Articles | Volume 20, issue 10
https://doi.org/10.5194/tc-20-5867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-5867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Subseasonal forecast improvements from sea ice concentration data assimilation in the Antarctic
Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey, USA
now at: Xinchang, Zhejiang, China
Mitchell Bushuk
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
Michael Winton
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
William Gregory
Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey, USA
Bill Hurlin
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
Liwei Jia
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
University Corporation for Atmospheric Research, Boulder, Colorado, USA
Feiyu Lu
Atmospheric and Oceanic Sciences Program, Princeton University, Princeton, New Jersey, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2026-5844, https://doi.org/10.5194/egusphere-2026-5844, 2026
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We present a new system to predict conditions in the Northeast Pacific Ocean up to one year in advance. By comparing past forecasts with ocean measurements, we show that we can accurately predict temperature and oxygen levels several months in advance. These predictions will soon be available to the public, enabling coastal communities, managers, and industries to make better proactive decisions.
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This preprint is open for discussion and under review for Ocean Science (OS).
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This paper examines the accuracy of forecasts of ocean oxygen and acidity, both critical for the healthy functioning of fish and marine ecosystems, along the continental shelf off the Northeast U.S. coastline. The forecasts are produced using a novel numerical model able to resolve fine oceanographic details. Comparison with a reference dataset shows that the model often accurately predicts oxygen and acidity up to several months in advance.
Xiao Liu, John P. Dunne, Liwei Jia, Charles A. Stock, Xiaosong Yang, Matthew J. Harrison, Liping Zhang, and Anthony Rosati
EGUsphere, https://doi.org/10.5194/egusphere-2026-4194, https://doi.org/10.5194/egusphere-2026-4194, 2026
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This study uses NOAA GFDL’s SPEAR model to improve predictions of how the ocean absorbs human emitted CO2. While standard models capture long-term trends, they struggle with yearly fluctuations. Researchers found that "initializing" the model with observed data significantly boosts their prediction accuracy for up to two years. This framework helps separate the predictions of natural variability from human-driven carbon changes, a vital step for verifying future marine carbon removal efforts.
Joseph Fogarty, Elie Bou-Zeid, Mitchell Bushuk, and Linette Boisvert
The Cryosphere, 18, 4335–4354, https://doi.org/10.5194/tc-18-4335-2024, https://doi.org/10.5194/tc-18-4335-2024, 2024
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We hypothesize that using a broad set of surface characterization metrics for polar sea ice surfaces will lead to more accurate representations in general circulation models. However, the first step is to identify the minimum set of metrics required. We show via numerical simulations that sea ice surface patterns can play a crucial role in determining boundary layer structures. We then statistically analyze a set of high-resolution sea ice surface images to obtain this minimal set of parameters.
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.
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.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
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This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang
The Cryosphere, 16, 1141–1156, https://doi.org/10.5194/tc-16-1141-2022, https://doi.org/10.5194/tc-16-1141-2022, 2022
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We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. The model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
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Short summary
Antarctic sea ice has shifted from its overall increasing trend in the past decades to the recent decline, which attracted attention from the research community. Providing more accurate subseasonal predictions of Antarctic sea ice is critical to managing the accelerating human activities. We demonstrate that by incorporating satellite observations of sea ice concentration with modeling, the subseasonal predictions of Antarctic sea ice can be improved significantly.
Antarctic sea ice has shifted from its overall increasing trend in the past decades to the...