ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks Geoscientific Model Development

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 11 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. ClimKern 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 from an abrupt 2×CO2 experiment to explore the sensitivity of feedback calculations to kernel choice. Interkernel spread exhibits considerable spatial heterogeneity, with the greatest spread in the surface albedo and cloud feedbacks occurring in the Arctic and Southern Ocean. In the global mean, the Planck and surface albedo feedbacks show the greatest interkernel variability. Our results highlight the importance of using multiple radiative kernels and standardizing feedback calculations in climate feedback, sensitivity, and polar amplification studies. As ClimKern continues to evolve, we hope others will contribute to its development to make it an even greater tool for the radiative feedback community.