摘要 : Decision -makers need reliable projections of future sea level change for risk assessment. Untangling the sources of uncertainty in sea level projections will help narrow the projection uncertainty. Here, we separate and quantify ... 展开 Decision -makers need reliable projections of future sea level change for risk assessment. Untangling the sources of uncertainty in sea level projections will help narrow the projection uncertainty. Here, we separate and quantify the contributions of internal variability, intermodel uncertainty, and scenario uncertainty to the ensemble spread of dynamic sea level (DSL) at both the basin and regional scales using Coupled Model Intercomparison Project phase 6 (CMIP6) and FGOALS-g3 large ensemble (LEN) data. For basin -mean DSL projections, intermodel uncertainty is the dominant contributor (.55%) in the near term (2021-40), midterm (2041-60), and long term (2081-2100) relative to the climatology of 1995-2014. Internal variability is of secondary importance in the near- and midterm until scenario uncertainty exceeds it in all basins except the Indian Ocean in the long term. For regional -scale DSL projections, internal variability is the dominant contributor (60%-100%) in the Pacific Ocean, Indian Ocean, and western boundary of the Atlantic Ocean, while intermodel uncertainty is more important in other regions in the near term. The contribution of internal variability (intermodel uncertainty) decreases (increases) in most regions from midterm to long term. Scenario uncertainty becomes important after emerging in the Southern, Pacific, and Atlantic Oceans. The signal-to-noise ratio (S/N) maps for regional DSL projections show that anthropogenic DSL signals can only emerge from a few regions. Assuming that model differences are eliminated, the perfect CMIP6 ensemble can capture more anthropogenic regional DSL signals in advance. These findings will help establish future constraints on DSL projections and further improve the next generation of climate models. 收起