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Coastal communities are increasingly vulnerable to long-term sea level rise and fluctuations driven by climate variability. While recent advances in coupled climate models enable sea level predictions several months in advance, further efforts are needed to assess and enhance seasonal prediction of coastal sea level. In this study, we evaluate seasonal prediction skill for large-scale and coastal sea level along the U.S. and Canadian West Coast using multiple forecast systems. Prediction skill peaks in the tropical Indo-Pacific and extends into the eastern North Pacific, declining from south to north along the coast. Using self-organizing maps (SOMs), a machine learning technique, we identify sources of large-scale sea level variability and predictability in the eastern tropical and North Pacific, closely linked to the El Niño–Southern Oscillation. Finally, we improve coastal sea level predictions from dynamical models by leveraging the connection between large-scale and coastal sea level through SOM-reconstructed and model-analog approaches.
Using tide gauge (TG) observations, we identify pronounced multidecadal fluctuations in sea level along the US Northeast Coast (USNEC) superimposed on a long-term increasing trend. This multidecadal sea level variability, largely arising from fluctuations in the buoyancy-driven Atlantic meridional overturning circulation (AMOC), substantially modulates the frequency of flood occurrences along the USNEC and serves as a source of multiyear predictability. Using an initialized dynamical downscaling decadal prediction system with a 1/12° ocean resolution, we demonstrate that flood frequency along the USNEC can be predicted on multiyear to decadal timescales. The long-term increasing trend in flood frequency, mainly driven by increasing greenhouse gases and associated radiative forcing changes, can be predicted a decade ahead. Furthermore, detrended flood frequency along the USNEC exhibits prediction skill for up to 3 years, as verified by TG observation. This multiyear prediction skill is achieved using prediction models that are initialized from our best estimate of observed AMOC.
Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction.
While the changes in ocean heat uptake in a warming climate have been well explored, the changes in response to climate mitigation efforts remain unclear. Using coupled climate model simulations, here we find that in response to a hypothesized reduction of greenhouse gases in the late 21st century, ocean heat uptake would significantly decline in all ocean basins except the North Atlantic, where a persistently weakened Atlantic meridional overturning circulation results in sustained heat uptake. These prolonged circulation anomalies further lead to interbasin heat exchanges, characterized by a sustained heat export from the Atlantic to the Southern Ocean and a portion of heat transfer from the Southern Ocean to the Indo-Pacific. Due to ocean heat uptake decline and interbasin heat export, the Southern Ocean experiences the strongest decline in ocean heat storage therefore emerging as the primary heat exchanger, while heat changes in the Indo-Pacific basin are relatively limited.
The rate of sea level rise (SLR) along the Southeast Coast of the U.S. increased significantly after 2010. While anthropogenic radiative forcing causes an acceleration of global mean SLR, regional changes in the rate of SLR are strongly influenced by internal variability. Here we use observations and climate models to show that the rapid increase in the rate of SLR along the U.S. Southeast Coast after 2010 is due in part to multidecadal buoyancy-driven Atlantic meridional overturning circulation (AMOC) variations, along with heat transport convergence from wind-driven ocean circulation changes. We show that an initialized decadal prediction system can provide skillful regional SLR predictions induced by AMOC variations 5 years in advance, while wind-driven sea level variations are predictable 2 years in advance. Our results suggest that the rate of coastal SLR and its associated flooding risk along the U.S. southeastern seaboard are potentially predictable on multiyear timescales.
