Thermal Remote Sensing

In contrast to the labour intensive field measurements approach, with advancements in sensor technology, Thermal Remote Sensing (TRS) has become a very comprehensive alternative featuring large scale thermal observation through the use of satellite and aircraft platforms. 

TRS has the ability to make use of all of the missing parameters that needed to be inferred or ignored during field measurements, which include albedo, surface emissivity, irradiative input to the system and surface moisture content in addition to the plethora of atmospheric dynamics and properties as suggested by Becker and Li (1995). 

Unfortunately, despite the added benefits to using TRS over field measurements, it has been met with an abundance of criticism as highlighted by Mirzaei and Haghighat (2010). TRS is very expensive, meaning experiments cannot be run for extended periods of time without large-scale funding, which isn't often available. Additionally, TRS can only measure Surface UHI (Figure 1) as it measures surface radiation, which means the atmospheric UHI would need to be inferred using mathematical models.  

There also exists the issue of unstable images due to being taken from constantly moving platforms and the interference of cloud coverage on readings. Considering cloud coverage plays a strong role in UHI formation, taking UHI readings during overcast where a strong UHI is expected isn't even an option. 

TRS has the benefit of covering a much wider observational range than field measurements and can be a very viable solution under favourable conditions but due to the position of the sensors, vertical profiles of temperature cannot be measured and large 3D structures could interfere with readings particularly because shadows will lower the surface UHI but the buildings themselves will be absorbing the thermal irradiance and re-radiating the heat back into the system warming the atmospheric UHI. This is not accounted for in TRS readings as atmospheric UHI can only be estimated from Surface UHI. This is concerning as the atmospheric UHI can vary greatly from surface UHI in some instances and may be underestimated for UHI intensities lower than 1 ºC, particularly due to the inaccuracies that are to be expected of thermal measurements taken from such a large distance.

Figure 1: Thermal Remote Sensing of surface thermal emissivity over a variety of cities.

Field Measurements

As you might come to expect, the UHI, when broken down to its key components is just a temperature difference between two locations. Of course there is a much deeper complex relationship between multiple atmospheric and physical forcings that bring out the true breadths and detail of the UHI, but such relationships aren’t important for simply identifying the UHI. 

Considering the UHI can fluctuate on a hourly basis, observations are often taken under favourable conditions as described by Anfield (2003) within calm/clear summer evenings. A well-known observational technique is to use a pair or more of fixed or mobile stations along a transect to measure near-surface air temperature between urban and rural regions. The technique was first implemented by a researcher named Luke Howard (1818) in the city of London but was later adopted as a very useful technique for UHI research. 

Measurements of Temperature are the only required variable for identifying a UHI but in order to develop a deeper understanding of the dynamics involved, measurements of Humidity, wind velocity, air pressure and pollution are also widely documented. Additionally, when taking transect measurements, researchers must take note of two very important variables, which include:

• Sky View Factor: A measure of the total visible sky at 180º from the surface position of interest. Measurements can be achieved affordably using a fisheye lens on a camera facing upwards from the surface (Figures 1, 2, 3), whereby the higher the percentage of visible sky in the image, there would be less trapping of heat in the system.

• Land Use Variable: These include the material make-up of the land where the measurements are taken (Tarmac, Glass, Soil, Water…), particularly with relation to Albedo and specific heat capacity. Higher Albedo and Lower Heat Capacity would suggest a weaker warming effect to be experienced in the vicinity. 

The largest issues associated with the use of fixed and mobile stations, suggested by Mirzaei and Haghighat (2010) is that they are very limited. They are time consuming and can only measure a limited number of parameters, hence requiring the remaining parameters to be ignored or estimated. Additionally, they would need to be run for very extended periods of time to weed out the effects of unexpected influences to the measurements (Pedestrians, Vehicles…). The techniques work well for identifying and potentially quantifying the intensity of the UHI but may only be suitable for generalisations based off of trends whilst dynamic relationships cannot be determined due to the missing parameters.

