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

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 res...

Descripción completa

Detalles Bibliográficos
Autores: Zha, Jinlin, Chuan, Ting, Qiu, Yuan, Wu, Jian, Zhao, Deming, Fan, Wenxuan, Lyu, Yan-Jun, Jiang, Hui-Ping, Deng, Kaiqiang, Andrés-Martín, M., Azorín-Molina, César, Chen, Deliang
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/373927
Acceso en línea:http://hdl.handle.net/10261/373927
Access Level:acceso abierto
Palabra clave:Near-surface wind speed
Wind power density
Dynamical downscaling
Central Asia
WRF
id ES_ed77f04a92d6515bc40d0437715ec430
oai_identifier_str oai:digital.csic.es:10261/373927
network_acronym_str ES
network_name_str España
repository_id_str
spelling Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate modelsZha, JinlinChuan, TingQiu, YuanWu, JianZhao, DemingFan, WenxuanLyu, Yan-JunJiang, Hui-PingDeng, KaiqiangAndrés-Martín, M.Azorín-Molina, CésarChen, DeliangNear-surface wind speedWind power densityDynamical downscalingCentral AsiaWRFWind 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.This work was supported by the Key Research and Development Program of China (2023YFF0805504), the National Natural Science Foundation of China (42375174, 42361134582), and the Yunnan Province Basic Research Project (202401AW070008, 202301AT070199). This work was also supported by the High-level Talent Project of Yunnan Province-Special Program for Young Talents, the High-level Talent Program of Yunnan University-Donglu Young Scholars, and the Chinese Jiangsu Collaborative Innovation Center for Climate Change China.Peer reviewedElsevierKeAi CommunicationsNational Key Research and Development Program (China)National Natural Science Foundation of ChinaYunnan ProvinceYunnan UniversityConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/373927reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.accre.2024.07.007https://doi.org/10.1016/j.accre.2024.07.007Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3739272026-05-22T06:33:51Z
dc.title.none.fl_str_mv Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
title Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
spellingShingle Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
Zha, Jinlin
Near-surface wind speed
Wind power density
Dynamical downscaling
Central Asia
WRF
title_short Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
title_full Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
title_fullStr Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
title_full_unstemmed Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
title_sort Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models
dc.creator.none.fl_str_mv Zha, Jinlin
Chuan, Ting
Qiu, Yuan
Wu, Jian
Zhao, Deming
Fan, Wenxuan
Lyu, Yan-Jun
Jiang, Hui-Ping
Deng, Kaiqiang
Andrés-Martín, M.
Azorín-Molina, César
Chen, Deliang
author Zha, Jinlin
author_facet Zha, Jinlin
Chuan, Ting
Qiu, Yuan
Wu, Jian
Zhao, Deming
Fan, Wenxuan
Lyu, Yan-Jun
Jiang, Hui-Ping
Deng, Kaiqiang
Andrés-Martín, M.
Azorín-Molina, César
Chen, Deliang
author_role author
author2 Chuan, Ting
Qiu, Yuan
Wu, Jian
Zhao, Deming
Fan, Wenxuan
Lyu, Yan-Jun
Jiang, Hui-Ping
Deng, Kaiqiang
Andrés-Martín, M.
Azorín-Molina, César
Chen, Deliang
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv National Key Research and Development Program (China)
National Natural Science Foundation of China
Yunnan Province
Yunnan University
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Near-surface wind speed
Wind power density
Dynamical downscaling
Central Asia
WRF
topic Near-surface wind speed
Wind power density
Dynamical downscaling
Central Asia
WRF
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/373927
url http://hdl.handle.net/10261/373927
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.accre.2024.07.007
https://doi.org/10.1016/j.accre.2024.07.007
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
KeAi Communications
publisher.none.fl_str_mv Elsevier
KeAi Communications
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869423507300941824
score 15.811543

Similares en LA Referencia