Download Impact of Climate Change on Annual Cooling and

IAQVEC 2016, 9th International Conference on Indoor Air Quality Ventilation & Energy Conservation In Buildings
Impact of Climate Change on Annual Cooling and Heating Load in Tokyo
Using Prototype of Near-Future Weather Data
Yusuke Arima1,*, Ryozo Ooka2 and Hideki Kikumoto2
1
The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo,
Japan
2
*
Corresponding email: [email protected]
ABSTRACT
During building design processes, energy simulations are often used to calculate building
energy consumption and to evaluate the indoor environment. In these simulations, regional
weather data, based on current or past weather events, are commonly used. However, most
buildings have existed for several decades, during which time climate conditions have gradually
changed due to global warming. Therefore, development of future weather data and assessment
of the impact on buildings are important for climate change mitigation and adaptation. In this
study, we dynamically downscale current (2006–2010) and near future (2031–2035) weather
data projected by a global climate model (GCM), MIROC4h, with a regional climate model in
order to obtain near- future weather data for building design. Our previous study focused on
only summer seasons, whereas in this paper we conduct dynamically downscaling throughout
the year. We then conduct building energy simulations using the bias-corrected weather data as
prototypes of current and near future weather data. In the simulation, we assessed the impact of
climate change on annual cooling and heating loads of a two-story detached house in Tokyo.
Five-year-averaged and maximum cooling and heating loads are calculated. According to the
analysis, the sensible cooling load increases by 12 % and the sensible heating load decreases by
8%. Consequently, annual sensible cooling and heating load increases by only 1 % from current
to future simulations. Latent cooling load increases by 11% and annual sensible and latent
heating and cooling load increases by 3 %. The increase in the maximum cooling (1 %) and
heating load (-4 %) rate is smaller than that of the averaged seasonal cooling (12 %) and heating
load (-8 %). The maximum latent heat load increases by 15 %, and consequently, the maximum
sensible and latent cooling and heating load increases by 8%.
KEYWORDS
Reference weather data, Design weather data, Global climate model, Dynamical downscaling,
Building energy simulation
INTRODUCTION
Climate change phenomena, such as global warming and urban heat island effects, cause serious
problems (Intergovernmental Panel on Climate Change, 2013). During architectural design
processes, energy simulations are often used to evaluate the indoor thermal environment and
energy consumption of buildings. In these simulations, it is common to use regional weather
data known as typical weather data, which is typically based on current or past weather events
(Cooperman et al. 2010). However, most buildings have existed for several decades, during
which time climate conditions have gradually changed. Therefore, development of future
weather data and effective assessment of climatic impacts on buildings have become very
important issues for both climate change mitigation and adaptation (Robert et al. 2012; Jentsch
et al. 2008).
In this study, we attempt to construct near-future weather data in order to assist architectural
design using numerical meteorological models. Climate data projected by global climate
models (GCMs) are available. Although GCMs can predict long-term global warming, they
cannot illustrate the details of local phenomena due to their coarse grid resolution (~100 km).
Therefore, we input GCM data into a regional climate model (RCM) as initial and boundary
conditions and downscale the data physically with the RCM; this process is known as dynamical
downscaling (Dickinson et al. 1989; Giorgi et al. 1989). The RCM uses nested regional
climate modeling and can analyze local climate with high resolution (~1 km). We employed the
Model for Interdisciplinary Research on Climate version 4 (MIROC4h) as the GCM and the
Weather Research and Forecasting (WRF) model as the RCM.
Similar previous studies have been conducted on future weather data and assessing the effect
of climate change on building energy consumption. For example, Belcher and Hacker (2005)
developed a method for producing future weather data, known as the morphing method, and
Crawley (2008) used the morphing method to derive future weather data from existing typical
weather data and calculate the impact of climate change on a small office building. In the
morphing method, future weather data is produced by transforming current observation data
using the difference between current and future weather conditions. Weather disturbances are
important weather data components for building energy simulations, particularly for estimating
peak demand. However, daily weather disturbances in future weather data obtained through the
morphing method are based on present-day observations. Disturbances observed in weather
data would be different in the future yet future weather data obtained through the morphing
method does not represent disturbances predicted by the GCM. On the other hand, daily
disturbances in future weather data using our dynamical downscaling method are based on
future climatic conditions predicted by the GCM. This is one of the advantages of our method
over the morphing method.
