Quantifying uncertainties for multi-model, multi-input ensembles of ice sheet and glacier change

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• Tamsin Edwards (King's College London)
• Thursday 31 July 2025, 14:00-15:00
• Seminar Room 2, Newton Institute.

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RCL - Representing, calibrating & leveraging prediction uncertainty from statistics to machine learning

Sea level projections to 2100 in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment report used Gaussian Process emulation of land ice changes, based on multi-model ensembles of ice sheet and glacier models (100s of simulations) that varied a small number of common parameters across models (Edwards et al., 2021).    The EU Horizon 2020 project PROTECT is aiming to extend and improve these projections with larger, more comprehensive datasets and methodological developments. We have generated large multi-model ensembles (1000s of simulations), that systematically explore tens of model uncertainties (parameters, structure, initial and boundary conditions), extending from the recent past to 2300. We use Gaussian Process emulation of these multi-model ensembles and calibrate the projections with observations. The aim is to produce more comprehensive and robust estimates of the land ice contribution to long-term sea level rise for the IPCC Seventh Assessment Report.

This talk is part of the Isaac Newton Institute Seminar Series series.

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