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APPENDIX 4.4.2-1
Climate Change Discussion
APPENDIX 4.4.2-1 Climate Change Discussion
1
1.0 CLIMATE CHANGE DISCUSSION
Climate is projected to change in the region of the Project. The infrastructure associated with the Project must be
robust enough to accommodate these projected changes in expected climate conditions. Adaptation to changing
climate conditions can either be dealt with in the Project design itself or through adaptive management. Adaptive
management considers what further information on changes to the expected conditions will be gathered and how
this information will be incorporated into Project operation and maintenance to make it more robust. Future
climate projections have already been considered as part of the Project design.
To understand future climate projections, current climate conditions near the Project must be understood. The
followings sections provide the approach for describing the current and future climate conditions, as well as a
discussion of the potential climate-infrastructure interactions for the Project.
1.1 Background and Approach
To understand how the climate has been changing, and may change in the future, climate trends were analysed
as follows:
Describing the current climate using available long-term (30 year) data;
Documenting how the climate has changed over the past 30 years in the Project region;
Discussing the range of future climate projections (2040 through 2069 and 2070 through 2099); and
Presenting a climate risk matrix.
The current climate conditions were defined using climate normals, which are long-term (usually 30-year)
averages of observed climate data. The standard period recommended by Environment and Climate Change
Canada (ECCC) for establishing climate normals is a 30-year period from 1981 through 2010. Current climate
trends are used to document how the climate has changed over the 30-year period in the Project area. Current
climate trends are characterized using existing climate data to identify apparent trends and assessing whether
these apparent trends are statistically significant.
The projected ranges of future climate were described using the outputs from General Circulation Models (GCMs)
accepted by the IPCC for various emission scenarios developed by the IPCC. The GCM projections are
accessed for the Project area using the PCIC Regional Analysis Tool (PCIC, 2015). The Regional Analysis Tool
provides multiple emissions scenarios for multiple models to provide an indication of the range of possible future
climate conditions. The Regional Analysis Tool currently only provides projections based on the IPCC Fourth
Assessment Report (AR4). The fifth assessment report (AR5) is the most current complete synthesis of climate
science, and any concerns and trends identified using AR4 remain consistent in AR5.
APPENDIX 4.4.2-1 Climate Change Discussion
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2.0 CURRENT CLIMATE
2.1 Approach for Describing Current Climate
For the purpose of this assessment, climate station selection was based on specific recommendations from
Environment and Climate Change Canada’s Canadian Climate Change Scenarios Network (CCCSN), which is
the Government of Canada’s interface for distributing global climate change scenarios and adaptation research.
This network provides useful guidance for selecting a climate station to represent an area of interest and how
climate data should be used when calculating trends (Canadian Climate Change Scenarios Network, 2009). The
following CCCSN criteria were selected for consideration:
Length of record (minimum 30 years of data);
Availability of a continuous record; and
Proximity to the area of interest.
In addition to the CCCSN criteria, the following selection factors were also considered to identify the station(s)
which best represent the Project site meteorologically:
Age of observations compared to the currently accepted normal period;
Latitude;
Elevation of station;
Geographic siting; and
Monthly data availability threshold of 90% for all years.
Given that a number of climate stations often fall within the boundaries of the study area of interest, it is often not
practical, from a detailed analysis perspective, to use all of the available climate stations within the study area.
The available climate data from each station must be compared to, and pass, the selection criteria outlined above.
Data from most climate stations is constrained by low numbers of observations, a limited life span for the station
(data quantity), and varying data quality.
The current climate temperature and precipitation were used to calculate the annual and seasonal current climate
normals and trends using the definitions provided in Table A4.4.2-1.
APPENDIX 4.4.2-1 Climate Change Discussion
3
Table A4.4.2-1: Definitions of Climate Indices
Climate Indices Definition
Total Precipitation Calculated as the sum of all the observed precipitation during the
selected annual period. Each annual value is averaged over the
30 years of the climate normal.
Seasonal Precipitation (Spring, Summer,
Fall, Winter)
Calculated as the sum of all the observed precipitation during the
selected season. Each annual value is averaged over the 30
years of the climate normal.
Number of Annual Dry Spells A dry spell is defined as a period of more than ten contiguous
days with no rain. This climate index counts the number of dry
spells during each annual period. Each annual value is averaged
over the 30 years of the climate normal.
Length of Dry Spells Calculated as the maximum length of all dry spells during the
selected annual period and then averaged over the 30 years of
the climate normal.
Average Annual Temperature Calculated as the average of all the observed temperatures
during the selected annual period. Each annual value is
averaged over the 30 years of the climate normal.
Seasonal Temperature (Spring, Summer,
Fall, Winter)
Calculated as the average of all the observed temperatures
during the selected seasonal period. Each annual value is
averaged over the 30 years of the climate normal.
Number of Annual Heat Waves A heat wave is defined as a period of more than three contiguous
days with maximum temperatures above 40°C. This climate
index counts the number of heat waves during each annual
period. Each annual value is averaged over the 30 years of the
climate normal.
Length of Heat Waves Calculated as the maximum length of all heat waves during the
selected annual period and then averaged over the 30 years of
the climate normal.
Maximum Daily Temperature Calculated as the maximum of all daily maximum temperatures
during the selected annual period and then averaged over the
30 years of the climate normal.
Number of Days with Freeze-Thaw Cycle A freeze-thaw cycle is defined as a day where the minimum daily
temperature is less than 0°C and the maximum daily temperature
is greater than 4°C. The climate index counts the number of
freeze-thaw cycles during each annual period. Each annual
value is averaged over the 30 years of the climate normal.
APPENDIX 4.4.2-1 Climate Change Discussion
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Climate Indices Definition
Number of Annual Cold Spells A cold spell is defined as a period of more than three contiguous
days with minimum temperatures below -15°C. This climate
index counts the number of cold spells during each annual
period. Each annual value is averaged over the 30 years of the
climate normal.
Length of Cold Spells Calculated as the maximum length of all cold spells during the
selected annual period and then averaged over the 30 years of
the climate normal.
Data will be used to calculate selected climate normals and trends (Table A4.4.2-1), using a methodology
developed by the Finnish Meteorological Institute (Salmi, Määttä, Anttila, Ruoho-Airola, & Amnell, 2002) to assess
climate changes predicted from long-term climate observations. Both annual and seasonal climate normals and
trends will be calculated for the mean temperature and total precipitation. The climate normal will be calculated
as the average of a given climate parameter over the selected period, and the climate trend was calculated as the
average change in the climate parameter per decade (i.e., the decadal trend or change). Potential trends in
temperature and precipitation will be evaluated by fitting a model to the data using the Sen’s nonparametric
model. The statistical significance of the observed trends will be determined using the Mann-Kendall test. The
Mann-Kendall test is applicable to the detection of a monotonic trend of a time series with no seasonal cycle. The
analysis uses a two-tail test to determine statistical significance at the 90th, 95th, 99th and 99.9th percentile levels.
