4.7 Article

Climate driven trends in London's urban heat island intensity reconstructed over 70 years using a generalized additive model

期刊

URBAN CLIMATE
卷 40, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.uclim.2021.100990

关键词

Climate; Generalized additive model; GAM; Extreme values; Time series; Urban heat island; UHI; Variability

资金

  1. UK Engineering and Physical Sciences Research Council (EPSRC) [EP/P002285/1]
  2. EPSRC [EP/P002285/1] Funding Source: UKRI

向作者/读者索取更多资源

This study utilizes a generalized additive model to observe UHI intensity in central London, finding significant variability in UHII seasonally and annually. Extreme value analysis shows that monthly mean maximum UHIIs are likely to exceed 2.75 degrees C once every 11 years. Despite a warmer background climate, London's UHII has not significantly changed across the analysis period (1950-2019).
Long-term urban heat island (UHI) observations are uncommon and where available, are generally unable to distinguish changing climate drivers from urban expansion; neither driver is treated independently. We overcome this limitation using a generalized additive model to learn the variability in UHI intensity (UHII) at a central London weather station (St James's Park) over a 10-year observation period (2010-2019). We then use the model to reconstruct 70 years (1950-2019) of monthly night-time UHII variability using ERA5 reanalysis data both as a reference in UHII calculation and for the predictors. We find considerable variability both seasonally and annually within the UHII time series (monthly mean maximum UHIIs are 1.4-2.9 degrees C). Applying extreme value analysis to the time series we show that monthly mean maximum UHIIs are likely to exceed 2.75 degrees C once every 11 years. Considering that most studies observe or model UHIIs for less than a year, they will likely misrepresent this UHII variability. Nevertheless, despite moving to a warmer background climate, London's UHII has not significantly changed across the period of analysis (1950-2019). The data-driven methods we create in this study are easily transferable to other cities.

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