Articles | Volume 29, issue 1
https://doi.org/10.5194/npg-29-17-2022
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the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/npg-29-17-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| Highlight paper
| 
07 Feb 2022
Review article | Highlight paper |
| 07 Feb 2022
| 07 Feb 2022
How many modes are needed to predict climate bifurcations? Lessons from an experiment
Bérengère Dubrulle
CORRESPONDING AUTHOR
Université Paris-Saclay, CEA, CNRS, SPEC, CEA Saclay 91191 Gif-sur-Yvette CEDEX, France
Invited contribution by Bérengère Dubrulle, recipient of the EGU Lewis Fry Richardson Medal 2021.
François Daviaud
Université Paris-Saclay, CEA, CNRS, SPEC, CEA Saclay 91191 Gif-sur-Yvette CEDEX, France
Davide Faranda
Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, IPSL, 91191 Gif-sur-Yvette CEDEX, France
London Mathematical Laboratory, 8 Margravine Gardens, London, W6 8RH, UK
Laboratoire de Météorologie Dynamique/Institut Pierre
Simon Laplace, Ecole Normale Superieure, PSL research University, 75005 Paris, France
Louis Marié
LOPS, UMR6523, Univ. Brest, CNRS, IFREMER, IRD, 29280 Plouzané, France
Brice Saint-Michel
Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, 2629HZ, Delft, the Netherlands
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Emmanouil Flaounas, Stavros Dafis, Silvio Davolio, Davide Faranda, Christian Ferrarin, Katharina Hartmuth, Assaf Hochman, Aristeidis Koutroulis, Samira Khodayar, Mario Marcello Miglietta, Florian Pantillon, Platon Patlakas, Michael Sprenger, and Iris Thurnherr
Weather Clim. Dynam., 6, 1515–1538, https://doi.org/10.5194/wcd-6-1515-2025, https://doi.org/10.5194/wcd-6-1515-2025, 2025
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Storm Daniel (2023) is one of the most catastrophic storms ever documented in the Mediterranean. Our results highlight the different dynamics and therefore the different predictability skill of precipitation, its extremes and impacts that have been produced in Greece and Libya, the two most affected countries. Our approach concerns an analysis of the storm by articulating dynamics, weather prediction, hydrological and oceanographic implications, climate extremes, and attribution theory.
Pradeebane Vaittinada Ayar, Stella Bourdin, Davide Faranda, and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 25, 4655–4672, https://doi.org/10.5194/nhess-25-4655-2025, https://doi.org/10.5194/nhess-25-4655-2025, 2025
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Tracking tropical cyclones (TCs) remains a matter of interest for investigating observed and simulated tropical cyclones. In this study, Random Forest (RF), a machine learning approach, is considered to track TCs. RF associates the TC occurrence or absence with different atmospheric configurations. Compared to trackers found in the literature, it shows similar performance for tracking TCs, better control over false alarms, more flexibility, and reveals key variables for TCs' detection.
Lia Rapella, Tommaso Alberti, Davide Faranda, and Philippe Drobinski
Weather Clim. Dynam., 6, 1339–1363, https://doi.org/10.5194/wcd-6-1339-2025, https://doi.org/10.5194/wcd-6-1339-2025, 2025
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Extreme weather events pose increasing challenges for aviation, including flight disruptions and infrastructure damage. This study examines the influence of anthropogenic climate change on four recent major storms across Europe, the USA, and East Asia. Our research underscores the growing intensity of extreme storms, driven by human-induced climate change, stressing the need to adapt aviation strategies to an increasingly hazardous environment.
Maxime Thiébaut, Louis Marié, Frédéric Delbos, and Florent Guinot
Wind Energ. Sci., 10, 1869–1885, https://doi.org/10.5194/wes-10-1869-2025, https://doi.org/10.5194/wes-10-1869-2025, 2025
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This study evaluates the impact of an enhanced sampling rate on turbulence measurements using the Vaisala WindCube v2.1 lidar profiler. A prototype configuration, sampling 4 times faster than the commercial setup, is compared to the commercial configuration, with reference measurements provided by a 2D sonic anemometer. The prototype lidar captures greater variance, resulting in turbulence estimates that are more closely aligned with the reference.
