Uncertainty quantification for a climatology of the frequency and spatial distribution of North Atlantic tropical cyclone landfalls

Journal of Advances in Modeling Earth Systems

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关键词: Bayesian hierarchical model;    tropical cyclone landfall;    uncertainty quantification;    geostatistics;    spatial modeling;    climatology;   

DOI  :  10.1002/2014MS000407

来源: Wiley

【 摘 要 】

Abstract

A spatially resolved climatology for the annual frequency of tropical cyclone (TC) landfalls along the Atlantic coast of North America is developed, and its uncertainty deriving from multiple sources is quantified. Historical landfall counts in piecewise-linear segments approximating the coastline are modeled using Poisson regression with spatial random effects. Predictors include index representations of the mean hurricane-season phases of the Southern Oscillation, the Atlantic Multidecadal Oscillation, and the North Atlantic Oscillation, with the effect of the latter also modeled spatially. This spatial generalized linear model for landfall frequency is used in conjunction with a data level accounting explicitly for the time-dependent uncertainty in the recorded landfall positions. The model performs skillfully in cross-validation exercises. The inferred effects of the climatic predictors are also consistent with current scientific understanding of the mechanisms through which related large-scale climatic variability affects the development and motion of Atlantic tropical cyclones. Sampling variability in the data over the short length of the observational record and observational error in the historical data are found to contribute substantially to the overall climatological uncertainty. The contribution from uncertainty in the underlying model parameters is negligible compared to these other sources. The model presented here could be used for applications in insurance and risk management, and adaptations could also be used to investigate changes in TC landfall climatology under an uncertain and changing climate.

【 授权许可】

CC BY-NC-ND   
© 2015. The Authors.

Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.