• AchutaRao, K., , and K. R. Sperber, 2006: ENSO simulation in coupled ocean–atmosphere models: Are the current models better? Climate Dyn., 27, 115, doi:10.1007/s00382-006-0119-7.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and et al. , 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Annamalai, H., , K. Hamilton, , and K. R. Sperber, 2007: The South Asian summer monsoon and its relationship with ENSO in the IPCC AR4 simulations. J. Climate, 20, 10711092, doi:10.1175/JCLI4035.1.

    • Search Google Scholar
    • Export Citation
  • Ashok, K., , Z. Guan, , and T. Yamagata, 2001: Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. Geophys. Res. Lett., 28, 44994502, doi:10.1029/2001GL013294.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, doi:10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Boos, W. R., , and Z. Kuang, 2010: Dominant control of the South Asian monsoon by orographic insulation versus plateau heating. Nature, 463, 218223, doi:10.1038/nature08707.

    • Search Google Scholar
    • Export Citation
  • Boos, W. R., , and J. V. Hurley, 2013: Thermodynamic bias in the multimodel mean boreal summer monsoon. J. Climate, 26, 22792287, doi:10.1175/JCLI-D-12-00493.1.

    • Search Google Scholar
    • Export Citation
  • Cai, W., , and T. Cowan, 2013: Why is the amplitude of the Indian Ocean dipole overly large in CMIP3 and CMIP5 climate models? Geophys. Res. Lett., 40, 12001205, doi:10.1002/grl.50208.

    • Search Google Scholar
    • Export Citation
  • Cai, W., , T. Cowan, , and M. Raupach, 2009: Positive Indian Ocean dipole events precondition southeast Australia bushfires. Geophys. Res. Lett., 36, L19710, doi:10.1029/2009GL039902.

    • Search Google Scholar
    • Export Citation
  • Cai, W., , X.-T. Zheng, , E. Weller, , M. Collins, , T. Cowan, , M. Lengaigne, , W. Yu, , and T. Yamagata, 2013: Projected response of the Indian Ocean dipole to greenhouse warming. Nat. Geosci., 6, 9991007, doi:10.1038/ngeo2009.

    • Search Google Scholar
    • Export Citation
  • Cai, W., , A. Santoso, , G. J. Wang, , E. Weller, , L. Wu, , K. Ashok, , Y. Masumoto, , and T. Yamagata, 2014: Increased frequency of extreme Indian Ocean dipole events due to greenhouse warming. Nature, 510, 254258, doi:10.1038/nature13327.

    • Search Google Scholar
    • Export Citation
  • Carton, J. A., , and B. S. Giese, 2008: A reanalysis of ocean climate using simple ocean data assimilation (SODA). Mon. Wea. Rev., 136, 29993017, doi:10.1175/2007MWR1978.1.

    • Search Google Scholar
    • Export Citation
  • Chang, C. Y., , J. A. Carton, , S. A. Grodsky, , and S. Nigam, 2007: Seasonal climate of the tropical Atlantic sector in the NCAR Community Climate System Model 3: Error structure and probable causes of errors. J. Climate, 20, 10531070, doi:10.1175/JCLI4047.1.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., and et al. , 2014: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1217–1308.

  • Davey, M., and et al. , 2002: STOIC: A study of coupled model climatology and variability in tropical ocean regions. Climate Dyn., 18, 403420, doi:10.1007/s00382-001-0188-6.

    • Search Google Scholar
    • Export Citation
  • de Szoeke, S. P., , and S.-P. Xie, 2008: The tropical eastern Pacific seasonal cycle: Assessment of errors and mechanisms in IPCC AR4 coupled ocean–atmosphere general circulation models. J. Climate, 21, 25732590, doi:10.1175/2007JCLI1975.1.

    • Search Google Scholar
    • Export Citation
  • Du, Y., , S.-P. Xie, , G. Huang, , and K. Hu, 2009: Role of air–sea interaction in the long persistence of El Niño-induced north Indian Ocean warming. J. Climate, 22, 20232038, doi:10.1175/2008JCLI2590.1.

    • Search Google Scholar
    • Export Citation
  • Du, Y., , W.-J. Cai, , and Y.-L. Wu, 2013: A new type of the Indian Ocean dipole since the mid-1970s. J. Climate, 26, 959972, doi:10.1175/JCLI-D-12-00047.1.

    • Search Google Scholar
    • Export Citation
  • Grodsky, S. A., , J. A. Carton, , S. Nigam, , and Y. M. Okumura, 2012: Tropical Atlantic biases in CCSM4. J. Climate, 25, 36843700, doi:10.1175/JCLI-D-11-00315.1.

    • Search Google Scholar
    • Export Citation
  • Guan, Z., , and T. Yamagata, 2003: The unusual summer of 1994 in East Asia: IOD teleconnections. Geophys. Res. Lett., 30, 1544, doi:10.1029/2002GL016831.

