Articles | Volume 386
https://doi.org/10.5194/piahs-386-55-2024
https://doi.org/10.5194/piahs-386-55-2024
Post-conference publication
 | 
19 Apr 2024
Post-conference publication |  | 19 Apr 2024

Investigation of Uncertainties in Multi-variable Bias Adjustment in Multi-model Ensemble

Saurabh Kelkar and Koji Dairaku

Cited articles

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Ayar, P. V., Vrac, M., Bastin, S., Carreau, J., Déqué, M., and Gallardo, C.: Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations, Clim. Dynam., 46, 1301–1329, https://doi.org/10.1007/s00382-015-2647-5, 2015. 
Berg, P., Feldmann, H., and Panitz, H.-J.: Bias correction of high resolution regional climate model data, J. Hydrol., 448–449, 80–92, https://doi.org/10.1016/j.jhydrol.2012.04.026, 2012. 
Bruyère, C. L., Done, J. M., Holland, G. J., and Fredrick, S.: Bias corrections of global models for regional climate simulations of high-impact weather, Clim. Dynam., 43, 1847–1856, https://doi.org/10.1007/s00382-013-2011-6, 2013. 
Casanueva, A., Bedia, J., Herrera, S., Fernández, J., and Gutiérrez, J. M.: Direct and component-wise bias correction of multi-variate climate indices: The percentile adjustment function diagnostic tool, Climatic Change, 147, 411–425, https://doi.org/10.1007/s10584-018-2167-5, 2018. 
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Short summary
Climate models are widely used to assess climate risks. Bias adjustment methods are often used to reduce biases in model simulations. Our research focuses on a bias adjustment method based on topography adjustment that considers the interactions between climate variables. Results indicate added bias over mountainous regions; however, they also show bias reductions over low-altitude regions, highlighting potential application over regions with limited observation data.