Access to Analysis and Climate Indices Tools for Climate Researchers and
End Users
Abstract
Researchers and end users using climate data face a challenge when they
analyze the data they need. Data volumes are increasing very rapidly,
and the ability to download all needed data is often no longer possible.
Most of the climate analysis tools for research and application needs
must use very large datasets, often distributed among several data
centres and into a large quantity of files. This is especially true when
they are stored in a federated architecture like the ESGF. One of these
tools is icclim (https://github.com/cerfacs-globc/icclim ), a flexible
python software package to calculate climate indices and indicators.
This tool adhere as much as possible to metadata conventions such as CF,
implementing also provenance information. It also aims at providing
increasing support for all FAIR aspects. It is designed with performance
and optimisation in mind, because the goal is to provide on-demand
calculations for users. It provides the implementation of most of the
international standard climate indices such as ECAD, ETCCDI, ET-SCI,
including the correct methodology for calculating percentile indices
using the bootstrapping method. It has been validated against R.Climdex
as well (https://cran.r-project.org/web/packages/climdex.pcic/index.html
). The new 5.x version of icclim is now based on functions from the
xclim python library, which was inspired by earlier versions of icclim,
but using xarray and dask for data access and processing. icclim is also
a candidate as the software to calculate climate indices for the C3S
toolbox (https://cds.climate.copernicus.eu/cdsapp#!/toolbox ). An
example of a complex analysis tool used in climate research and
adaptation studies is a tool to follow storm tracks. In the context of
climate change, it is important to know if storm tracks will change in
the future, in both their frequency and intensity. Storms can cause
significant societal impacts, hence it is important to assess future
patterns. These tools are 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 enable the use of those tools by a larger number of
researchers and end users. This project (IS-ENES3) has received funding
from the European Union’s Horizon 2020 research and innovation programme
under grant agreement N°824084.