The spatial and temporal structure of the urban heat island in Seoul, Korea, is investigated using near-surface temperature data measured at 31 automatic weather stations (AWSs) in the Seoul metropolitan area for the 1-yr period from March 2001 to February 2002. The urban heat island in Seoul deviates considerably from an idealized, concentric heat island structure, mainly because of the location of the main commercial and industrial sectors and the local topography. Relatively warm regions extend in the east–west direction and relatively cold regions are located near the northern and southern mountains. Several warm cores are observed whose intensity, size, and location are found to vary seasonally and diurnally. Similar to previous studies, the urban heat island in Seoul is stronger in the nighttime than in the daytime and decreases with increasing wind speed and cloud cover, but it is least developed in summer. The average maximum urban heat island intensity is 2.2°C over the 1-yr period and it is 3.4°C at 0300 local standard time (LST) and 0.6°C at 1500 LST. The reversed urban heat island is occasionally observed in the afternoon, but its intensity is very weak. An empirical orthogonal function (EOF) analysis is performed to find the dominant modes of variability in the Seoul urban heat island. In the analysis using temperature data that are averaged for each hour of the 1-yr period, the first EOF explains 80.6% of the total variance and is a major diurnal mode. The second EOF, whose horizontal structure is positive in the eastern part of Seoul and is negative in the western part, explains 16.0% of the total variance. This mode is related to the land use type and the diurnal pattern of anthropogenic heat release. In the analysis using temperature data at 0300 LST, the leading four modes explain 72.4% of the total variance. The first EOF reflects that the weakest urban heat island intensity is in summer. It is found that the urban heat island in Seoul is stronger on weekdays than weekends.

The maximum urban heat island (UHI) intensity in Seoul, Korea, is investigated using data measured at two meteorological observatories (an urban site and a rural site) during the period of 1973–96. The average maximum UHI is weakest in summer and is strong in autumn and winter. Similar to previous studies for other cities, the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is pronounced for clear skies. A multiple linear regression analysis is performed to relate the maximum UHI to meteorological elements. Four predictors considered in this study are the maximum UHI intensity for the previous day, wind speed, cloudiness, and relative humidity. The previous-day maximum UHI intensity is positively correlated with the maximum UHI, and the wind speed, cloudiness, and relative humidity are negatively correlated with the maximum UHI intensity. Among the four predictors, the previous-day maximum UHI intensity is the most ... Abstract The maximum urban heat island (UHI) intensity in Seoul, Korea, is investigated using data measured at two meteorological observatories (an urban site and a rural site) during the period of 1973–96. The average maximum UHI is weakest in summer and is strong in autumn and winter. Similar to previous studies for other cities, the maximum UHI intensity is more frequently observed in the nighttime than in the daytime, decreases with increasing wind speed, and is pronounced for clear skies. A multiple linear regression analysis is performed to relate the maximum UHI to meteorological elements. Four predictors considered in this study are the maximum UHI intensity for the previous day, wind speed, cloudiness, and relative humidity. The previous-day maximum UHI intensity is positively correlated with the maximum UHI, and the wind speed, cloudiness, and relative humidity are negatively correlated with the maximum UHI intensity. Among the four predictors, the previous-day maximum UHI intensity is the most ...

Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban‐focussed land surface models in over a decade. Here, in Phase 1 of the Urban‐PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information‐limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short‐wave radiation, sensible and latent heat fluxes, but little or no improvement in long‐wave radiation and momentum fluxes. Models with a simple urban representation (e.g., ‘slab’ schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid‐complexity models (e.g., ‘canyon’ schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three‐dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation‐only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban‐PLUMBER will evaluate models across 20 sites in different urban and regional climate zones.

This study identifies causative factors of the urban heat island (UHI) and quantifies their relative contributions to the daytime and nighttime UHI intensities using a mesoscale atmospheric model that includes a single-layer urban canopy model. A midlatitude city and summertime conditions are considered. Three main causative factors are identified: anthropogenic heat, impervious surfaces, and three-dimensional (3D) urban geometry. Furthermore, the 3D urban geometry factor is subdivided into three subfactors: additional heat stored in vertical walls, radiation trapping, and wind speed reduction. To separate the contributions of the factors and interactions between the factors, a factor separation analysis is performed. In the daytime, the impervious surfaces contribute most to the UHI intensity. The anthropogenic heat contributes positively to the UHI intensity, whereas the 3D urban geometry contributes negatively. In the nighttime, the anthropogenic heat itself contributes most to the UHI intensity, although it interacts strongly with other factors. The factor that contributes the second most is the impervious-surfaces factor. The 3D urban geometry contributes positively to the nighttime UHI intensity. Among the 3D urban geometry subfactors, the additional heat stored in vertical walls contributes most to both the daytime and nighttime UHI intensities. Extensive sensitivity experiments to anthropogenic heat intensity and urban surface parameters show that the relative importance and ranking order of the contributions are similar to those in the control experiment.

Urban heat island–induced circulation and convection in three dimensions are investigated theoretically and numerically in the context of the response of a stably stratified uniform flow to specified low-level heating that represents an urban heat island. In a linear, theoretical part of the investigation, an analytic solution for the perturbation vertical velocity in a three-dimensional, time-dependent, hydrostatic, nonrotating, inviscid, Boussinesq airflow system is obtained. The solution reveals a typical internal gravity wave field, including low-level upward motion downwind of the heating center. Precipitation enhancement observed downwind of urban areas may be partly due to this downwind upward motion. The comparison of two- and three-dimensional flow fields indicates that the dispersion of gravity wave energy into an additional dimension results in a faster approach to a quasi-steady state and a weaker quasi-steady flow well above the concentrated heating region in three dimensions. In a nonlinear, numerical modeling part of the investigation, extensive dry and moist simulations using a nonhydrostatic, compressible model with advanced physical parameterizations [Advanced Regional Prediction System (ARPS)] are performed. While the maximum perturbation vertical velocity in the linear internal gravity wave field exists in the downwind region close to the heating center, the maximum updraft in three-dimensional dry simulations propagates downwind and then becomes quasi stationary. In three-dimensional moist simulations, it is demonstrated that the downwind upward motion induced by an urban heat island can initiate moist convection and result in downwind precipitation. The cloud induced by the downwind upward motion grows rapidly to become deep convective clouds. Heavy rainfalls are localized in a region not far from the heating center by a convective precipitating system that is nearly stationary. The differences in results between two and three dimensions are explained by the presence of (moist) convergence in an additional dimension. The numerical simulation results indicate that the intensity and horizontal structure of the urban heat island affect those of circulation and convection and hence the distribution of surface precipitation.