1. Introduction
Understanding the influence of land cover on climate is crucial for improving global and regional environmental predictions (e.g., National Research Council 2005, 2012; Diffenbaugh 2009; Pielke et al. 2011). As an important type of land-use change, urbanization is known to induce substantial changes in local temperature. It has been elucidated in the classical paradigm of the urban heat island (UHI)—emerging urban built structures facilitate an increased absorption of heat by surfaces during the day and release of it at night, resulting in a weakening of nighttime cooling (e.g., Oke 1982). While the effect of nighttime warming is qualitatively robust, the detailed changes in temperature and wind induced by urbanization can vary significantly from city to city.
For example, examining the multidecadal trends over the Baltimore, Maryland, and Phoenix, Arizona, metropolitan areas, Brazel et al. (2000) demonstrates a weak daytime warming for the former but weak daytime cooling for the latter as measured by the classic Tu − Tr formula (urban temperature minus rural temperature). The weak daytime urban cooling relative to the rural area in Phoenix is reproducible by numerical simulations (Georgescu et al. 2011). The contrast between Baltimore and Phoenix arises from the fact that, for the latter, the growth of urban structures is at the expense of arid lands instead of lands with high vegetation coverage (Brazel et al. 2000, 2007; Georgescu et al. 2011). Yet, this explanation might not apply to other cities, which also exhibit daytime cooling despite not being located in arid regions [e.g., Vancouver, British Columbia, Canada, in Runnalls and Oke (2000) and Indianapolis, Indiana, in Carnahan and Larson (1990)].
These examples underscore the need for researchers not only to seek a universal mechanism for the influence of land cover on urban climate but also to explore intercity differences and attribute them to the detailed urban parameters of the individual cities. Detailed case studies for cities with unique landscapes and histories of urbanization add to this important line of investigation. In this spirit, this study uses a three-dimensional atmospheric model to perform numerical simulations in order to quantify the impact of urbanization on the local climate of Las Vegas, Nevada.
The choice of Las Vegas as the study area is motivated by three considerations. First, although it is one of the most important desert cities in the United States in terms of population and land area, the effect of urbanization on the local climate of Las Vegas has not been studied extensively when compared to other desert cities such as Phoenix. A detailed study and comparison of the results with existing studies for Phoenix [surveys of recent studies for Phoenix can be found in Brazel et al. (2000, 2007), Georgescu et al. (2009a), Myint et al. (2013), and Zheng et al. (2014)] and other large desert cities will help affirm the common features of the effect of urbanization for all those cities. For example, a recent observational study by Miller (2011) hints that the aforementioned daytime cooling also exists in Las Vegas.
Second, despite their similarity in population and size, Las Vegas differs from Phoenix in that the former has seen almost no agricultural development through its history. For Las Vegas, urbanization is simply the process of replacing shrubland with urban structures. The dominance of only two land surface classes makes it more straightforward to interpret the results of numerical experiments for the city.
Third, Las Vegas is surrounded by dramatic topography, which helps sustain a diurnal cycle of strong and coherent low-level wind over the city. This makes Las Vegas an ideal location to study the mechanical effect of urbanization on the wind field, which complements most previous studies that focused on temperature. Since previous studies have pointed to the possible role of the diurnal wind field in modifying the urban heat island (e.g., Morris et al. 2001; Takahashi et al. 2011; Lee et al. 2012), the numerical experiments also serve to explore how the thermodynamic and mechanical effects interact to shape the climate change induced by urbanization.
The numerical simulations in this work use the approach of dynamical downscaling (e.g., Leung et al. 2003; Mearns et al. 2012). A regional atmospheric model is driven by imposed, but time-varying, lateral boundary conditions constructed from large-scale observations. Multiple layers of nesting are adopted to allow a high degree of resolution for the innermost model domain that encompasses Las Vegas. Each set of numerical experiments is carried out by keeping all boundary conditions the same except changing the land surface boundary conditions within the metropolitan area. In this manner, the difference in the climate between two simulations can be solely attributed to the influence of land-use changes by urbanization. This complements observational studies in which it is more difficult to isolate the effects of land-use changes on local climate.
While many studies have used dynamical downscaling to simulate regional climate (e.g., Caldwell et al. 2009; Heikkila et al. 2011; Pan et al. 2011), the majority of them used a horizontal resolution of 15–30 km, which is not sufficient for resolving urban landscapes. Hence, a 3-km resolution is used over the metropolitan area in this study. This is comparable to recent numerical studies into the climatic impact of urbanization (e.g., Georgescu et al. 2009a,b; Kusaka et al. 2012) but longer (typically seasonal) simulations are performed in this study. The model resolution we use is close to existing high-resolution simulations of the influence of the urban landscape on short-term weather [e.g., Rozoff et al. (2003) for St. Louis, Missouri, and Meir et al. (2013) for New York City, New York]. Within this context, the study also serves a more general purpose of exploring the feasibility of dynamical downscaling toward the submeso- and urban scales but over longer time scales.
2. Numerical model and boundary conditions
a. Model domain and surface boundary condition
The Weather Research and Forecasting (WRF) Model (version 3.3.1; Skamarock et al. 2008) is used for the numerical simulations. The model domains are nested with 48-, 12-, and 3-km horizontal resolutions for the outermost, intermediate, and innermost domains, as shown in Fig. 1a. The innermost domain, shown enlarged in Fig. 1b, encompasses the Las Vegas metropolitan area (black shading indicates the urban coverage in 2006) and its surroundings (color contours represent topography). Several mountain ranges are located to the north, west, and south of the urban area. The topography supports a strong diurnal cycle of valley wind, which is examined in section 3e to understand how it is modified by the urban expansion. All simulations use 27 vertical levels (for thermal fields) with the top of model set at 50 hPa.
