Articles | Volume 22, issue 11
https://doi.org/10.5194/acp-22-7523-2022
© Author(s) 2022. 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-22-7523-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jun 2022
Research article |
| 10 Jun 2022
| 10 Jun 2022
The roles of the Quasi-Biennial Oscillation and El Niño for entry stratospheric water vapor in observations and coupled chemistry–ocean CCMI and CMIP6 models
Department of Physics, Ariel University, Ariel, Israel
Eastern R&D Center, Ariel, Israel
Chaim I. Garfinkel
The Fredy and Nadine Herrmann Institute of Earth Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
Sean Davis
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Antara Banerjee
NOAA Chemical Sciences Laboratory, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA
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Michael D. Himes, Natalya A. Kramarova, Krzysztof Wargan, Sean M. Davis, and Glen Jaross
Atmos. Meas. Tech., 19, 3601–3624, https://doi.org/10.5194/amt-19-3601-2026, https://doi.org/10.5194/amt-19-3601-2026, 2026
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Stratospheric water vapor (SWV) influences various atmospheric processes. While the Ozone Mapping and Profiler Suite Limb Profiler (OMPS LP) was not designed to measure SWV, we utilized near-coincident measurements by the Aura Microwave Limb Sounder (MLS) and OMPS LP to develop a machine learning method to measure SWV between 11.5–40.5 km. The LP-derived SWV closely agrees with MLS. Our results suggest OMPS LP can continue the global water vapor record following the MLS mission.
Dong-Chan Hong, Seok-Woo Son, Blanca Ayarzagüena, Amy H. Butler, Chaim I. Garfinkel, Peter Hitchcock, Yu-Kyung Hyun, and Jiankai Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2026-2798, https://doi.org/10.5194/egusphere-2026-2798, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
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This study investigates how Sudden Stratospheric Warming (SSW) influences surface climate. By comparing multi-model simulations, we isolated and quantified the role of SSWs. Results reveal that poleward mass transport during SSWs induces high pressure over the Arctic, driving changes in extratropical circulations. While SSWs alter the troposphere, chaotic internal weather variability can amplify or suppress their influence, explaining the differing surface impacts following SSWs.
Viktoria F. Sofieva, Monika E. Szelag, Natalya A. Kramarova, Robert Damadeo, Wolfgang Steinbrecht, Irina Petropavlovskikh, Corinne Vigouroux, Eliane Maillard Barras, Daniel Zawada, Kleareti Tourpali, Stacey M. Frith, Jeannette D. Wild, Sean M. Davis, Carlo Arosio, Mark Weber, Alexei Rozanov, Brian Auffarth, Lucien Froidevaux, Ryan Fuller, Doug Degenstein, Kimberlee Dube, Peter Effertz, Thierry Leblanc, Gérard Ancellet, Sophie Godin-Beekmann, Glen McConville, Richard Querel, Dan Smale, Marie-Renee DeBacker, Emmanuel Mahieu, and Ralf Sussmann
Atmos. Chem. Phys., 26, 7387–7405, https://doi.org/10.5194/acp-26-7387-2026, https://doi.org/10.5194/acp-26-7387-2026, 2026
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We present an updated evaluation of stratospheric ozone profile trends in the 60°S–60°N latitude range using long-term ground-based and satellite climate data records, as well as simulations by chemistry-climate models. Analyses confirm the statistically significant positive ozone trends in the upper stratosphere of ~1–3 % decade-1. The trends are close to zero in the middle stratosphere, and mostly negative in the lower stratosphere, but they are not statistically significant.
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
EGUsphere, https://doi.org/10.5194/egusphere-2026-2576, https://doi.org/10.5194/egusphere-2026-2576, 2026
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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.
Qian Lu, Jian Rao, Chunhua Shi, and Chaim I. Garfinkel
Atmos. Chem. Phys., 26, 5763–5780, https://doi.org/10.5194/acp-26-5763-2026, https://doi.org/10.5194/acp-26-5763-2026, 2026
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Stratospheric water vapor has an impact on global temperature changes. Tropical stratospheric water vapor exhibits a clear imprint of the Quasi-Biennial Oscillation (QBO). This study compares the water vapor variations associated with the QBO between boreal winter and summer, and the seasonal difference in the stratospheric water vapor distribution under different QBO phases is revealed.
Marta Abalos, Thomas Birner, Andreas Chrysanthou, Sean Davis, Alvaro de la Cámara, Sandip Dhomse, Hella Garny, Michaela I. Hegglin, Daan Hubert, Oksana Ivaniha, James Keeble, Marianna Linz, Daniele Minganti, Jessica Neu, David Plummer, Laura Saunders, Kasturi Shah, Gabriele Stiller, Kleareti Tourpali, Darryn Waugh, Nathan Luke Abraham, Hideharu Akiyoshi, Martyn P. Chipperfield, Patrick Jöckel, Béatrice Josse, Marion Marchand, Patrick Martineau, Olaf Morgenstern, Timofei Sukhodolov, Shingo Watanabe, and Yousuke Yamashita
Atmos. Chem. Phys., 26, 5249–5291, https://doi.org/10.5194/acp-26-5249-2026, https://doi.org/10.5194/acp-26-5249-2026, 2026
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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.
Chaim I. Garfinkel, Tiffany Shaw, Benny Keller, Edwin P. Gerber, Ian P. White, Martin Jucker, Wuhan Ning, Ori Adam, and Siming Liu
EGUsphere, https://doi.org/10.5194/egusphere-2026-1767, https://doi.org/10.5194/egusphere-2026-1767, 2026
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Midlatitude storm tracks are stronger over ocean basins than continents, and also stronger in the Southern Hemisphere than in the Northern Hemisphere. It is still not clear how Earth's land-ocean distribution, ocean heat transport, and orography, set up this structure. We use an intermediate complexity moist general circulation model to reveal substantial non-additivities in the response to these inhomogeneities, and then diagnose why.