    • Search Google Scholar
    • Export Citation
  • Guilyardi, E., 2006: El Niño-mean state-seasonal cycle interactions in a multi-model ensemble. Climate Dyn., 26, 329348, doi:10.1007/s00382-005-0084-6.

    • Search Google Scholar
    • Export Citation
  • Ham, Y. G., , and J. S. Kug, 2012: How well do current climate models simulate two types of El Niño? Climate Dyn., 39, 383398, doi:10.1007/s00382-011-1157-3.

    • Search Google Scholar
    • Export Citation
  • Hannachi, A., , I. T. Jolliffe, , and D. B. Stephenson, 2007: Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Climatol., 27, 11191152, doi:10.1002/joc.1499.

    • Search Google Scholar
    • Export Citation
  • Hashizume, M., , T. Terao, , and N. Minakawa, 2009: The Indian Ocean dipole and malaria risk in the highlands of western Kenya. Proc. Natl. Acad. Sci. USA, 106, 18571862, doi:10.1073/pnas.0806544106.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, doi:10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation
  • Hwang, Y.-T., , and D. M. M. Frierson, 2013: Link between the double-intertropical convergence zone problem and cloud biases over the Southern Ocean. Proc. Natl. Acad. Sci. USA, 110, 49354940, doi:10.1073/pnas.1213302110.

    • Search Google Scholar
    • Export Citation
  • Izumo, T., , C. Boyer Montegut, , J.-J. Luo, , S. K. Behera, , S. Masson, , and T. Yamagata, 2008: The role of the western Arabian Sea upwelling in Indian monsoon rainfall variability. J. Climate, 21, 56035623, doi:10.1175/2008JCLI2158.1.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and et al. , 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., , B. J. Soden, , and N.-C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917932, doi:10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., , and S. Manabe, 1995: Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean–atmosphere model. J. Climate, 8, 21812199, doi:10.1175/1520-0442(1995)008<2181:TMROTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kosaka, Y., , S.-P. Xie, , N.-C. Lau, , and G. A. Vecchi, 2013: Origin of seasonal predictability for summer climate over the northwestern Pacific. Proc. Natl. Acad. Sci. USA, 110, 75747579, doi:10.1073/pnas.1215582110.

    • Search Google Scholar
    • Export Citation
  • Kozar, M. E., , and V. Misra, 2013: Evaluation of twentieth-century Atlantic warm pool simulations in historical CMIP5 runs. Climate Dyn., 41, 23752391, doi:10.1007/s00382-012-1604-9.

    • Search Google Scholar
    • Export Citation
  • Latif, M., and et al. , 2001: ENSIP: The El Niño simulation intercomparison project. Climate Dyn., 18, 255276, doi:10.1007/s003820100174.

    • Search Google Scholar
    • Export Citation
  • Lau, K. M., , M. K. Kim, , and K. M. Kim, 2006: Asian summer monsoon anomalies induced by aerosol direct forcing: The role of the Tibetan Plateau. Climate Dyn., 26, 855864, doi:10.1007/s00382-006-0114-z.

    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., , and M. J. Nath, 2000: Impact of ENSO on the variability of the Asian–Australian monsoons as simulated in GCM experiments. J. Climate, 13, 42874309, doi:10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lee, T., , D. E. Waliser, , J.-L. F. Li, , F. W. Landerer, , and M. M. Gierach, 2013: Evaluation of CMIP3 and CMIP5 wind stress climatology using satellite measurements and atmospheric reanalysis products. J. Climate, 26, 58105826, doi:10.1175/JCLI-D-12-00591.1.

    • Search Google Scholar
    • Export Citation
  • Li, C., , and M. Yanai, 1996: The onset and interannual variability of the Asian summer monsoon in relation to land–sea thermal contrast. J. Climate, 9, 358375, doi:10.1175/1520-0442(1996)009<0358:TOAIVO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, G., , and S.-P. Xie, 2012: Origins of tropical-wide SST biases in CMIP multi-model ensembles. Geophys. Res. Lett., 39, L22703, doi:10.1029/2012GL053777.

    • Search Google Scholar
    • Export Citation
  • Li, G., , and S.-P. Xie, 2014: Tropical biases in CMIP5 multimodel ensemble: The excessive equatorial Pacific cold tongue and double ITCZ problems. J. Climate, 27, 17651780, doi:10.1175/JCLI-D-13-00337.1.

    • Search Google Scholar
    • Export Citation
  • Li, G., , S.-P. Xie, , and Y. Du, 2015: Climate model errors over the south Indian Ocean thermocline dome and their effect on the basin mode of interannual variability. J. Climate, doi:10.1175/JCLI-D-14-00810.1, in press.

