CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.
Bibliographic record
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. Accepted in submitted to Scientific Data 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 simulations_ouranos@ouranos.ca. 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".