Meghan Brehon, Susann Tegtmeier, Adam Bourassa, Sean M. Davis, Udo Grabowski, Tobias Kerzenmacher, and Gabriele Stiller
Atmos. Chem. Phys., 26, 3743–3764, https://doi.org/10.5194/acp-26-3743-2026, https://doi.org/10.5194/acp-26-3743-2026, 2026
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We used observations of water vapour to estimate vertical transport rates in the tropical stratosphere for 1995-2020 and analyze stratospheric variability. Our results find good agreement between our observation-based estimates and reanalysis upwelling and reveal that the variability is mainly driven by the Quasi-Biennial Oscillation (QBO) and El Niño Southern Oscillation (ENSO) with a clear signal in the upwelling time series coinciding with the recent QBO disruptions of 2015/16 and 2019/20.
Chaim I. Garfinkel, David Avisar, Scott M. Osprey, Doug Smith, Jian Rao, and Jonathon S. Wright
EGUsphere, https://doi.org/10.5194/egusphere-2026-759, https://doi.org/10.5194/egusphere-2026-759, 2026
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The Quasi-biennial Oscillation (QBO) dominates variability in the tropical stratosphere, & it impacts surface climate in several parts of the world. However, climate models have been shown to systematically under-estimate the influence of the QBO. Here, we re-evaluate this finding using much larger ensemble sizes than have been previously available based on four separate models. We find that the models are comparatively more successful in capturing QBO influences than reported by previous work.
Sean Davis, William Ball, Yue Jia, Gabriel Chiodo, Justin Alsing, James Keeble, Hideharu Akiyoshi, Carlo Arosio, Ewa Bednarz, Andreas Chrysanthou, Melanie Coldewey-Egbers, Robert Damadeo, Sandip Dhomse, Mohamadou Diallo, Simone Dietmuller, Roland Eichinger, Stacey Frith, Birgit Hassler, Michaela Hegglin, Daan Hubert, Patrick Jöckel, Béatrice Josse, Natalya Kramarova, Diego Loyola, Eliane Maillard Barras, Marion Marchand, Olaf Morgenstern, David Plummer, Robert Portmann, Karen Rosenlof, Alexei Rozanov, Viktoria Sofieva, Johannes Staehelin, Timofei Sukhodolov, Kleareti Tourpali, Ronald Van der A, H. J. Ray Wang, Krzysztof Wargan, Shingo Watanabe, Mark Weber, Jeannette Wild, Yousuke Yamashita, and Jerry Ziemke
EGUsphere, https://doi.org/10.5194/egusphere-2026-532, https://doi.org/10.5194/egusphere-2026-532, 2026
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This study investigates how tropical ozone levels have changed since 2000 in chemistry climate models and satellite observations to determine how well they agree with one another, and to see if current trends can help predict future levels. At some, satellite records disagree significantly on the magnitude of ozone changes. The study shows a connection between recent ozone trends and future ozone levels, suggesting that satellite measurements could help constrain future ozone changes.
Blanca Ayarzagüena, Amy H. Butler, Peter Hitchcock, Chaim I. Garfinkel, Zac D. Lawrence, Wuhan Ning, Philip Rupp, Zheng Wu, Hilla Afargan-Gerstman, Natalia Calvo, Alvaro de la Cámara, Martin Jucker, Gerbrand Koren, Daniel De Maeseneire, Gloria L. Manney, Marisol Osman, Masakazu Taguchi, Cory Barton, Dong-Chan Hong, Yu-Kyung Hyun, Hera Kim, Jeff Knight, Piero Malguzzi, Daniele Mastrangelo, Jiyoung Oh, Inna Polichtchouk, Jadwiga H. Richter, Isla R. Simpson, Seok-Woo Son, Damien Specq, and Tim Stockdale
Weather Clim. Dynam., 7, 411–437, https://doi.org/10.5194/wcd-7-411-2026, https://doi.org/10.5194/wcd-7-411-2026, 2026
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Sudden Stratospheric Warmings (SSWs) are known to follow a sustained wave dissipation in the stratosphere, which depends on both the tropospheric and stratospheric states. However, the relative role of each state is still unclear. Using a new set of subseasonal to seasonal forecasts, we show that the stratospheric state does not drastically affect the precursors of three recent SSWs, but modulates the stratospheric wave activity, with impacts depending on SSW features.
William J. M. Seviour, Justin Finkel, Philip Rupp, Regan Mudhar, Amy H. Butler, Chaim I. Garfinkel, Peter Hitchcock, Blanca Ayarzagüena, Dong-Chan Hong, Yu-Kyung Hyun, Hera Kim, Eun-Pa Lim, Daniel De Maeseneire, Gabriele Messori, Gerbrand Koren, Michael Sigmond, Isla R. Simpson, and Seok-Woo Son
EGUsphere, https://doi.org/10.5194/egusphere-2026-230, https://doi.org/10.5194/egusphere-2026-230, 2026
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Variability of the stratospheric polar vortex is thought to play a role in driving weather extremes, but quantifying this role for a given event has proved challenging. Using a new set of perturbed subseasonal forecast experiments from 7 modelling centres we determine the stratospheric contribution to the risk and severity of three recent extreme weather events. The forecast-based methodology that we develop is applicable to understanding a range of other drivers of weather extremes.
Wuhan Ning, Chaim I. Garfinkel, Judah Cohen, Ian P. White, and Jian Rao
Weather Clim. Dynam., 7, 277–295, https://doi.org/10.5194/wcd-7-277-2026, https://doi.org/10.5194/wcd-7-277-2026, 2026
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Whether the zonal structure of a polar vortex matters for surface climate is an open question, with much observational work showing a role but with limited modeling work and demonstration of a causal influence. Here, we isolate this influence using a moist general circulation model. We find that the surface responses differ qualitatively depending on the zonal asymmetries of the shifted polar vortex and concurrently occurring wave reflection events, and provide a mechanistic explanation for why.
