Canadian Regional Climate Model Research Articles

Projected changes to extreme wind and snow environmental loads for buildings and infrastructure across Canada

Wind and snow are major environmental loads that are often considered in the design of buildings and infrastructure. To ensure safety of existing structures and to develop guidelines for future developments, it is important to evaluate how these design loads will be impacted by the anticipated climate change. This study evaluates projected changes to selected return levels of wind speed and snow water equivalent (SWE) and associated wind pressure and ground snow loads across Canada for the future 2071–2100 period. Canadian Regional Climate Model (CRCM5) simulations driven by two Global Climate Models (GCMs) for two future emission scenarios are used. The CRCM5 projections suggest some increases in the future 50-year return levels of wind speed and pressure, mainly due to changes in inter-annual variability of annual maximum wind speed, particularly for the central and eastern regions. As for SWE loads, results suggest general decreases for southern Canada and increases for northern Canada in the 50-year return levels. However, the projections, particularly for wind loads, vary considerably with the driving GCM and the emission scenario, suggesting that larger ensembles including more RCMs and driving GCMs will be required to better quantify uncertainties to support development of climate-resilient design standards and codes.

Exploring a Variable‐Resolution Approach for Simulating Regional Climate in the Rocky Mountain Region Using the VR‐CESM

AbstractThe reliability of climate simulations and projections, particularly in the regions with complex terrains, is greatly limited by the model resolution. In this study we evaluate the variable‐resolution Community Earth System Model (VR‐CESM) with a high‐resolution (0.125°) refinement over the Rocky Mountain region. The VR‐CESM results are compared with observations, as well as CESM simulation at a quasi‐uniform 1° resolution (UNIF) and Canadian Regional Climate Model version 5 (CRCM5) simulation at a 0.11° resolution. We find that VR‐CESM is effective at capturing the observed spatial patterns of temperature, precipitation, and snowpack in the Rocky Mountains with the performance comparable to CRCM5, while UNIF is unable to do so. VR‐CESM and CRCM5 simulate better the seasonal variations of precipitation than UNIF, although VR‐CESM still overestimates winter precipitation whereas CRCM5 and UNIF underestimate it. All simulations distribute more winter precipitation along the windward (west) flanks of mountain ridges with the greatest overestimation in VR‐CESM. VR‐CESM simulates much greater snow water equivalent peaks than CRCM5 and UNIF, although the peaks are still 10–40% less than observations. Moreover, the frequency of heavy precipitation events (daily precipitation ≥ 25 mm) in VR‐CESM and CRCM5 is comparable to observations, whereas the same events in UNIF are an order of magnitude less frequent. In addition, VR‐CESM captures the observed occurrence frequency and seasonal variation of rain‐on‐snow days and performs better than UNIF and CRCM5. These results demonstrate the VR‐CESM's capability in regional climate modeling over the mountainous regions and its promising applications for climate change studies.

Dynamical downscaling with the fifth-generation Canadian regional climate model (CRCM5) over the CORDEX Arctic domain: effect of large-scale spectral nudging and of empirical correction of sea-surface temperature

As part of the CORDEX project, the fifth-generation Canadian Regional Climate Model (CRCM5) is used over the Arctic for climate simulations driven by reanalyses and by the MPI-ESM-MR coupled global climate model (CGCM) under the RCP8.5 scenario. The CRCM5 shows adequate skills capturing general features of mean sea level pressure (MSLP) for all seasons. Evaluating 2-m temperature (T2m) and precipitation is more problematic, because of inconsistencies between observational reference datasets over the Arctic that suffer of a sparse distribution of weather stations. In our study, we additionally investigated the effect of large-scale spectral nudging (SN) on the hindcast simulation driven by reanalyses. The analysis shows that SN is effective in reducing the spring MSLP bias, but otherwise it has little impact. We have also conducted another experiment in which the CGCM-simulated sea-surface temperature (SST) is empirically corrected and used as lower boundary conditions over the ocean for an atmosphere-only global simulation (AGCM), which in turn provides the atmospheric lateral boundary conditions to drive the CRCM5 simulation. This approach, so-called 3-step approach of dynamical downscaling (CGCM-AGCM-RCM), which had considerably improved the CRCM5 historical simulations over Africa, exhibits reduced impact over the Arctic domain. The most notable positive effect over the Arctic is a reduction of the T2m bias over the North Pacific Ocean and the North Atlantic Ocean in all seasons. Future projections using this method are compared with the results obtained with the traditional 2-step dynamical downscaling (CGCM-RCM) to assess the impact of correcting systematic biases of SST upon future-climate projections. The future projections are mostly similar for the two methods, except for precipitation.