(a) The computational domains and nesting for the WRF Model used in this study. (b) The topographic map (contours of elevation in ft) for the innermost model domain. The 2006 urban extent of Las Vegas is indicated in black shading. The land-use maps over the Las Vegas metropolitan area for (c) 1992 and (d) 2006, as used in the numerical simulations. Black, brown, and white grid boxes are those covered by urban land, barren surfaces with sparse vegetation, and background shrubland, respectively. The data for (c) and (d) are constructed from the NLCD1992 and NLCD2006 datasets, respectively. Degrees latitude and longitude are indicated on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) The computational domains and nesting for the WRF Model used in this study. (b) The topographic map (contours of elevation in ft) for the innermost model domain. The 2006 urban extent of Las Vegas is indicated in black shading. The land-use maps over the Las Vegas metropolitan area for (c) 1992 and (d) 2006, as used in the numerical simulations. Black, brown, and white grid boxes are those covered by urban land, barren surfaces with sparse vegetation, and background shrubland, respectively. The data for (c) and (d) are constructed from the NLCD1992 and NLCD2006 datasets, respectively. Degrees latitude and longitude are indicated on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) The computational domains and nesting for the WRF Model used in this study. (b) The topographic map (contours of elevation in ft) for the innermost model domain. The 2006 urban extent of Las Vegas is indicated in black shading. The land-use maps over the Las Vegas metropolitan area for (c) 1992 and (d) 2006, as used in the numerical simulations. Black, brown, and white grid boxes are those covered by urban land, barren surfaces with sparse vegetation, and background shrubland, respectively. The data for (c) and (d) are constructed from the NLCD1992 and NLCD2006 datasets, respectively. Degrees latitude and longitude are indicated on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The key controlling parameter in the numerical experiments is the surface boundary conditions, constructed from the land-use maps of different eras. To isolate the impact of urbanization, contrasting land-use maps from different years only within the Las Vegas metropolitan area are imposed. Elsewhere, the default land-use maps in WRF are retained. From the mid-1980s to the turn of the twenty-first century, developed area (with more than 10% coverage by impervious material) in Las Vegas has almost doubled in size (Xian et al. 2008). To approximately represent the beginning and end of this period of rapid growth, the National Land Cover Database 1992 (Vogelmann et al. 2001) and 2006 (Fry et al. 2011) land-use maps (hereafter referred to as NLCD1992 and NLCD2006) are used to construct the “1992” and “2006” surface boundary condition within the Las Vegas metropolitan area, as shown in Figs. 1c and 1d. The black area is urban land, brown is for barren surfaces with sparse vegetation, and white is the background shrubland (see appendix A for precise classification numbers in WRF). In addition to the 1992 and 2006 cases, an idealized case (nominally called the “1900” case) is also constructed with all urban grid boxes (black areas in Figs. 1c,d) reverted to shrubland. The differences between the 2006 and 1900 runs represent the effects of urbanization over the century.
The NLCD data have a much higher spatial resolution (30 m) than the grid resolution of WRF. The land surface categories in NLCD also differ in detail from the land surface classifications in the WRF Model. To construct the surface boundary conditions for the model as shown in Figs. 1c and 1d, a cross-reference table (see appendix A) is used to translate the NLCD land-use categories to the nearest equivalent WRF categories. The high-resolution NLCD pixels are then woven into the coarse-resolution WRF grid boxes using a simple majority rule as discussed in appendix A.
While the main simulations in this work use the NLCD 1992 and 2006 land-use maps, a set of sensitivity runs are performed with alternative land-use maps for 1986 and 2011, which we generated using Landsat satellite data (see appendix B). The sensitivity tests help assure the robustness of our conclusions with respect to the details of land-use data and the choices of the start and end years for the investigation.
b. Experimental design
For the main simulations, the Noah land surface model (Chen and Dudhia 2001) and an optional urban canopy model (UCM; Kusaka et al. 2001; Kusaka and Kimura 2004; Chen et al. 2006, 2011) are turned on in WRF. The UCM incorporates a set of parameters to describe the physical characteristics of the urban landscape such as building height, roughness length above canyon, urban fraction, and heat capacity of the roof, road, and walls. While these parameters are adjustable according to the specific landscape of a city, because of a lack of detailed data for Las Vegas, the default values in the UCM were retained. For example, building heights, urban fractions, and heat capacities of roof are chosen to be 7.5 m, 0.9, and 1.0 × 106 J K−1 m−3, respectively.
These choices are justified a posteriori by cross validating the model simulation with observations (see section 3a). The UCM calculates the effects of urban structures according to the input of the resolved meteorological variables from WRF and then feeds back to WRF with modified heat fluxes and diabatic heating, and so on. For example, for a desert city, a notable impact of the urban structures on the surface temperature is the shadow effect, which produces cooling during the day. Although most of the simulations in this study are run with the UCM on, a few sensitivity tests are performed with the UCM off.
For all simulations, we chose the Kain–Fritsch cumulus parameterization scheme for the intermediate and outermost domain, while the cumulus scheme was turned off for the innermost domain with 3-km resolution. The background for this choice can be found in our previous work (Sharma and Huang 2012), which showed how the simulated seasonal climatology depends on the model resolution and switching on and off of the cumulus scheme in WRF. For all domains, the WSM3 scheme is used for the parameterization of cloud microphysics, the Yonsei University (YSU) scheme for the planetary boundary layer, and the RRTM and Dudhia schemes for longwave and shortwave radiation, respectively.
To isolate the effect of urban land-use change, the 2006, 1992, and 1900 runs differ only in their surface boundary conditions over the Las Vegas metropolitan area. The lateral boundary conditions are all the same and are taken from the observations for 2006. Thus, it is understood that the 1992 run is not designed to exactly reproduce the climate of Las Vegas in that year but as a perturbation to the reference 2006 run when only the surface boundary conditions are replaced by the NCLD1992 data. The difference between the 2006 and 1992 (or 1900) runs can then be interpreted as the effect of urban land-use change over the intervening period.
Because the climate of Las Vegas has a large seasonal contrast, simulations for winter (defined as October–January) and summer (May–August) were done separately. The meteorological fields in the lateral boundary conditions are constructed from the 4-times daily NCEP Final Analysis (FNL) and are updated throughout the season. Thus, we have six major simulations: two runs (winter and summer) each for the 2006, 1992, and 1900 cases. A summary of all the runs is shown in Table 1.
Summary of the simulations performed in this study. Each run has a duration of 4 months in either winter (October–January) or summer (May–August).
3. Results of numerical experiments
a. Model validation
As a quick validation of the model simulation, the 2-m air temperature in the winter and summer runs for 2006 were first compared with the archived (by the National Climatic Data Center) observations from the meteorological station at McCarran International Airport in Las Vegas. The station, at 36°05′N, 115°09′W, is chosen because it is located near the center of the urban core of the city. Since the station does not perfectly coincide with a grid point of the model, the average over all grid points within a 0.06° radius of the station is taken as the model result for comparison. Figure 2a shows the hourly surface air temperature, averaged over winter (October 2005–January 2006) from the observation and two sets of WRF simulations with and without activating the urban canopy model. Figure 2b is the bias (model minus observation) deduced from Fig. 2a and using the run with the urban canopy model turned on. (The bias for the run without UCM is larger and is not shown.) Figures 2c and 2d are the counterparts of Figs. 2a and 2b but for summer (May–August 2006). For both seasons, the run without the UCM produces a greater bias. Specifically, it produces a diurnal cycle of temperature with excessive cooling after sunset or excessive heating after sunrise. Given the closer match of the WRF+UCM simulation to the observations, the UCM is kept on in all major simulations, and its default setting (which is adopted to produce the results in Fig. 2) is retained.
