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- Author or Editor: Jinlun Zhang x
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Abstract
A parallel ocean and ice model (POIM) in generalized orthogonal curvilinear coordinates has been developed for global climate studies. The POIM couples the Parallel Ocean Program (POP) with a 12-category thickness and enthalpy distribution (TED) sea ice model. Although the POIM aims at modeling the global ocean and sea ice system, the focus of this study is on the presentation, implementation, and evaluation of the TED sea ice model in a generalized coordinate system. The TED sea ice model is a dynamic thermodynamic model that also explicitly simulates sea ice ridging. Using a viscous plastic rheology, the TED model is formulated such that all the metric terms in generalized curvilinear coordinates are retained. Following the POP's structure for parallel computation, the TED model is designed to be run on a variety of computer architectures: parallel, serial, or vector. When run on a computer cluster with 10 parallel processors, the parallel performance of the POIM is close to that of a corresponding POP ocean-only model. Model results show that the POIM captures the major features of sea ice motion, concentration, extent, and thickness in both polar oceans. The results are in reasonably good agreement with buoy observations of ice motion, satellite observations of ice extent, and submarine observations of ice thickness. The model biases are within 8% in Arctic ice motion, within 9% in Arctic ice thickness, and within 14% in ice extent in both hemispheres. The model captures 56% of the variance of ice thickness along the 1993 submarine track in the Arctic. The simulated ridged ice has various thicknesses, up to 20 m in the Arctic and 16 m in the Southern Ocean. Most of the simulated ice is 1–3 m thick in the Arctic and 1–2 m thick in the Southern Ocean. The results indicate that, in the Atlantic–Indian sector of the Southern Ocean, the oceanic heating, mainly due to convective mixing, can readily exceed the atmospheric cooling at the surface in midwinter, thus forming a polynya. The results also indicate that the West Spitzbergen Current is likely to bring considerable oceanic heat (generated by lateral advection and vertical convection) to the Odden ice area in the Greenland Sea, an important factor for an often tongue-shaped ice concentration in that area.
Abstract
A parallel ocean and ice model (POIM) in generalized orthogonal curvilinear coordinates has been developed for global climate studies. The POIM couples the Parallel Ocean Program (POP) with a 12-category thickness and enthalpy distribution (TED) sea ice model. Although the POIM aims at modeling the global ocean and sea ice system, the focus of this study is on the presentation, implementation, and evaluation of the TED sea ice model in a generalized coordinate system. The TED sea ice model is a dynamic thermodynamic model that also explicitly simulates sea ice ridging. Using a viscous plastic rheology, the TED model is formulated such that all the metric terms in generalized curvilinear coordinates are retained. Following the POP's structure for parallel computation, the TED model is designed to be run on a variety of computer architectures: parallel, serial, or vector. When run on a computer cluster with 10 parallel processors, the parallel performance of the POIM is close to that of a corresponding POP ocean-only model. Model results show that the POIM captures the major features of sea ice motion, concentration, extent, and thickness in both polar oceans. The results are in reasonably good agreement with buoy observations of ice motion, satellite observations of ice extent, and submarine observations of ice thickness. The model biases are within 8% in Arctic ice motion, within 9% in Arctic ice thickness, and within 14% in ice extent in both hemispheres. The model captures 56% of the variance of ice thickness along the 1993 submarine track in the Arctic. The simulated ridged ice has various thicknesses, up to 20 m in the Arctic and 16 m in the Southern Ocean. Most of the simulated ice is 1–3 m thick in the Arctic and 1–2 m thick in the Southern Ocean. The results indicate that, in the Atlantic–Indian sector of the Southern Ocean, the oceanic heating, mainly due to convective mixing, can readily exceed the atmospheric cooling at the surface in midwinter, thus forming a polynya. The results also indicate that the West Spitzbergen Current is likely to bring considerable oceanic heat (generated by lateral advection and vertical convection) to the Odden ice area in the Greenland Sea, an important factor for an often tongue-shaped ice concentration in that area.
Abstract
Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean–atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings—namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice–ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently.
Abstract
Because sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean–atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings—namely, increasing the number of marine stratus clouds in the Arctic region and enabling realistic representation of the ice–ocean heat flux. Hindcasts were evaluated using sea ice concentration observations from the National Aeronautics and Space Administration (NASA) Team and Bootstrap algorithms. Results show that using PIOMAS initial sea ice thickness in addition to the physics modifications yielded significant improvement in the prediction of September Arctic sea ice extent along with increased interannual predictive skill. Significant local improvements in sea ice concentration were also seen in distinct regions for the change to PIOMAS initial thickness or the physics adjustments, with the most improvement occurring when these changes were applied concurrently.