Changes in Ocean Temperature in the Barents Sea in the Twenty-First Century

AbstractPossible modifications to ocean temperature in the Barents Sea induced by climate change are explored. The simulations were performed with a coupled ice–ocean model (CIOM) driven by the surface fields from the Canadian Regional Climate Model (CRCM) simulations. CIOM can capture the observed water volume inflow through the Barents Sea Opening. The CIOM simulation and observations suggest an increase in the Atlantic water volume inflow and heat transport into the Barents Sea in recent decades resulting from enhanced storm activity. While seasonal variations of sea ice and sea surface temperature in CIOM simulations are comparable with observations, CIOM results underestimate the sea surface temperature but overestimate ice cover in the Barents Sea, consistent with an underestimated heat transport through the Barents Sea Opening. Under the SRES A1B scenario, the loss of sea ice significantly increases the surface solar radiation and the ocean surface heat loss through turbulent heat fluxes and longwave radiation. Meanwhile, the lateral heat transport into the Barents Sea tends to increase. Thus, changes in ocean temperature depend on the heat balance of solar radiation, surface turbulent heat flux, and lateral heat transport. During the 130-yr simulation period (1970–2099), the average ocean temperature increases from 0° to 1°C in the southern Barents Sea, mostly due to increased lateral heat transport and solar radiation. In the northern Barents Sea, ocean temperature decreases by 0.4°C from the 2010s to the 2040s and no significant trend can be seen thereafter, when the surface heat flux is balanced by solar radiation and lateral heat transport and there is no notable net heat flux change.

Snow-atmosphere coupling and its impact on temperature variability and extremes over North America

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981–2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40–60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

The Role of Soil Moisture–Atmosphere Interaction on Future Hot Spells over North America as Simulated by the Canadian Regional Climate Model (CRCM5)

Soil moisture–atmosphere interactions play a key role in modulating climate variability and extremes. This study investigates how soil moisture–atmosphere coupling may affect future extreme events, particularly the role of projected soil moisture in modulating the frequency and maximum duration of hot spells over North America, using the fifth-generation Canadian Regional Climate Model (CRCM5). With this objective, CRCM5 simulations, driven by two coupled general circulation models (MPI-ESM and CanESM2), are performed with and without soil moisture–atmosphere interactions for current (1981–2010) and future (2071–2100) climates over North America, for representative concentration pathways (RCPs) 4.5 and 8.5. Analysis indicates that, in future climate, the soil moisture–temperature coupling regions, located over the Great Plains in the current climate, will expand farther north, including large parts of central Canada. Results also indicate that soil moisture–atmosphere interactions will play an important role in modulating temperature extremes in the future by contributing more than 50% to the projected increase in hot-spell days over the southern Great Plains and parts of central Canada, especially for the RCP4.5 scenario. This higher contribution of soil moisture–atmosphere interactions to the future increases in hot-spell days for RCP4.5 is related to the fact that the projected decrease in soil moisture caused the soil to remain in a transitional regime between wet and dry state that is conducive to soil moisture–atmosphere coupling. For the RCP8.5 scenario, on the other hand, the future projected soil state over the southern United States and northern Mexico is too dry to have an impact on evapotranspiration and therefore on temperature.

Investigating added value of regional climate modeling in North American winter storm track simulations

Extratropical Cyclone (EC) characteristics depend on a combination of large-scale factors and regional processes. However, the latter are considered to be poorly represented in global climate models (GCMs), partly because their resolution is too coarse. This paper describes a framework using possibilities given by regional climate models (RCMs) to gain insight into storm activity during winter over North America (NA). Recent past climate period (1981–2005) is considered to assess EC activity over NA using the NCEP regional reanalysis (NARR) as a reference, along with the European reanalysis ERA-Interim (ERAI) and two CMIP5 GCMs used to drive the Canadian Regional Climate Model—version 5 (CRCM5) and the corresponding regional-scale simulations. While ERAI and GCM simulations show basic agreement with NARR in terms of climatological storm track patterns, detailed bias analyses show that, on the one hand, ERAI presents statistically significant positive biases in terms of EC genesis and therefore occurrence while capturing their intensity fairly well. On the other hand, GCMs present large negative intensity biases in the overall NA domain and particularly over NA eastern coast. In addition, storm occurrence over the northwestern topographic regions is highly overestimated. When the CRCM5 is driven by ERAI, no significant skill deterioration arises and, more importantly, all storm characteristics near areas with marked relief and over regions with large water masses are significantly improved with respect to ERAI. Conversely, in GCM-driven simulations, the added value contributed by CRCM5 is less prominent and systematic, except over western NA areas with high topography and over the Western Atlantic coastlines where the most frequent and intense ECs are located. Despite this significant added-value on seasonal-mean characteristics, a caveat is raised on the RCM ability to handle storm temporal ‘seriality’, as a measure of their temporal variability at a given location. In fact, the driving models induce some significant footprints on the RCM skill to reproduce the intra-seasonal pattern of storm activity.