Nigel A. D. Richards, Natalya A. Kramarova, Stacey M. Frith, Sean M. Davis, and Yue Jia
Atmos. Meas. Tech., 19, 529–547, https://doi.org/10.5194/amt-19-529-2026, https://doi.org/10.5194/amt-19-529-2026, 2026
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The Montreal Protocol has led to a slow recovery in the Earth's ozone layer. To detect such changes, and to monitor the health of the ozone layer, long term global observations are needed. The OMPS Limb Profiler (LP) series of satellite sensors are designed to meet this need. We validate the latest version OMPS LP ozone profiles against other satellite and ground based measurements. We find that OMPS LP ozone is consistent with other data sources and is suitable for use in ozone trend studies.
Cristiana Stan, Saisri Kollapaneni, Andrea M. Jenney, Jiabao Wang, Zheng Wu, Cheng Zheng, Hyemi Kim, Chaim I. Garfinkel, and Ayush Singh
Geosci. Model Dev., 18, 7969–7985, https://doi.org/10.5194/gmd-18-7969-2025, https://doi.org/10.5194/gmd-18-7969-2025, 2025
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The diagnostics package is an open-source Python software package used for evaluating the Madden–Julian Oscillation teleconnections to the extratropics, as predicted by subseasonal-to-seasonal (S2S) forecast systems.
Mona Zolghadrshojaee, Susann Tegtmeier, Sean M. Davis, Robin Pilch Kedzierski, and Leopold Haimberger
Atmos. Chem. Phys., 25, 12213–12232, https://doi.org/10.5194/acp-25-12213-2025, https://doi.org/10.5194/acp-25-12213-2025, 2025
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The tropical tropopause layer (TTL) is a crucial region where the troposphere transitions into the stratosphere, influencing air mass transport. This study examines temperature trends in the TTL and lower stratosphere using data from weather balloons, satellites and reanalysis datasets. We found cooling trends in the TTL from 1980 to 2001, followed by warming from 2002 to 2023. These shifts are linked to changes in atmospheric circulation and impact water vapour transport into the stratosphere.
David Avisar and Chaim I. Garfinkel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4287, https://doi.org/10.5194/egusphere-2025-4287, 2025
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We use the Large Ensemble Single Forcing simulations to assess the role of individual forcings to the Mediterranean drying and to clarify the dynamical origin of the model’s prediction variability. A more pronounced North Atlantic warming hole, a stronger stratospheric polar vortex, and a larger poleward shift of the subtropical jet correlate with a stronger drying trend. Aerosols had a detectable influence on Mediterranean climate. Hence, their removal may have an impact in future decades.
Shenglong Zhang, Jiao Chen, Jonathon S. Wright, Sean M. Davis, Jie Gao, Paul Konopka, Ninghui Li, Mengqian Lu, Susann Tegtmeier, Xiaolu Yan, Guang J. Zhang, and Nuanliang Zhu
Atmos. Chem. Phys., 25, 10109–10139, https://doi.org/10.5194/acp-25-10109-2025, https://doi.org/10.5194/acp-25-10109-2025, 2025
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Swirling above summer storms, the Asian monsoon anticyclone functions as both gateway and gatekeeper to moisture entering the stratosphere. Although well monitored from space since 2005, many details of the anticyclone and the air that flows through it remain mysterious. Reanalyses, which combine model output and observations, may help to address how and why but only if they reliably capture the what and where of water vapor variations. Current reanalyses are beginning to meet these criteria.
Jonathon S. Wright, Shenglong Zhang, Jiao Chen, Sean M. Davis, Paul Konopka, Mengqian Lu, Xiaolu Yan, and Guang J. Zhang
Atmos. Chem. Phys., 25, 9617–9643, https://doi.org/10.5194/acp-25-9617-2025, https://doi.org/10.5194/acp-25-9617-2025, 2025
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Atmospheric reanalysis products reconstruct past states of the atmosphere. These products are often used to study winds and temperatures in the upper-level monsoon circulation, but their ability to reproduce composition fields like water vapor and ozone has been questionable at best. Here we report clear signs of improvement in both consistency across reanalyses and agreement with satellite observations, outline limitations, and suggest steps to further enhance the usefulness of these fields.
Ying Dai, Peter Hitchcock, Amy H. Butler, Chaim I. Garfinkel, and William J. M. Seviour
Weather Clim. Dynam., 6, 841–862, https://doi.org/10.5194/wcd-6-841-2025, https://doi.org/10.5194/wcd-6-841-2025, 2025
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Using a new database of subseasonal to seasonal (S2S) forecasts, we find that with a successful forecast of the sudden stratospheric warming (SSW), S2S models can capture the European precipitation signals after the 2018 SSW several weeks in advance. The findings indicate that the stratosphere represents an important source of S2S predictability for precipitation over Europe and call for consideration of stratospheric variability in hydrological prediction at S2S timescales.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 6, 171–195, https://doi.org/10.5194/wcd-6-171-2025, https://doi.org/10.5194/wcd-6-171-2025, 2025
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Variability in the extratropical stratosphere and troposphere is coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too weak; however downward coupling from the lower stratosphere to the near surface is too strong.
Kimberlee Dubé, Susann Tegtmeier, Adam Bourassa, Daniel Zawada, Douglas Degenstein, William Randel, Sean Davis, Michael Schwartz, Nathaniel Livesey, and Anne Smith
Atmos. Chem. Phys., 24, 12925–12941, https://doi.org/10.5194/acp-24-12925-2024, https://doi.org/10.5194/acp-24-12925-2024, 2024
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Greenhouse gas emissions that warm the troposphere also result in stratospheric cooling. The cooling rate is difficult to quantify above 35 km due to a deficit of long-term observational data with high vertical resolution in this region. We use satellite observations from several instruments, including a new temperature product from OSIRIS, to show that the upper stratosphere, from 35–60 km, cooled by 0.5 to 1 K per decade over 2005–2021 and by 0.6 K per decade over 1979–2021.
