Estimating Climate Feedbacks Using a Neural Network

A nonlinear method has been developed to estimate climate feedbacks based on the Neural Network (NN) taking advantage of its self-learning skills. The NN model developed here is trained using a reanalysis data set and predicts radiation flux globally from atmospheric and surface variables. The radiative feedbacks of temperature, water vapor, surface albedo, and cloud in the interannual climate variations estimated from the NN method are in agreement with those from a broadly used kernel method. However, the NN method demonstrates significant advantages: (1) it withdraws the linearity assumption of the kernel method and accounts for the nonlinear effects of the feedbacks. In the case of large climate perturbations, such as that in the Arctic caused by sea ice melt, the NN method achieves better radiation closure. (2) The method can directly calculate the radiative feedback of cloud and its components. We find that the high, middle, and low cloud feedback components analyzed from the NN method are linearly additive in the interannual climate variations, although there is a considerable nonlinear effect arising from the interactions between cloud and noncloud variables.