[LIVRO] Climate Change Scenario over Ontario Based on the Canadian Regional Climate Model (CRCM4 2) - Line Bourdages David Huard

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Climate Change Scenario over Ontario
Based on the Canadian Regional Climate
Model (CRCM4.2)
c©Ouranos, 2010
Climate Change Scenario over
Ontario Based on the Canadian
Regional Climate Model (CRCM4.2)
Authors: Line Bourdages, David Huard
Contributors: Diane Chaumont, Anne Frigon
Ouranos
May 5, 2010
Contents
Contents v
Executive Summary vi
Acknowledgements vii
1 Introduction 1
2 Recent Past Climate of Ontario 3
2.1 Observed Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Uncertainty in Observed Climate . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 CRCM Validation 13
3.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 CRCM Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Biases - Ontario Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Biases - Regional Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 Biases - Station Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4 Climate Change Scenarios 35
4.1 Methodology and Climate Indicators . . . . . . . . . . . . . . . . . . . . . . 36
4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5 IDF curves in future climate 105
5.1 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.3 Model Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6 Future Research and Conclusions 117
Bibliography 123
A Supplementary Figures 129
A.1 Comparison of rainfall observations with simulations . . . . . . . . . . . . . 130
v
vi Executive Summary
Executive Summary
This report presents the results from a study conducted by Ouranos for the Ministry
of Environment of Ontario (MOE). The main objective is to increase MOE’s knowledge
and scientific expertise on the current and future climates of the province, using 45-km
resolution Canadian Regional Climate Model (CRCM) output as well as observed climate
data.
The report first presents an overview of the climate context in Ontario as well as an
assessment of the skill of the CRCM simulation aev in simulating the recent past climate
on local and regional scales. Several variables are analyzed: daily mean, minimum and
maximum temperatures, mean diurnal range, mean daily precipitation rate and mean
snow depth. It is shown that some biases do exist in the CRCM and should be taken into
account when analyzing climate change scenarios.
In a second part, a series of 28 climate change indicators are analyzed over the
province and at 12 stations and 7 predefined regions. For each indicator, a mapped
portrait of the geographical distribution of the evolution under the SRES-A2 greenhouse
gas and emission scenario is shown for two future horizons: the 2040 to 2069 and 2070
to 2099 periods. The 1961 to 2099 long-term trends are also analyzed, and results are
discussed with regards to the interesting features pertaining to the individual indicators.
Results show interesting features, such as :
• General increases in temperature, which are reflected in a large number of indicators
(mean annual, minimum and maximum temperatures, mean temperature of the
warmest and coldest months and quarters, among others);
• Increase in most precipitation indicators, except for the mean precipitation of the
summer, where the spatial pattern shows large variability;
• Decrease in the length of the snow period;
An evaluation of return period of extreme rainfall events is also conducted over south-
ern Ontario with an analysis of Intensity-Duration-Frequency (IDF) curves. These results
are used to validate the CRCM’s simulation of extreme precipitation and show a projected
increase in future rainfall extremes, for the 2041-4070 horizon.
The validation and climate change scenario results are all based on a single CRCM
simulation (code-named aev), which implies that they are not accompanied by any quan-
titative or qualitative measure of uncertainty, probability or likelihood. Caution is there-
fore advised when using these results, as several additional key aspects need to be con-
sidered. Such aspects include statistical robustness and model representativeness, which
can be addressed by analyzing an ensemble of simulations from a variety of different
climate models (ideally of fine resolution and important complexity). The consideration
of a range of possible greenhouse gas and aerosol emission scenarios is also important to
assess the uncertainty associated with climate change projections.
Acknowledgements vii
Acknowledgements
This project has received funding support from the Ontario Ministry of the Environment.
Such support does not indicate endorsement by the Ministry of the contents of this ma-
terial.
The authors would like to acknowledge the Data Access Integration (DAI, see http:
//quebec.ccsn.ca/DAI/) Team for providing the observation station data and techni-
cal support. The DAI data download gateway is made possible through collaboration
among the Global Environmental and Climate Change Centre (GEC3), the Adaptation
and Impacts Research Division (AIRD) of Environment Canada, and the Drought Re-
search Initiative (DRI). The National Land and Water Information Service and Natural
Resources Canada are acknowledged for giving access to the historical gridded datasets
and the Ouranos Simulation Team is acknowledged as well, for generating and providing
the CRCM data. David Huard is also thankful for the review, suggestions and comments
provided by A. Mailhot.
Chapter 1
Introduction
It is now unequivocal that global climate is changing (e.g. Murphy et al. [2009], Solomon
et al. [2007]). Although the extent of the climate changes is not well known, the Interna-
tional Panel on Climate Change (IPCC fourth assessment report (AR4);Parry et al. [2007],
page 8) states that ”[o]bservational evidence from all continents [...] shows that many
natural systems are being affected by regional climate changes [...and that o]ther effects
[...] on natural and human environments are emerging, although many are difficult to
discern due to adaptation and non-climatic drivers.”
Global Climate Models (GCMs) are the primary tool used to study the global climate
and its evolution. They are based on the basic conservation equations and provide vari-
ables that are physically coherent in time, space and among themselves. This allows a
better understanding of the processes governing the general circulation and proves very
useful in the evaluation of climate change impacts in terms of the interrelations in the cli-
mate system. Due to their complexity and computational cost, the horizontal resolution
of GCMs is typically on the order of a few hundred kilometers. On a regional scale, such
a resolution is too coarse to allow the study of climate change in a way that is useable for
climate impacts study.
Two approaches are available to downscale the GCM results to a finer resolution and
thus permit regional scale studies: statistical and dynamical downscaling (IPCC Chap-
ter 11 ; Christensen et al. [2007]). In the former, statistical relationships are established
between observed variables (at the relevant scale) and larger (GCM) scale atmospheric
variables. This statistical relationship is then assumed to remain valid in a future climate
and applied against the GCM simulated output. Even though this method is computa-
tionally inexpensive and can be applied at any scale as long as observations are available,
the results can be physically incoherent and the validity of the relationship in the future
can be questioned.
Dynamical downscaling, on the other hand, involves the application of higher reso-
lution Regional Climate Models (RCMs) to downscale the GCM results at a finer scale.
To do so, GCM or reanalysis data is used as boundary conditionsaccording to their location.
50 Climate Change Scenarios
Mean Diurnal Temperature Range (dtr)
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.5: Deltas (absolute) and trend results for annual mean dtr.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. -0.75 0.00 0.23 -0.68 -0.75
Big Trout Lake -0.83 0.00 0.53 -0.72 -0.91
Timmins A. -0.49 0.00 0.14 -0.33 -0.60
Kenora A. -0.57 0.00 0.2 -0.27 -0.61
Sault St-Marie A. -0.36 0.02 0.00 -0.13 -0.41
North Bay A. -0.50 0.00 0.01 -0.23 -0.56
Wiarton A. -0.26 0.02 0.68 -0.19 -0.26
Ottawa A. -0.52 0.00 0.24 -0.32 -0.53
Toronto City Ctr -0.45 0.00 0.95 -0.28 -0.44
Toronto Pearson A. -0.40 0.00 0.95 -0.24 -0.40
London Int’l A. -0.14 0.35 0.9 -0.03 -0.10
Windsor A. -0.66 0.00 0.6 -0.41 -0.54
Region 7 -0.87 0.00 0.22 -0.68 -0.94
Region 6 -0.52 0.00 0.06 -0.26 -0.56
Region 5 -0.64 0.00 0.08 -0.49 -0.68
Region 4 -0.43 0.00 0.01 -0.20 -0.51
Region 3 -0.44 0.00 0.37 -0.24 -0.46
Region 2 -0.26 0.04 0.85 -0.14 -0.23
Region 1 -0.50 0.00 0.82 -0.25 -0.39
4.2. Results 51
Discussion
Mean Diurnal Temperature Range (dtr): Annual average of daily temperature ranges, i.e.
differences between daily maximum and minimum temperatures.
This indicator helps to assess how the increases in daily minimum and maximum
temperatures compare to each other. In the case where maximum temperature increases
more than minimum temperature, the dtr will show an increase. In the reverse case, the
dtr will decrease. Generally, the mean daily temperature range is projected to decrease
over Ontario, which is consistent with the change in minimum temperature being greater
than that of maximum temperature. This decrease shows a South - North gradient.
Considering the annual cycle of the 1980s to 2050s delta (Figure 4.8), however, the
picture is quite different. Indeed, during the winter and spring, dtr is expected to decrease
for all regions, whereas the summer is characterized by increases of dtr. The South
has an annual cycle with larger amplitude than the rest of Ontario, indicating that it is
expected to be the region most affected by these changes, which are projected to go from
approximately -3.5 to almost 4 ◦C in January and August, respectively. On average during
the year, values are slightly negative, resulting in the maps and results shown before.
Note that all stations but London International Airport show statistically significant
decreasing trends. Also, Sault-St-Marie and North Bay, as well as Region 4 in which they
are located show significant autocorrelation.
Figure 4.8: Annual cycle of the 1980s to 2050s delta of monthly mean daily temperature ranges
(◦C). Each of the seven regions of Ontario are represented by a color going from Blue (North) to
red (South) according to their location.
52 Climate Change Scenarios
Temperature Seasonality
1980s ; NLWIS (%) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.6: Deltas (absolute) and trend results for annual temperature seasonality
Location Trend P-value DW Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(%/100 years) (%) (%)
Moosonee A. -0.64 0.00 0.21 -0.50 -0.64
Big Trout Lake -0.53 0.00 0.24 -0.46 -0.54
Timmins A. -0.39 0.00 0.58 -0.32 -0.35
Kenora A. -0.26 0.00 0.11 -0.29 -0.22
Sault St-Marie A. -0.26 0.00 0.44 -0.22 -0.20
North Bay A. -0.31 0.00 1.00 -0.25 -0.27
Wiarton A. -0.23 0.00 0.99 -0.17 -0.16
Ottawa A. -0.32 0.00 0.90 -0.23 -0.27
Toronto City Ctr -0.30 0.00 0.84 -0.22 -0.23
Toronto Pearson A. -0.30 0.00 0.86 -0.22 -0.23
London Int’l A. -0.24 0.00 0.88 -0.17 -0.15
Windsor A. -0.27 0.00 0.01 -0.19 -0.16
Region 7 -0.59 0.00 0.21 -0.49 -0.60
Region 6 -0.28 0.00 0.18 -0.29 -0.24
Region 5 -0.46 0.00 0.27 -0.38 -0.43
Region 4 -0.31 0.00 0.73 -0.25 -0.27
Region 3 -0.29 0.00 0.94 -0.21 -0.22
Region 2 -0.25 0.00 0.87 -0.19 -0.18
Region 1 -0.25 0.00 0.36 -0.17 -0.15
4.2. Results 53
Discussion
Temperature Seasonality: Coefficient of variation of monthly mean temperatures, i.e. the
ratio between the standard deviation of monthly mean temperatures to mean monthly
mean temperature.
The contributions to the indicator from the change in standard deviation of the monthly
mean temperatures (σ) and the mean monthly mean temperature (µ) is not straight for-
ward. Indeed, if we look at the mathematical expression of the delta, where indices i and
f refer to the initial and final periods, respectively :
∆Seasonality =
σf
µ f
− σi
µi
=
σf µi − σiµ f
µiµ f
, (4.4)
the relationship is not simple to understand, by comparing changes in σ or µ.
In this indicator, temperatures are in Kelvin in order to remove discontinuities en-
countered when the denominator (mean monthly mean temperatures) tends to 0◦C. The
use of Kelvin degrees leads to temperature seasonality values that are relatively low and
those values were therefore multiplied by 100 on the maps to present the results in per-
cent. This was done to reproduce the NRCAN procedures.
This indicator represents a description of how variable the climate of a location is during
the year. For example, the North shows very large annual temperature range, with very
low winter temperatures (approx. -25◦C), and relatively temperate summer temperatures
(approx. 15◦C; see Figure 2.1). This leads to a large standard deviation of monthly mean
temperatures and therefore a large temperature seasonality (above 5% on the NLWIS
map).
The temperature seasonality is projected to decrease all over the Ontarian territory,
with larger decreases occurring in the North. Trends are statistically significant at all
stations and regions, but Windsor Airport shows some serial autocorrelation.
54 Climate Change Scenarios
Maximum Daily Mean Temperature
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.7: Deltas (absolute) and trend results for max. temperature of warmest month
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 4.73 0.00 0.57 3.16 5.67
Big Trout Lake 4.19 0.00 0.04 2.78 4.89
Timmins A. 5.37 0.00 0.93 3.58 5.89
Kenora A. 4.93 0.00 0.31 3.55 5.82
Sault St-Marie A. 5.27 0.00 0.86 4.00 5.30
North Bay A. 5.29 0.00 0.41 3.57 5.46
Wiarton A. 6.58 0.00 0.17 4.51 6.93
Ottawa A. 5.18 0.00 0.24 3.51 5.32
Toronto City Ctr 6.94 0.00 0.24 4.42 6.97
Toronto Pearson A. 7.03 0.00 0.28 4.44 7.06
London Int’l A. 7.03 0.00 0.60 5.12 7.57
Windsor A. 6.01 0.00 0.02 3.87 6.57
Region 7 4.33 0.00 0.26 2.88 4.94
Region 6 4.93 0.00 0.38 3.62 5.68
Region 5 4.91 0.00 0.73 3.53 5.62
Region 4 5.33 0.00 0.44 3.66 5.48
Region 3 5.61 0.00 0.06 3.79 5.80
Region 2 6.85 0.00 0.16 4.68 7.17
Region 1 6.14 0.00 0.06 4.31 6.80
4.2. Results 55
Discussion
Maximum Daily Mean Temperature: Maximum daily mean temperature of the year.
This indicator is a good representation of how the temperatures of the warmest month
of the year are projected to evolve. According to the aev simulation, this indicator is
projected to increase all over Ontario. The change is expected to be greatest in the South
and lowest in the North. The change is also projected to increase with time, with delta
values ranging from 2 to 6 ◦C at the 2050s horizon, and from 4 to 8 at the 2080s horizon.
Trends are statistically significant at all stations and regions. The Big Trout Lake
and Windsor Airport stations however show some autocorrelation. When looking more
closely at the time series (Figure 4.9;Big Trout Lake), it seems that the autocorrelation is
not due to non-linearity of the trend, which would result in a clear pattern in the resid-
uals. It seems that in this case, the patterns are due to interannual variability (decadal
cycles).
Figure 4.9: 1961 to 2099 time series of the maximum daily mean temperature of the warmest
month indicator for the Big Trout Lake station, from the CRCM simulation aev.
56 Climate Change Scenarios
Minimum Daily Mean Temperature
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.8: Deltas (absolute)and trend results for min. temperature of coldest month
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 8.30 0.00 0.14 5.54 8.51
Big Trout Lake 6.59 0.00 0.61 5.13 6.97
Timmins A. 7.18 0.00 0.06 5.50 7.42
Kenora A. 5.94 0.00 0.32 4.91 6.27
Sault St-Marie A. 7.68 0.00 0.15 6.57 7.75
North Bay A. 7.45 0.00 0.01 5.21 7.54
Wiarton A. 9.65 0.00 0.33 6.45 9.78
Ottawa A. 8.23 0.00 0.02 5.30 8.16
Toronto City Ctr 9.15 0.00 0.96 6.16 9.03
Toronto Pearson A. 9.20 0.00 0.93 6.21 9.05
London Int’l A. 8.97 0.00 0.16 6.00 8.96
Windsor A. 8.76 0.00 0.81 5.89 9.24
Region 7 7.06 0.00 0.97 5.17 7.38
Region 6 6.08 0.00 0.45 4.94 6.41
Region 5 7.22 0.00 0.10 5.53 7.46
Region 4 7.21 0.00 0.02 5.56 7.34
Region 3 8.22 0.00 0.03 5.62 8.17
Region 2 9.26 0.00 0.32 6.16 9.16
Region 1 9.07 0.00 0.68 5.86 9.12
4.2. Results 57
Discussion
Minimum Daily Mean Temperature: Minimum daily mean temperature of the year.
This indicator is a good representation of how the coldest temperatures of the coldest
month of the year are projected to evolve. According to the aev simulation, this indicator
is projected to increase all over Ontario. The change is expected to be greatest in the South
and the James Bay coast and lowest in northwestern Ontario. The change is also projected
to increase with time, with delta values ranging from 4 to 7 ◦C at the 2050s horizon, and
from 6 to 12 at the 2080s horizon. The minimum daily mean temperature of the coldest
month is therefore projected to increase more than the maximum daily mean temperature
of the warmest month, leading to a decrease in the annual temperature range.
Trends are statistically significant at all stations and regions. The Ottawa and North
Bay Airports, as well as their respective regions, show autocorrelation. When looking
more closely at the time series (Figure 4.10 ; Region 3), it seems that the autocorrelation
could be due to either a non-linearity of the climate change trend , interannual variability
or a combination of the two.
It is not possible to determine the cause of the non-linearities from a single simula-
tion and therefore the linear trend assumption is the simplest and most easily justified
estimate possible.
Figure 4.10: 1961 to 2099 time series of the minimum daily mean temperature of the coldest
month indicator for region 3, from the CRCM simulation aev.
58 Climate Change Scenarios
Mean Annual Temperature Range
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.9: Deltas (absolute), values and trend results for mean annual temperature range
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. -3.76 0.00 0.98 -2.38 -2.84
Big Trout Lake -2.27 0.01 0.91 -2.35 -2.08
Timmins A. -1.75 0.03 0.21 -1.92 -1.53
Kenora A. -1.04 0.22 0.94 -1.36 -0.45
Sault St-Marie A. -2.25 0.01 0.47 -2.56 -2.45
North Bay A. -2.12 0.01 0.11 -1.64 -2.08
Wiarton A. -2.97 0.00 0.14 -1.93 -2.85
Ottawa A. -3.12 0.00 0.22 -1.78 -2.84
Toronto City Ctr -2.18 0.01 0.51 -1.74 -2.06
Toronto Pearson A. -2.15 0.01 0.51 -1.76 -1.99
London Int’l A. -1.93 0.01 0.13 -0.88 -1.39
Windsor A. -2.87 0.00 0.96 -2.02 -2.68
Region 7 -2.91 0.00 0.46 -2.29 -2.43
Region 6 -1.13 0.17 0.67 -1.32 -0.73
Region 5 -2.50 0.00 0.22 -2.00 -1.83
Region 4 -1.80 0.01 0.19 -1.91 -1.86
Region 3 -2.53 0.00 0.33 -1.84 -2.37
Region 2 -2.33 0.00 0.15 -1.49 -1.99
Region 1 -3.06 0.00 0.84 -1.55 -2.32
4.2. Results 59
Discussion
Mean Annual Temperature Range: Difference between the annual maximum and minimum
daily temperatures (Pages 54 and 56).
This indicator represents the difference between the warmest and coldest days of the year.
As was mentioned before, the minimum daily mean temperature of the coldest month is
projected to increase more than the maximum daily mean temperature of the warmest
month, leading to a decrease in the annual temperature range. This is indeed what
is projected by simulation aev, with decreases over most of Ontario, except in western
Ontario (Region 6), where the decrease is small and even close to null (at 2080s horizon).
From the maps, increases seem to be non-linear in some cases. For example, Region 4,
5 and 6 see larger decreases at the 2050s horizon than the 2080s horizon. It is interesting
to note that this did not result in significant serial autocorrelation of the data. Figure 5
shows the time series, for Region 5. It can be seen that in this case, the seemingly non-
linear trend was the result of the presence of few particularly low values in the 2040-2069
period, which lowered the delta result. In this case, it can be seen that the trend would
be a better tool to use than the difference between the periods, as it is less affected by
interannual variability.
