Climate Change Canada

 

4.4.1.2: 2016 Fort McMurray wildfire

In early May 2016, a large wildfire burned almost 600,000 ha (a land area covering 6000 square kilometers) in northern Alberta. This fire resulted in the evacuation of all of the residents of Fort McMurray (over 80,000 people) and halted production in the oil sands (Government of Alberta, 2016). Insured losses are estimated at $3.5 billion (IBC, 2016). The total cost of the event is still being determined, but it is expected to be considerably higher.

The fire ignited near the Horse River amid very dry fuel conditions. High winds a few days later resulted in rapid spread and fire growth. A study has used event attribution to assess the influence of human-induced climate change on several measures of wildfire risk (see Box 4.2), albeit not extreme fire itself, in this region (Kirchmeier-Young et al., 2017a).

Like the previous example, the study used large ensembles of model simulations, in this case employing the Canadian Earth System Model (CanESM2). To assess human influence, the model was run with only natural forcings (solar and volcanic effects) and also with a combination of natural and anthropogenic forcings. The anthropogenic component includes GHG emissions, aerosols, atmospheric ozone changes, and land-use change.

Fire weather (see Box 4.2), fire behavior, and fire season measures were calculated to characterize fire risk from climate model output. To quantify the anthropogenic contribution, a risk ratio (NASEM, 2016) was calculated as the ratio of two probabilities: one for the event’s occurrence when the human component is included, and one for the occurrence of the same event with only natural factors. The risk ratio can be interpreted as how many times as likely the event is as a result of anthropogenic factors. For example, a risk ratio of 1 implies no change in the probability of occurrence, and a risk ratio of 2 implies the event is twice as likely, or that there has been a 100% increase in the probability of the event compared with the unperturbed climate.

Results of the analysis show that three of the fire risk indices — extreme values of the Fire Weather Index, high number of spread days, and long fire seasons — all show risk ratio values greater than 1, indicating extreme values of each measure of wildfire risk are more likely when anthropogenic warming is included. Risk ratios vary among the different fire risk indices analyzed. However, extreme values of all measures describing wildfire risk are more likely with anthropogenic forcing.

Increasing temperatures, like those observed across Canada, will lead to drier fuels, and thus increased fire potential, as well as longer fire seasons. It would require increases in precipitation well beyond what is expected with climate change to offset increasing temperatures in terms of the FWI indices (Flannigan et al., 2016). The study demonstrated that the extreme Alberta wildfire of 2016 occurred in a world where anthropogenic warming has increased fire risk, fire spread potential, and the length of fire seasons across parts of Alberta and Saskatchewan.

 

5.2: Snow cover

The portion of the year with snow cover decreased across most of Canada (very high confidence) as did the seasonal snow accumulation (medium confidence). Snow cover fraction decreased between 5% and 10% per decade since 1981 due to later snow onset and earlier spring melt. Seasonal snow accumulation decreased by 5% to 10% per decade since 1981 with the exception of southern Saskatchewan, and parts of Alberta and British Columbia (increases of 2% to 5% per decade).

It is very likely that snow cover duration will decline to mid-century across Canada due to increases in surface air temperature under all emissions scenarios. Scenario-based differences in projected spring snow cover emerge by the end of the century, with stabilized snow loss for a medium emission scenario but continued snow loss under a high emission scenario (high confidence). A reduction of 5% to 10% per decade in seasonal snow accumulation is projected through to mid-century for much of southern Canada; only small changes in snow accumulation are projected for northern regions of Canada (medium confidence).

Snow cover is a defining characteristic of the Canadian landscape for a few months each winter along the southern margins of the country and for up to nine or 10 months each year in the high Arctic. Snow is responsible for a cascade of interactions and feedbacks that affect the climate system, freshwater availability, vegetation, biogeochemical activity including exchanges of carbon dioxide and trace gases, and ecosystem services (Brown et al., 2017). To understand changes in snow, it is necessary to consider multiple variables, including snow cover fraction (SCF), which is affected by the timing of snow onset and snow melt, and the maximum seasonal snow water equivalent (SWEmax), the amount of water stored by snow and available for melt in spring. These variables affect the exchange of energy between the surface and the atmosphere (with important feedbacks to the global climate system) and freshwater availability, as nearly all Canadian watersheds are snow-dominated in the winter. Snow is critical to winter travel and tourism in many regions of the country and is a key requirement for the construction of winter roads that connect remote communities and mines, particularly in the Northwest Territories, northern Manitoba, and northern Ontario.