2.2 Current Climate Conditions
This section presents the existing climate conditions for the Project. The methodology used for the assessment is
provided in Section 2.1. This section presents the rationale for climate station selection, and provides a
characterization of the existing climate as well as an analysis of climate trends.
2.2.1 Station Description
There are 12 climate stations within 20 km of the Project; however, 6 of these stations did not contain a sufficient
amount of both temperature and precipitation data. The six remaining stations (Table A4.4.2-2) were considered
as possible sources of data for characterising the current climate and climate trends and are shown in Figure A-
4.4.2-1.
APPENDIX 4.4.2-1 Climate Change Discussion
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Table A4.4.2-2: Climate Stations Considered for Characterizing Current Climate
Station Name Climate
Station ID
Northing
(m N)
Easting
(m E)
Elevation
[m]
Distance
to Project
Centroid
[km]
Data
Availability
Richmond Nature Park 1106PF7 5446450.96 493216.99 3 5.2 1977 to 2015
Vancouver Intl A(1) 1108395/
1108447
5449118.77 486602.42 4.3 12.2 1953 to 2015
Surrey Newton 1107878 5442623.54 438197.93 73.2 13.9 1960 to 2000
Delta Tsawwassen
Beach
1102425 5428676.62 493174.86 2.4 15.1 1971 to 2015
Burquitlam Vancouver
Golf Course
1101200 5455440.27 5455440.27 122 16.9 1988 to 2005
Burnaby Simon
Fraser U
1101158 5458406.93 5056960.96 365.8 17.5 1965 to 2015
Note: (1) Vancouver Int’l A (climate ID 1108447) became Vancouver Intl A (climate ID 1108395) in 2013
The climate assessment completed for the Project used data from one climate station, namely Vancouver Intl A,
to describe current climate conditions, climate variability, and longer-term historical trends. Vancouver Intl A
climate station is located close to the Project with the longest dataset available that falls within the desired normal
period (1981 through 2010). Vancouver Intl A station also had a higher data completeness (over 90%) for
temperature and precipitation observations. For these reasons, Vancouver Intl A was selected to describe the
current climate and current climate trends. The remaining five stations were excluded based on geographic siting
and data availability. The selected climate station is shown in Attachment 1: Historical Climate Analysis.
Available daily meteorological data from the Vancouver Intl A station was collected for the period from 1981
through to 2010 (Environment Canada, 2015). Once the dataset passed the quality assurance/quality control
process (e.g., data checks, ranges, missing data), they were prepared for development of the long-term averages
and trend analysis.
The percentage of missing data at Vancouver Intl A station between 1981 and 2010 is approximately 0.3% for
temperature and 0.2% for total precipitation, rainfall and snowfall. All years have less than 10% of data missing
and therefore meet the CCCSN criteria outlined above.
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APPENDIX 4.4.2-1 Climate Change Discussion
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2.2.2 Current Climate Normals and Trends
The climate normals and current climate trends in climate were calculated for Vancouver Intl A climate station.
Both annual and seasonal normals and trends were calculated for the mean temperature and total precipitation.
The analysis resulted in three pieces of information for each climate parameter as follows:
Climate normal;
Climate trend; and
Statistical significance of the trend.
The climate normal is calculated as the average of a given climate parameter over the selected period; the
climate trend is calculated as the average change in the climate parameter per decade (i.e., the decadal trend or
change). The trends, calculated using Sen’s Slope Estimates (Salmi, Määttä, Anttila, Ruoho-Airola, & Amnell,
2002), are tested for significance at the 90th, 95th, 99th, and 99.9th percentile levels using the Mann-Kendall Test
(Salmi, Määttä, Anttila, Ruoho-Airola, & Amnell, 2002). A trend that is zero was classified as no apparent trend.
A trend that is not statistically significant at the 90th percentile was classified as being “not significant”. A trend is
determined to be statistically significant at the 95th percentile; there is a less than 5% chance that the observed
trend does not exist if the statistical test conditions are met. The normals and trends for each of the climate
indices are summarized in Table A4.4.2-3.
Table A4.4.2-3: Climate Normals and Current Climate Trends - Vancouver Intl A Climate Station
Climate Indices Vancouver Intl A (1981 to 2010)
1981 – 2010
Normals
Decadal
Change
Level of Statistical Significance
Total Precipitation [mm (equiv.)] 1191.2 -42.9 <90%; not statistically significant
Spring Total Precipitation [mm (equiv.)] 267.9 -21.1 <90%; not statistically significant
Summer Total Precipitation [mm (equiv.)] 126.1 -10.3 <90%; not statistically significant
Fall Total Precipitation [mm (equiv.)] 363.7 +1.1 <90%; not statistically significant
Winter Total Precipitation [mm (equiv.)] 433.6 -14.8 <90%; not statistically significant
Number of Period of More Than 10 Days with No
Rain [#]
4.6 +0.0 no apparent trend
Length of Dry Spells [days] 23.4 +0.0 no apparent trend
Average Annual Temperature [°C] 10.4 +0.2 <90%; not statistically significant
Average Spring Temperature [°C] 9.7 +0.0 no apparent trend
Average Summer Temperature [°C] 17.2 +0.3 significant at the 95th percentile
APPENDIX 4.4.2-1 Climate Change Discussion
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Climate Indices Vancouver Intl A (1981 to 2010)
1981 – 2010
Normals
Decadal
Change
Level of Statistical Significance
Average Fall Temperature [°C] 10.5 +0.1 <90%; not statistically significant
Average Winter Temperature [°C] 4.2 +0.2 <90%; not statistically significant
Number of Heat Waves or Periods of More Than 3
Days with Tmax > 30°C [#]
0.0 +0.0 no apparent trend
Length of Heat Waves [days] 0.1 +0.0 no apparent trend
Maximum Daily Temperature [°C] 29.0 +0.1 <90%; not statistically significant
Number of Days with Freeze-Thaw Cycle [#] 25.8 +0.0 no apparent trend
Number of Cold Spells or Periods of More Than 3
Days with Tmin < -15°C [#](1)
0.0 +0.0 no apparent trend
Length of Cold Spells [days](1) 0.0 +0.0 no apparent trend
Note: (1) Conditions for the cold spell, defined as three contiguous days with a minimum temperature below -15°C do not occur in the observations for Vancouver Intl A climate station (ID 1108447) between 1981 and 2010.
The analysis of Vancouver Intl A climate station shows no apparent temperature trends in the spring. The
summer, fall, winter, and annual temperatures show increasing trends; only the summer temperature trend is
statistically significant, at the 95th percentile. The total annual precipitation, as well as spring, summer, and
winter precipitation, show decreasing trends. The fall precipitation shows an increasing trend. None of the
precipitation trends analyzed are statistically significant above the 90th percentile. For the annual period, these
current climate trends indicate a current climate that is likely similar and slightly drier than previous periods
(e.g., a normal period centered on the 1970s).
3.0 FUTURE CLIMATE
3.1 Approach for Describing Future Climate
As an international body, the IPCC provides a common source of information relating to emission scenarios,
provides third-party reviews of models, and recommends approaches to document future climate projections. In
1988, the IPCC was formed by the World Meteorological Organisation (WMO) and the United Nations
Environment Program (UNEP) to review international climate change data. The IPCC is generally considered to
be the definitive source of information related to past and future climate change as well as climate science.
