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| Titel |
Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene |
| VerfasserIn |
J. C. Hargreaves, J. D. Annan  , R. Ohgaito, A. Paul, A. Abe-Ouchi |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1814-9324
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| Digitales Dokument |
URL |
| Erschienen |
In: Climate of the Past ; 9, no. 2 ; Nr. 9, no. 2 (2013-03-21), S.811-823 |
| Datensatznummer |
250018021
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| Publikation (Nr.) |
 copernicus.org/cp-9-811-2013.pdf |
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| Zusammenfassung |
Paleoclimate simulations provide us with an opportunity to critically
confront and evaluate the performance of climate models in simulating the
response of the climate system to changes in radiative forcing and other
boundary conditions. Hargreaves et al. (2011) analysed the reliability of
the Paleoclimate Modelling Intercomparison Project, PMIP2 model ensemble with respect to the MARGO sea surface temperature
data synthesis (MARGO Project Members, 2009) for the Last Glacial Maximum (LGM,
21 ka BP). Here we extend that work to include a new comprehensive
collection of land surface data (Bartlein et al., 2011), and introduce a
novel analysis of the predictive skill of the models. We include output from
the PMIP3 experiments, from the two models for which suitable data are
currently available. We also perform the same analyses for the PMIP2
mid-Holocene (6 ka BP) ensembles and available proxy data sets.
Our results are predominantly positive for the LGM, suggesting that as well
as the global mean change, the models can reproduce the observed pattern of
change on the broadest scales, such as the overall land–sea contrast and
polar amplification, although the more detailed sub-continental scale
patterns of change remains elusive. In contrast, our results for the
mid-Holocene are substantially negative, with the models failing to reproduce
the observed changes with any degree of skill. One cause of this problem
could be that the globally- and annually-averaged forcing anomaly is very
weak at the mid-Holocene, and so the results are dominated by the more
localised regional patterns in the parts of globe for which data are
available. The root cause of the model-data mismatch at these scales is
unclear. If the proxy calibration is itself reliable, then representativity
error in the data-model comparison, and missing climate feedbacks in the
models are other possible sources of error. |
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