Self-Hosted Ice Sheet Modeling: Elmer/Ice vs PISM vs ISSM

Introduction

Ice sheets contain enough water to raise global sea levels by over 65 meters, making their behavior under climate change one of the most consequential scientific questions of our time. Simulating ice sheet dynamics requires sophisticated numerical models that solve the Stokes equations for ice flow across continental scales — a computational challenge that demands high-performance computing and carefully validated software.

This guide compares three leading open-source ice sheet models: Elmer/Ice, PISM (Parallel Ice Sheet Model), and ISSM (Ice Sheet System Model). Each represents a different philosophy in ice dynamics modeling, from full-Stokes finite element solutions to hybrid shallow-ice/shelfy-stream approximations designed for parallel scalability.

Tool Overview

Why Self-Host Ice Sheet Models?

Running ice sheet simulations on your own infrastructure is essential for several scientific and practical reasons. Climate model outputs used as boundary conditions for ice sheet models are typically terabytes in size — transferring these datasets to cloud services incurs significant bandwidth costs and latency. By colocating ice sheet models with climate data archives, you eliminate data transfer as a bottleneck.

Reproducibility in glaciology requires versioned, containerized model deployments. Ice sheet models evolve rapidly as new physics (basal hydrology, fracture mechanics, ice-ocean interactions) are incorporated. A self-hosted deployment with Singularity or Docker containers pinned to specific model versions ensures that published results can be reproduced exactly — a requirement increasingly enforced by journals like The Cryosphere and Journal of Glaciology.

The computational demands of ice sheet modeling also favor dedicated hardware. A typical continental-scale simulation with 5 km resolution over Greenland requires 128-512 CPU cores for weeks of wall-clock time. Cloud spot instances may be cost-effective for one-off runs, but research groups running continuous ensemble simulations will find that purchasing dedicated HPC nodes pays for itself within a single funding cycle. For complementary environmental modeling, see our ocean circulation modeling guide.

For a broader view of scientific simulation platforms that share the same finite-element foundations, check our scientific simulation platforms guide.

Installing Elmer/Ice with Docker

Elmer/Ice is the glaciology extension of the Elmer finite element suite. The recommended deployment uses Singularity (widely available on HPC clusters) or Docker:

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# docker-compose.yml for Elmer/Ice development environment
version: "3.8"
services:
  elmerice:
    image: elmerfem/elmerice:latest
    container_name: elmerice-env
    volumes:
      - ./simulations:/workspace/simulations
      - ./meshes:/workspace/meshes
      - ./output:/workspace/output
    working_dir: /workspace
    environment:
      - OMP_NUM_THREADS=8
      - ELMER_HOME=/opt/elmer
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    entrypoint: ["/bin/bash"]
    stdin_open: true
    tty: true

Basic Elmer/Ice workflow for a simple diagnostic simulation:

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# Generate mesh from digital elevation model
ElmerGrid 1 2 mesh_directory/

# Run the ice flow solver
ElmerSolver ice_flow.sif

# Post-process with ParaView
paraview --script=visualize_results.py

Example .sif solver input file for a Stokes ice flow problem:

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! ice_flow.sif — Full-Stokes ice sheet diagnostic simulation
Header
  CHECK KEYWORDS Warn
  Mesh DB "." "mesh_directory"
  Include Path ""
  Results Directory "results"
End

Simulation
  Max Output Level = 5
  Coordinate System = Cartesian 3D
  Coordinate Mapping(3) = 1 2 3
  Simulation Type = Steady State
  Steady State Max Iterations = 50
End

! Ice rheology: Glen's flow law
Material 1
  Name = "Ice"
  Density = 910.0
  Viscosity Model = "Glen"
  Glen Exponent = 3.0
  Critical Shear Rate = 1.0e-10
End

Installing PISM on HPC Systems

PISM is designed for massive parallelism and can scale to thousands of cores:

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# Install PISM with PETSc on Linux
sudo apt-get install -y build-essential gfortran     libblas-dev liblapack-dev libnetcdf-dev     libfftw3-dev libgsl-dev

# Build PETSc (required dependency)
git clone https://gitlab.com/petsc/petsc.git
cd petsc
./configure --with-cc=mpicc --with-cxx=mpicxx --with-fc=mpif90     --download-fblaslapack --download-hdf5 --download-netcdf
make all

# Build PISM
git clone https://github.com/pism/pism.git
cd pism
mkdir build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX=/opt/pism
make -j 16
sudo make install

# Run a Greenland simulation
pismr -i greenland_5km.nc     -atmosphere给定 -surface给定     -o output.nc     -ys 2000 -ye 2100     -ts_file ts_output.nc     -extra_file extra_output.nc     -extra_times 0:1:100

Installing ISSM with MATLAB Integration

ISSM provides both MATLAB and Python interfaces, with Jupyter notebook workflows:

