Compute Canada resources
GEM (CRCM)
Citation
When using model results from GEM or CRCM4/5/6, cite the following two papers:
- Girard, C. et al. (2014). Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type. Mon. Wea. Rev., 142, 1183–1196, https://doi.org/10.1175/MWR-D-13-00255.1
- McTaggart‐Cowan, R. et al. (2019). Modernization of Atmospheric Physics Parameterization in Canadian NWP. JAMES, Vol. 11, Issue 11, p. 3593-3635. https://doi.org/10.1029/2019MS001781
Articles describing the dynamical core of GEM
- Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch and A. Staniforth, 1998 a: The operational CMC-MRB global environmental multiscale (GEM) model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 1373-1395.
- Côté, J., J.-G. Desmarais, S. Gravel, A. Méthot, A. Patoine, M. Roch and A. Staniforth, 1998 b: The operational CMC-MRB global environmental multiscale (GEM) model. Part II: Results. Mon. Wea. Rev., 126, 1397-1418.
Articles describing physics schemes of GEM & CRCM
PDF: Descriptions of GEM physics version 3.6 from 1998
Physics of GEM5:
Li and Barker correlated-k radiation:
- Li, J. and H.W. Barker, 2005: A radiation algorithm with correlated-k distribution. Part I: local thermal equilibrium. J. Atmos. Sci., 62, 286-309.
Kain and Fritsch convection:
- Kain, J.S. and J.M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and application in convective parameterization. J. Atmos. Sci., 47, 2784-2802.
Shallow convection: "ktrsnt":
- Bélair S, Mailhot J, Girard C, Vaillancourt P, 2005: Boundary-layer and shallow cumulus clouds in a medium-range forecast of a large-scale weather system. Mon Weather Rev., 133, 1938-1960
Sundquist condensation:
- Sundqvist, H., E. Berge and J.E. Kristjansson, 1989: Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model. Mon. Wea. Rev., 117, 1641-1657.
ISBA, land surface scheme:
- Bélair, S., L-P. Crevier, J. Mailhot, B. Bilodeau, and Y. Delage, 2003: Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part I: Warm Season Results. J. Hydromet., 4, Issue 2, 352-370
- Bélair, S., R. Brown, J. Mailhot, B. Bilodeau, and L.-P. Crevier, 2003: Operational Implementation of the ISBA Land Surface Scheme in the Canadian Regional Weather Forecast Model. Part II: Cold Season Results. J. Hydromet., 4, Issue 2, 371-386
CLASS, land surface scheme:
- D. L. Verseghy, 1991: CLASS--A Canadian Land Surface Scheme for GCMS: I. Soil Model. International Journal of Climatology IJCLEU, vol. p 111-133, p. 44
- D. L. Verseghy, N. A. McFarlane, and M. Lazare, 1993: CLASS-A Canadian land surface scheme for GCMS, II. Vegetation model and coupled runs. Int. J. Climatol., vol. 13, no. 4, pp. 347-370
- D. L. Verseghy, 2000: The Canadian Land Surface Scheme (CLASS): its history and future. Atmosphere-Ocean, vol. 38, no. 1, pp. 1-1
McFarlane gravity-wave drag:
- McFarlane, N.A., 1987: The effect of orographically excited gravity-wave drag on the circulation of the lower stratosphere and troposphere. J. Atmos. Sci., 44, 1175-1800
Orographic blocking:
- Zadra, A., M. Roch, S. Laroche and M. Charron, 2003: The subgrid scale orographic blocking parametrization of the GEM model. Atmos.-Ocean, 41, 155-170.
Geophysical fields (USGS aka GLC2000):
- E. Bartholomé and A. S. Belward, 2005: GLC2000: a new approach to global land cover mapping from Earth observation data. International Journal of Remote Sensing, 26:9, 1959-1977, DOI: 10.1080/01431160412331291297
CRCM5 evolving towards GEM4:
- Girard, C., A. Plante et al., 2014: Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type. Monthly Weather Review, DOI: 10.1175/MWR-D-13-00255.1
Click here to see the paper.
FLake:
- Mironov, D., Heise, E., Kourzeneva, E., Ritter, B., Schneider, N. and co-authors., 2010: Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Environ. Res.15, 218-230.
Prediction Particles Properties (P3):
- C. Jouan, J. A. Milbrandt, P. A. Vaillancourt, F. Chosson, and H. Morrison, 2020: Adaptation of the Predicted Particles Properties (P3) Microphysics Scheme for Large-Scale Numerical Weather Prediction. Weather and Forecasting, p. 2541�2565, DOI: https://doi.org/10.1175/WAF-D-20-0111.1
Storm tracking (group of Philippe Gachon)
- 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
- Radojevic, M, 2006: Activité des cyclones Extra-tropicaux simulés par le modèle canadien de circulation générale. Mémoire de maîtrise en Sciences de l’Atmosphère, Université du Québec à Montréal, Montréal, Québec, Canada, 119p.
- Rosu, C., 2005 : Les caractéristiques des cyclones et l'apport d'eau dans les bassins versants du Québec. Mémoire de maîtrise en Sciences de l’Atmosphère, Université du Québec à Montréal, Montréal, Québec,Canada, 118p.Sinclair, R.M., 1997: Objective identification of cyclones and their circulation intensity, and climatology,Weather and Forecasting, 12, 595–612.
