High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach

2.1 Model setup

The WRF model Version 3.6.1 was used to simulate the historical (2000–2015) and projected climate (RCP8.5) over western Canada with a convection-permitting resolution of 4 km. The WRF model is fully compressible and nonhydrostatic and uses the Advanced Research WRF (ARW) dynamical solvers. The model domain is composed of 699×639 grid points with 4 km horizontal resolution to cover western Canada from British Columbia and the Yukon to the west and the MRB and the SRB to the east as shown in Fig. 1. In total, the model domain covers 2800 km in the east–west direction and 2560 km in the north–south direction. The model's vertical coordinate comprised 37 stretched vertical levels topped at 50 hPa in the lower stratosphere. The model simulations employed several parameterization schemes, including the Thompson microphysics scheme (Thompson et al., 2008), the Yonsei University (YSU) planetary boundary-layer scheme, the Noah land surface model (Chen and Dudhia, 2001), and the CAM3 radiative transfer scheme (Collins et al., 2004). These physics schemes were chosen based on past good model performances using these schemes in cold regions (Liu et al., 2011, 2017; Rasmussen et al., 2014). Liu et al. (2011) did a comprehensive sensitivity study on the simulation of winter precipitation in the Colorado headwater region using various physics schemes. They found the Thompson et al. (2008) and Morrison et al. (2009) microphysics schemes have comparable skills and are superior to other schemes. The dependence of performance on land surface, PBL, and radiation parameterizations is moderate or weak due to the weak land surface coupling, shallow PBL, and weak solar radiative heating in the winter (Liu et al., 2011). The deep cumulus parameterization was turned off because with a 4 km horizontal resolution the model can explicitly resolve deep convection and simulate convective storms. The convection-permitting model produces precipitation more realistically by directly resolving convections. Also, because using cumulus parameterization schemes at this resolution often produces unrealistic convection (Westra et al., 2014), cumulus parameterization was switched off. Subgrid cloud cover was also disabled.

Figure 1The domain of WRF simulation. The black dots indicate the observation stations used in the evaluation of the simulations.

2.2 Numerical experiments

Two 15-year WRF simulations were conducted to simulate the regional climate under the historical and future climate using reanalysis and climate change forcing derived from CMIP5 ensembles, respectively. The control experiment (CTL), a retrospective/control simulation, aimed to reproduce the current climate statistics in terms of variability and mean state from 1 October 2000 to 30 September 2015. This control simulation was forced using 6-hourly 0.7^∘ ERA-Interim reanalysis data (Dee et al., 2011) directly. WRF simulation was directly forced by 4 km one-way nesting without an intermediate buffering coarse grid between the ERA-Interim reanalysis and WRF domain because the ∼75 km resolution reanalysis was shown to be adequate (Liu et al., 2017). The second simulation was a climate perturbation or sensitivity experiment following the PGW approach used in Colorado Headwaters work (Rasmussen et al., 2011, 2014). Climate projections from GCMs introduce large uncertainties because of the substantial inter-model variability among GCMs (Deser et al., 2012; Mearns et al., 2013), which can obscure the climate change response due to global warming. Using the PGW approach with GCM ensembles can overcome the inter-model variability and isolate radiative forcing and its associated circulation as the sole reason for the regional climate response. Using PGW methodology during a future period also requires less computation resource than a continuous simulation spanning a century. However, the PGW method also has its disadvantages and limitations. Addition of climate change signal onto the reanalysis field may introduce an imbalance to the lateral boundary forcing because the nonlinear terms are not necessarily additive to balance the dynamics (Misra and Kanamitsu, 2004). PGW also does not fully consider the nonlinear interaction between global warming and atmospheric circulation changes, thus, cannot estimate the changes in future storm frequency, storm intensity, and the positions of storm tracks, which all interact with the large-scale climate system beyond the model boundary and could not represented by simply adding thermodynamic and kinetic change to current weather and climate (Sato et al., 2007).

