Articles | Volume 2, issue 3
https://doi.org/10.5194/wcd-2-581-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-2-581-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jul 2021
Research article |
| 12 Jul 2021
| 12 Jul 2021
An unsupervised learning approach to identifying blocking events: the case of European summer
Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, UK
Apostolos Voulgarakis
Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, UK
School of Environmental Engineering, Technical University of Crete, Chania, Crete, 73100, Greece
Gerald Lim
Centre for Climate Research Singapore, 36 Kim Chuan Road, 537054, Singapore
Joanna Haigh
Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, UK
Grantham Institute, Imperial College London, SW7 2AZ, UK
Peer Nowack
Department of Physics, Imperial College London, South Kensington Campus, London, SW7 2BW, UK
Grantham Institute, Imperial College London, SW7 2AZ, UK
Climatic Research Unit, School of Environmental Sciences, Norwich, NR4 7TJ, UK
Data Science Institute, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
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Jingyu Wang, Gabriel Chiodo, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, Sandro Vattioni, and Timofei Sukhodolov
Atmos. Chem. Phys., 25, 17819–17844, https://doi.org/10.5194/acp-25-17819-2025, https://doi.org/10.5194/acp-25-17819-2025, 2025
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We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then examined the relations between ozone response and changes in temperature and circulation to identify the drivers of ozone change. The climate feedback of ozone is investigated through offline calculations and by comparing models with and without interactive chemistry. We find that ozone–climate interactions are important for Earth system models and thus should be considered in future model development.
Jianing Guo, Xiaoning Xie, Gunnar Myhre, Drew Shindell, Alf Kirkevåg, Trond Iversen, Apostolos Voulgarakis, Toshihiko Takemura, Ke Shang, Xinzhou Li, Zhengguo Shi, Yangang Liu, Xiaodong Liu, and Hong Yan
EGUsphere, https://doi.org/10.5194/egusphere-2025-5729, https://doi.org/10.5194/egusphere-2025-5729, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Central Asia has grown wetter in recent decades, but the drivers differ by season. We analyzed observations and climate model experiments to understand these changes and their future. Our analysis reveals that greenhouse gases from human activities drive winter wetting, whereas aerosol from Asia urbanization and industrialization enhances summer precipitation. As future reductions in air pollution, the region may experience drier summers and create new risks for regional water resources.
Paulo Ceppi, Sarah Wilson Kemsley, Hendrik Andersen, Timothy Andrews, Ryan J. Kramer, Peer Nowack, Casey J. Wall, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-5206, https://doi.org/10.5194/egusphere-2025-5206, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Recent decades have seen a marked decrease in global low-level cloud cover, leading to more sunlight heating the Earth. This trend is poorly understood, raising the concern that clouds may amplify global warming more than previously thought. We show that the cloud decrease is mostly caused by human forcing on climate, and that it agrees with previous estimates of how clouds respond to decreasing aerosol pollution, increasing greenhouse gas concentration, and their effects on global temperature.
Philipp Breul, Paulo Ceppi, and Peer Nowack
Atmos. Chem. Phys., 25, 11991–12005, https://doi.org/10.5194/acp-25-11991-2025, https://doi.org/10.5194/acp-25-11991-2025, 2025
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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.
Anastasios Rovithakis, Eleanor Burke, Chantelle Burton, Matthew Kasoar, Manolis G. Grillakis, Konstantinos D. Seiradakis, and Apostolos Voulgarakis
Nat. Hazards Earth Syst. Sci., 25, 3185–3200, https://doi.org/10.5194/nhess-25-3185-2025, https://doi.org/10.5194/nhess-25-3185-2025, 2025
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We used a land surface computer model to forecast how climate change will impact wildfires in Greece. Our results show a significant increase in future burnt area due to hotter, drier climate. Allowing vegetation to change with the climate lessens this increase overall, since fire is no longer igniting in areas already burnt, and it even led to projected decreases in the agricultural areas in the north of the country.
Oliver Perkins, Olivia Haas, Matthew Kasoar, Apostolos Voulgarakis, and James D. A. Millington
EGUsphere, https://doi.org/10.5194/egusphere-2025-3728, https://doi.org/10.5194/egusphere-2025-3728, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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Humans impact fire indirectly through climate change, but also directly through land use and different fire management strategies. We compare two recently-developed models of global burned area with very different assumptions about the role of direct human impacts on fire. We contrast their future projections and explore the implications of differences between them for climate change adaptation and fire science more broadly.
