Testing a stochastic weather generator for multivariate climate extremes in present climate across Europe

0518993 - ÚFA 2020 AT eng A - Abstrakt
Dabhi, H. - Rotach, M. - Dubrovský, Martin
Testing a stochastic weather generator for multivariate climate extremes in present climate across Europe.
IMC2019: abstracts. Innsbruck: Universität Innsbruck, 2019.
[International Mountain Conference 2019. 08.09.2019-12.09.2019, Innsbruck]
Institucionální podpora: RVO:68378289
Klíčová slova: climate extremes * climate model * stochastic weather generator * Europe climate
Obor OECD: Climatic research
https://www.uibk.ac.at/congress/imc2019/program/1-1-a.html.en

Climate change information required for impact modeling is of much finer spatial and temporal
scale than the climate models can provide. The highest spatial resolution provided by climate
models for Europe is 12.5 km while impact models require 100 m or less. Downscaling is a method
used to fill this gap. Among various approches available for downscaling, stochastic weather generators
have been widely used for impact anaysis in various fields such as agriculture, hydrology,
economics etc. Besides their widespread use, their potential to simulate extreme climate events
is largely unexplored. Extreme events like heat waves, droughts and wildfires often occur from
processes involving more than one weather variables. These kinds of events are called multivariate
exremes. The aim of this study is to evaluate the performance of a Richardson type 6 variate (precipitation,
minimum temperature, maximum temperature, solar radiation, relative humidity and
windspeed) single-site weather generator (SiSi) to simulate multivariate extremes. We evaluate the
weather generator at various sites in a mountainous catchment in the Austrian Alps. In addition
to that, we also include sites from different parts of Europe having varying climates, topography
and proximity. Results show that SiSi is able to simulate multivariate extremes generally well at
all sites. Among all extreme events, the weather generator has a tendency to underestimate the
extremes related to minimum temperature. The performance of SiSi doesn’t depend on the climate
type of a region or the elevation of a location. We conclude that the weather generator needs to be
trained at individual location which may require making different adjustments for each variable.
Trvalý link: http://hdl.handle.net/11104/0303990