Masatomo Fujiwara, Patrick Martineau, Jonathon S. Wright, Marta Abalos, Petr Šácha, Yoshio Kawatani, Sean M. Davis, Thomas Birner, and Beatriz M. Monge-Sanz
Atmos. Chem. Phys., 24, 7873–7898, https://doi.org/10.5194/acp-24-7873-2024, https://doi.org/10.5194/acp-24-7873-2024, 2024
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A climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations from four global atmospheric reanalyses is evaluated. The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are in the vertical advection, which does not show a structure consistent with the differences in heating.
Mona Zolghadrshojaee, Susann Tegtmeier, Sean M. Davis, and Robin Pilch Kedzierski
Atmos. Chem. Phys., 24, 7405–7419, https://doi.org/10.5194/acp-24-7405-2024, https://doi.org/10.5194/acp-24-7405-2024, 2024
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Satellite data challenge the idea of an overall cooling trend in the tropical tropopause layer. From 2002 to 2022, a warming trend was observed, diverging from earlier findings. Tropopause height changes indicate dynamic processes alongside radiative effects. Upper-tropospheric warming contrasts with lower-stratosphere temperatures. The study highlights the complex interplay of factors shaping temperature trends.
Sean M. Davis, Nicholas Davis, Robert W. Portmann, Eric Ray, and Karen Rosenlof
Atmos. Chem. Phys., 23, 3347–3361, https://doi.org/10.5194/acp-23-3347-2023, https://doi.org/10.5194/acp-23-3347-2023, 2023
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Ozone in the lower part of the stratosphere has not increased and has perhaps even continued to decline in recent decades. This study demonstrates that the amount of ozone in this region is highly sensitive to the amount of air upwelling into the stratosphere in the tropics and that simulations from a climate model nudged to historical meteorological fields often fail to accurately capture the variations in tropical upwelling that control short-term trends in lower-stratospheric ozone.
J. Douglas Goetz, Lars E. Kalnajs, Terry Deshler, Sean M. Davis, Martina Bramberger, and M. Joan Alexander
Atmos. Meas. Tech., 16, 791–807, https://doi.org/10.5194/amt-16-791-2023, https://doi.org/10.5194/amt-16-791-2023, 2023
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An instrument for in situ continuous 2 km vertical profiles of temperature below high-altitude balloons was developed for high-temporal-resolution measurements within the upper troposphere and lower stratosphere using fiber-optic distributed temperature sensing. The mechanical, electrical, and temperature calibration systems were validated from a short mid-latitude constant-altitude balloon flight within the lower stratosphere. The instrument observed small-scale and inertial gravity waves.
Sophie Godin-Beekmann, Niramson Azouz, Viktoria F. Sofieva, Daan Hubert, Irina Petropavlovskikh, Peter Effertz, Gérard Ancellet, Doug A. Degenstein, Daniel Zawada, Lucien Froidevaux, Stacey Frith, Jeannette Wild, Sean Davis, Wolfgang Steinbrecht, Thierry Leblanc, Richard Querel, Kleareti Tourpali, Robert Damadeo, Eliane Maillard Barras, René Stübi, Corinne Vigouroux, Carlo Arosio, Gerald Nedoluha, Ian Boyd, Roeland Van Malderen, Emmanuel Mahieu, Dan Smale, and Ralf Sussmann
Atmos. Chem. Phys., 22, 11657–11673, https://doi.org/10.5194/acp-22-11657-2022, https://doi.org/10.5194/acp-22-11657-2022, 2022
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An updated evaluation up to 2020 of stratospheric ozone profile long-term trends at extrapolar latitudes based on satellite and ground-based records is presented. Ozone increase in the upper stratosphere is confirmed, with significant trends at most latitudes. In this altitude region, a very good agreement is found with trends derived from chemistry–climate model simulations. Observed and modelled trends diverge in the lower stratosphere, but the differences are non-significant.
Zachary D. Lawrence, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Amy H. Butler, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Javier García-Serrano, Chaim I. Garfinkel, Neil P. Hindley, Liwei Jia, Martin Jucker, Alexey Y. Karpechko, Hera Kim, Andrea L. Lang, Simon H. Lee, Pu Lin, Marisol Osman, Froila M. Palmeiro, Judith Perlwitz, Inna Polichtchouk, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Irene Erner, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, https://doi.org/10.5194/wcd-3-977-2022, 2022
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Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Chen Schwartz, Chaim I. Garfinkel, Priyanka Yadav, Wen Chen, and Daniela I. V. Domeisen
Weather Clim. Dynam., 3, 679–692, https://doi.org/10.5194/wcd-3-679-2022, https://doi.org/10.5194/wcd-3-679-2022, 2022
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Eleven operational forecast models that run on subseasonal timescales (up to 2 months) are examined to assess errors in their simulated large-scale stationary waves in the Northern Hemisphere winter. We found that models with a more finely resolved stratosphere generally do better in simulating the waves in both the stratosphere (10–50 km) and troposphere below. Moreover, a connection exists between errors in simulated time-mean convection in tropical regions and errors in the simulated waves.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Cited articles
Avery, M. A., Davis, S. M., Rosenlof, K. H., Ye, H., and Dessler, A. E.: Large anomalies in lower stratospheric water vapour and ice during the 2015–2016 El Niño, Nat. Geosci., 10, 405–409, 2017. a
Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A training algorithm for optimal margin classifiers, in: Proceedings of the fifth annual workshop on Computational learning theory (COLT '92), Association for Computing Machinery, New York, NY, USA, 144–152, https://doi.org/10.1145/130385.130401, 1992. a
Breiman, L.: Random forests, Machine Learning, 45, 5–32, 2001. a
