Articles | Volume 14, issue 2
https://doi.org/10.5194/esd-14-507-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-507-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Hong Kong SAR, China
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Hong Kong SAR, China
Division of Environment and Sustainability, The Hong Kong University
of Science and Technology, Hong Kong SAR, China
Seung-Ki Min
Division of Environmental Science and Engineering,
Pohang University of Science and Technology, Pohang, South Korea
Institute for Convergence Research and Education in Advanced
Technology, Yonsei University, Incheon, South Korea
Yeon-Hee Kim
Division of Environmental Science and Engineering,
Pohang University of Science and Technology, Pohang, South Korea
Dong-Hyun Cha
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
Seok-Woo Shin
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
Joong-Bae Ahn
Department of Atmospheric Sciences, Pusan National University, Busan, South Korea
Eun-Chul Chang
Department of Atmospheric Sciences, Kongju National University, Gongju, South Korea
Young-Hwa Byun
Climate Change Research Team, National Institute of Meteorological Science, Seogwipo, South Korea
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Short summary
This study evaluates four bias correction methods (three univariate and one multivariate) for correcting multivariate heat-stress indices. We show that the multivariate method can benefit the indirect correction that first adjusts individual components before index calculation, and its advantage is more evident for indices relying equally on multiple drivers. Meanwhile, the direct correction of heat-stress indices by the univariate quantile delta mapping approach also has comparable performance.
This study evaluates four bias correction methods (three univariate and one multivariate) for...
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