Bias

There has been a great deal of controversy regarding the bias of temperature measuring devices used to document alleged global warming. The National Post has more:

Contaminated data
Hot cities, not CO2, cause urban thermometers to rise
Ross McKitrick, Financial Post Published: Wednesday, December 05, 2007

Below is the famous graph of "global average surface temperature," or "global temperature" for short. The data come from thermometers around the world, but between the thermometer readings and the final, famous, warming ramp, a lot of statistical modelling aims at removing known sources of exaggeration in the warming trend.

In a new article just published in the Journal of Geophysical Research -- Atmospheres, a co-author and I have concluded that the manipulations for the steep post-1980 period are inadequate, and the above graph is an exaggeration. Along the way, I have also found that the United Nations agency promoting the global temperature graph has made false claims about the quality of its data.

It's just so hard to believe that the UN could or would try to misrepresent the truth. After all, these are the folks who brought us the Oil For Food scam plan. Maybe its the cop in me, but I truly would like to see how many UN cronies are hooked up with these carbon offset investment firms that are headed by the likes of Al Gore.

The surface-measured data has many well-known problems. Over the post-war era, equipment has changed, station sites have been moved, and the time of day at which the data is collected has changed.

Many long-term weather records come from in or near cities, which have gotten warmer as they grow. Many poor countries have sparse weather-station records and few resources to ensure data quality. Fewer than one-third of the weather stations operating in the 1970s remain in operation.

Scientists readily acknowledged that temperature measurements are contaminated for the purpose of measuring climate change. But they argue that adjustments fix the problem. To deal with a false warming generated by urbanization, they have the "Urbanization Adjustment." To deal with biases due to changing the time of day when temperatures are observed, they have the "Time of Observation Bias Adjustment." And so forth.

Maybe they are working on "Change of Life Adjustments" to deal with sensors located near the homes of middle aged women. We could also sell them as "Hot Flash Offsets." Mope, you listening?

The papers describing the adjustments aim to construct data showing what the temperature would be in a region if nobody had ever lived there. If the adjustments are right, the final output should not be correlated with the extent of industrial development and variations in socioeconomic conditions. But in a 2004 study with climatologist Patrick Michaels, we found that the adjustment models were not removing the contamination patterns as claimed. If the contamination were removed, we estimated the average measured warming rate over land would decline by about half. Dutch meteorologists using different data and a different testing methodology had come to the same conclusions.

[...]Scientists who attribute warming to greenhouse gases argue that their climate models cannot reproduce the surface trends from natural variability alone. They then attribute it to greenhouse gases, since (they assume) all other human influences have been removed from the data by the adjustment models. If that has not happened, however, they cannot claim to be able to identify the role of greenhouse gases. Despite the vast number of studies involved, and the large number of contributors to the IPCC reports, the core message of the IPCC hinges on the assumption that their main surface climate data set is uncontaminated. And by the time they began writing the recent Fourth Assessment Report, they had before them a set of papers proving the data are contaminated.

How did they handle this issue? In the first draft of the IPCC report, they simply claimed that, while city data are distorted by urban warming, this does not affect the global averages. They cited two familiar studies to support their position and ignored the new counter-evidence. I submitted lengthy comments criticizing this section. In the second draft there was still no discussion, so again I put in lengthy comments. This time the IPCC authors wrote a response. They conceded the evidence of contamination, but in a stunning admission, said: "The locations of socioeconomic development happen to have coincided with maximum warming, not for the reason given by McKitrick and Mihaels [sic] (2004), but because of the strengthening of the Arctic Oscillation and the greater sensitivity of land than ocean to greenhouse forcing, owing to the smaller thermal capacity of land." Note the irony: Confronted with published evidence of an anthropogenic (but non-greenhouse) explanation for warming, they dismissed it with an unproven conjecture of natural causes. Who's the "denialist" now?

Furthermore, the claim is preposterous. The comparison of land and ocean is irrelevant since we were only talking about land areas. The Arctic Oscillation is a wind-circulation pattern that affects long-term weather trends in the Arctic. It certainly plays a role in explaining Arctic warming over the past few decades. But for IPCC lead authors to invoke it to explain a worldwide correlation between industrialization and warming patterns is nonsense.

Let's cut to the chase.

So there are two points to note here. First, the IPCC concedes the existence of a correlation pattern that shows its main data set is contaminated, and it has no coherent counterargument. Its claim that it is due to natural circulation changes contradicts its later (and prominently advertised) claims that recent warming patterns cannot be attributed to natural atmospheric circulation changes [emphasis mine].

This is rather important. The IPCC is pulling another Rwanda on us. Ignore the corpses, there's nothing going on here folks.

Second, the claim that our evidence is statistically insignificant is, in my opinion, a plain fabrication. The IPCC offered no supporting evidence. Confronted with two lines of independent evidence that the data set on which it bases its fundamental conclusions is contaminated, it conceded the point, but then dismissed it on the basis of non-existent counter-evidence.