Regional climate downscaling using convection-permitting models has a range of advantages over using models that rely on convection parameterization, including better convective precipitation simulation and the ability to compare regional climate changes directly related to global warming scenarios. Due to these benefits, convection-permitting PGW simulation (Liu et al., 2017) has been used in several recent studies to investigate the intensification of hourly precipitation extremes (Prein et al., 2017b), the decrease in overall precipitation frequency and light–moderate precipitation events over the contiguous US (CONUS) (Dai et al., 2017), the increase in rain-on-snow events in western North America (Musselman et al., 2018), and the change in cloud population (Rasmussen et al., 2017). The PGW forcing was derived from climate change signals from a 19-member ensemble mean of CMIP5 models. In particular, PGW 15-year (2000–2015) simulation was forced with the same period of 6 h ERA-Interim reanalysis as in CTL, plus a climate perturbation from the ensemble CMIP5 RCP8.5 projection:

where Δ_{CMIP5 RCP8.5} is the climate change signals derived from the CMIP5 multi-model (19 ensemble members) ensemble mean under the RCP8.5 emission scenario from 2071–2100 relative to 1976–2005. The choice of the model members and the details of the ensemble members of the 19 CMIP5 models are provided in Liu et al. (2017). Climate change signals are interpolated according to calendar date using the monthly Δ_{CMIP5 RCP8.5} data for both surface variables and three-dimensional field variables. The surface variables such as surface temperature, soil temperature, sea level pressure, and sea ice are incorporated into the PGW forcing by including the climate changes signals in the initial and boundary conditions for CTL. Similarly, PGW forcing perturbations were also added to the three-dimensional field variables, such as horizontal wind components, air temperature, specific humidity, and geopotential in the initial and boundary conditions of CTL.

The climate change signals in Fig. 2 show the circulation and thermodynamic changes in the PGW forcing for different seasons from the lower troposphere (750 hPa) to the jet-stream level (250 hPa). As shown in the third row of Fig. 2, the temperature increases at 750 hPa in the lower troposphere under RCP8.5. The warming is larger in the northwest and in the MRB than in the southwest and in the SRB, especially in autumn and winter. The warming ranges from 3 to 4 ^∘C in winter and spring and from 4 to 5 ^∘C in summer and autumn. Accompanying this warming is a moderate decrease (0.5 % to 2 %) in relative humidity throughout the domain, with a larger decrease in the south in summer and autumn. The change in geopotential height (GPH) at 750 hPa presents a pattern as thickness between the lower atmospheric isobaric surfaces, consistent with the temperature change, as the thickness is proportional to the average temperature of the layer. Accompanying this pattern of change in GPH, there is a weakening of the westerly flow in all seasons in the order of 0.5 to 1 ms⁻¹ at 750 hPa due to geostrophic balance. At the mid-troposphere level, the general pattern of change in GPH at 500 hPa is similar to that at 750 hPa but with larger values of 90–100 m, as shown in the second row of Fig. 2. For the upper level at 250 hPa, the increase in temperature ranges from 1 to 4 ^∘C, with stronger warming in the south, as shown in the top row of Fig. 2. The warming at 250 hPa is less than that at the lower levels, especially for the cold seasons, when the warming is only about 1 ^∘C. The geopotential height experiences the largest increase in summer and the smallest increase in winter.

Figure 2The climate change signal in the PGW forcing-derived from 19-member CMIP5 ensemble for each season (from left to right: spring, summer, autumn, and winter) at the upper levels (250 hPa, first row; 500 hPa, second row) and lower atmosphere (bottom two rows). The contours are the changes in geopotential height relative to current climate. The shadings are changes in temperature or moisture at each pressure level. The wind vectors denote the change in the mean wind at each level.

2.3 Verification data

The simulation evaluation was conducted against two gridded datasets for temperature and precipitation for the retrospective CTL simulation from 2000 to 2015. The NCEP North American Regional Reanalysis (NARR) (Mesinger et al., 2006) and the surface station observations from Environment Climate Change Canada were also used in basin-averaged evaluations. Several facts have to be noted when conducting intercomparison between models, reanalyses, and gridded observations. The gridded observation dataset makes interpolations based on station observation, which makes ANUSPLIN less reliable in regions with complex terrain such as in the Canadian Rockies and areas with sparse observations like in the northern territories. Though NARR assimilates precipitation unlike most atmospheric reanalyses, the poor coverage of observation in Canada makes it also less reliable outside the populated regions in the southern Canada. As a result, NARR's performance is worse than CaPA and ANUSPLIN over western Canada, especially in cold seasons (Wong et al., 2017). Because CaPA incorporates both station observation and radar precipitation, it produces better spatial distribution of precipitation than ANUSPLIN (Fortin et al., 2018). Additionally, the different horizontal resolutions between models also introduce large differences in elevation in mountainous terrains, which can make the temperature and precipitation evaluation on a common grid difficult as the elevation difference can cause large temperature and precipitation biases. Finally, ANUSPLIN and CaPA still cannot capture mountain weather processes well. These gridded datasets are mainly based on ground observation and CaPA assimilates radar observation. The sparse observation network cannot adequately cover the area to delineate the drastic change in temperature and precipitation and the elevation placement of sites tend to be in the valley. Radar observation is also hindered by the topography. Winter precipitation observation often suffers from undercatchment due to boundary-layer processes. Therefore, the evaluation of WRF-CTL must be considered with these limitations in mind.

