Radiative sensitivity quantified by a new set of radiation flux kernels based on the ERA5 reanalysis

Abstract. Radiative sensitivity, i.e., the response of the radiative flux to climate perturbations, is essential to understanding climate variability. The sensitivity kernels computed by radiative transfer models have been broadly used for assessing the climate forcing and feedbacks in global warming. As these assessments are largely focused on the top of atmosphere (TOA) radiation budget, less attention has been paid to the surface radiation budget or the associated surface radiative sensitivity kernels. Based on the fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis, we produce a new set of radiative kernels for both the TOA and surface radiative fluxes, which is made available at http://doi.org/10.17632/vmg3s67568.1 (H. Huang, 2022). By comparing with other published radiative kernels, we find that the TOA kernels are in agreement in terms of global mean radiative sensitivity and analyzed overall feedback strength. The unexplained residual in the radiation closure tests is found to be generally within 10 %, no matter which kernel dataset is used. The inter-kernel bias-induced uncertainty, as measured by the standard deviation of the global mean feedback parameter value, is typically no more than 10 % in the longwave and 20 % in the shortwave; this uncertainty is much smaller than the inter-climate model spread of the feedbacks. However, there exist more significant regional biases in kernel values, due to the dependence of radiative sensitivity on the atmospheric states, and this contributes to more significant radiation non-closure at the regional scale, such as in the Arctic and Southern Ocean regions. On the other hand, we find relatively larger discrepancies in the surface kernels. Although several kernels can achieve as good radiation closure compared to the TOA kernels, affirming the validity of kernel method for the surface radiation budget analysis, the non-closure residual in certain kernels may amount to over 100 % of the total radiation change. The intercomparison of the surface kernels reveals important biases, such as in the radiative sensitivity to air temperature in the lowermost atmospheric layers adjacent to the surface, which is of critical importance to the overall surface feedback strength.