Optimizing climate model selection in regional studies using an adaptive weather type based framework : a case study for extreme heat in Belgium
- Author
- Fien Serras, Kobe Vandelanotte (UGent) , Ruben Borgers, Bert Van Schaeybroeck (UGent) , Piet Termonia (UGent) , Matthias Demuzere (UGent) and Nicole P. M. van Lipzig
- Organization
- Project
- Abstract
- Selecting climate model projections is a common practice for regional and local studies. This process often relies on local rather than synoptic variables. Even when synoptic weather types are considered, these are not related to the variable or climate impact driver of interest. Therefore, most selection procedures may not sufficiently account for atmospheric dynamics and climate change impact uncertainties. This study outlines a selection methodology that addresses both these shortcomings. Our methodology first optimizes the Lamb Weather Type classification for the variable and region of interest. In the next step, the representation of the historical synoptic dynamics in Global Climate Models (GCMs) is evaluated and accordingly, low-performing models are excluded. In the last step, indices are introduced that quantify the climate change signals related to the impact of interest. Using these indices, a scoring method results in assessing the suitability of GCMs. To illustrate the applicability of the methodology, a case study of extreme heat in Belgium was carried out. This framework offers a comprehensive method for selecting relevant climate projections, applicable in model ensemble-based research for various climate variables and impact drivers.
- Keywords
- CMIP6, Model selection, Lamb Weather Type classification, Extreme heat, EARTH SYSTEM MODEL, VERSION, TEMPERATURE, PRECIPITATION, CMIP5, CLASSIFICATION, VARIABILITY, PROJECTIONS, MORTALITY, JENKINSON
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01J7WDF3Y3G2KZ7G1VNPMYRF9X
- MLA
- Serras, Fien, et al. “Optimizing Climate Model Selection in Regional Studies Using an Adaptive Weather Type Based Framework : A Case Study for Extreme Heat in Belgium.” CLIMATE DYNAMICS, vol. 62, 2024, pp. 9927–49, doi:10.1007/s00382-024-07432-7.
- APA
- Serras, F., Vandelanotte, K., Borgers, R., Van Schaeybroeck, B., Termonia, P., Demuzere, M., & van Lipzig, N. P. M. (2024). Optimizing climate model selection in regional studies using an adaptive weather type based framework : a case study for extreme heat in Belgium. CLIMATE DYNAMICS, 62, 9927–9949. https://doi.org/10.1007/s00382-024-07432-7
- Chicago author-date
- Serras, Fien, Kobe Vandelanotte, Ruben Borgers, Bert Van Schaeybroeck, Piet Termonia, Matthias Demuzere, and Nicole P. M. van Lipzig. 2024. “Optimizing Climate Model Selection in Regional Studies Using an Adaptive Weather Type Based Framework : A Case Study for Extreme Heat in Belgium.” CLIMATE DYNAMICS 62: 9927–49. https://doi.org/10.1007/s00382-024-07432-7.
- Chicago author-date (all authors)
- Serras, Fien, Kobe Vandelanotte, Ruben Borgers, Bert Van Schaeybroeck, Piet Termonia, Matthias Demuzere, and Nicole P. M. van Lipzig. 2024. “Optimizing Climate Model Selection in Regional Studies Using an Adaptive Weather Type Based Framework : A Case Study for Extreme Heat in Belgium.” CLIMATE DYNAMICS 62: 9927–9949. doi:10.1007/s00382-024-07432-7.
- Vancouver
- 1.Serras F, Vandelanotte K, Borgers R, Van Schaeybroeck B, Termonia P, Demuzere M, et al. Optimizing climate model selection in regional studies using an adaptive weather type based framework : a case study for extreme heat in Belgium. CLIMATE DYNAMICS. 2024;62:9927–49.
- IEEE
- [1]F. Serras et al., “Optimizing climate model selection in regional studies using an adaptive weather type based framework : a case study for extreme heat in Belgium,” CLIMATE DYNAMICS, vol. 62, pp. 9927–9949, 2024.
@article{01J7WDF3Y3G2KZ7G1VNPMYRF9X,
abstract = {{Selecting climate model projections is a common practice for regional and local studies. This process often relies on local rather than synoptic variables. Even when synoptic weather types are considered, these are not related to the variable or climate impact driver of interest. Therefore, most selection procedures may not sufficiently account for atmospheric dynamics and climate change impact uncertainties. This study outlines a selection methodology that addresses both these shortcomings. Our methodology first optimizes the Lamb Weather Type classification for the variable and region of interest. In the next step, the representation of the historical synoptic dynamics in Global Climate Models (GCMs) is evaluated and accordingly, low-performing models are excluded. In the last step, indices are introduced that quantify the climate change signals related to the impact of interest. Using these indices, a scoring method results in assessing the suitability of GCMs. To illustrate the applicability of the methodology, a case study of extreme heat in Belgium was carried out. This framework offers a comprehensive method for selecting relevant climate projections, applicable in model ensemble-based research for various climate variables and impact drivers.}},
author = {{Serras, Fien and Vandelanotte, Kobe and Borgers, Ruben and Van Schaeybroeck, Bert and Termonia, Piet and Demuzere, Matthias and van Lipzig, Nicole P. M.}},
issn = {{0930-7575}},
journal = {{CLIMATE DYNAMICS}},
keywords = {{CMIP6,Model selection,Lamb Weather Type classification,Extreme heat,EARTH SYSTEM MODEL,VERSION,TEMPERATURE,PRECIPITATION,CMIP5,CLASSIFICATION,VARIABILITY,PROJECTIONS,MORTALITY,JENKINSON}},
language = {{eng}},
pages = {{9927--9949}},
title = {{Optimizing climate model selection in regional studies using an adaptive weather type based framework : a case study for extreme heat in Belgium}},
url = {{http://doi.org/10.1007/s00382-024-07432-7}},
volume = {{62}},
year = {{2024}},
}
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