Brinkop, S., Dameris, M., Jöckel, P., Garny, H., Lossow, S., and Stiller, G.: The millennium water vapour drop in chemistry–climate model simulations, Atmos. Chem. Phys., 16, 8125–8140, https://doi.org/10.5194/acp-16-8125-2016, 2016. a
Calvo, N., Garcia, R., Randel, W., and Marsh, D.: Dynamical mechanism for the
increase in tropical upwelling in the lowermost tropical stratosphere during
warm ENSO events, J. Atmos. Sci., 67, 2331–2340, 2010. a
Davis, S. M., Liang, C. K., and Rosenlof, K. H.: Interannual variability of
tropical tropopause layer clouds, Geophys. Res. Lett., 40,
2862–2866, 2013. a
Davis, S. M., Rosenlof, K. H., Hassler, B., Hurst, D. F., Read, W. G., Vömel, H., Selkirk, H., Fujiwara, M., and Damadeo, R.: The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database: a long-term database for climate studies, Earth Syst. Sci. Data, 8, 461–490, https://doi.org/10.5194/essd-8-461-2016, 2016. a, b
Diallo, M., Riese, M., Birner, T., Konopka, P., Müller, R., Hegglin, M. I., Santee, M. L., Baldwin, M., Legras, B., and Ploeger, F.: Response of stratospheric water vapor and ozone to the unusual timing of El Niño and the QBO disruption in 2015–2016, Atmos. Chem. Phys., 18, 13055–13073, https://doi.org/10.5194/acp-18-13055-2018, 2018. a, b, c, d
Domeisen, D. I., Garfinkel, C. I., and Butler, A. H.: The teleconnection of El
Niño Southern Oscillation to the stratosphere, Rev. Geophys., 57,
5–47, 2019. a
Drdla, K. and Müller, R.: Temperature thresholds for chlorine activation and ozone loss in the polar stratosphere, Ann. Geophys., 30, 1055–1073, https://doi.org/10.5194/angeo-30-1055-2012, 2012. a
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., and Shevliakova, E.: The GFDL Earth System Model version 4.1 (GFDL‐ESM 4.1): Overall coupled model description and simulation characteristics, J. Adv. Model. Earth Sy., 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020. a, b
Durre, I., Vose, R. S., and Wuertz, D. B.: Overview of the integrated global
radiosonde archive, J. Climate, 19, 53–68, 2006. a
Dvortsov, V. L. and Solomon, S.: Response of the stratospheric temperatures and
ozone to past and future increases in stratospheric humidity, J.
Geophys. Res.-Atmos., 106, 7505–7514, 2001. a
Eyring, V., Arblaster, J., Cionni, I., Sedláček, J., Perlwitz, J.,
Young, P., Bekki, S., Bergmann, D., Cameron-Smith, P., Collins, W. J.,
Faluvegi, G., Gottschaldt, K.-D., Horowitz, L. W., Kinnison, D. E., Lamarque, J.-F., Marsh, D. R., Saint-Martin, D., Shindell, D. T., Sudo, K., Szopa, S., and Watanabe, S.: Long-term ozone changes and associated climate impacts in CMIP5 simulations, J. Geophys. Res. Atmos., 118, 5029–5060, https://doi.org/10.1002/jgrd.50316, 2013. a, b
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Forster, P. M. and Shine, K. P.: Stratospheric water vapor changes as a
possible contributor to observed stratospheric cooling, Geophys. Res. Lett.,
26, 3309–3312, https://doi.org/10.1029/1999GL010487, 1999. a, b
Free, M. and Seidel, D. J.: Observed El Niño-Southern Oscillation
temperature signal in the stratosphere, J. Geophys. Res., 114, D23108,
https://doi.org/10.1029/2009JD012420, 2009. a
Fueglistaler, S. and Haynes, P.: Control of interannual and longer-term
variability of stratospheric water vapor, J. Geophys. Res.,
110, D24108, https://doi.org//10.1029/2005JD006019, 2005a. a, b
Fueglistaler, S., Dessler, A., Dunkerton, T., Folkins, I., Fu, Q., and Mote,
P. W.: Tropical tropopause layer, Rev. Geophys., 47, RG1004, https://doi.org/10.1029/2008RG000267, 2009. a
Fujiwara, M., Vömel, H., Hasebe, F., Shiotani, M., Ogino, S.-Y., Iwasaki,
S., Nishi, N., Shibata, T., Shimizu, K., Nishimoto, E., Valverde Canossa, J. M., Selkirk, H. B., and Oltmans, S. J.: Seasonal to
decadal variations of water vapor in the tropical lower stratosphere observed
with balloon-borne cryogenic frost point hygrometers, J. Geophys.
Res.-Atmos., 115, D18304, https://doi.org/10.1029/2010JD014179, 2010. a
Garcia, R. R., Smith, A. K., Kinnison, D. E., Cámara, Á. D. L., and
Murphy, D. J.: Modification of the Gravity Wave Parameterization in the Whole
Atmosphere Community Climate Model: Motivation and Results, J.
Atmos. Sci., 74, 275–291, 2017. a
Garfinkel, C. I. and Hartmann, D. L.: The influence of the quasi-biennial
oscillation on the troposphere in winter in a hierarchy of models. Part II:
Perpetual winter WACCM runs, J. Atmos. Sci., 68,
2026–2041, 2011. a
Garfinkel, C. I., Shaw, T. A., Hartmann, D. L., and Waugh, D. W.: Does the
Holton–Tan mechanism explain how the quasi-biennial oscillation modulates
the Arctic polar vortex?, J. Atmos. Sci., 69, 1713–1733,
2012. a
Garfinkel, C. I., Harari, O., Ziskin Ziv, S., Rao, J., Morgenstern, O., Zeng, G., Tilmes, S., Kinnison, D., O'Connor, F. M., Butchart, N., Deushi, M., Jöckel, P., Pozzer, A., and Davis, S.: Influence of the El Niño–Southern Oscillation on entry stratospheric water vapor in coupled chemistry–ocean CCMI and CMIP6 models, Atmos. Chem. Phys., 21, 3725–3740, https://doi.org/10.5194/acp-21-3725-2021, 2021. a, b, c, d, e, f, g
Geller, M. A., Zhou, T., Shindell, D., Ruedy, R., Aleinov, I., Nazarenko, L.,
Tausnev, N., Kelley, M., Sun, S., Cheng, Y., Field, R. D., and Faluvegi, G.: Modeling the
QBO – Improvements resulting from higher-model vertical resolution, J. Adv. Model. Earth Sy., 8, 1092–1105, https://doi.org/10.1002/2016MS000699, 2016. a
Gettelman, A., Randel, W., Massie, S., Wu, F., Read, W., and Russell III, J.:
El Nino as a natural experiment for studying the tropical tropopause region,
J. Climate, 14, 3375–3392, 2001. a
Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K.,
Marsh, D. R., Tilmes, S., Vitt, F., Bardeen, C. G., McInerny, J., Liu, H.-L.,
Solomon, S. C., Polvani, L. M., Emmons, L. K., Lamarque, J.-F., Richter,
J. H., Glanville, A. S., Bacmeister, J. T., Phillips, A. S., Neale, R. B.,
Simpson, I. R., DuVivier, A. K., Hodzic, A., and Randel, W. J.: The Whole
Atmosphere Community Climate Model Version 6 (WACCM6), J. Geophys.
