Data Sources and Methodology

Climate Data

Geospatial data referencing monthly maximum 1-day precipitation, days with Tmax more than 30, and freeze thaw days are sourced from ClimateData.ca. The following parameters were used to select the information available on Climate Insight (all information is for illustrative purposes for a comprehensive review of climate data for your community, we encourage users to visit www.climatedata.ca):

Emission Scenario: High emission scenario (RCP 8.5)

This scenario assumes that greenhouse gas concentrations will continue to increase at approximately the same rate as they are increasing today. Under this scenario, the planet’s radiative forcing will have increased by 8.5 W/m2 by the year 2100, relative to 1750 (and continues to rise well after 2100). In the scientific literature, this scenario is referred to as “RCP8.5.” Of the four greenhouse gas pathways (RCP8.5, RCP6.0, RCP4.5, RCP2.6) used by the IPCC for its 5th Assessment Report, this pathway results in the most severe global warming and climate change.

Time Horizon

A future time period of interest over which the outputs of climate simulations are examined or for which future scenarios are produced. The climate science community tends to converge on common time horizons that are recommended by the World Meteorological Organization (WMO). The horizons typically encompass a 30- or 20- year period. For use on Climate Insight, three time horizons are available: 1971- 2001 (Baseline), 2041 – 2070 (Mid-century), and 2071 – 2100 (End of century).

Percentiles

Refers to the percentiles of the data being shown. The default options for percentiles shown on ClimateData.ca are the 10th, 50th and 90th percentiles. For Climate Insight, data is shown representing the 50th percentile value. This represents the 50th percentile value, also known as the median value. This represents the value where half of the model results are below this, and half are above this value. (Models is a term used to refer to the complete set of climate simulations or climate scenarios used for a given study. Because no one model can be considered best, it is standard practice in climate change studies to use the outputs of many models when studying the projected changes.)

Variable

The term climate variable is used to refer to a variable that can be measured directly in the field (at meteorological stations for example) or that is calculated by climate models. The three variables included on Climate Insight (as proxy information for climate hazards) include:

• Days with Maximum Temperature more than 30°C
  Maximum temperature describes the warmest temperature of the 24-hour day. Typically, but not always, the maximum temperatures occur during the day and so this variable is commonly referred to as the daytime high.
  The average highest temperature is an environmental indicator with many applications in agriculture, engineering, health, energy management, recreation, and more.
• Maximum 1-Day Precipitation
  Maximum 1-Day Total Precipitation describes the largest amount of precipitation (rain and snow combined) that falls within a single 24-hour day for the selected time period. This index is commonly referred to as the wettest day of the year.
• Freeze Thaw Days
  Freeze Thaw Days is a simple count of the days when the air temperature fluctuates between freezing and non-freezing temperatures on the same day. Freeze-thaw cycles can have major impacts on infrastructure. Water expands when it freezes, so the freezing, melting and re-freezing of water can, over time, cause significant damage to roads, sidewalks, and other outdoor structures.

Infrastructure Data

Geospatial layers showing infrastructure data, including public transit stops, bridges and tunnels, potable water, pedestrian and cycling, solid waste, wastewater, and stormwater is sourced from Statistics Canada’s Open Database of Infrastructure (ODI). As noted on the ODI website, the data for the current version (version 1.0) was collected between November 2022 and March 2023 from government open data sites. The ODI may not be an exhaustive dataset. For information on their methodology, visit:

https://www150.statcan.gc.ca/n1/pub/34-26-0003/342600032023001-eng.htm

The ODI infrastructure layers currently have gaps in the included infrastructure and associated metadata, and are provided, as is, so they can be used in locations where data is included. Gaps should not be taken to imply that the related infrastructure does not exist.

Equity Data

Geospatial layers labeled as the Social Vulnerability Index are representing the Canadian Index of Multiple Deprivation (CIMD). This index consists of variables such as income, newcomer status, age, home ownership, etc. Climate Insight uses this information, in tandem with climate data to illustrate areas with existing vulnerabilities which could be made worse by climate change. Please note that the SVI layers are static in time based on 2021 Canadian Census data; only climate layers change with time. For information on how this index was calculated, visit:

https://www150.statcan.gc.ca/n1/pub/45-20-0001/452000012023002-eng.htm

The Social Vulnerability Index layer, representing CIMD, currently has gaps and is provided, as is, so it can be used in locations where data exists. Gaps should not be taken to imply that no vulnerabilities exist in that area.

Combined Climate and Equity layers

Geospatial data combining the CIMD data with climate data (monthly maximum 1-day precipitation, days with Tmax more than 30, freeze thaw days) show the impact of climate parameters on communities already at risk. For information on how bivariate relationships are represented in ArcGIS, visit:

https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/bivariate-colors.htm

For more detailed information on the Map data and methodology please access Climate Insight Spatial Data Methodology