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Climate Data and Scenarios for Canada: Synthesis of Recent Observation and Modelling Results

1. Introduction

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Many aspects of Canada’s infrastructure, economy, and ecology are directly affected by climate variability and change. Observations provide information about historical climate and therefore the ‘baseline’ against which future change is compared. Future climate change information, needed to assess future impacts, plan adaptation measures, and develop mitigation policy, cannot be reliably obtained by extrapolation of observed historical changes. Quantitative longer-term applications of climate information require model-based projections driven by a range of greenhouse gas emission scenarios. This document provides a brief overview of the most up-to-date analysis of historical climate observations and future climate projections focusing specifically on Canada. The information presented here builds upon, and is fully consistent with, the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) Working Group I (IPCC, 2013). The current document is intended as a resource for dissemination of climate information with a specific focus on historical and future climate change across Canada. It is not intended to serve as a definitive reference or complete characterization, and readers are directed to the underlying data sources for more detailed and quantitative analyses specific to their climate impact, adaptation, or environmental assessment context.

Given the range of natural climate variability and uncertainties regarding future greenhouse gas emission pathways and climate response, changes projected by one climate model, or one individual emission scenario, should not be used in isolation. Rather, it is good practice to consider a range of projections from multiple climate models (ensembles) and emission scenarios. Although this does not allow one to estimate the probability of a particular climate change scenario, it does convey to users some of the uncertainties involved.

Along the same lines, one should not rely on an individual study or publication to inform on the potential impacts of climate change in Canada. Rather, it is the synthesis of information from a range of valid sources that forms the foundation for understanding climate change and quantitative impact assessment. Information presented in this document is based upon the peer reviewed scientific literature and major climate assessments available to date. The underlying data is publicly available and sources are noted.

Additional information on the use of climate scenarios has been produced for the Canadian adaptation community by the Ouranos Consortium on Regional Climatology and Adaptation to Climate Change (Charron, 2014). This publication may be valuable to those looking for further technical details and guidance on the use of climate scenarios.

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2. Historical climate change and variability in Canada

Climate everywhere varies from season to season, year to year, and decade to decade. This is a natural consequence of the complex interactions between processes in the atmosphere, ocean, and on land. Superimposed on this natural variability is the long-term shift or change in the mean state of the climate (what is commonly referred to as “climate change”). Long-term climate change is driven by both natural and human-caused, or anthropogenic, factors. The key anthropogenic contributors to long-term climate change are changes in atmospheric greenhouse gas concentrations and aerosol loadings. The Earth’s climate has experienced long-term changes in the past. However, it is “extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century” (IPCC, 2013).

Averaged globally, temperature has increased by approximately 0.85°C, over the period 1880 to 2012 (IPCC, 2013), although the warming has not been uniform in time or in space. Of particular note is that warming has been greater over high latitudes including Canada and Eurasia. Globally, as climate has warmed, extreme temperatures have also changed with increases in the frequency of hot days and heat waves and decreases in cold days (IPCC, 2013).

Because of natural variations on different time scales, historical changes in the climate need to be assessed over a long period of time. Changes in measurement techniques and instruments, in observing procedures, and in siting of the instruments do occur from time to time and can be reflected in the original climate records. As a result, the proper characterization of past climate change requires the use of homogenized climate data which have been adjusted to address artificial discontinuities which may be present in original historical records. Homogenized climate data sets account for possible artificial shifts imposed by non-climatic factors. For Canada, the adjusted data for some climate variables, including temperature and precipitation, are updated annually and are available publicly:

Adjusted and Homogenized Canadian Climate Data (AHCCD) for daily and monthly temperature and precipitation
Canadian Blended Precipitation, version 0 (CanBPv0)
Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) at 50 km resolution

Additionally, Environment Canada’s Climate Trends and Variations Bulletin (CTVB) summarizes recent Canadian climate data and presents it in a historical context. The CTVB makes use of the adjusted and homogenized Canadian climate datasets to present seasonal, annual, and long-term temperature and precipitation trends on the national and regional scales. The CTVB can be accessed from the climate trends and variations section of Environment Canada’s website.

In Canada, sufficient observations to generate national temperature estimates are available from 1948 onward, and a summary is shown in Figures 1 and 2. These results, when compared with global temperature trends calculated over the same time period, indicate that the rate of warming in Canada as a whole has been more than double that of the global mean, and that warming in northern Canada (i.e., north of 60°N) has been roughly three times the global mean. Longer term trends are available for some locations, especially for southern Canada, with data records extending back more than 100 years.

Figure 1

Figure 1 – Annual mean temperature anomalies and linear trends for the globe, all of Canada, southern Canada (i.e., south of 60°N), and northern Canada (i.e., north of 60°N) over the period 1948–2013 (relative to the 1961–1990 average). See inset for colour scheme. Global temperature anomalies were computed using HadCRUTv4. Canadian mean temperatures were computed using the CANGRD data set (updated from Zhang et al., 2000), which is based on homogenized temperature data from 338 stations in Canada.

Long description of Figure 1

This time series shows the mean temperature change over time for four different regions: Global (HadCRUT4), Canada, Southern Canada (south of 60°N), and Northern Canada (north of 60°N). All four series of lines show an increase in temperature over the period 1948–2013. Globally, the linear trend is +0.7°C. Canada as a whole has a linear trend of +1.6°C. Southern Canada has a linear trend of +1.3°C. Northern Canada has a linear trend of +2.2°C

Figure 2

Figure 2 – Linear trends in annual mean temperatures (°C) in Canada over the period 1948–2013, as computed from CANGRD data (updated from Zhang, et al., 2000). Note that the northern region has lower station density and as such higher uncertainty in gridded temperature anomalies.

Long description of Figure 2

This map of Canada shows the spatial trend in mean temperature over the 1948–2013 period. All areas of the country show some warming over this period of time, with the greatest warming in the North (2.5°C to 3.0°C range). The area with the least amount of warming is over Newfoundland and Labrador where much of the area shows temperature change that is not statistically significant.

To illustrate long-term changes in temperature at the local level, Table 1 provides estimates of linear trends in annual, summer, and winter mean temperatures for the 1900–2013 period for 16 selected Canadian cities where sufficient data is available (data is available from 1942, 1942, and 1946 for Whitehorse, Yellowknife, and Iqaluit, respectively, and trends for these cities are calculated accordingly). The cities were selected to include Canada’s three largest cities, the national capital, and all provincial and territorial capitals.

Table 1: Trends in annual, summer (June, July, August), and winter (December, January, February) mean temperatures for 16 selected Canadian cities. Trends are calculated over the 1900–2013 period (in °C/century), except for territorial capitals where the data record is shorter (see “Calculated Trend Period” column). Trends are computed from the homogenized monthly temperature dataset, but are not corrected to remove the effects of urbanization.
Canadian CityCalculated Trend PeriodAnnual Temp. Trend (°C / century)Summer (JJA) Temp. Trend (°C / century)Winter (DJF) Temp. Trend (°C / century)
Charlottetown, PE1900–20130.50.31.0
Edmonton, AB1900–20132.02.33.1
Fredericton, NB1900–20131.41.42.0
Halifax, NS1900–20131.21.61.4
Iqaluit, NU1946–20131.31.12.9
Montreal, QC1900–20132.01.42.7
Ottawa, ON1900–20131.71.02.6
Quebec City, QC1900–20130.60.01.1
Regina, SK1900-20131.91.53.1
St. John’s, NL1900–20130.61.20.9
Toronto, ON1900–20131.81.82.2
Vancouver, BC1900–20131.52.01.4
Victoria, BC1900–20130.60.61.1
Whitehorse1940-20132.10.26.0
Winnipeg, MB1900–20131.00.81.5
Yellowknife, NT1942–20134.02.27.4

Precipitation totals have also changed in Canada as illustrated in Figure 3, with most of the country (particularly the North) having experienced an increase in precipitation over the past century. There are regional exceptions however, such as the lack of significant change over the southern Prairies and northeastern Ontario. Seasonally, total precipitation has increased mainly in the north. In winter, decreasing trends are dominant in the southwestern part of the country (British Columbia, Alberta, and Saskatchewan). There is less evidence of significant changes in the south during spring, summer, and autumn. It should be noted that changes in annual precipitation do not directly relate to changes in water availability, particularly in critical summer periods (e.g., an increase in precipitation does not necessarily translate directly to an increase in water availability, as other factors are also involved).

