Articles | Volume 25, issue 4
https://doi.org/10.5194/acp-25-2365-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-25-2365-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opinion
| Highlight paper
| 
21 Feb 2025
Opinion | Highlight paper |
| 21 Feb 2025
| 21 Feb 2025
Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective
Institute of Theoretical Informatics, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Duncan Watson-Parris
CORRESPONDING AUTHOR
Scripps Institution of Oceanography, University of California San Diego, San Diego, CA, USA
Halicioglu Data Science Institute, University of California San Diego, San Diego, CA, USA
Related authors
Jingyu Wang, Gabriel Chiodo, Timofei Sukhodolov, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, and Sandro Vattioni
EGUsphere, https://doi.org/10.5194/egusphere-2025-340, https://doi.org/10.5194/egusphere-2025-340, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then looked at the relations between ozone response and temperature and circulation changes to identify drivers of the ozone change. The climate feedback of ozone is investigated by doing offline calculations and comparing models with and without interactive chemistry. We find that ozone-climate interactions are important for Earth System Models, thus should be considered in future model development.
Philipp Breul, Paulo Ceppi, and Peer Nowack
EGUsphere, https://doi.org/10.5194/egusphere-2025-221, https://doi.org/10.5194/egusphere-2025-221, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
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We explore how Pacific low-level clouds influence projections of regional climate change by adjusting a climate model to enhance low cloud response to surface temperatures. We find significant changes in projected warming patterns and circulation changes, under increased CO2 conditions. Our findings are supported by similar relationships across state-of-the-art climate models. These results highlight the importance of accurately representing clouds for predicting regional climate change impacts.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
EGUsphere, https://doi.org/10.5194/egusphere-2024-3702, https://doi.org/10.5194/egusphere-2024-3702, 2024
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This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight increases in anthropogenic emission are the primary driver of ozone increases both in the free troposphere and at the surface.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2024-2656, https://doi.org/10.5194/egusphere-2024-2656, 2024
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This study introduces a new method to reduce uncertainty in climate model projections of future precipitation patterns over land. By using advanced causal discovery techniques, our approach improves the reliability of precipitation projections under different global warming scenarios, supporting the development of more effective strategies to address the impacts of climate change.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
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Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
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This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Xiang Weng, Grant L. Forster, and Peer Nowack
Atmos. Chem. Phys., 22, 8385–8402, https://doi.org/10.5194/acp-22-8385-2022, https://doi.org/10.5194/acp-22-8385-2022, 2022
Short summary
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We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Peer Nowack, Lev Konstantinovskiy, Hannah Gardiner, and John Cant
Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, https://doi.org/10.5194/amt-14-5637-2021, 2021
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Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, https://doi.org/10.5194/wcd-2-581-2021, 2021
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Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
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Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Abdul Malik, Peer J. Nowack, Joanna D. Haigh, Long Cao, Luqman Atique, and Yves Plancherel
Atmos. Chem. Phys., 20, 15461–15485, https://doi.org/10.5194/acp-20-15461-2020, https://doi.org/10.5194/acp-20-15461-2020, 2020
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Solar geoengineering has been introduced to mitigate human-caused global warming by reflecting sunlight back into space. This research investigates the impact of solar geoengineering on the tropical Pacific climate. We find that solar geoengineering can compensate some of the greenhouse-induced changes in the tropical Pacific but not all. In particular, solar geoengineering will result in significant changes in rainfall, sea surface temperatures, and increased frequency of extreme ENSO events.
Lili Xia, Peer J. Nowack, Simone Tilmes, and Alan Robock
Atmos. Chem. Phys., 17, 11913–11928, https://doi.org/10.5194/acp-17-11913-2017, https://doi.org/10.5194/acp-17-11913-2017, 2017
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Ozone is a key air pollutant. We model two geoengineering schemes, stratospheric sulfur injection and solar irradiance reduction, to compare their impacts on atmospheric ozone concentrations. With the nearly identical global mean surface temperature reduction, solar dimming increases global average surface ozone concentration, while sulfate injection decreases it. This difference is due to different stratosphere–troposphere exchange of ozone and tropospheric ozone chemistry in the two scenarios.
Peer Johannes Nowack, Nathan Luke Abraham, Peter Braesicke, and John Adrian Pyle
Atmos. Chem. Phys., 16, 4191–4203, https://doi.org/10.5194/acp-16-4191-2016, https://doi.org/10.5194/acp-16-4191-2016, 2016
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Various forms of solar radiation management (SRM) have been proposed to counteract man-made climate change. However, all these countermeasures could have unintended side-effects. We add a novel perspective to this discussion by showing how atmospheric ozone changes under solar geoengineering could affect UV exposure and air pollution. This would have implications for human health and ecology. Atmospheric composition changes are therefore important to consider in the evaluation of any SRM scheme.
Jingyu Wang, Gabriel Chiodo, Timofei Sukhodolov, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, and Sandro Vattioni
EGUsphere, https://doi.org/10.5194/egusphere-2025-340, https://doi.org/10.5194/egusphere-2025-340, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then looked at the relations between ozone response and temperature and circulation changes to identify drivers of the ozone change. The climate feedback of ozone is investigated by doing offline calculations and comparing models with and without interactive chemistry. We find that ozone-climate interactions are important for Earth System Models, thus should be considered in future model development.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
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We compared smoke plume simulations from 11 global models to each other and to satellite smoke amount observations aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss rate assumptions vary enormously among models, causing uncertainties that require systematic in situ measurements to resolve.
Philipp Breul, Paulo Ceppi, and Peer Nowack
EGUsphere, https://doi.org/10.5194/egusphere-2025-221, https://doi.org/10.5194/egusphere-2025-221, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
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We explore how Pacific low-level clouds influence projections of regional climate change by adjusting a climate model to enhance low cloud response to surface temperatures. We find significant changes in projected warming patterns and circulation changes, under increased CO2 conditions. Our findings are supported by similar relationships across state-of-the-art climate models. These results highlight the importance of accurately representing clouds for predicting regional climate change impacts.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
EGUsphere, https://doi.org/10.5194/egusphere-2024-3702, https://doi.org/10.5194/egusphere-2024-3702, 2024
Short summary
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This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight increases in anthropogenic emission are the primary driver of ozone increases both in the free troposphere and at the surface.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2024-2656, https://doi.org/10.5194/egusphere-2024-2656, 2024
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This study introduces a new method to reduce uncertainty in climate model projections of future precipitation patterns over land. By using advanced causal discovery techniques, our approach improves the reliability of precipitation projections under different global warming scenarios, supporting the development of more effective strategies to address the impacts of climate change.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael J. Prather, Alexander T. Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Christopher J. Smith, Steven T. Turnock, Duncan Watson-Parris, and Paul J. Young
EGUsphere, https://doi.org/10.5194/egusphere-2024-2528, https://doi.org/10.5194/egusphere-2024-2528, 2024
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The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. In this paper, we review its contribution to AR6, and the wider understanding of the role of these species in climate and climate change. We identify remaining challenges concluding with recommendations aimed to improve the utility and uptake of climate model data to address the role of short-lived climate forcers in the Earth system.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
Short summary
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Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
Duncan Watson-Parris, Laura J. Wilcox, Camilla W. Stjern, Robert J. Allen, Geeta Persad, Massimo A. Bollasina, Annica M. L. Ekman, Carley E. Iles, Manoj Joshi, Marianne T. Lund, Daniel McCoy, Daniel Westervelt, Andrew Williams, and Bjørn H. Samset
EGUsphere, https://doi.org/10.5194/egusphere-2024-1946, https://doi.org/10.5194/egusphere-2024-1946, 2024
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In 2020, regulations by the International Maritime Organization aimed to reduce aerosol emissions from ships. These aerosols previously had a cooling effect, which the regulations might reduce, revealing more greenhouse gas warming. Here we find that while there is regional warming, the global 2020–2040 temperature rise is only +0.03°C. This small change is difficult to distinguish from natural climate variability, indicating the regulations have had a limited effect on observed warming to date.
