Articles | Volume 4, issue 2
https://doi.org/10.5194/wcd-4-287-2023
© Author(s) 2023. 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-4-287-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Apr 2023
Research article |
| 04 Apr 2023
| 04 Apr 2023
Improved extended-range prediction of persistent stratospheric perturbations using machine learning
Raphaël de Fondeville
CORRESPONDING AUTHOR
Swiss Data Science Center, ETH Zurich and EPFL, Lausanne, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Enikő Székely
Swiss Data Science Center, ETH Zurich and EPFL, Lausanne, Switzerland
Guillaume Obozinski
Swiss Data Science Center, ETH Zurich and EPFL, Lausanne, Switzerland
Daniela I. V. Domeisen
Institute of Earth Surface Dynamics, Université de Lausanne, Lausanne, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
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Zheng Wu, Bernat Jiménez-Esteve, Raphaël de Fondeville, Enikő Székely, Guillaume Obozinski, William T. Ball, and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, https://doi.org/10.5194/wcd-2-841-2021, 2021
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We use an advanced statistical approach to investigate the dynamics of the development of sudden stratospheric warming (SSW) events in the winter Northern Hemisphere. We identify distinct signals that are representative of these events and their event type at lead times beyond currently predictable lead times. The results can be viewed as a promising step towards improving the predictability of SSWs in the future by using more advanced statistical methods in operational forecasting systems.
Rachel W.-Y. Wu, Gabriel Chiodo, Inna Polichtchouk, and Daniela I. V. Domeisen
Atmos. Chem. Phys., 24, 12259–12275, https://doi.org/10.5194/acp-24-12259-2024, https://doi.org/10.5194/acp-24-12259-2024, 2024
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Strong variations in the strength of the stratospheric polar vortex can profoundly affect surface weather extremes; therefore, accurately predicting the stratosphere can improve surface weather forecasts. The research reveals how uncertainty in the stratosphere is linked to the troposphere. The findings suggest that refining models to better represent the identified sources and impact regions in the troposphere is likely to improve the prediction of the stratosphere and its surface impacts.
Lou Brett, Christopher J. White, Daniela I.V. Domeisen, Bart van den Hurk, Philip Ward, and Jakob Zscheischler
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Preprint under review for NHESS
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Compound events, where multiple weather or climate hazards occur together, pose significant risks to both society and the environment. These events, like simultaneous wind and rain, can have more severe impacts than single hazards. Our review of compound event research from 2012–2022 reveals a rise in studies, especially on events that occur concurrently, hot and dry events and compounding flooding. The review also highlights opportunities for research in the coming years.
Bastien François, Khalil Teber, Lou Brett, Richard Leeding, Luis Gimeno-Sotelo, Daniela I. V. Domeisen, Laura Suarez-Gutierrez, and Emanuele Bevacqua
EGUsphere, https://doi.org/10.5194/egusphere-2024-2079, https://doi.org/10.5194/egusphere-2024-2079, 2024
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Spatially compounding wind and precipitation (CWP) extremes can lead to severe impacts on society. We find that concurrent climate variability modes favor the occurrence of such wintertime spatially compounding events in the Northern Hemisphere, and can even amplify the number of regions and population exposed. Our analysis highlights the importance of considering the interplay between variability modes to improve risk management of such spatially compounding events.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Yu. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzaguena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
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Variability in the extratropical stratosphere and troposphere are coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too-weak, however downward coupling from the lower stratosphere to the near surface is too strong.
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The winter circulation in the stratosphere, a layer of the Earth’s atmosphere between 10 and 50 km height, is tightly linked to the circulation in the lower atmosphere determining our daily weather. This interconnection happens in the form of waves propagating in and between these two layers. Here, we use space–time spectral analysis to show that disruptions and enhancements of the stratospheric circulation modify the shape and propagation of waves in both layers.
Luca G. Severino, Chahan M. Kropf, Hilla Afargan-Gerstman, Christopher Fairless, Andries Jan de Vries, Daniela I. V. Domeisen, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 1555–1578, https://doi.org/10.5194/nhess-24-1555-2024, https://doi.org/10.5194/nhess-24-1555-2024, 2024
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Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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Weather Clim. Dynam., 5, 251–261, https://doi.org/10.5194/wcd-5-251-2024, https://doi.org/10.5194/wcd-5-251-2024, 2024
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Weather Clim. Dynam., 5, 231–249, https://doi.org/10.5194/wcd-5-231-2024, https://doi.org/10.5194/wcd-5-231-2024, 2024
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The stratosphere is a layer of Earth's atmosphere found above the weather systems. Changes in the stratosphere can affect the winds and the storm tracks in the North Atlantic region for a relatively long time, lasting for several weeks and even months. We show that the stratosphere can be important for weather forecasts beyond 1 week, but more work is needed to improve the accuracy of these forecasts for 3–4 weeks.
Maria Pyrina, Wolfgang Wicker, Andries Jan de Vries, Georgios Fragkoulidis, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3088, https://doi.org/10.5194/egusphere-2023-3088, 2024
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We investigate the atmospheric dynamics behind heatwaves, specifically of those occurring simultaneously across regions, known as concurrent heatwaves. We find that heatwaves are strongly modulated by Rossby wave packets, being Rossby waves whose amplitude has a local maximum and decays at larger distances. High amplitude Rossby wave packets increase the occurrence probabilities of concurrent and non-concurrent heatwaves by a factor of 15 and 18, respectively, over several regions globally.
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Wolfgang Wicker, Inna Polichtchouk, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 81–93, https://doi.org/10.5194/wcd-4-81-2023, https://doi.org/10.5194/wcd-4-81-2023, 2023
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Sudden stratospheric warmings are extreme weather events where the winter polar stratosphere warms by about 25 K. An improved representation of small-scale gravity waves in sub-seasonal prediction models can reduce forecast errors since their impact on the large-scale circulation is predictable multiple weeks ahead. After a sudden stratospheric warming, vertically propagating gravity waves break at a lower altitude than usual, which strengthens the long-lasting positive temperature anomalies.
