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Risks of summertime extreme thermal conditions in buildings as a result of climate change and exacerbation of urban heat islands
Building and Environment 78 (2014) 81e88
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Building and Environment journal homepage: www.elsevier.com/locate/buildenv
Risks of summertime extreme thermal conditions in buildings as a result of climate change and exacerbation of urban heat islands David J. Sailor* Department of Mechanical and Materials Engineering, Portland State University, Portland, OR, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 February 2014 Received in revised form 13 April 2014 Accepted 16 April 2014 Available online 28 April 2014
This study explores the role of global and local warming on indoor thermal environments of representative buildings in two warm climate cities in the U.S. (Chicago IL, and Houston TX). It uses downscaled climate change scenarios to drive whole-building model simulations of representative apartment buildings. Simulations were conducted under (a) current conditions; (b) conditions that include a global warming effect; and (c) conditions that include global warming with concurrent intensification of the urban heat island. Building thermal conditions are assessed for typical operating conditions, for conditions associated with failure of cooling equipment, and for complete power loss during a heat wave. Simulations show that warming by itself may have minimal effects on indoor thermal comfort in summer. For example, in Houston the Predicted Percent Dissatisfied (PPD) comfort metric was approximately 5e6% for current and future climate scenarios under normal operating conditions. Under conditions of AC failure, however, this increased to 61.9% for the current climate and 71.4% for the 2050 climate. In the case of Chicago PPD was between 6.2% and 7.9% for all climate scenarios when equipment operated normally. Under conditions of equipment failure, however, PPD increased to 34.1% for the current climate and 39.2% for the 2050 climate. In simulations for both cities, a complete power failure resulted in peak temperatures that were approximately 2 C cooler than the case of AC failure only. This is due to reduction in internal gains during a power blackout. Ó 2014 Elsevier Ltd. All rights reserved.
Keywords: Urban heat islands Heat waves Climate change Infiltration Thermal comfort
1. Introduction One of the fundamental purposes of buildings is to serve as protection from the ambient environment. Buildings provide shelter from wind and precipitation, but also act as buffers against heat in summer and cold in winter. Building energy codes and standards help to ensure that the building thermal envelope and the installed Heating, Ventilation, and Air-Conditioning (HVAC) systems are able to maintain the building’s interior environment within reasonable bounds. Such comfort boundaries are typically defined based on temperature and humidity limits (e.g., as specified in ANSI/ASHRAE Standard 55 [1]). Building designers and engineers employ complex whole-building energy simulation software that assists them in sizing and selecting HVAC equipment. These simulation models integrate information regarding building geometry, construction materials, and anticipated building use patterns (e.g., occupancy, lighting, and plug loads) with typical meteorological year (TMY) weather data to estimate building
* Tel.: þ1 503 725 4265; fax: þ1 503 725 8255. E-mail addresses: [email protected], [email protected]. http://dx.doi.org/10.1016/j.buildenv.2014.04.012 0360-1323/Ó 2014 Elsevier Ltd. All rights reserved.
performance under typical conditions (based on 30-years of historical weather data for the nearest airport weather station). A reasonable question is whether buildings designed and constructed to operate under climatic conditions of the past 30 years will be resilient to weather conditions experienced during the lifetime of the building and its installed equipment. Prompted by concerns of a warming climate, this manuscript addresses two questions: (1) to what extent is building thermal performance compromised when the building is exposed to significantly warmer conditions than it was designed for? and (2) how is this compromised performance further impacted when a heat wave is coincident with a major loss of power/HVAC equipment failure?
1.1. Local climate and the urban heat island effect Cities tend to be warmer than their natural (unbuilt) surroundings. This urban heat island (UHI) phenomenon is a result of a number of factors including the prevalence of thermally massive and low reflectivity surfaces, the general lack of surface moisture, and waste heat emissions from energy-consuming activities [2]. Urban heat islands are temporally and spatially complex. One can
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define a UHI based on differences in surface temperatures or air temperatures. Furthermore, air temperature heat islands can be defined at a range of vertical heights above the surface. It is the urban canopy air-temperature UHI that is most relevant with respect to direct effects on building occupants. For buildings located in or near the centre of a large city, the summertime urban canopy UHI tends to be largest in the early morning hours [2,3]. In fact, numerous studies have found remarkably similar results regarding summer differences in UHI magnitudes from day to night. For example, in an observationally-validated modelling study of the London heat island, Bohnenstengel et al. [4] found locations within the city centre to be 4e5 C warmer than rural locations in the early morning hours. The same study found that the UHI in early afternoon was no more than 1 C. In a similar study of London, Kolokotroni and Giridharan [5] found that summer daytime UHI magnitude was relatively small (<1 C) from 10 am to 6 pm, while throughout most of the night the UHI magnitude remained relatively constant at 2e3 C. Chan [6] found similar summertime results for Hong Kong: the nocturnal UHI was between 2 and 3 C while during the day it was consistently between 0.5 and 1.0 C. In a long-term analysis of 32 years of observational data for Buenos Aires, Camilloni and Barracand [7] found that night time UHI magnitudes were typically about 2 C while the daytime UHI was negligible. Likewise, in a study of summer (July 2006 and 2007) UHI in Bucharest, Cheval et al. [8] found daytime UHI in the range of 1 to þ1 C and night time UHI on the order of 2.5e3 C. So, as a general conclusion it is reasonable to state that near surface air temperature heat island magnitudes in summer are typically less than 1 C, while at night the UHI magnitude may approach 2e5 C. Actual UHI magnitudes depend on many factors including synoptic weather conditions (e.g., heat islands are typically greater during calm conditions).
1.3. Context for this study
1.2. Climate change
The building simulation software used in this study is EnergyPlus (v8.1) from the U.S. Department of Energy. EnergyPlus is a widely accepted simulation engine for modelling annual building energy consumption [18]. Released in April 2001, EnergyPlus replaced its predecessors BLAST and DOE-2 which had some technical and structural limitations. EnergyPlus takes as input information related to building location, geometry, and construction materials. It also allows the user to specify detailed schedules related to occupancy, lighting, plug loads, and thermostat set points. Once the building model (idf file) is fully defined it is coupled with a Typical Meteorological Year (TMY) data file. However, as desired, the default TMY file for a particular modelling location can be replaced with a user-modified weather file to reflect current local conditions, or test conditions. EnergyPlus also provides for extensive customizable output reports. It is relatively easy to extract thermal conditions (e.g., dry bulb and wet bulb temperatures) as well as hours that zone cooling set points are not met within each modelled zone. Furthermore, within its “Occupant Comfort Data Summary” EnergyPlus can track a number of thermal comfort metrics including Fanger’s Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) [1]. Detailed summaries of these and many other thermal comfort variables are provided in several recent review articles [19,20]. In a subset of simulations the failure of air conditioning was implemented in EnergyPlus by modifying the thermostat set point schedule to artificially allow indoor air temperatures to rise without the prospect of turning on air conditioning. Specifically, the set point for cooling was set to an artificially high and unrealistic level of 45 C for the period of failure. The case of a complete loss of power was simply accomplished by setting to zero all internal electric loads (lights, plugs, and HVAC) during the outage period. It should be noted that each simulation used EnergyPlus defaults for
Global climate change is likely to add to the UHI and to be magnified in cities in summer due to feedback mechanisms involving air conditioning of buildings [9e11]. Specifically, as the global climate warms energy use for air conditioning will increase and urban residents are likely to spend even more time indoors. These effects will interact with other risk factors related to building construction and insulation levels [12]. For example, Riberon [13] demonstrated that in the case of the 2003 heat wave in France individuals living on the top floor of uninsulated buildings had mortality risk that was roughly four times that of the general population. Further exacerbating these conditions is the continuing densification of urban populations. These trends will lead to increased waste heat emissions associated with air conditioning and will further increase summertime outdoor air temperatures. Diurnal variation of warming under climate scenarios is perhaps more important than the annual or even daily averages; although, it is far less studied. Most future climate assessment efforts focus on seasonal or annual increases in air temperature. Even the most detailed analyses resulting from downscaling of climate model simulations generally present only daily maximum and minimum temperatures. Results from such studies consistently suggest that minimum temperatures are expected to increase more than maximum temperatures, resulting in a decrease in the diurnal temperature range [14,15]. Nevertheless, it is possible that for some locations and some seasons a different trend may emerge. In any case, climate model predictions for changes in maximum and minimum temperatures can be used to construct hourly profiles of air temperatures under climate change scenarios [16].
