Bivariate bias correction of the regional climate model ensemble over the Adriatic region

Abstract

The main goal of this analysis is to provide an assessment of the fine grid regional climate model (RCM) ensemble over the broader Adriatic region, which is the domain with orographically complex terrain and a developed coastline. The results of this study are a beneficial complement to earlier work on using modified bivariate bias correction methods. This analysis was based on an ensemble of 12 combinations of three RCMs and four global climate models (GCMs) from 1971 to 2004, divided into calibration and validation parts. A comparison of one univariate and three versions of bivariate bias correction methods of summer and winter monthly temperature and precipitation was performed. To examine the effect of various methods, we used different marginal distributions and distributions of dependence (copula). Bivariate bias correction was conducted with parametric and empirical marginal distributions. In the quantile mapping (univariate) and the parametric bivariate method, gamma (precipitation) and normal (temperature) probability distributions were used. We documented the bias and the influence of bias correction methods on precipitation and temperature, including their climate change signals in historical data. The considered methods maintained the spatial distribution of trends from the native ensemble. If the relationship between variables was not distinct and the correlation coefficient was low, bivariate bias correction methods tended to weaken the correlation coefficient from the model. Two experiments on how the bias correction methods influence statistical measures of the considered variables and their relationships showed that the bivariate method with empirical distributions (eeG) performed slightly better than other methods. Although the eeG method performed better than the other methods, none of the considered methods was able to fully preserve the correlation coefficient between variables detected from observations. Considering two copulas (Gaussian and Student's t), we conclude that the effect of different copula types on method performance is small.

Očekivano toplinsko opterećenje Dubrovnika, Osijeka, Rijeke, Zadra i Zagreba prema projekcijama regionalnih klimatskih modela

“…In addition, some small-scale processes and physical properties are not distinguished by the models and give rise to systematic errors; therefore, it is important to adjust the modelled data to the measurements. In this paper, bias correction was performed using the quantile mapping method (e.g., Piani et al, 2010, Sokol Jurković et al, 2022. This adjustment was made on a monthly basis, which means that the observed and modelled daily data from a certain period were taken separately for each month, and the monthly means of modelled data corrected by this method match the monthly means of the measured values.…”