On using neural networks to predict mean Age-of-Air from long-lived tracers

Abstract. Climate models predict changes in the Brewer-Dobson circulation under a changing climate, which could have profound effects on tracer distributions and the radiative budget. Age-of-Air is an important concept for describing transport in the stratosphere and to understand and quantify global atmospheric circulation patterns such as the Brewer-Dobson circulation. Being an unobservable quantity, it must be inferred from other, directly observable quantities such as long-lived trace gases. It is therefore essential to have accurate ways of determining Age-of-Air through observations. These observations are subject to measurement noise, which is a long-known source of uncertainty when deriving Age-of-Air, as such uncertainties can affect the derived Age-of-Air significantly. We present a novel approach of using neural networks to derive Age-of-Air from long-lived trace gases. Multi-layer perceptrons can be used to predict model Age-of-Air with accuracy as little as a single month. The networks can be optimally trained according to expected measurement uncertainties. An unsupervised autoencoder is presented which is capable of achieving similar predictability almost without relying on model Age-of-Air inputs. This study presents an overview of these new approaches and discusses their capabilities, their accuracies and precisions mostly from a technical perspective regarding input parameters, regularization and predictions outside the training domain. Our approach allows us to derive Age-of-Air with accuracy of up to a single month under considerable measurement noise and over a wide altitude range. This accuracy is even retained when predicting values from a completely different period.