Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

Takeshi Doi Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Search for other papers by Takeshi Doi in
Current site
Google Scholar
PubMed
Close
,
Swadhin K. Behera Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Search for other papers by Swadhin K. Behera in
Current site
Google Scholar
PubMed
Close
, and
Toshio Yamagata Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan

Search for other papers by Toshio Yamagata in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0193.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Takeshi Doi, takeshi.doi@jamstec.go.jp

Abstract

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0193.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Takeshi Doi, takeshi.doi@jamstec.go.jp

Supplementary Materials

    • Supplemental Materials (PDF 12.19 MB)
Save
  • Akihiko, T., Y. Morioka, and S. K. Behera, 2014: Role of climate variability in the heatstroke death rates of Kanto region in Japan. Sci. Rep., 4, 5655, https://doi.org/10.1038/srep05655.

    • Crossref
    • 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, https://doi.org/10.1029/2001GL013294.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashok, K., Z. Guan, and T. Yamagata, 2003: A look at the relationship between the ENSO and the Indian Ocean dipole. J. Meteor. Soc. Japan, 81, 4156, https://doi.org/10.2151/jmsj.81.41.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., and S. J. Mason, 2011: Evaluation of IRI’s seasonal climate forecasts for the extreme 15% tails. Wea. Forecasting, 26, 545554, https://doi.org/10.1175/WAF-D-10-05009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behera, S. K., and T. Yamagata, 2003: Influence of the Indian Ocean dipole on the Southern Oscillation. J. Meteor. Soc. Japan, 81, 169177, https://doi.org/10.2151/jmsj.81.169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behera, S. K., R. Krishnan, and T. Yamagata, 1999: Unusual ocean-atmosphere conditions in the tropical Indian Ocean during 1994. Geophys. Res. Lett., 26, 30013004, https://doi.org/10.1029/1999GL010434.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Wang, and A. Kumar, 2013: Lagged ensembles, forecast configuration, and seasonal predictions. Mon. Wea. Rev., 141, 34773497, https://doi.org/10.1175/MWR-D-12-00184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crétat, J., P. Terray, S. Masson, K. P. Sooraj, and M. K. Roxy, 2016: Indian Ocean and Indian summer monsoon: Relationships without ENSO in ocean–atmosphere coupled simulations. Climate Dyn., 49, 14291448, https://doi.org/10.1007/s00382-016-3387-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crétat, J., P. Terray, S. Masson, and K. P. Sooraj, 2017: Intrinsic precursors and timescale of the tropical Indian Ocean Dipole: Insights from partially decoupled numerical experiment. Climate Dyn., 51, 13111332, https://doi.org/10.1007/s00382-017-3956-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dalcher, A., E. Kalnay, and R. N. Hoffman, 1988: Medium range lagged average forecasts. Mon. Wea. Rev., 116, 402416, https://doi.org/10.1175/1520-0493(1988)116<0402:MRLAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Déqué, M., 1997: Ensemble size for numerical seasonal forecasts. Tellus, 49A, 7486, https://doi.org/10.1034/j.1600-0870.1997.00005.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doi, T., S. K. Behera, and T. Yamagata, 2013: Predictability of the Ningaloo Niño/Niña. Sci. Rep., 3, 2892, https://doi.org/10.1038/srep02892.

  • Doi, T., S. K. Behera, and T. Yamagata, 2015: An interdecadal regime shift in rainfall predictability related to the Ningaloo Niño in the late 1990s. J. Geophys. Res. Oceans, 120, 13881396, https://doi.org/10.1002/2014JC010562.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doi, T., S. K. Behera, and T. Yamagata, 2016: Improved seasonal prediction using the SINTEX-F2 coupled model. J. Adv. Model. Earth Syst., 8, 18471867, https://doi.org/10.1002/2016MS000744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doi, T., A. Storto, S. K. Behera, A. Navarra, and T. Yamagata, 2017: Improved prediction of the Indian Ocean dipole mode by use of subsurface ocean observations. J. Climate, 30, 79537970, https://doi.org/10.1175/JCLI-D-16-0915.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eade, R., D. M. Smith, A. Scaife, E. Wallace, N. Dunstone, L. Hermanson, and N. Robinson, 2014: Do seasonal-to-decadal climate predictions underestimate the predictability of the real world? Geophys. Res. Lett., 41, 56205628, https://doi.org/10.1002/2014GL061146.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, M., M. J. Mcphaden, S. Xie, and J. Hafner, 2013: La Niña forces unprecedented Leeuwin Current warming in 2011. Nature, 3, 1277, https://doi.org/10.1038/srep01277.

