A Globally Consistent Reanalysis Of Hurricane Variability And Trends - AGU

[hatrack]

J. P. Kossin

Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison,
Madison, Wisconsin, USA

K. R. Knapp

National Climatic Data Center, NOAA,
Asheville, North Carolina, USA

D. J. Vimont

Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison,
Madison, Wisconsin, USA

R. J. Murnane

Risk Prediction Initiative, Bermuda Institute of Ocean Sciences,
Garrett Park, Maryland, USA

B. A. Harper

Systems Engineering Australia Pty. Ltd.,
Bridgeman Downs, Queensland, Australia


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1. Introduction
<2> The relationship between global warming and trends in hurricane activity is presently a topic of active research and debate, and much of the debate is rooted in questions about the suitability of the hurricane records that have been used to identify these trends . These “best track” records comprise global historical measures of hurricane position and intensity. Intensity is defined in terms of sustained surface wind speed, although the details of this definition can vary according to the protocols of individual forecast offices. Teams of forecasters update the best track data at the end of the hurricane season in each ocean basin using data collected during and after each hurricane's lifetime (tropical cyclones are known by different names in the various ocean basins, but here we are using the term “hurricane” in a generic sense). The variability of the available data combined with long time-scale changes in the availability and quality of observing systems, reporting policies, and the methods utilized to analyze the data make the best track records inhomogeneous by construction. Temporal consistency is sacrificed in favor of best possible absolute accuracy at every period during the lifetime of each hurricane.

<3> After the advent of global monitoring with geostationary satellites in the mid to late 1970's, metrics related to hurricane frequency are generally considered accurate, but the known lack of homogeneity in both the data and techniques applied in the post-analyses has resulted in skepticism regarding the consistency of the best track intensity estimates. As a first step toward addressing this shortcoming, we constructed a more homogeneous data record of hurricane intensity by first creating a new consistently analyzed global satellite data archive from 1983 to 2005 and then applying a new objective algorithm to the satellite data to form hurricane intensity estimates. Our new homogeneous record of hurricane intensity is denoted as the UW/NCDC (University of Wisconsin-Madison/National Climatic Data Center) record. Where the best track records sacrifice consistency in lieu of best possible absolute accuracy, our new record sacrifices best possible absolute accuracy for temporal consistency. It is important then to note that the UW/NCDC record serves as a complement to the best track, and not as a replacement.

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<8> The algorithm was cross-validated using a jackknife procedure: each storm was individually removed from the full training sample and EOF analysis was performed on the sub-sample of remaining storms. The regression was then trained on the sub-sample and tested on the storm that was left out. This was done for all storms in the sample and the cumulative errors were tallied. The independently-derived error distribution is shown in Figure 1 and demonstrates reasonable skill of the algorithm. Figure 1 can be compared directly to Figure 8 of Velden et al. <2006>, which is based on a very similar error analysis of operational Dvorak technique estimates from the National Hurricane Center . They compared Dvorak intensity estimates (from the period 1997–2003) with best track intensity that were contemporaneous with aircraft reconnaissance, and found that 90% of their (absolute) errors were less than 9 m s−1, 75% were less than 6 m s−1, and 50% were less than 3 m s−1. In comparison, 90% of the absolute errors of our algorithm were less than 12 m s−1, 75% were less than 8 m s−1, and 50% were less than 4 m s−1. Overall Root Mean Square (RMS) error was 6 m s−1 for the Dvorak estimates compared with 9 m s−1 using our algorithm.

<9> A physical explanation for the observed intensity trends in the best track has been posited using connections between upward trending tropical SST and maximum potential intensity (MPI) theory . Stated simply, the argument is that while there is no direct contemporaneous correlation between local SST and hurricane intensity (a hurricane can routinely spend its entire intensity evolution over relatively constant SST), an increase of SST does increase the maximum potential intensity, and over long enough time-scales this should be reflected by an increase on the extreme end of the hurricane intensity spectrum. To uncover this relationship, Emanuel <2005> used a Power Dissipation Index (PDI), which considers the cube of the maximum wind speed and thus accentuates the strongest cases, and Webster et al. <2005> considered the frequency of the most extreme intensities (Saffir-Simpson categories 4 and 5). It is important then that our algorithm capture maximum intensities well. Using the aircraft reconnaissance-based data introduced above, we compared the maximum intensity achieved by each individual storm to the maximum intensity estimated by our algorithm. The errors were normally distributed (bias = 0.005 m s−1, skewness = 0.34, and RMS error = 8.5 m s−1). The seasonally averaged maximum intensities have very small errors (RMS error = 2.7 m s−1) and the seasonal-mean time series of estimated maximum intensity correlates very strongly with the reconnaissance-based time series (r = 0.99). However, closer scrutiny revealed the potential for a problem — the algorithm does tend to under-estimate the strongest intensities. This result represents a potential weakness in the method, but we will provide countering evidence for the fidelity of the algorithm in the following section.

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[phantom power]

1. They appeared to conclude their algorithm has good fidelity.

Did they arrive at any conclusions regarding correlations between SST and maximum storm intensities?

[hatrack]

2. Definitely there, definitely positive

More to come - I'm writing in some haste.