Editorial Type:
Article
Article Type:
Research Article

Predictability of Two Types of El Niño Assessed Using an Extended Seasonal Prediction System by MIROC

Yukiko Imada Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan

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Hiroaki Tatebe Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan

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Masayoshi Ishii Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan

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Yoshimitsu Chikamoto International Pacific Research Center, University of Hawai‘i at Mānoa, Honolulu, Hawaii

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Masato Mori Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Miki Arai Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Masahiro Watanabe Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Masahide Kimoto Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan

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Print Publication:
01 Nov 2015
DOI:
https://doi.org/10.1175/MWR-D-15-0007.1
Page(s):
4597–4617
Restricted access

Abstract

Predictability of El Niño–Southern Oscillation (ENSO) is examined using ensemble hindcasts made with a seasonal prediction system based on the atmosphere and ocean general circulation model, the Model for Interdisciplinary Research on Climate, version 5 (MIROC5). Particular attention is paid to differences in predictive skill in terms of the prediction error for two prominent types of El Niño: the conventional eastern Pacific (EP) El Niño and the central Pacific (CP) El Niño, the latter having a maximum warming around the date line. Although the system adopts ocean anomaly assimilation for the initialization process, it maintains a significant ability to predict ENSO with a lead time of more than half a year. This is partly due to the fact that the system is little affected by the “spring prediction barrier,” because increases in the error have little dependence on the thermocline variability. Composite analyses of each type of El Niño reveal that, compared to EP El Niños, the ability to predict CP El Niños is limited and has a shorter lead time. This is because CP El Niños have relatively small amplitudes, and thus they are more affected by atmospheric noise; this prevents development of oceanic signals that can be used for prediction.

Denotes Open Access content.

Corresponding author address: Yukiko Imada, Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba-si, Ibaraki, 305-0052, Japan. E-mail: yimada@mri-jma.go.jp

Abstract

Predictability of El Niño–Southern Oscillation (ENSO) is examined using ensemble hindcasts made with a seasonal prediction system based on the atmosphere and ocean general circulation model, the Model for Interdisciplinary Research on Climate, version 5 (MIROC5). Particular attention is paid to differences in predictive skill in terms of the prediction error for two prominent types of El Niño: the conventional eastern Pacific (EP) El Niño and the central Pacific (CP) El Niño, the latter having a maximum warming around the date line. Although the system adopts ocean anomaly assimilation for the initialization process, it maintains a significant ability to predict ENSO with a lead time of more than half a year. This is partly due to the fact that the system is little affected by the “spring prediction barrier,” because increases in the error have little dependence on the thermocline variability. Composite analyses of each type of El Niño reveal that, compared to EP El Niños, the ability to predict CP El Niños is limited and has a shorter lead time. This is because CP El Niños have relatively small amplitudes, and thus they are more affected by atmospheric noise; this prevents development of oceanic signals that can be used for prediction.

Denotes Open Access content.

Corresponding author address: Yukiko Imada, Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba-si, Ibaraki, 305-0052, Japan. E-mail: yimada@mri-jma.go.jp
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  • Alexander, M. A., I. Blade, M. Newman, J. R. Lanzante, N.-C. Lau, and J. D. Scott, 2002: The atmospheric bridge: The influence of ENSO teleconnections on air–sea interaction over the global oceans. J. Climate, 15, 2205–2231, doi:10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • An, S. I., and B. Wang, 2001: Mechanisms of locking the El Niño and La Niña mature phases to boreal winter. J. Climate, 14, 2164–2176, doi:10.1175/1520-0442(2001)014<2164:MOLOTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Arakawa, A., and C. S. Konor, 1996: Vertical differencing of the primitive equations based on the Charney–Phillips grid in hybrid σ–p vertical coordinates. Mon. Wea. Rev., 124, 511–528, doi:10.1175/1520-0493(1996)124<0511:VDOTPE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi:10.1029/2006JC003798.

    • Search Google Scholar
    • Export Citation

  • Balmaseda, M. A., M. K. Davey, and D. L. T. Anderson, 1995: Decadal and seasonal dependence of ENSO prediction skill. J. Climate, 8, 2705–2715, doi:10.1175/1520-0442(1995)008<2705:DASDOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Barnston, A. G., M. K. Tippett, M. L. L’Heureux, S. Li, and D. G. DeWitt, 2012: Skill of real-time seasonal ENSO model predictions during 2002–11. Bull. Amer. Meteor. Soc., 93, 631–651, doi:10.1175/BAMS-D-11-00111.1.