In this study, we first dynamically downscaled current and future climate information projected
by MIROC4h for a 5-year period (2006-2010, 2031-2035). However, the output from weather
and climate models includes a systematical error, or bias, and the bias becomes problematic
when the output is used without any correction. Thus, we corrected for the bias of the weather
data. We conducted building energy simulations using these bias corrected weather data to
assess the impact of climate change on building energy loads. Our previous study (Arima et al.
2016; Kikumoto et al. 2014) focused only on summer seasons. In this paper, however, we
conducted simulations and climate change assessments throughout the years.
METHODS
In this study, we dynamically downscaled current (2006–2010) and near future (2031–2035)
weather data projected by a GCM (MIROC4h) with an RCM (WRF) to produce the weather
data for building energy simulations. MIROC4h reproduces global warming at a horizontal
scale of approximately 60 km (Nozawa et al. 2007; Sakamoto, 2012). For the current
simulations, we used the output of MIROC4h projected from 1981. For the near future
simulations, we use the output of MIROC4h projected from 2006. Future climate conditions
were projected by the GCM, assuming that concentrations of greenhouse gases such as CO2
will change in the future; these projected conditions are known as scenarios. The scenario
adopted by MIROC4h simulations is RCP4.5 (Meinshausen et al. 2012; Richard et al. 2010),
defined by the IPCC. There were no significant differences in radiative forcing among the
various RCPs (representative concentration pathways) for the near future, which was the focus
of this study.
We used WRF version 3.4 as the RCM (Skamarock et al. 2008). Figure 1 and Table 1 show the
nesting regions of the WRF. The target areas in this study were the Kanto region in Japan and,
in particular, Tokyo (in this study, Tokyo refers to Otemachi, which is located in the center of
Tokyo) and its surrounding area. We used four levels of nested regional climate modeling, in
which the first and fourth levels have horizontal spatial resolutions of 54 km and 2 km,
respectively. See Arima et al. (2016) for detailed information on the conditions of dynamical
downscaling.
Figure 1. Nesting region in the WRF simulation
Table 1. Weather component used as initial and boundary conditions in WRF simulation
Longitude, Latitude
0.5625º
Time
6h
Weather elements at 17 layers
Temperature, specific humidity, wind velocity, geopotential height
Surface
Surface temperature, sea surface pressure, sea surface temperature
17 layers (1000, 950, 900, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10 [hPa])
RESULTS
Dynamically Downscaling MIROC4h with the WRF
First, we compared the statistical value of the downscaled present-day output (2006–2010) of
MIROC4h (CURRENT) with that of observations (OBS) to confirm that it can reproduce
current climate conditions. Figure 2 shows the monthly average and bias of the 5-year mean of
each weather component. The monthly amount of bias is different in each month and a clear
annual trend cannot yet be confirmed, however, we confirmed the annual and seasonal average
of the bias. The cooling season is from May to October and the heating seasons are from January
to April and from November to December. Regarding temperature, the annual average bias is
1.49 ℃, and the values are similar and positive for both cooling and heating seasons (1.46 ℃
and 1.51 ℃). Concerning water vapor pressure, the annual average bias is 0.26 hPa and the
seasonal average bias is positive (0.57 hPa) in cooling seasons and slightly negative (-0.05 hPa)
in heating seasons. As for solar radiation, the annual average bias of the daily cumulative
amount is 3.17MJ/m2, and the seasonal average bias in cooling seasons (4.90 MJ/m2) is larger
than in heating seasons (1.44 MJ/m2).
