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A Bayesian Calibrated Deglacial History for the North American Ice Complex

A Bayesian Calibrated Deglacial History for the North American Ice Complex. Lev Tarasov, Radford Neal, and W. R. Peltier University of Toronto. Outline. Model Data Model + Data: Calibration methodology Some key results. Glacial modelling challenges and issues. Glacial Systems Model (GSM).

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A Bayesian Calibrated Deglacial History for the North American Ice Complex

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  1. A Bayesian Calibrated Deglacial History for the North American IceComplex Lev Tarasov, Radford Neal, and W. R. Peltier University of Toronto

  2. Outline Model Data Model + Data: Calibration methodology Some key results

  3. Glacial modelling challenges and issues

  4. Glacial Systems Model (GSM)

  5. Climate forcing • LGM monthly temperature and precipitation from 6 highest resolution PMIP runs • Mean and top EOFS • Total of 18 ensemble climate parameters

  6. Need constraints -> DATA

  7. Deglacial margin chronology • (Dyke, 2003) • 36 time-slices • +/- 50 km uncertainty • Margin buffer

  8. Relative sea-level (RSL) data

  9. VLBI and absolute gravity data

  10. Noisy data and non-linear system => need calibration and error bars

  11. Bayesian calibration • Sample over posterior probability distribution for the ensemble parameters given fits to observational data using Markov Chain Monte Carlo (MCMC) methods • Sampling also subject to additional volume and ice thickness constraints

  12. Large ensemble Bayesian calibration • Bayesian neural network integrates over weight space

  13. It works!

  14. RSL results, best fit models

  15. LGM characteristics

  16. LGM comparisons

  17. Maximum NW ice thickness • Green runs fail constraints • Blue runs pass constraints • Red runs are top 20% of blue runs

  18. Calibration favours fast flow

  19. Deglacial chronology

  20. Summary Glaciological results Large Keewatin ice dome Multi-domed structure due to geographically restricted fast flows Need strong ice calving and/or extensive ice-shelves in the Arctic to fit RSL data Need thin time-average Hudson Bay ice to fit RSL data Bayesian calibration method links data and physics (model) -> rational error bars

  21. Issues and challenges • Choice of ensemble parameters • Parameter set ended up being extended with time as troublesome regions were identified • Method could easily handle more parameters, so best to try to cover deglacial phase space from the start • Challenge of identifying appropriate priors for each parameter • Error model for RSL data • Noisy and likely site biased • Error model allows for site scaling and time-shifting • Heavy-tailed error model to limit influence of outliers • Neural network • Non-trivial to find appropriate configuration • Neural network for RSL was most complex: multi-layered and separate clusters for site location and time • Training takes a long time, predictions can be weak for distant regions • MCMC sampling • Can get stuck in local minima • “Unphysical” solutions cropped up => added constraints

  22. RSL data redundancy • Fairly close correspondence between fit to full RSL data set and fit to reduced 313 datapoint calibration data set (only the last 50 runs have been calibrated against the whole data set)

  23. RSL data fits • Data-points should generally provide lower envelope of true RSL history • Black: best overall fit with full constraints • Red: best overall fit to 313 data set and geodetic data with full constraints • Green: best fit to just 313 RSL data, no constraints • Blue: best fit to just full RSL data, no constraints

  24. NA LGM ice volume • Best fits required low volumes given global constraints • Possible indication of need for stronger Heinrich events

  25. Critical RSL site: SE Hudson Bay • Fitting this site required very strong regional desert-elevation effect (ie low value) and therefore thin and warm ice core • Atmospheric reorganization or weak Heinrich events? • Thin core results in low ice volumes

  26. Summary • Bayesian calibration • It works but is a non-trivial exercise • Need to ensure that parameter space is large enough • Phase space of model deglacial history must be quite bumpy • Tricky to define complete error bars • Calibration had tendency to find “wacky(?)” solutions • Glaciological results • Large Keewatin ice dome • Multi-domed structure due to geographically restricted fast flows • Need strong ice calving and/or extensive ice-shelves in the arctic to fit RSL data • Need thin time-average Hudson Bay ice to fit RSL data • Future work: • Faster (more diffusive computational kernal) ice-flow • Addition of hydrological constraints and other data (especially to better constrain south-central and NW sectors)

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