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APPENDIX 4.4.2-1

Climate Change Discussion

APPENDIX 4.4.2-1 Climate Change Discussion

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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.


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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.


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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


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


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


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.


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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


climate normal.

Length of Cold Spells Calculated as the maximum length of all cold spells during the


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.


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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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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


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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 [#]


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)


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


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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


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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


11

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.


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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.


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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


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.


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.


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.


17


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.


18


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).


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


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


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


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.


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


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


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.


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


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


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


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


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


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


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


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


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


Total Precipitation Calculated as the sum of all the observed precipitation during the

selected annual period. Each annual value is averaged over the


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


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


Seasonal Temperature (Spring, Summer,

Fall, Winter)

Calculated as the average of all the observed temperatures

during the selected seasonal period. Each annual value is


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


climate normal.

Length of Heat Waves Calculated as the maximum length of all heat waves during the


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



11


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


climate normal.

Length of Cold Spells Calculated as the maximum length of all cold spells during the


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.

appendix 4.4.2-1 climate change discussion€¦ · appendix 4.4.2-1 climate change discussion 4...

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