- Sinclair, R.M., 1997: Objective identification of cyclones and their circulation intensity, and climatology, Weather and Forecasting, 12, 595–612.
Storm tracking (code by K. Winger)
Hurricane tracking (code by K. Winger)
- Caron, L.-P., C. G. Jones and K. Winger, 2009: Simulating high-resolution Atlantic tropical cyclones using GEM: Part II. Blue Book of the Working Group on Numerical Experimentation (WGNE) of the World Weather Research Program (WWRP).
- Caron L-P, Jones CG, Winger K (2011) Impact of resolution and downscaling technique in simulating recent Atlantic tropical cyclone activity. Clim Dyn. 37(5–6):869–892 doi:10.1007/s00382-010-0846-7
- Dandoy, S., Pausata, F. S. R., Camargo, S., Laprise, R., Winger, K., and Emanuel, K. A. (2021) Atlantic Hurricane response to Sahara greening and reduced dust emissions during the mid-Holocene. DOI: 10.5194/cp-2020-112
Datasets
CaLDAS
- Carrera, M. L., Bélair, S., & Bilodeau, B. (2015). The Canadian Land Data Assimilation System (CaLDAS): Description and synthetic evaluation study. Journal of Hydrometeorology, 16(3), 1293–1314. https://doi.org/10.1175/JHM-D-14-0089.1
CaSPAr (products downloaded trough such as CaPA-fine)
- Mai, J., Kornelsen, K., Tolson, B., Fortin, V., Gasset, N., & Bouhemhem, D. et al. (2020). The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally. Bulletin Of The American Meteorological Society. doi: 10.1175/bams-d-19-0143.1
CMIP5 (CanESM, MPI-ESM)
- When using data from CMIP5 simulations "you are obligated to acknowledge CMIP5 and the participating modeling groups". Check under the following link for citation: http://cmip5.whoi.edu/?page_id=339
CMORPH
- Xie, P., Joyce, R., Wu, S., Yoo, S. H., Yarosh, Y., Sun, F., & Lin, R. (2017). Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. Journal of Hydrometeorology, 18(6), 1617–1641. https://doi.org/10.1175/JHM-D-16-0168.1
CRU
- Acknowledgement should usually be made by citing one or more of the papers referenced on the appropriate page. Suggestions can be found here: http://www.cru.uea.ac.uk/data
Daymet
- Thornton, P., Running, S., & White, M. (1997). Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal Of Hydrology, 190(3-4), 214-251. https://doi.org/10.1016/S0022-1694(96)03128-9
- Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. 2016. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1328
- Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840
- Thornton, P.E., R. Shrestha, M.M. Thornton, S.C. Kao, Y. Wei, and B.E. Wilson. 2020. Gridded daily weather data for North America with comprehensive uncertainty quantification – Daymet version 4. https://daymet.ornl.gov/files/Thornton_Daymet_V4_submitted_2021-01-20.pdf
CERA-20C
- Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K. and Janssen, P. (2016), A coupled data assimilation system for climate reanalysis. Q.J.R. Meteorol. Soc., 142: 65-78. doi:10.1002/qj.2629
- Laloyaux, P., de Boisséson, E., Dahlgren, P. (2016), CERA-20C: An Earth system approach to climate reanalysis. ECMWF newsletter, 150: 25-30. doi:10.21957/ffs36birj2
ERA5
- Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), date of access. https://cds.climate.copernicus.eu/cdsapp#!/home
- Hersbach, H, Bell, W, Berrisford, P, Horányi, A, J., M-S, Nicolas, J, Radu, R, Schepers, D, Simmons, A, Soci, C, Dee, D. (2019) Global reanalysis: goodbye ERA-Interim, hello ERA5; Newsletter Feature Article. ECMWF Newsletter, https://www.ecmwf.int/node/19027, 10.21957/vf291hehd7.
ERA5-Land
GLDAS
- Li, B., H. Beaudoing, and M. Rodell, NASA/GSFC/HSL (2018), GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree V2.0, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/LYHA9088MFWQ
- Li, B., Rodell, M., Sheffield, J., Wood, E. and Sutanudjaja, E. (2019). Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models, Scientific Reports, 9, 10746(2019), doi:10.1038/s41598-019-47219-z.
Globsnow
MSWEP
- Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., … Wood, E. F. (2017). Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences, 21(12), 6201–6217. https://doi.org/10.5194/hess-21-6201-2017
- Beck, H., Wood, E., Pan, M., Fisher, C., Miralles, D., & van Dijk, A. et al. (2019). MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment. Bulletin Of The American Meteorological Society, 100(3), 473-500. doi: 10.1175/bams-d-17-0138.1
MSWX
- Beck, H. E., van Dijk, A. I. J. M., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., and Miralles, D. G.MSWX: global 3‑hourly 0.1° bias-corrected meteorological data including near real-time updates and forecast ensembles. Submitted for publication, 2021.
SNODAS
- Barrett, A. (2003). National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special Report 11. Boulder: National Snow and Ice Data Center, p.19 pp.
- National Operational Hydrologic Remote Sensing Center (NOHRSC). 2004. Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1. Unmasked data. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. 2020. https://doi.org/10.7265/N5TB14TC
TRMM