CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,006 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».