Res.-Atmos., 124, 12380–12403, https://doi.org/10.1029/2019JD030943,
2019. a, b
Hardiman, S. C., Butchart, N., O'Connor, F. M., and Rumbold, S. T.: The Met Office HadGEM3-ES chemistry–climate model: evaluation of stratospheric dynamics and its impact on ozone, Geosci. Model Dev., 10, 1209–1232, https://doi.org/10.5194/gmd-10-1209-2017, 2017. a
Hatsushika, H. and Yamazaki, K.: Stratospheric drain over Indonesia and
dehydration within the tropical tropopause layer diagnosed by air parcel
trajectories, J. Geophys. Res.-Atmos., 108, 4610, https://doi.org/10.1029/2002JD002986, 2003. a
Hegglin, M. I. and Lamarque, J.-F.: The IGAC/SPARC Chemistry-Climate Model Initiative Phase-1 (CCMI-1) model data output, NCAS British Atmospheric Data Centre [data set], https://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/output (last access: 25 May 2022), 2015. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global
Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020. a, b
Hinton, G. E.: Connectionist learning procedures, Machine Learning, Artif. Intell., 40, 185–234, https://doi.org/10.1016/0004-3702(89)90049-0, 1989. a
Jöckel, P., Tost, H., Pozzer, A., Kunze, M., Kirner, O., Brenninkmeijer, C. A. M., Brinkop, S., Cai, D. S., Dyroff, C., Eckstein, J., Frank, F., Garny, H., Gottschaldt, K.-D., Graf, P., Grewe, V., Kerkweg, A., Kern, B., Matthes, S., Mertens, M., Meul, S., Neumaier, M., Nützel, M., Oberländer-Hayn, S., Ruhnke, R., Runde, T., Sander, R., Scharffe, D., and Zahn, A.: Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51, Geosci. Model Dev., 9, 1153–1200, https://doi.org/10.5194/gmd-9-1153-2016, 2016. a
Kawatani, Y., Lee, J. N., and Hamilton, K.: Interannual Variations of
Stratospheric Water Vapor in MLS Observations and Climate Model Simulations,
J. Atmos. Sci., 71, 4072–4085,
https://doi.org/10.1175/JAS-D-14-0164.1, 2014. a
Keeble, J., Hassler, B., Banerjee, A., Checa-Garcia, R., Chiodo, G., Davis, S., Eyring, V., Griffiths, P. T., Morgenstern, O., Nowack, P., Zeng, G., Zhang, J., Bodeker, G., Burrows, S., Cameron-Smith, P., Cugnet, D., Danek, C., Deushi, M., Horowitz, L. W., Kubin, A., Li, L., Lohmann, G., Michou, M., Mills, M. J., Nabat, P., Olivié, D., Park, S., Seland, Ø., Stoll, J., Wieners, K.-H., and Wu, T.: Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850 to 2100, Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, 2021. a
Konopka, P., Ploeger, F., Tao, M., and Riese, M.: Zonally resolved impact of
ENSO on the stratospheric circulation and water vapor entry values, J.
Geophys. Res.-Atmos., 121, 11486–11501, https://doi.org//10.1002/2015JD024698, 2016. a, b
Liang, C., Eldering, A., Gettelman, A., Tian, B., Wong, S., Fetzer, E., and
Liou, K.: Record of tropical interannual variability of temperature and water
vapor from a combined AIRS-MLS data set, J. Geophys. Res.-Atmos., 116, D06103, https://doi.org/10.1029/2010JD014841, 2011. a, b
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model
Predictions, in: Advances in Neural Information Processing Systems 30, edited
by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R.,
Vishwanathan, S., and Garnett, R., Curran Associates, Inc., pp. 4765–4774,
http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf (last access: 26 May 2022),
2017. a
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B.,
Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From local explanations
to global understanding with explainable AI for trees, Nature Machine
Intelligence, 2, 2522–5839, 2020. a
Martin, Z., Orbe, C., Wang, S., and Sobel, A.: The MJO–QBO Relationship in a
GCM with Stratospheric Nudging, J. Climate, 34, 4603–4624, 2021. a
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M.,
Lamarque, J., Matsumoto, K., Montzka, S., Raper, S., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The
RCP greenhouse gas concentrations and their extensions from 1765 to 2300,
Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011. a, b
Molnar, C.: Interpretable Machine Learning,
https://christophm.github.io/interpretable-ml-book/shap.html (last access: 26 May 2022),
2019. a
Morgenstern, O., Braesicke, P., O'Connor, F. M., Bushell, A. C., Johnson, C. E., Osprey, S. M., and Pyle, J. A.: Evaluation of the new UKCA climate-composition model – Part 1: The stratosphere, Geosci. Model Dev., 2, 43–57, https://doi.org/10.5194/gmd-2-43-2009, 2009. a
Morgenstern, O., Hegglin, M. I., Rozanov, E., O'Connor, F. M., Abraham, N. L., Akiyoshi, H., Archibald, A. T., Bekki, S., Butchart, N., Chipperfield, M. P., Deushi, M., Dhomse, S. S., Garcia, R. R., Hardiman, S. C., Horowitz, L. W., Jöckel, P., Josse, B., Kinnison, D., Lin, M., Mancini, E., Manyin, M. E., Marchand, M., Marécal, V., Michou, M., Oman, L. D., Pitari, G., Plummer, D. A., Revell, L. E., Saint-Martin, D., Schofield, R., Stenke, A., Stone, K., Sudo, K., Tanaka, T. Y., Tilmes, S., Yamashita, Y., Yoshida, K., and Zeng, G.: Review of the global models used within phase 1 of the Chemistry–Climate Model Initiative (CCMI), Geosci. Model Dev., 10, 639–671, https://doi.org/10.5194/gmd-10-639-2017, 2017. a, b
Mote, P. W., Rosenlof, K. H., McIntyre, M. E., Carr, E. S., Gille, J. C.,
Holton, J. R., Kinnersley, J. S., Pumphrey, H. C., Russell III, J. M., and
Waters, J. W.: An atmospheric tape recorder: The imprint of tropical
tropopause temperatures on stratospheric water vapor, J. Geophys.
