United States hurricane landfalls and damages: Can one- to five-year predictions beat climatology?

Abstract

This paper asks whether one-to five-year predictions of United States hurricane landfalls and damages improve upon a baseline expectation derived from the climatological record. The paper argues that the large diversity of available predictions means that some predictions will improve upon climatology, but for decades if not longer it will be impossible to know whether these improvements were due to chance or actual skill. A review of efforts to predict hurricane landfalls and damage on timescales of one to five years does not lend much optimism to such efforts in any case. For decision makers, the recommendation is to use climatology as a baseline expectation and to clearly identify hedges away from this baseline, in order to clearly distinguish empirical from non-empirical justifications for judgements of risk.

Historical Global Tropical Cyclone Landfalls*

“…Thus, our quantitative analysis of global hurricane landfalls is consistent with previous research focused on normalized losses associated with hurricanes that have found no trends once data are properly adjusted for societal factors (e.g., Pielke et al 2008;Crompton and McAneney 2008;Neumayer and Barthel 2011;Barthel and Neumayer 2012;Bouwer 2011;Raghavan and Rajesh 2003). …”

“…The active North Atlantic tropical cyclone (TC) seasons of 2004 and 2005 coupled with their considerable social and economic impacts of several major hurricane landfalls precipitated a spirited scientific debate on the implications of changing climate conditions on TC behavior (Emanuel 2005a,b;Pielke 2005;Trenberth 2005;Webster et al 2005). The current consensus is that an anthropogenic signal in historical TC activity metrics cannot be conclusively identified independent of historically documented variability (Knutson et al 2010).…”

A Regional Compound Poisson Process for Hurricane and Tropical Storm Damage

“…When fit to historical data, the proposed model reveals several interesting insights, some new and others confirming previous findings. For storm count, our analysis shows that colder SST anomalies are linked with higher storm counts (as noted in Pielke andLandsea, 1998 andKatz, 2002), and provides evidence for varying seasonal patterns by ENSO (as mentioned in Lu and Zeng, 2012). For storm path, a common spatial pattern is observed over all ENSO phases, with a noticeable higher tendency for El Niño and La Niña storms to hit northern locations.…”

“…El Niño -Southern Oscillation (ENSO) phases, which categorize sea surface temperatures (SST) in the tropic Pacific Ocean, can also have a notable impact on HTS behaviour (e.g., Pielke and Landsea, 1998;Camargo et al, 2007;Pielke, 2009), and should therefore be included as covariates for prediction. Cabras et al (2011) explored models with various ENSO and seasonal effects for both storm count and damage, and found that, while ENSO and seasonality have a noticeable impact on counts, only ENSO effects appear to impact damage.…”

Climatology in support of climate risk management

“…While acknowledging that modelling is an important tool for hurricane risk assessment, Levinosn et al (2010) address some of the issues that may exacerbate sampling problems for accurate characterization of hurricane parameters for design and operational applications and Vickery et al (2009c) discuss the uncertainty associated with hurricane wind speeds estimates for the US. Harper et al (2012) and Cook and Nicholls (2012) also touch on the way in which assumptions about hurricane dynamics can influence hurricane wind hazard estimation while Pielke (2009) compares the value of climatology over predictions in relation to risk estimation. Notwithstanding some of the assumptions associated with hurricane risk assessment, a range of studies report on risk assessment methods or risk estimates from numerical or statistical based analyses of hurricane related hazards including hurricane related storm surge in New York (Lin et al 2010) and risk to offshore wind turbines (Rose, 2012), US air force bases (Scheitlin et al, 2011) and the built environment in Florida (Hamid et al, 2011).…”