Articles | Volume 6, issue 4
https://doi.org/10.5194/wcd-6-1895-2025
© Author(s) 2025. 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-6-1895-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Dec 2025
Research article |
| 19 Dec 2025
| 19 Dec 2025
Summertime Arctic and North Atlantic–Eurasian circulation regimes under climate change
Johannes Max Müller
Alfred-Wegener Institute for Polar and Marine Research, Research Department Potsdam, Telegrafenberg A43, Potsdam, Germany
Oskar Andreas Landgren
Norwegian Meteorological Institute, Henrik Mohns Plass 1, Oslo, 0371, Norway
Alfred-Wegener Institute for Polar and Marine Research, Research Department Potsdam, Telegrafenberg A43, Potsdam, Germany
Related authors
No articles found.
Hannah Vickers, Priscilla Mooney, and Oskar Landgren
The Cryosphere, 19, 6907–6926, https://doi.org/10.5194/tc-19-6907-2025, https://doi.org/10.5194/tc-19-6907-2025, 2025
Short summary
Short summary
Rain-on-snow (ROS) events are becoming a common feature in winter in Svalbard due to climate warming. Understanding how ROS events are changing and how they will change in the coming decades is crucial to minimise their impacts. Using atmospheric reanalyses and climate projections we found contrasting trends between coastal and inland areas, and that the most dramatic future changes in ROS will occur in glaciated areas which will have considerable consequences for Svalbard's hydrology.
Sina Mehrdad, Sajedeh Marjani, Dörthe Handorf, and Christoph Jacobi
Weather Clim. Dynam., 6, 1491–1514, https://doi.org/10.5194/wcd-6-1491-2025, https://doi.org/10.5194/wcd-6-1491-2025, 2025
Short summary
Short summary
Wind flowing over mountains creates wave-like patterns aloft that can influence the atmosphere higher up in the stratosphere and mesosphere. In this study, we intensified these waves over specific regions like the Himalayas and Rocky Mountains and examined the resulting climate effects. We found that this can shift global wind patterns and even impact extreme events near the poles, showing how small regional changes in stratospheric wind patterns can influence the broader climate system.
Sina Mehrdad, Sajedeh Marjani, Dörthe Handorf, and Christoph Jacobi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3612, https://doi.org/10.5194/egusphere-2025-3612, 2025
Short summary
Short summary
We studied how strong wind disturbances caused by mountains can disturb the polar vortex, a large pool of cold air high above the North Pole. Using simulations, we boosted these wind disturbances over the Himalayas, North America, and East Asia. We found they can shift, weaken, and mix the vortex in different ways depending on the region. This helps explain how mountains influence the upper atmosphere and improve forecasts of extreme cold weather at the surface.
Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi
Weather Clim. Dynam., 5, 1223–1268, https://doi.org/10.5194/wcd-5-1223-2024, https://doi.org/10.5194/wcd-5-1223-2024, 2024
Short summary
Short summary
This study introduces a novel deep learning (DL) approach to analyze how regional radiative forcing in Europe impacts the Arctic climate. By integrating atmospheric poleward energy transport with DL-based clustering of atmospheric patterns and attributing anomalies to specific clusters, our method reveals crucial, nuanced interactions within the climate system, enhancing our understanding of intricate climate dynamics.
Xavier J. Levine, Ryan S. Williams, Gareth Marshall, Andrew Orr, Lise Seland Graff, Dörthe Handorf, Alexey Karpechko, Raphael Köhler, René R. Wijngaard, Nadine Johnston, Hanna Lee, Lars Nieradzik, and Priscilla A. Mooney
Earth Syst. Dynam., 15, 1161–1177, https://doi.org/10.5194/esd-15-1161-2024, https://doi.org/10.5194/esd-15-1161-2024, 2024
Short summary
Short summary
While the most recent climate projections agree that the Arctic is warming, differences remain in how much and in other climate variables such as precipitation. This presents a challenge for stakeholders who need to develop mitigation and adaptation strategies. We tackle this problem by using the storyline approach to generate four plausible and actionable realisations of end-of-century climate change for the Arctic, spanning its most likely range of variability.
