Heat waves (HWs) are natural hazards characterised by episodes of hot weather. However, in the absence of a universal definition a wide variety of definitions is applied. In this study, ten different air temperature (T) based HW definitions are applied to the urban region of Berlin, Germany, to investigate and compare the occurrence and duration of HWs, and their long‐term trends from 1893 to 2017. We studied how long‐term trends depend on different definition of HWs, as well as if long‐term mean values and trends differ between inner‐city and peripheral locations of Berlin. Generally, results show significant increases in HW occurrence and duration for most definitions, although large differences exist between them. Temporal agreement between the definitions is low, 15 episodes in 125 years are identified by all definitions as HWs. Inner‐city regions of Berlin are subject to more frequent and longer HWs than peripheral regions, if definitions based on daily minimum or mean T are applied. Results also show that trend estimations of HW characteristics for HW definitions with “extreme” values for their detection criteria (e.g., in terms of duration or threshold) are highly sensitive to the applied method. We conclude that depending on the question under investigation, different HW definitions might be optimal and hence attempts for the development of “universal” definitions need to take this into account.

The urban climate, especially the near-surface air temperature (T), is influenced to large amounts by urban surface properties on the local-scale. Landscape classification schemes, like the Local Climate Zone (LCZ) concept, classify neighbourhoods on this scale based on their surface properties, neglecting sub-scale heterogeneity in the urban structure and its potential effects on T. To quantify sub-scale T variability, a measurement campaign with eleven stationary T sensors was conducted within one LCZ (class 2 B , compact midrise with scattered trees) in Berlin, Germany, during 22 days in summer 2016. Correlation analyses were performed between observed spatial T differences and micro-scale morphometric parameters around the measurement sites, such as sky view factor and building surface fraction. The results show mean night-time T differences of up to 1 K between the different sites. On a clear, calm and dry day, the daytime difference reached 3 K. At night-time, the variability can be best explained by the building surface fraction within a radius of 50 m. Further, a nocturnal cooling influence of a neighbouring green space could be observed. The observed micro-scale T variability was smaller than T differences to other LCZ classes, highlighting the applicability of the LCZ concept.

Model‐based studies on urban heat islands can be seriously affected by errors in near‐surface air temperature (T2), especially if errors differ between cities and their rural surroundings. Furthermore, errors in T2 strongly depend on selected parameterisation schemes, in particular on the planetary boundary layer (PBL) scheme and the urban canopy model (UCM). We developed the Central Europe Refined analysis (CER), a dataset generated by dynamically downscaling a global atmospheric reanalysis with the Weather Research and Forecasting (WRF) model for Central Europe (30 km), Germany (10 km), and the region of Berlin‐Brandenburg (2 km). CER data were analysed to study urban–rural and intra‐urban differences in T2 for Berlin as well as to test the sensitivity of T2 against two different PBL schemes, a mosaic approach, and three UCMs with different levels of complexity. Results were evaluated using data from 22 weather stations. All tested configurations simulated T2 with small deviations from observations. The PBL schemes predominantly control the deviation of T2. From the tested PBL schemes, the Bougeault–Lacarrére scheme performed better than the Mellor–Yamada–Janjić scheme. The application of different UCMs and the mosaic approach also influenced the deviations, but not as strongly as the PBL schemes. The performance of the UCMs regarding the representation of intra‐urban and urban–rural differences showed that differences were largest when using a complex multi‐layer UCM. Overall, the simplest model showed lowest deviations. We conclude that more research on UCMs is required because complex UCMs showed potentials but did not outperform the simple slab model.

Cities typically exhibit higher air temperatures than their rural surroundings, a phenomenon known as the urban heat island (UHI) effect. Contrasting results are reported as to whether UHI intensity (UHII) is exacerbated or reduced during hot weather episodes (HWEs). This contrast is investigated for a four-year period from 2015 to 2018, utilising a set of observational data from high-quality meteorological stations, as well as from hundreds of crowdsourced citizen weather stations, located in the urban region of Berlin, Germany. It can be shown that if HWEs, defined here as the ten percent hottest days or nights during May-September, are identified via daytime conditions, or by night-time conditions at inner-city sites, then night-time UHII is exacerbated. However, if HWEs are identified via night-time conditions at rural sites, then night-time UHII is reduced. These differences in UHII change can be linked with prevalent weather conditions, namely radiation, cloud cover, wind speed, precipitation, and humidity. This highlights that, beside land cover changes, future changes in weather conditions due to climate change will control UHIIs, and thus heat-stress hazards in cities.

Episodes of hot weather and poor air quality pose significant consequences for public health. In this study, these episodes are addressed by applying the observational data of daily air temperature and ozone concentrations in an event-based risk assessment approach in order to detect individual heat and ozone events, as well as events of their co-occurrence in Berlin, Germany, in the years 2000 to 2014. Various threshold values are explored so as to identify these events and to search for the appropriate regressions between the threshold exceedances and mortality rates. The events are further analyzed in terms of their event-specific mortality rates and their temporal occurrences. The results reveal that at least 40% of all heat events during the study period are accompanied by increased ozone concentrations in Berlin, particularly the most intense and longest heat events. While ozone events alone are only weakly associated with increased mortality rates, elevated ozone concentrations during heat events are found to amplify mortality rates. We conclude that elevated air temperatures during heat events are one major driver for increased mortality rates in Berlin, but simultaneously occurring elevated ozone concentrations act as an additional stressor, leading to an increased risk for the regional population. ozone concentrations [17,21,22]. However, studies focusing on the relation between both stressors themselves, as well as their combined effect on human health, also reveal inconsistent results for different regions or their relative effect-contribution [13,17,23]. A comparison of 25 Italian cities revealed a strong effect modification by ozone concentrations to the air temperature-mortality association only in some cities, whereas other cities showed no effect modification [21]. Similar results were found in a Europe-wide study. However, they found no evidence for an effect modification when heat waves were considered instead of air temperature for any of the cities where the study was carried out [22]. Other investigations could not even find any interaction or confounding between air temperature and ozone to affect all-cause mortality [24,25]. Inconsistencies in the results are mainly attributed to the local-specific characteristics of the city under investigation (e.g., [21][22][23]). This underlines the need for a better understanding of the mechanisms between these environmental stressors and their resulting health effects.Physically, both health stressors are closely linked to each other, but the nature of the relationship between air temperature and ozone is highly complex and depends on a number of variables. The formation of ozone as a secondary pollutant is driven by the photochemical oxidation of precursors like volatile organic carbons, methane, and carbon monoxide [26][27][28]. Strong ozone forming can be observed during days of warm, cloud-free, and calm conditions [29][30][31]. Persistent atmospheric conditions of low winds speeds accompanied by high air temperature may enhance the accumulation ...