MALI (MPAS Albany Land Ice) is a land ice component of the MPAS framework, which uses unstructured Voronoi grids and adaptive encryption techniques to flexibly adjust the resolution of different regions. Historical background : MALI was developed by the MPAS team as part of the Model for Prediction Across Scales (MPAS) framework, aimed at providing a variable resolution ice sheet model to support Earth system modeling. The development of this model stems from the demand for resolution flexibility in ice sheet simulation. Technical features : • Using unstructured Voronoi grids to achieve variable resolution modeling • Adopting adaptive encryption technology, the resolution can be automatically increased in important areas • Coupling with Earth system models to simulate ice sheet climate interactions • Synchronize with climate models to achieve more realistic climate forcing • Integrate into the MPAS framework to share infrastructure and tools • Supports large-scale parallel computing and adapts to high-performance computing environments Core functions : • Variable resolution ice sheet simulation in Earth system modeling • Flexibly adjust resolution in different regions and optimize computing resource allocation • Coupling with Earth system models to simulate ice sheet climate interactions • Study the response of ice sheets to global climate change • Simulating the feedback effect of ice sheet changes on sea level and climate • Evaluate the evolution of ice sheets under different climate scenarios Application case : • Simulation of Ice Sheet Evolution under Global Climate Change Scenarios • High resolution simulation of the edge regions of the Greenland and Antarctic ice sheets • Research on the interaction between ice sheet and climate • Prediction and uncertainty assessment of sea level rise • Ice Sheet Components in Earth System Models • Analysis of the Impact of Different Resolutions on Ice Sheet Simulation Results Limitations : • High computational complexity, especially in high-resolution regions • High demand for computing resources, large-scale simulations require high-performance computing facilities • Model settings and parameter adjustments are relatively complex • Coupling with certain specific climate patterns may require additional development work • The choice of adaptive encryption strategy has a certain impact on the simulation results Input parameters : • Geometric data of ice sheet (thickness, surface elevation, etc.) • Climate forcing data (temperature, precipitation, etc.) • Physical parameters of ice (rheological parameters, thermal conductivity, etc.) • Grid resolution and adaptive encryption parameters • Boundary conditions (surface temperature, accumulation/ablation rate, etc.) • Initial conditions (initial velocity field, temperature field, etc.) Output result : • Ice sheet velocity field, thickness variation, and surface elevation variation • Ice sheet mass balance and material flux • The contribution of ice sheets to sea level rise • Comparison of simulation results in different resolution regions • Feedback process of ice sheet climate interaction