The VALUE perfect predictor experiment: evaluation of temporal variability

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378289%3A_____%2F19%3A00478292" target="_blank" >RIV/68378289:_____/19:00478292 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11310/19:10398771

Result on the web

<a href="https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5222" target="_blank" >https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.5222</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/joc.5222" target="_blank" >10.1002/joc.5222</a>

Result language

angličtina

Original language name

The VALUE perfect predictor experiment: evaluation of temporal variability

Original language description

Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short-term variations, spells and variability from interannual to long-term trends. The EU-COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long-term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10510 - Climatic research

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

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

Name of the periodical

International Journal of Climatology

ISSN

0899-8418

e-ISSN

—

Volume of the periodical

39

Issue of the periodical within the volume

9 Special Issue

Country of publishing house

GB - UNITED KINGDOM

Number of pages

33

Pages from-to

3786-3818

UT code for WoS article

000474001900007

EID of the result in the Scopus database

2-s2.0-85068558152