Consideration of land-use and land-cover changes in the projection of climate extremes over North America by the end of the twenty-first century

Changes in the essential climate extremes indices and surface variables for the end of the twenty-first century are assessed in this study based on two transient climate change simulations, with and without land-use and land-cover changes (LULCC), but identical atmospheric forcing. The two simulations are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by the Canadian Earth System Model for the (2006–2100)-Representative Concentration Pathway 4.5 (RCP4.5) scenario. For the simulation with LULCC, land-cover data sets are taken from the global change assessment model (GCAM) representing the RCP4.5 scenario for the period 2006–2100. LULCC in RCP4.5 scenario suggest significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to afforestation. CRCM5 climate projections imply a general warming by the end of the twenty-first century, especially over the northern regions in winter. CRCM5 projects more warm spell-days per year over most areas of the continent, and implicitly more summer days and tropical nights at the expense of cold-spell, frost and ice days whose number is projected to decrease by up to 40% by the end of the twenty-first century with respect to the baseline period 1971–2000. Most land areas north of 45°N, in all seasons, as well as the southeastern United States in summer, exhibit increases in mean precipitation under the RCP4.5 scenario. In contrast, central parts of the continent in summer and much of Mexico in all seasons show reduced precipitation. In addition, large areas of North America exhibit changes of 10 to 40% (depending on the season and geographical location) in the number of heavy precipitation days. Results also suggest that the biogeophysical effects of LULCC on climate, assessed through differences between the two simulations, lead to warmer regional climates, especially in winter. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Overall, at the seasonal scale, results show that intense afforestation may contribute to an additional 25% of projected changes.

Offline Implementation and Evaluation of the Canadian Small Lake Model with the Canadian Land Surface Scheme over Western Canada

Abstract The Canadian Small Lake Model (CSLM), version 2, was run with the Canadian Land Surface Scheme (CLASS), version 3.6.1, in an offline regional test over western Canada. Forcing data were derived from ERA-Interim and downscaled using the fifth-generation Canadian Regional Climate Model (CRCM5). The forcing precipitation field was adjusted using monthly data from the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) observation-based dataset. The modeled surface air temperature was evaluated against CANGRD data, the modeled albedo against MODIS data, and the modeled snow water equivalent against Canadian Meteorological Centre (CMC) and Global Snow Monitoring for Climate Research (GlobSnow) data. The lake simulation itself was evaluated using the Along Track Scanning Radiometer (ATSR) Reprocessing for Climate: Lake Surface Water Temperature and Ice Cover (ARC-Lake) dataset. Summer surface lake temperatures and the lake ice formation and breakup periods were well simulated, except for slight warm/cold summer/fall surface temperature biases, early ice breakup, and early ice formation, consistent with warm/cold biases in the climate simulation. Tests were carried out to investigate the sensitivity of the CSLM simulation to the default values assigned to the shortwave extinction coefficient and the average lake depth, and changing the former from 0.5 to 2.0 m−1 and the latter from 10.0 to 50.0 or 5.0 m had minimal effects on the simulation. Comparisons of the average annual variations of the simulated net shortwave radiation, turbulent fluxes, snowpack, and maximum and minimum daily surface temperatures between the land and the lake fractions for tundra, boreal, and southern regions showed patterns consistent with those expected. Finally, a test of the CSLM over the large resolved lakes in the model domain demonstrated a performance comparable to that for subgrid lakes.

