Was This July Really the “Hottest Month in History”?
No, but the “claim sounds scary until you read the fine print.” So, go out and enjoy your summer!
The annual “hottest month in history” headline has arrived again, complete with demands that the public accept it as proof of an imminent climate apocalypse.
Cue the red ink and hyperbole.
July was “a wild weather month,” setting a record as the warmest month ever for the contiguous United States, the National Oceanic and Atmospheric Administration said Monday.
Temperatures last month averaged 76.9 degrees Fahrenheit as above-average warmth blanketed the U.S., and several locations in Idaho, Utah, Wyoming and Montana saw all-time high temperature records since NOAA started keeping track in 1895.
Last month was 0.1 to 0.2 degrees warmer than July 1936 and July 2012, according to the agency. July also broke the record for the average low temperature, at 64.2 degrees, surpassing July 2022 by 0.7 degrees.
A heat wave that began in early July placed more than 185 million people under heat alerts, including residents of multiple major metropolitan areas, like Boston, New York City, Philadelphia and Washington, D.C. The Independence Day parade in the nation’s capital was among several events that had to be canceled or postponed due to the extreme heat.
Meteorologist Chris Martz says this claim is misleading.
“That claim is misleading for two reasons:
This is due to minimum temperatures only, which skew the “average.”
The real, measured thermometer data (area-weighted) don’t support this claim.”
I would like to explore the rest of his explanation for a few moments.
The reported warming trend appears to be driven mainly by rising nighttime low temperatures. Because climate “averages” combine daytime highs and nighttime lows, warmer nights can make the overall average look higher even when daytime high temperatures do not show the same increase.
Martz argues that raw thermometer readings, which are adjusted to account for the area each station represents, do not show as much recent warming as the adjusted records suggest.
To elaborate, temperature stations measure temperatures at specific locations. If we are trying to estimate the average temperature over a wide area like the U.S. or the Globe, it is advisable to use gridding or some more complicated form of spatial interpolation to assure that our results are representative of the underlying temperature field. For example, about a third of the available global temperature stations are in U.S. If we calculated global temperatures without spatial weighting, we’d be treating the U.S. as 33% of the world’s land area rather than ~5%, and end up with a rather biased estimate of global temperatures.
NOAA adjusts older weather-station records to account for non-climate changes, such as stations moving, equipment changing, or observers recording temperatures at different times of day. Its Pairwise Homogenization Algorithm (PHA) compares a station with nearby stations to identify and correct apparent breaks in the record.
Critics argue that this process can make early temperature records look too cool and recent warming look larger than it really was. One concern is that if an urban station affected by the urban heat-island effect is used as a comparison station, some of that artificial urban warming could influence adjustments applied to nearby rural stations. A peer-reviewed study found that this type of “urban blending” can occur in homogenized data sets.
Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.
Martz notes that rural stations, weighted so that densely monitored areas do not dominate the results, show more pronounced warm periods in the early and middle 1900s and a less steep warming trend in recent decades. The claim is that careful time-of-observation corrections do not automatically double-count hot days; instead, their main possible effect during a warm spell may occur near its end, when a cold front rapidly lowers temperatures.
Furthermore, the actual precision of the temperature determination for this claim is questionable.
In fact, the data show that if only daytime temperatures were taken into account, this would be only the 6th-hottest July. And it turns out Alaska, omitted by considering only the “contiguous U.S.,” was in the “coolest third” over the past century.
As former climate activist Lucy Biggers notes, the “claim sounds scary until you read the fine print.” So go out and enjoy your summer.
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