Robust predictors for seasonal Atlantic hurricane activity identified with causal effect networks

Pfleiderer, Peter; Schleussner, Carl-Friedrich; Geiger, Tobias; Kretschmer, Marlene

doi:https://doi.org/10.5194/wcd-1-313-2020

Articles | Volume 1, issue 2

https://doi.org/10.5194/wcd-1-313-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wcd-1-313-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 1, issue 2

Research article

|

13 Jul 2020

Research article |

| 13 Jul 2020

Robust predictors for seasonal Atlantic hurricane activity identified with causal effect networks

Peter Pfleiderer, Carl-Friedrich Schleussner, Tobias Geiger, and Marlene Kretschmer

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Final revised paper (published on 13 Jul 2020)
Supplement to the final revised paper
Preprint (discussion started on 13 Mar 2020)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'reviewer comments', Anonymous Referee #1, 08 Apr 2020
- AC1: 'Response to Anonymous Referee #1', Peter Pfleiderer, 28 May 2020
RC2: 'Review', Anonymous Referee #2, 09 Apr 2020
- AC2: 'Response to Anonymous Referee #2', Peter Pfleiderer, 28 May 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Peter Pfleiderer on behalf of the Authors (28 May 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (17 Jun 2020) by Silvio Davolio

RR by Anonymous Referee #2 (18 Jun 2020)

RR by Anonymous Referee #1 (25 Jun 2020)

ED: Publish subject to technical corrections (26 Jun 2020) by Silvio Davolio

AR by Peter Pfleiderer on behalf of the Authors (01 Jul 2020) Author's response Manuscript

Short summary

Seasonal outlooks of Atlantic hurricane activity are required to enable risk reduction measures and disaster preparedness. Many seasonal forecasts are based on a selection of climate signals from which a statistical model is constructed. The crucial step in this approach is to select the most relevant predictors without overfitting. Here we show that causal effect networks can be used to identify the most robust predictors. Based on these predictors we construct a competitive forecast model.