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Abstract:The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data. To achieve this goal, a time series of Landsat TM/ETM+/OLI-TIRS images was acquired from 1984 to 2016 and analyzed. In addition, nocturnal MODIS images acquired from 2005 to 2015 were used to evaluate the nighttime SUHI. In order to better analyze intense heat produced by urban core, SUHI intensity (SUHII) was computed by quantifying the difference of land surface temperature (LST) between urban and rural areas. The urban core SUHII appears more significant in winter seasons than during summer, while the pattern of SUHII becomes moderate during intermediate seasons. During winter, the average daytime SUHII gradually increased in the residential area of Casablanca and in some small peri-urban cities by more than 1 • C from 1984 to 2015. The industrial areas of the Casablanca region were affected by a significant rise in SUHII exceeding 15 • C in certain industrial localities. In contrast, daytime SUHII shows a reciprocal effect during summer with emergence of a heat island in rural areas and development of cool islands in urban and peri-urban areas. During nighttime, the SUHII remains positive in urban areas year-round with higher values in winter as compared to summer. The results point out that the seasonal cycle of daytime SUHII as observed in the Casablanca region is different from other mid-latitude cities, where the highest values are often observed in summer during the day.

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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.

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