The Alliance / Compute Canada resources

• When using results from simulations performed on The Alliance (ex. Compute Canada) clusters check under the following link for citation: https://alliancecan.ca/en/services/advanced-research-computing/acknowledging-alliance

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

For the usage of geophysical fields cite (depending of which dataset was used):

• USGS Land Cover
  Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example shown below.
        (Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey
  For example:
        "Vegetation, land-water mask and topology courtesy of the U.S. Geological Survey."
  
  
• CCI Land Cover & land-water mask (CCI-LC)
        https://www.esa-landcover-cci.org/?q=node/19
  
• GMTED (topography)      https://www.usgs.gov/coastal-changes-and-impacts/gmted2010

References

Click on the following link for GEM References.

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

• Copernicus Climate Change Service (C3S) (2019): ERA5-Land reanalysis. Copernicus Climate Change Service Climate Data Store (CDS), 2019-2020. https://cds.climate.copernicus.eu/cdsapp#!/home
• Muñoz Sabater, J. (2019). First ERA5-Land dataset to be released this spring. ECMWF Newsletter, 159, 8-9. https://www.ecmwf.int/node/19001

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

• Luojus, Kari; Pulliainen, Jouni; Takala, Matias; Lemmetyinen, Juha; Moisander, Mikko (2020): GlobSnow v3.0 snow water equivalent (SWE). PANGAEA, https://doi.org/10.1594/PANGAEA.911944

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

• Check under the following link: https://pmm.nasa.gov/data-access/citations