    • Search Google Scholar
    • Export Citation
  • Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean–atmosphere feedback analysis. J. Climate, 20, 44974525, doi:10.1175/JCLI4272.1.

    • Search Google Scholar
    • Export Citation
  • Liu, H., , C. Wang, , S.-K. Lee, , and D. B. Enfield, 2013: Atlantic warm pool variability in the CMIP5 simulations. J. Climate, 26, 53155336, doi:10.1175/JCLI-D-12-00556.1.

    • Search Google Scholar
    • Export Citation
  • Liu, L., , S.-P. Xie, , X.-T. Zheng, , T. Li, , Y. Du, , G. Huang, , and W.-D. Yu, 2014: Indian Ocean variability in the CMIP5 multi-model ensemble: The zonal dipole mode. Climate Dyn., 43, 17151730, doi:10.1007/s00382-013-2000-9.

    • Search Google Scholar
    • Export Citation
  • Mechoso, C. R., and et al. , 1995: The seasonal cycle over the tropical Pacific in coupled ocean–atmosphere general circulation models. Mon. Wea. Rev., 123, 28252838, doi:10.1175/1520-0493(1995)123<2825:TSCOTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Misra, V., , L. Marx, , M. Fennessey, , B. Kirtman, , and J. L. Kinter III, 2008: A comparison of climate prediction and simulation over tropical Pacific. J. Climate, 21, 36013611, doi:10.1175/2008JCLI1932.1.

    • Search Google Scholar
    • Export Citation
  • Murtugudde, R. G., , J. P. McCreary, , and A. J. Busalacchi, 2000: Oceanic processes associated with anomalous events in the Indian Ocean with relevance to 1997–1998. J. Geophys. Res., 105, 32953306, doi:10.1029/1999JC900294.

    • Search Google Scholar
    • Export Citation
  • Neelin, J. D., and et al. , 1992: Tropical air–sea interaction in general circulation models. Climate Dyn., 7, 73104, doi:10.1007/BF00209610.

    • Search Google Scholar
    • Export Citation
  • Ogata, T., , and S.-P. Xie, 2011: Semiannual cycle in zonal wind over the equatorial Indian Ocean. J. Climate, 24, 64716485, doi:10.1175/2011JCLI4243.1.

    • Search Google Scholar
    • Export Citation
  • Okumura, Y., , and S.-P. Xie, 2004: Interaction of the Atlantic equatorial cold tongue and African monsoon. J. Climate, 17, 35893602, doi:10.1175/1520-0442(2004)017<3589:IOTAEC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., , D. E. Parker, , E. B. Horton, , C. K. Folland, , L. V. Alexander, , D. P. Rowell, , E. C. Kent, , and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, doi:10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Richter, I., , and S.-P. Xie, 2008: On the origin of equatorial Atlantic biases in coupled general circulation models. Climate Dyn., 31, 587598, doi:10.1007/s00382-008-0364-z.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., , B. N. Goswami, , P. N. Vinayachandran, , and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360363.

    • Search Google Scholar
    • Export Citation
  • Schott, F. A., , and J. P. McCreary, 2001: The monsoon circulation of the Indian Ocean. Prog. Oceanogr., 51, 1123, doi:10.1016/S0079-6611(01)00083-0.

    • Search Google Scholar
    • Export Citation
  • Schott, F. A., , M. Dengler, , and R. Schoenefeldt, 2002: The shallow thermohaline circulation of the Indian Ocean. Prog. Oceanogr., 53, 57103, doi:10.1016/S0079-6611(02)00039-3.

    • Search Google Scholar
    • Export Citation
  • Schott, F. A., , S.-P. Xie, , and J. P. McCreary, 2009: Indian Ocean circulation and climate variability. Rev. Geophys., 47, RG1002, doi:10.1029/2007RG000245.

    • Search Google Scholar
    • Export Citation
  • Song, X., , and G. Zhang, 2009: Convection parameterization, tropical Pacific double ITCZ, and upper-ocean biases in the NCAR CCSM3. Part I: Climatology and atmospheric feedback. J. Climate, 22, 42994315, doi:10.1175/2009JCLI2642.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., , J. S. Ronald, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Ummenhofer, C. C., and et al. , 2011: Indian and Pacific Ocean influences on southeast Australian drought and soil moisture. J. Climate, 24, 13131336, doi:10.1175/2010JCLI3475.1.

    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and et al. , 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012, doi:10.1256/qj.04.176.

  • Vecchi, G. A., , and B. J. Soden, 2007: Global warming and the weakening of the tropical circulation. J. Climate, 20, 43164340, doi:10.1175/JCLI4258.1.

    • Search Google Scholar
    • Export Citation
  • Wahl, S., , M. Latif, , W. Park, , and N. Keenlyside, 2011: On the tropical Atlantic SST warm bias in the Kiel climate model. Climate Dyn., 36, 891906, doi:10.1007/s00382-009-0690-9.

