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A good knowledge of future coastal wind and wave resources in the context of climate change is crucial for the construction of offshore wind farms. In this study, the dataset of the coupled model intercomparison project phase 6 (CMIP6) was used to evaluate the future wind resources and wave conditions in the nearshore area of Guangdong of China. The long short-term memory (LSTM) algorithm was used to develop a statistical downscaling method to render high spatial resolution data. The Copula function was used to construct the joint probability distribution function. The key findings were as follows. First, over the whole Guangdong coastal area, the projection of wind speed (Hs) shows a generally increasing trend, the wave height (Ws) remains almost unchanged, and the growth is particularly pronounced in the western area. Second, it is clear that the joint probability distribution is less spread in the late 21st century; therefore, the distribution of wind energy will also be more suitable for the optimal operating of wind turbines. Third, the joint probability distribution results show that large wind and wave conditions possibly occur at the same time, which must be considered when determining worst-case conditions. Future work is required to use more models and scenarios from the ongoing CMIP6.


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Projections of offshore wind energy and wave climate in Guangdong’s nearshore area using CMIP6 simulations

Show Author's information Yiyong Dong1,2Jing Yuan1,2( )
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China
Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Tsinghua University, Beijing 100084, China

Abstract

A good knowledge of future coastal wind and wave resources in the context of climate change is crucial for the construction of offshore wind farms. In this study, the dataset of the coupled model intercomparison project phase 6 (CMIP6) was used to evaluate the future wind resources and wave conditions in the nearshore area of Guangdong of China. The long short-term memory (LSTM) algorithm was used to develop a statistical downscaling method to render high spatial resolution data. The Copula function was used to construct the joint probability distribution function. The key findings were as follows. First, over the whole Guangdong coastal area, the projection of wind speed (Hs) shows a generally increasing trend, the wave height (Ws) remains almost unchanged, and the growth is particularly pronounced in the western area. Second, it is clear that the joint probability distribution is less spread in the late 21st century; therefore, the distribution of wind energy will also be more suitable for the optimal operating of wind turbines. Third, the joint probability distribution results show that large wind and wave conditions possibly occur at the same time, which must be considered when determining worst-case conditions. Future work is required to use more models and scenarios from the ongoing CMIP6.

Keywords: long short-term memory (LSTM), climate change, statistical downscaling, wave climate, coupled model intercomparison project phase 6 (CMIP6), offshore wind energy, Copula

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Publication history

Received: 15 March 2023
Revised: 19 March 2023
Accepted: 20 March 2023
Published: 14 April 2023
Issue date: March 2023

Copyright

© The Author(s) 2023. Published by Tsinghua University Press.

Acknowledgements

This research is supported by the Fund Program of State Key Laboratory of Hydroscience and Engineering (No. 2022-KY-05). The author would like to acknowledge the reviewers for their comments and suggestions, which substantially improved the quality of the paper.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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