Technical University of Crete, School of Environmental Engineering, Chania, Greece
McMaster University, Department of Civil Engineering, Hamilton, ON, Canada
Abstract. Bias correction of climate variables has become a standard practice in Climate Change Impact (CCI) studies. While various methodologies have been developed, their majority assumes that the statistical characteristics of the biases between the modeled data and the observations remain unchanged in time. However, it is well known that this assumption of stationarity cannot stand in the context of a climate. Here, a method to overcome the assumption of stationarity and its drawbacks is presented. The method is presented as a pre-post processing procedure that can potentially be applied with different bias correction methods. The methodology separates the stationary and the non-stationary components of a time series, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method prevents the distortion and allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation, but also the higher and lower percentiles of the climate variable. Daily temperature time series obtained from five Euro CORDEX RCM models are used to illustrate the improvements of this method.
How to cite. Grillakis, M. G., Koutroulis, A. G., Daliakopoulos, I. N., and Tsanis, I. K.: Addressing the assumption of stationarityin statistical bias correction of temperature, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2016-52, 2016.
Received: 24 Oct 2016 – Discussion started: 27 Oct 2016
We present a methodology that adjusts the systematic errors of climate model simulated temperature towards observations. The method considers the separation of the stationary and the non-stationary components in order to apply adjustment only to the former. The results of a calibration-validation test show the good performance of the method. Additionally, results of the methodology on temperature projections, illustrate the preservation of the long-term statistics on the adjusted data.
We present a methodology that adjusts the systematic errors of climate model simulated...