3.1 Near-surface temperature

Surface air temperature is a key meteorological variable that directly affects the daily life of human beings, physiological development of field crops, agricultural product quality, and various hydrological processes. For humans, extreme and persistent hot days in summer can cause health issues including heat cramps, heat exhaustion, and heat stroke, especially for vulnerable populations such as the elderly. For agriculture, extreme hot spells of multiple days with a maximum temperature hovering above the cardinal maximum, the temperature at which crop growth ceases, can significantly reduce crop yields. At the other extreme, the effects of very cold temperatures range from a minor inconvenience for some to severe infrastructure damage and increased mortality for vulnerable populations. As the mean temperature changes, the extreme distribution of temperature also changes substantially, sometimes more than the changes in the mean. From the perspective of hydrology, the surface air temperature's simulation is also crucial for obtaining realistic evapotranspiration, energy exchange between the surface and atmosphere, and phase transition of water near the ground. Because of all these temperature effects, evaluating the surface air temperature simulation is critical in laying the foundation for applying the WRF-CTL and PGW simulations to hydrological modeling, climate projection, and climate change impact analysis.

Figure 3Seasonal mean (from top to bottom: spring, summer, autumn, and winter) daily mean temperature for spring (MAM), summer (JJA), autumn (SON), and winter (DJF) from 2000 to 2015 of ANUSPLIN (left column), WRF-CTL (middle column), and the difference (CTL–ANUSPLIN, right column). The Δ sign indicates the bias of WRF-CTL relative to ANUSPLIN.

3.2 Precipitation

Water resources are of strategic significance for the environment, agriculture, and society, especially for semi-arid regions in most of western Canada. Precipitation is an important component of water balance and is essential for hydrological modeling as all runoff comes from precipitation, either directly or indirectly. The ability of climate models to capture the temporal–spatial characteristics of observed precipitation is crucial for their application as input for hydrological models. GCMs' precipitation simulations are known to be one of the most challenging tasks for climate modelers as precipitation processes involve many subgrid-scale processes that have to be parameterized. Also, due to resolution limits, GCM's precipitation output has to be downscaled to be applicable in regional- and local-scale hydrological and ecological studies. The purpose of the WRF simulations is to dynamically downscale the current climate using ERA-Interim reanalysis and a future RCP8.5 climate projection based on the ensemble mean of 19 CMIP5 models. Especially, using a convection-permitting resolution, the WRF model avoids the utilization of convection parameterization, which introduces large biases and distortion in simulating convective precipitation systems. Figure S6 shows the CMIP5 GCM ensemble mean precipitation that differs from the observed pattern of seasonal precipitation distribution: the high-precipitation band near the British Columbia coast is much broader; the dry area between mountain ranges and a secondary peak on the eastern edge of the Canadian Rockies are missing. Both features are well captured by WRF-CTL. Due to the poor performance of GCM precipitation simulation and coarse-resolution reanalysis, we did not conduct a full evaluation of the WRF-CTL precipitation against any GCM output or reanalysis with coarse resolution (>25 km).

The number of global gridded precipitation datasets has grown in recent years with increasing coverage of satellites; however, the quality of the precipitation analysis is still limited by the number of observation stations over Canada, especially in the complex terrain in the Canadian Rockies and the northern territories, where only a few observation sites scatter across a vast domain. Wong et al. (2017) compared multiple precipitation products over Canada for various climatic zones and river basins against station observation and found the performances of CaPA and ANUSPLIN are generally superior to other datasets, even though both datasets perform poorly in the mountainous regions and northern territories. Furthermore, ANUSPLIN's coverage over the northern part of western Canada relies on a very limited number of stations and shows a large dry bias in the regions. CaPA, a reanalysis dataset, has been shown to have better overall spatial distribution of precipitation than ANUSPLIN (Fortin et al., 2018; Wong et al., 2017). Bearing these in mind, we conducted the evaluation of precipitation against two observation precipitation analysis datasets, ANUSPLIN and CaPA.

