the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jan 2025
Decadal tropospheric ozone radiative forcing estimations with offline radiative modelling and IAGOS aircraft observations
Abstract. We use an offline radiative transfer model driven by IAGOS aircraft observations, to estimate the tropospheric ozone radiative forcing (RF) at decadal time scale (two time intervals between 1994–2004 and 2011–2016 or 2019), over 11 selected Northern Hemispheric regions. On average, we found a systematic positive trend in the tropospheric ozone column (TOC) for both time intervals, even if trends are reduced in 2019 (∆TOC +2.5±1.7 DU, +9.3±7.7 %) with respect to 2011–2016 (∆TOC +3.6±2.0 DU, +14.9±11.5 %). The reduced TOC average trend in 2019 with respect to 2011–2016, originates mostly from decreases of the lower tropospheric ozone column (LTOC) trends and limited variations for upper tropospheric ozone column (UTOC) trends, in the tropics. These average reductions in TOC trends are not accompanied with reductions of the tropospheric ozone RF, between 2011–2016 (4.2±2.4 mW m-2 per year) and 2019 (3.8±3.6 mW m-2 per year). This disconnection depends by the smaller RF sensitivity to LTOC than UTOC changes. Correspondingly, the total tropospheric ozone RF sensitivity varies between 18.4±7.4 mW m-2 per DU, in 2011–2016, and 31.6±20.3 mW m-2 per DU, in 2019. About 84–85 % of the tropospheric ozone RF occurs in the longwave, with ~4–6 % larger values of this proportion in the tropics than in the extra-tropics. Our estimates are 60–90 % larger than the most recent global average tropospheric ozone RF estimates with online modelling. Our study underlines the importance of the evolution of ozone vertical profiles for the tropospheric ozone RF.
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RC1: 'Comment on egusphere-2024-3748', Anonymous Referee #1, 07 Feb 2025
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Radiative forcing estimations based on IAGOS mean tropospheric ozone profiles for different regions are provided with offline radiative modelling, enabling to assess the impact of the variability of the vertical distribution of tropospheric ozone on the radiative forcing. The radiative forcing calculations are important and follow a novel approach, that permits to make the distinction between shortwave and longwave RF for the different regions.
General comments
The manuscript deserves publication in ACP, but there is a need for further specifications or more details on the dataset used, some results (obtained values) should be better explained in comparison with other, previous studies, and some sensitivity analyses could be additionally performed.
The manuscript builds further on the Gaudel et al., 2020 (named G20) study, but some more details of interest for the analysis described here should be given: what were the selection criteria for defining the 11 regions (see Table 1)? Are the observations spatially representative for the defined region? Also, in contrast to the G20 study, in which tropospheric ozone trends are calculated based on Quantile Regression on monthly anomalies, tropospheric ozone decadal changes in this manuscript are estimated from the mean tropospheric ozone profiles for different periods, as shown in Figure 2. However, those mean tropospheric ozone profiles might be very dependent on the spatial and temporal distribution of the IAGOS observations over the region or over the time period. For instance, one period might be dominated by summertime observations, while the other period is mainly characterized by wintertime flights. Or, the large majority of the flights might be situated in the beginning of a time period for one region, but at the end of the time period for another region, making the comparison between the regions less meaningful. Also, during the early time period, most profiles might be originating from take-off/landing at the west side of the region, for instance, but on the east side of the region for one of the later periods. On top of that, there is clear temporal sampling difference between the two earlier periods and the year 2019, which will impact the mean tropospheric ozone profiles over the region as well. The impact of possible differences of the spatial and temporal sampling on the different mean tropospheric ozone profiles should, as a consequence, at least be mentioned or even better, somewhat assessed.
Related to this, I would expect to see also the standard deviations of the mean tropospheric ozone profiles included in Fig. 2, in the average LTOC and UTOC in Figure 3, and in the LT, UT, and T ozone percent differences in Figure 4. Only the uncertainties for the worldwide TOC, LTOC and UTOC differences are provided in the text (page 6) and in Table 2, but it is not mentioned how these uncertainties are obtained (statistical mean over the different regions I assume?).
Based on the standard deviations of the mean tropospheric ozone profiles in Fig. 2, one could perform a sensitivity analysis of the RF estimations on the input mean tropospheric ozone profile for each region. Given the comment on how spatial and temporal representative the mean tropospheric ozone profiles for each region are, this RF estimation sensitivity analysis would add an extra feature to your findings.
The obtained (global) RF estimates are compared with previous studies, but not with the values obtained in G20 (Fig. 6) for exactly the same regions, and one of your 2 periods, but with a different method. Why is this comparison not been made? I found this rather strange. It also turns out that your average values are 60 to 90% larger than previous global average estimates with online models, but no explanations for this rather large offset have been given. The authors should go more in depth on this.
As many studies in the TOAR Special Issue pointed out, there was a decrease of tropospheric ozone column amounts during the COVID-19 period (and still continuing today). Have the authors not considered to quantify the impact of this effect on the RF forcing estimations by including a more recent year(s) than 2019 in their analysis? The authors should make reference to this (post-)COVID impact on tropospheric ozone and comment on their choice.
Specific comments
- Line 13: add “the year 2019”
- Line 45: remove “a” before “tropospheric ozone”
- Line 109: have additionally been
- Fig. 2: apart from adding standard deviations, show the profiles up to 11 km, as the UT and T ozone columns are defined up to 11 km.
- Line 137: “2016” instead of “206”
- Lines 156-157: Just to give an example of my previous comment on the spatial or temporal sampling: How confident are you that the higher LTOC increase for Western North America in 2019 (+12.5%) than in 2011-2016 (+3.5%) compared to 1994-2004, is not due to the fact that the 2019 sample is dominantly made up by summertime months, compared to the 2011-2016 sample?
- Lines 186-189: Where do I have to note that the uncertainty of the decadal trends is increasing? In Table 3? But these are uncertainties over the regions, right? And also the trends themselves are increasing. Please clarify these statements.
- Line 194: 31.7 mW m-2 instead of W m-2
- Line 196: Try to give an explanation for this finding.
Citation: https://doi.org/10.5194/egusphere-2024-3748-RC1
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