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TOC > MSC data > CanSIPS > CanSIPS data in CSV on MSC Datamart

Canadian Seasonal to Inter-annual Prediction System (CanSIPS) Data in CSV Format

The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) is a long-term prediction system whose objective is to forecast the evolution of global climate conditions. CanSIPS is a multi-model ensemble (MME) system using two atmosphere-ocean-land coupled models developed by the Canadian Centre for Climate Modelling and Analysis (CCCma) and the Canadian Meteorological Centre (CMC). It is a fully coupled atmosphere-ocean-ice-land prediction system relying on the operationnal data assimilation infrastructure for the initial state of the atmosphere, sea surface temperature and sea ice. For further technical information about CanSIPS please refer to the technical note.

Principal components of CanSIPS

  • Assimilation mode: CanCM4 uses a continuous assimilation cycle for the following 3D atmospheric variables: temperature, wind and humidity. The oceanic variables: sea surface temperature and the sea ice are also assimilated by the system. The assimilated data are provided by the global atmospheric analysis available every 6 hours and the daily sea surface temperature and sea ice analysis. Also a 3D ocean temperature analysis is integrated to CanCM4 trial field before launching the integration. GEM-NEMO uses atmospheric initial condition of the Global Ensemble Prediction System (GEPS) which are generated from the Ensemble Kalman Filter (EnKF) with observations that are background-checked and bias-corrected by the Global Deterministic Prediction System (GDPS). The ocean and sea ice initial conditions come from the CMC GIOPS analysis. To initialize the land surface fields, the CMC Surface Prediction System (SPS) is run offline forced by the near-surface atmospheric and precipitation fields of the CMC analysis.

  • Forecast mode: The CanSIPS forecasts are based on a 10-member ensemble of forecasts produced with each of the two models for a total ensemble size of 20. Monthly to multi-seasonal forecasts extending to 12 months are issued on the first day of each month.

  • Hindcast mode: CanSIPS climatology is based on a series of retrograde forecasts (e. g. historical forecasts) covering the period 1981 to 2010. This climatology is very useful for interpreting realistic forecasts because real-time forecast anomalies are generated instead of raw forecasts.

Data address

MSC Datamart data can be automatically retrieved with the Advanced Message Queuing Protocol (AMQP) as soon as they become available. An overview and examples to access and use the Meteorological Service of Canada's open data is also available.

CanSIPS calculates a suite of oceanic and atmospheric indices (e.g. monsoon indices, PNA, NAO, El NINO, etc.) which can be used for seasonal climate forecasting and monitoring.

A more detailed description of the indices is available.

The files containing atmospheric and oceanic CanSIPS indices can be accessed at the following URLs :

Forecast files :

Hindcast files :

Observations :

File name nomenclature

NOTE: ALL HOURS ARE IN UTC.

The forecast and hindcast files have the following nomenclature :

  • YYYYMMDD00_indices_month_YYYY1MM1-YYYY2MM2.csv
  • YYYYMMDD00_indices_season_YYYY1MM1-YYYY2MM2.csv

where :

  • YYYY : Year of the forecast initiation
  • MMDD : Month and day of the forecast initiation
  • YYYY1MM1 : Year and month of the forecast start
  • YYYY2MM2 : Year and month of the forecast end

The observation files have the following nomenclature :

  • observed_indices_month_YYYY1MM1-YYYY2MM2.csv

where :

  • YYYY1MM1 : Year and month the index calculation starts
  • YYYY2MM2 : Year and month the index calculation ends

Observational indices based on the sea surface temperature anomalies are calculated with respect to the optimal interpolation SST dataset (OISST, Reynolds et al. 2002) while the remaining indices are calculated using ERA-interim reanalysis (Dee et al. 2011).

Support

If you have any questions about this data, please contact us.

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