outputs of GCMs,dynamical downscaling acquired using the WRF model can better capture the high-and low-value centres of NSWS,especially those of Central Asia's mountains.Meanwhile,the simulated NSWS bias was also reduced.For future changes in wind speed and wind energy,under the Representative Concentration Pathway 4.5(RCP4.5)scenario,NSWS during 2031-2050 is projected to decrease compared with that in 1986-2005.The magnitude of NSWS reduction during 2031-2050 will reach 0.1 m s-1,and the maximum reduction is projected to occur over the central and western regions(＞0.2 m s-1).Furthermore,future wind power density(WPD)can reveal nonstationarity and strong volatility,although a downward trend is expected during 2031-2050.In addition,the higher frequency of wind speeds at the turbine hub height exceeding 3.0 m s-1 can render the plain regions more suitable for wind energy development than the mountains from 2031 to 2050.This study can serve as a guide in gaining insights into future changes in wind energy across Central Asia and provide a scientific basis for decision makers in the formulation of policies for addressing climate change.

Near-surface wind speedWind power densityDynamical downscalingCentral AsiaWRF

Jin-Lin ZHA、Ting CHUAN、Yuan QIU、Jian WU、De-Ming ZHAO、Wen-Xuan FAN、Yan-Jun LYU、Hui-Ping JIANG、Kai-Qiang DENG、Miguel ANDRES-MARTIN、Cesar AZORIN-MOLINA、Deliang CHEN

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Key Laboratory of Atmospheric Environment and Processes in the Boundary Layer over the Low-Latitude Plateau Region,Department of Atmospheric Science,Yunnan University,Kunming 650091,China

b Key Laboratory of Regional Climate and Environment for Temperate East Asia,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China

Key Laboratory of Atmospheric Environment and Processes in the Boundary Layer Over the Low-Latitude Plateau Region,Department of Atmospheric Science,Yunnan University,Kunming 650091,China

c Center for Energy & Environmental Policy Research,Beijing Institute of Technology,Beijing 100081,China

d Key Laboratory of Regional Sustainable Development Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

e International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China

f School of Atmospheric Sciences,Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082,China

g Centro de Investigaciones Sobre Desertificación,Consejo Superior de Investigaciones Cientificas(CIDE,CSIC-UV-Generalitat Valenciana),Climate,Atmosphere and Ocean Laboratory(Climatoc-Lab),Moncada 46113,Spain

h Regional Climate Group,Department of Earth Sciences,University of Gothenburg,Gothenburg 40530,Sweden

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2024

10.1016/j.accre.2024.07.007

气候变化研究进展(英文版)
国家气候中心

气候变化研究进展(英文版)

影响因子：0.806
ISSN：1674-9278
年,卷(期)：2024.15(4)