Ross J. Herbert, Andrew I. L. Williams, Philipp Weiss, Duncan Watson-Parris, Elisabeth Dingley, Daniel Klocke, and Philip Stier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1689, https://doi.org/10.5194/egusphere-2024-1689, 2024
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Clouds exist at scales that climate models struggle to represent, limiting our knowledge of how climate change may impact clouds. Here we use a new km-scale global model representing an important step towards the necessary scale. We focus on how aerosol particles modify clouds, radiation, and precipitation. We find the magnitude and manner of responses tend to vary from region to region, highlighting the potential of global km-scale simulations and a need to represent aerosols in climate models.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
George Jordan, Florent Malavelle, Ying Chen, Amy Peace, Eliza Duncan, Daniel G. Partridge, Paul Kim, Duncan Watson-Parris, Toshihiko Takemura, David Neubauer, Gunnar Myhre, Ragnhild Skeie, Anton Laakso, and James Haywood
Atmos. Chem. Phys., 24, 1939–1960, https://doi.org/10.5194/acp-24-1939-2024, https://doi.org/10.5194/acp-24-1939-2024, 2024
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The 2014–15 Holuhraun eruption caused a huge aerosol plume in an otherwise unpolluted region, providing a chance to study how aerosol alters cloud properties. This two-part study uses observations and models to quantify this relationship’s impact on the Earth’s energy budget. Part 1 suggests the models capture the observed spatial and chemical evolution of the plume, yet no model plume is exact. Understanding these differences is key for Part 2, where changes to cloud properties are explored.
Peter Manshausen, Duncan Watson-Parris, Matthew W. Christensen, Jukka-Pekka Jalkanen, and Philip Stier
Atmos. Chem. Phys., 23, 12545–12555, https://doi.org/10.5194/acp-23-12545-2023, https://doi.org/10.5194/acp-23-12545-2023, 2023
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Aerosol from burning fuel changes cloud properties, e.g., the number of droplets and the content of water. Here, we study how clouds respond to different amounts of shipping aerosol. Droplet numbers increase linearly with increasing aerosol over a broad range until they stop increasing, while the amount of liquid water always increases, independently of emission amount. These changes in cloud properties can make them reflect more or less sunlight, which is important for the earth's climate.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
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This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David M. H. Sexton, Christopher Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John W. Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
Atmos. Chem. Phys., 23, 8749–8768, https://doi.org/10.5194/acp-23-8749-2023, https://doi.org/10.5194/acp-23-8749-2023, 2023
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Aerosol forcing of Earth’s energy balance has persisted as a major cause of uncertainty in climate simulations over generations of climate model development. We show that structural deficiencies in a climate model are exposed by comprehensively exploring parametric uncertainty and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. This provides a future pathway towards building models with greater physical realism and lower uncertainty.
Leighton A. Regayre, Lucia Deaconu, Daniel P. Grosvenor, David Sexton, Christopher C. Symonds, Tom Langton, Duncan Watson-Paris, Jane P. Mulcahy, Kirsty J. Pringle, Mark Richardson, Jill S. Johnson, John Rostron, Hamish Gordon, Grenville Lister, Philip Stier, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2022-1330, https://doi.org/10.5194/egusphere-2022-1330, 2022
Preprint archived
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We show that potential structural deficiencies in a climate model can be exposed by comprehensively exploring its parametric uncertainty, and that these deficiencies limit how much the model uncertainty can be reduced through observational constraint. Combined consideration of parametric and structural uncertainties provides a future pathway towards building models that have greater physical realism and lower uncertainty.
Haochi Che, Philip Stier, Duncan Watson-Parris, Hamish Gordon, and Lucia Deaconu
Atmos. Chem. Phys., 22, 10789–10807, https://doi.org/10.5194/acp-22-10789-2022, https://doi.org/10.5194/acp-22-10789-2022, 2022
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Extensive stratocumulus clouds over the south-eastern Atlantic (SEA) can lead to a cooling effect on the climate. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). Here, we investigated the source attribution of CCN in the SEA as well as the cloud responses. Our results show that aerosol nucleation contributes most to CCN in the marine boundary layer. In terms of emissions, anthropogenic sources contribute most to the CCN and cloud droplets.
Xiang Weng, Grant L. Forster, and Peer Nowack
Atmos. Chem. Phys., 22, 8385–8402, https://doi.org/10.5194/acp-22-8385-2022, https://doi.org/10.5194/acp-22-8385-2022, 2022
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We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
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Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
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Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Peer Nowack, Lev Konstantinovskiy, Hannah Gardiner, and John Cant
Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, https://doi.org/10.5194/amt-14-5637-2021, 2021
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Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, https://doi.org/10.5194/wcd-2-581-2021, 2021
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Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.
Shipeng Zhang, Philip Stier, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 10179–10197, https://doi.org/10.5194/acp-21-10179-2021, https://doi.org/10.5194/acp-21-10179-2021, 2021
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The relationship between aerosol-induced changes in atmospheric energetics and precipitation responses across different scales is studied in terms of fast (radiatively or microphysically mediated) and slow (temperature-mediated) responses. We introduced a method to decompose rainfall changes into contributions from clouds, aerosols, and clear–clean sky from an energetic perspective. It provides a way to better interpret and quantify the precipitation changes caused by aerosol perturbations.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
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Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Jim M. Haywood, Steven J. Abel, Paul A. Barrett, Nicolas Bellouin, Alan Blyth, Keith N. Bower, Melissa Brooks, Ken Carslaw, Haochi Che, Hugh Coe, Michael I. Cotterell, Ian Crawford, Zhiqiang Cui, Nicholas Davies, Beth Dingley, Paul Field, Paola Formenti, Hamish Gordon, Martin de Graaf, Ross Herbert, Ben Johnson, Anthony C. Jones, Justin M. Langridge, Florent Malavelle, Daniel G. Partridge, Fanny Peers, Jens Redemann, Philip Stier, Kate Szpek, Jonathan W. Taylor, Duncan Watson-Parris, Robert Wood, Huihui Wu, and Paquita Zuidema
Atmos. Chem. Phys., 21, 1049–1084, https://doi.org/10.5194/acp-21-1049-2021, https://doi.org/10.5194/acp-21-1049-2021, 2021
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Every year, the seasonal cycle of biomass burning from agricultural practices in Africa creates a huge plume of smoke that travels many thousands of kilometres over the Atlantic Ocean. This study provides an overview of a measurement campaign called the cloud–aerosol–radiation interaction and forcing for year 2017 (CLARIFY-2017) and documents the rationale, deployment strategy, observations, and key results from the campaign which utilized the heavily equipped FAAM atmospheric research aircraft.
