Hey! We’re Northflow, an AI-native infrastructure company working across climate, space, and research domains. Sharing this here first because we’re looking for the most relevant feedback.
climval is a typed Python library for composable climate model validation: comparing CMIP6 and CORDEX outputs against reference datasets like ERA5 and ERA5-Land.
pip install climval
We built it because the friction point kept coming up: reproducible validation metrics inside a Python pipeline, without ESMValTool’s configuration overhead for straightforward tasks. Saw the gap, built the tool and open-sourced it under Apache 2.0.
Out of the box: RMSE, MAE, Mean Bias, NRMSE, Pearson r, Taylor Skill Score, percentile bias (P5/P95). Custom metrics via subclass. HTML/JSON/Markdown export. Full CI on Python 3.10–3.12.
GitHub: GitHub - northflowlabs/climval: Composable climate model validation in Python: ERA5, CMIP6, CORDEX. pip install climval PyPI: Client Challenge
We’d appreciate and value feedback from people working with real CMIP6/ERA5 data, particularly on xarray integration, variable handling, and gaps in the metric set. This is v0.1.0 and actively in development. And we’ll be going through our projects to share more going forward.