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
Related authors
No articles found.
Wolfgang Wicker, Georgios Fragkoulidis, Nili Harnik, Emmanuele Russo, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-5678, https://doi.org/10.5194/egusphere-2026-5678, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
The horizontal structure of spatially compounding climate extremes can be described in terms of a zonal wavenumber. We derive this wavenumber by calibrating a simple dispersion relation with local flow properties and compare the structure of concurrent heatwaves in an idealized atmospheric model with reanalysis data. We find that the calibration can explain differences in concurrent heatwave frequency between summer and winter, and between the Northern and the Southern hemisphere.
Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Blanca Ayarzaguena, Marta Abalos, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Ying Dai, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Irene Erner, Javier García-Serrano, Neil P. Hindley, Peter Hitchcock, Martin Jucker, Alexey Yu. Karpechko, Hera Kim, Gerbrand Koren, Simon H. Lee, Robert W. Lee, Marisol Osman, Froila M. Palmeiro, Jian Rao, Chen Schwartz, Seok-Woo Son, Corwin J. Wright, and Rachel W.-Y. Wu
EGUsphere, https://doi.org/10.5194/egusphere-2026-6070, https://doi.org/10.5194/egusphere-2026-6070, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Weather at the surface is influenced by the stratosphere, where changes can affect conditions weeks later. The Northern and Southern Annular Modes (NAM and SAM) describe large-scale shifts in winds around the poles that influence storms, temperature and rainfall. We show that 22 forecasting models have systematic biases in representing stratosphere–troposphere coupling, limiting prediction skill. Improving these processes could extend useful forecasts of weather extremes.
Edgar Dolores-Tesillos, Praveen K. Pothapakula, Christian Zeman, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-5758, https://doi.org/10.5194/egusphere-2026-5758, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
We tested whether increasing climate-model resolution and changing how convection is represented improve the simulation of heatwaves. Using a hierarchy of global atmosphere-only simulations, we found that finer resolution does not consistently reduce heatwave biases. Heatwave persistence depends on regional interactions among convection, rainfall, land conditions, and large-scale circulation, showing that higher resolution alone is not enough.
Andries-Jan de Vries, Steven B. Feldstein, Georgios Fragkoulidis, Priyanka Yadav, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-5324, https://doi.org/10.5194/egusphere-2026-5324, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Short summary
Short summary
Much of the annual precipitation in the arid Middle East falls on only a few days per year, with relevance to drought and flooding. This study quantifies the separate and joint influence of the Madden-Julian Oscillation and midlatitude Rossby waves on daily precipitation variability in the Middle East during the wet season. We find that the tropical and extratropical forcing mechanisms operate largely independent of another and exert an approximately equal influence on precipitation anomalies.
Andries-Jan de Vries, Steven B. Feldstein, Jake W. Casselman, Georgios Fragkoulidis, Jos Lelieveld, and Daniela I. V. Domeisen
Weather Clim. Dynam., 7, 1479–1505, https://doi.org/10.5194/wcd-7-1479-2026, https://doi.org/10.5194/wcd-7-1479-2026, 2026
Short summary
Short summary
Precipitation in the arid Middle East experiences large yearly variation with direct relevance to drought and flooding. This study quantifies the influence of El Niño-Southern Oscillation on Middle East precipitation variability at local and regional scales during the cool season. Three mechanisms contribute to precipitation anomalies: a meridional shift in the subtropical jet, a barotropic Rossby wave response in the extratropics, and a baroclinic response in the regional tropical circulation.
Liping Wang, Gang Chen, Xiuyuan Ding, Haijun Yang, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-4150, https://doi.org/10.5194/egusphere-2026-4150, 2026
Short summary
Short summary
We investigate low-frequency Northern Hemisphere stratospheric variability using reanalysis and three multi-century climate model simulations. We identify an intermittent oscillation with a period of about 15 years. This signal is closely connected to the surface North Atlantic Oscillation and tends to precede it, whereas its link to North Pacific circulation is weaker. These results help improve our understanding of stratosphere-surface coupling on multi-year to multi-decadal timescales.
