KAPy (Klimaatlases in Python) – an open-source pipeline for the production of climate services

Many European countries have developed climate services in recent years presenting locally downscaled climate projections. While each such climate service has its own peculiarities, a high degree of overlap and duplication can also be found between these services: the basic workflow of the Danish “Klimaatlas”, starting from the EURO-CORDEX ensemble, bias-adjusting against local datasets, and producing indicators, can also be seen in climate services in other countries such as Norway and Sweden. However, although each of these climate services is doing nearly the same thing, there has traditionally been little exchange of code between even neighbouring countries. The KAPy (Klimaatlases in Python) project aims to provide a platform for climate services to learn from each other, reduce duplication and enable rapid development of new climate services in other regions. KAPy is a climate data processing pipeline built on an open-source software stack centred on the Python language. The workflow control tool Snakemake from the field of bioinformatics provides reproducibility and scalability,  xarray, xclim and xsdba provide core functionality, while the open-source paradigm enables collaboration and transparency. In addition to Klimaatlas Denmark, KAPy now also forms the core processing chain of a new climate service in Ghana, “Climate Atlas Ghana” and the current development of a climate service for Greece. To illustrate the ability of this tool to rapidly produce climate service information, I provide a detailed analysis of the efforts required to produce information that could be used for a hypothetical climate service for the town of Utrecht: starting from downloaded data, configuration, bias-adjustment and indicator production was completed in an afternoon working on a moderately sized terminal service. We also illustrate the wide applicability of the tool, showing its application from CMIP to convection-permitting models, and with a wide variety of outputs. We conclude with an open invitation to all to join the KAPy network as both users and developers, and thereby contribute to making climate services more transparent and widely accessible.