Comments on “New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model”

Abstract

The dramatic increase in computer power over the last decades has not fulfilled the ever-increasing needs of the climate and weather sciences. Improving the efficiency of numerical models is still topical. In their paper “New approach to calculation of atmospheric model physics: Accurate and fast neural network emulation of longwave radiation in a climate model,” Krasnopolsky et al. (2005, hereafter KFC05) develop the idea that artificial neural networks could accelerate model physics components in an atmospheric general circulation model (AGCM). As a first step, they apply it to the computation of longwave (LW) cooling rates and fluxes, which usually represent the main computational burden in an AGCM. KFC05 quote the studies previously performed at Laboratoire de Meteorologie Dynamique and at the European Centre for MediumRange Weather Forecasts (ECMWF) (Cheruy et al. 1996; Chevallier et al. 1998, 2000b; Chevallier and Mahfouf 2001). While referring to these earlier works is fair, KFC05 hardly discuss how their “new” method differs from the previous one, nor how the conclusions disagree. I wish to make such a discussion in the present note, as a complement to the KFC05 paper. The following sections successively tackle the method and the prospects.