Urban warming associated with concurrent global warming and urban growth will take place amid a backdrop of increasingly stressed electric utility grids and will, in some areas, result in increased frequency of utility system failures. Such events will have significant consequences for the health and comfort of building occupants [17]. This study explores the role of global and local warming on the indoor thermal environments of representative apartment buildings in two distinctly different warm climate cities. These scenarios are studied both in the context of typical operations and under the scenario of power outages and equipment failures during heat waves. 2. Methods Climate change scenarios are used in this study to construct whole-building model simulations of representative apartment buildings. In each case, the building design and sizing of cooling equipment are based on current building codes and Typical Meteorological Year-TMY weather data from local airports. Simulations are conducted under (a) current climate (CC) conditions; (b) conditions that include a global warming effect (2050); and (c) conditions that include global warming with a concurrent increase in the urban heat island magnitude (2050UHI). In each scenario, model analysis focusses on the hottest week of the summer in each city and explores the case of normal HVAC operations and the case of a system failure (e.g., no air conditioning during the episode) and a complete power outage. 2.1. Building energy simulation software
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Fig. 1. The reference apartment building used in this study. Top, middle, and bottom floors are illustrated explicitly. The middle floor is simulated in Energyplus using a multiplier of 2, yielding a total of 4 floors.
diurnal occupancy schedules, but that these schedules were not altered for any of the simulations. 2.2. Representative building description The US Department of Energy has developed Reference Buildings (formerly “Benchmark Buildings”) for use in studies of the performance of buildings across 16 representative climate zones in the US [21]. Archetype building input definition files are available for each of 16 building categories within each of the ASHRAE climate zones. This study used post-1980 construction versions of the “Lodging” building located in each city. This building has 4 floors, each with 8 apartments per floor. The building footprint is 46 m in the east-west direction and 17 m in the north-south direction for a total building floor area of 3128 m2 as illustrated in Fig. 1. The gross window to wall surface area ratio is 15%. The buildings are cooled to a set point of 23.9 C. Each building was simulated in two US cities representing distinctly different climate zones: Houston TX (ASHRAE climate zone 2A, “hot, humid”); and Chicago IL (ASHRAE climate zone 5A, “cool, humid”). These locations are highlighted in Fig. 2. 2.3. Climate scenarios Three climate scenarios were explored in this study: Current Climate (CC); Future Climate (2050); and Future Climate with Enhanced UHI (2050UHI). Descriptions of methods used to define these scenarios follow. 2.3.1. Current climate (CC) Weather data for current climate analyses for all building simulations are based on TMY3 files [22]. The TMY3 data represent
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typical weather conditions for a city based on hourly weather data from 1976 to 2005 (generally measured at the nearest airport weather station) and is the most common source of data for whole building energy simulations. The TMY methodology defines typical rather than average conditions for each site. It does so by first evaluating weather statistics for each month over the entire 30 year record. It then identifies for each month the single year from the weather history which had the most typical weather conditions within that month. The resulting TMY file simply combines these twelve months of weather data, using a suitable smoothing algorithm to stitch months of weather data from different years together (e.g., January from 2001, February from 1995, etc). 2.3.2. Future climate (2050) Climate change scenarios for the 2050 future climate were constructed using the downscaled global climate projections from the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project (CMIP5) from the IPCC Fifth Assessment (CMIP5). Bias-corrected daily values of maximum and minimum near-surface air temperatures were extracted for the period from 1970 though 2070 for the 1 degree latitude/longitude grid box corresponding to the location of each city studied: Chicago (41.88N, 87.63W); and Houston (29.76N, 95.38W). Downscaled results are available for the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) [23] from the IPCC. These scenarios represent nominal global-mean surface temperature increases by 2050 from 1.5 C for RCP2.6 (strong mitigation scenario) to 4.5 C for RCP8.5 (high greenhouse gas scenario). For the purpose of this analysis the RCP4.5 scenario was selected as a relatively conservative estimate of future warming. All available simulations from WRCP were therefore extracted for the RCP4.5 runs for the models listed in Table 1. Prior to using any of the 27 simulation runs listed in Table 1 the current-climate output were screened to identify the most accurate model runs for each city. As the focus of this study is summer and thermal comfort in particular, these models were screened based on their ability to replicate historical climate using the single metric of average annual Cooling Degree Days (base 18.3 C). This screening focused on the period corresponding to the monthly station normals available from NOAA for the period 1971e2000. The CDD climate normals for Chicago and Houston were 461 and 1607 C-days, respectively [24]. Any model run that could not replicate city-specific CDD to within 5% was discarded from the analysis. Table 2 summarizes the current climate (CDD) performance statistics for each city and model.
Fig. 2. ASHRAE 90.1 climate zones in the U.S. Chicago is in zone 5 and Houston is in zone 2.
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Table 1 WRCP CMIP5 Climate Modelling Group Models used in this study. All simulations are for the RCP4.5 scenario. WCRP CMIP5 Climate Modelling Group
Model ID
# of runs extracted
Canadian Centre for Climate Modelling and Analysis National Center for Atmospheric Research CSIRO NOAA Geophysical Fluid Dynamics Laboratory NOAA Geophysical Fluid Dynamics Laboratory Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo), and National Institute for Environmental Studies Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo), and National Institute for Environmental Studies Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)
CanESM2
5
CCSM4
2
CSIRO-MK3-6-0 GFDL-ESM2G
10 1
GFDL-ESM2M
1
MIROC-ESM
1
MIROC-ESM-CHEM
1
MPI-ESM-LR
3
MPI-ESM-MR
3
The CMIP5 model simulations that satisfied the current climate CDD test were then used to create hourly future climate summer air temperature profiles. Past studies of climate change impacts on building energy performance (e.g., [25,26]) often rely on monthly output from GCMs, and typically implement an “imposed offset” morphing method similar to that developed by Belcher et al. [27]. This morphing method essentially shifts and stretches the current hourly presentday TMY weather file based on predicted changes in monthly means of individual weather variables. More sophisticated methods use stochastic weather modelling techniques. Guan [28] reviewed these and other approaches. After considering the various strengths and weaknesses of each, Guan concluded that “the imposed offset method may be the most suitable method to be adopted.for building simulation”. In the present study a modification of the imposed offset approach was developed to take advantage of the availability of predicted maximum and minimum temperature data without introducing the complexity of a stochastic weather generator. The diurnal temperature range (DTR) for each CMIP5 model simulation was calculated as a simple difference (DTR ¼ Tmax Tmin) for each
Table 2 Current climate performance for each model simulation. Model ID
CanESM2 CCSM4 CSIRO-MK3-6-0 GFDL-ESM2G GFDL-ESM2M MIROC-ESM MIROC-ESM-CHEM MPI-ESM-LR MPI-ESM-MR TOTAL
Runs extracted for RCP4.5 scenario
5 2 10 1 1 1 1 3 3 27
Runs meeting 5% criteria for CDD Chicago
Houston
2 1 8 1 0 0 1 0 0 13
5 0 9 1 1 1 1 2 3 23
day of the future climate period (2036e2065). For every simulation run the sum of the squared deviations between daily DTR for that run and the average daily DTR across all models was calculated over the entire future climate period. This was used as the single metric to identify which simulation run was most representative of the mean behaviour of all runs. The single “best” simulation was then used to scale the TMY file. For the sake of this analysis the year 2050 was used as a representative future climate year. Temperature in any hour Ti,TMY,future was then calculated by:
Ti;TMY;future ¼
ðDTRGCM Þ $ T TTMY;min þ TGCM;min ðDTRTMY Þ i;TMY
(1)
So, the future minimum and maximum temperatures correspond to those of the GCM simulation and intermediate temperatures are scaled based on the hourly temperature profile from the TMY file. This is analogous to a morphing approach in which the current temperature record (TMY) is offset to match GCM modelled minimum temperatures and then scaled based on the modelled future diurnal range. To avoid discontinuities across days, a simple 4-h running average (average of current and prior 3 h of temperature values) was applied to the output generated by Eq. (1). The hourly temperature records in the TMY3 weather files were then modified for use in this study using the built-in weather conversion utility within EnergyPlus. 2.3.3. Future climate with UHI enhancement (2050UHI) As discussed above, past studies clearly note the significant variability of the diurnal UHI as a function of many parameters, most notably local wind speeds and cloud cover. Nevertheless, a clear trend is evident: during the summer, the daytime heat island is generally between 0 and 1 C while the night time heat island is generally between 2 and 3 C. The current climate scenario does not explicitly account for climate differences between the location of the building and the airport. Such differences will likely arise due both to spatial variability of the UHI effect and localized neighbourhood-scale microclimate variations (c.f. [29,30]). Thus, actual climate experienced by any specific building is not easily determined without detailed knowledge of the local neighbourhood environment. In fact, depending upon the characteristics of the environment surrounding both airport and building sites, the actual site temperature may be greater or less than that at the airport. Nevertheless, it is reasonable to expect that the UHI effect throughout the city will be magnified by climate change [11]. In order to explore the potential effects of this magnified UHI effect the 2050UHI scenario includes the base climate change projected for 2050 with a simple UHI perturbation. Based on prior studies the generic summertime UHI amplification is modelled as having a magnitude that varies linearly in time with a maximum of 2 C at 06:00 local time and a minimum of 1 C at 18:00 local time. This is clearly an oversimplification, but can provide some insight into the potential implications that would result from the combination of global climate change with an amplified local UHI. 3. Results Results are presented for both the driving climate scenarios constructed for building simulations as well as for indoor air temperature and several thermal comfort metrics for simulations in which building HVAC equipment functioned normally. Additional simulation results are presented for cases in which air conditioning failed during the hottest part of the summer, or there was a complete power outage to the building.
