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Purpose The current pandemic and ongoing climate risks highlight the limited capacity of various systems, including health and social ones, to respond to population-scale and long-term threats. Practices to reduce the impacts on the health and well-being of populations must evolve from a reactive mode to preventive, proactive and concerted actions beginning at individual and community levels. Experiences and lessons learned from the pandemic will help to better prevent and reduce the psychosocial impacts of floods, or other hydroclimatic risks, in a climate change context. Design/methodology/approach The present paper first describes the complexity and the challenges associated with climate change and systemic risks. It also presents some systemic frameworks of mental health determinants, and provides an overview of the different types of psychosocial impacts of disasters. Through various Quebec case studies and using lessons learned from past and recent flood-related events, recommendations are made on how to better integrate individual and community factors in disaster response. Findings Results highlight the fact that people who have been affected by the events are significantly more likely to have mental health problems than those not exposed to flooding. They further demonstrate the adverse and long-term effects of floods on psychological health, notably stemming from indirect stressors at the community and institutional levels. Different strategies are proposed from individual-centered to systemic approaches, in putting forward the advantages from intersectoral and multirisk researches and interventions. Originality/value The establishment of an intersectoral flood network, namely the InterSectoral Flood Network of Québec (RIISQ), is presented as an interesting avenue to foster interdisciplinary collaboration and a systemic view of flood risks. Intersectoral work is proving to be a major issue in the management of systemic risks, and should concern communities, health and mental health professionals, and the various levels of governance. As climate change is called upon to lead to more and more systemic risks, close collaboration between all the areas concerned with the management of the factors of vulnerability and exposure of populations will be necessary to respond effectively to damages and impacts (direct and indirect) linked to new meteorological and compound hazards. This means as well to better integrate the communication managers into the risk management team.

Polar lows (PLs) are maritime mesoscale cyclones associated with severe weather. They develop during marine cold air outbreaks near coastlines and the sea ice edge. Unfortunately, our knowledge about the mechanisms leading to PL development is still incomplete. This study aims to provide a detailed analysis of the development mechanisms of a PL that formed over the Norwegian Sea on 25 March 2019 using the output of a simulation with the sixth version of the Canadian Regional Climate Model (CRCM6/GEM4), a convection-permitting model. First, the life cycle of the PL is described and the vertical wind shear environment is analysed. Then, the horizontal wind divergence and the baroclinic conversion term are computed, and a surface pressure tendency equation is developed. In addition, the roles of atmospheric static stability, latent heat release, and surface heat and moisture fluxes are explored. The results show that the PL developed in a forward-shear environment and that moist baroclinic instability played a major role in its genesis and intensification. Baroclinic instability was initially only present at low levels of the atmosphere, but later extended upward until it reached the mid-troposphere. Whereas the latent heat of condensation and the surface heat fluxes also contributed to the development of the PL, convective available potential energy and barotropic conversion do not seem to have played a major role in its intensification. In conclusion, this study shows that a convection-permitting model simulation is a powerful tool to study the details of the structure of PLs, as well as their development mechanisms.

Abstract El Niño‐Southern Oscillation (ENSO) is often considered as a source of long‐term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to 7 years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1,600 years of bias‐corrected General Circulation Model outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all‐year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk. , Plain Language Summary El Niño‐Southern Oscillation (ENSO) is one of the most important global climate phenomena. It is well‐known to affect precipitation and temperature in many areas of the world. It is therefore very important for researchers (environmental and climate sciences, economics, etc.), risk managers, decision‐ and policy‐makers to understand the influence of ENSO on flooding. Previous studies analyzed the link between ENSO and flooding but because they were based upon 40 years of data, a lot of uncertainties remained as to how ENSO has any significance on flooding. In this study, we use outputs from a climate model large ensemble that provides 1,600 years of simulated data to determine the impacts of ENSO on flooding. But because it is very difficult to run traditional flood models on 1,600 years of data, we rather leverage a machine learning approach to accelerate computations in a context where the focus is on socioeconomic impacts. We find that ENSO is a significant driver of flooding in more regions than what was previously found. Finally, there appears to be a greater global influence of El Niño than La Niña on flooding. , Key Points We simulated an equivalent of 1,600 years of realistic flood events globally using a statistical model forced with climate model outputs We found a statistically significant ( α  = 0.05) influence of El Niño‐Southern Oscillation (ENSO) over 55% of land area for flood occurrence and over 69% for flood impact Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk

