Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme

Kue Bum Kim, Dawei Han, Hyun-Han Kwon

Research output: Contribution to journalArticle (Academic Journal)peer-review

15 Citations (Scopus)
332 Downloads (Pure)


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 realisations 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 southwest 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 maintain the ensemble spread within the natural variability of the observations.
Original languageEnglish
Pages (from-to)2019-2034
Number of pages16
JournalHydrology and Earth System Sciences
Issue number5
Early online date17 May 2016
Publication statusPublished - May 2016

Structured keywords

  • Water and Environmental Engineering


  • bias correction
  • RCM ensemble
  • spread
  • natural variability


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