Editorial Type:
Article
Article Type:
Research Article

How Well Do Global Climate Models Simulate the Variability of Atlantic Tropical Cyclones Associated with ENSO?

Hui Wang * NOAA/NWS/NCEP/Climate Prediction Center, College Park, and Innovim, Greenbelt, Maryland

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Lindsey Long * NOAA/NWS/NCEP/Climate Prediction Center, College Park, and Innovim, Greenbelt, Maryland

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Arun Kumar NOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland

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Wanqiu Wang NOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland

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Jae-Kyung E. Schemm NOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland

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Ming Zhao NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Gabriel A. Vecchi NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Timothy E. Larow Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida

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Young-Kwon Lim Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, and Goddard Earth Sciences Technology and Research, I.M. Systems Group, Greenbelt, Maryland

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Siegfried D. Schubert ** Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Daniel A. Shaevitz Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York

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Suzana J. Camargo Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Naomi Henderson Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Daehyun Kim Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Jeffrey A. Jonas Center for Climate System Research, Columbia University, and NASA Goddard Institute for Space Studies, New York, New York

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Kevin J. E. Walsh School of Earth Sciences, University of Melbourne, Parkville, Victoria, Australia

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Print Publication:
01 Aug 2014
Collections:
U.S. CLIVAR - Hurricanes and Climate
DOI:
https://doi.org/10.1175/JCLI-D-13-00625.1
Page(s):
5673–5692
Restricted access

Abstract

The variability of Atlantic tropical cyclones (TCs) associated with El Niño–Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. Climate Variability and Predictability Research Program (CLIVAR) Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multimodel ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Niño events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Niño and stronger activity during La Niña. For CP El Niño, there is a slight increase in the number of TCs as compared with EP El Niño. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Niño as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Niño, as well as the model bias in the mean circulation.

Corresponding author address: Dr. Hui Wang, NOAA/Climate Prediction Center, 5830 University Research Court, NCWCP, College Park, MD 20740. E-mail: hui.wang@noaa.gov

This article is included in the US CLIVAR Hurricanes and Climate special collection.

Abstract

The variability of Atlantic tropical cyclones (TCs) associated with El Niño–Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. Climate Variability and Predictability Research Program (CLIVAR) Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multimodel ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions during eastern Pacific (EP) and central Pacific (CP) El Niño events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Niño and stronger activity during La Niña. For CP El Niño, there is a slight increase in the number of TCs as compared with EP El Niño. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region during CP El Niño as in observations. The difference between the models and observations is likely due to the bias of the models in response to the shift of tropical heating associated with CP El Niño, as well as the model bias in the mean circulation.

Corresponding author address: Dr. Hui Wang, NOAA/Climate Prediction Center, 5830 University Research Court, NCWCP, College Park, MD 20740. E-mail: hui.wang@noaa.gov

This article is included in the US CLIVAR Hurricanes and Climate special collection.

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  • 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

  • Bell, G. D., and M. Chelliah, 2006: Leading tropical modes associated with interannual and multidecadal fluctuations in North Atlantic hurricane activity. J. Climate, 19, 590–612, doi:10.1175/JCLI3659.1.

    • Search Google Scholar
    • Export Citation

  • Camargo, S. J., 2013: Global and regional aspects of tropical cyclone activity in the CMIP5 models. J. Climate, 26, 9880–9902, doi:10.1175/JCLI-D-12-00549.1.

    • Search Google Scholar
    • Export Citation

  • Camargo, S. J., and S. E. Zebiak, 2002: Improving the detection and tracking of tropical cyclones in atmospheric general circulation models. Wea. Forecasting, 17, 1152–1162, doi:10.1175/1520-0434(2002)017<1152:ITDATO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Camargo, S. J., A. G. Barnston, and S. E. Zebiak, 2005: A statistical assessment of tropical cyclone activity in atmospheric general circulation models. Tellus, 57A, 589–604, doi:10.1111/j.1600-0870.2005.00117.x.

    • Search Google Scholar
    • Export Citation

  • Camargo, S. J., K. A. Emanuel, and A. H. Sobel, 2007: Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J. Climate, 20, 4819–4834, doi:10.1175/JCLI4282.1.

    • Search Google Scholar
    • Export Citation

  • Cocke, S., and T. E. LaRow, 2000: Seasonal predictions using a regional spectral model embedded within a coupled ocean–atmospheric model. Mon. Wea. Rev., 128, 689–708, doi:10.1175/1520-0493(2000)128<0689:SPUARS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Colbert, A. J., and B. J. Soden, 2012: Climatological variations in North Atlantic tropical cyclone tracks. J. Climate, 25, 657–673, doi:10.1175/JCLI-D-11-00034.1.

    • Search Google Scholar
    • Export Citation

  • Emanuel, K. A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. USA, 110, 12 219–12 224, doi:10.1073/pnas.1301293110.

