A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet

Lee, Ben Seiyon; Haran, Murali; Fuller, Robert W.; Pollard, David; Keller, Klaus

doi:10.1214/19-aoas1305

Title: A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet

Abstract

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, formore »« less

Authors:

Lee, Ben Seiyon ^[1]; Haran, Murali ^[1]; Fuller, Robert W. ^[1]; Pollard, David ^[1]; Keller, Klaus ^[1]

Pennsylvania State Univ., University Park, PA (United States)

Publication Date:: Mon Jun 01 00:00:00 EDT 2020

Research Org.:: Pennsylvania State Univ., University Park, PA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:: 1807560

Grant/Contract Number:: SC0016162

Resource Type:: Accepted Manuscript

Journal Name:: The Annals of Applied Statistics

Additional Journal Information:: Journal Volume: 14; Journal Issue: 2; Journal ID: ISSN 1932-6157

Publisher:: Institute of Mathematical Statistics

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; Antarctic ice sheet model; computer model calibration; Paleoclimate; sequential Monte Carlo; uncertainty quantification

Citation Formats


                    Lee, Ben Seiyon, Haran, Murali, Fuller, Robert W., Pollard, David, and Keller, Klaus. A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet.  United States: N. p., 2020. 
Web.  doi:10.1214/19-aoas1305.

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                    Lee, Ben Seiyon, Haran, Murali, Fuller, Robert W., Pollard, David, & Keller, Klaus. A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet.  United States.  https://doi.org/10.1214/19-aoas1305

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                    Lee, Ben Seiyon, Haran, Murali, Fuller, Robert W., Pollard, David, and Keller, Klaus. Mon .  
"A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet".  United States.  https://doi.org/10.1214/19-aoas1305.  https://www.osti.gov/servlets/purl/1807560.

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@article{osti_1807560,

  title        = {A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet},

  author       = {Lee, Ben Seiyon and Haran, Murali and Fuller, Robert W. and Pollard, David and Keller, Klaus},

  abstractNote = {We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. Here, this work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.},

  doi          = {10.1214/19-aoas1305},

  journal      = {The Annals of Applied Statistics},

  number       = 2,

  volume       = 14,

  place        = {United States},

  year         = {Mon Jun 01 00:00:00 EDT 2020},

  month        = {Mon Jun 01 00:00:00 EDT 2020}

}

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