Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-22T18:17:00.938Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  17 January 2023

Xavier-Andoni Tibau Open the ORCID record for Xavier-Andoni Tibau [Opens in a new window] ,
Christian Reimers ,
Andreas Gerhardus ,
Joachim Denzler ,
Veronika Eyring  and
Jakob Runge

Abstract

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

Keywords

Causal algorithmcausal discoveryclimate modelteleconnections
Type
Corrigendum
Information
Environmental Data Science , Volume 2 , 2023 , e4
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

The funding statement in the original article did not contain all relevant grants received by the authors. On publication, the funding statement read:

“J.R. has received funding from the European Research Council (ERC) Starting Grant CausalEarth under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 948112).”

The funding statement in the HTML and PDF versions of this article has now been updated to include the full details of grants received by the authors. It now reads:

“J.R. has received funding from the European Research Council (ERC) Starting Grant CausalEarth under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 948112). V.E. has received funding from the ERC Synergy Grant “Understanding and modeling the Earth System with Machine Learning (USMILE)” under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 855187).”

The author affiliations also contained some errors on publication. The first affiliation of author Veronika Eyring [2] contained a tilde rather than an umlaut above the word Atmosphäre. It read:

Institut für Physik der Atmosphãre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany

It has been corrected to read:

Deutsches Zentrum für Luft‐ und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

In addition, the author affiliations should all read with institution first rather than centre or department. The complete list of author affiliations is as follows:

Xavier-Andoni Tibau1,2,* , Christian Reimers1,3, Andreas Gerhardus1, Joachim Denzler1,3, Veronika Eyring2,4 and Jakob Runge1,5

1. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany

2. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

3. Friedrich Schiller University Jena, Computer Vision Group, Jena, Germany

4. University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany

5. Technische Universität Berlin, Faculty of Electrical Engineering and Computer Science, Berlin, Germany

*Corresponding author. E-mail:

We issue this corrigendum in order to be transparent about changes to the scholarly record. The article has been updated.

References

Tibau, X, Reimers, C, Gerhardus, A, Denzler, J, Eyring, V and Runge, J (2022). A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections. Environmental Data Science, 1, E12. doi:10.1017/eds.2022.11CrossRefGoogle Scholar