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Weather and Climate Dynamics An interactive open-access journal of the European Geosciences Union
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Abstracted/indexed

Abstracted/indexed
Preprints
https://doi.org/10.5194/wcd-2020-7
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-2020-7
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 13 Mar 2020

Submitted as: research article | 13 Mar 2020

Review status
A revised version of this preprint was accepted for the journal WCD and is expected to appear here in due course.

Robust Predictors for Seasonal Atlantic Hurricane Activity Identified with Causal Effect Networks

Peter Pfleiderer1,2,3, Carl-Friedrich Schleussner1,2,3, Tobias Geiger3,4, and Marlene Kretschmer5 Peter Pfleiderer et al.
  • 1Climate Analytics, Berlin, Germany
  • 2IRI THESys, Humboldt Universität Berlin, Berlin, Germany
  • 3Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 4German Meteorological Service, Climate and Environment Consultancy, Stahnsdorf, Germany
  • 5University of Reading, Department of Meteorology, Reading, UK

Abstract. Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April. Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting.

Peter Pfleiderer et al.

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peter Pfleiderer et al.

Model code and software

atlantic_ace_seasonal_forecast P. Pfleiderer https://doi.org/10.5281/zenodo.3708313

Peter Pfleiderer et al.

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Latest update: 07 Jul 2020
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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.
Seasonal outlooks of Atlantic hurricane activity are required to enable risk reduction measures...
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