Impact of anthropogenic heat emissions on London s temperatures

Transcription

1 Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. 140: , January 2014 B Impact of anthropogenic heat emissions on London s temperatures S. I. Bohnenstengel, a * I. Hamilton, b M. Davies, c and S. E. Belcher a,d a Department of Meteorology, University of Reading, UK b Energy Institute, University College London, UK c Bartlett School of Graduate Studies, University College London, UK d Met Office Hadley Centre, Exeter, UK *Correspondence to: S. I. Bohnenstengel, Department of Meteorology, University of Reading, PO Box 243, Earley Gate, Reading, RG6 6BB, UK. s.i.l.d.bohnenstengel@reading.ac.uk We investigate the role of the anthropogenic heat flux on the urban heat island of London. To do this, the time-varying anthropogenic heat flux is added to an urban surface-energy balance parametrization, the Met Office Reading Urban Surface Exchange Scheme (MORUSES), implemented in a 1 km resolution version of the UK Met Office Unified Model. The anthropogenic heat flux is derived from energydemand data for London and is specified on the model s 1 km grid; it includes variations on diurnal and seasonal time-scales. We contrast a spring case with a winter case, to illustrate the effects of the larger anthropogenic heat flux in winter and the different roles played by thermodynamics in the different seasons. The surface-energy balance channels the anthropogenic heat into heating the urban surface, which warms slowly because of the large heat capacity of the urban surface. About one third of this additional warming goes into increasing the outgoing long-wave radiation and only about two thirds goes into increasing the sensible heat flux that warms the atmosphere. The anthropogenic heat flux has a larger effect on screen-level temperatures in the winter case, partly because the anthropogenic flux is larger then and partly because the boundary layer is shallower in winter. For the specific winter case studied here, the anthropogenic heat flux maintains a well-mixed boundary layer through the whole night over London, whereas the surrounding rural boundary layer becomes strongly stably stratified. This finding is likely to have important implications for air quality in winter. On the whole, inclusion of the anthropogenic heat flux improves the comparison between model simulations and measurements of screen-level temperature slightly and indicates that the anthropogenic heat flux is beginning to be an important factor in the London urban heat island. Key Words: anthropogenic emissions; urban heat island; surface energy balance; diurnal cycle; London Received 5 October 2012; Revised 4 February 2013; Accepted 27 February 2013; Published online in Wiley Online Library 22 May 2013 Citation: Bohnenstengel SI, Hamilton I, Davies M, Belcher SE Impact of anthropogenic heat emissions on London s temperatures. Q. J. R. Meteorol. Soc. 140: DOI: /qj Introduction Anthropogenic heat emissions in cities due to human activities are likely to increase the urban heat island (UHI), whereby urban areas are warmer than rural areas. This potentially increases heat-related mortality rates, especially during hot summer periods (Milojevic et al., 2011), but can also offset cold night-time temperatures in winter. The present study aims at understanding the role of anthropogenic emissions for the UHI. We illustrate the c 2013 Royal Meteorological Society

2 688 S. I. Bohnenstengel et al. processes for spring and winter conditions in London. To do this, the UK Met Office Unified Model is applied to London using an urban surface-energy balance parametrization in the form of the Met Office Reading Urban Surface Exchange Scheme (MORUSES: Porson et al., 2010a), which was specifically configured for London (Bohnenstengel et al., 2011) and is extended here to include a representation of anthropogenic heat fluxes. Understanding the influence of anthropogenic emission on the urban surface-energy balance then helps to inform us of how energy might be used in urban areas to minimize its impact on urban temperatures. Anthropogenic heat and moisture sources include emissions from buildings and transportation. They appear in the surface-energy balance as further terms in the form of additional anthropogenic latent and sensible heat fluxes. Often these terms do not consider a diurnal cycle, either because they are thought to be small or because they are difficult to estimate. The anthropogenic emissions in London, which experiences a temperate climate, have been assumed to be small: the current version of the UK Met Office Unified Model uses values of the order of up to 20 W m 2 in urban areas that vary slightly with season but not on a diurnal cycle. These values compare well with the total anthropogenic emissions for the Greater London area derived by Iamarino et al. (2011). However, Iamarino et al. (2011) and Hamilton et al. (2009) also show the large variability of anthropogenic emissions on kilometre scales, with values of the order of 200 W m 2 in central London and emissions of the order of 10 W m 2 at the fringes of Greater London. We know that anthropogenic emissions vary spatially and diurnally in London; however, we