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Abstract Univariate quantile mapping (QM), a technique often used to statistically postprocess climate simulations, may generate physical inconsistency. This issue is investigated here by classifying physical inconsistency into two types. Type I refers to the attribution of an impossible value to a single variable, and type II refers to the breaking of a fixed intervariable relationship. Here QM is applied to relative humidity (RH) and its parent variables, namely, temperature, pressure, and specific humidity. Twelve sites representing various climate types across North America are investigated. Time series from an ensemble of ten 3-hourly simulations are postprocessed, with the CFSR reanalysis used as the reference product. For type I, results indicate that direct postprocessing of RH generates supersaturation values (>100%) at relatively small frequencies of occurrence. Generated supersaturation amplitudes exceed observed values in fog and clouds. Supersaturation values are generally more frequent and higher when RH is deduced from postprocessed parent variables. For type II, results show that univariate QM practically always breaks the intervariable thermodynamic relationship. Heuristic proxies are designed for comparing the initial bias with physical inconsistency of type II, and results suggest that QM generates a problem that is arguably lesser than the one it is intended to solve. When physical inconsistency is avoided by capping one humidity variable at its saturation level and deducing the other, statistical equivalence with the reference product remains much improved relative to the initial situation. A recommendation for climate services is to postprocess RH and deduce specific humidity rather than the opposite.

The hypothesis according to which higher sulphate concentrations favor ice clouds made of larger ice crystals is tested using data sets from the CloudSat and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. This is a potential consequence of the sulphate‐induced freezing inhibition (SIFI) effect, namely, the hypothesis that sulphates contribute to inhibit the onset of ice crystal formation by deactivating ice‐forming nuclei during Arctic winter. A simple index based on the backscattering at 532 nm and the color ratio from the CALIPSO lidar measurements is compared against in situ sulphate concentration time series and used as a proxy for this variable. An algorithm using the lidar data and the CloudSat radar microphysical retrievals is also developed for identifying cloud types, focusing on those supposedly favored by the SIFI effect. The analysis includes the effect of the lidar off‐nadir angle on the sulphate index and the cloud classification, the validation of the index, as well as the production of circum‐Arctic maps of the sulphate index and of the SIFI‐favored clouds fraction. The increase of the lidar off‐nadir angle is shown to cause an increase in the measured depolarization ratio and hence in the ability to detect ice crystals. The index correlates positively with both sulphates and sea salt concentrations, with a Pearson correlation coefficient ( ) varying from 0.10 to 0.42 for the different comparisons performed. Ultimate findings are the results of two correlation tests of the SIFI effect, which allow for a new outlook on its possible role in the Arctic troposphere during winter.

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

Abstract The path toward a warmer global climate is not smooth, but, rather, is made up of a succession of positive and negative temperature trends, with cooling having more chance to occur the shorter the time scale considered. In this paper, estimates of the probabilities of short-term cooling ( P cool ) during the period 2006–35 are performed for 5146 locations across Canada. Probabilities of cooling over durations from 5 to 25 yr come from an ensemble of 60 climate scenarios, based on three different methods using a gridded observational product and CMIP5 climate simulations. These methods treat interannual variability differently, and an analysis in hindcast mode suggests they are relatively reliable. Unsurprisingly, longer durations imply smaller P cool values; in the case of annual temperatures, the interdecile range of P cool values across Canada is, for example, ~2%–18% for 25 yr and ~40%–46% for 5 yr. Results vary slightly with the scenario design method, with similar geographical patterns emerging. With regards to seasonal influence, spring and winter are generally associated with higher P cool values. Geographical P cool patterns and their seasonality are explained in terms of the interannual variability over background trend ratio. This study emphasizes the importance of natural variability superimposed on anthropogenically forced long-term trends and the fact that regional and local short-term cooling trends are to be expected with nonnegligible probabilities.

Abstract Spatial analog techniques consist in identifying locations whose historical climate is similar to the anticipated future climate at a reference location. In the process of identifying analogs, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this study, six a priori suitable metrics are described (the standardized Euclidean distance, the Kolmogorov–Smirnov statistic, the nearest-neighbor distance, the Zech–Aslan energy statistic, the Friedman–Rafsky runs statistic, and the Kullback–Leibler divergence) and criteria are proposed and investigated in an attempt to identify the best metric for selecting spatial analogs. The case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM, version 4.2.3), from which three annual indicators (total precipitation, heating degree-days, and cooling degree-days) are calculated over 30-yr periods (1971–2000 and 2041–70). It is found that the six metrics identify comparable analog regions at a relatively large scale but that best analogs may differ substantially. For best analogs, it is shown that the uncertainty stemming from the metric choice does not generally exceed that stemming from the simulation or model choice. On the basis of the set of criteria considered in this study, the Zech–Aslan energy statistic stands out as the most recommended metric for analog studies, whereas the Friedman–Rafsky runs statistic is the least recommended.

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.

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.

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.