Figure 4.11: 1961 to 2099 time series of the annual temperature range indicator for region 5, from
the CRCM simulation aev.
60 Climate Change Scenarios
Mean Isothermality
1980s ; NLWIS (%) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.10: Deltas (absolute) and trend results for mean isothermality
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(%/100 years) % %
Moosonee A. 0.53 0.22 0.50 0.81 0.62
Big Trout Lake -0.15 0.71 0.58 0.42 -0.27
Timmins A. -0.28 0.50 0.83 0.63 -0.36
Kenora A. -0.60 0.14 0.85 0.63 -0.84
Sault St-Marie A. -0.40 0.35 0.16 0.59 -0.47
North Bay A. -0.39 0.41 0.12 0.61 -0.44
Wiarton A. -0.30 0.43 0.08 0.11 -0.54
Ottawa A. -0.53 0.24 0.42 0.20 -0.44
Toronto City Ctr -0.56 0.15 0.48 0.01 -0.56
Toronto Pearson A. -0.53 0.16 0.21 0.02 -0.51
London Int’l A. -0.57 0.11 0.22 -0.02 -0.70
Windsor A. -0.33 0.38 0.97 0.07 -0.44
Region 7 0.19 0.63 0.59 0.77 0.12
Region 6 -0.51 0.21 0.83 0.52 -0.77
Region 5 0.07 0.86 0.83 0.72 -0.01
Region 4 -0.27 0.56 0.12 0.78 -0.42
Region 3 -0.52 0.21 0.19 0.25 -0.41
Region 2 -0.40 0.28 0.85 0.05 -0.51
Region 1 -0.54 0.13 0.74 0.00 -0.76
4.2. Results 61
Discussion
Mean Isothermality: Ratio of the mean diurnal range (page 50) to the annual temperature
range (page 58). This ratio is multiplied by 100.
Generally, the mean daily temperature range (dtr) is projected to decrease over Ontario,
which is consistent with the change in minimum temperature being greater than that of
maximum temperature. This dtr decrease shows a South - North gradient.
Moreover, as was mentioned previously, the minimum daily mean temperature of the
coldest month is projected to increase more than the maximum daily mean temperature
of the warmest month, leading to a decrease in the annual temperature range.
The combination of these two indicators results in the mean isothermality, which is
simulated to first increase slightly at the 2050s horizon, and then decrease to either near
the 1980s levels (Northeast to Southeast Ontario, except for James Bay shore) or lower
(western and Region 1). On the Hudson and James Bay Shore, simulation aev projects an
increase of isothermality of up to 3%.
Note that no trend is found to be statistically significant at the 5% level for this indi-
cator.
62 Climate Change Scenarios
Mean Temperature of the Warmest Quarter
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.11: Deltas (absolute) and trend results for mean temp. of warmest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 3.68 0.00 0.24 2.32 3.90
Big Trout Lake 3.63 0.00 0.02 2.12 3.90
Timmins A. 4.25 0.00 0.92 2.71 4.51
Kenora A. 4.28 0.00 0.11 2.56 4.57
Sault St-Marie A. 4.86 0.00 0.74 3.08 5.07
North Bay A. 4.59 0.00 0.53 2.91 4.79
Wiarton A. 5.91 0.00 0.84 3.65 6.09
Ottawa A. 4.63 0.00 0.84 2.96 4.81
Toronto City Ctr 5.40 0.00 0.76 3.37 5.53
Toronto Pearson A. 5.37 0.00 0.70 3.36 5.52
London Int’l A. 5.75 0.00 1.00 3.64 5.95
Windsor A. 5.35 0.00 0.49 3.43 5.60
Region 7 3.62 0.00 0.03 2.11 3.83
Region 6 4.29 0.00 0.07 2.60 4.58
Region 5 4.05 0.00 0.30 2.53 4.32
Region 4 4.61 0.00 0.77 2.93 4.84
Region3 4.90 0.00 0.98 3.09 5.07
Region 2 5.64 0.00 0.88 3.54 5.82
Region 1 5.67 0.00 0.45 3.71 5.95
4.2. Results 63
Discussion
Mean Temperature of the Warmest Quarter: Mean daily temperature of the three consecutive
warmest months of each year, which represent a climatological definition of summer.
It was found (not shown) that the first month of the warmest quarter of the year remains
in June from the 1970-1999 reference period (NLWIS and CRCM datasets) at the 2050s
and 2080s horizons (CRCM). The warmest quarter of the year therefore consists of the
months of June, July and August throughout the period.
The indicator is projected to increase over all Ontario, with largest increases in the
South. Changes are expected to range between 1 to 4 ◦C and 3 to 7 ◦C for the 2050s and
2080s horizons, respectively. Trends are statistically significant at all stations and regions.
64 Climate Change Scenarios
Mean Temperature of the Coldest Quarter
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.12: Deltas (absolute) and trend results for mean temp. of coldest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 7.75 0.00 0.88 5.39 7.69
Big Trout Lake 6.99 0.00 0.79 5.12 6.94
Timmins A. 6.56 0.00 0.49 4.66 6.35
Kenora A. 5.83 0.00 0.98 4.50 5.48
Sault St-Marie A. 6.35 0.00 0.51 4.27 5.91
North Bay A. 6.45 0.00 0.26 4.34 6.17
Wiarton A. 6.99 0.00 0.46 4.48 6.61
Ottawa A. 6.48 0.00 0.34 4.27 6.16
Toronto City Ctr 6.94 0.00 0.71 4.47 6.58
Toronto Pearson A. 6.89 0.00 0.69 4.44 6.54
London Int’l A. 6.91 0.00 0.94 4.43 6.44
Windsor A. 6.89 0.00 0.91 4.51 6.33
Region 7 7.27 0.00 0.94 5.15 7.22
Region 6 5.96 0.00 0.79 4.54 5.64
Region 5 6.86 0.00 0.61 4.94 6.66
Region 4 6.48 0.00 0.44 4.43 6.14
Region 3 6.46 0.00 0.41 4.22 6.10
Region 2 6.86 0.00 0.77 4.41 6.46
Region 1 7.06 0.00 0.68 4.59 6.51
4.2. Results 65
Discussion
Mean Temperature of the Coldest Quarter: Mean daily temperature of the three consecutive
coldest months of each year, which represent a climatological definition of winter.
Figure 4.12 shows the first month of the coldest quarter, as calculated from first, NLWIS
on the 1970-1999 period and then by the CRCM simulation aev for the 1980s, 2050s
and 2080s horizons. For the 1970-1999 period, it can be seen that according tho the
NLWIS dataset, the first month of winter is December all over Ontario. In the CRCM
aev simulation, however, the first month is either December or January, depending on the
location. The month also varies depending on the period. For example, near Moosonee,
the winter is simulated to start in December in the 1980s, then shift to January at the 2050s
horizon. The Ottawa Region sees the same shift at the 2040s horizon, but the first month
goes back to December at the 2080s horizon. These variations in the coldest quarter
months need to be kept in mind when interpreting the current indicator. In fact, not only
the change in temperature, but also the change in coldest period of the year are assessed.
This indicator is projected to increase over all Ontario, with largest increases occurring
toward the North. Temperature changes are simulated to be between 4 and 6 ◦C and 5
and 10 ◦C for the 2050s and 2080s, respectively. Trends are statistically significant at all
stations and regions.
1970-1999 1970-1999 2040-2069 2070-2099
NLWIS aev aev aev
Figure 4.12: First Month of the coldest quarter.
66 Climate Change Scenarios
Mean Temperature of the Wettest Quarter
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.13: Deltas (absolute) and trend results for mean temp. of wettest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 2.57 0.00 0.12 1.92 1.92
Big Trout Lake 2.09 0.00 0.96 1.38 2.01
Timmins A. 0.72 0.24 0.76 0.26 0.12
Kenora A. 1.66 0.00 0.52 1.43 1.56
Sault St-Marie A. 0.53 0.48 0.53 0.52 0.97
North Bay A. -0.84 0.21 0.06 -0.84 -1.30
Wiarton A. 0.06 0.93 0.04 0.22 1.04
Ottawa A. -2.33 0.01 0.02 -2.86 -2.62
Toronto City Ctr -0.50 0.59 0.12 -0.44 0.90
Toronto Pearson A. -0.65 0.40 0.18 -0.56 0.74
London Int’l A. -0.63 0.48 0.03 -0.09 0.61
Windsor A. 0.36 0.66 0.29 0.05 1.64
Region 7 2.23 0.00 0.89 1.45 2.13
Region 6 1.37 0.00 0.53 0.88 1.07
Region 5 1.56 0.00 0.88 1.12 1.14
Region 4 -0.33 0.57 0.81 -0.06 -0.27
Region 3 -1.42 0.02 0.10 -1.70 -1.21
Region 2 -0.52 0.49 0.04 0.02 0.67
Region 1 -0.01 0.97 0.02 0.07 1.38
4.2. Results 67
Discussion
Mean Temperature of the Wettest Quarter: Mean temperature of the three consecutive months
of each year with largest precipitation .
This indicator is projected to increase in Northern Ontario (Regions 5, 6 and 7), to
decrease in the South (Regions 1, 2 and 3) but to remain relatively constant in between
those two sectors, i.e. in the vicinity of Region 4. Note however that trends are only
statistically significant in the Ottawa Region (3) and the North.
1970-1999 1970-1999 2040-2069 2070-2099
NLWIS aev aev aev
Figure 4.13: First Month of the wettest quarter
Figure 4.13 shows the first month of the wettest quarter, as calculated from first, NL-
WIS on the 1970-1999 period and then by the aev simulation for the 1980s, 2050s and
2080s. Looking at the NLWIS map over the 1980s, the wettest period shows significant
variability over Ontario. From the North down to a latitude near that of Sault-Ste-Marie,
the wettest period is indicated to start in June or July. In the South, however, the wet
period starts in July, September, October or November. This is likely due to the increased
precipitation during the fall due to lake-effect snow.
The aev simulation over the 1980s, on the other hand, shows the first month of the
wettest period decreasing from North to South, with the period starting in June/July in
the North, and shifting to May and then April in the South. This indicates significant dis-
crepancies between observed and simulated annual cycles of precipitation, as discussed
in the validation Section (3.4). It is interesting to note that there is some indication in the
aev simulation of lake-effect snow to the East of Lakes Superior and Huron, indicated by
the wettest season beginning in September. This feature is however of much lesser extent
than in the NLWIS dataset. Comparing the aev simulation over the 1980s to the 2050s
and 2080s horizons, the wettest period occurs earlier in the year as the climate warms.
The lake-effect snow features are also projected to gain importance in the annual cycle of
precipitation. Such changes in the considered quarter are to be kept in mind as they will
clearly affect the resulting temperature.
68 Climate Change Scenarios
Mean Temperature of the Driest Quarter
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.14: Deltas (absolute) and trend results for mean temp. of driest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 8.53 0.00 0.47 5.66 9.31
Big Trout Lake 7.32 0.00 0.05 5.70 6.87
Timmins A. 8.49 0.00 0.32 5.38 9.89
Kenora A. 6.26 0.00 0.47 5.55 6.83
Sault St-Marie A. 15.46 0.00 0.00 10.59 16.06
North Bay A. 11.68 0.00 0.00 8.38 12.83
Wiarton A. 18.39 0.00 0.05 13.93 17.52
Ottawa A. 13.11 0.00 0.02 8.25 13.14
Toronto City Ctr 15.18 0.00 0.28 10.29 14.78
Toronto Pearson A. 14.73 0.00 0.87 10.17 14.38
London Int’l A. 16.11 0.00 0.88 12.35 15.42
Windsor A. 14.39 0.00 0.04 9.96 12.82
Region 7 7.42 0.00 0.29 5.09 7.00
Region 6 7.26 0.00 0.71 5.23 7.31
Region 5 8.36 0.00 0.44 5.42 8.77
Region 4 11.75 0.00 0.09 8.00 12.62
Region 3 14.24 0.00 0.80 9.31 13.95
Region 2 16.23 0.00 0.87 12.00 15.66
Region 1 14.92 0.00 0.24 11.02 13.34
4.2. Results 69
Discussion
Mean Temperature of the Driest Quarter: Mean daily temperature of the three consecutive
driest months of each year. The driest months refer to those showing the smallest monthly
cumulative precipitation.
This indicator showsextremely large warming, especially in the South. Trends are statis-
tically significant over Ontario. Even if this result is surprising at first, closer investigation
of the concepts involved helps in understanding what is happening.
Figure 4.14 shows the first month of the driest quarter, as calculated from first, NLWIS
on the 1970-1999 period and then by the aev simulation for the 1980s, 2050s and 2080s.
Looking at the NLWIS map over the 1980s, the driest period generally starts in February
or January, with the former being the most common.
The simulated 1980s results, on the other hand, show a driest period starting generally
in January, except in the western part of Region 7 (North), where it starts in December.
Comparing the aev simulation over the 1980s to the 2050s and 2080s horizons, the period
remains the same for most Ontario, except in the South, where the driest period is simu-
lated to shift to the late summer (July to October). This drastic change will of course have
important effects on the current indicator, since the temperatures of the winter and late
summer are significantly different. This explains the very large temperature increases
(up to approximately 30◦C) in this region. In the North, since there is no drastic season
shifting, the simulated warming is due to the monthly temperature change.
There is some degree of autocorrelation at the Sault-Ste-Marie, North Bay, Ottawa and
Windsor stations, which is likely due to the non-linearity of the trend, which would result
in the seasonal shift.
1970-1999 1970-1999 2040-2069 2070-2099
NLWIS aev aev aev
Figure 4.14: First Month of the driest quarter.
70 Climate Change Scenarios
Heat Wave Annual Occurrences
1980s ; NLWIS (Ann. Occ.) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (Ann. Occ.)
Table 4.15: Deltas (absolute) and trend results for heat wave annual occurrences.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(Ann. occ./100 yrs) Ann. occ. Ann. occ.
Moosonee A. 1.25 0.00 0.00 0.49 2.19
Big Trout Lake 1.43 0.00 0.01 0.99 2.18
Timmins A. 2.00 0.00 0.09 1.18 2.67
Kenora A. 2.66 0.00 0.03 1.54 3.27
Sault St-Marie A. 3.13 0.00 0.00 1.70 3.94
North Bay A. 2.29 0.00 0.03 1.07 2.99
Wiarton A. 3.00 0.00 0.27 1.63 3.44
Ottawa A. 2.38 0.00 0.01 1.25 3.07
Toronto City Ctr 3.31 0.00 0.00 1.90 4.19
Toronto Pearson A. 3.53 0.00 0.01 2.00 4.33
London Int’l A. 4.00 0.00 0.24 2.51 4.39
Windsor A. 3.20 0.00 0.01 2.23 3.92
Region 7 1.55 0.00 0.01 0.81 2.01
Region 6 2.64 0.00 0.01 1.44 3.38
Region 5 2.07 0.00 0.01 1.13 2.72
Region 4 2.66 0.00 0.00 1.33 3.25
Region 3 2.94 0.00 0.11 1.55 3.58
Region 2 3.74 0.00 0.05 2.17 4.20
Region 1 3.62 0.00 0.19 2.43 4.16
4.2. Results 71
Discussion
Heat Wave Occurrences:
Heat-waves can be defined in various ways, depending on the purpose of the study In
the context of heat-related excess morbidity and mortality, several definitions have been
used. A study based in Montreal by Litvak et al. [2005] showed that criteria defining
heat-wave should be 3-day averages of maximum and minimum temperatures over 33
and 20 ◦C, respectively. In Toronto, a study by Pengelly and Cheng [2005], Pengelly et al.
[2007] attributed an ’annual mean burden of illness’ to a combination of air pollution and
hot weather, by using synoptic scale airmass classification and statistical downscaling
methods. In a climatological context, typically simpler definitions are used. For example,
on Environment Canada’s website (http://ontario.hazards.ca/maps/trends/wsdi-e.
html), heat waves are defined as “three or more consecutive days in which the maximum
temperature is greater than or equal to 32◦C ”.
Figure 4.15: Average number of days per year with max temperatures above 30◦C, based on data
from 1971-2000. Source: Environment Canada, 2003a. National Climate Data and Information
Archive. Available online: http://climate.weatheroffice.ec.gc.ca/climate_normals/index_e.html
A method, that uses an absolute threshold may not be appropriate for all locations. In
fact, a threshold like that of the Environment Canada definition has little utility in colder
regions (e.g Northern Ontario) as it is often only reached during a few days in the year
or not at all (Figure 4.15) and may not correspond to an appropriate heat-wave indicator.
72 Climate Change Scenarios
Also, as Smoyer-Tomic et al. [2003] (page 465) discuss in their study:
”[... Such an approach] implicitly assumes that all populations respond sim-
ilarly to each successive level of heat stress. Relative approaches [...] take
into account acclimatisation to weather and that responses to heat stress differ
depending on regional climate normals. Thus, what might be anomalous hot
weather in a cooler climate like Halifax might be within summer normals in a
warmer area like Toronto.”
In the current study, a large variety of locations and climates are studied, so a rel-
ative definition is more appropriate. Heat waves are therefore defined as a 3-day (or
longer) period with daily maximum temperature greater than the 1980s 99th percentile
of the daily maximum temperature. This percentile value corresponds to the mean of
the equivalent percentiles of 32◦C at the Lesley B. Pearson Airport (percentile 98.3) and
Toronto City Center (percentile 99.7) stations and is therefore close to the Environment
Canada definition for the Toronto area. The data used are produced by Environment
Canada, span the years 1961 to 2008 and are not homogenized. Figure 4.16 shows the
99th percentile equivalent temperature for Ontario as determined from the 1970 to 1999
NLWIS data (left) and the aev CRCM simulation (right).
1980s ; NLWIS 1980s ; aev
Figure 4.16: 99th percentile of the daily maximum temperature (◦C) for Ontario as determined
from the 1970 to 1999 NLWIS data (left) and the aev CRCM simulation (right)
4.2. Results 73
The 1980s distribution of the heat wave occurrence indicator is relatively uniform across
the province, due to the choice of a relative threshold. As can be seen from the maps and
table, the number of occurrences of heat waves per year is projected to increase all over
Ontario, but not uniformly. This change would range on average from 0 to 2.5 and from
1 to 5 occurrences per year at the 2050s and 2080s horizons, respectively. The greatest
changes would occur in the South and the smallest in the North. Positive trends are
statistically significant at all stations and regions.
Most regions and stations show significant autocorrelation. As can be seen on Fig-
ure 4.17, this autocorrelation is due to a non-linear trend in the data. In fact, according
to this time series, the positive trend in heat-wave occurrences seems to start around the
2000-2020 period for region 2, and even later for region 7. This results in patterns in the
residuals of the regression, i.e. autocorrelation.
Figure 4.17: 1961 to 2099 time series of the yearly occurrence of heat waves for regions 2 and 7,
from the CRCM simulation aev.
In a further study, the monthly distribution of heat-wave events, as well as their du-
ration would also be interesting to consider. In fact, Smoyer-Tomic et al. [2003] mention
that “Heat waves occurring earlier in the summer season have been shown to have a
greater impact on human mortality, before short-term acclimatisation to hot weather has
occurred (Kalkstein [1991], Kalkstein and Smoyer [1993])”.
