Articles | Volume 5, issue 4
https://doi.org/10.5194/wcd-5-1561-2024
© Author(s) 2024. 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-5-1561-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2024
Research article |
| 20 Dec 2024
| 20 Dec 2024
Causal relationships and predictability of the summer East Atlantic teleconnection
Julianna Carvalho-Oliveira
CORRESPONDING AUTHOR
Institute of Oceanography, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
International Max Planck Research School on Earth System Modelling, Max Planck Institute for Meteorology, Hamburg, Germany
Helmholtz-Zentrum Hereon, Institute of Coastal Systems – Analysis and Modeling, Geesthacht, Germany
present address: Leipzig Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Giorgia Di Capua
Earth System Analysis, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
Department of Water, Environment, Construction and Safety, Magdeburg–Stendal University of Applied Sciences, Magdeburg, Germany
Leonard F. Borchert
Institute of Oceanography, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Reik V. Donner
Department of Water, Environment, Construction and Safety, Magdeburg–Stendal University of Applied Sciences, Magdeburg, Germany
Earth System Analysis, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
Johanna Baehr
Institute of Oceanography, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Related authors
No articles found.
Anna K. Miesner, Daniel Krieger, and Leonard F. Borchert
EGUsphere, https://doi.org/10.5194/egusphere-2026-5065, https://doi.org/10.5194/egusphere-2026-5065, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Seasonal forecasts could help communities prepare for coastal flooding months in advance, but predicting storm surges at these timescales remains difficult. We show that large-scale weather patterns can improve seasonal storm surge forecasts by selecting climate-model predictions that better capture conditions linked to storm surges. This method reveals useful information that is hidden in the forecast and provides a pathway towards earlier and more reliable coastal hazard predictions.
Florian Börgel, Itzel Ruvalcaba Baroni, Leonie Barghorn, Leonard Borchert, Bronwyn Cahill, Cyril Dutheil, Leonie Esters, Małgorzata Falarz, Helena L. Filipsson, Matthias Gröger, Jari Hänninen, Magnus Hieronymus, Erko Jakobson, Mehdi Pasha Karami, Karol Kuliński, Taavi Liblik, H. E. Markus Meier, Gabriele Messori, Lev Naumov, Thomas Neumann, Piia Post, Gregor Rehder, Anna Rutgersson, and Georg Sebastian Voelker
Earth Syst. Dynam., 17, 415–450, https://doi.org/10.5194/esd-17-415-2026, https://doi.org/10.5194/esd-17-415-2026, 2026
Short summary
Short summary
This review explains how weather patterns, guided by the polar jet stream, influence the Baltic Sea’s climate and ecosystem. It covers the North Atlantic Oscillation, blocking events and other processes and discusses how they affect temperature, rainfall, and storms from days to decades. These shifts then impact oxygen levels, productivity, and acidification in the Baltic Sea. Physical links are fairly well known, but biogeochemical pathways remain uncertain.
Johanna Beckmann, Giorgia Di Capua, and Paolo Davini
Weather Clim. Dynam., 6, 1875–1894, https://doi.org/10.5194/wcd-6-1875-2025, https://doi.org/10.5194/wcd-6-1875-2025, 2025
Short summary
Short summary
Greenland blocking, which enhances ice sheet melting, has increased, but climate models fail to capture this trend. Analysis with reanalysis data and a seasonal forecast system shows that model improvements help, but they still miss the role of early North American snowmelt in shaping blocking patterns. This gap may explain the discrepancy and suggests that future projections could underestimate Greenland blocking and its impact on melting.
Natalia Castillo Bautista, Marco Gaetani, Leonard F. Borchert, Benjamin Poschlod, Lukas Brunner, Jana Sillmann, and Mario L. V. Martina
EGUsphere, https://doi.org/10.5194/egusphere-2025-5073, https://doi.org/10.5194/egusphere-2025-5073, 2025
Short summary
Short summary
When hot temperatures and drought occur together (compound events), they can cause harmful impacts on crops and society. Using six decades of climate data, we show that such compound events repeatedly occurred in three breadbaskets of the Northern Hemisphere. These events are linked to atmospheric circulation patterns that favor heat and dryness, which in turn interact to amplify the impact. Our study contributes to understand the drivers of these events to support climate impact assessment.
