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This study analyzes the uncertainty of seasonal (winter and summer) precipitation extremes as simulated by a recent version of the Canadian Regional Climate Model (CRCM) using 16 simulations (1961–1990), considering four sources of uncertainty from: (a) the domain size, (b) the driving Atmosphere–Ocean Global Climate Models (AOGCM), (c) the ensemble member for a given AOGCM and (d) the internal variability of the CRCM. These 16 simulations are driven by 2 AOGCMs (i.e. CGCM3, members 4 and 5, and ECHAM5, members 1 and 2), and one set of re-analysis products (i.e. ERA40), using two domain sizes (AMNO, covering all North America and QC, a smaller domain centred over the Province of Québec). In addition to the mean seasonal precipitation, three seasonal indices are used to characterize different types of variability and extremes of precipitation: the number of wet days, the maximum number of consecutive dry days, and the 95th percentile of daily precipitation. Results show that largest source of uncertainty in summer comes from the AOGCM selection and the choice of domain size, followed by the choice of the member for a given AOGCM. In winter, the choice of the member becomes more important than the choice of the domain size. Simulated variance sensitivity is greater in winter than in summer, highlighting the importance of the large-scale circulation from the boundary conditions. The study confirms a higher uncertainty in the simulated heavy rainfall than the one in the mean precipitation, with some regions along the Great Lakes—St-Lawrence Valley exhibiting a systematic higher uncertainty value.

An urban heat island (UHI) is a relative measure defined as a metropolitan area that is warmer than the surrounding suburban or rural areas. The UHI nomenclature includes a surface urban heat island (SUHI) definition that describes the land surface temperature (LST) differences between urban and suburban areas. The complexity involved in selecting an urban core and external thermal reference for estimating the magnitude of a UHI led us to develop a new definition of SUHIs that excludes any rural comparison. The thermal reference of these newly defined surface intra-urban heat islands (SIUHIs) is based on various temperature thresholds above the spatial average of LSTs within the city’s administrative limits. A time series of images from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) from 1984 to 2011 was used to estimate the LST over the warm season in Montreal, Québec, Canada. Different SIUHI categories were analyzed in consideration of the global solar radiation (GSR) conditions that prevailed before each acquisition date of the Landsat images. The results show that the cumulative GSR observed 24 to 48 h prior to the satellite overpass is significantly linked with the occurrence of the highest SIUHI categories (thresholds of +3 to +7 °C above the mean spatial LST within Montreal city). The highest correlation (≈0.8) is obtained between a pixel-based temperature that is 6 °C hotter than the city’s mean LST (SIUHI + 6) after only 24 h of cumulative GSR. SIUHI + 6 can then be used as a thermal threshold that characterizes hotspots within the city. This identification approach can be viewed as a useful criterion or as an initial step toward the development of heat health watch and warning system (HHWWS), especially during the occurrence of severe heat spells across urban areas.

Les inondations causent de lourds dommages tant économiques, sociaux qu'environnementaux, en plus d'avoir des effets sur la santé physique et psychologique des sinistrés.

Abstract Gridded estimates of precipitation using both satellite and observational station data are regularly used as reference products in the evaluation of basic climate fields and derived indices as simulated by regional climate models (RCMs) over the current period. One of the issues encountered in RCM evaluation is the fact that RCMs and reference fields are usually on different grids and often at different horizontal resolutions. A proper RCM evaluation requires remapping on a common grid. For the climate indices or other derived fields, the remapping can be done in two ways: either as a first-step operation on the original field with the derived index computed on the final/common grid in a second step, or to compute first the climate index on the original grid before remapping or regridding it as a last-step operation on the final/common grid. The purpose of this paper is to illustrate how the two approaches affect the final field, thus contributing to one of the Coordinated Regional Climate Downscaling Experiment (CORDEX) in Africa (CORDEX-Africa) goals of providing a benchmark framework for RCM evaluation over the West Africa monsoon area, using several daily precipitation indices. The results indicate the advantage of using the last-step remapping procedure, regardless of the mathematical method chosen for the remapping, in order to minimize errors in the indices under evaluation.

