ClimKern v1.1.2: a new Python package and kernel repository for calculating radiative feedbacks

Abstract. Climate feedbacks are a significant source of uncertainty in future climate projections and need to be quantified accurately and robustly. The radiative kernel method is commonly used to efficiently compute individual climate feedbacks from climate model or reanalysis output. Despite its popularity, it suffers from complications, including difficult-to-locate radiative kernels, inconsistent kernel properties, and a lack of standardized assumptions in radiative feedback calculations, limiting the robustness and reproducibility of climate feedback computations. We designed the ClimKern project to address these issues with a kernel repository and a separate but complementary Python package of the same name. We selected eleven sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. The functions contain helpful optional parameters while maintaining standard practices between calculations.

After documenting the kernels and ClimKern package, we test it with sample climate model output to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread shows considerable spatial heterogeneity, with the greatest spread in the Arctic and over the Southern Ocean. Considerable sensitivity to kernel choice is found even in the global means, with the surface albedo and cloud feedbacks showing the greatest spread across different kernels. Our results highlight the importance of using more than one radiative kernel and standardizing feedback calculations, like those offered by ClimKern, in climate feedback, climate sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it even more useful to the feedback community.