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Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions. Plain Language Summary: Precise simulations of snow depth (SD) are crucial for understanding changes in the Earth's energy balance and helping efforts to combat climate change. In this research, we evaluate how well the latest phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the daily SD across Canada. By comparing these simulations to various SD data sets, we looked at average daily SD, how long snow remains on the ground, and how quickly it accumulates or melts away, focusing on 11 major catchments in Canada. Our study found that the CMIP6 models tend to simulate more SD, by about 57.7% on average, and suggest that snow cover remains around 30.5 days longer than observed durations. Although three specific models excel in matching the annual peak of daily SD, we noticed biases that point to a need for improving how these models represent various land covers. Our findings highlight the importance of making these models more accurate by using more detailed information about land cover properties, which helps better predict SD and understand its role in climate change. Key Points: Coupled model intercomparison project phase 6 (CMIP6) simulations mostly overestimate the average daily SD values by a median of 57.7% (6.9 cm)The average snow cover duration is overestimated by 30.5 days compared to station data, confirmed by the CMC data setCMIP6 simulations relatively capture extremes well, with a median bias of 12.73% [ABSTRACT FROM AUTHOR]

Drugs that have been manufactured or packaged fraudulently are referred to as counterfeit/fake/spurious/falsified drugs because they either lack active ingredients or have the incorrect dosages. Counterfeiting of drugs has become a global issue with which the whole world is grappling. The World Health Organization states the frightening figure in which almost 10.5% of the medications worldwide are either subpar or fake. Although developing and low-income countries are the targets of the large-scale drug counterfeiting activities, fake/substandard drugs are also making their way into developed nations including the USA, Canada, and European countries. Counterfeiting of drugs is leading to not only economic loss but is also playing its part in the morbidity and mortality of patients. The recent COVID-19 pandemic fuelled the demand for certain categories of medicines such as antipyretics, remdesivir, corticosteroids, vaccines, etc., thus increasing the demand and manufacture of subpar/fake medicines. This review articulates the current trends and global impact of drug counterfeiting, current and potential measures for its prevention and the role of different stakeholders in tackling the menace of drug counterfeiting. [ABSTRACT FROM AUTHOR]

Canada is warming at double the rate of the global average caused in part to a fast-growing population and large land transformations, where urban surfaces contribute significantly to the urban heat island (UHI) phenomenon. The federal government released the strengthened climate plan in 2020, which emphasizes using nature-based solutions (NBSs) to combat the effects of UHI phenomenon. Here, the effects of two NBSs techniques are reviewed and analysed: increasing surface greenery/vegetation (ISG) and increasing surface reflectivity (ISR). Policymakers have the challenge of selecting appropriate NBSs to meet a wide range of objectives within the urban environment and Canadian-specific knowledge of how NBSs can perform at various scales is lacking. As such, this state-of-the-art review intends to provide a snapshot of the current understanding of the benefits and risks associated with the implantation of NBSs in urban spaces as well as a review of the current techniques used to model, and evaluate the potential effectiveness of UHI under evolving climate conditions. Thus, if NBSs are to be adopted to mitigate UHI effects and extreme summertime temperatures in Canadian municipalities, an integrated, comprehensive analysis of their contributions is needed. As such, developing methods to quantify and evaluate NBSs' performance and tools for the effective implementation of NBSs are required. [ABSTRACT FROM AUTHOR]