Figure 3

Figure 3 - Linear trends in annual total precipitation (expressed as percent change relative to the 1961–1990 climatology) for the period 1948–2012 for all of Canada (upper left) and for the period 1900–2012 for southern Canada (lower left). Trends are computed based on CANGRD datasets (updated from Zhang, et al., 2000). Note that the northern region has lower station density and as such higher uncertainty in gridded precipitation anomalies. Also note that precipitation climatology in the north is much smaller than in the south (i.e., the north receives much less precipitation, on average, than the south). As such, a large percentage increase in the north may only represent a small change in total precipitation amounts. The right panels show time series and their 11-year moving averages for Canada (upper right) and for southern Canada (lower right).

Long description of Figure 3

This figure contains four images (two maps and two graphs). The upper left panel shows the annual total precipitation trends map of Canada for the period 1948–2012. The Arctic Archipelago shows the greatest increase in total precipitation over the period. Only one small area in northern Ontario shows a slight decrease in total precipitation. Most of the change in precipitation from Saskatchewan to British Columbia was not statistically significant. The upper right panel shows the graph of annual total precipitation anomalies for Canada over the period 1948–2012. The start of the record is consistently drier than average. By the early 1970s, the anomalies are consistently wetter than average. The lower left panel shows the annual total precipitation trends map for the period 1900–2012, for southern Canada. The map indicates that the long-term trends for most of southern Canada have been towards more total precipitation, with only areas over Alberta and southern Saskatchewan, and a small area in northern Ontario showing no statistically significant trends. The lower right panel shows the graph of annual total precipitation anomalies for the period 1900–2012 for southern Canada. The graph shows strong drier than average conditions in the beginning of the record. By the mid-1960s, the annual values fluctuate around average, and only start to be consistently above average in the early 2000s.

The role of anthropogenic forcing in observed warming at global and continental scales has been a subject of intense study for many years. The most recent findings indicate that “it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century”, that “it is now very likely that human influence has contributed to observed global scale changes in the frequency and intensity of daily temperature extremes since the mid-20th century”, and that there is medium confidence that “anthropogenic influences have contributed to… intensification of heavy precipitation over land regions where data are sufficient” (IPCC, 2013).

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3. Future climate

The climate of the future will continue to experience natural variability, much as it has in the past. However, the background change in mean climate, already being driven by human activities, will continue at a rate that is determined primarily by current and future emissions of greenhouse gases and aerosols. Because future emissions are difficult to predict, it is necessary to use plausible scenarios, ranging from low to high emission pathways, to project future climate change. Global Earth System Models--which produce comprehensive computer simulations of the global climate system and the related carbon-cycle processes (see: Flato, 2011)--provide scientifically-based tools to make projections of future climate by simulating the response to atmospheric greenhouse gases and aerosols, land-use change, and other external forcings. Owing to uncertainties in the detailed representation of many complex climate processes, individual Earth System Models vary in their representation of these processes and will have biases of various kinds. Because of this, it is preferable to make use of a multi-model ensemble of projections for many applications. The average of a multi-model ensemble generally produces smaller historical errors than any individual model (Flato, et al., 2013) and the spread amongst models allows some quantification of uncertainty. The World Climate Research Programme,Footnote 1 (WCRP) coordinates multi-model climate projections via its Working Group on Coupled Modelling (WGCM) and the Coupled Model Intercomparison Project (CMIPFootnote 2). The results presented in the following sections are based on the CMIP5 results that were also featured in the Working Group I contribution to the IPCC Fifth Assessment Report (IPCC, 2013: see chapters 9, 11, and 12, and Annex I).

The CMIP5 projections make use of Representative Concentration Pathways (RCPs), which are designed to provide plausible future scenarios of anthropogenic forcing spanning a range from a low emission scenario characterized by active mitigation (RCP 2.6), through two intermediate scenarios (RCP 4.5 and RCP6), to a high emission scenario (RCP 8.5).Footnote 3 Figure 4 illustrates some of the assumptions underlying these scenarios. These scenarios make use of various combinations of projected population growth, economic activity, energy intensity, and socio-economic development. These, in turn, lead to calculations of energy consumption and related emissions and finally atmospheric concentrations of greenhouse gases and other climate forcings. These RCP scenarios serve as input to the Earth System Models, which simulate the climate system response and resulting climate conditions.

Figure 4

Figure 4 – Socioeconomic (top row), energy intensity (second row), greenhouse gas emission (third row), and ultimately greenhouse gas concentration (bottom row) assumptions underlying the representative concentration pathways (RCPs) used to drive future climate projections. From van Vuuren, et al., 2011, reproduced with permission.

Long description of Figure 4

This figure consists of 10 panels each depicting a graph showing projections from 2000 to 2100 showing how the projections are built up from socioeconomic to greenhouse gas concentrations. Each graph consists of four solid lines representing the four different forcing scenarios, known as Representative Concentration Pathways, used in the IPCC Fifth Assessment Report (RCP2.6, RCP4.5, RCP6, and RCP8.5). The top line of two panels represents socioeconomic variables. The upper left panel depicts population (in millions), starting at 6000 in 2000. RCP8.5 shows the greatest increase by the year 2100, from 6000 to 12000, and RCP4.5 shows the least growth from 6000 to about 8500. The upper right panel represents GDP ($2000), starting at 35 in the year 2000 for all RCPs, increasing to 300 for RCP2.6 (which is on the high end of the scale for the four RCPs), and about 150 for RCP6 (which is on the low side). The second row consists of two panels and shows projections of energy intensities, which are derived from the projections shown in the top row. The left panel in the second row represents primary energy consumption (EJ), starting around 400 in the year 2000, and increasing to 1750 in 2100 for RCP8.5 representing the high end of change, and about 750 for RCP6 on the low end of change. The right graph on the second row represents oil consumption (EJ), starting at 150 in 2000, peaking at 350 around 2070 then falling back to 150 by 2100 for RCP8.5, whereas the RCP2.6 trends fall throughout the period to around 50 by 2100. The third row consists of three panels showing greenhouse gas emissions projections, which are derived from the projections in the second row. The first panel shows CO2 emissions (GtC), which starts at 7.5 for all RCPs in 2000. The high end of the scale shows an increase to around 27.5 by 2100 for RCP8.5, and drops to 0 by 2080 for RCP2.6 on the low end of the scale. The second panel shows CH4 concentrations (TgCH4), starting at around 310 for all RCPs in 2000 and increasing to around 900 by 2100 for RCP8.5 on the high end of the scale, and around 130 for RCP2.6 on the low side. The third panel shows N2O emissions (TgN) starting around 7.5 in 2000 for all RCPs, and increasing to 15 for RCP8.5 by 2100, and decreasing to 5 for RCP2.6. The fourth row consists of three panels and represents the projected greenhouse gas concentrations, which are derived from the projections in the previous row. The first panel represents CO2 concentration (ppm), starting at around 370 in 2000 for all RCPs, and increasing to around 950 by 2100 for RCP8.5 (at the high end), or leveling out to around 400 for RCP2.6 by 2100 (at the low end). The second panel is CH4 concentration (ppb), starting around 1750 in 2000 for all RCPs, increasing to around 4000 by 2100 for RCP8.5, and decreasing to 1250 by 2100 for RCP2.6. The third panel represents the N2O concentration (ppb), starting at around 320 in 2000 for all RCPs, and increasing to 450 for RCP8.5 in 2100, or leveling off at 325 for RCP2.6 by 2100.

A new feature of the IPCC Fifth Assessment Report (AR5) is the Atlas of Global and Regional Climate Projections (Annex 1--IPCC, 2013), which provides a synthesis of results from the CMIP5 multi-model ensemble. For application to Canadian impact studies and adaptation planning, the regional boundaries of the Atlas are less than optimal: western Canada is combined with the western United States and Alaska, and eastern Canada is combined with Greenland and Iceland (but separated from western Canada). We have therefore generated multi-model ensemble results specific to Canada, using output from 29 CMIP5 models from which results were available for historical simulations, RCP2.6, RCP4.5, and RCP8.5 (results for RCP6.0 are also available, but from fewer models; so this scenario is not illustrated here). Further details on the models used in this document are presented in Table 2.