Marina Friedel, Gabriel Chiodo, Andrea Stenke, Daniela I. V. Domeisen, and Thomas Peter
Atmos. Chem. Phys., 22, 13997–14017, https://doi.org/10.5194/acp-22-13997-2022, https://doi.org/10.5194/acp-22-13997-2022, 2022
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In spring, winds the Arctic stratosphere change direction – an event called final stratospheric warming (FSW). Here, we examine whether the interannual variability in Arctic stratospheric ozone impacts the timing of the FSW. We find that Arctic ozone shifts the FSW to earlier and later dates in years with high and low ozone via the absorption of UV light. The modulation of the FSW by ozone has consequences for surface climate in ozone-rich years, which may result in better seasonal predictions.
Nora Bergner, Marina Friedel, Daniela I. V. Domeisen, Darryn Waugh, and Gabriel Chiodo
Atmos. Chem. Phys., 22, 13915–13934, https://doi.org/10.5194/acp-22-13915-2022, https://doi.org/10.5194/acp-22-13915-2022, 2022
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Polar vortex extremes, particularly situations with an unusually weak cyclonic circulation in the stratosphere, can influence the surface climate in the spring–summer time in the Southern Hemisphere. Using chemistry-climate models and observations, we evaluate the robustness of the surface impacts. While models capture the general surface response, they do not show the observed climate patterns in midlatitude regions, which we trace back to biases in the models' circulations.
Jake W. Casselman, Bernat Jiménez-Esteve, and Daniela I. V. Domeisen
Weather Clim. Dynam., 3, 1077–1096, https://doi.org/10.5194/wcd-3-1077-2022, https://doi.org/10.5194/wcd-3-1077-2022, 2022
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Using an atmospheric general circulation model, we analyze how the tropical North Atlantic influences the El Niño–Southern Oscillation connection towards the North Atlantic European region. We also focus on the lesser-known boreal spring and summer response following an El Niño–Southern Oscillation event. Our results show that altered tropical Atlantic sea surface temperatures may cause different responses over the Caribbean region, consequently influencing the North Atlantic European region.
Zachary D. Lawrence, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Amy H. Butler, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Javier García-Serrano, Chaim I. Garfinkel, Neil P. Hindley, Liwei Jia, Martin Jucker, Alexey Y. Karpechko, Hera Kim, Andrea L. Lang, Simon H. Lee, Pu Lin, Marisol Osman, Froila M. Palmeiro, Judith Perlwitz, Inna Polichtchouk, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Irene Erner, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, https://doi.org/10.5194/wcd-3-977-2022, 2022
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Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.
Rachel Wai-Ying Wu, Zheng Wu, and Daniela I.V. Domeisen
Weather Clim. Dynam., 3, 755–776, https://doi.org/10.5194/wcd-3-755-2022, https://doi.org/10.5194/wcd-3-755-2022, 2022
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Accurate predictions of the stratospheric polar vortex can enhance surface weather predictability. Stratospheric events themselves are less predictable, with strong inter-event differences. We assess the predictability of stratospheric acceleration and deceleration events in a sub-seasonal prediction system, finding that the predictability of events is largely dependent on event magnitude, while extreme drivers of deceleration events are not fully represented in the model.
Peter Hitchcock, Amy Butler, Andrew Charlton-Perez, Chaim I. Garfinkel, Tim Stockdale, James Anstey, Dann Mitchell, Daniela I. V. Domeisen, Tongwen Wu, Yixiong Lu, Daniele Mastrangelo, Piero Malguzzi, Hai Lin, Ryan Muncaster, Bill Merryfield, Michael Sigmond, Baoqiang Xiang, Liwei Jia, Yu-Kyung Hyun, Jiyoung Oh, Damien Specq, Isla R. Simpson, Jadwiga H. Richter, Cory Barton, Jeff Knight, Eun-Pa Lim, and Harry Hendon
Geosci. Model Dev., 15, 5073–5092, https://doi.org/10.5194/gmd-15-5073-2022, https://doi.org/10.5194/gmd-15-5073-2022, 2022
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This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.
Chen Schwartz, Chaim I. Garfinkel, Priyanka Yadav, Wen Chen, and Daniela I. V. Domeisen
Weather Clim. Dynam., 3, 679–692, https://doi.org/10.5194/wcd-3-679-2022, https://doi.org/10.5194/wcd-3-679-2022, 2022
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Eleven operational forecast models that run on subseasonal timescales (up to 2 months) are examined to assess errors in their simulated large-scale stationary waves in the Northern Hemisphere winter. We found that models with a more finely resolved stratosphere generally do better in simulating the waves in both the stratosphere (10–50 km) and troposphere below. Moreover, a connection exists between errors in simulated time-mean convection in tropical regions and errors in the simulated waves.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
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A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Zheng Wu, Bernat Jiménez-Esteve, Raphaël de Fondeville, Enikő Székely, Guillaume Obozinski, William T. Ball, and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, https://doi.org/10.5194/wcd-2-841-2021, 2021
Short summary
Short summary
We use an advanced statistical approach to investigate the dynamics of the development of sudden stratospheric warming (SSW) events in the winter Northern Hemisphere. We identify distinct signals that are representative of these events and their event type at lead times beyond currently predictable lead times. The results can be viewed as a promising step towards improving the predictability of SSWs in the future by using more advanced statistical methods in operational forecasting systems.
Amy H. Butler and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 453–474, https://doi.org/10.5194/wcd-2-453-2021, https://doi.org/10.5194/wcd-2-453-2021, 2021
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We classify by wave geometry the stratospheric polar vortex during the final warming that occurs every spring in both hemispheres due to a combination of radiative and dynamical processes. We show that the shape of the vortex, as well as the timing of the seasonal transition, is linked to total column ozone prior to and surface weather following the final warming. These results have implications for prediction and our understanding of stratosphere–troposphere coupling processes in springtime.