Periodically, the IPCC issues assessment reports summarising the most current state-of-climate science. The
AR4 (Solomon et al., 2007) was used as a reference in this report. The AR5 was released in 2013 and is the most
current complete synthesis of information regarding climate change. Any concerns identified using AR4 remains
APPENDIX 4.4.2-1 Climate Change Discussion
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in AR5, with consistent trends presented. However, a straightforward comparison between the reports is
challenging due to the changes in emission scenarios and models in AR5.
3.1.1 Global Climate Change Projections
Climate modelling involves the mathematical representation of global land, sea and atmosphere interactions over
a long period of time. These GCMs have been developed by various government agencies, but share a number
of common elements described by the IPCC (Solomon et al., 2007). The IPCC does not run the models, but does
act as a clearinghouse for the distribution and sharing of the model forecasts.
The IPCC data was accessed through the Regional Analysis Tool (PCIC, 2015) developed by the PCIC, a
regional climate service centre based at the University of Victoria, BC. Since the model outputs are susceptible to
inter-decadal variability, the model outputs are provided in 30-year blocks identified by the centre decade. The
following two blocks of climate forecast data were used to assess the range of projections for future climate for
the Project:
2050s - 2041 through 2070; and
2080s - 2071 through 2100.
These are the standard forecast data sets for the 21st century and both the 2050s and the 2080s will be reflective
of the Project decommissioning phase. While the majority of the Project time occurs during the 2020s (2011
through 2040), this climate projection data will not be assessed, as climate changes will not have been completely
manifested. Instead, since the operation phase of the Project (minimum 30 years) will extend past 2039, climate
is more appropriately described by the 2050s. Any projected changes in climate during the 2020s will be smaller
than the changes projected for the 2050s, and the 2050s will be representative of the conditions near the end of
operation and for conditions during decommissioning. The 2080s reflect a bounding condition should the
operational lifetime of the Project be extended beyond the minimum 30 years. By using the projected climate
change for the 2050s and 2080s, the period when the Project phases will be most sensitive to Project climate
change occurrences is included; the projected changes for the 2020s are already included.
Given the large grid size of a GCM projection, the data are representative of area averages and not necessarily
representative of a specific location contained within the grid box. Murdock and Spittlehouse (Murdock &
Spittlehouse, 2011) recommend that analyses involving GCM projections be based on descriptions of future
climate that have been presented in the context of change from the accepted baseline period (i.e., the models use
the 1961 through 1990 period as the baseline). Since the models may have an absolute bias, the predicted future
climate is compared to the predicted baseline using the same model. Also, because the models are most
effective at describing projections of change, projected changes from a modelled baseline are typically described
as a deviation from baseline, either in degrees Celsius (°C) for temperature, or percent (%) for precipitation. The
resulting change from the modelled baseline can then be used to estimate the future climate conditions in the
context of the actual current climate for the Project.
The current climate was analysed for the period from 1981 through 2010, a normal period occurring 20 years after
the modelled baseline of 1961 through 1990 from PCIC. In order to account for the difference in modelled
baseline and current climate, the projected changes in climate were scaled before being applied to the current
APPENDIX 4.4.2-1 Climate Change Discussion
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climate normals. The scaling approximated a constant decadal rate of change by dividing the projected model
change by the number of decades since the modelled baseline period (i.e., eight decades between the baseline
and the 2050s). This scaling was then multiplied by the number of decades between the current climate normal
and the desired future climate period (i.e., six decades between current climate normal and the 2050s). The
scaled changes are presented as changes in °C and changes in millimetres (mm) of precipitation for the current
climate.
Global climate models require extensive inputs in order to characterize the physical and social developments that
could alter climate in the future. In order to represent the wide range of the inputs possible to global climate
models, IPCC has established a series of socio-economic scenarios that help define the future levels of global
GHG emissions. While the IPCC identifies many scenarios, the following three are available from the PCIC
Regional Analysis Tool, namely A1B, A2 and B1:
Scenario A1B — the A1 family of scenarios describes a future world of very rapid economic growth, with a
global population that peaks in mid-century and declines thereafter, along with the rapid introduction of new
and more efficient technologies. The A1 family includes three groups of scenarios that describe alternative
directions in the energy system. The A1B group is distinguished by a balance across all sources of energy –
green and fossil;
Scenario A2 — the A2 scenario family describes a world with an underlying theme of self-reliance and
preservation of local identities. Fertility patterns across regions converge very slowly, which results in a
continuously increasing population. Economic development is regionally oriented, and per capita economic
growth and technological change are more fragmented and slower than for other scenarios; and
Scenario B1 — the B1 scenario family describes a convergent world with the same global population that
peaks in mid-century and declines thereafter (similar to the A1 scenarios). The B1 family has rapid change
in economic structures toward a service and information economy, with reductions in raw material intensity
and the introduction of clean and resource-efficient technologies. The emphasis is on global solutions to
economic, social and environmental sustainability, including improved equity, but without additional climate
initiatives.
These three socio-economic scenarios have been described more fully by IPCC in the Special Report on
Emission Scenarios (SRES) (Nakićenović & Swart, 2000). The IPCC considers each of the scenarios as equally
likely to occur. The PCIC Regional Analysis Tool used to provide information for this assessment is based on the
SRES emissions scenario combinations provided by the IPCC (PCIC, 2015). Data used in this assessment
relates to the A1B, A2 and B1 scenarios.
3.1.2 Regional Climate Change Projections
Climate simulations produced by these general circulation models vary because each model uses a different
combination of algorithms to describe and couple the earth’s atmospheric, oceanic and terrestrial processes. The
GCMs used in this analysis have been validated against observations, and the interpretations of their results have
been peer reviewed by the IPCC and others. Rather than selecting a single model, the climate change
projections from all the available models from AR4 (i.e., 136 unique sets of modelling results), using the PCIC
APPENDIX 4.4.2-1 Climate Change Discussion
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Regional Analysis Tool, were included in the analysis. This ensemble approach was used to delineate the
probable range of results and to better capture the actual outcome (an inherent unknown).
In the case of climate models, projections are not made at a location, but for a series of grid cells in the scale of
hundreds of kilometres in size. The PCIC Regional Analysis Tool provides GCM projections for a series of
defined regions. For this assessment the PCIC-defined Metro Vancouver Region was used because it
encompasses the Project area. The PCIC Regional Analysis Tool was then used to select the appropriate grid
information from the various GCMs in the ensemble.
3.1.3 Longer-term Effects of Climate Change
Longer-term effects of climate change on these factors (beyond 2100) are highly dependent on the emissions
scenario (A1B, A2, B1, etc.) being considered and are not provided by the PCIC. As a result, these results are
not discussed, as they are beyond the likely lifespan of the Project and are too variable to be considered reliable
at this time.
