0542977 - ÚFA 2022 DE eng A - Abstrakt
Dubrovský, Martin - Lhotka, Ondřej - Mikšovský, Jiří - Štěpánek, Petr - Meitner, Jan
SPAGETTA Weather Generator Linked with Climate Models May Produce Weather Series for Future Climate.
EGU General Assembly 2021 (vEGU21: Gather Online). Göttingen: European Geosciences Union, 2021.
[EGU General Assembly Conference 2021. 19.04.2021-30.04.2021, online]
Grant CEP: GA ČR(CZ) GA18-15958S
Institucionální podpora: RVO:68378289 ; RVO:86652079
Klíčová slova: climate change * stochastic weather generator SPAGETTA * climate model
Obor OECD: Climatic research; Climatic research (UEK-B)
Web výsledku:
https://meetingorganizer.copernicus.org/EGU21/EGU21-12704.htmlDOI: https://doi.org/10.5194/egusphere-egu21-12704

Stochastic weather generators (WGs) are tools for producing weather series, which are statistically
similar to the real world weather series. The synthetic series may represent both present and
changed (not only the future) climate. In the latter case, WG parameters derived from the
observed weather series are modified with climate change scenario, which is typically based on
RCM or GCM simulations. As the GCM/RCM simulations are very demanding on computer
resources, the numbers of simulations made for individual possible emission scenarios are
limited, especially for some (mostly the less probable ones) emission scenarios (e.g. RCP 2.6). Still,
many climate change impact studies try to give projections of the CC impacts assuming
uncertainties coming from all possible sources, including the modeling uncertainty and
uncertainties in emissions & climate sensitivity. To allow generation of weather series fitting the
projection of any GCM forced by any emission scenario, we use a pattern scaling approach, in
which the standardized climate change scenario (consisting of changes in climatic characteristic
related to 1ºC change in global mean temperature) derived from a given GCM is multiplied by a
change in global mean temperature (dTg) projected (for a selected emission scenario and climate
sensitivity) by a simple climate model MAGICC.
In our contribution, we will demonstrate the use of the generator (using SPAGETTA WG, which is
our multi-site multi-variate parametric daily WG) in probabilistic projection of future changes in
selected climatic characteristics of temperature (T) and precipitation (P); we will focus on spatial
hot/cold/dry/wet/hot-dry/hot-wet/cold-dry/cold-wet spells). Standardized climate change scenarios
will be derived from multiple GCMs (taken from CMIP5 database) and scaled by dTg projected by
MAGICC. Effects of the three above-named sources of uncertainty, as well as the effects of changes
in individual statistical characteristics (the means & the site-specific variabilities & the
characteristics of the temporal and spatial variability of both T and P) will be assessed.
Trvalý link: http://hdl.handle.net/11104/0320296