climdex-kit: An open software for climate index calculation, sharing and analysis towards tailored climate services

Abstract

The paper presents the open-source software climdex-kit which includes modules to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to ease the retrieval and interpretation of meaningful information for climate change studies and support the development of climate services for sectoral applications with flexible options for tailoring the expected outputs. It currently includes the computation of 38 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with multi-model ensembles of climate projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific capabilities for filtering, aggregating and visualizing insightful information out of the computed indices ensembles make this software rather unique in the still rich landscape of available akin libraries, and are thought to help users to produce tailored results improving the understanding and communication of future climate change. The package climdex-kit can be used directly in interactive Python shells or be integrated as a building block in more complex data processing workflows. New indices can be easily configured and the software can be re-used on the spatial and temporal domains required by the specific application. Moreover, with its utilities to publish the climate indices in open catalogues, this software can be a one stop shop for a FAIR and simplified computation and management of such complex multi-scenario settings. To show the functionalities of the package as well as its potential use for conducting regional climate assessments, the application of climdex-kit to an ensemble of climate model projections for the Trentino – South Tyrol region (north-eastern Italy) is presented. Examples of derived information for a selection of indices are reported and different visualization options discussed.