do not know their impact on urban temperatures. These measured anthropogenic heat flux values are very large compared with the net incoming radiation, particularly in winter. However, such large values are constrained to very small areas in the city centre. Nevertheless, their influence on the urban atmosphere deserves to be investigated in more detail with a view to improving the representation of the diurnal and seasonal cycle of anthropogenic emissions in the UK Met Office Unified Model. The first question this article addresses is how the diurnal cycle of anthropogenic emissions alters the individual terms of the surface-energy balance during winter and spring/summer conditions, since Bohnenstengel et al. (2011) demonstrated that London s UHI depends considerably on the surface sensible heat flux. We investigate a second question of more generic character, namely can anthropogenic emissions change the timing and strength of the UHI? Sailor (2011) summarizes three approaches to estimate anthropogenic emissions in urban areas. These are (i) an inventory approach, (ii) an estimate using energybudget closure and (iii) using building-energy models. The inventory approach gathers energy consumption data. Most inventory approaches make the assumption that the energy consumed is similar to the anthropogenic sensible heat released (Sailor, 2011), which neglects the time lag between consumption and heat release due to conduction through building walls, for instance. Most of these approaches also neglect the latent heat released, which is a valid assumption for London, where latent anthropogenic emissions are estimated to be below 7.3% (Iamarino et al., 2011). Allen et al. (2010) developed the Large-scale Urban Consumption of Energy (LUCY) model, which provides global anthropogenic emissions at arcmin 2 resolution. Their anthropogenic estimates for London compared well with the more detailed emissions for London of Hamilton et al. (2009), which are used in the present study. The energy-budget closure approach uses direct measurements of the terms in the surface-energy balance for a control volume. The residual of the net terms in and out of the control volume is then assumed to be the anthropogenic heat generated within the volume. The downsides are that this approach does not distinguish between the different anthropogenic sources and data generated by this approach are sparse. Also, the errors in each term accumulate into the anthropogenic term (Sailor, 2011). The third approach then uses building-energy models, which calculate the energy consumption within buildings. Of these models, EnergyPlus is probably the most well known (Bueno et al., 2011). This method characterizes the physical properties of the building and simulates the energy demand for services using assumptions of occupancy and service patterns. It attempts to combine the estimated sensible and latent emissions from building-scale models with the urban canopy over a geographic area. Dynamic simulation models such as EnergyPlus are very commonly used. Urban surface-energy balance parametrizations started to include anthropogenic terms in the urban surface-energy balance during the last decade. Masson et al. (2002) include anthropogenic heating into their TEB parametrization as an additional term in the surface-energy balance. They account for building-induced anthropogenic emissions by assuming an annual cycle for building temperature. They further add a prescribed traffic-induced anthropogenic heatsource term to the sensible heat-flux term in the canyon. Salamanca et al. (2010) take a more complex approach to account for building-induced anthropogenic emissions by including the building energy model (BEM) in their urban canopy parametrization (UCP). BEM takes into account several building energy sources, such as generation of heat by building occupants, radiation from the windows, heat exchange due to building ventilation and heat diffusion through the walls. Presently, this is the most complex approach to account for anthropogenic emissions in weather forecast models; however, it has the disadvantage that it needs very detailed high-resolution input data. In the present study, we follow the simpler approach of Masson et al. (2002) by including an anthropogenic heat-source term in the urban surface-energy balance. In the present study we include, for the first time, detailed anthropogenic emissions in the UK Met Office Unified Model generated for London. An inventory approach to estimate anthropogenic emissions (Hamilton et al., 2009) allows us to feed high-resolution spatially and temporally varying emissions into the urban surface-energy balance. To answer the questions raised earlier, we simulate two case studies: one in May 2008 analyzed by Bohnenstengel et al. (2011), which is characterized by a strong UHI and moderate advection, and the second simulating the London UHI on 10/11 December 2009, when a strong UHI occurred during a very locally driven meteorological situation. The comparison of case studies allows us to compare the impact of anthropogenic emissions in spring/summer and winter, as well as during advectively driven and locally driven meteorological situations.

