Inference Versus Imprint In Climate Modeling

Abstract

A statistical inference method known as ε-machine reconstruction is introduced as a modeling procedure for turbulent transport processes in a climate model. Observational data on the atmospheric boundary layer obtained with a radar wind profiler, a radio-acoustic sounding system, and a Raman lidar system was assembled to construct this type of model for use within the unresolved (sub-grid) scales of a numerical climate model. An ensemble of 500 single-column model runs using the inferred sub-grid turbulent transport models demonstrated comparable performance to an identical ensemble of runs using the standard, eddy-diffusivity parametrizations for the turbulent transport. The primary advantages of the ε-machine models are that they are a less biased modeling framework for complex processes such as turbulent transport, and that they are more memory efficient.