Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5)

Abstract

Abstract. Radiative sensitivity, i.e., the response of the radiative flux to climate perturbations, is essential to understanding climate change and variability. The sensitivity kernels computed by radiative transfer models have been broadly used for assessing the climate forcing and feedbacks for 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 (ERA5), we produce a new set of radiative kernels for both the TOA and surface radiative fluxes, which is made available at https://doi.org/10.17632/vmg3s67568 (Huang and Huang, 2023). By comparing these with other published radiative kernels, we find that the TOA kernels are generally 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 % of the total feedback, no matter which kernel dataset is used. The uncertainty in the TOA feedbacks caused by inter-kernel differences, as measured by the standard deviation of the global mean feedback parameter value, is much smaller than the inter-climate model spread of the feedback values. However, we find relatively larger discrepancies in the surface kernels. The newly generated ERA5 kernel outperforms many other datasets in closing the surface energy budget, achieving a radiation closure comparable to the TOA feedback decomposition, which confirms the validity of the kernel method for the surface radiation budget analysis. In addition, by investigating the ERA5 kernel values computed from the atmospheric states of different years, we notice some apparent interannual differences, which demonstrates the dependence of radiative sensitivities on the mean climate state and partly explains the inter-dataset kernel value differences. In this paper, we provide a detailed description of how ERA5 kernels are generated and considerations to ensure proper use of them in feedback quantifications.