Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7969-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7969-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
28 Oct 2025
Methods for assessment of models |
| 28 Oct 2025
| 28 Oct 2025
A Python diagnostics package for evaluation of Madden–Julian Oscillation (MJO) teleconnections in subseasonal-to-seasonal (S2S) forecast systems
Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USA
Saisri Kollapaneni
Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USA
Andrea M. Jenney
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, USA
Jiabao Wang
Center for Western Weather and Water Extremes, Scripps Institute of Oceanography, University of California, San Diego, California, USA
Department of Atmospheric and Environmental Sciences, University at Albany, SUNY, Albany, New York, USA
Cheng Zheng
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York, USA
Hyemi Kim
Department of Science Education, Ewha Womans University, Seoul, Republic of Korea
Chaim I. Garfinkel
The Fredy and Nadine Hermann Institute of Earth Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
Ayush Singh
College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon, USA
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Chaim I. Garfinkel, David Avisar, Scott M. Osprey, Doug Smith, Jian Rao, and Jonathon S. Wright
EGUsphere, https://doi.org/10.5194/egusphere-2026-759, https://doi.org/10.5194/egusphere-2026-759, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
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The Quasi-biennial Oscillation (QBO) dominates variability in the tropical stratosphere, & it impacts surface climate in several parts of the world. However, climate models have been shown to systematically under-estimate the influence of the QBO. Here, we re-evaluate this finding using much larger ensemble sizes than have been previously available based on four separate models. We find that the models are comparatively more successful in capturing QBO influences than reported by previous work.
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
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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.
William J. M. Seviour, Justin Finkel, Philip Rupp, Regan Mudhar, Amy H. Butler, Chaim I. Garfinkel, Peter Hitchcock, Blanca Ayarzagüena, Dong-Chan Hong, Yu-Kyung Hyun, Hera Kim, Eun-Pa Lim, Daniel De Maeseneire, Gabriele Messori, Gerbrand Koren, Michael Sigmond, Isla R. Simpson, and Seok-Woo Son
EGUsphere, https://doi.org/10.5194/egusphere-2026-230, https://doi.org/10.5194/egusphere-2026-230, 2026
This preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
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Variability of the stratospheric polar vortex is thought to play a role in driving weather extremes, but quantifying this role for a given event has proved challenging. Using a new set of perturbed subseasonal forecast experiments from 7 modelling centres we determine the stratospheric contribution to the risk and severity of three recent extreme weather events. The forecast-based methodology that we develop is applicable to understanding a range of other drivers of weather extremes.
Wuhan Ning, Chaim I. Garfinkel, Judah Cohen, Ian P. White, and Jian Rao
Weather Clim. Dynam., 7, 277–295, https://doi.org/10.5194/wcd-7-277-2026, https://doi.org/10.5194/wcd-7-277-2026, 2026
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Whether the zonal structure of a polar vortex matters for surface climate is an open question, with much observational work showing a role but with limited modeling work and demonstration of a causal influence. Here, we isolate this influence using a moist general circulation model. We find that the surface responses differ qualitatively depending on the zonal asymmetries of the shifted polar vortex and concurrently occurring wave reflection events, and provide a mechanistic explanation for why.
David Avisar and Chaim I. Garfinkel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4287, https://doi.org/10.5194/egusphere-2025-4287, 2025
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We use the Large Ensemble Single Forcing simulations to assess the role of individual forcings to the Mediterranean drying and to clarify the dynamical origin of the model’s prediction variability. A more pronounced North Atlantic warming hole, a stronger stratospheric polar vortex, and a larger poleward shift of the subtropical jet correlate with a stronger drying trend. Aerosols had a detectable influence on Mediterranean climate. Hence, their removal may have an impact in future decades.