Figure 1: Skyview experienced within a densely foliated rural station. This is a good example of a rural station which may be unsuitable for UHI measurements due to the potential for heat to be trapped within the dense foliage.

Figure 2: Skyview experienced within a city center. The skyview is obstructed by the high towering buildings, attributing to the increase in trapped heat.

Figure 3: Skyview experienced within a coastal region. The sky view is not highly obstructed, almost similar to what would be expected of a hilly grassland. The high sky visibility suggests low trapping of heat. Nevertheless, a UHI could still develop, albeit a weak one.

Introduction to Measuring the UHI

With some understanding of the UHI, some curiosity could be raised about the UHI present in your own city or living area. Unfortunately, aside from large, major cities, it isn't very likely to find a study having been carried out for your particular area of interest. Fortunately, simple measurements of UHI could be carried out without the need for complex instruments or expensive gear.

Some common considerations that need to be taken into account prior to committing to building a UHI database is whether the meteorological readings are to be collected using primary data gathering approaches, or downloaded from a meteorological archive such as the Met office or local weather stations.

Primary data:

Primary data in this field is very valuable because it adds to the flexibility of the research area. The UHI is very susceptible to changes in the vicinity's land use variables. Relying on weather stations would mean the UHI being measured would be that experienced within the vicinity of the weather stations, which may not accurately reflect the conditions experienced in a particular area of interest.

Secondary data:

Unlike Primary data, relying on an external archive provides the freedom to quickly build a developed picture of the UHI present in the relative vicinity as not only does it provide the data without the need for actual measurements, but it also allows for the use of a very large database, dating back much farther than what would realistically be available through primary data. Additionally, all of the data processing with secondary data will also need to be done with primary data. Some examples of free secondary data sources within the UK can be seen below:

• Weather Wunderground ~ 5 minute intervals (No Cloud Cover)
• World Weather Online ~ 3 hour intervals (Includes Cloud Cover)

Both approaches carry their own set of advantages and disadvantages but a combination of the two is likely to yield the most fruitful results. Regardless of whether a researcher opts for primary, secondary or a combination of the two, the methods of obtaining the measurements, as with most aspects of the UHI, can vary and are related to the aims of the study. Essentially, these can be broken down into two categories:

• Field measurements
• Remote sensing

Both of which will be discussed in more detail in their own respective posts.

Modelling Approaches at Different Scales

It should come as no surprise that not everyone would approach the UHI in the same manner. The interests of modelling a UHI to an architect for example is very likely to differ from that of a meteorologist, climatologist or business representative. As quoted by Parham A. Mirzaei, “The goal of a UHI study delineates the type of an adapted model”. 

The UHI is a complicated phenomenon and is not uniform over the entirety of its plume. Urban physics have a dynamic interaction which could range from minor influences such as human body thermal radiation up to city scale influences. The type of model that would be necessary to study the UHI is highly dependant on the scale of UHI formation and the goal of the study.

These can be broken down as follows:

Building-scale models:

Also known as building energy models (BEMs) operate under the notion of a building envelope existing as a closed system, isolated from the neighbouring buildings. External parameters such as temperature, moisture, solar and longwave radiation are incorporated into the model. BEM tools such as EnergyPlus are utilised to identify the influence of variations in the inputs, which then aids in identifying the potential effect of a warming climate on the building envelope in addition to the UHI influence. 

These types of models are very simplistic but are easily incorporated into larger scale models particularly when building energy performance is under investigation. 

~Further reading~

Micro-scale models:

A step up from BEMs, micro-scale models (MCMs) aim to identify the influence of the UHI at the urban canopy layer. Mainly of interest to architects, these include a range of models from computational fluid dynamics (CFD) models for wind flow patterns between buildings and streets to models dealing with the canopy energy budget (Urban canopy models, UCMs). Parameters of building orientation, pedestrian comfort, wind flow, vegetation, surface convection amongst other things could be investigated using MCMs, UCMs and CFDs. 

