1. Introduction
The Tokyo metropolitan area (TMA) is the largest urban agglomerate in the world (United Nations 2010). The summertime surface air temperature in Tokyo has increased by 1.7°C (100 yr)−1 during the past 80 yr from 1931 to 2010 (JMA 2011). This temperature increase in the TMA substantially exceeds the global average [i.e., 0.74°C (100 yr)−1; Solomon et al. (2007)] and the averaged value of that observed in the 17 less-urban stations in Japan [i.e., 0.9°C (100 yr)−1; JMA (2011)]. The frequency of extremely hot days (daily Tmax ≧ 35°C) and nights (daily Tmin ≧ 25°C) is increasing remarkably (JMA 2011). There is great concern over the effects of heat stress on inhabitants of the city because of the ramifications of both global warming associated with the increase in anthropogenic greenhouse gases and urban heat islands. For the local-scale climate, such as in the TMA, reductions in the effects of urban heat islands are expected to contribute to the mitigation of global warming. Thus, it is important to understand each of the impacts of future climate change and urbanization on future urban climate.
The effects of urban heat islands on observed surface temperature in the TMA have been investigated from observations or numerical simulations. Yamashita (1996), conducting an observation of air temperature on a moving train, indicated that the urban heat island intensity (UHII) on a clear calm summer night was approximately 3°C. Kimura and Takahashi (1991) investigated the UHII under typical summer conditions by using numerical experiments. They showed that the UHII at Tokyo is approximately 1°C in the daytime and 3°C at night. Kusaka et al. (2000) estimated the historical change as well as the spatial distribution of the daytime UHII by numerical simulations. They showed that the land-use change during the past 85 yr (1900–85) formed an urban heat island with an intensity of about 1°C in Tokyo and 3°–4°C in the downwind areas. Considering that the UHII in Tokyo is about 1°C in the daytime and 3°C at night on a clear calm summer day, the summertime temperature increase over the past 80 yr in Tokyo, 1.7°C (100 yr)−1, seems to be strongly influenced by urbanization.
On the other hand, several reports suggest that the recent surface temperature warming in Japanese cities can be largely attributed to global climate change rather than the development of an urban heat island. Fujibe (2009) statistically analyzed observational data over the past 27 yr (March 1979–February 2006) and investigated the past temperature trend at each observation station group categorized according to the population density within a few kilometers of a station. The temperature trend was about 0.5°C decade−1 for the group with high population densities (≥3000 km−2), including major cities in the TMA, while it was 0.3°–0.4°C decade−1 for groups with low population densities (<100 km−2). Adachi and Kimura (2010) estimated the contribution of urbanization on the warming in nighttime surface air temperature in the TMA by numerical experiments. They indicated that 10%–30% of the summer surface temperature increases over the past 10 yr (1980s–1990s) can be attributed to urbanization and the remaining 70%–90% to changes in the global climate.
Our understanding of past temperature increases has been gradually improving, as reported above. However, only a few studies have addressed the effects of global climate changes and urbanization relative to future local climates. Oleson et al. (2011) and McCarthy et al. (2010) investigated these effects on urban climates on a global scale. Oleson et al. (2011) analyzed the UHII change in the future climate using a general circulation model (GCM) with an urban model. Their results showed that the UHII at the end of the twenty-first century is weaker than that in the twentieth century despite assuming the same land-use distribution between present and future experiments. This is because the climate reaction differs depending on the land use properties. (Pielke et al. 2011). McCarthy et al. (2010) investigated the impact of global warming and the UHII on the frequency of hot nights in the enhanced CO2 climate using a GCM that included an urban land surface scheme. They reported that the frequency of hot nights cannot be attributed exclusively to the warming effects from a CO2 increase and an urban heat island estimated in the present climate because the climate change under doubled CO2 conditions modifies the urban heat island effect. The alteration in the urban heat island by global climate change was also reported by Hara et al. (2010), although they investigated a wintertime urban heat island in the future.
These previous studies describing the impacts of future global warming on urban climates are interesting. However, these studies lack a downscaling procedure to resolve the local climate change, except for Hara et al. (2010). In addition, future changes in population and urban configuration are not explicitly discussed in their experiments.