    • Search Google Scholar
    • Export Citation
  • Ferro, C. A., and D. B. Stephenson, 2011: Extremal dependence indices: Improved verification measures for deterministic forecasts of rare binary events. Wea. Forecasting, 26, 699713, https://doi.org/10.1175/WAF-D-10-05030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fichefet, T., and M. A. Morales Maqueda, 1997: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J. Geophys. Res., 102, 12 60912 646, https://doi.org/10.1029/97JC00480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Good, S. A., M. J. Martin, and N. A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans, 118, 67046716, https://doi.org/10.1002/2013JC009067.

    • Crossref
    • 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, https://doi.org/10.1029/2002GL016831.

    • Crossref
    • 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, https://doi.org/10.1073/pnas.0806544106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerling, M. P., and A. Kumar, 2002: Atmospheric response patterns associated with tropical forcing. J. Climate, 15, 21842203, https://doi.org/10.1175/1520-0442(2002)015,2184:ARPAWT.2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, C.-C., T. Li, LinHo, and J.-S. Kug, 2008a: Asymmetry of the Indian Ocean dipole. Part I: Observational analysis. J. Climate, 21, 48344848, https://doi.org/10.1175/2008JCLI2222.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, C.-C., T. Li, and J.-J. Luo, 2008b: Asymmetry of the Indian Ocean dipole. Part II: Model diagnosis. J. Climate, 21, 48494858, https://doi.org/10.1175/2008JCLI2223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horii, T., H. Hase, I. Ueki, and Y. Masumoto, 2008: Oceanic precondition and evolution of the 2006 Indian Ocean dipole. Geophys. Res. Lett., 35, L03607, https://doi.org/10.1029/2007GL032464.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudson, D., A. G. Marshall, Y. Yin, O. Alves, and H. H. Hendon, 2013: Improving intraseasonal prediction with a new ensemble generation strategy. Mon. Wea. Rev., 141, 44294449, https://doi.org/10.1175/MWR-D-13-00059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ikeda, T., S. Behera, Y. Morioka, N. Minakawa, M. Hashizume, A. Tsuzuki, R. Maharaj, and P. Kruger, 2017: Seasonally lagged effects of climatic factors on malaria incidence in South Africa. Sci. Rep., 7, 2458, https://doi.org/10.1038/s41598-017-02680-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joseph, S., A. K. Sahai, B. N. Goswami, P. Terray, S. Masson, and J. J. Luo, 2012: Possible role of warm SST bias in the simulation of boreal summer monsoon in SINTEX-F2 coupled model. Climate Dyn., 38, 15611576, https://doi.org/10.1007/s00382-011-1264-1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kang, S., and J. H. Yoo, 2006: Examination of multi-model ensemble seasonal prediction methods using a simple climate system. Climate Dyn., 26, 285294, https://doi.org/10.1007/s00382-005-0074-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and Coauthors, 2014: The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • 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, https://doi.org/10.1073/pnas.1215582110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, A., 2009: Finite samples and uncertainty estimates for skill measures for seasonal prediction. Mon. Wea. Rev., 137, 26222631, https://doi.org/10.1175/2009MWR2814.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, A., A. G. Barnston, and M. P. Hoerling, 2001: Seasonal predictions, probabilistic verifications, and ensemble size. J. Climate, 14, 16711676, https://doi.org/10.1175/1520-0442(2001)014<1671:SPPVAE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Latif, M., and Coauthors, 1998: A review of the predictability and prediction of ENSO. J. Geophys. Res., 103, 14 37514 393, https://doi.org/10.1029/97JC03413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopez, H., and B. P. Kirtman, 2014: WWBs, ENSO predictability, the spring barrier and extreme events. J. Geophys. Res. Atmos., 119, 10 11410 138, https://doi.org/10.1002/2014JD021908.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, B., and Coauthors, 2017: An extreme negative Indian Ocean dipole event in 2016: Dynamics and predictability. Climate Dyn., 51, 89100, https://doi.org/10.1007/s00382-017-3908-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, J. J., S. Masson, S. Behera, S. Shingu, and T. Yamagata, 2005: Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J. Climate, 18, 44744497, https://doi.org/10.1175/JCLI3526.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, J. J., S. Masson, S. Behera, and T. Yamagata, 2007: Experimental forecasts of the Indian Ocean dipole using a coupled OAGCM. J. Climate, 20, 21782190, https://doi.org/10.1175/JCLI4132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maclachlan, C., and Coauthors, 2015: Global Seasonal Forecast System version 5 (GloSea5): A high-resolution seasonal forecast system. Quart. J. Roy. Meteor. Soc., 141, 10721084, https://doi.org/10.1002/qj.2396.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madec, G., 2008: NEMO ocean engine, version 3.0. Institut Pierre-Simon Laplace Note du Pole de modélisation 27, 209 pp.

