Montréal, Canada
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Gennaretti F.,Aix - Marseille University | Huard D.,Ouranos Consortium | Naulier M.,CEA Cadarache Center | Savard M.,Geological Survey of Canada | And 3 more authors.
Climate Dynamics | Year: 2017

Northeastern North America has very few millennium-long, high-resolution climate proxy records. However, very recently, a new tree-ring dataset suitable for temperature reconstructions over the last millennium was developed in the northern Quebec taiga. This dataset is composed of one δ18O and six ring width chronologies. Until now, these chronologies have only been used in independent temperature reconstructions (from δ18O or ring width) showing some differences. Here, we added to the dataset a δ13C chronology and developed a significantly improved millennium-long multiproxy reconstruction (997–2006 CE) accounting for uncertainties with a Bayesian approach that evaluates the likelihood of each proxy model. We also undertook a methodological sensitivity analysis to assess the different responses of each proxy to abrupt forcings such as strong volcanic eruptions. Ring width showed a larger response to single eruptions and a larger cumulative impact of multiple eruptions during active volcanic periods, δ18O showed intermediate responses, and δ13C was mostly insensitive to volcanic eruptions. We conclude that all reconstructions based on a single proxy can be misleading because of the possible reduced or amplified responses to specific forcing agents. © 2017 The Author(s)


Rapaic M.,Service Meteorologique du Canada | Brown R.,Environment Canada | Markovic M.,Canadian Center for Meteorological and Environmental Prediction | Chaumont D.,Ouranos Consortium
Atmosphere - Ocean | Year: 2015

The spatial and temporal consistency of seasonal air temperature and precipitation in eight widely used gridded observation-based climate datasets (CANGRD, CRU-TS3.1, CRUTEM4.1, GISTEMP, GPCC, GPCP, HadCRUT3, and UDEL) and eight reanalyses (20CR, CFSR, ERA-40, ERA-Interim, JRA25, MERRA, NARR, and NCEP2) was evaluated over the Canadian Arctic for the 1950-2010 period. The evaluation used the CANGRD dataset, which is based on homogenized temperature and adjusted precipitation from climate stations, as a reference. Dataset agreement and bias were observed to exhibit important spatial, seasonal, and temporal variability over the Canadian Arctic with the largest spread occurring between datasets over mountain and coastal regions and over the Canadian Arctic Archipelago. Reanalysis datasets were typically warmer and wetter than surface observation-based datasets, with CFSR and 20CR exhibiting biases in total annual precipitation on the order of 300 mm. Warm bias in 20CR exceeded 12°C in winter over the western Arctic. Analysis of the temporal consistency of datasets over the 1950-2010 period showed evidence of discontinuities in several datasets as well as a noticeable increase in dataset spread in the period after approximately 2000. Declining station networks, increased automation, and the inclusion of new satellite data streams in reanalyses are potential contributing factors to this phenomenon. Evaluation of trends over the 1950-2010 period showed a relatively consistent picture of warming and increased precipitation over the Canadian Arctic from all datasets, with CANGRD giving moistening trends two times larger than the multi-dataset average related to the adjustment of the station precipitation data. The study results indicate that considerable care is needed when using gridded climate datasets in local or regional scale applications in the Canadian Arctic. © 2015 Taylor & Francis.


Huard D.,Ouranos Consortium | Chaumont D.,Ouranos Consortium | Logan T.,Ouranos Consortium | Sottile M.-F.,Ouranos Consortium | And 4 more authors.
Bulletin of the American Meteorological Society | Year: 2014

The government of the province of QueÉbec in Canada worked jointly with universities and Hydro-QueÉbec in 2002 to launch the Ouranos consortium with the objective of providing climate information and expertise in support of adaptation to climate change. Ouranos differed from most other climate service centers by merging operational climate modeling expertise, impacts, and adaptation expertise and climate analysis services in a single place. A dedicated scenario group was created, consisting of team specifically created to handle user requests for general climate information and climate change scenarios. The experience of Ouranos with and without a scenario group suggested that dedicated scenario professionals were a key ingredient to deliver effective climate services and maintain long-term healthy relationships between users and climate scientists. The consortium's first task was to was to provide Canada with a regional climate projections program.


News Article | December 16, 2016
Site: www.eurekalert.org

Emphasis should be on the energy needs of companies and communities rather than locally available resources. Furthermore, social acceptability and active involvement of the aboriginal communities are key to developing renewable energy in the North. These were two main takeaways that emerged from the workshop on defining energy solutions for northern regions held at INRS on December 9, 2016, in the wake of the international research cooperation agreement between Quebec and Iceland. The workshop, which brought together INRS (which initiated the agreement), Université Laval, Reykjavik University, University of Iceland, Landsvirkjun (Iceland's national power company), Hydro-Québec's Research Institute (IREQ), and Ouranos Consortium, was an opportunity for university partners and energy producers to discuss and define collaborative projects aimed at replacing fossil fuels with renewable energy, not only to preserve the environment but also to reduce price volatility. Participants highlighted the importance of improving: By pooling their complementary expertise in shallow geothermal energy, mobile energy system development, geothermal system engineering, and new materials research, partnership members want to provide private and public decision makers with valuable scientific and technical insight. To succeed, they plan to: Choosing the right materials for geothermal operations remains a constant challenge, which is why cooperation and knowledge sharing between universities, industry, and government is crucial to the growth of geothermal energy. That was the general consensus following the geothermal symposium held on December 8, 2016, at INRS's Eau Terre Environnement Research Centre. The event gave researchers from Iceland, Sweden, and Quebec an opportunity to review the state of knowledge in this multidisciplinary field. They concluded that: "Iceland's and Sweden's experience with geothermal energy in the Arctic Circle is paving the way for Quebec," asserts INRS professor Jasmin Raymond, holder of the Northern Geothermal Potential Research Chair.


