Publications scientifiques

Voici la liste des publications scientifiques par des membres du projet ARRIMÉ.

Une mise à jour sera faite prochainement pour vous permettre de faire une sélection par date, par auteur ou par sujet à partir de notre base de données des publications sous l’application ZOTERO.

Michaud A. (2026). Caractéristiques des vents et des rafales telles que simulées par un modèle régional du climat à très haute résolution. Maitrise en sciences de l’environnement, UQAM (en évaluation). Codirection : P. Gachon et M. Leduc.

Alpizar, M., Di Luca, A., Gachon, P. et al. Mesoscale Convective Systems in Northeastern North America: identification and evaluation with the convection-permitting version of the Canadian Regional Climate Model. Clim Dyn 64, 129 (2026). https://doi.org/10.1007/s00382-026-08102-6

Cloutier-Gervais, Maxine (2025). « Évaluation de la performance du modèle régional canadien du climat pour simuler les cyclones extratropicaux au nord-est Amérique du Nord » Mémoire. Montréal (Québec, Canada), Université du Québec à Montréal, Maîtrise en sciences de l’atmosphère.
WEB : https://archipel.uqam.ca/19368/

Marois, C. (2025). Intégration de facteurs de correction dans la modélisation conjointe des précipitations extrêmes pour l’estimation des courbes IDF en climat futur [Master’s thesis, Polytechnique Montréal].
WEB : https://publications.polymtl.ca/67714/
PDF : https://publications.polymtl.ca/67714/1/2025_CharlesMarois.pdf

De Meyer, V; Di Luca, A; Gachon, P. (2025). An Eulerian Evaluation of Intense Low-Pressure Systems over North America in CMIP6 and a Regional Climate Model]{An Eulerian Evaluation of Intense Low-Pressure Systems over North America in CMIP6 and a Regional Climate Model. Climate Dynamics

Ghosh, S., P. Lucas-Picher, P. Roy, P. Gachon, and A. Di Luca, 2025: Optimal Configuration of a Convection-Permitting Regional Climate Model in Simulating Precipitation Extremes: The Saguenay Flood. J. Hydrometeor.https://doi.org/10.1175/JHM-D-250011.s1.

Martin, A; Fournier, É; Jalbert, J. (2025). Statistical estimation of probable maximum precipitation. Hydrology and Earth System Sciences, 29, 4811-4824. https://doi.org/10.5194/hess-29-4811-2025

Martin, A. (2024). Estimation statistique de la précipitation maximale probable à l’aide de la loi de Pearson de type I [Master’s thesis, Polytechnique Montréal].
WEB : https://publications.polymtl.ca/59163/
PDF : https://publications.polymtl.ca/59163/1/2024_AnneMartin.pdf

Roberge, F., Di Luca, A., Laprise, R., Lucas-Picher, P., & Thériault, J. (2024). Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model. Geoscientific Model Development, 17(4), 1497–1510. https://doi.org/10.5194/gmd-17-1497-2024

Roy, P; Rondeau-Genesse, G; Jalbert, J; Fournier, É. (2024). Climate Scenarios of Extreme Precipitation Using a Combination of Parametric and Non-Parametric Bias Correction Methods. Canadian Water Resources Journal. 49(1): 23-39.https://doi.org/10.1080/07011784.2023.2220682

IPCC (2019). Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].

IPCC (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Full_Report.pdf

WMO (2021a). Global Annual to Decadal Climate Update, https://hadleyserver.metoffice.gov.uk/wmolc/WMO_GADCU_2020.pdf

WMO (2021b). State of the Global Climate 2020, WMO-No. 1264, World Meteorological Organization, https://library.wmo.int/doc_num.php?explnum_id=10618

Bush, E. and Lemmen, D.S., editors (2019). Canada’s Changing Climate Report; Government of Canada, Ottawa, ON. 444 p., https://changingclimate.ca/site/assets/uploads/sites/2/2019/04/CCCR_FULLREPORT-EN-FINAL.pdf

