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Recent hurricane losses in the New York Metropolitan area demonstrate its vulnerability to flood hazards. Long-term development and planning require predictions of low-probability high-consequence storm surge levels that account for climate change impacts. This requires simulating thousands of synthetic storms under a specific climate change scenario which requires high computational power. To alleviate this burden, we developed a machine learning-based predictive model. The training data set was generated using a high-fidelity hydrodynamic model including the effect of wind-generated waves. The machine learning model is then used to predict and compare storm surges over historical (1980–2000) and future (2080–2100) periods, considering the Representative Concentration Pathway 8.5 scenario. Our analysis encompassed 57 locations along the New York and New Jersey coastlines. The results show an increase along the southern coastline of New Jersey and inside Jamaica, Raritan, and Sandy Hook bays, while a decrease along the Long Island coastline and inland bays. [ABSTRACT FROM AUTHOR]

The authors define a spatio-statistical response of hurricane frequency to the solar cycle. Previous research indicates reduced (increased) hurricane intensities and frequency in the western (eastern) tropical Atlantic. However, no formal quantitative relationship has been spatially established between hurricane frequency and solar activity. The authors use a Bayesian hierarchical space-time model, an increasingly popular approach due to its advantage in facilitating regression modeling of space-time phenomena in the context of large data sets. Regional hurricane frequency over the period 1866-2010 is examined in response to September sunspot number (SSN) while controlling for other relevant climate factors. The response features a 13 % reduction in probability of annual hurricane occurrence for southeastern Cuba, the southern Bahama islands, Haiti, and Jamaica when the SSN is 80 sunspots. In contrast, hurricane risk in regions of the southeastern Atlantic is predicted to increase by 73 % when the SSN is 160 sunspots. The model can be ported to explore other relationships over contiguous space. [ABSTRACT FROM AUTHOR]