    • Search Google Scholar
    • Export Citation

  • Battisti, D. S., 1988: Dynamics and thermodynamics of a warming event in a coupled tropical atmosphere–ocean model. J. Atmos. Sci., 45, 2889–2919, doi:10.1175/1520-0469(1988)045<2889:DATOAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Behringer, D. W., M. Ji, and A. Leemaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev., 126, 1013–1021, doi:10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Bloom, S. C., L. Takacs, A. M. da Silva, and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 1256–1271, doi:10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Burgers, G., and J. van Oldenborgh, 2003: On the impact of local feedback in the central Pacific on the ENSO cycle. J. Climate, 16, 2396–2407, doi:10.1175/2766.1.

    • Search Google Scholar
    • Export Citation

  • Chikira, M., and M. Sugiyama, 2010: A cumulus parameterization with state-dependent entrainment rate. Part I: Description and sensitivity to temperature and humidity profiles. J. Atmos. Sci., 67, 2171–2193, doi:10.1175/2010JAS3316.1.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Fedorov, A. V., and S. G. Philander, 2000: Is El Niño changing? Science, 288, 1997–2002, doi:10.1126/science.288.5473.1997.

  • Guilyardi, E., 2006: El Niño–mean state–seasonal cycle interactions in a multi-model ensemble. Climate Dyn., 26, 329–348, doi:10.1007/s00382-005-0084-6.

    • Search Google Scholar
    • Export Citation

  • Hasumi, H., 2006: CCSR ocean component model (COCO) version 4.0. CCSR Rep. 25, 103 pp. [Available at http://ccsr.aori.u-tokyo.ac.jp/~hasumi/COCO/coco4.pdf.]

  • Hendon, H. H., E. Lim, G. Wang, O. Alves, and D. Hudson, 2009: Prospects for predicting two flavors of El Niño. Geophys. Res. Lett., 36, L19713, doi:10.1029/2009GL040100.

    • Search Google Scholar
    • Export Citation

  • Hirahara, S., M. Ishii, and Y. Fukuda, 2014: Centennial-scale sea surface temperature analysis and its uncertainty. J. Climate, 27, 57–75, doi:10.1175/JCLI-D-12-00837.1.

    • Search Google Scholar
    • Export Citation

  • Hoffman, R. N., and E. Kalnay, 1983: Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus, 35A, 100–118, doi:10.1111/j.1600-0870.1983.tb00189.x.

    • Search Google Scholar
    • Export Citation

  • Huang, B., J. Kinter, and P. Schopf, 2002: Ocean data assimilation using intermittent analyses and continuous model error correction. Adv. Atmos. Sci., 19, 965–992, doi:10.1007/s00376-002-0059-z.

    • Search Google Scholar
    • Export Citation

  • Imada, Y., and M. Kimoto, 2006: Improvement of thermocline structure that affect ENSO performance in a coupled GCM. SOLA, 2, 164–167, doi:10.2151/sola.2006-042.

    • Search Google Scholar
    • Export Citation

  • Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., 65, 287–299, doi:10.1007/s10872-009-0027-7.

    • Search Google Scholar
    • Export Citation

  • Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective analyses of SST and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. Int. J. Climatol., 25, 865–879, doi:10.1002/joc.1169.

    • Search Google Scholar
    • Export Citation

  • Ishii, M., M. Kimoto, K. Sakamoto, and S. Iwasaki, 2006: Steric sea level changes estimated from historical ocean subsurface temperature and salinity analyses. J. Oceanogr., 62, 155–170, doi:10.1007/s10872-006-0041-y.

    • Search Google Scholar
    • Export Citation

  • Jeong, H.-I., and Coauthors, 2012: Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter. Climate Dyn., 39, 475–493, doi:10.1007/s00382-012-1359-3.

    • Search Google Scholar
    • Export Citation

  • Jin, E. K., and Coauthors, 2008: Current status of ENSO prediction skill in coupled ocean–atmosphere models. Climate Dyn., 31, 647–664, doi:10.1007/s00382-008-0397-3.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kang, I.-S., S.-I. An, and F.-F. Jin, 2001: A systematic approximation of the SST anomaly equation for ENSO. J. Meteor. Soc. Japan, 79, 1–10, doi:10.2151/jmsj.79.1.

    • Search Google Scholar
    • Export Citation

  • Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting eastern Pacific and central Pacific types of ENSO. J. Climate, 22, 615–632, doi:10.1175/2008JCLI2309.1.

