2018, Volume 1, Issue 2
a Lamps, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
b Department of Earth and Space Science and Engineering, York University, Toronto, ON M3J 1P3, Canada
c NOVUS Environmental, Guelph, ON N1G 4T2, Canada
The urban environment and its infrastructure are vulnerable to climate change and the impacts of climate change are mostly local and, thus, adaptation should be highly location specific. Sub-
Keywords: downscaling, temporal disaggregation, temperature, precipitation, climatic design data, extreme climate indices
The IPCC's Fifth Assessment Report (AR5) estimates that global mean surface temperature is likely to be in the range of 2.6 to 4.8°C for 2081-
There is a need for more comprehensive research on potential impacts of climate change on infrastructure design. Typically, climatic design data are developed using historical climate data, assuming that the past climate conditions will still be representative over the future lifespan of the structure (Auld et al., 2008). While this assumption has worked in the past, it will become less valid as the climate changes. The recently updated climatic design values for 228 locations in Ontario (MMAH, 2015) were generated using observations from stations for a 25-
To address the need of the built environment and infrastructure sector in Ontario to adapt to the changing climate, this study explores a method to produce probabilistic projections for the 2050s and 2080s of the eight climatic design values associated with temperature and rainfall data. The projected eight climatic design values will be estimated at the 228 sites (see figure 1) listed in table 1 of the MMAH Supplementary Standard SB-
The paper continues in section 2 with brief descriptions of the downscaled daily data, reanalysis data, observations and the recently updated Ontario climatic design data which will be used in this study. In section 3, several methods of temporal downscaling and extreme value analysis are provided, followed by the results in section 4. The paper ends with the summary and conclusions in section 5.
2.1 Model Data
In this study, a 12-
2.2 Station data
The raw data used in this study include the historical observations of daily and hourly temperature data at 27 stations, climate normal, and climate extremes (Environment Canada, 2012) at 151 stations as well as the engineering climate data (ftp://ftp.tor.ec.gc.ca/Pub/Engineering_Climate_Dataset/IDF/) at 133 stations. The IDF (Intensity-
2.3 Reanalysis Datasets
Reanalysis data is the best alternative to observed data sets (Dee at al., 2011). There are several high resolution reanalysis datasets available. These include gridded fields dataset (CRU TS3.10, Harris, Jones, Osborn, & Lister, 2014), ECMWF ERA-
2.4 Current climatic design data
Recently updated climatic design data at 228 sites in Ontario is used as the reference data for model development and validation (MMAH, 2015). In this study, the following eight values are examined: Degree Days Below 18°C (also known as heating degree days, HDD), Annual Total Rainfall (RTot), Annual Total Precipitation(PrTot), 50-
3.1 Temporal disaggregation
3.1.1 Hourly temperature in January and July
A general location-
After conducting the clustering analysis, each day only belongs to one of the K clusters. A typical 24-
where β (β0,β1) is a vector denotes the estimator of the six coefficients of the model.
(3) Generate hourly temperature data by rescaling the pattern thus its minimum (maximum) value equals the daily minimum (maximum) temperature.
3.1.2 Hourly rainfall during May-
The model to identify wet and dry hours is constructed in the following steps:
After a threshold (R0) and the hourly rainfall (R) are given, the state of each hour is specified as "wet" (R>=R0) or "dry" (R<R0);
The fractions of wet hours in a day can be estimated (P1);
A relationship between the state of the current hour and the state of the preceding hours can be developed. Here, we denote P01 (or P11) as the probability of a wet hour conditioned on a preceding dry (or wet) hour, and P00 (or P10) as the probability of a dry hour conditioned on a preceding dry (or wet) hour, respectively. Based on the historical CFSR data, these frequencies can be easily estimated. In this study, we set R0=0.1mm/hour. For example, the result for the hourly August precipitation in the storm days at Pearson International Airport for the historical period (1981-
P1=0.6684, P00=0.8663, P01=0.1377, P10=0.0651, P11=0.9349.
