Urban heat island amplification estimates on global warming using an albedo model
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Urban heat island amplification estimates on global warming using an albedo model Alec Feinberg1 Received: 14 May 2020 / Accepted: 12 November 2020 © The Author(s) 2020 OPEN
Abstract In this paper, we provide nominal and worst-case estimates of radiative forcing due to the UHI effect using a Weighted Amplification Albedo Solar Urbanization model. This calculation is done with the help of reported findings from UHI footprint and heat dome studies that simplify estimates for UHI amplification factors. Using this method, we quantify a global warming range due to the UHI effect, including its extent. Forcing estimates varied approximately between 0.07 and 0.87 W/m2 representing 3% to 36% of global warming relative to the greenhouse gas forcing estimates between 1950 and 2019. Variations in our model are due to the urbanized area and associated UHI amplification estimate uncertainties. However, the model showed consistent values of about 0.16 W/m2/% solar effective amplified areas and 1.6 W/ m2/%Δalbedo for the urbanized coverage forcing values. The basic model is additionally used to quantify feedback warming due to Arctic sea ice loss. Feedback estimates contribute to the impact of UHI forcing assessments. From our median estimates, it is concluded that UHIs contribute significantly to global warming trends. The model is versatile and also provides UHI albedo reverse forcing assessments. The results provide insight into the UHI area effects from a new perspective using a global view albedo model compared to prior ground-based measurement studies. It also illustrates the utility of using effective UHI amplification estimates when assessing their warming effect on a global scale. Keywords Urban heat islands · Albedo modeling · UHI amplification effects · UHI heat dome · Cool roofs · Sea ice warming
1 Introduction There are few recent publications about possible UHI influences on global warming. Thus, more up-to-date related studies, including UHI amplification effects that will be discussed in this paper, could offer supporting data for climate change theories and solutions. One key paper often referred to is by McKitrick and Michael’s [1, 2], who found in 2004 and 2007 using regression trends on socioeconomic, geographical, and temperature indicators that the net warming bias at the global level may explain as much as half the observed land-based warming. Another independent study often quoted by De Laat and Maurellis [3] in 2006 found very similar results.
In 2007, IPCC [4] questioned these findings stating “the locations of greatest socioeconomic development are also those that have been most warmed by atmospheric circulation changes, which exhibit large-scale coherence.” Therefore, inferring that correlation to warming was not statistically significant but a result of atmospheric oscillations. In 2009, Schmidt [5] agreed and published a paper also suggesting that McKitrick and Michael’s observed correlations were probably spurious. However, in 2010, McKitrick responded with two publications, the first [6] enti
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