The ensemble climate model results include output representing a broad range of climate variables. For example, model output includes temperature, precipitation, snow depth, ocean pH and salinity, soil moisture, downwelling solar radiation, and many other quantities. As an example, a full listing of results from the Canadian model (CanESM2) is available. In this document we focus on temperature and precipitation changes in the Canadian context.

Table 2: Information on the CMIP5 models whose results were used to produce the climate scenario Figures 5–10.
Model NamePlace of OriginInstitution
BCC-CSM1-1ChinaBeijing Climate Centre, China Meteorological Administration
BCC-CSM1-1-mChinaBeijing Climate Centre, China Meteorological Administration
BNU-ESMChinaBeijing Normal University
CanESM2CanadaCanadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada
CCSM4USANational Centre for Atmospheric Research
CESM1-CAM5USANational Centre for Atmospheric Research
CESM1-WACCMUSANational Centre for Atmospheric Research
CNRM-CM5FranceCentre National de Recherches Météorologiques and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique
CSIRO-Mk3.6.0AustraliaQueensland Climate Change Centre of Excellence and Commonwealth Scientific and Industrial Research Organisation
EC-EarthEuropeA consortium of European institutions
FGOALS-g2ChinaState Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics
FIO-ESMChinaFirst Institute of Oceanography, State Oceanographic Administration
GFDL-CM3USANOAA Geophysical Fluid Dynamics Laboratory
GFDL-ESM2GUSANOAA Geophysical Fluid Dynamics Laboratory
GFDL-ESM2MUSANOAA Geophysical Fluid Dynamics Laboratory
GISS-E2-HUSANASA Goddard Institute for Space Studies
GISS-E2-RUSANASA Goddard Institute for Space Studies
HadGEM2-AOUKUK Met Office Hadley Centre
HadGEM2-ESUKUK Met Office Hadley Centre
IPSL-CM5A-LRFranceInstitut Pierre Simon Laplace
IPSL-CM5A-MRFranceInstitut Pierre Simon Laplace
MIROC-ESMJapanUniversity of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology
MIROC-ESM-CHEMJapanUniversity of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology
MIROC5JapanUniversity of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology
MPI-ESM-LRGermanyMax Planck Institute for Meteorology
MPI-ESM-MRGermanyMax Planck Institute for Meteorology
MRI-CGCM3JapanMeteorological Research Institute
NorESM1-MNorwayNorwegian Climate Centre
NorESM1-MENorwayNorwegian Climate Centre

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3.1 Temperature scenarios

In the following sections, multi-model climate change projections (relative to the 1986–2005 reference period) are shown for Canada. The format of the figures presented here is as consistent as possible with the analogous figures in the IPCC AR5 Atlas (IPCC, 2013--Annex I), referred to earlier, so as to allow direct comparison.

Time series of temperature anomalies, averaged over Canada covering the historical period (as simulated by the CMIP5 models) and the future (to year 2100), are shown in Figure 5. Results for three future forcing scenarios, RCP2.6, RCP4.5, and RCP8.5 are provided. The individual thin lines are the results of the individual models listed in Table 2 and the heavy line represents the multi-model ensemble average. Temperature anomaly is defined as the temperature relative to the 1986–2005 reference period. The range of values, quantified by the box and whisker plots to the right of each panel, results from both natural climate variability (as simulated by the models) and the differences in the detailed representation of physical processes in each model. As can be seen by comparing these plots to the global mean plots in the IPCC Atlas (IPCC, 2013–Annex I, pp. 1318–1319), the historical and projected changes for Canada are considerably larger (roughly 50 %) than for the global land area.
Figure 5

Figure 5 – Time series of historical and projected temperature change for the December, January, and February (left) and the June, July, and August (right) averages, as simulated by the CMIP5 multi-model ensemble. As in Annex I of the IPCC AR5 (IPCC, 2013), the individual curves represent the simulation results for individual models, while the heavy lines indicate the ensemble average. Results are shown for Canadian land areas only. Change is computed relative to the 1986–2005 period. The spread amongst models, evident in the thin curves, is quantified by the box and whisker plots to the right of each panel. They show, for the 2081–2100 period, the 5th, 25th, 50th (median), 75th, and 95th percentile values.

Long description of Figure 5

This figure has two graphs. The left graph represents a time series of historic and projected temperature change for winter (December, January, and February) as simulated by CMIP5 multi-model ensemble for grids covering Canada. A solid line represents the historic change from 1900 to 2005 and shows an increase from around -1.8°C to around 0°C. The RCP2.6 line then continues to increase until it reaches around 2°C in 2100. The RCP4.5 line continues on from 2005 to 2100 with an end point of around 4°C. The RCP8.5 projection shows a change of around 8.5°C by 2100. The right graph represents a time series of historic and projected temperature change for the summer (June, July, and August) as simulated by CMIP5 multi-model ensemble for grids covering Canada. A solid line represents the historic change from 1900 to 2005 and shows an increase from around -1°C to around 0°C. The RCP2.6 line then continues to increase until it reaches around 1.2°C by 2100. The RCP4.5 continues on from 2005 to 2100 with an end point of around 2.8°C. The RCP8.5 projection shows a change of around 6.3°C by 2100.

Even within Canada, climate change is not projected to be uniform, and so national average values may not be suitable for many applications. Figures 6 and 7 show maps of temperature change from the CMIP5 multi-model ensemble, based on the RCP4.5 scenario. Similar maps for the other RCP scenarios are available from the Canadian Climate Data and Scenarios website. RCP4.5 is used here for illustration purposes (as in the IPCC Atlas) and its use here does not imply that it is more probable than the other RCPs.
Figure 6

Figure 6 – Maps of winter temperature change projected by the CMIP5 multi-model ensemble for the RCP4.5 scenario, averaged over December–February. Change is computed relative to the 1986–2005 baseline period. As in the IPCC Atlas (IPCC, 2013), the top row shows results for the period 2016–2035, the middle row for 2046–2065, and the bottom row for 2081–2100. For each row the left panel shows the 25th percentile of simulated temperature change (25% of individual simulations show warming less than this), the middle panel the 50th percentile (median), and the right panel the 75th percentile. The color scale indicates temperature change in °C with positive change (warming) indicated by yellow through red colors and cooling by blue colors, consistent with the color scale used in the IPCC AR5 Annex I (IPCC, 2013).

Long description of Figure 6

This figure consists of 9 maps of Canada showing projected changes in temperature, driven by RCP4.5, for the winter (December, January, and February), organized in a 3 by 3 grid, with the 25th (left column), 50th (middle column) and 75th (right column) percentiles across the top of each column and the years 2016-2035 (first row), 2046-2065 (second row), and 2081-2100 (third row) down the side to indicate the years for each row. Each map shows an increase in temperature across the country, with the greatest warming in the north and over Hudson Bay. As the percentiles go up, the warming represented in the maps increases. The same is true for time, as the projection goes further into the future, the represented temperature increases, so the map in the top left (25th percentile for 2016-2035), shows the least amount of warming, whereas the bottom right map (75th percentile for 2081-2100), shows the greatest warming.

Figure 7

Figure 7 – Maps of summer temperature change projected by the CMIP5 multi-model ensemble for the RCP4.5 scenario, averaged over June–August. Change is computed relative to the 1986–2005 baseline period. As in the IPCC Atlas (IPCC, 2013), the top row shows results for the period 2016–2035, the middle row for 2046–2065, and the bottom row for 2081–2100. For each row the left panel shows the 25th percentile, the middle panel the 50th percentile (median), and the right panel the 75th percentile. The color scale indicates temperature change in °C with positive change (warming) indicated by yellow through red colors and cooling by blue colors, consistent with the color scale used in the IPCC AR5 Annex I (IPCC, 2013).

Long description of Figure 7

This figure consists of 9 maps of Canada showing projected changes in temperature, driven by RCP4.5, for the summer (June July, and August), organized in a 3 by 3 grid, with the 25th (left column), 50th (middle column) and 75th (right column) percentiles across the top of each column and the years 2016-2035 (first row), 2046-2065 (second row), and 2081-2100 (third row) down the side to indicate the years for each row. Each map shows an increase in temperature across the country, with the greatest warming in the south, and the least over the arctic. As the percentiles go up, the warming represented in the maps increases. The same is true for time, the further into the future the projection represents, the temperature change increases, so the map in the top left (25th percentile for 2016-2035), shows the least amount of warming, whereas the bottom right map (75th percentile for 2081-2100), shows the greatest warming.

