hide
Free keywords:
-
Abstract:
Assessing the impacts of future relative sea level rise requires projections consistent with historical observations. However, existing projections often do not align with past data, complicating adaptation planning, impact assessments, and communication. We present a spatial Bayesian model that generates local projections at tide gauge sites from historical records. The model integrates tide gauges, GPS, and satellite altimetry with past and future constraints on mountain glaciers, polar ice sheets, thermal expansion, ocean circulation, land water storage, and glacial history. By separating unforced ocean variability from long-term trends, we provide posterior estimates of sea level change and vertical land motion. The inclusion of local constraints reduces uncertainty in near-term local projections while producing global median projections and uncertainty ranges similar to those in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). The model enables projections of local relative sea level rise for any given global temperature trajectory, illustrated with three IPCC AR6 Working Group III pathways.