Abstract Bias adjustment of numerical climate model simulations involves several arguments wherein the notion of physical inconsistency is referred to, either for rejecting the legitimacy of bias adjustment in general or for justifying the necessity of sophisticated multivariate techniques. However, this notion is often mishandled, in part because the literature generally proceeds without defining it. In this context, the central objective of this study is to clarify and illustrate the distinction between physical inconsistency and multivariate bias, by investigating the effect of bias adjustment on two different kinds of intervariable relationships, namely a physical constraint expected to hold at every step of a time series and statistical properties that emerge with potential bias over a climatic timescale. To this end, 18 alternative bias adjustment techniques are applied on 10 climate simulations at 12 sites over North America. Adjusted variables are temperature, pressure, relative humidity and specific humidity, linked by a thermodynamic constraint. The analysis suggests on the one hand that a clear instance of potential physical inconsistency can be avoided with either a univariate or a multivariate technique, if and only if the bias adjustment strategy explicitly considers the physical constraint to be preserved. On the other hand, it also suggests that sophisticated multivariate techniques alone are not complete adjustment strategies in presence of a physical constraint, as they cannot replace its explicit consideration. By involving common bias adjustment procedures with likely effects on diverse basic statistical properties, this study may also help guide climate information users in the determination of adequate bias adjustment strategies for their research purposes.

Although the finance literature has devoted a lot of research into the development of advanced models for improving the pricing and hedging performance, there has been much less emphasis on approaches to measure dynamic hedging effectiveness. This article discusses a statistical framework based on regression analysis to measure the effectiveness of dynamic hedges for long-term investment guarantees. The importance of taking model risk into account is emphasized. The difficulties in reducing hedging risk to an appropriately low level lead us to propose a new perspective on hedging, and recognize it as a tool to modify the risk–reward relationship of the unhedged position.

Flood-related losses are on the rise in Canada and private insurance remains costly or unavailable in high-risk areas. Despite the introduction of overland flood insurance in 2015, following the federal government’s invitation to the insurance industry to participate in flood risk-sharing, federal and provincial disaster financial assistance programs still cover a large portion of these costs. As the risks increase, governments are questioning the sustainability of using taxpayers’ money to finance such losses, leaving municipalities with significant residual risk. The growing number of people and assets occupying flood-prone areas, including public infrastructure, has contributed to the sharp increase in flood damage costs. Based on a literature review and discussions with experts, this paper describes the municipal role in flood-risk management, and shows how provincial and federal financial assistance to municipalities for flood damage in British Columbia and Québec may be counterproductive in fostering flood-risk management at the municipal level. We conclude that municipalities can play a more proactive role in incorporating risk reduction as the key objective of disaster financial assistance and propose three specific policy instruments to help reduce the growing number of people living in flood zones: flood mapping, land-use planning, and the relocation of high-risk properties.

The UQAM Heatwave ERA5 Archive and Temperatures (U-HEAT) catalog is a global dataset of temperature and heatwave data spanning 1940 to 2022. The temperature data features the maximum daily 2-m temperature, the 90th percentile of the maximum daily 2-m temperature, and an indication as to whether a given location (grid point) is experiencing a heatwave or not on a given day. The heatwave data includes metrics such as the duration, the cumulated intensity and the maximum intensity of heatwaves occuring in the study period as well as their location (grid point) and start date. Both the temperature and the heatwave metrics data were calculated from the ERA5 data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). More information on the catalog can be found in the documentation and the README files. Le catalogue UQAM Heatwave ERA5 Archive and Temperatures (U-HEAT) est un jeu de données global de température et de vague de chaleur pour la période entre 1940 et 2022. Les données de température comprennent le maximum quotidien de la température à 2m, le 90e percentile du maximum quotidien de la température à 2m et une indication permettant de savoir si un lieu donné (point de grille) subit ou non une vague de chaleur pour un jour donné. Les données de vague de chaleur incluent des métriques comme la durée, l'intensité cumulée et l'intensité maximale de vagues de chaleur qui se sont produites durant la période d'étude en plus de leur emplacement (point de grille) et leur date de début. Les données de température et de vague de chaleur ont été calculées à partir du jeu de données ERA5 produit par le European Centre for Medium-Range Weather Forecasts (ECMWF). Le fichier de documentation et le fichier README peuvent être consultés pour obtenir plus d'information à propos du catalogue.

