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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Paulo Ceppi, Sarah Wilson Kemsley, Hendrik Andersen, Timothy Andrews, Ryan J. Kramer, Peer Nowack, Casey J. Wall, and Mark D. Zelinka
Atmos. Chem. Phys., 26, 4153–4171, https://doi.org/10.5194/acp-26-4153-2026, https://doi.org/10.5194/acp-26-4153-2026, 2026
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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.
Theodore R. Keeping, Mariam Zachariah, Olivia Haas, Μanolis Grillakis, Clair Barnes, Gareth D. Clay, Bikem Ekberzade, Orjeta Jaupaj, Andreia F. S. Ribeiro, Ricardo M. Trigo, Apostolos Voulgarakis, and Friederike E. L. Otto
EGUsphere, https://doi.org/10.5194/egusphere-2026-1193, https://doi.org/10.5194/egusphere-2026-1193, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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2025 broke wildfire records across European regions. For five extreme fire seasons, we identify the weather, vegetation and management conditions that drove such extreme fires. We found the change in the likelihood and intensity of similar driving “fire weather”, from before global warming to now, and from now to a likely future climate. Summer drought and hot, dry and windy conditions have shifted rapidly, with 2025’s conditions not rare in some regions despite devastating fires.
Fang Li, David Lawrence, Brendan Rogers, Chantelle Burton, Huilin Huang, Yiquan Jiang, Johannes Kaiser, Matthew Kasoar, Hanna Lee, Ruby Leung, Lars Nieradzik, Aihui Wang, Daniel Ward, Ligeer Ce, Yangchun Li, Zhongda Lin, Apostolos Voulgarakis, and Yongkang Xue
EGUsphere, https://doi.org/10.5194/egusphere-2025-6115, https://doi.org/10.5194/egusphere-2025-6115, 2025
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Extreme fire events are increasing, and fires are projected to rise across most regions, posing growing risks to ecosystems and society. As a key Earth system process, fire is now modeled in most Earth System Models (ESMs). FireMIP within CMIP7 will evaluate fire simulations in state‑of‑the‑art ESMs, project future fire changes, and provide quantitative, process‑based understanding of fire's role in the Earth system.
Laura A. Mansfield, Peer J. Nowack, Edmund M. Ryan, Oliver Wild, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-6046, https://doi.org/10.5194/egusphere-2025-6046, 2025
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We present a fast machine learning emulator that predicts how Earth’s surface temperature reacts within the first five years to changes in greenhouse gases and aerosol pollutants. It is trained on carefully designed simulations from a complex climate model, but can be run much faster. Our emulator can be used to show where the climate is most sensitive to different emissions and can help explore many possible future paths, making it easier to assess the climate effects of policy choices.
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
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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.
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
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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.
Cited articles
Barnes, E. A.: Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes, Geophys. Res. Lett., 40, 4734–4739, https://doi.org/10.1002/grl.50880, 2013. a
Barnes, E. A. and Polvani, L. M.: CMIP5 Projections of Arctic Amplification, of the North American/North Atlantic Circulation, and of Their Relationship, J. Climate, 28, 5254–5271, https://doi.org/10.1175/JCLI-D-14-00589.1, 2015. a
Barnes, E. A. and Screen, J. A.: The impact of Arctic warming on the midlatitude jet-stream: Can it? Has it? Will it?, WIREs Clim. Change, 6, 277–286, https://doi.org/10.1002/wcc.337, 2015. a