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3.1.1 Summary tables for temperature

Tables 3 and 4 provide values averaged over Canada and over each province and territory for the 50th (median), 25th and 75th percentiles of temperature change for the three future periods illustrated in the previous figures. These tables also provide the corresponding projections under RCP2.6 and RCP8.5. As Figures 6 and 7 clearly show, projected temperature changes are not constant across a province or territory, but the tables are provided to inform province- or territory-wide assessment activities that may need area-averaged information.

Table 3: Summary information for projected winter temperature change (in °C, relative to the 1986–2005 baseline period), averaged over December–February for three future periods and three RCPs. The table shows values for the 50th percentile (a), 25th percentile (b), and 75th percentile (c).

RCP2.6
(a) 50th Percentile2016–20352046–20652081–2100
Canada1.42.22.4
Alberta1.21.92.2
British Columbia1.11.71.8
Manitoba1.52.42.7
New Brunswick1.11.72.1
Newfoundland and Labrador1.32.22.3
Northwest Territories2.12.83.1
Nova Scotia1.01.51.9
Nunavut1.93.13.0
Ontario1.42.22.4
Prince Edward Island1.11.72.1
Quebec1.62.52.7
Saskatchewan1.32.22.5
Yukon1.82.12.3
RCP4.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada1.53.24.2
Alberta1.22.93.6
British Columbia1.12.43.1
Manitoba1.73.64.8
New Brunswick1.32.73.5
Newfoundland and Labrador1.32.94.1
Northwest Territories1.94.35.4
Nova Scotia1.22.32.9
Nunavut2.04.45.9
Ontario1.63.24.4
Prince Edward Island1.32.73.4
Quebec1.63.44.8
Saskatchewan1.43.34.2
Yukon1.53.34.1
RCP8.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada1.84.48.2
Alberta1.83.76.9
British Columbia1.53.15.7
Manitoba2.25.09.5
New Brunswick1.43.66.4
Newfoundland and Labrador1.54.37.7
Northwest Territories2.26.112.3
Nova Scotia1.33.05.4
Nunavut2.26.512.9
Ontario1.94.68.2
Prince Edward Island1.43.46.0
Quebec1.85.29.1
Saskatchewan2.04.38.1
Yukon1.94.48.1
RCP2.6
(b) 25th Percentile2016–20352046–20652081–2100
Canada0.91.61.5
Alberta0.61.31.3
British Columbia0.51.11.2
Manitoba0.91.81.5
New Brunswick0.81.31.4
Newfoundland and Labrador0.81.41.3
Northwest Territories1.32.01.8
Nova Scotia0.71.11.0
Nunavut1.42.22.0
Ontario0.81.51.4
Prince Edward Island0.81.31.3
Quebec0.91.61.5
Saskatchewan0.71.61.5
Yukon0.81.31.6
RCP4.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada1.02.53.2
Alberta0.72.22.1
British Columbia0.51.71.9
Manitoba1.02.83.5
New Brunswick0.72.02.9
Newfoundland and Labrador0.82.23.1
Northwest Territories1.23.14.1
Nova Scotia0.71.72.4
Nunavut1.43.44.7
Ontario0.92.43.1
Prince Edward Island0.81.82.7
Quebec1.02.63.6
Saskatchewan0.92.62.8
Yukon0.72.02.8
RCP8.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada1.23.77.2
Alberta1.02.75.5
British Columbia0.92.24.5
Manitoba1.33.77.4
New Brunswick1.03.05.6
Newfoundland and Labrador1.03.46.5
Northwest Territories1.74.89.4
Nova Scotia0.92.54.7
Nunavut1.75.410.5
Ontario1.23.46.9
Prince Edward Island1.02.75.3
Quebec1.34.08.0
Saskatchewan1.43.16.5
Yukon1.33.26.1
RCP2.6
(c) 75th Percentile2016–20352046–20652081–2100
Canada2.13.13.4
Alberta1.92.82.8
British Columbia1.72.42.5
Manitoba2.13.33.4
New Brunswick1.42.32.7
Newfoundland and Labrador1.83.03.3
Northwest Territories2.73.84.1
Nova Scotia1.32.12.5
Nunavut2.74.24.6
Ontario1.92.83.0
Prince Edward Island1.62.52.9
Quebec2.13.43.8
Saskatchewan2.03.03.1
Yukon2.53.03.1
RCP4.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada2.24.35.7
Alberta1.93.94.7
British Columbia1.53.13.7
Manitoba2.44.46.1
New Brunswick1.83.34.3
Newfoundland and Labrador2.03.74.9
Northwest Territories2.75.57.4
Nova Scotia1.62.93.7
Nunavut2.85.77.9
Ontario2.04.15.3
Prince Edward Island1.83.24.1
Quebec2.24.66.1
Saskatchewan2.14.15.5
Yukon2.04.35.1
RCP8.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada2.45.710.8
Alberta2.34.87.8
British Columbia2.03.96.6
Manitoba2.76.110.9
New Brunswick1.94.47.3
Newfoundland and Labrador2.25.29.0
Northwest Territories3.17.314.4
Nova Scotia1.63.66.2
Nunavut3.07.716.1
Ontario2.25.49.7
Prince Edward Island1.84.16.7
Quebec2.56.110.8
Saskatchewan2.45.59.1
Yukon2.45.310.0

Table 4: Summary information for projected summer temperature change (in °C, relative to the 1986–2005 baseline period), averaged over June–August for three future periods and three RCPs. The table shows values for the 50th percentile (a), 25th percentile (b), and 75th percentile (c).

RCP2.6
(a) 50th Percentile2016–20352046–20652081–2100
Canada1.01.51.4
Alberta1.11.51.4
British Columbia1.11.51.5
Manitoba1.21.51.5
New Brunswick1.01.51.4
Newfoundland and Labrador0.81.21.2
Northwest Territories1.11.51.4
Nova Scotia0.91.51.3
Nunavut1.01.41.2
Ontario1.11.41.3
Prince Edward Island0.91.61.4
Quebec0.91.41.3
Saskatchewan1.21.51.5
Yukon1.11.41.3
RCP4.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada1.12.02.6
Alberta1.12.22.7
British Columbia1.22.22.7
Manitoba1.22.23.0
New Brunswick1.12.12.5
Newfoundland and Labrador0.81.72.2
Northwest Territories1.12.12.4
Nova Scotia1.01.92.4
Nunavut0.91.82.4
Ontario1.12.12.9
Prince Edward Island1.12.02.5
Quebec1.01.92.6
Saskatchewan1.22.32.8
Yukon1.11.92.4
RCP8.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada1.22.95.4
Alberta1.43.25.9
British Columbia1.33.05.6
Manitoba1.43.46.3
New Brunswick1.23.05.4
Newfoundland and Labrador1.02.54.6
Northwest Territories1.22.95.1
Nova Scotia1.12.74.9
Nunavut1.12.64.8
Ontario1.33.16.0
Prince Edward Island1.22.95.1
Quebec1.22.85.3
Saskatchewan1.43.46.3
Yukon1.12.64.9
RCP2.6
(b) 25th Percentile2016–20352046–20652081–2100
Canada0.70.90.8
Alberta0.80.90.8
British Columbia0.81.00.9
Manitoba0.81.00.9
New Brunswick0.81.00.8
Newfoundland and Labrador0.50.70.6
Northwest Territories0.70.90.9
Nova Scotia0.71.00.9
Nunavut0.50.70.6
Ontario0.81.00.9
Prince Edward Island0.71.00.8
Quebec0.60.90.8
Saskatchewan0.91.00.8
Yukon0.70.90.8
RCP4.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada0.71.41.7
Alberta0.91.51.8
British Columbia0.91.52.0
Manitoba0.91.71.9
New Brunswick0.71.52.0
Newfoundland and Labrador0.51.21.5
Northwest Territories0.81.41.7
Nova Scotia0.71.51.9
Nunavut0.51.11.3
Ontario0.81.61.8
Prince Edward Island0.71.62.0
Quebec0.61.41.7
Saskatchewan0.91.71.9
Yukon0.71.51.7
RCP8.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada0.92.24.2
Alberta1.12.34.4
British Columbia0.92.34.3
Manitoba1.02.74.9
New Brunswick1.02.44.4
Newfoundland and Labrador0.81.93.9
Northwest Territories0.92.14.0
Nova Scotia0.92.24.2
Nunavut0.61.83.4
Ontario1.02.64.7
Prince Edward Island1.02.44.2
Quebec0.92.24.1
Saskatchewan1.12.64.9
Yukon0.71.93.8
RCP2.6
(c) 75th Percentile2016–20352046–20652081–2100
Canada1.52.02.0
Alberta1.42.12.1
British Columbia1.42.12.2
Manitoba1.72.42.2
New Brunswick1.42.11.8
Newfoundland and Labrador1.21.81.7
Northwest Territories1.62.22.1
Nova Scotia1.21.81.8
Nunavut1.52.02.1
Ontario1.52.22.0
Prince Edward Island1.32.11.9
Quebec1.32.01.9
Saskatchewan1.62.32.2
Yukon1.52.02.0
RCP4.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada1.42.63.4
Alberta1.42.63.5
British Columbia1.52.83.6
Manitoba1.52.94.1
New Brunswick1.42.63.5
Newfoundland and Labrador1.32.33.0
Northwest Territories1.52.73.4
Nova Scotia1.52.43.2
Nunavut1.42.53.2
Ontario1.42.83.6
Prince Edward Island1.42.53.3
Quebec1.32.63.3
Saskatchewan1.52.73.9
Yukon1.52.63.4
RCP8.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada1.63.66.6
Alberta1.63.86.8
British Columbia1.63.86.8
Manitoba1.84.27.8
New Brunswick1.63.76.3
Newfoundland and Labrador1.33.25.9
Northwest Territories1.63.76.8
Nova Scotia1.53.45.9
Nunavut1.53.46.6
Ontario1.63.96.9
Prince Edward Island1.63.46.0
Quebec1.53.56.3
Saskatchewan1.74.07.5
Yukon1.53.76.4