Raphael Harry Köhler, Ralf Jaiser, and Dörthe Handorf
Weather Clim. Dynam., 4, 1071–1086, https://doi.org/10.5194/wcd-4-1071-2023, https://doi.org/10.5194/wcd-4-1071-2023, 2023
Short summary
Short summary
This study explores the local mechanisms of troposphere–stratosphere coupling on seasonal timescales during extended winter in the Northern Hemisphere. The detected tropospheric precursor regions exhibit very distinct mechanisms of coupling to the stratosphere, thus highlighting the importance of the time- and zonally resolved picture. Moreover, this study demonstrates that the ICOsahedral Non-hydrostatic atmosphere model (ICON) can realistically reproduce troposphere–stratosphere coupling.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Johannes Riebold, Andy Richling, Uwe Ulbrich, Henning Rust, Tido Semmler, and Dörthe Handorf
Weather Clim. Dynam., 4, 663–682, https://doi.org/10.5194/wcd-4-663-2023, https://doi.org/10.5194/wcd-4-663-2023, 2023
Short summary
Short summary
Arctic sea ice loss might impact the atmospheric circulation outside the Arctic and therefore extremes over mid-latitudes. Here, we analyze model experiments to initially assess the influence of sea ice loss on occurrence frequencies of large-scale circulation patterns. Some of these detected circulation changes can be linked to changes in occurrences of European temperature extremes. Compared to future global temperature increases, the sea-ice-related impacts are however of secondary relevance.
Daniel Gliksman, Paul Averbeck, Nico Becker, Barry Gardiner, Valeri Goldberg, Jens Grieger, Dörthe Handorf, Karsten Haustein, Alexia Karwat, Florian Knutzen, Hilke S. Lentink, Rike Lorenz, Deborah Niermann, Joaquim G. Pinto, Ronald Queck, Astrid Ziemann, and Christian L. E. Franzke
Nat. Hazards Earth Syst. Sci., 23, 2171–2201, https://doi.org/10.5194/nhess-23-2171-2023, https://doi.org/10.5194/nhess-23-2171-2023, 2023
Short summary
Short summary
Wind and storms are a major natural hazard and can cause severe economic damage and cost human lives. Hence, it is important to gauge the potential impact of using indices, which potentially enable us to estimate likely impacts of storms or other wind events. Here, we review basic aspects of wind and storm generation and provide an extensive overview of wind impacts and available indices. This is also important to better prepare for future climate change and corresponding changes to winds.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
Short summary
Short summary
A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
Short summary
Short summary
The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Klaus Dethloff, Wieslaw Maslowski, Stefan Hendricks, Younjoo J. Lee, Helge F. Goessling, Thomas Krumpen, Christian Haas, Dörthe Handorf, Robert Ricker, Vladimir Bessonov, John J. Cassano, Jaclyn Clement Kinney, Robert Osinski, Markus Rex, Annette Rinke, Julia Sokolova, and Anja Sommerfeld
The Cryosphere, 16, 981–1005, https://doi.org/10.5194/tc-16-981-2022, https://doi.org/10.5194/tc-16-981-2022, 2022
Short summary
Short summary
Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.
Cited articles
Babanov, B. A., Semenov, V. A., Akperov, M. G., Mokhov, I. I., and Keenlyside, N. S.: Occurrence of Winter Atmospheric Circulation Regimes in Euro-Atlantic Region and Associated Extreme Weather Anomalies in the Northern Hemisphere, Atmospheric and Oceanic Optics, 36, 522–531, https://doi.org/10.1134/S1024856023050056, 2023. a
Barnes, E. A. and Polvani, L.: Response of the Midlatitude Jets, and of Their Variability, to Increased Greenhouse Gases in the CMIP5 Models, Journal of Climate, 26, 7117–7135, https://doi.org/10.1175/JCLI-D-12-00536.1, 2013. a, b
Benestad, R. E., Mezghani, A., Lutz, J., Dobler, A., Parding, K. M., and Landgren, O. A.: Various ways of using empirical orthogonal functions for climate model evaluation, Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, 2023. a