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.

The West Africa rainfall regime constitutes a considerable challenge for Regional Climate Models (RCMs) due to the complexity of dynamical and physical processes that characterise the West African Monsoon. In this paper, daily precipitation statistics are evaluated from the contributions to the AFRICA-CORDEX experiment from two ERA-Interim driven Canadian RCMs: CanRCM4, developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) and CRCM5, developed at the University of Québec at Montréal. These modelled precipitation statistics are evaluated against three gridded observed datasets—the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM), and the Africa Rainfall Climatology (ARC2)—and four reanalysis products (ECMWF ERA-Interim, NCEP/DOE Reanalysis II, NASA MERRA and NOAA-CIRES Twentieth Century Reanalysis). The two RCMs share the same dynamics from the Environment Canada GEM forecast model, but have two different physics’ packages: CanRCM4 obtains its physics from CCCma’s global atmospheric model (CanAM4), while CRCM5 shares a number of its physics modules with the limited-area version of GEM forecast model. The evaluation is focused on various daily precipitation statistics (maximum number of consecutive wet days, number of moderate and very heavy precipitation events, precipitation frequency distribution) and on the monsoon onset and retreat over the Sahel region. We find that the CRCM5 has a good representation of daily precipitation statistics over the southern Sahel, with spatial distributions close to GPCP dataset. Some differences are observed in the northern part of the Sahel, where the model is characterised by a dry bias. CanRCM4 and the ERA-Interim and MERRA reanalysis products overestimate the number of wet days over Sahel with a shift in the frequency distribution toward smaller daily precipitation amounts than in observations. Both RCMs and reanalyses have difficulties in reproducing the local onset date over the Sahel region. Nevertheless, the large-scale features of the monsoon precipitation evolution over West Africa are well reproduced by the RCMs, whereas the northern limit of the rainy bands is less accurately reproduced. Both RCMs exhibit an overall good representation of the local retreat index over the Sahel region.

Changes in extreme precipitation should be one of the primary impacts of climate change (CC) in urban areas. To assess these impacts, rainfall data from climate models are commonly used. The main goal of this paper is to report on the state of knowledge and recent works on the study of CC impacts with a focus on urban areas, in order to produce an integrated review of various approaches to which future studies can then be compared or constructed. Model output statistics (MOS) methods are increasingly used in the literature to study the impacts of CC in urban settings. A review of previous works highlights the non-stationarity nature of future climate data, underscoring the need to revise urban drainage system design criteria. A comparison of these studies is made difficult, however, by the numerous sources of uncertainty arising from a plethora of assumptions, scenarios, and modeling options. All the methods used do, however, predict increased extreme precipitation in the future, suggesting potential risks of combined sewer overflow frequencies, flooding, and back-up in existing sewer systems in urban areas. Future studies must quantify more accurately the different sources of uncertainty by improving downscaling and correction methods. New research is necessary to improve the data validation process, an aspect that is seldom reported in the literature. Finally, the potential application of non-stationarity conditions into generalized extreme value (GEV) distribution should be assessed more closely, which will require close collaboration between engineers, hydrologists, statisticians, and climatologists, thus contributing to the ongoing reflection on this issue of social concern.

Since the 1980s, populations of the Asian tiger mosquito Aedes albopictus have become established in south-eastern, eastern and central United States, extending to approximately 40°N. Ae. albopictus is a vector of a wide range of human pathogens including dengue and chikungunya viruses, which are currently emerging in the Caribbean and Central America and posing a threat to North America.

