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).
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.
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.
Time Series Lesson 2
15 hours ago