OBS (2006‐2010)
Bias
2.80
30
2.40
25
2.00
20
1.60
15
1.20
10
0.80
5
0.40
0
0.00
1
2
3
4
5
6
7
8
month
9
10
11
Bias
30
1.40
25
0.70
20
0.00
15
‐0.70
10
‐1.40
5
‐2.10
0
12
‐2.80
1
2
3
4
5
6
7
8
month
9
10
11
12
b) Water vapor pressure [hPa]
a) Temperature [℃]
OBS (2006‐2010)
CURRENT (2006‐2010)
BIAS
25
10
20
8
15
6
10
4
5
2
0
difference [MJ]
day cumlative amount of solar radiation [MJ]
CURRENT (2006‐2010)
difference [hPa]
35
water vapor pressure [hPa]
CURRENT (2006‐2010)
difference [℃]
temperature increase [℃]
OBS (2006‐2010)
0
1
2
3
4
5
6
7
8
9
10
11
12
month
c) Solar radiation [MJ/m2]
Figure 2. Monthly average and the bias of 5-year mean of each weather components
Next, we compared the results of current and future weather predictions. Figure 3 shows the
monthly average of current and future simulations and the difference in each weather
component. The difference between water vapor pressure simulations seems to show an annual
trend whereby the increase in cooling seasons is larger than that in heating seasons. However,
the difference between temperature and solar radiation simulations does not seem to indicate a
clear annual trend. We confirmed the annual and seasonal average of the differences. The annual
difference of temperature data is 0.54 ℃, and the difference during cooling seasons (0.62 ℃)
is slightly larger than that in heating seasons (0.46 ℃). Water vapor pressure increases by 0.52
hPa throughout the years, and the difference in cooling seasons (0.91 hPa) is larger than that in
heating seasons (0.13 hPa). The annual averaged daily cumulative amount of solar radiation
increases by 0.05 MJ/m2, and the difference in cooling seasons is negative (-0.27 MJ/m2),
whereas in heating seasons it is positive (0.37 MJ/m2).
Difference
1.50
30
1.20
25
0.90
20
0.60
15
0.30
10
0.00
5
‐0.30
0
‐0.60
1
2
3
4
5
6
7
month
8
9
a) temperature [℃]
10
11
12
FUTURE (2031‐2035)
Differece
35
2.50
30
2.00
25
1.50
20
1.00
15
0.50
10
0.00
5
‐0.50
‐1.00
0
1
2
3
4
5
6
7
month
8
9
10
11
b) water vapor pressure [hPa]
12
difference [hPa]
35
CURRENT (2006‐2010)
water vapor pressure [hPa]
FUTURE (2031‐2035)
difference [℃]
temperature increase [℃]
CURRENT (2006‐2010)
FUTURE (2031‐2035)
Differece
30
1.50
25
1.00
20
0.50
15
0.00
10
‐0.50
5
‐1.00
difference [MJ/m2]
day cumlative solar radiation [MJ/m2]
CURRENT (2006‐2010)
‐1.50
0
1
2
3
4
5
6
7
8
9
10
11
12
month
c) solar radiation [MJ/m2]
Figure 3. Monthly averages of current and future 5-year mean values for each weather
components and the difference between them
Maximum and minimum values are also important for designing weather data to estimate the
maximum cooling and heating load. Table 2 shows the maximum and minimum values of each
weather component for both current and future weather simulations. The maximum and
minimum values are defined as the highest above 1 % and the lowest below 1 %, respectively,
over 5 years. Regarding temperature, the maximum value increases by up to 0.93 ℃ and the
minimum value increases by between 0.42 ℃ and 0.83 ℃. With regards to water vapor pressure,
the maximum value increases by between 1.56 hPa and 2.05 hPa and the minimum value
increases by 0.11 hPa to 0.37 hPa, which is lower than the increase of the maximum value.
Regarding solar radiation, the maximum day cumulative amount does not show a significant
change.