As shown in Figs. 4 and S4, the WRF-CTL simulation captures the main precipitation distribution pattern in the observed precipitation from CaPA and ANUSPLIN, respectively: high precipitation near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies in summer. WRF-CTL's spatial pattern more closely resembles CaPA's and bears noticeable difference to ANUSPLIN's, especially over the eastern ranges of the Canadian Rockies. Both CaPA and WRF-CTL are significantly wetter than ANUSPLIN, especially in the mountainous region and northern part. Compared to ANUSPLIN in Fig. S4, WRF-CTL's wet bias mainly resides over the mountain ranges by the Pacific Ocean and in the Canadian Rockies. This wet bias associated with topography is as high as 1.7 mm d⁻¹ and more prominent in winter and spring. It must be considered, though, that gridded observation analyses often underestimate precipitation over mountains, where data are scarce, through interpolation from available lower-elevation observations. East of the Canadian Rockies, there are moderate wet biases (about 0.5–0.9 mm d⁻¹) across the Prairies and the boreal forest. In terms of WRF-CTL's relative bias in reference to ANUSPLIN, there is significant wet bias (+90 %) in the northern domain, including the MRB for all seasons. For the SRB, a large dry relative bias occurs in winter due to low observed precipitation during this season. However, according to the evaluation by Wong et al. (2017), ANUSPLIN underestimates annual precipitation by 10 % to 50 % from the south to north of western Canada relative to gauge observation in the region from 2002 to 2012. Thus, the large wet bias of WRF-CTL relative to ANUSPLIN in the north is largely due to the large dry biases of ANUSPLIN there.

Figure 4Seasonal mean (from top to bottom: spring, summer, autumn, and winter) daily precipitation from CaPA (first column) and WRF-CTL (second column), and their absolute (third column) and relative differences in percentage (fourth column). The Δ sign indicates the bias of WRF-CTL relative to CaPA.

Relative to CaPA, the wet bias of WRF-CTL is generally less in magnitude and less correlated with topography because CaPA assimilates GEM forecast and remote sensing data to better represent orographic precipitation than analysis data, which rely heavily on rain gauges located at lower elevations. The wet bias along the British Columbia coastal mountain ranges and the Canadian Rockies are prominent in spring, autumn, and winter. East of the Canadian Rockies, the wet bias is located mainly over the SRB and southern MRB in spring and summer. There are also regions of dry biases in the region surrounding the MRB and SRB in spring, summer, and autumn. In winter the difference between CTL and CaPA is small east of the Canadian Rockies. It is noteworthy that, according to Wong et al. (2017), the WRF-CTL wet bias relative to CaPA's east of the Canadian Rockies may be partly attributed to CaPA's relatively small dry bias (10 %) relative to station observation.

In summary, the WRF-CTL simulation captures well the spatial distribution of precipitation in all seasons. WRF-CTL's agreement with CaPA is more widespread and consistent. There are wet biases in WRF-CTL over the mountainous region compared to both ANUSPLIN and CaPA. According to the evaluation of Wong et al. (2017), both ANUSPLIN and CaPA show wet bias in the mountainous region compared to station observation, but this may be because the stations are usually situated at low altitudes and thus fail to capture the representative areal precipitation due to the topography. East of the Canadian Rockies, WRF-CTL shows a wet bias relative to ANUSPLIN and CaPA, although both the observation and reanalysis datasets show dry bias from 2002 to 2012 in the region, especially in the northern part.

Figure 5The monthly mean precipitation/temperature averaged over the Mackenzie River basin (a, c) and Saskatchewan River basin (b, d) from 2000 to 2015 from WRF-CTL (black curve) and an ensemble of observation/reanalyses of temperature (NARR, blue; ANUSPLIN, red) and precipitation (NARR, blue; ANUSPLIN, red; CaPA, green).

