A Neural Network Model for Shortwave Radiative Feedback Quantification
AbstractA neural network model is developed to measure shortwave radiative
feedbacks. This model is trained with the atmospheric data of the
fifth-generation European Centre for Medium-Range Weather Forecasts
Reanalysis to emulate the atmospheric shortwave radiation fluxes, which
thus allows direct and computationally efficient evaluation of the
radiative feedbacks following the straightforward perturbation method.
This model is tested for computing the radiative feedbacks of surface
albedo, water vapor and cloud in the Arctic climate change. In both
idealized and real climate change scenarios, the model can accurately
quantify the albedo feedback compared to the truth computed by a
radiative transfer model, while the linear kernel method severely
overestimates the albedo feedback during Arctic warming. The neural
network model can capture the nonlinearity in the surface albedo
feedback and its dependency on the cloud amount.