Romain Pilon, Andries Jan De Vries, and Daniela I. V. Domeisen
Weather Clim. Dynam., 7, 857–872, https://doi.org/10.5194/wcd-7-857-2026, https://doi.org/10.5194/wcd-7-857-2026, 2026
Short summary
Short summary
South Pacific cloud bands are vital rain sources. Using historical weather data, we investigated how atmospheric waves from the midlatitudes shape these cloud bands. We found that long-lasting cloud bands require sustained high-altitude waves to continuously steer tropical moisture southward. These persistent events occur strictly during the summer. Understanding this dynamic link is essential for improving climate models and predicting how regional rainfall patterns may change in the future.
Rachel Wai-Ying Wu, Dominik Büeler, Maria Pyrina, Jonathan Day, Adel Imamovic, Vincent Humphrey, and Daniela I.V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-2465, https://doi.org/10.5194/egusphere-2026-2465, 2026
Short summary
Short summary
Compound heat-drought events threaten health, agriculture, and energy. Using ECMWF sub-seasonal hindcasts, we find that physical coupling increases forecast skill by ~10 % relative to assuming event independence. However, skill drops significantly where land-atmosphere interactions – namely soil moisture feedbacks – are misrepresented. We identify European regions where these forecasts are most reliable and highlight physical biases as the primary barrier to accuracy elsewhere.
Lara C. Mercier, Hilla Afargan-Gerstman, Matthew D. K. Priestley, Jens H. Christensen, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-1805, https://doi.org/10.5194/egusphere-2026-1805, 2026
Preprint archived
Short summary
Short summary
The study examines how extreme extratropical cyclones in the North Atlantic will change in a warming climate using historical and future projections of CMIP6 models. It shows that while near-surface temperature gradients are weakening, increased moisture is likely to make future storms more intense, suggesting a shift toward moisture-driven dynamics. Our findings provide new insights for improving long-term climate adaptation and risk management for high-impact weather in the midlatitudes.
Mélanie Coleman, Andries-Jan de Vries, Caroline Roberts, and Daniela I. V. Domeisen
EGUsphere, https://doi.org/10.5194/egusphere-2026-1827, https://doi.org/10.5194/egusphere-2026-1827, 2026
Short summary
Short summary
This study compares flood impacts between countries in the European Union (EU) and in the Middle East and North Africa (MENA). We find that floods cause four times more fatalities in the MENA region, while they inflict more economic losses in the EU. By comparing recorded impacts and survey data available in the EU only, we find that the population overestimates flood risk in northern EU countries but underestimates it in southern EU countries relative to other natural hazards.
Pauline Rivoire, Sonia Dupuis, Antoine Guisan, Pascal Vittoz, and Daniela I. V. Domeisen
Nat. Hazards Earth Syst. Sci., 26, 1183–1205, https://doi.org/10.5194/nhess-26-1183-2026, https://doi.org/10.5194/nhess-26-1183-2026, 2026
Short summary
Short summary
Our study investigates the temperature, precipitation, humidity, and soil moisture conditions leading to the browning of the European forests in summer. Using a Random Forest model and satellite measurements of vegetation greenness, we identify key conditions to predict forest damage. We conclude that hot and dry conditions in spring and summer are adverse conditions. The conditions during the preceding year can also have an impact, with the relevant period varying by forest type.
