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A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections

Tibau Alberdi, Xavier Andoni and Reimers, Christian and Gerhardus, Andreas and Denzler, Joachim and Eyring, Veronika and Runge, Jakob (2022) A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections. Environmental data science, 1, e12. Cambridge University Press. doi: 10.1017/eds.2022.11. ISSN 2634-4602.

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Official URL: https://www.cambridge.org/core/journals/environmental-data-science/article/spatiotemporal-stochastic-climate-model-for-benchmarking-causal-discovery-methods-for-teleconnections/0E066B8813BA2281D2B95279EF3272B4


Teleconnections that link climate processes at widely separated spatial locations form a key component of the climate system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always reveal the dynamical mechanisms between different climatological processes. More recently, causal discovery methods based either on time series at grid locations or on modes of variability, estimated through dimension-reduction methods, have been introduced. A major challenge in the development of such analysis methods is a lack of ground truth benchmark datasets that have facilitated improvements in many parts of machine learning. Here, we present a simplified stochastic climate model that outputs gridded data and represents climate modes and their teleconnections through a spatially aggregated vector-autoregressive model. The model is used to construct benchmarks and evaluate a range of analysis methods. The results highlight that the model can be successfully used to benchmark different causal discovery methods for spatiotemporal data and show their strengths and weaknesses. Furthermore, we introduce a novel causal discovery method at the grid level and demonstrate that it has orders of magnitude better performance than the current approaches. Improved causal analysis tools for spatiotemporal climate data are pivotal to advance process-based understanding and climate model evaluation.

Item URL in elib:https://elib.dlr.de/188646/
Document Type:Article
Title:A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Tibau Alberdi, Xavier AndoniUNSPECIFIEDhttps://orcid.org/0000-0002-7239-1421UNSPECIFIED
Reimers, ChristianComputer Vision Group, Friedrich-Schiller-Universität Jena, Germanyhttps://orcid.org/0000-0003-1127-136XUNSPECIFIED
Gerhardus, AndreasUNSPECIFIEDhttps://orcid.org/0000-0003-1868-655XUNSPECIFIED
Denzler, JoachimFSU Jenahttps://orcid.org/0000-0002-3193-3300UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Runge, JakobUNSPECIFIEDhttps://orcid.org/0000-0002-0629-1772UNSPECIFIED
Date:27 September 2022
Journal or Publication Title:Environmental data science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:e12
Publisher:Cambridge University Press
Keywords:Causal algorithm causal discovery climate model teleconnections
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Buildup Data Science Jena
Location: Jena
Institutes and Institutions:Institute of Data Science
Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Tibau Alberdi, Xavier Andoni
Deposited On:28 Nov 2022 14:10
Last Modified:28 Nov 2022 14:10

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