3.1.4 Understanding Climate Projections and their Limitations
GCMs have inherent limitations that are important to bear in mind when evaluating variability and the rate of
climate change (i.e., when comparing future projections to historical observations). These limitations are
dependent on the research institution’s approach to overcoming model uncertainty. Since no one model or
climate scenario can be viewed as completely accurate, the IPCC recommends that climate change assessments
use as many models and climate scenarios as possible. For this reason, the multi-model ensemble approach
described above was used to account for these uncertainties and limitations.
3.1.4.1 Spatial and Temporal Scales
Due to limitations on computing power, the GCM outputs are limited to grid cells of 1 to 2.5 degrees (°)
(approximately 110km – 275km) and a small number of vertical layers in both the atmosphere and the ocean.
These grid cells represent a mathematically defined ’region’ rather than a specific geographic location and are
different for many models. Although the appropriate grid cells were selected to represent the Project location, and
the data extracted from the appropriate grid cell, this scale is much larger than that of most weather processes
such as convective thunderstorms. In addition, local changes in topography cannot be represented at this scale.
Temporally, the GCM simulations are run at monthly time scales, and only monthly average temperature and
precipitation are available as outputs.
The process of ‘downscaling’ is a method to overcome the spatial and temporal scale limitations. Downscaling
may decrease uncertainty for regions where the regional topography or geography is complex compared to the
GCM grid-scale, or where diurnal fluctuations in local meteorology are important. While this technique can
improve comparisons between historical observations and simulations of past climate for a specific GCM, it does
not address uncertainty in the models, as noted in the following sections.
APPENDIX 4.4.2-1 Climate Change Discussion
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3.1.4.2 Unpredictable Events
Climate model simulations represent average conditions and typically do not consider the influence of inherently
unpredictable stochastic or episodic events (e.g., volcanic eruptions, earthquakes, tsunamis). In other words,
events of a certain magnitude tend to occur at a certain frequency; however, their actual magnitude and timing is
unknown and currently not predictable within a specific GCM’s outputs.
Although large events are rare, they have the potential to invalidate climate model projections both globally and
regionally. For example, the 1991 eruption of Mount Pinatubo is well known to have decreased the average
planetary surface temperature by approximately 1°C for at least one year; this change represents a significant
offset to predictions of approximately 3°C of warming over the next century. The Pinatubo eruption ranks as a “6
out of 8” on the logarithmic-based volcanic explosivity index and events such as Pinatubo have return periods on
the order of 100 years. Larger events have return periods of 1,000 years or more; however, their plumes can
reach altitudes of greater than 40 km and inject sufficient amounts of sulphur into the stratosphere to suppress
global temperature from years to decades (Robock, Marquardt, & Kravitz, 2009).
3.1.4.3 Changes to our Understanding of the Processes
The earth’s system processes and feedbacks are very complex, and therefore have to be approximated in the
GCM model simulations. In these instances, mathematical parameterisations of these processes are required to
reduce the computational burden within the simulations. Each of these independent processes that drive climate
change can be assigned a rank based on the current level of scientific understanding (LOSU). The contribution of
aerosols in the GCMs is an example of this uncertainty. Aerosols were ranked as very low LOSU in the 2001
IPCC report and were upgraded to a medium-to-low LOSU in the 2007 IPCC report (Forster & Ramaswamy,
2007).
In addition, new discoveries can change the inputs to the GCMs and the interrelationship of these drivers within
each GCM. For example, the 1988 discovery of Prochlorococcus spp. (cyanobacteria), the most abundant
photosynthetic organism (i.e., a photosynthetic picoplankton) in the ocean, led to a change in the understanding
of ocean biology, the carbon cycle and atmospheric carbon dioxide (CO2) (Moore, Rocap, & Chisholm, 1998).
Similarly, the 2001 discovery of ubiquitous atmospheric N2-fixation by the marine cyanobacterium Trichodesmium
spp. (also called ‘sea sawdust’) changed the understanding of the effects of ocean biology and our understanding
of the earth’s nitrogen cycle (Berman-Frank et al., 2001).
3.2 Future Climate Conditions
The future climate for the Project site has been described using the climate projections for the Metro Vancouver
Region defined in the PCIC Regional Analysis Tool. The data were obtained from PCIC for all the available AR4
scenarios. The historic modelled baseline period used by PCIC is 1961 through 1990, which differs from the
current climate period of 1981 through 2010 used in Section 1.0. It is important to note that this modelled
baseline is not necessarily representative of the local conditions and does not correspond to the observed data
but, as outlined in Section 3.1, is used by the GCM projections to estimate changes in climate. This assessment
presents the data obtained for the historic baseline period (1961 through 1990), as well as the A1B, A2, and B1
socio-economic scenarios for the 2050s (2040 through 2069) and 2080s (2070 through 2099) time periods from
PCIC are presented in this assessment.
APPENDIX 4.4.2-1 Climate Change Discussion
13
A scatter plot analysis is widely used for describing future climate projections and illustrates the distribution of the
future climate conditions predicted by the models. The figures illustrate the projected change in temperature on
the vertical axis and the projected change in precipitation on the horizontal axis. The resulting scatter plots are
divided into four quadrants, with values in the upper right quadrant, representing change to a warmer and wetter
climate, while values in the lower left quadrant represents a change to a cooler and drier climate. In addition, the
current climate trends are added to the scatter plot figures to illustrate whether the models are predicting changes
that are consistent with current climate observations, or whether future trends are different.
The model projections generally fall in the upper right quadrant of the plots, suggesting a future climate that will
be warmer and wetter; however, some of the model projections suggest a future climate that will be warmer and
drier, and these forecasts are most similar to the observed current climate trends at Vancouver Intl A climate
station, as per Table A4.4.2-3, the annual historical climate trends for temperature and precipitation were not
found to be statistically significant. Comparisons of the future climate projections for the Project area for the
2050s and the 2080s periods, as well as the change in climate that would occur if the observed current climate
changes continue forward into the future (i.e., the black diamond on the scatter plot graphs), are shown as scatter
plots on Figure A4.4.2-2. For reference, the current climate normal is where the axes intersect. The current
climate trend shown in the figure is calculated using the Vancouver Intl A climate station data.
Figure A4.4.2-2: Scatter Plots Showing the 2050s and 2080s Annual Projections for the Project Region
APPENDIX 4.4.2-1 Climate Change Discussion
14
In general, the climate in the Project region is projected to be warmer and possibly wetter for the 2050s and
2080s time horizons when compared to the current climate period. This is a change from the trends currently
observed at Vancouver Intl A. It is not unusual for current climate trends to differ from the projected future trends.
The projected current climate trends do not account for changes in the anthropogenic forcing or variations in the
observed record between the current climate conditions and projected future climate conditions. The mean of the
projected annual temperature and precipitation for all models and the three SRES scenarios are provided in Table
A4.4.2-4, measured from the observed climate baseline.
Table A4.4.2-4: Summary of Projected Climate Change for the Project Area
SRES Scenario Time Period Annual Average
Temperature (°C)
Total Annual Precipitation
(mm[equiv.])
