---
title: "Iceland Glacier Projections — Delivery Overview"
subtitle: "OGGM hindcast and CMIP5/CMIP6 projections for Landsvirkjun"
author: "Fabien Maussion & Patrick Schmitt"
date: 2026-07-07
format:
html:
output-file: delivery_overview.html
toc: true
embed-resources: true
---
## What this is
This delivery contains state-of-the-art OGGM hindcast and future projections for all
572 Icelandic glaciers, run to 2100 and 2300 under the CMIP5 and CMIP6 climate model
ensembles, together with the notebooks used to select, validate, and analyse them.
The projections are intended to support your team's work on how projected changes in
the Atlantic Meridional Overturning Circulation (AMOC) may influence Icelandic glacier
evolution — see `Landsvirkjun consultancy` (google doc) for the original scope of
this work.
## Configuration used
Two climate forcings were calibrated and validated: **CARRA** and **ERA5** (1991-2026).
**CARRA (trapezoidal bed shape, lambda=14) is the delivered configuration** used
throughout the main projections. **ERA5 (lambda=8) was selected as the best-performing
configuration for this climate forcing** and is kept
alongside it for comparison — the two configurations differ only modestly in their
projected volume loss. The full reasoning and validation evidence behind this choice is
in notebook `00`, below.
## Contents & how to read this delivery
Each notebook below is linked two ways: an **nbviewer** link (read directly in your
browser, no setup needed) and the underlying **.py script** (a plain, `# %%`-commented
Python file — the notebook `.ipynb` next to it was generated from this script and is
the exact same content, just executed). Either can be opened directly in Jupyter if you
want to re-run or adapt the analysis.
1. **[00 — Experiment selection & validation](https://nbviewer.org/urls/cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/00_experiment_selection_and_validation.ipynb)**
([.py script](https://cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/00_experiment_selection_and_validation.py)) —
the calibration sweep (climate forcing × bed shape × mass-balance model) validated
against glacier area, Hugonnet mass change, DEM-differenced volume change, and SMB
maps, ending in the decision to ship CARRA, lambda=14.
2. **[01 — Comparison with reference models](https://nbviewer.org/urls/cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/01_model_comparison.ipynb)**
([.py script](https://cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/01_model_comparison.py)) —
how our Iceland-specific calibration compares to three independent published
projections (OGGM standard, GloGEM, PyGEM-OGGM) for the same glaciers.
3. **[02 — Projections analysis](https://nbviewer.org/urls/cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/02_projections_analysis.ipynb)**
([.py script](https://cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/02_projections_analysis.py)) —
**the main deliverable.** Total Iceland glacier volume to 2100 and 2300, the same
projections re-grouped by global temperature level and by AMOC index, and total
glacier runoff by AMOC index (following the same method as the
[OGGM-EDU tutorial on glacier water resources](https://edu-notebooks.oggm.org/oggm-edu/glacier_water_resources_projections.html)).
This is the notebook to start from if you want an initial analysis of the
projections themselves, or a template to build your own.
4. **[03 — AMOC index](https://cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections/03_amoc_index/)**
— two standalone CLI scripts (`amoc_index_timeseries.py`, `amoc_index_classify.py`,
not notebooks) that build the AMOC-strength classification used in notebook `02`,
plus `amoc_index_table_README.txt` documenting the full methodology and the
already-computed output tables. See *Data* below for what is and isn't shipped here.
## Downloading this delivery
Everything in this README — notebooks, scripts, figures, and data — is hosted at:
You can browse it directly in a browser, or download the whole delivery as a single
archive:
```bash
wget https://cluster.klima.uni-bremen.de/~fmaussion/share/iceland_projections.tar.gz
tar -xzf iceland_projections.tar.gz
```
The archive is about 4.1 GB (compressed).
## Key results





More figures are in the `figures/` folder, and each notebook produces the ones
relevant to its own analysis.
## Data
All data referenced by these notebooks ships alongside them, organised under `data/`:
```
data/
historical/ — CARRA and ERA5 historical OGGM runs (1991-2026)
projections/ — merged CMIP5/CMIP6 projection NetCDFs (2100 and 2300), CARRA only
supporting/ — observations (Hugonnet, SMB maps), reference-model CSVs,
global-temperature-level tables, and the AMOC index's own
supporting files (subpolar-gyre mask, Weijer et al. streamfunction)
```
Two things are **not** shipped, to keep this delivery a manageable size:
- **The full calibration sweep** behind notebook `00` (~15 configurations across bed
shape and mass-balance model choices) — only the shortlisted configurations' data is
included. The full sweep's data can be made available on request.
- **The raw CMIP6/CMIP5 GCM `tas` files** used by `amoc_index_timeseries.py` to build
the AMOC index from scratch (73+ files, too large for this delivery). The
already-computed output (`i_tas_timeseries.csv`, `amoc_index_table.csv`) is included,
so `amoc_index_classify.py` and notebook `02` run standalone. To regenerate the index
from scratch, the GCM files are publicly available at:
- CMIP6:
- CMIP5:
## Running this delivery
The notebooks use only relative paths (`data/`, `figures/`, `../03_amoc_index/`), so
they must be run from within this folder. A standard scientific Python environment is
enough: `xarray`, `pandas`, `numpy`, `matplotlib`, `pyyaml`, and `jupytext` if you want
to convert the `.py` scripts back to notebooks yourself
(`jupytext --to notebook 02_projections_analysis.py`). The AMOC index scripts
additionally need `regionmask`, `scipy`, and `cf_xarray`/`xesmf`-adjacent packages only
if re-run from raw GCM data.
## About this delivery
These notebooks and scripts were built with the assistance of Claude (Anthropic's AI
coding assistant), under our direction and review. If your team wants to extend this
analysis — different scenario groupings, per-ice-cap breakdowns, other hydro variables
(melt vs. precipitation split, peak-water timing per ice cap), or anything else the
underlying NetCDF data supports — Claude Code can be pointed at this project directory
to pick up the analysis directly; the notebooks and this README are written to give it
(and any future collaborator, human or otherwise) enough context to continue from here.