Urban Heat Islands and Their Relation to Urban Ecology

Module Authored by G. Bothun, Department of Physics, University of Oregon

Introductory Flash Movie - See the Author!!

Module Learning Objectives

To use some data to characterize the magnitude of the UHI phenomena.
To understand the energy balance and energy flows at the urban/rural interface
To develop mitigation strategies

Components of the Urban Heat Island

Clearly, an Urban Heat Island (UHI) occurs as a result of converting green space into asphalt/concrete. The absorption and emission profiles of asphalt/concrete or substantially different than green space and/or soil.
In particular, asphalt/concrete have relatively low specific heats but relatively high thermal conductivity. This physics means that concrete/asphalt can absorb heat and increase their temperature relatively fast and once they have become heated, they can effectively radiate that excess heat to the atmosphere for some time.
Because it takes a few hours for this material to absorb the incident sunshine, the temperature contrast between the urban and rural areas usually maximizes in the late afternoon. The actual value of the temperature contrast varies as a function of concentration of materials on the landscape. This is summarized in the Figure below:

Figure 1: Cross Sectional Profile of a typical summertime UHI

The 7 degree maximum temperature differential shown above is the typical average contrast seen in well studied urban areas such as Atlanta GA, Washington DC or London England.

First Student Exercise: Using the image below, deconstruct that 2D contour map of the London England into a 1D cross sectional profile similar to Figure 1. Note that you will have to convert from degrees C to degrees F.

Measuring the amplitude of the UHI

Characterization of the amplitude of the UHI effect is done by making daily temperature measurements between the urban and rural areas. The data sets below are daily maximum (high) and daily minimum (low) temperatures for December, January and February (representing winter) and June, July and August (representing summer) for two years for downtown Wilkes-Barre, PA, and the nearby, less urban Wilkes-Barre/Scranton International Airport:

Second Student Exercise: Using the data links above, before the following analysis:

Determine the average difference in winter and summer time high temperatures between the Urban (downtown) and Rural (airport locations). You will want to average all of the daily data to do this.
Determine the average difference in winter and summer low temperatures between Urban an Rural Areas.
Determine if there is a correlation between the temperature difference and the actual rural temperature itself.
Based on this analysis, under which scenario (e.g. summertime high, wintertime low, etc) is the UHI effect the strongest?
Does the actual UHI effect (e.g. the temperature difference) dependent on ambient rural temperature or does the UHI represent a constant offset independent of actual air temperature? If its the former, suggest a reason. If its the latter, suggest a reason why the UHI effect would be temperature independent.

Scaling the UHI with Population Increases

Now that you have used data to determine the amplitude of the UHI for a specific location, it is of interest to examine scaling behavior. That is, how much does the temperature differential increase with the size or area of the city.
The data below refer to some averages for cities in europe.

Third Student Exercise:

Using the data shown above, predict what the temperature differential would be for a city size of 20,000,000 and, god forbid, 100,000,000.
Is this relation linear or non-linear? That is, if you double the size of the city, does the temperature difference also double?
What factors do you think are limiting the overall amplitude of the temperature differential?

Clearly the relation between city size and maximum temperature difference is complex and depends upon the landscape the city is set against. For instance, a large metropolitan area in a desert, probably has a smaller signature than a similar size metropolis set in a cow pasture.
The data below, show the trends in the UHI effect, in units of degrees C or F per decade, for different urban environments for which sufficient data exists to define a trend. Note that this data is averaged annually and does not refer to the maximum tempearture differential as we have used previously. Thus, for instance, Los Angeles shows a growth rate of .8 degrees F per decade meaning that every 10 years, the average annual temperature in the LA metro area increases by .8 degrees relative to the average annual temperature in the surrounding (rural) environment.

Fourth Student Exercise:

Suggest a set of reasons why the growth of the UHI effect is so much larger in Los Angeles and San Diego than in San Francisco or Baltimore.

Pima County Arizona, where Tucson is located, is a rapidly sprawling urban disaster in an arid, naturally hot climate. The figure below shows the very good correlation between average annual temperature and the county population. In this case, its actual average temperature which plotted and not tempearture differential between the rural and urban environments. That part is coming next.

Firth Student Exercise:

Using the above graph, determine what the average mean temperature will be for Pima county when it reaches population levels of 1.6 million and 3.2 million.
Comment on the reliabilty of your extrapolations. Do you think some other factors might come in to play to reduce the amplitude of the future UHI effect as population in the area increases?

The figure below shows the period of 1969-1998 which compares the average annual temperature between the urban areas of Pima county (e.g. Tucson) and the surrounding rural area (which is mosly inhabited by cacti and prairie dogs).

Sixth Student Exercise:

Using the above trends which have been fit to the data, estimate the rural/urban temperature difference in the years 2030 and 2060?
Comment on the possible effect this might have on urban weather in this area.

Lastly, the image below as constructed using data from small towns in Austrailia. This study is unique as its the only one that has studied small scale systems. The formula that shown as the fit to the data is a non-linear formula.

Seventh Student Exercise:

Using the formula, determine the maximum urban/rural temperature differences for population sizes of 100,000; 500,000; 2,000,000 and 10,000,000.
Compare those estimates to the ones you did earlier from different data sets. Based on that comparison, do you think the UHI as defined on small scales, accurately connects to the large scale population centers. If so, why might this be the case. If not, why don't small population centers scale up to large ones in their UHI behavior?

A Changed Energy Balance
In the broadest physical sense, the alteration from green space/soil due concrete and asphalt.
In this way the local energy balance has been altered due to the presence of these artificial materials that are now embedded in the natural landscape. The figure below is a representation of artifically induced energy flows that can occur
These we will specify as: QI = Incident (direct and diffuse) solar radiation, QR = Reflected solar radiation, QLu = Upward surface emission of long-wave length radiation, QLd = Downward atmospheric emission of long-wave length radiation, Q* = Net solar energy (positive or zero) = QI - QR, and QL = Net infra-red energy = QLd-QLu (generally negative or zero) Other energy terms identify energy moving about the Earth and between the surface and the atmosphere. These types of energy are involved in modifying the temperature at various locations. They are: QF = Anthropogenic energy such as from industry, transportation, heating and AC QE = Latent heat from evaporation of water from trees, soil, bodies of water, etc. QH = Sensible heat carried by vertical and horizontal air motion, including wind QS = Storage heat flux within the system (ground, buildings, etc.).