Climate Change Canada

 

3.3.2: Sources of confidence and uncertainty

Confidence in climate model projections arises from many sources. First, climate models are solidly based on physical laws and scientific understanding of physical processes. Second, climate model results are evaluated in detail by comparing model output to observations. The model evaluation chapter in the most recent IPCC Assessment report provides many examples (Flato et al., 2013). Third, some of the models used to make climate projections are also used to make seasonal climate predictions whose skill is routinely evaluated (e.g., Kirtman et al., 2013, Merryfield et al., 2013; Sigmond et al., 2013; Kharin et al., 2017).

There are, however, uncertainties that have to be considered when using model projections. These uncertainties stem from the fact that models cannot simulate all physical processes exactly (and therefore must make approximations), and from internal variability in both the simulated and the real climate system (see Chapter 2, Box 2.5). The uncertainty due to approximations of physical processes can be reduced, in principle, and models continue to improve in this regard (Flato et al., 2013). However, it is impossible to reduce the uncertainty from internal variability that is superimposed on the underlying forced climate change. In addition, there is uncertainty about what future climate forcing (e.g., future GHG emissions) will be, which is accounted for by making projections with a range of forcing scenarios. These sources of uncertainty vary in importance depending on the time and space scale under consideration — generally, uncertainties diminish at larger spatial scales as internal variability “averages out” to a certain degree when one considers larger regions (e.g., Haw-kins and Sutton, 2009). This also means that uncertainty is larger when one looks at small regions or specific locations. In addition, at longer time scales (say, by the end of the 21st century), uncertainty is dominated by differences in the forcing scenarios and internal variability is, by comparison, much smaller.

 

3.3.3: Global-scale climate projections

As described in Section 3.2, climate projections are a result of driving climate models with different future forcing scenarios (RCPs, in the case of CMIP5). These projections include the response of the climate system to external forcing (e.g., changing GHG concentrations), internal variability, and uncertainties associated with differences between models. These effects can be separated, to some extent, by drawing upon projections from multiple models (e.g., Collins et al., 2013). The multi-model average provides an estimate of the response of the climate system to forcing, since internal variability and model differences are “averaged out” to a large extent (see Box 3.2). The upper panel of Figure 3.6 shows the change over time in global mean surface air temperature, as simulated by the CMIP5 models, spanning the period from 1950 to 2100. The heavy lines indicate the multi-model average, and the shaded band represents the range of model results around this average. Within this shaded band, each individual model result would look like one of the individual coloured lines in Figure 3.5, but for clarity this collection of individual lines is shown as a shaded band. The high emission scenario (RCP8.5) results are shown by the red line and the orange shaded band, whereas the low emission scenario (RCP2.6) results are shown by the blue line and the blue shaded band.

There are two key points illustrated in the upper panel of Figure 3.6. First, when looking at projected climate change, the spread across models (the vertical extent of the shaded bands) is smaller in the near term (to around 2040) than it is toward the end of the 21st century, indicating that model uncertainty has a larger effect further into the future. (Internal variability also contributes to the width of the shaded bands, as described previously, but the size of this contribution is not expected to change significantly in the future.) Second, the differences among forcing scenarios are small in the near term, but become large toward the end of the 21st century (as illustrated by the growing separation between results for the low emission scenario [RCP2.6] and high emission scenario [RCP8.5]). For simplicity, the medium emission scenarios (RCP4.5 and RCP6.0) are not shown in the main part of the figure, but their end-of-century results are shown on the right-hand side of the top panel for comparison.

The spatial patterns of projected temperature and precipitation change are shown in the bottom panel. The large difference in mean change between the high and low emission scenarios is clearly evident in the maps (darker colours indicate larger change), but there is a marked similarity in pattern. For temperature, changes are larger over land than over the adjacent ocean, and are larger at high latitudes, particularly over the Arctic, an illustration of Arctic amplification. As a result, projected warming in Canada is roughly double the global mean. For precipitation, the pattern of change is more complex, with the polar and equatorial regions projected to have increased annual precipitation, whereas precipitation decreases are projected for much of the subtropics (roughly 24º to 35º north and south latitude). For southern Canada, the projected change in precipitation is rather small, but projected increases are larger further north. (Changes in annual mean precipitation do not translate directly into changes in seasonal snow cover or water availability.

On average, the models project a future global mean temperature change (relative to the 1986–2005 reference period) of about 1ºC for the low emission scenario (RCP2.6) and 3.7ºC for the high emission scenario (RCP 8.5) by the late 21st century, with a 5%–95% range of about 1ºC above and below the multi-model average. This change is over and above the 0.6ºC change that had already occurred from 1850 to the reference period. Therefore, the average projected change under the low emission scenario is consistent with the global temperature target in the Paris Agreement of limiting global warming to between 1.5ºC and 2.0ºC, although the projected range from all models extends both below and above this target. The low emission (RCP2.6) scenario requires emissions of CO2 to peak almost immediately and reduce to near zero before the end of the century. Recent studies (e.g., Millar et al., 2017) provide more detailed analysis of scenarios that will limit warming to 1.5ºC, and these also involve very rapid and deep emission reductions.

More details regarding future projections, with a focus on Canada, are provided in other chapters of this report. Confidence in climate change projections varies by region and by climate variable. So, for example, confidence in temperature change is higher than confidence in precipitation change. This is in large part because temperature change is a direct consequence of radiative forcing, whereas precipitation change is affected by a number of complex interactions, including changes in the water-holding capacity of a warming atmosphere, in global atmospheric circulation, in evaporation, and in other factors (e.g., Shepherd, 2014). Changes in snow and ice are a consequence of changes in both temperature and precipitation and are discussed in more detail in Chapter 5. Freshwater availability (see Chapter 6) and ocean changes are also affected by changes in temperature and precipitation, as well as by other factors.

 

3.3.4: Compatible emissions

Earth system models can be run in two different ways: one in which GHG concentrations are set and another in which GHG emissions are set (both are available as part of the RCP datasets). Concentration-driven simulations allow scientists to assess the difference, from one model to another, in how the climate responds to identical changes in GHG concentrations in the atmosphere. This helps separate the response of the climate system to a change in forcing (e.g., change in GHG concentrations) from the effect of carbon-cycle feedbacks involving the terrestrial and oceanic biospheres. The response of these natural carbon sinks to atmospheric CO2 levels and to climate change will influence anthropogenic emissions compatible with a given CO2 pathway. Therefore, an interesting aspect of these concentration-forced simulations is that global anthropogenic emissions can be computed — emissions that are compatible with the prescribed concentration pathway (e.g., Jones et al., 2013). The range in compatible CO2 emissions between different models provides a measure of the uncertainty inherent in representing carbon-cycle feedbacks in models. Figure 3.7 illustrates results from compatible emission calculations and shows that, while there is some variation, this group of models is consistent. For the RCP 2.6 scenario in which temperature is stabilized below about 2ºC, the models have compatible emissions that start reducing immediately and reach near zero well before the end of the century.