Hilla Afargan-Gerstman, Iuliia Polkova, Lukas Papritz, Paolo Ruggieri, Martin P. King, Panos J. Athanasiadis, Johanna Baehr, and Daniela I. V. Domeisen
Weather Clim. Dynam., 1, 541–553, https://doi.org/10.5194/wcd-1-541-2020, https://doi.org/10.5194/wcd-1-541-2020, 2020
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We investigate the stratospheric influence on marine cold air outbreaks (MCAOs) in the North Atlantic using ERA-Interim reanalysis data. MCAOs are associated with severe Arctic weather, such as polar lows and strong surface winds. Sudden stratospheric events are found to be associated with more frequent MCAOs in the Barents and the Norwegian seas, affected by the anomalous circulation over Greenland and Scandinavia. Identification of MCAO precursors is crucial for improved long-range prediction.
Daniela I. V. Domeisen, Christian M. Grams, and Lukas Papritz
Weather Clim. Dynam., 1, 373–388, https://doi.org/10.5194/wcd-1-373-2020, https://doi.org/10.5194/wcd-1-373-2020, 2020
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We cannot currently predict the weather over Europe beyond 2 weeks. The stratosphere provides a promising opportunity to go beyond that limit by providing a change in probability of certain weather regimes at the surface. However, not all stratospheric extreme events are followed by the same surface weather evolution. We show that this weather evolution is related to the tropospheric weather regime around the onset of the stratospheric extreme event for many stratospheric events.
Bernat Jiménez-Esteve and Daniela I. V. Domeisen
Weather Clim. Dynam., 1, 225–245, https://doi.org/10.5194/wcd-1-225-2020, https://doi.org/10.5194/wcd-1-225-2020, 2020
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Atmospheric predictability over Europe on subseasonal to seasonal timescales remains limited. However, the remote impact from the El Niño–Southern Oscillation (ENSO) can help to improve predictability. Research has suggested that the ENSO impact in the North Atlantic region is affected by nonlinearities. Here, we isolate the nonlinearities in the tropospheric pathway through the North Pacific, finding that a strong El Niño leads to a stronger and distinct impact compared to a strong La Niña.
Matthias Fischer, Daniela I. V. Domeisen, Wolfgang A. Müller, and Johanna Baehr
Earth Syst. Dynam., 8, 129–146, https://doi.org/10.5194/esd-8-129-2017, https://doi.org/10.5194/esd-8-129-2017, 2017
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In a climate projection experiment with the Max Planck Institute Earth System Model (MPI-ESM), we find that a decline in the Atlantic Ocean meridional heat transport (OHT) is accompanied by a change in the seasonal cycle of the total OHT and its components. We found a northward shift of 5° and latitude-dependent shifts between 1 and 6 months in the seasonal cycle that are mainly associated with changes in the meridional velocity field rather than the temperature field.
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Benjamin Doiteau, Florian Pantillon, Matthieu Plu, Laurent Descamps, and Thomas Rieutord
Weather Clim. Dynam., 5, 1409–1427, https://doi.org/10.5194/wcd-5-1409-2024, https://doi.org/10.5194/wcd-5-1409-2024, 2024
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The predictability of Mediterranean cyclones is investigated through a large dataset of 1960 cyclones tracks, ensuring robust statistical results. The motion speed of the cyclone appears to determine the predictability of its location. In particular, the location of specific slow cyclones concentrated in the Gulf of Genoa is remarkably well predicted. It is also shown that the intensity of deep cyclones, occurring in winter, is particularly poorly predicted in the Mediterranean region.
Lisa Degenhardt, Gregor C. Leckebusch, and Adam A. Scaife
Weather Clim. Dynam., 5, 587–607, https://doi.org/10.5194/wcd-5-587-2024, https://doi.org/10.5194/wcd-5-587-2024, 2024
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This study investigates how dynamical factors that are known to influence cyclone or windstorm development and strengthening also influence the seasonal forecast skill of severe winter windstorms. This study shows which factors are well represented in the seasonal forecast model, the Global Seasonal forecasting system version 5 (GloSea5), and which might need improvement to refine the forecast of severe winter windstorms.
David Martin Straus, Daniela I. V. Domeisen, Sarah-Jane Lock, Franco Molteni, and Priyanka Yadav
Weather Clim. Dynam., 4, 1001–1018, https://doi.org/10.5194/wcd-4-1001-2023, https://doi.org/10.5194/wcd-4-1001-2023, 2023
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The global response to the Madden–Julian oscillation (MJO) is potentially predictable. Yet the diabatic heating is uncertain even within a particular episode of the MJO. Experiments with a global model probe the limitations imposed by this uncertainty. The large-scale tropical heating is predictable for 25 to 45 d, yet the associated Rossby wave source that links the heating to the midlatitude circulation is predictable for 15 to 20 d. This limitation has not been recognized in prior work.
Kristian Strommen, Tim Woollings, Paolo Davini, Paolo Ruggieri, and Isla R. Simpson
Weather Clim. Dynam., 4, 853–874, https://doi.org/10.5194/wcd-4-853-2023, https://doi.org/10.5194/wcd-4-853-2023, 2023
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We present evidence which strongly suggests that decadal variations in the intensity of the North Atlantic winter jet stream can be predicted by current forecast models but that decadal variations in its position appear to be unpredictable. It is argued that this skill at predicting jet intensity originates from the slow, predictable variability in sea surface temperatures in the sub-polar North Atlantic.
Juan Camilo Acosta Navarro and Andrea Toreti
Weather Clim. Dynam., 4, 823–831, https://doi.org/10.5194/wcd-4-823-2023, https://doi.org/10.5194/wcd-4-823-2023, 2023
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Droughts and heatwaves have become some of the clearest manifestations of a changing climate. Near-term adaptation strategies can benefit from seasonal predictions, but these predictions still have limitations. We found that an intrinsic property of multi-system forecasts can serve to better anticipate extreme high-temperature and low-precipitation events during boreal summer in several regions of the Northern Hemisphere with different levels of predictability.
Julianna Carvalho-Oliveira, Giorgia di Capua, Leonard Borchert, Reik Donner, and Johanna Baehr
EGUsphere, https://doi.org/10.5194/egusphere-2023-1412, https://doi.org/10.5194/egusphere-2023-1412, 2023
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We demonstrate with a causality analysis that an important recurrent summer atmospheric pattern, the so-called East Atlantic teleconnection, is 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.