Noémie Ehstand, Reik V. Donner, Cristóbal López, Marcelo Barreiro, and Emilio Hernández-García
Nonlin. Processes Geophys., 32, 367–382, https://doi.org/10.5194/npg-32-367-2025, https://doi.org/10.5194/npg-32-367-2025, 2025
Short summary
Short summary
The Madden–Julian Oscillation (MJO) is a large-scale tropical wave of enhanced and suppressed rainfall, slowly moving eastward along the equator and influencing the weather and climate globally. We study the MJO using a simplified model designed to capture its large-scale features. We introduce new, more realistic model inputs, show that this enhanced model successfully replicates key characteristics of the MJO, and identify some of its limitations.
Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke
Nat. Hazards Earth Syst. Sci., 25, 2081–2096, https://doi.org/10.5194/nhess-25-2081-2025, https://doi.org/10.5194/nhess-25-2081-2025, 2025
Short summary
Short summary
We developed a simple and effective model to predict storm surges in the German Bight, using wind data and a multiple linear regression approach. Trained on historical data from 1959 to 2022, our storm surge model demonstrates high predictive skill and performs as well as more complex models, despite its simplicity. It can predict both moderate and extreme storm surges, making it a valuable tool for future climate change studies.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Daniel Krieger, Sebastian Brune, Johanna Baehr, and Ralf Weisse
Nat. Hazards Earth Syst. Sci., 24, 1539–1554, https://doi.org/10.5194/nhess-24-1539-2024, https://doi.org/10.5194/nhess-24-1539-2024, 2024
Short summary
Short summary
Previous studies found that climate models can predict storm activity in the German Bight well for averages of 5–10 years but struggle in predicting the next winter season. Here, we improve winter storm activity predictions by linking them to physical phenomena that occur before the winter. We guess the winter storm activity from these phenomena and discard model solutions that stray too far from the guess. The remaining solutions then show much higher prediction skill for storm activity.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
Short summary
Short summary
Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Giorgia Di Capua, Dim Coumou, Bart van den Hurk, Antje Weisheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam., 4, 701–723, https://doi.org/10.5194/wcd-4-701-2023, https://doi.org/10.5194/wcd-4-701-2023, 2023
Short summary
Short summary
Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns, and this interaction can explain weather patterns in the Northern Hemisphere during summer. In this analysis we detect these tropical–extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well atmospheric models can reproduce the observed patterns. We find a good agreement although these relationships are weaker in model data.
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483, https://doi.org/10.5194/esd-14-457-2023, https://doi.org/10.5194/esd-14-457-2023, 2023
Short summary
Short summary
We carry out an assessment of an ensemble of general climate models (CMIP6) based on the ability of the models to represent the key physical processes that are important for representing European climate. Filtering the models with the assessment leads to more models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
Daniel Krieger, Sebastian Brune, Patrick Pieper, Ralf Weisse, and Johanna Baehr
Nat. Hazards Earth Syst. Sci., 22, 3993–4009, https://doi.org/10.5194/nhess-22-3993-2022, https://doi.org/10.5194/nhess-22-3993-2022, 2022
Short summary
Short summary
Accurate predictions of storm activity are desirable for coastal management. We investigate how well a climate model can predict storm activity in the German Bight 1–10 years in advance. We let the model predict the past, compare these predictions to observations, and analyze whether the model is doing better than simple statistical predictions. We find that the model generally shows good skill for extreme periods, but the prediction timeframes with good skill depend on the type of prediction.
Yiyu Zheng, Maria Rugenstein, Patrick Pieper, Goratz Beobide-Arsuaga, and Johanna Baehr
Earth Syst. Dynam., 13, 1611–1623, https://doi.org/10.5194/esd-13-1611-2022, https://doi.org/10.5194/esd-13-1611-2022, 2022
Short summary
Short summary
El Niño–Southern Oscillation (ENSO) is one of the dominant climatic phenomena in the equatorial Pacific. Understanding and predicting how ENSO might change in a warmer climate is both societally and scientifically important. We use 1000-year-long simulations from seven climate models to analyze ENSO in an idealized stable climate. We show that ENSO will be weaker and last shorter under the warming, while the skill of ENSO prediction will unlikely change.