Barnes, E. A., Dunn-Sigouin, E., Masato, G., and Woollings, T.: Exploring recent trends in Northern Hemisphere blocking, Geophys. Res. Lett., 41, 638–644, https://doi.org/10.1002/2013GL058745, 2014. a, b
Barriopedro, D., García-Herrera, R., and Trigo, R.: Application of blocking diagnosis methods to General Circulation Models. Part I: A novel detection scheme, Clim. Dynam., 35, 1373–1391, https://doi.org/10.1007/s00382-010-0767-5, 2010. a, b, c, d
Benjamini, Y. and Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, J. Roy. Stat. Soc. B Met., 57, 289–300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x, 1995. a
Black, E., Blackburn, M., Harrison, G., Hoskins, B., and Methven, J.: Factors contributing to the summer 2003 European heatwave, Weather, 59, 217–223, https://doi.org/10.1256/wea.74.04, 2004. a, b
Brunner, L., Hegerl, G. C., and Steiner, A. K.: Connecting Atmospheric Blocking to European Temperature Extremes in Spring, J. Climate, 30, 585–594, https://doi.org/10.1175/JCLI-D-16-0518.1, 2017. a
Cassou, C.: Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation, Nature, 455, 523–527, https://doi.org/10.1038/nature07286, 2008. a, b, c
Cattiaux, J., Vautard, R., Cassou, C., Yiou, P., Masson-Delmotte, V., and Codron, F.: Winter 2010 in Europe: A cold extreme in a warming climate, Geophys. Res. Lett., 37, 20, https://doi.org/10.1029/2010GL044613, 2010. a
Cattiaux, J., Douville, H., and Peings, Y.: European temperatures in CMIP5: origins of present-day biases and future uncertainties, Clim. Dynam., 41, 2889–2907, https://doi.org/10.1007/s00382-013-1731-y, 2013. a
Chen, G., Lu, J., Burrows, D. A., and Leung, L. R.: Local finite-amplitude wave activity as an objective diagnostic of midlatitude extreme weather, Geophys. Res. Lett., 42, 10952–10960, https://doi.org/10.1002/2015GL066959, 2015. a
Christidis, N., Jones, G., and Stott, P.: Dramatically increasing chance of extremely hot summers since the 2003 European heatwave, Nat. Clim. Change, 5, 46–50, https://doi.org/10.1038/nclimate2468, 2014. a, b
Coumou, D., Di Capua, G., Vavrus, S., Wang, L., and Wang, S.: The influence of Arctic amplification on mid-latitude summer circulation, Nat. Commun., 9, 2959, https://doi.org/10.1038/s41467-018-05256-8, 2018. a
Croci-Maspoli, M., Schwierz, C., and Davies, H. C.: A Multifaceted Climatology of Atmospheric Blocking and Its Recent Linear Trend, J. Climate, 20, 633–649, https://doi.org/10.1175/JCLI4029.1, 2007. a
Crum, F. X. and Stevens, D. F.: A Case Study of Atmospheric Blocking Using Isentropic Analysis, Mon. Weather Rev., 116, 223–241, https://doi.org/10.1175/1520-0493(1988)116<0223:ACSOAB>2.0.CO;2, 1988. a
Davini, P. and D'Andrea, F.: From CMIP3 to CMIP6: Northern Hemisphere Atmospheric Blocking Simulation in Present and Future Climate, J. Climate, 33, 10021–10038, https://doi.org/10.1175/JCLI-D-19-0862.1, 2020. a
Davini, P., Cagnazzo, C., Gualdi, S., and Navarra, A.: Bidimensional Diagnostics, Variability, and Trends of Northern Hemisphere Blocking, J. Climate, 25, 6496–6509, https://doi.org/10.1175/JCLI-D-12-00032.1, 2012. a
Diao, Y., Li, J., and Luo, D.: A New Blocking Index and Its Application: Blocking Action in the Northern Hemisphere, J. Climate, 19, 4819–4839, https://doi.org/10.1175/JCLI3886.1, 2006. a
Diday, E. and Simon, J. C.: Clustering Analysis, Springer, Berlin, Heidelberg, 47–94, https://doi.org/10.1007/978-3-642-67740-3_3, 1980. a, b
Diffenbaugh, N. S., Singh, D., Mankin, J. S., Horton, D. E., Swain, D. L., Touma, D., Charland, A., Liu, Y., Haugen, M., Tsiang, M., and Rajaratnam, B.: Quantifying the influence of global warming on unprecedented extreme climate events, P. Natl. Acad. Sci. USA, 114, 4881–4886, https://doi.org/10.1073/pnas.1618082114, 2017. a
Drouard, M. and Woollings, T.: Contrasting Mechanisms of Summer Blocking Over Western Eurasia, Geophys. Res. Lett., 45, 12,040–12,048, https://doi.org/10.1029/2018GL079894, 2018. a