Controlling thermal environments in urban areas may be one of the ways to adapt to heat stress due to global climate changes on a regional level. Thus, the thermal changes in urban heat islands and those attributed to global climate change relative to surface air temperatures need to be thoroughly understood. Nevertheless, little is known about the extent to which the two factors above affect the future temperature change. In this study, the impacts of changes in the global climate and urbanization on future climate in the TMA through the 2070s are estimated using a regional climate model with an urban canopy model.
In section 2 of this article, the experimental design and urban parameters used in the urban canopy model are described. Section 3 presents the results of the impacts of urbanization and global climate change on the past and future urban climates. The adaptation to global warming in urban areas is discussed in section 4. Section 5 provides the conclusions and remarks.
2. Methodology
a. Model descriptions
This study estimates the impacts of global climate change on future urban climate in the TMA based on dynamical downscaling using five global climate projections and their ensemble provided by GCMs. The numerical model used for the downscaling is TERC-RAMS, which is a modified version of the Regional Atmospheric Modeling System (RAMS; Pielke et al. 1992) developed at the Terrestrial Environment Research Centre of the University of Tsukuba, Japan (TERC; Adachi et al. 2009). The model domains are shown in Fig. 1a. The horizontal grid spacings of the outer and inner domains are 15 and 3 km, respectively. The outer domain covers central Japan with a grid of 40 × 40 points. The inner domain covers the entire area of the TMA with a grid of 70 × 72 points.
(a) Model domain and (b) distributions of AMeDAS observation points. The solid contours in (a) and (b) indicate the elevation with an interval of 500 m. Black and white stars in (b) indicate the Nerima and Kumagaya AMeDAS stations, respectively. The italic letters and dashed lines in (b) denote the prefectural names and boundaries, respectively.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
The land surface process is calculated according to the methods of Tremback and Kessler (1985) and Avissar and Pielke (1989). The urban effects on the atmosphere are expressed by a single-layer urban canopy model (Kusaka et al. 2001; Kusaka and Kimura 2004). The canopy model estimates prognostic variables of the surface temperatures and heat fluxes from three surfaces (i.e., roofs, walls, and roads). Precipitation is calculated by the Arakawa–Schubert convective parameterization scheme (Arakawa and Schubert 1974) and the microphysics scheme of Walko et al. (1995). The Nakajima radiation scheme is applied to the calculation of radiation processes (Nakajima et al. 2000). The integration was conducted from 26 July to 1 September for each experiment, and the first 6 days are a spinup period.
b. Experimental design
1) Validation of the dynamical downscaling method
First of all, the downscaling method used for the urban climate projection is applied to the past climate change to validate the method. The four experiments shown in Table 1 are executed to estimate the ranges of changes in past surface temperatures due to changes in the global climate and land use. The two 7-yr periods from 1984 to 1990 and 1994 to 2000 are defined as periods I and II, respectively.
Descriptions of numerical experiments for past climate change used to validate the PGW method. Periods I and II are defined as 1984–90 and 1994–2000, respectively.
The runs of CTL_p1 and CTL_p2 indicate the hindcast simulations of these two periods, respectively. The initial and boundary conditions for these hindcast simulations are given the meteorological fields (i.e., zonal and meridional winds, air temperature, geopotential height, and relative humidity) in the pressure levels and sea surface temperature from the reanalysis data of the Japanese 25-Year Reanalysis Project (JRA-25) and the Japan Meteorological Agency’s (JMA) Climate Data Assimilation System (JCDAS; Onogi et al. 2007). Details about land-use data and urban parameters used in the model are given in section 2c. The LAND_past simulation is almost the same as CTL_p1 but with the land-use data of 1997 instead of those of 1987. The difference between LAND_past and CTL_p1 corresponds to the contribution of land-use changes on temperature increases between periods I and II. On the other hand, PGW_past is a downscale experiment applying the pseudo–global warming downscaling (PGW-DS) method to past climate change.
The schematic view of the PGW method is shown in Fig. 2. PGW-DS is almost the same as the conventional method of dynamical downscaling (DDS) except for the manner in which the initial and boundary conditions are given. The boundary conditions for PGW-DS are not directly given by the meteorological fields in the targeted year calculated by GCM. The boundary conditions of PGW_past, except for relative humidity, were given as the total of 6-hourly reanalysis data in the specific year of period I and the monthly climate difference between periods I and II. Both sets of data, 6-hourly data and the monthly difference, were obtained from JRA-25 (Fig. 2a). The PGW method assumes that the relative humidity in the future climate is equal to that in the present climate, because the global trend in relative humidity is quite small during recent decades (e.g., Dai 2006; Soden et al. 2005).
