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• Spatially averaged near surface temperature. • REI for the near surface temperature and wind speed. • DTR, the minimum diurnal temperature and the maximum diurnal temperature. • UHI intensity and diurnal cycle. 6.3.1 Effect of urban growth on the spatially average near surface potential temperature Table 6.6 shows the maximum variation in the near surface (z = 10 m) domain averaged potential temperature for the three series of simulations, for both night time (04:00) and daytime (12:00). This quantity was calculated as the difference in domain averaged near surface potential temperature of the most urbanised compared to the least urbanised simulation. Very little variation was found in the daytime near surface potential temperature for the series where the radial extent of the urban area is varied (RADIUS), whereas a much larger variation was seen for the DENSITY series. For both the RADIUS and DENSITY series a larger variation was seen during night time. Table 6.6: Comparison of the maximum variation in near surface potential temperature (%) for daytime and night time for each series of model runs. Maximum variation in near surface Maximum variation in near surface potential temperature (%) at 04:00 potential temperature (%) at 12:00 RADIUS series 0.22 0.03 DENSITY series 0.20 0.13 COMBINED series 0.11 0.01 For the COMBINED series, which represented both changes in radial extent and in urban grid cell density, the maximum change across the series in the mean potential temperature at 12:00 remained very small (0.01%) whereas the maximum change at night time was 0.11%. These results must however be understood in the context of the much smaller variation in the mean urban land cover fractions which was covered by this series. For this 186
eason the near surface potential temperature was analysed as a function of the mean urban land cover fraction. This variable is calculated as the spatially averaged urban land cover fraction across the whole domain, and therefore can be used as an index to quantify the amount of urban land cover within the domain and enable the comparison of the different series of simulations, both in this Chapter and in Chapter 7. Figure 6.6 shows the near surface potential temperature averaged across the whole domain for the urbanised domains of the COMBINED, RADIUS and DENSITY series of model runs at 04:00, as a function of the spatially averaged urban land use cover. All series show a linear form of increase in the near surface potential temperature with an R 2 value between 0.97 and 0.99. The rate of increase is very similar for the COMBINED and RADIUS series, but larger for the DENSITY series. For all series the total change at 04:00 in mean near surface potential temperature between the least urbanised domain and the most urbanised domain is less than 1 K; however it must be noted that this is the mean change across the entire domain. 187
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MODELLING THE IMPACT OF URBANISATIO
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Abstract Urban areas have well docu
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Abbreviations aLMo Meteo Swiss oper
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4.2.2 Impact of the urban area on t
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List of figures Figure 2.1: Scales
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Figure 4.21: Diurnal variation of t
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Figure 6.16: Mean horizontal wind s
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Chapter 1: Introduction Urban areas
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with trends derived from a statisti
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scheme, and therefore it was necess
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The effects of future urban expansi
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2.1 Urban climate modification Urba
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latitudes (Taha 1997), and this is
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100 - 200 m 1 - 2 km 10 - 20 km 100
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epresentatively sample the underlyi
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over the city centre, convergent fl
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Cold islands can also occur in urba
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of the atmosphere. For example, it
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amount of solar radiation absorbed
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(Peterson 1969; Changnon 1992; Coll
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2.3 Justification of modelling appr
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Trusilova (2006) carried out a stud
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constant flux surface layer in the
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the vertical distribution of vegeta
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using different building parameters
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Table 2.2: SWOT analysis for the ME
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Fraction of urban land cover [%] Fi
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The built up area of London can cur
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Figure 2.5(a-f): Builtup area of Lo
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• Mainly high density development
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climate of London, and currently on
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For mid-latitude cities, of which L
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warming of 0.04 °C per decade, in
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out differences in the urban heat i
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adopted in previous studies ranges
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y the implementation of a specific
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A prognostic equation is used to re
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At the surface the temperature is c
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The boundary conditions used for th
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are defined in the file m1tini_TAPE
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The terms representing the impact o
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3.2.1 Calculation of dynamical effe
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θ k ∆z / 2 H Siu (Equation 3.12)
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3.3 Implementation of BEP in METRAS
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difficulties in the linking of the
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Table 3.3: Description of how the 2
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3.5.2 Orography data Orography data
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epresenting suburban development a
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Table 3.7 shows a summary of the MI
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3.6 Summary In this PhD study the m
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temperature, air pressure and air d
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Potential temperature [K] Figure 4.