Abstract The contraction of species range is one of the most significant symptoms of biodiversity loss worldwide. While anthropogenic activities and habitat alteration are major threats for several species, climate change should also be considered. For species at risk, differentiating the effects of human disturbances and climate change on past and current range transformations is an important step towards improved conservation strategies. We paired historical range maps with global atmospheric reanalyses from different sources to assess the potential effects of recent climate change on the observed northward contraction of the range of boreal populations of woodland caribou ( Rangifer tarandus caribou ) in Quebec (Canada) since 1850. We quantified these effects by highlighting the discrepancies between different southern limits of the caribou's range (used as references) observed in the past and reconstitutions obtained through the hindcasting of the climate conditions within which caribou are currently found. Hindcasted southern limits moved ~105 km north over time under all reanalysis datasets, a trend drastically different from the ~620 km reported for observed southern limits since 1850. The differences in latitudinal shift through time between the observed and hindcasted southern limits of distribution suggest that caribou range recession should have been only 17% of what has been observed since 1850 if recent climate change had been the only disturbance driver. This relatively limited impact of climate reinforces the scientific consensus stating that caribou range recession in Quebec is mainly caused by anthropogenic drivers (i.e. logging, development of the road network, agriculture, urbanization) that have modified the structure and composition of the forest over the past 160 years, paving the way for habitat‐mediated apparent competition and overharvesting. Our results also call for a reconsideration of past ranges in models aiming at projecting future distributions, especially for endangered species.

This catalogue includes seasonal mean precipitation fields from simulations and reanalysis used for the calculation of the Spatial Spin-Up Distance (SSUD). A total of seven simulation were conducted using the convection-permitting configuration (2.5-km grid spacing) of version 6 of the Canadian Regional Climate Model (CRCM6/GEM5; hereafter denoted as GEM2.5 for simplicity). The CRCM6/GEM5 version used here is based on version 5.0.2 of the Global Environmental Multiscale model (GEM5). GEM2.5 simulations were driven directly by the ERA5 reanalysis or by 12-km (GEM12) simulations also performed using the CRCM6/GEM5 model (which was in turn driven by the ERA5 reanalysis). The catalogue comprises seasonal mean precipitation fields from all seven GEM2.5 simulations, from the ERA5 reanalysis and from two GEM12 intermediate simulations (GEM12_SUN and GEM12_P3). Seasonal means were calculated using the common convention: December, January and February (DJF) for winter; March, April and May (MAM) for spring; June, July and August (JJA) for summer; and September, October and November (SON) for fall. Precipitations fields are given in mm/h.

