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.