Consistency Between Zonal Mean Stratospheric and Total Column Ozone Trends (2000&ndash;2024)

Auffarth, Brian; Weber, Mark; Rozanov, Alexei; Arosio, Carlo; Burrows, John P.; Coldewey-Egbers, Melanie; Davis, Sean M.; Degenstein, Doug; Dubé, Kimberlee; Frith, Stacey M.; Froidevaux, Lucien; Loyola, Diego; Fioletov, Vitali E.; Sofieva, Viktoria; van der A, Ronald; Wild, Jeannette D.

doi:10.5194/egusphere-2026-2576

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https://doi.org/10.5194/egusphere-2026-2576

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11 May 2026

| 11 May 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Consistency Between Zonal Mean Stratospheric and Total Column Ozone Trends (2000–2024)

Brian Auffarth, Mark Weber, Alexei Rozanov, Carlo Arosio, John P. Burrows, Melanie Coldewey-Egbers, Sean M. Davis, Doug Degenstein, Kimberlee Dubé, Stacey M. Frith, Lucien Froidevaux, Diego Loyola, Vitali E. Fioletov, Viktoria Sofieva, Ronald van der A, and Jeannette D. Wild

Abstract. This study presents an updated assessment of stratospheric and total column ozone trends over the 2000–2024 period using six merged limb-profile datasets and six merged total ozone datasets. Long-term changes were quantified using a multiple linear regression framework that accounts for dynamical and chemical variability. In addition to standard regressors (solar cycle, QBO, ENSO, stratospheric aerosol optical depth), we include Arctic and Antarctic Oscillation indices and the eddy heat flux in each hemisphere as proxies for dynamic variability. Volcanic (and wildfire) aerosol forcing is represented by separate proxies for three periods dominated by the major volcanic events of El Chichón, Pinatubo, and post-2000 volcanic eruptions, including Hunga-Tonga. These period-specific proxies are employed to better account for varying dynamical ozone responses that largely depend on the season and location of the eruptions. All profile datasets consistently show positive trends in the upper stratosphere, with the strongest ozone recovery in southern mid-latitudes, in agreement with other studies. In the lower stratosphere, trends remain weak, spatially heterogeneous, and predominantly negative. A comparison of stratospheric column trends derived from profile data with total ozone trends shows close agreement across latitude bands. Within the trend uncertainties, total column trends since 2000 are largely driven by stratospheric ozone changes, while tropospheric contributions to zonal-mean total ozone trends (the difference between total and stratospheric column trends) appear negligible. The extended regression framework improves the representation of recent dynamical variability and provides an updated perspective on stratospheric ozone recovery through 2024.

Received: 04 May 2026 – Discussion started: 11 May 2026

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RC1:
'Comment on egusphere-2026-2576', Anonymous Referee #1, 20 May 2026 reply
General Comments
This manuscript provides an updated assessment of ozone trends during the 2000–2024 period using several merged ozone profile and total column ozone datasets together with a multiple linear regression (MLR) technique. The results show that the MLR can better capture dynamical variability in ozone when additional dynamical regressors are included, especially for the SH polar region in September. The analyses with additional dynamical regressors also indicate more pronounced ozone recovery in the SH lower stratosphere, even though the positive trends remain within the uncertainty range (2σ) of the results obtained without the additional dynamical regressors.
The topic is relevant to the scientific questions within the scope of ACP. The approach and methodology are sound. There are, however, several minor issues and questions that should be addressed or clarified. After these issues are resolved, I strongly recommend publication.
Specific Comments
Page 4, Figure 1: The instrument datasets listed for creating GOZCARDS are SAGE-I, SAGE-II, HALOE, and Aura MLS. However, UARS MLS and ACE-FTS were also included in the GOZCARDS ozone database according to Lucien et al. (2015) (reference listed in Table 3). Please verify whether these two instruments were unintentionally omitted from Figure 1 or whether they are no longer included in the latest version of the dataset.

Page 5, Figure 2: OMPS-NP data have been used in several merged datasets, such as SBUV/NASA (MOD), SBUV/NOAA (COH), and MSR-2. For clarification, more detailed information should be provided to distinguish the different OMPS-NP satellites. For example, it would be better to replace “OMPS-NP” with “OMPS-NP (Suomi-NPP)” for the SBUV/NASA (MOD) dataset. More detailed information should also be provided for the “OMPS” instrument used in MSR-2.

