Articles | Volume 26, issue 8
https://doi.org/10.5194/acp-26-5249-2026
© 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/acp-26-5249-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of stratospheric transport in three generations of Chemistry-Climate Models
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, 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, Emeriville, CA, USA
Daniele Minganti
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Jessica Neu
NASA Jet Propulsion Laboratory, Pasadena, CA, USA
David Plummer
Climate Research Division, Environment and Climate Change Canada, Montréal, Canada
Laura Saunders
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Kasturi Shah
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, 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, Leeds, 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, 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
Marion Marchand
LATMOS, Institut Pierre‐Simon Laplace, Sorbonne Université/CNRS/UVSQ, Paris, France
Patrick Martineau
Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
Olaf Morgenstern
National Institute for Water and Atmospheric Research (NIWA), Te Whanganui-a-Tara / Wellington, Aotearoa New Zealand
School of Physical and Chemical Sciences, University of Canterbury, Ōtautahi / Christchurch, Aotearoa New Zealand
now at: 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
Download
- Final revised paper (published on 21 Apr 2026)
- Supplement to the final revised paper
- Preprint (discussion started on 13 Jan 2026)
- Supplement to the preprint
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
- RC1: 'Comment on egusphere-2025-6549', Anonymous Referee #1, 17 Feb 2026
- RC2: 'Comment on egusphere-2025-6549', Kris Wargan, 19 Feb 2026
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AC1: 'Response to reviewers', Marta Abalos, 14 Mar 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6549/egusphere-2025-6549-AC1-supplement.pdf
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AC2: 'Response to reviewers', Marta Abalos, 14 Mar 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6549/egusphere-2025-6549-AC2-supplement.pdf
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Marta Abalos on behalf of the Authors (14 Mar 2026)
Author's response
Author's tracked changes
Manuscript
ED: Publish subject to technical corrections (23 Mar 2026) by Petr Šácha
AR by Marta Abalos on behalf of the Authors (13 Apr 2026)
Author's response
Manuscript
Editorial statement
Correct simulation of stratospheric transport in Chemistry-Climate Models is a key requirement for their usefulness for future climate prediction. This study reports a comprehensive intercomparison of the simulation of the recent history of stratospheric transport in three generations of Chemistry-Climate Models, validating them against state-of-the-art observational datasets, clearly identifying major biases and discussing potential causes and impacts. The biases persist over model generations and, by some measures, increase for the most recent generation. These results have important implications for the climate science community, including model builders and those seeking to strengthen the underpinning science through process studies and new observations.
Correct simulation of stratospheric transport in Chemistry-Climate Models is a key requirement...
Short summary
Accurate representation of stratospheric transport in Chemistry-Climate Models is essential for reliable climate projections. This study evaluates three generations of models using observational data and reanalyses, identifying persistent biases and their potential causes. Some biases persist or even worsen in newer models. These findings highlight key limitations and inform efforts to improve models and advance understanding through process-based studies and enhanced observations.
Accurate representation of stratospheric transport in Chemistry-Climate Models is essential for...
Altmetrics
Final-revised paper
Preprint
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 ..."