#99 (Steve Bloom) — I’m not sure about the progress of reanalysis for HURDAT, or the other sources of data for that matter. I gather from your comment (“available in easy spreadsheet format”) that this is why I’ve only used HURDAT. However, to correct errors noted below, I’ve supplemented it with data from UNISYS. However UNISYS has its own inconsistencies with HURDAT from 1911-1913 (5 missing storms?).

#115 (Judith Curry) — The 1915, data starts on September 7, and therefore misses Tropical Storm #1, Hurricane #2, and most of Hurricane #3 (82% by energy). Both hurricanes were category 3.

For 1926, I corrected the storm number for Hurricane #5 (originally listed as #4, so there was one long #4 over two time sequences). For 1972, I corrected the day on Sept 8 for Hurricane Dawn (originally listed as Sept 0). There were other corrections to pressure and location which wouldn’t affect my analysis. I update my data for post-2004 storms using the National Hurricane Center reports (TCR).

Bender, I agree. I guess there are two possibilities that spring to mind:

1. Unknown linear forcings that operate on long timescales

- or -

2. Coupled non-linear response to linear forcings even if we do know about them, producing a time series with fractal properties as an output

In either case, a 1/f null hypothesis would be appropriate (fractional gaussian noise?). I haven’t read through it in detail, but a cursory glance would suggest Professor Liu’s work makes it difficult to ignore this issue.

I don’t think a lack of information is the problem

Where do you think political motivation comes from, if not information?

The topic of self-similarity interests me in this case, but the problem is the available data spans too short a time period, so it seems difficult to assess the issue. One of my early questions (last para, comment #141) was whether there was much in the peer-reviewed literature in terms of such an assessment, and the RPSr. article interests me because it is an example of where such issues have been considered. It does seem that there is at least one paper that supports the notion that the hurricane count does display self similar behaviour.

As illustrated by Professor Liu’s research, the use of linear trends as a tool to attribute the reason for climate variability and change (i.e. human and natural) and their extrapolation into the future to communicate hurricane and other climate risks to policymakers, is seriously inadequate.

Bingo. If the use of linear trend analysis (top-right panel in opening graphic) is inadequate, then is a distorted analysis with no statistics (top-left) better? In some contexts, such as a newspaper, possibly. In a scientific or science-policy paper – NO!