scholarly journals DEVELOPMENT OF STATISTICAL BIAS CORRECTION METHOD FOR CLIMATE MODEL TYPHOONS AND ENSAMBLE FUTURE STORM SURGE PROJECTION DUE TO CLIMATE CHANGE

2015 ◽  
Vol 71 (2) ◽  
pp. I_1507-I_1512
Author(s):  
Tomohiro YASUDA ◽  
Nariaki KATAHIRA ◽  
Nobuhito MORI ◽  
Hajime MASE ◽  
Yoko SHIBUTANI
Keyword(s):  
Climate Change ◽  
Storm Surge ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Statistical Bias ◽  
Bias Correction Method ◽  
Statistical Bias Correction

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.

scholarly journals Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 990
Author(s):  
Joana Martins ◽  
Helder Fraga ◽  
André Fonseca ◽  
João Andrade Santos
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Model ◽  
Correction Method ◽  
Maximum Temperature ◽  
Lower Sensitivity ◽  
Climate Projections ◽  
Quantile Mapping ◽  
Bias Correction Method ◽  
Wine Region

The implications of weather and climate extremes on the viticulture and winemaking sector can be particularly detrimental and acquire more relevance under a climate change context. A four-member ensemble of the Regional Climate Model-Global Climate Model chain simulations is used to evaluate the potential impacts of climate change on indices of extreme temperature and precipitation, as well as on agroclimatic indices of viticultural suitability in the Douro Wine Region, Portugal, under current and future climate conditions, following the RCP8.5 anthropogenic radiative forcing scenario. Historical (1989–2005) and future (2051–2080) periods are considered for this purpose. Although model outputs are bias-corrected to improve the accuracy of the results, owing to the sensitivity of the climatic indicators to the specific bias correction method, the performance of the linear and quantile mapping methods are compared. The results hint at the importance of choosing the most accurate method (quantile mapping), not only in replicating extremes events but also in reproducing the accumulated agroclimatic indices. Significant differences between the bias correction methods are indeed found for the number of extremely warm days (maximum temperature > 35 °C), number of warm spells, number of warm spell days, number of consecutive dry days, the Dryness Index, and growing season precipitation. The Huglin Index reveals lower sensitivity, thus being more robust to the choice of the method. Hence, an unsuitable bias correction method may hinder the accuracy of climate change projections in studies heavily relying on derived extreme indices and agroclimatic indicators, such as in viticulture. Regarding the climate change signal, significant warming and drying trends are projected throughout the target region, which is supported by previous studies, but also accompanied by an increase of intensity, frequency, and duration of extreme events, namely heatwaves and dry spells. These findings thereby corroborate the need to adopt timely and effective adaptation strategies by the regional winemaking sector to warrant its future sustainability and enhance climate resilience.

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 Climate model bias correction and the role of timescales

2011 ◽  
Vol 15 (3) ◽  
pp. 1065-1079 ◽  
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 timescale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a statistical bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

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