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.
- C. Girard, A. Plante et al. (2011): GEM4.1: A non-hydrostatic atmospheric model (Euler equations)
- C. Girard, A. Plante et al. (2011): GEM4.2: A non-hydrostatic atmospheric model (Euler equations)
- C. Girard, A. Plante et al.: Options for Semi-Lagrangian Trajectory Calculations
Vertical staggering:
- Girard, C., Plante, A., Desgagné, M., McTaggart-Cowan, R., Côté, J., Charron, M., Gravel, S., Lee, V., Patoine, A., Qaddouri, A. and Roch, M., 2014. Staggered vertical discretization of the Canadian Environmental Multiscale (GEM) model using a coordinate of the log-hydrostatic-pressure type. Monthly Weather Review, 142(3), pp.1183-1196. https://doi.org/10.1175/MWR-D-13-00255.1
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):
- Morrison, H. and Milbrandt, J.A., 2015. Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. Journal of the Atmospheric Sciences, 72(1), pp.287-311.
- Morrison, H., Milbrandt, J.A., Bryan, G.H., Ikeda, K., Tessendorf, S.A. and Thompson, G., 2015. Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part II: Case study comparisons with observations and other schemes. Journal of the Atmospheric Sciences, 72(1), pp.312-339.
- 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. Journal of the Atmospheric Sciences, 73(3), pp.975-995.
Cholette, M., Morrison, H., Milbrandt, J.A. and Thériault, J.M., 2019. Parameterization of the bulk liquid fraction on mixed-phase particles in the predicted particle properties (P3) scheme: Description and idealized simulations. Journal of the Atmospheric Sciences, 76(2), pp.561-582.
- Milbrandt, J.A., Morrison, H., Dawson II, D.T. and Paukert, M., 2021. A triple-moment representation of ice in the Predicted Particle Properties (P3) microphysics scheme. Journal of the Atmospheric Sciences, 78(2), pp.439-458.
Cloud fraction:
- 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
Geophysical fields
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
ANUSPLIN
- Hutchinson, M. F., McKenney, D. W., Lawrence, K., Pedlar, J. H., Hopkinson, R. F., Milewska, E., & Papadopol, P. (2009). Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003. Journal of Applied Meteorology and Climatology, 48(4), 725–741. https://doi.org/10.1175/2008JAMC1979.1
- Hopkinson, R. F., Mckenney, D. W., Milewska, E. J., Hutchinson, M. F., Papadopol, P., & Vincent, A. L. A. (2011). Impact of aligning climatological day on gridding daily maximum-minimum temperature and precipitation over Canada. Journal of Applied Meteorology and Climatology, 50(8), 1654–1665. https://doi.org/10.1175/2011JAMC2684.1
- McKenney, D. W., Hutchiinson, M. F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R. F., Price, D., & Owen, T. (2011). Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 92(12), 1611–1622. https://doi.org/10.1175/2011BAMS3132.1
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 V4
Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0453-3
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
ECCC hourly wind-bias-adjusted precipitation
- Smith, C. D., Mekis, E., Hartwell, M., and Ross, A. (2022): The hourly wind-bias adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-208, in review.
ERA-Interim
To cite the source of the data, you may use the following data citation (as part of the bibliography):
European Centre for Medium-range Weather Forecast (ECMWF) (2011): The ERA-Interim reanalysis dataset, Copernicus Climate Change Service (C3S) (accessed <insert date of access here>), available from https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim
If no specific advice is given by the journals, it is usually recommended that the above data citation is put in the acknowledgements section.
More information can be found under:
https://confluence.ecmwf.int/display/CKB/ERA-Interim%3A+documentation#ERAInterim:documentation-HowtoacknowledgeandciteERA-Interim
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.
GLEAM v3
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., & Verhoest, N. E. C. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5), 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017
Globsnow
MODIS - MCD43C3.061
Schaaf, C. B., Liu, J., Gao, F., & Strahler, A. H. (2011). Aqua and terra MODIS albedo and reflectance anisotropy products. In Remote Sensing and Digital Image Processing (Vol. 11, pp. 549–561). Springer International Publishing. https://doi.org/10.1007/978-1-4419-6749-7_24
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d’Entremont, R. P., Hu, B., … Roy, D. (2002). First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1–2), 135–148. https://doi.org/10.1016/S0034-4257(02)00091-3
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.
NEXRAD Stage IV
Fulton, R. A., Breidenbach, J. P., Seo, D.-J., & Miller, D. A. (1998). The WSR-88D Rainfall Algorithm. Weather and Forecasting, 13(2), 377–395. https://doi.org/https://doi.org/10.1175/1520-0434(1998)013<0377:TWRA>2.0.CO;2
Lin, Y., & Mitchell, K. E. (2005). The NCEP Stage II/IV hourly precipitation analyses: development and applications. 19th Conference on Hydrology, American Meteorological Society, CA. https://ams.confex.com/ams/pdfpapers/83847.pdf
PERSIANN-CCS-CDR
Sadeghi, M., Nguyen, P., Naeini, M. R., Hsu, K., Braithwaite, D., & Sorooshian, S. (2021). PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies. Scientific Data, 8(1). https://doi.org/10.1038/s41597-021-00940-9
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