Haochi Che, Philip Stier, Hamish Gordon, Duncan Watson-Parris, and Lucia Deaconu
Atmos. Chem. Phys., 21, 17–33, https://doi.org/10.5194/acp-21-17-2021, https://doi.org/10.5194/acp-21-17-2021, 2021
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The south-eastern Atlantic is semi-permanently covered by some of the largest stratocumulus clouds and is influenced by one-third of the biomass burning emissions from African fires. A UKEMS1 model simulation shows that the absorption effect of biomass burning aerosols is the most significant on clouds and radiation. The dominate cooling and rapid adjustments induced by the radiative effects of biomass burning aerosols result in an overall cooling in the south-eastern Atlantic.
Abdul Malik, Peer J. Nowack, Joanna D. Haigh, Long Cao, Luqman Atique, and Yves Plancherel
Atmos. Chem. Phys., 20, 15461–15485, https://doi.org/10.5194/acp-20-15461-2020, https://doi.org/10.5194/acp-20-15461-2020, 2020
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Solar geoengineering has been introduced to mitigate human-caused global warming by reflecting sunlight back into space. This research investigates the impact of solar geoengineering on the tropical Pacific climate. We find that solar geoengineering can compensate some of the greenhouse-induced changes in the tropical Pacific but not all. In particular, solar geoengineering will result in significant changes in rainfall, sea surface temperatures, and increased frequency of extreme ENSO events.
Max Heikenfeld, Peter J. Marinescu, Matthew Christensen, Duncan Watson-Parris, Fabian Senf, Susan C. van den Heever, and Philip Stier
Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, https://doi.org/10.5194/gmd-12-4551-2019, 2019
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We present tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing clouds in different types of datasets. It provides a flexible new way to include the evolution of individual clouds in a wide range of analyses. It is developed as a community project to provide a common basis for the inclusion of existing tracking algorithms and the development of new analyses that involve tracking clouds and other features in geoscientific research.
Øivind Hodnebrog, Gunnar Myhre, Bjørn H. Samset, Kari Alterskjær, Timothy Andrews, Olivier Boucher, Gregory Faluvegi, Dagmar Fläschner, Piers M. Forster, Matthew Kasoar, Alf Kirkevåg, Jean-Francois Lamarque, Dirk Olivié, Thomas B. Richardson, Dilshad Shawki, Drew Shindell, Keith P. Shine, Philip Stier, Toshihiko Takemura, Apostolos Voulgarakis, and Duncan Watson-Parris
Atmos. Chem. Phys., 19, 12887–12899, https://doi.org/10.5194/acp-19-12887-2019, https://doi.org/10.5194/acp-19-12887-2019, 2019
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Different greenhouse gases (e.g. CO2) and aerosols (e.g. black carbon) impact the Earth’s water cycle differently. Here we investigate how various gases and particles impact atmospheric water vapour and its lifetime, i.e., the average number of days that water vapour stays in the atmosphere after evaporation and before precipitation. We find that this lifetime could increase substantially by the end of this century, indicating that important changes in precipitation patterns are excepted.
Duncan Watson-Parris, Nick Schutgens, Carly Reddington, Kirsty J. Pringle, Dantong Liu, James D. Allan, Hugh Coe, Ken S. Carslaw, and Philip Stier
Atmos. Chem. Phys., 19, 11765–11790, https://doi.org/10.5194/acp-19-11765-2019, https://doi.org/10.5194/acp-19-11765-2019, 2019
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The vertical distribution of aerosol in the atmosphere affects its ability to act as cloud condensation nuclei and changes the amount of sunlight it absorbs or reflects. Common global measurements of aerosol provide no information about this vertical distribution. Using a global collection of in situ aircraft measurements to compare with an aerosol–climate model (ECHAM-HAM), we explore the key processes controlling this distribution and find that wet removal plays a key role.
George S. Fanourgakis, Maria Kanakidou, Athanasios Nenes, Susanne E. Bauer, Tommi Bergman, Ken S. Carslaw, Alf Grini, Douglas S. Hamilton, Jill S. Johnson, Vlassis A. Karydis, Alf Kirkevåg, John K. Kodros, Ulrike Lohmann, Gan Luo, Risto Makkonen, Hitoshi Matsui, David Neubauer, Jeffrey R. Pierce, Julia Schmale, Philip Stier, Kostas Tsigaridis, Twan van Noije, Hailong Wang, Duncan Watson-Parris, Daniel M. Westervelt, Yang Yang, Masaru Yoshioka, Nikos Daskalakis, Stefano Decesari, Martin Gysel-Beer, Nikos Kalivitis, Xiaohong Liu, Natalie M. Mahowald, Stelios Myriokefalitakis, Roland Schrödner, Maria Sfakianaki, Alexandra P. Tsimpidi, Mingxuan Wu, and Fangqun Yu
Atmos. Chem. Phys., 19, 8591–8617, https://doi.org/10.5194/acp-19-8591-2019, https://doi.org/10.5194/acp-19-8591-2019, 2019
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Effects of aerosols on clouds are important for climate studies but are among the largest uncertainties in climate projections. This study evaluates the skill of global models to simulate aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentrations (CDNCs). Model results show reduced spread in CDNC compared to CCN due to the negative correlation between the sensitivities of CDNC to aerosol number concentration (air pollution) and updraft velocity (atmospheric dynamics).
Ina Tegen, David Neubauer, Sylvaine Ferrachat, Colombe Siegenthaler-Le Drian, Isabelle Bey, Nick Schutgens, Philip Stier, Duncan Watson-Parris, Tanja Stanelle, Hauke Schmidt, Sebastian Rast, Harri Kokkola, Martin Schultz, Sabine Schroeder, Nikos Daskalakis, Stefan Barthel, Bernd Heinold, and Ulrike Lohmann
Geosci. Model Dev., 12, 1643–1677, https://doi.org/10.5194/gmd-12-1643-2019, https://doi.org/10.5194/gmd-12-1643-2019, 2019
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We describe a new version of the aerosol–climate model ECHAM–HAM and show tests of the model performance by comparing different aspects of the aerosol distribution with different datasets. The updated version of HAM contains improved descriptions of aerosol processes, including updated emission fields and cloud processes. While there are regional deviations between the model and observations, the model performs well overall.
Lili Xia, Peer J. Nowack, Simone Tilmes, and Alan Robock
Atmos. Chem. Phys., 17, 11913–11928, https://doi.org/10.5194/acp-17-11913-2017, https://doi.org/10.5194/acp-17-11913-2017, 2017
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Ozone is a key air pollutant. We model two geoengineering schemes, stratospheric sulfur injection and solar irradiance reduction, to compare their impacts on atmospheric ozone concentrations. With the nearly identical global mean surface temperature reduction, solar dimming increases global average surface ozone concentration, while sulfate injection decreases it. This difference is due to different stratosphere–troposphere exchange of ozone and tropospheric ozone chemistry in the two scenarios.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
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In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Peer Johannes Nowack, Nathan Luke Abraham, Peter Braesicke, and John Adrian Pyle
Atmos. Chem. Phys., 16, 4191–4203, https://doi.org/10.5194/acp-16-4191-2016, https://doi.org/10.5194/acp-16-4191-2016, 2016
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Various forms of solar radiation management (SRM) have been proposed to counteract man-made climate change. However, all these countermeasures could have unintended side-effects. We add a novel perspective to this discussion by showing how atmospheric ozone changes under solar geoengineering could affect UV exposure and air pollution. This would have implications for human health and ecology. Atmospheric composition changes are therefore important to consider in the evaluation of any SRM scheme.