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Figure 6The mean annual cycle for WRF-CTL (black), NARR (red), and ANUSPLIN (blue) over the Mackenzie River basin (a) and Saskatchewan River basin (b). Monthly basin-averaged precipitation over the Saskatchewan River basin from WRF-CTL, the WRF-CONUS control run, and the ensemble of observation datasets (NARR, ANUSPLIN, CaPA).

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Figure 7The mean annual cycle of monthly precipitation for WRF-CTL (black), NARR (red), and ANUSPLIN (blue) over the Mackenzie River basin (a) and Saskatchewan River basin (b).

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3.3 Basin-averaged statistics

The evaluation of the simulation over the two major river basins focuses on the model performance in simulating the seasonal and interannual variations of the two key variables for hydrology: temperature and precipitation. To validate the WRF simulation results in the MRB and SRB, we compared them with several existing observation and reanalysis products. Figure 5 shows the time series of basin-averaged temperature (top) and precipitation (bottom) in the MRB (left) and SRB (right) for the simulation period, together with different observation and reanalysis datasets (NARR, ANUSPLIN, CaPA). Figure 6 shows the mean annual temperature cycle from WRF-CTL (black), NARR (red), and ANUSPLIN (blue) for the MRB. Figure 7 shows the annual cycle of precipitation for the two basins.

4.1 Near-surface temperature

The daily mean temperature simulated by WRF-CTL and WRF-PGW, and the warming in WRF-PGW relative to CTL are presented in Fig. 8 together with the projected warming by CMIP5 ensemble (2071–2100 to 1976–2005). The temperature increase in WRF-PGW is larger in the northeastern domain and smaller in the southwest, generally reducing the northeast–southwest temperature gradient in CTL climatology in all seasons. The warming is the greatest in winter, with a 10 ^∘C increase in the northeastern quadrant. In the Prairie, the largest warming occurs in the spring. This larger warming over the Prairies is related to the shift of the daily mean temperature from below freezing in early and mid-spring to above freezing, likely causing amplified warming through snow-albedo feedback. The mean temperature in the Yukon and NWT will be similar to those currently experienced in Saskatchewan and Alberta in spring and summer, which has great implications for the length of the growing season in the northern territories. The winter temperature in the coldest region of the domain will be as warm as the central Canadian Prairies in the current climate. The higher temperatures in the boreal forest region will greatly increase the probability of wildfire, water stress, and insect pests, threatening the boreal forest ecosystem, which could eventually be replaced by grassland and parkland (Stralberg et al., 2018). As shown in Figs. 8 and S5, CMIP5 ensemble projection indicates a larger warming for all seasons than WRF projection and different spatial pattern for spring and summer. In spring WRF has larger warming (about 7 ^∘C) in the Canadian Prairies and about 6 ^∘C warming in the north comparing to CMIP5 has a warming of 9 ^∘C in the north and about 5 ^∘C in the Canadian Prairies. In summer, WRF shows a 5 ^∘C warming in most of the domain except for the northeastern corner (about 6 ^∘C warmer); CMIP5 shows a much stronger warming in the southern domain, about 7 ^∘C, south of 55^∘ N. The stronger summer warming in CMIP5 over the southern domain is consistent with the decrease in summer precipitation in the region.

Figure 9From top to the bottom, spring, summer, autumn, and winter seasonal mean daily precipitation are shown for WRF-CTL (first column), WRF-PGW (second column), the difference (PGW–CTL, third column), and percentage difference over CTL (fourth column). On the right-hand side, the projected changes from the CMIP5 ensemble for precipitation (2071–2100 to 1976–2005, fifth column) and in percentage (sixth column) are shown.