Kerry Emanuel, Tommaso Alberti, Stella Bourdin, Suzana J. Camargo, Davide Faranda, Emmanouil Flaounas, Juan Jesus Gonzalez-Aleman, Chia-Ying Lee, Mario Marcello Miglietta, Claudia Pasquero, Alice Portal, Hamish Ramsay, Marco Reale, and Romualdo Romero
Weather Clim. Dynam., 6, 901–926, https://doi.org/10.5194/wcd-6-901-2025, https://doi.org/10.5194/wcd-6-901-2025, 2025
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Storms strongly resembling hurricanes are sometimes observed to form well outside the tropics, even in polar latitudes. They behave capriciously, developing very rapidly and then dying just as quickly. We show that strong dynamical processes in the atmosphere can sometimes cause it to become much colder locally than the underlying ocean, creating the conditions for hurricanes to form but only over small areas and for short times. We call the resulting storms "CYCLOPs".
Davide Faranda, Lucas Taligrot, Pascal Yiou, and Nada Caud
EGUsphere, https://doi.org/10.5194/egusphere-2025-2222, https://doi.org/10.5194/egusphere-2025-2222, 2025
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We developed a free online game called ClimarisQ to help people better understand climate change and extreme weather. By playing the game, users learn how decisions about the environment, money, and public opinion affect future risks. We studied how players reacted and found that the game makes climate issues easier to grasp and encourages discussion. This shows that interactive tools like games can support learning and action on climate and environmental challenges.
Robin Noyelle, Davide Faranda, Yoann Robin, Mathieu Vrac, and Pascal Yiou
Weather Clim. Dynam., 6, 817–839, https://doi.org/10.5194/wcd-6-817-2025, https://doi.org/10.5194/wcd-6-817-2025, 2025
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Properties of extreme meteorological and climatological events are changing under human-caused climate change. Extreme event attribution methods seek to estimate the contribution of global warming in the probability and intensity changes of extreme events. Here we propose a procedure to estimate these quantities for the flow analogue method, which compares the observed event to similar events in the past.
Valerio Lembo, Gabriele Messori, Davide Faranda, Vera Melinda Galfi, Rune Grand Graversen, and Flavio Emanuele Pons
EGUsphere, https://doi.org/10.5194/egusphere-2025-2189, https://doi.org/10.5194/egusphere-2025-2189, 2025
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Hemispheric heatwaves have fundamental implications for ecosystems and societies. They are studied together with the large-scale atmospheric dynamics, through the lens of the poleward heat transports by planetary-scale waves. Extremely weak transports of heat towards the Poles are found to be associated with hemispheric heatwaves in the Northern Hemisphere mid-latitudes. Therefore, we conclude that heat transports are a clear indicator, and possibly a precursor of hemispehric heatwaves.
Ferran Lopez-Marti, Mireia Ginesta, Davide Faranda, Anna Rutgersson, Pascal Yiou, Lichuan Wu, and Gabriele Messori
Earth Syst. Dynam., 16, 169–187, https://doi.org/10.5194/esd-16-169-2025, https://doi.org/10.5194/esd-16-169-2025, 2025
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Explosive cyclones and atmospheric rivers are two main drivers of extreme weather in Europe. In this study, we investigate their joint changes in future climates over the North Atlantic. Our results show that both the concurrence of these events and the intensity of atmospheric rivers increase by the end of the century across different future scenarios. Furthermore, explosive cyclones associated with atmospheric rivers last longer and are deeper than those without atmospheric rivers.
David L. McCann, Adrien C. H. Martin, Karlus A. C. de Macedo, Ruben Carrasco Alvarez, Jochen Horstmann, Louis Marié, José Márquez-Martínez, Marcos Portabella, Adriano Meta, Christine Gommenginger, Petronilo Martin-Iglesias, and Tania Casal
Ocean Sci., 20, 1109–1122, https://doi.org/10.5194/os-20-1109-2024, https://doi.org/10.5194/os-20-1109-2024, 2024
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This paper presents the results of the first scientific campaign of a new method to remotely sense the small-scale, fast-evolving dynamics that are vital to our understanding of coastal and shelf sea processes. This work represents the first demonstration of the simultaneous measurement of current and wind vectors from this novel method. Comparisons with other current measuring systems and models around the dynamic area of the Iroise Sea are presented and show excellent agreement.