Abstract The increasing atmospheric nitrous oxide (N 2 O) concentration stems from the development of agriculture. However, N 2 O emissions from global rice‐based ecosystems have not been explicitly and systematically quantified. Therefore, this study aims to estimate the spatiotemporal magnitudes of the N 2 O emissions from global rice‐based ecosystems and determine different contribution factors by improving a process‐based biogeochemical model, TRIPLEX‐GHG v2.0. Model validation suggested that the modeled N 2 O agreed well with field observations under varying management practices at daily, seasonal, and annual steps. Simulated N 2 O emissions from global rice‐based ecosystems exhibited significant increasing trends from 0.026 ± 0.0013 to 0.18 ± 0.003 TgN yr −1 from 1910 to 2020, with ∼69.5% emissions attributed to the rice‐growing seasons. Irrigated rice ecosystems accounted for a majority of global rice N 2 O emissions (∼76.9%) because of their higher N 2 O emission rates than rainfed systems. Regarding spatial analysis, Southern China, Northeast India, and Southeast Asia are hotspots for rice‐based N 2 O emissions. Experimental scenarios revealed that N fertilizer is the largest global rice‐N 2 O source, especially since the 1960s (0.047 ± 0.010 TgN yr −1 , 35.24%), while the impact of expanded irrigation plays a minor role. Overall, this study provides a better understanding of the rice‐based ecosystem in the global agricultural N 2 O budget; further, it quantitively demonstrated the central role of N fertilizer in rice‐based N 2 O emissions by including rice crop calendars, covering non‐rice growing seasons, and differentiating the effects of various water regimes and input N forms. Our findings emphasize the significance of co‐management of N fertilizer and water regimes in reducing the net climate impact of global rice cultivation. , Plain Language Summary Nitrous oxide (N 2 O) is a greenhouse gas with ∼300 times greater effect on climate warming than carbon dioxide. Global croplands represent the largest source of anthropogenic N 2 O emissions. However, the contribution of global rice‐based cropping ecosystems to the N 2 O budget remains largely uncertain because of inconsistent observed results. Inspired by the increasing availability of reliable global data sets, we improved and applied a process‐based biogeochemical model by describing the dynamics of various microbial activities to simulate N 2 O emissions from rice‐based ecosystems on a global scale. Model simulations showed that 0.18 million tons of N 2 O‐N were emitted from global rice‐based N 2 O emissions in the 2010s, which was five times larger than that in the 1910s. In the context of regional contribution, southern China, northern India, and Southeast Asia are responsible for more than 80% of the total emissions during 1910–2020. Results suggest that N fertilizer is the most important rice‐N 2 O source quantitively and that increasing irrigation exerts a buffering effect. This study confirmed the potential mitigating effect of co‐managing N fertilizer and irrigation on mitigating rice‐based N 2 O emissions globally. , Key Points N 2 O emissions from global rice‐based ecosystem increased from 0.026 to 0.18 TgN yr −1 between 1910 and 2020 Irrigated rice‐based ecosystems showed larger N 2 O fluxes than rainfed rice globally due to higher N fertilizer use and frequent aerations N fertilizer represents the largest N 2 O source, and co‐management of N fertilizer and flooding regimes is important for mitigation

Abstract. The changing Arctic climate is creating increased economic, transportation, and recreational activities requiring reliable and relevant weather information. However, the Canadian Arctic is sparsely observed, and processes governing weather systems in the Arctic are not well understood. There is a recognized lack of meteorological data to characterize the Arctic atmosphere for operational forecasting and to support process studies, satellite calibration/validation, search and rescue operations (which are increasing in the region), high-impact weather (HIW) detection and prediction, and numerical weather prediction (NWP) model verification and evaluation. To address this need, Environment and Climate Change Canada commissioned two supersites, one in Iqaluit (63.74∘ N, 68.51∘ W) in September 2015 and the other in Whitehorse (60.71∘ N, 135.07∘ W) in November 2017 as part of the Canadian Arctic Weather Science (CAWS) project. The primary goals of CAWS are to provide enhanced meteorological observations in the Canadian Arctic for HIW nowcasting (short-range forecast) and NWP model verification, evaluation, and process studies and to provide recommendations on the optimal cost-effective observing system for the Canadian Arctic. Both sites are in provincial/territorial capitals and are economic hubs for the region; they also act as transportation gateways to the north and are in the path of several common Arctic storm tracks. The supersites are located at or next to major airports and existing Meteorological Service of Canada ground-based weather stations that provide standard meteorological surface observations and upper-air radiosonde observations; they are also uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites. The suite of in situ and remote sensing instruments at each site is completely automated (no on-site operator) and operates continuously in all weather conditions, providing near-real-time data to operational weather forecasters, the public, and researchers via obrs.ca. The two sites have similar instruments, including mobile Doppler weather radars, multiple vertically profiling and/or scanning lidars (Doppler, ceilometer, water vapour), optical disdrometers, precipitation gauges in different shielded configurations, present weather sensors, fog monitoring devices, radiation flux sensors, and other meteorological instruments. Details on the two supersites, the suites of instruments deployed, the data collection methods, and example case studies of HIW events are discussed. CAWS data are publicly accessible via the Canadian Government Open Data Portal (https://doi.org/10.18164/ff771396-b22c-4bc3-844d-38fc697049e9, Mariani et al., 2022a, and https://doi.org/10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78, Mariani et al., 2022b); this dataset is being used to improve our understanding of synoptic and fine-scale meteorological processes in the Arctic and sub-Arctic, including HIW detection and prediction and NWP verification, assimilation, and processes.