Blanca Ayarzagüena, Amy H. Butler, Peter Hitchcock, Chaim I. Garfinkel, Zac D. Lawrence, Wuhan Ning, Philip Rupp, Zheng Wu, Hilla Afargan-Gerstman, Natalia Calvo, Alvaro de la Cámara, Martin Jucker, Gerbrand Koren, Daniel De Maeseneire, Gloria L. Manney, Marisol Osman, Masakazu Taguchi, Cory Barton, Dong-Chan Hong, Yu-Kyung Hyun, Hera Kim, Jeff Knight, Piero Malguzzi, Daniele Mastrangelo, Jiyoung Oh, Inna Polichtchouk, Jadwiga H. Richter, Isla R. Simpson, Seok-Woo Son, Damien Specq, and Tim Stockdale
Weather Clim. Dynam., 7, 411–437, https://doi.org/10.5194/wcd-7-411-2026, https://doi.org/10.5194/wcd-7-411-2026, 2026
Short summary
Short summary
Sudden Stratospheric Warmings (SSWs) are known to follow a sustained wave dissipation in the stratosphere, which depends on both the tropospheric and stratospheric states. However, the relative role of each state is still unclear. Using a new set of subseasonal to seasonal forecasts, we show that the stratospheric state does not drastically affect the precursors of three recent SSWs, but modulates the stratospheric wave activity, with impacts depending on SSW features.
Quentin Duchemin, Maria Grazia Zanoni, Marius G. Floriancic, Hansjörg Seybold, Guillaume Obozinski, James W. Kirchner, and Paolo Benettin
Geosci. Model Dev., 18, 8663–8678, https://doi.org/10.5194/gmd-18-8663-2025, https://doi.org/10.5194/gmd-18-8663-2025, 2025
Short summary
Short summary
We introduce GAMCR (Generalized Additive Models for Catchment Responses), a data-driven model that estimates how catchments respond to individual precipitation events. We validate GAMCR on synthetic data and demonstrate its ability to investigate the characteristic runoff responses from real-world hydrologic series. GAMCR provides new data-driven opportunities to understand and compare hydrological behavior across different catchments.
Cristiana Stan, Saisri Kollapaneni, Andrea M. Jenney, Jiabao Wang, Zheng Wu, Cheng Zheng, Hyemi Kim, Chaim I. Garfinkel, and Ayush Singh
Geosci. Model Dev., 18, 7969–7985, https://doi.org/10.5194/gmd-18-7969-2025, https://doi.org/10.5194/gmd-18-7969-2025, 2025
Short summary
Short summary
The diagnostics package is an open-source Python software package used for evaluating the Madden–Julian Oscillation teleconnections to the extratropics, as predicted by subseasonal-to-seasonal (S2S) forecast systems.
Monika Feldmann, Daniela I. V. Domeisen, and Olivia Martius
Weather Clim. Dynam., 6, 1089–1106, https://doi.org/10.5194/wcd-6-1089-2025, https://doi.org/10.5194/wcd-6-1089-2025, 2025
Short summary
Short summary
Severe thunderstorm outbreaks are a source of major damage across Europe. Using historical data, we analysed the large-scale weather patterns leading to these outbreaks in eight different regions. Three types of regions emerge: those limited by temperature, those limited by saturation, and those overall favourable for thunderstorms, consistent with their associated weather patterns and the general climate. These findings help explain regional differences and provide a basis for future forecast improvements.
Wolfgang Wicker, Emmanuele Russo, and Daniela I. V. Domeisen
Weather Clim. Dynam., 6, 965–979, https://doi.org/10.5194/wcd-6-965-2025, https://doi.org/10.5194/wcd-6-965-2025, 2025
Short summary
Short summary
Heatwaves are becoming more frequent, but the contribution by atmospheric circulation changes is unclear. Experiments with an idealized model that simulates atmospheric dynamcis, but excludes clouds, radiation, and moisture, show how a poleward storm track shift increases the eastward phase speed of Rossby waves and reduces mid-latitude heatwave frequency. A comparison with real data for the Southern Hemisphere is attempted.
Annie Y.-Y. Chang, Shaun Harrigan, Maria-Helena Ramos, Massimiliano Zappa, Christian M. Grams, Daniela I. V. Domeisen, and Konrad Bogner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3411, https://doi.org/10.5194/egusphere-2025-3411, 2025
Short summary
Short summary
This study presents a machine learning-aided hybrid forecasting framework to improve early warnings of low flows in the European Alps. It combines weather regime information, streamflow observations, and model simulations (EFAS). Even using only weather regime data improves predictions over climatology, while integrating different data sources yields the best result, emphasizing the value of integrating diverse data sources.