(a) A comparison of the diurnal cycle of 2-m air temperature from the observation at Las Vegas’s McCarran International Airport (black) and the WRF simulations with (red) and without (green) an activated urban canopy model. All three are the average over the winter of 2006 (October 2005–January 2006). The time of day is indicated on the x axis. (b) The model bias, defined as the run with UCM minus observations, from (a). (c),(d) As in (a),(b) but for summer 2006 (May–August 2006).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) A comparison of the diurnal cycle of 2-m air temperature from the observation at Las Vegas’s McCarran International Airport (black) and the WRF simulations with (red) and without (green) an activated urban canopy model. All three are the average over the winter of 2006 (October 2005–January 2006). The time of day is indicated on the x axis. (b) The model bias, defined as the run with UCM minus observations, from (a). (c),(d) As in (a),(b) but for summer 2006 (May–August 2006).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) A comparison of the diurnal cycle of 2-m air temperature from the observation at Las Vegas’s McCarran International Airport (black) and the WRF simulations with (red) and without (green) an activated urban canopy model. All three are the average over the winter of 2006 (October 2005–January 2006). The time of day is indicated on the x axis. (b) The model bias, defined as the run with UCM minus observations, from (a). (c),(d) As in (a),(b) but for summer 2006 (May–August 2006).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Additional model validations for summer 2006 are shown using the surface air temperature from three more stations in Nevada: Henderson Executive Airport (35°58′N, 115°08′W; Figs. 3a,b), Nellis Air Force Base (36°15′N, 115°02′W; Figs. 3c,d), and North Las Vegas Airport (36°12′N, 115°11′W; Figs. 3e,f). They are located at, respectively, the south, northeast, and northwest edges of the city. Only the run with the UCM is shown. From Figs. 2 and 3, the model shows a cold bias during the day and a less pronounced warm bias in late night and early morning. Nevertheless, with the UCM, the simulations capture the essential structure of the diurnal cycle of temperature.
As in Figs. 2c and 2d, but for the comparison of the simulation with the UCM (red) to observations (black) for summer 2006 at (a),(b) Henderson Executive Airport, (c),(d) Nellis Air Force Base, and (e),(f) North Las Vegas Airport. The bias, defined as model minus observations, is shown in blue for each station.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Figs. 2c and 2d, but for the comparison of the simulation with the UCM (red) to observations (black) for summer 2006 at (a),(b) Henderson Executive Airport, (c),(d) Nellis Air Force Base, and (e),(f) North Las Vegas Airport. The bias, defined as model minus observations, is shown in blue for each station.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Figs. 2c and 2d, but for the comparison of the simulation with the UCM (red) to observations (black) for summer 2006 at (a),(b) Henderson Executive Airport, (c),(d) Nellis Air Force Base, and (e),(f) North Las Vegas Airport. The bias, defined as model minus observations, is shown in blue for each station.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The model biases as shown in Figs. 2a, 2b, 3b, 3d, and 3f exhibit a common pattern of a cold bias in the afternoon and a near-zero or slightly warm bias late at night and early in the morning. Using the hourly model output and station observations over the season, a standard Student’s t test for each specific hour of the day shows that the cold bias in the afternoon is statistically significant above the 95% level. While this bias remains in the model even with an active UCM, a further cancellation of the bias is expected by taking the difference between two runs, which differ only in their surface boundary conditions, to extract the urban effect.
In addition to seasonal mean, we also compared the standard deviation of the daily fluctuation of surface air temperature between the model and observations. Since the simulation is performed for only one particular year, for our purposes we define the high-frequency anomaly as the departure from the slow seasonal trend constructed from a quadratic fit, in the form of α + βt + γt2, where t is time, to the daily model output. This is done for each specific hour of the day. The observations are treated in a similar manner. We find a reasonable agreement between model and observations. For example, for McCarran station in winter, the ratio of the standard deviation of the model simulation to that of observations falls between 0.85 and 1.15 for all hours of the day (and between 0.9 and 1.1 for the majority of the hours). This exercise also reveals that the aforementioned seasonal trend is very similar between the model and observations except for a systematic shift (i.e., a difference in the α parameter in the quadratic fit), which is essentially the seasonal mean bias as already shown in Fig. 2b.
b. Climatology of Las Vegas
Figures 4 and 5 show the climatology of Las Vegas at four different times of the day for winter and summer, respectively, from the 2006 simulations. The color map shows the seasonally averaged 2-m air temperature and the vectors are 10-m wind. In both seasons, the metropolitan area (the 2006 urban extent is marked by the black boundary) is warmer than the desert area to its east and southeast at night and early in the morning. In the afternoon, the temperature over the city is similar to, or even slightly cooler than, that in the desert area. (Here, only the desert areas to the south and southeast of the city are considered because the areas to the north and northwest of the city are of higher elevation and are expected to be colder than the city most of the time.)
The climatology at different times of day of the surface air temperature (color shading scale at right) and 10-m velocity [arrows with scale indicated in the bottom-left corner of (c)] constructed from the winter 2006 simulation: (a) 2100, (b) 0300, (c) 0900, and (d) 1500 LT. Shown are the averages from October 2005 to January 2006. The 2006 urban extent of Las Vegas is outlined in black, and latitude and longitude are marked on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The climatology at different times of day of the surface air temperature (color shading scale at right) and 10-m velocity [arrows with scale indicated in the bottom-left corner of (c)] constructed from the winter 2006 simulation: (a) 2100, (b) 0300, (c) 0900, and (d) 1500 LT. Shown are the averages from October 2005 to January 2006. The 2006 urban extent of Las Vegas is outlined in black, and latitude and longitude are marked on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The climatology at different times of day of the surface air temperature (color shading scale at right) and 10-m velocity [arrows with scale indicated in the bottom-left corner of (c)] constructed from the winter 2006 simulation: (a) 2100, (b) 0300, (c) 0900, and (d) 1500 LT. Shown are the averages from October 2005 to January 2006. The 2006 urban extent of Las Vegas is outlined in black, and latitude and longitude are marked on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 4, but from the summer 2006 simulation. Shown are the averages from May to August 2006.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 4, but from the summer 2006 simulation. Shown are the averages from May to August 2006.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 4, but from the summer 2006 simulation. Shown are the averages from May to August 2006.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
In winter, during the night and early in the morning a downslope valley wind blows from the northwest through the valley between the two mountains to the north and northwest of Las Vegas. It is strong enough that from 2100 to 0900 LT this northwesterly wind dominates the near-surface circulation over Las Vegas. This topographically induced circulation disappears during the day. It is also largely absent in summer. The climatology presented in Figs. 4 and 5 is from the 2006 simulations in which Las Vegas is fully covered by urban structures. How the temperature and wind over the city change when the urban land cover is partially (1992) or totally (1900) removed is examined next.
