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Journal Article

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

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons49546

Bothe,  O.
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons37193

Jungclaus,  J. H.
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons37386

Zanchettin,  D.
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Fulltext (public)

cp-9-2471-2013.pdf
(Publisher version), 2MB

cp-9-2471-2013-supplement.pdf
(Publisher version), 3MB

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Citation

Bothe, O., Jungclaus, J. H., & Zanchettin, D. (2013). Consistency of the multi-model CMIP5/PMIP3-past1000 ensemble. Climate of the Past, 9, 2471-2487. doi:10.5194/cp-9-2471-2013.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-B329-5
Abstract
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.