A Preliminary Calibrated Deglaciation Chronology for the Eurasian Ice Complex Lev Tarasov¹, W.R. Peltier², R. Gyllencreutz³, O. Lohne³, J. Mangerud³, and.

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A Preliminary Calibrated Deglaciation Chronology for the Eurasian Ice Complex Lev Tarasov¹, W.R. Peltier², R. Gyllencreutz³, O. Lohne³, J. Mangerud³, and J.-I. Svensen³ (1) Memorial University of Newfoundland, (2) University of Toronto, (3) University of Bergen

Inferred Greenland temperature (based ss09 chronology)

A lot of physics is missing in pure geophysical (Legoblock) reconstructions

No meaningful error bars! => no meaningful interpretation Solution: 3 components:

#1: Glacial Systems Model (GSM)  3D thermo- mechanically coupled ice-sheet model, 0.5 * 1.0 (lat/long) model resolution  VM2 viscosity model  detailed surface mass-balance and ice-calving modules  global gravitationally self-consistent RSL solver  fully coupled surface drainage solver

Climate forcing  Last Glacial Maximum (LGM) precipitation and temperature from 4 (6 for N. A.) highest resolution Paleo Model Intercomparison Project GCM runs  Mean and EOF fields  Present day observed fields

120 kyr climate forcing

Lots of ensemble parameters  3(5 for North America) ice dynamical  13(16) regional precipitation  LGM precipitation EOFs most significant for North America  4(4) ice calving  4(4) temperature  4(2) ice margins  1 model version  = 29 (32)

Need constraints -> #2: DATA

Initial European deglacial margin chronology (Saarnisto and Lunkka, 2003)  margin forcing: surface mass- balance near ice margin adjusted to fit chronology  applied up to km buffer zone

New margin chronology (Oct/06)

RSL site weighting (U. of Toronto RSL database)

Noisy data and non-linear system => need #3: calibration and error bars

Bayesian calibration  Sample over posterior probability distribution for the ensemble parameters given fits to observational data using Markov Chain Monte Carlo (MCMC) methods  Other constraint:  Minimize margin forcing

Large ensemble Bayesian calibration  Bayesian neural network integrates over weight space  Self-regularized  Can handle local minima  O(1 million) model sampling

Results  from 1 calibration iteration with new margin  923 model runs 2 million MCMC samplings

Overall best RSL fitting models  Key:  Black and red: new margin, 2 best runs  Green: old (Saarnisto and Lunkka) margin  most sites have data only back to no more than -12 to -14 kyr  Generally good fits except for Scotland and Norway

Problem with Norwegian coast  Key:  Black and red: as before  Green: red run with further modifications to the margin chronology: -11 kyr - > -13 kyr, 12/11/10 - > 11.8/11.4/10.7 kyr  margin chronology and/or fjord resolution?

Mean ensemble -20 kyr ice velocity and surface elevation

ICE-5G -20 kyr ice surface elevation

-20kyr: Ensemble mean - ICE-5G

1 sigma range for LGM ice thickness!

Ensemble mean present-day rate of uplift

1 sigma range of ensemble rate of uplift

Deglacial eustatic sea-level chronology

Summary  European contributions:  LGM: m eustatic  mwp1-a: m eustatic ( m old margin)  Strong dependence on margin chronology (given apparent uncertainties)  likely problems with western margin chronology for Fennoscandia and/or sub-grid issues related to lack of resolution of fjords  physical model + calibration against data => meaningful error bars/probability distributions  Significant differences with ICE-5G => CAN'T IGNORE CLIMATE and ICE DYNAMICS