    • Search Google Scholar
    • Export Citation

  • Keenlyside, N. S., M. Latif, J. Jugclaus, L. Kornblueh, and E. Roeckner, 2008: Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 453, 84–88, doi:10.1038/nature06921.

    • Search Google Scholar
    • Export Citation

  • Keppenne, C. L., M. Rienecker, N. P. Kurkowski, and D. A. Adamec, 2005: Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction. Nonlinear Processes Geophys., 12, 491–503, doi:10.5194/npg-12-491-2005.

    • Search Google Scholar
    • Export Citation

  • Kim, H.-M., P. J. Webster, and J. A. Curry, 2009: Impact of shifting patterns of Pacific Ocean warming on North Atlantic tropical cyclones. Science, 325, 77–80, doi:10.1126/science.1174062.

    • Search Google Scholar
    • Export Citation

  • Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Niño events: Cold tongue El Niño and warm pool El Niño. J. Climate, 22, 1499–1515, doi:10.1175/2008JCLI2624.1.

    • Search Google Scholar
    • Export Citation

  • Larkin, N. K., and D. E. Harrison, 2005: On the definition of El Niño and associated seasonal average U.S. weather anomalies. Geophys. Res. Lett., 32, L13705, doi:10.1029/2005GL022738.

    • Search Google Scholar
    • Export Citation

  • Latif, M., T. P. Barnett, M. A. Cane, M. Flugel, N. E. Graham, H. Von Storch, J. S. Xu, and S. E. Zebiak, 1994: A review of ENSO prediction studies. Climate Dyn., 9, 167–179, doi:10.1007/BF00208250.

    • Search Google Scholar
    • Export Citation

  • Lau, N.-C., and M. J. Nath, 1996: The role of the “atmospheric bridge” in linking tropical Pacific ENSO events to extratropical SST anomalies. J. Climate, 9, 2036–2057, doi:10.1175/1520-0442(1996)009<2036:TROTBI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lee, T., J. P. Boulanger, A. Foo, L. L. Fu, and R. Giering, 2000: Data assimilation by an intermediate coupled ocean-atmosphere model: Application to the 1997-1998 El Niño. J. Geophys. Res., 105, 26 063–26 087, doi:10.1029/2000JC900118.

    • Search Google Scholar
    • Export Citation

  • Lim, E.-P., H. H. Hendon, D. Hudson, G. Wang, and O. Alves, 2009: Dynamical forecasts of inter–El Niño variations of tropical SST and Australian spring rainfall. Mon. Wea. Rev., 137, 3796–3810, doi:10.1175/2009MWR2904.1.

    • Search Google Scholar
    • Export Citation

  • Magnusson, L., M. A. Balmaseda, S. Corti, F. Molteni, and T. Stockdale, 2013: Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors. Climate Dyn., 41, 2393–2409, doi:10.1007/s00382-012-1599-2.

    • Search Google Scholar
    • Export Citation

  • Neelin, J., D. Battisti, A. Hirst, F.-F. Jin, T. W. T. Yamagata, and S. Zebiak, 1998: ENSO theory. J. Geophys. Res., 103, 14 261–14 290, doi:10.1029/97JC03424.

    • Search Google Scholar
    • Export Citation

  • Pohlmann, H., J. H. Jungclaus, A. Kohl, D. Stammer, and J. Marotzke, 2009: Initializing decadal climate predictions with the GECCO oceanic synthesis: Effect on the North Atlantic. J. Climate, 22, 3926–3938, doi:10.1175/2009JCLI2535.1.

    • Search Google Scholar
    • Export Citation

  • Smith, D. M., S. Cusack, A. W. Colman, C. K. Folland, G. R. Harris, and J. M. Murphy, 2007: Improved surface temperature prediction for the coming decade from a global climate model. Science, 317, 796–799, doi:10.1126/science.1139540.

    • Search Google Scholar
    • Export Citation

  • Tatebe, H., and Coauthors, 2012: The initialization of the MIROC Climate Models with hydrographic data assimilation for decadal prediction. J. Meteor. Soc. Japan, 90A, 275–294, doi:10.2151/jmsj.2012-A14.

    • Search Google Scholar
    • Export Citation

  • Torrence, C., and P. J. Webster, 1998: The annual cycle of persistence in the El Niño/Southern Oscillation. Quart. J. Roy. Meteor. Soc., 124, 1985–2004, doi:10.1002/qj.49712455010.