Mark J. Rodwell and Heini Wernli
Weather Clim. Dynam., 4, 591–615, https://doi.org/10.5194/wcd-4-591-2023, https://doi.org/10.5194/wcd-4-591-2023, 2023
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Midlatitude storms and their downstream impacts have a major impact on society, yet their prediction is especially prone to uncertainty. While this can never be fully eliminated, we find that the initial rate of growth of uncertainty varies for a range of forecast models. Examination of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) suggests ways in which uncertainty growth could be reduced, leading to sharper and more reliable forecasts over the first few days.
Alberto Caldas-Alvarez, Hendrik Feldmann, Etor Lucio-Eceiza, and Joaquim G. Pinto
Weather Clim. Dynam., 4, 543–565, https://doi.org/10.5194/wcd-4-543-2023, https://doi.org/10.5194/wcd-4-543-2023, 2023
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We evaluate convection-permitting modelling (CPM) simulations for the greater Alpine area to assess its added value compared to a 25 km resolution. A new method for severe precipitation detection is used, and the associated synoptic weather types are considered. Our results document the added value of CPM for precipitation representation with higher intensities, better rank correlation, better hit rates, and an improved amount and structure, but with an overestimation of the rates.
Wolfgang Wicker, Inna Polichtchouk, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 81–93, https://doi.org/10.5194/wcd-4-81-2023, https://doi.org/10.5194/wcd-4-81-2023, 2023
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Sudden stratospheric warmings are extreme weather events where the winter polar stratosphere warms by about 25 K. An improved representation of small-scale gravity waves in sub-seasonal prediction models can reduce forecast errors since their impact on the large-scale circulation is predictable multiple weeks ahead. After a sudden stratospheric warming, vertically propagating gravity waves break at a lower altitude than usual, which strengthens the long-lasting positive temperature anomalies.
Takumi Matsunobu, Christian Keil, and Christian Barthlott
Weather Clim. Dynam., 3, 1273–1289, https://doi.org/10.5194/wcd-3-1273-2022, https://doi.org/10.5194/wcd-3-1273-2022, 2022
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This study quantifies the impact of poorly constrained parameters used to represent aerosol–cloud–precipitation interactions on precipitation and cloud forecasts associated with uncertainties in input atmospheric states. Uncertainties in these parameters have a non-negligible impact on daily precipitation amount and largely change the amount of cloud. The comparison between different weather situations reveals that the impact becomes more important when convection is triggered by local effects.
Melissa L. Breeden, John R. Albers, and Andrew Hoell
Weather Clim. Dynam., 3, 1183–1197, https://doi.org/10.5194/wcd-3-1183-2022, https://doi.org/10.5194/wcd-3-1183-2022, 2022
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We use a statistical dynamical model to generate precipitation forecasts for lead times of 2–6 weeks over southwest Asia, which are needed to support humanitarian food distribution. The model signal-to-noise ratio is used to identify a smaller subset of forecasts with particularly high skill, so-called subseasonal forecasts of opportunity (SFOs). Precipitation SFOs are often related to slowly evolving tropical phenomena, namely the El Niño–Southern Oscillation and Madden–Julian Oscillation.
Michael K. Tippett, Chiara Lepore, and Michelle L. L’Heureux
Weather Clim. Dynam., 3, 1063–1075, https://doi.org/10.5194/wcd-3-1063-2022, https://doi.org/10.5194/wcd-3-1063-2022, 2022
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The El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) are phenomena that affect the weather and climate of North America. Although ENSO hails from from the tropical Pacific and the AO high above the North Pole, the spatial patterns of their influence on a North American tornado environment index are remarkably similar in computer models. We find that when ENSO and the AO act in concert, their impact is large, and when they oppose each other, their impact is small.
Rachel Wai-Ying Wu, Zheng Wu, and Daniela I.V. Domeisen
Weather Clim. Dynam., 3, 755–776, https://doi.org/10.5194/wcd-3-755-2022, https://doi.org/10.5194/wcd-3-755-2022, 2022
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Accurate predictions of the stratospheric polar vortex can enhance surface weather predictability. Stratospheric events themselves are less predictable, with strong inter-event differences. We assess the predictability of stratospheric acceleration and deceleration events in a sub-seasonal prediction system, finding that the predictability of events is largely dependent on event magnitude, while extreme drivers of deceleration events are not fully represented in the model.
Martin Wegmann, Yvan Orsolini, Antje Weisheimer, Bart van den Hurk, and Gerrit Lohmann
Weather Clim. Dynam., 2, 1245–1261, https://doi.org/10.5194/wcd-2-1245-2021, https://doi.org/10.5194/wcd-2-1245-2021, 2021
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Northern Hemisphere winter weather is influenced by the strength of westerly winds 30 km above the surface, the so-called polar vortex. Eurasian autumn snow cover is thought to modulate the polar vortex. So far, however, the modeled influence of snow on the polar vortex did not fit the observed influence. By analyzing a model experiment for the time span of 110 years, we could show that the causality of this impact is indeed sound and snow cover can weaken the polar vortex.
Cameron Bertossa, Peter Hitchcock, Arthur DeGaetano, and Riwal Plougonven
Weather Clim. Dynam., 2, 1209–1224, https://doi.org/10.5194/wcd-2-1209-2021, https://doi.org/10.5194/wcd-2-1209-2021, 2021
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While the assumption of Gaussianity leads to many simplifications, ensemble forecasts often exhibit non-Gaussian distributions. This work has systematically identified the presence of a specific case of
non-Gaussianity, bimodality. It has been found that bimodality occurs in a large portion of global 2 m temperature forecasts. This has drastic implications on forecast skill as the minimum probability in a bimodal distribution often lies at the maximum probability of a Gaussian distribution.