The biggest limitation would be the relatively small domain size (a few hundred meters) coupled with the steep computational costs.

~Further reading~

City-scale models:

The most well known scale of UHI modelling. Of biggest interest to meteorologists and climatologists, city-scale models operate over very large domains using meso-scale tools to identify the impact of pollution reduction approaches and surface ventilation strategies on the UHI in addition to the  natural meteorological influences on the UHI. They are based not he governing equations of fluid dynamics and incorporate other fundamentally important models such as those for: Cloud cover, soil moisture absorption and thermal radiation.

A large limitation of such city-scale models is they are modelled on very coarse cells giving a weak resolution within the surface layer (Hence the strong need for MSCs). Additionally, they cannot be easily extended into other regions due to being developed very specifically for their designated location and city structure.

~Further reading~

Figure 1: A schematic view of the UHI at multiple scales. (Source)

Models are developed to tackle particular issues related to the UHI. Most models serve a particular purpose and cannot extend to multiple scales of UHI formation. As a result, all scales carry their own share of importance with respect to who in particular is interested. Regardless of personal interest, the UHI in its entirety can only really be understood in a region where all scales have thoroughly been investigated.

Modelling Heat Islands - Parameters and Considerations

The Urban Heat Island, similar to most atmospheric phenomenon develops under a set of patterned conditions. It’s highly unlikely that the UHI would form randomly, but the main challenge associated with the UHI is that the conditions for formation are not easily quantifiable. 

In order to model a phenomenon, a thorough understanding of the characteristics of formation is required as the parameters that need to be incorporated into the model are highly dependant on them. Additionally, the conditions for mitigation or UHI decay also need to be understood as they should not be overlooked. Essentially, conditions for formation and depletion are two sides of the same coin, just sitting on opposite extremes. 

To date, the most important meteorological characteristics that have been determined to influence the UHI (positively or negatively) include:

• Wind speed
• Cloud coverage
• Relative Humidity (RH)
• Water vapour content (WVC)
• Atmospheric pressure ~ More specifically, Anticyclonic conditions

The reason WVC and RH are treated as two separate meteorological conditions is because there is a considerable difference in how the UHI responds to the actual vapour content of the atmosphere and simply how close the system is to saturation, irrespective of the vapour required for it to reach saturation. Regardless of the vapour content, the UHI appears to respond more strongly to how close the system is to saturation. This has not been tested in very warm regions and hence the effect of a very large water vapour content under average RH is still uncertain, which is why it is being considered.

It may seem that these conditions differ from Arnfield’s suggestions for UHI formation and that is particularly because Arnfield dealt primarily with optimising UHI formation whereas in the case of models, we are dealing with all parameters that have been shown to exhibit some form of influence on the UHI. 

Nocturnal UHI Statistical Model in Hamburg (Hoffmann et al., 2011)

A good example of a model developed to model the nocturnal UHI in relation to meteorological conditions is that of Hoffmann et al. (2011) in Hamburg. They worked to model the UHI whereby the parameters would be identified through the use of a generalised least squares method to form a statistical model. To identify the significant meteorological variables to include in their model, a regression based statistical model was developed to test each meteorological condition (X) under a 2-tailed t-test. The explained variance of R^2 was then used to test the strength of the relationship.

 ∆Tu−r =  aX + b

Note: ∆Tu−r refers to UHI intensity (Urban - Rural)

They concluded the most influential conditions were:

• Wind speed (Negative correlation) ~ This was discussed in a previous post (2 posts ago)
• Cloud coverage (Negative correlation)
• Relative Humidity (RH) (Positive correlation)

Wind was found to replace the air within the system, hence weakening the UHI. Cloud coverage prevented escaping heat from cooling the rural region, which then lowered the contrast between the urban and rural stations. High relative humidity meant more vapour would condense in the upper atmosphere releasing latent heat.

They decided to exclude the effects of WVC and Atmospheric pressure due to their observed weak influence on system as part of the study.