  • Mason, S. J., and N. E. Graham, 2002: Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quart. J. Roy. Meteor. Soc., 128, 21452166, https://doi.org/10.1256/003590002320603584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masson, S., P. Terray, G. Madec, J. J. Luo, T. Yamagata, and K. Takahashi, 2012: Impact of intra-daily SST variability on ENSO characteristics in a coupled model. Climate Dyn., 39, 681707, https://doi.org/10.1007/s00382-011-1247-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molteni, F., and Coauthors, 2011: The new ECMWF seasonal forecast system (system 4). ECMWF Tech. Memo. 656, 51 pp.

  • Morioka, Y., J. V. Ratnam, W. Sasaki, and Y. Masumoto, 2013: Generation mechanism of the South Pacific subtropical dipole. J. Climate, 26, 60336045, https://doi.org/10.1175/JCLI-D-12-00648.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morioka, Y., S. Masson, P. Terray, C. Prodhomme, S. K. Behera, and Y. Masumoto, 2014: Role of tropical SST variability on the formation of subtropical dipoles. J. Climate, 27, 44864507, https://doi.org/10.1175/JCLI-D-13-00506.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morioka, Y., F. Engelbrecht, and S. K. Behera, 2015: Potential sources of decadal climate variability over southern Africa. J. Climate, 28, 86958709, https://doi.org/10.1175/JCLI-D-15-0201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morioka, Y., B. Taguchi, and S. K. Behera, 2017: Eastward propagating decadal temperature variability in the South Atlantic and Indian Oceans. J. Geophys. Res. Oceans, 122, 56115623, https://doi.org/10.1002/2017JC012706.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morioka, Y., T. Doi, and S. K. Behera, 2018a: Decadal climate predictability in the southern Indian Ocean captured by SINTEX-F using a simple SST-nudging scheme. Sci. Rep., 8, 1029, https://doi.org/10.1038/s41598-018-19349-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morioka, Y., T. Doi, A. Storto, S. Masina, and S. K. Behera, 2018b: Role of subsurface ocean in decadal climate predictability over the South Atlantic. Sci. Rep., 8, 8523, https://doi.org/10.1038/s41598-018-26899-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, J. M., 1990: Assessment of the practical utility of extended range ensemble forecasts. Quart. J. Roy. Meteor. Soc., 116, 89125, https://doi.org/10.1002/qj.49711649105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oettli, P., S. K. Behera, and T. Yamagata, 2018: Climate based predictability of oil palm tree yield in Malaysia. Sci. Rep., 8, 2271, https://doi.org/10.1038/s41598-018-20298-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ogata, T., T. Doi, Y. Morioka, and S. Behera, 2018: Mid-latitude source of the ENSO-spread in SINTEX-F ensemble predictions. Climate Dyn., https://doi.org/10.1007/s00382-018-4280-6, in press.