3 Impact of Anthropogenic Heat Emissions Anthropogenic emissions 2.1. Anthropogenic emissions in London The anthropogenic energy use (domestic, non-domestic, metabolic emissions and transport) is derived from energydemand statistics for London and is then converted into hourly anthropogenic heat fluxes for each 1 km 2 UK Unified Model grid box using a standardized emission profile. The total annual, domestic, non-domestic and transport anthropogenic energy data are provided at a census output level (i.e. Middle Layer Super Output Area (MLSOA)) in kw h 1. A method of accounting for these emissions is detailed in Davies et al. (2008a), which uses national statistics of domestic and non-domestic energy demand (including gas, electricity, oil and solid fuels), transport fuel demand and flows in London and the metabolic release of heat of the resident and workplace population (Davies et al., 2008b). Individual daily and hourly emission profiles for each energy sector are developed using available daily demand profiles for a whole year, updated to the selected periods in May 2008 and December Hourly anthropogenic releases for each Unified Model grid box are made using estimates of diurnal profiles for each energy sector (Davies et al., 2008b; Hamilton et al., 2009). The sum of the anthropogenic energy from all mentioned energy sectors is apportioned to each grid box of the Unified Model domain by overlaying the grid on the MLSOA boundaries and apportioning the total to each grid box within the boundary. The emission profiles are converted into a heat-emission flux using the technique set out in Hamilton et al. (2009). They use the national daily energy-demand profiles and transport-movement profile to estimate the total daily energy expenditure within each output area. These daily total emission profiles are then converted into an hourly emission by applying the hourly demand profiles for each energy source. Finally, the total anthropogenic emission E total is divided by the total subgridscale urban land-use area A i within each Unified Model grid box i to define an hourly average anthropogenic heat flux Q A,i in W m 2. The anthropogenic heat flux Q A,i (t) for a grid box i at a time t is then given by the following set of equations: Q A,i (t) = E total,i (t)/a i, (1) E total,i (t) = E d,i f d (t) + E nd,i f nd (t) + E tr,i f tr (t) + E m,i f m (t), (2) with the functions f d, f nd, f tr and f m being the hourly emission profiles for domestic (d), non-domestic (nd), transport (tr) and metabolic (m) emissions. For all functions f S, it is assumed that 8760 t=1 f S(t) = 1, where S stands for each sector, i.e. d (domestic), nd (non-domestic), tr (transport) and m (metabolic), respectively. E d, E nd, E tr and E m are the annual demand for each energy sector in kw h Implementation of anthropogenic emissions in MORUSES The surface-energy balance provides the forcing to the atmosphere. MORUSES calculates the thermodynamic urban surface-energy balance separately for roof and street canyon tiles (Porson et al., 2010a,b) in the UK Met Office Unified Model. There are a range of approaches, such as Figure 1. Model domain showing the subgrid-scale urban land-use fractions for London and surroundings within each grid box and the locations of UCL temperature sensors listed in Table 1. This figure is available in colour online at wileyonlinelibrary.com/journal/qj using fixed internal building temperatures to simulate the impact of domestic heating contributions (Masson, 2000). However, MORUSES is developed for use in a numerical weather prediction model; we are only interested in the overall surface fluxes from the street canyon and the roof that provide the forcing to the lowest atmospheric model level at 5 m and the way in which anthropogenic emissions alter this surface-energy balance. Therefore, we assume in MORUSES that the anthropogenic heat from buildings is mostly released via ventilation into the street canyon. Therefore, the anthropogenic heating term Q A is added to the surface-energy balance of the canyon tile as follows: C dt c = R N Q H Q E + Q A G. (3) dt Here C is the areal heat capacity of the canopy (J K 1 m 2 ) and T c is the surface temperature. Their product represents the thermal inertia of the urban canopy. R N is the net radiation in W m 2, Q H represents the sensible heat flux (W m 2 ), Q E the latent heat flux, Q A the additional anthropogenic emissions term (W m 2 )andgthe heat flux into the underlying ground (W m 2 ). A detailed description of the individual terms of the surface-energy balance is provided in Porson et al. (2010a). Figure 1 shows the urban land-use distribution for the model domain, which is 100 km 100 km in size. Figure 2 shows the corresponding spatial pattern of the anthropogenic heat flux Q A at its morning peak on 10 December 2009 and Figure 2 shows the corresponding diurnal cycle of the anthropogenic emissions at the city centre and the fringes of London for the May and December case studies. In both cases, the anthropogenic emissions peak in the city centre and decrease in roughly concentric circles towards the fringes of London. Values in the city centre exceed 400 W m 2 (Figure 2) and values at the fringes of London are of the order of 60 W m 2 in December. As can be seen from Figure 2, anthropogenic heat emissions correlate loosely with London s urban land-use fraction. The correlation factor is around 0.44 due to some smaller urban land-use fractions being associated with higher anthropogenic heat fluxes towards the fringes of London, where light industrial land use leads to higher anthropogenic emissions. The urban parametrization varies the building

