Utilizing a FAMOS hierarchy of sea ice models to identify their physical limitations

Transcription

1 Utilizing a FAMOS hierarchy of sea ice models to identify their physical limitations What is the main goal of a coordinated FAMOS sea ice modeling effort: q To improve synoptic to seasonal hindcasts and forecasts? q To better understand limitations on longer term coupled simulations and projections? q To gain a better understanding of physics? q All of the above

2 Constraints within a FAMOS sea ice model hierarchy Stand-alone model Most constrained system assimilated stand-alone constrained at the coupling channel 6-hour reanalysis Ocean Sea Ice possible assimilation of ice and ocean boundary condition -Oceanic constraint -Coupling channels between component models -Component models Least constrained system

3 Constraints within a FAMOS sea ice model hierarchy Stand-alone global model Most constrained system assimilated stand-alone constrained at the coupling channel 6-hour reanalysis global Ocean Sea Ice -Oceanic constraint -Coupling channels between component models -Component models Least constrained system

4 Constraints within a FAMOS sea ice model hierarchy Coupled model Most constrained system assimilated boundary condition constrained away from the coupling channel stand-alone Atmosphere possible assimilation above PBL global Ocean Sea Ice land ice sheet Runoff Routing coupled model boundary condition -Atmospheric and Oceanic constraints -Coupling channels between component models -Component models Least constrained system

5 Constraints within a FAMOS sea ice model hierarchy Coupled global model Most constrained system assimilated constrained away from the coupling channel stand-alone Atmosphere Changes in composition of atmosphere including Carbon Dioxide and Sulfates global Ocean Sea Ice land ice sheet Runoff Routing coupled model coupled global model -Atmospheric constraint -Coupling channels between component models -Component models Least constrained system

6 Utilizing a Utilizing FAMOS a FAMOS hierarchy hierarchy of of models sea ice models More constrained Less constrained Regional models Global models Regional coupled atmosphere-land models Global coupled iceocean-atmosphereland models evaluate individual extreme events evaluate statistics of internal variability full feedbacks of atmosphere- interaction

7 Some suggested physically-based metrics for which observations exist In concert with a sparse set of metric on the basic performance of sea ice simulations, can we identify physically-based metrics meaningful across multiple model configurations, such as: o Melt timing and melt rate

8 The trajectory of sea ice thickness during the melt season is defined by surface, bottom, and lateral melt. I Ftop.. / lead I' fi.w,., melt season How does the partitioning between these affect the summer minimum? \\ Glat Gbot _... '---"--- G tøp.,..'" ocean model levels Steele, 1992 Z n ----

9 Some suggested physically-based metrics for which observations exist In concert with a sparse set of metric on the basic performance of sea ice simulations, can we identify physically-based metrics meaningful across multiple model configurations, such as: o Melt timing and melt rate o Sea ice mechanics scaling o And should these consider atmospheric processes, e.g. radiation scheme? What would the combined results from a FAMOS model hierarchy reveal using such metrics?