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
Atmos. Chem. Phys., 25, 7991–8028, https://doi.org/10.5194/acp-25-7991-2025, https://doi.org/10.5194/acp-25-7991-2025, 2025
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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 that increases in anthropogenic emissions are the primary driver of ozone increases both in the free troposphere and at the surface.
Joao C. M. Teixeira, Chantelle Burton, Douglas I. Kelley, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-3066, https://doi.org/10.5194/egusphere-2025-3066, 2025
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Burnt areas produced by wildfires around the world are decreasing, especially in tropical regions, but many climate models fail to show this trend. Our study looks at whether adding a measure of human development to a fire model could improve its representation of these processes. We found that including these factors helped the model better match observations in many regions. This shows that human activity plays a key role in shaping fire trends.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025, https://doi.org/10.5194/esd-16-607-2025, 2025
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Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.
Peer Nowack and Duncan Watson-Parris
Atmos. Chem. Phys., 25, 2365–2384, https://doi.org/10.5194/acp-25-2365-2025, https://doi.org/10.5194/acp-25-2365-2025, 2025
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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.
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.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
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.
Christopher D. Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis
Atmos. Chem. Phys., 24, 1025–1039, https://doi.org/10.5194/acp-24-1025-2024, https://doi.org/10.5194/acp-24-1025-2024, 2024
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Human-driven emissions of air pollutants, mostly caused by burning fossil fuels, impact both the climate and human health. Millions of deaths each year are caused by air pollution globally, and the future trends are uncertain. Here, we use a global climate model to study the effect of African pollutant emissions on surface level air pollution, and resultant impacts on human health, in several future emission scenarios. We find much lower health impacts under cleaner, lower-emission futures.
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.
Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-136, https://doi.org/10.5194/bg-2023-136, 2023
Revised manuscript not accepted
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Representing socio-economic impacts on fires is crucial to underpin the confidence in global fire models. Introducing these into INFERNO, reduces biases and improves the modelled burnt area (BA) trends when compared to observations. Including socio-economic factors in the representation of fires in Earth System Models is important for realistically simulating BA, quantifying trends in the recent past, and for understanding the main drivers of those at regional scales.
Christopher D. Wells, Matthew Kasoar, Nicolas Bellouin, and Apostolos Voulgarakis
Atmos. Chem. Phys., 23, 3575–3593, https://doi.org/10.5194/acp-23-3575-2023, https://doi.org/10.5194/acp-23-3575-2023, 2023
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The climate is altered by greenhouse gases and air pollutant particles, and such emissions are likely to change drastically in the future over Africa. Air pollutants do not travel far, so their climate effect depends on where they are emitted. This study uses a climate model to find the climate impacts of future African pollutant emissions being either high or low. The particles absorb and scatter sunlight, causing the ground nearby to be cooler, but elsewhere the increased heat causes warming.
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.
João C. Teixeira, Gerd A. Folberth, Fiona M. O'Connor, Nadine Unger, and Apostolos Voulgarakis
Geosci. Model Dev., 14, 6515–6539, https://doi.org/10.5194/gmd-14-6515-2021, https://doi.org/10.5194/gmd-14-6515-2021, 2021
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Fire constitutes a key process in the Earth system, being driven by climate as well as affecting climate. However, studies on the effects of fires on atmospheric composition and climate have been limited to date. This work implements and assesses the coupling of an interactive fire model with atmospheric composition, comparing it to an offline approach. This approach shows good performance at a global scale. However, regional-scale limitations lead to a bias in modelling fire emissions.
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.
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.
Yawei Qu, Apostolos Voulgarakis, Tijian Wang, Matthew Kasoar, Chris Wells, Cheng Yuan, Sunil Varma, and Laura Mansfield
Atmos. Chem. Phys., 21, 5705–5718, https://doi.org/10.5194/acp-21-5705-2021, https://doi.org/10.5194/acp-21-5705-2021, 2021
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The meteorological effect of aerosols on tropospheric ozone is investigated using global atmospheric modelling. We found that aerosol-induced meteorological effects act to reduce modelled ozone concentrations over China, which brings the simulation closer to observed levels. Our work sheds light on understudied processes affecting the levels of tropospheric gaseous pollutants and provides a basis for evaluating such processes using a combination of observations and model sensitivity experiments.
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.
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Short summary
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.
Atmospheric blocking events are complex large-scale weather patterns which block the path of the...