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, Gianmarco Mengaldo, and Robert Vautard
Weather Clim. Dynam., 5, 959–983, https://doi.org/10.5194/wcd-5-959-2024, https://doi.org/10.5194/wcd-5-959-2024, 2024
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We introduce ClimaMeter, a tool offering real-time insights into extreme-weather events. Our tool unveils how climate change and natural variability affect these events, affecting communities worldwide. Our research equips policymakers and the public with essential knowledge, fostering informed decisions and enhancing climate resilience. We analysed two distinct events, showcasing ClimaMeter's global relevance.
Lucas Fery and Davide Faranda
Weather Clim. Dynam., 5, 439–461, https://doi.org/10.5194/wcd-5-439-2024, https://doi.org/10.5194/wcd-5-439-2024, 2024
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In this study, we analyse warm-season derechos – a type of severe convective windstorm – in France between 2000 and 2022, identifying 38 events. We compare their frequency and features with other countries. We also examine changes in the associated large-scale patterns. We find that convective instability has increased in southern Europe. However, the attribution of these changes to natural climate variability, human-induced climate change or a combination of both remains unclear.
Emma Holmberg, Gabriele Messori, Rodrigo Caballero, and Davide Faranda
Earth Syst. Dynam., 14, 737–765, https://doi.org/10.5194/esd-14-737-2023, https://doi.org/10.5194/esd-14-737-2023, 2023
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We analyse the duration of large-scale patterns of air movement in the atmosphere, referred to as persistence, and whether unusually persistent patterns favour warm-temperature extremes in Europe. We see no clear relationship between summertime heatwaves and unusually persistent patterns. This suggests that heatwaves do not necessarily require the continued flow of warm air over a region and that local effects could be important for their occurrence.
Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, https://doi.org/10.5194/wcd-3-1311-2022, 2022
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We analyze the atmospheric circulation leading to impactful extreme events for the calendar year 2021 such as the Storm Filomena, Westphalia floods, Hurricane Ida and Medicane Apollo. For some of the events, we find that climate change has contributed to their occurrence or enhanced their intensity; for other events, we find that they are unprecedented. Our approach underscores the importance of considering changes in the atmospheric circulation when performing attribution studies.
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022, https://doi.org/10.5194/ascmo-8-155-2022, 2022
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The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Miriam D'Errico, Flavio Pons, Pascal Yiou, Soulivanh Tao, Cesare Nardini, Frank Lunkeit, and Davide Faranda
Earth Syst. Dynam., 13, 961–992, https://doi.org/10.5194/esd-13-961-2022, https://doi.org/10.5194/esd-13-961-2022, 2022
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Climate change is already affecting weather extremes. In a warming climate, we will expect the cold spells to decrease in frequency and intensity. Our analysis shows that the frequency of circulation patterns leading to snowy cold-spell events over Italy will not decrease under business-as-usual emission scenarios, although the associated events may not lead to cold conditions in the warmer scenarios.
Davide Faranda, Mathieu Vrac, Pascal Yiou, Flavio Maria Emanuele Pons, Adnane Hamid, Giulia Carella, Cedric Ngoungue Langue, Soulivanh Thao, and Valerie Gautard
Nonlin. Processes Geophys., 28, 423–443, https://doi.org/10.5194/npg-28-423-2021, https://doi.org/10.5194/npg-28-423-2021, 2021
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Machine learning approaches are spreading rapidly in climate sciences. They are of great help in many practical situations where using the underlying equations is difficult because of the limitation in computational power. Here we use a systematic approach to investigate the limitations of the popular echo state network algorithms used to forecast the long-term behaviour of chaotic systems, such as the weather. Our results show that noise and intermittency greatly affect the performances.
Gabriele Messori and Davide Faranda
Clim. Past, 17, 545–563, https://doi.org/10.5194/cp-17-545-2021, https://doi.org/10.5194/cp-17-545-2021, 2021
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The palaeoclimate community must both analyse large amounts of model data and compare very different climates. Here, we present a seemingly very abstract analysis approach that may be fruitfully applied to palaeoclimate numerical simulations. This approach characterises the dynamics of a given climate through a small number of metrics and is thus suited to face the above challenges.
