Evaluation of a stochastic weather generator in simulating univariate and multivariate climate extremes in different climate zones across Europe

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378289%3A_____%2F21%3A00539657" target="_blank" >RIV/68378289:_____/21:00539657 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/86652079:_____/21:00542780

Výsledek na webu

<a href="http://oadoi.org/10.1127/metz/2020/1021" target="_blank" >http://oadoi.org/10.1127/metz/2020/1021</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1127/metz/2020/1021" target="_blank" >10.1127/metz/2020/1021</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of a stochastic weather generator in simulating univariate and multivariate climate extremes in different climate zones across Europe

Popis výsledku v původním jazyce

Stochastic weather generators have been increasingly used as downscaling tools for climate change impact assessments. In spite of their widespread use, their potential to simulate climate extremes – especially multivariate extremes – is largely unexplored. The aim of this study is to assess the ability of the Richardson type six-variate weather generator SiSi to simulate the frequency of various univariate as well as multivariate extremes with focus on extremes related to the non-normally distributed weather variables relative humidity and wind speed. A total of 83 sites with different elevation and proximity to each other – thereby defining a European, a country (Austria) and a local (catchment) scale – and diverse climates across Europe are selected. Results show that SiSi is able to simulate univariate and multivariate extremes generally and equally well in all climate zones. The results depend on the nature of the individual variables involved in the extreme events. Among all the extreme events, the weather generator has a tendency to underestimate the extremes related tonminimum temperature. The first-order auto-regressive (AR(1)) model used for modeling non-precipitation variables assumes the distribution of variables to be Gaussian. This assumption has been enforced in this study by transforming each non-precipitation variable to a normal distribution, but nevertheless the weather generator consistently underestimates the cold extremes. This is due to the multimodal nature of the distribution of temperature. The AR(1) model is not able to reproduce the multimodality of the distributions. The performance of SiSi does not depend on the climate type of a region or the proximity of sites to one another, rather it depends on the characteristics of a variable at an individual location.

Název v anglickém jazyce

Evaluation of a stochastic weather generator in simulating univariate and multivariate climate extremes in different climate zones across Europe

Popis výsledku anglicky

Stochastic weather generators have been increasingly used as downscaling tools for climate change impact assessments. In spite of their widespread use, their potential to simulate climate extremes – especially multivariate extremes – is largely unexplored. The aim of this study is to assess the ability of the Richardson type six-variate weather generator SiSi to simulate the frequency of various univariate as well as multivariate extremes with focus on extremes related to the non-normally distributed weather variables relative humidity and wind speed. A total of 83 sites with different elevation and proximity to each other – thereby defining a European, a country (Austria) and a local (catchment) scale – and diverse climates across Europe are selected. Results show that SiSi is able to simulate univariate and multivariate extremes generally and equally well in all climate zones. The results depend on the nature of the individual variables involved in the extreme events. Among all the extreme events, the weather generator has a tendency to underestimate the extremes related tonminimum temperature. The first-order auto-regressive (AR(1)) model used for modeling non-precipitation variables assumes the distribution of variables to be Gaussian. This assumption has been enforced in this study by transforming each non-precipitation variable to a normal distribution, but nevertheless the weather generator consistently underestimates the cold extremes. This is due to the multimodal nature of the distribution of temperature. The AR(1) model is not able to reproduce the multimodality of the distributions. The performance of SiSi does not depend on the climate type of a region or the proximity of sites to one another, rather it depends on the characteristics of a variable at an individual location.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Meteorologische Zeitschrift

ISSN

0941-2948

e-ISSN

1610-1227

Svazek periodika

30

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

25

Strana od-do

127-151

Kód UT WoS článku

000643536700003

EID výsledku v databázi Scopus

2-s2.0-85105343465