c. Effect of urbanization on surface air temperature
Figures 6 and 7 show the difference in 2-m air temperature defined as the 2006 run minus the 1900 run. In the latter, all of the grid boxes with urban land cover (black area in Fig. 1d) are reverted to shrubland. The 2006 minus 1900 difference (i.e., the effect of urbanization) is similar in winter (Fig. 6) and summer (Fig. 7). To illustrate the contrast between day and night, the difference at 1300 LT (Figs. 6a and 7a) and 0300 LT (Figs. 6b and 7b) is shown. A weak cooling is found during the day while a much stronger warming is found at night. (Note that the contour interval for the 1300 LT plots is much smaller, and some background noise remains in those plots.) Also notable is that the effect of urbanization on surface air temperature is generally local: the temperature change outside the 2006 urban boundary is relatively small.
The 2006 minus 1900 difference in the 2-m temperature averaged over winter (October–January) from the 2006 and 1900 simulations at (a) 1300 and (b) 0300 LT. A smaller color range (shown at right) is used for (a) because of the weaker daytime cooling compared to the strong nighttime warming in (b). To focus on areas with more significant changes in temperature, the areas in (a) with changes in temperature < 0.04°C are masked in white. In (b), the threshold is 0.5°C. The black border outlines the 2006 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference in the 2-m temperature averaged over winter (October–January) from the 2006 and 1900 simulations at (a) 1300 and (b) 0300 LT. A smaller color range (shown at right) is used for (a) because of the weaker daytime cooling compared to the strong nighttime warming in (b). To focus on areas with more significant changes in temperature, the areas in (a) with changes in temperature < 0.04°C are masked in white. In (b), the threshold is 0.5°C. The black border outlines the 2006 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference in the 2-m temperature averaged over winter (October–January) from the 2006 and 1900 simulations at (a) 1300 and (b) 0300 LT. A smaller color range (shown at right) is used for (a) because of the weaker daytime cooling compared to the strong nighttime warming in (b). To focus on areas with more significant changes in temperature, the areas in (a) with changes in temperature < 0.04°C are masked in white. In (b), the threshold is 0.5°C. The black border outlines the 2006 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 6, but for the average over summer (May–August).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 6, but for the average over summer (May–August).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 6, but for the average over summer (May–August).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Figures 8 and 9 are similar to Figs. 6 and 7 except that they are for the 2006 minus 1992 difference in 2-m temperature as inferred from the 2006 and 1992 runs. For reference, the boundary of the urban area of 1992 is marked by boldface black lines whereas that of 2006 is marked by gray lines. For the convenience of later discussion, the area within the 1992 urban boundary is labeled as domain 1, or D1, and the area that turned from nonurban to urban between 1992 and 2006 is labeled as domain 2, or D2. First, similar to the case of 2006 minus 1900, the change in temperature induced by urbanization is local, as it is concentrated mostly within D2. Second, within D2, the seasonal and diurnal variations of the urban effect in Figs. 8 and 9 are similar to their counterparts in Figs. 6 and 7. Weak cooling is found during the day and more significant warming is found at night. This behavior is similar during the winter and summer.
As in Fig. 6, but for the 2006 minus 1992 difference in the 2-m air temperature in winter (October–January), as deduced from the 2006 and 1992 simulations. The black border outlines the 1992 urban extent and gray border outlines the 2006 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 6, but for the 2006 minus 1992 difference in the 2-m air temperature in winter (October–January), as deduced from the 2006 and 1992 simulations. The black border outlines the 1992 urban extent and gray border outlines the 2006 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 6, but for the 2006 minus 1992 difference in the 2-m air temperature in winter (October–January), as deduced from the 2006 and 1992 simulations. The black border outlines the 1992 urban extent and gray border outlines the 2006 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 7, but for the 2006 minus 1992 difference in the 2-m air temperature in summer (May–August).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 7, but for the 2006 minus 1992 difference in the 2-m air temperature in summer (May–August).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 7, but for the 2006 minus 1992 difference in the 2-m air temperature in summer (May–August).
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Figure 10 summarizes the changes in the diurnal cycle of the 2-m air temperature induced by the urbanization of Las Vegas. The red and black curves in Fig. 10a are for winter and summer from the 2006 minus 1992 difference averaged over domain 2. Both seasons see a clear warming—the classic urban heat island—during the night and early morning but also a weak cooling during the day. The blue and green curves in Fig. 10a are the counterparts of the red and black examples but for the average over domain 1, that is, the urban core of Las Vegas that was already urban in 1992 (and remains so in 2006). Only a very weak warming at night is found over this area, indicating that the effect of the land-use change over domain 2 is largely confined to within that domain.