Gabriele Messori, Nili Harnik, Erica Madonna, Orli Lachmy, and Davide Faranda
Earth Syst. Dynam., 12, 233–251, https://doi.org/10.5194/esd-12-233-2021, https://doi.org/10.5194/esd-12-233-2021, 2021
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Atmospheric jets are a key component of the climate system and of our everyday lives. Indeed, they affect human activities by influencing the weather in many mid-latitude regions. However, we still lack a complete understanding of their dynamical properties. In this study, we try to relate the understanding gained in idealized computer simulations of the jets to our knowledge from observations of the real atmosphere.
Cited articles
Andersen, K. K.: High resolution record of Northern Hemisphere climate
extending into the last interglacial period, Nature, 431, 147–151, 2004. a
Bayr, T., Dommenget, D., and Latif, M.: Walker circulation controls ENSO
atmospheric feedbacks in uncoupled and coupled climate model simulations,
Clim. Dynam., 54, 2831–2846, 2020. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., et al.:
Presentation and evaluation of the IPSL-CM6A-LR climate model, J. Adv. Model Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural
network unified physics parameterization, Geophys. Res. Lett., 45,
6289–6298, 2018. a
Brenowitz, N. D. and Bretherton, C. S.: Spatially Extended Tests of a Neural
Network Parametrization Trained by Coarse-Graining, J. Adv. Model. Earth Sy., 11, 2728–2744, 2019. a
Brunetti, M., Kasparian, J., and Vérard, C.: Co-existing climate attractors in a coupled aquaplanet, Clim. Dynam., 53, 6293–6308, 2019. a
Cappanera, L., Debue, P., Faller, H., Kuzzay, D., Saw, E.-W., Nore, C.,
Guermond, J.-L., Daviaud, F., Wiertel-Gasquet, C., and Dubrulle, B.:
Turbulence in realistic geometries with moving boundaries: when simulations
meet experiments, Comput. Fluids, 214, 104750, https://doi.org/10.1016/j.compfluid.2020.104750, 2021. a
Cassou, C. and Cattiaux, J.: Disruption of the European climate seasonal clock in a warming world, Nat. Clim. Change, 6, 589–594, 2016. a
Dubrulle, B.: Beyond Kolmogorov cascades, J. Fluid Mech., 867,
P1, https://doi.org/10.1017/jfm.2019.98, 2019. a, b, c
Eyink, G. L.: Turbulence Theory,
available at: https://www.ams.jhu.edu/~eyink/Turbulence/notes.html (last access: 2 February 2022), Course notes, The Johns Hopkins University, 2007–2008. a
Falasca, F., Bracco, A., Nenes, A., and Fountalis, I.: Dimensionality Reduction
and Network Inference for Climate Data Using δ-MAPS: Application to
the CESM Large Ensemble Sea Surface Temperature, J. Adv.
Model. Earth Sy., 11, 1479–1515, 2019. a
Faller, H., Geneste, D., Chaabo, T., Cheminet, A., Valori, V., Ostovan, Y.,
Cappanera, L., Cuvier, C., Daviaud, F., Foucaut, J.-M., et al.: On the
nature of intermittency in a turbulent von Karman flow, J. Fluid
Mech., 914, A2, https://doi.org/10.1017/jfm.2020.908, 2021. a, b, c
Faranda, D., Sato, Y., Saint-Michel, B., Wiertel, C., Padilla, V., Dubrulle,
B., and Daviaud, F.: Stochastic Chaos in a Turbulent Swirling Flow, Phys.
Rev. Lett., 119, 014502, https://doi.org/10.1103/PhysRevLett.119.014502, 2017. a, b, c
Faranda, D., Vrac, M., Yiou, P., Pons, F. M. E., Hamid, A., Carella, G.,
Ngoungue Langue, C., Thao, S., and Gautard, V.: Enhancing geophysical flow
machine learning performance via scale separation, Nonlinear Proc.
