Monsoon-induced biases of climate models over the tropical Indian Ocean

TitleMonsoon-induced biases of climate models over the tropical Indian Ocean
Publication TypeJournal Article
Year of Publication2015
AuthorsLi G., Xie SP, Du Y.
JournalJournal of Climate
Date Published2015/04
Type of ArticleArticle
ISBN Number0894-8755
Accession NumberWOS:000352487300008
Keywordsasian summer monsoon; cmip5 multimodel; cold-tongue; coupled ocean; double-itcz; el-nino; ensemble; equatorial; general-circulation models; pacific; sea-surface temperature; seasonal cycle

Long-standing biases of climate models limit the skills of climate prediction and projection. Overlooked are tropical Indian Ocean (IO) errors. Based on the phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble, the present study identifies a common error pattern in climate models that resembles the IO dipole (IOD) mode of interannual variability in nature, with a strong equatorial easterly wind bias during boreal autumn accompanied by physically consistent biases in precipitation, sea surface temperature (SST), and subsurface ocean temperature. The analyses show that such IOD-like biases can be traced back to errors in the South Asian summer monsoon. A southwest summer monsoon that is too weak over the Arabian Sea generates a warm SST bias over the western equatorial IO. In boreal autumn, Bjerknes feedback helps amplify the error into an IOD-like bias pattern in wind, precipitation, SST, and subsurface ocean temperature. Such mean state biases result in an interannual IOD variability that is too strong. Most models project an IOD-like future change for the boreal autumn mean state in the global warming scenario, which would result in more frequent occurrences of extreme positive IOD events in the future with important consequences to Indonesia and East Africa. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) characterizes this future IOD-like projection in the mean state as robust based on consistency among models, but the authors' results cast doubts on this conclusion since models with larger IOD amplitude biases tend to produce stronger IOD-like projected changes in the future.

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