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3.2 Precipitation

In this section, multi-model climate change projections (relative to the 1986–2005 reference period) are shown for precipitation in Canada. The format of these figures is as consistent as possible with the analogous figures in the IPCC AR5 Atlas (IPCC, 2013--Annex I) referred to earlier so as to allow direct comparison.

Time series of precipitation anomaly (as a percentage relative to the 1986–2005 mean), averaged over Canada and covering the historical period (as simulated by the CMIP5 models) and the future (to year 2100), are shown in Figure 8. Results for three future forcing scenarios, RCP2.6, RCP4.5, and RCP8.5, are provided. The individual thin lines are the results of the individual models listed in Table 2, and the heavy line represents the multi-model ensemble average. The range of values, quantified by the box and whisker plots to the right of each panel, results from both natural climate variability (as simulated by the models) and the differences in the detailed representation of physical processes in each model.

Figure 8

Figure 8 – Time series of historical and projected precipitation change for December–February (left) and June–August (right) average, as simulated by the CMIP5 multi-model ensemble. As in Annex I of the IPCC AR5 (IPCC, 2013), the individual curves represent the simulation results for individual models, while the heavy lines indicate the ensemble average. Results are shown for Canadian land areas only. Change is computed as a percentage relative to the 1986–2005 period. The spread amongst models, evident in the thin curves, is quantified by the box and whisker plots to the right of each panel. They show, for the 2081–2100 period, the 5th, 25th, 50th (median), 75th and 95th percentile values.

Long description of Figure 8

This figure has two graphs. The left graph represents a time series of historic and projected precipitation changes for the winter (December, January, and February) as simulated by CMIP5 multi-model ensemble for grids covering Canada. A solid line represents the historic change from 1900 to 2005 and shows an increase from around -2% to around 0%. The RCP2.6 line then continues to increase until it reaches around 10% in 2100. The RCP4.5 line continues on from 2005 to 2100 with an end point of around 18%. The RCP8.5 projection shows a change of around 37% by 2100. The right graph represents a time series of historic and projected precipitation change for the summer (June, July, and August) as simulated by CMIP5 multi-model ensemble for grids covering Canada. A solid line represents the historic change from 1900 to 2005 and shows almost no change, at around 0%. The RCP2.6 line then continues to increase until it reaches around 5%. The RCP4.5 line is almost identical to the RCP2.6 line. The RCP8.5 projection shows a change of around 8% by 2100.

As was shown for temperature in Figures 6 and 7, Figures 9 and 10 show maps of precipitation change from the CMIP5 multi-model ensemble, based on the RCP4.5 scenario. Similar maps for the other RCP scenarios are available from the Canadian Climate Data and Scenarios website. RCP4.5 is used here for illustration purposes (as in the IPCC Atlas) and its use here does not imply that it is more probable than the other RCPs.

Figure 9

Figure 9 – Maps of winter precipitation change projected by the CMIP5 multi-model ensemble for the RCP4.5 scenario, averaged over December–February. Change is computed relative to the 1986–2005 baseline period. As in the IPCC Atlas (IPCC, 2013), the top row shows results for the period 2016–2035, the middle row for 2046–2065, and the bottom row for 2081–2100. For each row the left panel shows the 25th percentile, the middle panel the 50th percentile (median), and the right panel the 75th percentile. The colour scale indicates precipitation change in % with positive change (increased precipitation) indicated by green colours and decrease by yellow to brown colours, consistent with the colour scale used in the IPCC AR5 Annex I (IPCC, 2013).

Long description of Figure 9

This figure consists of 9 maps of Canada showing projected changes in precipitation, driven by RCP4.5, for the winter (December, January, and February), organized in a 3 by 3 grid, with the 25th (left column), 50th (middle column) and 75th (right column) percentiles across the top of each column and the years 2016-2035 (first row), 2046-2065 (second row), and 2081-2100 (third row) down the side to indicate the years for each row. Each map shows most of the country with a projected increase in precipitation, with the 2016-2035/25th percentile map showing some slight drying over the southern parts of the country. The greatest increase in the precipitation is in the north and over Hudson Bay. As the percentiles go up, the changes in percent of precipitation increase. The same is true for time, the further into the future the projection represents, there is an increase in the amount of precipitation represented in the maps, so the map in the top left (25th percentile for 2016-2035), has the least amount of change in precipitation, whereas the bottom right map (75th percentile for 2081-2100), shows the greatest increase in precipitation.

Figure 1

Figure 10 – Maps of summer precipitation change projected by the CMIP5 multi-model ensemble for the RCP4.5 scenario, averaged over June–August. Change is computed relative to the 1986–2005 baseline period. As in the IPCC Atlas (IPCC, 2013), the top row shows results for the period 2016–2035, the middle row for 2046–2065, and the bottom row for 2081–2100. For each row the left panel shows the 25th percentile, the middle panel the 50th percentile (median), and the right panel the 75th percentile. The colour scale indicates precipitation change in % with positive change (increased precipitation) indicated by green colours and decrease by yellow to brown colours, consistent with the colour scale used in the IPCC AR5 Annex I (IPCC, 2013).

Long description of Figure 10

This figure consists of 9 maps of Canada showing projected changes in precipitation, driven by RCP4.5, for the summer (June, July and August), organized in a 3 by 3 grid, with the 25th (left column), 50th (middle column) and 75th (right column) percentiles across the top of each column and the years 2016-2035 (first row), 2046-2065 (second row), and 2081-2100 (third row) down the side to indicate the years for each row. Each of the three 25th percentile maps (2016-2035, 2046-2065, and 2081-2100) shows some decrease in precipitation over most of the country, with the 2016-2035 map showing almost all the country in the 0-10% decrease category. By 2081-2100, only the provinces from Ontario westward are in that category, and the east and the north are in the 0-10% increasing precipitation category. Again the highest percentile map for the most distant future projection shows the greatest change, with areas in the Arctic projected to increase by 20-30%, and with southern Canada only expected to have a 0-10% increase by 2100.

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3.2.1 Summary tables for precipitation

Tables 5 and 6 provide values averaged over Canada and over each province and territory for the 50th (median), 25th, and 75th percentiles of precipitation change for the three future periods illustrated in Figures 9 and 10. These tables also provide the corresponding projections under RCP2.6 and RCP8.5. As the figures clearly show, projected precipitation changes are not constant across a province or territory, but the tables are provided to inform province- or territory-wide assessment activities that may need area-averaged information.

