Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling

Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this s...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	He, J.Y. [verfasserIn] Chan, P.W. [verfasserIn] Li, Q.S. [verfasserIn] Tong, H.W. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty

Übergeordnetes Werk:	Enthalten in: Renewable & sustainable energy reviews - Amsterdam [u.a.] : Elsevier Science, 1997, 188
Übergeordnetes Werk:	volume:188

DOI / URN:	10.1016/j.rser.2023.113865

Katalog-ID:	ELV065290054

Internformat


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100	1		\|a He, J.Y. \|e verfasserin \|0 (orcid)0000-0001-5280-4287 \|4 aut
245	1	0	\|a Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
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520			\|a Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5.
650		4	\|a Offshore wind energy
650		4	\|a Offshore wind resource map
650		4	\|a Dynamical downscaling
650		4	\|a Wind resource variability
650		4	\|a Wind speed extrapolation accuracy
650		4	\|a Climate projection uncertainty
700	1		\|a Chan, P.W. \|e verfasserin \|4 aut
700	1		\|a Li, Q.S. \|e verfasserin \|0 (orcid)0000-0002-4822-2863 \|4 aut
700	1		\|a Tong, H.W. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Renewable & sustainable energy reviews \|d Amsterdam [u.a.] : Elsevier Science, 1997 \|g 188 \|h Online-Ressource \|w (DE-627)320599035 \|w (DE-600)2019940-5 \|w (DE-576)25948511X \|x 1879-0690 \|7 nnns
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912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
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912			\|a GBV_ILN_165
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912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
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912			\|a GBV_ILN_4037
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912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
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912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
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912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 52.56 \|j Regenerative Energieformen \|j alternative Energieformen \|q VZ
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Indexfelder