    • Crossref
    • Export Citation
  • Philander, S., 1989: El Niño, La Niña, and the Southern Oscillation. S. G. Philander, Ed., Academic Press, 293 pp.

  • Prodhomme, C., P. Terray, S. Masson, T. Izumo, T. Tozuka, and T. Yamagata, 2014: Impacts of Indian Ocean SST biases on the Indian monsoon: As simulated in a global coupled model. Climate Dyn., 42, 271290, https://doi.org/10.1007/s00382-013-1671-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prodhomme, C., P. Terray, S. Masson, G. Boschat, and T. Izumo, 2015: Oceanic factors controlling the Indian summer monsoon onset in a coupled model. Climate Dyn., 44, 9771002, https://doi.org/10.1007/s00382-014-2200-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prodhomme, C., L. Batté, F. Massonnet, P. Davini, O. Bellprat, V. Guemas, and F. J. Doblas-Reyes, 2016: Benefits of increasing the model resolution for the seasonal forecast quality in EC-Earth. J. Climate, 29, 91419162, https://doi.org/10.1175/JCLI-D-16-0117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ratnam, J. V., T. Doi, and S. K. Behera, 2017: Dynamical downscaling of SINTEX-F2v CGCM seasonal retrospective austral summer forecasts over Australia. J. Climate, 30, 32193235, https://doi.org/10.1175/JCLI-D-16-0585.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, H.-L., F.-F. Jin, B. Tian, and A. A. Scaife, 2016: Distinct persistence barriers in two types of ENSO. Geophys. Res. Lett., 43, 10 97310 979, https://doi.org/10.1002/2016GL071015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 16091625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496, https://doi.org/10.1175/2007JCLI1824.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richardson, D. S., 2003: Economic value and skill. Forecast Verification: A Practitioner’s Guide in Atmospheric Science, I. T. Jolliffe and D. B. Stephenson, Eds., Wiley, 165–187.

  • Roeckner, E., and Coauthors, 2003: The atmospheric general circulation model ECHAM5. Part I: Model description. Max-Planck-Institut für Meteorologie Rep. 349, 140 pp., https://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf.

  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saji, N. H., and T. Yamagata, 2003: Possible impacts of Indian Ocean dipole mode events on global climate. Climate Res., 25, 151169, https://doi.org/10.3354/cr025151.

    • Crossref
    • 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, https://doi.org/10.1038/43854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanna, A., A. Borrelli, S. Materia, P. Athanasiadis, A. Bellucci, P. G. Fogli, E. Scoccimarro, and S. Gualdi, 2015: The new CMCC–Seasonal Prediction System. CMCC Rep. RP0253, 13 pp., https://www.cmcc.it/publications/rp0253-the-new-cmcc-seasonal-prediction-system.

  • Sasaki, W., K. J. Richards, and J. J. Luo, 2012: Role of vertical mixing originating from small vertical scale structures above and within the equatorial thermocline in an OGCM. Ocean Modell., 57–58, 2942, https://doi.org/10.1016/j.ocemod.2012.09.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, W., K. J. Richards, and J. J. Luo, 2013: Impact of vertical mixing induced by small vertical scale structures above and within the equatorial thermocline on the tropical Pacific in a CGCM. Climate Dyn., 41, 443453, https://doi.org/10.1007/s00382-012-1593-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, W., T. Doi, K. J. Richards, and Y. Masumoto, 2014: Impact of the equatorial Atlantic sea surface temperature on the tropical Pacific in a CGCM. Climate Dyn., 43, 25392552, https://doi.org/10.1007/s00382-014-2072-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, W., T. Doi, K. J. Richards, and Y. Masumoto, 2015: The influence of ENSO on the equatorial Atlantic precipitation through the Walker circulation in a CGCM. Climate Dyn., 44, 191202, https://doi.org/10.1007/s00382-014-2133-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and Coauthors, 2014: Skillful long-range prediction of European and North American winters. Geophys. Res. Lett., 41, 25142519, https://doi.org/10.1002/2014GL059637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, J., and Coauthors, 2000: Dynamical seasonal prediction. Bull. Amer. Meteor. Soc., 81, 25932606, https://doi.org/10.1175/1520-0477(2000)081<2593:DSP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storto, A., S. Dobricic, S. Masina, and P. Di Pietro, 2011: Assimilating along-track altimetric observations through local hydrostatic adjustment in a global ocean variational assimilation system. Mon. Wea. Rev., 139, 738754, https://doi.org/10.1175/2010MWR3350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storto, A., S. Masina, and S. Dobricic, 2014: Estimation and impact of nonuniform horizontal correlation length scales for global ocean physical analyses. J. Atmos. Oceanic Technol., 31, 23302349, https://doi.org/10.1175/JTECH-D-14-00042.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takaya, Y., and Coauthors, 2017a: Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting. Climate Dyn., 48, 313333, https://doi.org/10.1007/s00382-016-3076-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takaya, Y., and Coauthors, 2017b: Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): Atmosphere–land–ocean–sea ice coupled prediction system for operational seasonal forecasting. Climate Dyn., 50, 751765, https://doi.org/10.1007/s00382-017-3638-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Terray, P., S. Masson, and K. Kakitha, 2011: Role of the frequency of coupling in the simulation of the monsoon-ENSO relationship in a global coupled model. European Geosciences Union General Assembly, Vienna, Austria, European Geosciences Union, 13355.

