The Role of Rural Variability in Urban Heat Island Determination for Phoenix, Arizona

Hawkins, Timothy W.; Brazel, Anthony J.; Stefanov, William L.; Bigler, Wendy; Saffell, Erinanne M.

doi:10.1175/1520-0450(2004)043<0476:trorvi>2.0.co;2

Cited by 158 publications

(100 citation statements)

References 20 publications

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“…The heat island intensity is also likely to be a function of the characteristics of the rural surface (e.g., Hawkins et al 2004). Figure 4 also shows the simulated heat island with a rural surface composed of needleleaf evergreen trees rather than grassland.…”

Section: ͑1͒mentioning

confidence: 99%

An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations

Oleson

Bonan

Feddema

et al. 2008

Journal of Applied Meteorology and Climatology

102

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In a companion paper, the authors presented a formulation and evaluation of an urban parameterization designed to represent the urban energy balance in the Community Land Model. Here the robustness of the model is tested through sensitivity studies and the model's ability to simulate urban heat islands in different environments is evaluated. Findings show that heat storage and sensible heat flux are most sensitive to uncertainties in the input parameters within the atmospheric and surface conditions considered here. The sensitivity studies suggest that attention should be paid not only to characterizing accurately the structure of the urban area (e.g., height-to-width ratio) but also to ensuring that the input data reflect the thermal admittance properties of each of the city surfaces. Simulations of the urban heat island show that the urban model is able to capture typical observed characteristics of urban climates qualitatively. In particular, the model produces a significant heat island that increases with height-to-width ratio. In urban areas, daily minimum temperatures increase more than daily maximum temperatures, resulting in a reduced diurnal temperature range relative to equivalent rural environments. The magnitude and timing of the heat island vary tremendously depending on the prevailing meteorological conditions and the characteristics of surrounding rural environments. The model also correctly increases the Bowen ratio and canopy air temperatures of urban systems as impervious fraction increases. In general, these findings are in agreement with those observed for real urban ecosystems. Thus, the model appears to be a useful tool for examining the nature of the urban climate within the framework of global climate models.

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Section: ͑1͒mentioning

confidence: 99%

An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations

Oleson

Bonan

Feddema

et al. 2008

Journal of Applied Meteorology and Climatology

102

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“…Fujibe (2008;hereafter F08) analyzed the data of AMeDAS (Automated Meteorological Data Acquisition System) for 27 years, and found anomalous temperature trends even at slightly urbanized sites, with population density of less than 300 km 2 . The surface temperature can also be influenced by the microscale environment of the observation site (Hawkins et al 2004;Mahmood et al 2006;Runnalls and Oke 2006;Pielke et al 2007). Kondo (2009) documented the temperature increase associated with a decrease of wind speed at some stations in Japan, as a result of decreasing exposure that controls ventilation and vertical heat diffusion.…”

Section: Introductionmentioning

confidence: 99%

Relation between Long-term Temperature and Wind Speed Trends at Surface Observation Stations in Japan

Fujibe¹

2009

SOLA

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In order to detect microscale influences on observed surface air temperature changes, 29 years' data on the AMeDAS network were used to identify the relation between temperature trends and wind speed trends, which were regarded as reflecting the changes of site exposure. The results were found to differ according to time of the day and the mean wind speed of the site. For stations with relatively high mean wind speed ( 1.5 m s 1 ), a significant negative correlation is found between daytime temperature trends and wind speed trends. For stations with low wind speed (< 1.0 m s 1 ), evening temperature trends are negatively correlated with wind speed trends, whereas daytime temperature trends tend to be positively correlated with them. These facts give statistical evidence of the dependence of temperature trends on microscale environmental changes, with complications according to the degree of site exposure.

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“…Because (a) local climates can vary significantly within a metropolitan area and (b) the signal of an urban heat island greatly depends on the available sites [14,39], including this valuable metadata is crucial for accurately quantifying the results of the research conducted. Oke [40] noted that if a simple, common classification system were in place, scientists with no direct experience with the sites used in a particular study could gain an appreciation of site characteristics and have an objective basis on which to include or exclude that data.…”

Section: Methodsmentioning

confidence: 99%

“…Because of the varied surface conditions between urban and rural zones, urban areas worldwide have yielded warmer conditions when compared to nearby rural areas [13][14][15]. This phenomenon, known as the urban heat island, results from differential thermal storage between rural and urban areas [16] and is most prominent at night and under synoptic high-pressure systems [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Challenges Associated with Classifying Urban Meteorological Stations: The Oklahoma City Micronet Example

Schroeder¹,

Basara²,

Illston³

2010

TOASCJ

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During 2007During -2008, a dense network of meteorological stations was deployed across the Oklahoma City metropolitan area to collect real-time, research-quality observations of atmospheric variables throughout the urban environment: the Oklahoma City Micronet (OKCNET). Because surface characteristics can be vastly different between rural and urban areas as well as throughout a city, significant variability exists in the local microclimates observed by meteorological stations deployed in an urban area. As such, documenting the characteristics near any site (i.e., metadata) is critical to fully understand the overall representativeness of the site and the associated evolution of atmospheric conditions. To date, a universal classification system for urban meteorological stations does not exist. Thus, this study utilized four different methodologies to classify OKCNET sites and increase the metadata for the individual sites and the overall network. The results demonstrated that while each classification system had specific merits, significant challenges existed in establishing consistent metadata for the sites due to (a) limitations associated with the methodologies and (b) the heterogeneity of surface conditions. In particular, stations deployed within the transition zones form urban to suburban and suburban to rural posed the greatest challenges in establishing consistent metadata for the sites.

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The Role of Rural Variability in Urban Heat Island Determination for Phoenix, Arizona

Cited by 158 publications

References 20 publications

An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations

An Urban Parameterization for a Global Climate Model. Part II: Sensitivity to Input Parameters and the Simulated Urban Heat Island in Offline Simulations

Relation between Long-term Temperature and Wind Speed Trends at Surface Observation Stations in Japan

Challenges Associated with Classifying Urban Meteorological Stations: The Oklahoma City Micronet Example

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