{"id":14688,"date":"2013-02-13T05:40:44","date_gmt":"2013-02-13T10:40:44","guid":{"rendered":"http:\/\/www.realclimate.org\/?p=14688"},"modified":"2013-02-13T13:56:16","modified_gmt":"2013-02-13T18:56:16","slug":"urban-heat-islands-and-u-s-temperature-trends","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2013\/02\/urban-heat-islands-and-u-s-temperature-trends\/","title":{"rendered":"Urban Heat Islands and U.S. Temperature Trends"},"content":{"rendered":"
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Guest Commentary by Zeke Hausfather and Matthew Menne (NOAA)<\/small><\/p>\n

The impact of urban heat islands (UHI) on temperature trends has long been a contentious area<\/a>, with some studies finding no effect<\/a><\/span> of urbanization on large-scale temperature trend and others finding large effects in certain regions<\/a><\/span>. The issue has reached particular prominence on the blogs, with some claiming that the majority of the warming in the U.S.<\/a> (or even the world<\/a>) over the past century can be attributed to urbanization. We therefore set out to undertake a thorough examination of UHI in the Conterminous United States (CONUS), examining multiple ‘urban’ proxies, different methods of analysis, and temperature series with differing degrees of homogenization and urban-specific corrections (e.g. the GISTEMP nightlight method; Hansen et al, 2010<\/a><\/span>). The paper reporting our results has just been published in the Journal of Geophysical Research.
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In our paper (Hausfather et al, 2013)<\/a><\/span> (pdf<\/a>, alt. site<\/a>), we found that urban-correlated biases account for between 14 and 21% of the rise in unadjusted minimum temperatures since 1895 and 6 to 9% since 1960. Homogenization of the monthly temperature data via NCDC’s Pairwise Homogenization Algorithm (PHA) removes the majority of this apparent urban bias, especially over the last 50 to 80 years. Moreover, results from the PHA using all available station data and using only data from stations classified as rural are broadly consistent, which provides strong evidence that the reduction of the urban warming signal by homogenization is a consequence of the real elimination of an urban warming bias present in the raw data rather than a consequence of simply forcing agreement between urban and rural station trends through a ‘spreading’ of the urban signal to series from nearby stations.<\/p>\n

Homogenization is a somewhat complex term for a conceptually simple idea. Climate variations tend not to be purely local so changes in temperatures over long time spans (longer than a month) will be highly spatially correlated. Any major changes over time in individual stations that are not reflected in nearby stations are likely due to local (rather than regional) effects such as station moves, instrument changes, time of observation changes, or even such things as a tree growing over the thermometer stand. By removing any artifacts of individual station records not shared with other stations in their region, we can get a more accurate estimate of regional climate changes.<\/p>\n

The conterminous United States (CONUS) has some of the most dense, publicly available digital surface temperature data in the world with over 7000 Cooperative Observer (Coop) stations reporting daily maximum and minimum temperature. This provides a unique resource to compare subsets of stations with various characteristics (e.g. urban form, sensor types, etc.) without suffering bias due to differing spatial coverage, a factor that often complicates global-scale studies of UHI. The Coop Program also maintains accurate station location data (roughly 30 meter accuracy), which allows for the accurate indexing of Coop stations against high-resolution spatial datasets that are useful for identifying urban and rural areas. <\/p>\n

We use four datasets to classify stations as urban or rural, all with 1 km spatial resolution:
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