Researchers use MITgcm to help improve seasonal US Gulf and East Coast sea level forecasts.

Reporting by Helen Hill for MITgcm

A new study led by the University of Hawaii Sea Level Center (UHSLC) suggests that a forecasting approach based on an ocean-only model—powered by the MIT General Circulation Model (MITgcm) through the Estimating the Circulation and Climate of the Ocean (ECCO) framework—can potentially improve seasonal sea level predictions along the U.S. Gulf and East Coasts.

Forecasting seasonal sea level changes is challenging in coastal regions like the Northwest Atlantic Ocean, where complex bathymetry, strong boundary currents, and atmospheric variability interact. Traditional climate models, such as ECMWF’s SEAS5, depend heavily on forecasted atmospheric conditions, which have limited predictability at seasonal leads. The new study, led by Xue Feng and co-authors—including researchers from NASA, NOAA, and international climate centers—assessed an approach called ocean dynamic persistence. Instead of relying on uncertain atmosphere forecasts, this method uses observation-constrained initial conditions derived from the ECCO State Estimate and average atmospheric conditions. Published in Ocean Science, the results suggest that simulating how the ocean’s initial conditions respond to the climatological atmosphere can be used to improve the prediction of monthly sea level anomalies several months in advance.

“The ocean evolves more slowly than the atmosphere, and some oceanic processes can carry sea level signals over long distances for months at a time,” said lead author Xue Feng. “We wanted to see if that ‘memory’ could be translated into more skillful seasonal predictions.”

Team members at NASA’s Jet Propulsion Laboratory created the forecast by initializing MITgcm-based ECCO simulations monthly from 1992 to 2017 and running them forward for 12 months using climatological atmospheric forcing. This setup highlights the ocean’s capacity to retain and propagate sea level signals over time. The resulting forecasts were compared to observed sea level data and to predictions from both a damped persistence model and ECMWF’s SEAS5. Validated against water level observations, at a lead time of four months, the dynamic persistence forecasts showed the highest anomaly correlation coefficients at 22 out of 39 coastal locations—particularly south of Cape Hatteras.

“We hope our findings encourage other modeling centers to try this approach with their own ocean models,” said co-author Tong Lee, research scientist at NASA JPL. “Direct comparisons between ocean dynamic persistence and coupled climate forecasts can reveal how much predictability comes from the ocean initialization versus from the post-initialization air-sea coupling processes.”

The study also highlights MITgcm’s ability to represent the large-scale ocean circulation robustly and efficiently. By leveraging ECCO’s observation-constrained state estimates, this ocean-only forecasting approach is less computationally demanding than running coupled climate models—yet it provides useful skills especially for regions where atmospheric predictability is low.

The authors note that while the approach improves correlation skill, it may underestimate variability due to the use of climatological forcing and ECCO’s relatively coarse resolution. “Dynamic persistence effectively raises the bar for what we should expect from seasonal sea level forecasts,” added Matthew Widlansky, associate director of the UHSLC and co-author of the study. “It shows that we can extract meaningful predictive skill from the ocean initial conditions alone. However, further assessment by operational climate modeling centers is needed before it can be considered in a forecast product.”

Future work could involve exploring how to generate ensemble predictions from an ocean dynamic persistence framework and configuring real-time forecasts.

Questions/ comments email: Xue

Image: Pexels

About the Researcher

Xue Feng is a Postdoctoral Researcher in the University of Hawaii Sea Level Center attached to the School of Ocean and Earth Science and Technology at the University of Hawai’i at Manoa, Her research interests include ocean variability and climate dynamics. When not working, she enjoys reading and hiking.

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