Page 6, Table 1: Similar to comment #2, more detailed information is needed for “OMPS” in the instrument column for MSR-2. In addition, for NOAA/SBUV (COH), the OMPS-NP instrument from NOAA-20 is missing in Table 1, although it appears in Figure 2.

Page 8, Figure 8: It is not clear from either the figure caption or the text in Section 3.1 what “reference” value was used to define the differences before calculating the trends in those differences. In other words, was the reference based on MLS Level-3 or Level-2 data (e.g., by first converting individual profiles)? This information would help readers better interpret the sign of the trends in the differences and the impact of coordinate conversion on the derived ozone trends.

Lines 153-154, “It ranges from 0.5 to 0.75%/decade between 60⁰S and 60⁰N and above 20km”. This statement is applicable for 2000 to 2024 period, but not for 1985 to 1995 period (Figure 4a). The sentence should be revised for clarity.

Lines 156-157, “and the results are shown for the period after 1996 (ODS peak) and 2000 in Figure 5.” This sentence implies that the total ozone data periods shown in Figure 5 are 1996–2024 and 2000–2024 (red and blue lines). However, the Figure 5 caption indicates “until 1995 and since 2000” (i.e., 1985–1995 and 2000–2024). Please clarify the wording for consistency.

Page 10, Equation (2), there are typos in the “inflection time” corresponding to the linear trend coefficients, β13(z,t)(t-t1), and β16(z,t)(t-t2). The terms t1, and t2 should be corrected to t0, and t1, respectively.

Lines 174-175, A special term, L_Gap(t), is used for the 1996-1999 period due to “the sparse sampling of the merged ozone profile datasets”. During this period, the main contributing satellite measurements came from SAGE II and HALOE (see Figure 1). Since there were no major volcanic eruptions (Figure A1) or extended periods of instrument problems during that time, could the authors elaborate further on the cause of sparse sampling, and did the sparse measurements mainly occur in specific latitude or altitude regions?

Page 14, Figure 6. The regression coefficients of the proxy terms are shown as functions of latitude and altitude, but there is no information regarding the statistical significance of those coefficients. It would be helpful to use hatching to indicate insignificant coefficients (similar to Figure 3). This would provide additional insight into the remarkably different impacts of the three volcanic aerosol proxies (Figure 6i, j, k). A related question concerns Figure 6k, the El Chichon aerosol proxy appears to result in ozone increases in the mid-latitude lower stratosphere (below 20km). Is this feature an artifact of MLR fit or has it been reported in previous studies?

Line 285, “SH trends are lower and remain at about +0.5DU/decade”. The trend value appears to be a typo. It should be approximately 1 to 1.5 DU/decade (or ~0.5 %/decade).

Line 354, “in % per decade Fig. A7) can can be compared”. Please correct the typos, for example, “in % per decade (Fig. A7) can be compared”.

Lines 359-360, “The larger variations in SH trend profiles, ….. is due to the smaller positive trends seen in GOZCADRS compared to all other merged datasets.” The smaller positive GOZCARDS trends mainly occur above 25 km. Below 20 km the larger variations in trend profiles are mainly associated with SAGE-SCIA-OMPS and SAGE-OSIRIS-OMPS. For clarification, I recommend revising the sentence to specify the altitude range, for example, “The larger variations in SH trend profiles for altitudes above 25 km, …, is due to the smaller positive trends seen in GOZCARDS….”.

Lines 417-418, “For the stratospheric column calculations, data were screened out if more than three of the five layers above the tropopause had missed data”. Conventionally there should be no missing data above the tropopause when calculating stratospheric column ozone. Which five layers were used for screening criteria, and how were the missing data treated (e.g. interpolation) before calculating the stratospheric column ozone?