AMAP (2021). Arctic Climate Change Update 2021: Key Trends and Impacts. Summary for Policymakers. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway. 16 pp. https://www.amap.no/documents/download/6730/inline

The Global Risks Report (2021). The Global Risks Report 2021, 16th Edition, the World Economic Forum. ISBN: 978-2-940631-24-7, https://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2021.pdf

WMO (2021c). Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019). WMO-No. 1267, 90 p., https://library.wmo.int/doc_num.php?explnum_id=10769

BAC (Bureau d’assurance du Canada) (2015). « La gestion financière du risque d’inondation »,assets.ibc.ca/Documents/Natural%20Disasters/The_Financial_Management_of_Flood_Risk_FR.pdf

BAC (Bureau d’assurance du Canada) (2019). Options de gestion des coûts de propriétés résidentielles les plus à risque d’inondation au Canada : un rapport du Groupe de travail national sur le risque financier d’inondation. http://assets.ibc.ca/Documents/Studies/IBCFlood-Options-PaperFR.pdf.

IPCC [Intergovernmental Panel on Climate Change] (2013). Climate Change 2013: The Physical Science Basis; Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, (ed.) T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley; Cambridge University Press, Cambridge, United Kingdom and New York, NY, p. 3–29, https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_SPM_FINAL.pdf

IPCC (2018). Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. https://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_SPM_version_report_LR.pdf

O’Neill, B., Oppenheimer, M., Warren, R. et al. (2017). IPCC reasons for concern regarding climate change risks. Nature Clim Change 7, 28–37. https://doi.org/10.1038/nclimate3179

UNDRR (2019). Global Assessment Report on Disaster Risk Reduction, Geneva, Switzerland, United Nations Office for Disaster Risk Reduction (UNDRR). https://gar.undrr.org/sites/default/files/reports/2019-05/full_gar_report.pdf

Alexander D. (2021). Cascading Disasters: Multiple Risk Reduction and Resilience. In: Eslamian S., Eslamian F. (eds) Handbook of Disaster Risk Reduction for Resilience. Springer, Cham. https://doi.org/10.1007/978-3-030-61278-8_8

 Council of Canadian Academies (2019). Canada’s Top Climate Change Risks, Ottawa (ON): The Expert Panel on Climate Change Risks and Adaptation Potential, Council of Canadian Academies. https://cca-reports.ca/wp-content/uploads/2019/07/Report-Canada-top-climate-change-risks.pdf

Catto, J. L., and Dowdy, A. (2021). Understanding compound hazards from a weather system perspective, Weather and Climate Extremes, 32, 100313, ISSN 2212-0947, https://doi.org/10.1016/j.wace.2021.100313.

Messmer, M. and Simmonds, I. (2021). Global analysis of cyclone-induced compound precipitation and wind extreme events, Weather and Climate Extremes, 32, 100324, ISSN 2212-0947, https://doi.org/10.1016/j.wace.2021.100324.

Gutowski, W. J., Jr, Ullrich, P. A., Hall, A., Leung, L. R., O’Brien, T. A., Patricola, C. M., Arritt, R. W., Bukovsky, M. S., Calvin, K. V., Feng, Z., Jones, A. D., Kooperman, G. J., Monier, E., Pritchard, M. S., Pryor, S. C., Qian, Y., Rhoades, A. M., Roberts, A. F., Sakaguchi, K., Urban, N., & Zarzycki, C. (2020). The Ongoing Need for High-Resolution Regional Climate Models: Process Understanding and Stakeholder Information, Bulletin of the American Meteorological Society, 101(5), E664-E683. Retrieved Jun 1, 2021, from https://journals.ametsoc.org/view/journals/bams/101/5/bams-d-19-0113.1.xml

Westra, S., Fowler, H.J., Evans, J.P., Alexander, L.V., Berg, P., Johnson, F., Kendon, E.J., Lenderink, G., Roberts, N.M., (2014). Future changes to the intensity and frequency of short-duration extreme rainfall. Rev. Geophys. 52, 522–555. https://doi.org/10.1002/2014RG000464.