    • Search Google Scholar
    • Export Citation

  • Enfield, D. B., and D. A. Mayer, 1997: Tropical Atlantic sea surface temperature variability and its relation to El Niño–Southern Oscillation. J. Geophys. Res., 102, 929–945, doi:10.1029/96JC03296.

    • Search Google Scholar
    • Export Citation

  • Fitzpatrick, P. J., J. A. Knaff, C. W. Landsea, and S. V. Finley, 1995: Documentation of a systematic bias in the aviation model’s forecast of the Atlantic tropical upper-tropospheric trough: Implications for tropical cyclone forecasting. Wea. Forecasting, 10, 433–446, doi:10.1175/1520-0434(1995)010<0433:DOASBI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Goldenberg, S. B., and L. J. Shapiro, 1996: Physical mechanisms for the association of El Niño and West African rainfall with Atlantic major hurricane activity. J. Climate, 9, 1169–1187, doi:10.1175/1520-0442(1996)009<1169:PMFTAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nuñez, and W. M. Gray, 2001: The recent increase in Atlantic hurricane activity: Causes and implications. Science, 293, 474–479, doi:10.1126/science.1060040.

    • Search Google Scholar
    • Export Citation

  • Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part I: El Niño and 30 mb quasi-biennial oscillation influences. Mon. Wea. Rev., 112, 1649–1668, doi:10.1175/1520-0493(1984)112<1649:ASHFPI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643, doi:10.1175/BAMS-83-11-1631.

    • 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

  • Kim, S. T., and J.-Y. Yu, 2012: The two types of ENSO in CMIP5 models. Geophys. Res. Lett., 39, L11704, doi:10.1029/2012GL052006.

  • Knutson, T. R., and Coauthors, 2013: Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Climate, 26, 6591–6617, doi:10.1175/JCLI-D-12-00539.1.

    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. J. Climate, 23, 3057–3076, doi:10.1175/2010JCLI3497.1.

    • Search Google Scholar
    • Export Citation

  • Kumar, A., and M. P. Hoerling, 1995: Prospects and limitations of seasonal atmospheric GCM predictions. Bull. Amer. Meteor. Soc., 76, 335–345, doi:10.1175/1520-0477(1995)076<0335:PALOSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Landsea, C. W., 2000: El Niño/Southern Oscillation and the seasonal predictability of tropical cyclones. El Niño and the Southern Oscillation: Multiscale Variability and Global and Regional Impacts, H. F. Diaz and V. Markgraf, Eds., Cambridge University Press, 149–182.

  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 3576–3592, doi:10.1175/MWR-D-12-00254.1.

    • Search Google Scholar
    • Export Citation

  • Landsea, C. W., G. A. Vecchi, L. Bengtsson, and T. R. Knutson, 2010: Impact of duration thresholds on Atlantic tropical cyclone counts. J. Climate, 23, 2508–2519, doi:10.1175/2009JCLI3034.1.

    • Search Google Scholar
    • Export Citation

  • LaRow, T. E., 2013: The impact of SST bias correction on North Atlantic hurricane retrospective forecasts. Mon. Wea. Rev., 141, 490–498, doi:10.1175/MWR-D-12-00152.1.

    • Search Google Scholar
    • Export Citation

  • LaRow, T. E., Y.-K. Lim, D. W. Shin, E. P. Chassignet, and S. Cocke, 2008: Atlantic basin seasonal hurricane simulations. J. Climate, 21, 3191–3206, doi:10.1175/2007JCLI2036.1.

    • Search Google Scholar
    • Export Citation

  • McPhaden, M. J., T. Lee, and D. McClurg, 2011: El Niño and its relationship to changing background conditions in the tropical Pacific Ocean. Geophys. Res. Lett., 38, L15709, doi:10.1029/2011GL048275.

    • Search Google Scholar
    • Export Citation

  • Molod, A., L. Takacs, M. Suarez, J. Bacmeister, I.-S. Song, and A. Eichmann, 2012: The GEOS-5 Atmospheric General Circulation Model: Mean climate and development from MERRA to Fortuna. NASA Tech. Rep. NASA/TM-2012-104606, Vol. 28, 117 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/tm28.pdf.]

  • NOAA, cited 2013: Atlantic hurricane outlook and summary archive. [Available online at http://www.cpc.ncep.noaa.gov/products/outlooks/hurricane-archive.shtml.]

  • Peng, M. S., B. Fu, T. Li, and D. E. Stevens 2012: Developing versus nondeveloping disturbances for tropical cyclone formation. Part I: North Atlantic. Mon. Wea. Rev., 140, 1047–1066, doi:10.1175/2011MWR3617.1.

    • Search Google Scholar
    • Export Citation

  • Pielke, R. A., Jr., and C. W. Landsea, 1999: La Niña, El Niño, and Atlantic hurricane damages in the United States. Bull. Amer. Meteor. Soc., 80, 2027–2033, doi:10.1175/1520-0477(1999)080<2027:LNAENO>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

  • 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, 1609–1625, doi:10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 Data Assimilation System—Documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Tech. Rep. NASA/TM-2008-104606, Vol. 27, 92 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf.]