Table 2. Maximum and minimum values of each weather component for current and future
simulations
a) Maximum
0.5%
34.42
34.81
34.42
35.28
0.00
0.47
29.25
29.73
30.81
31.60
1.56
1.87
3.48
3.57
3.48
3.56
0.00
-0.01
b) Minimum
1%
0.5%
3.99
3.30
4.58
3.91
0.60
0.61
2.59
2.34
2.71
2.47
0.11
0.13
1%
CURRENT (TEMP) [℃]
FUTURE (TEMP) [℃]
DIFFERENCE (TEMP) [℃]
CURRENT (WATER) [hPa]
FUTURE (WATER) [hPa]
DIFFERENCE (WATER) [hPa]
CURRENT (SOLAR) [MJ]
FUTURE (SOLAR) [MJ]
DIFFERENCE (SOLAR) [MJ]
CURRENT (TEMP) [℃]
FUTURE (TEMP) [℃]
DIFFENRECE (TEMP) [℃]
CURRENT (WATER) [hPa]
FUTURE (WATER) [hPa]
DIFFRENCE (WATER) [hPa]
0.2%
0.1%
35.56
36.49
0.93
30.24
32.28
2.05
3.65
3.64
-0.01
0.2%
36.27
37.07
0.80
30.73
32.63
1.90
3.68
3.68
0.00
0.1%
2.55
3.31
0.77
1.99
2.25
0.26
2.05
2.89
0.83
1.79
2.16
0.37
Conditions of Building Energy Simulation
We conducted annual building energy simulations for the current period (2006-2010) and nearfuture period (2031-2035) in order to estimate the impact of climate change on building cooling
and heating load. We used the TRaNsient SYstem Simulation Tool (TRNSYS) to estimate the
building energy load (The University of Wisconsin). The target was a two-story detached house
model IBEC defined as a standard Japanese house for building energy simulations, with a total
floor space of 120 m2. The location was assumed as Tokyo. Input data were current and future
bias-corrected weather data obtained from dynamical downscaling (Arima et al. 2016), referred
to as CURRENT and FUTURE respectively. The rooms for air conditioning were the living
room and dining kitchen (LDK), bedroom, and two children’s rooms, and the schedule of air
conditioning is shown in Table 3. See Arima et al. (2016) for more detailed information on
building energy simulation conditions and the bias correction method used in this study.
Table 3. Air conditioning setting
a) Cooling (5–10)
ROOM
Setting temperature [℃]/ Schedule (5–10)
relative humidity [%]
LDK
27 /60
6:00–10:00, 12:00–
14:00, 16:00–24:00
BEDROOM
28 /60
21:00–23:00 (sleeping)
CHILD ROOM 1 27 /60
20:00–21:00, 22:00–
24:00
CHILD ROOM 2 27 /60
18:00–19:00, 21:00–
24:00
b) Heating (1–4, 11–12)
Setting
Schedule (1–4, 11–12)
temperature [℃]
22
6:00–10:00, 12:00–
14:00, 16:00–24:00
22
22
20:00–21:00, 22:00–
24:00
18:00–19:00, 21:00–
23:00
Building Energy Simulation Results
Table 4 shows the 5-year averaged annual cooling and heating load of current (CURRENT) and
future (FUTURE) simulations, and any increase or decrease between the two (DIF). It is clear
that global warming has increased the energy demand in cooling seasons and decreased it in
heating seasons. In cooling seasons, the sensible cooling load increases by 12 % and the sensible
heat load decreases by 8 %. Consequently, the annual sensible cooling and heating load
increases by only 1 % from current to future simulations. The latent cooling load increases by
11% and the total annual sensible and latent heating and cooling load increases by 3%.
Table 4. 5–year averaged annual cooling and heating load [MJ]
Case
CURRENT
FUTURE
DIF
Cooling (sensible)
5.61ൈ103
6.26ൈ103
6.58ൈ102 (12 %)
Case
CURRENT
FUTURE
DIF
Cooling (latent)
2.73ൈ103
3.03ൈ103
2.96ൈ102 (11 %)
a) Sensible heat load
Heating
Annual heating and cooling
7.34ൈ103
1.31ൈ104
6.84ൈ103
1.31ൈ104
2
-5.55ൈ10 (-8 %)
1.03ൈ102 (1 %)
b) Sensible and latent heat load
Annual heating and cooling (sensible and latent)
1.57ൈ104
1.61ൈ104
4.00ൈ102 (3 %)
Next, we assessed the impact of climate change on the maximum cooling and heating load.
