We report two developments concerning the feedback analysis that is critical for understanding climate sensitivity and its difference in climate models. First, we developed a set of radiative sensitivity kernels for analyzing the radiative feedbacks with regard to the surface and top-of-atmosphere radiation budgets together. This kernel dataset is developed by conducting partial radiative perturbations with multi-years’ EAR-interim reanalysis atmospheric profiles and thus is especially suitable for analyzing the observed climate feedbacks. In addition, we explored a Neural Network (NN) method to estimate climate feedbacks. The NN method can calculate cloud and non-cloud radiative feedbacks directly while accounting for the nonlinearity effects between feedback variables. Comparisons are made between the two methods when applied to analyzing the radiative feedback in inter-annual climate variations.