ECtuner: a semi-automatic GCM tuning tool and its application to the EC-Earth4 model

The approximate representation of subgrid-scale processes  in atmospheric General Circulation Models through parameterizations - such as convection and cloud microphysics - introduces significant parametric uncertainty. As a consequence, model tuning remains a crucial step in model development and in recent years the tuning procedure has evolved from an exclusively manual, expert-guided task, into a more rigorous scientific phase essential for reducing systematic biases and for constraining the global energy balance. We introduce ECtuner, a semi-automatic optimization software tool in python for the tuning of GCMs, developed for the tuning of the EC-Earth4 GCM. ECtuner uses global optimization algorithms to minimize a cost function based on the weighted distance between simulated fields and multiple observational targets. The tool identifies an optimal parameter set that best aligns the model with the present-day climate by computing the sensitivity of radiative fluxes to various atmospheric parameters from a set of perturbed simulations where one parameter is changed at a time. ECtuner offers flexibility, including a choice of global minimization algorithm, the introduction of a penalty for distance from default parameters and a choice of different tuning targets (such as TOA or surface fluxes), which can be weighted by season or latitudinal band. We present some results from the application of the tool to the tuning of the EC-Earth4 model, demonstrating how a significant reduction in the global TOA net imbalance can be achieved in AMIP simulations with a small change in essential tuning parameters.