Statistical bias correction method applied on CMIP5 datasets over the Indian region during the summer monsoon season for climate change applications

2016 ◽  
Vol 131 (1-2) ◽  
pp. 471-488 ◽  
Author(s):  
V. Prasanna
Keyword(s):  
Climate Change ◽  
Summer Monsoon ◽  
Bias Correction ◽  
Monsoon Season ◽  
Correction Method ◽  
Indian Region ◽  
Statistical Bias ◽  
Summer Monsoon Season ◽  
Bias Correction Method ◽  
Statistical Bias Correction

scholarly journals Application of Statistical Bias correction method to the Yoshino River Basin

2014 ◽  
Vol 70 (4) ◽  
pp. I_193-I_198 ◽  
Author(s):  
CHOTHANDA NYUNT ◽  
TOSHIO KOIKE ◽  
AKIO YAMAMOTO ◽  
TOSHIHORO NEMOTO ◽  
MASARU KITSUREGAWA
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Correction Method ◽  
Statistical Bias ◽  
Bias Correction Method ◽  
Statistical Bias Correction

scholarly journals Analysis of Rainfall-Runoff Characteristics on Bias Correction Method of Climate Change Scenarios

2015 ◽  
Vol 31 (3) ◽  
pp. 241-252 ◽  
Author(s):  
Donghyuk Kum ◽  
Younsik Park ◽  
Young Hun Jung ◽  
Min Hwan Shin ◽  
Jichul Ryu ◽  
...  
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Correction Method ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Bias Correction Method

scholarly journals The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

2015 ◽  
Vol 19 (10) ◽  
pp. 4055-4066 ◽  
Author(s):  
A. Gobiet ◽  
M. Suklitsch ◽  
G. Heinrich
Keyword(s):  
Climate Change ◽  
Model Uncertainty ◽  
Bias Correction ◽  
Climate Change Signal ◽  
Model Errors ◽  
Statistical Bias ◽  
Data Set ◽  
Dependent Model ◽  
Temperature Climate ◽  
Statistical Bias Correction

Abstract. This study discusses the effect of empirical-statistical bias correction methods like quantile mapping (QM) on the temperature change signals of climate simulations. We show that QM regionally alters the mean temperature climate change signal (CCS) derived from the ENSEMBLES multi-model data set by up to 15 %. Such modification is currently strongly discussed and is often regarded as deficiency of bias correction methods. However, an analytical analysis reveals that this modification corresponds to the effect of intensity-dependent model errors on the CCS. Such errors cause, if uncorrected, biases in the CCS. QM removes these intensity-dependent errors and can therefore potentially lead to an improved CCS. A similar analysis as for the multi-model mean CCS has been conducted for the variance of CCSs in the multi-model ensemble. It shows that this indicator for model uncertainty is artificially inflated by intensity-dependent model errors. Therefore, QM also has the potential to serve as an empirical constraint on model uncertainty in climate projections. However, any improvement of simulated CCSs by empirical-statistical bias correction methods can only be realized if the model error characteristics are sufficiently time-invariant.

scholarly journals Climate model bias correction and the role of timescales

2010 ◽  
Vol 7 (5) ◽  
pp. 7863-7898 ◽  
Author(s):  
J. O. Haerter ◽  
S. Hagemann ◽  
C. Moseley ◽  
C. Piani
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Properties ◽  
Statistical Bias ◽  
Negative Effects ◽  
Climate Model Bias ◽  
Corrected Data ◽  
Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

Sign in / Sign up

Export Citation Format

Share Document