Lou Brett, Christopher J. White, Daniela I. V. Domeisen, Bart van den Hurk, Philip Ward, and Jakob Zscheischler
Nat. Hazards Earth Syst. Sci., 25, 2591–2611, https://doi.org/10.5194/nhess-25-2591-2025, https://doi.org/10.5194/nhess-25-2591-2025, 2025
Short summary
Short summary
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
Earth Syst. Dynam., 16, 1029–1051, https://doi.org/10.5194/esd-16-1029-2025, https://doi.org/10.5194/esd-16-1029-2025, 2025
Short summary
Short summary
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 Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, 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
Weather Clim. Dynam., 6, 171–195, https://doi.org/10.5194/wcd-6-171-2025, https://doi.org/10.5194/wcd-6-171-2025, 2025
Short summary
Short summary
Variability in the extratropical stratosphere and troposphere is 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.
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
Short summary
Short summary
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.
Michael Schutte, Daniela I. V. Domeisen, and Jacopo Riboldi
Weather Clim. Dynam., 5, 733–752, https://doi.org/10.5194/wcd-5-733-2024, https://doi.org/10.5194/wcd-5-733-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
We combine climate projections from 30 climate models with a climate risk model to project winter windstorm damages in Europe under climate change. We study the uncertainty and sensitivity factors related to the modelling of hazard, exposure and vulnerability. We emphasize high uncertainties in the damage projections, with climate models primarily driving the uncertainty. We find climate change reshapes future European windstorm risk by altering damage locations and intensity.
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
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Molly E. Menzel, Darryn W. Waugh, Zheng Wu, and Thomas Reichler
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
Short summary
Short summary
Recent work exploring the tropical atmospheric circulation response to climate change has revealed a disconnect in the latitudinal location of two features, the subtropical jet and the Hadley cell edge. Here, we investigate if the surprising result from coupled climate model and meteorological reanalysis output is consistent across model complexity.
Hilla Afargan-Gerstman, Dominik Büeler, C. Ole Wulff, Michael Sprenger, and Daniela I. V. Domeisen
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
Short summary
Short summary
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
Preprint withdrawn
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Gabriel Chiodo, Marina Friedel, Svenja Seeber, Daniela Domeisen, Andrea Stenke, Timofei Sukhodolov, and Franziska Zilker
Atmos. Chem. Phys., 23, 10451–10472, https://doi.org/10.5194/acp-23-10451-2023, https://doi.org/10.5194/acp-23-10451-2023, 2023
Short summary
Short summary
Stratospheric ozone protects the biosphere from harmful UV radiation. Anthropogenic activity has led to a reduction in the ozone layer in the recent past, but thanks to the implementation of the Montreal Protocol, the ozone layer is projected to recover. In this study, we show that projected future changes in Arctic ozone abundances during springtime will influence stratospheric climate and thereby actively modulate large-scale circulation changes in the Northern Hemisphere.
Jake W. Casselman, Joke F. Lübbecke, Tobias Bayr, Wenjuan Huo, Sebastian Wahl, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 471–487, https://doi.org/10.5194/wcd-4-471-2023, https://doi.org/10.5194/wcd-4-471-2023, 2023
Short summary
Short summary
El Niño–Southern Oscillation (ENSO) has remote effects on the tropical North Atlantic (TNA), but the connections' nonlinearity (strength of response to an increasing ENSO signal) is not always well represented in models. Using the Community Earth System Model version 1 – Whole Atmosphere Community Climate Mode (CESM-WACCM) and the Flexible Ocean and Climate Infrastructure version 1, we find that the TNA responds linearly to extreme El Niño but nonlinearly to extreme La Niña for CESM-WACCM.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