• Water vapour content (WVC) (0.2%)
• Atmospheric pressure (7.6%)

The linear regression model was then constructed:

∆Tu−r = aFF + bCC + cRH + d

Whereby:

FF = Windspeed

CC = Cloud Coverage

RH = Relative Humidity

a, b, c, d are fixed parameters to be determined through the least squares method.

The datasets used with respect to the meteorological observations were obtained from the German Meteorological Service (DWD), ERA40 and Regional Climate Model (RCM) results.

The figure below illustrates a frequency distribution of the UHI observed within Hamburg vs values calculated through the model. It was suggested within the paper that the overestimation of UHI was likely due to overestimations in the cloud coverage and relative humidity datasets that were obtained. Additionally, calibrations would need to be made to filter some of the biases in the system. Another factor to consider if applying a model such as this to future climates is the effect of increases in unstable climatic conditions that would rise due to warmer climates.

Figure 1: A frequency distribution plot of observed (Black Asterisk) and modelled UHI intensity using measurements (Black points) and ERA40 (Grey points) for the period of 1985 - 1999. Error bars indicate 95% confidence intervals due to unexplained variance. 

I highly recommend giving this paper a read for a more in depth understanding of the model’s design, capabilities and application. It should be noted that this is a city-scale model, which is ideal for contribution to climate models when appropriate. UHIs also form at smaller scales, each with their own set of approaches and challenges, which will be discussed in the next post. 


Probably...

Does the UHI Affect Climate Models?

Initially I had planned for this update to be on discussing the different approaches to modelling the UHI and some of the difficulties associated with it over the years. I decided to push the topic to next week as i've come across a few people recently who seem confused about how we claim to predict climate change whilst we know of the existence of a phenomenon (UHI) that could blatantly be skewing the readings.

I've touched on this a little in my last post but I thought i'd dedicate a short post to this as I've come across a very useful video lecture by Kevin Cowtan, a computational Scientist from the university of York, to help explain why climate models could be trusted regardless of the effects of the UHI.

The lecture also makes good use of visuals to help explain the UHI, which may solidify some of the points from the previous posts to those who may still be uncomfortable with the topic. It could also act a brief introduction to some of the considerations that are made when developing models with regards to corrections to datasets.

Effect of Wind on UHI

When thinking about the UHI from a physical perspective, it’s not surprising that one way to remove a mass of warm air from a system is to just move it somewhere else. Arnfield (2003) also suggests that an increase in wind activity will result in a decrease in UHI intensity primarily due to the fact that the warm air is moved away from the heat source and is replaced by cooler rural air. 

The main issue with Urban regions is that they don’t respond to atmospheric stimuli in the same manner as rural regions. Most weather stations are placed on rural land to avoid being contaminated by UHI alterations, which means temperatures, humidity and wind activity are archived without significant influence by the UHI. The UHI does scale with overall climate warming though, as a warming climate will evenly warm urban and rural regions by a similar margin. This also means wind speed measurements are optimistic (Appear faster than they actually are) as they don’t account for urban obstructions. The wind speed would vary depending on where it is being experienced within the city meaning some congested regions may have urban thermal plumes whilst others cool by unhindered winds.

In urban regions, wind is obstructed by all of the unnatural alterations to the landscape. High buildings, narrow streets, crevasses and such. This means that wind activity will be weaker and less efficient at removing the warm air from the system as seen in figure (1). It has also been suggested by Kershaw et al., (2010) that emphasis would need to be placed on urban morphology, wind speed and cloud cover during the summer evenings, whereas in the winter, the system is mainly controlled by large scale climate. 

Obstructions to wind cause a region of turbulent flow, which both slows the wind and weaken the transfer of air. Source

Wind speed does play a large role at mitigating the UHI but the degree of UHI depletion is inversely proportional to the morphology complexity. This could be taken into consideration as a mitigation strategy during urban development by being conscientious about flow patterns and obstructions. Of course, this does rely on meteorology to solve an anthropogenic formation, which to some regard is a very green approach, but also unpredictable and not necessarily reliable. 

