Anthropogenic climate forcing will cause the global mean sea level to rise over the 21st century. However, regional sea level is expected to vary across ocean basins, superimposed by the influence of natural internal climate variability.Here, we address the detection of dynamic sea level (DSL) changes by combining the perspectives of a single and a multi-model ensemble approach (the 50-member CanESM5 and a 27-model ensemble, respectively, all retrieved from the CMIP6 archive), under three CMIP6 projected scenarios: SSP1-2.6, SSP3-7.0 and SSP5-8.5. The ensemble analysis takes into account four key metrics: signal (S), noise (N), S/N ratio, and time of emergence (ToE). The results from both sets of ensembles agree in the fact that regions with higher S/N (associated with smaller uncertainties) also reflect earlier ToEs. The DSL signal is projected to emerge in the Southern Ocean, Southeast Pacific, Northwest Atlantic, and the Arctic. Results common for both sets of ensemble simulations show that while S progressively increases with increased projected emissions, N, in turn, does not vary substantially among the SSPs, suggesting that uncertainty arising from internal climate variability has little dependence on changes in the magnitude of external forcing. Projected changes are greater and quite similar for the scenarios SSP3-7.0 and SSP5-8.5 and considerably smaller for the SSP1-2.6, highlighting the importance of public policies towards lower emission scenarios and of keeping emissions below a certain threshold.