4 690 S. I. Bohnenstengel et al. (c) Figure 2. Spatial pattern of anthropogenic heat emissions. Colours indicate anthropogenic emissions in W m 2 on the Unified Model grid on 10 December 2009 at 9 am local time and on 7 May 2006 at 9 am local time. Isolines (blue in the online journal) indicate urban land-use fractions for London. (c) Corresponding diurnal cycle of anthropogenic heat emissions for the city centre (black lines) and the fringes (grey lines) of London for 7/8 May 2008 and 10/11 December Thick lines depict May and thin lines December. This figure is available in colour online at wileyonlinelibrary.com/journal/qj morphology on the grid scale at 1 km and therefore accounts for different urban land-use types such as industrial or residential areas via the building morphology. London s subgrid-scale urban land-use fractions and the planar and frontal area index describing the density and height of buildings for London are correlated with a factor of However, the anthropogenic emissions show a correlation factor of 0.44 with the building morphology parameters. Therefore, the building morphology and urban land-use fraction only partly explain anthropogenic emission values. However, higher urbanization generally leads to higher anthropogenic heat fluxes in most parts of London, with the highest anthropogenic heat fluxes associated with central London. They are slightly lower in May at the fringes and similar to those in December in the city centre, according to Figure 2. However, the spatial pattern is similar to the one in December. Anthropogenic emissions depend not only on the degree of urbanization but also on the urban land-use type, such as residentially or commercially used districts. A similar dependence on urban land-use type was found for Singapore by Quah and Roth (2012). The timing of the diurnal cycle (Figure 2) of anthropogenic heat fluxes is the same in these scenarios; however, the amplitude varies between May and December. As shown in Figure 2, the anthropogenic forcing term shows a pronounced diurnal cycle with two peaks in both periods. In May the anthropogenic heat flux starts to increase at sunrise around 5 am and peaks for the first time around 9 am, about 2.5 h after sunrise. The anthropogenic heat flux then drops until noon, to a value of just under 100 W m 2 in the city centre. It then reaches a second peak about half an hour after sunset, around 9 pm. The evening heat flux is slightly lower than the morning emissions. During peak hours, the anthropogenic heat flux reaches values of over 400 W m 2 (Figure 2). This equals about half of the net solar radiation flux in May. Outside the city centre, the diurnal cycle follows the same pattern as inside, although the emissions are only about a third of those in the centre. In December, the timing of the diurnal cycle of the anthropogenic heat flux is similar to that in May, although the amplitude is higher near the fringes. The timing of the peak heat flux is, however, different compared with sunrise and sunset in December, when the sun rises around 8 am and sets around 4 pm. Hence, the first peak of the anthropogenic heat flux in the morning nearly coincides with sunrise, while the evening peak is delayed by 5 h compared with sunset. In contrast, in May the anthropogenic heat flux peaks in the morning after sunrise; in the evening at sunset. Diurnally, through the year, the pattern and timing of anthropogenic

5 Impact of Anthropogenic Heat Emissions 691 release is dependent on the magnitude of the source. In the summer, transport emissions dominate, while in the winter heating emissions dominate. The seasonal trend is also primarily driven by the summer/winter balance of urban heating, as it will be highest in the winter and lowest in the summer in London. These differences provide an interesting experiment into the mechanism of anthropogenic heating in an urban boundary layer. 3. Selection of case studies flux [Wm 2 ] Two cases were selected: one in spring and one in winter. Both case studies were selected for conditions with little cloud and with weak synoptic forcing, and so both cases have pronounced UHIs. The UK Met Office Unified Model was run with the urban surface-energy balance scheme MORUSES, which was configured for London with a horizontal resolution of 1 km for a domain of 100 km 2 (Figure 1), with London in the centre of the domain (Bohnenstengel et al., 2011). For each case study, two simulations were set up: an urban and a rural simulation. The urban simulation uses realistic land-use information including urban areas and information about the building morphology, while the rural simulation replaces the urban land-use in each grid box with the remaining subgridscale land-use classes in this grid box by scaling them up. Alternatively, if a grid box is covered by 100% urban landuse, urban areas are replaced by grass. Comparing the urban with the rural simulation determines the urban increment in the surface-energy balance or temperatures caused by London. The spring case study is centred on 7/8 May This case was analyzed in detail in Bohnenstengel et al. (2011): a strong UHI occurred during the night of 7/8 May 2008 while a high-pressure system was dominating the weather over the UK with clear skies at night. Moderate winds led to a well-mixed boundary layer and an UHI pattern that was shifted slightly downwind from the urban land-use in London. The winter case study is centred on the UHI observed during the night of 10/11 December 2009 and has not been analyzed before. During this period, the UK was under the influence of a high-pressure system with about 1030 hpa sitting over the UK and very low wind conditions. The UK experienced cold temperatures around freezing point during the simulated period. These conditions led to the formation of some fog and low clouds in and around London in the early morning hours of 11 December It is particularly difficult to distinguish low clouds from fog in satellite images. The scattered fog or low clouds peaked around 6 am and cleared up around noon on 11 December, according to satellite images (personal communication with Aurore Porson and Adrian Lock, UK Met Office). This makes comparison with observations difficult. The lowlevel fog affects the surface-energy balance via the long-wave radiation term. Therefore, it is expected that the simulated screen-level temperatures will differ from the measured ones if fog is simulated but not observed in some locations or vice versa, because the Unified Model simulates fog over London around the same time with a peak around 8 am. Unfortunately, there