Abstract
High-resolution sea ice modeling is becoming widely available for both operational forecasts and climate studies. In traditional Eulerian grid-based models, smallscale sea ice kinematics represent the most prominent feature of high-resolution simulations, and with rheology models such as viscous-plastic (VP) and Maxwell elasto-brittle (MEB), sea ice models are able to reproduce multi-fractal sea ice deformation and linear kinematic features that are seen in high-resolution observational datasets. In this study, we carry out modeling of sea ice with multiple grid resolutions by using the Community Earth System Model (CESM) and a grid hierarchy (22, 7.3, and 2.4 km grid stepping in the Arctic). By using atmospherically forced experiments, we simulate consistent sea ice climatology across the three resolutions. Furthermore, the model reproduces reasonable sea ice kinematics, including multi-fractal spatial scaling of sea ice deformation that partially depends on atmospheric circulation patterns and forcings. By using high-resolution runs as references, we evaluate the model's effective resolution with respect to the statistics of sea ice kinematics. Specifically, we find the spatial scale at which the probability density function (PDF) of the scaled sea ice deformation rate of low-resolution runs matches that of high-resolution runs. This critical scale is treated as the effective resolution of the coarse-resolution grid, which is estimated to be about 6 to 7 times the grid's native resolution.We show that in our model, the convergence of the elastic-viscous-plastic (EVP) rheology scheme plays an important role in reproducing reasonable kinematics statistics and, more strikingly, simulates systematically thinner sea ice than the standard, non-convergent experiments in landfast ice regions of the Canadian Arctic Archipelago. Given the wide adoption of EVP and subcycling settings in current models, it highlights the importance of EVP convergence, especially for climate studies and projections. The new grids and the model integration in CESM are openly provided for public use.
| Original language | English |
|---|---|
| Pages (from-to) | 603-628 |
| Number of pages | 26 |
| Journal | Geoscientific Model Development |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 29 2021 |
| Externally published | Yes |
Funding
Financial support. This research has been supported by the Na- Acknowledgements. The authors would like to thank the editors and referees for their invaluable efforts to improve the paper. This work is partially supported by the National Key R & D Program of China (no. 2017YFA0603902), the General Program of National Science Foundation of China (no. 42030602), and the Tsinghua University Initiative Scientific Research Program (no. 2019Z07L01001). This work is also partially supported by the Center for High-Performance Computing and System Simulation, Pilot National Laboratory for Marine Science and Technology (Qingdao). The authors would also like to thank the National Supercomputing Center at Wuxi for the computational and technical support during the numerical experiments.