Subdaily precipitation gauging stations are limited and unevenly distributed across Canada. To support the design of sustainable stormwater infrastructure, especially in the data-sparse regions of Canada, this study presents a novel, gridded intensity–duration–frequency (IDF) dataset of precipitation storms of 5, 10, 15, 30, and 60 min and 1, 2, 6, 12, and 24 h durations and 2, 5, 10, 25, 50, and 100 year return periods. The dataset has been prepared using atmospheric variable (AVs) estimates from two reanalysis products: the North American Regional Reanalysis (NARR) and ERA-Interim. A state-of-the-art machine-learning algorithm, named a support vector machine (SVM), is used to establish the link between AVs and extreme precipitation magnitudes. First, the most relevant AVs shaping precipitation extremes in different parts of Canada are identified, and preliminary estimates of gridded IDFs are produced. The preliminary IDF estimates are corrected for systematic distribution of spatial errors to obtain corrected gridded IDF estimates. Modeled gridded IDF estimates are compared with observations and are found to exhibit a root mean squared error varying between 5% and 25% across different regions of Canada. The gridded IDFs are also found to capture the observed spatial pattern of extreme precipitation reasonably well. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between historically observed changes in extreme precipitation magnitudes and temperature (Pex-T relationship) at multiple locations in Canada. The focus is on understanding the behavior of these relationships with regards to key storm characteristics such as its duration, season of occurrence, and location. To do so, three locations are chosen such that they have large amounts of moisture available near them whereas four locations are chosen such that they are located in the land-locked regions of Canada and subsequently have no nearby moisture source available on them. To investigate the effect of different storm durations on Pex-T relationship, storms of durations: 5, 10, 15, 30 min, 1, 2, 6, 12, 24 h are considered. Finally, Pex-T relationship is analyzed separately for summer and winter seasons to quantify the influence of seasons. Results indicate strong influences of storm duration, season of occurrence, and location on observed precipitation scaling rates. Drastic intensification of precipitation extremes with temperature is obtained for shorter duration precipitation events than for longer duration precipitation events, in summers than in the winters. Furthermore, in summertime, increases in the intensity of convection driven precipitation extremes is found highest at locations away from large waterbodies. On the other hand, in wintertime most drastic increases in extreme precipitation are obtained at locations near large waterbodies. These findings contribute towards increasing the current understanding of precipitation extremes in the context of rapidly increasing global temperatures. [ABSTRACT FROM AUTHOR]

Surface Urban Heat Island (SUHI) is an urban climate phenomenon that is expected to respond to future climate and land-use land-cover change. It is important to further our understanding of physical mechanisms that govern SUHI phenomenon to enhance our ability to model future SUHI characteristics under changing geophysical conditions. In this study, SUHI phenomenon is quantified and modelled at 20 cities distributed across Canada. By analyzing MODerate Resolution Imaging Spectroradiometer (MODIS) sensed surface temperature at the cities over 2002–2012, it is found that 16 out of 20 selected cities have experienced a positive SUHI phenomenon while 4 cities located in the prairies region and high elevation locations have experienced a negative SUHI phenomenon in the past. A statistically significant relationship between observed SUHI magnitude and city elevation is also recorded over the observational period. A Physical Scaling downscaling model is then validated and used to downscale future surface temperature projections from 3 GCMs and 2 extreme Representative Concentration Pathways in the urban and rural areas of the cities. Future changes in SUHI magnitudes between historical (2006–2015) and future timelines: 2030s (2026–2035), 2050s (2046–2055), and 2090s (2091–2100) are estimated. Analysis of future projected changes indicate that 15 (13) out of 20 cities can be expected to experience increases in SUHI magnitudes in future under RCP 2.6 (RCP 8.5). A statistically significant relationship between projected future SUHI change and current size of the cities is also obtained. The study highlights the role of city properties (i.e. its size, elevation, and surrounding land-cover) towards shaping their current and future SUHI characteristics. The results from this analysis will help decision-makers to manage Canadian cities more efficiently under rapidly changing geophysical and demographical conditions. [ABSTRACT FROM AUTHOR]

Climate change has led to prolonged, more frequent, intense, and severe extreme weather events, such as summertime heatwaves, creating many challenges on the economy and society and human health and energy resources. For example, the 2010 and 2018 heatwave in Quebec, Canada, resulted in about 280 and 93 heat-related deaths, and there were around 500 fatalities due to overheated indoor environments in 2021 around entire Canada. Therefore, it is imperative to understand and evaluate the overheating conditions in buildings, for which selecting suitable future reference weather data under climate change is one of the first critical steps. This study evaluated a reference year selection method in terms of typical and extreme reference years based on future climate datasets to assess both outdoor and indoor overheating in the future. The future climate data were collected from the Coordinated Regional Downscaling Experiment (CORDEX) program. Three Canadian cities (Montreal, Toronto, Vancouver) were selected for the overheating evaluation during three selected periods (2001–2020, 2041–2060, 2081–2100). The CORDEX climate projections were first bias-corrected by the multivariate quantile mapping correction method with the observational data. Then, the typical and extreme reference year data were generated as well as climate data from the design summer year for comparison. The performance of the reference year selection method was evaluated by comparing the maximum, minimum, and average overheating hours for the 20-years data of each period. This study demonstrates that the multivariate quantile mapping bias correction method can improve the reliability of future climate data making it one of the most important steps for any future weather projection study. Besides, the reference year selection method could efficiently capture maximum and minimum monthly overheating hours providing the upper and lower boundary of possible outdoor and indoor overheating conditions.. In contrast, neither the severest nor the typical monthly outdoor and indoor overheating conditions could be predicted by the design summer year method. Finally, owing to the effects of climate change, average monthly overheating hours normally increase by around one time (from 50% to 150%) until the mid-term future (2041–2060) and by around two to three times (even up to nine times for some scenarios) during the long-term future (2081–2100). [ABSTRACT FROM AUTHOR]