Geoph., 28, 423–443, 2021. a
Ferreira, D., Marshall, J., and Rose, B.: Climate Determinism Revisited:
Multiple Equilibria in a Complex Climate Model, J. Climate, 24, 992–1012, https://doi.org/10.1175/2010JCLI3580.1, 2011. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by Stocker, T., Qin, D.,
Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y.,
Bex, V., and Midgley, P., Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, 741–866, https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Forster, D., Nelson, D. R., and Stephen, M. J.: Large-distance and long-time
properties of a randomly stirred fluid, Phys. Rev. A, 16, 732–749,
https://doi.org/10.1103/PhysRevA.16.732, 1977. a, b
Frisch, U. and Parisi, G.: On the singularity structure of fully developed
turbulence, in: Turbulence and Predictability in Geophysical Fluid Dynamics
and Climate Dynamics, edited by Gil, M., Benzi, R., and Parisi, G.,
Elsevier, Amsterdam, North-Holland, 84–88, ISBN 9780444869364, 1985. a
Frisch, U., Lesieur, M., and Schertzer, D.: Comments on the quasi-normal
Markovian approximation for fully-developed turbulence, J. Fluid Mech., 97, 181–192, https://doi.org/10.1017/S0022112080002492, 1980. a, b
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
machine learning break the convection parameterization deadlock?, Geophys.
Res. Lett., 45, 5742–5751, 2018. a
Gettelman, A., Gagne, D. J., Chen, C.-C., Christensen, M., Lebo, Z., Morrison, H., and Gantos, G.: Machine Learning the Warm Rain Process,
J. Adv. Model. Earth Syst., 13, e2020MS002268, https://doi.org/10.1029/2020MS002268, 2021. a
Ghil, M., Read, P., and Smith, L.: Geophysical flows as dynamical systems: the influence of Hide's experiments, Astron. Geophys., 51, 4.28–4.35, https://doi.org/10.1111/j.1468-4004.2010.51428.x, 2010. a
Grover, A., Kapoor, A., and Horvitz, E.: A deep hybrid model for weather
forecasting, in: Proceedings of the 21th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10–13 August 2015, 379–386, ACM, https://doi.org/10.1145/2783258.2783275, 2015. a
Guimberteau, M., Zhu, D., Maignan, F., Huang, Y., Yue, C.,
Dantec-Nédélec, S., Ottlé, C., Jornet-Puig, A., Bastos, A.,
Laurent, P., et al.: ORCHIDEE-MICT (v8. 4.1), a land surface model for the
high latitudes: model description and validation, Geosci. Model Dev., 11, 121–163, 2018. a
Haupt, S. E., Cowie, J., Linden, S., McCandless, T., Kosovic, B., and Alessandrini, S.: Machine Learning for Applied Weather Prediction, in: 2018
IEEE 14th International Conference on e-Science (e-Science), Amsterdam, The Netherlands, 29 October–1 November 2018, 276–277, https://doi.org/10.1109/eScience.2018.00047, 2018. a
Herring, J. R., Schertzer, D., Lesieur, M., Newman, G. R., Chollet, J. P., and Larcheveque, M.: A comparative assessment of spectral closures as applied to passive scalar diffusion, J. Fluid Mech., 124, 411–437,
https://doi.org/10.1017/S0022112082002560, 1982. a, b
Hohenegger, C., Schlemmer, L., and Silvers, L.: Coupling of convection and
circulation at various resolutions, Tellus A, 67, 26678, https://doi.org/10.3402/tellusa.v67.26678, 2015. a
Irving, D., Hobbs, W., Church, J., and Zika, J.: A mass and energy conservation analysis of drift in the CMIP6 ensemble, J. Climate, 34, 3157–3170, 2021. a
Iyer, K. P., Sreenivasan, K. R., and Yeung, P. K.: Scaling exponents saturate
in three-dimensional isotropic turbulence, Phys. Rev. Fluids, 5, 054605,
https://doi.org/10.1103/PhysRevFluids.5.054605, 2020. a, b
Kolmogorov, A. N.: The local structure of turbulence in incompressible viscous fluids for very large Reynolds number, Proc. R. Soc. A, 434, 9–13, https://doi.org/10.1098/rspa.1991.0075, 1991. a
Kolmogorov, A. N.: A refinement of previous hypotheses concerning the local
structure of turbulence in a viscous incompressible fluid at high Reynolds
number, J. Fluid Mech., 13, 82, https://doi.org/10.1017/S0022112062000518, 1962. a
