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Abstract. Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM, the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.

Abstract Meteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land–sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000–2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of (i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and (ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain.

Objectives: We propose a novel approach to examine vulnerability in the relationship between heat and years of life lost and apply to neighborhood social disparities in Montreal and Paris. Methods: We used historical data from the summers of 1990 through 2007 for Montreal and from 2004 through 2009 for Paris to estimate daily years of life lost social disparities (DYLLD), summarizing social inequalities across groups. We used Generalized Linear Models to separately estimate relative risks (RR) for DYLLD in association with daily mean temperatures in both cities. We used 30 climate scenarios of daily mean temperature to estimate future temperature distributions (2021–2050). We performed random effect meta-analyses to assess the impact of climate change by climate scenario for each city and compared the impact of climate change for the two cities using a meta-regression analysis. Results: We show that an increase in ambient temperature leads to an increase in social disparities in daily years of life lost. The impact of climate change on DYLLD attributable to temperature was of 2.06 (95% CI: 1.90, 2.25) in Montreal and 1.77 (95% CI: 1.61, 1.94) in Paris. The city explained a difference of 0.31 (95% CI: 0.14, 0.49) on the impact of climate change. Conclusion: We propose a new analytical approach for estimating vulnerability in the relationship between heat and health. Our results suggest that in Paris and Montreal, health disparities related to heat impacts exist today and will increase in the future.

Abstract The interdependence between climatic variables should be taken into account when developing climate scenarios. For example, temperature‐precipitation interdependence in the Arctic is strong and impacts on other physical characteristics, such as the extent and duration of snow cover. However, this interdependence is often misrepresented in climate simulations. Here we use two two‐dimensional (2‐D) methods for statistically adjusting climate model simulations to develop plausible local daily temperature ( T mean ) and precipitation ( Pr ) scenarios. The first 2‐D method is based on empirical quantile mapping (2Dqm) and the second on parametric copula models (2Dcopula). Both methods are improved here by forcing the preservation of the modeled long‐term warming trend and by using moving windows to obtain an adjustment specific to each day of the year. These methods were applied to a representative ensemble of 13 global climate model simulations at 26 Canadian Arctic coastal sites and tested using an innovative cross‐validation approach. Intervariable dependence was evaluated using correlation coefficients and empirical copula density plots. Results show that these 2‐D methods, especially 2Dqm, adjust individual distributions of climatic time series as adequately as one common one‐dimensional method (1Dqm) does. Furthermore, although 2Dqm outperforms the other methods in reproducing the observed temperature‐precipitation interdependence over the calibration period, both 2Dqm and 2Dcopula perform similarly over the validation periods. For cases where temperature‐precipitation interdependence is important (e.g., characterizing extreme events and the extent and duration of snow cover), both 2‐D methods are good options for producing plausible local climate scenarios in Canadian Arctic coastal zones. , Key Points We improved two methods for adjusting T mean , Pr , and their dependence in scenarios Methods are tested at Arctic coastal sites where T mean ‐ Pr dependence is crucial Both methods improve the plausibility of the local climate scenarios

The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate–demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose ( Chen caeruslescens atlantica ), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.

Abstract Climate change can cause negative impacts to the agricultural sector by increasing pest damage to crops. The European corn borer (ECB) is a major insect pest of corn in North America. Its speed of development could potentially accelerate under a warmer climate, leading to an earlier development of the first generation and an increase in the number of generations per year. The main objective of this study was to assess the potential impacts of climate change on ECB management for the future period 2041–2070 in Quebec, Canada, using bioclimatic modelling and climate analogues. First flight of ECB moths could occur about 15 days earlier in the season in 2041–2070 compared to the reference period 1970–1999. The window for insecticide interventions may be reduced under climate change by 15.6% to 27.8% for univoltine ECB and by 13.8% to 52.7% for bivoltine ECB. Climate change could promote the development of an additional generation in the southern region for both races, considering temperature increases and factors inducing the overwintering diapause. ECB management could become more costly both economically and environmentally under the future climate, and it should be revised according to the results of this study.