A1B 1981 - 2010 Climate +10.4 +1191.2
2050s +11.9 (+1.5) +1218.2 (+27.0)
2080s +12.8 (+2.4) +1240.5 (+49.3)
A2 1981 - 2010 Climate +10.4 +1191.2
2050s +12.1 (+1.6) +1217.3 (+26.1)
2080s +13.0 (+2.5) +1245.1 (+53.9)
B1 1981 - 2010 Climate +10.4 +1191.2
2050s +12.0 (+1.5) +1219.8 (+28.6)
2080s +13.3 (+2.8) +1240.8 (+49.5)
All Scenarios 1981 - 2010 Climate +10.4 +1191.2
2050s +11.8 (+1.3) +1218.1 (+26.9)
2080s +12.3 (+1.9) +1234.2 (+43.0)
Notes:
Scaled projected changes, relative to the current climate, are provided in brackets. The All Scenarios projected changes are based on PCIC outputs and not an average of the three SRES Scenarios listed above.
APPENDIX 4.4.2-1 Climate Change Discussion
15
4.0 CLIMATE INFRASTRUCTURE INTERACTIONS
4.1 Climate Change and Infrastructure
While the projected climate normal for the 2050s and the 2080s show slightly different trends than presented in
the current climate (i.e., warmer and wetter compared to warmer and possibly drier (Figure A4.4.2-2), climate
change may result in a climatological environment that is different from the current climatological environment
(e.g., changes in the intensity and frequency of precipitation). Such changes may affect future operations and
may affect the operation of infrastructure associated with the Project. A qualitative assessment of how the
changing climate may affect Project aspects and components has been completed by identifying interactions
between the proposed infrastructure and selected climate factors.
Based on the climate parameters and climate data analysed, climate factors have been developed to further
analyse the potential climate infrastructure interactions for the Project region. The climate factors include
changes to precipitation (i.e., focused on rainfall), along with temperature and extreme events (e.g., storms).
These factors are further subdivided into specific event-type factors that describe long-term changes such as
increasing winter temperatures, or extreme events such as increased storms which have the potential for
lightning, high winds, and intense precipitation. Where climate projections are not available, literature values are
referenced to discuss the projected change in climate. For example, the monthly time scale of climate model
projections is not able to capture changes in the frequency of rain events, and thus literature is referenced. The
climate factors, climate factor trend, and justification for the trend are provided in Table A4.4.2-5.
APPENDIX 4.4.2-1 Climate Change Discussion
16
Table A4.4.2-5: Climate Factor Trends
Climate Factor Description Trend Comments on Future Trends
Pre
cip
ita
tio
n
Drought Increasing Projections indicate an increase in the frequency and/or
intensity of droughts (Warren & Lemmen, 2014).
The multi-model ensemble suggests increasing
temperatures and precipitation. The change in precipitation
distribution could lead to more drought events.
Amount of
precipitation
Increasing Declining winter precipitation in Western Canada but
increasing annual total precipitation in the spring and fall
seasons is projected (Warren & Lemmen, 2014).
Average annual precipitation may increase by 4 to 17%
from 1961-1990 levels (BC Ministry of Environment, 2016).
The multi-model ensemble suggests a slight increase in the
amount of seasonal and annual precipitation.
Frequency of heavy
rain fall events
Increasing An increase in the frequency of rain events is projected for
the Province of British Columbia (Solomon et al., 2007).
Amount of rainfall per
event
Increasing As extreme precipitation events are projected to increase,
the amount of rainfall per event is projected to increase.(BC
Ministry of Environment, 2016).
Changes in snowfall Decreasing There has been a shift in precipitation type, with decreasing
snow fall and increasing precipitation as temperatures
increase (Warren & Lemmen, 2014).
The multi-model ensemble suggests an increase in the
amount of winter precipitation but does not differentiate
between snow and rain.
Changes in
snowpack
Decreasing Reduced snow cover is expected with projected increased
winter temperatures leading to projected reduced snowpack
(Lemmen, Warren, & Lacroix, 2008). Decreases in the
duration of snow cover is also projected for the west coast
of North America (Warren & Lemmen, 2014).
The multi-model ensemble suggests an increasing trend in
winter temperatures, which may cause a decrease in the
snowpack.
APPENDIX 4.4.2-1 Climate Change Discussion
17
Climate Factor Description Trend Comments on Future Trends
Te
mp
era
ture
Freeze-thaw Increasing In some areas of BC, freeze-thaw events are projected to
increase (BC Ministry of Environment, 2016).
The multi-model ensemble suggests a slight increase in the
amount of winter precipitation and winter temperatures,
which can lead to an increase in freeze-thaw cycles.
High temperatures Increasing Average annual temperatures in BC are projected to
increase by 1.7°C to 4.5°C from 1961-1990 temperatures
(BC Ministry of Environment, 2016).
The multi-model ensemble suggests temperatures are
increasing, leading to the possibility for higher
temperatures.
Low temperatures Decreasing Warmer and/or fewer cold days and nights are projected
over most land areas (Solomon et al., 2007).
The multi-model ensemble suggests temperatures
increases for all seasons indicating low temperature events
will likely decrease in frequency.
Warmer winter Increasing An increasing trend in warmer winters is projected
(Lemmen et al., 2008).
The multi-model ensemble suggests temperature increases
for winter.
Heat waves Increasing An increase in heat waves is considered to be very likely,
with an increased number, intensity and duration (Solomon
et al., 2007).
The multi-model ensemble suggests higher temperatures,
allowing for the possibility of increase in heat waves.
APPENDIX 4.4.2-1 Climate Change Discussion
18
Climate Factor Description Trend Comments on Future Trends
Oth
er
Eve
nts
Increase in extreme
events (e.g., storms)
Increasing Extreme events, including warm temperature extremes,
heavy precipitation events and storm events (i.e. wind, ice,
lightning), are likely to increase in frequency and intensity
(Solomon et al., 2007).
Lightning Unknown Projected trends in lightning are uncertain since it occurs at
a small spatial scale. There is insufficient evidence to
determine whether it will increase or decrease in intensity
and frequency (Solomon et al., 2007). There is a potential
for an increase inferred from the increased frequency and
intensity of storm events.
Wind Variable Potential for an increase inferred from increased frequency
and intensity of storms (Blunden & Arndt, 2017).
Large-scale atmospheric circulations are projected to
experience changes, such as a poleward shift and
strengthening of westerly winds in the Northern
Hemisphere (Blunden & Arndt, 2017).
Rainfall on snowpack
Increasing
Projected increases in rainfall, as described above, may
lead to more rainfall on snowpack. The projected increases
in temperature will decrease the time for snowpack
accumulation (Lemmen, Warren, Lacroix, & Bush, 2007).
The multi-model ensemble suggests the potential for
decreased snowpack due to the increase in temperature
and the potential for increased precipitation, meaning the
potential for more rainfall on the available snowpack.
Sea level rise Increasing Sea levels are projected to increase by 0.47 to 1.9 metres
by 2100 (BC Ministry of Environment, 2011).
