We have now published a new milestone (MS9), which marks the development of a comprehensive and statistically rigorous toolbox for identifying, quantifying, and interpreting emergent or observational constraints in climate projections. Observational constraints are statistical methods that integrate information from an ensemble of simulations across different climate models and available observations to better estimate future changes in a given variable and reduce the associated uncertainty. Such approaches are now considered in estimates delivered in IPCC reports.

To use such approaches within the TipESM project to reduce the estimate and uncertainty of future changes in the key tipping elements analysed, a toolbox has been developed. It has been named ClimLoco1.0 since it is the first version, but it might be further developed in the future. Its full description is provided in the published paper Portmann et al. (2025), which includes a Zenodo record of the associated Python code.

Read more in the milestone report: https://zenodo.org/records/20800197

Read Portmann et al. (2025) ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint, Geoscientific Model Development, Vol. 18, No. 22, pp. 9015-9038. https://doi.org/10.5194/gmd-18-9015-2025