Compute Canada ressources and GEM

Compute Canada resources

When using results from simulations performed on Compute Canada clusters check under the following link for citation: https://www.computecanada.ca/research-portal/accessing-resources/acknowledging-compute-canada

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

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

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

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

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

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

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

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

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.

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

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.

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

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

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.

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