are no fog measurements in central London and we can therefore only speculate that fog might have affected the comparison between the simulation and the observations. The wind direction during these two days flux [Wm 2 ] Hour[UTC] Hour[UTC] Figure 3. Surface-energy balance for simulations with and without anthropogenic heat emissions for the city centre for 7 May 2008 and 10 December Black solid lines depict anthropogenic heat emissions, solid coloured lines refer to the simulations including anthropogenic heat emissions and dashed coloured lines refer to simulations without anthropogenic heat emissions, while dotted lines refer to the rural simulation. In the online article, green lines depict net short-wave radiation fluxes, magenta lines depict net long-wave radiative fluxes, red lines depict latent heat fluxes, blue lines depict sensible heat fluxes and yellow lines depict the sum of storage of heat into the urban slab and the ground heat-flux term. This figure is available in colour online at wileyonlinelibrary.com/journal/qj shifted from northwesterlies to northerly winds and wind speeds were very low, about 1 m s 1. Under such conditions, a strong local urban heat-island signal developed in the screen-level temperatures at night. 4. Impact of anthropogenic emissions on the urban surface-energy balance In order to understand the physical mechanisms that give rise to the urban and anthropogenic increment, we analyse the energy balance for the city centre of London, where anthropogenic emissions are very high. Consider first the balance of terms during the May case in the absence of anthropogenic heat flux. As shown in Figure 3, the dominant balance during the night, at the beginning of the period, is between loss of energy at the surface from outgoing long-wave radiation and cooling of the urban fabric. During the morning transition, the incoming solar flux is largely balanced by an increased storage in the urban

6 692 S. I. Bohnenstengel et al. (c) (d) (e) (f) (g) Figure 4. Diurnal cycle of screen-level temperature at seven UCL locations from 1800 on 7 May to 1800 on 8 May Dash dotted lines refer to the rural simulation, dotted lines the urban simulation, dashed lines the urban simulation including anthropogenic emissions and solid lines the UCL measurements. Vertical lines indicate measurement and model uncertainty. The latitude and longitude coordinates of each station are listed in Table1. fabric. Over time this warms the urban fabric. The timescale is determined by the heat capacity of the urban fabric. Once the urban fabric is warmer than the overlying air, this drives a positive heat flux into the atmosphere. In the rural simulation, the heat capacity of the vegetated land is considerably lower than in the urban environment. As a consequence, the rural surface warms up faster and drives a positive sensible heat into the atmosphere earlier than in the urban simulations. In contrast to the urban areas, the rural surface-energy balance shows a positive latent heat flux during the day, while the latent heat flux is suppressed in the urban simulation in central London. Through the afternoon, as the incoming solar radiation drops, the loss of heat from the urban fabric by outgoing long-wave radiation and sensible heat flux is balanced by cooling of the urban fabric. Again, the large heat capacity of the urban fabric means that it is some time beyond sunset before the urban fabric temperature drops below the air temperature and the sensible heat flux drops to zero. In contrast, the small rural heat capacity causes the sensible heat flux to drop to zero about an hour before sunset. The large heat capacity of the urban fabric therefore acts like a thermal flywheel, which reduces and delays the diurnal variations in sensible heat flux. This pattern of variation is seen in idealized modelling (Harman & Belcher, 2006) and in other data (Porson et al., 2010a,b).

7 Impact of Anthropogenic Heat Emissions 693 (c) (d) (e) (f) Figure 5. Diurnal cycle of screen-level temperature at six UCL locations from 0000 on 10 December 2009 to 1800 on 11 December Dash dotted lines refer to the rural simulation, dotted lines the urban simulation, dashed lines the urban simulation including anthropogenic emissions and solid lines the UCL measurements. Vertical lines indicate measurement and model uncertainty. The dashed lines in Figure 3 show that the anthropogenic heat flux is not transferred directly into the atmosphere. Instead it is channelled by the surfaceenergy balance into the storage term, which then warms the urban fabric. The temperature of the urban fabric then rises, driving an increased loss of energy as outgoing long-wave radiation and as heat flux into the atmosphere. Hence again there is a time lag in the response of the sensible heat flux to variations in anthropogenic heat fluxes, which is determined by the heat capacity of the urban fabric. During daytime, the sensible heat flux is increased by about 60 W m 2 and the outgoing long-wave radiation is increased by about 10 W m 2. The inclusion of anthropogenic emissions also leads to a longer-term effect by storing more energy in the urban fabric during the morning, which in turn leads to a positive sensible heat flux being sustained later into the night. Figure 3 shows the results for the December case. All the natural terms in the energy balance are smaller, because of the smaller net drive from the incoming solar radiation (note the different scales in Figure 3 and ). The dominant balance is similar to the May case study, although the daytime sensible heat flux remains negative throughout the day. The anthropogenic heat flux has a larger proportional effect in December because the natural terms are all smaller than in May. The basic pattern of effects of the anthropogenic heat flux is similar to the May case, although there are interesting differences due to the different magnitude of the anthropogenic heat flux relative to the other terms and the different phasing of the peak in anthropogenic heat flux relative to sunrise and sunset. The morning peak in anthropogenic heat fluxes again drives higher storage terms over the whole morning. This drives up the temperature of the urban fabric, which drives greater outgoing long-wave radiation, by up to about 25 W m 2, and changes the sensible heat flux from negative to positive. Although it peaks at only about 50 Wm 2, as we shall see, this difference leads to substantial changes to the UHI intensity. 