Krasnopolsky, V. M. and Fox-Rabinovitz, M. S.: Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction, Neural Networks, 19, 122–134, 2006. a
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Chalikov, D. V.: New approach
to calculation of atmospheric model physics: Accurate and fast neural network emulation of longwave radiation in a climate model, Mon. Weather Rev., 133, 1370–1383, 2005. a
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Belochitski, A. A.: Using
ensemble of neural networks to learn stochastic convection parameterizations for climate and numerical weather prediction models from data simulated by a
cloud resolving model, Adv. Artif. Neural Syst., 2013, 485913, https://doi.org/10.1155/2013/485913, 2013. a
Le Clec'h, S., Charbit, S., Quiquet, A., Fettweis, X., Dumas, C., Kageyama, M., Wyard, C., and Ritz, C.: Assessment of the Greenland ice sheet–atmosphere feedbacks for the next century with a regional atmospheric model coupled to an ice sheet model, Cryosphere, 13, 373–395, https://doi.org/10.5194/tc-13-373-2019, 2019. a
Liu, J. N., Hu, Y., He, Y., Chan, P. W., and Lai, L.: Deep neural network
modeling for big data weather forecasting, in: Information Granularity, Big
Data, and Computational Intelligence, Springer, 389–408, https://doi.org/10.1007/978-3-319-08254-7_19, 2015. a
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20, 130–141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2, 1963. a
Margazoglou, G., Grafke, T., Laio, A., and Lucarini, V.: Dynamical Landscape and Multistability of a Climate Model, P. R. Soc. A, 477, 20210019,
https://doi.org/10.1098/rspa.2021.0019, 2021. a, b
Marié, L.: Transport de moment cinétique et de champ magnétique par un
écoulement tourbillonnaire turbulent: Influence de la rotation, PhD
thesis, Université Denis Diderot – Paris VII, France, available at: https://tel.archives-ouvertes.fr/tel-00007755 (last access: 2 February 2022), 2003. a
Olsen, M. A., Schoeberl, M. R., and Nielsen, J. E.: Response of stratospheric
circulation and stratosphere-troposphere exchange to changing sea surface
temperatures, J. Geophys. Res.-Atmos., 112, D16104, 2007. a
Paladin, G. and Vulpiani, A.: Anomalous scaling laws in multifractal objects, Phys. Rep., 156, 147–225, 1987. a
Pathak, J., Lu, Z., Hunt, B. R., Girvan, M., and Ott, E.: Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data, Chaos, 27, 121102, https://doi.org/10.1063/1.5010300, 2017. a
Pathak, J., Hunt, B., Girvan, M., Lu, Z., and Ott, E.: Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing
approach, Phys. Rev. Lett., 120, 024102, https://doi.org/10.1103/PhysRevLett.120.024102, 2018. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. A. Sci., 115, 9684–9689, 2018. a
Ravelet, F., Marie, L., Chiffaudel, A., and Daviaud, F.: Multistability and memory effect in a highly turbulent flow: Experimental evidence for a global bifurcation, Phys. Rev. Lett., 93, 164501, https://doi.org/10.1103/PhysRevLett.93.164501, 2004. a
Richardson, L. F. and Walker, G. T.: Atmospheric diffusion shown on a
distance-neighbour graph, P. R. Soc. Lond. A-Conta., 110, 709–737, https://doi.org/10.1098/rspa.1926.0043, 1926. a
Russill, C.: Climate change tipping points: origins, precursors, and debates, WIREs Clim. Change, 6, 427–434, https://doi.org/10.1002/wcc.344, 2015. a
Russill, C. and Nyssa, Z.: The tipping point trend in climate change
communication, Glob. Environ. Change, 19, 336–344, https://doi.org/10.1016/j.gloenvcha.2009.04.001, 2009. a
Saint-Michel, B., Dubrulle, B., Marié, L., Ravelet, F., and Daviaud, F.: Evidence for forcing-dependent steady states in a turbulent swirling flow, Phys. Rev. Lett., 111, 234502, https://doi.org/10.1103/PhysRevLett.111.234502, 2013. a, b, c
Sarnthein, M., Winn, K., Jung, S. J. A., Duplessy, J.-C., Labeyrie, L., Erlenkeuser, H., and Ganssen, G.: Changes in East Atlantic Deepwater Circulation over the last 30,000 years: Eight time slice reconstructions,