Maximum heat load is defined as the topmost 0.5 % among the annual heat loads for a period
of 5 years (43805 hours), shown in Table 5. Regarding sensible heat load, the maximum sensible
cooling load increases by only 1 %, and maximum heating load decreases by 4 %. Maximum
latent cooling load increase by 15 % and sensible and latent cooling load increase by 8 %.
Table 5. Maximum cooling and heating load [kW] over a 5 year period
Case
MAX_CURRENT
MAX_FUTURE
MAX_DIF
Case
MAX_CURRENT
a) Sensible heat load
Sensible cooling load
Heating load
2.33
2.65
2.37
2.53
0.03 (1 %)
0.12 (-4 %)
b) Sensible and latent heat load
Latent cooling load
Sensible and latent cooling load
0.97
2.68
MAX_FUTURE
MAX_DIF
1.11
0.15 (15 %)
2.88
0.21 (8 %)
CONCLUSIONS
In this study, we dynamically downscaled the GCM (MIROC4h) and assessed the bias and
climate change information in the downscaled weather data. In current simulations, we confirm
the bias on the weather data. The temperature bias in cooling seasons (1.46 ℃) is close to that
in heating seasons (1.51 ℃). Regarding water vapor pressure, the seasonal average bias is
positive (0.57 hPa) in cooling seasons and slightly negative (-0.05 hPa) in heating seasons. As
for solar radiation, the bias in cooling seasons (4.90 MJ) is larger than that in heating seasons
(1.44 MJ). In future simulations, we confirm the climate change information for each weather
components. The temperature increase in cooling seasons (0.62℃) is slightly larger than that in
heating seasons (0.46 ℃), and the increase of water vapor pressure in cooling seasons (0.91
hPa) is much larger than that in heating seasons (0.13 hPa). Solar radiation slightly increases in
heating seasons (0.37 MJ) and decreases in cooling seasons (-0.27 MJ). We also confirmed the
increase in the maximum value of each weather component. Results showing that the increase
of maximum water vapor pressure (1.56 to 2.63 hPa) is larger than the increase of minimum
water vapor pressure (0.11 to 0.74 hPa) are noteworthy.
We also assessed the impact of climate change on the annual building energy demand of a twostory detached house in Tokyo using near future weather data dynamically downscaled from
MIROC4h. Regarding the sensible cooling load, the sum of the annual cooling and heating load
increases by only 1 % from current to future simulations. The annual latent cooling load
increases by 11 % and the total cooling and heating load increases by 3 %. In addition, we
assessed the impact of climate change on the maximum cooling and heating load. Regarding
the sensible cooling load, the maximum cooling load increases by only 1 % and the heating load
decreases by 4 %. The increase in the rate of maximum cooling (1 %) and heating load (-4 %)
is smaller than that of the averaged seasonal cooling (12 %) and heating load (-8 %). The
maximum latent cooling load increases by 15 % and the total cooling and heating load increases
by 8 %. The impact of climate change on sensible cooling and heating therefore cancel each
other out. However, the increase in the annual and maximum values of water vapor pressure is
large in cooling seasons. Consequently, the future increase in the latent cooling load is not
negligible.
ACKNOWLEDGEMENTS
This study represents part of the research conducted by the working group on the near-future
standard weather data using global climate modeling (project general manager Ryozo Ooka)
(Working Group for Future Standard Weather Data using GCM results,AIJ, 2014). The authors
are deeply grateful to WG members and express their gratitude to staff at Kimoto laboratory in
the Atmosphere and Ocean Research Institute, University of Tokyo, who provided the
MIROC4h data for this study. Part of this work was supported by JSPS KAKENHI Grant
Number 24226013 “Development of a meteorological information platform with high spatial
resolution for the urban environment and disaster reduction” (Project general manager: Ryozo
Ooka).
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