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| Titel |
Probabilistic calibration of a Greenland Ice Sheet model using spatially resolved synthetic observations: toward projections of ice mass loss with uncertainties |
| VerfasserIn |
W. Chang, P. J. Applegate, M. Haran, K. Keller |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 5 ; Nr. 7, no. 5 (2014-09-05), S.1933-1943 |
| Datensatznummer |
250115713
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| Publikation (Nr.) |
 copernicus.org/gmd-7-1933-2014.pdf |
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| Zusammenfassung |
| 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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