Articles | Volume 20, issue 5
Hydrol. Earth Syst. Sci., 20, 2019–2034, 2016
https://doi.org/10.5194/hess-20-2019-2016
Hydrol. Earth Syst. Sci., 20, 2019–2034, 2016
https://doi.org/10.5194/hess-20-2019-2016

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 et al.

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Cited articles

Addor, N. and Fischer, E. M.: The influence of natural variability and interpolation errors on bias characterization in RCM simulations, J. Geophys. Res.-Atmos., 120, 10180–10195, https://doi.org/https://doi.org/10.1002/2014JD022824, 2015.
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Baigorria, G. A., Jones, J. W., Shin, D.-W., Mishra, A., and O'Brien, J. J.: Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs, Clim. Res., 34, 211–222, https://doi.org/10.3354/cr00703, 2007.
Bates, B., Kundzewicz, Z. W., Wu, S., and Palutikof, J.: Climate change and water, Intergovernmental Panel on Climate Change (IPCC), 2008.
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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.