Spatial and Temporal Variability Patterns of the Urban Heat Island in São Paulo

Fernanda Silva, Karla Longo, Felipe de Andrade
2017 Environments  
The spatial and temporal variability patterns of the urban heat island (UHI) in the metropolitan area of Sao Paulo (MASP) were investigated using hourly temperature observations for a 10-year period from January 2002 to December 2011. The empirical orthogonal function (EOF) and cluster analysis (CA) techniques for multivariate analysis were used to determine the dominant modes of UHI variability and to identify the homogeneity between the temperature observations in the MASP. The EOF method was
more » ... used to obtain the spatial patterns (T-mode EOF) and to define temporal variability (S-mode EOF). In the T-mode, three main modes of variability were recognized. The first EOF explained 66.7% of the total variance in the air temperature, the second explained 24.0%, and the third explained 7.8%. The first and third EOFs were associated with wind movement in the MASP. The second EOF was considered the most important mode and was found to be related to the level of urbanization in the MASP, the release of heat stored in the urban canopy and the release of heat by anthropogenic sources, thus representing the UHI pattern in the MASP. In the S-mode, two modes of variability were found. The first EOF explained 49.4% of the total variance in the data, and the second explained 30.9%. In the S-mode, the first EOF represented the spatial pattern of the UHI and was similar to the second EOF in the T-mode. CA resulted in the identification of six homogeneous groups corresponding to the EOF patterns observed. The standard UHI according to the scale and annual seasons for the period from 2002 to 2010 presented maximum values between 14:00 and 16:00 local time (LT) and minimum values between 07:00 and 09:00 LT. Seasonal analysis revealed that spring had the highest maximum and minimum UHI values relative to the other seasons. Environments 2017, 4, 27 2 of 26 geometry of the urban surface (canyon-type configuration), which traps energy; the size of the population; the release of anthropogenic heat through vehicle traffic, industrial processes, human and animal metabolism and energy consumption; and a high concentration of energy-absorbing pollutants (gases and aerosols) in the urban atmosphere. Furthermore, the spatial patterns of UHIs can be strongly influenced by surface characteristics, such as green areas, water bodies and local topographies [1, 2, [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] . The most widely recognized method of studying the spatial and temporal variability patterns of UHIs is by applying statistical techniques to observational meteorological databases [20] [21] [22] [23] [24] . This approach can analyze differences between urban and rural areas by comparing the intensity of the day and night UHI periods and by identifying variations at seasonal, interannual and decadal scales [25, 26] . The metropolitan area of São Paulo (MASP) in southeast Brazil is among the top ten most highly populated cities and is one of the largest urban areas in the world. Previous attempts to determine the UHI patterns of the MASP have revealed that feedback occurs between the UHI and sea breezes [27] [28] [29] . According to Freitas et al. [29], the MASP's UHI induces the formation of an active convergence zone in the city and accelerates the movement of the sea breeze front radially towards the urban center. The mixture of dry and warm urban air, with relatively damp and cold maritime air, favors convective instability and the development of intense convective cells, transporting a significant amount of humidity from the surface to upper levels, and only when the heat island dissipates, the sea breeze progresses beyond the city. The net effect is the retention of the sea breeze over the metropolitan area for up to two hours [29] . The UHI predominates during daytime, with the maximum and minimum intensities in the afternoon and morning, respectively [30] . However, during November, December, and January the minimum UHI intensity occurs at night. July and September exhibit the highest intensity, and June and November the lowest [30] . To the best of our knowledge, previous studies of the UHI of the MASP are all based on studies of climatological time series [31] and analyses of the effects of urbanization and morphology on temperature patterns [30, 32, 33] . Recent studies have also included remote sensing [34] and atmospheric modelling [29, 35] . Other approaches have investigated the UHI according to its effects on comfort and human health; for example, areas with a more intense UHI (land surface temperature >32 • C) have a higher incidence of the disease dengue fever than other urban areas [36] . Nonetheless, the spatial and temporal patterns of urban temperatures and UHIs are not well understood for the largest metropolitan area of South America. The main goal of this study was to determine the spatial and temporal structures of the UHI of the MASP. Specifically, we chose to investigate the diurnal, seasonal and interannual patterns, with a focus on the dominant modes of variability in air temperature anomaly data, to determine the characteristic similarities and differences in the MASP. Multivariate statistical analyses were performed using observational data. The study area, datasets and a review of the statistical analysis methods are described in Section 2, the results are presented and discussed in Section 3, and a summary and conclusions are provided in Section 4. Materials and Methods Study Area and Datasets São Paulo city, in the southeast Brazil, is one of the most populated and largest urban conglomerates in South America (Figure 1a) . The MASP consists of 39 cities (Figure 1b) , and it is the largest hub of national wealth and the sixth largest city in the world. The MASP occupies a land area of 944 km 2 and has a population of approximately 20 million (IBGE/2011).
doi:10.3390/environments4020027 fatcat:674j6jmwrnhptc4uex6dmtbzfm