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Citation: https://doi.org/10.5194/egusphere-2026-2576-RC1
RC2:
'Comment on egusphere-2026-2576', Anonymous Referee #2, 11 Jun 2026 reply
Based on (pre-dominantly) satellite ozone retrievals of total ozone and stratospheric (profile) ozone, the study calculates global trends since 2000 using the standard LOTUS and a more enhanced MLR. A rather consistent global picture of (total) ozone recovery trends is found, with the strongest recovery at SH high latitudes. These positive total ozone trends are mostly driven by mainly positive stratospheric ozone trends, except in the lower stratosphere, where weak and pre-dominantly negative trends are obtained. I really congratulate the authors for this very thorough and detailed study, clearly written. The authors tackle almost all related topics, such as the trend differences between both MLR models, different trend estimation approaches, relative drifts, ozone unit conversion from L2 vs. L3 data, stratospheric ozone trends calculated from stratospheric ozone columns vs. integrating ozone layer trends, impact of tropospheric ozone trends on total ozone trends, etc.
The study will be very important for the next WMO Ozone Assessment report, and I would therefore recommend the (quick) publication of the manuscript, after providing some feedback to some minor points I want to raise:
perhaps add in the title that trends are (predominantly) calculated from satellite ozone retrievals

line 60 ends with two full stops.

lines 136-138: “At 25 km, a negative trend of around 1 % per decade between April and August in the northern hemisphere (NH) and between January and July in the southern hemisphere (SH) is noted. The significance of these trends gets smaller with altitude, showing the most significant trends at 25 km.” Give a possible reason/explanation for this finding.

Section 3.2: can you make some (general) conclusions on the relative drifts of the individual merged datasets w.r.t. the MIM? This might be interesting to link to some “deviating” trends that were found for some of the datasets (e.g. the ones containing OSIRIS) in Fig. 13.

In Figs. 4 and 5, you show the drift uncertainties for two different time periods, but you don’t mention why these different time periods are considered and if the drift uncertainties are different between those two time periods. If you include it, describe it!

line 158: the highest total ozone drift uncertainties can be seen at high latitudes and in the tropics. Why is that? Again due to the higher uncertainties in the total ozone measurements there? Explain!

lines 238-245: I’m not very familiar with the concept of accumulating proxies over their dynamically active season and could not find how it is done in earlier studies (Weber et al., 2018, 2022). How is it done in practice? For example, a winter accumulated proxy is obtained by using the original Dec value as the Dec MM, the original Dec value + the original Jan value as the Jan MM, etc?

line 266: add a right bracket after Fig. A1

lines 270-272: it’s a good idea to run the MLR model over the entire time period available, also when only calculation trends from 2000-2024. But I was wondering if the trends would differ significantly if only the 2000-2024 period would be regressed? Have you tried this sensitivity experiment?

Fig. 7: can you make the so-called thick lines thicker? They are hard to distinguish from the thin lines on my printout.

Fig. 8: The dataset lines are very hard to see.

Fig. 9, caption: the climatological thermal tropopauses are marked by full lines, not dashed lines. You might make those full lines thicker as well.

Lines 360-361: any explanation for the smaller SH positive trends seen in GOZCARDS, which seem to get closer to the trends of the other data when the MLR is applied on deseasonalized ozone data?

Line 362 and caption Fig. 10: the joint-distribution uncertainties are marked by full lines (not dashed lines)

Fig. 12: can the thick lines be made thicker?

Lines 393-396 and 405-406: why are the trends from the datasets containing OSIRIS stronger than trends from other merged datasets? What’s wrong with OSIRIS? Are these datasets also drifting from the MIM (see remark on section 3.2).

Caption Fig. 13: all error bars are full lines, not dashed or dotted on my printout.

line 418: how are the five layers above the tropopause defined?

line 473 ends with two full stops.

Although the paper is predominantly based on satellite ozone retrievals, I somewhat miss the link with trends calculated from ground-based datasets. For the total ozone trends, you could point out if and where the satellite-retrieved total ozone trends differ from the WOUDC trends. And for the stratospheric (profile) ozone trends, you might compare those with the trend estimates appearing in Jonas et al., ACP, 2026 (https://doi.org/10.5194/acp-26-8089-2026) and Mirallie et al., egusphere, 2026 (https://doi.org/10.5194/egusphere-2026-113).

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Citation: https://doi.org/10.5194/egusphere-2026-2576-RC2

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Short summary

This study examines the trends of stratospheric and total ozone between 2000 and 2024, using long-term satellite datasets. Our results show positive trends in the upper stratosphere and a strong ozone recovery in the Southern Hemisphere, while changes in lower altitudes remain mostly small. The total and stratospheric trends show very similar results, indicating that tropospheric ozone contributes little to total column changes, while the stratospheric column is the dominant driver.


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