Abstract

Abstract. We introduce MPAS-Albany Land Ice (MALI) v6.0, a new variable-resolution land ice model that uses unstructured Voronoi grids on a plane or sphere. MALI is built using the Model for Prediction Across Scales (MPAS) framework for developing variable-resolution Earth system model components and the Albany multi-physics code base for the solution of coupled systems of partial differential equations, which itself makes use of Trilinos solver libraries. MALI includes a three-dimensional first-order momentum balance solver (Blatter–Pattyn) by linking to the Albany-LI ice sheet velocity solver and an explicit shallow ice velocity solver. The evolution of ice geometry and tracers is handled through an explicit first-order horizontal advection scheme with vertical remapping. The evolution of ice temperature is treated using operator splitting of vertical diffusion and horizontal advection and can be configured to use either a temperature or enthalpy formulation. MALI includes a mass-conserving subglacial hydrology model that supports distributed and/or channelized drainage and can optionally be coupled to ice dynamics. Options for calving include “eigencalving”, which assumes that the calving rate is proportional to extensional strain rates. MALI is evaluated against commonly used exact solutions and community benchmark experiments and shows the expected accuracy. Results for the MISMIP3d benchmark experiments with MALI's Blatter–Pattyn solver fall between published results from Stokes and L1L2 models as expected. We use the model to simulate a semi-realistic Antarctic ice sheet problem following the initMIP protocol and using 2 km resolution in marine ice sheet regions. MALI is the glacier component of the Energy Exascale Earth System Model (E3SM) version 1, and we describe current and planned coupling to other E3SM components.

Highlights

  • During the past decade, numerical ice sheet models (ISMs) have undergone a renaissance relative to their predecessors

  • We find that Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) using the Albany-LI Blatter–Pattyn velocity solver is able to resolve the MISMIP3d experiments satisfactorily compared to the Pattyn et al (2013) benchmark results when using a grid resolution of 500 m with grounding line parameterization

  • We have described MPAS-Albany Land Ice (MALI), a higher-order, thermomechanically coupled ice sheet model using unstructured Voronoi meshes

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Summary

Introduction

Numerical ice sheet models (ISMs) have undergone a renaissance relative to their predecessors. 2013; Hewitt, 2013; Hoffman and Price, 2014; Bueler and van Pelt, 2015; and ice damage, fracture, and calving, e.g., Åström et al, 2014; Bassis and Ma, 2015; Borstad et al, 2016; Jiménez et al, 2017), and the coupling between ISMs and Earth system models (ESMs) (e.g., Ridley et al, 2005; Vizcaíno et al, 2008, 2009; Fyke et al, 2011; Lipscomb et al, 2013) These “next-generation” ISMs have been applied to community-wide experiments focused on assessing (i) the sensitivity of ISMs to idealized and realistic boundary conditions and environmental forcing and (ii) the potential future contributions of ice sheets to sea level rise (see, e.g., Pattyn et al, 2013; Nowicki et al, 2013a, b; Bindschadler et al, 2013; Shannon et al, 2013; Edwards et al, 2014b). We discuss each of these components in more detail

MPAS Framework
Model meshes
The Albany software library
Conservation of momentum
Reduced-order equations
First-order velocity solver and coupling
Shallow ice approximation velocity solver
Conservation of mass
Conservation of energy
Temperature formulation
Subglacial hydrology
Distributed drainage
Channelized drainage
Drainage component coupling
Numerical implementation
Coupling to ice sheet model
Verification and real-world application
Iceberg calving
Optimization
Simulation analysis
Model verification and benchmarks
Halfar analytic solution
EISMINT
Enthalpy benchmarks
Experiment A
Experiment B
ISMIP-HOM
MISMIP3d
Realistic application
Coupling to Energy Exascale Earth System Model
10 Model performance
Findings
11 Conclusions and future work

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