Related subject area
Subject: Climate and Earth System | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
Yichen Jia, Hendrik Andersen, and Jan Cermak
Atmos. Chem. Phys., 24, 13025–13045, https://doi.org/10.5194/acp-24-13025-2024, https://doi.org/10.5194/acp-24-13025-2024, 2024
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We present a near-global observation-based explainable machine learning framework to quantify the response of cloud fraction (CLF) of marine low clouds to cloud droplet number concentration (Nd), accounting for the covariations with meteorological factors. This approach provides a novel data-driven method to analyse the CLF adjustment by assessing the CLF sensitivity to Nd and numerous meteorological factors as well as the dependence of the Nd–CLF sensitivity on the meteorological conditions.
Cited articles
Abramowitz, G. and Bishop, C. H.: Climate model dependence and the ensemble dependence transformation of CMIP projections, J. Climate, 28, 2332–2348, https://doi.org/10.1175/JCLI-D-14-00364.1, 2015. a
Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019. a
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and the hydrologic cycle, Nature, 419, 224–232, https://doi.org/10.1038/nature01092, 2002. a
Andersen, H., Cermak, J., Fuchs, J., Knutti, R., and Lohmann, U.: Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks, Atmos. Chem. Phys., 17, 9535–9546, https://doi.org/10.5194/acp-17-9535-2017, 2017. a
Andersen, H., Cermak, J., Zipfel, L., and Myers, T. A.: Attribution of Observed Recent Decrease in Low Clouds Over the Northeastern Pacific to Cloud-Controlling Factors, Geophys. Res. Lett., 49, 1–10, https://doi.org/10.1029/2021gl096498, 2022. a
Andrews, T., Forster, P. M., Boucher, O., Bellouin, N., and Jones, A.: Precipitation, radiative forcing and global temperature change, Geophys. Res. Lett., 37, L14701, https://doi.org/10.1029/2010GL043991, 2010. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A. L., Dufresne, J. L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a
Beucler, T., Pritchard, M., Gentine, P., and Rasp, S.: Towards physically-consistent, data-driven models of convection, IEEE Xplore, 3987–3990 pp., https://doi.org/10.1109/IGARSS39084.2020.9324569, 2020. a
Beucler, T., Gentine, P., Yuval, J., Gupta, A., Peng, L., Lin, J., Yu, S., Rasp, S., Ahmed, F., O'gorman, P. A., Neelin, J. D., Lutsko, N. J., and Pritchard, M.: Climate-invariant machine learning, Sci. Adv., 10, eadj7250, https://doi.org/10.1126/sciadv.adj7250, 2024. a, b
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023. a
Bishop, C. H. and Abramowitz, G.: Climate model dependence and the replicate Earth paradigm, Clim. Dynam., 41, 885–900, https://doi.org/10.1007/s00382-012-1610-y, 2013. a
Bishop, C. M.: Pattern recognition and machine learning, Springer Science+Business Media, ISBN 978-0387-31073-2, 2006. a
Bouabid, S., Sejdinovic, D., and Watson-Parris, D.: FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation, arXiv, 1–64 pp., http://arxiv.org/abs/2307.10052 (last access: 10 March 2024), 2023. a
Bouallègue, Z. B., Weyn, J. A., Clare, M. C. A., Dramsch, J., Dueben, P., and Chantry, M.: Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers, Art. Intell. Earth Syst., 3, e230027, https://doi.org/10.1175/aies-d-23-0027.1, 2024. a
Bracegirdle, T. J. and Stephenson, D. B.: On the robustness of emergent constraints used in multimodel climate change projections of arctic warming, J. Climate, 26, 669–678, https://doi.org/10.1175/JCLI-D-12-00537.1, 2013. a
Bretherton, C. S. and Caldwell, P. M.: Combining emergent constraints for climate sensitivity, J. Climate, 33, 7413–7430, https://doi.org/10.1175/JCLI-D-19-0911.1, 2020. a
Breul, P., Ceppi, P., and Shepherd, T. G.: Revisiting the wintertime emergent constraint of the southern hemispheric midlatitude jet response to global warming, Weather Clim. Dynam., 4, 39–47, https://doi.org/10.5194/wcd-4-39-2023, 2023. a
Brient, F. and Schneider, T.: Constraints on climate sensitivity from space-based measurements of low-cloud reflection, J. Climate, 29, 5821–5835, https://doi.org/10.1175/JCLI-D-15-0897.1, 2016. a
Brunner, L., McSweeney, C., Ballinger, A. P., Befort, D. J., Benassi, M., Booth, B., Coppola, E., Vries, H. D., Harris, G., Hegerl, G. C., Knutti, R., Lenderink, G., Lowe, J., Nogherotto, R., O'Reilly, C., Qasmi, S., Ribes, A., Stocchi, P., and Undorf, S.: Comparing Methods to Constrain Future European Climate Projections Using a Consistent Framework, J. Climate, 33, 8671–8692, https://doi.org/10.1175/JCLI-D-19-0953.1, 2020a. a, b
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020b. a
Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B. D., and Sanderson, B. M.: Statistical significance of climate sensitivity predictors obtained by data mining, Geophys. Res. Lett., 41, 1803–1808, https://doi.org/10.1002/2014gl059205, 2014. a
Caldwell, P. M., Zelinka, M. D., and Klein, S. A.: Evaluating Emergent Constraints on Equilibrium Climate Sensitivity, J. Climate, 31, 3921–3942, https://doi.org/10.1175/JCLI-D-17-0631.1, 2018. a, b, c
Camps-Valls, G., Gerhardus, A., Ninad, U., Varando, G., Martius, G., Balaguer-Ballester, E., Vinuesa, R., Diaz, E., Zanna, L., and Runge, J.: Discovering causal relations and equations from data, Vol. 1044, 1–68 pp., https://doi.org/10.1016/j.physrep.2023.10.005, 2023. a
Carslaw, K. S., Lee, L. A., Reddington, C. L., Mann, G. W., and Pringle, K. J.: The magnitude and sources of uncertainty in global aerosol, Faraday Discussions, 165, 495, https://doi.org/10.1039/c3fd00043e, 2013a. a, b, c
Carslaw, K. S., Lee, L. A., Reddington, C. L., Pringle, K. J., Rap, A., Forster, P. M., Mann, G. W., Spracklen, D. V., Woodhouse, M. T., Regayre, L. A., and Pierce, J. R.: Large contribution of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67–71, https://doi.org/10.1038/nature12674, 2013b. a
Ceppi, P., Brient, F., Zelinka, M., and Hartmann, D. L.: Cloud feedback mechanisms and their representation in global climate models, Wiley Interdisciplinary Reviews: Climate Change, 8, 1–21, https://doi.org/10.1002/wcc.465, 2017. a
Ceppi, P., Myers, T. A., Nowack, P., Wall, C. J., and Zelinka, M. D.: Implications of a Pervasive Climate Model Bias for Low-Cloud Feedback, Geophys. Res. Lett., 51, e2024GL110525, https://doi.org/10.1029/2024GL110525, 2024. a, b
Cesana, G. V. and Genio, A. D. D.: Observational constraint on cloud feedbacks suggests moderate climate sensitivity, Nat. Clim. Change, 11, 213–220, https://doi.org/10.1038/s41558-020-00970-y, 2021. a
Chadburn, S. E., Burke, E. J., Cox, P. M., Friedlingstein, P., Hugelius, G., and Westermann, S.: An observation-based constraint on permafrost loss as a function of global warming, Nat. Clim. Change, 7, 340–344, https://doi.org/10.1038/nclimate3262, 2017. a
Charlesworth, E., Plüger, F., Birner, T., Baikhadzhaev, R., Abalos, M., Abraham, N. L., Akiyoshi, H., Bekki, S., Dennison, F., Jäckel, P., Keeble, J., Kinnison, D., Morgenstern, O., Plummer, D., Rozanov, E., Strode, S., Zeng, G., Egorova, T., and Riese, M.: Stratospheric water vapor affecting atmospheric circulation, Nat. Commun., 14, 3925, https://doi.org/10.1038/s41467-023-39559-2, 2023. a