Schematic view of the PGW method.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
The difference between PGW_past and CTL_p1 indicates the impact of large-scale climate change on local climate without land-use change. The sum of the two components of global climate change (PGW_past-CTL_p1) and land-use change (LAND_past-CTL_p1) corresponds to the total of the downscaled climate difference in the TMA, which is expected to agree with that estimated by the conventional DDS method (i.e., CTL_p2-CTL_p1).
The PGW method has already been applied to future climate projections in other regions (Sato et al. 2007; Kawase et al. 2009; Hara et al. 2010). PGW-DS reduces the GCM bias by the use of the climate difference estimated by the GCM. In addition, it reduces the sampling error caused by the interannual or interdecadal variability because the daily weather in the future climate has characteristics similar to those of the corresponding days (control days) in the present climate in this method. This fact enables the comparison between two shorter periods in present and future climates. On the other hand, the suitability of the PGW-DS method is not adequately understood for the projection of the change in amplitude of the variability of the weather. For example, several studies indicate changes in the numbers of typhoons for the future climate (e.g., Sugi et al. 2009; Yamada et al. 2010). However, that is not considered in the PGW method. This issue should be investigated in future studies. Kawase et al. (2008) validated this method by applying it to the past decadal-scale variation of the baiu rainband in East Asia. However, the validity of the method has not been assessed for an urban climate. Therefore, the PGW method is applied to past climate in order to confirm its suitability.
2) Estimation of impacts of global climate change and urbanization on future urban climate
Table 2 shows a list of numerical experiments designed to evaluate the impact of global climate change and urbanization on future climate change. The control experiment is the hindcast simulation of period II, namely, CTL_p2. In this case, LAND_2070s is the sensitivity experiment of future land-use change until the 2070s, and it is basically the same as CTL_p2 except for the land-use data. Details of the land-use and urban parameters used for LAND_2070s are given in section 2c.
Descriptions of numerical experiments for future climate change.
Six other experiments were conducted (i.e., PGW-DS for the 2070s). The boundary conditions of PGW-DS for future climate are given by superimposing climate differences estimated by the GCM projection on the 6-hourly reanalysis data (JRA-25) in period II (Figs. 2b and 2c). The future climate difference is obtained from the climatological difference of the monthly mean in August between the 20C3M (from 1991 to 2000) and A1B (from 2071 to 2080) scenarios in the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emissions Scenarios (SRES). Since the future climate difference is highly dependent on the GCM projections, in this study, we use five climate differences projected by selected GCMs.
The climate differences between the present and future for the PGW-DS runs were obtained from the following five GCMs: Bjerknes Centre for Climate Research Bergen Climate Model, version 2.0 (BCCR BCM2.0), Commonwealth Scientific and Industrial Research Organisation, Mark version 3.0 (CSIRO Mk3.0), Geophysical Fluid Dynamics Laboratory Climate Model, version 2.0 (GFDL CM2.0), Istituto Nazionale di Geofisica e Vulcanologia’s (INGV) Decadal and Interdecadal Climate Variability: Scales Interactions Experiments (SINTEX), version G (SXG), and Meteorological Research Institute Coupled General Circulation Model, version 2.3.2a (MRI-CGCM2.3.2). These GCMs showed the high reproducibility of the Pacific high that affects the summertime climate in the TMA. The boundary conditions of PGW-multi are given by the multimodel mean of the five GCM projections. These five projections were selected by the following procedures: 1) 17 GCMs with horizontal grid spacings of less than 3° were chosen from 23 GCMs from phase 3 of the Coupled Model Intercomparison Project (CMIP3); 2) for each GCM, the root-mean-square errors (RMSEs) of U and V in the 850-hPa layer above Japan (20°–50°N, 120°–155°E) were calculated between 20C3M and the observation [40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis: ERA-40] in August from 1980 to 1999; and 3) the GCMs with the five smallest RMSEs were selected. These five GCMs cover four-fifths of the range of uncertainty in surface temperature change defined by 23 GCMs. Kawase et al. (2009) indicated that the average of the multiple PGW-DS runs can be replaced by a single PGW experiment using the boundary data of an ensemble mean of multiple GCMs. This advantage allows a reduction in the computational costs. However, we conducted the individual PGW-DS runs to estimate the intermodel variability.
