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The lower Athabasca River (Canada) has experienced notable declines in streamflow and increasing oil sands development since the 1970s. This study investigates the potential impacts of climate change on navigability using both observed historical and projected future flows derived via hydrological simulations driven by an ensemble of statistically downscaled general circulation model climate data. Our use of proposed indices that form the Aboriginal Navigation Index (ANI) and a new index based on percentage over threshold (POT) occurrences yielded novel insights into anticipated changes to the flow regime. Comparisons of near (2041–2070) and far (2071–2100) future periods with the historical baseline (1981–2010) yielded results that project significant reductions in the 500 m3 s−1 POT during the fall navigability period spanning weeks 34 to 43, as well as reductions in the integrated ANIFall. These results indicate that challenging navigational conditions may become more frequent in the second half of the 21st century, not only during this fall period but also earlier into the summer, due to a shift in the flow regime, with potentially severe impacts on the users of the river channels. Our assessment approach is transferable to other regional study areas and should be considered in water management and environmental flow frameworks. [ABSTRACT FROM AUTHOR]

Winter roads are seasonal roads that only exist during the winter – they run over frozen land and frozen lakes and rivers. Many northern communities in Canada rely on them for their yearly supplies of bulk goods, including fuel and building supplies, which are too costly to ship by air. Because of a warming climate, a progressive shortening of the operational time windows is observed, and is predicted to continue based on climate model projections. Compared to all-season roads, winter roads are less well understood; they are also unevenly managed across Canada. This state of affairs represents a liability for Northerners and could be addressed via the systematic characterization of individual roads. This would help the assessment of community vulnerability and costs for remediation measures. It would also guide decision-making and prioritization. [ABSTRACT FROM AUTHOR]

This study evaluates the uncertainty of four components of the hydroclimatic modelling chain on flood projections over 96 basins covering contrasting hydrometeorological regimes located in Canada and Mexico. Two ensembles of climate simulations are considered, a large ensemble of 22 global climate model simulations and a smaller ensemble of three high-resolution regional climate model simulations. The other components are two post-processing techniques, three lumped hydrological models and six probability distributions. These four sources are assessed through a method of variance decomposition applied to six flood indicators over a reference period and two future periods: 1976–2005, 2041–2070 and 2070–2099. Systematic differences are observed between basins with contrasting flood-generating processes. Snow-dominated basins consistently show larger variance contributions from hydrological models, while rain-dominated basins show climate simulations as their dominant source. These results underline the need to consider the variability of each component's uncertainty contribution and its link to hydroclimatic conditions and dominant processes. [ABSTRACT FROM AUTHOR]

Infrastructure and transportation systems on which northern communities rely are exposed to a variety of climatic hazards over a broad range of scales. Efforts to adapt these systems to the rapidly warming Arctic climate require high-quality climate projections. Here, a state-of-the-art regional climate model is used to perform simulations at 4-km resolution over the eastern and central Canadian Arctic. These include, for the first time over this region, high-resolution climate projections extending to the year 2040. Validation shows that the model adequately simulates base climate variables, as well as variables hazardous to northern engineering and transportation systems, such as degrading permafrost, extreme rainfall, and extreme wind gust. Added value is found over coarser resolution simulations. A novel approach integrating climate model output and machine learning is used for deriving fog—an important, but complex hazard. Hotspots of change to climatic hazards over the next two decades (2021–2040) are identified. These include increases to short-duration rainfall intensity extremes exceeding 50%, suggesting Super–Clausius–Clapeyron scaling. Increases to extreme wind gust pressure are projected to reach 25% over some regions, while widespread increases in active layer thickness and ground temperature are expected. Overall fog frequency is projected to increase by around 10% over most of the study region by 2040, due to increasing frequency of high humidity conditions. Given that these changes are projected to be already underway, urgent action is required to successfully adapt northern transportation and engineering systems located in regions where the magnitude of hazards is projected to increase. [ABSTRACT FROM AUTHOR]

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45–52 ∘ N, 119–123 ∘ W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 ∘ C hotter than a 1-in-1000-year heat wave would have been in 1850–1900, when global mean temperatures were 1.2 ∘ C cooler than today. Looking into the future, in a world with 2 ∘ C of global warming (0.8 ∘ C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future. [ABSTRACT FROM AUTHOR]

Freezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events. Plain Language Summary: Freezing rain events, or ice storms, can have major impacts on electrical infrastructure, agriculture, and road and air travel. Despite these impacts, relatively little research has been done on how these events may change as the Earth warms. We therefore examine several climate model simulations to determine how the frequency of freezing rain may change at different levels of future global warming. We focus in particular on how sensitive the projected changes are to the method used to identify freezing rain in the model output, as well as to the choice of climate model used to produce the projections. We find strong agreement among methods and models on a decrease in freezing rain frequency over much of the United States (from Texas northeastward to Maine) and an increase in freezing rain frequency over portions of western Canada (Alberta, Saskatchewan, Manitoba). In many other areas, however, the different methods and simulations disagree on the direction of projected change. Our findings highlight the importance of using many different climate models, rather than single simulations, to paint a clearer picture of the level of certainty in projections of freezing rain in the context of global warming. Key Points: Freezing rain is projected to increase in frequency over portions of western and central Canada and decrease over most of the United StatesThe sign of projected changes is not highly sensitive to the precipitation‐type algorithm used to diagnose freezing rainThe choice of driving global climate model is a key source of uncertainty in both the sign and magnitude of projected changes [ABSTRACT FROM AUTHOR]

With the objective of trying to understand the adaptability of agriculture across the Canadian Prairies under climate change, simple‐to‐use agroclimatic indices were calculated for the base climate period of 1981 to 2010 and for both the medium (RCP4.5) and high (RCP8.5) emission projections extending to the distant future (2071–2100). The agroclimatic indices included the Effective Growing Degree Days (EGDDs), Growing Season Length (GSL), the Climate Moisture Index (CMI), and the Temperature Humidity Index (THI). For climate change in 30‐year periods, these indices were calculated as multi‐model ensembles of six Global Climate Models recommended under the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the study area. We found that the GSL, EGDDs, CMI, and THI were amplified above the values of the base climate period in the order of 40–50 days, 600–1200 heat units, −100 to −120 mm and 3–4 THI units by the close of the distant future (2071–2100) under the RCP4.5 and RCP8.5, respectively. This amplification has implications on where crop and livestock production could become more suitable or riskier in future. Opportunities include expanding crop and livestock production to more northerly regions which currently have insufficient heat units, a short growing season and unfavourable temperature humidity thresholds for livestock production. Moisture deficits will continue to be the greatest risk during the growing season under climate change scenarios but options exist to implement adaptive measures such as staggering seeding times to take advantage of moisture availability in the spring and autumn seasons and crop substitution. This study has relevance for policy and program formulation and implementation in Canada's agricultural regions and potentially, other areas of the world with similar climate change outcomes. [ABSTRACT FROM AUTHOR]

Changes in climate are altering the historical characteristics of the streamflow regime and affecting the performance of water systems. Here, the role of representing natural streamflow conditions in quantification of water system vulnerability under changing climate is evaluated in the Oldman River Basin, Canada. Four hydrological models are developed using point- and grid-based climate data and considering lumped and semi-distributed representations of the watershed. These hydrological models are then coupled with a reservoir water allocation model. Using an ensemble of climate model projections fed into these integrated models, changes in the water system's behaviour are evaluated. Although intensified and earlier peak flows and more critical water deficits are projected, the estimated risks of failure strongly depend on the considered hydrological model configuration. The divergence among models' projections for water deficit can be as high as 300%. Therefore, usage of all configurations is recommended to revise the reservoir operational policies. [ABSTRACT FROM AUTHOR]

Changes in temperature and precipitation are expected to alter the seasonal distribution of surface water supplies in snowmelt-dominated watersheds. A realistic assessment of future climate change and inter-annual variability is required to meet a growing demand for water supplies in all major use sectors. This study focuses on changes in climate and runoff in the North Saskatchewan River Basin (NSRB) above Edmonton, AB, Canada, using the MESH (Modélisation Environnementale communautaire—Surface Hydrology) model. The bias-corrected ensemble of Canadian Regional Climate Model (CanRCM4) data is used to drive MESH for two 60-year time periods, a historical baseline (1951–2010) and future projection (2041–2100), under Representative Concentration Pathway (RCP) 8.5. The precipitation is projected to increase in every season, there is significant trend in spring (0.62) and fall (0.41) and insignificant in summer (0.008). Winter extreme minimum temperature and summer extreme maximum temperature are increasing by 2–3 °C in the near future and 5–6 °C in the far future. Annual runoff increases by 19% compared to base period. The results reveal long-term hydrological variability enabling water resource managers to better prepare for climate change and extreme events to build more resilient systems for future water demand in the NSRB. [ABSTRACT FROM AUTHOR]