 Ban, N., Caillaud, C., Coppola, E. et al. (2021). The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: evaluation of precipitation. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-05708-w

 Di Luca, A., Argüeso, D., Sherwood, S., & Evans, J. P. (2021). Evaluating precipitation errors using the environmentally conditioned intensity-frequency decomposition method. Journal of Advances in Modeling Earth Systems, 13, e2020MS002447. https://doi.org/10.1029/2020MS002447

Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S., & Caillaud, C. (2021). Convection-permitting modeling with regional climate models: Latest developments and next steps. Wiley Interdisciplinary Reviews: Climate Change, e731. https://doi.org/10.1002/wcc.731

Argüeso, D., Romero, R., & Homar, V. (2020). Precipitation features of the maritime continent in parameterized and explicit convection models. Journal of Climate, 33, 2449–2466. https://doi.org/10.1175/JCLI-D-19-0416.1

 Cholette, M., Laprise, R. et Thériault, J. M. (2015). Perspectives for very high-resolution climate simulations with nested models: Illustration of potential in simulating St. Lawrence river valley channelling winds with the fifth-generation Canadian regional climate model. Climate, 3(2), 283-307.

 Bao, J., & Sherwood, S. C. (2019). The role of convective self-aggregation in extreme instantaneous versus daily precipitation. Journal of Advances in Modeling Earth Systems, 11, 19–33. https://doi.org/10.1029/2018MS001503

 Semie, A. G., & Bony, S. (2020). Relationship between precipitation extremes and convective organization inferred from satellite observations. Geophysical Research Letters, 47, e2019GL086927. https://doi.org/10.1029/2019GL086927.

Adinolfi, M., Raffa, M., Reder, A., & Mercogliano, P. (2021). Evaluation and expected changes of summer precipitation at convection permitting scale with COSMO-CLM over alpine space. Atmosphere, 12, 54. https://doi.org/10.3390/atmos12010054

 Ban N, Schmidli J, Schär C (2014). Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J Geophys Res Atmos 119:7889–7907. https://doi.org/10.1002/2014JD021478

Lind, P., Lindstedt, D., Kjellström, E., & Jones, C. (2016). Spatial and temporal characteristics of summer precipitation over central Europe in a suite of high-resolution climate models. Journal of Climate, 29, 3501– 3518. https://doi.org/10.1175/JCLI-D-15-0463.1

Lind, P., Belušić, D., Christensen, O. B., Dobler, A., Kjellström, E., Landgren, O., Lindstedt, D., Matte, D., Pedersen, R. A., Toivonen, E., & Wang, F. (2020). Benefits and added value of convection-permitting climate modeling over Fenno-Scandinavia. Climate Dynamics, 55(7), 1893– 1912. https://doi.org/10.1007/s00382-020-05359-3

Roberts, N. (2008). Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model, Meteorol. Appl., 15, 163–169.

Mendoza, P. A., Mizukami, N., Ikeda, K., Clark, M. P., Gutmann, E. D., Arnold, J. R., & Rajagopalan, B. (2016). Effects of different regional climate model resolution and forcing scales on projected hydrologic changes. Journal of Hydrology, 541, 1003-1019

Meredith, E. P., Rust, H. W., and Ulbrich, U. (2018). A classification algorithm for selective dynamical downscaling of precipitation extremes, Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018.

Ban, N., Schmidli, J. & Schär, C. (2015). Heavy precipitation in a changing climate: does short-term summer precipitation increase faster? Geophys. Res. Lett. 42, 1165–1172.

Rousseau, A.N., Klein, I.M., Freudiger, D., Gagnon, P., Frigon, A., Ratté-Fortin, C. (2014). Development of a methodology to evaluate probable maximum precipitation (PMP) under changing climate conditions: application to southern Quebec, Canada. J. Hydrol. 519, 3094–3109. http://dx.doi.org/10.1016/j.jhydrol.2014.10.053.