D.J. Sailor / Building and Environment 78 (2014) 81e88 Table 3 Summary statistics under current climate and future climate scenarios for Chicago and Houston for the period of JuneeAugust.
Current Summer max T ( C) Future Summer max T ( C) Change in Summer max T ( C) Current Summer min T ( C) Future Summer min T ( C) Change in Summer min T ( C)
Chicago
Houston
27.6 33.5 þ5.9 16.7 20.3 þ3.6
33.5 35.0 þ1.5 23.2 25.2 þ2.0
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For this hottest period of the summer the climate change scenario for Houston has a maximum temperature that is only about 1 C warmer than the current climate scenario, while the minimum temperature is roughly 5 C warmer than the minimum temperature for the current climate scenario. For Chicago, the maximum temperature under the climate change scenario is roughly 6 C warmer than the current climate, while the minimum temperature is only 3 C warmer.
3.2. Building thermal performance with and without AC 3.1. Climate scenarios Future climate scenarios were selected from WCRP CMIP5 simulations using the most representative simulation for each city using the methods discussed above. For Chicago the most representative scenario was that of the CSIRO (rcp45, csiro-mk3-6-0.10). For Houston it was a simulation from CanESM2 (rcp45, CanESM2.1). Summary statistics for the current and future climate scenarios are given in Table 3. Thus, for Chicago, the climate change scenario suggests a larger increase in maximum summer temperatures than in minimum summer temperatures. This is in contrast to the more common finding that climate change will result in an increase in the diurnal temperature range for most locations. This might be due in part to local geographydproximity to Lake Michigan and possibly to seasonally-specific changes in model-predicted large-scale atmospheric flows (e.g., the location of the jet stream). The results for Houston, however, do show a modest reduction in summer time diurnal temperature range as is typically expected. A sample of temperature profiles over a 4-day summer period in each city (centred on the hottest simulation hour) is presented in Fig. 3. For Houston, this hottest hour under the 2050 climate change scenario occurs on August 1 at 1700 local time with a temperature of 40.4 C. For Chicago it is 38.7 C and occurs on July 16 at 1600 local time.
When considering normal operations of HVAC systems during a warm summer period it is instructive to investigate maximum values of zone air temperature and several common thermal comfort metricsdFanger’s Predicted Mean Vote (PMV) and Fanger’s Predicted Percent Dissatisfied (PPD). For reference, a PMV value of zero corresponds to thermal neutrality (e.g., comfortable). For the purposes of this study the focus was on the core zone of the south-facing apartment on the top floor of the apartment building in each simulation. PMV is based on surveys that categorize comfort sensation on a scale from 3 (too cold) to þ3 (too hot). The recommended comfort level is 0.5 < PMV < þ0.5. As implemented in EnergyPlus, PMV can exceed the standard limits of 3 simply reflecting the predicted sensation of very hot or very cold conditions. The PPD value ranges from 0 to 100% and represents the proportion of any population who will be dissatisfied with the thermal environment. Indoor air temperature in the top-floor south-facing apartment is shown in Fig. 4 for both Chicago and Houston under all three climate scenarios for both normal and failed AC operations. For Chicago, indoor air temperature never exceeds the set point temperature under any climate conditions tested as long as air conditioning is operating properly. For Houston, however, there is a period in the late afternoon each day when the air conditioning
Fig. 3. Sample hourly plots of current and future (2050) climate outdoor air temperatures for Houston and Chicago for the 4 day period centred on the hottest hour of the simulation.
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Fig. 4. Indoor air temperature evolution in the south-facing top-floor apartment for all simulations. Data are presented for the three day period encompassing the hottest outdoor temperature period.
system is unable to maintain set point temperatures under either of the climate change scenarios (2050 and 2050UHI). In those instances the peak temperature in the apartment reaches 26 C which is roughly 2 C warmer than the set point temperature. In both cities, however, when the air conditioning system fails during a hot spell apartment temperatures rapidly rise over a 3-day period to levels that would be considered extremely uncomfortable. In Chicago, for instance, under the current climate condition, and air conditioning failure leads to a max/min temperature on the 3rd day of 34 and 29 C. For the 2050UHI case, when the air conditioning fails the apartment in Chicago reaches max/min temperatures on the 3rd day of roughly 39 and 34 C, respectively. For Houston, there is a similar warming when the air conditioning fails. However, in this case, the maximum temperature on the 3rd day is similar for all three climate scenarios: 40, 39, and 38 C respectively, for the 2050UHI, 2050, and CC cases. For Houston, the effect of air conditioning failure on minimum temperatures shows more dependence on the climate scenario. For the current climate the case of air conditioning failure leads to a minimum temperature on the 3rd day of about 30 C, while the minimum temperatures for the 2050 and 2050UHI cases are about 34 and 35 C, respectively. Table 4 Thermal comfort metrics for Chicago simulations (south-facing apartment on top floor) for the current climate, 2050 simulation, and 2050 simulation with enhanced UHI. Each result is presented for normal operations and for a scenario in which air conditioning fails during the hottest 3 day period (July 16e18). Performance metric
Max Indoor Air T ( C) Max PMV Avg. PMV Avg. PPD
Current
2050
2050UHI
Summaries of indoor air temperature and thermal comfort conditions for Chicago and Houston are given in Tables 4 and 5, respectively. 3.3. Building thermal performance under complete power loss While failure of the AC system during a major heat wave as discussed above represents a worst case scenario, it may be more likely that the whole building would lose power during an extreme event. In such a case one would expect that reduction of internal heating associated with lighting and other electric devices in the building would result in less extreme indoor air temperature conditions. So, for comparison with the AC equipment failures discussed above, Fig. 5 presents cases of normal operations, AC failure, and total electric power loss to the building. These results for the warmest of the climate change scenarios (2050UHI) indicate that in terms of indoor thermal comfort the case of complete power loss is preferable to that of AC system failure. The indoor temperature difference for these cases is roughly 2 C in both cities. Further, while indoor air temperature maxima are lower than the outdoor
Table 5 Thermal comfort metrics for Houston simulations (south-facing apartment on top floor) for the current climate, 2050 simulation, and 2050 simulation with enhanced UHI. Each result is presented for normal operations and for a scenario in which air conditioning fails during the hottest 3 day period (Aug 1e3). Performance metric
Normal
Failure
Normal
Failure
Normal
Failure
23.9 0.2 0.4 7.9
34.4 3.1 0.6 34.1
23.9 0.1 0.3 6.5
37.8 4.5 1.1 39.2
23.9 0.1 0.2 6.2
39.0 4.9 1.3 42.5
Max Indoor Air T ( C) Max PMV Avg. PMV Avg. PPD
Current
2050
2050UHI
Normal
Failure
Normal
Failure
Normal
Failure
24.4 0.0 0.2 6.2
38.5 4.4 2.0 61.9
25.4 0.3 0.1 5.9
39.4 4.9 2.6 71.4
26.1 0.6 0.1 6.0
40.5 5.3 3.0 74.7
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Fig. 5. Ambient and indoor air temperature evolution for the south-facing top-floor apartment (Apt) for the 2050UHI scenario under conditions of normal operations, AC failure, and total power outage (Blackout). Data are presented for the single hottest day for each city.