Surface observations of snow depth from climate monitoring stations (such observations are referred to as “in situ data”) are not well suited for detecting trends and variability in snow cover because they measure snow only at individual points (Brown and Braaten, 1998). Snow depth can vary significantly at the local scale because of interactions with vegetation and topography (typically driven by winds), which means single point measurements may not capture the mean snow depth on the landscape (Neumann et al., 2003). In addition, climate stations are exceptionally sparse above 55º north latitude in Canada and are biased to lower elevations in mountainous areas and in coastal areas in the sub-Arctic and Arctic. It is, therefore, challenging to use the conventional Canadian climate observing network for a national-scale assessment of snow. Satellite observations and land surface models are available that provide daily, spatially continuous data across all of Canada, extending back for decades. These products have a coarse spatial resolution (25–50 km), which presents problems for alpine areas and regions with mixed land cover. Researchers have made significant efforts to determine the agreement among datasets to ensure robust analysis of trends (Mudryk et al., 2018).

 

5.2.1: Observed changes in snow cover

Based on an analysis of multiple datasets covering 1981–2015, SCF (characterized as the proportion of days within each month that snow was present on the ground) decreased by 5% to 10% across most of Canada during most seasons (Mudryk et al., 2018), notably, for eastern Canada in spring (April/May/ June) and most of the Canadian land area in the fall (October/November/December). This loss of snow cover is consistent with previous studies using in situ datasets covering a longer time period (Brown and Braaten, 1998; Vincent et al., 2015), but the 1981–2015 period is characterized by stronger reductions in snow cover during the snow onset period for eastern Canada in response to enhanced fall warming (consistent with Brown et al., 2018). Decreasing SCF trends over high latitudes of Canada are consistent with documented reductions in annual snow cover duration (SCD; the number of days with snow cover) across circumpolar Arctic land areas of two to four days per decade (approximately 1% to 2% per decade, assuming 250 days mean snow cover) (Brown et al., 2017). Some studies (Derksen and Brown, 2012; Derksen et al., 2016; Brutel-Vuilmet et al., 2013; Hernández-Henríquez et al., 2015; Mudryk et al., 2017) identified spring snow cover losses slightly stronger, because different datasets and time periods were considered. Despite these differences, all studies consistently show reductions in spring SCF.

Analysis of surface temperature from a blend of six atmospheric reanalysis datasets showed that warming trends over the 1981–2015 period are found in all Canadian land areas with SCF reductions (Mudryk et al., 2018). Cooling trends in winter and spring are associated with the regions of increasing SCF. Observations from climate stations in the regions where SCF trends increased over 1981–2015 also show decreased maximum snow depth and SCD over the longer 1950–2012 period (Vincent et al., 2015), so the positive trends over 1981–2015 reflect nature variability in regional surface temperatures and precipitation.

While SCF information is important for identifying changes in where snow covers the ground, from a water-resources perspective, it is important to understand how much water is stored in the form of snow. This is determined from the pre-melt SWEmax. SWEmax declined by 5% to 10% across much of Canada during the period 1981–2015, according to the multi-dataset analysis shown in Figure 5.3 (Mudryk et al., 2018). This is consistent with snow depth trends from surface measurements (Brown and Braaten, 1998; Vincent at al., 2015) and other observational studies (for example, Mudryk et al., 2015). Increases in SWEmax are evident across parts of British Columbia, Alberta, and southern Saskatchewan. The influences of temperature and precipitation changes need to be separated to understand the driving mechanisms behind trends in SWEmax (Raisanen, 2008; Brown and Mote, 2009; Mankin and Diffenbaugh, 2014; Sospedra-Alfonso and Merryfield, 2017).