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1. Forecast system version

Identifier code: cwao_ CanESM5.1p1bc

First operational forecast run: 30 June 2024

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Greenhouse gasesCMIP6 historical (before 2014 inclusive); CMIP6SSP2-4.5 (2015 and after) as described in O'Neill et al. 2016 http://www.geosci-model-dev.net/9/3461/2016/
OzoneTemporally and spatially varying following Checa-Garcia et al., 2018 https://doi.org/10.1002/2017GL0767702018 
Tropospheric aerosolsParameterized using a prognostic scheme for bulk concentrations of natural and anthropogenic aerosols, including sulfate, black and organic carbon, sea salt, and mineral dust; parameterizations for emissions, transport, gas-phase and aqueous-phase chemistry; and dry and wet deposition accounting for interactions with simulated meteorology
Volcanic aerosolsCMIP6 volcanic stratospheric aerosols according to Thomason et al. 2018 https://doi.org/10.5194/essd-10-469-20182018 
Solar forcingCMIP6 solar forcing according to Matthes et al. 2017 https://gmd.copernicus.org/articles/10/2247/2017/2017 

Detailed documentation:

Checa-Garcia et al., 2018: Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database. Geophysical Research Letters, 45, 3264-3273, https://doi.org/10.1002/2017GL076770

Matthes, K., et al. 2017: Solar forcing for CMIP6 (v3.2). Geoscientific Model Development, 10, 2247–2302. https://doi.org/10.5194/gmd‐10‐2247‐2017

O'Neill, B. C., et al. 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9, 3461–3482, http://www.geosci-model-dev.net/9/3461/2016/

Thomason, L. W., et al. 2018: A global space‐based stratospheric aerosol climatology: 1979–2016. Earth System Science Data, 10, 469–492. https://doi.org/10.5194/essd‐10‐469‐2018

4. Initialization and initial condition (IC) perturbations

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Horizontal and vertical resolution of perturbations:  

Perturbations are primarily on spatial scales <1000 km, vertical structure not directly specified.

Perturbations in +/- pairs: 

No, all perturbations represent random samples from a distribution.

Detailed documentation:

Merryfield, W. J., et al. 2013: The Canadian seasonal to interannual prediction system. Part I: Models and initialization. Monthly Weather Review, 141, 2910–2945, https://doi.org/10.1175/MWR-D-12-00216.1

Sospedra-Alfonso, R., et al. 2024: Evaluation of soil moisture in the Canadian Seasonal to Interannual Prediction System, Version 2.1 (CanSIPSv2.1). Journal of Applied Meteorology and Climatology, 63, 143-164, https://doi.org/10.1175/JAMC-D-23-0034.1

Sospedra-Alfonso, R., et al. 2016: Representation of snow in the Canadian Seasonal to Interannual Prediction System: Part I. Initialization. Journal of Hydrometeorology, 17, 1467–1488, https://doi.org/10.1175/JHM-D-14-0223.1 

4.2 Ocean and cryosphere


HindcastForecast
Ocean initialization
  • SST nudged to OISSTv2 with 3 day time constant
  • Subsurface potential temperature and salinity nudged to ORAS5 with 30 day time constant near surface, transitioning to 360 days at large depths
  • Sea ice concentration nudged to merged HadISST2.2 and Canadian Ice Service chart data with 3 day time constant
  • Sea ice thickness nudged to Dirkson et al. (2017) SMv3 statistical model with 3 day time constant
  • Ocean surface forced by assimilating atmosphere in coupled assimilation runs
  • SST nudged to ECCC GDPS SST analysis with 3 day time constant
  • Subsurface potential temperature and salinity nudged to ECCC GIOPS with 30 day time constant near surface, transitioning to 360 days at large depths
  • Sea ice concentration nudged to merged HadISST2.2 and Canadian Ice Service chart data with 3 day time constant
  • Sea ice thickness nudged to Dirkson et al. (2017) SMv3 statistical model with 3 day time constant
  • Ocean surface forced by assimilating atmosphere in coupled assimilation runs
Ocean IC perturbationsAssimilation runs begun from different initial conditionsAssimilation runs begun from different initial conditions
Unperturbed control forecast?NoNo

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 Dirkson, A., et al. 2017: Impacts of sea ice thickness initialization on seasonal Arctic sea ice predictions. Journal of Climate, 30, 1001–1017, https://doi.org/10.1175/JCLI-D-16-0437.1

Lin, H., et al. 2020: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). Weather and Forecasting, 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1 

Sospedra-Alfonso, R., et al. 2021: Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5). Geoscientific Model Development, 14, 6863–6891, https://doi.org/10.5194/gmd-14-6863-2021

5. Model Uncertainties perturbations:

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6. Forecast system and hindcasts

Forecast frequency12-month forecast is produced on the last day of each month
Forecast ensemble size20 (ensemble members 1-10 initialized at 00Z on last day of month, 11-20 at 00Z on 5th to last day of month)
Hindcast years1980-2023
Hindcast ensemble size20 (ensemble members 1-10 initialized at 00Z on first day of month, 11-20 at 00Z on 5th to last day of previous month)
On-the-fly or static hindcast set?static
Calibration (bias correction) period1991-2020

7. Other relevant information

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8. Where to find more information

https://weather.gc.ca/saisons/index_e.html

https://climate-scenarios.canada.ca/?page=seasonal-forecasts 

https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/tech_notes/technote_cansips-300_20240611_e.pdf