c. Land-use and anthropogenic heat data
The land-use data from 1987 and 1997 are created using the fine land cover data (FLCD) from the Digital National Land Information (DNLI) dataset, published by the Ministry of Land, Infrastructure, Transport, and Tourism. The FLCD categorizes land use into 11 or 12 types for every 100-m square. However, each grid cell in the TERC-RAMS simulation consists of only two subgrid tiles representing the urban and vegetation areas. The vegetation area is assumed to be grassland. The urban ratio in a grid cell is defined as the ratio of the total area categorized as a building or traffic in FLCD to the entire grid cell area. Figure 3a illustrates the distribution of the urban ratio in 1997 (period II). The surface heat and moisture fluxes are calculated on the basis of the tiles of both the urban and vegetative structures, and then these fluxes in a grid cell are given by the weighted averages of the fluxes from the two subgrid tiles according to the urban ratio (Kimura 1989). Surface parameters for urban and vegetation areas are summarized in Table 3.
Distributions of urban ratios in (a) 1997 and (b) the 2070s and (c) the anthropogenic heat flux and (d) building height in period II (from 1994 to 2000).
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
Surface parameters.
The anthropogenic heat (AH) release is based on the AH value in August reported in NIRE (1997), which is estimated using energy consumption rates. The AH in period II [hereafter AH (period II)] was calculated by averaging the National Institute for Resources and Environment’s (NIRE) AH values at 1-km resolution on a model grid cell with 3-km resolution (Fig. 3c). Since the AH data in period I based on observations or statistics are not available, it was estimated by following the two steps described below assuming that the AH data are basically proportional to the floor spaces. 1) The tentative AH (period I) is calculated by multiplying AH (period II) by the ratio of floor spaces in period I to that in period II. 2) The adjustment of AH (period I) is made separately for each prefecture using a multiplication factor during the two periods of total energy consumption of its own. The energy consumption ratio for periods I and II is calculated on the basis of the consumption of electric power, gas, and petroleum (Statistical Research and Training Institute 2000; Petroleum Association of Japan 2000). As a result of these procedures, the total amount of AH (period I) was adjusted to be 0.70 times that in period II throughout the entire TMA.
In the model, the diurnal cycle of AH release is also considered. The AH has the minimum value at 0300 local time (LT) and the maximum value at 1700 LT. The diurnal cycle is expected to be spatially inhomogeneous. However, it is assumed to be horizontally uniform in this study because there is no usable observation.
The building height used in the urban canopy model was obtained from the data file of the appraised land value data in 1987 and 1997 provided in DNLI. The building height in the center part of the TMA is above 16 m on average over the 3-km mesh, while most of that in the surrounding area is 6–7 m (Fig. 3d).
A detailed projection of land use in the future is not available at this time, although land use changes as a result of social situations, such as economic progress and urban planning. In this study, we assume a developing urban scenario to estimate the possible maximum impact caused by future changes in land use. Recently, the building height in the central TMA has shown a trend toward verticalization. On the other hand, the surrounding urban area has basically expanded horizontally over the last 30 yr. Therefore, the future land use for LAND_2070s is estimated for two types of urban growth: the multistoried building area (MBA) and the low-rise building area (LBA). In this paper, the MBA is defined as grid cells with building heights exceeding four floors (corresponding to 16 m high) as of 1997, while other grid cells are defined as the LBA.
The building height in the MBA is assumed to increase at one-half the rate of the decadal extension averaged in MBA from 1987 to 1997, although the urban ratio is the same as that in 1997. Future changes in land use in the LBA were estimated with the following assumptions: 1) the total urban area in the entire LBA increases each decade at a rate of one-half of the decadal increment from 1987 to 1997 and 2) the future growth of the urban area is distributed over the entire LBA in proportion to the current vegetation ratio in each grid cell with an altitude less than 150 m. The distribution of the estimated urban ratio in the 2070s is illustrated in Fig. 3b. The energy consumption for both types of urban structures is assumed to increase in proportion to the increment of floor space.
Figure 4 indicates the change in the mean urban ratio in the inner model domain, namely, the TMA, as well as population changes in the TMA and Japan. The population of the TMA was calculated by the total of that in the Tokyo metropolis and six prefectures (shown in Fig. 1b). The changes in population and urban ratio indicate a similar tendency until 2005. The population is projected to decrease after reaching a peak in the near future. On the other hand, the estimated urban ratio indicated with a black dotted line continues to increase in the opposite direction of the population change. The estimation of the urban ratio corresponds to the statistics until 2006. The assumptions for future land use seem to be the most extreme scenario in possible changes in land use over the next 70 years.
