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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 5 | Copyright
Geosci. Model Dev., 7, 1933-1943, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 05 Sep 2014

Development and technical paper | 05 Sep 2014

Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties

W. Chang1, P. J. Applegate2, M. Haran1, and K. Keller3 W. Chang et al.
  • 1Department of Statistics, University of Chicago, Chicago, IL 60637, USA
  • 2Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA 16802, USA
  • 3Department of Geosciences, Pennsylvania State University, University Park, PA 16802, USA

Abstract. Computer models of ice sheet behavior are important tools for projecting future sea level rise. The simulated modern ice sheets generated by these models differ markedly as input parameters are varied. To ensure accurate ice sheet mass loss projections, these parameters must be constrained using observational data. Which model parameter combinations make sense, given observations? Our method assigns probabilities to parameter combinations based on how well the model reproduces the Greenland Ice Sheet profile. We improve on the previous state of the art by accounting for spatial information and by carefully sampling the full range of realistic parameter combinations, using statistically rigorous methods. Specifically, we estimate the joint posterior probability density function of model parameters using Gaussian process-based emulation and calibration. This method is an important step toward calibrated probabilistic projections of ice sheet contributions to sea level rise, in that it uses data–model fusion to learn about parameter values. This information can, in turn, be used to make projections while taking into account various sources of uncertainty, including parametric uncertainty, data–model discrepancy, and spatial correlation in the error structure. We demonstrate the utility of our method using a perfect model experiment, which shows that many different parameter combinations can generate similar modern ice sheet profiles. This result suggests that the large divergence of projections from different ice sheet models is partly due to parametric uncertainty. Moreover, our method enables insight into ice sheet processes represented by parameter interactions in the model.

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