Determination of the urban heat island intensity in villages and its connection to land cover in three European climate zones

Abstract

Although urban heat islands (UHIs) have been found in many cities throughout the world, work on smaller settlements is still limited, especially concerning variations connected to climate zones. Meteorological stations are often regarded as rural when located in a village or small town, and any temperature bias is assumed negligible. In this paper, we therefore present air temperature variations and their connection to land cover in 3 European villages, boreal Haparanda, temperate Geisenheim, and Mediterranean Cazorla, all of them hosting long temperature records that might be biased. The villages were equipped with temperature sensors, and the surrounding areas were digitized to compare UHI effects and to evaluate the contribution of land cover on local cooling and warming. This sensor network reveals significant village UHIs in all 3 climate zones, with seasonal maximum intensities decreasing from north (1.4°C) to south (0.9°C). During summer, urban warming is most emphasized in minimum temperatures in boreal Haparanda and temperate Geisenheim but weakest in Mediterranean Cazorla, presumably because of limited plant transpiration due to high insolation and drought stress. Urban warming is correlated with building density in all 3 settlements and shows little seasonal variation. Even though the mountain river passing Cazorla substantially cools ambient temperatures at distances <100 m, mitigation of warming through water bodies is limited in the Central and Northern European sites. Our results suggest to treat rural instrumental station data with care and to avoid using data recorded in villages as unbiased reference records to adjust measurements from larger cities.

High-Resolution Temperature Variability Reconstructed from Black Pine Tree Ring Densities in Southern Spain

“…Such early-calibration-period decreases are fairly common [62] and are typically related to increased observational temperature uncertainties due to changes in instrumentation, data gaps, station relocations, etc. [44,[63][64][65][66][67][68][69][70][71]. The recent, post-1980 correlation decline, seen in the longer seasonal means (FMAM&S and FMAM&SO), is more relevant, however, and deserves further attention before producing a formal temperature reconstruction based on the CNP MXD data.…”

“…On the other hand, Cazorla is the closest, and is therefore likely the most representative station for the calibration of high-elevation CNP MXD data (Table 2), which again supports its use. An assessment of the underlying reasons of the deviating 1970s temperatures, be it changes in the station's environment and instrumentation or (real) spatial variability, would require studying the station history and metadata, and monitoring current temperatures at historical sites [66,67], which is beyond the scope of this paper. From a tree-ring perspective, the 1970s proxy-target difference reported here adds uncertainty to any reconstructed lower frequency deviation derived from the CNP MXD data.…”

Integration of a Building Energy Model in an Urban Climate Model and its Application

“…Here, the weather station is situated closer to villages and located on a small hill where cold air may flow downhill, being replaced by warmer near-surface air from above. As even small villages may exhibit the UHI effect (Dienst et al 2018(Dienst et al , 2019, the nocturnal atmosphere at Lindenberg is less stable than that at Kaniswall. In summer at Lindenberg, the model-derived air temperature shows good agreement with observations, with a small overestimation during the night and at noon.…”

Impact Mechanism and Improvement Strategy on Urban Ventilation, Urban Heat Island and Urban Pollution Island: A Case Study in Xiangyang, China

“…Rapid urbanization has been changing urban forms and functions continuously (Stone, 2009), as manifested in increasing surface roughness (Chen, Liang & Dirmeyer, 2019), decreasing ventilation potential, the expansion of impervious surfaces (Yang, Chen et al, 2019) and the weakening of surface transpiration (Dienst, Lindé n, & Esper, 2018). These changes make it difficult to effectively evaluate the energy dissipation and waste emissions generated by populations and industries over short periods of time (Kuang et al, 2015).…”