Reduced complexity climate models are useful tools for uncertainty quantification given their flexibility, computational efficiency and suitability for large-ensemble frameworks necessary for statistical estimation. Here we combine recent results from coupled climate model ensembles (CMIP5 and CMIP6) with results from a large perturbed parameter experiment using Hector-BRICK, to analyze how polar land ice contributions expand the uncertainties in probabilistic sea-level rise projections. We present harmonized probabilistic sea-level rise projections that account for structural model differences, parametric uncertainties (e.g. climate sensitivity) and contributions from polar land ice sources. The combination of the simple model (Hector-BRICK) with results from the global models ensembles (CMIP5/CMIP6) helps to fill the gap between computationally expensive process-based models and simpler statistical estimation techniques based on Bayesian calibration with observational constraints. Results are well-suited for multi-sector analysis and systems that are particularly vulnerable to extreme and deeply uncertain sea-level rise scenarios.