Tim Rohrschneider, Johanna Baehr, Veit Lüschow, Dian Putrasahan, and Jochem Marotzke
Ocean Sci., 18, 979–996, https://doi.org/10.5194/os-18-979-2022, https://doi.org/10.5194/os-18-979-2022, 2022
Short summary
Short summary
This paper presents an analysis of wind sensitivity experiments in order to provide insight into the wind forcing dependence of the AMOC by understanding the behavior of its depth scale(s).
Marcel Meyer, Iuliia Polkova, Kameswar Rao Modali, Laura Schaffer, Johanna Baehr, Stephan Olbrich, and Marc Rautenhaus
Weather Clim. Dynam., 2, 867–891, https://doi.org/10.5194/wcd-2-867-2021, https://doi.org/10.5194/wcd-2-867-2021, 2021
Short summary
Short summary
Novel techniques from computer science are used to study extreme weather events. Inspired by the interactive 3-D visual analysis of the recently released ERA5 reanalysis data, we improve commonly used metrics for measuring polar winter storms and outbreaks of cold air. The software (Met.3D) that we have extended and applied as part of this study is freely available and can be used generically for 3-D visualization of a broad variety of atmospheric processes in weather and climate data.
Cited articles
Athanasiadis, P. J., Yeager, S., Kwon, Y.-O., Bellucci, A., Smith, D. W., and Tibaldi, S.: Decadal predictability of North Atlantic blocking and the NAO, npj Climate and Atmospheric Science, 3, 1–10, 2020. a
Bastos, A., Janssens, I. A., Gouveia, C. M., Trigo, R. M., Ciais, P., Chevallier, F., Penuelas, J., Rödenbeck, C., Piao, S., Friedlingstein, P., and Running, S. W.: European land CO2 sink influenced by NAO and East-Atlantic Pattern coupling, Nat. Commun., 7, 1–9, 2016. a
Cassou, C., Terray, L., Hurrell, J. W., and Deser, C.: North Atlantic winter climate regimes: Spatial asymmetry, stationarity with time, and oceanic forcing, J. Climate, 17, 1055–1068, 2004. a
Cassou, C., Terray, L., and Phillips, A. S.: Tropical Atlantic influence on European heat waves, J. Climate, 18, 2805–2811, 2005. a
Collins, M.: Climate predictability on interannual to decadal time scales: The initial value problem, Clim. Dynam., 19, 671–692, 2002. a
Comas-Bru, L. and Hernández, A.: Reconciling North Atlantic climate modes: revised monthly indices for the East Atlantic and the Scandinavian patterns beyond the 20th century, Earth Syst. Sci. Data, 10, 2329–2344, https://doi.org/10.5194/essd-10-2329-2018, 2018. a
Di Capua, G., Kretschmer, M., Donner, R. V., van den Hurk, B., Vellore, R., Krishnan, R., and Coumou, D.: Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach, Earth Syst. Dynam., 11, 17–34, https://doi.org/10.5194/esd-11-17-2020, 2020a. a
Di Capua, G., Runge, J., Donner, R. V., van den Hurk, B., Turner, A. G., Vellore, R., Krishnan, R., and Coumou, D.: Dominant patterns of interaction between the tropics and mid-latitudes in boreal summer: causal relationships and the role of timescales, Weather Clim. Dynam., 1, 519–539, https://doi.org/10.5194/wcd-1-519-2020, 2020b. a, b, c
Domeisen, D. I., Badin, G., and Koszalka, I. M.: How predictable are the Arctic and North Atlantic Oscillations? Exploring the variability and predictability of the Northern Hemisphere, J. Climate, 31, 997–1014, 2018. a
Duchez, A., Frajka-Williams, E., Josey, S. A., Evans, D. G., Grist, J. P., Marsh, R., McCarthy, G. D., Sinha, B., Berry, D. I., and Hirschi, J. J.: Drivers of exceptionally cold North Atlantic Ocean temperatures and their link to the 2015 European heat wave, Environ. Res. Lett., 11, 074004, https://doi.org/10.1088/1748-9326/11/7/074004, 2016. a
European Centre for Medium-Range Weather Forecasts (ECMWF): The Reanalysis of the 20th Century, ECMWF Forecasts [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-20th-century, retrieved prior to discontinuation on 1 June 2023. a
Gastineau, G. and Frankignoul, C.: Influence of the North Atlantic SST variability on the atmospheric circulation during the twentieth century, J. Climate, 28, 1396–1416, 2015. a