    • Search Google Scholar
    • Export Citation
  • Wang, C., , L. Zhang, , S.-K. Lee, , L. Wu, , and C. R. Mechoso, 2014: A global perspective on CMIP5 climate model biases. Nat. Climate Change, 4, 201205, doi:10.1038/nclimate2118.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., , A. M. Moore, , J. P. Loschnigg, , and R. R. Leben, 1999: Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature, 401, 356360, doi:10.1038/43848.

    • Search Google Scholar
    • Export Citation
  • Weller, E., , and W. Cai, 2013a: Asymmetry in the IOD and ENSO teleconnection in a CMIP5 model ensemble and its relevance to regional rainfall. J. Climate, 26, 51395149, doi:10.1175/JCLI-D-12-00789.1.

    • Search Google Scholar
    • Export Citation
  • Weller, E., , and W. Cai, 2013b: Realism of the Indian Ocean dipole in CMIP5 models: The implications for climate projections. J. Climate, 26, 66496659, doi:10.1175/JCLI-D-12-00807.1.

    • Search Google Scholar
    • Export Citation
  • Wittenberg, A. T., , A. Rosati, , N.-C. Lau, , and J. J. Ploshay, 2006: GFDL’s CM2 global coupled climate models. Part III: Tropical Pacific climate and ENSO. J. Climate, 19, 698722, doi:10.1175/JCLI3631.1.

    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1973: An equatorial jet in the Indian Ocean. Science, 181, 262264, doi:10.1126/science.181.4096.262.

  • Xie, S.-P., , H. Annamalai, , F. A. Schott, , and J. P. McCreary, 2002: Structure and mechanisms of south Indian Ocean climate variability. J. Climate, 15, 864878, doi:10.1175/1520-0442(2002)015<0864:SAMOSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., , K. Hu, , J. Hafner, , H. Tokinaga, , Y. Du, , G. Huang, , and T. Sampe, 2009: Indian Ocean capacitor effect on Indo–western Pacific climate during the summer following El Niño. J. Climate, 22, 730747, doi:10.1175/2008JCLI2544.1.

    • Search Google Scholar
    • Export Citation
  • Yamagata, T., , S. K. Behera, , J.-J. Luo, , S. Masson, , M. Jury, , and S. A. Rao, 2004: Coupled ocean–atmosphere variability in the tropical Indian Ocean. Earth’s Climate: The Ocean–Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 189–211.

  • Yang, J., , Q. Liu, , S.-P. Xie, , Z. Liu, , and L. Wu, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708, doi:10.1029/2006GL028571.

    • Search Google Scholar
    • Export Citation
  • Yu, J.-Y., , and C. R. Mechoso, 1999: Links between annual variations of Peruvian stratocumulus clouds and of SST in the eastern equatorial Pacific. J. Climate, 12, 33053318, doi:10.1175/1520-0442(1999)012<3305:LBAVOP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., , and H. Wang, 2006: Toward mitigating the double ITCZ problem in NCAR CCSM3. Geophys. Res. Lett., 33, L06709, doi:10.1029/2005GL025229.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., , S.-P. Xie, , G. A. Vecchi, , Q. Liu, , and J. Hafner, 2010: Indian Ocean dipole response to global warming: Analysis of ocean–atmospheric feedbacks in a coupled model. J. Climate, 23, 12401253, doi:10.1175/2009JCLI3326.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., , S.-P. Xie, , Y. Du, , L. Liu, , G. Huang, , and Q. Liu, 2013: Indian Ocean dipole response to global warming in the CMIP5 multi-model ensemble. J. Climate, 26, 60676080, doi:10.1175/JCLI-D-12-00638.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, Y., , J.-L. Lin, , and T. Shinoda, 2012: The equatorial Pacific cold tongue simulated by IPCC AR4 coupled GCMs: Upper ocean heat budget and feedback analysis. J. Geophys. Res., 117, C05024, doi:10.1029/2011JC007746.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 145 144 26
PDF Downloads 127 127 20

Monsoon-Induced Biases of Climate Models over the Tropical Indian Ocean

View More View Less
  • 1 State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, and Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
  • | 2 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
  • | 3 State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
© Get Permissions
Restricted access

Abstract

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.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-14-00740.s1.

Corresponding author address: Gen Li, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, CAS, 164 West Xingang Road, Guangzhou 510301, China. E-mail: ligen@scsio.ac.cn

Abstract

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.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-14-00740.s1.

Corresponding author address: Gen Li, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, CAS, 164 West Xingang Road, Guangzhou 510301, China. E-mail: ligen@scsio.ac.cn

Supplementary Materials

    • Supplemental Materials (DOCX 3.07 MB)
Save