74 Climate Change Scenarios
Annual Precipitation
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.16: Deltas (relative) and trend results for mean annual precipitation.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 151.63 0.00 0.24 12.26 20.01
Big Trout Lake 86.72 0.00 0.41 11.57 15.26
Timmins A. 129.07 0.00 0.03 8.57 15.85
Kenora A. 67.30 0.00 0.59 4.20 9.68
Sault St-Marie A. 114.89 0.00 0.12 3.66 14.27
North Bay A. 126.26 0.00 0.72 7.03 10.53
Wiarton A. 123.14 0.00 0.17 10.86 11.04
Ottawa A. 134.26 0.00 0.20 7.83 13.15
Toronto City Ctr 97.27 0.000.24 9.22 9.31
Toronto Pearson A. 98.39 0.00 0.15 9.34 9.11
London Int’l A. 97.93 0.00 0.31 9.58 7.88
Windsor A. 103.86 0.00 0.84 10.33 9.57
Region 7 103.68 0.00 0.41 12.99 18.84
Region 6 64.70 0.00 0.30 4.62 9.67
Region 5 109.85 0.00 0.90 8.40 14.80
Region 4 116.04 0.00 0.69 6.28 13.44
Region 3 120.94 0.00 0.11 8.29 10.99
Region 2 102.12 0.00 0.23 9.55 8.77
Region 1 98.58 0.00 0.27 9.01 9.12
4.2. Results 75
Discussion
Annual Precipitation: Annual cumulative precipitation.
Annual precipitation is projected to increase over all Ontario, with largest increases oc-
curring in the North East, near the Hudson and James Bay shores with deltas up to 35%
in the 2070-2099 period. The lowest changes are simulated to occur in Western Ontario.
Deltas vary between 0 to 25% and 5 to 35% at the 2050s and 2080s horizons and trends
are statistically significant at the 5% level at all stations and regions.
Figure 4.18 shows the annual cycle of the 1980s to 2050s deltas of monthly mean
precipitation (relative to the 1980s levels) for the seven Ontario regions. There is large
monthly variability in the deltas, with values ranging between -40 to 60%. Geographically,
the annual cycle is uniform, with greater increases in the winter than the summer. The
cycle is of greater amplitude in the South than the North. On the annual average, the
balance is positive, leading to the results presented in the maps and table.
Figure 4.18: Annual cycle of the 1980s to 2050s delta of monthly mean daily precipitation rate
(%). Each of the seven regions of Ontario are represented by a color going from Blue (North) to
red (South) according to their location.
76 Climate Change Scenarios
Precipitation of Wettest Month
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.17: Deltas (relative) and trend results for mean precip. of the wettest month.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 20.66 0.00 0.51 11.14 13.59
Big Trout Lake 7.79 0.05 0.29 8.06 5.14
Timmins A. 10.51 0.05 0.87 4.08 12.29
Kenora A. 13.57 0.02 1.00 7.02 5.24
Sault St-Marie A. 24.11 0.00 0.13 5.84 23.28
North Bay A. 14.13 0.01 0.83 4.13 14.66
Wiarton A. 22.44 0.00 0.75 15.24 17.47
Ottawa A. 12.86 0.01 0.95 3.45 13.64
Toronto City Ctr 17.39 0.00 0.58 9.70 13.16
Toronto Pearson A. 15.73 0.00 0.62 9.17 11.87
London Int’l A. 17.72 0.00 0.32 13.82 10.86
Windsor A. 14.55 0.02 0.27 12.27 12.02
Region 7 13.64 0.00 0.50 11.44 11.72
Region 6 12.38 0.00 0.56 5.85 7.16
Region 5 18.35 0.00 0.82 7.45 13.48
Region 4 15.11 0.00 0.77 4.46 14.82
Region 3 16.75 0.00 0.53 6.29 14.23
Region 2 17.45 0.00 0.96 12.14 12.50
Region 1 12.30 0.03 0.46 11.37 11.47
4.2. Results 77
Discussion
Precipitation of Wettest Month: Mean cumulative precipitation of the month of the year
with the largest precipitation.
Maximum monthly precipitation is projected to increase throughout most of Ontario at
the 2050s and 2080s horizons, except for scattered regions where the change is near null.
Changes are projected to range generally from -2.5 to 20% and -2.5 to 30% for the 2050s
and 2080s, respectively, with rare locations where the change is projected to be down to
-10%. Geographically, the changes are simulated to be relatively uniform over Ontario.
Trends are statistically significant at the 5% level at all stations and regions.
78 Climate Change Scenarios
Precipitation of Driest Month
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.18: Deltas (relative) and trend results for mean precip. of the driest month.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 7.26 0.00 0.43 36.81 49.75
Big Trout Lake 2.73 0.00 0.80 24.87 21.61
Timmins A. 10.85 0.00 0.80 46.89 62.02
Kenora A. 2.31 0.00 0.02 19.96 15.47
Sault St-Marie A. 8.18 0.00 0.19 22.53 34.69
North Bay A. 8.10 0.00 0.67 34.06 27.54
Wiarton A. 2.83 0.21 0.46 10.24 1.39
Ottawa A. 8.56 0.00 0.37 17.66 14.66
Toronto City Ctr 4.20 0.08 0.31 9.85 3.50
Toronto Pearson A. 5.03 0.05 0.57 11.25 5.71
London Int’l A. 2.13 0.40 0.80 11.80 -1.80
Windsor A. 4.47 0.06 0.88 11.82 6.70
Region 7 3.09 0.00 0.83 27.39 28.23
Region 6 3.00 0.00 0.31 19.50 20.62
Region 5 7.61 0.00 0.24 35.60 49.99
Region 4 9.09 0.00 0.81 32.11 40.60
Region 3 5.48 0.01 0.20 16.00 8.27
Region 2 2.92 0.18 0.82 9.72 0.49
Region 1 1.91 0.44 0.78 6.15 -2.51
4.2. Results 79
Discussion
Precipitation of Driest Month: Mean cumulative precipitation of the month of the year with
the smallest precipitation.
Minimum monthly precipitation is projected to increase throughout most of Ontario at
the 2050s and 2080s horizons, except for the South at the later horizon, where changes
range from -25 to 2.5%.
Changes are projected to range generally from 0 to as much as 75%. Spatially, the
changes are simulated to be relatively uniform over Ontario. Trends are statistically sig-
nificant at the 5% level in the northern regions and stations, but not at the Wiarton,
Toronto City Center, London and Windsor stations, which are located in the area of
smaller changes.
80 Climate Change Scenarios
Precipitation Seasonality
1980s ; NLWIS (%) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.19: Deltas (absolute) and trend results for mean precipitation Seasonality.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(%/100 years) (%) (%)
Moosonee A. -4.69 0.02 0.21 -2.84 -6.43
Big Trout Lake -7.18 0.00 0.44 -0.55 -7.74
Timmins A. -7.45 0.00 0.93 -5.85 -6.09
Kenora A. -1.50 0.51 0.67 -0.16 -3.14
Sault St-Marie A. 0.97 0.63 0.81 -1.26 2.53
North Bay A. -4.06 0.04 0.67 -5.23 -0.90
Wiarton A. 1.78 0.38 0.11 2.09 4.86
Ottawa A. -5.21 0.01 0.50 -3.51 -0.58
Toronto City Ctr -0.47 0.80 0.11 2.11 3.36
Toronto Pearson A. -1.16 0.54 0.13 1.64 2.52
London Int’l A. 0.92 0.64 0.08 4.48 3.91
Windsor A. -0.67 0.76 0.14 2.12 2.17
Region 7 -6.14 0.00 0.57 -0.59 -6.41
Region 6 -3.08 0.08 0.42 -1.31 -3.02
Region 5 -4.34 0.01 0.22 -3.44 -5.32
Region 4 -3.58 0.03 0.43 -4.03 -2.15
Region 3 -1.79 0.28 0.17 -1.13 2.12
Region 2 0.61 0.69 0.13 3.00 3.80
Region 1 -0.44 0.81 0.20 3.03 2.58
4.2. Results 81
Discussion
Precipitation Seasonality: Coefficient of variation of monthly cumulative precipitation, i.e.
the ratio of the standard deviation of monthly cumulative precipitation to the mean
monthly cumulative temperature.
As was mentioned in the Temperature seasonality Section (page 52), the contributions
to this result from the change in standard deviation of the monthly precipitation (σ) and
the mean monthly precipitation (µ) is not straight forward. Indeed, if we look at the
mathematical expression of the delta, where indices i and f refer to the initial and final
periods, respectively :
∆Seasonality =
(
σf
µ f
− σi
µi
)
∗ µi
σi
=
σf µi − σiµ f
σiµ f
, (4.5)
the relationship is not simple to understand, by comparing changes in σ or µ.
Precipitation seasonality values are relatively small and were therefore multiplied by
100 on the maps. This was done to reproduce the indicator from the NRCAN dataset.
This indicator represents a description of how variable the climate of a location is
during the year.
There is large variability in the projections of change of this indicator. At the 2050s
horizon, precipitation seasonality is simulated to remain relatively constant in the North,
to decrease in the East and to increase or remain the same in the South. For the 2080s, the
simulation indicates that the indicator will decrease in most of Northern Ontario down
to the great Lakes, but will increase in the South.
The northern and northeastern stations show statistically significant trends (Regions
4, 5 and 7). The regions of small or increasing precipitation seasonality, however, show
non-significant trends.
82 Climate Change Scenarios
Precipitation of Wettest Quarter
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.20: Deltas (relative) and trend results for meanprecip. of the wettest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 52.15 0.00 0.14 9.66 12.47
Big Trout Lake 21.86 0.00 0.55 11.69 7.43
Timmins A. 28.10 0.01 0.81 3.75 10.60
Kenora A. 28.84 0.01 0.46 3.97 6.99
Sault St-Marie A. 57.59 0.00 0.24 3.92 18.34
North Bay A. 37.27 0.00 0.54 3.95 10.90
Wiarton A. 58.09 0.00 0.31 14.21 16.86
Ottawa A. 30.65 0.00 0.42 3.33 11.00
Toronto City Ctr 41.32 0.00 0.44 11.90 12.61
Toronto Pearson A. 40.55 0.00 0.29 11.72 11.88
London Int’l A. 44.56 0.00 0.34 15.46 11.75
Windsor A. 39.29 0.00 0.99 13.96 12.13
Region 7 32.49 0.00 0.37 12.81 11.85
Region 6 22.36 0.01 0.72 3.23 6.51
Region 5 34.67 0.00 0.35 4.98 9.27
Region 4 36.71 0.00 0.72 3.51 12.30
Region 3 45.66 0.00 0.24 7.53 12.76
Region 2 45.17 0.00 0.25 13.58 12.73
Region 1 38.70 0.00 0.89 13.58 11.89
4.2. Results 83
Discussion
Precipitation of Wettest Quarter: Cumulative precipitation of the three consecutive months
of the year with the largest precipitation.
As was presented on page 66 (Mean Temperature of the wettest quarter), Figure 4.19
shows the first month of the wettest quarter, as calculated from first, NLWIS on the 1970-
1999 period and then by the aev simulation for the 1980s, 2050s and 2080s. Following is
a reminder of the main features described previously.
Looking at the NLWIS map over the 1980s, the wettest period shows significant vari-
ability over Ontario. From the North down to a latitude near that of Sault-Ste-Marie, the
wettest period is indicated to start in June or July. In the South, however, the wettest
period starts in July, September, October or November. This is likely due to the increased
precipitation during the fall due to lake-effect snow.
The aev simulation over the 1980s, on the other hand, shows the first month of the
wettest period decreasing from North to South, with the period starting in June/July in
the North, and shifting to May and then April in the South. This indicates significant dis-
crepancies between observed and simulated annual cycles of precipitation, as discussed
in the validation Section (3.4). Comparing the aev simulation over the 1980s to the 2050s
and 2080s horizons, the wettest period occurs earlier in the year as the climate warms.
The lake-effect snow features are also projected to gain importance in the annual cycle of
precipitation shifting the wettest quarter of the year to the fall.
Where these issues are important to keep in mind in a climate change context, the
results pertaining to this indicator are relatively straightforward. The indicator is in fact
projected to increase over most of Ontario, with statistically significant positive trends at
all stations and regions. In brief, this indicates that despite important shifts in the annual
cycle of precipitation, the wettest quarter of the year is expected to have larger amounts
of precipitation.
1970-1999 1970-1999 2040-2069 2070-2099
NLWIS aev aev aev
Figure 4.19: First Month of the wettest quarter
84 Climate Change Scenarios
Precipitation of Driest Quarter
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.21: Deltas (relative) and trend results for mean precip. of the driest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 26.19 0.00 0.38 26.66 42.28
Big Trout Lake 13.52 0.00 0.17 19.80 33.07
Timmins A. 33.21 0.00 0.32 32.46 42.62
Kenora A. 10.11 0.00 0.58 13.14 18.45
Sault St-Marie A. 22.73 0.00 0.52 20.66 24.32
North Bay A. 28.70 0.00 0.94 28.99 22.04
Wiarton A. 12.59 0.02 0.01 11.39 6.45
Ottawa A. 32.40 0.00 0.32 15.07 16.40
Toronto City Ctr 15.08 0.01 0.12 4.83 4.53
Toronto Pearson A. 17.85 0.00 0.11 6.26 6.21
London Int’l A. 8.05 0.20 0.06 3.57 1.08
Windsor A. 15.55 0.01 0.39 10.80 7.36
Region 7 14.90 0.00 0.35 21.30 34.62
Region 6 12.34 0.00 0.44 13.97 19.96
Region 5 24.99 0.00 0.36 23.39 37.51
Region 4 27.04 0.00 0.88 26.59 28.75
Region 3 22.40 0.00 0.11 12.69 9.92
Region 2 12.37 0.03 0.04 5.97 3.63
Region 1 11.59 0.05 0.66 5.44 3.79
4.2. Results 85
Discussion
Precipitation of Driest Quarter: Cumulative precipitation of the three consecutive months
of the year with smallest precipitation.
As presented on page 68 (Mean temperature of the driest quarter), Figure 4.20 shows the
first month of the driest quarter, as calculated from first, NLWIS on the 1970-1999 period
and then by the aev simulation for the 1980s, 2050s and 2080s. Looking at the NLWIS
map over the 1980s, the driest period generally starts in February or January, with the
former being the most common.
The simulated 1980s results, on the other hand, show a driest period starting generally
in January, except in the western part of Region 7 (North), where it starts in December.
Comparing the aev simulation over the 1980s to the 2050s and 2080s horizons, the pe-
riod remains the same for most Ontario, except in the South, where the driest period is
simulated to shift to the late summer (July to October).
This indicator is relatively similar to the Precipitation of the driest month indicator
(page 78). Precipitation of the driest quarter is projected to increase throughout most of
Ontario at the 2050s and 2080s horizons, except for the South where changes are small
and sometimes negative.
Changes are projected to range generally from -10 to as much as 65%. Trends are
statistically significant at the 5% level at all stations and regions, except for the London
International Airport station.
1970-1999 1970-1999 2040-2069 2070-2099
NLWIS aev aev aev
Figure 4.20: First Month of the driest quarter.
86 Climate Change Scenarios
Precipitation of Warmest Quarter
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.22: Deltas (relative) and trend results for mean precip. of the warmest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 29.41 0.01 0.83 11.26 6.80
Big Trout Lake 8.03 0.39 0.30 10.11 1.20
Timmins A. -19.14 0.14 0.52 -4.87 -6.60
Kenora A. -22.16 0.08 0.87 -5.84 -9.38
Sault St-Marie A. -58.02 0.00 0.89 -20.99 -25.11
North Bay A. -59.01 0.00 0.72 -16.92 -24.64
Wiarton A. -69.39 0.00 0.02 -18.58 -30.82
Ottawa A. -61.48 0.00 0.04 -15.32 -19.92
Toronto City Ctr -71.29 0.00 0.06 -16.39 -25.44
Toronto Pearson A. -68.18 0.00 0.07 -15.46 -24.70
London Int’l A. -90.64 0.00 0.02 -22.62 -32.31
Windsor A. -62.53 0.00 0.01 -20.28 -25.33
Region 7 17.40 0.02 0.81 10.70 5.73
Region 6 -22.80 0.02 0.70 -7.25 -10.41
Region 5 -5.98 0.48 0.60 -1.03 -3.69
Region 4 -42.89 0.00 1.00 -14.12 -18.49
Region 3 -63.36 0.00 0.03 -16.21 -24.82
Region 2 -80.74 0.00 0.02 -20.04 -30.27
Region 1 -71.89 0.00 0.02 -22.92 -26.53
4.2. Results 87
Discussion
Precipitation of Warmest Quarter: Cumulative precipitation of the three consecutive warmest
months of the year.
As was mentioned on page page 62 (Mean temperature of the warmest quarter), for both
the NLWIS dataset and simulation aev, as well as for all periods, the first month of the
summer is always June. This means that the warmest quarter of the year consists of
the months of June, July and August. This indicator thus represents the summertime
precipitation, which is projected to increase in the North and decrease in the South by as
much as 25 and 40%, respectively.
In the intermediate region, changes are expected to be small. Stations in this area (Big
Trout Lake, Timmins, Kenora) show trends that are not statistically different from zero.
All other stations show trends that are significant to the 5% level.
Southern regions and stations show some degree of autocorrelation. Closer look at the
time series (Figure 4.21 ; Region 1) shows that there is significant interannual variability
and possibly a leveling off of the trend during the 21st century. It is not possible to
accurately assess the shape of the trend from a single simulation, as the components
from the trend and the interannual variability anddecadal cycles cannot be separated.
Figure 4.21: 1961 to 2099 time series of the precipitation of the warmest quarter indicator for
Region 1, from the CRCM simulation aev.
88 Climate Change Scenarios
Precipitation of Coldest Quarter
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.23: Deltas (relative) and trend results for mean precip. of the coldest quarter.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 34.39 0.00 0.89 19.65 45.19
Big Trout Lake 16.89 0.00 0.53 17.37 31.89
Timmins A. 41.83 0.00 0.69 21.38 42.39
Kenora A. 13.67 0.00 0.45 24.73 24.66
Sault St-Marie A. 58.48 0.00 0.97 23.12 53.20
North Bay A. 54.09 0.00 0.79 23.40 40.70
Wiarton A. 68.31 0.00 0.51 37.89 39.63
Ottawa A. 73.40 0.00 0.34 26.57 40.34
Toronto City Ctr 68.90 0.00 0.82 28.41 31.90
Toronto Pearson A. 68.83 0.00 0.75 30.01 32.35
London Int’l A. 67.12 0.00 0.93 27.98 27.75
Windsor A. 53.65 0.00 0.28 23.67 20.91
Region 7 18.44 0.00 0.82 20.85 34.98
Region 6 16.05 0.00 0.73 24.09 25.51
Region 5 30.34 0.00 0.18 19.75 35.04
Region 4 48.98 0.00 0.85 21.47 43.50
Region 3 69.78 0.00 0.60 27.35 38.60
Region 2 68.95 0.00 0.82 31.08 31.88
Region 1 62.48 0.00 0.41 24.37 24.16
4.2. Results 89
Discussion
Precipitation of Coldest Quarter: Cumulative precipitation of the three consecutive month
of the year with the lowest monthly mean temperatures.
Figure 4.22 shows the first month of such a quarter, as calculated from first, NLWIS on the
1970-1999 period and then by the CRCM simulation aev for the 1980s, 2050s and 2080s
horizons. As was mentioned on page 64 (Mean Temperature of the coldest quarter), it
can be seen that for the NLWIS dataset, the first month of winter is December all over
Ontario. In the CRCM simulation, however, the first month is either December or January,
depending on the location. The month also varies depending on the period. For example,
near Moosonee, the winter is simulated to start in December in the 1980s, then shift to
January at the 2050s horizon. The Ottawa region sees the same shift at the 2040s horizon,
but the first month of the coldest quarter then goes back to December at the 2080s horizon.
These variations in the coldest quarter months need to be kept in mind when inter-
preting the current indicator. In fact, this indicator represents the precipitation of the
winter months, where the winter is not defined by three specific months, but rather by a
temperature criterion.
