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.

Basic information

Original name

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

Authors

HOLTANOVÁ, Eva (203 Czech Republic); Thomas MENDLIK (40 Austria); Jan KOLÁČEK (203 Czech Republic, guarantor, belonging to the institution); Ivanka HOROVÁ (203 Czech Republic, belonging to the institution) and Jiří MIKŠOVSKÝ (203 Czech Republic)

Edition

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

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

10103 Statistics and probability

Country of publisher

Germany

Confidentiality degree

is not subject to a state or trade secret

References:

Full Text

Impact factor

Impact factor: 5.240

RIV identification code

RIV/00216224:14310/19:00109164

Organization unit

Faculty of Science

DOI

https://doi.org/10.5194/gmd-12-735-2019

UT WoS

000459175500001

EID Scopus

2-s2.0-85062036906

Keywords in English

global climate model; regional climate model; functional data analysis

Abstract

In the original language

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.

Links

MUNI/A/1204/2017, interní kód MU

Name: Matematické statistické modelování 2 (Acronym: MaStaMo2)

Investor: Masaryk University, Category A

Cite

HOLTANOVÁ, Eva; Thomas MENDLIK; Jan KOLÁČEK; Ivanka HOROVÁ and Jiří MIKŠOVSKÝ. Similarities within a multi-model ensemble: functional data analysis framework. Geoscientific Model Development. Göttingen: Copernicus Publications, 2019, vol. 12, No 2, p. 735-747. ISSN 1991-959X. Available from: https://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 = {https://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 and Jiří MIKŠOVSKÝ. Similarities within a multi-model ensemble: functional data analysis framework. \textit{Geoscientific Model Development}. Göttingen: Copernicus Publications, 2019, vol.~12, No~2, p.~735-747. ISSN~1991-959X. Available from: https://doi.org/10.5194/gmd-12-735-2019.

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