Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions

Abstract

In this contribution air temperature differences among Local Climate Zone (LCZ) categories are analysed with special consideration of varying synoptic conditions. Analyses are based upon an LCZ mapping for the urban area of Augsburg (Bavaria, Southern Germany) and hourly air temperature data from a comprehensive logger network. Quality checked air temperature measurements have been stratified according to season, hour of the day and weather situation. For resulting subsamples thermal differences among LCZs have been determined and appropriate statistical tests have been applied. Results confirm that built up LCZs feature higher temperatures than natural LCZs and that most distinct differences among LCZs appear under undisturbed synoptic conditions. With increasing cloudiness and in particular with increasing wind speed differences among LCZs diminish. But, even for strongly disturbed synoptic conditions statistical significance of the influence of LCZs on thermal characteristics could be assured. Thus, our findings provide clear evidence that detectable thermal differences among LCZs are not restricted to "ideal" synoptic conditions but occur as well under disturbed conditions. However, to assure not only the statistical but also the climatological and in particular the bioclimatological and human health related relevance of the documented differences among LCZs further studies incorporating appropriate metrics are intended. Keywords urban climate, local climate zones, urban air temperature, urban meteorological network, urban heat island

How well does the local climate zone scheme discern the thermal environment of Toulouse (France)? An analysis using numerical simulation data

“…Following the first assessment of the LCZ scheme by Stewart et al . () using screen‐height temperatures observed in Nagano (Japan), Vancouver (Canada), and Uppsala (Sweden), studies have been performed in multiple European cities, such as Dublin (Alexander and Mills, ), Olomouc (Lehnert et al, ), Szeged (Skarbit et al, ), Berlin (Fenner et al, ), and Augsburg (Beck et al, ) to reveal distinct air temperature signals for stations located in different LCZs. However, one major drawback of using station “point” measurements is the inability to capture spatial variations in temperature in detail.…”

Contrasting changes of urban heat island intensity during hot weather episodes

“…Secondly, UHII for each station was aggregated to an arithmetic mean value for daytime (13-16 h UTC+1) and night-time (01-04 h UTC+1) intervals each day. UHII was analysed separately for daytime and night-time periods, as it shows a distinct diurnal cycle with largest UHIIs at night (Oke 1982, Chow and Roth 2006, Erell and Williamson 2007, Fenner et al 2014, Beck et al 2018. A discussion on the selected time intervals and study period is given in supplementary Discussion D1.…”

“…With projected future increase in frequency, duration, and intensity of heat waves globally (Meehl and Tebaldi 2004, Fischer and Schär 2010, Russo et al 2014, as well as projected ongoing urbanization (United Nations 2015), the question whether UHI intensities (UHIIs) are exacerbated during such episodes is of high relevance for risk assessment. Beside influences of size, morphology, and contiguity of each city onto UHIIs, both in air as well as surface temperatures (Arnfield 2003, Debbage and Shepherd 2015, Zhou et al 2017, UHIIs are largely determined by weather conditions, with dry, clear, and calm conditions favouring large UHIIs (Morris et al 2001, Kim and Baik 2005, Erell and Williamson 2007, Arnds et al 2017, Beck et al 2018.…”

Towards the determination of driving factors of varying LST-LCZ relationships: A case study over 25 cities

“…Local climate zoning for all case study cities was performed following the standardized "WUDAPT-workflow" (Bechtel & Daneke, 2012;Bechtel et al, 2015). This approach consists of two main steps (Beck et al, 2018): 1) generating so-called "training areas" (TA) representing typical surface structure morphology for each LCZ, 2) applying the properties of these TAs to assign each pixel of selected Landsat images to its corresponding LCZ by a random forest algorithm implemented in the SAGA open source GIS software .…”

“…Cai et al (2017) in Yangtze River Delta (China) showed, as well, that the air temperature has considerable connection with LCZs. Beck et al (2018) investigated the relations between air temperature and LCZs in Augsburg (Germany) under various synoptic conditions by using a comprehensive logger network. Their results confirmed conformity between air temperature and LCZs.…”