Similar to the daily precipitation simulation procedure (Wilks & Wilby, 1999), a uniform random number u within the range of [0, 1] is generated for each simulated hour; whether the next hour in the sequence is wet or dry, it is determined by comparing the value of u and the conditional probabilities. If the previous hour (t-
The models used to generate daily rainfall amounts include the two-
3.2 Extreme values analysis
There are several theoretical extreme value distribution functions (De Haan & Ferreira, 2007). The Gumbel distribution is used by Environment and Climate Change Canada (ECCC) to calculate the return period values listed in the MMAH Supplementary Standard SB-
where a1, b1 and c1 are the parameters estimated by the available return period values such as 10-
Based on the GCM projections from the 12-
3.3.1 2.5% and 1% temperatures
The 1% and 2.5% January temperatures are the values used in the design of heating systems and the 2.5% percentile of July temperature is used in the design of cooling systems (MMAH, 2015). These temperature indicators are based on hourly temperatures generated by the methods described in 3.1. At each of the 228 locations, there are 22,320 hourly data (24-
3.3.2 Heating degree days(HDD)
HDD could be deemed as an indicator of building energy use in heating seasons. The calculation of HDD is straightforward. First, the downscaled high-
where N is the number of days in the year, T_18=18°C, is the reference temperature to which the degree-
3.3.3 Annual Total Precipitation and Rainfall
Annual total precipitation and total rainfall of a year are calculated by adding all daily precipitations and rainfall in the year, respectively. Then, the annual values are spatially linearly interpolated on the 228 locations. Next, 30-
3.3.4 Return Period Values
We calculated the return period values of RX1D and R15min, and used the median of the ensemble as the results.
4.1 January and July temperature change projection
Minimum temperature in January, maximum temperature in July, annual heating degree days (HDD) and cooling degree days (CDD) affect the design of heating system of buildings. Before analyzing these variables in details, we investigated their long term trend. The result shows that annual, January minimum and July maximum temperature steadily increased since 1980. As winter temperature continue increased, heating degree days significantly decreased since 1980 (not shown). Under RCP 6.0, temperatures and total precipitation are projected to significantly increase in Ontario (Zhu et al., 2017).
4.2 Indicators derived from daily data
As mentioned in section 3, Degree-
After the bias correction, the values in the base period (1990s) are the same as those in the recently updated codes (MMAH 2015). The values in the 2050s and 2080s are the projected data. Figure 4 shows the ensemble mean of HDD, PrTot and RTot for the three periods. It is observed that HDD may significantly decrease in the 2050s and 2080s relative to the values in the 1990s across all the 228 locations; PrTot and RTot may significantly increase in the 2050s and 2080s; relative to the 2050s, RTot may decrease in the 2080s at some locations.
Averaging over the 228 locations, HDD may decrease by 13.5% and 20.8% in the 2050s and 2080s, respectively; the decreasing amplitude varies across the province with a range of 450-
PrTot may increase by 5.1% and 8.2%, and RTot may increase by 10.4% and 16.8% in the 2050s and 2080s, respectively. PrTot may increase more dramatically in the north than in the southern regions with a range of 30-
4.3 Indicators derived from hourly temperature
The 1% and 2.5% January temperatures are the values used in the design of heating systems and the 2.5% percentile of July temperature is used in the design of cooling systems (MMAH, 2015). These three indicators are derived from the hourly temperatures which are generated using the equations (1-
4.4 Indicators of extreme rainfall events
Based on the IDF model proposed by Bernard (1932), the 10-
5. Summary and discussion
In this study, eight temperature and precipitation related climate indices are projected across the province of Ontario in Canada in the 2050s and 2080s based on an ensemble of downscaled CMIP5 data under the IPCC AR5 RCP6.0 scenario. These indices are often used as climate indicators in design of infrastructures. Four of them are based on daily data, and the other four indicators are based on sub-
To generate sub-
The results show that as global warming continues, heating degree-
The projections of HDD provided in this study could set the stage for future research on building energy use projections in Ontario, as HDD could be deemed as an indicator of building energy use in heating seasons. However, drawing practical conclusions for building energy use would require us to pose more specific questions in conjunction with other energy/thermal performance indicators. For example, of many building properties, the type of building could play a significant role on how climate change might impact building energy use, demanding different climate change adaption strategy (Shen 2017). Among other energy/thermal performance indices, overheating hours should be also considered (Gupta & Gregg 2012) in tandem with HDD from not only energy use perspective but also thermal performance of building, which could adversely affect thermal comfort of occupants.
Despite the fact that climate models are helpful to estimate some climate extreme variables, quantifying the impact of climate change on the climatic design values is still an extremely challenging task. It is more challenging to estimate the precipitation related variables than temperature related variables. The uncertainty in projections of the extreme climatic values for climatic design data calculation may come from many different sources, such as the imperfection of GCM models, spatial-
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