Predictability of Arctic sea-ice linear kinematic features in high-resolution ensemble simulations
Linear kinematic features (LKFs) in sea ice, potentially important for short-term forecast users as for climate simulations, emerge as viscous-plastic sea ice models are used at high (<10km) resolution. Here we analyze the short-range (up to 10 days) potential predictability of LKFs in Arctic sea...
| Main Authors: | , , , |
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| Format: | Conference Object |
| Language: | unknown |
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2016
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| Subjects: | |
| Online Access: | https://epic.awi.de/id/eprint/40996/ https://epic.awi.de/id/eprint/40996/1/poster_PolarPrediction_NY_v6.pdf https://www.arcus.org/sipn/meetings/workshops/may-2016/posters https://hdl.handle.net/10013/epic.47969 https://hdl.handle.net/10013/epic.47969.d001 |
| Summary: | Linear kinematic features (LKFs) in sea ice, potentially important for short-term forecast users as for climate simulations, emerge as viscous-plastic sea ice models are used at high (<10km) resolution. Here we analyze the short-range (up to 10 days) potential predictability of LKFs in Arctic sea ice using an ocean/sea-ice model with a grid point separation of ~4.5 km. We analyze the sensitivity of predictability to idealized initial perturbations, resembling uncertainties in sea ice analyses, and to growing uncertainty of the atmospheric forcing caused by the chaotic nature of the atmosphere. For the latter we use different members of ECMWF ensemble forecasts to drive ocean/sea-ice forecasts. For our analysis, we diagnose LKFs occurrence and investigate different sea ice characteristics. On the 10-day-time scale, the model has lower predictive skill for LKFs and deformation than for sea-ice thickness and concentration. |
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