Urban Spatial Patterns and Heat Exposure in the Mediterranean City of Tel Aviv

Mandelmilch, Moshe; Ferenz, Michal; Mandelmilch, Noa; Potchter, Oded

doi:10.3390/atmos11090963

Cited by 22 publications

(17 citation statements)

References 48 publications

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“…These adverse effects might be amplified by increasing urbanization (Grimm et al, 2008;United Nations, 2019), transported or local air pollution (Tressol et al, 2008;Solberg et al, 2008), and urban heat island effect (Mandelmilch et al, 2020). Specifically, Cramer et al (2018) noted that the combination of the aforementioned impacts in the Mediterranean basin may exacerbate their magnitude or could produce successive, more frequent stress periods, which the least resilient countries would find difficult to cope with.…”

Section: Societal Impactsmentioning

confidence: 99%

ESD Reviews: Extreme Weather and Societal Impacts in the Eastern Mediterranean

Hochman

Marra

Messori

et al. 2021

Preprint

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Abstract. Gaining a holistic understanding of extreme weather, from its physical drivers to its impacts on society and ecosystems, is key to supporting future risk reduction and preparedness measures. Here, we provide an overview of the state-of-the-art, knowledge gaps and key open questions in the study of extreme weather events over the vulnerable eastern Mediterranean. This region is situated in a transition zone between subtropical and mid-latitude climates. Extreme weather is mainly governed by the large-scale atmospheric circulation and its interaction with regional synoptic systems, i.e., Cyprus Lows, Red Sea Troughs, Persian Troughs, ‘Sharav’ Lows, and high-pressure systems. Complex orographic features further play an important role in the generation of extreme weather. Most extreme weather events, including heavy precipitation, cold spells, floods and wind storms, are associated with a Cyprus Low or Active Red Sea Trough, whereas heat waves are related with either the Persian Trough and Sub-Tropical High-pressure systems in summer, or the ‘Sharav’ Low during spring time. Heat waves and droughts are projected to significantly increase in both frequency and intensity. In future decades, changes in heavy precipitation frequency and intensity may vary in sign and magnitude depending on the scale, severity and region of interest. There are still relatively large uncertainties concerning the physical understanding and the projected changes of cold spells, wind storms and compound events, as these types of events received comparatively little attention in the literature. We further identify knowledge gaps that relate to the societal impacts of extreme weather. These gaps mainly relate to the effects extreme weather may have on mortality, morbidity and infrastructure in the eastern Mediterranean. Research is currently limited in this context, and we call to strengthen the database of analyzed case-studies. We trust that this can only be suitably accomplished by inter-disciplinary and international regional collaborations, in spite of political unrest.

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Section: Societal Impactsmentioning

confidence: 99%

ESD Reviews: Extreme Weather and Societal Impacts in the Eastern Mediterranean

Hochman

Marra

Messori

et al. 2021

Preprint

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“…The variability in the climate elements in tropical and subtropical coastal cities is observed in a lot of research, such as [67][68][69][70][71][72], but parallel to the coastline (urban waterfront) it is observed in a few [73,74], including solar radiation [75,76], or associated with wind and thermal comfort [77], which may demonstrate the influence of the verticalization of the skyscrapers on the spatial conformation of the Ta, mainly, and RH, especially in Brazil [78]. In other words, the vertical barrier of the buildings tends to dry and warm air as one moves away from the waterfront, but the RH variations are always a result of the water content in the air (saturated content), which is a result of the performance of the upper climate scales.…”

Section: Meteorological Conditions In Urban Areamentioning

confidence: 99%

Thermo-Hygrometric Variability on Waterfronts in Negative Radiation Balance: A Case Study of Balneário Camboriú/SC, Brazil

et al. 2021

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Extensive urbanization around the world has resulted in the consumption of massive vegetated areas and natural resources. To this end, one strategy for urban development is to consolidate urban areas. In Balneário Camboriú/SC, Brazil, this trend has transformed the city into a vertical built-up area on its coastal strip, accommodating a large amount of buildings both in terms of quantity and number of floors. This research aims to quantify the thermo-hygrometric fluctuation on the waterfront of Balneário Camboriú, in negative radiation balance. To acquire the data on air temperature (Ta) and relative humidity (RH), two mobile transects and measuring at two fixed points were made in a situation of negative radiation balance on 26 August 2019, in the winter period of the Southern Hemisphere. The collection work began at 06:00:00 a.m. (before sunrise, the peak of the negative radiation balance), on Atlântica Avenue (waterfront) and Brasil Avenue (parallel to the waterfront). It was verified that the Ta varied from 16.0 °C to 19.0 °C, and the RH remained over 80% during the entire route. At the meteorological shelters, the temperature presented a variation from 14.4 °C to 17.7 °C, and the RH ranged from 79.6% to 91.3% between the two points. The spatial variability in the Ta and RH along the paths travelled and at the fixed points is directly related to the land cover, represented especially by the buildings’ verticalization and data collection time.

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“…The direct way to determine LCZ classes is to match the calculated values of LCZ parameters with their reference ranges. This method has been most widely used to distinguish LCZs based on the LCZ parameters provided by Stewart & Oke (2012) or custom parameters (Agathangelidis et al, 2019;Bartesaghi Koc et al, 2018, 2017Cai et al, 2019;Emmanuel & Loconsole, 2015;Jin et al, 2020;Leconte et al, 2017Leconte et al, , 2015Mandelmilch et al, 2020;Mitraka et al, 2015;Nassar et al, 2016;Ndetto & Matzarakis, 2015;Perera & Emmanuel, 2018;Shi et al, 2018;Thomas et al, 2014;Villadiego & Velay-Dabat, 2014;Wang et al, 2018b;Zheng et al, 2018). In addition, some studies have used other methods to determine LCZ types, such as the score assignment method (Lelovics et al, 2014;Unger et al, 2014), the decision-making algorithm (Chen et al, 2020b;Quan, 2019;Zhao et al, 2019a), the multi-dimensional linear interpolation method (Quan et al, 2017), the Naive Bayes algorithm (Hammerberg et al, 2018), the random forest algorithm (Hu et al, 2019), and the k-means method (Hidalgo et al, 2019;Kwok et al, 2019;Zhan et al, 2018).…”

Section: Classification Rulesmentioning

confidence: 99%