Bi, D., Dix, M., Marsland, S., O'Farrell, S., Sullivan, A., Bodman, R., Law, R., Harman, I., Srbinovsky, J., Rashid, H. A., Dobrohotoff, P., Mackallah, C., Yan, H., Hirst, A., Savita, A., Dias, F. B., Woodhouse, M., Fiedler, R., and Heerdegen, A.: Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model, Journal of Southern Hemisphere Earth Systems Science, 70, 225–251, https://doi.org/10.1071/ES19040, 2020. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, Journal of Advances in Modeling Earth Systems, 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Brunner, L., Schaller, N., Anstey, J., Sillmann, J., and Steiner, A. K.: Dependence of Present and Future European Temperature Extremes on the Location of Atmospheric Blocking, Geophysical Research Letters, 45, 6311–6320, https://doi.org/10.1029/2018GL077837, 2018. a
Byun, Y.-H., Lim, Y.-J., Sung, H. M., Kim, J., Sun, M., and Kim, B.-H.: NIMS-KMA KACE1.0-G model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2241, 2019. a
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, Geophysical Research Letters, 37, 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, Climate Dynamics, 41, 2889–2907, https://doi.org/10.1007/s00382-013-1731-y, 2013. a, b, c
Coumou, D. and Rahmstorf, S.: A decade of weather extremes, Nature Climate Change, 2, 491–496, https://doi.org/10.1038/nclimate1452, 2012. a
Crasemann, B., Handorf, D., Jaiser, R., Dethloff, K., Nakamura, T., Ukita, J., and Yamazaki, K.: Can preferred atmospheric circulation patterns over the North-Atlantic-Eurasian region be associated with arctic sea ice loss?, Polar Science, 14, 9–20, https://doi.org/10.1016/j.polar.2017.09.002, 2017. a, b, c
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Dawson, A. and Palmer, T. N.: Simulating weather regimes: impact of model resolution and stochastic parameterization, Climate Dynamics, 44, 2177–2193, https://doi.org/10.1007/s00382-014-2238-x, 2015. a
Dorrington, J. and Strommen, K. J.: Jet Speed Variability Obscures Euro-Atlantic Regime Structure, Geophysical Research Letters, 47, e2020GL087907, https://doi.org/10.1029/2020GL087907, 2020. a
Dorrington, J., Strommen, K., and Fabiano, F.: Quantifying climate model representation of the wintertime Euro-Atlantic circulation using geopotential-jet regimes, Weather Clim. Dynam., 3, 505–533, https://doi.org/10.5194/wcd-3-505-2022, 2022. a
Dunne, J., Horowitz, L., Adcroft, A., Ginoux, P., Held, I., John, J., Krasting, J., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C., Zadeh, N., Balaji, V., Blanton, C., Dunne, K., Dupuis, C., Durachta, J., Dussin, R., and Zhao, M.: The GFDL Earth System Model version 4.1 (GFDL-ESM 4.1): Overall coupled model description and simulation characteristics, Journal of Advances in Modeling Earth Systems, 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020. a
Fabiano, F., Christensen, H. M., Strommen, K., Athanasiadis, P., Baker, A., Schiemann, R., and Corti, S.: Euro-Atlantic weather Regimes in the PRIMAVERA coupled climate simulations: impact of resolution and mean state biases on model performance, Climate Dynamics, 54, 5031–5048, https://doi.org/10.1007/s00382-020-05271-w, 2020. a, b
Falkena, S. K., de Wiljes, J., Weisheimer, A., and Shepherd, T. G.: Revisiting the identification of wintertime atmospheric circulation regimes in the Euro-Atlantic sector, Quarterly Journal of the Royal Meteorological Society, 146, 2801–2814, https://doi.org/10.1002/qj.3818, 2020. a, b, c
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.: Balancing Europe's wind-power output through spatial deployment informed by weather regimes, Nature Climate Change, 7, 557–562, https://doi.org/10.1038/nclimate3338, 2017. a, b, c
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a
Hannachi, A., Straus, D. M., Franzke, C. L., Corti, S., and Woollings, T.: Low-frequency nonlinearity and regime behavior in the Northern Hemisphere extratropical atmosphere, Reviews of Geophysics, 55, 199–234, https://doi.org/10.1002/2015RG000509, 2017. a