The wintertime precipitation is projected to increase all over Ontario, as well as for
both horizons. The change is relatively uniform over the province, with values ranging
from 10 to 30% and 15 to 75% increases at the 2050s and 2080s horizons. Trends are
statistically significant at the 5% level for all stations and regions.
1970-1999 1970-1999 2040-2069 2070-2099
NLWIS aev aev aev
Figure 4.22: First month of the coldest quarter.
90 Climate Change Scenarios
Freezing degree-days
1980s ; NLWIS (dd) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.24: Deltas (relative) and trend results for number of freezing degree-days.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(dd/100 years) (%) (%)
Moosonee A. -1128 0.00 0.31 -28.72 -41.74
Big Trout Lake -1112 0.00 0.99 -24.18 -34.37
Timmins A. -930 0.00 0.90 -28.26 -40.72
Kenora A. -883 0.00 0.58 -24.35 -33.56
Sault St-Marie A. -794 0.00 0.81 -32.86 -45.53
Notrh Bay A. -811 0.00 0.81 -31.71 -45.23
Wiarton A. -809 0.00 0.16 -39.93 -55.95
Ottawa A. -763 0.00 0.74 -34.03 -48.55
Toronto City Ctr -778 0.00 0.92 -40.42 -55.81
Toronto Pearson A. -786 0.00 0.02 -39.76 -55.08
London Int’l A. -755 0.00 0.66 -41.23 -56.40
Windsor A. -699 0.00 0.71 -45.34 -60.21
Region 7 -1152 0.00 0.35 -24.29 -35.34
Region 6 -886 0.00 0.75 -24.90 -34.50
Region 5 -1004 0.00 0.02 -27.21 -38.74
Region 4 -854 0.00 0.98 -29.97 -42.80
Region 3 -763 0.00 0.98 -35.25 -49.67
Region 2 -774 0.00 0.25 -40.15 -55.51
Region 1 -715 0.00 0.25 -45.50 -60.47
4.2. Results 91
Discussion
Freezing degree-days: Annual cumulative of daily mean temperatures below 0◦C.
The aev simulation projects that the number of freezing degree-days will decrease over
all Ontario, with the largest relative decreases occurring in the South and the lowest in
the North. Delta values are projected to range from -20 to -55% at the 2050s horizon and
from -30 to -65% at the 2080s horizon. Negative trends are statistically significant at the
5% level at all stations and regions.
92 Climate Change Scenarios
Summertime soil moisture
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.25: Deltas (relative) and trend results for mean summertime soil moisture.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100years) (%) (%)
Moosonee A. -19.48 0.00 0.67 -2.22 -5.20
Big Trout Lake -46.08 0.00 0.01 -3.97 -8.84
Timmins A. -57.94 0.00 0.85 -8.37 -11.65
Kenora A. -59.45 0.00 0.00 -7.17 -14.62
Sault St-Marie A. -39.34 0.00 0.03 -11.05 -9.77
North Bay A. -51.81 0.00 0.27 -8.86 -11.23
Wiarton A. -12.49 0.00 0.09 -0.40 -3.13
Ottawa A. -27.76 0.00 0.08 -2.22 -2.64
Toronto City Ctr -20.68 0.00 0.08 -2.69 -5.91
Toronto Pearson A. -22.86 0.00 0.14 -2.93 -6.37
London Int’l A. -22.94 0.00 0.09 -3.64 -6.03
Windsor A. 36.67 0.00 0.00 2.68 2.38
Region 7 -40.19 0.00 0.00 -4.35 -9.22
Region 6 -67.36 0.00 0.00 -10.17 -16.01
Region 5 -46.01 0.00 0.02 -7.03 -10.77
Region 4 -52.50 0.00 0.04 -10.36 -11.92
Region 3 -35.85 0.00 0.07 -5.15 -7.56
Region 2 -24.25 0.00 0.09 -3.27 -6.48
Region 1 21.63 0.00 0.00 1.12 1.05
4.2. Results 93
Discussion
Summertime soil moisture: Summertime (June, July and August) mean of total (liquid and
solid) soil moisture.
This indicator is highly dependent on the parameterized soil features across Ontario.
In version 4.2.3 of the CRCM, the land surface scheme is provided by the Canadian
Land Surface Scheme (CLASS version 2.7 ; Verseghy et al. [1993]). Figure 4.23 shows the
1970-1999 average contribution in soil moisture from the three modeled soil layers: the
surface, middle and deep layers with respective thicknesses of 10, 25 and 375 cm. Soil
moisture is considered for layers (or parts thereof) located above the depth of bedrock.
Depending on the soil properties (e.g. porosity, fraction of clay, depth of bedrock) defined
at each grid point, the soil moisture will vary across the province, leading to breaks in the
geographical pattern of soil properties and moisture. Such transitions can be noticed in
the figures. For example in northeastern Ontario, roughly following the James Bay shore,
wetlands have soil properties (e.g. high porosity) that allow for a larger soil moisture
content. This produces a sharp elongated feature present in all three layers that is also
apparent in the deltas.
Surface layer Middle layer Deep layer
Figure 4.23: Total (liquid and solid) summertime soil moisture (mm) for surface, middle and
deep layers, as simulated by the CRCM (simulation aev) over the 1970-1999 period. Note that
colorbars vary between panels.
Simulation aev projects that summertime soil moisture will decrease over most of
Ontario, with greatest changes in the vicinity of Region 6, where decreases are simulated
to reach 30% at the 2080s horizon. Note that this region has relatively low soil moisture
in the model’s present climate, thus increasing the relative change. The Windsor station,
and Region 1 in which it is located, show relatively large statistically significant positive
trends. This results is due to a problem in the initialization of the CRCM’s soil moisture
with GCM climate data. From the soil property database, the CRCM at 45-km resolution
94 Climate Change Scenarios
sees the Windsor region as characterized by a few grid points with a deep layer of 375
cm, compared to a relatively shallower deep layer in the surroundings (less than 150 cm).
These small scale features are not resolved by the GCM and the soil moisture for that
deep layeris therefore initialized inappropriately in the CRCM. This problem will be
addressed in the next version of the model. For the Windsor and Region 1 calculations,
removing the 10 first years of data leads to trends of 21.79 mm/100years (p-value = 0.05)
and 9.37 mm/100years (p-value=0.18), respectively. These values are still positive, because
of the relatively large amount of moisture in the deep layer, which is projected to increase
in the future.
4.2. Results 95
need an empty page here
96 Climate Change Scenarios
Maximum Daily Snow Water Equivalent (SWE)
1980s ; NLWIS (mm) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (%)
Table 4.26: Deltas (relative) and trend results for maximum daily SWE.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(mm/100 years) (%) (%)
Moosonee A. 1.19 0.88 0.56 1.57 -0.50
Big Trout Lake 3.11 0.62 0.06 -3.70 2.60
Timmins A. 2.73 0.75 0.27 0.74 -5.68
Kenora A. 0.35 0.97 0.58 -2.08 -0.78
Sault St-Marie A. -2.69 0.66 0.81 0.34 -4.13
North Bay A. -16.82 0.03 0.08 -0.43 -14.81
Wiarton A. -45.98 0.00 0.10 -16.10 -36.48
Ottawa A. -53.78 0.00 0.23 -20.02 -33.75
Toronto City Ctr -52.93 0.00 0.36 -27.41 -42.81
Toronto Pearson A. -52.79 0.00 0.28 -26.23 -41.87
London Int’l A. -55.26 0.00 0.50 -29.86 -45.42
Windsor A. -42.80 0.00 0.06 -35.58 -49.70
Region 7 3.73 0.47 0.02 -1.92 4.84
Region 6 2.75 0.63 0.49 0.40 -0.12
Region 5 -0.52 0.92 0.92 -0.05 -3.56
Region 4 -5.86 0.42 0.15 0.66 -7.89
Region 3 -48.47 0.00 0.01 -17.68 -33.07
Region 2 -52.19 0.00 0.34 -24.29 -41.76
Region 1 -45.73 0.00 0.17 -36.09 -48.50
4.2. Results 97
Discussion
Maximum Daily Snow Water Equivalent (SWE): Daily Maximum SWE found each year.
At the 2050s horizon, maximum daily swe is projected to decrease in the South but to
remain relatively constant North of Sault-Ste-Marie (trends not statistically different than
zero). This feature is simulated to expand toward the North at the 2080s horizon, where
all Regions but Region 7 (North) are projected to see their maximum daily swe decrease,
to as much as 70% in Region 1. Note that this region has relatively small SWE thus
increasing the relative change values. Region 7 has projections of positive trend in max-
imum daily swe. Note however that trends are non-significant in the Northern stations
and regions. No station is located in the region of relatively large increase in snow water
equivalent and the significance of the trend is therefore not reported.
98 Climate Change Scenarios
First Part of Snow Period (July 1st to Dec 31st)
1980s ; NLWIS (days) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (days)
Table 4.27: Deltas (abs.) and trend results for first part of snow period.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(days/100 years) days days
Moosonee A. -20.97 0.00 0.23 -13.20 -21.18
Big Trout Lake -14.71 0.00 0.06 -13.21 -15.50
Timmins A. -16.52 0.00 0.75 -12.53 -18.49
Kenora A. -12.05 0.00 0.34 -10.87 -9.04
Sault St-Marie A. -14.04 0.00 0.64 -10.44 -14.56
North Bay A. -15.69 0.00 0.17 -11.26 -17.18
Wiarton A. -11.77 0.00 1.00 -11.32 -13.40
Ottawa A. -13.02 0.00 0.09 -11.58 -12.56
Toronto City Ctr -6.38 0.00 0.10 -8.46 -8.92
Toronto Pearson A. -7.20 0.00 0.34 -8.48 -9.37
London Int’l A. -4.62 0.00 0.03 -7.60 -7.94
Windsor A. 0.00 0.00 0.00 -5.06 -5.92
Region 7 -17.33 0.00 0.06 -13.77 -16.95
Region 6 -12.28 0.00 0.26 -10.08 -10.32
Region 5 -17.43 0.00 0.63 -12.64 -17.95
Region 4 -16.24 0.00 0.88 -11.84 -17.32
Region 3 -12.69 0.00 0.36 -10.62 -12.90
Region 2 -8.02 0.00 0.24 -8.77 -9.66
Region 1 0.00 0.00 0.00 -4.56 -5.23
4.2. Results 99
Discussion
First Part of Snow Period (July 1st to Dec 31st): Number of days between July 1st and
December 31st with snow depth greater than 10 cm.
This indicator is calculated based on snow depth greater than 10 cm. This choice is
made because of the treatment of this variable in the CRCM below this 10 cm threshold.
In fact, in the CRCM grids where the simulated snow depth is 10 cm or less, it is supposed
that the tile is partially covered by snow.
The readily available snow depth datasets did not cover the full 1970 to 1999 period
and therefore for consistency with the rest of the results only the deltas were mapped.
This indicator is projected to decrease over all Ontario, with values ranging from -2.5
to -20 days in the 2050s and from -5 to -30 in the 2080s. Decreases in the length of the
first part of the snow period are projected to be largest in the North and lowest in the
southern and western parts of Ontario. Negative trends are statistically significant at the
5% level at all stations and regions. Note that Windsor and Region 1 in which it is located
show statistically significant trends of zero slope. This is due to the presence in the data
of repeated zero-day values (Figure 4.24), because of the impossibility of the data to show
periods of length smaller than zero. The trend result is not accurate, but is still found to
be significant. Autocorrelation at those stations and Regions are due to the same feature.
Figure 4.24: 1961 to 2099 time series of the length of the snow period (July 1st to Dec 31st) for
Region 1, from the CRCM simulation aev.
100 Climate Change Scenarios
Second Part of Snow Period (Jan 1st to June 30th)
1980s ; NLWIS (days) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (days)
Table 4.28: Deltas (abs.) and trend results for second part of snow period.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(days/100 years) days days
Moosonee A. -7.95 0.00 0.22 -3.36 -6.27
Big Trout Lake -11.91 0.00 0.22 -10.73 -10.33
Timmins A. -7.14 0.00 0.36 -1.74 -9.91
Kenora A. -7.56 0.00 0.90 -2.76 -7.82
Sault St-Marie A. -11.11 0.00 0.25 -4.39 -14.38
North Bay A. -12.30 0.00 0.10 -3.20 -14.73
Wiarton A. -34.27 0.00 0.02 -15.94 -40.93
Ottawa A. -23.56 0.00 0.00 -9.40 -27.79
Toronto City Ctr -44.56 0.00 0.05 -20.97 -45.02
Toronto Pearson A. -43.75 0.00 0.03 -20.66 -45.28
London Int’l A. -46.44 0.00 0.35 -23.83 -46.26
Windsor A. -50.00 0.00 0.57 -25.73 -40.58
Region 7 -11.39 0.00 0.17 -7.63 -9.45
Region 6 -7.81 0.00 0.24 -2.83 -7.72
Region 5 -8.41 0.00 0.11 -4.41 -8.89
Region 4 -10.38 0.00 0.06 -2.99 -12.88
Region 3 -26.51 0.00 0.00 -11.59 -30.35
Region 2 -42.23 0.00 0.08 -20.56 -44.11
Region 1 -50.97 0.00 0.92 -28.81 -43.32
4.2. Results 101
Discussion
Second Part of Snow Period (Jan 1st to June 30th): Number of days between January 1st and
June 30th with snow depth greater than 10 cm.As mentioned in the previous section, this
indicator is calculated based on snow depth greater than 10 cm due to the treatment of
snow depth in the CRCM. The readily available snow depth datasets did not cover the
full 1970 to 1999 period and therefore for consistency with the rest of the results only the
deltas were mapped.
The length of the second part of the snow period (after January 1st) is projected to
decrease over all Ontario. The changes are simulated to range between 0 to -35 days at
the 2050s horizon and between -2.5 and -55 days for the 2080s. This decrease is largest
in southern Ontario, lowest in central regions (Regions 4, 5 and 6) and increasing again
towards the North around the Big Trout Lake area.
Autocorrelation is significant in Region 3. This is due to a strong non-linearity in the
data (Figure 4.25, where the magnitude of the trend seems to increase significantly at the
end of the 21st century. Here again, it cannot be determined if this non-linearity is due to
the trend or interannual variability.
Figure 4.25: 1961 to 2099 time series of the length of the second part of the snow period (Jan 1st
to June 30th) for Region 3, from the CRCM simulation aev.
102 Climate Change Scenarios
Length of Snow Period (Jan 1st to Dec 31st)
1980s ; NLWIS (days) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (days)
Table 4.29: Deltas (abs.) and trend results for length of total snow period.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(days/100 years) days days
MoosoneeA. -28.49 0.00 0.00 -16.56 -27.44
Big Trout Lake -26.24 0.00 0.00 -23.94 -25.83
Timmins A. -23.21 0.00 0.15 -14.27 -28.40
Kenora A. -20.00 0.00 0.12 -13.63 -16.86
Sault St-Marie A. -23.81 0.00 0.14 -14.83 -28.93
North Bay A. -27.41 0.00 0.69 -14.46 -31.92
Wiarton A. -49.31 0.00 0.02 -27.27 -54.33
Ottawa A. -38.49 0.00 0.17 -20.98 -40.35
Toronto City Ctr -53.85 0.00 0.02 -29.42 -53.94
Toronto Pearson A. -53.60 0.00 0.01 -29.14 -54.64
London Int’l A. -54.17 0.00 0.18 -31.43 -54.20
Windsor A. -52.85 0.00 0.28 -30.79 -46.50
Region 7 -28.28 0.00 0.01 -21.40 -26.40
Region 6 -19.85 0.00 0.02 -12.90 -18.04
Region 5 -24.90 0.00 0.06 -17.04 -26.83
Region 4 -25.26 0.00 0.28 -14.83 -30.20
Region 3 -40.56 0.00 0.04 -22.22 -43.25
Region 2 -52.13 0.00 0.04 -29.34 -53.77
Region 1 -54.29 0.00 0.33 -33.37 -48.55
4.2. Results 103
Discussion
Length of Snow Period (Jan 1st to Dec 31st): Number of days between January 1st and
December 31st with snow depth greater than 10 cm. This indicator is calculated as the
sum of the two previous indicators (pages 98 and 100).
As mentioned previously, this indicator is calculated based on snow depth greater
than 10 cm due to the treatment of snow depth in the CRCM.
The total length of the snow period is projected by simulation aev to decrease over
the whole Ontarian territory. Changes range from -2.5 to -40 and -10 to -65 days at the
2050s and 2080s horizons, respectively.
Comparing the total snow period to its individual parts, a few interesting features can
be noticed. In the South, the End of the snow season (Jan 1st to June 30th) decreases more
than the beginning part. In the North, however, the largest decrease occurs during the
first part of the snow period (July 1st to Dec 31st). The lowest decreases occur in Regions
4, 5 and 6
Autocorrelation is significant in Regions 2, 3, 6 and 7. Figure 4.26 shows the indicator
times series for the 1961 to 2100 period, for Region 7 (blue) and 2 (red). From these
time series, it seems that in this case, the autocorrelation is due mainly to interannual
variability as no clear break in the trend can be noticed.
Figure 4.26: 1961 to 2099 time series of the length of the snow period for Region 2 (red) and 7
(blue), from the CRCM simulation aev.
Chapter 5
IDF curves in future climate
Intensity-Duration-Frequency (IDF) curves are used in engineering to assess the return
periods of extreme rainfall events. This chapter covers the computation of IDF curves for
the South Ontario region using climate simulations generated by the Canadian Regional
Climate Model (CRCM_v4.2) [Music and Caya, 2007]. The model simulation covers the
period from 1961 to 2100 and IDF curves are generated for the present climate (1961-2000)
and the future climate (2041-2070). Model simulations for the present climate are com-
pared to meteorological station data provided by Environment Canada (EC) to measure
the model’s ability to simulate rainfall adequately.
The study restricts itself to event durations of 1, 2, 6, 12 and 24 hours. IDF curves
based on observations typically also include events of durations 5, 10, 15 and 30 minutes.
This restriction is due to the time step of the model, which corresponds to 15 minutes
for 45 km resolution runs. Extreme events with a temporal resolution below or near that
time scale (15 minutes to an hour) are likely to be misrepresented by the model. Thus
while results for one hour events are included in the study, they are strongly affected by
the limited temporal resolution of the model and their reliability is uncertain.
The region under study is the South of the province of Ontario, delimited North by
the 44.5◦parallel. The region counts 37 stations with more than 20 years of data and 44
grid cells of the CRCM (see Figure 5.2). In the following, station data are used to evaluate
model simulations in the present climate. Future climate simulations are then used to
assess projected changes in the occurrence of extreme rainfall events. The methodology
presented below for estimating IDF curves follows the work of Mailhot et al. [2007]. Note
that although the acronym IDF is used throughout the document, figures often show
Depth-Duration-Frequency (DDF) curves, where the ordinate axis has units of mm.
105
106 IDF curves in future climate
5.1 Data processing
Station data processing
Daily extreme rainfall over durations of 1, 2, 6, 12 and 24 hours for all Ontario stations
were obtained from the Data Access Integration portal (DAI, see http://quebec.ccsn.
ca/DAI/). The daily time series are processed to extract the annual maxima occurring
between May 1st and October 31st (184 days). Winter months are neglected due to the
considerable amount of missing data stemming from the occurrence of solid precipita-
tions, not measured by all climatological stations. The time series of annual maxima
during the period from May to October are denoted in the following as MOAM (May to
October Annual Maximum) time series.
MOAM values are discarded whenever there is more than 34 days of missing data over
the 184-day period. Stations which count less than 20 such valid years are not included
in the study. Figure 5.1 shows the location of all valid and invalid stations in Ontario. In
the region of study, there are 37 stations with time series of at least 20 years for durations
equal to 1, 2, 6,and 12 hours, and 31 stations for 24 hours. The longest valid series spans
57 years.
