A method to preserve trends in quantile mapping bias correction of climate modeled temperature
By M. Grillakis, A. Koutroulis, Ioannis N. Daliakopoulos, Ioannis K. Tsanis. 2017
74 citations recorded by the source corpus.
Abstract. Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).
Subfields: Atmospheric Science, Economics and Econometrics, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/d35a0c25c5718ad72372e2be3e6527ad5a088d5e
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Citations in the corpus
- Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method (2021, background): “When applied to future projections, EQM has been shown to significantly distort future climate change signals (Grillakis et al. 2017; Maraun et al. 2017) and exaggerate or deflate extreme trends, introducing additional uncertainty into bias-corrected data (Cannon et al.”
- Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin (2017, unknown): “Correction schemes for rainfall estimates are developed for climate models (Maraun, 2016; Grillakis et al., 2017; Switanek et al., 2017), for radar approaches (Cecinati et al.”