Dunn-Sigouin, E., Son, S.-W., and Lin, H.: Evaluation of Northern Hemisphere Blocking Climatology in the Global Environment Multiscale Model, Mon. Weather Rev., 141, 707–727, https://doi.org/10.1175/MWR-D-12-00134.1, 2013. a
Elliot, R. and Smith, T.: A study of the effect of large blocking highs on the general circulation in the northern hemisphere westerlies, J. Meteorol., 6, 67–85, 1949. 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. a
Fabiano, F., Meccia, V. L., Davini, P., Ghinassi, P., and Corti, S.: A regime view of future atmospheric circulation changes in northern mid-latitudes, Weather Clim. Dynam., 2, 163–180, https://doi.org/10.5194/wcd-2-163-2021, 2021. a, b, c
Francis, J. A. and Vavrus, S. J.: Evidence linking Arctic amplification to extreme weather in mid-latitudes, Geophys. Res. Lett., 39, 6, https://doi.org/10.1029/2012GL051000, 2012. a
Francis, J. A. and Vavrus, S. J.: Evidence for a wavier jet stream in response to rapid Arctic warming, Environ. Res. Lett., 10, 014005, https://doi.org/10.1088/1748-9326/10/1/014005, 2015. a
Gibson, P., Pitman, A., Lorenz, R., and Perkins-Kirkpatrick, S.: The Role of Circulation and Land Surface Conditions in Current and Future Australian Heat Waves, J. Climate, 30, 9933–9948, https://doi.org/10.1175/JCLI-D-17-0265.1, 2017a. a
Gibson, P. B., Pitman, A. J., Lorenz, R., and Perkins-Kirkpatrick, S. E.: The Role of Circulation and Land Surface Conditions in Current and Future Australian Heat Waves, J. Climate, 30, 9933–9948, https://doi.org/10.1175/JCLI-D-17-0265.1, 2017b. 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, 3, https://doi.org/10.1029/2003GL018747, 2004. a
Grotjahn, R. and Zhang, R.: Synoptic Analysis of Cold Air Outbreaks over the California Central Valley, J. Climate, 30, 9417–9433, https://doi.org/10.1175/JCLI-D-17-0167.1, 2017. a
Hassanzadeh, P., Kuang, Z., and Farrell, B. F.: Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM, Geophys. Res. Lett., 41, 5223–5232, https://doi.org/10.1002/2014GL060764, 2014. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hewitson, B. and Crane, R.: Self-Organizing Maps: Applications to synoptic climatology, Clim. Res., 22, 13–26, https://doi.org/10.3354/cr022013, 2002. a
Hoskins, B. J., McIntyre, M. E., and Robertson, A. W.: On the use and significance of isentropic potential vorticity maps, Q. J. Roy. Meteor. Soc., 111, 877–946, https://doi.org/10.1002/qj.49711147002, 1985. a
Huang, C. S. Y. and Nakamura, N.: Local Finite-Amplitude Wave Activity as a Diagnostic of Anomalous Weather Events, J. Atmos. Sci., 73, 211–229, https://doi.org/10.1175/JAS-D-15-0194.1, 2015. a
Huguenin, M. F., Fischer, E. M., Kotlarski, S., Scherrer, S. C., Schwierz, C., and Knutti, R.: Lack of Change in the Projected Frequency and Persistence of Atmospheric Circulation Types Over Central Europe, Geophys. Res. Lett., 47, e2019GL086132, https://doi.org/10.1029/2019GL086132, e2019GL086132 2019GL086132, 2020. a
Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M., Kyselý, J., and Tveito, O. E.: Classifications of atmospheric circulation patterns: recent advances and applications, Ann. NY Acad. Sci., 1146, 105–52, 2008. a
Jézéquel, A., Yiou, P., and Radanovics, S.: Role of circulation in European heatwaves using flow analogues, Clim. Dynam., 50, 1145–1159, https://doi.org/10.1007/s00382-017-3667-0, 2017. a, b
Johnson, N.: How many ENSO flavors can we distinguish?, J. Climate, 26, 4816–4827, https://doi.org/10.1175/JCLI-D-12-00649.1, 2013. a
Kennedy, D., Parker, T., Woollings, T., Harvey, B., and Shaffrey, L.: The response of high-impact blocking weather systems to climate change, Geophys. Res. Lett., 43, 7250–7258, 2016. a
Kohonen, T.: Self-organized formation of topologically correct feature maps, Biol. Cybern., 43, 59–69, https://doi.org/10.1007/BF00337288, 1982. a
