THE NEED FOR DATA/MODEL INTEGRATION Retrodiction/prediction is meaningless without meaningful error bars or probability distribution of model results Major challenge for climate change assessments Are PMIP/observation discrepancies due to faulty boundary conditions or to problems in the models? even dynamical process modelling needs constraints on boundary conditions for under-constrained systems need data/physics integration -> calibrate model against observational data

Criteria for calibration methodology Complicated under-constrained non-linear system with threshold behavior effectively large number of poorly constrained model parameters Large set of diverse noisy constraint data Data and & model limitations -> need a fundamentally probabilistic approach bumpy phase and likelihood spaces (shown below) further rule out gradient-based approaches such as adjoint (eg. 4D var) methods -> stochastic methodology accurate propagation of data uncertainties -> Bayesian approach => Markov Chain Monte Carlo

Bayesian calibration Sample over posterior probability distribution for the ensemble parameters given fits to observational data using Markov Chain Monte Carlo (MCMC) methods Other constraints: Minimize margin forcing LGM ice volume bounds Hudson Bay glaciated at -25 kyr Post MCMC scoring: Marine Limits Strandlines

Large ensemble Bayesian calibration Bayesian neural network integrates over weight space Self-regularized Can handle local minima

It works

Bumpy likelihood space

Another data/model link: guide future data collection

North American Climate and meltwater phasing -> meltwater/iceberg discharge is a critical link between cryosphere and climate system

The meltwater link Need more marine observations to corroborate/refute results What happens to a meltwater plume in the Arctic Ocean? Mixing dynamics in the GIN Seas?

A couple of other interim results The calibration tends to favour an ice volume for North America that is too low to meet global LGM eustatic constraints -> can be addressed by strong H2/H1/mwp1-a events -> also starting to give consideration to larger marine components (especially given recent HOTRAX data)

Where to: Completion of interim global calibrated deglacial ice/meltwater chronology EMIC/GCM recursion to get climatological self-consistency Calibration of glacial inception ice & climate with the glacial systems model coupled to a reduced AOGCM Ditto for deglaciation -> forward in time: P(future cryospheric evolution) eventual calibration of full glacial cycle ice & climate