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Precipitation and temperature are among major climatic variables that are used to characterize extreme weather events, which can have profound impacts on ecosystems and society. Accurate simulation of these variables at the local scale is essential to adapt urban systems and policies to future climatic changes. However, accurate simulation of these climatic variables is difficult due to possible interdependence and feedbacks among them. In this paper, the concept of copulas was used to model seasonal interdependence between precipitation and temperature. Five copula functions were fitted to grid (approximately 10 km × 10 km) climate data from 1960 to 2013 in southern Ontario, Canada. Theoretical and empirical copulas were then compared with each other to select the most appropriate copula family for this region. Results showed that, of the tested copulas, none of them consistently performed the best over the entire region during all seasons. However, Gumbel copula was the best performer during the winter season, and Clayton performed best in the summer. More variability in terms of best copula was found in spring and fall seasons. By examining the likelihoods of concurrent extreme temperature and precipitation periods including wet/cool in the winter and dry/hot in the summer, we found that ignoring the joint distribution and confounding impacts of precipitation and temperature lead to the underestimation of occurrence of probabilities for these two concurrent extreme modes. This underestimation can also lead to incorrect conclusions and flawed decisions in terms of the severity of these extreme events.

Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in terms of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. The rain on snow index has been widely used, but it neglects rain-only events which are expected to be more frequent in the future. In this study, we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model large ensemble (CRCM5-LE). These climate data were used as input in Precipitation Runoff Modelling System (PRMS) hydrological model to simulate the future evolution of high flows in three watersheds in southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in north-eastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10 mm and temperature higher than 5 ∘C were necessary historical conditions to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated with two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP regime) and the Atlantic Ocean (South regime). These hydrometeorological extreme events will still be associated with the same atmospheric patterns in the near future. The future evolution of the index will be modulated by the internal variability of the climate system, as higher Z500 on the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high-flow generation. This study shows the value of the CRCM5-LE dataset in simulating hydrometeorological extreme events in eastern Canada and better understanding the uncertainties associated with internal variability of climate.

This chapter presents current knowledge of observed and projected impacts from extreme weather events, based on recorded events and their losses, as well as studies that project future impacts from anthropogenic climate change. The attribution of past changes in such impacts focuses on the three key drivers: changes in extreme weather hazards that can be due to natural climate variability and anthropogenic climate change, changes in exposure and vulnerability, and risk reduction efforts. The chapter builds on previous assessments of attribution of extreme weather events, to drivers of changes in weather hazard, exposure and vulnerability. Most records of losses from extreme weather consist of information on monetary losses, while several other types of impacts are underrepresented, complicating the assessment of losses and damages. Studies into drivers of losses from extreme weather show that increasing exposure is the most important driver through increasing population and capital assets. Residual losses (after risk reduction and adaptation) from extreme weather have not yet been attributed to anthropogenic climate change. For the Loss and Damage debate, this implies that overall it will remain difficult to attribute this type of losses to greenhouse gas emissions. For the future, anthropogenic climate change is projected to become more important for driving future weather losses upward. However, drivers of exposure and especially changes in vulnerability will interplay. Exposure will continue to lead to risk increases. Vulnerability on the other hand may be further reduced through disaster risk reduction and adaptation. This would reduce additional losses and damages from extreme weather. Yet, at the country scale and particularly in developing countries, there is ample evidence of increasing risk, which calls for significant improvement in climate risk management efforts.

In the context of global warming, changes in extreme weather and climate events are expected, particularly those associated with changes in temperature and precipitation regimes and those that will affect coastal areas. The main objectives of this study were to establish the number of extreme events that have occurred in northeastern New Brunswick, Canada in recent history, and to determine whether their occurrence has increased. By using archived regional newspapers and data from three meteorological stations in a national network, the frequency of extreme events in the study area was established for the time period 1950–2012. Of the 282 extreme weather events recorded in the newspaper archives, 70% were also identified in the meteorological time series analysis. The discrepancy might be explained by the synergistic effect of co-occurring non-extreme events, and increased vulnerability over time, resulting from more people and infrastructure being located in coastal hazard zones. The Mann Kendall and Pettitt statistical tests were used to identify trends and the presence of break points in the weather data time series. Results indicate a statistically significant increase in average temperatures and in the number of extreme events, such as extreme hot days, as well as an increase in total annual and extreme precipitation. A significant decrease in the number of frost-free days and extreme cold days was also found, in addition to a decline in the number of dry days.

