Preprints
https://doi.org/10.5194/esd-2022-33
https://doi.org/10.5194/esd-2022-33
 
12 Aug 2022
12 Aug 2022
Status: a revised version of this preprint is currently under review for the journal ESD.

Direct and Indirect Application of Univariate and Multivariate Bias Corrections on Heat-stress Indices based on Multi-RCM Simulations

Liying Qiu1, Eun-Soon Im1,2, Seung-Ki Min3, Yeon-Hee Kim3, Dong-Hyun Cha4, Seok-Woo Shin4, Joong-Bae Ahn5, Eun-Chul Chang6, and Young-Hwa Byun7 Liying Qiu et al.
  • 1Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
  • 2Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, China
  • 3Division of Environmental Science and Engineering, Pohang University of Science and Technology, South Korea
  • 4Ulsan National Institute of Science and Technology, South Korea
  • 5Pusan National University, South Korea
  • 6Kongju National University, South Korea
  • 7National Institute of Meteorological Science, South Korea

Abstract. Statistical bias correction (BC) is a widely used tool to post-process climate model biases for heat-stress impact studies, which are often based on indices calculated from multiple dependent variables. This study compares five bias correction methods (four univariate and one multivariate) with two applying strategies (direct and indirect) for correcting two heat-stress indices with different dependencies on temperature and relative humidity, using multiple Regional Climate Model simulations over South Korea. It would be helpful for reducing the ambiguity involved in the practical application of BC for climate modeling as well as end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence and therefore benefit the indirect correction of heat-stress indices that depend on the adjustment of individual components, especially those relying equally on multiple drivers. On the other hand, the direct correction of multivariate indices using the Quantile Delta Mapping univariate approach can also produce a comparable performance in the corrected heat-stress indices. However, our results also indicate that attention should be paid to the non-stationarity of bias brought by climate sensitivity in the modeled data, which may affect the bias-corrected results unsystematically. Careful interpretation of the correction process is required for an accurate heat-stress impact assessment.

Liying Qiu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2022-33', Anonymous Referee #1, 14 Sep 2022
    • AC1: 'Reply on RC1', Liying Qiu, 13 Oct 2022
  • RC2: 'Comment on esd-2022-33', Anonymous Referee #2, 17 Sep 2022
    • AC2: 'Reply on RC2', Liying Qiu, 14 Oct 2022
      • AC3: 'Reply on AC2', Liying Qiu, 14 Oct 2022

Liying Qiu et al.

Liying Qiu et al.

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Short summary
This study evaluates five bias correction methods (four 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 indices calculation, and its advantage is more evident for index relying equally on multiple drivers. Meanwhile, the direct correction of heat-stress indices by univariate Quantile Delta Mapping approach also has a comparable performance.
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