  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 2158–2208, doi:10.1175/JCLI-D-12-00823.1.

  • Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci., 45, 1228–1251, doi:10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Schemm, J.-K. E., and L. Long, 2009: Dynamic hurricane season prediction experiment with the NCEP CFS CGCM. Extended Abstracts, NOAA CTB Joint Seminar Series, Calverton, MD, IGES/COLA, NOAA, Vol. 3, 27–29. [Available online at http://www.nws.noaa.gov/ost/climate/STIP/FY09CTBSeminars/CollectionVol_FY09CTBJS.pdf.]

    • Search Google Scholar
    • Export Citation

  • Schmidt, G. A., and Coauthors, 2014: Configuration and assessment of GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst., 6, 141–184, doi:10.1002/2013MS000265.

    • Search Google Scholar
    • Export Citation

  • Stevenson, S. L., 2012: Significant changes to ENSO strength and impacts in the twenty-first century: Results from CMIP5. Geophys. Res. Lett., 39, L17703, doi:10.1029/2012GL052759.

    • Search Google Scholar
    • Export Citation

  • Tang, B. H., and J. D. Neelin, 2004: ENSO influence on Atlantic hurricanes via tropospheric warming. Geophys. Res. Lett., 31, L24204, doi:10.1029/2004GL021072.

    • Search Google Scholar
    • Export Citation

  • Tory, K. J., S. S. Chand, J. L. McBride, H. Ye, and R. A. Dare, 2013: Projected changes in late-twenty-first-century tropical cyclone frequency in 13 coupled climate models from phase 5 of the Coupled Model Intercomparison Project. J. Climate, 26, 9946–9959, doi:10.1175/JCLI-D-13-00010.1.

    • Search Google Scholar
    • Export Citation

  • U.S. CLIVAR, 2011: U.S. CLIVAR launches two new working groups. U.S. Clivar Variations, Vol. 9, No. 1, U.S. CLIVAR Office, Washington, DC, 12 pp. [Available online at http://www.usclivar.org/sites/default/files/Variations-V9N1-1.pdf.]

    • Search Google Scholar
    • Export Citation

  • Vecchi, G. A., and B. J. Soden, 2007: Increased tropical Atlantic wind shear in model projections of global warming. Geophys. Res. Lett., 34, L08702, doi:10.1029/2006GL028905.

    • Search Google Scholar
    • Export Citation

  • Vecchi, G. A., M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I. M. Held, and R. Gudgel, 2011: Statistical–dynamical predictions of seasonal North Atlantic hurricane activity. Mon. Wea. Rev., 139, 1070–1082, doi:10.1175/2010MWR3499.1.

    • Search Google Scholar
    • Export Citation

  • Villarini, G., and G. A. Vecchi, 2012: Twenty-first-century projections of North Atlantic tropical storms from CMIP5 models. Nat. Climate Change, 2, 604–607, doi:10.1038/nclimate1530.

    • Search Google Scholar
    • Export Citation

  • Villarini, G., G. A. Vecchi, T. R. Knutson, and J. A. Smith, 2011: Is the recorded increase in short-duration North Atlantic tropical storms spurious? J. Geophys. Res., 116, D10114, doi:10.1029/2010JD015493.

    • Search Google Scholar
    • Export Citation

  • Wang, H., J.-K. E. Schemm, A. Kumar, W. Wang, L. Long, M. Chelliah, G. D. Bell, and P. Peng, 2009: A statistical forecast model for Atlantic seasonal hurricane activity based on the NCEP dynamical seasonal forecast. J. Climate, 22, 4481–4500, doi:10.1175/2009JCLI2753.1.

    • Search Google Scholar
    • Export Citation

  • Wang, H., A. Kumar, W. Wang, and B. Jha, 2012: U.S. summer precipitation and temperature patterns following the peak phase of El Niño. J. Climate, 25, 7204–7215, doi:10.1175/JCLI-D-11-00660.1.

    • Search Google Scholar
    • Export Citation

  • Wang, H., Y. Pan, A. Kumar, and W. Wang, 2013: Modulation of convectively coupled Kelvin wave activity in the tropical Pacific by ENSO. Acta Meteor. Sin., 27, 295–307, doi:10.1007/s13351-013-0306-5.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.

  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • 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, 511–514, doi:10.1038/nature08316.

    • Search Google Scholar
    • Export Citation

  • Zhao, M., and I. M. Held, 2010: An analysis of the effect of global warming on the intensity of Atlantic hurricanes using a GCM with statistical refinement. J. Climate, 23, 6382–6393, doi:10.1175/2010JCLI3837.1.

    • Search Google Scholar
    • Export Citation

  • Zhao, M., I. M. Held, S.-J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate, 22, 6653–6678, doi:10.1175/2009JCLI3049.1.

    • Search Google Scholar
    • Export Citation
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