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Wind speed [ms -1 ] Figure 4.2: Ver
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4.2.1 Horizontal cross sections of
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affected, as the flow bends around
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Downwind of the city on the other h
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the city, which is expected since t
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Potential temperature [K] Figure 4.
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simulation this effect is verticall
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differences are expected during day
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tend to cool the surface, but can a
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Figure 4.15 shows the vertical prof
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appropriate value for these paramet
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0.61) and that of conventional grav
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Difference in potential temperature
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educed near surface temperatures of
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fraction was increased in steps of
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works. In the control simulation th
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characterised by the albedo, therma
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is higher by a similar amount. The
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(a) Potential temperature [K] (c) T
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Figure 4.25 shows the vertical sect
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tests also reproduced well document
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Chapter 5: Evaluation of the urbani
Page 149 and 150: Fraction of urban land cover [%] Fi
Page 151 and 152: For the summers of 1995-2000 the an
Page 153 and 154: Table 5.2: Selected periods of simu
Page 155 and 156: commonly used as a rural reference
Page 157 and 158: newly urbanised version of the METR
Page 159 and 160: adiation trapping in the street can
Page 161 and 162: the second day of simulation (31 st
Page 163 and 164: Air temperature ( o C) 35 30 25 20
Page 165 and 166: the model. The smaller percentage o
Page 167 and 168: Wind speed (ms -1 ) Wind direction
Page 169 and 170: London Heathrow Airport (LHR) Wind
Page 171 and 172: A full set of hourly wind speed and
Page 173 and 174: contain both roofs exposed to direc
Page 175 and 176: Chapter 6: The effects of urban lan
Page 177 and 178: simulations with two hypothetical s
Page 179 and 180: dependent on the r(i,j), the radial
Page 181 and 182: centre of the domain was more than
Page 183 and 184: 6.2 Effects of the current state of
Page 185 and 186: During daytime the shape of the are
Page 187 and 188: The calculation was performed for t
Page 189 and 190: ecause finding a rural reference po
Page 191 and 192: development (GLA 2006) and differen
Page 193 and 194: 6.2.4 Wind speed and direction Klai
Page 195 and 196: Wind speed and direction (ms -1 ) F
Page 197 and 198: “urban” cells to be affected by
Page 199: the urban surface. Many other studi
Page 203 and 204: Mean potential temperature at 12:00
Page 205 and 206: where A(x) is the area affected by
Page 207 and 208: Table 6.8: REI for the RADIUS, DENS
Page 209 and 210: COMBINED series also show a reducti
Page 211 and 212: for all runs in the series, and cor
Page 213 and 214: The runs are then combined into one
Page 215 and 216: to be more linear than the smaller
Page 217 and 218: The total reduction in mean wind sp
Page 219 and 220: The REI showed that for all the var
Page 221 and 222: Higher temperatures in the urban ar
Page 223 and 224: Over the next ten years the populat
Page 225 and 226: 7.1.1 EXPANSION series The first sc
Page 227 and 228: Another cause of this form of expan
Page 229 and 230: 7.2 Effects of urbanisation for the
Page 231 and 232: Mean near surface potential tempera
Page 233 and 234: UHI intensity (K) 4 3.5 3 2.5 2 1.5
Page 235 and 236: temperature, an increase in the mea
Page 237 and 238: 7.2.3 Wind speed The effect of the
Page 239 and 240: 7.3 Effects of urbanisation for the
Page 241 and 242: mean near surface potential tempera
Page 243 and 244: 7.3.2 Wind speed Changes in the mea
Page 245 and 246: expansion strategies such as the re
Page 247 and 248: should also be considered to avoid
Page 249 and 250: models, for example Martilli et al.
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cities (Baker et al. 2002). Already
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uses, painting the roofs white to r
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The full influence of different met
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References Abercrombie, P. (1945).
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Bottema, M. (1997). "Urban Roughnes
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Craig, K. J. and R. D. Bornstein (2
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Grimmond, C. S. B. and T. R. Oke (2
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Jin, M. and J. M. Shepherd (2005).
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Lemonsu, A. and V. Masson (2002). "
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Nitis, T., Z. B. Klaic and N. Mouss
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alance in urban areas - Recent adva
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Seaman, N. L. (2000). "Meteorologic
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Therry, G. and P. Lacarrere (1983).
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Yague, C., E. Zurita and A. Martine
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