Abstract/Summary

Downward counterfactual analysis – or quantitatively estimating how our observed history could have been worse – is increasingly being used by the re/insurance industry to identify, quantify, and mitigate against as-yet-unrealised “grey-swan” catastrophic events. While useful for informing site-specific adaptation strategies, the extraction of probabilistic information remains intangible from such downside-only focused analytics. We hypothesise that combined upward and downward counterfactual analysis (i.e., how history could have been either better or worse) may allow us to obtain probabilistic information from counterfactual research if it can be applied objectively and without bias. Here we test this concept of objective counterfactual analysis by investigating how initial-condition-driven track variability of events in our North Atlantic Hurricane (NAHU) record may affect present-day probabilistic views of US landfall risk. To do this, we create 10,000 counterfactual NAHU histories from NCEP GEFS v2 initial-condition ensemble reforecast data for the period 1985-2016 and compare the statistics of these counterfactual histories to a model-based version of our single observational history. While the methodology presented herein attempts to produce the histories as objectively as possible, there is clear – and, ultimately, intuitively understandable – systematic underprediction of US NAHU landfall frequency in the counterfactual histories. This limits the ability to use the data in real-world applications at present. However, even with this systematic under-prediction, it is interesting to note both the magnitude of volatility and spatial variability in hurricane landfalls in single cities and wider regions along the US coastline, which speaks to the potential value of objective counterfactual analysis once methods have evolved.