CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.

Paquin, Dominique; Giguère, Michel; McCray, Christopher; Asselin, Olivier; Gauthier, Charles; Matte, Dominic; Labonté, Marie-Pier; Bourgault, Pascal

doi:10.5281/zenodo.17465777

CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.

Paquin, Dominique; Giguère, Michel; McCray, Christopher; Asselin, Olivier; Gauthier, Charles; Matte, Dominic; Labonté, Marie-Pier; Bourgault, Pascal

Publisher: Zenodo
DOI: 10.5281/zenodo.17465777
Repository: zenodo.org
Language: en

Abstract

CRCM-CMIP Data reference Paquin, D., C. McCray, C. B. Gauthier, M. Giguère, O. Asselin, P .Bourgault, M.-P. Labonté and D. Matte. The CRCM5-CMIP6 Ouranos’ ensemble : A dynamically-downscaled ensemble of CMIP6 simulations over North America. Published in Scientific Data https://doi.org/10.1038/s41597-025-06289-7. Ouranos : Canadian Regional Climate Model – version 5 Martynov et al. 2013, Separovic et al. 2013 Based on GEM 3.3.3.1 Configuration NAM-11 CORDEX North American domain at 0.11° 695x668 grid points including a 20-point sponge (and halo) zone surrounding the domain, 5-minute time steps, xlat1=28.525 xlon2=145.955. 56 vertical levels and a top at 10 hPa. 17 surface levels and a bottom at 15 m. Spectral Nudging A spectral nudging is applied to the horizontal wind component with a half-response wavelength of 1177km and a relaxation time of 13.34 h. The nudging strength is set to zero from the surface to a height of 500 hPa and increases linearly onward to the top of the model’s simulated atmosphere (10 hPa). Parameterization Atmosphere Precipitation: modified Sundqvist (1998); precipitation partition Bourgouin (2000) ; Implicit vertical diffusion. Shallow convection: Kuo (1965) transient shallow, Non‐cloudy boundary layer formulation. Deep convection: Kain-Fritsch (1990); Radiation: Li & Barker (2005) Surface CLASS3.5c (Verseghy, 1993) Lake model: FLake Ocean Prescribed SST & sea ice fraction Aerosol Prescribed Data Access Due to its large size, the full dataset can't yet be shared publicly. A subset of the variables are stored on Ouranos' THREDDS server. - Annual files : https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html- Aggregated datasets : https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html Other variables can be provided upon request by writing to [email protected]. All data are available through a CC-BY 4.0 license. Acknowlegments Developed by the ESCER Centre at UQAM (Université du Québec à Montréal) with the collaboration of Environment and Climate Change Canada (ECCC). CRCM5; Martynov et al. 2013, Separovic et al. 2013 The CRCM5 data has been generated and supplied by Ouranos. CRCM5 computations were made on the supercomputers beluga and narval managed by Calcul Québec and the Digital Research Alliance of Canada. The operation of this supercomputer received financial support from Innovation, Science and Economic Development Canada and the Ministère de l’Économie et de l’Innovation du Québec. Some references for CRCM5 Asselin, M. Leduc, D. Paquin, K. Winger, A. Di Luca, M. Bukovsky, B. Music, and M. Giguère (2022). On the Intercontinental Transferability of Regional Climate Model Response to Severe Forestation. MDPI's Climate https://doi.org/10.3390/cli10100138 Bresson, E., R. Laprise, D. Paquin, J. M. Thériault, R. de Elia, 2017: Evaluating CRCM5 ability to simulate mixed precipitation. Atmosphere-Ocean. 55(2); 79-93. http://dx.doi.org/10.1080/07055900.2017.1310084 Leduc, M., A. Mailhot, A. Frigon, J.-L. Martel, R. Ludwig, G.B. Brietzke, M. Giguère, F. Brissette, R. Turcotte, M. Braun, (2019) 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). Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/JAMC-D-18-0021.1 Martynov A, R Laprise, L Sushama, K Winger, L Separovic, B Dugas. 2013. Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation. Clim Dyn 41:2973-3005. https://doi.org/10.1007/s00382-013-1778-9 Martynov A, L Sushama, R Laprise, K Winger, B Dugas. 2012. Interactive lakes in the Canadian regional climate model version 5: the role of lakes in the regional climate of North America. Tellus A 64, 016226. https://doi.org/10.3402/tellusa.v64i0.16226. Martynov A, L Sushama, R Laprise. 2010. Simulation of temperate freezing lakes by one-dimensional lake models: performance assessment for interactive coupling with regional climate models. Boreal Env Res 15:143-164. Matte, D., Thériault, J. M., & Laprise, R. (2019). Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model. Climate Dynamics, 53(1), 1125–1141. https://doi.org/10.1007/s00382-018-4231-2 McCray, C. D., D. Paquin, J. M. Thériault, É. Bresson (2022). A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations. Journal of Geophysical Research -Atmospheres https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935 McCray, D. C., J. M. Thériault, D. Paquin, É. Bresson, 2022. Quantifying the impact of precipitation-type algorithm selection on the representation of freezing rain in an ensemble of regional climate model simulations. Journal of Applied Meteorology and Climatology. https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml McCray, C.D., G. Schmidt, D. Paquin, M. Leduc, Z. Bi, M. Radiyat, C. Silverman, M. Spitz, B. Brettschneider (2023). Changing Nature of High-Impact Snowfall Events in Eastern North America. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2023JD038804 Mironov D, E Heise, E Kourzeneva, B Ritter, N Schneider, A Terzhevik. 2010. Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Env Res 15:218-230. Mittermeier, M., E. Bresson, D. Paquin, R. Ludwig, 2021 A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada). Atmosphere-Ocean. https://doi.org/10.1080/07055900.2021.1992341 Riette S, D Caya. 2002. Sensitivity of short simulations to the various parameters in the new CRCM spectral nudging. – In: RITCHIE, H. (Ed.): Research activities in Atmospheric and Oceanic Modeling, WMO/TD No. 1105, Report No. 32: 7.39–7.40. Pérez Bello, A., A. Mailhot and D. Paquin, 2021 The response of daily and sub-daily extreme precipitations to changes in surface and dew point temperatures. Journal of Geophysical Research – Atmospheres http://dx.doi.org/10.1029/2021JD034972 Pérez Bello, A., A. Mailhot, D. Paquin and D. Paquin-Ricard (2022). Temperature-precipitation scaling rates: a rainfall event-based perspective. Journal of Geophysical Research – Atmospheres. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037873 Separovic L, A Alexandru, R Laprise, A Martynov, L Sushama, K Winger, K Tete, M Valin. 2013. Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model. Clim Dyn 41:3167-3201. DOI 10.1007/s00382-013-1737-5. St-Pierre, M., J. Thériault and D. Paquin, 2019. Influence of the model spatial resolution on atmospheric conditions leading to freezing rain in regional climate simulations. Atmosphere-Ocean, https://doi.org/10.1080/07055900.2019.1583088.