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., Wyman, B., Xiang, B., Zhang, R., Anderson, W., Balaji, V., Donner, L., Dunne, K., Durachta, J., Gauthier, P. P. G., Ginoux, P., Golaz, J.-C., Griffies, S. M., Hallberg, R., Harris, L., Harrison, M., Hurlin, W., John, J., Lin, P., Lin, S.-J., Malyshev, S., Menzel, R., Milly, P. C. D., Ming, Y., Naik, V., Paynter, D., Paulot, F., Ramaswamy, V., Reichl, B., Robinson, T., Rosati, A., Seman, C., Silvers, L. G., Underwood, S., and Zadeh, N.: Structure and Performance of GFDL's CM4.0 Climate Model, Journal of Advances in Modeling Earth Systems, 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019. 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, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Horton, R. M., Coffel, E. D., Winter, J. M., and Bader, D. A.: Projected changes in extreme temperature events based on the NARCCAP model suite, Geophysical Research Letters, 42, 7722–7731, https://doi.org/10.1002/2015GL064914, 2015. a
Hoskins, B. and Woollings, T.: Persistent Extratropical Regimes and Climate Extremes, Current Climate Change Reports, 1, 115–124, https://doi.org/10.1007/s40641-015-0020-8, 2015. a
Levine, X. J., Williams, R. S., Marshall, G., Orr, A., Seland Graff, L., Handorf, D., Karpechko, A., Köhler, R., Wijngaard, R. R., Johnston, N., Lee, H., Nieradzik, L., and Mooney, P. A.: Storylines of summer Arctic climate change constrained by Barents–Kara seas and Arctic tropospheric warming for climate risk assessment, Earth Syst. Dynam., 15, 1161–1177, https://doi.org/10.5194/esd-15-1161-2024, 2024. a, b, c, d, e
Li, X., Zheng, J., Wang, C., Lin, X., and Yao, Z.: Unraveling the roles of jet streams on the unprecedented hot July in Western Europe in 2022, npj Climate and Atmospheric Science, 7, 323, https://doi.org/10.1038/s41612-024-00880-2, 2024. a
Lorenz, E.: Empirical Orthogonal Functions and Statistical Weather Prediction, Science Report 1, Massachusetts Institute of Technology, https://wind.mit.edu/~emanuel/Lorenz/EdLorenz/Empirical_Orthogonal_Functions_1956.pdf (last access: 4 December 2025), 1956. a
Madonna, E., Li, C., Grams, C. M., and Woollings, T.: The link between eddy-driven jet variability and weather regimes in the North Atlantic-European sector, Quarterly Journal of the Royal Meteorological Society, 143, 2960–2972, https://doi.org/10.1002/qj.3155, 2017. a, b
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D., Haak, H., Hagemann, S., Hedemann, C., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM 1.2) and its response to increasing CO2, Journal of Advances in Modeling Earth Systems, 11, https://doi.org/10.1029/2018MS001400, 2019. a, b
McWethy, D., Gray, S., Higuera, P., Littell, J., Pederson, G., Ray, A., and Whitlock, C.: Climate and terrestrial ecosystem change in the U. S. Rocky Mountains and Upper Columbia Basin: Historical and future perspectives for natural resource management, National Park Service U.S. Department of the Interior, Natural Resource Report NPS/GRYN/NRR—2010/260, https://rewilding.org/wp-content/uploads/2012/05/McWethy_et_al_2010_NPS_NRR260_Climate_Terrestrial_Change_RockyMtns_UprColBasin.pdf, (last access: 4 December 2025), 2010. a
Müller, J.: ravenclaw00/WCD_ACR_paper: EGUSPHERE Atmospheric Circulation Regimes, Version 1.0, Zenodo [software], https://doi.org/10.5281/zenodo.17820047, 2025. a
Olmo, M., Cos, P., Muñoz, Á. G., Altava-Ortiz, V., Barrera-Escoda, A., Campos, D., Soret, A., and Doblas-Reyes, F.: Cross-Time-Scale Analysis of Year-Round Atmospheric Circulation Patterns and Their Impacts on Rainfall and Temperatures in the Iberian Peninsula, Journal of Climate, 37, 5525–5541, https://doi.org/10.1175/JCLI-D-23-0735.1, 2024. a, b
Osman, M. B., Coats, S., Das, S. B., McConnell, J. R., and Chellman, N.: North Atlantic jet stream projections in the context of the past 1,250 years, Proceedings of the National Academy of Sciences, 118, e2104105118, https://doi.org/10.1073/pnas.2104105118, 2021. a
Palmer, T. N.: A nonlinear dynamical perspective on climate change, Weather, 48, 314–326, https://doi.org/10.1002/j.1477-8696.1993.tb05802.x, 1993. a, b, c
Pfleiderer, P., Schleussner, C.-F., Kornhuber, K., and Coumou, D.: Summer weather becomes more persistent in a 2 °C world, Nature Climate Change, 9, 666–671, https://doi.org/10.1038/s41558-019-0555-0, 2019. a
