Observed and global climate model based changes in wind power potential over the Northern Hemisphere during 1979–2016

Tian, Qun; Huang, Gang; Hu, Kaiming; Niyogi, Dev

doi:10.1016/j.energy.2018.11.027

Cited by 88 publications

(71 citation statements)

References 49 publications

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“…2. The MME_6 fails to reproduce the decreasing trends during 1961-2005 over the entire China, with the smaller magnitude of wind decrease and opposite trends over some regions compared with the observation, which is also found in CMIP3 and CMIP5 models (Chen et al, 2012;Jiang, 2009;Tian et al, 2019). The maximum monthly wind speed in MME_6 occurs in winter, which is inconsistent with the observation where the maximum in spring.…”

Section: Summary and Discussioncontrasting

confidence: 61%

“…Over the Bay of Bengal region, it is found that the CMIP5 models fail to represent the current wind climate satisfactorily (Krishnan & Bhaskaran, 2019). The relatively poor skill especially for the long-term temporal trends over the Northern Hemisphere is also found in Tian et al (2019).…”

Section: Introductionmentioning

confidence: 98%

“…The wind climate has a major impact on water cycles (McMahon et al, 2013; Rayner, 2007; Roderick et al, 2007), the wind energy industry (Ding et al, 2020; Kulkarni & Huang, 2014; Tian et al, 2019; Zeng et al, 2019), and wind‐related natural disasters, such as tornados, typhoons (Cao & Wang, 2013; Tamura & Cao, 2012), and storm surges (Debernard & Røed, 2008). Therefore, it is of great importance to understand the changes of the wind speed, especially for China with the largest new and cumulative wind power capacities in the world (Renewables, 2019; Sönnichsen, 2020).…”

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: Summary and Discussioncontrasting

confidence: 61%

Section: Introductionmentioning

confidence: 98%

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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“…By analyzing the observations at 822 surface weather stations, Vautard et al (2010) showed that the surface wind speed has been reduced by 5%-15% over almost all continents in the Northern Hemisphere (NH) midlatitudes over . A recent study also showed a decrease in surface wind speeds and wind power potential in most of NH over the past four decades (Tian et al 2019) exerting adverse impacts on windgenerated electricity (Sherman et al 2017) and the dispersion of air pollutants (Li et al 2019b), reducing evaporation (McVicar et al 2012), and affecting soil erosion (Zhang et al 2019). However, the mechanisms related to the observed wind stilling are not well understood (Wu et al 2018, Zeng et al 2018.…”

Section: Introductionmentioning

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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Alternating Magnetic Field‐Enhanced Triboelectric Nanogenerator for Low‐Speed Flow Energy Harvesting

Zhang

Gao

et al. 2023

Adv Funct Materials

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Low‐speed flow energy, such as breezes and rivers, which are abundant in smart agriculture and smart cities, faces significant challenges in efficient harvesting as an untapped sustainable energy source. This study proposes an alternating magnetic field‐enhanced triboelectric nanogenerator (AMF‐TENG) for low‐speed flow energy harvesting, and demonstrates its feasibility through experimental results. AMF‐TENG's minimum cut‐in speed is 1 m s−1, thereby greatly expanding its wind energy harvesting range. When the wind speed is 1–5 m s−1, the open‐circuit voltage (VOC) is 20.9–179.3 V. The peak power is 0.68 mW at 5 m s−1. In a durability test of 100 K cycles, the VOC decreases from 188.4 to 174.2 V but remain at 92.5% of the initial value. furthermore, the AMF‐TENG can harvest low‐speed flow energy from the natural environment to power temperature and humidity sensors and wireless light intensity sensor in smart agriculture. This study provides a promising method for low‐speed flow energy harvesting in distributed applications.

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Observed and global climate model based changes in wind power potential over the Northern Hemisphere during 1979–2016

Cited by 88 publications

References 49 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

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

Alternating Magnetic Field‐Enhanced Triboelectric Nanogenerator for Low‐Speed Flow Energy Harvesting

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