−100 −95 −90 −85 −80 −75 −70
40
42
44
46
48
50
52
54
56
58
for station data (only 31 for the 24 hours
duration) and 44 series for CRCM simulations (control and future).
Stationarity
Series Duration [h]
1 2 6 12 24
Stations 34 ( 92%) 34 ( 92%) 36 ( 97%) 33 ( 89%) 30 ( 97%)
Control 41 ( 93%) 42 ( 95%) 41 ( 93%) 44 (100%) 44 (100%)
Future 44 (100%) 43 ( 98%) 42 ( 95%) 41 ( 93%) 41 ( 93%)
Homogeneity
Series Duration [h]
1 2 6 12 24
Stations 34 ( 92%) 36 ( 97%) 37 ( 100%) 36 ( 97%) 26 ( 84%)
Control 41 ( 93%) 41 ( 93%) 41 ( 93%) 44 (100%) 44 (100%)
Future 44 (100%) 41 ( 93%) 43 ( 98%) 40 ( 91%) 43 ( 98%)
Independence
Series Duration [h]
1 2 6 12 24
Stations 35 ( 95%) 34 ( 92%) 36 ( 97%) 34 ( 92%) 26 ( 84%)
Control 43 ( 98%) 43 ( 98%) 44 (100%) 44 (100%) 44 (100%)
Future 42 ( 95%) 42 ( 95%) 44 (100%) 43 ( 98%) 44 (100%)
5.2. Model Evaluation 109
time series and station time series are then used to estimate rainfall depth associated
with return periods of 2, 5, 10, 25 and 50 years. Results are shown in figures 5.3 and 5.4
(figures for the other durations are included in the appendix).
10 20 30 40 50 60 70 80 90 100 110
10
20
30
40
50
60
70
80
90
100
110
2−hr MOAM − model present (mm)
2−
hr
 M
O
A
M
 −
 s
ta
tio
n 
(m
m
)
 
 
2 yr
5 yr
10 yr
25 yr
50 yr
Figure 5.3: Grid box scale comparison of observations with the CRCM control run for two-hour
duration and various return periods.
As expected, the extreme values over two hours simulated by the model are far from
the observed values. Mailhot et al. [2007] explains the discrepancies by the hypothesis
that thunderstorms responsible for most extremes over two hours are convective and
have a small spatial footprint, smaller than the CRCM grid scale. Meteorological systems
responsible for the 24h extreme events have larger scales, large enough to be reasonably
well simulated by regional climate models.
Another factor must be kept in mind when comparing observations to simulations.
Rainfall gauges typically measure precipitation over a small area, eg. 400 cm2, while
the CRCM simulates rainfall over an area of 45 by 45 km. Observations are hence point
measurements, while simulations are area averaged values. The temporal variability of
rainfall at a particular point is expected to be larger than the temporal variability of
rainfall averaged over a large area. The same is true for extremes. In this sense, we
should not expect a perfect match between observations and simulations, simply because
they represent different quantities.
Finally, observation biases must also be considered; rain gauges do not record 100% of
the rain actually falling on the ground. The under catch is due to a number of influences,
but in the extreme rainfall context, the most significant is probably the under catch due to
rain being deflected from the gauge by high wind speeds. This effect reduces the observed
precipitation extremes, and hence the bias between modelled and projected rainfall.
The results from this simulation are sufficiently different from those of Mailhot et al.
110 IDF curves in future climate
20 40 60 80 100 120 140 160
20
40
60
80
100
120
140
160
24−hr MOAM − model present (mm)
24
−
hr
 M
O
A
M
 −
 s
ta
tio
n 
(m
m
)
 
 
2 yr
5 yr
10 yr
25 yr
50 yr
Figure 5.4: Grid box scale comparison of observations with the CRCM control run for 24-hour
duration and various return periods.
[2007] to raise a number of questions. Mailhot et al. [2007] based their study over South-
ern Québec on outputs from CRCM 3.7.1, driven by CGCM2 (abi simulations). Can the
differences be attributed to the region? the RCM ? the GCM ? A preliminary analysis (not
shown) indicates that the abi simulation used in the Québec study overestimates rainfall
compared to observations, and compared to the current generation of models. Also, the
abi runs displays significant regional differences in terms of extreme rainfall between
Southern Québec and Southern Ontario, something that is not seen in the aev run used
for this study, nor in observational data. It would seem, then, that changes in the model
structure which led to an improvement of the mean rainfall patterns did adversely affect
extreme rainfall simulations.
The large differences between station and model rainfall require a mechanism to trans-
fer information from one scale to another. The same approach used by Mailhot et al.
[2007], namely areal reduction factors (ARF), is applied here. ARF factors are defined as
ARF(T, d) =
x(g)
p (T, d)
x(s)
p (T, d)
(5.1)
where x(s)
p (T, d) and x(g)
p (T, d) are the average regional rainfall depths for event durations
d and return period T at the station and model grid scale respectively. These average re-
gional rainfall depths are estimated by regionalizing the parameters of the GEV distribu-
tion over the region of interest [Hosking and Wallis, 1997]. ARF are hence factors linking
the average aerial rainfall at the model (grid box) scale, to the average aerial rainfall at
the station scale. Figure 5.5 shows clearly that the model underestimates extreme rainfall
for all durations compared to observations, and that the effect is more pronounced for
5.2. Model Evaluation 111
short durations. As discussed earlier, this is to be expected due to the type of storms
generating the extremes.
CRCM future vs control climate
Figures 5.6 and 5.7 show the differences between estimated rainfall depths for the control
and future climate for the two and 24-hour duration (figures for the other durations are
included in the appendix). The results show clearly that the future climate simulation has
larger extremes than the control climate. Preliminary tests performed with other model
simulations show however that there is a significant variability in future rainfall which
cannot be caught by a single model run. In other words, ensembles of simulations should
be used whenever possible to improve the reliability of indicators based on future rainfall
projections.
0 5 10 15 20 25 30 35 40 45 50
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Return period (years)
A
R
F
 
 
2 hr
6 hr
12 hr
24 hr
Figure 5.5: Areal reduction factor (ARF) between regional average rainfall depth values at the
grid box scale and the station scale in CRCM’s control climate for various durations.
112 IDF curves in future climate
10 12 14 16 18 20 22 24
10
12
14
16
18
20
22
24
2−hr MOAM − model present (mm)
2−
hr
 M
O
A
M
 −
 m
od
el
 fu
tu
re
(m
m
)
 
 
2 yr
5 yr
10 yr
25 yr
50 yr
Figure 5.6: Grid box scale comparison of CRCM’s future run with the control run for 2-hour
duration.
30 40 50 60 70 80 90 100
30
40
50
60
70
80
90
100
24−hr MOAM − model present (mm)
24
−
hr
 M
O
A
M
 −
 m
od
el
 fu
tu
re
(m
m
)
 
 
2 yr
5 yr
10 yr
25 yr
50 yr
Figure 5.7: Grid box scale comparison of CRCM’s future run with the control run for 24-hour
duration.
5.3. Model Projections 113
5.3 Model Projections
Following Mailhot et al. [2007], IDF curves are computed using regionalized GEV param-
eters. Regionalization pools data sets together to obtain more reliable statistics than what
could be obtained using single site analyses. In a sense, uncertainty due to short time
series is compensated by a larger spatial extent. Regionalization is done following Hosk-
ing and Wallis [1997] using the L-moments of the rainfall series. Specifically, regional
L-moments are simply the mean value of each individual series L-moments, weighted by
the series length. These regionalized L-moments are then used to estimate the parameters
of the regionalized Generalized Extreme Value (GEV) distribution.
Regional Homogeneity
Regional GEV parameters only make sense if the stations are homogeneous over the re-
gion. Regional homogeneity measures of the station data, the control and future simula-
tions were computed using three methods [Hosking and Wallis, 1997]. H1 is the weighted
standard deviation of the L-CV (coefficient of variation), H2 is the average distance from
sites to the regional average on a graph of L-CV vs.to the RCM, which
produces, in the same way as the GCMs, spatially and temporally coherent variables but
1
2 Introduction
at a finer resolution. This method has the disadvantage of being computationally very
costly and requires significant expertise and storage resources.
As both downscaling techniques show advantages and limits, it can be interesting to
consider the use of the two, or even a combination of both. Such dynamical-statistical
approaches are evolving as promising techniques for regional scale climate change as-
sessment (e.g. Mailhot et al. [2007]) and can contribute to improve the representation of
climate variables and to better evaluate the associated uncertainties.
In an effort to acquire and enhance knowledge and scientific expertise on the effects of
climate change in Ontario, the current sudy is conducted by Ouranos to provide infor-
mation on climate downscaling for a set of climate variables (indicators). This is done
by analyzing the latest Canadian Regional Climate Model (CRCM; version 4.2.3) ouput
data at 45 km resolution, regarding the geographical distributions and trends of major
climate change indicators (e.g. daily mean temperature, annual precipitation and mean
snow depth) at district/county scales.
This report presents an overview of the situation regarding CRCM climate modeling
over Ontario. Chapter 2 first presents an overview of the climate context in Ontario.
Then, an assessment of the CRCM’s ability to simulate Ontario’s climate in local and re-
gional perspectives is presented in Chapter 3. The simulated evolution of various climate
indicators is presented in Chapter 4 for the 2040-2069 and 2070-2099 horizons. Chapter 5
focuses on Intensity-Duration-Frequency (IDF) curves in southern Ontario, their compu-
tation from observed and simulated data, validation and projections in the future. Finally,
Chapter 6 includes concluding remarks and a discussion of the next steps to take for a
further assessment of the evolution of climate in Ontario.
A copy of the data generated during this project has been made available publicly by
Ouranos at http://www.ouranos.ca/Ontario/Results_html/index.htm.
Chapter 2
Recent Past Climate of Ontario
In this Chapter, a graphic overview of the climate of Ontario in the recent past is presented
to provide visual support for the validation of Regional Climate Model (RCM) data and
climate projection analysis.
Six variables from two datasets are used to represent the recent past: daily mean,
minimum and maximum temperatures, diurnal temperature range (defined as the range
between the daily minimum and maximum temperatures), mean daily precipitation rate
and mean snow depth. Temperature fields and precipitation are taken from the Na-
tional Land and Water Information Services (NLWIS) daily 10 km resolution gridded
data [Hutchinson et al., 2009]. Snow depth is evaluated with the 0.3◦ resolution interpo-
lated dataset from Brown et al. [2003], hereafter referred to as B03.
Climate observations do not represent the real climate perfectly, due to measurement
errors and uncertainties, undersampling in remote areas, interpolation method and erro-
neous data, among others. Ideally, various sets of observed data would be used for the
assessment of recent past climate and for the model validation. Such datasets are how-
ever difficult to come about, especially when it comes to gridded and interpolated data
which are more readily used for the validation of climate model output.
This Chapter first presents in Section 2.1 a portrait of the recent past climate of On-
tario. The uncertainty in observed climate is then assessed in Section 2.2 by comparing
the NLWIS results to the Climatic Research Unit (CRU ; version 2) dataset [Mitchell and
Jones, 2005], interpolated on the 45 km-resolution Canadian Regional Climate Model
(CRCM) grid. Only one gridded dataset was used for snow depth so no dataset com-
parison is conducted. Specifications of the datasets are given in Table 2.1, along with
references where more details can be found.
2.1 Observed Climate
Figures 2.1 to 2.6 present seasonal maps of the various meteorological fields over Ontario,
during the period referred to as the recent past. This period corresponds to the 1971 to
3
4 Recent Past Climate of Ontario
Original Period
Dataset
Resolution
Variables
and Domain
Reference
T. min, T. max, 1961-2003
NLWIS 10 km
precip. Canada
Hutchinson et al. [2009]
CRUv.2 on T. min,T. max
45 km 0.5◦ T. mean,T. range
1911-2000
Mitchell and Jones [2005]
CRCM grid and precip.
Global
1979-1997
B03 0.3◦ snow depth
N. America
Brown et al. [2003]
Table 2.1: Specifications of the different datasets used in the validation of the CRCM.
2000 and 1979 to 1997 periods for the NLWIS and B03 datasets, respectively. Each variable
is represented by its average (top panel), as well as its interquartile range (IQR ; bottom
panel) over the recent past period. The latter quantity is defined as the range between
the 25th and the 75th percentiles, and can be interpreted as an indicator of interannual
variability of the field. Note that in the case of precipitation and snow depth, the color
scale is reversed to represent the field in a more intuitive way (e.g. a wetter climate or
one with larger snow depth (colder) is represented in blue). Special care should be given
to the analysis of the various fields in Northern Ontario, as this region is significantly un-
dersampled (maps of the distribution of observation stations can be found in Hutchinson
et al. [2009] and Brown et al. [2003]).
The 30-year mean generally shows obvious seasonal patterns. For example, mean,
minimum and maximum temperatures are largest in Summer and smallest in Winter
(Figures 2.1 to 2.3). Diurnal temperature ranges (Figure 2.4) show a maximum in the
Spring, whereas precipitation rate peaks in the Summer and Fall (Figure 2.5). Snow
depth is practically null in Summer and Fall, as expected (Figure 2.6). Spatial patterns
are also easily identified. For example, temperatures generally decrease with increasing
latitude. Daily temperature ranges show interesting West-East gradients in Summer and
Winter. Also, areas affected by lake-effect snow can be identified by local maxima of
precipitation downwind from the Great Lakes, especially in the Fall and Winter.
The interannual variability (represented by the 30-year IQR) of the variables is a lot
less uniform across Ontario than the 30-year mean. Seasonal and spatial patterns can be
identified in the temperature fields but their explanation is not simple or intuitive. For
example, large interannual variability in the mean and minimum temperature fields is
observed North of Lake Superior during Winter and Spring. This is likely due to the
presence of lakes and depending on their freezing and thawing cycles that can differ
from year to year. This behaviour could also be related to the interannual variability of
the onset and disappearance of snow cover.
2.1. Observed Climate 5
Interannual variability in precipitation and snow depth is generally proportional to
the absolute value of the field, so that areas with important precipitation rates or snow
accumulation are generally found to have important interannual variability.
The presence of a trend in the recent past climate can affect significantly the inter-
annual variability. Spatial and Temporal variation in trends could therefore result in
complex spatial and temporal patterns in interquartile range. The calculation of trends
necessitates long-term time series to be statistically and climatologically significant. In
fact, interdecadal cycles in climate can affect short-term trends to a large extent. The re-
cent past period analyzed here (30 years) is considered too short to perform a valid trend
analysis.
Figure 2.1: NLWIS observations of average (top panel) and IQR (bottom panel) of mean daily
temperature over the 1971 to 2000 period. Seasons are, from left to right: Winter, Spring, Summer
and Fall ; units are ◦C.
6 RecentL-skewness, and H3 is the average
distance from sites to the regional average on a graph of L-skewness vs. L-kurtosis. A
value greater than one is an indication of regional heterogeneity. Results are shown in
table 5.2 and confirm the homogeneity of the data sets under consideration.
DDF curves projection in the future climate
The depth-duration-frequency (DDF) curve for the control and future climate simulations
are shown in figure 5.8. As expected, rainfall extremes are projected to increase in the
future climate. To translate this increase at the station scale, ARF factors are used and
Table 5.2: Homogeneity measures of station and CRCM’s control and future MOAM rainfall time
series.
Durations [h]
1 2 6 12 24
St
at
io
n H1 -0.05 -0.08 -0.07 -0.20 -0.25
H2 -0.05 -0.09 -0.07 -0.22 -0.27
H3 0.16 1.12 -0.25 0.04 0.55
C
on
tr
ol H1 -2.32 -0.10 -0.12 -0.06 -0.16
H2 -3.08 -0.11 -0.13 -0.07 -0.18
H3 0.43 0.17 0.20 -2.31 -0.22
Fu
tu
re H1 -0.28 -0.13 -0.07 -0.13 -0.28
H2 -0.47 -0.14 -0.07 -0.14 -0.32
H3 -0.69 -0.04 -0.05 -1.79 -1.94
114 IDF curves in future climate
applied to simulated future rainfall using the constant ARF approach. The future station
rainfall DDF is compared with station data DDF in figure 5.9. The 24-hour events are
the most affected in this future climate projection, with today’s 50-year return period
events increasing in frequency to about 20 years. Results at the 25- and 50-year time
scale are however plagued with considerable uncertainty due, among other factors, to
the short time series (30 years) used in the analysis. Preliminary results obtained from a
similar model run (aet) suggest that while the increasing trend in rainfall extremes is a
robust feature, the projected increase in rainfall depth of 25- and 50-year events cannot be
determined with accuracy. In other words, a single model run is insufficient to provide a
reliable projection of rainfall intensities at those time scales.
On a related note, the model’s projections at time scales of one and two hours are so
far from observed values that their predictive value is doubtful. This is understandable
given that the model’s time step is of 15 minutes, and that there are very few time steps
in a two hour frame to build a lot of rainfall variability, and a fortiori, extremes. Readers
should thus interpret figure 5.9 knowing that the confidence in the results increases from
bottom right to top left.
Uncertainty considerations
The generation of extreme values by simulations is not something that receives any ex-
plicit treatment in the formulation of climate models. Furthermore, the performance of
climate models is not evaluated on the characteristics of its extreme events, but rather on
the basis of aggregated average values. For those two reasons, the analysis of products
derived from simulated extremes, such as IDF curves, are likely to be far more uncertain
that other climatic indicators based on averaged values. In this sense, the projected DDF
curves presented in this chapter are of “research” quality, meaning that they are subject
to changes as models and analysis methods improve. Another factor to take into consid-
eration is the fact that as the climate warms, extreme events may happen more frequently
during the winter, that is, outside the May to October period used in this analysis.
5.3. Model Projections 115
10
0
10
1
10
2
10
20
30
40
50
60
70
80
Return period (years)
R
ai
nf
al
l d
ep
th
 (
m
m
)
 
 
2 hr
6 hr
12 hr
24 hr
Figure 5.8: Depth-Duration-Frequency curve showing differences between CRCM’s control sim-
ulation (filled markers) and the future simulation (white markers) for return periods of 2-, 5-, 10-,
25- and 50-year, and various durations (2-, 6-, 12- and 24-h).
116 IDF curves in future climate
10
0
10
1
10
2
20
40
60
80
100
120
140
Return period (years)
R
ai
nf
al
l d
ep
th
 (
m
m
)
 
 
2 hr
6 hr
12 hr
24 hr
Figure 5.9: Depth-Duration-Frequency curve showing differences between station rainfall (filled
markers) and CRCM’s future station rainfall (white markers) estimated using the constant ARF
approach for return periods of 2-, 5-, 10-, 25- and 50-year, and various durations (2-, 6-, 12- and
24-h).
Chapter 6
Future Research and Conclusions
This report presented an overview of the climate context in Ontario. First, this was
done by presenting the historical climate of Ontario as well as the ability of the CRCM
simulation (code-named aev, generated at 45-km resolution on the AMNO domain) to
reproduce it over the recent past period. It was shown that some biases do exist in the
CRCM simulation aev (partially due to biases in driving data) and should be taken into
account when analyzing climate change scenarios. For example, indicators that consider
the annual cycle of precipitation (e.g. temperature and precipitation of the wettest, driest
quarters) should be handled with care as this feature is not well simulated by the CRCM
simulation aev. Indicators that describe annual means of temperature, on the other hand,
are more reliable since this climatological feature is better simulated. Biases in the sim-
ulation can represent a problem if one needs to use modeled output directly as input
to impact assessment models (e.g. hydrologic and forest fire models). Some debiasing
techniques have been developed (Schmidli et al. [2007], Deque [2007]) but they need to be
adapted to each specific application, by taking the variables, statistics, and the area into
consideration.
