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All IPCC definitions taken from Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I, Glossary, pp. 941-954. Cambridge University Press.

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Microsite influences on the temperature record are minimal

What the science says...

Select a level... Basic Intermediate

Poor weather stations actually show a cooler trend compared to well sited stations. This is due to instrumentation changes. When this is taken into account, there's negligible difference between poor and well sited stations.

Climate Myth...

It's microsite influences

U.S. weather stations have been located next to exhaust fans of air conditioning units, surrounded by asphalt parking lots, on blistering-hot rooftops, and near sidewalks and buildings that absorb and radiate heat. 89 percent of the stations fail to meet the National Weather Service’s own siting requirements that stations must be 30 metres away from an artificial heating or radiating/reflecting heat source. (Watts 2009)

The website enlisted an army of volunteers, travelling across the U.S. photographing weather stations. The point of this effort was to document cases of microsite influence - weather stations located near car parks, air conditioners and airport tarmacs and anything else that might impose a warming bias. While photos can be compelling, the only way to quantify any microsite influence is through analysis of the data. This has been done in On the reliability of the U.S. Surface Temperature Record (Menne 2010), published in the Journal of Geophysical Research. The trends from poorly sited weather stations are compared to well-sited stations. The results indicate that yes, there is a bias associated with poor exposure sites. However, the bias is not what you expect.

Weather stations are split into two categories: good (rating 1 or 2) and bad (ratings 3, 4 or 5). Each day, the minimum and maximum temperature are recorded. All temperature data goes through a process of homogenisation, removing non-climatic influences such as relocation of the weather station or change in the Time of Observation. In this analysis, both the raw, unadjusted data and homogenised, adjusted data are compared. Figure 1 shows the comparison of unadjusted temperature from the good and bad sites. The top figure (c) is the maximum temperature, the bottom figure (d) is the minimum temperature. The black line represents well sited weather stations with the red line representing poorly sited stations.

Maximum and Minimum Temperature Anomaly for good and bad sites
Figure 1. Annual average maximum and minimum unadjusted temperature change calculated using (c) maximum and (d) minimum temperatures from good and poor exposure sites (Menne 2010).

Poor sites show a cooler maximum temperature compared to good sites. For minimum temperature, the poor sites are slightly warmer. The net effect is a cool bias in poorly sited stations. Considering all the air-conditioners, BBQs, car parks and tarmacs, this result is somewhat a surprise. Why are poor sites showing a cooler trend than good sites?

The cool bias occurs primarily during the mid and late 1980s. Over this period, about 60% of USHCN sites converted from Cotton Region Shelters (CRS otherwise known as Stevenson Screens) to electronic Maximum/Minimum Temperature Systems (MMTS). MMTS sensors are attached by cable to an indoor readout device. Consequently, limited by cable length, they're often located closer to heated buildings, paved surfaces and other artificial sources of heat.

Investigations into the impact of the MMTS on temperature data have found that on average, MMTS sensors record lower daily maximums than their CRS counterparts, and, conversely, slightly higher daily minimums (Menne 2009). Only about 30% of the good sites currently have the newer MMTS-type sensors compared to about 75% of the poor exposure locations. Thus it's MMTS sensors that are responsible for the cool bias imposed on poor sites.

When the change from CRS to MMTS are taken into account, as well as other biases such as station relocation and Time of Observation, the trend from good sites show close agreement with poor sites.

Maximum and Minimum Temperature Anomaly for good and bad sites
Figure 2: Comparison of U.S. average annual (a) maximum and (b) minimum temperatures calculated using USHCN version 2 adjusted temperatures. Good and poor site ratings are based on

Does this latest analysis mean all the work at has been a waste of time? On the contrary, the laborious task of rating each individual weather station enabled Menne 2010 to identify a cool bias in poor sites and isolate the cause. The role of is recognised in the paper's acknowledgements in which they "wish to thank Anthony Watts and the many volunteers at for their considerable efforts in documenting the current site characteristics of USHCN stations." A net cooling bias was perhaps not the result the volunteers were hoping for but improving the quality of the surface temperature record is surely a result we should all appreciate.

Intermediate rebuttal written by John Cook

Update July 2015:

Here is a related lecture-video from Denial101x - Making Sense of Climate Science Denial

Last updated on 15 July 2015 by pattimer. View Archives

Printable Version  |  Offline PDF Version  |  Link to this page

Argument Feedback

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Comments 1 to 7:

  1. An idle thought....we tend to think of 'bad' stations giving higher readings than 'good' stations as they respond to surrounding heat emitters such as buildings. In cold weather the opposite can occur...the 'bad' stations will respond more slowly to increasing T due to the thermal inertia of those same buildings. In other words, the T swing max-min could be greater in the 'bad' stations but dis-appears in the averaging process. So maybe there is an element of self-compensation here which could be determined by looking at more detailed data.
  2. Questions: 1) The belgian media jumped on a result similar to what is posted on of summer 2009 (no source reference in Belgian media) in which it seems that a lot of american surface stations have a deviations of more than 1°C and that for some "accurate" surface stations the temperature has been decline the last 100 years. Is this already incorporated in the graphs above? (No post date of the graphs). 2) Why do your graphs only go back to 1980 while other stations have data going back more than 100 years on the graphs of
  3. psilax, Re. #2, a good question. I looked up the Menne article and the Watts 2009 article to which it is a response. Watts suggests two driving causes for the upward bias of temperatures: The change in paint spec in 1979 and the introduction of the new type of thermometers in the 1980s. I suspect that Menne, et al, restricted their period of reporting to that which Watts was concerned about.
  4. For what it's worth, Watts put his three different boxes closer to each other than I would have, and put the newer paint spec one in the middle. That puts the one he expects to find higher temperatures in between two, too-close, bodies that both reflect SW and radiate LW. I can't predict what difference that made, but for someone really concerned about siting of instruments, he introduces his own siting problems.
  5. Capital Weather Gang (now part of Washington Post) noted that the NWS in Sterling VA noted that 2011 is the "warmest summer on record" through Aug 17th at National Airport (KDCA). The problem is that it isn't. National has fresh dark gravel spread around their thermometer site (new google satellite compared to old) and they are consistently high by 2-3 degrees on radiational cooling mornings. The anomaly doesn't show up as much on windy or cloudy mornings. National's lows are also 5 to 10 degrees higher than neighboring weatherbug sites (mostly at schools) but that is a river and heat island influence that hasn't changed much over the years. If the washpost link above doesn't work (their site is sometimes unruly), the NWS discussion was also quoted here;
  6. This needs to be updated to include the results of the BEST (Berkely Eartth Surface Temperature) study, which shows that there was no significant influence on the measured long-term temperature due to the siting of temperature stations. And that the temperature rises from BEST's analysis match the other temperature analyses closely.
  7. The change of the temperature merely in 1 degree has a huge impact on climate change issue. To find the way to limit, control and slow down the process of global warming the scientists do need the accurate information. So no matter how close of the good and bad station data is, we should set up the station in the place that can get the closest result to the actual temperature on earth. The factors which affect the measurement of the temperature should be find out.

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