Clavet-Gaumont, J., Huard, D., Frigon, A., Koenig, K., Slota, P., Rousseau, A., Klein, I., Thiémonge, N., Houdré, F., Perdikaris, J., Turcotte, R., Lafleur, J., Larouche, B. (2017). Probable maximum flood in a changing climate: an overview for Canadian basins. Journal of Hydrology: Regional Studies 13. 1–25. DOI:10.1016/j.ejrh.2017.07.003

Vergara‐Temprado, J., Ban, N., & Schär, C. (2021). Extreme Sub‐Hourly Precipitation Intensities Scale Close to the Clausius‐Clapeyron Rate Over Europe. Geophysical Research Letters, 48(3), e2020GL089506.

 Heikkilä, U., Sandvik, A., and Sorteberg, A. (2011). Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model, Clim. Dynam., 37, 1551–1564.

Torma, C., Giorgi, F., and Coppola, E. (2015). Added value of regional climate modeling over areas characterized by complex terrain – Precipitation over the Alps, J. Geophys. Res.-Atmos., 120, 3957–3972.

Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., and Zahn, M. (2011). Regional climate models add value to global model data: a review and selected examples, B. Am. Meteorol. Soc., 92, 1181–1192.

Kay, A. L., Rudd, A. C., Davies, H. N., Kendon, E. J., & Jones, R. G. (2015). Use of very high resolution climate model data for hydrological modelling: baseline performance and future flood changes. Climatic Change, 133(2), 193-208.

Reszler, C., Switanek, M. B., & Truhetz, H. (2018). Convection-permitting regional climate simulations for representing floods in small-and medium-sized catchments in the Eastern Alps. Natural Hazards and Earth System Sciences, 18(10), 2653-2674.

WMO (2009). Manual on Estimation of Probable Maximum Precipitation (PMP). WMO N°1045, World Meteorological Organization, 2009.

Paquin, D., A. Frigon, and K. Kunkel (2016). Evaluation of total precipitable water from CRCM4 using the NVAP-MEaSUREs Dataset and ERA-Interim reanalysis data. Atmos.–Ocean, 54, 541–548, https://doi.org/10.1080/07055900.2016.1230043.

Beauchamp, J., Leconte, R., Trudel, M., & Brissette, F. (2013). Estimation of the summerfall PMP and PMF of a northern watershed under a changed climate. Water Resources Research, 49(6), 3852– 3862. doi:10.1002/wrcr.20336.

Micovic, Z. (2016). Chapter 3: Stochastic approach to flood hazard determination. Version préliminaire d’un document pour publication dans : Sensitivity Analysis in Earth Observation Modelling (pp.213-234). Edition: 1. Chapter: 11. Publisher: Elsevier. Editors: George P. Petropoulos, Prashant K. Srivastava, https://doi.org/10.1016/B978-0-12-803011-0.00011-2

Kaddissi, C., M.-C. Simard (2021). Hydrologie statistique et sécurité des ouvrages en climat changeant. Fiche d’identification de projet : NOUV-PR-049. Hydro-Québec. 12 janvier 2021.

 Hershfield DM. (1965). Method for Estimating Probable Maximum Precipitation. American Water Works Association. 57(8):965‒972.

Shepherd, T.G., Boyd, E., Calel, R.A. et al. (2018). Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change 151, 555–571 https://doi.org/10.1007/s10584-018-2317-9

Maraun D. et al (2017). Towards process-informed bias correction of climate change simulations. Nat Clim Chang 7:764–773.

Shepherd T.G. (2014). Atmospheric circulation as a source of uncertainty in climate change projections. Nat Geosci 7:703–708.