Chen, Z., Liu, Y., and Sun, H.: Physics-informed learning of governing equations from scarce data, Nat. Commun., 12, 6136, https://doi.org/10.1038/s41467-021-26434-1, 2021. a
Chen, Z., Zhou, T., Chen, X., Zhang, W., Zhang, L., Wu, M., and Zou, L.: Observationally constrained projection of Afro-Asian monsoon precipitation, Nat. Commun., 13, 2552, https://doi.org/10.1038/s41467-022-30106-z, 2022. a
Cox, P. M.: Emergent Constraints on Climate-Carbon Cycle Feedbacks, Curr. Clim. Change Rep., 5, 275–281, https://doi.org/10.1007/s40641-019-00141-y, 2019. a, b, c
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C., Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability, Nature, 494, 341–344, https://doi.org/10.1038/nature11882, 2013. a
Cox, P. M., Huntingford, C., and Williamson, M. S.: Emergent constraint on equilibrium climate sensitivity from global temperature variability, Nature, 553, 319–322, https://doi.org/10.1038/nature25450, 2018. a
Deangelis, A. M., Qu, X., Zelinka, M. D., and Hall, A.: An observational radiative constraint on hydrologic cycle intensification, Nature, 528, 249–253, https://doi.org/10.1038/nature15770, 2015. a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate change projections: The role of internal variability, Clim. Dynam., 38, 527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012. a
Dessler, A., Ye, H., Wang, T., Schoeberl, M., Oman, L., Douglass, A., Butler, A., Rosenlof, K., Davis, S., and Portmann, R.: Transport of ice into the stratosphere and the humidification of the stratosphere over the 21st century, Geophys. Res. Lett., 43, 2323–2329, https://doi.org/10.1002/2016GL067991, 2016. a
Dessler, A. E. and Forster, P. M.: An estimate of equilibrium climate sensitivity from interannual variability, J. Geophys. Res.-Atmos., 123, 1–12, https://doi.org/10.1029/2018JD028481, 2018. a
Dietmüller, S., Ponater, M., and Sausen, R.: Interactive ozone induces a negative feedback in CO2-driven climate change simulations, J. Geophys. Res-.Atmos., 119, 1796–1805, https://doi.org/10.1002/2013JD020575, 2014. a
Donat, M. G., Pitman, A. J., and Angélil, O.: Understanding and Reducing Future Uncertainty in Midlatitude Daily Heat Extremes Via Land Surface Feedback Constraints, Geophys. Res. Lett., 45, 10627–10636, https://doi.org/10.1029/2018GL079128, 2018. a
Douville, H.: Robust and perfectible constraints on human-induced Arctic amplification, Commun. Earth Environ., 4, 283, https://doi.org/10.1038/s43247-023-00949-5, 2023. a
Douville, H., Qasmi, S., Ribes, A., and Bock, O.: Global warming at near-constant tropospheric relative humidity is supported by observations, Commun. Earth Environ., 3, 237, https://doi.org/10.1038/s43247-022-00561-z, 2022. a
Dueben, P. D. and Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev., 11, 3999–4009, https://doi.org/10.5194/gmd-11-3999-2018, 2018. 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., 106, 7505–7514, https://doi.org/10.1029/2000JD900637, 2001. a
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 (data available at: https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/, last access: 29 May 2024). a
Eyring, V., Cox, P., Flato, G., Gleckler, P., Abramowitz, G., Caldwell, P., Collins, W., Gier, B., Hall, A., Hoffman, F., Hurtt, G., Jahn, A., Jones, C., Klein, S., Krasting, J., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G., and Pen, M.: Taking climate model evaluation to the next level, Nat. Clim. Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019. a, b, c
Fuchs, J., Cermak, J., and Andersen, H.: Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning, Atmos. Chem. Phys., 18, 16537–16552, https://doi.org/10.5194/acp-18-16537-2018, 2018. a
Fueglistaler, S., Bonazzola, M., Haynes, P. H., and Peter, T.: Stratospheric water vapor predicted from the Lagrangian temperature history of air entering the stratosphere in the tropics, J. Geophys. Res.-Atmos., 110, D10S16, https://doi.org/10.1029/2004JD005516, 2005. a
Fueglistaler, S., Dessler, A. E., Dunkerton, T. J., Folkins, I., Fu, Q., and Ote, P. W.: Tropical tropopause layer, Rev. Geophys., 47, RG1004, https://doi.org/10.1029/2008RG000267, 2009. a
Giorgi, F. and Mearns, L. O.: Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “Reliability Ensemble Averaging” (REA) method, J. Climate, 15, 1141–1158, https://doi.org/10.1175/1520-0442(2003)016<0883:COCOAU>2.0.CO;2, 2002. a
Gregory, J. M., Ingram, W. J., Palmer, M. A., Jones, G. S., Stott, P. A., Thorpe, R. B., Lowe, J. A., Johns, T. C., and Williams, K. D.: A new method for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett., 31, L03205, https://doi.org/10.1029/2003GL018747, 2004. a
Gryspeerdt, E., Povey, A. C., Grainger, R. G., Hasekamp, O., Hsu, N. C., Mulcahy, J. P., Sayer, A. M., and Sorooshian, A.: Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions, Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, 2023. a
Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127, 2006. a, b
Hall, A., Cox, P., Huntingford, C., and Klein, S.: Progressing emergent constraints on future climate change, Nat. Clim. Change, 9, 269–278, https://doi.org/10.1038/s41558-019-0436-6, 2019. a, b, c
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020 (data available at: https://crudata.uea.ac.uk/cru/data/hrg/, last access: 29 May 2024). a, b
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional climate predictions, B. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hegerl, G. C., Ballinger, A. P., Booth, B. B., Borchert, L. F., Brunner, L., Donat, M. G., Doblas-Reyes, F. J., Harris, G. R., Lowe, J., Mahmood, R., Mignot, J., Murphy, J. M., Swingedouw, D., and Weisheimer, A.: Toward Consistent Observational Constraints in Climate Predictions and Projections, Front. Climate, 3, 678109, https://doi.org/10.3389/fclim.2021.678109, 2021. a, b, c
Held, I.: Simplicity amid complexity, Science, 343, 1206–1207, https://doi.org/10.1126/science.1248447, 2014. a
Hess, P., Drüke, M., Petri, S., Strnad, F. M., and Boers, N.: Physically constrained generative adversarial networks for improving precipitation fields from Earth system models, Nat. Mach. Intellig., 4, 828–839, https://doi.org/10.1038/s42256-022-00540-1, 2022. a
Hickman, S. H. M., Griffiths, P. T., Nowack, P. J., and Archibald, A. T.: Short-term forecasting of ozone air pollution across Europe with transformers, Environ. Data Sci., 2, e43, https://doi.org/10.1017/eds.2023.37, 2023. a
Hoerl, A. E. and Kennard, R. W.: Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12, 55–67, https://doi.org/10.2307/1271436, 1970. a, b
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J. C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.: The art and science of climate model tuning, B. Am. Meteorol. Soc., 98, 589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a, b
Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., and Yang, H.: Machine learning and artificial intelligence to aid climate change research and preparedness, Environ. Res. Lett, 14, 124007, https://doi.org/10.1088/1748-9326/ab4e55, 2019. a
Huntingford, C., Cox, P. M., Williamson, M. S., Clarke, J. J., and Ritchie, P. D. L.: Emergent constraints for the climate system as effective parameters of bulk differential equations, Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023, 2023. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021. a
Joshi, M. M., Charlton, A. J., and Scaife, A. A.: On the influence of stratospheric water vapor changes on the tropospheric circulation, Geophys. Res. Lett., 33, L09806, https://doi.org/10.1029/2006GL025983, 2006. a
Joshi, M. M., Webb, M. J., Maycock, A. C., and Collins, M.: Stratospheric water vapour and high climate sensitivity in a version of the HadSM3 climate model, Atmos. Chem. Phys., 10, 7161–7167, https://doi.org/10.5194/acp-10-7161-2010, 2010. a
Kaltenborn, J., Charlotte, L., Ramesh, V., Brouillard, P., Gurwicz, Y., Nagda, C., Runge, J., Nowack, P., and Rolnick, D.: ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning, Advances in Neural Information Processing Systems, 36, https://papers.nips.cc/paper_files/paper/2023/hash/44a6769fe6c695f8dfb347c649f7c9f0-Abstract-Datasets_and_Benchmarks.html (last access: 2 January 2025), 2023. a
Kang, S. M., Yu, Y., Deser, C., Zhang, X., Kang, I. S., Lee, S. S., Rodgers, K. B., and Ceppi, P.: Global impacts of recent Southern Ocean cooling, P. Natl. Acad. Sci. USA, 120, e2300881120, https://doi.org/10.1073/pnas.2300881120, 2023. a
Karpechko, A. Y., Maraun, D., and Eyring, V.: Improving antarctic total ozone projections by a process-oriented multiple diagnostic ensemble regression, J. Atmos. Sci., 70, 3959–3976, https://doi.org/10.1175/JAS-D-13-071.1, 2013. a
Kashinath, K., Mustafa, M., Albert, A., Wu, J. L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., Hassanzadeh, P., and Prabhat: Physics-informed machine learning: Case studies for weather and climate modelling, P. Trans. Roy. Soc. A, 379, 20200093, https://doi.org/10.1098/rsta.2020.0093, 2021. a
Kasoar, M., Voulgarakis, A., Lamarque, J.-F., Shindell, D. T., Bellouin, N., Collins, W. J., Faluvegi, G., and Tsigaridis, K.: Regional and global temperature response to anthropogenic SO2 emissions from China in three climate models, Atmos. Chem. Phys., 16, 9785–9804, https://doi.org/10.5194/acp-16-9785-2016, 2016. 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
Klein, S. A. and Hall, A.: Emergent Constraints for Cloud Feedbacks, Curr. Clim. Change Rep., 1, 276–287, https://doi.org/10.1007/s40641-015-0027-1, 2015. a
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review, Surv. Geophys., 38, 1307–1329, https://doi.org/10.1007/s10712-017-9433-3, 2017. a, b
Knutti, R.: The end of model democracy?, Clim. Change, 102, 395–404, https://doi.org/10.1007/s10584-010-9800-2, 2010. a
Knutti, R., Rugenstein, M. A. A., and Hegerl, G. C.: Beyond equilibrium climate sensitivity, Nat. Geosci., 10, 727–736, https://doi.org/10.1038/ngeo3017, 2017. a
Kroll, C. A. and Schmidt, A.: Indirect stratospheric moisture increase after a Pinatubo-magnitude eruption can be comparable to direct increase after 2022 Hunga, Commun. Earth Environ., 5, 497, https://doi.org/10.1038/s43247-024-01651-w, 2024. a
Kurth, T., Subramanian, S., Harrington, P., Pathak, J., Mardani, M., Hall, D., Miele, A., Kashinath, K., and Anandkumar, A.: FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators, Association for Computing Machinery, Inc, ISBN 9798400701900, https://doi.org/10.1145/3592979.3593412, 2023. a
Kwiatkowski, L., Bopp, L., Aumont, O., Ciais, P., Cox, P. M., Laufkötter, C., Li, Y., and Séférian, R.: Emergent constraints on projections of declining primary production in the tropical oceans, Nat. Clim. Change, 7, 355–358, https://doi.org/10.1038/nclimate3265, 2017. a
Labe, Z. M. and Barnes, E. A.: Detecting Climate Signals Using Explainable AI With Single-Forcing Large Ensembles, J. Adv. Model. Earth Syst., 13, e2021MS002464, https://doi.org/10.1029/2021ms002464, 2021. a
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: Learning skillful medium-range global weather forecasting, Science, 382, 1416–1421, 2023. a
Leach, N. J., Jenkins, S., Nicholls, Z., Smith, C. J., Lynch, J., Cain, M., Walsh, T., Wu, B., Tsutsui, J., and Allen, M. R.: FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration, Geosci. Model Dev., 14, 3007–3036, https://doi.org/10.5194/gmd-14-3007-2021, 2021. a
Li, G., Xie, S. P., He, C., and Chen, Z.: Western Pacific emergent constraint lowers projected increase in Indian summer monsoon rainfall, Nat. Clim. Change, 7, 708–712, https://doi.org/10.1038/nclimate3387, 2017. a
Lipat, B. R., Tselioudis, G., Grise, K. M., and Polvani, L. M.: CMIP5 models' shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent, Geophys. Res. Lett., 44, 5739–5748, https://doi.org/10.1002/2017GL073151, 2017. a
Lorenz, R., Herger, N., Sedláček, J., Eyring, V., Fischer, E. M., and Knutti, R.: Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America, J. Geophys. Res.-Atmos., 123, 4509–4526, https://doi.org/10.1029/2017JD027992, 2018. a, b
Mansfield, L., Nowack, P., Kasoar, M., Everitt, R., Collins, W., and Voulgarakis, A.: Predicting global patterns of long-term climate change from short-term simulations using machine learning, npj Clim. Atmos. Sci., 3, 44, https://doi.org/10.1038/s41612-020-00148-5, 2020. a
Marshall, L. R., Schmidt, A., Schurer, A. P., Abraham, N. L., Lücke, L. J., Wilson, R., Anchukaitis, K., Hegerl, G., Johnson, B., Otto-Bliesner, B. L., Brady, E. C., Khodri, M., and Yoshida, K.: Last Millennium Volcanic Forcing and Climate Response using SO2 Emissions, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1322, 2024. a
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz, D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a global model, J. Adv. Model. Earth Syst., 4, M00A01, https://doi.org/10.1029/2012MS000154, 2012. a, b
McCoy, D. T., Eastman, R., Hartmann, D. L., and Wood, R.: The change in low cloud cover in a warmed climate inferred from AIRS, MODIS, and ERA-interim, J. Climate, 30, 3609–3620, https://doi.org/10.1175/JCLI-D-15-0734.1, 2017. a
Ming, A., Hitchcock, P., and Haynes, P.: The double peak in upwelling and heating in the tropical lower stratosphere, J. Atmos. Sci., 73, 1889–1901, https://doi.org/10.1175/JAS-D-15-0293.1, 2016. a
Mulholland, D. P., Haines, K., Sparrow, S. N., and Wallom, D.: Climate model forecast biases assessed with a perturbed physics ensemble, Clim. Dynam., 49, 1729–1746, https://doi.org/10.1007/s00382-016-3407-x, 2017. a
Murphy, J. M., Sexton, D. M. H., Barnett, D. N., Jones, G. S., Webb, M. J., Collins, M., and Stainforth, D. A.: Quantification of modelling uncertainties in a large ensemble of climate change simulations, Nature, 430, 768–772, https://doi.org/10.1038/nature02771, 2004. a
Myers, T. A. and Norris, J. R.: Reducing the uncertainty in subtropical cloud feedback, Geophys. Res. Lett., 43, 2144–2148, https://doi.org/10.1002/2015GL067416, 2016. a
Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., and Caldwell, P. M.: Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity, Nat. Clim. Change, 11, 501–507, https://doi.org/10.1038/s41558-021-01039-0, 2021. a
Nowack, P., Braesicke, P., Haigh, J., Abraham, N. L., Pyle, J., and Voulgarakis, A.: Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations, Environ. Res. Lett., 13, 104016, https://doi.org/10.1088/1748-9326/aae2be, 2018a. a, b
Nowack, P., Abraham, N. L., Braesicke, P., and Pyle, J. A.: The impact of stratospheric ozone feedbacks on climate sensitivity estimates, J. Geophys. Res.-Atmos., 123, 4630–4641, https://doi.org/10.1002/2017JD027943, 2018b. a
Nowack, P., Ong, Q. Y. E., Braesicke, P., Haigh, J. D., Luke, A., Pyle, J., and Voulgarakis, A.: Machine learning parameterizations for ozone: climate model transferability, in: Conference Proceedings of the 9th International Conference on Climate Informatics, Paris, France, 263–268 pp., https://spiral.imperial.ac.uk/entities/publication/ab6ee0d6-7552-455b-b635-62a1ffbadf2e (last access: 2 January 2020), 2019. a
Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal networks for climate model evaluation and constrained projections, Nat. Commun., 11, 1415, https://doi.org/10.1038/s41467-020-15195-y, 2020. a, b
Nowack, P., Konstantinovskiy, L., Gardiner, H., and Cant, J.: Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability, Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, 2021. a
Nowack, P., Ceppi, P., Davis, S. M., Chiodo, G., Ball, W., Diallo, M. A., Hassler, B., Jia, Y., Keeble, J., and Joshi, M.: Response of stratospheric water vapour to warming constrained by satellite observations, Nat. Geosci., 16, 577–583, https://doi.org/10.1038/s41561-023-01183-6, 2023. a, b, c, d, e, f, g
Nowack, P. J., Abraham, N. L., Maycock, A. C., Braesicke, P., Gregory, J. M., Joshi, M. M., Osprey, A., and Pyle, J. A.: A large ozone-circulation feedback and its implications for global warming assessments, Nat. Clim. Change, 5, 41–45, https://doi.org/10.1038/nclimate2451, 2015. a
Nowack, P. J., Braesicke, P., Abraham, N. L., and Pyle, J. A.: On the role of ozone feedback in the ENSO amplitude response under global warming, Geophys. Res. Lett., 44, 3858–3866, https://doi.org/10.1002/2016GL072418, 2017. a
O'Gorman, P. A.: Sensitivity of tropical precipitation extremes to climate change, Nat. Geosci., 5, 697–700, https://doi.org/10.1038/ngeo1568, 2012. a
O'Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., and van Vuuren, D. P.: A new scenario framework for climate change research: The concept of shared socioeconomic pathways, npj Clim. Change, 122, 387–400, https://doi.org/10.1007/s10584-013-0905-2, 2014. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
O'Reilly, C. H., Befort, D. J., and Weisheimer, A.: Calibrating large-ensemble European climate projections using observational data, Earth Syst. Dynam., 11, 1033–1049, https://doi.org/10.5194/esd-11-1033-2020, 2020. a
O'Reilly, C. H., Brunner, L., Qasmi, S., Nogherotto, R., Ballinger, A. P., Booth, B., Befort, D. J., Knutti, R., Schurer, A. P., Ribes, A., Weisheimer, A., Coppola, E., and McSweeney, C.: Assessing observational constraints on future European climate in an out-of-sample framework, Clim. Atmos. Sci., 7, 95, https://doi.org/10.1038/s41612-024-00648-8, 2024. a
Pendergrass, A. G.: The Global-Mean Precipitation Response to CO2-Induced Warming in CMIP6 Models, Geophys. Res. Lett., 47, e2020GL089964, https://doi.org/10.1029/2020GL089964, 2020. a
Qasmi, S. and Ribes, A.: Reducing uncertainty in local climate projections, Sci. Adv., 8, eabo6872, https://doi.org/doi.org/10.1126/sciadv.abo6872, 2022. a
Qu, X., Hall, A., Klein, S. A., and Deangelis, A. M.: Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors, Geophys. Res. Lett., 42, 7767–7775, https://doi.org/10.1002/2015GL065627, 2015. a
Rasp, S. and Thuerey, N.: Data-Driven Medium-Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench, J. Adv. Model. Earth Syst., 13, e2020MS002405, https://doi.org/10.1029/2020MS002405, 2021. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent sub-grid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Reichler, T. and Kim, J.: How Well Do Coupled Models Simulate Today's Climate?, B. Am. Meteorol. Soc., 819, 303–311, https://doi.org/10.1175/BAMS-89-3-303, 2008. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Ribes, A., Qasmi, S., and Gillett, N. P.: Making climate projections conditional on historical observations, Sci. Adv., 7, eabc0671, https://doi.org/10.1126/sciadv.abc0671, 2021. a
Ribes, A., Boé, J., Qasmi, S., Dubuisson, B., Douville, H., and Terray, L.: An updated assessment of past and future warming over France based on a regional observational constraint, Earth Syst. Dynam., 13, 1397–1415, https://doi.org/10.5194/esd-13-1397-2022, 2022. a
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.: Detecting and quantifying causal associations in large nonlinear time series datasets, Sci. Adv., 5, aau4996, https://doi.org/10.1126/sciadv.aau4996, 2019. a
Räisänen, J., Ruokolainen, L., and Ylhäisi, J.: Weighting of model results for improving best estimates of climate change, Clim. Dynam., 35, 407–422, https://doi.org/10.1007/s00382-009-0659-8, 2010. a
Saltelli, A.: A short comment on statistical versus mathematical modelling, Nat. Commun., 19, 3870, https://doi.org/10.1038/s41467-019-11865-8, 2019. a
Sanderson, B. M., Knutti, R., and Caldwell, P.: Addressing interdependency in a multimodel ensemble by interpolation of model properties, J. Climate, 28, 5150–5170, https://doi.org/10.1175/JCLI-D-14-00361.1, 2015. a
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017. a
Sanderson, B. M., Pendergrass, A. G., Koven, C. D., Brient, F., Booth, B. B. B., Fisher, R. A., and Knutti, R.: The potential for structural errors in emergent constraints, Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021, 2021. a
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., and Eyring, V.: Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?, Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, 2020. a, b
Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth system modeling 2.0: a blueprint for models that learn from observations and targeted high-resolution simulations, Geophys. Res. Lett., 44, 12396–12417, https://doi.org/10.1002/2017GL076101, 2017. a
Scott, R. C., Myers, T. A., and Norris, J. R.: Observed Sensitivity of Low Cloud Radiative Effects to Meteorological Perturbations over the Global Oceans, J. Climate, 33, 7717–7734, https://doi.org/10.1175/JCLI-D-19-1028.1., 2020. a
Shao, Y., Bishop, C. H., Hobeichi, S., Nishant, N., Abramowitz, G., and Sherwood, S.: Time Variability Correction of CMIP6 Climate Change Projections, J. Adv. Model. Earth Syst., 16, e2023MS003640, https://doi.org/10.1029/2023MS003640, 2024. a
Sherwood, S., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An assessment of Earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a, b, c, d
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42, https://doi.org/10.1038/nature12829, 2014. a, b