Res.-Atmos., 101, 3989–4006, 1996a. a
Mote, P. W., Rosenlof, K. H., McIntyre, M. E., Carr, E. S., Gille, J. C.,
Holton, J. R., Kinnersley, J. S., Pumphrey, H. C., Russell III, J. M., and
Waters, J. W.: An atmospheric tape recorder: The imprint of tropical
tropopause temperatures on stratospheric water vapor, J. Geophys.
Res.-Atmos., 101, 3989–4006, 1996b. a
National Centre for Atmospheric Research: CCMI Phase 1, NCAR [data set], https://www.earthsystemgrid.org/project/CCMI1.html, last access: 25 May 2021. a
Niwano, M., Yamazaki, K., and Shiotani, M.: Seasonal and QBO variations of
ascent rate in the tropical lower stratosphere as inferred from UARS HALOE
trace gas data, J. Geophys. Res.-Atmos., 108, 4794, https://doi.org/10.1029/2003JD003871, 2003. a
Orbe, C., Waugh, D. W., Yang, H., Lamarque, J.-F., Tilmes, S., and Kinnison,
D. E.: Tropospheric transport differences between models using the same
large-scale meteorological fields, Geophys. Res. Lett., 44,
1068–1078, 2017. a
Orbe, C., Yang, H., Waugh, D. W., Zeng, G., Morgenstern , O., Kinnison, D. E., Lamarque, J.-F., Tilmes, S., Plummer, D. A., Scinocca, J. F., Josse, B., Marecal, V., Jöckel, P., Oman, L. D., Strahan, S. E., Deushi, M., Tanaka, T. Y., Yoshida, K., Akiyoshi, H., Yamashita, Y., Stenke, A., Revell, L., Sukhodolov, T., Rozanov, E., Pitari, G., Visioni, D., Stone, K. A., Schofield, R., and Banerjee, A.: Large-scale tropospheric transport in the Chemistry–Climate Model Initiative (CCMI) simulations, Atmos. Chem. Phys., 18, 7217–7235, https://doi.org/10.5194/acp-18-7217-2018, 2018. a
Orr, G. B. and Müller, K.-R.: Neural Networks: Tricks of the Trade, in: Lecture Notes in Computer Science, 2nd edn., edited by: Montavon, G., Orr, G. B., and Müller, K.-R.,
Springer Berlin, Heidelberg, ISBN 978-3-642-35288-1, ISBN 978-3-642-35289-8, https://doi.org/10.1007/978-3-642-35289-8, 2003. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: machine learning, Python. J. Mach. Learn. Res., 12, 2825–2830, http://jmlr.org/papers/v12/pedregosa11a.html (last access 25 May 2022), 2011. a, b
Randel, W. J., Wu, F., and Gaffen, D. J.: Interannual variability of the
tropical tropopause derived from radiosonde data and NCEP reanalyses, J. Geophys. Res., 105, 15509–15523, https://doi.org/10.1029/2000JD900155, 2000. a
Randel, W. J., Wu, F., Voemel, H., Nedoluha, G. E., and Forster, P.: Decreases
in stratospheric water vapor after 2001: Links to changes in the tropical
tropopause and the Brewer-Dobson circulation, J. Geophys.
Res.-Atmos., 111, D12312, https://doi.org/10.1029/2005JD006744, 2006. a
Randel, W. J., Garcia, R. R., Calvo, N., and Marsh, D.: ENSO influence on zonal
mean temperature and ozone in the tropical lower stratosphere, Geophys.
Res. Lett., 36, L15822, https://doi.org/10.1029/2009GL039343, 2009. a
Rao, J., Garfinkel, C. I., and White, I. P.: Impact of the Quasi-Biennial
Oscillation on the Northern Winter Stratospheric Polar Vortex in CMIP5/6
Models, J. Climate, 33, 4787–4813, https://doi.org/10.1175/JCLI-D-19-0663.1,
2020a. a, b, c
Reid, G. C. and Gage, K. S.: Interannual variations in the height of the
tropical tropopause, J. Geophys. Res.-Atmos., 90,
5629–5635, 1985. a
Richter, J. H., Anstey, J. A., Butchart, N., Kawatani, Y., Meehl, G. A.,
Osprey, S., and Simpson, I. R.: Progress in Simulating the Quasi-Biennial
Oscillation in CMIP Models, J. Geophys. Res.-Atmos.,
125, e2019JD032362, https://doi.org/10.1029/2019JD032362, 2020. a, b, c
Robrecht, S., Vogel, B., Tilmes, S., and Müller, R.: Potential of future stratospheric ozone loss in the midlatitudes under global warming and sulfate geoengineering, Atmos. Chem. Phys., 21, 2427–2455, https://doi.org/10.5194/acp-21-2427-2021, 2021. a
Scherllin-Pirscher, B., Deser, C., Ho, S.-P., Chou, C., Randel, W., and Kuo, Y.-H.: The vertical and spatial structure of ENSO in the upper troposphere and lower stratosphere from GPS radio occultation measurements, Geophys. Res. Lett., 39, L20801, https://doi.org/10.1029/2012GL053071, 2012. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A.,
Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y-Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.:
Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System
Processes in Present-Day and Future Climate, J. Adv. Model.