Table 5: Summary information for projected winter precipitation change (in % change from the 1986–2005 baseline period), averaged over December–February for three future periods and three RCPs. The table shows values for the 50th percentile (a), 25th percentile (b), and 75th percentile (c).

RCP2.6
(a) 50th Percentile2016–20352046–20652081–2100
Canada5.49.19.1
Alberta3.16.77.9
British Columbia1.66.47.5
Manitoba5.310.79.0
New Brunswick4.86.73.5
Newfoundland and Labrador3.25.86.3
Northwest Territories7.111.910.9
Nova Scotia2.82.53.0
Nunavut7.213.615.4
Ontario5.38.97.9
Prince Edward Island3.05.25.3
Quebec6.210.29.9
Saskatchewan4.18.18.4
Yukon7.39.711.0
RCP4.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada5.912.917.6
Alberta5.910.811.6
British Columbia4.38.710.8
Manitoba6.412.716.5
New Brunswick5.28.911.9
Newfoundland and Labrador5.09.514.5
Northwest Territories6.815.419.5
Nova Scotia2.85.48.7
Nunavut8.819.028.7
Ontario5.712.916.4
Prince Edward Island5.37.510.8
Quebec6.514.921.2
Saskatchewan5.711.011.7
Yukon5.914.114.7
RCP8.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada7.218.137.8
Alberta4.310.820.4
British Columbia3.410.017.9
Manitoba6.616.228.9
New Brunswick5.811.419.0
Newfoundland and Labrador4.812.023.2
Northwest Territories8.219.742.9
Nova Scotia3.78.313.9
Nunavut10.929.166.4
Ontario6.617.531.8
Prince Edward Island5.310.717.1
Quebec7.520.739.8
Saskatchewan5.312.122.2
Yukon5.915.829.9
RCP2.6
(b) 25th Percentile2016–20352046–20652081–2100
Canada-0.51.92.0
Alberta-1.82.52.0
British Columbia-2.61.51.7
Manitoba0.43.53.6
New Brunswick1.11.00.9
Newfoundland and Labrador-2.4-0.11.0
Northwest Territories1.25.25.1
Nova Scotia-0.1-2.20.3
Nunavut1.54.56.0
Ontario0.23.22.7
Prince Edward Island1.2-2.32.1
Quebec0.42.83.5
Saskatchewan-1.02.12.9
Yukon-0.83.44.7
RCP4.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada0.16.29.1
Alberta0.65.75.7
British Columbia-0.62.73.4
Manitoba0.67.89.2
New Brunswick-0.73.67.0
Newfoundland and Labrador-0.32.96.5
Northwest Territories2.09.712.4
Nova Scotia-1.82.94.1
Nunavut2.212.017.1
Ontario1.87.610.0
Prince Edward Island-1.22.55.1
Quebec0.97.513.2
Saskatchewan0.15.66.7
Yukon0.78.58.4
RCP8.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada0.410.524.8
Alberta0.25.212.2
British Columbia-1.42.69.0
Manitoba1.210.017.0
New Brunswick-0.26.013.1
Newfoundland and Labrador0.66.514.2
Northwest Territories2.512.929.7
Nova Scotia1.73.07.7
Nunavut2.219.747.5
Ontario1.610.921.6
Prince Edward Island1.43.48.3
Quebec1.714.729.3
Saskatchewan0.46.213.1
Yukon1.09.718.9
RCP2.6
(c) 75th Percentile2016–20352046–20652081–2100
Canada12.417.017.3
Alberta9.913.112.6
British Columbia7.612.714.0
Manitoba10.617.015.3
New Brunswick10.112.49.9
Newfoundland and Labrador8.111.812.9
Northwest Territories13.018.818.3
Nova Scotia6.27.07.8
Nunavut16.824.426.0
Ontario10.814.913.4
Prince Edward Island8.78.88.0
Quebec11.617.216.9
Saskatchewan8.114.112.7
Yukon12.315.317.8
RCP4.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada12.120.226.4
Alberta10.815.217.6
British Columbia9.514.417.4
Manitoba12.218.724.5
New Brunswick10.115.219.1
Newfoundland and Labrador11.716.120.8
Northwest Territories11.922.627.3
Nova Scotia6.19.314.4
Nunavut15.728.740.4
Ontario11.218.223.5
Prince Edward Island8.111.315.1
Quebec12.221.628.7
Saskatchewan9.916.717.1
Yukon10.719.321.2
RCP8.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada13.926.752.8
Alberta8.717.928.8
British Columbia8.417.827.1
Manitoba12.323.641.8
New Brunswick11.317.328.6
Newfoundland and Labrador10.620.734.8
Northwest Territories13.527.955.1
Nova Scotia6.712.521.1
Nunavut18.739.889.8
Ontario12.123.941.7
Prince Edward Island9.313.822.1
Quebec13.929.552.3
Saskatchewan10.618.730.6
Yukon12.423.243.2

Table 6: Summary information for projected summer precipitation change (in % change from the 1986–2005 baseline period), averaged over June–August for three future periods and three RCPs. The table shows values for the 50th percentile (a), 25th percentile (b), and 75th percentile (c).

RCP2.6
(a) 50th Percentile2016–20352046–20652081–2100
Canada2.85.05.2
Alberta3.34.45.9
British Columbia1.33.43.7
Manitoba0.22.22.9
New Brunswick2.81.13.9
Newfoundland and Labrador3.54.84.3
Northwest Territories4.57.46.8
Nova Scotia2.82.23.6
Nunavut4.66.25.9
Ontario0.52.61.2
Prince Edward Island0.61.92.7
Quebec2.54.14.2
Saskatchewan1.92.74.4
Yukon5.07.46.8
RCP4.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada2.25.16.5
Alberta2.32.34.1
British Columbia0.20.70.9
Manitoba0.62.02.2
New Brunswick3.03.94.5
Newfoundland and Labrador3.35.15.9
Northwest Territories3.57.810.1
Nova Scotia3.04.46.4
Nunavut3.18.110.8
Ontario0.42.73.3
Prince Edward Island3.13.96.1
Quebec2.65.25.1
Saskatchewan0.61.21.4
Yukon4.58.912.0
RCP8.5
(a) 50th Percentile2016–20352046–20652081–2100
Canada3.06.410.6
Alberta1.72.82.4
British Columbia0.72.10.1
Manitoba0.81.7-1.1
New Brunswick3.24.27.8
Newfoundland and Labrador3.56.611.5
Northwest Territories4.511.117.8
Nova Scotia1.74.36.8
Nunavut5.211.322.9
Ontario0.71.3-0.5
Prince Edward Island2.55.46.3
Quebec3.05.66.5
Saskatchewan0.51.7-1.9
Yukon4.513.221.1
RCP2.6
(b) 25th Percentile2016–20352046–20652081–2100
Canada-3.2-1.5-1.4
Alberta-2.9-1.4-0.3
British Columbia-4.0-2.8-2.3
Manitoba-4.2-2.5-2.7
New Brunswick-3.4-2.7-0.6
Newfoundland and Labrador-0.20.80.3
Northwest Territories-1.01.80.4
Nova Scotia-3.9-3.8-2.9
Nunavut-2.4-0.8-0.9
Ontario-3.8-2.6-3.4
Prince Edward Island-3.7-4.3-1.4
Quebec-2.10.2-0.2
Saskatchewan-3.1-2.4-1.1
Yukon0.92.82.0
RCP4.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada-3.4-1.7-0.4
Alberta-3.5-4.2-2.9
British Columbia-4.7-5.0-5.4
Manitoba-5.4-4.7-4.5
New Brunswick-2.20.2-0.7
Newfoundland and Labrador-0.60.72.2
Northwest Territories-1.61.83.8
Nova Scotia-2.2-3.6-1.3
Nunavut-2.70.63.7
Ontario-4.5-3.0-2.0
Prince Edward Island-3.1-1.4-2.1
Quebec-1.30.60.5
Saskatchewan-5.3-4.6-4.4
Yukon1.14.06.2
RCP8.5
(b) 25th Percentile2016–20352046–20652081–2100
Canada-2.9-0.60.7
Alberta-5.1-3.4-7.8
British Columbia-5.1-4.0-8.3
Manitoba-4.1-4.5-9.1
New Brunswick-2.3-1.3-1.1
Newfoundland and Labrador-1.72.25.3
Northwest Territories-1.74.48.9
Nova Scotia-4.5-2.2-3.0
Nunavut-1.04.012.6
Ontario-3.5-3.8-8.2
Prince Edward Island-6.0-2.4-1.8
Quebec-1.60.80.0
Saskatchewan-4.9-4.8-9.2
Yukon0.27.412.0
RCP2.6
(c) 75th Percentile2016–20352046–20652081–2100
Canada8.912.012.4
Alberta9.110.012.0
British Columbia7.09.29.9
Manitoba4.57.78.4
New Brunswick5.88.610.1
Newfoundland and Labrador7.39.88.8
Northwest Territories10.013.513.1
Nova Scotia7.38.09.2
Nunavut10.813.413.8
Ontario4.67.97.6
Prince Edward Island8.17.76.6
Quebec7.28.69.5
Saskatchewan7.78.710.1
Yukon9.712.612.9
RCP4.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada8.512.614.5
Alberta8.48.410.9
British Columbia5.46.37.8
Manitoba5.27.58.7
New Brunswick8.29.111.3
Newfoundland and Labrador7.810.710.5
Northwest Territories9.415.117.4
Nova Scotia7.69.29.8
Nunavut9.517.119.1
Ontario5.37.48.3
Prince Edward Island9.412.29.8
Quebec6.910.510.9
Saskatchewan5.77.49.1
Yukon8.714.717.6
RCP8.5
(c) 75th Percentile2016–20352046–20652081–2100
Canada9.314.221.3
Alberta8.09.212.9
British Columbia7.18.18.0
Manitoba6.67.47.4
New Brunswick6.69.914.8
Newfoundland and Labrador7.911.218.6
Northwest Territories10.918.227.5
Nova Scotia5.88.515.3
Nunavut11.220.134.2
Ontario5.16.25.9
Prince Edward Island5.99.415.6
Quebec7.510.814.1
Saskatchewan6.39.08.4
Yukon9.818.630.8