Changes in the population and urban ratio. The white and gray bars show the population of Japan and the TMA (Tokyo metropolis and six prefectures shown in Fig. 1b), respectively. The data before 2005 are the statistical value, and those after 2010 are the estimation provided by the National Institute of Population and Social Security Research. The solid gray line indicates the urban ratios averaged in the TMA (inner model domain) based on the FLCD. The dotted line is the estimated urban ratio in the future developing urban scenario used in this study.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
3. Results
a. Validation of the PGW-DS for the past climate change
The 7-yr-averaged monthly mean surface air temperatures of the observations (the Automated Meteorological Data Acquisition System: AMeDAS) and the hindcast simulations in August are shown in Fig. 5. Surface temperature in the central part of Tokyo exceeds 27°C, which is the highest in the metropolitan area. The warmer area expands inland into Saitama and Gunma Prefectures. On the other hand, lower temperatures are shown in Ibaraki and Chiba, mostly because of cooling by the sea breeze. The model has high reproducibility for the surface temperature distribution, although the model temperature has a lower bias than is found in the observations. Figure 6 indicates the diurnal variation of surface air temperature averaged in period II. The diurnal variation of simulated surface temperature accurately captures the characteristics of the observation, although it shows lower bias in the nighttime.
Distributions of the surface air temperature in August: (a) observations and (b) CTL_p1 averaged in period I (from 1984 to 1990), as well as (c) observations and (d) CTL_p2 averaged in period II (from 1994 to 2000).
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
Diurnal variation of surface air temperature averaged in period II. The observed diurnal variation is calculated by averaging the temperatures among the AMeDAS stations shown in Fig. 1b, while that of the model results is calculated using the CTL_p2 data on the nearest grid cell to each AMeDAS station.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
Figure 7 is an illustration of the changes in the surface temperature between periods I and II in each prefecture. The observed and simulated temperature changes in each prefecture were calculated by averaging the value of the AMeDAS observations and that of grid cells including the AMeDAS station, respectively. The observed temperature change is small, within the range of 0.21°–0.48°C, as shown by the black bars. The simulations range from 0.49° to 0.70°C in the conventional DDS method (Fig. 7a) and from 0.49° to 0.67°C in the PGW method (Fig. 7b). Although the estimated temperature changes are larger than those from the observations, the model represents a tendency among prefectures. In addition, the temperature change calculated by the PGW method is in a similar range to that obtained with the conventional DDS method. This result reveals that the PGW method is applicable for estimating temperature changes in the future climate.
Surface temperature change between period I (from 1984 to 1990) and period II (from 1994 to 2000). The black bar shows the observed temperature change, which is calculated by averaging the temperatures among the AMeDAS stations located in each prefecture and at altitudes of less than 200 m (shown by circles and stars in Fig. 1b). The gray bar indicates the temperature change associated with the land-use change estimated by the difference between LAND_past and CTL_p1. The white bar is the temperature change related to synoptic climate change estimated by (a) subtracting the land-use change component (gray bar) from the temperature change derived from the conventional DDS method (CTL_p2 – CTL_p1) and (b) applying the PGW method (PGW_past – CTL_p1). The model results were calculated using the data on the nearest grid cell to each AMeDAS station.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
b. Future urban climate change in the Tokyo metropolitan area
Histograms of the hourly surface temperature at the AMeDAS stations of Nerima and Kumagaya are shown in Fig. 8. Nerima is a city in the Tokyo metropolis, and Kumagaya is an inner city where the highest temperature on record was observed. The locations of these stations are illustrated in Fig. 1b. The histogram of CTL_p2 (red line) has a similar shape to that of the observations (black line). The model result captures the variability of the observed hourly temperature quite well.
