Applied Science & Research Standing Committee Action from IICWG-VII Co-Chairs Lars-Anders Breivik & Pablo Clemente-Colón

2 SC6.2: Re-identify ice service/national Science representatives for all member nations. For those services which do not have Science sections, Heads of Ice Service will be contacted to propose appropriate national representatives as a contact Responsible: Science Committee Co-Chairs Target Date(s): December 2006 Status: Open Applied Science and Research IICWG-7 Action Items

3 National representatives, draft list Canada: Roger DeAbreu Denmark: (Søren Andersen) Finland: Jari Haapala Germany: Iceland: Ingibjörg Jónsdóttir Japan: Norway: Lars-Anders Breivik Russia: Sweden: USA: Pablo Clemente-Colón Lithuania: UK: John Stark Australia: Tony Worby WCRP / CliC: (Tony Worby will make contact)

4 SC6.7: Each national member requested to give a brief summary of Top Research priorities and plans to facilitate identification of complementary &/or common activities and areas for collaboration. An updated list will be put on the IICWG web page Responsible: Science Committee Co-Chairs (Breivik) Target Date(s): December 2006 Status: Open Applied Science and Research IICWG-7 Action Items

5 SC6.8: Develop a process for rotation of Committee Co-Chairs. Flett stepping down as Co-Chair for IICWG-7. Clements-Colon step in. Suggested procedure: Rotation with a change every 2. meeting (3 years ?) Co-Chairs from Canada/USA and Europe(+) Overlap in rotation Responsible: All Target Date(s): IICWG-8 Status: OPEN Applied Science and Research IICWG-7 Action Items

SC7.1: IICWG, IPY data assimilation and modelling project: Send out to each project partner template for project outline (Carrieres). This shall be completed and sent back within December Responsible: Tom Carrieres Target Date: December 2006 Status: Closed (see 7.2)

Applied Science and Research IICWG-7 Action Items SC7.2: IICWG, IPY data assimilation and modelling project: Prepare and arrange a dedicated workshop. ice modelling and data assimilation Place: met.no, Oslo Time: May 2006: Responsible: Breivik with support from Carrieres Target Date: May 2006 Status: Closed

Status on sea ice data assimilation Report from IICWG work shop Oslo May, 2007

IICWG - data assimilation history Tromsø 2002, ice modeling and DA defined as a main priority St Petersburg 2003, White paper Hamburg 2004, dedicated science workshop Ottawa 2005, IPY –proposal Helsinki 2006, … Oslo 2007, Dedicated workshop

Sea Ice – Ocean modeling, status

Sea ice – modeling, status Relying on a two-dimensional continuum hypothesis for description of the ice, and uses Viscous-Plastic rheology. None of the groups are using discrete element models, or other Lagrangian types of models, despite that some of the forecast systems are operating with a few km horizontal resolution. –climate applications has been the main constraint. How complex a model system is needed for the ice forecasts (uncoupled ice, coupled ice-ocean, fully coupled atmosphere- ice-ocean, or a hybrid in between those) depends on the type and length of the forecast, and on what kind of physical processes that are the most important for that specific case.

Data assimilation challenge From a limited set of surface observations - obtain a well balanced 4-dimensional ice-ocean model field First approach: Simple nudging of Ice concentration and SST towards observations (externally analyzed fields) satellite Model + 0 hModel h

Main challenge Specifying the background (model) error covariances for a coupled ice-ocean analysis Especially important for: –Unobserved variables (e.g. under-ice ocean variables) –when the horizontal coverage of ice observations is not complete over the analysis domain (e.g. SAR or AVHRR data) Ignoring important spatial and multivariate covariances can lead to imbalances or artificial spatial discontinuities in the analyzed model state.

Facing the main challenge: The groups are working on more advanced methods for estimating model error covariances: EnKF SEIK

Example of estimating background error covariances using EnKF for one ice-season Local covariance between SST and OWF Averaged (spatially and temporally) covariance between OWF  Ocean Temperature Alain Caya, Meteorological Research Division, Environment Canada

Observations 1)Ice concentration analysis 2)Ice drift vectors 3)High resolution data OSI SAF, AMSR-E, ice conc October IFREMER, AMSR-E, 3day ice drift, May FIMR, SAR & Insitu Ice Thickness charts

Observation errors Example: use of Ice drift data: Recommendations: Specify accurate start and end times of displacement vectors Specify elaborate error statistics 3-day coarse resolution IFREMER -> 1-day fine resolution DNSC ->->

Very brief summary Not as far as hoped for in 2002 (Tromsø) However Promising results in advanced methods (3DVAR, EnKF, SEIK), Long term R&D focus is needed Simple pragmatic approaches for short term forecast systems are implemented and works To be followed up by new workshop in fall 2008 (at DMI)