Secondly, a series of climate change indicators were analyzed over Ontario, and at
12 stations and 7 regions. For each indicator, a map of the geographical distribution of
the evolution was shown for two future horizons : the 2040 to 2069 and 2070 to 2099
periods. 1961 to 2099 long-term trends were also analyzed, and results pertaining to each
indicators were discussed. Results showed interesting features, such as :
• General increases in temperature, which were reflected in a large number of indi-
cators (mean annual, minimum and maximum temperatures, mean temperature of
the warmest and coldest months and quarters, among others);
• Increase in most precipitation indicators, except for the mean precipitation of the
summer, where the spatial pattern shows large variability;
• Decrease in the length of the snow period;
• Increase in future rainfall extremes, from analysis of IDF curves.
117
118 Future Research and Conclusions
These validation and climate change scenario results were all based exclusively on
simulation aev. This implies that they are not accompanied by any quantitative or qual-
itative measure of uncertainty, probability or likelihood of the scenario. As stated in the
IPCC Fourth Assessment Report (AR4 ; Meehl et al. [2007], page 797):
”Uncertainty in predictions of anthropogenic climate change arises at all stages
of the modelling process [...] At each step, uncertainty in the true signal of cli-
mate change is introduced both by errors in [the greenhouse gas and emission
scenario,] the representation of Earth system processes in models [...] and by
internal climate variability [...]. The effects of internal variability can be quanti-
fied by running models many times from different initial conditions, provided
that simulated variability is consistent with observations. The effects of uncer-
tainty in the knowledge of Earth system processes can be partially quantified
by constructing ensembles of models that sample different parameterizations
of these processes. However, some processes may be missing from the set of
available models, and alternative parameterizations of other processes may
share common systematic biases. Such limitations imply that distributions
of future climate responses from ensemble simulations are themselves subject
to uncertainty (Smith [2002]), and would be wider were uncertainty due to
structural model errors accounted for.”
An assessment of the uncertainty associated with the climate change scenario, fol-
lowing methodologiessuch as those mentioned in the IPCC AR4 report, is of utmost
importance in the context of decision making and should be the next natural step to un-
dertake. Uncertainties in climate change projections should be evaluated in terms of the
noise (e.g., natural climate variability). This noise in fact depends on a number of factors,
such as the spatial scale, the variable and the statistics considered. Here, we must stress
that noise (and uncertainty) will increase: 1) as we move from a global to a regional scale,
2) as our interest shifts from temperature to precipitation, for example, and 3) as we go
from mean annual to mean seasonal state and to extremes (e.g. Murphy et al. [2009]).
Some sensitivity analyses of the CRCM have been performed to get a grasp on the ro-
bustness of the climate change signal at the regional scale (de Elia et al. [2008], de Elia and
Côté [2010]). It is also important that users acquire some knowledge on the sensitivity
of their own impact model to their various inputs and parameter settings. With the use
of ensemble climate simulations, and an evaluation of uncertainties, this knowledge will
become helpful in evaluating the confidence on the end result.
Future Research and Conclusions 119
Understanding this need for the evaluation of uncertainties in the context of decision
making and policy development, it is interesting to show some examples of results from
past and ongoing research at Ouranos. Such examples can indeed give insight on how to
understand and describe the limitations of the results from the current study.
As was mentioned several times in this report, the study of ensembles of climate sim-
ulations can help provide quantitative estimates of the uncertainty on climate scenarios.
The use of several simulations from the same model, but using different initial condi-
tions can help assess the contribution from natural climate variability, whereas the use of
simulations from several different models can help quantify the contribution from mod-
eling uncertainty. The uncertainty from the emissions scenario can be evaluated by using
simulations in which the hypothesized scenario varies.
Figure 6.1 presents an example in which several GCM simulations (from the IPCC 4th
assessment report (AR4)) are analyzed. The study was conducted at Ouranos for southern
Quebec, showing the scatter diagram of the temperature versus the precipitation changes
between the 1961-1990 and 2041-2070 periods. Each point, color and shape represent
a simulation, GCM and emission scenario and the 50, 75 and 95% circles represent the
distribution ellipses of the simulations, i.e. the median, 75th and 95th percentiles. This
type of analysis allows the study of the contribution to uncertainty of the various sources
discussed previously.
∆
P
re
ci
p(
%
)
∆Temp(◦C)
Figure 6.1: Dispersion diagram of the precipitation change (%) versus the temperature change
(◦C) for southern Quebec between the 1961-1990 and 2041-2070 periods. Each point, color and
shape represent a simulation, GCM and emission scenario from IPCC AR4. The 50, 75 and 95%
circles represent the surface probability density, i.e. the median, 75th and 95th percentiles. Source:
Ouranos (D. Chaumont, personal communication.)
In terms of the production of climate scenarios, statistical quantities such as the in-
120 Future Research and Conclusions
terquartile range, percentiles or standard deviation of modeled results can be used to
illustrate the uncertainty.
Such an approach has been used by the Ouranos Climate Simulation Team (de Elia
and Côté [2010]) to present ranges of possible changes. Figure 6.2 shows one of such
examples, where the change in wintertime precipitation is presented as the mean of a
17 CRCM simulation ensemble (AMNO domain, SRES A2, 45-km resolution), along with
the associated inter-projection standard deviation.
a)
b)
Figure 6.2: Ensemble mean relative wintertime precipitation change (a ; % ) and inter-projection
standard deviation (b ; %) over North America between the 1961 - 1990 and 2041 - 2070 periods.
The statistics were calculated from an ensemble of 17 CRCM simulations at 45-km resolution
on the ’AMNO’ domain and based on SRES A2 GHG emission scenario. White areas indicate
locations where precipitations were below the detection threshold. Adapted from de Elia and
Côté [2010].
Future Research and Conclusions 121
Finally, the following example from Ouranos [2010] (Figure 6.3) shows the 1900 to
2099 projected evolution of changes in wintertime temperature (◦C) and precipitation
(%), relative to the 1900-1969 reference period. This analysis was conducted over North-
ern Quebec, with 130 GCM simulations from IPCC AR4 (16 GCMs, 3 GHG and aerosol
emission scenarios (SRES), many simulations per GCM-SRES combination). Shown on
the figure are the median (solid line), the interquartile range (dashed line) and the range
between the 5th and 95th percentiles (grey area).
Figure 6.3: 1900 to 2099 projected evolution of Northern Quebec changes in wintertime tem-
perature (top ; ◦C) and precipitation (bottom ; %), relative to the 1900-1969 reference period.
The analysis was conducted with 130 GCM simulations from IPCC AR4 (16 GCMs, 3 GHG and
aerosol emission scenarios (SRES), many simulations per GCM-SRES combination). The median
(solid line), interquartile range (dashed line) and range between the 5th and 95th percentiles (grey
area) are shown. Adapted from Ouranos [2010].
These three cases are only examples of the various ways how uncertainty in climate
modeling can be quantified and used in a decision making context. The dissemination of
this type of information is becoming increasingly common, as a result of the increasing
demand from users in the climate impact assessment disciplines. Research in this field
is still ongoing at Ouranos and elsewhere, where tools are being developed to provide
uncertainty information that can be easily and efficiently transmitted to climate scenario
users.
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Appendix A
Supplementary Figures
129
130 Supplementary Figures
A.1 Comparison of rainfall observations with simulations
10 20 30 40 50 6070 80 90 100 110
10
20
30
40
50
60
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110
1−hr MOAM − model present (mm)
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(m
m
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2 yr
5 yr
10 yr
25 yr
50 yr
Figure A.1: Grid box scale comparison of observations with the CRCM control run for one hour
duration.
20 40 60 80 100 120 140
20
40
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140
6−hr MOAM − model present (mm)
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Figure A.2: Grid box scale comparison of observations with the CRCM control run for six hours
duration.
A.1. Comparison of rainfall observations with simulations 131
30 40 50 60 70 80 90 100 110 120 130
30
40
50
60
70
80
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110
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12−hr MOAM − model present (mm)
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(m
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5 yr
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Figure A.3: Grid box scale comparison of observations with the CRCM control run for twelve
hours duration.
6 7 8 9 10 11 12 13 14
6
7
8
9
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11
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13
14
1−hr MOAM − model present (mm)
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m
)
 
 
2 yr
5 yr
10 yr
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Figure A.4: Grid box scale comparison of the future run with the CRCM control run for one hour
duration.
132 Supplementary Figures
15 20 25 30 35 40 45
15
20
25
30
35
40
45
6−hr MOAM − model present (mm)
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m
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2 yr
5 yr
10 yr
25 yr
50 yr
Figure A.5: Grid box scale comparison of the future run with the CRCM control run for six hour
duration.
20 25 30 35 40 45 50 55 60 65 70
20
25
30
35
40
45
50
55
60
65
70
12−hr MOAM − model present (mm)
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2 yr
5 yr
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Figure A.6: Grid box scale comparison of the future run with the CRCM control run for twelve
hours duration.Past Climate of Ontario
Figure 2.2: NLWIS observations of average (top panel) and IQR (bottom panel) of mean daily
minimum temperature over the 1971 to 2000 period. Seasons are, from left to right: Winter, Spring,
Summer and Fall ; units are ◦C.
Figure 2.3: NLWIS observations of average (top panel) and IQR (bottom panel) of mean daily
maximum temperature over the 1971 to 2000 period. Seasons are, from left to right: Winter,
Spring, Summer and Fall ; units are ◦C.
2.1. Observed Climate 7
Figure 2.4: NLWIS observations of average (top panel) and IQR (bottom panel) of mean daily
temperature range over the 1971 to 2000 period. Seasons are, from left to right: Winter, Spring,
Summer and Fall ; units are ◦C.
Figure 2.5: NLWIS observations of average (top panel) and IQR (bottom panel) of mean daily
precipitation over the 1971 to 2000 period. Seasons are, from left to right: Winter, Spring, Summer
and Fall ; units are mm/day.
8 Recent Past Climate of Ontario
Figure 2.6: B03 observations of average (top panel) and IQR (bottom panel) of mean daily snow
depth over the 1979 to 1997 period. Seasons are, from left to right: Winter, Spring, Summer and
Fall; units are cm.
2.2. Uncertainty in Observed Climate 9
2.2 Uncertainty in Observed Climate
CRU vs NLWIS
Uncertainty of the observed climate data is analyzed for each variable as the comparison
between the NLWIS and CRU datasets. The CRU data were interpolated (using the near-
est grid point) from their original 0.5◦ resolution onto the CRCM’s (Canadian Regional
Climate Model) 45-km grid. Results are presented in Figures 2.7 to 2.11. The two panels
represent the difference in the absolute value (top panel) and the interquartile range over
the recent past period (bottom panel), which is indicative of the interannual variability.
Differences are presented as the CRU results subtracted from the NLWIS results, that
is from each NLWIS value is subtracted the value associated with the closest CRU grid
point.
Generally, the NLWIS and CRU datasets are very similar in the absolute values of the
variables, but significant differences appear in the calculation of the IQR. Differences are
likely due to the different interpolation techniques. Obvious problems appear around
Lake Nipigon, Chapleau and to the East of the Great Lakes, in areas affected by lake
induced snow squalls. The North also has significant differences, probably because of
interpolation in undersampled area and the fact that both datasets were not produced
using the same station data. Such differences between the two datasets indicate that
interannual variability is a relatively noisy field and that special care should be given to
this uncertainty when validating simulated data.
Figure 2.7: Difference between NLWIS and CRU daily mean temperature (top panel) and inter-
annual variability of mean temperature (bottom panel). Data used cover the 1971 to 2000 period
and units are ◦C.
10 Recent Past Climate of Ontario
Figure 2.8: Difference between NLWIS and CRU daily mean minimum temperature (top panel)
and IQR of mean minimum temperature (bottom panel). Data used cover the 1971 to 2000 period
and units are ◦C.
Figure 2.9: Difference between NLWIS and CRU daily mean maximum temperature (top panel)
and IQR of mean maximum temperature (bottom panel). Data used cover the 1971 to 2000 period
and units are ◦C.
2.2. Uncertainty in Observed Climate 11
Figure 2.10: Difference between NLWIS and CRU daily mean diurnal temperature range (top
panel) and IQR of mean diurnal temperature range (bottom panel). Data used cover the 1971 to
2000 period and units are ◦C.
Figure 2.11: Difference between NLWIS and CRU daily mean precipitation rate (top panel) and
IQR of mean daily precipitation rate (bottom panel). Data used cover the 1971 to 2000 period and
units are mm/day.
12 Recent Past Climate of Ontario
Snow dataset (B03) considerations
In the case of snow depth, only one gridded dataset was readily available and no direct
comparison was conducted. However, two aspects of the dataset can be considered to
assess the reliability of the snow depth product.
Firstly, in undersampled areas, the dataset tends to a first guess estimate produced
from a simple snow accumulation, aging and melt model [Brown et al., 2003], that is not
parameterized to represent the local climate.
Secondly, the observations used in the B03 dataset are likely to misrepresent the actual
climate to a certain extent. In fact, snow depth observations are generally conducted in
airport fields, which are open and exposed terrains. Amount of shade and wind are two
factors that significantly affect the snow melt and therefore this will possibly result in an
accelerated snow melt in the observations compared to the regional climate.
This feature of the snow depth observations is dependent on the surface of the con-
sidered region. For example, open and exposed airport fields have wind and shade
conditions that are closest to those of regions covered by agricultural fields rather than
dense forest. In the case of forested areas, this is likely to result in an overestimation of
the snow melt in the dataset with respect to the actual situation, leading to an earlier
and faster spring melt. For the agricultural region, this overestimation is likely to be less
important [R. Brown, personal communication, 2009].
Chapter 3
CRCM Validation
Before using Regional Climate Model (RCM) simulations to analyze signals of climate
change and to establish climate change projections, it is important to investigate the abil-
ity of the model to accurately simulate the current climate. Biases between simulated and
observed climates can then be used to understand the limitations of the analyses, as well
as to adjust the climate scenarios, when appropriate.
This Chapter first describes in Sections 3.1 and 3.2 the methodology and the simulated
climate data from the Canadian Regional Climate Model (CRCM). Then, the validation
results are presented under three sections. Section 3.3 shows the results on the Ontario
map for a broad view of the situation over the full study region. Then, in Section 3.4,
a regional characterization is applied to assess the simulation of the annual cycle of the
studied variables. Finally, the CRCM is validated in Section 3.5 against observations from
twelve stations across Ontario to provide insight on the ability of the model to represent
localized climates.
3.1 Methodology
Validation of the CRCM is done with observational data over the recent past period.
Generally, 30-year periods are considered for validation. In this study, the recent past
period is represented by 1971 to 2000, except when considering the B03 dataset where the
recent past period is 1979 to 1997, due to the availability of the observational data.
Differences between observed and simulated climates can come from three sources:
1) uncertainties in the climate observations, 2) internal natural variability of the climate
system and 3) imperfections of climate models. In the case of climate data simulated by
RCMs, the bias at a regional scale can be partially due to the bias in the driving data (see
for example Plummer et al. [2006]). Furthermore, an additional factor to consider in this
validation process is the difference of spatial scale between station observations and the
45km× 45km tiles from the CRCM output considered.
Uncertainties in observed climate arise from the uncertainty of the measuring tech-
13
14 CRCM Validation
nique, and in the present case where interpolated gridded datasets are used, on the inter-
polation technique and undersampling. This contribution to uncertainties was discussed
previously (Section 2.2).
The second source is the contribution to the variability that is related to the climate
system’s natural variability. As described by the United Kingdom Climate Projections
(UKCP ; Murphy et al. [2009], pp 26-27):
"[...] Climate, at a global scale and even more at a local scale, can vary substantially
from one period(for example, a decade or more) to the next, even in the absence of
any human influences. [...] [N]atural internal variability, arises from the chaotic na-
ture of the climate system, ranging from individual storms which affect our regional
weather to large scale variations over periods of seasons to years. [...] Natural inter-
nal variability will continue in future, and be superimposed on longer-term changes
due to man’s activities. [...] Climate models provide realistic simulations of a number
of key aspects of natural internal variability in the observed climate. By running the
climate model many times with different initial conditions (a so-called initial con-
dition ensemble) we can estimate the statistical nature of this natural variability on
a range of space and time scales, and hence quantify the consequent uncertainty in
projections. [...] [For example, when looking at ] global temperatures projected from
a three-member initial condition ensemble, [...] although each experiment shows the
same general warming, individual years can be quite different, due to the effect of
natural internal variability. [...] A common way of reducing the effect of uncertainty
due to natural variability on the projections is to average changes over a 30-yr period
[...] ."
Imperfections of climate models come from an incomplete understanding of processes
in the Earth’s climate system and an inadequate representation of these processes in the
climate models (e.g., sub-grid scale parameterizations). Some imperfections lead to un-
certainties (because of limited physical knowledge, for example), and others lead to errors
(biases) in models. The uncertainties in modelling are usually considered by using an en-
semble of simulations consisting in various GCMs (Global Climate Models) and RCMs,
where each model represents physical processes differently. In the case of model imper-
fections, some are sometimes known, leading to biases that are expected. For example,
in the CRCM’s current version 4.2.3, the misrepresentation of wetlands in Northern On-
tario, near the Hudson and James Bays shoreline, is expected to lead to biases in total
precipitation and temperature. Also, it is expected that the lake-effect snow to the East
of the Great Lakes will not be well resolved by the model at its current 45-km resolution.
Such known model features are to be accounted for when considering CRCM simulated
current or future climates.
Several variables were chosen for the validation of the CRCM : daily mean, minimum
and maximum temperatures, daily diurnal temperature range, daily precipitation rate as
3.1. Methodology 15
well as snow depth. Each variable is validated for two statistical quantities: the mean and
interquartile range (IQR) over the study period.
Finally, the 30-year validation period is considered relatively short to allow for a re-
liable trend analysis. Natural variability is such that decadal and other time scale cycles
can affect the trends significantly, even more so at the seasonal scale. A preliminary
trend analysis of gridded observations and CRCM data was however conducted using
Sen’s non-parametric approach [1968] and the Mann-Kendall statistical significance test
[Kendall, 1975]. This method was chosen over a least-square method because it is less af-
fected by outliers. The statistical significance of the trend was found to be relatively low
(at the 5% level) in most cases. As a result of these considerations, trend calculations over
the recent past period were judged to be inconclusive and were left out of the analysis.
16 CRCM Validation
3.2 CRCM Simulation
Validation in this study is made with the aev simulation of the CRCM 4.2.3 (Music and
Caya [2007], Caya and Laprise [1999]). This simulation was performed using the post-2000
Special Report on Emission Scenario (SRES, Nakicenovic and Swart [2000]) A2 green-
house gas and aerosol projected evolution and was driven by atmospheric fields from
the Canadian Global Climate Model 3.1 (CGCM3.1v2 member #5; Scinocca et al. [2008],
McFarlane et al. [2005], Flato and Boer [2001]), also following the SRES-A2 greenhouse
gas and aerosol projected evolution. The regional domain covers North America (AMNO
with 200x192 grid points) with a horizontal grid-size mesh of 45 km (polar-stereographic
projection, true at 60◦N). Version 4.2.3 of the CRCM is coupled with a lake model for
the Laurentian Great Lakes [Goyette et al., 2000]. Model output is archived on a 3-hourly
basis, over the entire 1961 to 2100 period and is validated with observations over the 1971-
2000 recent past 30-year period. A summary of the simulation specifications is presented
in Table 3.1.
Note that no ensemble approach is conducted here and it is therefore necessary to
keep in mind that the aev simulation results represent only one possible realization of
the past and projected climates. Further study is necessary in order to better evaluate the
potential of the model to simulate the observed climate. This should be planned in the
validation process by using more than a single simulation.
