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https://doi.org/10.5194/egusphere-2025-6549
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-6549
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 Jan 2026
| 13 Jan 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Evaluation of stratospheric transport in three generations of Chemistry-Climate Models
Abstract. The representation of stratospheric transport in Chemistry-Climate Models (CCMs) is key for accurately reproducing and projecting the evolution of the ozone layer and other radiatively relevant trace gases. We evaluate stratospheric transport in CCMs that have participated in three model intercomparison initiatives (CCMVal-2, CCMI-1, and CCMI-2022) over the last ~15 years using modern satellite datasets and reanalyses. Key long-standing model biases persist across generations, with some worsening in recent simulations. Transport remains overly fast in the models, with a global mean age of air young bias of ~1 year for the CCMI-2022 median. It is argued that this bias could be associated with too fast tropical upwelling in the lower stratosphere, insufficient horizontal mixing and/or excessive vertical diffusion. In the springtime southern polar stratosphere, the final warming is delayed (~3 weeks), downwelling is underestimated (~25 %), and the depth of the ozone minimum is overestimated (~10 DU) on average in the most recent models. The tropopause is too high in all generations, and the tropical cold point tropopause is too warm in the latest generation (~1–2 K). Long-term trends in transport and over 1980–1999 are consistent across model generations and highlight the crucial role of ozone depletion in contributing to accelerate the Brewer-Dobson circulation and delaying the southern polar vortex breakdown.
How to cite. Abalos, M., Birner, T., Chrysanthou, A., Davis, S., de la Cámara, A., Dhomse, S., Garny, H., Hegglin, M. I., Hubert, D., Ivaniha, O., Keeble, J., Linz, M., Minganti, D., Neu, J., Plummer, D., Saunders, L., Shah, K., Stiller, G., Tourpali, K., Waugh, D., Abraham, N. L., Akiyoshi, H., Chipperfield, M. P., Jöckel, P., Josse, B., Morgenstern, O., Sukhodolov, T., Watanabe, S., and Yamashita, Y.: Evaluation of stratospheric transport in three generations of Chemistry-Climate Models, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-6549, 2026.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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- RC1: 'Comment on egusphere-2025-6549', Anonymous Referee #1, 17 Feb 2026 reply
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RC2: 'Comment on egusphere-2025-6549', Kris Wargan, 19 Feb 2026
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My comments are in the attachment.
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Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Spain
Thomas Birner
Ludwig-Maximilians-University, Munich, Germany
Andreas Chrysanthou
School of Earth and Environment, University of Leeds, Leeds, UK
Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece
Institute of Geosciences, Spanish National Research Council (IGEO-CSIC), Madrid, Spain
Sean Davis
NOAA Chemical Sciences Laboratory, Boulder, CO USA
Alvaro de la Cámara
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Spain
Sandip Dhomse
School of Earth and Environment, University of Leeds, Leeds, UK
National Centre for Earth Observation (NCEO), University of Leeds, Leeds, UK
Hella Garny
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Michaela I. Hegglin
Institute of Climate and Energy Systems - Stratosphere (ICE-4), Forschungszentrum Juelich, Juelich, Germany
Department of Meteorology, University of Reading, Reading, UK
Institute for Atmospheric and Environmental Research, University of Wuppertal, Wuppertal, Germany
Daan Hubert
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Oksana Ivaniha
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Spain
James Keeble
Lancaster University, Lancaster, UK
Marianna Linz
Reflective Emeryville, CA USA
Daniele Minganti
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Jessica Neu
NASA JPL, Pasadena, CA, USA
David Plummer
Climate Research Division, Environment and Climate Change Canada, Montréal, Canada
Laura Saunders
Department of Physics, University of Toronto, Canada
Kasturi Shah
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
Gabriele Stiller
Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing, Karlsruhe, Germany
Kleareti Tourpali
Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece
Darryn Waugh
Johns Hopkins University, Baltimore, MD, USA
Nathan Luke Abraham
National Centre for Atmospheric Science, UK
Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
Hideharu Akiyoshi
National Institute for Environmental Studies, Tsukuba, Japan
Martyn P. Chipperfield
School of Earth and Environment, University of Leeds, Leeds, UK
National Centre for Earth Observation (NCEO), University of Leeds, Leeds, UK
Patrick Jöckel
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Béatrice Josse
Météo-France, CNRS, Univ. Toulouse, CNRM, Toulouse, France
Olaf Morgenstern
Deutscher Wetterdienst, Offenbach, Germany
Timofei Sukhodolov
Physikalisch-Meteorologisches Observatorium Davos / World Radiation Center, Davos, Switzerland
Shingo Watanabe
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
Advanced Institute for Marine Ecosystem Change (WPI-AIMEC), Tohoku University, Sendai, Japan
Yousuke Yamashita
National Institute for Environmental Studies, Tsukuba, Japan
Short summary
Chemistry-climate models are widely used to understand stratospheric ozone and its interactions with climate. We evaluate the most recent generations of models against modern observations. We find that important long-standing errors remain, and some have increased in recent models. Transported too fast in the stratosphere, and the strong winter circulation around the polar region lasts too long. These results highlight where models must improve to better assess past and future changes.
