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WIREs Clim Change
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Urban heat island effects on estimates of observed climate change

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Urban heat islands are a result of the physical properties of buildings and other structures, and the emission of heat by human activities. They are most pronounced on clear, calm nights; their strength depends also on the background geography and climate, and there are often cool islands in parks and less‐developed areas. Some old city centers no longer show warming trends relative to rural neighbourhoods, because urban development has stabilised. This article reviews the effects that urban heat islands may have on estimates of global near‐surface temperature trends. These effects have been reduced by avoiding or adjusting urban temperature measurements. Comparisons of windy weather with calm‐weather air temperature trends for a worldwide set of observing sites suggest that global near‐surface temperature trends have not been greatly affected by urban warming trends; this is supported by comparisons with marine surface temperatures. The use of dynamical‐model‐based reanalyses to estimate urban influences has been hindered by the heterogeneity of the data input to the reanalyses and by biases in the models. However, improvements in reanalyses are increasing their utility for assessing the surface air temperature record. High‐resolution climate models and data on changing land use offer potential for future assessment of worldwide urban warming influences. The latest assessments of the likely magnitude of the residual urban trend in available global near‐surface temperature records are summarized, along with the uncertainties of these residual trends. Copyright © 2010 John Wiley & Sons, Ltd.

Figure 1.

Globally averaged anomalies, relative to 1961–1990, of land surface air temperature (LSAT), sea surface temperature (SST), and night marine air temperature. Also shown are 90% uncertainty ranges ( ± 1.65 standard errors) for LSAT and SST. The anomalies and uncertainties have been smoothed with a 21‐point binomial filter to highlight decadal and longer term variations.

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Figure 2.

Globally, annually averaged land surface air temperature (LSAT) anomalies, relative to 1961–1990, and their 95% confidence ranges ( ± 1.96 standard errors), since 1850. The anomalies (black line) and uncertainties (shaded bands) have been smoothed with a 21‐point binomial filter to highlight decadal and longer term variations. The inner dark grey band and the light grey band surrounding it represent the cumulative uncertainties arising respectively from random and sampling errors, and incomplete coverage. The outer black band represents the additional uncertainties arising from changes in thermometer exposure and from urbanization; it is narrow in recent years because the data are referenced to 1961–1990. Successive uncertainties were combined in quadrature. The impact of urbanization on the uncertainties can be seen from the asymmetry in the black bands. Brohan et al.17 double counted the urbanization uncertainty but this has been corrected here. The overall uncertainty exceeds that for the LSAT curve in Figure 1 because here the two hemispheres are weighted equally, increasing the emphasis on the data‐sparse Southern hemisphere, whereas Figure 1 is based on area‐weighting of available data. (Updated from Brohan et al.17).

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