Ødemark, K., Müller, M., & Tveito, O. E. (2021). Changing Lateral Boundary Conditions for Probable Maximum Precipitation Studies: A Physically Consistent Approach, Journal of Hydrometeorology, 22(1), 113-123. https://journals.ametsoc.org/view/journals/hydr/22/1/jhm-d-20-0070.1.xml

Ohara, N., M. L. Kavvas, S. Kure, Z. Q. Chen, S. Jang, and E. Tan (2011). Physically based estimation of maximum precipitation over American River Watershed, California. J. Hydrol. Eng., 16, 351–361, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000324

Hiraga Y., Y. Iseri, M. D. Warner, C. D. Frans, A. M. Duren, J. F. England, M. Levent Kavvas (2021). Estimation of Long-duration Maximum Precipitation during a winter season for large basins dominated by Atmospheric Rivers using a Numerical Weather Model, Journal of Hydrology, 598, 126224, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2021.126224.

Singh, M. S., and O’Gorman, P. A. (2014). Influence of microphysics on the scaling of precipitation extremes with temperature, Geophys. Res. Lett., 41, 6037– 6044, doi:10.1002/2014GL061222.

Salas, J.D., M. L. Anderson, S. M. Papalexiou, et F. Frances (2020). PMP and Climate Variability and Change: A Review. Journal of Hydrologic Engineering v25 n12 (20201201).

Ben Alaya, M. A., F. Zwiers, and X. Zhang (2018). Probable maximum precipitation: Its estimation and uncertainty quantification using bivariate extreme value analysis. J. Hydrometeor., 19, 679–694, https://doi.org/10.1175/JHM-D-17-0110.1.

Ishida, K., N. Ohara, M.L. Kavvas, Z.Q. Chen, M.L. Anderson (2018). Impact of air temperature on physically-based maximum precipitation estimation through change in moisture holding capacity of air. Journal of Hydrology, 556, 1050-1063, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2016.10.008.

Salas JD, Gavilan G, Salas FR, et al. (2014). Uncertainty of the PMP and PMF. Handbook of engineering hydrology. Ch 57, 2.

Singh A, Singh VP, Byrd AR. (2018). Computation of probable maximum precipitation and its uncertainty. Int J Hydro.;2(4):504-514. DOI: 10.15406/ijh.2018.02.00118.

 van der Wiel K., et al. (2020). Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions. Environ. Res. Lett. 15 034050.

Fery L., Dubrulle B., Podvin B., Pons F., et Faranda D. (2021). Learning a weather dictionary of atmospheric patterns using Latent Dirichlet Allocation. hal-03258523, https://hal.archivesouvertes.fr/hal-03258523/file/Fery_et_al_LDA_NG.pdf

 McTaggart‐Cowan et al. (2019). Modernization of atmospheric physics parameterization in Canadian NWP. J. of Advances in Modeling Earth Systems, 11.

Milbrandt, J. A., and Morrison, H. (2016). Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part III: Introduction of multiple free categories. J. Atmos. Sci. 73, 975–995. doi:10.1175/JAS-D-15-0204.1.

 Verseghy, D. (2017). CLASS – The Canadian land surface scheme, Climate Research Division, Science and Technology Branch, Environment Canada.

Eyring et al. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958.

Mearns LO et al. (2017). The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder, CO. https://doi.org/10.5065/D6SJ1JCH

 Diaconescu, E., Gachon, P., Laprise, R. et Scinocca, J. (2016). Evaluation of precipitation indices over North America from various configurations of regional climate models. Atmosphere-Ocean, 54(4), 418–439. http://dx.doi.org/10.1080/07055900.2016.1185005

Lucas-Picher, P., Laprise, R. & Winger, K. (2017). Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions. Clim Dyn 48, 2611–2633 https://doi.org/10.1007/s00382-016-3227-z

 Leduc M et al. (2019). The ClimEx project: a 50- member ensemble of climate change projections at 12-km resolution over Europe and northeastern north America with the Canadian regional climate model (CRCM5) J. Appl. Meteorol. Climatol. 58 663–93.

Hersbach, H et al., (2020). The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.3803.

