Open Science
For me open science means making results, code, and data usable and not just accessible – for the scientific community as well as society.
I try to follow best practices such as open access publishing and permissive licensing, coding style guides and documentation, metadata conventions and the FAIR data principles, as well as version control and transparency in data and code usage in general.
You are free to use any of the material I provide following the rules of the indicated licenses (mostly CC BY). In addition I’d be thankful if you let me know so that I can keep track (but you don’t have to).
If you publish scientific work based on material I provide please consider citing some of my papers and putting me in the acknowledgements if appropriate.
Code
Sub-grid variability: code accompanying Brunner et al 2025
In our 2025 paper we developed the concepts of sub-grid variability and sub-gird anomaly to quantify the information missed at coarse (CMIP6-like) resolutions compared to new km-scale models. The code to calculate these metrics and to recreate the figures from the paper GitHub or in the code freeze for the paper on Zenodo.
Scripts for calculating ETCCDI indices
A collection of bash scripts to calculate the indices suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI) based on the Climate Data Operators (cdo) on GitHub
Running window bias: data and code accompanying Brunner et al 2024
In our 2024 paper we identify and discuss a bias merging from the use of too long seasonal windows when defining temperature extremes – the running window bias. The code and the most important datasets to recreate the figures from the paper and to play around with are freely available on GitHub.
Model Learning: Data and Code for identifying climate models
Model learning combines the terms climate model and machine learning providing a framework to disdinguish models and observations based on output maps. The backgrounds are described in Brunner and Sippel (2023), the data and code version used for the publication are available from Zotero, and the most recent version of the code can be found on GitHub.
Global blocking detection
During my PhD I implemented a blocking detection algorithm in Python and manly based on xarray. It enables the classification of atmospheric blocks based on global geopotential height fields and following different definitions from the literature. A detailed description can be found in section 3.2 (page 25f) of my PhD-thesis. The code is freely available under a MIT license on GitHub: https://github.com/lukasbrunner/blocking
Climate model Weighting by Independence and Performance (ClimWIP) – Standalone
The Climate model Weighting by Independence and Performance (ClimWIP) package is based on earlier work by people from the Climate Physics group at ETH Zurich (mainly Reto Knutti, Jan Sedláček, and Ruth Lorenz). Ruth Lorenz and I have implemented the current version in Python and it is freely available under a GPLv3.0 on GitHub: https://github.com/lukasbrunner/ClimWIP
Climate model Weighting by Independence and Performance (ClimWIP) – ESMValTool
Supported by the eScience Center and the ESMValTool team (in particular Peter Kalverla) we have also added the ClimWIP method as a diagnostic to the ESMValTool, including several recipes to reproduce some of our published studies: ClimWIP recipes
Data
Annual ETCCDI Extreme Indices For ICON-Sapphire And IFS-FESOM (nextGEMS Cycle 4)
ETCCDI indices calculated from two km-scale global models developed within the nextGEMS project (https://nextgems-h2020.eu/): ICON-Sapphire (Hohenegger et al. 2023) and IFS-FESOM (Rackow et al. 2025). The indices are based on the 30-year production simulations of nextGEMS, cycle 4 with a spatial resolution of about 10km (Segura et al. 2025). Here, we provide them in the 29-year period 2021-2049 (as the first year, 2020, is incomplete for IFS), driven by the high-emission pathway SSP3-7.0. The original data and the derived indices are available on the unstructured HEALPix grid (Górski et al. 2005). HEALPix organises data at discrete resolutions or zoom levels. Here, the highest resolved zoom level 9 (about 13km grid spacing corresponding to about 3 million grid cells globally) and the intermediate (“CMIP6-like”) zoom level 6 (about 102km, 50’000 grid cells) are provided.
Provided on World Data Center for Climate
CMIP6 next generation data archive
I have lead work to post-process CMIP6 data from ESGF into a quality-controlled, highly consistent database termed ETH Zurich CMIP6 next generation archive (CMIP6ng). Due to technical reasons and because we do not have dedicated funding for it the data is not directly available online. I do no longer work at ETH but they normally provide download options (ftp, rsync) for the data upon request: cmip6-archive@env.ethz.ch. A documentation is available here.
Gridded radio occultation data
During my PhD I created a dataset of gridded radio occultation (RO) climatologies from 2006 to 2016. I used them to investigate atmospheric blocking and related impacts but they might also be useful of other applications. A detailed description of the data and the processing can be found in section 4.3 (page 35f) of my PhD-thesis. The data are freely available from the Climate Change Center Austria (CCCA):