Constantin Ardilouze, Damien Specq, Lauriane Batté, and Christophe Cassou
Weather Clim. Dynam., 2, 1033–1049, https://doi.org/10.5194/wcd-2-1033-2021, https://doi.org/10.5194/wcd-2-1033-2021, 2021
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Forecasting temperature patterns beyond 2 weeks is very challenging, although occasionally, forecasts show more skill over Europe. Our study indicates that the level of skill varies concurrently for two distinct forecast systems. It also shows that higher skill occurs when forecasts are issued during specific patterns of atmospheric circulation that tend to be particularly persistent.
These results could help forecasters estimate a priori how trustworthy extended-range forecasts will be.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
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This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Zheng Wu, Bernat Jiménez-Esteve, Raphaël de Fondeville, Enikő Székely, Guillaume Obozinski, William T. Ball, and Daniela I. V. Domeisen
Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, https://doi.org/10.5194/wcd-2-841-2021, 2021
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We use an advanced statistical approach to investigate the dynamics of the development of sudden stratospheric warming (SSW) events in the winter Northern Hemisphere. We identify distinct signals that are representative of these events and their event type at lead times beyond currently predictable lead times. The results can be viewed as a promising step towards improving the predictability of SSWs in the future by using more advanced statistical methods in operational forecasting systems.
Alberto Caldas-Alvarez, Samiro Khodayar, and Peter Knippertz
Weather Clim. Dynam., 2, 561–580, https://doi.org/10.5194/wcd-2-561-2021, https://doi.org/10.5194/wcd-2-561-2021, 2021
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The prediction capabilities of GPS, operational (low-resolution) and targeted (high-resolution) radiosondes for data assimilation in a Mediterranean heavy precipitation event at different model resolutions are investigated. The results show that even if GPS provides accurate observations, their lack of vertical information hampers the improvement, demonstrating the need for assimilating radiosondes, where the location and timing of release was more determinant than the vertical resolution.
Joel Lisonbee and Joachim Ribbe
Weather Clim. Dynam., 2, 489–506, https://doi.org/10.5194/wcd-2-489-2021, https://doi.org/10.5194/wcd-2-489-2021, 2021
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Why do some monsoon seasons start early, while others start late? For the Australian monsoon, some previous research suggested the El Niño–Southern Oscillation in the months before the onset influenced the monsoon timing. This research tests if this is still correct and if other large-scale climate patterns also influenced onset timing. We found that a strong La Niña pattern usually coincided with an early onset but weak La Niña and El Niño patterns did not show a consistent pattern.
John R. Albers, Amy H. Butler, Melissa L. Breeden, Andrew O. Langford, and George N. Kiladis
Weather Clim. Dynam., 2, 433–452, https://doi.org/10.5194/wcd-2-433-2021, https://doi.org/10.5194/wcd-2-433-2021, 2021
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Weather variability controls the transport of ozone from the stratosphere to the Earth’s surface and water vapor from oceanic source regions to continental land masses. Forecasting these types of transport has high societal value because of the negative impacts of ozone on human health and the role of water vapor in governing precipitation variability. We use upper-level wind forecasts to assess the potential for predicting ozone and water vapor transport 3–6 weeks ahead of time.
Lesetja E. Lekoloane, Mary-Jane M. Bopape, Tshifhiwa Gift Rambuwani, Thando Ndarana, Stephanie Landman, Puseletso Mofokeng, Morne Gijben, and Ngwako Mohale
Weather Clim. Dynam., 2, 373–393, https://doi.org/10.5194/wcd-2-373-2021, https://doi.org/10.5194/wcd-2-373-2021, 2021
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We analysed a tornadic supercell that tracked through the northern Highveld region of South Africa for 7 h. We found that atmospheric conditions were conducive for tornado-associated severe storms over the region. A 4.4 km resolution model run by the South African Weather Service was able to predict this supercell, including its timing. However, it underestimated its severity due to underestimations of other important factors necessary for real-world development of these kinds of storms.
Raphael Portmann, Juan Jesús González-Alemán, Michael Sprenger, and Heini Wernli
Weather Clim. Dynam., 1, 597–615, https://doi.org/10.5194/wcd-1-597-2020, https://doi.org/10.5194/wcd-1-597-2020, 2020
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In September 2018 an intense Mediterranean cyclone with structural similarities to a hurricane, a so-called medicane, caused severe damage in Greece. Its development was uncertain, even just a few days in advance. The reason for this was uncertainties in the jet stream over the North Atlantic 3 d prior to cyclogenesis that propagated into the Mediterranean. They led to an uncertain position of the upper-level disturbance and, as a result, of the position and thermal structure of the cyclone.
Peter Pfleiderer, Carl-Friedrich Schleussner, Tobias Geiger, and Marlene Kretschmer
Weather Clim. Dynam., 1, 313–324, https://doi.org/10.5194/wcd-1-313-2020, https://doi.org/10.5194/wcd-1-313-2020, 2020
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Seasonal outlooks of Atlantic hurricane activity are required to enable risk reduction measures and disaster preparedness. Many seasonal forecasts are based on a selection of climate signals from which a statistical model is constructed. The crucial step in this approach is to select the most relevant predictors without overfitting. Here we show that causal effect networks can be used to identify the most robust predictors. Based on these predictors we construct a competitive forecast model.
Kirsten J. Mayer and Elizabeth A. Barnes
Weather Clim. Dynam., 1, 247–259, https://doi.org/10.5194/wcd-1-247-2020, https://doi.org/10.5194/wcd-1-247-2020, 2020
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Tropical storms are key for harnessing midlatitude weather prediction skill 2–8 weeks into the future. Recently, stratospheric activity was shown to impact tropical storminess and thus may also be important for midlatitude prediction skill on these timescales. This work analyzes two forecast systems to assess whether they capture this additional skill. We find there is enhanced prediction out through week 4 when both the tropical and stratospheric phenomena are active.
Christian Barthlott and Andrew I. Barrett
Weather Clim. Dynam., 1, 207–224, https://doi.org/10.5194/wcd-1-207-2020, https://doi.org/10.5194/wcd-1-207-2020, 2020
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The mesoscale convective system (MCS) that affected Germany at Pentecost 2014 was one of the most severe for decades. However, the predictability of this system was very low. By moving the model domain by just one grid point changed whether the MCS was successfully simulated or not. The decisive factor seems to be small differences in the initial track of the convection: cooler air near the coast inhibited development there, but tracks slightly more inland found more favorable conditions.