Kornhuber, K., Osprey, S., Coumou, D., Petri, S., Petoukhov, V., Rahmstorf, S., and Gray, L.: Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern, Environ. Res. Lett., 14, 054002, https://doi.org/10.1088/1748-9326/ab13bf, 2019. a
Kornhuber, K., Coumou, D., Vogel, E., Lesk, C., Donges, J., Lehmann, J., and Horton, R.: Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions, Nat. Clim. Change, 10, 1–6, https://doi.org/10.1038/s41558-019-0637-z, 2020. a
Lejenäs, H. and Økland, H.: Characteristics of northern hemisphere blocking as determined from a long time series of observational data, Tellus A, 35A, 350–362, https://doi.org/10.1111/j.1600-0870.1983.tb00210.x, 1983. a, b, c
Liniger, M. A. and Davies, H. C.: Seasonal differences in extratropical potential vorticity variability at tropopause levels, J. Geophys. Res.-Atmos., 109, D17, https://doi.org/10.1029/2004JD004639, 2004. a
Liu, Y. and Weisberg, R. H.: Patterns of ocean current variability on the West Florida Shelf using the self-organizing map, J. Geophys. Res.-Oceans, 110, C6, https://doi.org/10.1029/2004JC002786, 2005. a
Mann, M. E., Rahmstorf, S., Kornhuber, K., Steinman, B. A., Miller, S. K., Petri, S., and Coumou, D.: Projected changes in persistent extreme summer weather events: The role of quasi-resonant amplification, Science Advances, 4, 10, https://doi.org/10.1126/sciadv.aat3272, 2018. a
Mansfield, L. A., Nowack, P. J., Kasoar, M., Everitt, R. G., Collins, W. J., and Voulgarakis, A.: Predicting global patterns of long-term climate change from short-term simulations using machine learning, npj Climate and Atmospheric Science, 3, 44, https://doi.org/10.1038/s41612-020-00148-5, 2020. a, b
Michelangeli, P.-A., Vautard, R., and Legras, B.: Weather Regimes: Recurrence and Quasi Stationarity, J. Atmos. Sci., 52, 1237–1256, https://doi.org/10.1175/1520-0469(1995)052<1237:WRRAQS>2.0.CO;2, 1995. a, b, c
Mioduszewski, J. R., Rennermalm, A. K., Hammann, A., Tedesco, M., Noble, E. U., Stroeve, J. C., and Mote, T. L.: Atmospheric drivers of Greenland surface melt revealed by self-organizing maps, J. Geophys. Res.-Atmos., 121, 5095–5114, https://doi.org/10.1002/2015JD024550, 2016. a
Mitchell, D., Kornhuber, K., Huntingford, C., and Uhe, P.: The day the 2003 European heatwave record was broken, The Lancet Planetary Health, 3, e290–e292, https://doi.org/10.1016/s2542-5196(19)30106-8, 2019. a
Nakamura, N. and Huang, C. S. Y.: Atmospheric blocking as a traffic jam in the jet stream, Science, 361, 42–47, https://doi.org/10.1126/science.aat0721, 2018. a
Nowack, P., Braesicke, P., Haigh, J., Abraham, N., 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, 2018. 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
Nowack, P. J., Braesicke, P., Luke Abraham, N., 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
Palmer, T. N.: A Nonlinear Dynamical Perspective on Climate Prediction, J. Climate, 12, 575–591, https://doi.org/10.1175/1520-0442(1999)012<0575:ANDPOC>2.0.CO;2, 1999. a
Rex, D. F.: Blocking Action in the Middle Troposphere and its Effect upon Regional Climate, Tellus, 2, 275–301, https://doi.org/10.1111/j.2153-3490.1950.tb00339.x, 1950. a, b, c
Robine, J.-M., Cheung, S. L. K., Roy, S. L., Oyen, H. V., Griffiths, C., Michel, J.-P., and Herrmann, F. R.: Death toll exceeded 70,000 in Europe during the summer of 2003, C. R. Biol., 331, 171–178, https://doi.org/10.1016/j.crvi.2007.12.001, 2008. a
Saffioti, C., Fischer, E. M., and Knutti, R.: Improved Consistency of Climate Projections over Europe after Accounting for Atmospheric Circulation Variability, J. Climate, 30, 7271–7291, https://doi.org/10.1175/JCLI-D-16-0695.1, 2017. a
Sánchez-Benítez, A., Barriopedro, D., and García-Herrera, R.: Tracking Iberian heatwaves from a new perspective, Weather and Climate Extremes, 28, 100238, https://doi.org/10.1016/j.wace.2019.100238, 2019. a