APPENDIX 4.4.2-1 Climate Change Discussion
19
The facilities and infrastructure associated with the Project have an estimated minimum operational lifetime of
30 years and will be removed during the decommissioning phase. Table A.4.4.2-6 presents a climate risk matrix,
which provides a summary of the potential climate-infrastructure interactions by physical work or activity
associated with the Project. This climate risk matrix was provided to all other technical disciplines to identify all
possible climate-infrastructure interactions and the climate factors behind the interactions.
Table A.4.4.2-6: Climate Risk Matrix
Physical Work or
Activity
Climate Factors Description of Potential Interaction with Climate
Change
Construction Phase
Site preparation and
removal of existing marine
infrastructure
Extreme events,
Precipitation
Extreme events may impact construction, but the
events are under the current climate conditions and are
not likely influenced by climate change.
Increases in extreme events (e.g. storms) and high
intensity precipitation events may result in a potential
interaction with all construction activities described
(i.e., the dredging, land and river- based ground
stabilization, and piling work activities). Extreme events
could cause delays, disrupting transportation as a result
of road washouts, or damage equipment from lightning,
and storm surges (high waves). High waves could
further impact shoreline enhancement as it would
cause erosion.
Flooding could cause delays to activities and has the
potential to impact slope stability creating unsafe
working conditions.
Dredging of dredge area
In river ground
stabilization and piling
works
Land based ground
stabilization and piling
works
Construction of
associated offshore
facilities
Marine transportation of
construction materials and
equipment
Road transportation of
construction materials and
equipment
Shoreline enhancement of
the previously disturbed
shoreline
Operation Phase
LNG carrier/bunker vessel
loading
Extreme events,
sea level rise
Increases in extreme events (e.g. storms) may result in
a potential interaction with the LNG carrier/bunker
APPENDIX 4.4.2-1 Climate Change Discussion
20
Physical Work or
Activity
Climate Factors Description of Potential Interaction with Climate
Change
Berthing/departure of
vessels
vessel loading, the berthing/departure of vessels and
marine shipping from the Project site to Sand Heads.
Strong winds, heavy rainfall, high waves, and lightning
can physically impact equipment and cause disruptions.
Worker safety can also be at risk while these activities
are occurring during these events.
Marine shipping from the
Project site to Sand
Heads
Maintenance dredging Extreme events,
precipitation, sea
level rise
An increase in extreme events, as well as an increase
in frequency and/or intensity of precipitation events may
cause delays in dredging. Expected sea level rise may
also cause delays depending on amount of change
experienced.
Maintaining marine
security area
Extreme events Extreme events may impact the maintenance of marine
security areas, as worker safety may be at risk during
severe storms and during floods.
Accidents and
malfunctions during
operation
Extreme events Response to accidents or malfunctions during
operations may be disrupted and/or delayed as a result
of extreme events (e.g. storms), and/or high intensity
precipitation events that could impact access roads.
Decommissioning Phase
Removal of associated
offshore Facilities
Extreme events,
Temperature,
Precipitation
Extreme events may impact activities in the
decommissioning phase, however, due to the short
time frame (approximately 2 months), decommissioning
activities are not likely to be influenced by climate
change.
Climate change may impact clean-up and reclamation
activities. Changes in temperature and precipitation
may impact the flora and fauna species used when
revegetating the area.
Removal of associated
onshore Facilities
Marine transportation of
decommissioning
materials and equipment
APPENDIX 4.4.2-1 Climate Change Discussion
21
4.2 Sea Level Change and Infrastructure
With melting polar ice due to increased temperatures, it is predicted that sea levels will continue to rise, with a
possibility of increased or changing coastal erosion. The Project site is located on the Fraser River; therefore,
changes in sea level and coastal erosion dynamics have the potential to affect the Project directly. Global sea
level rise is projected to increase by 26 to 98 centimetres by 2100 according to climate models (BC Ministry of
Environment, 2016). A study undertaken by Thomson et al. (Thomson, Bornhold, & Mazzotti, 2008) presents an
examination of the factors affecting relative and absolute sea level in coastal BC, and presents estimates of future
sea level change. The study presents sea level height by the year 2100 relative to 2007 levels (RSL2100).
The RSL2100 was predicted using two eustatic sea level rises by the year 2100, the IPCC-AR4 mean eustatic sea
level rise of 30 ±12 centimetres (cm) and a high predicted eustatic sea level rise of 100 ±30 cm. The tide gauge
closest to the Project, where sea level predictions were made in the study, was New West (49.200°N,
122.910°W), located approximately 12 km northeast of the Project site along the Fraser River. The predicted
RSL2100 using the mean sea level rise was -13 cm, with a possible range of -45 cm to 20 cm. The predicted
RSL2100 using the high predicted sea level rise was 57 cm, with a possible range of 14 cm to 100 cm.
Each tide gauge has a category ranking, which is a letter grade in which A denotes the most reliable estimate and
F a non-reliable estimate. The New West ranking is an F, which denotes a non-reliable estimate. Therefore, a
secondary tide gauge was considered. The next closest tide gauge to the Project was Vancouver (49.287°N,
123.110°W), located approximately 17 km north-northwest of the Project in the Burrard Inlet. The category
ranking for Vancouver was a B, which indicates a more reliable estimate. The predicted RSL2100 using the mean
sea level rise was 19 cm, with a possible range of 7 cm to 31 cm. The predicted RSL2100 using the high predicted
sea level rise was 89 cm, with a possible range of 58 cm to 119 cm. Since the Project is expected to be
completed by the 2050s it is expected that rising sea levels of this amount will have little direct effect on the
Project operation phase. The Project design considered a sea level rise of 0.3 m over the design life of the
Project, conservatively based on the BC MoE ‘Guidelines for Management of Coastal Flood Hazard Land Use’
(BC Ministry of Environment, 2011), which recommends a global sea level rise of 1.0 m by the year 2100. The
sea level rise considered over the Project lifetime is comparable to the sea level rise predicted by 2100 at the
nearby tide gauges. The Project closure plan will comprise the removal of surface infrastructure and; therefore, it
is expected that the predicted rising sea level will have little effect on Project closure.
4.3 Summary of Climate Infrastructure Interactions
As discussed in Section 4.1 of this appendix, changes in climate may affect future operations and infrastructure
associated with the Project. Since both the construction and decommissioning phases occur during short time
frames, this summary focuses on the long-term operations phase that is expected to occur for 30 years. The
Climate Risk Matrix (Table A-4.4.2-6) described potential climate-infrastructure interactions by physical work or
activity associated with the Project. Extreme events were identified as potentially impacting all activities occurring
during the operations phase (i.e. vessel loading, berthing, and shipping, maintenance dredging, maintaining the
security areas, and accidents and malfunctions during operation). Increases in the frequency and intensity of
extreme events is projected for the next century which includes temperature and precipitation extremes, as well
as storm events (i.e. wind, ice, and lightning) (Solomon et al., 2007). Increases in these events may result in a
potential interaction with these activities as strong winds, heavy rainfall, high waves and lightning can physically
impact equipment and can cause not only delays and disruptions but also a complete shutdown of operations.