ambient temperatures night time temperatures indoors remain several degrees warmer than ambient.
4. Discussion and conclusions This study demonstrates the importance of designing buildings to account for future ambient conditions, and highlights the risks to building occupants associated with heat waves that may occur simultaneously with loss of power or air conditioning system failures. The results clearly show that in some instances, apartment buildings designed to meet comfort needs based on historical climate conditions will be resilient to climate change. Specifically, the modelled apartment building in Chicago was able on average to maintain thermal comfort under all climate conditions tested as long as the air conditioning system worked. In fact, due to the relatively cool default set point temperature settings employed it is likely that the average resident in the Chicago building would feel that the conditions were slightly too cool for all climate conditions modelled (0.4 < PMV < 0.2) but within the recommended comfort level (0.5 < PMV < þ0.5). For Houston, the current-climate-sized air conditioning system was unable to maintain the set point temperature under conditions of climate change. While peak apartment temperatures remained generally under 26 C and the thermal comfort of the apartment was generally considered to be satisfactory, this result does highlight the importance of designing new buildings to be resilient to future climates rather than the status quodwhich is to design based on the past 30-year climate record. Under the 2050UHI scenario the maximum value of PMV in the Houston simulations does rise to levels that are higher than recommended. In both cities, maximum indoor air temperatures exceeded 40 C for the case of air conditioning failure under the 2050 climate. In both cities studied, it was found that under hot summer conditions interior air temperatures rise to very uncomfortable levels within the first day of the air conditioning system failure. In fact, for both cities, the average PMV under conditions of the system failure were consistently above the comfort thresholds (>þ0.5) for all climate conditions. As expected, the worst scenario was the 2050UHI climate case in which the air conditioning system failure in Chicago and Houston led to average PPD values of 42.5 and 74.7%, respectively. In all cases, the failure of the air conditioning system led to minimum night time temperatures that were elevated above the concurrent outdoor ambient temperatures. In fact, for Houston, the minimum night time apartment temperature for the 2050UHI case was above 33 C on the 2nd day of failure and above 35 C on
the third. This compares with outdoor ambient temperatures which dropped to about 30 C on both of these days. For Chicago, the minimum night time apartment temperature for this case was roughly 30 C, as compared with the ambient minimum temperature which was approximately 25 C. These results are particularly important in considering the risks of heat-related mortality and morbidity. Specifically, while maximum daytime temperature is one key determinant of heat-related health outcomes, the minimum nocturnal temperature is also important due to the role it plays in enabling the body to recover at night during a multi-day heat episode [31]. An important next step in the investigation of the health risks of power failures during extreme heat events will be to evaluate the total exposure (indoor and outdoor) of vulnerable populations, and to link such information to epidemiological assessments of relative risk. It will likely be important to include estimates not only of hourly temperature conditions, but also of concurrent humidity and pollutant levels to which the vulnerable population is exposed. The climate change scenarios explored in this study have been developed to represent reasonable estimates of possible future climates. Results presented are not forecasts; rather, they are possible outcomes based on some of the most trusted modelling efforts currently available. Also, due to the general lack of information regarding changes in near-surface humidity in future climate scenarios, humidity was kept constant in this analysis. While this is clearly a limitation of the present study, it is reasonable to conclude that the approach taken here should capture the primary effects of climate change on indoor thermal comfort. Nevertheless, future work should explore sensitivities to potential changes in ambient humidity. Furthermore, results from the two case study cities investigated here may not be representative of the range of results that might be found for other cities. Thus, future analyses should consider additional climate zones.
Acknowledgements The author wishes to acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thanks the climate modelling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was supported in part by the U.S. Department of Energy under award DE-EE0003870.
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References [1] ANSI/ASHRAE, Standard 55-2010 e Thermal environmental conditions for human occupancy (ANSI approved), in, ASHRAE, 2010. p. 40. [2] Oke TR. The energetic basis of the urban heat island. Q J Royal Meteorol Soc 1982;108:1e24. [3] Runnalls KE, Oke TR. Dynamics and controls of the near-surface heat island of Vancouver British Columbia. Phys Geogr 2000;21(4):283e304. [4] Bohnenstengel SI, Evans S, Clark PA, Belcher SE. Simulations of the London urban heat island. Q J Royal Meteorol Soc 2011;137(659):1625e40. [5] Kolokotroni M, Giridharan R. Urban heat island intensity in London: an investigation of the impact of physical characteristics on changes in outdoor air temperature during summer. Sol Energy 2008;82(11):986e98. [6] Chan ALS. Developing a modified typical meteorological year weather file for Hong Kong taking into account the urban heat island effect. Build Environ 2011;46(12):2434e41. [7] Camilloni I, Barrucand M. Temporal variability of the Buenos Aires, Argentina, urban heat island. Theor Appl Climatol 2012;107(1e2):47e58. [8] Cheval S, Dumitrescu A, Bell A. The urban heat island of Bucharest during the extreme high temperatures of July 2007. Theor Appl Climatol 2009;97(3e4): 391e401. [9] Sailor DJ, Lu L. A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas. Atmos Environ 2004;38(17): 2737e48. [10] Sailor DJ, Pavlova AA. Air conditioning market saturation and long-term response of residential cooling energy demand to climate change. Energy 2003;28(9):941e51. [11] Fernando HJS, Dimitrova R, Sentic S. Climate change meets urban environment. In: NATO advanced research workshop on climate change, human health and national security, 28e30 April 2011. Dordrecht, Netherlands: Springer Netherlands; 2012. pp. 115e33. [12] Mavrogianni A, Wilkinson P, Davies M, Biddulph P, Oikonomou E. Building characteristics as determinants of propensity to high indoor summer temperatures in London dwellings. Build Environ 2012;55:117e30. [13] Riberon J, Vandentorren S, Bretin P, Zeghnoun A, Salines G, Cochet C, et al. Building and urban factors in heat related deaths during the 2003 heat wave in France. In: Healthy buildings: creating a healthy indoor environment for people, HB 2006, June 4, 2006eJune 8, 2006. Lisboa, Portugal: International Society of Indoor Air Quality and Climate; 2006. pp. 323e6. [14] IPCC. Climate change 2001: impacts, adaptation and vulnerability. London: Cambridge University Press; 2001. [15] Wild M. How well do IPCC-AR4/CMIP3 climate models simulate global dimming/brightening and twentieth-century daytime and nighttime warming? J Geophys Res Part D Atmos 2010;115(D10):00e11.
