Saturday, June 28, 2008

Hurricanes and Temperature are Indeed Associated

There is apparently considerable climate science that can be cited to show there's a clear association between global warming and either the number of hurricanes in any given season or their intensity. See, for example, Hurricanes and Global Warming - Is There a Connection?, written by a number of climate scientists who run RealClimate.org. There is both basic science and computer modeling that can be used to predict what should occur under certain warming scenarios.

I'm generally inclined to trust scientific consensus and published science, particularly if it's peer-reviewed, unless I can advance a seriously strong argument explaining why I do not. Nevertheless, there's nothing like analyzing data first hand. Because I understand this, and because I understand some people out there don't trust some published science at all under the pretext of "conflicts of interest," I've acquired the habit of writing posts where I walk the reader through very accessible analyses of publicly available data. I combine this with a very lenient comment policy. My pledge is to only remove comments that clearly violate Blogger's content policy.

I already did this type of analysis in my post titled Anthropogenic Global Warming is Absolutely Occurring. This time I will look into the claim that global warming might have had an effect in the number of named storms in the Atlantic Basin, given that some people appear to doubt this claim. In doing so, I will try to go over additional details of the methodology which I might have left out in my previous post.

I will use data on the number of named storms from 1851 to 2006 provided by NOAA. I will use ocean surface temperature data for the northern hemisphere provided by the Climatic Research Unit of the University of East Anglia. For accuracy, since we're interested in the hurricane season, I will use June-November averages for each year.

Let's start by putting these two data sets in a chart, side by side. This will be Figure 1, which also shows trend lines for both temperature and storm trends. The trend lines are third-order polynomial fits (easily produced with Excel).

hurricanes global warming

The reader will note that both trends are pointing upward, at least for the last 60 years. This is not what we are interested in, however. We want to control for the fact that there could be a coincidence of upward trends. That's where the third-order polynomial fits come in.

The polynomial fits provide us a time-based model of each trend. For any given year they tell us what the "expected" temperature and number of storms should be. Of course, a given year might have more or less storms than expected. It will also have a higher or lower temperature than expected. In the end, what we want to find out is whether years with higher temperature than expected tend to have more storms than expected, and vice versa.

By subtracting trend line equation values from observed values, residuals of temperature and storms can be produced for each year. These residuals represent how different from "expected" an observed value is in a given year. Residuals are generally time-independent. In our case, if you produce a scatter chart of year vs. temperature residual or storm residual, you will see the scatter trend is entirely flat. This is a basic confirmation that should be done after getting the set of residuals.

Figure 2 is a scatter chart of temperature residuals vs. storm residuals. The trend of this scatter should be flat, unless there's association between temperature and number of storms.

hurricanes global warming

What we see in Figure 2 is that if we try to fit a linear trend to the scatter, we do get a positive slope of 3.43. Now, we need to verify that we can state, with statistical confidence, that the slope is actually positive. In this case it is. The 95% confidence interval of the slope is 0.25 to 6.61. This is not a slam dunk finding like the one for the correlation between cumulative CO2 emissions and temperature, but it is statistically significant, which means an association between temperature and number of storms is demonstrated.

Given the methodology used, this result cannot be explained as a coincidental trend.

There are some peculiarities about the data which are interesting. For example, it is clear that the 2005 Atlantic season was an unusual one, even after controlling for the time trend of named storms. It could be placed in a group of seasons that only occur every 50 years or so. Evidently, the fact that the seasons that came after 2005 did not measure up is inconsequential to the finding that temperature associates with the number of named storms.

We can, however, pose the following question: What sort of temperature increase would be required for the average season to be like the 2005 season? Given the slope of the scatter in Figure 2, it would seem that a temperature anomaly of 4.05 degrees (C) would be required for this. The current temperature anomaly is about 0.6 degrees (C), so such an eventuality appears to be far off. Or is it?

I ran a second residual correlation analysis of temperature vs. number of named storms one year later. This actually produces a considerably steeper slope (6.36) and the confidence interval is entirely positive even at 99.993% confidence. I can't really explain why this would be the case. But here's the thing. If we were to take this new slope at face value, a temperature anomaly of 2.18 degrees (C) would be enough to make the average season similar to the 2005 season.

5 comments:

llewelly said...

A few issues with your fine statistical analysis. First, I note you use June-Novemeber NH ocean temps. While the majority of Atlantic hurricanes occur between June and November, the database you use does include storms which occur outside of that period. This may be insignificant; I believe there are only 37 out-of-season storms in your data. Second, there have been extensive changes in observational techniques. Your statistical analysis cannot easily disentangle changes in observational techniques because, to a first approximation, both the improvements in observational techniques and the rise in NH ocean temps are due to the same cause: expanding human industry. A few papers related to observational issues: A Reanalysis of the 1911-20 Atlantic Hurricane Database, Landsea et al, 2008 , Counting Atlantic Tropical Cyclones back to 1900, Landsea 2007 , Can we detect trends in extreme tropical cyclones? Landsea, et al, 2006 , The Atlantic hurricane database re-analysis project: Documentation for the 1851-1910 alterations and additions to the HURDAT database, Landsea, et al 2004 , Chronological Listing of Tropical Cyclones affecting North Florida and Coastal Georgia 1565-1899, Sandrik, et al, 2003 .

A trouble is that the prime authoritiy in this area - Chris Landsea - was one of the last hurricane scientists to admit global warming is occurring, and even today tries to avoid admitting it is human caused (though he has done so a time or two.) He's behaved in many ways as if he's very, very opposed to the idea that AGW might result in more intense hurricanes. But it's not at all clear to what extent this has affected his peer-reviewed work. In any case, although I haven't done the math, I don't think the numbers of missed storms he suggests can make the connection go away - they seem only to modestly reduce the confidence in the connection.

Joseph said...

Hi, llewelly. I suspect that an even narrower range (Jul-Aug) would be better than a Jun-Nov average. That's because while there are storms outside that range, data from months with less activity probably adds noise.

About the changes in observation techniques coinciding with temperature observation changes, I expect that the detrending would take care of it.

llewelly said...


Hi, llewelly. I suspect that an even narrower range (Jul-Aug) would be better than a Jun-Nov average. That's because while there are storms outside that range, data from months with less activity probably adds noise.

June has a storm only about 1 year out of 3 - so I can understand leaving it out in an effort to reduce noise. November is also not active. However - September is the most active month of the year, and October is usually more active than July. (See here . ) So using a Jul-Aug would probably be a bad idea.

Joseph said...

FYI - there's a follow-up to this post here.
The highlight: This chart.

santa claws said...

A trouble is that the prime authoritiy in this area - Chris Landsea - was one of the last hurricane scientists to admit global warming is occurring, and even today tries to avoid admitting it is human caused (though he has done so a time or two.) He's behaved in many ways as if he's very, very opposed to the idea that AGW might result in more intense hurricanes. But it's not at all clear to what extent this has affected his peer-reviewed work. In any case, although I haven't done the math, I don't think the numbers of missed storms he suggests can make the connection go away - they seem only to modestly reduce the confidence in the connection.Water Damage