Simulation aev
Time step 15 min
Resolution 45 km
CRCM version CRCM 4.2.3
Driving data CGCM3.1v2 #5
Domain AMNO
Forcing frequency 6h
Archiving frequency 3h
Period of simulation 1961 - 2100
SRES (post-2000) A2
Figure 3.1: Description of the CRCM simulation used in the study (left) and presentation of the
AMNO (North America) domain (right) on which the simulation was conducted.
3.3. Biases - Ontario Overview 17
3.3 Biases - Ontario Overview
Validation of the climate as simulated by the CRCM is made against the observational
datasets presented in Chapter 2 over the recent past period. In the case where two
datasets were available (all but snow depth), only the results validated against the NL-
WIS dataset are shown. In fact, Section 2.2 already shows the differences between the two
observational datasets and presenting all the validation results would be redundant.
In this section, each variable is presented in a way similar to that of Section 2.1, but
here as differences between the CRCM simulation aev and the observational dataset cho-
sen for that variable. Results are presented on the CRCM’s 45-km grid, subtracting the
mean of the —NLWIS or B03— observations within 0.5◦ of each CRCM grid point. Fig-
ures 3.2 to 3.7 show the biases in 30-yr mean (top panel) and interquartile range (bottom
panel) for daily mean, minimum and maximum temperature, daily temperature range,
daily precipitation and snow depth, respectively. All color scales are adjusted so that neg-
ative, null and positive biases appear in blue, green and red, respectively. The magnitude
of the bias is indicated by the intensity of the color, as shown in the colorbars. Regions
and periods where the CRCM accurately simulates the climate will therefore easily be
recognized by their colors in the green shades.
Result overview
It is clear from Figures 3.3 to 3.7 that biases vary greatly over regions and seasons, and
differently for each variable. Bias features can be separated in four categories: statistical
quantity, spatial and temporal variations, as well as uncertainty in observations.
Statistical Quantity
Bias patterns vary significantly between the mean and IQR. For the temperature fields,
there is no obvious simple correlation between the bias in the two statistical quantities.
As for the precipitation and snow depth, biases in the mean and interquartile range seem
to be proportional (see for example Figure 3.6, in the Winter or Figure 3.7 during the
Winter and Fall).
Temporal variations
Seasonal differences in the biases are apparent in most variables and statistical quantities
but are most evident in the bias of the climate mean over the recent past period. Some
variables have biases of the mean that seem constant during the year (e.g. mean daily
temperature, Figure 3.2, top row), whereas others show significant differences of their
biases depending on the season (e.g. mean daily precipitation, Figure 3.6, top row).18 CRCM Validation
Spatial Variations
There are significant differences in the biases over the study region. The Northern and
Southern parts of Ontario often show significantly different biases, as can be seen in
Figure 3.4 during the Spring and Summer. In this case, daily mean maximum temperature
biases are of opposite signs in the North and South. The same comment applies to both
the mean and IQR.
Uncertainty in Observations
On top of the actual biases in the CRCM simulated climate, a contribution from the uncer-
tainty in the observed climate should be considered. In fact, as shown in Section 2.2, some
regions show significant dispersion in the observations, such as the undersampled North
and the region downwind from the Great Lakes, for example. This type of uncertainty is
particularly important for the IQR, as was discussed in Section 2.2.
The maintenance of the current state of the monitoring network, and ideally an im-
provement in undersampled areas should be considered an important aspect of climate
science policy. In fact, the reduction of uncertainties in observations would allow a better
understanding of climate processes as well as improve the confidence in model validation
results.
Figure 3.2: Seasonal biases in CRCM simulated mean (top) and IQR (bottom) of daily mean
temperature compared to NLWIS data. Seasons are, from left to right, Winter, Spring, Summer
and Fall. Data used cover the 1971 to 2000 period and units are ◦C.
3.3. Biases - Ontario Overview 19
Figure 3.3: Seasonal biases in CRCM simulated mean (top) and IQR (bottom) of daily mean
minimum temperature compared to NLWIS data. Seasons are, from left to right, Winter, Spring,
Summer and Fall. Data used cover the 1971 to 2000 period and units are ◦C.
Figure 3.4: Seasonal biases in CRCM simulated mean (top) and IQR (bottom) of daily mean
maximum temperature compared to NLWIS data. Seasons are, from left to right, Winter, Spring,
Summer and Fall. Data used cover the 1971 to 2000 period and units are ◦C.
20 CRCM Validation
Figure 3.5: Seasonal biases in CRCM simulated mean (top) and IQR (bottom) of daily mean
diurnal temperature range compared to NLWIS data. Seasons are, from left to right, Winter,
Spring, Summer and Fall. Data used cover the 1971 to 2000 period and units are ◦C.
Figure 3.6: Seasonal biases in CRCM simulated mean (top) and IQR (bottom) of daily mean pre-
cipitation rate compared to NLWIS data. Seasons are, from left to right, Winter, Spring, Summer
and Fall. Data used cover the 1971 to 2000 period and units are mm/day.
3.3. Biases - Ontario Overview 21
Figure 3.7: Seasonal biases in CRCM simulated mean (top) and IQR (bottom) of mean snow
depth compared to B03 data. Seasons are, from left to right, Winter, Spring, Summer and Fall.
Data used cover the August 1979 to June 1997 period and units are cm.
22 CRCM Validation
3.4 Biases - Regional Validation
As mentioned at the beginning of this chapter, three aspects are to be considered in the
validation: the uncertainty in the climate observations, the internal natural variability of
the climate system and the imperfection of climate models.
Section 3.3 showed how all these bias features are superposed and the resulting pat-
terns are complex and difficult to analyze. In order to better assess the biases, it is useful
to present the results under a different point of view.
The biases of the variables and statistical quantities are produced here on a regional
basis, so that spatial variations are smoothed and the annual cycle more evident. Four
regions have been defined for the validation of the CRCM over Ontario, as shown on
Figure 3.8: 1) Hudson and James Bays (hereafter referred to as HJB) area, 2) North-West,
3) East and 4) South. These regions were chosen after the study of Figures 3.2 to 3.7 to
represent the major features of the biases. Only land grid points within the four regions
as well as the Ontario borders are considered.
Figure 3.8: Definition of regions used in the validation of the CRCM over Ontario: 1) Hudson
and James Bays (HJB) area, 2) North-West , 3) East and 4) South.
3.4. Biases - Regional Validation 23
Annual Cycle Validation
The validation of the variables’ annual cycle can give insight into the understanding
of certain biases. Figures 3.9 to 3.11 show the observed and simulated annual cycles
of the five studied variables (daily mean, minimum and maximum temperatures, diurnal
temperature range, precipitation ad snow depth). The simulated cycle (CRCM simulation
aev) is shown in blue (bold) and the observations are shown in red. When available, both
observed datasets are shown.
Temperatures
The annual cycles of the 30-year mean and IQR are relatively well simulated by the CRCM
for daily mean, minimum and maximum temperatures (Figures 3.9 to 3.11). The cycle in
IQR is more noisy but the observed and simulated curves are relatively close together.
In Figure 3.10b, the 30-year mean of daily temperature range is not well simulated
but the IQR is similar in the observed and simulated results. Note that there is significant
uncertainty between the observation datasets. The annual cycle of the simulated diurnal
temperature range is difficult to assess. In the North-West and HJB regions, the observed
decrease in daily diurnal temperature range in the Fall is not well reproduced by the
model. For those two regions, as well as the East, the CRCM produces a strong Spring
increase that is not observed. Taking the example of the Spring for the HJB region, daily
minimum (Figures 3.3 and 3.9b) and maximum (Figure 3.4 and 3.10a) temperatures are
underestimated and overestimated, respectively. This leads to a large overestimation
of the diurnal temperature range, as can be observed in Figures 3.5 and 3.10b. In the
South and East during the Spring, both daily minimum and maximum temperatures are
underestimated (Figures 3.3 and 3.4), with the former having the largest bias. The result
is an increase in the simulated diurnal temperature range bias.
Precipitation
In the HJB and North-West regions, the simulated and observed annual cycles in pre-
cipitation (Figure 3.11a) are shifted, with the CRCM simulating the Spring increase in
precipitation too early during the year. This leads to an overestimation and underestima-
tion of the precipitation amounts in the Spring and Fall, respectively (Figure 3.6). In the
Southern and Eastern regions, the observed annual cycle has a smaller amplitude than
that of the cycle simulated by the CRCM.
In terms of the IQR, the simulated cycle is also shifted in the North-West, with the
simulated peak occurring earlier than what is observed. This field is however significantly
more noisy and its interpretation more difficult than that of the mean.
Snow depth
Figure 3.11 b shows that the start of the snow season is well simulated by the CRCM for
the North, East and South regions. In the HJB region, however, the CRCM has an early
24 CRCM Validation
start compared to the observations which can be related to the positive snow depth bias
already present in the Fall for this region (Figure 3.7). This bias can be partially explained
by the cold CRCM temperature bias in northern Ontario during the Fall (Figures 3.2
and 3.9).
The snow depth peaks seem to occur generally earlier in the observations, except in
the HJB region, where the simulated and observed peaks correspond.
The end of the snow season is well simulated in the North-West and HJB regions
but occurs too late for the East and South region, the latter having the largest shift.
This can partially explain the positive snow depth bias in the Spring in the East and
South (Figure 3.7). As discussed in Section 2.2, the misrepresentation of climate by the
observations could partially explain the difference in snow melt between the CRCM and
the B03 dataset. The North-West and HJB regions have lower density forests than the
South and East (and faster snow melt), which is better represented by the open-area
observation stations. Similar commentsapply to the IQR cycle, which follows relatively
closely the mean.
In the South region, the CRCM seems to largely overestimate snow depth throughout
Winter. This behaviour is also noted in the HJB region, but to a lesser extent. In the
North-West region, the CRCM shows a generally systematic underestimation of snow
depth. In the CRCM, snow water equivalent is computed and then converted into snow
depth through the parameterization of snow density, which varies according to age and
composition of the snow cover. One must bear in mind that the snow depth comparison
is thus strongly dependent on the snow cover density.
3.4. Biases - Regional Validation 25
a)
b)
Figure 3.9: Annual cycle of 30-year mean (left) and IQR (right) of a) daily mean temperature
and b) daily mean minimum temperature. Observed (NLWIS ; red) and simulated (CRCM ; blue)
cycles are shown and the four panels present, from top to bottom, the HJB, North-West, East and
South regions.
26 CRCM Validation
a)
b)
Figure 3.10: Annual cycle of 30-year mean (left) and IQR (right) of a) daily mean maximum
temperature and b) mean diurnal temperature range. Observed (NLWIS ; red) and simulated
(CRCM ; blue) cycles are shown and the four panels present, from top to bottom, the HJB, North-
West, East and South regions.
3.4. Biases - Regional Validation 27
a)
b)
Figure 3.11: Annual cycle of 30-year mean (left) and IQR (right) of a) daily mean precipitation
rate and b) mean snow depth. Observed (NLWIS for precipitation and B03 for snow depth ; red)
and simulated (CRCM ; blue) cycles are shown and the four panels present, from top to bottom,
the HJB, North-West, East and South regions.
28 CRCM Validation
3.5 Biases - Station Validation
In order to assess the performance of the CRCM on a more local basis, validation against
twelve surface stations in Ontario is conducted. The station data was provided by En-
vironment Canada via the Data Access Integration (DAI) portal. Table 3.1 presents the
geographic coordinates of each station, which are also mapped on Figure 3.12 along with
their respective number of full available years of data between 1971 and 2000. Note that
most southern stations have the full 30-year period but the two most northerly stations
are missing 9 and 6 years, respectively. Such gaps are likely to affect the validation re-
sults, especially since the missing years are consecutive and in the last decade (1992-2000
for Big trout Lake and 1994-1998 for Moosonee Airport). In fact, the climate change sig-
nal is not necessarily uniform over Ontario, and natural variability is expected to show
significant variations on decadal or even longer time scales [Murphy et al., 2009].
Station name Station number Latitude Longitude Altitude Nb years
(◦) (◦) (m) (1971-2000)
Big Trout Lake 6010738 53.83 -89.87 224.1 21
Kenora A. 6037775 50.12 -91.90 383.4 30
London Int’l A. 6144475 43.03 -81.15 278 30
Moosonee A. 6075428 51.29 -80.61 9.1 24
North Bay A. 6085700 46.36 -79.42 370.3 30
Ottawa Int’l A. 6106000 45.32 -75.67 114 30
Sault Ste Marie A. 6057592 46.48 -84.51 192 30
Timmins Victor Power A. 6078285 48.57 -81.38 294.7 30
Toronto City Center A. 6158665 43.63 -79.39 76.80 30
Toronto Pearson Int’l A. 6158733 43.68 -79.63 173.40 30
Wiarton A. 6119500 44.75 -81.11 222.2 29
Windsor A. 6139525 42.28 -82.96 189.6 30
Table 3.1: List of the 12 validation surface stations and their geographic coordinates.
Validation of the CRCM is conducted for the same variables as presented in previous
sections: temperature (daily mean, minimum, maximum and range), as well as precip-
itation and snow depth. These fields, calculated from the 1971-2000 observed station
data, are compared with CRCM’s simulation aev on two statistical quantities: the 30-year
mean and interquartile range.The number of years included in the observation calculation
depends on each stations’ data availability (see Figure 3.12).
3.5. Biases - Station Validation 29
Figure 3.12: Location of the twelve surface stations considered for validation, along with the
number of available years of data between 1971 and 2000.
Results
Figures 3.13 and 3.14 show the validation results between the 12 observing stations and
the average of the CRCM’s grid points located within a 1◦× 1◦ box centered on the
station (from 3 to 6 CRCM grids). Rows a) to f) show the different variables: daily
minimum, maximum and mean temperatures, daily temperature range, precipitation and
snow depth. The two panels show the mean (left) and IQR (right) over the study period.
The y and x axes corresponding to the observed and simulated fields, respectively and
the color and symbols represent the station and season, according to the legend shown
in Figure 3.12 and Table 3.2. Note that the stations’ colors tend to red and blue if they are
located in the South (warm) or North (cold), respectively. This allows the identification of
spatial patterns in the plots. Seasonality is indicated by grouping of points with similar
symbols. The distance of each symbol from the x = y line is inversely proportional to
the ability of the CRCM to simulate the recent past. In fact, in the case where the CRCM
would perfectly simulate the observed climate, all points would fall on this line.
Attention should be given to the axis scales, as they are related to the magnitude of
30 CRCM Validation
the bias. For example, in Figure 3.13 d, the biases in daily mean temperature range may
seem significantly greater than those of minimum, maximum and mean temperature.
This is partly due to the temperature range’s shorter scale, which makes the biases more
apparent.
Season Symbol
Winter (DJF) �
Spring (MAM) 4
Summer (JJA) ©
Automn (SON) ×
Table 3.2: Symbols used to represent the four seasons in the station validation results.
Results are similar to those of the previous sections. The 30-year mean values of
mean, minimum and maximum temperatures (Figure 3.13a, b and c) are relatively well
simulated by the CRCM, with the greatest biases in the South (red) during the Winter
(�). Biases in IQR are less consistent but the ensemble of symbols (for all regions and
seasons) is centered on the x = y line.
Mean daily temperature range is not well represented by the CRCM in either the
mean or IQR. This is likely due to the combination of biases in both the minimum and
maximum temperatures, as discussed in Section 3.4.
The CRCM simulates the mean precipitation relatively well. The spread of the colors
on the graph indicates an underestimation in the North and overestimation in the South.
The IQR of precipitation is generally underestimated by the CRCM.
Snow depth shows a clear regional pattern, with overestimation of the simulated mean
and IQR in the South and underestimation in the North. Spring and Summer data points
are located near the zero value, and have small biases. When comparing the snow depth
results to those presented in the maps of Section 3.3, it is easy to note that the region
of positive bias near the Hudson and James Bays during the Winter and Spring is not
represented here. This is due to the choice of the stations, none of which are found in
that area.
The biases in interquartile range of values over the study period indicate a general
overestimation by the CRCM in the case of minimum, maximum and mean temperatures,
as well as temperature range. In the case of precipitation, IQR is underestimated by the
CRCM, whereas mean snow depth IQR biases show important regional dependence (as
indicated in Figure 3.14f by the color separation in the cloud of symbols). In fact, the IQR
in seasonal mean snow depth is overestimated by the CRCM in the South (red shades)
and underestimated in the North (blue shades). As was previously mentioned, the IQR
is indicative of the interannual variability of the variables.
3.5. Biases - Station Validation 31
Figure 3.13: Validation results of the CRCM co-located with the 12 stations. Rows a) to d)
present mean, minimum, and maximum temperatures and daily temperature range, respectively.The two panels show the mean (left) and IQR (right) over the study period, which depends on the
station (see Figure 3.12). The y and x axes corresponding to the observed and simulated fields,
respectively and the color and symbol represent the station(see Figure 3.12) and season (Winter
(DJF) : �, Spring (MAM) : 4, Summer (JJA) : ◦ , Automn (SON) : ×). Units are ◦C.
32 CRCM Validation
Figure 3.14: Continuation of Figure 3.13, for daily precipitation (e) and snow depth (f). The two
panels show the mean (left) and IQR (right) over the 1971-2000 study period. The number of years
included depends on the station(see Figure 3.12). The y and x axes corresponding to the observed
and simulated fields, respectively and the color and symbol represent the station(see Figure 3.12)
and season (Winter (DJF) : �, Spring (MAM) :4, Summer (JJA) : ◦ , Automn (SON) : ×). Units
are mm/day and cm for the precipitation rate and snow depth, respectively.
3.5. Biases - Station Validation 33
Trend validation - an example
As discussed previously in Section 3.1, the validation of the trend is left out of the anal-
ysis. In fact, the trend is highly dependent on natural variability of the climate as is
illustrated by the following example. Figure 3.15 shows an example time series of mean
annual temperature, taken from the Toronto City Center station (number 6158665). Time
series are indicated in 2 different colors : the orange line represents the full 1971-2000
time period, whereas the black dash-dot line only covers the 1971-1995 period. Table 3.3
presents the comparison of the mean, trend and interquartile range, calculated first with
the full 1971-2000 dataset and then with a subsample, removing the last five available
years. In this particular case, removing the last five years of data results in a decrease
of approximately 10% in the calculated trend for annual mean temperature. Here, we
find that both trends are not significant at the 10% level. It is therefore necessary to be
aware of such possible effects in the trend validation. The mean and more particularly
the IQR validation are also likely to be affected by the number of years considered. In
the Toronto City Center example, the mean decreases by only 1% while the IQR drops
by 17% when when the sample period is shortened from 30 to 25 years. This result in-
dicates, as was discussed in Section 2.1, that the IQR is sensitive to the number of years
considered, especially in cases where trends are likely to be significant. Over the 30-year
periods considered in this study, the validation of this field is therefore less reliable than
that of the mean.
Figure 3.15: Example time series of mean annual temperature (◦C), for the Toronto City Center
station (number 6158665). Two periods are shown : 1971-2000 in orange and 1971-1995 in black
dash-dot line.
34 CRCM Validation
1971-2000 1971-1995 % difference
Mean 8.29 ◦C 8.21 ◦C -0.97%
Trend 2.1◦C/100year 1.9◦C/100year -9.95%
P-value 0.13 0.21 0.8
IQR 1.09◦C 0.90◦C -17.63%
Table 3.3: Comparison of mean, trend (and associated p-value) and interquartile range calcu-
lations of annual mean temperature, for the Toronto Pearson Airport station, using a dataset
covering the 1971-2000 and 1971-1995 periods (shown in Figure 3.15). Differences are presented
with respect to the full period 30-year period.