Full text

Page 1 of 6 Comprehensive list of output variables Variable types include instantaneous (I) values at the archival time step, means between archival times (M), and miNimum (N) or maXimum (X) values between archival times. For variable types M, N and X, the values of the time dimension correspond to the center of the time interval over which the minimum, maximum or average is calculated. For instance, the precipitation ux (pr) is given at times 0:30, 1:30, 2:30, etc., which correspond to the average precipitation ux over the time intervals 0:00-1:00, 1:002:00, 2:00-3:00, etc., respectively. The latter interval bounds are provided via the variable “time_bnds” present in the time series of variables of type M, N or X. Daily and monthly means are calculated from hourly and 3-hourly output. For some variables, only monthly means are archived. For 3D atmospheric variables, data is provided on 11 pressure levels : 1000, 975, 950, 925, 900, 850, 800, 700, 600, 500 and 250 hPa. For 3D soil variables, data is provided at the 17 soil levels down to a depth of 15 m. However, variables related to soil moisture should be used with caution below a depth of 2 m, because deep-soil moisture cannot be considered to be in equilibrium in this ensemble. The status column shows the availability of the variables via THREDDS: available at time of writing (T), available later (L), or not available (blank). When a variable is shared on THREDDS at a coarser frequency than what was originally saved, the THREDDS frequency is indicated in parenthesis. Variable Description Frequency Realm Type Status baresoilFrac Bare Soil Fraction 3-hr Land 2D I cape Convective Available Potential Energy 3-hr Atmo 2D I L clt Total Cloud Fraction 1-hr Atmo 2D M L clwvi Condensed Water Path 3-hr Atmo 2D I T ct Air Pressure at Cloud Top 3-hr Atmo 2D I dds Near-Surface Dewpoint Depression 3-hr Atmo 2D I dedt Tendency of Integrated Cloud Water and Ice monthly Atmo 2D M dfq Horizontal Divergence of Water Vapor Flux monthly Atmo 2D M drdt Tendency of Integrated Water Vapor monthly Atmo 2D M Page 2 of 6 Variable Description Frequency Realm Type Status ebq Residual Term of Atmosphere Water Budget monthly Atmo 2D M evspsbl Evaporation 1-hr Atmo 2D M T evspsblland Water Evaporation from Land 3-hr Land 2D M hs Surface Upward Latent Heat Flux 3-hr Atmo 2D M T hfss Surface Upward Sensible Heat Flux 3-hr Atmo 2D M L hrmax Maximum Near-Surface Relative Humidity 3-hr Atmo 2D X hrmin Minimum Near-Surface Relative Humidity 3-hr Atmo 2D N hurs Near-Surface Relative Humidity 1-hr Atmo 2D I T hus Specic Humidity 3-hr (6-hr) Atmo 3D I L huss Near-Surface Specic Humidity 1-hr Atmo 2D I T lrt Lake Floor Temperature 3-hr Lake 2D I lif Lake Ice Fraction 3-hr Lake 2D I lit Lake Ice Thickness 3-hr Lake 2D I lmlt Lake Mixed-Layer Temperature 3-hr Lake 2D I lmlthick Lake Mixed-Layer Thickness 3-hr Lake 2D I mrfsl Soil Layer Frozen Water Content monthly Land 3D I mrfso Soil Frozen Water Content 3-hr Land 2D I L mrlsl Water Content of Soil Layer monthly Land 3D I Page 3 of 6 Variable Description