[Editor’s Note: We don’t usually publish such a long excerpt as this, but this is a very important, and very substantive, article. It adds to the many reasons to question seriously the reliability and scientific objectivity of the UN Intergovernmental Panel on Climate Change and the magnitude of recent warming.–ECB]
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 modeling 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 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.
The graph comes from data collected in weather stations around the world. Other graphs come from weather satellites and from networks of weather balloons that monitor layers of the atmosphere. These other graphs don’t show as much warming as the weather-station data, even though they measure at heights where there is supposed to be even more greenhouse-gas-induced warming than at the surface. The discrepancy is especially clear in the tropics.
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.
How do we know these adjustments are correct? In most studies, the question is simply not asked. A few studies argue that the adjustments must be adequate since adjacent rural and urban samples give similar results. But closer inspection shows some of these papers don’t actually give similar results at all, or when they do they define “rural” so broadly that it includes partly urbanized places. Other studies say the adjustments must be adequate because trends on windy nights look the same as trends on calm nights. But the long list of data problems includes issues just as serious under both windy and calm conditions.
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.
In response to criticisms of our paper, I began assembling a more complete database, covering all available land areas and a more extensive set of climatological and economic indicators. Meantime, in 2005, I was asked to serve as an external reviewer for the IPCC report, which was released earlier this year. I accepted, in part to address the data-contamination problem.
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.
The final version of the report, published in May, 2007, included the following paragraph (Chapter 3, page 244):
McKitrick and Michaels (2004) and [Dutch meteorologists] de Laat and Maurellis (2006) attempted to demonstrate that geographical patterns of warming trends over land are strongly correlated with geographical patterns of industrial and socioeconomic development, implying that urbanization and related land surface changes have caused much of the observed warming. However, the locations of greatest socioeconomic development are also those that have been most warmed by atmospheric circulation changes (Sections 3.2.2.7 and 3.6.4), which exhibit large-scale coherence. Hence, the correlation of warming with industrial and socioeconomic development ceases to be statistically significant. In addition, observed warming has been, and transient greenhouse-induced warming is expected to be, greater over land than over the oceans (Chapter 10), owing to the smaller thermal capacity of the land.
In the first sentence, the phrase “attempted to demonstrate” should be replace with “showed.” This kind of slanted wording arises when organizations like the IPCC fail to control the biases of their lead authors.
The above paragraph acknowledges the correlation between warming trends and socioeconomic development. But it claims it is a mere coincidence, due to unspecified “atmospheric circulation changes.” The two cited sections discuss some natural circulation patterns, but do not show they overlap with the pattern of industrialization — the topic simply does not come up. And the de Laat and Maurellis paper refuted that explanation, anyway.
The IPCC authors also claimed that, in view of the natural circulation changes, “the correlation of warming with industrial and socioeconomic development ceases to be statistically significant.” Statistical significance is a precise scientific term, and a claim that results are insignificant requires specific numerical evidence. The effects in the underlying papers were all statistically significant. The IPCC’s claim to the contrary is false.
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. 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.
This is no mere tiff among dueling experts. The IPCC has a monopoly on scientific advising to governments concerning climate change. Governments who never think to conduct due diligence on IPCC reports send delegates to plenary meetings at which they formally “accept” the conclusions of IPCC reports. Thereafter they are unable — legally and politically — to dissent from its conclusions. In the years ahead, people around the world, including here in Canada, could bear costs of climate policies running to hundreds of billions of dollars, based on these conclusions. And the conclusions are based on data that the IPCC lead authors concede exhibits a contamination pattern that undermines their interpretation of it, a problem they concealed with untrue claims.
Our new paper presents a new, larger data set with a more complete set of socioeconomic indicators. We showed that the spatial pattern of warming trends is so tightly correlated with indicators of economic activity that the probability they are unrelated is less than one in 14 trillion. We applied a string of statistical tests to show that the correlation is not a fluke or the result of biased or inconsistent statistical modelling. We showed that the contamination patterns are largest in regions experiencing real economic growth. And we showed that the contamination patterns account for about half the surface warming measured over land since 1980.
In other words, we have confirmed, on new and stronger grounds, that the IPCC’s global surface-temperature data is exaggerated, with a large warming bias. Claims about the amount of surface warming since 1980, and its attribution to anthropogenic greenhouse-gas emissions, should be reassessed using uncontaminated data. And governments that rely on the IPCC for advice should begin asking why it was allowed to suppress earlier evidence of this problem.
Excerpted from NationalPost.com.
Featured Image Courtesy of Julie A. Wenskoski/Freedigitalphotos.net