Global change induced biomass growth offsets carbon released via increased forest fire and respiration of the central Canadian boreal forest

AbstractNorthern boreal forests are sensitive to many effects of global change. This is of particular concern due to the proportionally greater climate change projected for the area in which these forests occur. One of the sensitive areas is the Far North of Ontario (FNO), consisting of one of the world's largest remaining tracts of unmanaged boreal forest, the world's third largest area of wetland, and the most southerly area of tundra. We studied past, present, and potential future carbon (C) balance of FNO forests using the Integrated Terrestrial Ecosystem Carbon Model and the Canadian Regional Climate Model with stand‐replacing fire disturbance. The forced simulations of past (1901–2004) C balances indicated that vegetation C stock remained stable, while soil C stock gradually declined (−0.07 t C ha−1 yr−1, p < 0.001), resulting in an overall significant decrease in total ecosystem C balance (−0.07 t C ha−1 yr−1, p < 0.001). Two Representative Concentration Pathways (RCPs), RCP8.5 and RCP4.5, simulations of future (2007–2100) C balances indicated that the carbon dioxide fertilization and climate growth‐enhancing effects of global change will outweigh C loss through increased ecosystem respiration, disturbance, and changes in forest age class structure resulting in an increase in total FNO ecosystem C stock by mid‐21st century. However, the projected simulations also indicated that the relative sizes of forest C stocks will change, with relatively less in the soil and more in vegetation, increasing fuel loads and making the entire ecosystem susceptible to forest fire and insect disturbances.

Evaluation of CLASS Snow Simulation over Eastern Canada

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

Impacts of climate changes on ocean surface gravity waves over the eastern Canadian shelf

A numerical study is conducted to investigate the impact of climate changes on ocean surface gravity waves over the eastern Canadian shelf (ECS). The “business-as-usual” climate scenario known as Representative Concentration Pathway RCP8.5 is considered in this study. Changes in the ocean surface gravity waves over the study region for the period 1979–2100 are examined based on 3 hourly ocean waves simulated by the third-generation ocean wave model known as WAVEWATCHIII. The wave model is driven by surface winds and ice conditions produced by the Canadian Regional Climate Model (CanRCM4). The whole study period is divided into the present (1979–2008), near future (2021–2050) and far future (2071–2100) periods to quantify possible future changes of ocean waves over the ECS. In comparison with the present ocean wave conditions, the time-mean significant wave heights (H s ) are expected to increase over most of the ECS in the near future and decrease over this region in the far future period. The time-means of the annual 5% largest H s are projected to increase over the ECS in both near and far future periods due mainly to the changes in surface winds. The future changes in the time-means of the annual 5% largest H s and 10-m wind speeds are projected to be twice as strong as the changes in annual means. An analysis of inverse wave ages suggests that the occurrence of wind seas is projected to increase over the southern Labrador and central Newfoundland Shelves in the near future period, and occurrence of swells is projected to increase over other areas of the ECS in both the near and far future periods.

Impact of Lateral Boundary Conditions on Regional Analyses

Abstract Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-Interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large-scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging significantly improved the circulation through the lateral boundaries, which translated in a much better agreement with observations.

Evaluating the Ability of CRCM5 to Simulate Mixed Precipitation

ABSTRACTPrecipitation episodes in the form of freezing rain and ice pellets represent natural hazards affecting eastern Canada during the cold season. These types of precipitation mainly occur in the St. Lawrence River valley and the Atlantic provinces of Canada. This study aims to evaluate the ability of the fifth-generation Canadian Regional Climate Model (CRCM5), using a 0.11° horizontal grid mesh, to hindcast mixed precipitation when driven by reanalyses produced by the European Centre for Medium-range Weather Forecasts (ERA-Interim) for a 35-year period. In general, the CRCM5 simulation slightly overestimates the occurrence of freezing rain, but the geographical distribution is well reproduced. The duration of freezing rain events and accompanying surface winds in the Montréal region are reproduced by CRCM5. A case study is performed for an especially catastrophic freezing-rain event in January 1998; the model succeeds in simulating the intensity and duration of the episode, as well as the propitious meteorological environment. Overall, the model is also able to reproduce the climatology and a specific event of freezing rain and ice pellets.

