Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 20; p. 4076
• Main Authors: Long, Yunxia, Xu, Changchun, Liu, Fang, Liu, Yongchang, Yin, Gang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2021
• Subjects: 21st century; Arid Region of Northwest China; Arid regions; Arid zones; Autumn; basins; China; Climate change; Climate models; CMIP6; Correlation coefficient; Correlation coefficients; data collection; Datasets; Energy industry; Energy resources; Global climate; Global climate models; latitude; longitude; Mountain regions; Mountainous areas; Mountains; multi-model ensemble; Remote sensing; Simulation; Spatial distribution; Spatial variations; Spring; Spring (season); SSPs; Standard deviation; Summer; Surface wind; surface wind speed; Trends; Wind power; Wind speed; Winter; Europe; China
• DOI: 10.3390/rs13204076
• ISSN: 2072-4292

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 p = 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.