DOI: 10.5281/zenodo.14045269
title: Data from Glacier Model Intercomparison Project Phase 3 (GlacierMIP3)

description: This dataset presents the data from the third phase of  GlacierMIP (GlacierMIP3: Equilibration of glaciers under different climate states). It includes regional glacier volume and area projections as submitted by the glacier modelling groups.  Additionally, it features post-processed and aggregated data derived from GlacierMIP3, or in combination with other studies, which is used for the analyses and visualisations presented in the following manuscript: 

Zekollari*, H., Schuster*, L., Maussion, F., Hock, R., Marzeion, B., Rounce, D. R., Compagno, L., Fujita, K., Huss, M., James, M., Kraaijenbrink, P. D. A., Lipscomb, W. H., Minallah, S., Oberrauch, M., Van Tricht, L., Champollion, N., Edwards, T., Farinotti, D., Immerzeel, W., Leguy, G., Sakai, A. (under review): Glacier preservation doubled by limiting warming to 1.5°C. Preprint available at https://doi.org/10.31223/X51T5W, 2024.*Harry Zekollari and Lilian Schuster contributed equally to this dataset and the manuscript above.If you use the data, please cite this Zenodo dataset and the above study. More info in README_data.pdf. For information about the GlacierMIP3 experimental design, please refer to the GlacierMIP3_protocol.pdf. The code used to generate the postprocessed data and to conduct the analyses for the manuscript mentioned above is available at https://github.com/GlacierMIP/GlacierMIP3.

To assist potential data users, we have included a jupyter notebook (gmip3_data_example_use_cases.ipynb) that guides you through some simple use cases. This notebook can be directly run when clicking on this link. Please note that you will need to log in to your Google account and, if you haven't already done so, install Google Colaboratory. The data will then be automatically downloaded to your account.

We may adapt the data structure and improve the documentation during the review phase. If you have any questions or suggestions, please contact us (lilian.schuster@uibk.ac.at, harry.zekollari@vub.be).
description: This work was funded by the following: DOC Fellowship of the Austrian Academy of Sciences at the Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, No. 25928 (to LS); European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101003687, PROVIDE (to LS and FM); European Union’s Horizon 2020 research and innovation programme – PROTECT, grant agreement number 869304 (to HZ, BM, MH, and MJ); European Research Council (ERC) under the European Union's Horizon Framework research and innovation programme, grant agreement No 101115565, ICE3 (to HZ); Research foundation – Flanders (FWO) through an Odysseus Type II project, grant agreement G0DCA23N, GlaciersMD (to HZ and LVT); VUB ZAP Startkrediet, ICEFIELD (to HZ); Norwegian Research Council Project 324131 (to RH); ERC-2022-ADG No 01096057 GLACMASS (to RH); National Aeronautics and Space Administration grants 80NSSC20K1296 and 80NSSC20K1595 (to RH and DR); Swiss National Science Foundation grant nr. 200021_184634 (to LC); Dutch Research Council (NWO) Talent Programme Veni, GREENPEAKS, file number VI.Veni.222.019 (to PK); NSF National Center for Atmospheric Research, which is a major facility sponsored by the U.S. National Science Foundation under Cooperative Agreement No. 1852977 (to WL and SM); Computing and data storage resources for CISM simulations were supported by the Derecho system (doi:10.5065/qx9a-pg09) provided by the NSF National Center for Atmospheric Research (NCAR), sponsored by the National Science Foundation (to WL and SM); World Climate Research Programme (WCRP) Climate and Cryosphere Project (CliC) who funded GlacierMIP workshops.