Shiogama, H., Watanabe, M., Kim, H., and Hirota, N.: Emergent constraints on future precipitation changes, Nature, 602, 612–616, https://doi.org/10.1038/s41586-021-04310-8, 2022. a
Simpson, I. R., McKinnon, K. A., Davenport, F. V., Tingley, M., Lehner, F., Fahad, A. A., and Chen, D.: Emergent constraints on the large-scale atmospheric circulation and regional hydroclimate: Do they still work in CMIP6 and how much can they actually constrain the future?, J. Climate, 34, 6355–6377, https://doi.org/10.1175/JCLI-D-21-0055.1, 2021. a
Sippel, S., Mitchell, D., Black, M. T., Dittus, A. J., Harrington, L., Schaller, N., and Otto, F. E.: Combining large model ensembles with extreme value statistics to improve attribution statements of rare events, Weather Clim. Extrem., 9, 25–35, https://doi.org/10.1016/j.wace.2015.06.004, 2015. a
Sippel, S., Zscheischler, J., Mahecha, M. D., Orth, R., Reichstein, M., Vogel, M., and Seneviratne, S. I.: Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics, Earth Syst. Dynam., 8, 387–403, https://doi.org/10.5194/esd-8-387-2017, 2017. 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
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
Stuber, N., Ponater, M., and Sausen, R.: Why radiative forcing might fail as a predictor of climate change, Clim. Dynam., 24, 497–510, https://doi.org/10.1007/s00382-004-0497-7, 2005. a
Tebaldi, C., Mearns, L. O., Nychka, D., and Smith, R. L.: Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations, Geophys. Res. Lett., 31, L24213, https://doi.org/10.1029/2004GL021276, 2004. a
Thackeray, C. W. and Hall, A.: An emergent constraint on future Arctic sea-ice albedo feedback, Nat. Clim. Change, 9, 972–978, https://doi.org/10.1038/s41558-019-0619-1, 2019. a
Thackeray, C. W., Hall, A., Zelinka, M. D., and Fletcher, C. G.: Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6, J. Climate, May, 3889–3905, https://doi.org/10.1175/JCLI-D-20-0703.1, 2021. a
Thackeray, C. W., Hall, A., Norris, J., and Chen, D.: Constraining the increased frequency of global precipitation extremes under warming, Nat. Clim. Change, 12, 441–448, https://doi.org/10.1038/s41558-022-01329-1, 2022. a
Thomas, C., Voulgarakis, A., Lim, G., Haigh, J., and Nowack, P.: An unsupervised learning approach to identifying blocking events: the case of European summer, Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, 2021. a
Tian, B.: Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias, Geophys. Res. Lett., 42, 4133–4141, https://doi.org/10.1002/2015GL064119, 2015. a
Tokarska, K. B., Stolpe, M. B., Sippel, S., Fischer, E. M., Smith, C. J., Lehner, F., and Knutti, R.: Past warming trend constrains future warming in CMIP6 models, Sci. Adv., 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549, 2020. a
Watson-Parris, D.: Machine learning for weather and climate are worlds apart, Philosophical Transactions of the Royal Society A: Mathematical, Phys. Eng. Sci., 379, 20200098, https://doi.org/10.1098/rsta.2020.0098, 2021. a
Watson-Parris, D., Rao, Y., Olivié, D., Seland, Ã., Nowack, P., Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J., Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard, L., and Roesch, C.: ClimateBench v1.0: A benchmark for data-driven climate projections, J. Adv. Model. Earth Syst., 14, e2021MS002954, https://doi.org/10.1029/2021ms002954, 2022. a
Watt-Meyer, O., Dresdner, G., McGibbon, J., Clark, S. K., Henn, B., Duncan, J., Brenowitz, N. D., Kashinath, K., Pritchard, M. S., Bonev, B., Peters, M. E., and Bretherton, C. S.: ACE: A fast, skillful learned global atmospheric model for climate prediction, arXiv:2310.02074 [physics.ao-ph], https://arxiv.org/abs/2310.02074 (last access: 2 January 2025), 2023. a
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system models, J. Geophys. Res.-Biogeosci., 119, 794–807, https://doi.org/10.1002/2013JG002591, 2014. a
Wenzel, S., Eyring, V., Gerber, E. P., and Karpechko, A. Y.: Constraining future summer austral jet stream positions in the CMIP5 ensemble by process-oriented multiple diagnostic regression, J. Climate, 29, 673–687, https://doi.org/10.1175/JCLI-D-15-0412.1, 2016. a
Wilkinson, S., Nowack, P., and Joshi, M.: Observations-based machine learning model constrains uncertainty in future regional warming projections., EGU General Assembly 2023, Vienna, Austria, 24–28 April 2023, EGU23-7869, https://doi.org/10.5194/egusphere-egu23-7869, 2023. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, Volume 91 in the International Geophysics Series, A series of monographs and textbooks edited by: Dmowska, R., Hartmann, D., and Rossby, H. T., 2nd edn., Elsevier and Academic Press, 2006, ISBN 13: 978-0-12-751966-1, 2006. a
Wills, R. C., Dong, Y., Proistosecu, C., Armour, K. C., and Battisti, D. S.: Systematic Climate Model Biases in the Large-Scale Patterns of Recent Sea-Surface Temperature and Sea-Level Pressure Change, Geophys. Res. Lett., 49, e2022GL100011, https://doi.org/10.1029/2022GL100011, 2022. a
Winkler, A. J., Myneni, R. B., Alexandrov, G. A., and Brovkin, V.: Earth system models underestimate carbon fixation by plants in the high latitudes, Nat. Commun., 10, 885, https://doi.org/10.1038/s41467-019-08633-z, 2019a. a
Winkler, A. J., Myneni, R. B., and Brovkin, V.: Investigating the applicability of emergent constraints, Earth Syst. Dynam., 10, 501–523, https://doi.org/10.5194/esd-10-501-2019, 2019b. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a, b
Zhou, C., Zelinka, M. D., Dessler, A. E., and Klein, S. A.: The relationship between interannual and long-term cloud feedbacks, Geophys. Res. Lett., 42, 10463–10469, https://doi.org/10.1002/2015GL066698, 2015. a
Executive editor
Emergent constraints are becoming popular for the identification of statistical relationships in observed current and past climate data that can be used to guide projections of future climate states. Their application is not without controversy, however, due to uncertainties about whether these relationships are indeed climate-invariant. This Opinion introduces an argument that Machine Learning tools can be useful for identifying climate-invariant relationships in historical data, especially those that are more complex, that can be expected to remain consistent under future climate scenarios.
Emergent constraints are becoming popular for the identification of statistical relationships in...
Short summary
In our article, we review uncertainties in global climate change projections and current methods using Earth observations as constraints, which is crucial for climate risk assessments and for informing society. We then discuss how machine learning can advance the field, discussing recent work that provides potentially stronger and more robust links between observed data and future climate projections. We further discuss the challenges of applying machine learning to climate science.
In our article, we review uncertainties in global climate change projections and current methods...
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