Philipp, A., Della-Marta, P. M., Jacobeit, J., Fereday, D. R., Jones, P. D., Moberg, A., and Wanner, H.: Long-Term Variability of Daily North Atlantic–European Pressure Patterns since 1850 Classified by Simulated Annealing Clustering, Journal of Climate, 20, 4065–4095, https://doi.org/10.1175/JCLI4175.1, 2007. a, b
Philipp, A., Beck, C., Huth, R., and Jacobeit, J.: Development and comparison of circulation type classifications using the COST 733 dataset and software, International Journal of Climatology, 36, 2673–2691, https://doi.org/10.1002/joc.3920, 2016. a
Proshutinsky, A., Dukhovskoy, D., Timmermans, M.-L., Krishfield, R., and Bamber, J. L.: Arctic circulation regimes, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373, 20140160, https://doi.org/10.1098/rsta.2014.0160, 2015. a
Ray, A., Barsugli, J., Averyt, K., Deheza, V., and Udall, B.: Climate Change in Colorado: Developing a Synthesis of the Science to Support Water Resources Management and Adaptation, AGU Fall Meeting Abstracts, https://wwa.colorado.edu/sites/default/files/2021-07/Climate_Change_CO_Report_2014_FINAL.pdf, (last access: 4 December 2025), 2008. a
Riebold, J.: On the linkage between future Arctic sea ice retreat, the large-scale atmospheric circulation and temperature extremes over Europe, doctoral thesis, Universität Potsdam, https://doi.org/10.25932/publishup-60488, 2023. a
Riebold, J., Richling, A., Ulbrich, U., Rust, H., Semmler, T., and Handorf, D.: On the linkage between future Arctic sea ice retreat, Euro-Atlantic circulation regimes and temperature extremes over Europe, Weather Clim. Dynam., 4, 663–682, https://doi.org/10.5194/wcd-4-663-2023, 2023. a, b
Røste, J. and Landgren, O. A.: Impacts of dynamical downscaling on circulation type statistics in the Euro-CORDEX ensemble, Climate Dynamics, 59, 2445–2466, https://doi.org/10.1007/s00382-022-06219-y, 2022. a
Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987. a, b
Scaife, A. A. and Smith, D.: A signal-to-noise paradox in climate science, npj Climate and Atmospheric Science, 1, 28, https://doi.org/10.1038/s41612-018-0038-4, 2018. 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, Environmental Research Letters, 13, 054015, https://doi.org/10.1088/1748-9326/aaba55, 2018. a
Screen, J. A. and Simmonds, I.: The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 464, 1334–1337, https://doi.org/10.1038/nature09051, 2010. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1391, 2018. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a
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, Journal of Advances in Modeling Earth Systems, 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Luca, A. D., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S. M., Wehner, M., Zhou, B., and Allan, R.: Weather and Climate Extreme Events in a Changing Climate, Cambridge University Press, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021. a
Shepherd, T. G.: A Common Framework for Approaches to Extreme Event Attribution, Current Climate Change Reports, 2, 28–38, https://doi.org/10.1007/s40641-016-0033-y, 2016. a, b
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., Senior, C. A., Sobel, A. H., Stainforth, D. A., Tett, S. F. B., Trenberth, K. E., van den Hurk, B. J. J. M., Watkins, N. W., Wilby, R. L., and Zenghelis, D. A.: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change, Climatic Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018. a, b, c
Simpson, I. R., Shaw, T. A., and Seager, R.: A Diagnosis of the Seasonally and Longitudinally Varying Midlatitude Circulation Response to Global Warming, Journal of the Atmospheric Sciences, 71, 2489–2515, https://doi.org/10.1175/JAS-D-13-0325.1, 2014. a, b
Smith, D. M., Gillett, N. P., Simpson, I. R., Athanasiadis, P. J., Baehr, J., Bethke, I., Bilge, T. A., Bonnet, R., Boucher, O., Findell, K. L., Gastineau, G., Gualdi, S., Hermanson, L., Leung, L. R., Mignot, J., Müller, W. A., Osprey, S., Otterå, O. H., Persad, G. G., Scaife, A. A., Schmidt, G. A., Shiogama, H., Sutton, R. T., Swingedouw, D., Yang, S., Zhou, T., and Ziehn, T.: Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP), Frontiers in Climate, 4, 955414, https://doi.org/10.3389/fclim.2022.955414, 2022. a
