I will hazard (!) a guess that most of that 90% is explained, if at all, by economic and political factors — not climate. Economic damages from Katrina appear to have resulted mostly from ancillary phenomena, such as the peculiar political cultures of the U.S Congress (earmarking in lieu of benefit-cost analysis to allocate ACE resources) and New Orleans (’nuff said).

I also want to admit that if a MJ SST and an ASO SST both came to the door, I’d just pay for the pizza. There are a lot more policy levers iurking in the unexplained variance.

I will hazard (!) a guess that most of that 90% is explained, if at all, by economic and political factors — not climate. Economic damages from Katrina appear to have resulted mostly from ancillary phenomena, such as the peculiar political cultures of the U.S Congress (earmarking in lieu of benefit-cost analysis to allocate ACE resources) and New Orleans (’nuff said).

I also want to admit that if a MJ SST and an ASO SST both came to the door, I’d just pay for the pizza. If higher SSTs cause more intense storms (my boneheaded economist’s interpretation of Elsner’s posts), averaging over time and space (whether MJ or ASO) seems to me to be an inefficient way to discover it. If I were July, I’d sue for discrimination.

Interestingly, knocking out 1958 increases the r2 to 0.1544 even better than omitting 2006 (0.1309). Omitting 2005 gives an r2 of 0.0323, but most values are between 0.06 and 0.09. The mean r2, although I’m not at all sure that it means anything, is 0.0857.

The values follow:

Year Damage MJ LN(Damage) r2 omitting year
2006 $250,000,000 26.88 19.33697148 0.1309
2005 107,350,000,000 27.62 25.39936036 0.0323
2004 48,985,385,716 26.69 24.61478784 0.0779
2003 3,966,169,543 26.38 22.10106662 0.0846
2002 1,055,578,444 26.31 20.77735474 0.0827
1999 7,930,494,729 26.44 22.79398126 0.085
1998 4,937,282,449 27.27 22.32008091 0.0925
1996 6,313,192,709 26.71 22.56590736 0.0841
1995 7,444,021,043 26.91 22.730677 0.0829
1992 57,663,865,630 26.38 24.77789657 0.0946
1991 3,044,037,453 26.1 21.83645058 0.0828
1989 15,322,273,457 25.95 23.45257339 0.1023
1985 10,822,277,643 26 23.10487259 0.0959
1984 285,333,505 26.12 19.46916925 0.0733
1983 7,469,100,008 26.98 22.73404035 0.083
1980 1,602,040,183 27.05 21.19454377 0.1064
1979 12,533,467,223 26.87 23.25166828 0.0787
1976 486,444,597 25.82 20.00263357 0.0631
1975 2,791,286,883 25.75 21.74976857 0.082
1974 970,296,296 25.53 20.69311204 0.0644
1972 17,540,611,499 26.1 23.58778469 0.098
1971 593,886,695 25.94 20.20219911 0.0688
1970 5,627,670,656 26.75 22.45096146 0.0848
1969 21,225,180,492 26.99 23.77845407 0.0725
1968 592,857,495 26.21 20.20046462 0.0788
1967 4,016,468,362 26.21 22.11366884 0.0845
1965 20,710,396,948 26.12 23.75390168 0.099
1964 15,675,871,032 26.44 23.47538849 0.0862
1961 14,209,129,737 26.24 23.37715053 0.0913
1960 29,619,654,069 26.6 24.11170397 0.0819
1959 691,173,599 26.09 20.35390158 0.0747
1958 509,818,712 27.3 20.04956575 0.1544
1957 3,764,307,219 26.29 22.04882968 0.0843
1956 577,494,764 26.18 20.17420993 0.0775
1955 23,274,260,349 26.39 23.87061388 0.089
1954 35,671,450,726 26.65 24.29761651 0.0799
1950 4,408,121,956 26.11 22.20671458 0.0854

Thanks for your comments. You write, “Is there such a postulate lurking here, or is this a case of a conclusion hunting for supporting data?”

I am no climatologist, but I have asked Jim about the basis for expecting MJ SSTs to be correlated with damages, but not ASO SSTs, when hurricanes form, strike, and cause damage.

Thanks!

Thanks. I accept the value of a “random sum” approach. But lets be clear, the point is to deconvolve a relationship. I don’t think that the intensity component of your “random sum” model is stable or shows a strong relationship. This doesn’t give me much faith in your overall model, much less making an overall argument about SSTs versus damage, for which I assert that the simple relationship I discussed in part 1 is indeed relevant.

We can of course agree to disagee and let readers decide which arguments they find compelling. I have posted another entry on this and highlighted your blog.

Thanks!

At issue is the fractional change in hurricane potential destruction for the fraction of hurricanes hitting our coast that is related to Atlantic SST. A regression analysis of the log of annual damage total on SST simply does not get the job done as the analysis conflates number of loss events with loss amounts. Instead, and I repeat for the 3rd time, it is much better to model the number of loss events separately from the magnitude of losses. This is exactly what we do in Jagger et al. (2007). In this way we keep all the data (even years with no hurricane damage) and it answers the question–what happens to damages if SSTs increase. Conditional on the historical data and our model, increasing SSTs will not influence the number of loss events but they will influence the magnitude of the loss given an event.

For a more detailed and technical discussion of why you are wrong about our work jump over to our blog http://hurricaneclimate.blogspot.com/

Best,
Jim

y = -4.1096 + 0.9986x
…….. (0.78)…….(0.08)
…….. [33.9]…….[0.12]

Adjusted R^2 = 0.06

The coefficient for SST is nonsignificant at p<.10. I’d be hard-pressed to argue that there was anything going on here.

I ma catching up with this thread late, but it seems to me that SST could still predict damages, but average SST doesn’t. The problem is that if we looked hard enough we could always find some version of SST that would “do the job.”

I’m a humble country economist, not a climatologist. But that means I start from the theoretical postulate before applying statistics to data. Is there such a postulate lurking here, or is this a case of a conclusion hunting for supporting data?

Thanks very much for your input. I certainly agree 100% with your view of statistical significance. But in this case recognize also that the question that is answered by Elsner’s work (Jagger et al.) is predicated on a conditional — experiencing storms of $250M or more within the season. If knowing when seasons are likely to be benign is important, then one would’t want to throw out years with little damage even though SSTs are high. This year is a good example of why that matters.

Because of this the graph does NOT answer the question — what happens to damages if SSTs increase?

SSTs are not a reliable predictor of damages. Have a look again at this graph:

http://sciencepolicy.colorado.edu/prometheus/archives/climate_change/000963what_does_the_histor.html#comments

Thanks!