5. Atmospheric response to the anthropogenic heat flux 5.1. Screen-level temperature and boundary-layer depth The simulated screen-level temperatures are plotted against measurements for a rural simulation, a simulation with London s urban land-use and a simulation with urban landuse and anthropogenic emissions. We make the assumption that the mixing is sufficient that temperature differences between surface level and above roof level are small (Oke, 2006) in order to compare the measurements with simulated temperatures. Figure 4 shows the May case and Figure 5 the December case. This allows us to determine an urban increment as well as an anthropogenic increment in the screen-level temperatures. MORUSES calculates temperatures at 10 m height, which are then interpolated down to 1.5 m height assuming

8 694 S. I. Bohnenstengel et al. Table 1. Longitude and latitude of the UCL temperature sensors. Location Longitude Latitude Height (m) SE NW EE SW NE WW WW Monin Obukhov theory. The temperature at 1.5 m height then represents the fraction-weighted average of the subgridscale land-use tile temperatures at screen level. Figure 4 compares simulated screen-level temperatures with observations made with the University College London (UCL) temperature sensor network at seven locations (given in Table 1and in Figure 1) for the May case. As described in Bohnenstengel et al. (2011), including the urban land-use leads to good agreement between simulated and measured screen-level temperatures, with a temperature increment of up to 5 K during the night. Daytime values show almost no difference between rural and urban areas. The reason is that the night-time boundary layer is shallow and so increases to the sensible heat flux associated with urban land-use will warm less air by a higher temperature; by day the boundary layer is deep and so increases to the sensible heat flux associated with urban land-use will warm more air by a lower temperature. The anthropogenic temperature increment, forced by the anthropogenic heat flux, is much smaller than the urban temperature increment. The night-time temperatures are warmed by a further 0.5 K to 1 K, which improves the agreement between the model and the observations, although this improvement lies within the uncertainty range of both MORUSES and the measurements. Daytime temperatures are hardly affected. There is a difference between the observed and modelled temperatures during the morning transition (Bohnenstengel et al., 2011). The addition of the anthropogenic heat flux does not explain this difference. The simulated temperatures for both cases are evaluated against measurements at screen-level height at several locations in and around London. Both case studies were evaluated against UCL temperature measurements assuming an uncertainty of 1 K for the measurements and a model uncertainty of 1.3 and 1.5 K after 6 and 36 h simulation time, respectively (Jorge Bornemann, UK Met Office, personal communication) with a spin-up time of 24 h. Locations for all stations can be found in Table 1 and Figure 1. A detailed description of the UCL temperature sensors can be found in Bohnenstengel et al. (2011). Figure 5 shows a comparison of screen-level temperatures from midnight on 10 December until 6 pm on 11 December. In general, Figure 5, (d) and (e) show reasonable agreement, within an uncertainty of K, between the measurements and the model. The temperatures measured and simulated in Figure 5, (c) and (f) do not agree well, especially during the cooling phase at night. In general, then, the agreement between MORUSES and UCL is not as good as for the May case. Two reasons for this are as follows. Firstly, the measurements are much noisier than for the May case. This noisy behaviour is not reflected in the simulations. The fog or low clouds that developed are very patchy and, although the model did produce fog for this case, it is unlikely that it is in exactly the correct locations. Since fog alters the net long-wave radiation and therefore screen-level temperatures, this is likely to compromise the comparisons. It is difficult to determine exactly when and where the fog formed: satellite images show patchy fog during the early morning hours of 11 December. Secondly, the meteorological situation in December was extremely locally driven, with wind speeds around 1 m s 1. It is therefore likely that the UCL sensors reflect a more local temperature within the street canyon, while MORUSES simulates a temperature above the street canyon. In this case the assumption that there is sufficient mixing for temperature differences between surface level and above roof level to be small might be violated. With only a very small positive sensible heat flux during the night, the urban canopy is likely to be stably stratified, suppressing mixing and leading to a very small footprint of the measurements. MORUSES, however, reflects a temperature averaged over a 1km 2 region. Given these caveats, the comparison between measurements and simulations looks sufficiently promising to use this case to study the generation of an UHI and the role of the anthropogenic heat flux in a winter setting. The anthropogenic heat flux has a negligible effect at sites with low urban fraction, such as shown in Figure 5 and, but a more pronounced effect for sites with a high urban fraction, such as seen in Figure 5(d), (e) and (f), where the anthropogenic increment reaches 1.5 K, whichisprobablyjustabovetheuncertaintyrangeofthe measurements. It seems that anthropogenic emissions tend to have a measurable impact on temperatures in locally driven situations. The spatial pattern of the anthropogenic increment in screen-level temperatures for the May case is shown in Figure 6. Screenshots of the anthropogenic temperature