The effect of sea surface temperature bias in the PMIP2 AOGCMs on mid-Holocene Asian monsoon enhancement
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The effect of bias on control simulation is a significant issue for climate change modeling studies. We investigated the effect of the sea surface temperature (SST) bias in present day (0 ka) Atmosphere–Ocean Coupled General Circulation Model (AOGCM) simulations on simulations of the mid-Holocene (6 ka, i.e., 6,000 years before present) Asian monsoon enhancement. Because changes in ocean heat transport have a negligible effect on the 6 ka Asian monsoon (Ohgaito and Abe-Ouchi in Clim Dyn 29(1):39–50, 2007), we used an Atmospheric General Circulation Model (AGCM) rather than an AOGCM. Simulations using the AGCM coupled to a mixed layer ocean model (MLM) were conducted for 0 and for 6 ka with different ocean heat transport estimated from the climatological SST of the 0 ka simulations from nine Paleoclimate Modeling Intercomparison Project (PMIP) phase 2 (PMIP2) AOGCMs (henceforth “MA” is used to refer to experiments using the AGCM coupled with the MLM). No correlation between MA and the corresponding PMIP2 was seen in the 0 ka precipitation and it was not very strong for the 6 ka precipitation enhancement. Thus, the influences from the different AGCMs play a substantial role on the 0 ka precipitation and the 6 ka precipitation enhancement. The sensitivity experiments indicated that it was the pattern of the 0 ka SST bias which played a dominant role in the 0 ka precipitation and the 6 ka precipitation enhancement, not the difference in the mean value of the SST bias. The distributions of the 6 ka precipitation enhancements for the nine PMIP2 AOGCMs and nine MA experiments were compared. These showed that the effects of SST bias on 6 ka precipitation enhancement among the AOGCMs were not negligible. The effects of biases among the AGCMs were not negligible either, but of comparable size. That is, improvements in both the SST bias and the AGCM contribute to simulate better 6 ka monsoon.
KeywordsMid-Holocene GCM Asian monsoon
First of all, we would like to thank the MIROC model developers. We are most grateful to the PMIP modeling groups for providing their data for analysis, and to the Laboratoire des Sciences du Climat et de l’Environnement for collecting and archiving the model data. The PMIP2/MOTIF Data Archive is supported by CEA, CNRS, the EU project MOTIF (EVK2-CT-2002-00153) and the Programme National d’Etude de la Dynamique du Climat. Further information is available at http://pmip2.lsce.ipsl.fr/ and http://motif.lsce.ipsl.fr/. We also thank WL Chan for his help on proofreading. Finally, we would like to acknowledge the valuable comments from two reviewers whose suggestions have inspired us to improve this work.
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