Articles | Volume 21, issue 4
https://doi.org/10.5194/os-21-1663-2025
© 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/os-21-1663-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Aug 2025
Research article |
| 05 Aug 2025
| 05 Aug 2025
Indications of improved seasonal sea level forecasts for the United States Gulf Coast and East Coast using ocean dynamic persistence
Xue Feng
CORRESPONDING AUTHOR
Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology (SOEST), University of Hawai`i at Mānoa, Honolulu, HI, USA
Matthew J. Widlansky
CORRESPONDING AUTHOR
Cooperative Institute for Marine and Atmospheric Research, School of Ocean and Earth Science and Technology (SOEST), University of Hawai`i at Mānoa, Honolulu, HI, USA
Department of Oceanography, SOEST, University of Hawai`i at Mānoa, Honolulu, HI, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Magdalena A. Balmaseda
European Centre for Medium-Range Weather Forecasts, Reading, UK
European Centre for Medium-Range Weather Forecasts, Reading, UK
Gregory Dusek
National Ocean Service, NOAA, Silver Spring, MD, USA
William Sweet
National Ocean Service, NOAA, Silver Spring, MD, USA
Malte F. Stuecker
Department of Oceanography, SOEST, University of Hawai`i at Mānoa, Honolulu, HI, USA
International Pacific Research Center, SOEST, University of Hawai`i at Mānoa, Honolulu, HI, USA
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John R. Albers, Matthew Newman, Magdalena A. Balmaseda, William Sweet, Yan Wang, and Tongtong Xu
Ocean Sci., 21, 1761–1785, https://doi.org/10.5194/os-21-1761-2025, https://doi.org/10.5194/os-21-1761-2025, 2025
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Providing early warning of coastal flooding is an emerging priority for the National Oceanic and Atmospheric Administration. We assess whether current operational forecast models can provide the basis for predicting the risks of higher-than-normal coastal sea level values up to 6 weeks in advance. For many United States coastal locations, models have sufficient prediction skill to be used as the basis for the development of a high tide flooding prediction system on subseasonal timescales.
Jozef Skákala, David Ford, Keith Haines, Amos Lawless, Matthew J. Martin, Philip Browne, Marcin Chrust, Stefano Ciavatta, Alison Fowler, Daniel Lea, Matthew Palmer, Andrea Rochner, Jennifer Waters, Hao Zuo, Deep S. Banerjee, Mike Bell, Davi M. Carneiro, Yumeng Chen, Susan Kay, Dale Partridge, Martin Price, Richard Renshaw, Georgy Shapiro, and James While
Ocean Sci., 21, 1709–1734, https://doi.org/10.5194/os-21-1709-2025, https://doi.org/10.5194/os-21-1709-2025, 2025
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UK marine data assimilation (MDA) involves a closely collaborating research community. In this paper, we offer both an overview of the state of the art and a vision for the future across all of the main areas of UK MDA, ranging from physics to biogeochemistry to coupled DA. We discuss the current UK MDA stakeholder applications, highlight theoretical developments needed to advance our systems, and reflect upon upcoming opportunities with respect to hardware and observational missions.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian L. E. Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Dae-Won Kim, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana N. Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
Earth Syst. Dynam., 16, 1103–1134, https://doi.org/10.5194/esd-16-1103-2025, https://doi.org/10.5194/esd-16-1103-2025, 2025
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Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability, and extreme events. The 10-year-long high-resolution simulations for the 2000s, 2030s, 2060s, and 2090s were initialized from a coarser-resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Fiona Raphaela Spuler, Marlene Kretschmer, Magdalena Alonso Balmaseda, Yevgeniya Kovalchuk, and Theodore G. Shepherd
EGUsphere, https://doi.org/10.5194/egusphere-2024-4115, https://doi.org/10.5194/egusphere-2024-4115, 2025
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Large-scale atmospheric dynamics modulate the occurrence of extreme events and can be leveraged to improve their predictability. In this paper, we introduce a generative machine learning method to identify dynamical drivers of a relevant impact variable in the form of targeted circulation regimes. Applying the method to study extreme precipitation over Morocco, we show that these regimes are more predictive of the impact while maintaining their own predictability and physical consistency.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
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Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Thomas P. Collings, Niall D. Quinn, Ivan D. Haigh, Joshua Green, Izzy Probyn, Hamish Wilkinson, Sanne Muis, William V. Sweet, and Paul D. Bates
Nat. Hazards Earth Syst. Sci., 24, 2403–2423, https://doi.org/10.5194/nhess-24-2403-2024, https://doi.org/10.5194/nhess-24-2403-2024, 2024
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Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study applies a new approach to estimating the likelihood of coastal flooding around the world. The method uses data from observations and computer models to create a detailed map of where these coastal floods might occur. The approach can predict flooding in areas for which there are few or no data available. The results can be used to help prepare for and prevent this type of flooding.
Eric de Boisséson and Magdalena Alonso Balmaseda
Ocean Sci., 20, 265–278, https://doi.org/10.5194/os-20-265-2024, https://doi.org/10.5194/os-20-265-2024, 2024
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Marine heatwaves are long periods of extremely warm ocean surface temperatures. Predicting such events a few months in advance would help decision-making to mitigate their impacts on marine ecosystems. This work investigates how well operational seasonal forecasts can predict marine heatwaves. Results show that such events can be predicted a few months in advance in the tropics but that extending the predictability skill to other regions will require additional work on the forecast models.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
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We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
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Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
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We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager
Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, https://doi.org/10.5194/esd-12-1393-2021, 2021
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A large ensemble of simulations with 100 members has been conducted with the state-of-the-art CESM2 Earth system model, using historical and SSP3-7.0 forcing. Our main finding is that there are significant changes in the variance of the Earth system in response to anthropogenic forcing, with these changes spanning a broad range of variables important to impacts for human populations and ecosystems.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
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High ice shelf melting in the Amundsen Sea has attracted many observational campaigns in the past decade. One method to combine observations with numerical models is the adjoint method. After 20 iterations, the cost function, defined as a sum of the weighted model–data difference, is reduced by 65 % by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. This study demonstrates adjoint-method optimization with explicit representation of ice shelf cavity circulation.
Kyung-Sook Yun, Axel Timmermann, and Malte F. Stuecker
Earth Syst. Dynam., 12, 121–132, https://doi.org/10.5194/esd-12-121-2021, https://doi.org/10.5194/esd-12-121-2021, 2021
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Changes in the Hadley and Walker cells cause major climate disruptions across our planet. What has been overlooked so far is the question of whether these two circulations can shift their positions in a synchronized manner. We here show the synchronized spatial shifts between Walker and Hadley cells and further highlight a novel aspect of how tropical sea surface temperature anomalies can couple these two circulations. The re-positioning has important implications for extratropical rainfall.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
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Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
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Short summary
Forecasting sea level changes months in advance along the Gulf Coast and East Coast of the United States is challenging. Here, we present a method that uses past ocean states to forecast future sea levels, while assuming no knowledge of how the atmosphere will evolve other than its typical annual cycle near the ocean's surface. Our findings indicate that this method improves sea level outlooks for many locations along the Gulf Coast and East Coast, especially south of Cape Hatteras.
Forecasting sea level changes months in advance along the Gulf Coast and East Coast of the...