[16] Fatichi S, Ivanov VY, Caporali E. Simulation of future climate scenarios with a weather generator. Adv Water Resour 2011;34(4):448e67. [17] Wiegler L. New York feels the heat [power system restoration]. Eng Technol 2006;1(6):26e7. [18] Crawley DB, Lawrie LK, Pedersen CO, Winkelmann FC, Witte MJ, Strand RK, et al. Energyplus: new, capable, and linked. J Archit Plan Res 2004;21(4):292e 302. [19] Carlucci S, Pagliano L. A review of indices for the long-term evaluation of the general thermal comfort conditions in buildings. Energy Build 2012;53:194e 205. [20] Taleghani M, Tenpierik M, Kurvers S, Van Den Dobbelsteen A. A review into thermal comfort in buildings. Renew Sustain Energy Rev 2013;26:201e15. [21] Torcellini P, Deru M, Griffith B, Benne K, Halverson M, Winiarski D, et al. DOE commercial building benchmark models. National Renewable Energy Laboratory; 2008. p. 15. [22] Wilcox S, Marion W. User’s manual for TMY3 data sets. Golden, CO: National Renewable Energy Laboratory; 2008. [23] Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 2011;109(1e2):213e41. [24] NOAA. Climatography of the United States, No 81, monthly station normals of temperature, precipitation, and heating and cooling degree days 1971e2000. Asheville, NC: National Oceanagraphic and Atmospheric Administration; 2002. [25] Chan ALS. Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong. Energy Build 2011;43(10):2860e8. [26] Jentsch MF, Bahaj AS, James PAB. Climate change future proofing of buildingsgeneration and assessment of building simulation weather files. Energy Build 2008;40(12):2148e68. [27] Belcher SE, Hacker JN, Powell DS. Constructing design weather data for future climates. Build Serv Eng Res Technol 2005;26(1):49e61. [28] Guan L. Preparation of future weather data to study the impact of climate change on buildings. Build Environ 2009;44(4):793e800. [29] Erell E, Williamson T. Intra-urban differences in canopy layer air temperature at a mid-latitude city. Int J Climatol 2007;27(9):1243e55. [30] Liang C, Ng E, Xipo A, Chao R, Lee M, Una W, et al. Sky view factor analysis of street canyons and its implications for daytime intra-urban air temperature differentials in high-rise, high-density urban areas of Hong Kong: a GIS-based simulation approach. Int J Climatol 2012;32(1):121e36. [31] Palecki MA, Changnon SA, Kunkel KE. The nature and impacts of the July 1999 heat wave in the midwestern United States: learning from the lessons of 1995. Bull Am Meteorol Soc 2001;82(7):1353e67.
Contents lists available at ScienceDirect
Building and Environment journal homepage: www.elsevier.com/locate/buildenv
Risks of summertime extreme thermal conditions in buildings as a result of climate change and exacerbation of urban heat islands David J. Sailor* Department of Mechanical and Materials Engineering, Portland State University, Portland, OR, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 February 2014 Received in revised form 13 April 2014 Accepted 16 April 2014 Available online 28 April 2014
This study explores the role of global and local warming on indoor thermal environments of representative buildings in two warm climate cities in the U.S. (Chicago IL, and Houston TX). It uses downscaled climate change scenarios to drive whole-building model simulations of representative apartment buildings. Simulations were conducted under (a) current conditions; (b) conditions that include a global warming effect; and (c) conditions that include global warming with concurrent intensification of the urban heat island. Building thermal conditions are assessed for typical operating conditions, for conditions associated with failure of cooling equipment, and for complete power loss during a heat wave. Simulations show that warming by itself may have minimal effects on indoor thermal comfort in summer. For example, in Houston the Predicted Percent Dissatisfied (PPD) comfort metric was approximately 5e6% for current and future climate scenarios under normal operating conditions. Under conditions of AC failure, however, this increased to 61.9% for the current climate and 71.4% for the 2050 climate. In the case of Chicago PPD was between 6.2% and 7.9% for all climate scenarios when equipment operated normally. Under conditions of equipment failure, however, PPD increased to 34.1% for the current climate and 39.2% for the 2050 climate. In simulations for both cities, a complete power failure resulted in peak temperatures that were approximately 2 C cooler than the case of AC failure only. This is due to reduction in internal gains during a power blackout. Ó 2014 Elsevier Ltd. All rights reserved.
Keywords: Urban heat islands Heat waves Climate change Infiltration Thermal comfort
1. Introduction One of the fundamental purposes of buildings is to serve as protection from the ambient environment. Buildings provide shelter from wind and precipitation, but also act as buffers against heat in summer and cold in winter. Building energy codes and standards help to ensure that the building thermal envelope and the installed Heating, Ventilation, and Air-Conditioning (HVAC) systems are able to maintain the building’s interior environment within reasonable bounds. Such comfort boundaries are typically defined based on temperature and humidity limits (e.g., as specified in ANSI/ASHRAE Standard 55 [1]). Building designers and engineers employ complex whole-building energy simulation software that assists them in sizing and selecting HVAC equipment. These simulation models integrate information regarding building geometry, construction materials, and anticipated building use patterns (e.g., occupancy, lighting, and plug loads) with typical meteorological year (TMY) weather data to estimate building
* Tel.: þ1 503 725 4265; fax: þ1 503 725 8255. E-mail addresses: [email protected], [email protected]. http://dx.doi.org/10.1016/j.buildenv.2014.04.012 0360-1323/Ó 2014 Elsevier Ltd. All rights reserved.
performance under typical conditions (based on 30-years of historical weather data for the nearest airport weather station). A reasonable question is whether buildings designed and constructed to operate under climatic conditions of the past 30 years will be resilient to weather conditions experienced during the lifetime of the building and its installed equipment. Prompted by concerns of a warming climate, this manuscript addresses two questions: (1) to what extent is building thermal performance compromised when the building is exposed to significantly warmer conditions than it was designed for? and (2) how is this compromised performance further impacted when a heat wave is coincident with a major loss of power/HVAC equipment failure?
1.1. Local climate and the urban heat island effect Cities tend to be warmer than their natural (unbuilt) surroundings. This urban heat island (UHI) phenomenon is a result of a number of factors including the prevalence of thermally massive and low reflectivity surfaces, the general lack of surface moisture, and waste heat emissions from energy-consuming activities [2]. Urban heat islands are temporally and spatially complex. One can
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define a UHI based on differences in surface temperatures or air temperatures. Furthermore, air temperature heat islands can be defined at a range of vertical heights above the surface. It is the urban canopy air-temperature UHI that is most relevant with respect to direct effects on building occupants. For buildings located in or near the centre of a large city, the summertime urban canopy UHI tends to be largest in the early morning hours [2,3]. In fact, numerous studies have found remarkably similar results regarding summer differences in UHI magnitudes from day to night. For example, in an observationally-validated modelling study of the London heat island, Bohnenstengel et al. [4] found locations within the city centre to be 4e5 C warmer than rural locations in the early morning hours. The same study found that the UHI in early afternoon was no more than 1 C. In a similar study of London, Kolokotroni and Giridharan [5] found that summer daytime UHI magnitude was relatively small (<1 C) from 10 am to 6 pm, while throughout most of the night the UHI magnitude remained relatively constant at 2e3 C. Chan [6] found similar summertime results for Hong Kong: the nocturnal UHI was between 2 and 3 C while during the day it was consistently between 0.5 and 1.0 C. In a long-term analysis of 32 years of observational data for Buenos Aires, Camilloni and Barracand [7] found that night time UHI magnitudes were typically about 2 C while the daytime UHI was negligible. Likewise, in a study of summer (July 2006 and 2007) UHI in Bucharest, Cheval et al. [8] found daytime UHI in the range of 1 to þ1 C and night time UHI on the order of 2.5e3 C. So, as a general conclusion it is reasonable to state that near surface air temperature heat island magnitudes in summer are typically less than 1 C, while at night the UHI magnitude may approach 2e5 C. Actual UHI magnitudes depend on many factors including synoptic weather conditions (e.g., heat islands are typically greater during calm conditions).
