Variability of North Atlantic hurricanes: seasonal versus individual-event features

TL;DR

This paper investigates the variability of North Atlantic hurricanes by analyzing individual storm features, especially the power dissipation index (PDI), revealing a power-law distribution and the influence of sea surface temperature on extreme events.

Contribution

It introduces a novel approach focusing on single-event characteristics like PDI and models their distribution with a gamma function, highlighting the impact of sea surface temperature on extreme storm activity.

Findings

PDI follows a power-law distribution for individual hurricanes.

The gamma distribution effectively models the entire PDI range.

A 0.49°C increase in sea surface temperature correlates with a 50% rise in extreme PDI events.

Abstract

Tropical cyclones are affected by a large number of climatic factors, which translates into complex patterns of occurrence. The variability of annual metrics of tropical-cyclone activity has been intensively studied, in particular since the sudden activation of the N Atl in the mid 1990's. We provide first a swift overview on previous work by diverse authors about these annual metrics for the NAtl basin, where the natural variability of the phenomenon, the existence of trends, the drawbacks of the records, and the influence of global warming have been the subject of interesting debates. Next, we present an alternative approach that does not focus on seasonal features but on the characteristics of single events [Corral et al Nature Phys 6, 693, 2010]. It is argued that the individual-storm power dissipation index (PDI) constitutes a natural way to describe each event, and further, that the PDI statistics yields a robust law for the occurrence of tropical cyclones in terms of a power law. In this context, methods of fitting these distributions are discussed. As an important extension to this work we introduce a distribution function that models the whole range of the PDI density (excluding incompleteness effects at the smallest values), the gamma distribution, consisting in a power-law with an exponential decay at the tail. The characteristic scale of this decay, represented by the cutoff parameter, provides very valuable information on the finiteness size of the basin, via the largest values of the PDIs that the basin can sustain. We use the gamma fit to evaluate the influence of sea surface temperature (SST) on the occurrence of extreme PDI values, for which we find an increase around 50 % in the values of these basin-wide events for a 0.49 degC SST average difference. ...