Worker safety is also a concern during extreme events such as severe storms and flooding. Extreme events may
APPENDIX 4.4.2-1 Climate Change Discussion
22
need to be assessed in greater detail as the frequency and severity of these events is projected to increase
during the lifespan of the Project.
The construction and closure phases of the Project are considered to be resilient to changing climate conditions
since both occur during time frames too short to be significantly impacted by climate change, especially
considering the large range of weather conditions experienced seasonally. During the long-term operations of the
Project, the Project infrastructure is considered to be resilient to sea level rise given the conservative design
considerations. However, additional planning or modification of operating procedures may be required as extreme
events increase in frequency and severity. Operations may be delayed or stopped during extreme events
regardless of any infrastructure design modifications to accommodate these projected changes in climate
conditions. These events can be addressed through operational responses or through adaptive management as
they continue to increase.
APPENDIX 4.4.2-1 Climate Change Discussion
23
5.0 REFERENCES
BC Ministry of Environment. (2011). Guidelines for Management of Coastal Flood Hazard Land Use. Retrieved
from http://www.env.gov.bc.ca/wsd/public_safety/flood/pdfs_word/coastal_flooded_land_guidelines.pdf
BC Ministry of Environment. (2016). Indicators of Climate Change for British Columbia 2016 Update. Retrieved
from https://www2.gov.bc.ca/assets/gov/environment/research-monitoring-and-
reporting/reporting/envreportbc/archived-reports/climate-change/climatechangeindicators-
13sept2016_final.pdf
Berman-Frank, I., Lundgren, P., Chen, Y. B., Küpper, H., Kolber, Z., Bergman, B., & Falkowski, P. (2001).
Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium
Trrichodesmium. Science, 294(5546), 1534–1537. https://doi.org/10.1126/science.1064082
Blunden, J., & Arndt, D. S. (2017). State of the Climate in 2016. Bull. Amer. Meteor. Soc., 98(8), Si-S277.
https://doi.org/10.1175/2017BAMSStateoftheClimate.1
Canadian Climate Change Scenarios Network. (2009). Canadian Climate Change Scenarios Network Workshop,
Environment Canada, Toronto, Ontario.
Salmi, T., Määttä , A., Anttila, P., Ruoho-Airola, T., & Amnell, T. (2002). Detecting Trends of Annual Values of
Atmospheric Pollutants by the Mann-Kendall Test and Sen’s Slope Estimates – The Excel Template
Application MakeSens. Publications on Air Quality, (31).
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., & Tignor, M. (2007). Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
(H. L. Miller, Ed.). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Thomson, R. E., Bornhold, B. D., & Mazzotti, S. (2008). An Examination of the Factors Affecting Relative and
Absolute Sea Level in Coastal British Columbia (p. 49). Canadian Technical Report of Hydrography and
Ocean Sciences 260.
Warren, F. J., & Lemmen, D. S. (2014). Canada in a Changing Climate: Sector Perspectives on Impacts and
Adaptation. Government of Canada.
BC Ministry of Environment. (2011). Guidelines for Management of Coastal Flood Hazard Land Use. Retrieved
from http://www.env.gov.bc.ca/wsd/public_safety/flood/pdfs_word/coastal_flooded_land_guidelines.pdf
BC Ministry of Environment. (2016). Indicators of Climate Change for British Columbia 2016 Update. Retrieved
from https://www2.gov.bc.ca/assets/gov/environment/research-monitoring-and-
reporting/reporting/envreportbc/archived-reports/climate-change/climatechangeindicators-
13sept2016_final.pdf
Berman-Frank, I., Lundgren, P., Chen, Y. B., Küpper, H., Kolber, Z., Bergman, B., & Falkowski, P. (2001).
Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium
Trrichodesmium. Science, 294(5546), 1534–1537. https://doi.org/10.1126/science.1064082
Blunden, J., & Arndt, D. S. (2017). State of the Climate in 2016. Bull. Amer. Meteor. Soc., 98(8), Si-S277.
https://doi.org/10.1175/2017BAMSStateoftheClimate.1
APPENDIX 4.4.2-1 Climate Change Discussion
24
Canadian Climate Change Scenarios Network. (2009). Canadian Climate Change Scenarios Network Workshop,
Environment Canada, Toronto, Ontario.
Environment Canada. (2015). Historical Climate Data. Retrieved from http://climate.weather.gc.ca
Forster, P., & Ramaswamy, V. (2007). Changes in Atmospheric Constituents and in Radiative Forcing. The
Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change. Retrieved from https://www.ipcc.ch/pdf/assessment-
report/ar4/wg1/ar4-wg1-chapter2.pdf
Lemmen, D. S., Warren, F. J., & Lacroix, J. (2008). From Impacts to Adaptation: Canada in a Changing Climate
2007. (E. Bush, Ed.). Climate Change Impacts and Adaptation Division, Ottawa, ON, Canada.
Lemmen, D. S., Warren, F. J., Lacroix, J., & Bush, E. (2007). From Impacts to Adaptation: Canada in a Changing
Climate. Government of Canada.
Moore, L. R., Rocap, G., & Chisholm, S. W. (1998). Physiology and molecular phylogeny of coexisting
Prochlorococcus Ecotypes. Nature, 393. Retrieved from
http://www.soest.hawaii.edu/oceanography/courses/OCN626/moore%20et%20al.pdf
Murdock, T. Q., & Spittlehouse, D. L. (2011, December 23). Selecting and Using Climate Change Scenarios for
British Columbia. Retrieved from
https://www.pacificclimate.org/sites/default/files/publications/Murdock.ScenariosGuidance.Dec2011.pdf
Nakićenović, N., & Swart, R. (2000). Special Report of Emissions Scenarios, A Special Report of Working Group
III of the Intergovernmental Panel on Climate Change. Retrieved from https://www.ipcc.ch/pdf/special-
reports/emissions_scenarios.pdf
PCIC. (2015). Regional Analysis Tool. Retrieved from http://www.pacificclimate.org/analysis-tools/regional-
analysis-tool. Accessed October 13, 2015.
Robock, A., Marquardt, A., & Kravitz, B. (2009). Benefits, Risks, and Costs of Stratospheric Geoengineering.
Geophysical Research Letters, 36. https://doi.org/10.1029/2009GL039209
Salmi, T., Määttä , A., Anttila, P., Ruoho-Airola, T., & Amnell, T. (2002). Detecting Trends of Annual Values of
Atmospheric Pollutants by the Mann-Kendall Test and Sen’s Slope Estimates – The Excel Template
Application MakeSens. Publications on Air Quality, (31).
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., & Tignor, M. (2007). Contribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
(H. L. Miller, Ed.). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Thomson, R. E., Bornhold, B. D., & Mazzotti, S. (2008). An Examination of the Factors Affecting Relative and
Absolute Sea Level in Coastal British Columbia (p. 49). Canadian Technical Report of Hydrography and
Ocean Sciences 260.