1.3. Context for this study
1.2. Climate change
The building simulation software used in this study is EnergyPlus (v8.1) from the U.S. Department of Energy. EnergyPlus is a widely accepted simulation engine for modelling annual building energy consumption [18]. Released in April 2001, EnergyPlus replaced its predecessors BLAST and DOE-2 which had some technical and structural limitations. EnergyPlus takes as input information related to building location, geometry, and construction materials. It also allows the user to specify detailed schedules related to occupancy, lighting, plug loads, and thermostat set points. Once the building model (idf file) is fully defined it is coupled with a Typical Meteorological Year (TMY) data file. However, as desired, the default TMY file for a particular modelling location can be replaced with a user-modified weather file to reflect current local conditions, or test conditions. EnergyPlus also provides for extensive customizable output reports. It is relatively easy to extract thermal conditions (e.g., dry bulb and wet bulb temperatures) as well as hours that zone cooling set points are not met within each modelled zone. Furthermore, within its “Occupant Comfort Data Summary” EnergyPlus can track a number of thermal comfort metrics including Fanger’s Predicted Mean Vote (PMV) and Predicted Percent Dissatisfied (PPD) [1]. Detailed summaries of these and many other thermal comfort variables are provided in several recent review articles [19,20]. In a subset of simulations the failure of air conditioning was implemented in EnergyPlus by modifying the thermostat set point schedule to artificially allow indoor air temperatures to rise without the prospect of turning on air conditioning. Specifically, the set point for cooling was set to an artificially high and unrealistic level of 45 C for the period of failure. The case of a complete loss of power was simply accomplished by setting to zero all internal electric loads (lights, plugs, and HVAC) during the outage period. It should be noted that each simulation used EnergyPlus defaults for
Global climate change is likely to add to the UHI and to be magnified in cities in summer due to feedback mechanisms involving air conditioning of buildings [9e11]. Specifically, as the global climate warms energy use for air conditioning will increase and urban residents are likely to spend even more time indoors. These effects will interact with other risk factors related to building construction and insulation levels [12]. For example, Riberon [13] demonstrated that in the case of the 2003 heat wave in France individuals living on the top floor of uninsulated buildings had mortality risk that was roughly four times that of the general population. Further exacerbating these conditions is the continuing densification of urban populations. These trends will lead to increased waste heat emissions associated with air conditioning and will further increase summertime outdoor air temperatures. Diurnal variation of warming under climate scenarios is perhaps more important than the annual or even daily averages; although, it is far less studied. Most future climate assessment efforts focus on seasonal or annual increases in air temperature. Even the most detailed analyses resulting from downscaling of climate model simulations generally present only daily maximum and minimum temperatures. Results from such studies consistently suggest that minimum temperatures are expected to increase more than maximum temperatures, resulting in a decrease in the diurnal temperature range [14,15]. Nevertheless, it is possible that for some locations and some seasons a different trend may emerge. In any case, climate model predictions for changes in maximum and minimum temperatures can be used to construct hourly profiles of air temperatures under climate change scenarios [16].
Urban warming associated with concurrent global warming and urban growth will take place amid a backdrop of increasingly stressed electric utility grids and will, in some areas, result in increased frequency of utility system failures. Such events will have significant consequences for the health and comfort of building occupants [17]. This study explores the role of global and local warming on the indoor thermal environments of representative apartment buildings in two distinctly different warm climate cities. These scenarios are studied both in the context of typical operations and under the scenario of power outages and equipment failures during heat waves. 2. Methods Climate change scenarios are used in this study to construct whole-building model simulations of representative apartment buildings. In each case, the building design and sizing of cooling equipment are based on current building codes and Typical Meteorological Year-TMY weather data from local airports. Simulations are conducted under (a) current climate (CC) conditions; (b) conditions that include a global warming effect (2050); and (c) conditions that include global warming with a concurrent increase in the urban heat island magnitude (2050UHI). In each scenario, model analysis focusses on the hottest week of the summer in each city and explores the case of normal HVAC operations and the case of a system failure (e.g., no air conditioning during the episode) and a complete power outage. 2.1. Building energy simulation software
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Fig. 1. The reference apartment building used in this study. Top, middle, and bottom floors are illustrated explicitly. The middle floor is simulated in Energyplus using a multiplier of 2, yielding a total of 4 floors.
diurnal occupancy schedules, but that these schedules were not altered for any of the simulations. 2.2. Representative building description The US Department of Energy has developed Reference Buildings (formerly “Benchmark Buildings”) for use in studies of the performance of buildings across 16 representative climate zones in the US [21]. Archetype building input definition files are available for each of 16 building categories within each of the ASHRAE climate zones. This study used post-1980 construction versions of the “Lodging” building located in each city. This building has 4 floors, each with 8 apartments per floor. The building footprint is 46 m in the east-west direction and 17 m in the north-south direction for a total building floor area of 3128 m2 as illustrated in Fig. 1. The gross window to wall surface area ratio is 15%. The buildings are cooled to a set point of 23.9 C. Each building was simulated in two US cities representing distinctly different climate zones: Houston TX (ASHRAE climate zone 2A, “hot, humid”); and Chicago IL (ASHRAE climate zone 5A, “cool, humid”). These locations are highlighted in Fig. 2. 2.3. Climate scenarios Three climate scenarios were explored in this study: Current Climate (CC); Future Climate (2050); and Future Climate with Enhanced UHI (2050UHI). Descriptions of methods used to define these scenarios follow. 2.3.1. Current climate (CC) Weather data for current climate analyses for all building simulations are based on TMY3 files [22]. The TMY3 data represent
83
typical weather conditions for a city based on hourly weather data from 1976 to 2005 (generally measured at the nearest airport weather station) and is the most common source of data for whole building energy simulations. The TMY methodology defines typical rather than average conditions for each site. It does so by first evaluating weather statistics for each month over the entire 30 year record. It then identifies for each month the single year from the weather history which had the most typical weather conditions within that month. The resulting TMY file simply combines these twelve months of weather data, using a suitable smoothing algorithm to stitch months of weather data from different years together (e.g., January from 2001, February from 1995, etc). 2.3.2. Future climate (2050) Climate change scenarios for the 2050 future climate were constructed using the downscaled global climate projections from the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project (CMIP5) from the IPCC Fifth Assessment (CMIP5). Bias-corrected daily values of maximum and minimum near-surface air temperatures were extracted for the period from 1970 though 2070 for the 1 degree latitude/longitude grid box corresponding to the location of each city studied: Chicago (41.88N, 87.63W); and Houston (29.76N, 95.38W). Downscaled results are available for the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) [23] from the IPCC. These scenarios represent nominal global-mean surface temperature increases by 2050 from 1.5 C for RCP2.6 (strong mitigation scenario) to 4.5 C for RCP8.5 (high greenhouse gas scenario). For the purpose of this analysis the RCP4.5 scenario was selected as a relatively conservative estimate of future warming. All available simulations from WRCP were therefore extracted for the RCP4.5 runs for the models listed in Table 1. Prior to using any of the 27 simulation runs listed in Table 1 the current-climate output were screened to identify the most accurate model runs for each city. As the focus of this study is summer and thermal comfort in particular, these models were screened based on their ability to replicate historical climate using the single metric of average annual Cooling Degree Days (base 18.3 C). This screening focused on the period corresponding to the monthly station normals available from NOAA for the period 1971e2000. The CDD climate normals for Chicago and Houston were 461 and 1607 C-days, respectively [24]. Any model run that could not replicate city-specific CDD to within 5% was discarded from the analysis. Table 2 summarizes the current climate (CDD) performance statistics for each city and model.
Fig. 2. ASHRAE 90.1 climate zones in the U.S. Chicago is in zone 5 and Houston is in zone 2.