4.2 Precipitation

The comparison of WRF-PGW and WRF-CTL precipitation is shown in Fig. 9. Generally speaking, the precipitation will increase in most of the domain. In most places, WRF-PGW shows an increase in precipitation of about 15 %–30 % in all seasons compared with WRF-CTL. Near the British Columbia coast, the magnitude of the increase can be as large as 2 mm d⁻¹. This substantial increase in precipitation in British Columbia's coastal mountains is related to the larger water vapor loading in PGW and the stronger effective orographic lifting to produce precipitation in that region. The change in precipitation is the least in summer, when parts of the Prairies receive less precipitation in PGW than in CTL. With almost no increase in summer precipitation and the much larger evapotranspiration in the Canadian Prairies in PGW than in CTL, the water availability during the growing season will be challenging for the Canadian Prairies. The dynamic downscaling by WRF is less pessimistic for growing season water availability in the Canadian Prairies than CMIP5 ensemble projection in Figs. 9 and S6, which shows a much larger decrease (−10 %–20 %) in precipitation in summer in the southern part of the domain including the SRB and southern MRB. In the northeastern portion of the domain, northern Manitoba and NWT, the precipitation increase could be as large as 0.5–1 mm d⁻¹, with an increase of about 40 % in autumn and winter. The Yukon and central–northern British Columbia are expected to have a 40 % increase in precipitation in winter due to the higher loading of water vapor in a warmer climate. In addition to wetter projection for the Canadian Prairies in summer, the WRF projection also shows a larger increase in precipitation near the British Columbia coast, along the eastern mountain ranges of the Canadian Rockies throughout the year in Fig. 9. These regions have significant orographic lifting, which is better represented in WRF than GCMs, to initiate convection and/or precipitation and convert the higher water vapor concentration in a warmer climate to higher precipitation. GCMs' poor representation of orography may be the reason that less precipitation increases are generated over these terrains.

4.3 Basin-averaged changes compared to CMIP5

Here we compare the regional-averaged temperature and precipitation for two major river basins and for output from CMIP5 vs. 4 km WRF. Figures 10 and 11 show the temperature and precipitation changes between 1976–2005 and 2071–2100 as projected by CMIP5 ensembles and those of WRF-CTL (2000–2015) and PGW with 2070–2100 climate forcing simulation.

Figure 10Annual cycle of temperature and precipitation projected by CMIP5 ensemble. The orange bars indicate the basin-averaged temperature of the current climate (1976–2005). The red bars represent the basin-averaged temperature at the end of the 21st century under RCP8.5 (2076–2100). The white bars denote the change in temperature at the end of the century relative to the current climate. The dark green bars indicate the basin-averaged precipitation of the current climate. The shallow green bars represent the basin-averaged precipitation at the end of the 21st century under RCP8.5. The white bars denote the change in precipitation.

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Figure 11Annual cycle of temperature and precipitation projected by WRF-PGW (2001–2015). The pink bars indicate the basin-averaged temperature of current climate. The red bars represent the basin-averaged temperature at the end of the 21st century under RCP8.5. The white bars denote the change in temperature at the end of the century relative to the current climate. The dark blue bars indicate the basin-averaged precipitation of the current climate. The shallow blue bars represent the basin-averaged precipitation at the end of the 21st century under RCP8.5. The white bars denote the change in precipitation.

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In Figs. 10 and 11, the historical runs are shown in the red/orange columns for temperature for CMIP5/WRF-CTL; the future runs equivalent to the end of the 21st century are shown in the light red/orange columns for the temperature of CMIP5-RCP8.5/WRF-PGW, and the difference between the future simulation and the historical simulation are represented by the white columns. In general, temperature will increase for all months for both CMIP5 and WRF in both basins. The temperature increases in most months for the SRB are smaller in the WRF simulation than they are in CMIP5, especially in summer. The temperature increases in the MRB are about 3 ^∘C smaller in WRF in December and February and about 2 ^∘C smaller in summer. For the MRB, the temperature increase simulated by WRF is smaller than the CMIP5 ensemble mean for most months.

The historical precipitations are shown in the dark blue/green columns in Figs. 10 and 11; the future precipitations equivalent to the end of the 21st century are shown in the light blue/green columns, and the differences between the future run and the historical run are represented by the white columns. The projected changes from the CMIP5 ensemble and WRF show seasonally dependent differences. In the MRB, the precipitation increase in WRF-PGW simulation is lower in April and May and higher in other months compared to that in CMIP5. For the SRB the ensemble of the CMIP5 RCP8.5 projection shows that the winter and early spring precipitation will experience a large increase and the warm season (May–September) precipitation will decrease, especially in July. In contrast to this, WRF shows a large increase in precipitation in June and smaller decreases in precipitation in July and August, with moderate increases in other months in the SRB. Due to this difference in the annual cycle of precipitation change in the SRB, the dynamical downscaling by WRF-PGW shows an increase in precipitation before July, whereas the CMIP5 ensemble projection increases the precipitation before May.