  • Terray, P., S. Masson, C. Prodhomme, M. K. Roxy, and K. P. Sooraj, 2016: Impacts of Indian and Atlantic Oceans on ENSO in a comprehensive modeling framework. Climate Dyn., 46, 25072533, https://doi.org/10.1007/s00382-015-2715-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Terray, P., K. P. Sooraj, S. Masson, R. P. M. Krishna, G. Samson, and A. G. Prajeesh, 2017: Towards a realistic simulation of boreal summer tropical rainfall climatology in state-of-the-art coupled models: Role of the background snow-free land albedo. Climate Dyn., 50, 34133439, https://doi.org/10.1007/s00382-017-3812-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tompkins, A. M., and Coauthors, 2017: The Climate-System Historical Forecast Project: Providing open access to seasonal forecast ensembles from centers around the globe. Bull. Amer. Meteor. Soc., 98, 22932301, https://doi.org/10.1175/BAMS-D-16-0209.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Valcke, S., A. Caubel, R. Vogelsang, and D. Declat, 2004: OASIS3 Ocean Atmosphere Sea Ice Soil user’s guide. CERFACS Tech. Rep. TR/CMGC/04/68, 70 pp.

  • Vecchi, G. A., and Coauthors, 2014: On the seasonal forecasting of regional tropical cyclone activity. J. Climate, 27, 79948016, https://doi.org/10.1175/JCLI-D-14-00158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vernieres, G., M. M. Rienecker, R. Kovach, and C. L. Keppenne, 2012: The GEOS-iODAS: Description and evaluation. NASA Tech. Rep. NASA/TM-2012-104606/VOL30, 73 pp.

  • Vinayachandran, P. N., N. H. Saji, and T. Yamagata, 1999: Response of the equatorial Indian Ocean to an unusual wind event during 1994. Geophys. Res. Lett., 26, 16131616, https://doi.org/10.1029/1999GL900179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and T. Li, 2003: Atmosphere–warm ocean interaction and its impacts on Asian–Australian monsoon variation. J. Climate, 16, 11951211, https://doi.org/10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2.

    • Crossref
    • 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, 93117, https://doi.org/10.1007/s00382-008-0460-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., and C. D. Hoyos, 2010: Beyond the spring barrier? Nat. Geosci., 3, 152153, https://doi.org/10.1038/ngeo800.

  • Xie, P., and P. A. Arkin, 1996: Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. J. Climate, 9, 840858, https://doi.org/10.1175/1520-0442(1996)009<0840:AOGMPU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, C., and T. Yamagata, 2015: Impacts of IOD, ENSO and ENSO Modoki on the Australian winter wheat yields in recent decades. Sci. Rep., 5, 17252, https://doi.org/10.1038/srep17252.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1095 384 10
PDF Downloads 665 152 3