(a) The diurnal cycle of the 2006 minus 1992 difference in the 2-m air temperature, averaged over the season and over D1, the urban area that existed in 1992 (the area within the black border in Fig. 8), and D2, the area where urbanization occurred between 1992 and 2006 (the area outside the black border but within the gray border in Fig. 8). The calculations are performed for winter (October–January) and summer (May–August) separately as indicated in the legend in the panel. (b) As in (a), but for the 2006 minus 1900 difference, averaged over the 2006 urban extent of Las Vegas (the whole area within the black border in Fig. 6). The time of the day is indicated along the x axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) The diurnal cycle of the 2006 minus 1992 difference in the 2-m air temperature, averaged over the season and over D1, the urban area that existed in 1992 (the area within the black border in Fig. 8), and D2, the area where urbanization occurred between 1992 and 2006 (the area outside the black border but within the gray border in Fig. 8). The calculations are performed for winter (October–January) and summer (May–August) separately as indicated in the legend in the panel. (b) As in (a), but for the 2006 minus 1900 difference, averaged over the 2006 urban extent of Las Vegas (the whole area within the black border in Fig. 6). The time of the day is indicated along the x axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) The diurnal cycle of the 2006 minus 1992 difference in the 2-m air temperature, averaged over the season and over D1, the urban area that existed in 1992 (the area within the black border in Fig. 8), and D2, the area where urbanization occurred between 1992 and 2006 (the area outside the black border but within the gray border in Fig. 8). The calculations are performed for winter (October–January) and summer (May–August) separately as indicated in the legend in the panel. (b) As in (a), but for the 2006 minus 1900 difference, averaged over the 2006 urban extent of Las Vegas (the whole area within the black border in Fig. 6). The time of the day is indicated along the x axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Figure 10b is similar to Fig. 9a but for the 2006 minus 1992 difference. Shown is the average of the surface air temperature over the entire urban area that exists in 2006 (which was entirely shrubland in 1900); red and black are used to represent winter and summer. With a much greater area that undergoes land-use change, the overall characteristics of the changes in temperature in Fig. 9b are similar to those in Fig. 9a. Between 2.5° and 3°C warming is found in the evening and early morning, and 0.2°–0.3°C cooling is found during the day.
The strong nighttime warming and weak daytime cooling in the simulation are consistent with the observational study for Las Vegas performed by Miller (2011). The weak daytime urban cooling relative to the rural area in the simulations for Las Vegas is broadly consistent with that found in observational and modeling studies for Phoenix (Brazel et al. 2000, 2007; Georgescu et al. 2011), which suggests that this behavior is common to cities located in desert or semiarid regions.
In the model simulations, the direct effect of urban land-use change on the thermal field is largely confined to within a few model levels near the surface. As an example, Fig. 11 shows the vertical profiles of the seasonal mean potential temperature θ at 0300 LT averaged over the core of Las Vegas from the summer 2006 (blue) and summer 1900 (red) runs. With some spatial variation of surface elevation within the city, we chose to show θ at the η level [η ≡ (p − pT)/(ps − pT), where ps and pT are the pressure at the surface and the top of the model] of the model instead of the pressure level because the former closely follows the terrain, which allows an easier interpretation of the results. The mean surface pressure over the city changes very little from the 1900 to 2006 runs, which further allows a meaningful comparison of the two runs at the η level. The area averaging is carried out only over the grid points with ps > 920 hPa, which excludes the locations at a higher elevation. With this setup, the first four η levels (marked as 1–4 along the ordinate in Fig. 11) correspond approximately to 30, 110, 210, and 340 m above ground. (Note that WRF uses a staggered grid in the vertical direction with the thermal fields located at the half grid; the first η level for θ is already substantially above the ground.) We find that the influence of surface warming during summer nights diminishes above the third η level.
The vertical profile of potential temperature at 0300 LT over the lowest few η levels and averaged from the summer 2006 (blue) and summer 1900 (red) runs. The lowest four η levels correspond approximately to 30, 110, 210, and 340 m above ground. See text for details.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The vertical profile of potential temperature at 0300 LT over the lowest few η levels and averaged from the summer 2006 (blue) and summer 1900 (red) runs. The lowest four η levels correspond approximately to 30, 110, 210, and 340 m above ground. See text for details.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The vertical profile of potential temperature at 0300 LT over the lowest few η levels and averaged from the summer 2006 (blue) and summer 1900 (red) runs. The lowest four η levels correspond approximately to 30, 110, 210, and 340 m above ground. See text for details.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
We have performed the 1992 and 2006 simulations because of the availability of the NLCD data for those two particular years. While it would be interesting to push the start year farther back (other than using our idealized 1900 case with no urban land at all), the construction of a reliable land-use map for the 1980s or 1970s is a challenge in its own right because of the decreasing availability of satellite observations backward in time. Nevertheless, knowing that the choices of the end points of the period and the specific land-use maps might affect the trend, we performed a set of sensitivity tests by running the summer case using the 1986 and 2011 land-use maps constructed from Landsat satellite observations. The details are shown in appendix B. With the longer span of the period of urbanization and the use of an alternative set of land-use maps, the 2011 minus 1986 trend exhibits the same key features of a strong nighttime warming and a weak daytime cooling over the areas that experienced urbanization. This affirms the robustness of our basic conclusions on temperature.
d. Energy budget at the surface
The changes in local climate induced by urbanization are closely related to the changes in the physical properties of the surface (e.g., Oke 1982), which alter the energy budget at the surface. Figure 12 summarizes the changes, calculated from the 2006 minus 1900 difference, for winter and summer in the major terms in the surface energy balance—net longwave and shortwave radiation, sensible heat flux, and latent heat flux (all defined as positive upward), all of which are averaged over the 2006 extent of the urban area (the black area in Fig. 1d). The counterpart of Fig. 12 for the 2006 minus 1992 difference (as averaged over the area where urbanization took place between 1992 and 2006) is very similar to Fig. 12 and is not shown.
The 2006 minus 1900 difference in the major terms of the surface heat or energy flux, defined as positive upward, for (a) winter (October–January) and (b) summer (May–August). Shown are the diurnal cycles of each flux averaged over the season and over the 2006 urban extent of Las Vegas. The net upward longwave (shortwave) radiation values are shown in red (blue), and sensible (latent) heat fluxes are shown in black (green); the sum of the four is in brown.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference in the major terms of the surface heat or energy flux, defined as positive upward, for (a) winter (October–January) and (b) summer (May–August). Shown are the diurnal cycles of each flux averaged over the season and over the 2006 urban extent of Las Vegas. The net upward longwave (shortwave) radiation values are shown in red (blue), and sensible (latent) heat fluxes are shown in black (green); the sum of the four is in brown.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference in the major terms of the surface heat or energy flux, defined as positive upward, for (a) winter (October–January) and (b) summer (May–August). Shown are the diurnal cycles of each flux averaged over the season and over the 2006 urban extent of Las Vegas. The net upward longwave (shortwave) radiation values are shown in red (blue), and sensible (latent) heat fluxes are shown in black (green); the sum of the four is in brown.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
For both winter and summer, the change in latent heat flux is minor given that in the model the surface is generally dry either pre- or post-urbanization. This is distinctive from the situation with nondesert cities where a conversion from grass or agricultural land to urban structures can substantially reduce surface evaporation. It is worth noting that Miller (2011) showed evidence of an upward trend in dewpoint temperature over Las Vegas in the post-1970s era. That our model did not simulate this trend could be interpreted in several ways. First, while urbanization generally leads to increased irrigation for a desert city, this effect is not yet properly represented in the model. Second, with the 3-km horizontal resolution, the subkilometer details in urban landscape (e.g., the contrast between park and pavement) are not resolved by the model. Third, in the observations, part of the trend could be due to other influences (e.g., large-scale climate change) unrelated to urbanization. Since we have designed the numerical experiment to isolate the effect of land-use changes (by replacing only the surface boundary conditions but keeping the lateral boundary conditions the same), the large-scale influence is also not included in the simulations.