Precipitation in summer and late autumn for the SRB either remains unchanged or shows a decrease for both the WRF-CTL and CMIP5 ensembles. This seasonal difference in precipitation change indicates that the Canadian Prairies and the southern boreal forest biomes will likely see a slight decline in precipitation minus evapotranspiration during the summer months, possibly affecting soil moisture for farming and forest fires. Because the precipitation increases substantially in spring in both the SRB and MRB, when combined with large temperature increases in spring, western Canada may experience more frequent rain-on-snow events that can cause severe flooding. This projection calls for thorough investigations that combine the high-resolution regional climate simulation and state-of-the-art hydrological modeling to quantify the probability of catastrophic flooding in spring over western Canada (Li et al., 2017).

4.4 Daily precipitation frequency distribution

For both hydrological applications and societal impacts, the temporal precipitation distribution and precipitation intensity distribution are as important as the total amount of precipitation. For example, more high-intensity precipitation events tend to cause flash flooding and sharp spikes of runoff, while lower effective precipitation during warm seasons increases the possibility of drought and fire.

The probability density function (PDF) of precipitation shows the distribution of precipitation amounts among both light and intense precipitation events. Figure 12 shows the probability density function of daily precipitation for the simulation of WRL-CTL and WRF-PGW and observation from CaPA and ANUSPLIN in the MRB (top two rows) and SRB (bottom two rows). In the top panel, the precipitation intensity is shown with a linear scale on the x axis and a logarithmic scale on the y axis for a probability density function (PDF) to show the detail in high-end precipitation. Compared to that of ANUSPLIN and CaPA, the WRF-CTL-simulated precipitation shows a heavy tail on the high end of the distribution, indicating that the bias in WRF-CTL mean precipitation relative to ANUSPLIN and CaPA in the MRB is largely caused by a larger number of heavy precipitation events. WRF-PGW future simulation shows an even higher distribution for extreme precipitation events, indicating that these events will become even more severe under future climate conditions. In the lower panel, both log −X and log −Y are used for precipitation and probability density, enabling us to zoom in on the probability distribution of the light-to-moderate events. The red curve, the WRF-PGW future climate simulation, is now underneath CTL, ANUSPLIN, and CaPA curves in events lower than 5 mm d⁻¹, especially in summer (JJA). This means that the MRB is expected to experience fewer moderate precipitation events in addition to an increase in the probability of high-intensity precipitation.

Figure 12Daily precipitation probability density function in the Mackenzie River basin (top two rows) and Saskatchewan River basin (bottom two rows) for WRF (CTL, PGW) and CaPA and ANUSPLIN with linear–log and log–log axes for density (y) and amount (x).

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For the SRB, the bottom two rows of Fig. 12, the difference between WRF-CTL (blue curve), ANUSPLIN, and CaPA is less than that in the MRB. This difference is consistent with the spatial distribution of precipitation bias in Figs. 4 and S4, where the bias in the SRB is much smaller than it is in the MRB. WRF-PGW shows that heavy precipitation events increase and that their distribution trends towards more extreme intensive events in all seasons, especially in summer. Similarly to the MRB, there is also a decrease in moderate precipitation events, shown in the log −log plot in the second row. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in total amounts in summer for both basins may not reflect the real change in flooding risk and water availability for agriculture. Although the total amount of precipitation is expected to increase in the future, there will be less water for agriculture because extreme precipitation will contribute more to runoff than soil moisture, reducing its accessibility to crops.

As seen in Fig. 12, the intervals between light to moderate precipitation events increase, because the total summer precipitation slightly increases and heavy precipitation events significantly increase, while the atmosphere needs more time to replenish water vapor (Dai et al., 2017; Trenberth et al., 2003). Dry spells also increase in frequency because both evaporation and the intervals between precipitation events increase. The intensification of droughts will have a wide-reaching impact beyond the agricultural sector: conditions are likely to be ideal for wildfires, like those experienced across the western provinces and territories from 2014 to 2018.

Figure 13Three hourly precipitation distribution from station observation in the MRB (left) and SRB (right) and those corresponding to WRF-CTL and WRF-PGW simulations.