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# Clone ISSM repository
git clone https://github.com/ISSMteam/ISSM.git
cd ISSM

# Configure with MATLAB and MPI support  
./configure --prefix=/opt/issm     --with-matlab-dir=/usr/local/MATLAB/R2024a     --with-mpi-libflags="-lmpi"     --enable-development

make -j 16
make install

# Verify installation
cd $ISSM_DIR/test/NightlyRun
matlab -nodisplay -r "runme('Greenland'); exit"

Performance Benchmarks and Scaling Considerations

Ice sheet model performance varies dramatically with the chosen physics approximation. Full-Stokes models like Elmer/Ice solve the complete momentum balance, requiring a nonlinear viscosity iteration and linear system solve at each time step. For a Greenland-scale simulation at 5 km resolution, Elmer/Ice typically achieves 0.5-1 model-years per wall-clock hour on 256 cores, with strong scaling efficiency of ~70 percent at 512 cores.

PISM’s hybrid approach (combining shallow-ice and shallow-shelf approximations) trades physical completeness for computational speed. The same Greenland simulation at 5 km resolution runs approximately 2-3 times faster in PISM than in full-Stokes mode — but at the cost of less accurate representation of fast-flowing outlet glaciers where vertical shear gradients are significant. PISM’s use of PETSc’s distributed linear algebra provides excellent scaling to 4,000+ cores on leadership-class supercomputers.

ISSM offers a unique advantage: it can switch between approximation levels within a single simulation, applying full-Stokes physics only in fast-flowing regions while using cheaper approximations in slow-moving interior ice. This adaptive approach provides a best-of-both-worlds compromise, achieving 75-85 percent of full-Stokes accuracy at roughly double the speed. However, it requires mesh design expertise — poorly chosen refinement regions can negate the performance advantage.

Frequently Asked Questions

Which model should I use for my specific research question?

For studies focused on fast-flowing outlet glaciers or ice streams where vertical shear is critical — such as Jakobshavn Isbræ in Greenland or Pine Island Glacier in Antarctica — Elmer/Ice’s full-Stokes solver provides the highest fidelity. For continental-scale, multi-millennial simulations (paleoclimate or long-term future projections), PISM’s hybrid approach offers the necessary computational efficiency. If you need to study both fast ice streams and slow interior flow in a single simulation, ISSM’s anisotropic mesh refinement with adaptive approximations is the most pragmatic choice.

Do I need an HPC cluster to run these models?

For educational or exploratory work, all three models can run simplified test cases on a workstation with 16+ cores and 32 GB RAM. The ISSM and Elmer/Ice source distributions include small benchmark problems designed for desktop execution. However, production-quality science — continental-scale simulations at resolutions below 10 km — absolutely requires HPC resources. A minimum of 128 cores and 256 GB RAM is typical for Greenland simulations, with Antarctic-scale problems requiring 512+ cores.

How do I validate my ice sheet model setup?

The ice sheet modeling community maintains several standard benchmarking initiatives. The ISMIP6 (Ice Sheet Model Intercomparison Project for CMIP6) provides standardized boundary conditions, initialization procedures, and validation metrics. The MISMIP and MISMIP+ experiments provide analytical and semi-analytical solutions for marine ice sheet dynamics. Always run your model through these benchmarks before publishing results — reviewers will expect it.

What’s the difference between the enthalpy method and full thermomechanical coupling?

PISM’s enthalpy method tracks both temperature and liquid water content (for temperate ice) as a single conserved quantity, making it computationally efficient but less accurate near the pressure melting point where water content affects rheology nonlinearly. Elmer/Ice and ISSM solve the full heat equation with phase change tracking, providing higher accuracy at the expense of additional computational cost. For cold ice (much of Antarctica and interior Greenland), the difference is negligible. For temperate glaciers (Alaska, Patagonia, Antarctic Peninsula), full coupling is recommended.

How do I handle the spin-up problem for paleoclimate simulations?

Ice sheets have memory spanning tens of thousands of years — you cannot start a simulation from present-day geometry and expect accurate results within a century. The standard approach uses PISM for a computationally efficient multi-millennial spin-up (driven by paleoclimate reconstructions), then switches to a higher-fidelity model for the period of interest. ISSM supports reading PISM output as initial conditions, and Elmer/Ice can interpolate between different mesh structures using the ElmerGrid tools.

Can these models be used for glacier (non-ice-sheet) simulations?

Yes, all three can model mountain glaciers and ice caps, though with caveats. Elmer/Ice is the most flexible for alpine glaciers due to its unstructured mesh capability and sophisticated basal sliding laws. PISM works well for ice caps (Vatnajökull, Devon Ice Cap) but becomes inefficient for valley glaciers where high grid resolution is needed in narrow domains. ISSM’s anisotropic mesh refinement is well-suited to glacier-scale problems where you need high resolution along flowlines but coarser resolution in adjacent bedrock.

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