As a final side note, the obstructions do have their benefits. During stormy weather, they weaken the gale force winds and lower the risk of objects being hurled into the air under steady streams of winds. Some thought may need to be put into the geographical climate of a region before a decision to welcome unhindered wind pathways into a city is made. 

In all honesty though, I was pretty excited when I was warned to be wary of cats falling out of the sky whilst I still lived in Plymouth. You'd be surprised what strong winds can lift into the air. The real questions we can pose: Do I wish to remain cool and refreshed in the summer breeze? Or perhaps feel safe walking around without a titanium umbrella? Maybe consider moving somewhere less windy... 

The Urban Cool Valley

It should come to no surprise that as with most things, the UHI isn't the most predictable phenomenon. As the UHI refers to urban regions maintaining a warmer temperature than their neighbouring rural regions, there must be some circumstances where the situation is reversed.

If an urban region was found to be cooler than its neighbouring rural region, this is often referred to as a Negative Heat Island, primarily due to the equation for UHI being:

UHI = Urban Temperature - Rural Temperature

Also known as Urban Cool Valleys (UCV) or Urban Cool Islands (UCI), the phenomenon is very under-explored. A UCV is difficult to properly measure or come across and they don't have many negative implications compared to the UHI, which makes seeking them out both uninspiring and redundant. Nevertheless, it's could still be useful to understand them when dealing with UHI as they are technically two halves of the same phenomenon.

Formation:

There still exists no accepted consensus on the mechanisms for UCV formation. Not many studies have come across significant UCVs as most measurements are taken at times where you'd expect the UHI to be strongest (Arnfield's classification). Despite this, the UCV has been identified with some thought put into the mechanisms for formation in some UHI research expeditions. Two in particular:

Evaporative Cooling:

Proposed by Debbage and Shepherd (2015), the UCV is likely to form due to evaporative cooling within cities with poor drainage systems. Unlike rural regions, water accumulating in these cities under wet conditions would not be able to drain efficiently. As a result, thermal radiation would be weakened due to the water's high thermal heat capacity and evaporative cooling would further cool the urban regions compared to rural regions where the water would drain into the water table. 

Relative Cooling:

Proposed by Zhao et. al. (2014), the UCV is not a result of a cooling mechanism, but rather, a slower rate of warming in comparison to rural areas. As urban regions have varying surfaces that maintain high relative heat capacities, it would take more energy to raise their temperatures by a degree. During the day, the urban region is warmed more slowly than the rural regions and hence appears to be cooler when in truth, it is just lagging behind slightly.  During the evening, it would release its energy at a slower rate than the rural region hence creating a relative UHI. Due to this buffering effect slowed warming and cooling, the urban temperature range is also weaker than the rural range.

To help visualise this explanation, i've designed an illustrative chart on Matlab as seen below. 

An illustrative chart highlighting the differences in the degree at which urban and rural regions are warmed throughout the day. The urban peaks and troughs slightly lag behind and are less extreme than the rural region. UCV is seen during mid-day whilst UHI is seen during the evening. 

Neither proposition has been disproven as of yet, nor do they clash with one another. If Evaporative cooling is in fact correct, this could help with developing techniques to mitigate the UHI, whilst the Relative cooling proposition would help model the UHI development and create statistical probability charts for UHI formation using information on surface absorption potential. As of yet, this is all speculation but as mentioned earlier, the UCV could prove very useful if utilized correctly considering it, in and of itself is the most natural form of UHI mitigation. 

Impacts of Atmospheric and Surface UHI

It should come as no surprise that anthropogenic modifications could have the potential for some negative side-effects. Up to this point, this series has focused on creating familiarity with what the Urban Heat Island actually is and how it should be viewed as a phenomenon. Nevertheless, the UHI does have its own set of repercussions and those need to be considered as scientific interest alone is not a considerable driving force for researching into the phenomenon. To fully comprehend the implications of the UHI, a new UHI formation would need to be introduced first.

