| 30 Jul 2026
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.
This paper discusses the use of neural networks to produce stratospheric mean age of air from a set of long-lived trace gases. I know very little about neural networks so I can’t evaluate if the methods used are the best suited for this task. But the basic techniques are well described and at a level that a non-expert can at least gain an understanding of how they are used. Neural networks are certainly a powerful data analysis tool and it’s great to see how they can be implemented in the context of AoA, at least for model output.
As the authors mention, the estimation of AoA from trace gas measurements is not fully constrained and involves certain assumptions and corrections to the data. While measurement uncertainty, or noise in the context of the neural network, is emphasized in this study, I think this is only one of a number of important aspects of how well we can calculate AoA. Ideally, the use of a neural network can provide an increased robustness to the conversion of multiple trace gas distributions to AoA distributions. I’m not necessarily convinced by this paper that the neural network technique can improve on existing AoA estimates from trace gases but it’s important to explore this possibility and hope to improve the calculations in the future.
I realize this paper is an initial discussion of the use of neural networks to calculate AoA from model output but I was hoping for some exploration of the possibilities and potential issues with the use of a more limited observational dataset. Sparse sampling, missing data or fewer different trace gases measured are examples of the complications involved with a measurement dataset. A new satellite data set is mentioned and a brief description of how to use in situ measurements near the tropopause to help calibrate the neural network, although that section wasn’t entirely clear to me.
In general, I support the publication of this paper with consideration of the minor comments listed below. The methods are described well and the results are clearly explained. Some of the figures could use better labels to help the reader as mentioned below.
Specific comments:
Line 387: change ‘inherent’ to ‘inherit’
Line 390: Do you mean ‘zonal’ here or ‘meridional’?
Fig. 8: The y-scale is too large to see differences between these plots. They also should be labeled differently so it’s easy to see what they represent rather than ‘Performances 2011’ on each one. For instance, 8a could be labeled ‘Trained with time dependence’, 8b ‘Trained with latitude as a feature’, etc.
Lines 436-7: Some missing parentheses and the e.g. statement is awkward.
Lines 438-9: I assume this is referring to the chemistry climate model predictions of AoA and the trends. I’m not sure about such a broad statement and what ‘relying on new observations’ means in this context. Mostly free running CCMs with greenhouse gas emission trends predict a decreasing trend of AoA. The emission trend could be called new observations but I don’t think that’s what’s referred to here.
Lines 440-50: I would expect some of this variability in performance to be due to the phase of the QBO relative to the seasonal cycle. The QBO has a significant impact on AoA and each cycle can be phased somewhat differently at different levels with the seasonal cycle. This could be checked fairly easily.
Line 567: ‘monotonic’ instead of ‘monotonous’
Line 589: ‘dependent’ instead of ‘depending’
Fig. 15: Again, some headers on the plots, in this case the year would be appropriate, would help the reader.
Line 612: ‘observationally-derived AoA’