As you may have heard, the long-awaited peer-reviewed analysis of the results of the SurfaceStations.org project has finally been released. I can’t wait to see the dueling headlines. Some will argue that the take-home message should be: Poor Station Siting Strongly Effects Temperature Trend Measurements, and will laugh at the idea that we can say with sufficient accuracy what has happened to our climate. Others will argue that the take-home message should be: Poor Station Siting Has No Effect on Temperature Trend Measurements, and will laugh at all the effort expended on a null result. Both sides will find solid evidence for their points of view in the paper. How can that be? How can one paper support opposing conclusions?
First off, when you see a plot of historic temperature anomalies averaged over the United States or over the globe, you’re almost certainly looking at something derived from “mean temperatures”. The daily mean temperature is simply the average of the maximum and minimum temperatures.
Siting issues will affect maximum and minimum temperatures differently. For example, a shade tree overhanging the instrument shelter will make the maximum temperatures cooler and the minimum temperatures warmer. An air conditioner near the instrument shelter will make both maximum and minimum temperatures warmer, but probably have the biggest effect on minimum temperatures.
It’s possible that such siting problems might alter the temperature trend measured at a particular station, but clearly there will be a big effect if the siting characteristics change over time. Add an air conditioning unit, and temperatures go up. Add a tree, and the diurnal temperature range (the difference between the daily maximum and minimum temperatures) goes down.
Big siting changes started in the late 1980s, as the thermometer of choice went from a standard liquid-in-glass thermometer to MMTS, a sensor that required electricity. Quite frequently, the measurement site ended up closer to buildings because that’s where the electricity was.
In an ideal world, you’ll have records of all these changes, and you’ll have observations taken simultaneously at the old and new sites so that you’ll know what difference it makes. But as you may have noticed, ours is not an ideal world.
Correcting for such changes is a job that falls to the National Climatic Data Center (NCDC). Their techniques for making adjustments to the data to correct for such problems have become more and more sophisticated over the years. First, they make the obvious correction for time-of-observation bias (if your daily observation time is in the afternoon, you’ll double-count maxes; if it’s in the morning, you’ll double-count mins), then the rest of it is based on comparing a station to its neighbors and looking for sudden jumps or steadily diverging records. If the difference is big enough, they estimate the difference and adjust for it; “big enough” is fairly small if the jump happens to coincide with a documented change of the station characteristics, but the bar is set higher if there was no known change.
The Fall et al. 2011 paper used the SurfaceStations.org ratings of the quality of the individual USHCN stations and asked some basic questions: Does the temperature measured at a site depend on the siting quality? Does the trend measured over time depend on siting quality? Do the NCDC adjustments correct for differences in siting quality? Note that Fall et al. didn’t attempt to distinguish from effects due to siting quality itself and effects due to instrument changes, nor did they look at larger-scale effects like urbanization.
Here, in brief, are the answers: The poorest sites tend to be warmer. The minimum temperatures are warming faster at poorer sites than at better sites. The maximum temperatures are warming slower at poorer sites than at better sites. The adjustments reduce the differences by about half. The two effects are roughly equal and opposite so the mean temperature is rising at about the same rate across sites of different quality while the diurnal temperature range shows the biggest difference across sites.
On the one hand, this seems to be confirmation of the quality of the temperature record. All types of sites show the same mean temperature trend, so there’s no change necessary to our estimates of observed historical temperature trends in the United States.
On the other hand, there are several warning flags raised by this study. First, station siting is indeed important for the maximum and minimum temperature measurements. Second, the adjustments are only partly correcting the temperature record. Third, since the adjustments use data from all surrounding stations, there’s the danger that the mean trends are dominated by data from the poorer stations. (Less than ten percent of the USHCN stations are sited well enough to be considered appropriate for climate trend measurements.) Finally, and perhaps most important, are we really so lucky that the rest of the world would also have its poorly-sited stations have erroneous maximum and minimum temperature trends that just happen to be equal and opposite to each other?
So if you thought that the temperature record in the US was lousy, well for mean temperatures in particular it may not be too bad. (Note: regional conditions may vary.) And if you thought that the adjusted station data had eliminated effects due to poor siting, well for most temperature variables there’s still a ways to go. I’m glad we now have the Climate Reference Network, which should at least guarantee accurate trend measurements going forward.
By the way, if you’ve downloaded the paper you may have noticed that I’m one of the authors. My involvement was mainly in the statistical analysis, and I’ll discuss the statistics of the paper in another post very soon. There’s also a lot more in the paper than my brief summary implies, and I’ll go into some of those issues later too.
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