The underestimated magnitude and decline trend in near‐surface wind over China

Jiang, Ying; Xu, Xiyan; Liu, Hanwu; Xuguang, Dong; Wang, Wenben; Jia, Guixiao

doi:10.1002/asl.791

Cited by 11 publications

(9 citation statements)

References 28 publications

(40 reference statements)

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“…Generally, the surface wind speed trends are not well simulated by the CMIP3 (Jiang, 2009), CMIP5 (Chen et al, 2012; Tian et al, 2019), and the new generation models of CMIP (CMIP6), which is maybe caused by the following: (1) The surface boundary conditions of CMIP GCMs do not include surface roughness, which can increase the friction force on the surface and weaken the wind speed due to urbanization, the trees and forest, and other types of underlying surface (Vautard et al, 2010), and (2) the current GCMs have a relatively low ability to represent some features of the atmospheric flow (Chen et al, 2012). For example, the CMIP5 GCMs fail to reproduce the slowed East Asia monsoon (Saha et al, 2014), which may result in the decline trend of surface wind speed in China (Jiang et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

“…By comparing the climatology of the East Asian winter monsoon simulated by 41 CMIP5 and 24 CMIP3 GCMs, Wei et al (2013) revealed that CMIP5 models perform better in simulating the near‐surface wind speed. Jiang et al (2017, 2018) and Jiang, Luo, and Zhao (2009) applied CMIP3 and CMIP5 GCMs to validate and project the surface wind speed over China, and the results show that these GCMs can capture the spatial patterns of annual and seasonal mean wind speed, but underestimate their decline trends.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Shi

2020

JGR Atmospheres

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Surface wind speed has great impacts on the economy, environment, and society around the world. Based on 24 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6), this paper assesses the historical surface wind speed over China, and quantifies the advancements of CMIP6 over CMIP5. In addition, future changes of surface wind speed under the Shared Socioeconomic Pathway scenarios of 1-2.6, 2-4.5, and 5-8.5 (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are also provided, by using 18 out of the 24 models with all three scenarios. Results show that multimodel ensemble mean of CMIP6 can well capture the spatial distributions of the annual, summer, and winter surface wind speed and generally performs better than both the median of the individual CMIP6 GCMs and multimodel ensemble mean of CMIP5 in monthly, seasonal, and annual wind climatology. However, CMIP6 GCMs fail to reproduce the observed decreasing trends, in terms of magnitude and/or sign. Meanwhile, the annual cycle of the surface wind speed simulated by CMIP6 GCMs falsely demonstrates the maximum in winter instead of spring. In the context of global warming, surface wind speed is projected to decrease in most parts of China in the middle and late 21st century under the three scenarios. Moreover, the trends of wind speed averaged over China will decrease significantly in both annual and winter under all three scenarios, but it will increase significantly in summer under SSP5-8.5.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Shi

2020

JGR Atmospheres

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“…Future changes in NWS over China have also been investigated. For example, Jiang et al [14,15] applied CMIP3 and CMIP5 databases to validation and projection of future NWS over China, and they found that CMIP3 and CMIP5 GCMs could realistically capture the spatial patterns of annual and seasonal mean wind speed, but that they tended to underestimate the trends of decline. Abolude et al [16] assessed the potential status of future wind power over China using CMIP5 models.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

Deng

Hua

Fan³

2021

Atmosphere

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The characteristics of near-surface wind speed (NWS) are important to the study of dust storms, evapotranspiration, heavy rainfall, air pollution, and wind energy development. This study evaluated the performance of 30 models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) through comparison with observational NWS data acquired in China during a historical period (1975–2014), and projected future changes in NWS under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) based on an optimal multi-model ensemble. Results showed that most models reproduced the spatial pattern of NWS for all seasons and the annual mean, although the models generally overestimated NWS magnitude. All models tended to underestimate the trends of decline of NWS for all seasons and the annual mean. On the basis of a comprehensive ranking index, the KIOST-ESM, CNRM-ESM2-1, HadGEM3-GC31-LL, CMCC-CM2-SR5, and KACE-1-0-G models were ranked as the five best-performing models. In the projections of future change, nationally averaged NWS for all months was weaker than in the historical period, and the trends decreased markedly under all the different scenarios except the winter time series under SSP2-4.5. Additionally, the projected NWS over most regions of China weakened in both the early period (2021–2060) and the later period (2061–2100).

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“…This may affect projections of future surface winds and associated fluxes over land surfaces. Climate simulations in the Coupled Model Intercomparison Project Phase 3 and 5 (CMIP3 and CMIP5) generally underestimate the rate of decline in surface wind speed by an order of magnitude (Jiang et al 2017).…”

Section: Discussionmentioning

confidence: 99%

“…However, the mechanisms related to the observed wind stilling are not well understood (Wu et al 2018, Zeng et al 2018. Moreover, climate models have limited ability for simulating the long-term trend in surface wind speeds (Jiang et al 2017, Tian et al 2019, indicating the need for a clear understanding of the mechanisms of wind stilling.…”

Section: Introductionmentioning

confidence: 99%

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Chen

Jeong

Park

et al. 2020

Environ. Res. Lett.

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Wind stilling has been observed in many regions across the Northern Hemisphere; however, the related mechanisms are not well understood. Analyses of the wind speed variations in South Korea during 1993-2015 in this study reveal that the annual-mean surface wind speeds at rural stations have increased by up to 0.41 m s −1 decade −1 , while those at urban stations have decreased by up to −0.63 m s −1 decade −1 . The local wind speed variations are found to be negatively correlated with the population density at the corresponding observation sites. Gustiness analyses show the increase in local surface roughness due to urbanization can explain the observed negative wind speed trends at urban stations as the urbanization effect overwhelms the positive wind speed trend due to climate change. The observed negative wind speed trend in urban areas are not found in the regional climate model simulations in the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA) as these models do not take into account the impact of urbanization on wind variations during the period. This study suggests that urbanization can play an important role in the recent wind stilling in rapidly developing regions such as South Korea. Our results suggest that future climate projections in CORDEX-EA may overestimate wind speeds in urban areas, and that future regional climate projections need to consider the effects of urbanization for a more accurate projection of wind speeds.

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The underestimated magnitude and decline trend in near‐surface wind over China

Cited by 11 publications

References 28 publications

Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Evaluation and Projection of Surface Wind Speed Over China Based on CMIP6 GCMs

Evaluation and Projection of Near-Surface Wind Speed over China Based on CMIP6 Models

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

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