Abstract
Abstract. Atlantic hurricane activity varies substantially from year to year and so does the associated damage. Longer-term forecasting of hurricane risks is a key element to reduce damage 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 interpretable late-spring precursors of seasonal Atlantic hurricane activity. Based on these precursors we construct statistical seasonal forecast models with competitive skill compared to operational forecasts. In particular, we present a skilful prediction model to forecast July to October tropical cyclone activity at the beginning of April. Our approach highlights the potential of applying causal effect network analysis to identify sources of predictability on seasonal timescales.
Highlights
Tropical cyclones (TCs) are among the most damaging weather events in many tropical and subtropical regions (Munich Re, 2020)
We identify the regions in the tropical Atlantic that are correlated with accumulated cyclone energy (ACE) in our target region during the hurricane season (July to October)
We identify robust precursors amongst all potential precursor regions identified in step 2 by constructing a causal effect network (CEN) using the so-called PCMCI algorithm
Summary
Tropical cyclones (TCs) are among the most damaging weather events in many tropical and subtropical regions (Munich Re, 2020). Applying risk reduction measures to the direct damage of TCs is challenging and is expected to become even more so with global warming and sea level rise (Woodruff et al, 2013). Preparedness for the secondary impacts could, be improved if reliable forecasts of the potential risks of the upcoming hurricane season are available (Murphy et al, 2001). Several academic institutes provide seasonal hurricane forecasts for the Atlantic basin (Klotzbach et al, 2019). A variety of forecasting methods have been applied, ranging from purely statistical forecasts to forecasts based on numerical global climate model simulations and hybrid approaches (Klotzbach et al, 2019, 2017). The Barcelona Super Computing Center collects and publishes seasonal forecasts from universities, private entities and government agencies each year (https://seasonalhurricanepredictions.bsc.es/ predictions, last access: 1 July 2020)
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