    • Search Google Scholar
    • Export Citation

  • Wang, B., and Coauthors, 2009: Advance and prospectus of seasonal prediction: Assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). Climate Dyn., 33, 93–117, doi:10.1007/s00382-008-0460-0.

    • Search Google Scholar
    • Export Citation

  • Wang, C., and J. Picaut, 2004: Understanding ENSO physics—A review. Earth's Climate: The Ocean–Atmosphere Interaction, Geophys. Monogr., Vol. 147, Amer. Geophys. Union, 21–48.

  • Wang, G., and H. H. Hendon, 2007: Sensitivity of Australian rainfall to inter–El Nino variations. J. Climate, 20, 4211–4226, doi:10.1175/JCLI4228.1.

    • Search Google Scholar
    • Export Citation

  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 6312–6335, doi:10.1175/2010JCLI3679.1.

    • Search Google Scholar
    • Export Citation

  • Watanabe, M., M. Chikira, Y. Imada, and M. Kimoto, 2011: Convective control of ENSO simulated in MIROC. J. Climate, 24, 543–562, doi:10.1175/2010JCLI3878.1.

    • Search Google Scholar
    • Export Citation

  • Webster, P. J., 1995: The annual cycle and the predictability of the tropical coupled ocean-atmosphere system. Meteor. Atmos. Phys., 56, 33–55, doi:10.1007/BF01022520.

    • Search Google Scholar
    • Export Citation

  • Weng, H., K. Ashok, S. K. Behera, S. A. Rao, and T. Yamagata, 2007: Impacts of recent El Niño Modoki on dry/wet conditions in the Pacific rim during boreal summer. Climate Dyn., 29, 113–129, doi:10.1007/s00382-007-0234-0.

    • Search Google Scholar
    • Export Citation

  • Yang, S., and X. Jiang, 2014: Prediction of eastern and central Pacific ENSO events and their impacts on East Asian climate by the NCEP climate forecast system. J. Climate, 27, 4451–4472, doi:10.1175/JCLI-D-13-00471.1.

    • Search Google Scholar
    • Export Citation

  • Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511–515, doi:10.1038/nature08316.

    • Search Google Scholar
    • Export Citation

  • Yin, Y., O. Alves, and P. R. Oke, 2011: An ensemble ocean data assimilation system for seasonal prediction. Mon. Wea. Rev., 139, 786–808, doi:10.1175/2010MWR3419.1.

    • Search Google Scholar
    • Export Citation

  • Zebiak, S. E., and M. A. Cane, 1987: A model El Niño–Southern Oscillation. Mon. Wea. Rev., 115, 2262–2278, doi:10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Zhang, W., F.-F. Jin, J. Li, and H.-L. Ren, 2011: Contrasting impacts of two-type El Niño over the western North Pacific during boreal autumn. J. Meteor. Soc. Japan, 89, 563–569, doi:10.2151/jmsj.2011-510.

    • Search Google Scholar
    • Export Citation

  • Zhang, W., F.-F. Jin, H.-L. Ren, J. Li, and J.-X. Zhao, 2012: Differences in teleconnection over the North Pacific and rainfall shift over the USA associated with two types of El Niño during boreal autumn. J. Meteor. Soc. Japan, 90, 535–552, doi:10.2151/jmsj.2012-407.

    • Search Google Scholar
    • Export Citation

  • Zhu, J., G. Zhou, R.-H. Zhang, and Z. Sun, 2011: On the role of oceanic entrainment temperature (Te) in decadal changes of El Niño/Southern Oscillation. Ann. Geophys., 29, 529–540, doi:10.5194/angeo-29-529-2011.

    • Search Google Scholar
    • Export Citation

  • Zhu, J., B. Huang, L. Marx, J. L. Kinter III, M. A. Balmaseda, R.-H. Zhang, and Z.-Z. Hu, 2012: Ensemble ENSO hindcasts initialized from multiple ocean analyses. Geophys. Res. Lett., 39, L09602, doi:10.1029/2012GL051503.

    • Search Google Scholar
    • Export Citation

  • Zhu, J., B. Huang, M. A. Balmaseda, J. L. Kinter III, P. Peng, Z.-Z. Hu, and L. Marx, 2013: Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization. Climate Dyn., 41, 2785–2795, doi:10.1007/s00382-013-1965-8.

    • Search Google Scholar
    • Export Citation
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