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3.3 Extremes

For many climate change impacts, changes in the frequency and magnitude of extreme events are more important than changes in mean values. There are many extremes that have been analyzed in the climate science literature, but by way of illustration we focus here on two basic quantities: changes in annual maximum temperature (i.e., the hottest temperature of the year) and changes in annual maximum 24-hour precipitation. Because global climate models operate with time steps of roughly half an hour, daily minimum, maximum, and mean values can be computed and the projected changes provide an indication of changes that might be anticipated in the future. An important caveat, especially for precipitation, is that the spatial resolution of global climate models remains relatively coarse (typically 100–250 km), and so the precipitation extremes in a model represent averages over an area of several thousand square kilometres. Additionally, climate models may not have all of the physical processes that produce local intense rainstorms. These limitations must be kept in mind when making comparisons to individual meteorological station measurements.

A common way to illustrate changes in climate extremes is to compute the ‘return period’ of events of a particular magnitude for different time periods. The return period is the long-term average interval between recurrences of extreme values. Figure 11 shows projected return periods for annual maximum temperature and the annual maximum amount of precipitation within a 24-hour period. These plots indicate that the recurrence time, or return period, for these extremes is projected to decrease, for both quantities, in the future. That is, extremes of a particular magnitude will become more frequent. For example, the lower right panel of Figure 11 indicates that, under the RCP8.5 forcing scenario, an annual maximum daily temperature that would currently be attained once every 10 years, on average, will become an annual event by the end of the century.

Figure 11

Figure 11 – Projected return periods (in years) for -twentieth century 10-, 20-, and 50-year return values of annual maximum 24-hour precipitation (upper panel) and annual maximum temperature (lower panel) over Canada as simulated by GCMs contributing to the CMIP5 for three RCPs (RCP2.6, left; RCP4.5, middle; RCP8.5, right). Values are computed based on Kharin et al., 2013.

Long description of Figure 11
This figure consists of 6 graphs arranged in a grid of 3 columns by 2 rows. Each graph shows three lines representing three different return period-events over the period 2000-2100. The top row of three graphs shows the 24-hour precipitation extremes, and the bottom row shows annual maximum temperature extremes. The three columns of graphs represent each three forcing scenarios (RCP2.6, RCP4.5, and RCP8.5). For all the graphs, 50-year, 20-year and 10-year events all become more frequent over time, with those in the RCP2.6 scenarios showing the least change and RCP8.5 showing the greatest change.

As with mean temperature and precipitation, changes in climate extremes are not uniform across the globe, or even across Canada. Figure 12 shows projected changes in precipitation extremes for different regions of Canada, along with estimates of the uncertainty range around the projected return periods.

Figure 12

Figure 12 – Projected changes (in %) in 20-year return values of annual maximum 24-hour precipitation rates (i.e., precipitation extremes). The bar plots show results for regionally-averaged projections for three time horizons: 2016–2035, 2046–2065, and 2081–2100, as compared to the 1986–2005 baseline period. The blue, green, and red bars represent results for RCP2.6, RCP4.5, and RCP8.5, respectively. Projections are based on GCMs contributing to CMIP5 and the analysis is described in Kharin et al., 2013.

Long description of Figure 12

This figure is a map of Canada. Superimposed over the map are 5 bar plots showing projected changes (in %) in the 20-year return value of annual maximum 24-hour precipitation rates. Each bar plot represents a region of Canada (Canada as a whole, Northern Canada, Eastern Canada, Prairies, and B.C.-Yukon) and has three different colours to represent three different scenarios (RCP2.6, RCP4.5, and RCP8.5), grouped together in the three averaged time periods (2016-2035, 2046-2065, and 2081-2100). The Canada plot shows the average bar range from around 5% for the early period to little change in the RCP2.6, a 10% change for RCP4.5 and an average of 36% change in RCP8.5. The Northern Canada plot shows around a 5% change in the 2016-2035 period for all three scenarios, with an increase change of 8% for RCP2.6, 14% for RCP4.5 and 30% for RCP8.5 by 2081-2100. The Eastern Canada plot starts with a change of around 5% for all three scenarios in the first period, increasing to about 6% for RCP2.6, 13% for RCP4.5 and 25% for RCP8.5 by 2086-2100. The Prairies plot shows around a 6% change for RCP2.6, 5% for both RCP4.5 and RCP8.5 in the 2016-2035 period, remains unchanged for RCP2.6, increasing to 7% for RCP4.5, and increasing to 18% for RCP8.5 for the last period. The B.C.-Yukon plot shows a 4% change for RCP2.6, a 6% change for RCP4.5 and a 5% change for RCP8.5 for the first period. These values increase to 8% for RCP2.6, 13% for RCP4.5, and 36% for RCP8.5 by 2086-2100.

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3.4 Higher resolution

For many applications, climate changes projected by fairly coarse resolution global climate models may suffice. However, there are applications for which much more spatial detail is necessary. This is particularly true for applications in which a secondary model (such as an agricultural crop model or a basin-scale hydrological model) must be driven by climate model output. In such cases, higher-resolution regional downscaling may be required.

There are two general categories of downscaling: dynamical downscaling, using a regional climate model; and statistical downscaling, using empirical relationships between larger-scale meteorological variables and the local variables of interest. It is beyond the scope of the present document to provide a comprehensive review, and more detail regarding these methods can be found in the literature (see: (Hewitson & Crane, 1996; Murphy, 1999; Wilby & Wigley, 1997; Wilby, et al., 1998; Wilby, et al., 2004; and Schmidli, et al., 2006). However, by way of example, we provide here some results from two Environment Canada resources.

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3.4.1 Canadian regional climate model

A new regional climate model, CanRCM4, has been developed based on the ‘physics’ used in the Canadian Earth System Model (CanESM2). This model has been used to produce downscaled climate information at 50 km and 25 km resolution for domains covering North America, the Arctic, Africa, and Europe as part of an international downscaling effort. A wide array of daily and monthly output from this model is available.

Figure 13 compares precipitation simulated by CanRCM4 (at 25 km resolution) to that simulated by the Canadian global model, CanESM2. The spatial detail provided by dynamical downscaling is readily apparent.

Figure 13

Figure 13 - Comparison of regional climate model (CanRCM4, left) with global climate model (CanESM2, right) simulation of precipitation for the RCP8.5 forcing scenario. Upper row shows results for December–February, lower row shows results for June–August. The results show a change in precipitation as the difference between the 2096-2100 and the 2006-2010 averages. The spatial detail afforded by the high-resolution (25 km) regional model may be useful for many applications.

