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Developing the next generation ECCC seasonal forecasting system

Developing the next generation ECCC seasonal forecasting system. Hai Lin RPN, ECCC FORCAII, Beijing Climate Center April 24-26, 2017 Acknowledgements: contributions of many RPN/CMC and CCCma colleagues. Outlines. Early generations of the ECCC seasonal system

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Developing the next generation ECCC seasonal forecasting system

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  1. Developing the next generation ECCC seasonal forecasting system Hai Lin RPN, ECCC FORCAII, Beijing Climate Center April 24-26, 2017 Acknowledgements: contributions of many RPN/CMC and CCCma colleagues

  2. Outlines • Early generations of the ECCC seasonal system • Current operational system: CanSIPS • Contribution to NMME • The GEM coupled model • Hindcast verifications • Summary

  3. Seasonal forecasts in CMC Sept. 1995 – 2003: based on 12-member ensemble of 2 atmospheric models (GCM2, SEF) (2-tier system). Multi-model approach: 1 climate models and 1 NWP models; Forecast range limited to 3 months 2003 – Nov. 2011: based on 40-member ensemble of 4 atmospheric models (GCM2, GCM3, SEF, GEM) (2-tier system). Multi-model approach: 2 climate models and 2 NWP models; Forecast range limited to 4 months Since Dec. 2011: based on 20-member ensemble of 2 coupled atmosphere-ocean-land physical climate models (CanSIPS); Multi-model approach: 2 climate models; Forecast range up to 12 months.

  4. Current Seasonal System (CanSIPS) • 2-coupled models (CCCma models) • AGCM3 (T63L31) -> CanCM3 • AGCM4 (T63L35) -> CanCM4 • coupled with CanOM4 (1.41°× 0.94° / L40) ocean model • 1-tier system • 10 members for each model • 1-12 month forecast issued every month • Hindcast period: 1980-2010

  5. CanSIPS contribution to the NMME

  6. NMME Model History Forecast release months Aug 2011 Aug 2012 Year 1 Year 2 NCEP CFSv1 NCEP CFSv1 NCEP CFSv2 IRI ECHAM4f IRI ECHAM4i GFDL CM2.1 NASA GEOS5 NCAR CCSM3 CMC1/CanCM3 CMC2/CanCM4 IRI ECHAM4f IRI ECHAM4i Aug 2013 Aug 2014 Year 3 Year 4 GFDL FLOR NCAR CCSM4

  7. NMME Operational Centers Research Centers EC GFDL NASA NCAR NCEP 8th of each month Hindcasts Real-time Forecasts Research Community User/applications Community

  8. Niño3.4 Plumes (Nov-Jun 2014-15) Ensemble means for each model

  9. Real time verifications – summary statistics init Aug 2011 – Jul 2012 init Aug 2012 – Jul 2013

  10. Plan for next system • Multi-model (currently already CanCM3+CanCM4) • Climate model + NWP • Development of GEM coupled model

  11. Coupled GEM model Coupled system of • Atmosphere + Land • Ocean • Sea ice

  12. Atmospheric Model • Global Environmental Multiscale (GEM) • Horizontal resolution: 256 x 128 (~1.4° × 1.4° ) • 79 levels, top at 0.075 hPa • Time step: 1 hour • Land surface scheme: ISBA • Deep convection scheme: Kain-Fritsch (KFC)

  13. Ocean + Sea ice • NEMO • Horizontal resolution: 1° × 1° , 1/3 degree meridionally near the equator • 50 levels • Time step: 30 minutes • coupled with sea ice --- CICE

  14. GEM-NEMO Seasonal System • Two components: 1) Hindcast (model statistics, verification) 2) Real time forecast

  15. Hindcast: initial conditions • Atmosphere: ERA-interim pefield of GEPS to generate 10 members (random isotropic perturbations) • Ocean: ORAP5 from ECMWF: ocean T, S, H, U, V • Land: off-line SPS forced by ERA-interim atmosphere • Sea ice concentration: ORAP5 • Sea ice thickness: ORAP5

  16. Hindcast • 12 month integrations • 10 members • 31 years of 1980-2010

  17. Real-time • Start at beginning of each month, 10 members • 12 month integrations • Atmosphere: 10 members from ENKF of GEPS • Land: SPS forced by CMC analysis • Ocean: CMC GIOPS • Sea ice concentration: CMC GIOPS • Sea ice thickness: CMC GIOPS

  18. May 1 Start

  19. DJF T2m lead=1 month

  20. JJA T2m lead=1 month

  21. Nov 1 Start

  22. DJF PR lead=0

  23. JJA PR lead=0

  24. Lead months Lead months Lead months Lead months

  25. NAO skill 1 for JFM, 2 for FMA, etc

  26. PNA skill

  27. MJO skill

  28. Seasonal forecast system • Current operational system (CanSIPS): CM3+CM4 • Possible next system option 1: GEM+CM4 option 2: GEM+CM3+CM4

  29. TAS, Land, seasonal mean, vs. ERA-Interim GEM+CM4 CanSIPS GEM GEM+CM3+CM4 CM4 CM3

  30. TAS, N.America, seasonal mean, vs. ERA-Interim

  31. PR, Globe, seasonal mean, vs. GPCP2.3

  32. PR, N.America, monthly mean, vs. GPCP2.3

  33. Summary • ECCC continues to develop MME in seasonal forecasting • GEM in general has a higher skill for T2m and PR than CM3 and CM4. CM3 has the least skill. • Better MJO and NAO skill in GEM • Combining GEM with CM4 gives better skill than CanSIPS. Adding CM3 does not improve the skill.

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