Chemistry-climate models are widely used to understand stratospheric ozone and its interactions...
The paper by Abalos et al. compares stratospheric transport in three generations of climate models (CCMVal-2, CCMI-1, CCMI-2022) and evaluates different transport characterictics by comparison to satellite observations. The authors show that several long-standing model biases persist across the generations of models, even worsening in the most recent simulations for some transport features. In particular, stratospheric mean age of air is too low indicating too fast transport in the models, and this bias is largest for CCMI-2022. Also, the spring-time polar vortex break-up (final warming) date is delayed, strongest in most recent models, and this appears to be related to an overestimation of the ozone minimum in the simulations. Long-term trends show a robust acceleration of the stratospheric circulation for all model generations, with the trends before 2000 likely related to ozone depletion.
Overall, I find this a great model intercomparison paper which presents and evaluates different sophisticated and detailed transport diagnostics. The paper clearly falls within the scope of the journal and will be of much interest to a broad readership. Moreover, the paper is very well written, the results are concisely presented and the figures are clear and high-quality. In my opinion, particularly the identification of climate model biases and their evolution over different generations of models is urgently needed. And here the paper is doing a great job, pinpointing these biases in a very clear manner and discussing their causes and impacts, to the degree possible within such an extensive intercomparison. In summary, I do strongly recommend publication and have only a few specific and technical comments, which hopefully help to further improve the paper.
Specific comments:
L128ff: From the following two paragraphs it seems important that different merged satellite data products are used for this study. If so, it would be helpful to describe here more clearly the merging techniques, and in particular differences between the data products.
L160: I'd mention already here "... version 3.5/3.6, as proposed by Saunders et al. (2025, 10.5194/acp-25-4185-2025)."
L288ff: The increase in age of air bias for the newest model generation is remarkable. Any ideas/hypotheses regarding potential causes? This could be briefly added here or in the discussion/conclusions section.
Figures 3, 4, 9, 10, 18, 19, 20: The yellow lines for CCMVal-2 are very hard to see in some cases.
L324: I'm somewhat unsure about "The mass flux in our observational references...". Can reanalyses really be seen as observational references for upward mass flux? Since the residual circulation is not directly constrained by data assimilation, and substantial differences in its strength and structure are found among reanalyses (e.g. Abalos et al., 2015, 10.1002/2015JD023182; Fujiwara et al., 2024, 10.5194/acp-24-7873-2024), a brief discussion of these discrepancies could be appropriate here.
L338: "... the reduced spread is a common feature of the newest model generation found across metrics." Isn't Fig. 1 rather showing larger spread in age for the newest models? Please clarify what is meant here.
L353ff: I'm wondering about the comparison between mass flux results in Figs. 3 and 4. Why are relative differences between models different in the two diagnostics? For instance, in Fig. 4 the CCMI-2022 models show strongest upwelling throughout the profile while in Fig. 3 this is not the case in some layers (e.g. below 70hPa). It would be good to provide some further explanation to avoid confusion.
L389: It seems to me that the upwelling at lowest stratospheric levels is for some models faster than in ERA5. So I don't fully see the "consistency" between upwelling and CPT differences mentioned here.
L439: Any idea why the inter-model spread in mixing efficiency changes so much between model generations?
L637: I find the reduced spread in age trends at lower levels for CCMI-2022 particularly interesting. Any idea why?
L650: The CCMI-2022 models here (Fig. 18 b/c) show a flip in the hemispheric difference of age change, with more negative trends before 2000 in the SH and afterwards in the NH (c.f. Strahan et al., 2020, 10.1029/2020GL088567; Ploeger and Garny, 2022, 10.5194/acp-22-5559-2022). Some related discussion of the robustness of hemispheric differences in age trends in different model generations could be interesting.
Figure 19 a-c: Are the reanalysis trends significantly different from zero? Given the usually strong inter-annual variability in reanalysis upwelling, I guess that this is not the case at all levels. Adding a significance indicator to the plot would be helpful for interpretation of differences.
L703: Given the fact that the newest models (CCMI-2022) show only very weak correlation between age and mixing efficiency changes (r=0.25, Fig. 21b), are the results here really robust? A more detailed discussion could be helpful.
Figure 23: For better comparability, I'd find it better to use a common reference period for all datasets used (e.g. 1995-2005).
Technical corrections:
L192: Reference not properly linked.
L284: gin situ --> in situ
L348: Maybe better "satellite-based mean age data"?
Figure 7, caption: add "(black line)" after "comparison".
L517: "...will be discussed..."
L547: "...can imply"
Figure 16: An entry for ACE is missing in the legend.
L679: Is it really meant that differences between profiles in Fig. 20b and Fig. 19b at upper levels are less than 0.5 percent, or is it rather meant that trend values differ by less than 0.5 percent per decade?
L690: Check the wording following "... and there is evidence ..."
L696: "contributions" to what?
L709: Bracket after "Figure 22"
L722: Check wording in "...and this it is likely ..."