Rain-on-snow events over North America based on two Canadian regional climate models

This study evaluates projected changes to rain-on-snow (ROS) characteristics (i.e., frequency, rainfall amount, and runoff) for the future 2041–2070 period with respect to the current 1976–2005 period over North America using six simulations, based on two Canadian RCMs, driven by two driving GCMs for RCP4.5 and 8.5 emission pathways. Prior to assessing projected changes, the two RCMs are evaluated by comparing ERA-Interim driven RCM simulations with available observations, and results indicate that both models reproduce reasonably well the observed spatial patterns of ROS event frequency and other related features. Analysis of current and future simulations suggest general increases in ROS characteristics during the November–March period for most regions of Canada and for northwestern US for the future period, due to an increase in the rainfall frequency with warmer air temperatures in future. Future ROS runoff is often projected to increase more than future ROS rainfall amounts, particularly for northeastern North America, during snowmelt months, as ROS events usually accelerate snowmelt. The simulations show that ROS event is a primary flood generating mechanism over most of Canada and north-western and -central US for the January–May period for the current period and this is projected to continue in the future period. More focused analysis over selected basins shows decreases in future spring runoff due to decreases in both snow cover and ROS runoff. The above results highlight the need to take into consideration ROS events in water resources management adaptation strategies for future climate.

An assessment of Pinus contorta seed production in British Columbia: Geographic variation and dynamically-downscaled climate correlates from the Canadian Regional Climate Model

The ecological and economic importance of lodgepole pine (Pinus contorta Douglas ex Louden) in British Columbia (BC) has heightened interest in the adaptability and effective management of the species, especially as climate changes. The relationship between climate and the seed production of natural populations is a key management issue that has yet to be assessed. The purpose of this study is to determine if variation in P. contorta seed yield is related to the climate of BC.Regional differences in the seed production of lodgepole pine were examined using 1924 archived seedlot collections across 18 different natural stand seed planning zones (SPZs) in BC. The relationship between climate variation and the seed production of P. contorta was then evaluated using dynamically-downscaled output from the Canadian Regional Climate Model (CRCM).Seed production is relatively consistent across SPZs spanning a wide range of climate regimes, with the exception of Nass Skeena Transition (NST) where seed yield is an order of magnitude higher than elsewhere. Significant temporal correlations between overall trends in seed production and both temperature and precipitation were found using the CRCM output. However, only three of the 18 SPZs showed a significant overall trend in mean annual seed yield based on cone collections made between 1963 and 2013, suggesting that the reproductive capacity of natural populations is well adapted to decadal-scale climate change. Tolerance to significant variation in climate likely plays an important role in explaining the ability of this species to thrive well outside its natural range.

Modeling soil organic carbon in corn ( Zea mays L.)-based systems in Ohio under climate change

Soil organic carbon (SOC) is a key indicator of soil quality. Knowledge of the effects of land management and climate change on SOC stocks is of vital importance in creating future sustainable land use systems. This study presents both the promise and current challenges of modeling SOC in mineral soils under climate change. Soils data from two long-term agricultural research sites in Ohio under no-till (NT) and plow-till (PT) management, the RothC soil C model, and climate data from the Canadian Regional Climate Model were used to project future SOC content in agricultural soils using low-emissions (LE) and high-emissions (HE) climate change scenarios. It was hypothesized that from 2015 to 2070, SOC levels in soils under NT management in Ohio will show increasing trends under the LE scenario, decreasing trends in NT under the HE scenario, and decreasing trends in PT under both scenarios, with lower levels of SOC for both treatments under the HE scenario. The results of this study projected total SOC content in the topsoil layers (0 to 25 cm [0 to 10 in] at Wooster and 0 to 23 cm [0 to 9 in] at Hoytville) to decrease at all sites and under all management and climate projections, with the exception of NT at Wooster and Hoytville and PT at Wooster under the LE scenario. Starting at 32.4 Mg C ha⁻¹ (14.5 tn C ac⁻¹) in 1962 at Wooster, by 2070, soil under NT management is projected to have 45.4 and 32.1 Mg C ha⁻¹ (20.3 and 14.3 tn C ac⁻¹) for LE and HE scenarios, respectively, while PT management starting at 31.5 Mg C ha⁻¹ (14.1 tn C ac⁻¹) would have 29.4 and 21 Mg C ha⁻¹ (13.1 and 9.4 tn C ac⁻¹) for LE and HE scenarios, respectively. Starting at 65.2 Mg C ha⁻¹ (29.1 tn C ac⁻¹) in 1963 at Hoytville, by 2070, soil under NT management would have 65.9 and 51 Mg C ha⁻¹ (29.4 and 22.8 tn C ac⁻¹) for LE and HE scenarios, respectively, and PT starting at 63.5 Mg C ha⁻¹ (28.3 tn C ac⁻¹) would have 36.9 and 28.7 Mg C ha⁻¹ (16.5 and 12.8 tn C ac⁻¹) for LE and HE scenarios, respectively.

