The global ocean is losing its “memory” under global warming

Using future projections from the latest generation of Earth system models, a recent study published in Scientists progress found that most of the world’s ocean steadily loses its “memory” from year to year under global warming.

Compared to the atmosphere’s rapid weather fluctuations, the slowly varying ocean exhibits strong persistence, or “memory,” which means that tomorrow’s ocean temperature is likely to be much like today’s, with only slight changes. As a result, ocean memory is often used to predict ocean conditions.

The decline in ocean memory appears as a collective response across climate models to human-induced warming. As greenhouse gas concentrations continue to rise, this decline in memory will become increasingly evident.

“We discovered this phenomenon by examining the similarity of ocean surface temperature from year to year as a simple measure of ocean memory,” said lead author and researcher Hui Shi. at the Farallon Institute in Petaluma, California. “It’s almost like the ocean is developing amnesia.”

Ocean memory is related to the thickness of the upper layer of the ocean, known as the mixed layer. Deeper mixed layers have greater heat content, which imparts more thermal inertia which translates into memory. However, the mixed layer over most oceans will become shallower in response to continued anthropogenic warming, causing ocean memory to decline.

“Other processes, such as changes in ocean currents and changes in the exchange of energy between the atmosphere and the ocean, also contribute to changes in ocean memory, but the decrease in depth of the mixed layer and the resulting decline in memory is occurring in all regions of the globe, and this makes it an important factor to consider for future climate predictions,” said Robert Jnglin Wills, a researcher at the ‘University of Washington in Seattle, Washington, and research co-author.

Along with declining ocean memory, thinning of the mixed layer also increases random fluctuations in sea surface temperature. Else in the future, the fraction of useful signals for prediction decreases greatly.

“The reduced memory of the ocean as well as the increase in random fluctuations suggest intrinsic changes in the system and new challenges in predicting warming,” said Fei-Fei Jin, professor of atmospheric sciences at the University of ‘Hawai’i at the Manoa School of Ocean and Earth Science and Technology, and research co-author.

Ocean memory loss not only impacts the prediction of physical variables, but could also influence how we manage sensitive marine ecosystems.

“Reduced memory means less time in advance to make a forecast. This could hamper our ability to predict and prepare for ocean changes, including marine heatwaves, which are known to cause sudden changes and pronounced in ocean ecosystems around the world,” said Michael Jacox, a research scientist at NOAA Fisheries’ Southwest Fisheries Science Center in Monterey, Calif., and co-author of the research.

In fisheries management, the biological parameters used for stock assessment are estimated assuming a stable environment represented by the recent past. Reduced ocean memory could make this estimate inaccurate and calls for new approaches in ecosystem-based fisheries management to include real-time ocean monitoring and other similar efforts. The decline of ocean memory is also likely to have impacts on populations of biological resources. Depending on whether species are adapted to constant or more variable environmental conditions, the future evolutions of their population can be better estimated and predicted by taking into account the loss of oceanic memory.

In addition to ocean forecasting, the forecasting of land-based impacts on temperature, precipitation as well as extreme events could also be affected by the decline of ocean memory due to their dependence on the persistence of sea surface temperature as a what a source of predictability. As ocean memory continues to decline, researchers will likely be challenged to seek alternative predictors for skillful predictions.

Teresa H. Sadler