Paleoceanography, 9, 209–267, https://doi.org/10.1029/93PA03301, 1994. a
Sarraf, C., Jaouen, R., Djeridi, H., and Billard, J. Y.: Investigation of thickness effects on 2D NACA symmetric foils, in: Europe Oceans 2005, Brest, France, 20–23 June 2005, 2, 1298–1303, https://doi.org/10.1109/OCEANSE.2005.1513247, 2005. a
Scher, S. and Messori, G.: Predicting weather forecast uncertainty with machine learning, Q. J. Roy. Meteorol. Soc., 144, 2830–2841, 2018. a
Schertzer, D. and Lovejoy, S. (Eds.): Non-Linear Variability in Geophysics – Scaling and Fractals, Kluwer, Dordrecht, The Netherlands, ISBN 9789401074667, 1991. a
Schertzer, D. and Lovejoy, S.: Multifractals, generalized scale invariance and complexity in Geophysics, Int. J. Bifurcat. Chaos, 21, 3417–3456, https://doi.org/10.1142/S0218127411030647, 2011. a, b
Serra, M., Sathe, P., Beron-Vera, F., and Haller, G.: Uncovering the edge of the polar vortex, J. Atmos. Sci., 74, 3871–3885, 2017. a
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo,
W.-c.: Deep learning for precipitation nowcasting: A benchmark and a new model, in: Advances in neural information processing systems, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., 5617–5627, Curran Associates Inc., ISBN 9781510860964, 2017. a
Sprenger, M., Schemm, S., Oechslin, R., and Jenkner, J.: Nowcasting foehn wind events using the adaboost machine learning algorithm, Weather Forecast., 32, 1079–1099, 2017. a
Stommel, H.: Thermohaline convection with two stable regimes of flow, Tellus, 13, 224–230, 1961. a
Thalabard, S., Saint-Michel, B., Herbert, E., Daviaud, F., and Dubrulle, B.: A statistical mechanics framework for the large-scale structure of turbulent
von Kármán flows, New J. Phys., 17, 063006, https://doi.org/10.1088/1367-2630/17/6/063006, 2015. a, b, c
Vautard, R.: Multiple weather regimes over the North Atlantic: Analysis of precursors and successors, Mon. Weather Rev., 118, 2056–2081, 1990. a
Vrac, M., Vaittinada Ayar, P., and Yiou, P.: Trends and variability of seasonal weather regimes, Int. J. Climatol., 34, 472–480, 2014. a
Weeks, E. R., Tian, Y., Urbach, J. S., Ide, K., Swinney, H. L., and Ghil, M.: Transitions Between Blocked and Zonal Flows in a Rotating Annulus with Topography, Science, 278, 1598–1601, https://doi.org/10.1126/science.278.5343.1598, 1997. a
Weyn, J. A., Durran, D. R., and Caruana, R.: Can machines learn to predict weather? Using deep learning to predict gridded 500-hPa geopotential height
from historical weather data, J. Adv. Model. Earth Sys., 11, 2680–2693, 2019. a
Wu, X., Yang, H., Waugh, D. W., Orbe, C., Tilmes, S., and Lamarque, J.-F.: Spatial and temporal variability of interhemispheric transport times, Atmos. Chem. Phys., 18, 7439–7452, https://doi.org/10.5194/acp-18-7439-2018, 2018. a
Shi, X., Chen, Z., Wang, H., Yeung, D.-T., Wong, W.-K., and Woo, W.-C.:
Convolutional
LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, in: Advances in Neural
Information Processing Systems 28 (NIPS 2015), edited by: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R., Curran Associates Inc., ISBN 9781510825024, 2015. a
Yang, B., Zhang, Y., Qian, Y., Song, F., Leung, L. R., Wu, P., Guo, Z., Lu, Y., and Huang, A.: Better monsoon precipitation in coupled climate models due to bias compensation, NPJ Clim. Atmos. Sci., 2, 43, https://doi.org/10.1038/s41612-019-0100-x, 2019.
a
Yuval, J. and O’Gorman, P. A.: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions, Nat.
Commun., 11, 3295, https://doi.org/10.1038/s41467-020-17142-3, 2020. a
Short summary
Present climate models discuss climate change but show no sign of bifurcation in the future. Is this because there is none or because they are in essence too simplified to be able to capture them? To get elements of an answer, we ran a laboratory experiment and discovered that the answer is not so simple.
Present climate models discuss climate change but show no sign of bifurcation in the future. Is...
