Abstract
During the last decades, global and regional climate models have been widely used for the estimation of future climate conditions. Unfortunately, the models’ estimated values present important biases relative to the observed values, especially when the estimations refer to extremes. Consequently, several researchers have studied several statistical methods that are able to minimize the biases between climate models and observed values. The present study evaluates a new statistical method for bias correction: The triangular irregular network (TIN)-copula method. This method is a combination of the triangular irregular networks and the copula theory. In the present research, the new method is applied to ten Mediterranean stations and its results are compared with the bias-corrected values of three other widely used methods: The delta, the scaling, and the empirical quantile mapping methods. The analysis was made for maximum mean temperature (TMX) and minimum mean temperature (TMN) as well as for extreme precipitation (R99). According to the results, the TIN-copula method is able to correct extreme temperature and precipitation values, estimated by regional climate models, with high accuracy. Additionally, it is proven that the TIN-copula method is a useful tool for bias correction as it presents several advantages compared with the other methods, and it is recommended for future works.
Highlights
General circulation models (GCMs) and regional climate models (RCMs) constitute widely used tools for analyzing the Earth’s climate and for estimating future climate conditions [1]
Bias correction methods are preferred by scientists as they are applicable, flexible for correcting the simulation
Considering the extended use of bias correction methodologies, this study aims to compare the Considering the extended use of bias correction methodologies, this study aims to compare the results of three widely used methods with the results of a new statistical method for bias correction, results of three widely used methods with the results a newthe statistical methodoffor bias correction, the triangular irregular network (TIN)-copula method.ofHence, main objective this study is to theevaluate triangular irregular network (TIN)-copula method
Summary
General circulation models (GCMs) and regional climate models (RCMs) constitute widely used tools for analyzing the Earth’s climate and for estimating future climate conditions [1]. Both the increasing frequency of extreme climate episodes as well as their severe impacts in several fields (society, economy, agriculture) have made the use of climate models necessary. The pre-process is achieved with the use of statistical methods, namely bias correction methods. The purpose of these methods is to minimize the biases between climate models’ data and observations. Bias correction methods are preferred by scientists as they are applicable, flexible for correcting the simulation
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