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4.5 Hourly precipitation extremes

The future distribution of subdaily precipitation extremes in western Canada is of particular concern, as they can cause flash floods and landslides, which damage human infrastructure and result in injuries and deaths. Here we compare the 3-hourly precipitation rate distribution among station observation, WRF-CTL, and WRF-PGW in the two basins and then investigate the changes in the hourly precipitation rate distribution. The 3-hourly precipitation rate is first compared to observation to evaluate the extreme precipitation simulation at 3 h intervals. The 3-hourly precipitation histograms for extreme precipitation events in the MRB and SRB are shown in Fig. 13. WRF simulations are compared with EC station observations in Fig. 1 because these station observations are closer to the ground truth than the gridded observational products for which the spatial resolution is 10 km at most and are shown to have biases (Wong et al., 2017). Only the moderate to extreme values of precipitation distribution are shown here by cutting it off at a precipitation rate of 5 mm/3 h. WRF-CTL's precipitation distribution (the blue columns) is close to that of the station observation (the gray columns) in the SRB in spring, summer, and autumn, whereas WRF-CTL produces more light to moderate precipitation events than observation in the MRB in most seasons except spring. These results indicate that the WRF simulation captures the local precipitation extremes in all seasons well, except winter in the SRB. WRF also shows a wet bias in light to moderate rain events in the MRB in all seasons but spring, while WRF-PGW simulations (the red columns) show a significant increase in the frequency of high-intensity rainfall events across seasons.

For the MRB, the WRF-CTL 3-hourly precipitation events are much more frequent than those captured by observation in the 5 mm/3 h range but comparable and less frequent at a higher rate in autumn and winter. In spring WRF-CTL shows fewer extreme precipitation events than observation. In summer WRF-CTL shows more extreme events than observation at most precipitation bins. For autumn, spring, and winter WRF-PGW sees a significant increase, 50 %, 150 %, and 300 % for 5 mm/3 h, respectively, in precipitation events. The change in the number of extreme precipitation events in the MRB in the 5–10 mm/3 h range is negligible in summer but significant at higher precipitation rates.

Figure 14Hourly extreme precipitation frequency density over western Canada from station observation, WRF-CTL and PGW. The bottom panel shows the ratio between PGW and CTL for events with different intensities.

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For the SRB, the WRF-CTL agrees well with observation in spring, summer, and autumn in terms of moderate to extreme 3 h precipitation events, but significantly underestimates the extreme precipitation events in winter. In spring, autumn, and winter WRF-PGW shows significant increases in extreme precipitation events. In summer WRF-PGW shows a small decrease in precipitation events at 5 mm/3 h and only moderate increases for higher rates. It is also worth mentioning that extreme events are much more numerous in the SRB than in the MRB, especially in spring and summer because the seasonal mean precipitation is higher in the SRB.

Figure 15(a–c) Extreme statistics of daily maximum temperature in summer WRF-CTL vs. WRF-PGW, 95th percentile. (d–f) Extreme statistics of daily minimum temperature in winter WRF-CTL vs. WRF-PGW, 5th percentile.

Figure 14 shows the changes in hourly precipitation distribution between surface observation and WRF simulations at a 1 h interval. The black line represents observation data collected from 232 surface stations in the SRB and MRB from Environment Climate Change Canada (Website: http://climate.weather.gc.ca/index_e.html, last access: 18 December 2018). The blue and red bars are the closest grid points to these stations extracted every hour (in total 113 952 time steps in 13 years) from the WRF domain for CTL and PGW runs, respectively. Despite the spatial scarcity and data quality associated with station observation, the results do provide some evaluation of the WRF-simulated hourly rainfall, from small to extreme. The majority of hourly precipitation simulated by WRF-CTL is close to that in observations, within the range of 1–10 mm h⁻¹. In this range, future rainfall shows little increase compared to that under the current climate, with even a slight decrease in the amount of light rainfall. The higher hourly rainfall at the high end of the distribution (>10 mm h⁻¹), although comprising only 0.5 % of density in total events, shows a dramatic increase by a probability of 1.5 to 3 times in frequency in the future warmer climate. Notably, the density in the high-end distribution is much higher in the station observation than in CTL because the denominator for observed density, the total number of events, is significantly less in observation, although the absolute number of high-intensity events is comparable or higher in WRF-CTL. In addition to a greater likelihood of the high end of extreme rainfall occurring, a slight decrease in light rainfall is also evident, supporting previous findings from other modeling studies (Cubasch et al., 2013; Easterling et al., 2000; Karl et al., 2006).