Chapter 4
Climate Change Scenarios
Uncertainties in climate model simulations were discussed in Chapter 3 as a way to ex-
plain the discrepancies between observed and simulated recent past variables. In the
same way, uncertainties in future climate estimations can come from three sources : nat-
ural variability of the climate system, imperfections of climate models, and uncertainties
in the future greenhouse gas (GHG) and aerosol emissions scenario.
Firstly, the climate system’s internal climate variability, linked to the chaotic nature
of the climate system, is irreducible. This noise can hide a signal (a climate change sig-
nal, for example), and imposes that we use a detectability threshold, such as a signal to
noise ratio. Secondly, as was earlier discussed, imperfect models generate biases in their
estimation of present climate and possibly different responses to the increase of GHG.
There remains a need to improve climate models through their development ; this work
is ongoing worldwide. This uncertainty imposes the necessity to use an ensemble of
climate models in order to increase robustness and quality in the future climate estima-
tion. Thirdly, future evolution in society, technology and environmental choices, among
others, is unpredictable and represents an irreducible uncertainty. It was found that glob-
ally, greenhouse gas and aerosol emission scenarios do not significantly influence climate
change projections up to the 2050s (Figure 4.1 ; IPCC (Meehl et al. [2007])). The effect of
the emission scenario is more important as we move toward the end of the 21st century.
At Ouranos, considering the computer power available to perform regional climate
simulations, it was decided to focus on the SRES-A2 future emissions scenario. Also,
with most recent GHG emissions, it seems that the A2 emissions scenario (considered as
a relatively pessimistic scenario) is even slightly below what is evaluated and expected in
the short range [Raupach et al., 2007, Meinshausen and Hare, 2008, Sheehan, 2008].
This Chapter presents a climate change scenario produced from the CRCM simulation
aev over Ontario (see Section 3.2). Section 4.1 describes the methodology used to produce
the climate change scenario, as well as the list of the studied climate indicators. The
results of the scenario are then presented in Section 4.2, along with a short discussion
35
36 Climate Change Scenarios
Figure 4.1: Multi-model global averages of surface warming (relative to 1980–1999) for the sce-
narios A2, A1B and B1, shown as continuations of the 20th century simulations. The pink line
is for the experiment where concentrations were held constant at year 2000 values. The bars at
right indicate the best estimate (solid line within each bar) and the likely range assessed for the
six SRES marker scenarios. Adapted from IPCC (Meehl et al. [2007]).
associated to each individual indicator. Note that since only a single simulation, aev, is
analyzed, the results presented here should be handled with great care, understanding
clearly their limitations. In fact, where it was possible to determine the simulation bias
with respect to the observation over the recent past period (Section 3.3), it is however not
possible, from a single simulation, to make an evaluation of uncertainties due to natural
variability, model imperfections and emission scenario.
4.1 Methodology and Climate Indicators
Climate change scenarios are produced using two different approaches : differences be-
tween future and reference periods, and long-term trend calculations. This Section con-
tains three parts: the first two present the different approaches used to produce the
climate change scenario and the last summarizes the studied climate indicators.
Climate Change Calculations (Deltas)
This climate scenario approach consists of calculating mean climate values for each in-
dicator on simulated reference and future periods. The difference (absolute or relative,
depending on the indicator) between those future and reference periods is computed to
4.1. Methodology and Climate Indicators 37
produce what is called the climate change scenario or ’delta’. This change is then applied
to the recent past historical data to produce a climate scenario.
In the current study, climate change scenarios have been calculated for two horizons:
the 2040-2069 and 2070-2099 periods, hereafter referred to as the 2050s and the 2080s.
The reference dataset is the 10km-resolution NLWIS gridded dataset, over the 1970-1999
period (1980s). In order for this method to be appropriate, it is assumed that biases in
the modeled results for the historical climate are maintained in the futureclimate. The
accuracy of this assumption has been questioned by Christensen et al. [2008], who showed
that, in some cases, a correlation between the magnitude of the temperature and the bias
could be found.
Long-term Trend Calculations
The evaluation of trends in climate data requires the consideration of several statistical
concepts. In order to illustrate these concepts and to describe the issues relevant in this
study, simple examples will be presented.
Considering a model of the evolution of a variable without noise
y = at + b, (4.1)
where a is the trend and b is the intercept. This model can be illustrated by a graphic
example, as in Figure 4.2 a, which shows a straight line with in this case a slope (trend)
of 0.01 per time step and an intercept of zero. In reality, such model representations are
inappropriate, since climate variability is inherently noisy. A better representation would
therefore include a component of noise ε, giving :
y = at + b + ε, (4.2)
such as in Figure 4.2 b.
With the assumption that the data corresponds to such a model, trends are then cal-
culated using Sen’s slope estimator (Sen [1968]) and Mann-Kendall’s τ statistics (Kendall
[1975]). This non-parametric method has the advantage over a linear regression to be less
affected by outliers. The significance is assessed with the p-value, with values below 0.05
being considered as statistically significant at the 5% level. On Figure 4.2, the p-value is
indicated as P and it can be seen that in both panels, the trend is statistically significant,
with Panel a) being the most significant (p-value of 3.5x10−49), due to its lack of noise.
One important consideration to discuss at this point is the possibility that the residual
noise ε may not be random. This would correspond to a model of the form, where ε1 is
time dependent (red noise) and ε2 is random (white noise),
y = at + b + ε1(t) + ε2 (4.3)
and can be due to either serial autocorrelation of the residuals or the presence of a non-
linear trend. These two cases are illustrated in Figure 4.3 a and b, respectively. The
38 Climate Change Scenarios
a)
b)
Figure 4.2: Simple model representation of a linear trend with intercept zero. Panel a) and b)
have no noise and random noise, respectively.
autocorrelation of the residuals is tested using the Durbin-Watson statistics [Durbin et al.,
1950, 1951], presented in the Figures as DW. Durbin-Watson values lower than 0.05 in-
dicate the presence of an autocorrelation in the residuals. It can then be seen that both
panels in Figure 4.3 show autocorrelation of the residuals, which can be easily identified
by non-random patterns of the points relative to the trend line. In Figure 4.2, panel b
shows no autocorrelation (DW of 0.77) as the noise was defined as random values be-
tween -0.5 and 0.5. Panel a, however shows highly autocorrelated residuals. This is due
to the fact that all residuals have zero values and are therefore highly correlated in time
(correlation of 1). Autocorrelation requires a complex treatment, especially because of its
two different causes (trend non-linearity and serial correlation). The DW-test is therefore
used to assess the goodness-of-fit of the model, that is its ability to reproduce the data.
Trend non-linearities require assumptions on the shape of the trend. In the context of
4.1. Methodology and Climate Indicators 39
climate data from a single simulation, it is not appropriate to make such approximations.
Indeed, as was described in the Copenhagen Diagnosis report (Allison et al. [2009], page
49):
”[...] Despite the certainty of a long-term warming trend in response to rising
greenhouse gases, there is no expectation that the warming will be monotonic
and follow the emissions pathway on a year-to-year basis. This is because nat-
ural variability and the 11-year solar cycle, as well as sporadic volcanic erup-
tions, generate short-term variations superimposed on the long term trend
[Lean and Rind, 2009]. Even under a robust century-long warming trend of
around 4◦C, we still expect to see the temperature record punctuated by iso-
lated but regular ten-year periods of no trend, or even modest cooling [East-
erling and Wehner, 2009]”.
Indicators
Table 4.1 lists the indicators analyzed in this Chapter, along with a brief description. A
more detailed description of each indicator will be provided in the result presentation.
A great number of those climate indicators are available from 10-km gridded data set
produced by Natural Resources Canada (NRCAN; publicly available online at http://
cfs.nrcan.gc.ca/subsite/glfc-climate (accessed February 19th 2010)), similar to that
of NLWIS. The definitions of the climate indicators correspond to that used to produce
the NRCAN grids, thus allowing consistency between the indicators between the current
study and the literature. The indicators for which the recent past estimates were provided
by the NRCAN dataset are indicated in Table 4.1 by a star. For quick reference, the page
on which the presentation of the indicator can be found is indicated in the same table.
40 Climate Change Scenarios
a)
b)
Figure 4.3: Model representation of a linear trend with intercept zero on which is superimposed
correlated noise (top) and of a non-linear trend on which is superimposed uncorrelated noise
(bottom)
4.1. Methodology and Climate Indicators 41
Table 4.1: List of indicators and their brief description. Stars indicate fields that are available
from the NRCAN dataset (http : //c f s.nrcan.gc.ca/subsite/gl f c − climate), for the 1970-1999
recent past period. Indicator are described in more detail in the result presentation.
No. Page Indicator Description
1 p.44 Mean min. temperature Avg. of daily min temperatures
2 p.46 Mean max. temperature Avg. of daily max temperatures
3 p.48 Mean temperature* Avg. of daily mean temperatures
4 p.50 Mean diurnal range* Avg of daily temperature ranges
5 p.52 Temperature seasonality* 100*Coefficient of variation of
monthly mean temp. (in Kelvin)
6 p.54 Max. daily mean temperature —
7 p.56 Min. daily mean temperature —
8 p.58 Annual temperature range* 6-7
9 p.60 Isothermality* 4/8
10 p.66 Mean temp. of wettest quarter* Avg. temp. of 3 wettest months
11 p.68 Mean temp. of driest quarter* Avg. temp. of 3 driest months
12 p.62 Mean temp. of warmest quarter* Avg. temp. of 3 warmest months
13 p.64 Mean temp. of coldest quarter* Avg. temp. of 3 coldest months
14 p.70 Heat wave number of occurrences —
15 p.74 Annual precipitation(pcp)* Sum of monthly pcp
16 p.76 Pcp of wettest month* Highest monthly pcp
17 p.78 Pcp of driest month* Lowest monthly pcp
18 p.80 Pcp seasonality* 100*Coefficient of variation of
monthly pcp
19 p.82 Pcp of wettest quarter* Total pcp of three wettest months
20 p.84 Pcp of driest quarter* Total pcp of three driest months
21 p.86 Pcp of warmest quarter* Total pcp of three warmest months
22 p.88 Pcp of coldest quarter* Total pcp of three coldest months
23 p.92 Summertime mean of total Mean over June, July and August
soil moisture (liquid and solid)
24 p.96 Max daily snow water equivalent —
25 p.90 Freezing degree-days Number of degree-days below 0◦C
26 p.98 First part of snow period Number of days (July 1st-Dec 31st)
with snow depth ≥ 10cm
27 p.100 Second part snow period Number of days (Jan 1st-June 30th)
with snow depth ≥ 10cm
28 p.102 Length snow period 26+27
42 Climate Change Scenarios
4.2. Results 43
4.2 Results
Climate change results are presented for each climate indicator in two ways: distribution
over Ontario as well as long-term trend calculations. First, a series of three maps of
Ontario are presented. On the left, the first map shows the recent past (1980s) distribution
of the indicator as calculated with the NLWIS gridded dataset. The middle and right
maps then show the climate change scenario (delta) for the indicator between the 1980s
and 2050s, and the 1980s and 2080s periods, respectively.
Following the maps is a table presenting the climate scenario results forthe 12 stations
presented in Section 3.5 (Table 3.1 and Figure 3.12) as well as 7 regions of Ontario, which
are shown in Figure 4.4. Only land grid points within the seven regions as well as the
Ontario borders are considered. The results presented in the table include the long-term
(1961-2099) trend, significance (P-value) and autocorrelation (as well as goodness-of-fit)
estimate (DW), as was described in the methodology section, along with the 1980s to
2050s and 1980s to 2080s change (delta) in the climate indicator. Time series that show
autocorrelation (DW less than 5%) and trends that are not statistically significant (p-value
less than 5%) are indicated in the table in blue and red, respectively. These results should
be taken with care.
Following the presentation of results for each indicator is a discussion of the rele-
vant concepts and descriptions, as well as any interesting features. When appropriate,
additional figures are presented to support argumentation.
Figure 4.4: Definition of regions used for the calculations of the climate scenarios over Ontario.
44 Climate Change Scenarios
Mean Daily Minimum Temperature
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.2: Deltas(absolute) and trend results for annual mean daily min. temperature.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 5.64 0.00 0.38 3.67 5.64
Big Trout Lake 5.42 0.00 0.30 3.55 5.38
Timmins A. 5.31 0.00 0.76 3.40 5.39
Kenora A. 4.99 0.00 0.96 3.10 4.90
Sault St-Marie A. 5.22 0.00 0.98 3.28 5.21
North Bay A. 5.28 0.00 0.53 3.28 5.32
Wiarton A. 5.86 0.00 0.30 3.74 5.87
Ottawa A. 5.42 0.00 0.97 3.39 5.43
Toronto City Ctr 5.78 0.00 0.39 3.72 5.79
Toronto Pearson A. 5.78 0.00 0.34 3.70 5.76
London Int’l A. 5.75 0.00 0.40 3.71 5.71
Windsor A. 5.75 0.00 0.82 3.76 5.70
Region 7 5.49 0.00 0.13 3.50 5.44
Region 6 5.03 0.00 0.89 3.14 4.96
Region 5 5.39 0.00 0.90 3.49 5.41
Region 4 5.26 0.00 0.78 3.31 5.31
Region 3 5.43 0.00 0.75 3.40 5.41
Region 2 5.76 0.00 0.43 3.69 5.73
Region 1 5.88 0.00 0.53 3.82 5.79
4.2. Results 45
Discussion
Mean Daily Minimum Temperature: Annual average of daily minimum temperatures.
Annual mean minimum temperature is projected to increase all over Ontario. The warm-
ing is projected to be between 3 and 4 degrees at the 2050s horizon. For the 2070-2099
period, the warming is projected to be between 4 and 6 degrees, with the lowest values
occurring in the westernmost part of Ontario. Trends are significant for all 12 stations
and 7 regions.
Figure 4.5 shows the annual cycle of the 1980s to 2050s delta of monthly mean daily
minimum temperatures for the seven Ontario regions. It is clear that mean daily mini-
mum temperature deltas are highly variable on a monthly basis, a piece of information
that was hidden in the annual mean. In fact, where the annual mean minimum temper-
ature deltas are relatively uniform (within 2 degrees) across Ontario, the monthly deltas
can range from 2 to as much as 9 ◦C in the South during the summer.
Annual cycles are also spatially variable, as can be noticed by comparing the southern-
and northernmost regions, which peak at different times of the year. Indeed, in the
southern regions (red), monthly mean minimum temperature deltas are greatest at the
end of the summer, whereas in the North (blue), the maximum occurs in the winter.
Figure 4.5: Annual cycle of the 1980s to 2050s delta of monthly mean daily minimum tempera-
tures (◦C). Each of the seven regions of Ontario are represented by a color going from Blue (North)
to red (South) according to their location.
46 Climate Change Scenarios
Mean Daily Maximum Temperature
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.3: Deltas (absolute), values and trend results for annual mean daily max. temperature.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 4.83 0.00 0.98 2.99 4.88
Big Trout Lake 4.53 0.00 0.92 2.83 4.46
Timmins A. 4.81 0.00 0.49 3.07 4.79
Kenora A. 4.33 0.00 0.93 2.83 4.29
Sault St-Marie A. 4.82 0.00 0.85 3.15 4.79
North Bay A. 4.76 0.00 0.65 3.05 4.76
Wiarton A. 5.58 0.00 0.20 3.55 5.61
Ottawa A. 4.84 0.00 0.82 3.07 4.89
Toronto City Ctr 5.32 0.00 0.60 3.44 5.34
Toronto Pearson A. 5.34 0.00 0.20 3.45 5.36
London Int’l A. 5.58 0.00 0.49 3.67 5.62
Windsor A. 5.12 0.00 0.99 3.35 5.16
Region 7 4.56 0.00 0.48 2.82 4.50
Region 6 4.45 0.00 0.85 2.89 4.40
Region 5 4.73 0.00 0.77 3.00 4.73
Region 4 4.80 0.00 0.74 3.11 4.80
Region 3 4.93 0.00 0.86 3.15 4.96
Region 2 5.46 0.00 0.57 3.55 5.50
Region 1 5.38 0.00 0.29 3.57 5.40
4.2. Results 47
Discussion
Mean Daily Maximum Temperature: Annual average of daily maximum temperatures.
Mean daily maximum temperature is expected to increase over Ontario, with warming
from 2 to 4 degrees and from 4 to 6 degrees at the 2050s and 2080s horizons, respectively.
The warming of the daily maximum temperature is projected to be more important in the
South than North. Trends are significant for all 12 stations and 7 regions.
Figure 4.6 shows the annual cycle of the 1980s to 2050s delta of monthly mean daily
maximum temperatures for the seven Ontario regions. There is large monthly variability
in the delta, with values ranging between 3 and 9 ◦C.
Spatially, the annual cycle is uniform, with greater increases in the winter than the
summer. Southern regions typically have larger monthly deltas than the northern regions,
except during the months of October, November and December.
Increase in annual mean daily minimum temperatures are greater than that of daily
maximum temperatures. This is however not always the case in the monthly distributions.
Figure 4.6: Annual cycle of the 1980s to 2050s delta of monthly mean daily maximum tempera-
tures (◦C). Each of the seven regions of Ontario are represented by a color going from Blue (North)
to red (South) according to their location.
48 Climate Change Scenarios
Mean Daily Mean Temperature
1980s ; NLWIS (◦C) 2050s vs 1980s ; aev 2080s vs 1980s ; aev (◦C)
Table 4.4: Deltas (absolute) and trend results for annual mean daily mean temperature.
Location Trend P-value DW Delta Delta
1961-2099 2050s vs 1980s 2080s vs 1980s
(◦C/100 years) (◦C) (◦C)
Moosonee A. 5.19 0.00 0.51 3.33 5.21
Big Trout Lake 4.96 0.00 0.29 3.17 4.91
Timmins A. 4.97 0.00 0.50 3.17 5.03
Kenora A. 4.64 0.00 0.94 2.92 4.56
Sault St-Marie A. 4.96 0.00 0.97 3.16 4.94
North Bay A. 4.96 0.00 0.54 3.13 5.01
Wiarton A. 5.65 0.00 0.47 3.60 5.66
Ottawa A. 5.03 0.00 0.86 3.17 5.08
Toronto City Ctr 5.47 0.00 0.36 3.52 5.47
Toronto Pearson A. 5.46 0.00 0.47 3.51 5.46
London Int’l A. 5.55 0.00 0.26 3.59 5.52
Windsor A. 5.39 0.00 0.72 3.52 5.37
Region 7 5.00 0.00 0.25 3.16 4.96
Region 6 4.71 0.00 0.98 2.97 4.64
Region 5 5.00 0.00 0.92 3.21 5.02
Region 4 4.96 0.00 0.71 3.15 5.01
Region 3 5.11 0.00 0.89 3.22 5.12
Region 2 5.52 0.00 0.57 3.55 5.51
Region 1 5.54 0.00 0.62 3.63 5.50
4.2. Results 49
Discussion
Mean Daily Mean Temperature: Annual average of daily mean temperatures.
Mean annual temperature is projected to increase all over Ontario, between 2 and 4 ◦C,
and between 4 and 6 ◦C for the 2050s and 2080s horizons, respectively. Positive trends
are significant for all 12 stations and 7 regions. Warming is more important in the East
than the West.
Figure 4.6 shows the annual cycle of the 1980s to 2050s delta of monthly mean daily
mean temperatures for the seven Ontario regions. The annual cycle shows contributions
from both daily minimum and maximum temperatures. For the southern regions, for
example, this results in two distinct peaks, in the summer and winter, which correspond
to the contributions from increases in daily minimum and maximum temperature, re-
spectively.
Figure 4.7: Annual cycle of the 1980s to 2050s delta of monthly mean daily mean temperatures
(◦C). Each of the seven regions of Ontario are represented by a color going from Blue (North) to
red (South)