Abstract Quantile mapping (QM) is a technique often used for statistical post‐processing (SPP) of climate model simulations, in order to adjust their biases relative to a selected reference product and/or to downscale their resolution. However, when QM is applied in univariate mode, there is a risk of generating other problems, like intervariable physical inconsistency (PI). Here, such a risk is investigated with daily temperature minimum ( T min ) and maximum ( T max ), for which the relationship T min > T max would be inconsistent with the definition of the variables. QM is applied to an ensemble of 78 daily CMIP5 simulations over Hudson Bay for the application period 1979–2100, with Climate Forecast System Reanalysis (CFSR) selected as the reference product during the calibration period 1979–2010. This study's specific objectives are as follows: to investigate the conditions under which PI situations are generated; to test whether PI may be prevented simply by tuning some of the QM technique's numerical choices; and to compare the suitability of alternative approaches that hinder PI by design. Primary results suggest that PI situations appear preferentially for small values of the initial (simulated) diurnal temperature range (DTR), but the differential between the respective biases of T min and T max also plays an important role; one cannot completely prevent the generation of PI simply by adjusting QM parameters and options, but forcing preservation of the simulated long‐term trends generates fewer PI situations; for avoiding PI between T min and T max , the present study supports a previous recommendation to directly post‐process T max and DTR before deducing T min .

The objective of this study was to estimate economic losses associated with milk performance detriments under different climate scenarios. A dataset containing milk records of Holstein and daily temperature–humidity indexes compiled over 6 yr in two contrasting climatic dairy regions of Quebec [eastern (EQ) and southwestern Quebec (SWQ)] was used to develop equations for modeling milk performance. Milk performance, including milk, fat, protein, and lactose yields of dairy herds (kg·d −1 ), were then projected considering six plausible climate scenarios during a climatic reference period (REF: 1971–2000) and two future periods (FUT1: 2020–2049; FUT2: 2050–2079). Economic losses were assessed by comparing future to reference milk prices based on components. Results indicated that fat and protein yields could decline in the future, thus resulting in economic losses ranging from $5.34 to $7.07 CAD·hL −1 in EQ and from $5.03 to $6.99 CAD·hL −1 in SWQ, depending on the amplitude of future temperature and humidity changes and on whether it is milk quota or cow number which is adjusted to avoid under-quota production. The projected increase in occurrence and duration of heat stress episodes under climate change could result in substantial financial harm for producers, thereby reinforcing the necessity of implementing heat-abatement strategies on dairy farms.

Abstract Timothy ( Phleum pratense L.) is expected to be more affected by climate change than other forage grasses. Therefore, alternatives to timothy, such as tall fescue [ Schedonorus arundinaceus (Shreb.) Dumort.], meadow fescue [ S. pratensis (Huds.) P. Beauv.], or meadow bromegrass ( Bromus biebersteinii Roem. & Schult.) should be explored. Our objective was to simulate and compare the yield and nutritive value of four alfalfa ( Medicago sativa L.)–grass mixtures and annual crops grown on two virtual dairy farms representative of eastern Canada under future climate conditions. The Integrated Farm System Model (IFSM) was used for these projections under the reference (1971–2000), near future (2020–2049), and distant future (2050–2079) climates for two climatically contrasting agricultural areas in eastern Canada (eastern Quebec; southwestern Quebec). In both future periods, annual forage dry matter (DM) yields of the four alfalfa–grass mixtures are projected to increase because of additional harvests, with greater DM yield increases projected in the colder area than in the warmer area. In both areas, the highest yield increase is projected for alfalfa–tall fescue mixture and the lowest for alfalfa–timothy mixture. The nutritive value of all mixtures should increase due to a greater proportion of alfalfa. In both areas, yields of silage and grain corn ( Zea mays L.), and soybean [ Glycine max (L.) Merr.] are projected to increase, but not those of wheat ( Triticum aestivum L.) and barley ( Hordeum vulgare L.). Tall fescue, meadow bromegrass, and meadow fescue are adequate alternatives to timothy grown in association with alfalfa under future climate conditions. , Core Ideas Forage yields of alfalfa–grass mixtures are projected to increase due to additional harvests. Mixture with tall fescue is projected to increase the most and timothy the least. Tall fescue, meadow fescue, and meadow bromegrass are valuable alternatives to timothy. Nutritive value is projected to increase due to more alfalfa in the mixture. Corn and soybean grain yields are projected to increase but not those of wheat and barley.

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.