increment are shown for 7 May 2008 at 6 am and 10 pm local time. The spatial pattern of the anthropogenic increment follows the pattern of the night-time UHI (Bohnenstengel et al., 2011, their figure 3 and figure 9), although its maximum values are restricted to the small area where the anthropogenic heat flux peaks (cf. Figure 2). During the morning the anthropogenic increment reaches nearly 2 K in the city centre, decreasing to 0.8 K for the surrounding areas of the city and 0.4 K in the suburbs. At 10 pm, the overall pattern remains the same but the temperature increment only reaches 1.8 K to the west of the city centre and the overall impact is slightly smaller. At both times a plume forms with higher temperatures, also found downwind of London. The differences between 6 am and 10 pm can be explained partly by the larger emissions during the morning in comparison with the evening and by the lower boundary-layer depth in the early morning. The anthropogenic increment in screen-level temperature in the December case is generally larger than in the May case, especially in the evening (Figure 7).The urban temperature increment reaches up to 4 K in the evening for the urban simulation including anthropogenic heat fluxes. In contrast to May, the atmospheric response to emissions is largest in the evening. The reason is that in the winter the boundary layer is shallow and so the anthropogenic heat flux through the day is restricted to warming a shallow layer of air near the surface. In the December case, the daytime boundary

9 Impact of Anthropogenic Heat Emissions 695 Figure 6. Anthropogenic increment in screen-level temperatures for 6 am local time on 7 May 2008 and 10 pm local time on 7 May This figure is available in colour online at wileyonlinelibrary.com/journal/qj layer is up to 400 m deep and the night-time boundary layer roughly 100 m for the urban simulation with anthropogenic heat flux (Figure 8). While the temperature profiles from the rural simulation indicate a well-mixed boundary layer of up to 400 m around noon only, and otherwise a stable stratified boundary layer, the urban simulation has a slightly deeper daytime boundary layer of up to 500 m depth and is otherwise less stably stratified (Figure 8). However, with the anthropogenic emissions, a well-mixed shallow boundary layer exists even during the night and is of the order of 200 m deep. Hence, in this December case study the anthropogenic emissions not only increase the UHI but also change the mixing properties of the urban boundary layer. For comparison, the boundary layer in May is much deeper, nearly 2000 m during the daytime and a well-mixed layer of the order of up to 300 m during the night (Figure 8). Hence, the anthropogenic emissions are mixed over a much smaller volume in December. This is an important outcome with regards to air-quality forecasts, which depend on accurately predicted mixing properties of the boundary layer. The December case also shows very detailed structures on very short length-scales. The low wind speeds in December, however, allow for a very local response to anthropogenic emissions. This is in contrast to the spatial structure in May (Figure 6), when temperatures are more blurred through the mixing associated with advection. Figure 7. Anthropogenic increment in screen-level temperatures for 6 am local time on 10 December 2009 and 10 pm local time on 10 December This figure is available in colour online at wileyonlinelibrary.com/journal/qj 5.2. Frequency distribution of urban and rural temperatures Figure 9 summarizes the frequency distribution of screen-level temperatures for the inner 10 km 2 area around the London city centre for the rural, urban and urban plus anthropogenic simulations from 6 am on 7 May 2008 until 6 am on 8 May 2008, concentrating on a complete night. All three simulations show the same peak in the frequency distribution for high temperatures around 295 K: these are daytime temperatures. The occurrence of these temperatures does not change between the three simulations. This underlines the finding from analyzing the diurnal cycle of the screen-level temperatures that daytime temperatures barely differ between urban and rural areas, because the boundary layer is deep. At night, however, when the boundary layer is shallow, larger differences appear in the frequency distribution. The urban simulation shows that temperatures are likely to be about 4 K warmer than in the rural simulation; the lowest temperatures are shifted by 3 K for the urban and 4 K for the anthropogenic simulation compared with the rural simulation. Also, the urban simulation including anthropogenic emissions shows that night-time temperatures are likely to be 5.5 K higher than in the rural simulation and 1.5 K higher than in the urban simulation for the most likely night-time temperatures indicated by the night-time peaks. Hence, the anthropogenic

10 696 S. I. Bohnenstengel et al. Figure 9. Frequency distribution of screen-level temperatures for the inner km 2 area of London for the rural simulation, the urban simulation and the urban simulation with anthropogenic emissions based on temperature data for 6 am on 7 May until 6 am on 8 May and 6 am on 10 December until 6 am on 11 December in Figure 8. Vertical potential temperature profiles over the city centre of London for December and May. Dark grey lines depict profiles at noon, light grey lines depict profiles at 2100 UTC and black lines depict profiles at 0300 UTC. Solid lines refer to the rural simulation, dashed lines the urban simulations and dash dotted lines the urban simulations with anthropogenic heat fluxes included. emissions increase screen-level temperatures just beyond the uncertainty range of the model when analyzing the whole inner km 2 London domain. In December a similar pattern is found in the frequency distribution for 10 and 11 December 2009 (Figure 9) for the rural simulation. The peak around K indicates that daytime temperatures barely differ between rural, urban and urban plus anthropogenic simulations. At night, the urban simulation gives temperatures 1 K warmer for the most likely