Quantifying the uncertainty of regime shifts in the paleoclimate via physics-informed emulation and expert knowledge

The absence of data in the existing instrumental record significantly limits our ability to comprehend and forecast tipping points in the Greenland Ice Sheet (GrIS) and Subpolar Gyre (SPG). Multidirectional approaches are therefore required to capture the complexity of systemic changes and support future early warning efforts. In this study we discuss the ongoing work of the research project VERIFY: Out Of Sample Testing For Early Warning Systems Using Past Climate. We combine computational experiments, with physics-informed emulation and insights from expert elicitation to better understand dirvers of paleoclimate regime shifts in the GrIS. Employing uncertainty quantification methods, we make use of machine learning surrogate models to approximate the system's response. Surrogate models can accurately mimic input-output relationships of complex and computationaly expensive models, providing the opportunity to produce large ensembles for fully exploring the range of plausible model inputs. The goal is to understand what drives the exceedance of critical thresholds through the integration of computational experiments, machine learning and current scientific knowledge.