Cited articles
Allen, M. R. and Robertson, A. W.: Distinguishing modulated oscillations from
coloured noise in multivariate datasets, Clim. Dynam., 12, 775–784,
1996. a
Baldwin, M. P. and Dunkerton, T. J.: Stratospheric Harbingers of Anomalous
Weather Regimes, Science, 294, 581–584, https://doi.org/10.1126/science.1063315, 2001. a
Baldwin, M. P., Ayarzagüena, B., Birner, T., Butchart, N., Butler, A. H.,
Charlton-Perez, A. J., Domeisen, D. I., Garfinkel, C. I., Garny, H., Gerber,
E. P., Hegglin, M. I., Langematz, U., and Pedatella, N. M.: Sudden
Stratospheric Warmings, Rev. Geophys., 59, 1–37,
https://doi.org/10.1029/2020RG000708, 2021. a, b, c
Bancalá, S., Krüger, K., and Giorgetta, M.: The Preconditioning of Major
Sudden Stratospheric Warmings, J. Geophys. Res.-Atmos.,
117, D04101, https://doi.org/10.1029/2011JD016769, 2012. a, b
Barshan, E., Ghodsi, A., Azimifar, Z., and Jahromi, M. Z.: Supervised Principal
Component Analysis: Visualization, Classification and Regression on Subspaces
and Submanifolds, Pattern Recognition, 44, 1357–1371,
https://doi.org/10.1016/j.patcog.2010.12.015, 2011. a, b
Belkin, M. and Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and
Data Representation, Neural Comput., 15, 1373–1396,
https://doi.org/10.1162/089976603321780317, 2003. a
Birner, T. and Albers, J. R.: Sudden Stratospheric Warmings and Anomalous
Upward Wave Activity Flux, Scientific Online Letters on the Atmosphere, 13,
8–12, https://doi.org/10.2151/sola.13A-002, 2017. a
Black, R. X. and Mcdaniel, B. A.: Diagnostic Case Studies of the Northern
Annular Mode, J. Climate, 17, 3990–4004, 2004. a
Blume, C. and Matthes, K.: Understanding and forecasting polar stratospheric variability with statistical models, Atmos. Chem. Phys., 12, 5691–5701, https://doi.org/10.5194/acp-12-5691-2012, 2012. a
Blume, C., Matthes, K., and Horenko, I.: Supervised Learning Approaches to
Classify Sudden Stratospheric Warming Events, J. Atmos. Sci., 69, 1824–1840, https://doi.org/10.1175/JAS-D-11-0194.1, 2012. a, b, c
Bushuk, M., Giannakis, D., and Majda, A. J.: Reemergence Mechanisms for North
Pacific Sea Ice Revealed through Nonlinear Laplacian Spectral Analysis,
J. Climate, 27, 6265–6287, https://doi.org/10.1175/JCLI-D-13-00256.s1, 2014. a
Butler, A. H. and Gerber, E. P.: Optimizing the Definition of a Sudden
Stratospheric Warming, J. Climate, 31, 2337–2344,
https://doi.org/10.1175/JCLI-D-17-0648.1, 2018. a
Butler, A. H., Seidel, D. J., Hardiman, S. C., Butchart, N., Birner, T., and
Match, A.: Defining Sudden Stratospheric Warmings, B. Am.
Meteorol. Soc., 96, 1913–1928, https://doi.org/10.1175/BAMS-D-13-00173.1,
2015. a
Butler, A. H., Sjoberg, J. P., Seidel, D. J., and Rosenlof, K. H.: A sudden stratospheric warming compendium, Earth Syst. Sci. Data, 9, 63–76, https://doi.org/10.5194/essd-9-63-2017, 2017. a
Charlton, A. J. and Polvani, L. M.: A New Look at Stratospheric Sudden
Warmings. Part I: Climatology and Modeling Benchmarks, J. Climate,
20, 449–469, 2007. a
Cohen, J., Coumou, D., Hwang, J., Mackey, L., Orenstein, P., Totz, S., and
Tziperman, E.: S2S Reboot: An Argument for Greater Inclusion of Machine
Learning in Subseasonal to Seasonal Forecasts, Wires Clim. Change, 10, 1–15, https://doi.org/10.1002/wcc.567, 2019. a
Coifman, R. R. and Lafon, S.: Diffusion Maps, Appl. Comput.
Harmon. A., 21, 5–30, https://doi.org/10.1016/j.acha.2006.04.006, 2006. a
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F.,
and Zucker, S. W.: Geometric diffusions as a tool for harmonic analysis and
structure definition of data: Diffusion maps, P. Natl.
Acad. Sci. USA, 102, 7426–7431,
https://doi.org/10.1073/pnas.0500334102, 2005. a
Collins, M., Chandler, R. E., Cox, P. M., Huthnance, J. M., Rougier, J., and
Stephenson, D. B.: Quantifying Future Climate Change, Nat. Clim. Change,
6, 403–409, 2012. a
Coughlin, K. and Gray, L. J.: A Continuum of Sudden Stratospheric Warmings,
J. Atmos. Sci., 66, 531–540,
https://doi.org/10.1175/2008JAS2792.1, 2009. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011 (data available at: https://apps.ecmwf.int/datasets/data/interim-full-daily, last access: 24 March 2021). a, b
DelSole, T. and Tippett, M. K.: Laplacian Eigenfunctions for Climate Analysis,
J. Climate, 28, 7420–7436, https://doi.org/10.1175/JCLI-D-15-0049.1, 2015. a, b
Domeisen, D. I., Martius, O., and Jiménez-Esteve, B.: Rossby Wave Propagation
into the Northern Hemisphere Stratosphere: The Role of Zonal Phase Speed,
Geophys. Res. Lett., 45, 2064–2071, https://doi.org/10.1002/2017GL076886,
2018. a
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzagüena, B.,
Baldwin, M. P., Dunn-Sigouin, E., Furtado, J. C., Garfinkel, C. I.,
Hitchcock, P., Karpechko, A. Y., Kim, H., Knight, J., Lang, A. L., Lim,
E. P., Marshall, A., Roff, G., Schwartz, C., Simpson, I. R., Son, S. W., and
Taguchi, M.: The Role of the Stratosphere in Subseasonal to Seasonal
Prediction: 1. Predictability of the Stratosphere, J. Geophys.
