Intensity or Frequency?

I have previously argued that – in my estimation – there's a strong causal association between sea-surface temperatures and the number of named storms (or tropical cyclones) in the Atlantic Basin. Statistically, the association is quite significant, and graphically, it is evident once you apply very simple smoothing filters.

This is not the prevailing scientific view, which essentially says that the intensity of storms should increase with global warming, and the frequency of storms should actually decline. This prevailing view, largely based on computer modeling and not observations, is best summarized by the IPCC in 4AR WGI 10.3.6.3.

I was wondering if both could be true: global warming increases the frequency and intensity of tropical cyclones. How can we test this idea using available observations? You can't just look at, say, Accumulated Cyclone Energy (ACE.) If the frequency of storms increases, ACE should also increase, even if the average intensity of each storm doesn't change.

It occurred to me that a much better test would be to look at the ratio of hurricanes to all named storms, and the ratio of major hurricanes to storms. I've done this, but I'll leave it as an exercise for the reader. It's a very easy analysis. You can use the named storm count data from the Hurricane Research Division of NOAA. If you have concerns that tropical storms were under-counted in the past relative to hurricanes (a reasonable assumption), you can use data starting in 1944, which is when systematic aircraft recognizance started. But remember, causality matters more than the trend in this case.

To make a long story short, observations do not appear to support the view that global warming will cause storm intensity to increase. The historical data is telling me the opposite of what the IPCC claims. What, if anything, am I missing? Could it be that things will work differently in the future?

Urban Heat Island Effect - A Model

In the addendum of my last post on the Urban Heat Island (UHI) Effect, I noted that GHCN v2 apparently does contain data that we can use to verify the existence of the effect, even though UHI doesn't seem to have a discernible impact on global temperature trends. This is interesting because it's at odds with some well known findings from the literature, such as Peterson et al. (2003), and it addresses a "mystery" of sorts about the instrumental temperature record.

I wrote some code in order to carry out a more thorough analysis of a possibly systematic effect in the raw data, hypothesizing the effect depends on the size of the station's associated town. Basically, I divided stations in population size groups, using 1.25-fold increments. That is, the first group consists of towns whose population is between 10,000 and 12,500. The second group has between 12,500 and 15,625 people, and so on. The last group consists of towns with populations between 15.8 million and 19.7 million. For each group, I got a global temperature series, in a way equivalent to how GHCN Processor would produce them. This is what I came up with:



This is a highly significant effect. It doesn't even make sense to post a confidence level, because it's exceedingly close to 100%.

It is obvious from the graph, nonetheless, that the number of cities declines rapidly with population size. It's a good idea in these cases to look at a logarithmic scale of the X axis.



This logarithmic relationship is clearly a good candidate for segmented regression. When the population is less than about 1.04 million, there is no discernible effect. A linear regression of the left-hand "segment" has a slight downward slope, which is not statistically significant. The average temperature slope between 1880 and 2009 is 0.0056°C/year (which is what the red line represents.)

We can thus derive a straightforward model for UHI, applicable to the GHCN v2 raw data file, which follows.

C = -0.0039·[ln(P + 1) - ln(1042)]
Where:

• C is a correction (in °C/year) that should be added to the temperature slope of a station only if the population of its associated town is greater than 1.04 million.
• P is the population of the town associated with the station, in thousands.

Urban Heat Island Effect - Probably Negligible

Previously I had discussed the difference between rural and urban temperature stations in the U.S. Commenter steven argued that population assessments (R, S and U) in GHCN v2 might be outdated and – in general – not very good proxies of what we really want to measure.

I then compared rural stations in the Mid-West (a low-population-density region of the U.S.) with all rural stations. There wasn't a major difference between these two sets of stations either. Commenter steven was not convinced, however. He posted some satellite pictures of rural stations that are located in what appear to be sub-urban areas.

How could we measure the impact of human populations on station temperature with the data available to us? It's clearly not enough to express doubt and speculate about what might be going on.

Here's what I came up with. There's a vegetation property in the station metadata. If you look at stations in regions that are forested (FO), marshes (MA) or deserts (DE), they appear to be actually rural. I looked at a subset of such stations in Google Maps, and they are not close to human settlements, with few exceptions. The GHCN Processor command I used to obtain a temperature series is the following.

ghcnp -dt mean -include "population_type=='R' && (vegetation=='FO' || vegetation=='MA' || vegetation=='DE')" -o /tmp/global-rural-plus.csv

575 stations fit these characteristics. For comparison, I got temperature series for big cities (population > 0.5 million), and small towns and cities (population <= 0.5 million.) I calculated 12-year moving averages in each case, which is what you see in the figure below.



There might be some differences, but they are always small, and we've compared several different stations sets now, globally and at the U.S. level.

An argument could also be made that small human settlements increase the albedo of an area, so they might have a cooling effect.

Addendum (4/5/2010)

Here's an actual UHI finding of interest. I compared cities of population over 2 million with towns whose population is between 10,000 and 15,000. The difference is more pronounced in this case.



The overall effect is still negligible, nevertheless. The number of cities decreases exponentially with population size.