Histograms of hourly surface temperature at the Nerima and Kumagaya stations. The histograms were calculated from the observations (black), CTL_p2 (red), LAND_2070s (green), and PGW-multi (blue). The model results were calculated using the data on the nearest grid cell to each AMeDAS station.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
Figure 8 shows histograms of LAND_2070s (green line) and PGW-multi (blue line), as well as CTL_p2. The differences in the histograms between LAND_2070s and CTL_p2 reveal changes in the histogram due to changes in land use in the 2070s. The effect of the changes in land use in Nerima is quite small because the urban area around the Nerima AMeDAS station was saturated as of 1997 and the increase in building height is negligible there. In contrast to that in Nerima, the surface temperature at the Kumagaya station is estimated to increase by about 0.5°C as a result of changes in land use. The differences in the histograms between PGW-multi and CTL_p2 reveal the change in the histogram due to large-scale climate changes in the future. The intensity of warming by large-scale climate changes is about 2°C at both stations.
The monthly mean surface temperature changes until the 2070s in five prefectures were estimated separately for both factors: changes in land use and those in the global climate (Fig. 9). The contribution of urbanization (gray bars) to the change in surface temperature is less than 0.65°C in all prefectures and is lowest in Tokyo. The average of the urbanization impact among the five prefectures is 0.55°C. The warming caused by the global climate change, estimated by the PGW-multi run, reaches approximately 2.18°–2.35°C (shown by white bars); namely, it is 4 times the warming due to urbanization. As for the TMA under the A1B scenario in the 2070s, the global climate change has a higher potential to increase the local surface temperature than the future urbanization.
Surface temperature change from the 1990s to the 2070s. The gray bar shows the temperature change due to future urbanization (LAND_2070s–CTL_p2), the white bar indicates the temperature change due to synoptic climate change (PGW-multi–CTL_p2), and the thin color bars show the temperature change estimated using one of the following GCM projections: PGW-bccr (green), PGW-csiro (red), PGW-gfdl (blue), PGW-ingv (yellow), and PGW-mri (black).
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
The thin color bars in Fig. 9 show the estimated temperature change due to the global climate change using the different boundary conditions given by the five GCMs. The results depend on the GCM projection and vary greatly within the range of 1.26°–3.58°C around the value of the PGW-multi run. In other words, the range of variability due to the projection is 1.98°C. The range of uncertainty is approximately 4 times that from changes in land use.
The model parameters in the regional climate model, especially for those related to land surface, could be other major sources of uncertainty for estimating the surface temperature. The comparison between the model and observations shows that the model accurately represents the present surface temperature (Figs. 5–7), while there is no certainty that every model parameter is nearly correct. The temperature change is not likely to be seriously affected by the uncertainty of the model parameters because the range of uncertainty should be similar between the present and the future.
4. Discussion
The temperature increase is estimated to be only about 0.5°C due to the further expansion of the TMA, assuming the scenario described above. On the other hand, the temperature increase caused by global climate change as a result of anthropogenic greenhouse gases averages about 2°C in the downscaling projections from five different GCMs. The contribution of future urbanization to surface temperature is small compared to that of global climate change, but it is not negligible. This is the result in the case of the future TMA, which is already a mature urban area. The effect of future urbanization may be much larger in developing urban cities.
The United Nations (2009) reported that the projected population in more developed regions (MDRs) will undergo little change over the next 60 yr and the ratio of the population in 2070 to that in 2000 will be only 1.18, while the ratios are projected to be 3.34 and 1.8 at least less developed countries (LLDCs) and less developed countries (LDCs), respectively (Fig. 10b). It is well known that the UHII tends to be roughly proportional to a logarithm of its population (Oke 1973). Since the growth rate of the population is small and the current population is very large in the MDR including the TMA, the growth ratio of the UHII should be much smaller than that in most cities in LDCs and LLDCs. Several cities in LDCs and LLDCs would have a great potential to urbanize much more rapidly than the urbanization that the TMA experienced over the last century. Some of them may have prominent intensifications in the UHII and warming in addition to that occurring as a result of global climate change.
(a) Surface temperature change in the TMA and (b) population change. The black bar in (a) shows the UHII as of period II (CTL_p2–NOURB), the gray bar is the surface temperature change due to urbanization until the 2070s (LAND_2070s – CTL_p2), and the white bar indicates the temperature change due to global climate change (PGW-multi – CTL_p2). The black bars in (b) indicate the ratio of population in 1950 to that in 2000; the gray bars show the ratio of population in 2070 to that in 2000.