Res.-Atmos., 125, e2019JD030920, https://doi.org/10.1029/2019JD030920,
2020a. a, b
Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzagüena, B.,
Baldwin, M. P., Dunn-Sigouin, E., Furtado, J. C., Garfinkel, C. I.,
Hitchcock, P., Karpechko, A. Y., Kim, H., Knight, J., Lang, A. L., Lim,
E. P., Marshall, A., Roff, G., Schwartz, C., Simpson, I. R., Son, S. W., and
Taguchi, M.: The Role of the Stratosphere in Subseasonal to Seasonal
Prediction: 2. Predictability Arising From Stratosphere-Troposphere Coupling,
J. Geophys. Res.-Atmos., 125, e2019JD030923,
https://doi.org/10.1029/2019JD030923, 2020b. a
Eliassen, A. and Palm, E.: On the transfer of energy in stationary mountain
waves, Geofysiske Publikasjoner – Geophysica Norvegica, 22, 1–23, 1960. a
Friedman, J., Tibshirani, R., and Hastie, T.: The elements of statistical
learning: Data mining, inference, and prediction, Springer,
https://doi.org/10.1007/b94608, 2009. a
Garfinkel, C. I., Schwartz, C., Domeisen, D. I. V., Son, S.-W., Butler, A. H.,
and White, I. P.: Extratropical atmospheric predictability from the
quasi-biennial oscillation in subseasonal forecast models, J.
Geophys. Res.-Atmos., 123, 7855–7866, 2018. a
Ghil, M., Allen, M. R., Dettinger, M. D., Ide, K., Kondrashov, D., Mann, M. E.,
Robertson, A. W., Saunders, A., Tian, Y., Varadi, F., and Yiou, P.: Advanced
spectral methods for climatic time series, Rev. Geophys., 40, 1–41,
https://doi.org/10.1029/2000RG000092, 2002. a
Hannachi, A., Mitchell, D., Gray, L., and Charlton-Perez, A.: On the Use of
Geometric Moments to Examine the Continuum of Sudden Stratospheric Warmings,
J. Atmos. Sci., 68, 657–674,
https://doi.org/10.1175/2010JAS3585.1, 2011. a
Hitchcock, P., Shepherd, T. G., Taguchi, M., Yoden, S., and Noguchi, S.:
Lower-Stratospheric radiative damping and polar-night jet oscillation events,
J. Atmos. Sci., 70, 1391–1408,
https://doi.org/10.1175/JAS-D-12-0193.1, 2013b. a
Jucker, M. and Reichler, T.: Dynamical Precursors for Statistical Prediction of
Stratospheric Sudden Warming Events, Geophys. Res. Lett., 45,
13124–13132, https://doi.org/10.1029/2018GL080691, 2018. a
Karpechko, A. Y.: Predictability of Sudden Stratospheric Warmings in the ECMWF
Extended-range Forecast System, Mon. Weather Rev., 146, 1063–1075,
https://doi.org/10.1175/MWR-D-17-0317.1, 2018. a
Karpechko, A. Y., Hitchcock, P., Peters, D. H., and Schneidereit, A.:
Predictability of Downward Propagation of Major Sudden Stratospheric
Warmings, Q. J. Roy. Meteor. Soc., 143,
1459–1470, https://doi.org/10.1002/qj.3017, 2017a. a
Karpechko, A. Y., Hitchcock, P., Peters, D. H., and Schneidereit, A.:
Predictability of downward propagation of major sudden stratospheric
warmings, Q. J. Roy. Meteor. Soc., 143,
1459–1470, https://doi.org/10.1002/qj.3017, 2017b. a
Knapp, T. R.: Canonical correlation analysis: A general parametric
significance-testing system, Psychol. Bull., 85, 410–416, 1978. a
Kodera, K., Kuroda, Y., and Pawson, S.: Stratospheric Sudden Warmings and
Slowly Propagating Zonal-mean Zonal Wind Anomalies, J. Geophys.
Res.-Atmos., 105, 12351–12359, https://doi.org/10.1029/2000JD900095,
2000. a
Kretschmer, M., Runge, J., and Coumou, D.: Early Prediction of Extreme
Stratospheric Polar Vortex States based on Causal Precursors, Geophys.
Res. Lett., 44, 8592–8600, https://doi.org/10.1002/2017GL074696, 2017. a, b, c, d
Kuroda, Y. and Kodera, K.: Variability of the Polar Night Jet in the Northern
and Southern Hemispheres, J. Geophys. Res.-Atmos., 106,
20703–20713, https://doi.org/10.1029/2001JD900226, 2001. a
Kuroda, Y. and Kodera, K.: Role of the Polar-night Jet Oscillation on the
formation of the Arctic Oscillation in the Northern Hemisphere winter,
J. Geophys. Res.-Atmos., 109, D11112,
https://doi.org/10.1029/2003JD004123, 2004. a, b
Lawrence, Z. D. and Manney, G. L.: Characterizing Stratospheric Polar Vortex
Variability With Computer Vision Techniques, J. Geophys. Res.-Atmos., 123, 1510–1535, https://doi.org/10.1002/2017JD027556, 2018. a
Lawrence, Z. D. and Manney, G. L.: Does the Arctic Stratospheric Polar Vortex
Exhibit Signs of Preconditioning prior to Sudden Stratospheric Warmings?,
J. Atmos. Sci., 77, 611–632,
https://doi.org/10.1175/JAS-D-19-0168.1, 2020. a
Limpasuvan, V., Thompson, D. W., and Hartmann, D. L.: The Life Cycle of the
Northern Hemisphere Sudden Stratospheric Warmings, J. Climate, 17,
2584–2596, https://doi.org/10.1175/1520-0442(2004)017<2584:TLCOTN>2.0.CO;2, 2004. a
Lu, C. and Ding, Y.: Analysis of Isentropic Potential Vorticities for the
Relationship Between Stratospheric Anomalies and the Cooling Process in
China, Sci. Bull., 60, 726–738, https://doi.org/10.1007/s11434-015-0757-4, 2015. a
Lu, C., Zhou, B., and Ding, Y.: Decadal Variation of the Northern Hemisphere
Annular Mode and its Influence on the East Asian Trough, J.