Earth Sy., 11, 4182–4227, 2019. a, b
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A.,
O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora,
L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E.,
Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T.,
Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J.,
Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A.,
Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat,
S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A.,
Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.:
UKESM1: Description and Evaluation of the U.K. Earth System Model, J.
Adv. Model. Earth Sy., 11, 4513–4558,
https://doi.org/10.1029/2019MS001739, 2019. a, b
Simpson, I. R., Shepherd, T. G., and Sigmond, M.: Dynamics of the lower
stratospheric circulation response to ENSO, J. Atmos.
Sci., 68, 2537–2556, 2011. a
Smalley, K. M., Dessler, A. E., Bekki, S., Deushi, M., Marchand, M., Morgenstern, O., Plummer, D. A., Shibata, K., Yamashita, Y., and Zeng, G.: Contribution of different processes to changes in tropical lower-stratospheric water vapor in chemistry–climate models, Atmos. Chem. Phys., 17, 8031–8044, https://doi.org/10.5194/acp-17-8031-2017, 2017. a, b, c, d, e
Solomon, S.: Stratospheric ozone depletion: A review of concepts and history,
Rev. Geophys., 37, 275–316, 1999. a
Solomon, S., Garcia, R. R., Rowland, F. S., and Wuebbles, D. J.: On
the depletion of Antarctic ozone, Nature, 321, 755–758,
https://doi.org/10.1038/321755a0, 1986. a
Solomon, S., Rosenlof, K. H., Portmann, R. W., Daniel, J. S., Davis,
S. M., Sanford, T. J., and Plattner, G.-K.: Contributions of
Stratospheric Water Vapor to Decadal Changes in the Rate of Global Warming,
Science, 327, 1219–1223, https://doi.org/10.1126/science.1182488, 2010. a, b
Stenke, A. and Grewe, V.: Simulation of stratospheric water vapor trends: impact on stratospheric ozone chemistry, Atmos. Chem. Phys., 5, 1257–1272, https://doi.org/10.5194/acp-5-1257-2005, 2005. a
Tian, E. W., Su, H., Tian, B., and Jiang, J. H.: Interannual variations of water vapor in the tropical upper troposphere and the lower and middle stratosphere and their connections to ENSO and QBO, Atmos. Chem. Phys., 19, 9913–9926, https://doi.org/10.5194/acp-19-9913-2019, 2019. a
Tian, W., Chipperfield, M. P., and Lü, D.: Impact of increasing
stratospheric water vapor on ozone depletion and temperature change, Adv. Atmos. Sci., 26, 423–437, 2009. a
Tilmes, S., Lamarque, J.-F., Emmons, L. K., Kinnison, D. E., Marsh, D., Garcia, R. R., Smith, A. K., Neely, R. R., Conley, A., Vitt, F., Val Martin, M., Tanimoto, H., Simpson, I., Blake, D. R., and Blake, N.: Representation of the Community Earth System Model (CESM1) CAM4-chem within the Chemistry-Climate Model Initiative (CCMI), Geosci. Model Dev., 9, 1853–1890, https://doi.org/10.5194/gmd-9-1853-2016, 2016. a
Vapnik, V., Guyon, I., and Hastie, T.: Support vector machines, Machine Learning,
20, 273–297, 1995. a
World Meteorological Organization: Scientific Assessment of Ozone Depletion:
2010, Global Ozone Research and Monitoring Project Rep. No. 52, https://library.wmo.int/index.php?id=5230&lvl=notice_display#.Yo88x6jP23A (last access: 31 May 2022), 2011. a
Ye, H., Dessler, A. E., and Yu, W.: Effects of convective ice evaporation on interannual variability of tropical tropopause layer water vapor, Atmos. Chem. Phys., 18, 4425–4437, https://doi.org/10.5194/acp-18-4425-2018, 2018. a
Yook, S., Thompson, D. W. J., Solomon, S., and Kim, S.: The Key Role of Coupled Chemistry – Climate Interactions in Tropical Stratospheric Temperature Variability, J. Climate, 33, 7619–7629, https://doi.org/10.1175/JCLI-D-20-0071.1, 2020. a
Yuan, W., Geller, M. A., and Love, P. T.: ENSO influence on QBO modulations of
the tropical tropopause, Q. J. Roy. Meteor.
Soc., 140, 1670–1676, https://doi.org/10.1002/qj.2247, 2014. a, b
Yukimoto, S., Adachi, Y., Hosaka, M., Sakami, T., Yoshimura, H., Hirabara, M.,
Tanaka, T. Y., Shindo, E., Tsujino, H., Deushi, M., Mizuta, R., Yabu, S., Obata, A., Nakano, H., Koshiro, T., Ose, T., and Kitoh, A.: A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3 – Model Description and Basic Performance, J. Meteorol. Soc. Jpn. Ser. II, 90, 23–64, https://doi.org/10.2151/jmsj.2012-A02, 2012. a
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological
Research Institute Earth System Model version 2.0, MRI-ESM2. 0: Description
and basic evaluation of the physical component, J. Meteorol.
Soc. Jpn. Ser. II, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019. a, b
Short summary
Stratospheric water vapor is important for Earth's overall greenhouse effect and for ozone chemistry; however the factors governing its variability on interannual timescales are not fully known, and previous modeling studies have indicated that models struggle to capture this interannual variability. We demonstrate that nonlinear interactions are important for determining overall water vapor concentrations and also that models have improved in their ability to capture these connections.
Stratospheric water vapor is important for Earth's overall greenhouse effect and for ozone...
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