4.5 Analyzing the Radiative Feedbacks Using Kernel and Neural Network Methods

Monday, 9 July 2018: 4:30 PM
Regency E/F (Hyatt Regency Vancouver)
Yi Huang, McGill Univ., Montreal, Canada; and T. Zhu

Handout (2.4 MB)

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.

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