Global warming is expected to affect both the frequency and severity of extreme weather events, though projections of the response of these events to climate warming remain highly uncertain. The range of changes reported in the climate modelling literature is very large, sometimes leading to contradictory results for a given extreme weather event. Much of this uncertainty stems from the incomplete understanding of the physics of extreme weather processes, the lack of representation of mesoscale processes in coarse-resolution climate models, and the effect of natural climate variability at multi-decadal time scales. However, some of the spread in results originates simply from the variety of scenarios for future climate change used to drive climate model simulations, which hampers the ability to make generalizations about predicted changes in extreme weather events. In this study, we present a meta-analysis of the literature on projected future extreme weather events in order to quantify expected changes in weather extremes as a function of a common metric of global mean temperature increases. We find that many extreme weather events are likely to be significantly affected by global warming. In particular, our analysis indicates that the overall frequency of global tropical cyclones could decrease with global warming but that the intensity of these storms, as well as the frequency of the most intense cyclones could increase, particularly in the northwestern Pacific basin. We also found increases in the intensity of South Asian monsoonal rainfall, the frequency of global heavy precipitation events, the number of North American severe thunderstorm days, North American drought conditions, and European heatwaves, with rising global mean temperatures. In addition, the periodicity of the El Niño–Southern Oscillation may decrease, which could, in itself, influence extreme weather frequency in many areas of the climate system.

Right after a devastating multi-year drought, a number of flood events with unprecedented spatial extent hit different parts of Iran over the 2-week period of March 17th to April 1st, 2019, causing a human disaster and substantial loss of assets and infrastructure across urban and rural areas. Here, we investigate natural (e.g., rainfall, snow accumulation/melt, soil moisture) and anthropogenic drivers (e.g., deforestation, urbanization, and management practices) of these events using a range of ground-based data and satellite observations. These drivers can range from exceptionally extreme rainfall intensities, to cascades of several extreme and moderate events, and various anthropogenic interventions that exacerbated flooding. Our results reveal strong compounding impacts of natural drivers and anthropogenic triggers in escalating flood risks to unprecedented levels. We argue that a new form of floods, i.e. anthropogenic floods, is becoming more common and should be recognized during the “Anthropocene”. This specific form of floods refers to high to extreme streamflow/runoff events that are primarily caused, or largely exacerbated, by anthropogenic drivers. We demonstrate how the growing risk of anthropogenic floods can be assessed using a wide range of climatic and non-climatic satellite and in-situ data.

Climate change is already increasing the severity of extreme weather events such as with rainfall during hurricanes. But little research to date investigates if, and to what extent, there are social inequalities in climate change-attributed extreme weather event impacts. Here, we use climate change attribution science paired with hydrological flood models to estimate climate change-attributed flood depths and damages during Hurricane Harvey in Harris County, Texas. Using detailed land-parcel and census tract socio-economic data, we then describe the socio-spatial characteristics associated with these climate change-induced impacts. We show that 30 to 50% of the flooded properties would not have flooded without climate change. Climate change-attributed impacts were particularly felt in Latina/x/o neighborhoods, and especially so in Latina/x/o neighborhoods that were low-income and among those located outside of FEMA's 100-year floodplain. Our focus is thus on climate justice challenges that not only concern future climate change-induced risks, but are already affecting vulnerable populations disproportionately now.

Abstract In a rapidly changing world, what is today an unprecedented extreme may soon become the norm. As a result, extreme‐related disasters are expected to become more frequent and intense. This will have widespread socio‐economic consequences and affect the ability of different societal groups to recover from and adapt to rapidly changing environmental conditions. Therefore, there is the need to decipher the relation between genesis of unprecedented events, accumulation and distribution of risk, and recovery trajectories across different societal groups. Here, we develop an analytical approach to unravel the complexity of future extremes and multiscalar societal responses—from households to national governments and from immediate impacts to longer term recovery. This requires creating new forms of knowledge that integrate analyses of the past—that is, structural causes and political processes of risk accumulation and differentiated recovery trajectories—with plausible scenarios of future environmental extremes grounded in the event‐specific literature. We specifically seek to combine the physical characteristics of the extremes with examinations of how culture, politics, power, and policy visions shape societal responses to unprecedented events, and interpret the events as social‐environmental extremes. This new approach, at the nexus between social and natural sciences, has the concrete advantage of providing an impact‐focused vision of future social‐environmental risks, beyond what is achievable within conventional disciplinary boundaries. In this paper, we focus on extreme flooding events and the societal responses they elicit. However, our approach is flexible and applicable to a wide range of extreme events. We see it as the first building block of a new field of research, allowing for novel and integrated theoretical explanations and forecasting of social‐environmental extremes. , Key Points We conceptualize unprecedented extremes as social‐environmental processes shaped by institutional, political, and economic change As social‐environmental extremes become more frequent, there is an urgency to unravel their genesis and the possible societal responses This approach is the first building block of a new field of research in social‐environmental extreme event forecasts , Plain Language Summary The world is seeing increases in a range of extreme events, and this increase may continue or even accelerate in the future, due to anthropogenic climate change. Furthermore, it is often those who are already vulnerable that experience the biggest impacts from these extremes. Yet, there is little understanding of the possible societal responses to unprecedented events. This underscores the urgency of creating innovative approaches to develop plausible scenarios of societal responses and, in turn, mitigate hazards and reduce vulnerability and exposure to extreme events. In this commentary, we develop a truly interdisciplinary conceptual approach to better understand how different societal groups might interact with and respond to future unprecedented extreme events. We combine social science theories describing how different societal groups are affected by, and recover from, extreme events with projections from the literature identifying plausible areas at risk of unprecedented occurrences and local analyses of past extreme events. We see this as the first building block of a new field of research in forecasting social‐environmental extremes that could support governments, civil protection agencies, and civil society organizations to ensure a fairer, improved response to future events.

