DOI: 10.5061/dryad.3r2280gph
title: Data from: Improving access and use of climate projections for ecological research through the use of a new Python tool

description: Over the past decade, the use of future climate projections from the
 Coupled Model Intercomparison Project (CMIP) has become central in
 biodiversity science. Pre-packaged datasets containing future projections
 of the widely used bioclimatic variables, for different times and
 socio-economic pathways, have contributed immensely to the study of
 climate change implications for biodiversity. However, these datasets lack
 the flexibility to obtain projections to other target years, and the use
 of raw data requires coding and spatial information systems expertise. The
 Python tool, chelsa-cmip6, developed by Karger et. al 2023 provides the
 flexibility needed by allowing users to generate bioclimatic variables for
 the time of their choice provided the selected general circulation model
 and socioeconomic pathway combination exists. This is a fantastic step
 forward in bringing flexibility to the use of climate datasets in
 biodiversity and will allow for more widespread use of data provided by
 CMIP6. We hope it also will prompt the development of more user-friendly
 tools for the study of the effects of climate change on biodiversity.
description: This is a jupiter notebook that allows the user to check if the
 desired combination of GCM and SSP is available for the selected time and
 area, and then uses the chelsa-cmip6 tool to create bioclimatic variables
 based on the user-selected scenarios. This notebook was created to
 facilitate use of the chelsa-cmip6 Python tool by Karger et al
 2023.
description: # Improving access and use of climate projections for ecological research
 through the use of a new Python tool
 \# Improving access and
 use of climate projections for ecological research through the use of a
 new Python tool \--- *Brief summary of contents* Data archive for:
 Improving access and use of climate projections for ecological research
 through the use of a new Python tool by Andrea Paz, Thomas Lauber, Thomas
 W. Crowther and Johan van den Hoogen This archive contains an example
 notebook script that allows the user to check if the desired combination
 of GCM and SSP is available for the selected time and area, and then uses
 the chelsa-cmip6 Python tool (Karger et al 2023) to create bioclimatic
 variables based on the user-selected scenarios. The bioclimatic variables
 can then be converted to GeoTiffs, downloaded and used in different
 applications through R, Python, Google Earth Engine, or others. This
 Notebook can be opened locally or uploaded to Google Colab, Deepnote, etc.
 If uploaded to a platform make sure that the required files are also
 uploaded to the same folder. \## Description of the data and file
 structure The main file is a Jupyter notebook (chelsa_cmip6.ipynb) that
 requires the modules.py and the requirements.txt files. The
 requirements.txt file contains the required python packages that need to
 be installed prior to running the Jupyter Notebook. This can be done from
 the Jupyter Notebook by running the first cell. The modules.py file
 contains all functions that are required for running the Jupyter Notebook
 successfully. These functions are loaded by running the second cell in the
 Jupyter Notebook. \### Schema of data archive \```bash \|- README.md
 +---chelsa_cmip6 \| chelsa_cmip6.ipynb \| modules.py \| requirements.txt
 \``` \### File details \#### Details for: `chelsa_cmip6.ipynb` \--- \*
 Description: The Jupyter Notebook that allows to check for which
 combination of GCM and SSP the user can generate bioclimatic variables
 using the chelsa-cmip6 Python tool (Karger et al 2023). The tool will only
 work if both historical and future experiments are available for all input
 variables. Therefore, the user can first check which combinations of GCM
 members and SSP scenarios are available. Then, the bioclimatic variables
 can be created using the chelsa-cmip6 Python tool (Karger et al 2023) for
 the chosen combination of model members, SSP scenarios, a region of
 interest and the future periods of interest. In a last step, the
 bioclimatic variables can then be converted to GeoTiffs. \* Format(s):
 .ipynb \#### Details for: `modules.py` \--- \* Description: This python
 script contains all the functions needed for the Jupyter Notebook. The
 functions are separated from the Notebook to facilitate the user inputs.
 They include functions to (i) check the required GCM and SSP combinations,
 (ii) plot a region of interest, (iii) run the chelsa-cmip6 Python tool
 (Karger et al 2023) and (iv) convert the NetCDF files into GeoTiffs. \*
 Format(s): .py \#### Details for: `requirements.txt` \--- \* Description:
 This text file contains the required python packages. The required python
 packages can be installed using this requirements files. \* Format(s):
 .txt \## References Karger, D. N., Chauvier, Y., & Zimmermann, N. E.
 (2023). chelsa‐cmip6 1.0: a python package to create high resolution
 bioclimatic variables based on CHELSA ver. 2.1 and CMIP6 data. Ecography,
 e06535.