Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

Mirakbari, Maryam; Mesbahzadeh, Tayyebeh; Sardoo, Farshad Soleimani; Miglietta, Mario Marcello; Krakauer, Nir Y.; Alipour, Nahid

doi:10.1111/nrm.12254

Cited by 6 publications

(2 citation statements)

References 70 publications

(103 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Copulas were also used in a few studies considering bias correction of temperature and precipitation data, each variable was corrected separately but at different locations (e.g., Alidoost et al, 2017;Lazoglou and Anagnostopoulou, 2019). In addition to bias correction, copulas in climatology are used to investigate trends in extreme precipitation and temperature, as in Mirakbari et al (2020). In the work by Laux et al (2011), the focus was on the positive pairs of observed and modelled precipitation, and with the usage of empirical copulas, statistical downscaling of precipitation over the complex terrain in the Alpine region of Germany was performed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Jurković

Güttler

Pasarić

2022

Intl Journal of Climatology

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

“…In addition to bias correction, copulas in climatology are used to investigate trends in extreme precipitation and temperature, as in Mirakbari et al . (2020). In the work by Laux et al .…”

Section: Introductionmentioning

confidence: 99%