Surface Heat Islands (SHI):

Up to this point, the UHI has been discussed from the perspective of the atmosphere with respect to formation and implications. When working with the UHI, unless explicitly stated otherwise, it will often be with regards to Atmospheric Heat Islands. In contrast, a form of UHI known as a Surface Heat Island (SHI) could also form in regions with high exposure to solar irradiance and high thermal absorption properties. 

The SHI refers to the increase in the temperature of the land surface as a result of direct exposure to to solar influence. By replacing the natural environmental surfaces with anthropogenic surfaces, the thermodynamic properties of the surface can shift. Greenery are much more proficient at dissipating the influence of the added solar input through the use of transpiration and moisture retention. Anthropogenic surfaces such as Asphalt, steel and glass have a high tendency to absorb heat and as a result would become much warmer than their green counterparts. Additionally, lower albedo surfaces also add to this effect as anthropogenic surfaces have a tendency to be dark (Roads). 

Impacts of UHI:

Urban heat islands, both atmospheric and surface have been characterised for being hazardous in more extreme circumstances. Atmospheric Heat Islands could represent an increase in temperatures of up to 12°C. On warm summer afternoons, a rise in temperature of such a degree, particularly near the equator could have dangerous implications towards one's health. The potential for heat stroke and dehydration alone should highlight the importance of mitigating the phenomenon. 

The increase in atmospheric temperature also increases the use of cooling mechanisms to cool the environment, which in turn releases more waste heat as a result, further fueling the UHI. Additionally, the increase in electricity consumption would induce a larger requirement for electricity generation, which in most countries is primarily achieved through fossil fuels. This results in an increase in waste pollution into the atmosphere, which could result in the development of photochemical smog, acid rain and further fuel climate change. Additionally heat sensitive equipment could also suffer as a result of the increased temperatures.

The surface heat island could also pose a threat as the temperatures experienced could reach up to (27–50°C) warmer than the atmospheric temperatures. This does not apply to shaded areas as direct sunlight is required for the SHI to form. Under shade, the temperatures experienced will reflect that of the atmospheric temperatures. Many urban surfaces have a high retention for heat, which could also lead to burn incidents for outdoor workers. 

Storm water runoff has also been found to be heated by potent surface heat islands on pavements and rooftops. High SHI could raise the stormwater temperature from 21°C to over 35°C, which when washed into freshwater communities within lakes, rivers, ponds or other sources populated by freshwater ecosystems, could result in serious repercussions to temperature sensitive communities. Water temperature has an effect on metabolic functions of some aquatic organisms and rapid changes in temperature as a result of heated water inflow could be very stressful to the communities, leading to sickness and even death.

The UHI is not naturally known to form extreme conditions regularly, but nevertheless, the potential for such exists and the repercussions do need to be taken into consideration. I decided to dedicate this post to help promote understanding for why the UHI, despite being often ignored, deserves recognition and attention. The effects are no doubt of lower significance in comparison to climate change or atmospheric pollution but the UHI still deserves to receive the attention it needs to be properly understood and dealt with. 

UHI Basics

Following up from last week's post, the fundamentals of heat islands in general should be somewhat familiar before progressing into that of urban heat islands. As a recap, the UHI refers primarily to the increase in the atmospheric temperature within an urbanised region as a result of anthropogenic modifications to the surrounding landscape. The temperatures experienced could vary depending on the degree of urbanisation in conjunction with some meteorological conditions such as cloud cover and wind activity.

The phenomenon is not particularly difficult to come across and has been studied across multiple research expeditions. The following lists a few notable examples of highly regarded research papers into UHI dynamics.