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
Figure 10a indicates the surface temperature change in the TMA from CTL_p2 in the cases of LAND_2070s and PGW-multi as well as an additional experiment without urban areas (NOURB case). The NOURB is basically the same as CTL_p2 except that the urban subgrid tiles in the land-use dataset were replaced by those of grasslands. The absolute value of the temperature difference between NOURB and CTL_p2 corresponds to the current UHII. The UHII in the TMA is estimated to be about 1°C, as in the 1990s, on average among the model grid cells that include AMeDAS stations (shown by a black bar in Fig. 10a). The UHII in the 2070s is estimated to increase to about 0.5°C due to future urban development. Then, the total UHII in the 2070s is expected to reach approximately 1.5°C.
The horizontal distributions of past and possible future UHII and temperature increases due to global warming are shown in Fig. 11. The past UHII is estimated to be as high as about 2.7°C in Tokyo, which is consistent with previous studies considering the use of recent urban data in this study. The distribution of possible future UHII expands to the suburbs compared to that of the past UHII. The temperature increase due to global warming is almost horizontally uniform, with a slight dependency on topography. On the other hand, the UHII has a prominent large intensity in the center of the TMA, and its magnitude is comparable to the warming due to global climate change, even for the past UHII. This means that the reduction of the UHII can regionally suppress the warming caused by global climate change by the effects of greenhouse gases even in a mature urban area such as the TMA. A reduction of the UHII can be achieved by local decision makers by controlling the urbanization and modifying urban structures. In the LDCs and LLDCs, it is more important to plan the future urban structure and land use of surrounding areas to reduce the UHII. In other words, the mitigation of heat island effects and the control of land use can be one of the effective ways to adapt to future warming caused by the global climate change.
Distributions of the UHII for the (a) present urban and (b) the future possible urban conditions, and (c) temperature warming due to global climate change (PGW-multi – CTL_p2). The present UHII is calculated by the difference in surface temperature between the CTL_p2 and NOURB runs. The possible UHII was estimated as the total of the present UHII and further UHII due to future urbanization (LAND_2070s–CTL_p2).
Citation: Journal of Applied Meteorology and Climatology 51, 8; 10.1175/JAMC-D-11-0137.1
5. Conclusions and remarks
This study is an investigation of the impact of global climate change due to an increase in anthropogenic greenhouse gases and future urbanization on the surface temperature until the 2070s in the TMA.
The surface temperature of the TMA in the 2070s under the SRES A1b scenario was projected to show warming of about 2°C associated with the increase in anthropogenic greenhouse gases and of about 0.5°C due to the land-use changes from the values of the 1990s. In the mature metropolitan area, the TMA, the impact of global climate change on urban climate was shown to be greater than that of future urbanization. On the other hand, the total UHII in the 2070s due to past and future urbanization reached about 1.5°C, since the UHII averaged in the TMA had risen to about 1°C as of the 1990s. Therefore, mitigation of the urban heat island should be one of the effective ways of adapting to the future warming caused by global climate change. In less developed regions, temperature increases due to future urbanization would have larger impacts on the future local climate than in more developed regions. Thus, adequate land-use planning around cities could be one of the ways to adapt to global warming by suppressing urban heat islands.
For further study focusing on the estimation of future temperature change, it would be useful to assume detailed urban scenarios based upon a consistent policy with mitigation of global warming as well as population change. In addition, the contributions of other urban properties, such as albedo, urban greening, and aerosols, need to be assessed.
Dynamical downscale simulations using five different GCM projections indicated that the estimated temperature change varied greatly among the GCM projections, with a range of 1.98°C. Since the future projection of local climate is directly affected by GCM projections, the uncertainties caused by GCM projections need to be estimated in order to apply downscaling of the projections to any decisions regarding ways to adapt to warming in future urban climate. Dynamical downscaling using multiple GCMs and regional climate models (RCMs) could be an effective method for estimating or reducing the uncertainty.
The PGW-DS method is one option for reducing the bias in GCM projections. Although the PGW-DS method is verified through application to the past climate change in this study and that by Kawase et al. (2009), validation of the method is still limited. It would be desirable to validate the method further by applying it to past climate change in various targeted regions and seasons as well as by focusing on the suitability of the method for the projection of variability with a shorter time scale than the monthly mean.
Acknowledgments
This study was supported by the Global Environment Research Fund (S-5-3 and S-5-2) of the Ministry of the Environment, Japan. The authors are grateful to Dr. Hiroaki Kondo and Dr. Yukihiro Kikegawa for providing anthropogenic heat data and Mr. Masahiro Ueno and Ms. Mari Kimura for their support in gathering the statistical data related to energy consumption.
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