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Modeling the intra-urban nocturnal summertime air temperature fields at a daily basis in a city with complex topography [1]

['Moritz Burger', 'Oeschger Centre For Climate Change Research', 'University Of Bern', 'Bern', 'Institute Of Geography', 'Moritz Gubler', 'Institute For Lower Secondary Education', 'Bern University Of Teacher Education', 'Stefan Brönnimann']

Date: 2022-12 

Estimation of nocturnal air temperature variability using LUR models

The present study aims to model nocturnal UHI intensities and map summertime nighttime air temperatures in a city with complex terrain using a LUR approach, whereas three different model structures and four CAD variables are tested. Hereby, one crucial step is the selection of the critical meteorological and land use variables. It has been shown in various studies, that large UHI intensities are found during “calm and clear” (low wind speed and clear sky) nights after radiation intense days [4,18–21]. This research leads to similar findings for the city of Bern, but with the addition, that wind direction may also play an important role depending on the location of the rural reference station. Here, the rural reference station is located in the northeast of the city (Fig 1B), and if the winds originate from this direction, air masses close to the reference station were likely to be exchanged faster than in the city, due to blocking by buildings or orography. Hence, the temperature differences between urban sites and the rural reference station in Zollikofen are even larger during nights with northerly winds than during calm nights. Conversely, winds from southeast or southwest lead to strongly declining UHI intensities (Fig 3E). Additionally, little attention has so far been given to the inclusion of precipitation variables in similar LUR modeling studies, since the focus has mainly been on conditions favoring large UHI intensities [4,12,14]. However, the weakening effect of precipitation on the UHI intensity in general is well documented [19,22] and should be incorporated if longer timespans are investigated. Here, a Boolean precipitation variable instead of a numerical variable is used, which can be justified by the fact that precipitation events during the night likely vanishes a substantial share of the urban heat, independently of the amount of precipitation.

The land use variables that are significant in this study differ slightly from a previous study in the same city, which only focused on land use variables and heatwave episodes [17]. While the main pattern of open and vegetated areas having a cooling effect is similar, the cooling effect of forested areas and dense vegetation is more explicit in this study. Contrariwise, the cooling effect of water seems to be less pronounced if the entire summer is analyzed, which might be due to the water temperatures not significantly being cooler than the air temperatures during the night. Within the densely built areas, unsealed garden areas and vegetation lead to lower local temperatures (Figs 4 and S2–S9). Another important difference compared to the previous study is that altitude is directly incorporated in the model with the variable AD. This allows for a modeling of warm hilltops and slopes during calm and clear nights (Fig 4A, 4C and 4D), which is an important feature since such inversion patterns are a typical characteristic of the local climate of Bern [37]. However, when analyzing such rural, hilly areas, it is important to keep in mind that only a few rural stations were available for model calibration and validation, making uncertainties larger in these locations of the study area.

The evaluation with additional data (Table 6) shows that the model performance (R2 and RMSE) is similar (summer 2018) or a bit lower (summer 2021) compared to the calibration data (Table 4). The air temperatures are rather underestimated during 2018 (MB -0.21 to -0.29 K) and overestimated during 2021 (MB +0.41 to +0.43 K; Table 6). One likely reason for these differences can be found in the fact that Switzerland experienced a very hot and record dry summer 2018 and a rather cool and very wet summer 2021 [52,53]. The differences in air temperature and precipitation might thus have led to a very different state of the vegetation during these summers, having larger evaporative cooling capacities during summer 2021, which may have caused lower UHI intensities. In this study, the land use variables including vegetation are treated as static and thus do not account for changes due to inter-annual variability of meteorological background conditions. An inclusion of such an additional variable accounting for the year to year changes in the state of the vegetation (e.g. NDVI) would be an interesting supplement for future studies.

The presented LUR models are subject to additional limitations being worthwhile to be mentioned. As such, shallow depressions may cause air temperature differences of several K during nighttime [25], even within a city. The application of the models on a shallow depression shows that none of the models is able to represent such small-scale air temperature differences in an adequate manner (Table 7). This might be due to the rather large buffer radii of the land use variables (up to 1000 m; Table 1) or the cooling effect of the CAD variables being to weak: A detailed analysis of the CAD variables shows that they are only able to capture about 0.6 K of the cooling in the Egelsee depression, whereas differences of up to 3 K are reached under favorable conditions. Another limitation is that the models tested here are static and do not take into account weather patterns from previous days and resulting variables depending on those, such as soil moisture, which may influence the present day air temperature patterns [20]. Lastly, the exposition of a location to the wind is not incorporated, since no land use variable was found which was able to represent this aspect.

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[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000089

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