• A review on the generation, determination and mitigation of the urban heat island.
• Rizwan Ahmed Memon, Dennis Y.C. Leung, Liu Chunho (2008)
• Urban Heat Island Dynamics in Montreal and Vancouver
• T. R. Oke. (1974)
• City Size and the Urban Heat Island
• T. R. Oke (1973)

Nevertheless, as described by Oke (1974), The exact manner in which the thermal and radiative properties of the urban environment could operate in conjunction to form an urban climate is somewhat poorly understood. This fact remains true despite the plethora of studies that have delved into the subject. A prominent issue with urban heat islands is they are not consistent and tend to fluctuate regularly. Obtaining measurements requires ample effort and time and hence, identifying the conditions necessary for the formation of the UHI is of considerable importance. To date, the most reliable classification system is that imposed by Arnfield (2003):

• UHI is strongest during the evening
• UHI is stronger during the warmer half of the year
• UHI decreases with increasing wind speed
• UHI decreases with increasing cloud coverage

Despite Arnfield's suggestions for the optimal conditions to measure the UHI, they are not absolute and have been proven to be incorrect in certain circumstances. As mentioned earlier, considering the UHI is very dependant on the urban architecture and meteorological conditions, it's fairly optimistic to assume his classification could be used for most areas outside of his geographic scope. Some other conditions have also been poorly explored, such as humidity and the potential effect of traveling fronts (migrating air masses).

On average, the measured UHI rests at 1-2 ºC warmer than the surrounding rural regions but could reach up to 12 ºC in extreme conditions. To get a better understanding of what would need to happen for the conditions to become extreme, we would benefit from exploring the conditions associated with the UHI's formation.

Formation:

The UHI forms as a result of heat ineffectively escaping from an area over a span of time. To put this in context, we could compare the urban heat island to a normal heat island that could be found in densely populated rural regions (forests). 

Heat absorption:

• Urban regions are usually riddled with dark surfaces and efficient heat absorbing materials. Tarmac is a good example of this. 
• High albedo surfaces such as windows and reflective surfaces can also intensify heating in microscale regions (Figure 1).
• Pollutants can evoke cloud formation aiding in heat trapping.

Moisture: (Water has a high heat capacity and evaporation cools the surroundings)

• Urban regions tend to be more dry than forested rural regions. As most of the moisture is not retained in the system, evaporative cooling is lower.

Heat sources:

• Urban regions are warmed during the day by solar activity but also continue to be warmed uniformly due to technological machinery. Waste heat can accumulate and coupled with escaping heat being trapped, the regions could be warmed more efficiently. Buildings also extend much higher, increasing the surface area for heat absorption and release.
• Rural regions are only warmed by radiation from other warmed surfaces. Primary heating is limited to solar activity. Also less efficient at trapping heat than urban regions.

Wind activity:

Wind forcing has a tendency to blow warmed air from one region to another allowing for the air to be recycled and remove trapped air from a system.

• Urban regions are much more unnaturally structured. Wind is easily obstructed and slowed by having to veer around buildings and streets (figure 1). The weaker wind forcing lowers the degree at which the UHI is dissipated.
• Rural regions feature many gaps between structures allowing the wind to flow more smoothly.

Figure 1: A schematic view of the UHI at multiple scales in addition to the obstructions imposed by the urban architecture on wind forcing. (Source)

Summary

• The UHI is still poorly understood in all forms outside of a conceptual understanding
• The UHI tends to fluctuate and can vary from city to city depending on the architecture.
• Best conditions to measure the UHI are during calm, clear, summer evenings. 
• The thermal properties of the urban surfaces emphasize heat absorption and less efficient heat loss
• Urban moisture levels are lower on average, which increases UHI
• Urban areas obstruct wind forcing and hence the air is not easily regulated.
• Urban pollutants release condensation nuclei which aid cloud formation and hence, heat trapping

~Further reading~

I hope this post has helped in solidifying any concerns with regards to what the UHI is and what the key drivers for its formation are. Many researchers have attempted to mitigate the UHI by removing the conditions for formation as opposed to developing techniques for removal. These approaches will be discussed in future posts.

An Introduction To Heat Islands - An Unnatural Phenomenon?

