Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney.

The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999-2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06-0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized.

Holistic approach to assess co-benefits of local climate mitigation in a hot humid region of Australia.

Overheated outdoor environments adversely impact urban sustainability and livability. Urban areas are particularly affected by heat waves and global climate change, which is a serious threat due to increasing heat stress and thermal risk for residents. The tropical city of Darwin, Australia, for example, is especially susceptible to urban overheating that can kill inhabitants. Here, using a modeling platform supported by detailed measurements of meteorological data, we report the first quantified analysis of the urban microclimate and evaluate the impacts of heat mitigation technologies to decrease the ambient temperature in the city of Darwin. We present a holistic study that quantifies the benefits of city-scale heat mitigation to human health, energy consumption, and peak electricity demand. The best-performing mitigation scenario, which combines cool materials, shading, and greenery, reduces the peak ambient temperature by 2.7 °C and consequently decreases the peak electricity demand and the total annual cooling load by 2% and 7.2%, respectively. Further, the proposed heat mitigation approach can save 9.66 excess deaths per year per 100,000 people within the Darwin urban health district. Our results confirm the technological possibilities for urban heat mitigation, which serves as a strategy for mitigating the severity of cumulative threats to urban sustainability.