Similarities within a multi-model ensemble: functional data
analysis framework

J 2019

Similarities within a multi-model ensemble: functional data analysis framework

HOLTANOVÁ, Eva, Thomas MENDLIK, Jan KOLÁČEK, Ivanka HOROVÁ, Jiří MIKŠOVSKÝ et. al.

Základní údaje

Originální název

Similarities within a multi-model ensemble: functional data analysis framework

Autoři

HOLTANOVÁ, Eva (203 Česká republika), Thomas MENDLIK (40 Rakousko), Jan KOLÁČEK (203 Česká republika, garant, domácí), Ivanka HOROVÁ (203 Česká republika, domácí) a Jiří MIKŠOVSKÝ (203 Česká republika)

Vydání

Geoscientific Model Development, Göttingen, Copernicus Publications, 2019, 1991-959X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Full Text

Impakt faktor

Impact factor: 5.240

Kód RIV

RIV/00216224:14310/19:00109164

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.5194/gmd-12-735-2019

UT WoS

000459175500001

Klíčová slova anglicky

global climate model; regional climate model; functional data analysis

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 3. 2020 15:42, Mgr. Marie Šípková, DiS.

Anotace

V originále

Despite the abundance of available global climate model (GCM) and regional climate model (RCM) outputs, their use for evaluation of past and future climate change is often complicated by substantial differences between individual simulations and the resulting uncertainties. In this study, we present a methodological framework for the analysis of multi-model ensembles based on a functional data analysis approach. A set of two metrics that generalize the concept of similarity based on the behavior of entire simulated climatic time series, encompassing both past and future periods, is introduced. To our knowledge, our method is the first to quantitatively assess similarities between model simulations based on the temporal evolution of simulated values. To evaluate mutual distances of the time series, we used two semimetrics based on Euclidean distances between the simulated trajectories and based on differences in their first derivatives. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm. Using the layout graphs, the data are ordered on a two-dimensional plane which enables an unambiguous interpretation of the results. The method is demonstrated using two illustrative cases of air temperature over the British Isles (BI) and precipitation in central Europe, simulated by an ensemble of EURO-CORDEX RCMs and their driving GCMs over the 1971–2098 period. In addition to the sample results, interpretational aspects of the applied methodology and its possible extensions are also discussed.

Návaznosti

MUNI/A/1204/2017, interní kód MU

Název: Matematické statistické modelování 2 (Akronym: MaStaMo2)

Investor: Masarykova univerzita, Matematické statistické modelování 2, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

HOLTANOVÁ, Eva, Thomas MENDLIK, Jan KOLÁČEK, Ivanka HOROVÁ a Jiří MIKŠOVSKÝ. Similarities within a multi-model ensemble: functional data analysis framework. Geoscientific Model Development. Göttingen: Copernicus Publications, 2019, roč. 12, č. 2, s. 735-747. ISSN 1991-959X. Dostupné z: https://dx.doi.org/10.5194/gmd-12-735-2019.

@article{1498536,
   author = {Holtanová, Eva and Mendlik, Thomas and Koláček, Jan and Horová, Ivanka and Mikšovský, Jiří},
   article_location = {Göttingen},
   article_number = {2},
   doi = {http://dx.doi.org/10.5194/gmd-12-735-2019},
   keywords = {global climate model; regional climate model; functional data analysis},
   language = {eng},
   issn = {1991-959X},
   journal = {Geoscientific Model Development},
   title = {Similarities within a multi-model ensemble: functional data analysis framework},
   url = {https://www.geosci-model-dev.net/12/735/2019/gmd-12-735-2019.html},
   volume = {12},
   year = {2019}
}

TY  - JOUR
ID  - 1498536
AU  - Holtanová, Eva - Mendlik, Thomas - Koláček, Jan - Horová, Ivanka - Mikšovský, Jiří
PY  - 2019
TI  - Similarities within a multi-model ensemble: functional data analysis framework
JF  - Geoscientific Model Development
VL  - 12
IS  - 2
SP  - 735-747
EP  - 735-747
PB  - Copernicus Publications
SN  - 1991959X
KW  - global climate model
KW  - regional climate model
KW  - functional data analysis
UR  - https://www.geosci-model-dev.net/12/735/2019/gmd-12-735-2019.html
L2  - https://www.geosci-model-dev.net/12/735/2019/gmd-12-735-2019.html
N2  - Despite the abundance of available global climate model (GCM) and regional climate model (RCM) outputs, their use for evaluation of past and future climate change is often complicated by substantial differences between individual simulations and the resulting uncertainties. In this study, we present a methodological framework for the analysis of multi-model ensembles based on a functional data analysis approach. A set of two metrics that generalize the concept of similarity based on the behavior of entire simulated climatic time series, encompassing both past and future periods, is introduced. To our knowledge, our method is the first to quantitatively assess similarities between model simulations based on the temporal evolution of simulated values. To evaluate mutual distances of the time series, we used two semimetrics based on Euclidean distances between the simulated trajectories and based on differences in their first derivatives. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm. Using the layout graphs, the data are ordered on a two-dimensional plane which enables an unambiguous interpretation of the results. The method is demonstrated using two illustrative cases of air temperature over the British Isles (BI) and precipitation in central Europe, simulated by an ensemble of EURO-CORDEX RCMs and their driving GCMs over the 1971–2098 period. In addition to the sample results, interpretational aspects of the applied methodology and its possible extensions are also discussed.
ER  -

HOLTANOVÁ, Eva, Thomas MENDLIK, Jan KOLÁČEK, Ivanka HOROVÁ a Jiří MIKŠOVSKÝ. Similarities within a multi-model ensemble: functional data analysis framework. \textit{Geoscientific Model Development}. Göttingen: Copernicus Publications, 2019, roč.~12, č.~2, s.~735-747. ISSN~1991-959X. Dostupné z: https://dx.doi.org/10.5194/gmd-12-735-2019.

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