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Table 1 WRCP CMIP5 Climate Modelling Group Models used in this study. All simulations are for the RCP4.5 scenario. WCRP CMIP5 Climate Modelling Group
Model ID
# of runs extracted
Canadian Centre for Climate Modelling and Analysis National Center for Atmospheric Research CSIRO NOAA Geophysical Fluid Dynamics Laboratory NOAA Geophysical Fluid Dynamics Laboratory Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo), and National Institute for Environmental Studies Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (University of Tokyo), and National Institute for Environmental Studies Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)
CanESM2
5
CCSM4
2
CSIRO-MK3-6-0 GFDL-ESM2G
10 1
GFDL-ESM2M
1
MIROC-ESM
1
MIROC-ESM-CHEM
1
MPI-ESM-LR
3
MPI-ESM-MR
3
The CMIP5 model simulations that satisfied the current climate CDD test were then used to create hourly future climate summer air temperature profiles. Past studies of climate change impacts on building energy performance (e.g., [25,26]) often rely on monthly output from GCMs, and typically implement an “imposed offset” morphing method similar to that developed by Belcher et al. [27]. This morphing method essentially shifts and stretches the current hourly presentday TMY weather file based on predicted changes in monthly means of individual weather variables. More sophisticated methods use stochastic weather modelling techniques. Guan [28] reviewed these and other approaches. After considering the various strengths and weaknesses of each, Guan concluded that “the imposed offset method may be the most suitable method to be adopted.for building simulation”. In the present study a modification of the imposed offset approach was developed to take advantage of the availability of predicted maximum and minimum temperature data without introducing the complexity of a stochastic weather generator. The diurnal temperature range (DTR) for each CMIP5 model simulation was calculated as a simple difference (DTR ¼ Tmax Tmin) for each
Table 2 Current climate performance for each model simulation. Model ID
CanESM2 CCSM4 CSIRO-MK3-6-0 GFDL-ESM2G GFDL-ESM2M MIROC-ESM MIROC-ESM-CHEM MPI-ESM-LR MPI-ESM-MR TOTAL
Runs extracted for RCP4.5 scenario
5 2 10 1 1 1 1 3 3 27
Runs meeting 5% criteria for CDD Chicago
Houston
2 1 8 1 0 0 1 0 0 13
5 0 9 1 1 1 1 2 3 23
day of the future climate period (2036e2065). For every simulation run the sum of the squared deviations between daily DTR for that run and the average daily DTR across all models was calculated over the entire future climate period. This was used as the single metric to identify which simulation run was most representative of the mean behaviour of all runs. The single “best” simulation was then used to scale the TMY file. For the sake of this analysis the year 2050 was used as a representative future climate year. Temperature in any hour Ti,TMY,future was then calculated by:
Ti;TMY;future ¼
ðDTRGCM Þ $ T TTMY;min þ TGCM;min ðDTRTMY Þ i;TMY
(1)
So, the future minimum and maximum temperatures correspond to those of the GCM simulation and intermediate temperatures are scaled based on the hourly temperature profile from the TMY file. This is analogous to a morphing approach in which the current temperature record (TMY) is offset to match GCM modelled minimum temperatures and then scaled based on the modelled future diurnal range. To avoid discontinuities across days, a simple 4-h running average (average of current and prior 3 h of temperature values) was applied to the output generated by Eq. (1). The hourly temperature records in the TMY3 weather files were then modified for use in this study using the built-in weather conversion utility within EnergyPlus. 2.3.3. Future climate with UHI enhancement (2050UHI) As discussed above, past studies clearly note the significant variability of the diurnal UHI as a function of many parameters, most notably local wind speeds and cloud cover. Nevertheless, a clear trend is evident: during the summer, the daytime heat island is generally between 0 and 1 C while the night time heat island is generally between 2 and 3 C. The current climate scenario does not explicitly account for climate differences between the location of the building and the airport. Such differences will likely arise due both to spatial variability of the UHI effect and localized neighbourhood-scale microclimate variations (c.f. [29,30]). Thus, actual climate experienced by any specific building is not easily determined without detailed knowledge of the local neighbourhood environment. In fact, depending upon the characteristics of the environment surrounding both airport and building sites, the actual site temperature may be greater or less than that at the airport. Nevertheless, it is reasonable to expect that the UHI effect throughout the city will be magnified by climate change [11]. In order to explore the potential effects of this magnified UHI effect the 2050UHI scenario includes the base climate change projected for 2050 with a simple UHI perturbation. Based on prior studies the generic summertime UHI amplification is modelled as having a magnitude that varies linearly in time with a maximum of 2 C at 06:00 local time and a minimum of 1 C at 18:00 local time. This is clearly an oversimplification, but can provide some insight into the potential implications that would result from the combination of global climate change with an amplified local UHI. 3. Results Results are presented for both the driving climate scenarios constructed for building simulations as well as for indoor air temperature and several thermal comfort metrics for simulations in which building HVAC equipment functioned normally. Additional simulation results are presented for cases in which air conditioning failed during the hottest part of the summer, or there was a complete power outage to the building.
D.J. Sailor / Building and Environment 78 (2014) 81e88 Table 3 Summary statistics under current climate and future climate scenarios for Chicago and Houston for the period of JuneeAugust.
Current Summer max T ( C) Future Summer max T ( C) Change in Summer max T ( C) Current Summer min T ( C) Future Summer min T ( C) Change in Summer min T ( C)
Chicago
Houston
27.6 33.5 þ5.9 16.7 20.3 þ3.6
33.5 35.0 þ1.5 23.2 25.2 þ2.0
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For this hottest period of the summer the climate change scenario for Houston has a maximum temperature that is only about 1 C warmer than the current climate scenario, while the minimum temperature is roughly 5 C warmer than the minimum temperature for the current climate scenario. For Chicago, the maximum temperature under the climate change scenario is roughly 6 C warmer than the current climate, while the minimum temperature is only 3 C warmer.
3.2. Building thermal performance with and without AC 3.1. Climate scenarios Future climate scenarios were selected from WCRP CMIP5 simulations using the most representative simulation for each city using the methods discussed above. For Chicago the most representative scenario was that of the CSIRO (rcp45, csiro-mk3-6-0.10). For Houston it was a simulation from CanESM2 (rcp45, CanESM2.1). Summary statistics for the current and future climate scenarios are given in Table 3. Thus, for Chicago, the climate change scenario suggests a larger increase in maximum summer temperatures than in minimum summer temperatures. This is in contrast to the more common finding that climate change will result in an increase in the diurnal temperature range for most locations. This might be due in part to local geographydproximity to Lake Michigan and possibly to seasonally-specific changes in model-predicted large-scale atmospheric flows (e.g., the location of the jet stream). The results for Houston, however, do show a modest reduction in summer time diurnal temperature range as is typically expected. A sample of temperature profiles over a 4-day summer period in each city (centred on the hottest simulation hour) is presented in Fig. 3. For Houston, this hottest hour under the 2050 climate change scenario occurs on August 1 at 1700 local time with a temperature of 40.4 C. For Chicago it is 38.7 C and occurs on July 16 at 1600 local time.
When considering normal operations of HVAC systems during a warm summer period it is instructive to investigate maximum values of zone air temperature and several common thermal comfort metricsdFanger’s Predicted Mean Vote (PMV) and Fanger’s Predicted Percent Dissatisfied (PPD). For reference, a PMV value of zero corresponds to thermal neutrality (e.g., comfortable). For the purposes of this study the focus was on the core zone of the south-facing apartment on the top floor of the apartment building in each simulation. PMV is based on surveys that categorize comfort sensation on a scale from 3 (too cold) to þ3 (too hot). The recommended comfort level is 0.5 < PMV < þ0.5. As implemented in EnergyPlus, PMV can exceed the standard limits of 3 simply reflecting the predicted sensation of very hot or very cold conditions. The PPD value ranges from 0 to 100% and represents the proportion of any population who will be dissatisfied with the thermal environment. Indoor air temperature in the top-floor south-facing apartment is shown in Fig. 4 for both Chicago and Houston under all three climate scenarios for both normal and failed AC operations. For Chicago, indoor air temperature never exceeds the set point temperature under any climate conditions tested as long as air conditioning is operating properly. For Houston, however, there is a period in the late afternoon each day when the air conditioning
Fig. 3. Sample hourly plots of current and future (2050) climate outdoor air temperatures for Houston and Chicago for the 4 day period centred on the hottest hour of the simulation.