author_variant	j h jh p c pc q l ql h t ht
matchkey_str	article:18790690:2023----::apnftrofhrwnrsucsnhsuhhnsaneciaehn
hierarchy_sort_str	2023
bklnumber	52.56
publishDate	2023
allfields	10.1016/j.rser.2023.113865 doi (DE-627)ELV065290054 (ELSEVIER)S1364-0321(23)00723-2 DE-627 ger DE-627 rda eng 620 VZ 52.56 bkl He, J.Y. verfasserin (orcid)0000-0001-5280-4287 aut Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5. Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty Chan, P.W. verfasserin aut Li, Q.S. verfasserin (orcid)0000-0002-4822-2863 aut Tong, H.W. verfasserin aut Enthalten in Renewable & sustainable energy reviews Amsterdam [u.a.] : Elsevier Science, 1997 188 Online-Ressource (DE-627)320599035 (DE-600)2019940-5 (DE-576)25948511X 1879-0690 nnns volume:188 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.56 Regenerative Energieformen alternative Energieformen VZ AR 188
spelling	10.1016/j.rser.2023.113865 doi (DE-627)ELV065290054 (ELSEVIER)S1364-0321(23)00723-2 DE-627 ger DE-627 rda eng 620 VZ 52.56 bkl He, J.Y. verfasserin (orcid)0000-0001-5280-4287 aut Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5. Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty Chan, P.W. verfasserin aut Li, Q.S. verfasserin (orcid)0000-0002-4822-2863 aut Tong, H.W. verfasserin aut Enthalten in Renewable & sustainable energy reviews Amsterdam [u.a.] : Elsevier Science, 1997 188 Online-Ressource (DE-627)320599035 (DE-600)2019940-5 (DE-576)25948511X 1879-0690 nnns volume:188 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.56 Regenerative Energieformen alternative Energieformen VZ AR 188
allfields_unstemmed	10.1016/j.rser.2023.113865 doi (DE-627)ELV065290054 (ELSEVIER)S1364-0321(23)00723-2 DE-627 ger DE-627 rda eng 620 VZ 52.56 bkl He, J.Y. verfasserin (orcid)0000-0001-5280-4287 aut Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5. Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty Chan, P.W. verfasserin aut Li, Q.S. verfasserin (orcid)0000-0002-4822-2863 aut Tong, H.W. verfasserin aut Enthalten in Renewable & sustainable energy reviews Amsterdam [u.a.] : Elsevier Science, 1997 188 Online-Ressource (DE-627)320599035 (DE-600)2019940-5 (DE-576)25948511X 1879-0690 nnns volume:188 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.56 Regenerative Energieformen alternative Energieformen VZ AR 188
allfieldsGer	10.1016/j.rser.2023.113865 doi (DE-627)ELV065290054 (ELSEVIER)S1364-0321(23)00723-2 DE-627 ger DE-627 rda eng 620 VZ 52.56 bkl He, J.Y. verfasserin (orcid)0000-0001-5280-4287 aut Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5. Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty Chan, P.W. verfasserin aut Li, Q.S. verfasserin (orcid)0000-0002-4822-2863 aut Tong, H.W. verfasserin aut Enthalten in Renewable & sustainable energy reviews Amsterdam [u.a.] : Elsevier Science, 1997 188 Online-Ressource (DE-627)320599035 (DE-600)2019940-5 (DE-576)25948511X 1879-0690 nnns volume:188 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.56 Regenerative Energieformen alternative Energieformen VZ AR 188
allfieldsSound	10.1016/j.rser.2023.113865 doi (DE-627)ELV065290054 (ELSEVIER)S1364-0321(23)00723-2 DE-627 ger DE-627 rda eng 620 VZ 52.56 bkl He, J.Y. verfasserin (orcid)0000-0001-5280-4287 aut Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5. Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty Chan, P.W. verfasserin aut Li, Q.S. verfasserin (orcid)0000-0002-4822-2863 aut Tong, H.W. verfasserin aut Enthalten in Renewable & sustainable energy reviews Amsterdam [u.a.] : Elsevier Science, 1997 188 Online-Ressource (DE-627)320599035 (DE-600)2019940-5 (DE-576)25948511X 1879-0690 nnns volume:188 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.56 Regenerative Energieformen alternative Energieformen VZ AR 188
language	English
source	Enthalten in Renewable & sustainable energy reviews 188 volume:188
sourceStr	Enthalten in Renewable & sustainable energy reviews 188 volume:188
format_phy_str_mv	Article
bklname	Regenerative Energieformen alternative Energieformen
institution	findex.gbv.de
topic_facet	Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty
dewey-raw	620
isfreeaccess_bool	false
container_title	Renewable & sustainable energy reviews
authorswithroles_txt_mv	He, J.Y. @@aut@@ Chan, P.W. @@aut@@ Li, Q.S. @@aut@@ Tong, H.W. @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320599035
dewey-sort	3620
id	ELV065290054
language_de	englisch
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author	He, J.Y.
spellingShingle	He, J.Y. ddc 620 bkl 52.56 misc Offshore wind energy misc Offshore wind resource map misc Dynamical downscaling misc Wind resource variability misc Wind speed extrapolation accuracy misc Climate projection uncertainty Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
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topic_title	620 VZ 52.56 bkl Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling Offshore wind energy Offshore wind resource map Dynamical downscaling Wind resource variability Wind speed extrapolation accuracy Climate projection uncertainty
topic	ddc 620 bkl 52.56 misc Offshore wind energy misc Offshore wind resource map misc Dynamical downscaling misc Wind resource variability misc Wind speed extrapolation accuracy misc Climate projection uncertainty
topic_unstemmed	ddc 620 bkl 52.56 misc Offshore wind energy misc Offshore wind resource map misc Dynamical downscaling misc Wind resource variability misc Wind speed extrapolation accuracy misc Climate projection uncertainty
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title	Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
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title_full	Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
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title_sort	mapping future offshore wind resources in the south china sea under climate change by regional climate modeling
title_auth	Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
abstract	Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5.
abstractGer	Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5.
abstract_unstemmed	Cost-effective deployment of wind energy systems benefits from a thorough understanding of wind resources. Therefore, it is imperative to figure out how the spatiotemporal patterns of wind resources will evolve in a warmer future world. Based on a regional climate model system named RegCM4.7, this study investigates the future offshore wind resources under climate change in a tropical ocean, namely the South China Sea (SCS). First, 25-km-resolution wind field information over the SCS is retrieved from a regional climate model driven by 22 global climate models. Then, the simulated wind fields are validated against the reanalysis dataset ERA5 and the Cross-Calibrated Multi-Platform satellite dataset. Subsequently, the spatial distribution and temporal variation of wind resources under climate change are comprehensively assessed, and the inter-annual and intra-annual wind resource variability is quantified by the robust coefficient of determination. Last, the significance, robustness, and uncertainty of climate change signals are evaluated. It is projected that the wind power density will increase by 10–20 % in the northern SCS but decrease by 10–30 % in the southern SCS in 2081–2100 (compared to 1985–2004) under the Representative Concentration Pathway 8.5.
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title_short	Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling
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author2	Chan, P.W. Li, Q.S. Tong, H.W.
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doi_str	10.1016/j.rser.2023.113865
up_date	2024-07-06T22:30:45.376Z
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Mapping future offshore wind resources in the South China Sea under climate change by regional climate modeling

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