Giorgetta, M., Jungclaus, J., Reick, C., Legutke, S., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K., Gayler, V., Haak, H., Hollweg, H.-D., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Müller, W., Notz, D., Raddatz, T., Rast, S., Roeckner, E., Salzmann, M., Schmidt, H., Schnur, R., Segschneider, J., Six, K., Stockhause, M., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.: CMIP5 simulations of the Max Planck Institute for Meteorology (MPI-M) based on the MPI-ESM-LR model: The piControl experiment, served by ESGF, https://doi.org/10.1594/WDCC/CMIP5.MXELpc, 2012. a
Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J., Böttinger, M., Brovkin, V., Crueger, T., Esch, M., Fieg, K., Glushak, K., Gayler, V., Haak, H., Hollweg, H.-D., Ilyina, T., Kinne, S., Kornblueh, L., Matei, D., Mauritsen, T., Mikolajewicz, U., Mueller, W., Notz, D., Pithan, F., Raddatz, T., Rast, S., Redler, R., Roeckner, E., Schmidt, H., Schnur, R., Segschneider, J., Six, K. D., Stockhause, M., Timmreck, C., Wegner, J., Widmann, H., Wieners, K.-H., Claussen, M., Marotzke, J., and Stevens, B.: Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5, J. Adv. Model. Earth Sy., 5, 572–597, 2013. a
Hallam, S., Josey, S. A., McCarthy, G. D., and Hirschi, J. J.-M.: A regional (land–ocean) comparison of the seasonal to decadal variability of the Northern Hemisphere jet stream 1871–2011, Clim. Dynam., 59, 1897–1918, 2022. a
Jungclaus, J., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D., Mikolajewicz, U., Notz, D., and Von Storch, J.: Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model, J. Adv. Model. Earth Sy., 5, 422–446, 2013. a
Karmouche, S., Galytska, E., Runge, J., Meehl, G. A., Phillips, A. S., Weigel, K., and Eyring, V.: Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6, Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, 2023. a
Klavans, J. M., Cane, M. A., Clement, A. C., and Murphy, L. N.: NAO predictability from external forcing in the late 20th century, npj Climate and Atmospheric Science, 4, 1–8, 2021. a
Kretschmer, M., Zappa, G., and Shepherd, T. G.: The role of Barents–Kara sea ice loss in projected polar vortex changes, Weather Clim. Dynam., 1, 715–730, https://doi.org/10.5194/wcd-1-715-2020, 2020. a
Kretschmer, M., Adams, S. V., Arribas, A., Prudden, R., Robinson, N., Saggioro, E., and Shepherd, T. G.: Quantifying causal pathways of teleconnections, B. Am. Meteorol. Soc., 102, E2247–E2263, 2021. a
Mariotti, A., Baggett, C., Barnes, E. A., Becker, E., Butler, A., Collins, D. C., Dirmeyer, P. A., Ferranti, L., Johnson, N. C., Jones, J., Kirtman, B. P., Lang, A. L., Molod, A., Newman, M., Robertson, A. W., Schubert, S., Waliser, D. E., and Albers, J.: Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond, B. Am. Meteorol. Soc., 101, E608–E625, 2020. a, b
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz, D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a global model, J. Adv. Model. Earth Sy., 4, M00A01, https://doi.org/10.1029/2012MS000154, 2012. a
Mishra, N., Prodhomme, C., and Guemas, V.: Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe, Clim. Dynam., 52, 4207–4225, 2019. a
Moore, G., Renfrew, I., and Pickart, R. S.: Multidecadal mobility of the North Atlantic oscillation, J. Climate, 26, 2453–2466, 2013. a
Neddermann, N.-C., Müller, W. A., Dobrynin, M., Düsterhus, A., and Baehr, J.: Seasonal predictability of European summer climate re-assessed, Clim. Dynam., 51, 1–18, 2018. a
Oliveira, J. C., Zorita, E., Koul, V., Ludwig, T., and Baehr, J.: Forecast opportunities for European summer climate ensemble predictions using Self-Organising Maps, in: Proceedings of the 10th International Conference on Climate Informatics, Oxford University, 23–25 September 2020, 67–71, https://doi.org/10.1145/3429309.3429319, 2020. a, b
O'Reilly, C. H., Weisheimer, A., Woollings, T., Gray, L. J., and MacLeod, D.: The importance of stratospheric initial conditions for winter North Atlantic Oscillation predictability and implications for the signal-to-noise paradox, Q. J. Roy. Meteor. Soc., 145, 131–146, 2019. a