As a consequence of global warming, buildings in Canada and around the globe are expected to face unprecedented climate over their design lives. The Moisture Index (MI) is a climate-based indicator currently used in the National Building Code of Canada (NBCC) to guide the design of wall assemblies for acceptable durability performance. In this study, future changes in MI are calculated across Canada under 2 and 3.5 °C of future global warming. Results indicate that the coastal and great lakes regions of Canada will have increased MI, whereas prairies and northern regions will in the future have decreased values of MI. Furthermore, it is demonstrated that drastically different projected future MI changes can be obtained with different values of drying potential normalization factor, and it is suggested that this value is chosen by carefully analyzing the historical and future projected drying potential values. Finally, our analysis shows that wall assemblies at an increased number of reference locations given in the NBCC, importantly those falling in the densely populated south-western Ontario region, will need to be designed with additional safety measures such as a capillary break in the future for them to achieve satisfactory moisture performance over their design lives. • Potential moisture risks of building envelopes under a changing climate over Canada. • Projected increases in atmospheric moisture loading requires moisture resilient building design. • Drastic differences in Moisture Index can be obtained with different normalization factors. • Many additional locations will need a capillary break in the future to achieve satisfactory moisture performance. [ABSTRACT FROM AUTHOR]

Rainfall Intensity–Duration–Frequency (IDF) curves are among the most essential datasets used in water resources management across the globe. Traditionally, they are derived from observations of historical rainfall, under the assumption of stationarity. Change of climatic conditions makes use of historical data for development of IDFs for the future unreliable, and in some cases, may lead to underestimated infrastructure designs. The IDF_CC tool is designed to assist water professionals and engineers in producing IDF estimates under changing climatic conditions. The latest version of the tool (Version 4) provides updated IDF curve estimates for gauged locations (rainfall monitoring stations) and ungauged sites using a new gridded dataset of IDF curves for the land mass of Canada. The tool has been developed using web-based technologies and takes the form of a decision support system (DSS). The main modifications and improvements between version 1 and the latest version of the IDF_CC tool include: (i) introduction of the Generalized Extreme value (GEV) distribution; (ii) updated equidistant matching algorithm (QM); (iii) gridded IDF curves dataset for ungauged location and (iv) updated Climate Models. [ABSTRACT FROM AUTHOR]

Sustainable building practices are rooted in the need for reliable information on the long-term performance of building materials; specifically, the expected service-life of building materials, components, and assemblies. This need is ever more evident given the anticipated effects of climate change on the built environment and the many governmental initiatives world-wide focused on ensuring that structures are not only resilient at their inception but also, can maintain their resilience over the long-term. The Government of Canada has funded an initiative now being completed at the National Research Council of Canada's (NRC) Construction Research Centre on "Climate Resilience of Buildings and Core Public infrastructure". The outcomes from this work will help permit integrating climate resilience of buildings into guides and codes for practitioners of building and infrastructure design. In this paper, the impacts of climate change on buildings are discussed and a review of studies on the durability of building envelope materials and elements is provided in consideration of the expected effects of climate change on the longevity and resilience of such products over time. Projected changes in key climate variables affecting the durability of building materials is presented such that specifications for the selection of products given climate change effects can be offered. Implications in regard to the maintainability of buildings when considering the potential effects of climate change on the durability of buildings and its components is also discussed. [ABSTRACT FROM AUTHOR]