Afin de mieux comprendre la distribution géographique des facilitateurs et des obstacles à la participation sociale des Québécois âgés, cette étude visait à documenter l’Indice du potentiel de participation sociale (IPPS) selon les zones métropolitaines, urbaines et rurales. Des analyses de données secondaires, dont l’Enquête transversale sur la santé des collectivités canadiennes, ont permis de développer et de cartographier un indice composé de facteurs environnementaux associés à la participation sociale, pondérés par une analyse factorielle. En zones métropolitaines, l’IPPS était supérieur au centre qu’en périphérie, compte tenu d’une concentration accrue d’aînés et des transports. Bien qu’atténuée, la configuration était similaire en zones urbaines. En zone rurale, un IPPS élevé était associé à une concentration d’aînés et un accès aux ressources accru, sans configuration spatiale. Pour favoriser la participation sociale, l’IPPS soutient que les transports et l’accès aux ressources doivent respectivement être améliorés en périphérie des métropoles et en zone rurale., AbstractTo better understand the geographic distribution of facilitators of, and barriers to, social participation among older Quebecers, this study aimed to document the Social Participation Potential Index (SPPI; Indice du potentiel de participation sociale) in metropolitan, urban and rural areas. Secondary data analyses, including the Canadian Community Health Survey, were used to develop and map a composite index of environmental factors associated with social participation, weighted by factor analysis. In metropolitan areas, the SPPI was higher in the center than in the periphery, due to an increased concentration of seniors and transportation. Although reduced, the pattern was similar in urban areas. In rural areas, a higher SPPI was associated with an increased concentration of older adults and access to resources, showing no spatial pattern. To promote social participation, the SPPI suggests that transportation and access to resources must be improved in the periphery of metropolitan areas and in rural areas, respectively.

Abstract Background Evidence continues to demonstrate that certain marginalised populations are disproportionately affected by COVID-19. While many studies document the impacts of COVID-19 on social inequalities in health, none has examined how public health responses to the pandemic have unfolded to address these inequities in Canada. The purpose of our study was to assess how social inequalities in health were considered in the design and planning of large-scale COVID-19 testing programs in Montréal (Québec, Canada). Methods Part of the multicountry study HoSPiCOVID, this article reports on a qualitative case study of large-scale testing for COVID-19 in Montréal. We conducted semi-structured interviews with 19 stakeholders involved in planning large-scale testing or working with vulnerable populations during the pandemic. We developed interview guides and a codebook using existing literature on policy design and planning, and analysed data deductively and inductively using thematic analysis in NVivo. Results Our findings suggest that large-scale COVID-19 testing in Montréal did not initially consider social inequalities in health in its design and planning phases. Considering the sense of urgency brought by the pandemic, participants noted the challenges linked to the uptake of an intersectoral approach and of a unified vision of social inequalities in health. However, adaptations were gradually made to large-scale testing to improve its accessibility, acceptability, and availability. Actors from the community sector, among others, played an important role in supporting the health sector to address the needs of specific subgroups of the population. Conclusions These findings contribute to the reflections on the lessons learned from COVID-19, highlighting that public health programs must tackle structural barriers to accessing healthcare services during health crises. This will be necessary to ensure that pandemic preparedness and response, including large-scale testing, do not further increase social inequalities in health.