ClimKern: a new Python package and kernel repository for calculating radiative feedbacks in the Arctic

The radiative kernel method is widely used in the polar amplification community for calculating radiative feedbacks. The “kernels,” which are precalculated radiative sensitivities to changes in climate fields (e.g., temperature, specific humidity), are commonly shared among research groups. Despite the method’s popularity, there is a lack of studies quantifying the importance of kernel choice or methodological differences in calculating radiative feedbacks. To address this, we have developed an open-source Python package called ClimKern, which incorporates an extensive collection of radiative kernels and simplifies radiative feedback calculations using any model output. ClimKern standardizes the assumptions overlooked in feedback calculations, defines a set of best practices for future studies, and gives users access to 12 different radiative kernels. We show that kernel choice is a non-negligible consideration when calculating radiative feedbacks; this is especially true in the Arctic and Southern Ocean, where interkernel spread is considerably larger. In these regions, shortwave feedbacks show strong sensitivity to kernel choice, creating a situation where different conclusions may be reached regarding the relative importance of Arctic amplification mechanisms, depending on the kernel used. We conclude by inviting others to contribute to ClimKern to facilitate more robust and reproducible polar amplification studies.