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| Titel |
Reducing uncertainties in projections of Antarctic ice mass loss |
| VerfasserIn |
G. Durand, F. Pattyn |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1994-0416
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| Digitales Dokument |
URL |
| Erschienen |
In: The Cryosphere ; 9, no. 6 ; Nr. 9, no. 6 (2015-11-09), S.2043-2055 |
| Datensatznummer |
250116865
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| Publikation (Nr.) |
 copernicus.org/tc-9-2043-2015.pdf |
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| Zusammenfassung |
| Climate model projections are often aggregated into multi-model averages of
all models participating in an intercomparison project, such as the Coupled
Model Intercomparison Project (CMIP). The "multi-model" approach provides a
sensitivity test to the models' structural choices and implicitly assumes
that multiple models provide additional and more reliable information than a
single model, with higher confidence being placed on results that are common to
an ensemble. A first initiative of the ice sheet modeling community, SeaRISE,
provided such multi-model average projections of polar ice sheets'
contribution to sea-level rise. The SeaRISE Antarctic numerical experiments
aggregated results from all models devoid of a priori selection, based on the
capacity of such models to represent key ice-dynamical processes. Here, using
the experimental setup proposed in SeaRISE, we demonstrate that correctly
representing grounding line dynamics is essential to infer future Antarctic
mass change. We further illustrate the significant impact on the ensemble
mean and deviation of adding one model with a known bias in its ability of
modeling grounding line dynamics. We show that this biased model can hardly
be identified from the ensemble only based on its estimation of volume
change, as ad hoc and untrustworthy parametrizations can force any modeled grounding
line to retreat. However, tools are available to test parts of the response
of marine ice sheet models to perturbations of climatic and/or oceanic origin
(MISMIP, MISMIP3d). Based on recent projections of Pine Island Glacier mass
loss, we further show that excluding ice sheet models that do not pass the
MISMIP benchmarks decreases the mean contribution and standard deviation of
the multi-model ensemble projection by an order of magnitude for that
particular drainage basin. |
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