O’Reilly, C. H., Woollings, T., Zanna, L., and Weisheimer, A.: The impact of tropical precipitation on summertime Euro-Atlantic circulation via a circumglobal wave train, J. Climate, 31, 6481–6504, 2018. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F., Laloyaux, P., Tan, D. G., Peubey, C., Thépaut, J.-N., Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: An atmospheric reanalysis of the twentieth century, J. Climate, 29, 4083–4097, 2016. a
Rieke, O., Greatbatch, R. J., and Gollan, G.: Nonstationarity of the link between the Tropics and the summer East Atlantic pattern, Atmos. Sci. Lett., 22, e1026, https://doi.org/10.1002/asl.1026, 2021. a
Rousi, E., Selten, F., Rahmstorf, S., and Coumou, D.: Changes in North Atlantic atmospheric circulation in a warmer climate favor winter flooding and summer drought over Europe, J. Climate, 34, 2277–2295, 2021. a
Rousi, E., Kornhuber, K., Beobide-Arsuaga, G., Luo, F., and Coumou, D.: Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia, Nat. Commun., 13, 1–11, 2022. a
Runge, J.: Causal network reconstruction from time series: From theoretical assumptions to practical estimation, Chaos: An Interdisciplinary Journal of Nonlinear Science, 28, 075310, https://doi.org/10.1063/1.5025050, 2018. a
Runge, J., Petoukhov, V., and Kurths, J.: Quantifying the strength and delay of climatic interactions: The ambiguities of cross correlation and a novel measure based on graphical models, J. Climate, 27, 720–739, 2014. a
Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., Hartman, D., Marwan, N., Paluš, M., and Kurths, J.: Identifying causal gateways and mediators in complex spatio-temporal systems, Nat. Commun., 6, 1–10, 2015. a
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.: Detecting and quantifying causal associations in large nonlinear time series datasets, Sci. Adv., 5, eaau4996, https://doi.org/10.1126/sciadv.aau4996, 2019. a, b
Schuhen, N., Schaller, N., Bloomfield, H. C., Brayshaw, D. J., Lledó, L., Cionni, I., and Sillmann, J.: Predictive Skill of Teleconnection Patterns in Twentieth Century Seasonal Hindcasts and Their Relationship to Extreme Winter Temperatures in Europe, Geophys. Res. Lett., e2020GL092360, https://doi.org/10.1029/2020GL092360, 2022. a
Siew, P. Y. F., Li, C., Sobolowski, S. P., and King, M. P.: Intermittency of Arctic–mid-latitude teleconnections: stratospheric pathway between autumn sea ice and the winter North Atlantic Oscillation, Weather Clim. Dynam., 1, 261–275, https://doi.org/10.5194/wcd-1-261-2020, 2020. a, b
Spirtes, P., Glymour, C. N., and Scheines, R.: Causation, prediction, and search, MIT press, https://doi.org/10.7551/mitpress/1754.001.0001, 2000. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth system model: ECHAM6, J. Adv. Model. Earth Sy., 5, 146–172, 2013. a
Weisheimer, A., Decremer, D., MacLeod, D., O'Reilly, C., Stockdale, T. N., Johnson, S., and Palmer, T. N.: How confident are predictability estimates of the winter North Atlantic Oscillation?, Q. J. Roy. Meteor. Soc., 145, 140–159, 2019. a
Woollings, T., Franzke, C., Hodson, D., Dong, B., Barnes, E. A., Raible, C., and Pinto, J.: Contrasting interannual and multidecadal NAO variability, Clim. Dynam., 45, 539–556, 2015. a
Short summary
We demonstrate with a causal analysis that an important recurrent summer atmospheric pattern, the so-called East Atlantic teleconnection, was influenced by the extratropical North Atlantic in spring during the second half of the 20th century. This causal link is, however, not well represented by our evaluated seasonal climate prediction system. We show that simulations able to reproduce this link show improved surface climate prediction credibility over those that do not.
We demonstrate with a causal analysis that an important recurrent summer atmospheric pattern,...