In both seasons, during the day, urbanization leads to a decrease in the net upward shortwave radiation that is due to the decrease in surface albedo from 0.26 for shrubland to 0.17 for urban land. The excessive absorption of solar radiation is redistributed as an overall increase in the upward longwave radiation through the whole day but particularly at night. Note that the weaker but still positive increase in the upward longwave radiation during the day does not contradict the fact that temperature decreases slightly during the daytime, because emissivity increases from 0.88 for shrubland to close to 0.98 for urban land. (The value of 0.98 is as provided by the output of the model and is understood to be the effective emissivity, taking into account the increased area for emission given the existence of buildings and other urban structures. Without the geometric effect of urban structures as parameterized in the urban canopy model in WRF, merely turning shrubland to a flat concrete slab would not lead to such a significant increase in emissivity.)
The changes in the sensible heat flux exhibit a more complicated seasonal and diurnal dependence. In winter, the change is very small at night. This is not unexpected since the atmospheric boundary layer is very stable during cold winter nights and would likely remain stable even with some warming induced by urbanization. An increase in the sensible heat flux is found during the daytime in winter. Since the surface actually cools slightly during the day, this is likely related to more complicated dynamical processes in the boundary layer, which would require a separate study. In the summer, the boundary layer is closer to neutral or unstable such that the change in sensible heat flux appears to have a more direct relation to the change in surface temperature. The sensible heat flux increases during the night when the surface temperature increases, and decreases during the day when the surface cools slightly. The changes in the total upward energy flux (sum of all flux terms discussed above) are also shown in Fig. 12. For both seasons, an increase in the total flux at night and a decrease during the day is found. This diurnal contrast is more pronounced in summer.
e. Changes in near-surface wind and implications
The effect of urbanization on the local atmospheric velocity has so far received less attention than the effect on temperature. Using the WRF simulations, analysis of the changes in the 10-m wind induced by the land cover changes is performed. Since the topographically induced diurnal circulation over Las Vegas exhibits a particularly coherent pattern at night and in the early morning (see Figs. 3 and 4), the study focuses on those times. Figure 13 shows the 2006 minus 1900 difference in the 10-m wind speed (as contours) and velocity (as arrows) at 2100 and 0300 LT, for winter and summer.
The 2006 minus 1900 difference in the wind speed (contours with color scale at right) and velocity [arrows with scale indicated in the bottom-left corner in (d)] averaged over the season: (a) 2100 LT in summer, (b) 2100 LT in winter, (c) 0300 LT in summer, and (d) 0300 LT in winter. The contour interval for the change in wind speed is 0.2 m s−1. The black border outlines the 2006 urban extent of Las Vegas. The latitude and longitude are indicated on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference in the wind speed (contours with color scale at right) and velocity [arrows with scale indicated in the bottom-left corner in (d)] averaged over the season: (a) 2100 LT in summer, (b) 2100 LT in winter, (c) 0300 LT in summer, and (d) 0300 LT in winter. The contour interval for the change in wind speed is 0.2 m s−1. The black border outlines the 2006 urban extent of Las Vegas. The latitude and longitude are indicated on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference in the wind speed (contours with color scale at right) and velocity [arrows with scale indicated in the bottom-left corner in (d)] averaged over the season: (a) 2100 LT in summer, (b) 2100 LT in winter, (c) 0300 LT in summer, and (d) 0300 LT in winter. The contour interval for the change in wind speed is 0.2 m s−1. The black border outlines the 2006 urban extent of Las Vegas. The latitude and longitude are indicated on the axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Comparing those panels with the climatology in Figs. 4 and 5, the results show that (i) the change in wind speed is almost uniformly negative and (ii) the change in velocity is predominantly in the direction opposite to the direction of the climatological velocity vector. These two characteristics remain robust if the 2006 minus 1992 difference (in which the area of urbanization is much smaller), as shown in Fig. 14, is considered. The results strongly indicate that the change in the near-surface wind is due to a simple mechanical effect of the retardation of the climatological wind by the emerging buildings and urban structures. This increase in surface friction can be quantified, for example, by the classical surface roughness scale u*, which is indeed significantly higher over the urban areas (not shown).
As in Fig. 13, but for the 2006 minus 1992 difference in the wind speed and velocity and with the gray border outlining the 2006 urban extent and the black border outlining the 1992 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 13, but for the 2006 minus 1992 difference in the wind speed and velocity and with the gray border outlining the 2006 urban extent and the black border outlining the 1992 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 13, but for the 2006 minus 1992 difference in the wind speed and velocity and with the gray border outlining the 2006 urban extent and the black border outlining the 1992 urban extent of Las Vegas.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Given the overall reduction of the wind speed (but relatively minor changes in wind direction) induced by urbanization, one may further infer its impact on the temperature itself. Previous studies suggest that local wind circulation generally provides ventilation to alleviate the urban heat island effect (e.g., Takahashi et al. 2011; Lee et al. 2012, 2014). The ventilation is accomplished by the advection of warmer air out of the city, or advection of cooler air into the city. If both the strength and direction of the wind remain the same during urban expansion, the increased temperature gradient between the urban and rural areas combined with an unchanged velocity field would imply a strengthening of the temperature advection. If so, the ventilation effect increases with an increase in the urban heat island effect itself.
The situation for Las Vegas from the simulations is more complicated. During the nighttime, while the temperature of the city increases, the wind speed decreases as a result of urbanization. The two would have the opposite effects on the strength of temperature advection. During the day, the desert areas surrounding the city can have a higher temperature than the city itself. With the retardation of the wind, there could be a reduction of the advection of hotter air into the city, a cooling effect. Although these conjectures cannot be unequivocally affirmed within the framework of the WRF Model simulations (given that in the model one cannot artificially hold either wind or temperature constant), a diagnostic analysis of the temperature advection term provides some useful insights.
