Statistical Modelling of Urban Heat Islands under Present and Future Climate

Berk, Sarah (2023) Statistical Modelling of Urban Heat Islands under Present and Future Climate. Doctoral thesis, University of East Anglia.

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Abstract

The urban heat island (UHI) is a well-observed phenomenon, where temperature in a city is usually warmer than the surrounding rural area. The properties of the UHI are influenced by both the climate and the morphology of the city. It follows therefore, that a changing climate is expected to result in consequences for characteristics of the UHI.

Modelling the future climate of cities remains a challenge as the resolution of global climate models is too coarse to capture the scale of a city, and regional climate models are computationally expensive. To address these limitations, statistical or machine learning models can prove effective. Focusing on cities in the tropics and subtropics and those with a population of less than 1 million, this research explores the relationship between the UHI effect and climate. Satellite data, with global coverage, is used to quantify the surface UHI (SUHI) of the chosen cities using a novel physics-based machine learning model fitted to the current observations, including predictive climate variables.

With use of this machine learning model and global climate model projections, changes in the SUHI under 2 °C global warming from preindustrial are examined. Based the 50th percentile of Earth System Model outputs, the model projects 81% of the selected cities will have an increase in the annual mean SUHI to varying extents up to 1.9 °C, with an increase of over 1 °C for 14% of cities. In the warmest 3 months of the year, SUHIs in the selected cities in China are shown to have increases in magnitude of 0.8 °C, further exacerbating uncomfortable temperatures for city residents during these months.

This new approach has potential to better inform adaptation and mitigation policies in vulnerable cities, especially in south and east Asia.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Depositing User:	Chris White
Date Deposited:	09 Apr 2024 10:06
Last Modified:	09 Apr 2024 10:06
URI:	https://ueaeprints.uea.ac.uk/id/eprint/94873
DOI:

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