Floods have potentially devastating consequences on populations, industries and environmental systems. They often result from a combination of effects from meteorological, physiographic and anthropogenic natures. The analysis of flood hazards under a multivariate perspective is primordial to evaluate several of the combined factors. This study analyzes spring flood-causing mechanisms in terms of the occurrence, frequency, duration and intensity of precipitation as well as temperature events and their combinations previous to and during floods using frequency analysis as well as a proposed multivariate copula approach along with hydrometeorological indices. This research was initiated over the Richelieu River watershed (Quebec, Canada), with a particular emphasis on the 2011 spring flood, constituting one of the most damaging events over the last century for this region. Although some work has already been conducted to determine certain causes of this record flood, the use of multivariate statistical analysis of hydrologic and meteorological events has not yet been explored. This study proposes a multivariate flood risk model based on fully nested Archimedean Frank and Clayton copulas in a hydrometeorological context. Several combinations of the 2011 Richelieu River flood-causing meteorological factors are determined by estimating joint and conditional return periods with the application of the proposed model in a trivariate case. The effects of the frequency of daily frost/thaw episodes in winter, the cumulative total precipitation fallen between the months of November and March and the 90th percentile of rainfall in spring on peak flow and flood duration are quantified, as these combined factors represent relevant drivers of this 2011 Richelieu River record flood. Multiple plausible and physically founded flood-causing scenarios are also analyzed to quantify various risks of inundation.

Projections from the Canadian Regional Climate Model (CRCM) for the southern part of the province of Québec, Canada, suggest an increase in extreme precipitation events for the 2050 horizon (2041–2070). The main goal of this study consisted in a quantitative and qualitative assessment of the impact of the 20 % increase in rainfall intensity that led, in the summer of 2013, to overflows in the “Rolland-Therrien” combined sewer system in the city of Longueuil, Canada. The PCSWMM 2013 model was used to assess the sensitivity of this overflow under current (2013) and future (2050) climate conditions. The simulated quantitative variables (peak flow, QCSO, and volume discharged, VD) served as the basis for deriving ecotoxicological risk indices and event fluxes (EFs) transported to the St. Lawrence (SL) River. Results highlighted 15 to 500 % increases in VD and 13 to 148 % increases in QCSO by 2050 (compared to 2013), based on eight rainfall events measured from May to October. These results show that (i) the relationships between precipitation and combined sewer overflow variables are not linear and (ii) the design criteria for current hydraulic infrastructure must be revised to account for the impact of climate change (CC) arising from changes in precipitation regimes. EFs discharged into the SL River will be 2.24 times larger in the future than they are now (2013) due to large VDs resulting from CC. This will, in turn, lead to excessive inputs of total suspended solids (TSSs) and tracers for numerous urban pollutants (organic matter and nutrients, metals) into the receiving water body. Ecotoxicological risk indices will increase by more than 100 % by 2050 compared to 2013. Given that substantial VDs are at play, and although CC scenarios have many sources of uncertainty, strategies to adapt this drainage network to the effects of CC will have to be developed.

Climate change is likely to affect windthrow risks at northern latitudes by potentially changing high wind probabilities and soil frost duration. Here, we evaluated the effect of climate change on windthrow risk in eastern Canada’s balsam fir (Abies balsamea [L.] Mill.) forests using a methodology that accounted for changes in both wind speed and soil frost duration. We used wind speed and soil temperature projections at the regional scale from the CRCM5 regional climate model (RCM) driven by the CanESM2 global climate model (GCM) under two representative concentration pathways (RCP4.5, RCP8.5), for a baseline (1976–2005) and two future periods (2041–2070, 2071–2100). A hybrid mechanistic model (ForestGALES) that considers species resistance to uprooting and wind speed distribution was used to calculate windthrow risk. An increased risk of windthrow (3 to 30%) was predicted for the future mainly due to an increased duration of unfrozen soil conditions (by up to 2 to 3 months by the end of the twenty-first century under RCP8.5). In contrast, wind speed did not vary markedly with a changing climate. Strong regional variations in wind speeds translated into regional differences in windthrow risk, with the easternmost region (Atlantic provinces) having the strongest winds and the highest windthrow risk. Because of the inherent uncertainties associated with climate change projections, especially regarding wind climate, further research is required to assess windthrow risk from the optimum combination of RCM/GCM ensemble simulations.