The first term on the rhs is the 3D advection of potential temperature by the resolved velocity from the WRF Model. It represents the main effect of ventilation by the wind. For conciseness, the term is not split further into the contributions from the horizontal and vertical advection but the former generally dominates (not shown). The second term on the rhs is the convergence of the vertical potential temperature flux by unresolved turbulence in the boundary layer. Its value is calculated by WRF with the boundary layer parameterization scheme. The diabatic forcing includes the latent heat release due to moist convection or cloud/fog formation (both are very rare occurrences at the lower to middle boundary layer under the very dry climate of Las Vegas) and the atmospheric absorption of longwave and shortwave radiation.
Figure 15a shows the diurnal cycle of the climatological value of the advection term [the first term on the rhs of Eq. (2)], averaged over the 2006 urban extent of Las Vegas, from the 2006 and 1900 simulations for winter. (Winter was chosen as the wind pattern is less organized in summer.) At night, the advection of colder air through the northwest corridor into the city is reflected in the negative value of the θ-advection term. With urbanization, cooling by advection is found to become stronger. Since the wind speed |V| is decreased, this suggests that the increase in |∇θ| due to urban warming is significant enough to overcome the reduced wind speed such that the ventilation effect still increases with urbanization. The positive climatological value of the advection term during the day reflects the tendency for the warmer air in the surrounding desert areas to intrude into the city. Since during the day the temperature of the city changes only slightly by urbanization, the reduction of the wind speed by the mechanical effect should decrease this intrusion of warm air. This is indeed the case, as shown in Fig. 15a (note that red line falls below the blue line during the daytime).
(a) The diurnal cycle of the θ-advection term averaged over the winter season (October–January) and over the 2006 urban extent of Las Vegas (see text for detail) from the 2006 (red) and 1900 (blue) runs. (b) The 2006 minus 1900 difference in the θ-advection term (green), convergence of the vertical potential temperature flux by boundary layer turbulence (red), and radiative forcing (blue), averaged in time and space in the same manner as in (a). All calculations are performed at p = 900 hPa. The time of day is indicated along the bottom x axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) The diurnal cycle of the θ-advection term averaged over the winter season (October–January) and over the 2006 urban extent of Las Vegas (see text for detail) from the 2006 (red) and 1900 (blue) runs. (b) The 2006 minus 1900 difference in the θ-advection term (green), convergence of the vertical potential temperature flux by boundary layer turbulence (red), and radiative forcing (blue), averaged in time and space in the same manner as in (a). All calculations are performed at p = 900 hPa. The time of day is indicated along the bottom x axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
(a) The diurnal cycle of the θ-advection term averaged over the winter season (October–January) and over the 2006 urban extent of Las Vegas (see text for detail) from the 2006 (red) and 1900 (blue) runs. (b) The 2006 minus 1900 difference in the θ-advection term (green), convergence of the vertical potential temperature flux by boundary layer turbulence (red), and radiative forcing (blue), averaged in time and space in the same manner as in (a). All calculations are performed at p = 900 hPa. The time of day is indicated along the bottom x axes.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
The 2006 minus 1900 difference (red line minus blue line in Fig. 15a) in the θ-advection term in winter is shown as the green line in Fig. 15b. Also imposed are the 2006 minus 1900 differences in the convergence of turbulent potential temperature flux [second term on the r.h.s. of Eq. (2)] and radiative heating [from the Q term in Eq. (2)]. As explained before, the heating due to moist convection or the formation of clouds/fog is small at 900 hPa and is not shown. The radiative heating term in Fig. 15b is small, which is understandable since urbanization is not expected to significantly change the atmospheric absorption (by changing either the atmospheric composition or cloudiness) of longwave/shortwave radiation.
The change in the turbulent heat flux convergence is small at night, likely because the boundary layer is very stable during winter nights and remains undisturbed even with the surface warming attributable to urbanization. During the day, the heating due to turbulent flux convergence increases with urbanization. This is consistent with the decrease in the positive temperature advection into the city during the daytime, as shown in Fig. 15a. Since the air in the middle boundary layer is cooler but the temperature at the surface changes only slightly with urbanization, the static stability of the lower boundary layer decreases, which favors more turbulent heat transfer upward. Within this context, the changes in the first and second terms on the rhs of Eq. (2) partially cancel each other.
The preceding analysis suggests that the changes in the wind field by the mechanical effect of urbanization can have a secondary influence on the temperature of the city. This effect is noticeable for Las Vegas because there exists a strong and coherent diurnal circulation pattern over its metropolitan area. The details of the wind’s effect on temperature vary from city to city and will be an interesting aspect for future studies on urban climate.
4. Discussion
a. Remarks on numerical experiment
While this study has focused on Las Vegas, the results contribute to a more general understanding of the diverse behaviors of local climate change due to urbanization for different types of cities as mentioned in the introduction. Within this context, Las Vegas serves as a unique sample since it is a large desert city with very little agriculture within or surrounding its metropolitan area. To help future numerical studies for multicity comparisons, it is useful to discuss the strengths and limitations of the approach used in this work.
By fixing the model parameters and lateral boundary conditions but only replacing the surface boundary conditions in the twin simulations, our approach is designed to isolate the impact that comes from urban land-use changes alone. It is understood that the model does not simulate the absolute climate change that includes other large-scale influences unrelated to urbanization. While this work and previous studies have shown an urban cooling trend relative to rural areas for both Phoenix and Las Vegas, the two cities have seen different absolute climate changes in recent decades. For Phoenix, both the urban core and its surrounding rural areas have warmed but the urban core has warmed less. A similar large-scale warming trend is absent for Las Vegas. To distinguish the urban and nonurban influences on local climate in future numerical studies, at the minimum we need to quadruple the number of runs in this study by considering the four combinations of 1992 and 2006 surface boundary conditions with 1992 and 2006 lateral boundary conditions (and hope that 1992 and 2006 are not outliers in terms of decadal variability).
Although our use of 3-km horizontal resolution for seasonal climate simulations is comparable to the state of the art, such a resolution still does not fully resolve the detailed surface heterogeneity within the urban area. This could cause not only inaccuracies in the representation of physical processes in the model (e.g., the contrast between park and pavement is lost if the two are lumped into one grid box) but also issues with validation. The scattered in situ measurements that are available within an urban area are potentially influenced by the subkilometer urban landscapes surrounding the stations. It will be useful to further test the convergence of model simulation toward the observations when the resolution of the model is refined to the subkilometer range.
b. Future work
The preceding discussion points to the need for more model validation in order to improve future simulations. Limited in part by the availability of the NLCD land-use maps used in our simulations, the validation performed in section 3a was restricted to the summer and winter in 2006. This could be improved if more simulations that span multiple years (and adopt custom-made land-use maps for the specific years) are performed and validated against observations. Having multiple years of runs will also allow us to quantify not only the effect of urbanization but also the influence of large-scale interannual-to-decadal variability unrelated to land-use changes. Likewise, simulations for multiple desert cities will help affirm our conclusions based on Las Vegas alone. We are pursuing both lines of improvement as future work.