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Fig. 4. Indoor air temperature evolution in the south-facing top-floor apartment for all simulations. Data are presented for the three day period encompassing the hottest outdoor temperature period.
system is unable to maintain set point temperatures under either of the climate change scenarios (2050 and 2050UHI). In those instances the peak temperature in the apartment reaches 26 C which is roughly 2 C warmer than the set point temperature. In both cities, however, when the air conditioning system fails during a hot spell apartment temperatures rapidly rise over a 3-day period to levels that would be considered extremely uncomfortable. In Chicago, for instance, under the current climate condition, and air conditioning failure leads to a max/min temperature on the 3rd day of 34 and 29 C. For the 2050UHI case, when the air conditioning fails the apartment in Chicago reaches max/min temperatures on the 3rd day of roughly 39 and 34 C, respectively. For Houston, there is a similar warming when the air conditioning fails. However, in this case, the maximum temperature on the 3rd day is similar for all three climate scenarios: 40, 39, and 38 C respectively, for the 2050UHI, 2050, and CC cases. For Houston, the effect of air conditioning failure on minimum temperatures shows more dependence on the climate scenario. For the current climate the case of air conditioning failure leads to a minimum temperature on the 3rd day of about 30 C, while the minimum temperatures for the 2050 and 2050UHI cases are about 34 and 35 C, respectively. Table 4 Thermal comfort metrics for Chicago simulations (south-facing apartment on top floor) for the current climate, 2050 simulation, and 2050 simulation with enhanced UHI. Each result is presented for normal operations and for a scenario in which air conditioning fails during the hottest 3 day period (July 16e18). Performance metric
Max Indoor Air T ( C) Max PMV Avg. PMV Avg. PPD
Current
2050
2050UHI
Summaries of indoor air temperature and thermal comfort conditions for Chicago and Houston are given in Tables 4 and 5, respectively. 3.3. Building thermal performance under complete power loss While failure of the AC system during a major heat wave as discussed above represents a worst case scenario, it may be more likely that the whole building would lose power during an extreme event. In such a case one would expect that reduction of internal heating associated with lighting and other electric devices in the building would result in less extreme indoor air temperature conditions. So, for comparison with the AC equipment failures discussed above, Fig. 5 presents cases of normal operations, AC failure, and total electric power loss to the building. These results for the warmest of the climate change scenarios (2050UHI) indicate that in terms of indoor thermal comfort the case of complete power loss is preferable to that of AC system failure. The indoor temperature difference for these cases is roughly 2 C in both cities. Further, while indoor air temperature maxima are lower than the outdoor
Table 5 Thermal comfort metrics for Houston simulations (south-facing apartment on top floor) for the current climate, 2050 simulation, and 2050 simulation with enhanced UHI. Each result is presented for normal operations and for a scenario in which air conditioning fails during the hottest 3 day period (Aug 1e3). Performance metric
Normal
Failure
Normal
Failure
Normal
Failure
23.9 0.2 0.4 7.9
34.4 3.1 0.6 34.1
23.9 0.1 0.3 6.5
37.8 4.5 1.1 39.2
23.9 0.1 0.2 6.2
39.0 4.9 1.3 42.5
Max Indoor Air T ( C) Max PMV Avg. PMV Avg. PPD
Current
2050
2050UHI
Normal
Failure
Normal
Failure
Normal
Failure
24.4 0.0 0.2 6.2
38.5 4.4 2.0 61.9
25.4 0.3 0.1 5.9
39.4 4.9 2.6 71.4
26.1 0.6 0.1 6.0
40.5 5.3 3.0 74.7
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Fig. 5. Ambient and indoor air temperature evolution for the south-facing top-floor apartment (Apt) for the 2050UHI scenario under conditions of normal operations, AC failure, and total power outage (Blackout). Data are presented for the single hottest day for each city.
ambient temperatures night time temperatures indoors remain several degrees warmer than ambient.
4. Discussion and conclusions This study demonstrates the importance of designing buildings to account for future ambient conditions, and highlights the risks to building occupants associated with heat waves that may occur simultaneously with loss of power or air conditioning system failures. The results clearly show that in some instances, apartment buildings designed to meet comfort needs based on historical climate conditions will be resilient to climate change. Specifically, the modelled apartment building in Chicago was able on average to maintain thermal comfort under all climate conditions tested as long as the air conditioning system worked. In fact, due to the relatively cool default set point temperature settings employed it is likely that the average resident in the Chicago building would feel that the conditions were slightly too cool for all climate conditions modelled (0.4 < PMV < 0.2) but within the recommended comfort level (0.5 < PMV < þ0.5). For Houston, the current-climate-sized air conditioning system was unable to maintain the set point temperature under conditions of climate change. While peak apartment temperatures remained generally under 26 C and the thermal comfort of the apartment was generally considered to be satisfactory, this result does highlight the importance of designing new buildings to be resilient to future climates rather than the status quodwhich is to design based on the past 30-year climate record. Under the 2050UHI scenario the maximum value of PMV in the Houston simulations does rise to levels that are higher than recommended. In both cities, maximum indoor air temperatures exceeded 40 C for the case of air conditioning failure under the 2050 climate. In both cities studied, it was found that under hot summer conditions interior air temperatures rise to very uncomfortable levels within the first day of the air conditioning system failure. In fact, for both cities, the average PMV under conditions of the system failure were consistently above the comfort thresholds (>þ0.5) for all climate conditions. As expected, the worst scenario was the 2050UHI climate case in which the air conditioning system failure in Chicago and Houston led to average PPD values of 42.5 and 74.7%, respectively. In all cases, the failure of the air conditioning system led to minimum night time temperatures that were elevated above the concurrent outdoor ambient temperatures. In fact, for Houston, the minimum night time apartment temperature for the 2050UHI case was above 33 C on the 2nd day of failure and above 35 C on
the third. This compares with outdoor ambient temperatures which dropped to about 30 C on both of these days. For Chicago, the minimum night time apartment temperature for this case was roughly 30 C, as compared with the ambient minimum temperature which was approximately 25 C. These results are particularly important in considering the risks of heat-related mortality and morbidity. Specifically, while maximum daytime temperature is one key determinant of heat-related health outcomes, the minimum nocturnal temperature is also important due to the role it plays in enabling the body to recover at night during a multi-day heat episode [31]. An important next step in the investigation of the health risks of power failures during extreme heat events will be to evaluate the total exposure (indoor and outdoor) of vulnerable populations, and to link such information to epidemiological assessments of relative risk. It will likely be important to include estimates not only of hourly temperature conditions, but also of concurrent humidity and pollutant levels to which the vulnerable population is exposed. The climate change scenarios explored in this study have been developed to represent reasonable estimates of possible future climates. Results presented are not forecasts; rather, they are possible outcomes based on some of the most trusted modelling efforts currently available. Also, due to the general lack of information regarding changes in near-surface humidity in future climate scenarios, humidity was kept constant in this analysis. While this is clearly a limitation of the present study, it is reasonable to conclude that the approach taken here should capture the primary effects of climate change on indoor thermal comfort. Nevertheless, future work should explore sensitivities to potential changes in ambient humidity. Furthermore, results from the two case study cities investigated here may not be representative of the range of results that might be found for other cities. Thus, future analyses should consider additional climate zones.
Acknowledgements The author wishes to acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thanks the climate modelling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was supported in part by the U.S. Department of Energy under award DE-EE0003870.
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