The Australian Alps are the highest mountain range in Australia, which are important for biodiversity, energy generation and winter tourism. Significant increases in temperature in the past decades has had a huge impact on biodiversity and ecosystem in this region. In this study, observed temperature is used to assess how temperature changed over the Australian Alps and surrounding areas. We also use outputs from two generations of NARCliM (NSW and Australian Regional Climate Modelling) to investigate spatial and temporal variation of future changes in temperature and its extremes. The results show temperature increases faster for the Australian Alps than the surrounding areas, with clear spatial and temporal variation. The changes in temperature and its extremes are found to be strongly correlated with changes in albedo, which suggests faster warming in cool season might be dominated by decrease in albedo resulting from future changes in natural snowfall and snowpack. The warming induced reduction in future snow cover in the Australian Alps will have a significant impact on this region.

The biogeophysical effects of severe forestation are quantified using a new ensemble of regional climate simulations over North America and Europe. Following the protocol outlined for the Land-Use and Climate Across Scales (LUCAS) intercomparison project, two sets of simulations are compared, FOREST and GRASS, which respectively represent worlds where all vegetation is replaced by trees and grasses. Three regional climate models were run over North America. One of them, the Canadian Regional Climate Model (CRCM5), was also run over Europe in an attempt to bridge results with the original LUCAS ensemble, which was confined to Europe. Overall, the CRCM5 response to forestation reveals strong inter-continental similarities, including a pronounced wintertime and springtime warming concentrated over snow-masking evergreen forests. Crucially, these northern evergreen needleleaf forests populate lower, hence sunnier, latitudes in North America than in Europe. Snow masking reduces albedo similarly over both continents, but stronger insolation amplifies the net shortwave radiation and hence warming simulated over North America. In the summertime, CRCM5 produces a mixed response to forestation, with warming over northern needleleaf forests and cooling over southern broadleaf forests. The partitioning of the turbulent heat fluxes plays a major role in determining this response, but it is not robust across models over North America. Implications for the inter-continental transferability of the original LUCAS results are discussed.

Background: Although floods may have important respiratory health impacts, few studies have examined this issue. This study aims to document the long-term impacts of the spring floods of 2019 in Quebec by (1) describing the population affected by the floods; (2) assessing the impacts on the respiratory system according to levels of exposure; and (3) determining the association between stressors and respiratory health. Methods: A population health survey was carried out across the six most affected regions 8–10 months post-floods. Data were collected on self-reported otolaryngology (ENT) and respiratory symptoms, along with primary and secondary stressors. Three levels of exposure were examined: flooded, disrupted and unaffected. Results: One in ten respondents declared being flooded and 31.4% being disrupted by the floods. Flooded and disrupted participants reported significantly more ENT symptoms (adjusted odds ratio (aOR): 3.18; 95% CI: 2.45–4.14; aOR: 1.76; 95% CI: 1.45–2.14) and respiratory symptoms (aOR: 3.41; 95% CI: 2.45–4.75; aOR: 1.45; 95% CI: 1.10–1.91) than the unaffected participants. All primary stressors and certain secondary stressors assessed were significantly associated with both ENT and respiratory symptoms, but no “dose–response” gradient could be observed. Conclusion: This study highlights the long-term adverse effects of flood exposure on respiratory health.