The treatment of the urban effect in our simulations relies on the parameterization scheme in the UCM. With more urban parameterization schemes being developed in the last decade (e.g., Best and Grimmond 2015), it will be useful to test how the choice of the urban scheme or UCM affects the results of our simulations. Even with the same UCM we used, the procedure for weaving the high-resolution NLCD pixels into a WRF grid may also affect the results. While we used a simple “majority rule” as described in appendix A, a “mosaic approach” that incorporates more information from the secondary and other minor land-use categories into a WRF grid box has also been suggested (Li et al. 2013). With its large urban area and relatively simple composition of land-use categories, Las Vegas will serve as an ideal test bed for different UCM and urban schemes.
The 3-km horizontal resolution used in our simulations is still too coarse to resolve individual buildings and street canyons. How or whether this will have a net effect on the simulated temperature and wind over a WRF grid box remains to be investigated. A potential approach toward the resolution of true urban landscape is to further nest a high-resolution large eddy simulation within critical subdomains of the WRF (e.g., Talbot et al. 2012). Such microscale simulations could serve as benchmarks to calibrate the submesoscale model used in this study to increase the reliability of using the latter for long-term climate simulations.
5. Concluding remarks
In this study, numerical simulations using the WRF Model produce the classical nighttime warming by urbanization but also a weak daytime cooling of surface air temperature over Las Vegas. The daytime cooling is consistent with observations for Las Vegas (Miller 2011) and is reminiscent of a similar feature for Phoenix from observation and numerical simulations (Brazel et al. 2000; Georgescu et al. 2011). This suggests that, despite the differences in the detailed land coverage (e.g., unlike Phoenix, Las Vegas has almost no agricultural land in its history), daytime cooling is a common characteristic of the effect of urbanization on the climate of desert cities.
An analysis of the surface energy balance in the simulations indicates that the decrease in surface albedo and increase in the effective emissivity due to urbanization play major roles in shaping the influence of urbanization on local climate. In the model, the slight daytime cooling is facilitated by the shadow effect and the increase in the effective area of emission of infrared radiation with the presence of urban structures. Since those processes are parameterized in the UCM, it will be useful to test how this conclusion depends on the detail of the urban parameterization schemes.
An equally interesting finding from the numerical simulations is the mechanical effect, by the increase in the effective surface roughness due to emerging urban structures, on the near-surface wind field. For Las Vegas, the result of this effect is a reduction of the wind speed with relatively minor changes in the wind direction. Thus, the diurnal circulation over Las Vegas is weakened. This change, in turn, has a secondary effect on the temperature. The complexity of this secondary effect remains to be further explored for Las Vegas and other cities with different types of local circulations.
Acknowledgments
This work is supported by NASA Grant NNX12AM88G and NSF Grant AGS-0934592. A seed fund from the Global Institute of Sustainability of Arizona State University supported the first author in spring 2013. Two of the authors (SK and HPH) appreciate useful conversations with Drs. Ashish Sharma, Taewoo Lee, and Matei Georgescu. The authors appreciate the comments by anonymous reviewers that helped improve the manuscript.
APPENDIX A
Cross Reference of the Land-Use Categories in NLCD and WRF
While the classification of land cover in WRF includes 24 categories, the NLCD2006 and NLCD1992 data have 16 and 21 categories, respectively. The cross references listed in Table A1 were adopted to convert the NLCD categories to their corresponding WRF categories. The categories not listed in Table A1 together account for less than 1% of the land coverage over the greater Las Vegas area. For simplicity, they are all converted to the dominant background land category of shrubland. The resolution of the NLCD data is much higher (at 30 m) compared to the 3-km resolution for the innermost domain of WRF in the simulations. To map the NLCD data onto the WRF grid, a “democracy” scheme is used for counting the numbers of NLCD pixels in each WRF grid box and picking the most dominant land type to overtake the WRF grid box.
Cross references between the land-use categories in the WRF Model and the two NLCD datasets used in this study. All other categories not listed in this table together account for less than 1% of the land cover over the greater Las Vegas region.
APPENDIX B
Additional Simulations Using Alternative Land-Use Maps
To test the sensitivity of the results of numerical simulation on the details of land-use maps and the start and end years for the period of urbanization, we performed a pair of additional runs for summer using the 1986 and 2011 land-use maps over Las Vegas area as generated from Landsat satellite observations (http://landsat.usgs.gov). The high-resolution Landsat images are processed and weaved into the WRF Model grid in the same fashion as described in appendix A. The changes (defined as 2011 minus 1986) in the 2-m air temperature induced by urbanization for day and night from the new runs are shown in Fig. B1 using the format of Fig. 9. The change in the diurnal cycle of 2-m air temperature averaged over the season is shown in Fig. B2 using the format of Fig. 10. The results from the additional runs are qualitatively similar to their counterparts from the 2006 and 1992 runs based on the NLCD land-use maps. Strong nighttime warming and weak daytime cooling are shown in Figs. B1 and B2. With an additional decade of urbanization spanned by the years between 1986 and 2011 (as compared to 1992 and 2006), in Fig. B1 the “ring” around the city within which urbanization occurred is thicker than its counterpart in Fig. 9. The climate change induced by urban land-use change is relatively local and concentrated in the areas where urbanization occurred.
As in Fig. 9, but for the difference in 2-m temperature from summer 2011 minus summer 1986 using the additional runs described in this appendix.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 9, but for the difference in 2-m temperature from summer 2011 minus summer 1986 using the additional runs described in this appendix.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
As in Fig. 9, but for the difference in 2-m temperature from summer 2011 minus summer 1986 using the additional runs described in this appendix.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Similar to the black curve in Fig. 10a, but for the summer 2011 minus summer 1986 difference from the additional runs described in this appendix.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Similar to the black curve in Fig. 10a, but for the summer 2011 minus summer 1986 difference from the additional runs described in this appendix.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
Similar to the black curve in Fig. 10a, but for the summer 2011 minus summer 1986 difference from the additional runs described in this appendix.
Citation: Journal of Applied Meteorology and Climatology 54, 11; 10.1175/JAMC-D-15-0003.1
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