Long description of Figure 13

This figure consists of four maps of simulated precipitation over North America. All the results are shown for the simulations driven by the RCP8.5 forcing scenario, and are the differences between the 2096-2100 and the 2006-2010 averages. The upper row represents winter (December, January and February) values, while the lower row shows summer (June, July, and August) values. The left images show the regional climate model (CanRCM4) results which present the contour lines in much finer detail than the right images of the coarser global climate model (CanESM2). The upper row shows more precipitation in the western United States and B.C. and in the mid-west through the Maritimes. The rest of Canada and most of the United States show little change in precipitation. The summer maps show an increase in precipitation over the territories, the Maritimes and Quebec, the Eastern Seaboard and the southern states of the United States. There is a decrease in precipitation over B.C. and Alberta.

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3.4.2 Statistically downscaled results from CMIP5 models

Statistical downscaling makes use of empirically-derived relationships between large and small scales, and allows for a range of relevant climate quantities to be estimated. An important underlying assumption is that the empirical relationships are unaltered by a changing climate. While this may be a limiting assumption, it is offset to some degree by the fact that these approaches reduce the effect of systematic biases that may be present in global and regional climate models. The reduction of systematic biases is essential for the projection of some extreme indicators that are based on threshold crossing, for example, heating or cooling degree days. Environment Canada has worked with the Pacific Climate Impacts Consortium (PCIC) to develop statistically downscaled climate scenarios based on the CMIP5 global climate projections and regional climate projections (NARCAPP and CorDEXFootnote 4). The projections for Canada are available via the PCIC Data Portal. Figure 14 shows the potential utility of statistical downscaling for projecting climate extremes. Projected changes in heating degree-days and cooling degree-days in Canada are shown for three future periods (see figure caption for further details).

Figure 14

Figure 14 – Illustration of potential utility of statistically downscaled projections of extremes. Projected changes in cooling (left panel) and heating (right panel) degree days (in degree-days) are shown for the 2016–2035 (top), 2046–2065 (middle), and 2081–2100 (bottom) periods. Projected changes are relative to the 1986–2005 mean estimated from the multi-model ensemble shown in Table 7 and downscaled using BCCAQ.

Long description of Figure 14

This figure shows 6 maps. The left 3 maps represent Heating Degree Days (HDD), and the right 3 maps represent Cooling Degree Days (CDD), with each row representing the different time periods (2016-2035, 2046-2065, and 2081-2100). The 2016-2035 HDD map starts with the south having -250 to -500 HDD, and the north having -500 to -1000 HDD. By 2086-2100 the south is anywhere between -500 to -1250 HDD and the north is between -1250 and -1500 HDD. The 2016-2035 CDD map starts with the southern Prairies and southern Ontario with about 50-100 CDD. By 2086-2100 the southern Prairies and southern Ontario have increased to between 200 and 300 CDD and the area indicating at least some CDD covers almost of the provinces.

Table 7: Information on the CMIP5 models whose results were used to produce Figure 14.
Model NamePlace of OriginInstitution
ACCESS1.0AustraliaCommonwealth Scientific and Industrial Research Organisation and Bureau of Meteorology
CanESM2CanadaCanadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada
CCSM4USANational Centre for Atmospheric Research
CNRM-CM5FranceCentre National de Recherches Météorologiques and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique
CSIRO-Mk3.6.0AustraliaQueensland Climate Change Centre of Excellence and Commonwealth Scientific and Industrial Research Organisation
GFDL-ESM2GUSANOAA Geophysical Fluid Dynamics Laboratory
HadGEM2-CCUKUK Met Office Hadley Centre (additional realizations contributed by Instituto Nacional de Pesquisas Espaciais, Brazil)
HadGEM2-ESUKUK Met Office Hadley Centre (additional realizations contributed by Instituto Nacional de Pesquisas Espaciais, Brazil)
INM-CM4RussiaInstitute for Numerical Mathematics
MIROC5JapanUniversity of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology
MPI-ESM-LRGermanyMax Planck Institute for Meteorology
MRI-CGCM3JapanMeteorological Research Institute

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4. Further reading

As stated in the introduction, this document is intended as a reference to illustrate some of the key historical and projected changes in climate in Canada. This report focuses on average temperature and precipitation changes as well as some key weather extremes for Canada. This is not intended as a comprehensive analysis of all climate change indicators, nor is it meant to provide technical guidance on the use of climate change scenarios. More detailed information on climate data, projections, and scenarios for Canada are available at Environment Canada’s Canadian Climate Data and Scenarios website.

As was noted in section 1, we would direct readers looking for technical guidance with scenarios to the Ouranos Guidebook (Charron, 2014). Similarly, an in-depth analysis of a variety of climate change indicators, specific to Canada, can be found in the Natural Resources Canada report, “Canada in a Changing Climate: Sector Perspectives on Impacts and Adaptation; Chapter 2: An Overview of Canada’s Changing Climate” (Bush et al., 2014). Finally, the primary literature referenced throughout this paper collectively forms an excellent resource for more in-depth information on methods, analyses, and context related to the material presented herein. The IPCC Assessment Reports are generally accepted as the most authoritative source on climate change at a global scale. At the time of publication of this document, the Fifth Assessment Report was the most recent of these reports issued by the IPCC.

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5. References

Bush, E. J. et al., 2014. An overview of Canada's changing climate. In: F. J. Warren & D. S. Lemmen, eds. Canada in a changing climate: Sector perspectives on impacts and adaptation. Ottawa: Government of Canada, p. 23–64.

Charron, I., 2014. A Guidebook on Climate Scenarios: Using Climate Information to Guide Adaptation Research and Decisions, Montreal, Canada: Ouranos.

Flato, G., 2011. Earth System Models: An Overview. WIREs Climate Change, Volume 2, p. 783–800.

Flato, G. et al., 2013. Evaluation of Climate Models. In: T. F. Stocker, et al. eds. Climate Change 2013: The Physical Science Basis. Cambridge, UK and New York, USA: Cambridge University Press, p. 741–866.

Hewitson, B. C. & Crane, R. G., 1996. Climate downscaling: Techniques and application. Climate Research, Volume 7, p. 85–95.

Hopkinson, R. F. et al., 2011. Impact of aligning climatological day on gridding daily maximum-minimum temperature and precipitation over Canada. Journal of Applied Meteorology and Climatology, Volume 50, p. 1654–1665.

IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK; and New York, USA: Cambridge University Press.

IPCC, 2013. Summary for Policymakers. In: T. F. Stocker, et al. eds. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press, p. 27.

Kharin, V. V., Zwiers, F. W., Zhang, X. & Wehner, M., 2013. Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, Volume 119, p. 345–357.

McKenney, D. W. et al., 2011. Customized spatial climate models for North America. Bulletin of the American Meteorological Society, Volume 92, p. 1611–1622.

Moss, R. H. et al., 2010. The next generation of scenarios for climate change research and assessment. Nature, Volume 463, p. 747–756.

Murphy, J., 1999. An evaluation of statistical and dynamical techniques for downscaling local climate. Journal of Climate, Volume 12, p. 2256–2284.

Schmidli, J., Frei, C. & Vidale, P. L., 2006. Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods. International Journal of Climatology, 26(5), p. 679–689.

Taylor, K. E., Stouffer, R. J. & Meehl, G. A., 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, Volume 93, p. 485–498.

van Vuuren, D. P. et al., 2011. The representative concentration pathways: An overview. Climatic Change, Volume 109, p. 5–31.

Vincent, L. A. et al., 2012. A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis. Journal of Geophysical Research, p. D18110; 13 pp..

Wilby, R. L. et al., 2004. Guidelines for use of climate scenarios developed from statistical downscaling methods,s.l.: Supporting material of the Intergovernmental Panel on Climate Change; available from the DDC of IPCC TGCIA, 27.

Wilby, R. L. & Wigley, T. M. L., 1997. Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography, Volume 21, p. 530–548.

Wilby, R. L. et al., 1998. Statistical downscaling of General Circulation Model output: A comparison of methods. Water Resources Research, Volume 34, p. 2995–3008.

Zhang, X., Vincent, L. A., Hogg, W. D. & Niitsoo, A., 2000. Temperature and precipitation trends in Canada during the 20th century. Atmosphere-Ocean, Volume 38, pp. 395-429.

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