Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble

We present an assessment of the probabilistic and climatological consistency of the CMIP5/PMIP3 ensemble simulations for the last millennium relative to proxy-based reconstructions under the paradigm of a statistically indistinguishable ensemble. We evaluate whether simulations and reconstru...

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Main Authors: O. Bothe, J. H. Jungclaus, D. Zanchettin
Format: Article
Language:English
Published: Copernicus Publications 2013-11-01
Series:Climate of the Past
Online Access:http://www.clim-past.net/9/2471/2013/cp-9-2471-2013.pdf
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spelling doaj-bb54b66c474f4f4d868d9a4733a0e42d2020-11-24T23:05:08ZengCopernicus PublicationsClimate of the Past1814-93241814-93322013-11-01962471248710.5194/cp-9-2471-2013Consistency of the multi-model CMIP5/PMIP3-past1000 ensembleO. Bothe0J. H. Jungclaus1D. Zanchettin2Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, GermanyMax Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, GermanyMax Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, GermanyWe present an assessment of the probabilistic and climatological consistency of the CMIP5/PMIP3 ensemble simulations for the last millennium relative to proxy-based reconstructions under the paradigm of a statistically indistinguishable ensemble. We evaluate whether simulations and reconstructions are compatible realizations of the unknown past climate evolution. A lack of consistency is diagnosed in surface air temperature data for the Pacific, European and North Atlantic regions. On the other hand, indications are found that temperature signals partially agree in the western tropical Pacific, the subtropical North Pacific and the South Atlantic. Deviations from consistency may change between sub-periods, and they may include pronounced opposite biases in different sub-periods. These distributional inconsistencies originate mainly from differences in multi-centennial to millennial trends. Since the data uncertainties are only weakly constrained, the frequently too wide ensemble distributions prevent the formal rejection of consistency of the simulation ensemble. The presented multi-model ensemble consistency assessment gives results very similar to a previously discussed single-model ensemble suggesting that structural and parametric uncertainties do not exceed forcing and internal variability uncertainties.http://www.clim-past.net/9/2471/2013/cp-9-2471-2013.pdf
collection DOAJ
language English
format Article
sources DOAJ
author O. Bothe
J. H. Jungclaus
D. Zanchettin
spellingShingle O. Bothe
J. H. Jungclaus
D. Zanchettin
Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
Climate of the Past
author_facet O. Bothe
J. H. Jungclaus
D. Zanchettin
author_sort O. Bothe
title Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
title_short Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
title_full Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
title_fullStr Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
title_full_unstemmed Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble
title_sort consistency of the multi-model cmip5/pmip3-past1000 ensemble
publisher Copernicus Publications
series Climate of the Past
issn 1814-9324
1814-9332
publishDate 2013-11-01
description We present an assessment of the probabilistic and climatological consistency of the CMIP5/PMIP3 ensemble simulations for the last millennium relative to proxy-based reconstructions under the paradigm of a statistically indistinguishable ensemble. We evaluate whether simulations and reconstructions are compatible realizations of the unknown past climate evolution. A lack of consistency is diagnosed in surface air temperature data for the Pacific, European and North Atlantic regions. On the other hand, indications are found that temperature signals partially agree in the western tropical Pacific, the subtropical North Pacific and the South Atlantic. Deviations from consistency may change between sub-periods, and they may include pronounced opposite biases in different sub-periods. These distributional inconsistencies originate mainly from differences in multi-centennial to millennial trends. Since the data uncertainties are only weakly constrained, the frequently too wide ensemble distributions prevent the formal rejection of consistency of the simulation ensemble. The presented multi-model ensemble consistency assessment gives results very similar to a previously discussed single-model ensemble suggesting that structural and parametric uncertainties do not exceed forcing and internal variability uncertainties.
url http://www.clim-past.net/9/2471/2013/cp-9-2471-2013.pdf
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