Climate Data Analysis Tools Along With Pre-Computed Climate Indices Datasets to Support Climate Action

AGU 2022

Abstract: Many end users of climate change information often need specialized products to perform their research or data analysis. Climate indices, like the standard ones defined by ECA&D and ETCCDI, cover most of the general needs. However, datasets provided on the climate data infrastructure ESGF are climate model output and only provide standard variables, such as temperature and precipitation, and not climate indices, such as the number of summer days or the number of days with heavy precipitation.

A python package to calculate climate indices, called icclim https://github.com/cerfacs-globc/icclim , is currently developed within the H2020 IS-ENES3 project, with a strong collaboration with the xclim https://github.com/Ouranosinc/xclim developer team. This package is using xarray and dask for very fast execution and smaller memory footprint. It also aims at providing increasing support for all FAIR aspects. This tool is integrated in the IS-ENES C4I 2.0 platform (https://climate4impact.eu/ ), using a Jupyter notebook collection in a SWIRRL environment (Software for Interactive Reproducible Research Labs https://gitlab.com/KNMI-OSS/swirrl ). Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like C4I enables the use of those tools by a larger number of researchers and end users.

Providing those users datasets of climate indices pre-computed on CMIP6 simulations would be very valuable for those users. Of course all specific needs cannot be taken into account (such as specific seasons, specific reference periods, etc.), but the most general ones can be fulfilled. The European Open Science Cloud (EOSC) is providing computing and storage resources through the EGI-ACE project, enabling the possibility to compute several climate indices. In this EGI-ACE Use Case, icclim will be used to compute 49 standard climate indices on a large number of CMIP6 simulations, starting with the most used ones. It could also be extended to ERA5 reanalysis, CORDEX and CMIP5 datasets. The resulting climate indices datasets will later be made available in the C4I portal. Preliminary results of the climate indices dataset will be presented.

This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.