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Toward GELATO6 in EC-Earth3

Picture from Matthieu Chevallier. Toward GELATO6 in EC-Earth3. V. Guemas, D. Salas-Mélia, M. Chevallier virginie.guemas@ic3.cat , david.salas@meteo.fr , matthieu.chevallier@meteo.fr. A multi-category sea ice model since 1996. Number of categories to be selected in GELATO namelist. Melting.

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Toward GELATO6 in EC-Earth3

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  1. Picture from Matthieu Chevallier Toward GELATO6 in EC-Earth3 V. Guemas, D. Salas-Mélia, M. Chevallier virginie.guemas@ic3.cat, david.salas@meteo.fr, matthieu.chevallier@meteo.fr

  2. A multi-category sea ice model since 1996 Number of categories to be selected in GELATO namelist Melting c, h c3 c1 c*2/3, h*2/3 h1 GELATO LIM3 CICE h3 Ocean grid cell c c h/2 h Sea ice grid cell LIM2 c => concentration, h => thickness

  3. GELATO in CNRM-CM since 1999 ARPEGE-climat atmosphere model Number of categories to be selected in GELATO namelist Distribution within GELATO NEMO

  4. History of GELATO development Old history: • Development of Gelato (multi-category model) initiated in 1996 • Dynamics + redistribution by rafting and ridging (1998) • Coupling with OPA (1998) and ARPEGE-Climat (1999) • Elastic-Viscous-Plastic = EVP rheology (2000) + incremental remapping (2002), Hunke & Dukowicz (1997) CMIP5 (2009-2010): many new developments • Interactive prognostic salinity Salt uptake : follows Cox and Weeks (1988) Desalination processes adapted from Vancoppenolle et al., O. Mod. (2009) • Enthalpy model H = H(T,S) and Cp=Cp(T,S) • Vertical Heat Diffusion (VHD) Ice thermal conductivity k is a function of T,S (Pringle et al., 2007) • Revised snow albedo (adapted from Curry et al. (2001)) • New tracers can now easily be added : sea ice age COMBINE (2011-2012) • Development of a surface melt pond scheme

  5. GELATO surface scheme Melt ponds + snow + ice albedo Explicit resolution of melt ponds Snow Melt ponds advected drainage Salinity profile NEMO

  6. Use in forced mode embedded into NEMO Chevallier et al 2013 Validation sea ice thickness against Lindsay (2010) Bias : -0.14 to -0.46 m RMSE : 0.64 to 1m Correlation : 0.71 to 0.83 GELATO tested with DFS4.3, ERA-interim, CORE forcings

  7. Use in coupled mode within CNRM-CM Performance : 4min/year with 4 nodes (48 procs) on BULL in ORCA1 • CMIP5 : 9000 years of simulation within CNRM-CM5 (Voldoire et al 2013) performed at CNRM • Decadal prediction activities: 3000 years of simulation performed at CERFACS (Germe et al 2014) • Seasonal prediction : best skill scores (Chevallier and Salas y Mélia 2012, Chevallier et al 2013) Ensemble mean Correlation skill: CNRM-CM5 : 0.6 CFSv2 : 0.4 CanSIPS < 0.2 Observations Ensemble range

  8. Toward an operational use for weather prediction SURFEX is a surface model used operationally in Numerical Weather Prediction by the HIRLAM European consortium GELATO has been included in the next release of SURFEX crucial for weather prediction in Northern Europe

  9. The developers David Salas y Mélia : Matthieu Chevallier : Aurore Voldoire : Stéphane Sénési : Virginie Guemas : Initial developer and leader of the current development team Validation and inclusion of new processes 1D tests, technical aspects (portability on different platforms, parallelisation …) Inclusion in EC-Earth

  10. In NEMO3.3.1-stand-alone from EC-Earth3.0.1 ORCA1

  11. Timeline Next month: • Evaluating GELATO6.0.47 within NEMO3.3.1 from EC-Earth3.0.1 in the ORCA025 stand-alone configuration • Testing GELATO6.0.47 within EC-Earth3.0.1 in coupled mode in configuration ORCA1 Later: Testing and evaluation GELATO6.0.47 within EC-Earth3.0.1 coupled mode in configuration ORCA025

  12. Thanks a lot for you attention Any question to be directed to: virginie.guemas@ic3.cat, david.salas@meteo.fr, matthieu.chevallier@meteo.fr

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