Articles | Volume 20, issue 5
https://doi.org/10.5194/hess-20-2019-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2019-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 May 2016
Research article |
| 17 May 2016
Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme
Kue Bum Kim
Water and Environmental Management Research Centre,
Department of Civil Engineering, University of Bristol, Bristol, UK
Department of Civil Engineering, Chonbuk National
University, Jeonju-si, Jeollabuk-do, South Korea
Dawei Han
Water and Environmental Management Research Centre,
Department of Civil Engineering, University of Bristol, Bristol, UK
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Cited
15 citations as recorded by crossref.
- Unravelling the potential of global streamflow reanalysis in characterizing local flow regime T. Zhao et al. 10.1016/j.scitotenv.2022.156125
- Climate Data to Support the Adaptation of Buildings to Climate Change in Canada A. Gaur & M. Lacasse 10.3390/data7040042
- Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. 10.1016/j.jhydrol.2021.127261
- Possible NPP changes and risky ecosystem region identification in China during the 21st century based on BCC-CSM2 C. Zhang et al. 10.1007/s11442-020-1778-8
- Performance evaluation of six RCMs for precipitation and temperature in a semi-arid region S. Al-Hilali et al. 10.1007/s40808-024-02006-2
- Can Bias Correction of Regional Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall Variability? E. Rocheta et al. 10.1175/JCLI-D-16-0654.1
- A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate Y. Kim et al. 10.1016/j.wace.2024.100688
- How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts? T. Zhao et al. 10.1175/JCLI-D-16-0652.1
- Evaluation of the performance of Euro-CORDEX Regional Climate Models for assessing hydrological climate change impacts in Great Britain: A comparison of different spatial resolutions and quantile mapping bias correction methods E. Pastén-Zapata et al. 10.1016/j.jhydrol.2020.124653
- A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks Y. Kim et al. 10.1029/2021GL095729
- Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models B. So et al. 10.1016/j.jhydrol.2017.07.061
- Challenges and potential solutions in statistical downscaling of precipitation J. Chen & X. Zhang 10.1007/s10584-021-03083-3
- Evaluation of global ensemble prediction models for forecasting medium to heavy precipitations A. Abdolmanafi et al. 10.1007/s00703-020-00731-8
- WITHDRAWN: Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. 10.1016/j.hydroa.2021.100109
- Uncertainty of Intensity–Duration–Frequency (IDF) curves due to varied climate baseline periods S. Fadhel et al. 10.1016/j.jhydrol.2017.02.013
15 citations as recorded by crossref.
- Unravelling the potential of global streamflow reanalysis in characterizing local flow regime T. Zhao et al. 10.1016/j.scitotenv.2022.156125
- Climate Data to Support the Adaptation of Buildings to Climate Change in Canada A. Gaur & M. Lacasse 10.3390/data7040042
- Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. 10.1016/j.jhydrol.2021.127261
- Possible NPP changes and risky ecosystem region identification in China during the 21st century based on BCC-CSM2 C. Zhang et al. 10.1007/s11442-020-1778-8
- Performance evaluation of six RCMs for precipitation and temperature in a semi-arid region S. Al-Hilali et al. 10.1007/s40808-024-02006-2
- Can Bias Correction of Regional Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall Variability? E. Rocheta et al. 10.1175/JCLI-D-16-0654.1
- A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate Y. Kim et al. 10.1016/j.wace.2024.100688
- How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts? T. Zhao et al. 10.1175/JCLI-D-16-0652.1
- Evaluation of the performance of Euro-CORDEX Regional Climate Models for assessing hydrological climate change impacts in Great Britain: A comparison of different spatial resolutions and quantile mapping bias correction methods E. Pastén-Zapata et al. 10.1016/j.jhydrol.2020.124653
- A Novel Spatial Downscaling Approach for Climate Change Assessment in Regions With Sparse Ground Data Networks Y. Kim et al. 10.1029/2021GL095729
- Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models B. So et al. 10.1016/j.jhydrol.2017.07.061
- Challenges and potential solutions in statistical downscaling of precipitation J. Chen & X. Zhang 10.1007/s10584-021-03083-3
- Evaluation of global ensemble prediction models for forecasting medium to heavy precipitations A. Abdolmanafi et al. 10.1007/s00703-020-00731-8
- WITHDRAWN: Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective K. Kim et al. 10.1016/j.hydroa.2021.100109
- Uncertainty of Intensity–Duration–Frequency (IDF) curves due to varied climate baseline periods S. Fadhel et al. 10.1016/j.jhydrol.2017.02.013
Saved (preprint)
Latest update: 21 Nov 2024
Short summary
A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.
A primary advantage of using model ensembles for climate change impact studies is to represent...
