Temporal modes and spatial patterns of urban air temperatures and limitations of heat adaptation. (October 2020)

• Record Type: Journal Article
• Authors: Schlink, Uwe; Mohamdeen, Abdelrhman; Raabe, Armin
• Is Part Of: Environmental modelling & software. Volume 132(2020)
• Publication Date: 2020-10
• Subjects: Urban air temperature -- Empirical orthogonal functions -- EMVI-met -- Climate change -- Adaptation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104773
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A hot-spot region of climate change is the Middle East where temperatures actually have a rising tendency and this will increase in future. To mitigate the progressing thermal burden urban planning has to develop adaptation measures. On the basis of micrometeorological simulations for a quarter in Cairo we suggest a decomposition of air temperatures into two temporal and two spatial patterns, respectively explaining 97% and 94% of the temperature variability. We find that land-use has a significant impact on the spatial temperature distribution and should be modified for the purpose of heat adaptation. However, just 13% of the spatial temperature variability are explained by land-use, which is a quite limited impact. Regional weather conditions are the dominant factor for the spatial as well as the temporal development of urban heat. Highlights: Spatiotemporal fields of urban air temperatures are characterized by temporal modes and spatial patterns. Patterns occur due to strong auto-correlations in urban atmospheric data and can be utilized for adaptation measures. The diurnal cycle explains 73% of temporal temperature variability. 13% of the spatial temperature variability depend on land-use and land cover (LULC). Adaptation strategies based on LULC changes have limited effectiveness in air temperatures.