Abstract Background During Spring 2019, many regions in Quebec (Canada) experienced severe floods. As much as 5,245 households were flooded and 7,452 persons were evacuated, causing extensive material and human damages. A large population-based study was therefore conducted to examine medium-term effects of this natural disaster on health and well-being. Methods Six to eight months post-floods, households located in the flooded zones (in one of the 6 Quebec regions the most severely affected) were randomly invited to participate to a telephone or a web-based survey (response rate=15.3%). Several psychological health outcomes were examined, including psychological distress (based on the 6-item Kessler Scale, score 0-24) and post-traumatic stress (based on the 15-item Impact of Event Scale, score 0-75). These outcomes were compared among 3 levels of exposure using Chi-square test: flooded (floodwater in ≥ 1 liveable room), disrupted (floodwater in non-liveable areas, loss of utilities, loss of access to services, or evacuation), and unaffected. Results Of the 3,437 participating households, 349 (10.2%) were flooded and 1230 (35.8%) were disrupted (but not flooded) during the 2019 floods. A steep gradient was observed for moderate/severe symptoms of post-traumatic stress (score ≥ 26) according to the level of exposure to flooding (unaffected: 3.0%; disrupted: 14.6%; flooded: 44.1%; p < 0.0005). For psychological distress (score ≥ 7), the baseline level (i.e. unaffected group) was 7.3% while it reached 15.0% and 38.4% in the disrupted and the flooded groups, respectively (p < 0.0005). Conclusions This study is among the largest to examine the psychological impacts of flooding. The magnitude of effects observed in flooded households is consistent with the literature and calls for stronger social and economic measures to support flood victims. Such support should help coping with initial stress, but also alleviating secondary stressors classically observed in post-flood settings. Key messages Psychological impacts of floods may persist for several months and may be observed in both flooded and disrupted people. Stronger social and economic measures are needed to better support flood victims, not only in the short but also in the longer term.

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.

The paper describes the development of predictive equations of windthrow for five tree species based on remote sensing of wind-affected stands in southwestern New Brunswick (NB). The data characterises forest conditions before, during and after the passing of extratropical cyclone Arthur, July 4–5, 2014. The five-variable logistic function developed for balsam fir (bF) was validated against remote-sensing-acquired windthrow data for bF-stands affected by the Christmas Mountains windthrow event of November 7, 1994. In general, the prediction of windthrow in the area agreed fairly well with the windthrow sites identified by photogrammetry. The occurrence of windthrow in the Christmas Mountains was prominent in areas with shallow soils and prone to localised accelerations in mean and turbulent airflow. The windthrow function for bF was subsequently used to examine the future impact of windthrow under two climate scenarios (RCP’s 4.5 and 8.5) and species response to local changes anticipated with global climate change, particularly with respect to growing degree-days and soil moisture. Under climate change, future windthrow in bF stands (2006–2100) is projected to be modified as the species withdraws from the high-elevation areas and NB as a whole, as the climate progressively warms and precipitation increases, causing the growing environment of bF to deteriorate.

Abstract Atmospheric blockings are generally associated with large-scale high-pressure systems that interrupt west-to-east atmospheric flow in mid and high latitudes. Blockings cause several days of quasi-stationary weather conditions, and therefore can result in monthly or seasonal climate anomalies and extreme weather events on the affected regions. In this paper, the long-term coupled CERA-20C reanalysis data from 1901 to 2010 are used to evaluate the links between blocking events over the North Atlantic north of 35° N, and atmospheric and oceanic modes of climate variability on decadal time scales. This study indicates more frequent and longer lasting blocking events than previous studies using other reanalyses products. A strong relationship was found between North Atlantic blocking events and North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO) and Baffin Island–West Atlantic (BWA) indices, in fall, winter and spring. More blocking events occur during the negative phases of the NAO index and positive phases of the BWA mode. In some situations, the BWA patterns provide clearer links with the North Atlantic blocking occurrence than with the NAO alone. The correlation between the synchronous occurrences of AMO and blocking is generally weak, although it does increase for a lag of about 6–10 years. Convergent cross mapping (CCM) furthermore demonstrates a significant two-way causal effect between blocking occurrences and the NAO and BWA indices. Finally, while we find no significant trends in blocking frequencies over the last 110 years in the Northern Hemisphere, these events become longer lasting in summer and fall, and more intense in spring in the North Atlantic.