Meteorol. Res., 30, 584–597, https://doi.org/10.1007/s13351-016-5105-3, 2016. a
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model
Predictions, Proceedings of the 31st International Conference on Neural
Information Processing Systems, Long Beach, CA, USA, 4768–4777,
https://doi.org/10.1016/j.inffus.2019.12.012,
2017. a
Maycock, A. C. and Hitchcock, P.: Do split and displacement sudden
stratospheric warmings have different annular mode signatures?, Geophys.
Res. Lett., 42, 10943–10951, https://doi.org/10.1002/2015GL066754, 2015. a
McDermott, P. L. and Wikle, C. K.: A Model-based Approach for Analog
Spatio-temporal Dynamic Forecasting, Environmetrics, 27, 70–82,
https://doi.org/10.1002/env.2374, 2016. a
McIntyre, M. E. and Palmer, T. N.: Breaking Planetary Waves in the
Stratosphere, Nature, 305, 593–600, 1983. a
Minokhin, I., Fletcher, C. G., and Brenning, A.: Forecasting Northern Polar
Stratospheric Variability with Competing Statistical Learning Models,
Q. J. Roy. Meteor. Soc., 143, 1816–1827,
https://doi.org/10.1002/qj.3043, 2017. a
Mitchell, D. M., Charlton-Perez, A. J., and Gray, L. J.: Characterizing the
Variability and Extremes of the Stratospheric Polar Vortices Using 2D Moment
Analysis, J. Atmos. Sci., 68, 1194–1213,
https://doi.org/10.1175/2010JAS3555.1, 2011. a
Rao, J., Garfinkel, C. I., and White, I. P.: Predicting the Downward and
Surface Influence of the February 2018 and January 2019 Sudden Stratospheric
Warming Events in Subseasonal to Seasonal (S2S) Models, J.
Geophys. Res.-Atmos., 125, e2019JD031919, https://doi.org/10.1029/2019JD031919, 2020. a
Rongcai, R. and Cai, M.: Polar Vortex Oscillation Viewed in an Istentropic
Potential Velocity Coordinate, Adv. Atmos. Sci., 23,
884–900, 2006. a
Rougier, J. and Goldstein, M.: Climate Simulators and Climate Projections,
Annu. Rev. Stat. Appl., 1, 103–123, 2014. a
Runde, T., Dameris, M., Garny, H., and Kinnison, D. E.: Classification of
Stratospheric Extreme Events According to their Downward Propagation to the
Troposphere, Geophys. Res. Lett., 43, 6665–6672,
https://doi.org/10.1002/2016GL069569, 2016. a
Sauer, T., Yorke, J. A., and Casdagli, M.: Embedology, J. Stat.
Phys., 65, 579–616, 1991. a
Sigmond, M., Scinocca, J. F., Kharin, V. V., and Shepherd, T. G.: Enhanced
Seasonal Forecast Skill Following Stratospheric Sudden Warmings, Nat.
Geosci., 6, 98–102, https://doi.org/10.1038/ngeo1698, 2013. a
Székely, E., Giannakis, D., and Majda, A. J.: Extraction and predictability of
coherent intraseasonal signals in infrared brightness temperature data,
Clim. Dynam., 46, 1473–1502, https://doi.org/10.1007/s00382-015-2658-2, 2016. a
Takens, F.: Detecting strange attractors in turbulence, in: Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics, edited by: Rand, D. and Young, L. S., 898, Springer, Berlin, Heidelberg, https://doi.org/10.1007/BFb0091924, 1981. a
Tibshirani, R.: Regression Shrinkage and Selection via the Lasso, Source:
J. R. Stat. Soc. B, 58,
267–288, 1996. a
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J.,
Kang, H. S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I.,
Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A.,
Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W.,
Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D.,
Woolnough, S., Wu, T., Won, D. J., Xiao, H., Zaripov, R., and Zhang, L.: The
subseasonal to seasonal (S2S) prediction project database, B.
Am. Meteorol. Soc., 98, 163–173,
https://doi.org/10.1175/BAMS-D-16-0017.1, 2017 (data available at: https://apps.ecmwf.int/datasets/data/s2s, last access: 22 July 2021). a, b
White, I. P., Garfinkel, C. I., Gerber, E. P., Jucker, M., Hitchcock, P., and
Rao, J.: The Generic Nature of the Tropospheric Response to Sudden
Stratospheric Warmings, J. Climate, 33, 5589–5610,
https://doi.org/10.1175/JCLI-D-19-0697.1, 2020. a
Wu, R. W.-Y., Wu, Z., and Domeisen, D. I. V.: Differences in the sub-seasonal predictability of extreme stratospheric events, Weather Clim. Dynam., 3, 755–776, https://doi.org/10.5194/wcd-3-755-2022, 2022. a
Wu, Z., Jiménez-Esteve, B., de Fondeville, R., Székely, E., Obozinski, G., Ball, W. T., and Domeisen, D. I. V.: Emergence of representative signals for sudden stratospheric warmings beyond current predictable lead times, Weather Clim. Dynam., 2, 841–865, https://doi.org/10.5194/wcd-2-841-2021, 2021. a
Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014. a
Short summary
We propose a fully data-driven, interpretable, and computationally scalable framework to characterize sudden stratospheric warmings (SSWs), extract statistically significant precursors, and produce machine learning (ML) forecasts. By successfully leveraging the long-lasting impact of SSWs, the ML predictions outperform sub-seasonal numerical forecasts for lead times beyond 25 d. Post-processing numerical predictions using their ML counterparts yields a performance increase of up to 20 %.
We propose a fully data-driven, interpretable, and computationally scalable framework to...