Warren, F. J., & Lemmen, D. S. (2014). Canada in a Changing Climate: Sector Perspectives on Impacts and
Adaptation. Government of Canada.
o:\final\2013\1422\13-1422-0049\1314220049-162-a-rev2\1314220049-162-a-rev2-appendix 4.4.2-1 climate change 20mar_19.docx
APPENDIX 4.4.2-1 Climate Change Discussion
ATTACHMENT 1
Historical Climate Analysis
ATTACHMENT 1 Historical Climate Analysis
1
1.0 HISTORICAL CLIMATE TRENDS Historical changes in climate have been described as the trend in the observed data from Vancouver Intl A
climate station (ID 1108447) between 1981 and 2010. There is approximately 0.3% of the temperature data and
0.2% of the precipitation data missing from this station for this period. All years all have less than 10% of data
missing. This is the data used to define the climate normal, which represents the expected climate for the Project
area.
The historical trend is the slope of a regression line fit to the historical data. In addition to having a slope, each
regression line has a level of statistical significance. The statistical significance of a trend line indicates whether a
trend is robust or not. Typically, trends that are not statistically significant are ignored because it is not possible to
know whether it is an upward or downward trend. The level of statistical significance is expressed as a degree of
confidence in percentiles. Usually, a trend that has a statistical significance of less than the 90th percentile is not
considered to be a statistically significant trend.
Figure A-4.4.2A-1 presents the historical data and trends. The graph shows the variation in year to year
observations, along with the climate normal (i.e., the average of the 30 years of observations, and the trend
derived from the observed data. In the figure shown, no trend is apparent for the average annual temperature.
Figure A-4.4.2A-1: Historical Temperature Analysis for Vancouver Intl A Climate Station - Annual
Similar figures are shown below for the remaining climate factors discussed in Section 4.4.2 (Greenhouse Gas
Management) of the Application, and also provide a listing of the statistical significance of the climate factors
presented.
ATTACHMENT 1 Historical Climate Analysis
2
Figure A-4.4.2A-2: Historical Temperature Analysis for Vancouver Intl A Climate Station – Spring
Figure A-4.4.2A-3: Historical Temperature Analysis for Vancouver Intl A Climate Station – Summer
ATTACHMENT 1 Historical Climate Analysis
3
Figure A-4.4.2A-4: Historical Temperature Analysis for Vancouver Intl A Climate Station – Fall
Figure A-4.4.2A-5: Historical Temperature Analysis for Vancouver Intl A Climate Station – Winter
ATTACHMENT 1 Historical Climate Analysis
4
Figure A-4.4.2A-6: Historical Temperature Analysis for Vancouver Intl A Climate Station – Number of Periods of More Than 3 Days with Maximum Temperature Above 30°C (Heat Waves)
Figure A-4.4.2A-7: Historical Temperature Analysis for Vancouver Intl A Climate Station – Length of Heat Waves
ATTACHMENT 1 Historical Climate Analysis
5
Figure A-4.4.2A-8: Historical Temperature Analysis for Vancouver Intl A Climate Station – Maximum Daily Temperature
Figure A-4.4.2A-9: Historical Temperature Analysis for Vancouver Intl A Climate Station – Number of Days with a Freeze-Thaw Cycle
ATTACHMENT 1 Historical Climate Analysis
6
Figure A-4.4.2A-10: Historical Temperature Analysis for Vancouver Intl A Climate Station – Number of Periods of More Than 3 Days with Minimum Temperature Below -15°C (Cold Spells)
Figure A-4.4.2A-11: Historical Precipitation Analysis for Vancouver Intl A Climate Station – Annual
ATTACHMENT 1 Historical Climate Analysis
7
Figure A-4.4.2A-12: Historical Precipitation Analysis for Vancouver Intl A Climate Station – Spring
Figure A-4.4.2A-13: Historical Precipitation Analysis for Vancouver Intl A Climate Station – Summer
ATTACHMENT 1 Historical Climate Analysis
8
Figure A-4.4.2A-14: Historical Precipitation Analysis for Vancouver Intl A Climate Station – Fall
Figure A-4.4.2A-15: Historical Precipitation Analysis for Vancouver Intl A Climate Station – Winter
ATTACHMENT 1 Historical Climate Analysis
9
Figure A-4.4.2A-16: Historical Precipitation Analysis for Vancouver Intl A Climate Station – Number of Periods of More Than 10 days With No Rain (Dry Spells)
Figure A-4.4.2A- 17 Historical Precipitation Analysis for Vancouver Intl A Climate Station – Length of Dry Spells
ATTACHMENT 1 Historical Climate Analysis
10
2.0 DEFINITION OF CLIMATE INDICES Table A-4.4.2A-1 defines how each of the climate indices was calculated.
Table A-4.4.2A-1 Definitions of Climate Indices
Climate Indices Definition
Total Precipitation Calculated as the sum of all the observed precipitation during the
selected annual period. Each annual value is averaged over the
30 years of the climate normal.
Seasonal Precipitation (Spring, Summer,
Fall, Winter)
Calculated as the sum of all the observed precipitation during the
selected season. Each annual value is averaged over the 30
years of the climate normal.
Number of Annual Dry Spells A dry spell is defined as a period of more than ten contiguous
days with no rain. This climate index counts the number of dry
spells during each annual period. Each annual value is averaged
over the 30 years of the climate normal.
Length of Dry Spells Calculated as the maximum length of all dry spells during the
selected annual period and then averages over the 30 years of
the climate normal.
Average Annual Temperature Calculated as the average of all the observed temperatures
during the selected annual period. Each annual value is
averaged over the 30 years of the climate normal.
Seasonal Temperature (Spring, Summer,
Fall, Winter)
Calculated as the average of all the observed temperatures
during the selected seasonal period. Each annual value is
averaged over the 30 years of the climate normal.
Number of Annual Heat Waves A heat wave is defined as a period of more than three contiguous
days with maximum temperatures above 40°C. This climate
index counts the number of heat waves during each annual
period. Each annual value is averaged over the 30 years of the
climate normal.
Length of Heat Waves Calculated as the maximum length of all heat waves during the
selected annual period and then averaged over the 30 years of
the climate normal.
Maximum Daily Temperature Calculated as the maximum of all daily maximum temperatures
during the selected annual period and then averaged over the 30
years of the climate normal.
ATTACHMENT 1 Historical Climate Analysis
11
Climate Indices Definition
Number of Days with Freeze-Thaw Cycle A freeze-thaw cycle is defined as a day where the minimum daily
temperature is less than 0°C and the maximum daily temperature
is greater than 4°C. The climate index counts the number of
freeze-thaw cycles during each annual period. Each annual
value is averaged over the 30 years of the climate normal.
Number of Annual Cold Spells A cold spell is defined as a period of more than three contiguous
days with minimum temperatures below -15°C. This climate
index counts the number of cold spells during each annual
period. Each annual value is averaged over the 30 years of the
climate normal.
Length of Cold Spells Calculated as the maximum length of all cold spells during the
selected annual period and then averaged over the 30 years of
the climate normal.
Note: No length of cold spells are calculated as there are no annual cold spells for Vancouver Intl A climate station (ID 1108447) between 1981 and 2010.