Sousa, P. M., Trigo, R. M., Barriopedro, D., Soares, P. M. M., and Santos, J. A.: European temperature responses to blocking and ridge regional patterns, Climate Dynamics, 50, 457–477, https://doi.org/10.1007/s00382-017-3620-2, 2018. a
Stendel, M., Francis, J., White, R., Williams, P. D., and Woollings, T.: Chapter 15 – The jet stream and climate change, in: Climate Change, third edition edn., edited by: Letcher, T. M., Elsevier, https://doi.org/10.1016/B978-0-12-821575-3.00015-3, pp. 327–357, 2021. a
Stephenson, D. B., Hannachi, A., and O'Neill, A.: On the existence of multiple climate regimes, Quarterly Journal of the Royal Meteorological Society, 130, 583–605, https://doi.org/10.1256/qj.02.146, 2004. a
Straus, D. M., Corti, S., and Molteni, F.: Circulation Regimes: Chaotic Variability versus SST-Forced Predictability, Journal of Climate, 20, 2251–2272, https://doi.org/10.1175/JCLI4070.1, 2007. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, Journal of Geophysical Research-Atmospheres, 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Timmermans, M.-L. and Marshall, J.: Understanding Arctic Ocean Circulation: A Review of Ocean Dynamics in a Changing Climate, Journal of Geophysical Research-Oceans, 125, e2018JC014378, https://doi.org/10.1029/2018JC014378, 2020. a
Trenberth, K. E., Fasullo, J. T., and Shepherd, T. G.: Attribution of climate extreme events, Nature Climate Change, 5, 725–730, https://doi.org/10.1038/nclimate2657, 2015. a, b, c
Tveito, O. E. and Huth, R.: Circulation-type classifications in Europe: results of the COST 733 Action, International Journal of Climatology, 7, 2671–2672, 2016. a
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1, Journal of Advances in Modeling Earth Systems, 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019. a, b
Volodin, E., Mortikov, E., Gritsun, A., Lykossov, V., Galin, V., Diansky, N., Gusev, A., Kostrykin, S., Iakovlev, N., Shestakova, A., and Emelina, S.: INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.12321, 2019. a
Wang, Q., Danilov, S., Sidorenko, D., and Wang, X.: Circulation Pathways and Exports of Arctic River Runoff Influenced by Atmospheric Circulation Regimes, Frontiers in Marine Science, 8, 707593, https://doi.org/10.3389/fmars.2021.707593, 2021. a
Welch, B.: THE GENERALIZATION OF 'STUDENT'S' PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED, Biometrika, 34, 28–35, https://doi.org/10.1093/biomet/34.1-2.28, 1947. a
Wiel, K., Bloomfield, H., Lee, R., Stoop, L., Blackport, R., Screen, J., and Selten, F.: The influence of weather regimes on European renewable energy production and demand, Environmental Research Letters, 14, 094010, https://doi.org/10.1088/1748-9326/ab38d3, 2019. a
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations, Journal of Advances in Modeling Earth Systems, 10, 357–380, https://doi.org/10.1002/2017MS001115, 2018. a
Woollings, T. and Blackburn, M.: The North Atlantic Jet Stream under Climate Change and Its Relation to the NAO and EA Patterns, Journal of Climate, 25, 886–902, https://doi.org/10.1175/JCLI-D-11-00087.1, 2012. a, b
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, Journal of the Meteorological Society of Japan. Ser. II, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019. a
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth System Model: ACCESS-ESM1.5, Journal of Southern Hemisphere Earth Systems Science, 70, 193–214, https://doi.org/10.1071/ES19035, 2020. a
Short summary
Weather and climate is determined by large-scale air pressure patterns, which can be grouped into a small number of representative patterns. We use two different statistical methods and data from 20 climate models to detect significant changes in a future scenario of strong warming. We focus on the summer season as this is important for both human activities and natural ecosystems. We find an increase in high pressure above Europe and low pressure above the Arctic ocean.
Weather and climate is determined by large-scale air pressure patterns, which can be grouped...
