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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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.