Frequency Realm Type Status mrro Total Runoff 3-hr Land 2D M T mrros Surface Runoff 3-hr Land 2D M T mrso Total Soil Moisture Content 3-hr Land 2D I mrsos Moisture in Upper Portion of Soil Column 3-hr Land 2D I L pr Precipitation Flux 1-hr, daily Atmo 2D M T prc Convective Precipitation 1-hr Atmo 2D M L prdc Deep Convective Precipitation monthly Atmo 2D M prfr Freezing Rain 1-hr Atmo 2D M T prlp/prra Liquid Precipitation/Rainfall Flux 3-hr Atmo 2D M T prrp Refrozen Rain 3-hr Atmo 2D M prsn Snowfall Flux 3-hr Atmo 2D M T prw Water Vapor Path 3-hr Atmo 2D I T ps Surface Air Pressure 1-hr Atmo 2D I T psl Sea Level Pressure 1-hr Atmo 2D I T pw Precipitable Water 3-hr Atmo 2D I rhsmax Surface Daily Maximum Relative Humidity daily Atmo 2D X rhsmin Surface Daily Minimum Relative Humidity daily Atmo 2D N rlds Surface Downwelling Longwave Radiation 1-hr Atmo 2D M T Page 4 of 6 Variable Description Frequency Realm Type Status rls Net LW Surface Radiation 1-hr Atmo 2D M rlus Surface Upwelling Longwave Radiation 1-hr Atmo 2D M L rlut TOA Outgoing Longwave Radiation 1-hr Atmo 2D M L rsaa Shortwave Radiation Absorbed by Atmosphere 1-hr Atmo 2D M rsds Surface Downwelling Shortwave Radiation 1-hr Atmo 2D M T rsdt TOA Incident Shortwave Radiation 1-hr Atmo 2D M L rss Net SW Surface Radiation 1-hr Atmo 2D M rsus Surface Upwelling Shortwave Radiation 1-hr Atmo 2D M L rsut TOA Outgoing Shortwave Radiation 1-hr Atmo 2D M L sfcWindmax Daily Maximum Near-Surface Wind Speed daily Atmo 2D X L sic Sea Ice Area Fraction 3-hr Ocean 2D I sit Sea Ice Thickness 3-hr Ocean 2D I snc Snow Area Fraction 3-hr Land 2D I snd Snow Depth 3-hr Land 2D I L snm Surface Snow Melt 3-hr Land 2D M L snw Surface Snow Amount 3-hr Land 2D I T ta Air Temperature 3-hr (6-hr) Atmo 3D I L tas Near-Surface Air Temperature 1-hr, daily Atmo 2D I T Page 5 of 6 Variable Description Frequency Realm Type Status tasmax Daily Maximum Near-Surface Temperature daily Atmo 2D X T tasmin Daily Minimum Near-Surface Temperature daily Atmo 2D N T tke Turbulent Kinetic Energy monthly Atmo 2D I ts Surface Temperature 3-hr Atmo 2D I L tsl Temperature of Soil 3-hr (6-hr) Land 3D I L tsmax Maximum Near-Surface Temperature 3-hr Atmo 2D X tsmin Minimum Near-Surface Temperature 3-hr Atmo 2D N tso Sea Surface Temperature 3-hr Ocean 2D I ttop Air Temperature at Cloud Top 3-hr Atmo 2D I ua Eastward Wind 3-hr (6-hr) Atmo 3D I L uas Eastward Near-Surface Wind 1-hr Atmo 2D I T uqvc Vertically Integrated East. Comp. of Specic Humidity Flux 3-hr Atmo 2D M uvmax Maximum Near-Surface Wind Speed 3-hr Atmo 2D X va Northward Wind 3-hr (6-hr) Atmo 3D I L vas Northward Near-Surface Wind 1-hr Atmo 2D I T volmrfsl Soil Layer Volumetric Frozen Water Content 3-hr Land 3D I volmrliqsl Soil Layer Volumetric Liquid Water Content 3-hr Land 3D I vqvc Vertically Integrated North Comp. of Specic Humidity Flux 3-hr Atmo 2D M Page 6 of 6 Variable Description Frequency Realm Type Status wa Upward air velocity 3-hr (6-hr) Atmo 3D I L zg Geopotential Height 3-hr (6-hr) Atmo 3D I L zmla Height of Boundary Layer 3-hr Atmo 2D I L