Spatialization of the SNOWPACK snow model for the Canadian Arctic to assess Peary caribou winter grazing conditions

Peary caribou is the northernmost designatable unit for caribou species, and its population has declined by about 70% over the last three generations. The Committee on the Status of Endangered Wildlife in Canada identified difficult grazing conditions through the snow cover as being the most significant factor contributing to this decline. This study focuses on a spatially explicit assessment tool using snow model simulations (Swiss SNOWPACK model driven in an off-line mode by spatialized meteorological forcing data generated by the Canadian Regional Climate Model) to characterize snow conditions for Peary caribou grazing in the Canadian Arctic. The life cycle of Peary caribou has been subdivided into three critical periods: summer foraging and fall breeding (July–October), winter foraging (November–March), and spring calving (April–June). Winter snow conditions are analyzed and snow simulations compared to Peary caribou island counts to identify a snow parameter that could potentially act as a proxy for grazing conditions and explain fluctuations in Peary caribou numbers. This analysis concludes that caribou counts are affected by simulated snow density values >300 kg m−3. A software tool mapping possibly favorable and unfavorable grazing conditions based on snow is proposed at a regional scale across the Canadian Arctic Archipelago. Specific output examples are given to show the utility of the tool, mapping pixels with cumulative snow thickness above densities of 300 kg m−3, where cumulative seasonal thicknesses >7000 cm are considered unfavorable.

Impacts of boreal hydroelectric reservoirs on seasonal climate and precipitation recycling as simulated by the CRCM5: a case study of the La Grande River watershed, Canada

Located in northern Quebec, Canada, eight hydroelectric reservoirs of a 9782-km2 maximal area cover 6.4% of the La Grande watershed. This study investigates the changes brought by the impoundment of these reservoirs on seasonal climate and precipitation recycling. Two 30-year climate simulations, corresponding to pre- and post-impoundment conditions, were used. They were generated with the fifth-generation Canadian Regional Climate Model (CRCM5), fully coupled to a 1D lake model (FLake). Seasonal temperatures and annual energy budget were generally well reproduced by the model, except in spring when a cold bias, probably related to the overestimation of snow cover, was seen. The difference in 2-m temperature shows that reservoirs induce localized warming in winter (+0.7 ± 0.02 °C) and cooling in the summer (−0.3 ± 0.02 °C). The available energy at the surface increases throughout the year, mostly due to a decrease in surface albedo. Fall latent and sensible heat fluxes are enhanced due to additional energy storage and availability in summer and spring. The changes in precipitation and runoff are within the model internal variability. At the watershed scale, reservoirs induce an additional evaporation of only 5.9 mm year−1 (2%). We use Brubaker’s precipitation recycling model to estimate how much of the precipitation is recycled within the watershed. In both simulations, the maximal precipitation recycling occurs in July (less than 6%), indicating weak land-atmosphere coupling. Reservoirs do not seem to affect this coupling, as precipitation recycling only decreased by 0.6% in July.

The intraannual variability of land‐atmosphere coupling over North America in the Canadian Regional Climate Model (CRCM5)

AbstractThis study investigates the intraannual variability of soil moisture‐temperature coupling over North America. To this effect, coupled and uncoupled simulations are performed with the fifth‐generation Canadian Regional Climate Model (CRCM5), driven by ERA‐Interim. In coupled simulations, land and atmosphere interact freely; in uncoupled simulations, the interannual variability of soil moisture is suppressed by prescribing climatological values for soil liquid and frozen water contents. The study also explores projected changes to coupling by comparing coupled and uncoupled CRCM5 simulations for current (1981–2010) and future (2071–2100) periods, driven by the Canadian Earth System Model. Coupling differs for the northern and southern parts of North America. Over the southern half, it is persistent throughout the year while for the northern half, strongly coupled regions generally follow the freezing line during the cold months. Detailed analysis of the southern Canadian Prairies reveals seasonal differences in the underlying coupling mechanism. During spring and fall, as opposed to summer, the interactive soil moisture phase impacts the snow depth and surface albedo, which further impacts the surface energy budget and thus the surface air temperature; the air temperature then influences the snow depth in a feedback loop. Projected changes to coupling are also season specific: relatively drier soil conditions strengthen coupling during summer, while changes in soil moisture phase, snow depth, and cloud cover impact coupling during colder months. Furthermore, results demonstrate that soil moisture variability amplifies the frequency of temperature extremes over regions of strong coupling in current and future climates.