 Muñoz-Sabater, J., Dutra, E., Agustí-panareda, A., Albergel, C., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., & Rodríguez-fernández, N. J. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data Discussions [Preprint], March, 1–50. https://doi.org/https://doi.org/10.5194/essd-2021-82

C3S (2021). The Climate Data Store of the European Copernicus project: https://cds.climate.copernicus.eu/#!/home

Coppola, E., Raffaele, F., Giorgi, F. et al. (2021). Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble. Climate Dynamics https://doi.org/10.1007/s00382-021-05640-z

Alfieri, L., B. Bisselink, F. Dottori, G. Naumann, A. de Roo, P. Salamon, K. Wyser, and L. Feyen (2017). Global projections of river flood risk in a warmer world, Earth’s Future, 5, 171–182, doi:10.1002/2016EF000485.

 Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A. (2019). ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019.

Poan, E.D., Gachon, P., Laprise, R., Aider, R. et Dueymes, G. (2018). Investigating added value of regional climate modeling in North American winter storm tracks simulations. Climate Dynamics, 50(5-6), 1799–1818. http://dx.doi.org/10.1007/s00382-017-3723-9

Dee D.P. et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597.

 Zadra, A., Caya, D., Côté, J., Dugas, B., Jones, C., Laprise, R., Winger, K., & Caron, L. P. (2008). The next Canadian regional climate model. La Physique au Canada, 64, 75– 83.

Wazneh H., Gachon P., Laprise R., De Vernal A., and Tremblay B. (2021). Atmospheric blocking events in the North Atlantic: trends and links to climate anomalies and teleconnections. Climate Dynamics. 56: 2199-2221. http://dx.doi.org/https://doi.org/10.1007/s00382-020-05583-x

Berry, G., Reeder, M.J., Jakob, C. (2011). A global climatology of atmospheric fronts. Geophys. Res. Lett. 38 https://doi.org/10.1029/2010GL046451.

Hewson, T.D. (1998). Objective fronts. Meteorol. Appl. 5, 37–65.

Dowdy, A., Catto (2017). J. Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences. Sci Rep 7, 40359. https://doi.org/10.1038/srep40359

Zhang, X., Alexander, L., Hegerl, G.C., Jones, P., Tank, A.K., Peterson, T.C., Trewin, B., Zwiers, F.W. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev. Clim. Change 2 (6), 851–870. https://doi.org/10.1002/wcc.147

Brönnimann, S., Rajczak, J., Fischer, E. M., Raible, C. C., Rohrer, M., and Schär, C. (2018). Changing seasonality of moderate and extreme precipitation events in the Alps, Nat. Hazards Earth Syst. Sci., 18, 2047–2056, https://doi.org/10.5194/nhess-18-2047-2018

Rhoades A. M., M. D. Risser, D. A. Stone, M. F. Wehner, A. D. Jones (2021). Implications of warming on western United States landfalling atmospheric rivers and their flood damages, Weather and Climate Extremes, 32, 100326, ISSN 2212-0947, https://doi.org/10.1016/j.wace.2021.100326

Guay C., Minville M. et Chartier I. (2022). HSAMI+ : Revisiting Hydro-Québec’s operational hydrological model for flow forecasting and climate change impact studies. Journal of Hydrology (soumis).

Fortin, J.P., Turcotte, R., Massicotte, S., Moussa, R., Fitzback, J., Villeneuve, J.P., (2001). A distributed watershed model compatible with remote sensing and GIS data, Part 1: Description of the model. Journal of Hydrologic Engineering, 6(2), 91-99.

Geoslope (2021). Heat and mass transfer modeling with GeoStudio. GEOSLOPE International Ltd.

GoldSim Technology Group. (2017). GoldSim: Using Simulation to Move Beyond the Limitations of Spreadsheet Models. White Paper: www.goldsim.com

Brasseur, O. (2001). Development and Application of a Physical Approach to Estimating Wind Gusts, Monthly Weather Review, 129(1), 5-25.

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