Ying Dai, Peter Hitchcock, Amy H. Butler, Chaim I. Garfinkel, and William J. M. Seviour
Weather Clim. Dynam., 6, 841–862, https://doi.org/10.5194/wcd-6-841-2025, https://doi.org/10.5194/wcd-6-841-2025, 2025
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Using a new database of subseasonal to seasonal (S2S) forecasts, we find that with a successful forecast of the sudden stratospheric warming (SSW), S2S models can capture the European precipitation signals after the 2018 SSW several weeks in advance. The findings indicate that the stratosphere represents an important source of S2S predictability for precipitation over Europe and call for consideration of stratospheric variability in hydrological prediction at S2S timescales.
Yalalt Nyamgerel, Yeongcheol Han, Soon Do Hur, Hyemi Kim, Songyi Kim, Jangil Moon, Barbara Stenni, and Jeonghoon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2025-2408, https://doi.org/10.5194/egusphere-2025-2408, 2025
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This research explores climate patterns recorded in Antarctic ice over the past two centuries. By analyzing ice layers, we identified connections between Antarctica's climate and tropical ocean conditions. Results show changing influences over time and highlight the Indian Ocean's key role in Antarctic snowfall. This improves understanding of how polar and tropical climates interact, crucial for future climate predictions.
Qian Lu, Jian Rao, Chunhua Shi, and Chaim I. Garfinkel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1123, https://doi.org/10.5194/egusphere-2025-1123, 2025
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Stratospheric water vapor has been proven to have significant climate effects as a greenhouse gas. Tropical stratospheric water vapor exhibits a clear imprint of the Quasi-Biennial Oscillation (QBO). This study compares the water vapor variations associated with the QBO between boreal winter and summer, and the seasonal difference in the water vapor QBO signals is revealed.
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
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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.
Cheng Zheng, Yutian Wu, Mingfang Ting, and Clara Orbe
Atmos. Chem. Phys., 24, 6965–6985, https://doi.org/10.5194/acp-24-6965-2024, https://doi.org/10.5194/acp-24-6965-2024, 2024
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Trace gases and aerosols in the Arctic, which typically originate from midlatitude and tropical emission regions, modulate the Arctic climate via their radiative and chemistry impacts. Thus, long-range transport of these substances is important for understanding the current and the future change of Arctic climate. By employing chemistry–climate models, we explore how year-to-year variations in the atmospheric circulation modulate atmospheric long-range transport into the Arctic.
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
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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.
Raphaël de Fondeville, Zheng Wu, Enikő Székely, Guillaume Obozinski, and Daniela I. V. Domeisen
Weather Clim. Dynam., 4, 287–307, https://doi.org/10.5194/wcd-4-287-2023, https://doi.org/10.5194/wcd-4-287-2023, 2023
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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 %.
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.
Shlomi Ziskin Ziv, Chaim I. Garfinkel, Sean Davis, and Antara Banerjee
Atmos. Chem. Phys., 22, 7523–7538, https://doi.org/10.5194/acp-22-7523-2022, https://doi.org/10.5194/acp-22-7523-2022, 2022
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Stratospheric water vapor is important for Earth's overall greenhouse effect and for ozone chemistry; however the factors governing its variability on interannual timescales are not fully known, and previous modeling studies have indicated that models struggle to capture this interannual variability. We demonstrate that nonlinear interactions are important for determining overall water vapor concentrations and also that models have improved in their ability to capture these connections.
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
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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.
Andrea M. Jenney, David A. Randall, and Elizabeth A. Barnes
Weather Clim. Dynam., 2, 653–673, https://doi.org/10.5194/wcd-2-653-2021, https://doi.org/10.5194/wcd-2-653-2021, 2021
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Storm activity in the tropics is one of the key phenomena that provide weather predictability on an extended timescale of about 10–40 d. The influence of tropical storminess on places like North America is sensitive to the overall average state of the climate system. In this study, we try to unpack the reasons why climate models do not agree on how the influence of these storms on weather over the North Pacific and North America will change in the future.
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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.
The diagnostics package is an open-source Python software package used for evaluating the...
