Probabilistic Projections of Sea-Level Rise and Global Mean Temperature using Hector
Reduced complexity earth system models are useful tools for quantifying uncertainty, given their flexibility, computational efficiency, and suitability for the large‐ensemble frameworks necessary for statistical estimation. A team including researchers from Pacific Northwest National Laboratory coupled a new version of the reduced complexity model, Hector, with a 1‐D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea-level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. They applied a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters, using observational and historical information from global surface temperature, thermal expansion, and other contributors to sea-level change, to analyze the effects of different sources of information on extreme sea-level rise projections.
Different observational constraints can yield similar temperatures but drastically different sea-level rise projections, particularly for extreme sea-level rise scenarios. Results pave the way for new research linking global climate uncertainties (e.g., climate sensitivity) with local-scale flood risk analysis.
Using observational and historical information from global surface temperature, thermal expansion, and other contributors to sea-level change, the research team applied Bayesian calibration to quantify model uncertainties surrounding model parameters and analyzed the effects of different sources of information on extreme sea-level rise projections. They found that the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea-level change play lesser roles. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5, year-2100 temperature decreases 0.3 K). Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate, while thermal expansion data allow for constrained sea-level projections. Different combinations of observational constraints can yield very similar year-2100 temperatures but drastically different SLR projections. This is particularly important for extreme sea-level projections.