Xu Z.,Maurice Lamontagne Institute | Savard J.-P.,Ouranos Consortium | Lefaivre D.,Maurice Lamontagne Institute
Atmosphere - Ocean | Year: 2015

This study demonstrates that long-term climate model solutions can be efficiently converted to storm surge time series at points of interest (POIs) for the future. The all-source Green's function (ASGF) regression model is used for this conversion. In addition to being data assimilative, the ASGF regression model can also simulate storm surges at a POI faster than the traditional modelling approach by orders of magnitude. This is demonstrated using the tidal gauge at Sept-Îles (Quebec, Canada) in the Gulf of St. Lawrence as the POI. First the ASGF regression model is used to assimilate 32 years of tidal gauge data, producing a continuous hindcast of storm surges and a set of best-estimate regression parameters. Second, the ASGF regression model with the best-estimate parameters is used to convert a Canadian Regional Climate Model solution (CRCM/AHJ) to an hourly time series of storm surges from 1961 to 2100. Gumbel's extreme value analysis (EVA) is then applied to the time series as a whole and also to tri-decadal segments. The tri-decadal approach is used to investigate whether there is any progressive shortening or lengthening of storm surge return periods as a result of future climate change. A method for correcting for bias due to the forcing field at the EVA level is also demonstrated. © 2015 © 2015 Taylor & Francis.


Grenier P.,Ouranos Consortium | Parent A.-C.,Laval University | Huard D.,Ouranos Consortium | Anctil F.,Laval University | Chaumont D.,Ouranos Consortium
Journal of Applied Meteorology and Climatology | Year: 2013

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. © 2013 American Meteorological Society.


Guay C.,Hydro - Quebec | Minville M.,Hydro - Quebec | Braun M.,Ouranos Consortium
Canadian Water Resources Journal | Year: 2015

This paper presents the methodology and results of a vast study on climate change impacts on hydrology for the province of Québec for the 2050 horizon. A climate ensemble was first built with simulations from the World Climate Research Programme (WCRP)'s Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset, the North American Regional Climate Change Assessment Program (NARCCAP) and the Canadian Regional Climate Model (CRCM) operational runs. Direct outputs and post-processed data from the climate simulations were used as input to the calibrated HSAMI hydrological model in order to produce a large ensemble of hydrological projections for 305 Québec watersheds. Simulations results indicate that increases in mean annual streamflow are projected for the whole province, with greater changes (up to 14%) in the north. The intra-annual distribution of streamflow also changes, with higher winter flows and lower summer flows as well as apparently earlier spring floods. The maximum height of the snow cover and the number of days with snow on the ground are likely to diminish for the 2050 horizon for the southern half of the province, while the northern half will see more snow, but a shorter snow season as well. © 2015 Canadian Water Resources Association.


Chen J.,Wuhan University | St-Denis B.G.,Ouranos Consortium | Brissette F.P.,University of Québec | Lucas-Picher P.,University of Québec | Lucas-Picher P.,University of Quebec at Montréal
Journal of Hydrometeorology | Year: 2016

Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability. © 2016 American Meteorological Society.


Roy-Dufresne E.,McGill University | Logan T.,Ouranos Consortium | Simon J.A.,McGill University | Chmura G.L.,McGill University | Millien V.,McGill University
PLoS ONE | Year: 2013

The white-footed mouse (Peromyscus leucopus) is an important reservoir host for Borrelia burgdorferi, the pathogen responsible for Lyme disease, and its distribution is expanding northward. We used an Ecological Niche Factor Analysis to identify the climatic factors associated with the distribution shift of the white-footed mouse over the last 30 years at the northern edge of its range, and modeled its current and potential future (2050) distributions using the platform BIOMOD. A mild and shorter winter is favouring the northern expansion of the white-footed mouse in Québec. With more favorable winter conditions projected by 2050, the distribution range of the white-footed mouse is expected to expand further northward by 3° latitude. We also show that today in southern Québec, the occurrence of B. burgdorferi is associated with high probability of presence of the white-footed mouse. Changes in the distribution of the white-footed mouse will likely alter the geographical range of B. burgdorferi and impact the public health in northern regions that have yet to be exposed to Lyme disease. © 2013 Roy-Dufresne et al.


Simon J.A.,McGill University | Marrotte R.R.,McGill University | Desrosiers N.,Ministere du Developpement Durable | Fiset J.,University of Montréal | And 15 more authors.
Evolutionary Applications | Year: 2014

Lyme borreliosis is rapidly emerging in Canada, and climate change is likely a key driver of the northern spread of the disease in North America. We used field and modeling approaches to predict the risk of occurrence of Borrelia burgdorferi, the bacteria causing Lyme disease in North America. We combined climatic and landscape variables to model the current and future (2050) potential distribution of the black-legged tick and the white-footed mouse at the northeastern range limit of Lyme disease and estimated a risk index for B. burgdorferi from these distributions. The risk index was mostly constrained by the distribution of the white-footed mouse, driven by winter climatic conditions. The next factor contributing to the risk index was the distribution of the black-legged tick, estimated from the temperature. Landscape variables such as forest habitat and connectivity contributed little to the risk index. We predict a further northern expansion of B. burgdorferi of approximately 250-500 km by 2050 - a rate of 3.5-11 km per year - and identify areas of rapid rise in the risk of occurrence of B. burgdorferi. Our results will improve understanding of the spread of Lyme disease and inform management strategies at the most northern limit of its distribution. © 2014 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd.

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