| 01 Oct 2025
Quantifying and addressing the uncertainties in tropospheric ozone and OH in a global chemistry transport model
Abstract. The major physical and chemical processes governing the abundance of atmospheric oxidants such as ozone and hydroxyl radicals (OH) are largely understood, but quantitative assessment of their importance in different environments remains challenging. Atmospheric chemistry transport models allow exploration of these processes on a global scale, but weaknesses in process representation in these models introduces uncertainty, and model intercomparisons show considerable diversity even in representing current atmospheric composition. Formal constraint of models with atmospheric observations is needed to provide more critical insight into the causes of model weaknesses. In this study we perform a global sensitivity analysis on a chemistry transport model using Gaussian process emulation and identify the processes contributing most to uncertainty in tropospheric ozone and OH. We then explore the use of atmospheric measurements to calibrate the model and identify weaknesses in process representation and understanding. We find that the largest uncertainties are associated with photochemical kinetic data and with factors governing photolysis rates and surface deposition. Calibration constrains the uncertainty in key processes, informing model development and improving comparisons with observations, but we show that it is also valuable in identifying structural errors in models. We show that surface ozone measurements alone provide insufficient constraint, and we highlight the importance of applying a broad range of different observational metrics. While this study is exploratory in nature, focussing on a limited number of constraints, we clearly demonstrate the value of rigorous calibration for providing important new insight into key processes and their representation in atmospheric models.
Review of egusphere-2025-4534
Wild & Ryan
“Quantifying and addressing the uncertainties in tropospheric ozone and OH in a global chemistry transport model”
This thoughtful and novel study by Wild and Ryan centers on using a chemistry-transport model to understand how the uncertainties in our current knowledge of the physics and chemistry in the model translate into uncertainties in the model generated tropospheric O3 and OH (the major sink of CH4). The current state of understanding is readily seen in the recent model intercomparison projects (MIPs), where the budgets for O3 and CH4 show wide spreads. So, yes, this is an important area of study for both air quality and climate.
The authors perform an intense series of objective sensitivity simulations with the CTM involving 60 some identified uncertainties and reduce this to 36 for more serious study. This is the most thorough such analysis that this reviewer has seen. The authors then use “Gaussian process emulators” to further study the full range of coupled uncertainties in order to map out cost functions for matching some key large-scale observational constrains (surface and 500 hPa O3, total tropospheric O3 burden, CH4 lifetime).
This work is a valuable contribution to the community’s understanding of the how and why our models do not always give us what we want. The only major problem is the denseness of the manuscript and the difficulty in keeping track of the specific uncertainties. I have the following suggestions for minor rewrites.
Starting with Table 1. This table provides the core data and assumptions used here. The problem is that the reader has little chance of remembering all the 3 letter abbreviations later in the paper. Can the authors come up with a reasonable, abbreviated table for the 36 key uncertainties that could be included in the legend of some of the later figures? Shorthand examples:
fir BB all
iso biogenic C5H8
hna Henry’s HNO3
xfm X-section HCHO
Some of the paragraphs are very dense and difficult to follow. Would it be possible to segment these, maybe with bulleted points to give us the sequence of the logic?
L5: “even when we all agree on the physical or chemical rate constants” Yes, indeed, the diversity is distressing.
L39: But these studies did not look at photochemistry since they did not do cross sections!
L112: Yes, it is good you are dealing with uncertainties in the numbers we often assume are fixed.
L160: Thanks for doing the cross sections.
L163: is it good to see a clear definition of you uncertainty ranges. You might also note than when you report an uncertainty factor, it is boeh / and x, effectively you are treating the internal range as lon-normal??
L172 of the 2-sigma (95% range) uncertainty. Your uncertainty prob distrib is not stopped at 2 sigma.
Fig. 1: I really like Fig. 1 and would expect near perfect symmetry in the up/down perturbations, but items like VOC emissions are asymmetric, presumably because you did log normal with a large factor. You should comment on that.
L255: I am a bit worried about the apportionments based on log-normal ranges/
Fig 2: This really needs a legend with a somewhat expanded explanation for the 3 letter designations.
Fig 3: ditto
Fig.7: Ditto. This paddle figure is challenging for me, but OK
L325ff: You are worried about the vertical resolution in comparing surface O3, but I think the observations are MDA8 or daytime and so a thin nocturnal BL is not a problem. I would be more worried about the horizontal resolution because you cannot resolve urban centers.
L341ff: This is an interesting statement about structural errors. We all certainly have them, But, do they apply in a similar way to all models/
Fig 7: What is the color code here? More important, why does everything seem to be pushed away from the prior?
L411: Nice case study, but too bad it did not work out better.
Fig 8: Again, why is everything (except ddf & ddg) pushed far off the prior? Some discussion.
L476ff: Why not do trop O3 burden as NH and SH, since there are distinct differences with separate causes presumably
L482: Note that errors in J-NO2 would do the same thing?
L492: Great, major conclusion!