When it comes down to discussing the potential impacts of anthropogenic modifications to the natural landscape, it’s a common opinion to be opposed to said changes in hopes of protecting the native wildlife but not very many people consider the implications these modifications could have on the surrounding atmosphere itself.

The Urban Heat Island refers to the increase in the temperatures experienced within an urban region in comparison to its rural counterpart. The key mistake people tend to make here is falling into the misconception that simply developing in a region will force a UHI to develop but this is more of an issue with understanding the nature of heat islands in general. Development itself does not cause the UHI, but rather our approach to developments and urbanisation do. To properly understand what an Urban Heat Island is, it would help to look into a few key fundamentals of meteorology first.

Schematic diagram of the formation of the Heat Island. The UHI experiences a plume-like formation which extends from the epicentre of the most urbanised region. Temperatures experienced in each respective region is subject to the degree of development that has taken place in sad region.

All matter absorb and radiate heat at different rates depending upon their physical characteristics. Specific heat capacity, albedo (reflectivity) and exposure to other heat sources can affect the absorption and release of heat into the system; all of which are strongly modified towards the warmer end of the spectrum within urbanised regions. Within a temperate grassland for example, the energy absorbed at the surface can easily be radiated outwards during the evening allowing for the atmosphere to cool naturally. This follows the system of the Terrestrial Heat Budget very well but this is where most people tend to flub with their interpretations of the urban heat island.

The Terrestrial heat budget highlighting the balance of incoming and outgoing energy from the terrestrial system.

Non-Urban Heat Islands

To understand how Heat Islands work in our anthropogenic biome (Cities), we would benefit from exploring the effects on other "Natural" biomes.

Grasslands are very efficient at losing heat as one of the key features for heat island formation; Physical Heat trapping, is absent. As a result, they operate as an ideal example of the geographical climate in that region. Heat radiated is easily lost into the upper atmospheres (with slight absorption and re-radiation downwards by clouds and within the stratosphere and so on…). They also lack external heat sources due to being very flat. This in turn means they aren’t likely to be heated externally during the evening whilst heat is continually being lost through thermal radiation.

In contrast to a grassland, or desert (Which is somewhat similar to a grassland for the same reasons), biomes with heavy foliage such as jungles and forests do in fact experience heat islands of their own but are not studied in detail due to not posing much risk to humans or the environment. Absorbed heat can often have difficulty escaping due to being re-absorbed and reradiated by the surrounding foliage. As a result, a small plume like formation develops within the entangled regions of these areas where the temperature is slightly warmer than it naturally would if it was an open space.

Some may argue that these regions aren’t as effectively warmed as open regions due to all of the foliage but that’s not entirely true. The degree at which heat is absorbed is more closely linked to the specific heat capacity and albedo of the surface exposed and most jungle and forest trees experience similar if not more absorption due to these factors in particular (As seen below). 

List of albedo experienced by different exposed surfaces. Higher albedo signifies higher reflectivity and lower heat absorption. Source (Oke, 1992, Ahrens, 2006)

 The other factor is that warmed air cannot be removed as effectively due to wind obstructions but this will be discussed in more detail in some later posts to help focus on the topic in a bit more detail.

Urbanisation simply produces an exaggerated form of this phenomenon meaning that the Urban Heat Island (UHI), despite occupying a very unnatural region is a natural side-effect. 

I plan to delve much deeper into how the UHI forms and its varying intensities and so on in the following posts but I felt it would be appropriate to begin by putting this phenomenon into its natural context. I believe it would be easier to understand the UHI by viewing it as a natural atmospheric side effect rather than an unusual hazard plaguing urban societies.

Despite the fact that I’ve defended the UHI as being a natural response to an unnatural situation, this does not mean that it carries no repercussions and these will be discussed in detail further down the line. I hope to spread my current knowledge of the UHI through this series of posts and hope to also expand my own understanding of the UHI and the atmosphere in general as we progress further into this series.