Abstract The Maritime Continent is one of the most challenging regions for atmospheric models. Processes that modulate deep convection are poorly represented in models, which affects their ability to simulate precipitation features accurately. Thus, future projections of precipitation over the region are prone to large uncertainties. One of the key players in modeling tropical precipitation is the convective representation, and hence convection-permitting experiments have contributed to improve aspects of precipitation in models. This improvement creates opportunities to explore the physical processes that govern rainfall in the Maritime Continent, as well as their role in a warming climate. Here, we examine the response to climate change of models with explicit and parameterized convection and how that reflects in precipitation changes. We focus on the intensification of spatial contrasts as precursors of changes in mean and extreme precipitation in the tropical archipelago. Our results show that the broad picture is similar in both model setups, where islands will undergo an increase in mean and extreme precipitation in a warmer climate and the ocean will see less rain. However, the magnitude and spatial structure of such changes, as well as the projection of rainfall percentiles, are different across model experiments. We suggest that while the primary effect of climate change is thermodynamical and it is similarly reproduced by both model configurations, dynamical effects are represented quite differently in explicit and parameterized convection experiments. In this study, we link such differences to horizontal and vertical spatial contrasts and how convective representations translate them into precipitation changes.

Abstract This study investigates the seasonality of near‐surface wind speeds associated with extratropical cyclones (ETCs) over northeastern North America using a global reanalysis data set during 1979–2020. As opposed to most studies that emphasize winter storms, ETCs during the fall exhibit significantly stronger 10‐m winds over this region due to the slightly stronger continental cyclones and significantly weaker low‐level stability during that time of the year. Also, ETCs favor inland lakes and Hudson Bay during the low‐ice‐content fall season, leading to lower surface roughness. Combining these results, we derive simple linear regressions to predict the 10‐m wind speed given three variables: 850‐hPa wind speed, low‐level Richardson number, and surface roughness length. This formula captures the observed seasonality and serves as a valuable tool for cyclone near‐surface wind risk assessment. , Plain Language Summary Extratropical cyclones can bring powerful winds that can cause severe damage to infrastructure. We find that cyclones with severe winds are the most frequent in the fall season over continental northeastern North America. Three reasons are found responsible: stronger continental cyclones, weaker low‐level atmospheric stability, and the lower surface roughness over lakes and Hudson Bay, where cyclones frequently occur in fall. A simple formula that can effectively assess the near‐surface wind speeds associated with cyclones is derived based on these results. , Key Points Extratropical‐cyclone‐associated 10‐m wind speeds are the strongest in the fall season over northeastern North America Besides stronger continental cyclones and 850‐hPa winds, weaker low‐level stability in fall favors stronger 10‐m wind speeds in this region Linear regression using 850‐hPa wind, Richardson number, and surface roughness well predicts cyclones' 10‐m wind speeds and seasonality

Carbon allocation is an important mechanism through which plants respond to environmental changes. To enhance our understanding of maximizing carbon uptake by controlling planting densities, the carbon allocation module of a process-based model, TRIPLEX-Management, was modified and improved by introducing light, soil water, and soil nitrogen availability factors to quantify the allocation coefficients for different plant organs. The modified TRIPLEX-Management model simulation results were verified against observations from northern Jiangsu Province, China, and then the model was used to simulate dynamic changes in forest carbon under six density scenarios (200, 400, 600, 800, 1000, and 1200 stems ha−1). The mean absolute errors between the predicted and observed variables of the mean diameter at breast height, mean height, and estimated aboveground biomass ranged from 15.0% to 26.6%, and were lower compared with the original model simulated results, which ranged from 24.4% to 60.5%. The normalized root mean square errors ranged from 0.2 to 0.3, and were lower compared with the original model simulated results, which ranged from 0.3 to 0.6. The Willmott index between the predicted and observed variables also varied from 0.5 to 0.8, indicating that the modified TRIPLEX-Management model could accurately simulate the dynamic changes in poplar (Populus spp.) plantations with different densities in northern Jiangsu Province. The density scenario results showed that the leaf and fine root allocation coefficients decreased with the increase in stand density, while the stem allocation increased. Overall, our study showed that the optimum stand density (approximately 400 stems ha−1) could reach the highest aboveground biomass for poplar stands and soil organic carbon storage, leading to higher ecological functions related to carbon sequestration without sacrificing wood production in an economical way in northern Jiangsu Province. Therefore, reasonable density control with different soil and climate conditions should be recommended to maximize carbon sequestration.