Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.153 IF 5.153
  • IF 5-year value: 5.460 IF 5-year
    5.460
  • CiteScore value: 7.8 CiteScore
    7.8
  • SNIP value: 1.623 SNIP 1.623
  • IPP value: 4.91 IPP 4.91
  • SJR value: 2.092 SJR 2.092
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 123 Scimago H
    index 123
  • h5-index value: 65 h5-index 65
Volume 20, issue 5
Hydrol. Earth Syst. Sci., 20, 2019–2034, 2016
https://doi.org/10.5194/hess-20-2019-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 2019–2034, 2016
https://doi.org/10.5194/hess-20-2019-2016
© Author(s) 2016. This work is distributed under
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 Kim1, Hyun-Han Kwon2, and Dawei Han1 Kue Bum Kim et al.
  • 1Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol, Bristol, UK
  • 2Department of Civil Engineering, Chonbuk National University, Jeonju-si, Jeollabuk-do, South Korea

Abstract. This study presents a novel bias correction scheme for regional climate model (RCM) precipitation ensembles. A primary advantage of using model ensembles for climate change impact studies is that the uncertainties associated with the systematic error can be quantified through the ensemble spread. Currently, however, 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. Since the observation is only one case of many possible realizations due to the climate natural variability, a successful bias correction scheme should preserve the ensemble spread within the bounds of its natural variability (i.e. sampling uncertainty). To demonstrate a new bias correction scheme conforming to RCM precipitation ensembles, an application to the Thorverton catchment in the south-west of England is presented. For the ensemble, 11 members from the Hadley Centre Regional Climate Model (HadRM3-PPE) data are used and monthly bias correction has been done for the baseline time period from 1961 to 1990. In the typical conventional method, monthly mean precipitation of each of the ensemble members is nearly identical to the observation, i.e. the ensemble spread is removed. In contrast, the proposed method corrects the bias while maintaining the ensemble spread within the natural variability of the observations.

Publications Copernicus
Download
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...
Citation