2. Brief report from some institutes on the status of ice shelf – ocean modeling

3. AWI – Alfred Wegner Institute (Hartmut Hellmer)

4. A consistent dataset of global ocean bathymetry, incl. ice shelf cavities Potter and Paren (1985) Makinson and Nicholls (1999) S-2004 (Marks and Smith, 2006) Lythe et al. (2001)

5. Cavity representation in sigma coordinates (e.g. Filchner-Ronne) October 2009

6. BAS – British Antarctic Survey (Adrian Jenkins)

8. GFDL – Geophysical Fluid Dynamics Laboratory, NOAA (Olga Sergienko, Dan Goldberg)

9. GISS – Goddard Institute for Space Studies, NASA (Gavin Schmidt)

10. JPL – Jet Propulsion Lab, NASA (Eric Rignot, Eric Larour)

11. ISSM- Current implementation. 2D-3D higher-order modeling: Hutter’s SIA, MacAyeal’s shelfy-stream, Pattyn’s higher order 3D, full Navier-Stokes 3D. Multi-model: different models are connected using Rigid Body Motion connectors, or method of penalties. Multi-scale: anisotropic mesh adaptation, Yams (INRIA, Pascal Frey). 09/30/09ISSM11

12. ISSM – ECCO2 09/30/09ISSM12 ECCO2 framework (using MIT GCM) is used to provide a coupling with the ocean. –melting under ice shelf cavities and at ice front implemented by Schodlock, 2005. –Other processes are being implemented (sub-glacial discharge, submarine melting of calving faces (Rignot et al., 2009)).

13. LANL – Los Alamos National Laboratory (Bill Lipscomb, Matthew Hecht)

14. Glimmer-CISM The Glimmer ice sheet model (Rutt et al. 2009) was developed by Tony Payne et al. in the U.K. with the goal of coupling dynamic ice sheets to global climate models. The Glimmer ice sheet model (Rutt et al. 2009) was developed by Tony Payne et al. in the U.K. with the goal of coupling dynamic ice sheets to global climate models. NSF has supported development of a Community Ice Sheet Model (CISM) based on Glimmer. NSF has supported development of a Community Ice Sheet Model (CISM) based on Glimmer. – Jesse Johnson’s wiki: http://websrv.cs.umt.edu/isis DOE has supported coupling of Glimmer to the Community Climate System Model (CCSM). DOE has supported coupling of Glimmer to the Community Climate System Model (CCSM). – CCSM Land ice working group (meeting Feb. 2010 in Boulder): – CCSM Land ice working group (meeting Feb. 2010 in Boulder): http://www.ccsm.ucar.edu/working_groups/Land+Ice/ The U.S. and U.K. groups have recently combined efforts: The U.S. and U.K. groups have recently combined efforts: – BerliOS repository: http://developer.berlios.de/projects/glimmer-cism/ – 6-member steering committee (M. Hagdorn, J. Johnson, W. Lipscomb, T. Payne, S. Price, I. Rutt)

15. HyPOP: Momentum Grid and Tracer Grid Two Grids momentum layer edge tracer layer edge x,y z

16. Nansen Center, Bergen (Helge Drange, Mats Bentsen)

17. ODU – Old Dominion University (Mike Dinniman)

18. Ocean/Ice Shelf Modeling w/ ROMS ROMS (Regional Ocean Modeling System) Hydrostatic, free-surface, primitive equation OGCM in terrain-following coordinates Thus, easy to implement ice shelf processes, but concerns about pressure gradient force errors at the ice shelf front Typically used for regional Ocean/Ice Shelf models (see next slide) and process studies, but circum- Antarctic models in development Ice shelf code development in ROMS has not been implemented into main branch, but this will change with a new community effort (https://projects.arcs.org.au/trac/ciomp)https://projects.arcs.org.au/trac/ciomp

19. Examples of Regional ROMS Models w/ Ice Shelves Bellingshausen Sea/George VI-Wilkins Ice Shelves: Effects of CDW transport on basal melt (Dinniman/Klinck – ODU) Prydz Bay/Amery Ice Shelf: Ice shelf mass balance (including frazil ice deposition), bottom water formation (Galton-Fenzi – UTAS/ACECRC) Ross Sea/Ross Ice Shelf: Basal melt, water mass changes, MCDW transport (Dinniman/Klinck/Hüsrevo ğ lu – ODU) Weddell Sea/Larsen C Ice Shelf: Tidal effects on basal melt (Mueller/Padman – OSU/ESR) Weddell Sea/Ronne-Filchner Ice Shelf: Baroclinic/Barotropic tides (Robertson – UNSW/AFDA)

20. Two part discussion on ice-shelf – ocean interaction 1. Understanding: Status of understanding physical processes 2. Modeling: Status of a having a predictive capability