This is the first of a series of articles in which Cornwall Alliance board member and senior fellow Dr. Roy W. Spencer explains the importance of distinguishing urban heat island effect from global temperature increase and of recognizing that by choosing to live in warmer regions of the world and in cities rather than in cooler and rural or wilderness areas, people demonstrate their preference for warmer temperatures.

After years of dabbling in this issue, John Christy and I have finally submitted a paper to Journal of Applied Meteorology and Climatology entitled, “Urban Heat Island Effects in U.S. Summer Surface Temperature Data, 1880-2015“.

I feel pretty good about what we’ve done using the GHCN data. We demonstrate that, not only do the homogenized (“adjusted”) dataset not correct for the effect of the urban heat island (UHI) on temperature trends, the adjusted data appear to have even stronger UHI signatures than in the raw (unadjusted) data. This is true of both trends at stations (where there are nearby rural and non-rural stations… you can’t blindly average all of the stations in the U.S.), and it’s true of the spatial differences between closely-space stations in the same months and years.

The bottom line is that an estimated 22% of the U.S. warming trend, 1895 to 2023, is due to localized UHI effects.

And the effect is much larger in urban locations. Out of 4 categories of urbanization based upon population density (0.1 to 10, 10-100, 100-1,000, and >1,000 persons per sq. km), the top 2 categories show the UHI temperature trend to be 57% of the reported homogenized GHCN temperature trend. So, as one might expect, a large part of urban (and even suburban) warming since 1895 is due to UHI effects. This impacts how we should be discussing recent “record hot” temperatures at cities. Some of those would likely not be records if UHI effects were taken into account.

Yet, those are the temperatures a majority of the population experiences. My point is, such increasing warmth cannot be wholly blamed on climate change.

One of the things I struggled with was how to deal with stations having sporadic records. I’ve always wondered if one could use year-over-year changes instead of the usual annual-cycle-an-anomaly calculations, and it turns out you can, and with extremely high accuracy. (John Christy says he did it many years ago for a sparse African temperature dataset). This greatly simplifies data processing, and you can use all stations that have at least 2 years of data.

Now to see if the peer review process deep-sixes the paper. I’m optimistic.

This piece originally appeared at DrRoySpencer.com and has been republished here with permission.