night-time temperatures than the rural temperatures. This is much smaller than for the May case. However, we observe a 4 K shift for the lowest temperatures at night for the urban simulation. This shift of night-time temperatures is of the same order of magnitude as in May. The anthropogenic heat flux yields a further warming of about 1 K. From Figure 7 it was shown that the anthropogenic temperature increment can exceed 3 K in places in December. While the rural simulation shows a distribution with two peaks as in May, both urban simulations show a distribution with only a single peak. One reason for this shift is the smaller diurnal temperature range in December than in May due to the lower heat fluxes. The additional heat flux from the urban area and the anthropogenic heat is large enough to shift the two-peak rural distribution into a single-peak distribution for the urban and anthropogenic simulations in December Urban heat island Figure 10 and shows the evolution of the canopylayer urban heat island at a city centre location for the May and December cases, respectively. In both cases the UHI grows sharply from noon and levels out at sunset. Following sunset, the UHI calculated with the urban simulation (without the anthropogenic heat flux) drops slightly until sunrise, when it drops sharply. Hence the UHI persists over

11 Impact of Anthropogenic Heat Emissions 697 anthropogenic heat source is more pronounced in December than in May, when anthropogenic emissions are large compared with the sensible heat flux caused by storage of heat in the urban canopy. We then determined how anthropogenic emissions change the diurnal cycle of the UHI in London. The anthropogenic emissions have a more pronounced impact on temperatures in December than in May. The reason is that the anthropogenic emissions account for a relatively larger proportion of heat available in urban areas in December than in May due to the lower incoming energy in December. Further, the boundary layer is shallower in December and the anthropogenic emissions are mixed over a much smaller volume. They increase temperatures beyond the uncertainty range of measurements by up to 1.5 K in central parts of London in December on a calm cloud-free day. However, in May their impact is smaller compared with the incoming solar energy. The anthropogenic emissions increase the amplitude of the UHI and slightly extend the period of the UHI in December. The reason is that they not only affect temperatures directly, in the form of an anthropogenic sensible heat flux, but also affect them indirectly by altering storage and consequently the outgoing long-wave and sensible heat flux terms. Due to the buffer effect of the storage term in the surfaceenergy balance, the anthropogenic emissions have a delayed effect on screen-level temperatures, which maintains higher night-time temperatures in London. With anthropogenic heat fluxes likely to increase in the future, it seems that the London UHI is likely to be increasingly affected by anthropogenic heat fluxes. Acknowledgements Figure 10. Diurnal cycle of the UHI at a city centre location for the urban simulation (solid line) and the urban simulation including anthropogenic emissions (dashed line) for 7/8 May 2009 and 10/11 December Vertical black lines indicate sunrise and sunset. a longer period in the winter case. The simulation with the anthropogenic heat flux remains more nearly constant through the night. At this location, the anthropogenic heat flux increases the UHI by 1 K in May and 1.5 K in December. Similar values were found by Ruy and Baik (2012) for an idealized study quantifying the factors contributing to the UHI. 6. Conclusions In this study, diurnally varying anthropogenic emissions were included in the Met Office Unified Model for the first time based on realistic emission datasets for London. Anthropogenic heat emissions are locally considerably larger than assumed; however, these large values, of the order of several hundred W m 2, are restricted to small areas in London. We then evaluated the improved MORUSES setup against screen-level temperatures measured in London to test the impact of heat emissions on urban temperatures. Therefore, two case studies were chosen to compare winter and springtime impacts of anthropogenic emissions on the UHI in London. Including anthropogenic emissions improves the simulation of screen-level temperatures just beyond the uncertainty range. The impact of the This work was undertaken within the LUCID project and ClearfLo project and was funded by EPSRC grant EP/E016448/1 and NERC grant NE/H00324X/1. Building input parameters were provided from CASA (UCL Centre for Advance Spatial Analysis) using the GLUD database. We are grateful to the Met Office for making the MetUM available and to the National Centre of Atmospheric Science (NCAS) Computational Modelling Support (CMS) for providing technical support. We are grateful to Drs Aurore Porson and Adrian Lock from the UK Met Office for providing information on fog during December Thanks go to Jorge Bornemann (UK Met Office) for advice on model uncertainty. We thank the two anonymous reviewers for their helpful comments. References Allen L, Lindberg F, Grimmond CSB Global to city scale urban anthropogenic heat flux; model and variability. Int. J. Climatol. 31: Bohnenstengel SI, Evans S, Clark PA, Belcher SE Simulations of the London urban heat island.q. J. R. Meteorol. Soc. 137: Bueno B, Norford L, Pigeon G, Britter R Combining a detailed building energy model with physically-based urban canopy model. Boundary-Layer Meteorol. 140: Davies M, Steadman P, Oreszczyn T. 2008a. Strategies for the modification of the urban climate and the consequent impact on building energy use. Energy Policy 36: Davies M, Hamilton I, Steadman P, Stone A, Ridley I, Evans S. 2008b. London s anthropogenic heat emissions implications for building design. In World Renewable Energy Congress X.WREN: Glasgow,UK. Hamilton IG, Davies M, Steadman P, Stone A, Ridley I, Evans S The significance of the anthropogenic heat emissions of London s

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