Scaife, A. A., Woollings, T., Knight, J., Martin, G., and Hinton, T.: Atmospheric Blocking and Mean Biases in Climate Models, J. Climate, 23, 6143–6152, https://doi.org/10.1175/2010JCLI3728.1, 2010. a
Schaller, N., Sillmann, J., Anstey, J., Fischer, E. M., Grams, C. M., and Russo, S.: Influence of blocking on Northern European and Western Russian heatwaves in large climate model ensembles, Environ. Res. Lett., 13, 054015, https://doi.org/10.1088/1748-9326/aaba55, 2018. a
Scherrer, S., Croci-Maspoli, M., Schwierz, C., and Appenzeller, C.: Two-dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro-Atlantic region, Int. J. Climatol., 26, 233–249, https://doi.org/10.1002/joc.1250, 2006. a, b, c, d
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U. K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Shepherd, T. G.: Atmospheric circulation as a source of uncertainty in climate change projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/ngeo2253, 2014. a
Sheridan, S. C. and Lee, C. C.: The self-organizing map in synoptic climatological research, Prog. Phys. Geogr., 35, 109–119, https://doi.org/10.1177/0309133310397582, 2011. a
Singh, D., Swain, D. L., Mankin, J. S., Horton, D. E., Thomas, L. N., Rajaratnam, B., and Diffenbaugh, N. S.: Recent amplification of the North American winter temperature dipole, J. Geophys. Res.-Atmos., 121, 9911–9928, https://doi.org/10.1002/2016JD025116, 2016. a
Skific, N. and Francis, J.: Self-Organizing Maps: A Powerful Tool for the Atmospheric Sciences, https://doi.org/10.5772/54299, 2012. a, b
Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J.,
Nauels, A., Xia, Y., Bex, V., and Midgley, P. (Eds.): IPCC, 2013: Climate
Change 2013: The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, New York, USA, 2013. a
Stott, P. A., Stone, D. A., and Allen, M. R.: Human contribution to the European heatwave of 2003, Nature, 432, 610–614, https://doi.org/10.1038/nature03089, 2004. a
Strommen, K., Mavilia, I., Corti, S., Matsueda, M., Davini, P., von Hardenberg, J., Vidale, P.-L., and Mizuta, R.: The Sensitivity of Euro-Atlantic Regimes to Model Horizontal Resolution, Geophys. Res. Lett., 46, 7810–7818, https://doi.org/10.1029/2019GL082843, 2019. a, b, c
Thomas, C.: Ground Truth Dataset for European summer blocking events 1979–2019, Zenodo [data set], https://doi.org/10.5281/zenodo.4436206, 2021. a
Thomas, C., Lim, G., and Nowack, P.: SOM-BI Scripts, Zenodo [code], https://doi.org/10.5281/zenodo.4436225, 2021. a
Tibaldi, S. and Molteni, F.: On the operational predictability of blocking, Tellus A, 42, 343–365, https://doi.org/10.1034/j.1600-0870.1990.t01-2-00003.x, 1990.
a, b, c
Ullmann, A., Fontaine, B., and Roucou, P.: Euro-Atlantic weather regimes and Mediterranean rainfall patterns: Present-day variability and expected changes under CMIP5 projections, Int. J. Climatol., 34, 8, https://doi.org/10.1002/joc.3864, 2014. a, b, c
Vautard, R.: Multiple Weather Regimes over the North Atlantic: Analysis of Precursors and Successors, Mon. Weather Rev., 118, 2056–2081, https://doi.org/10.1175/1520-0493(1990)118<2056:MWROTN>2.0.CO;2, 1990. a, b
Verdecchia, M., Visconti, G., D'Andrea, F., and Tibaldi, S.: A Neural Network Approach for blocking recognition, Geophys. Res. Lett., 23, 2081–2084, https://doi.org/10.1029/96GL01810, 1996. a
Wittek, P., Gao, S. C., Lim, I. S., and Zhao, L.: somoclu: An Efficient Parallel Library for Self-Organizing Maps, J. Stat. Softw., 78, 1–21, https://doi.org/10.18637/jss.v078.i09, 2017. a
Xu, G., Osborn, T. J., Matthews, A. J., and Joshi, M. M.: Different atmospheric moisture divergence responses to extreme and moderate El Niños, Clim. Dynam., 47, 393–410, https://doi.org/10.1007/s00382-015-2844-2, 2016. a
Zwiers, F. W. and von Storch, H.: Taking Serial Correlation into Account in Tests of the Mean, J. Climate, 8, 336–351, https://doi.org/10.1175/1520-0442(1995)008<0336:TSCIAI>2.0.CO;2, 1995. a
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...
