Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6
Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/20/4076 |
| _version_ | 1827678668743245824 |
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| author | Yunxia Long Changchun Xu Fang Liu Yongchang Liu Gang Yin |
| author_facet | Yunxia Long Changchun Xu Fang Liu Yongchang Liu Gang Yin |
| author_sort | Yunxia Long |
| collection | DOAJ |
| description | Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of <i>p</i> = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter. |
| first_indexed | 2024-03-10T06:14:17Z |
| format | Article |
| id | doaj.art-68f600e9c0ac4b418ad355be30218d08 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:14:17Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-68f600e9c0ac4b418ad355be30218d082023-11-22T19:53:48ZengMDPI AGRemote Sensing2072-42922021-10-011320407610.3390/rs13204076Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6Yunxia Long0Changchun Xu1Fang Liu2Yongchang Liu3Gang Yin4MOE Key Laboratory of Oasis Ecology, College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, ChinaMOE Key Laboratory of Oasis Ecology, College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, ChinaMOE Key Laboratory of Oasis Ecology, College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaMOE Key Laboratory of Oasis Ecology, College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, ChinaNear surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of <i>p</i> = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter.https://www.mdpi.com/2072-4292/13/20/4076surface wind speedCMIP6Arid Region of Northwest ChinaSSPsmulti-model ensemble |
| spellingShingle | Yunxia Long Changchun Xu Fang Liu Yongchang Liu Gang Yin Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6 Remote Sensing surface wind speed CMIP6 Arid Region of Northwest China SSPs multi-model ensemble |
| title | Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6 |
| title_full | Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6 |
| title_fullStr | Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6 |
| title_full_unstemmed | Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6 |
| title_short | Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6 |
| title_sort | evaluation and projection of wind speed in the arid region of northwest china based on cmip6 |
| topic | surface wind speed CMIP6 Arid Region of Northwest China SSPs multi-model ensemble |
| url | https://www.mdpi.com/2072-4292/13/20/4076 |
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