Editorial Type:
Article
Article Type:
Research Article

Resolution Dependence of Future Tropical Cyclone Projections of CAM5.1 in the U.S. CLIVAR Hurricane Working Group Idealized Configurations

Michael Wehner Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California

Search for other papers by Michael Wehner in
Current site
Google Scholar
PubMed Close
,
Prabhat Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California

Search for other papers by Prabhat in
Current site
Google Scholar
PubMed Close
,
Kevin A. Reed National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Kevin A. Reed in
Current site
Google Scholar
PubMed Close
,
Dáithí Stone Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California

Search for other papers by Dáithí Stone in
Current site
Google Scholar
PubMed Close
,
William D. Collins Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California

Search for other papers by William D. Collins in
Current site
Google Scholar
PubMed Close
, and
Julio Bacmeister National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Julio Bacmeister in
Current site
Google Scholar
PubMed Close
Print Publication:
15 May 2015
Collections:
U.S. CLIVAR - Hurricanes and Climate
DOI:
https://doi.org/10.1175/JCLI-D-14-00311.1
Page(s):
3905–3925
Restricted access

Abstract

The four idealized configurations of the U.S. CLIVAR Hurricane Working Group are integrated using the global Community Atmospheric Model version 5.1 at two different horizontal resolutions, approximately 100 and 25 km. The publicly released 0.9° × 1.3° configuration is a poor predictor of the sign of the 0.23° × 0.31° model configuration’s change in the total number of tropical storms in a warmer climate. However, it does predict the sign of the higher-resolution configuration’s change in the number of intense tropical cyclones in a warmer climate. In the 0.23° × 0.31° model configuration, both increased CO2 concentrations and elevated sea surface temperature (SST) independently lower the number of weak tropical storms and shorten their average duration. Conversely, increased SST causes more intense tropical cyclones and lengthens their average duration, resulting in a greater number of intense tropical cyclone days globally. Increased SST also increased maximum tropical storm instantaneous precipitation rates across all storm intensities. It was found that while a measure of maximum potential intensity based on climatological mean quantities adequately predicts the 0.23° × 0.31° model’s forced response in its most intense simulated tropical cyclones, a related measure of cyclogenesis potential fails to predict the model’s actual cyclogenesis response to warmer SSTs. These analyses lead to two broader conclusions: 1) Projections of future tropical storm activity obtained by a direct tracking of tropical storms simulated by coarse-resolution climate models must be interpreted with caution. 2) Projections of future tropical cyclogenesis obtained from metrics of model behavior that are based solely on changes in long-term climatological fields and tuned to historical records must also be interpreted with caution.

Denotes Open Access content.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Michael Wehner, Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720. E-mail: mfwehner@lbl.gov

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

Abstract

The four idealized configurations of the U.S. CLIVAR Hurricane Working Group are integrated using the global Community Atmospheric Model version 5.1 at two different horizontal resolutions, approximately 100 and 25 km. The publicly released 0.9° × 1.3° configuration is a poor predictor of the sign of the 0.23° × 0.31° model configuration’s change in the total number of tropical storms in a warmer climate. However, it does predict the sign of the higher-resolution configuration’s change in the number of intense tropical cyclones in a warmer climate. In the 0.23° × 0.31° model configuration, both increased CO2 concentrations and elevated sea surface temperature (SST) independently lower the number of weak tropical storms and shorten their average duration. Conversely, increased SST causes more intense tropical cyclones and lengthens their average duration, resulting in a greater number of intense tropical cyclone days globally. Increased SST also increased maximum tropical storm instantaneous precipitation rates across all storm intensities. It was found that while a measure of maximum potential intensity based on climatological mean quantities adequately predicts the 0.23° × 0.31° model’s forced response in its most intense simulated tropical cyclones, a related measure of cyclogenesis potential fails to predict the model’s actual cyclogenesis response to warmer SSTs. These analyses lead to two broader conclusions: 1) Projections of future tropical storm activity obtained by a direct tracking of tropical storms simulated by coarse-resolution climate models must be interpreted with caution. 2) Projections of future tropical cyclogenesis obtained from metrics of model behavior that are based solely on changes in long-term climatological fields and tuned to historical records must also be interpreted with caution.

Denotes Open Access content.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Michael Wehner, Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720. E-mail: mfwehner@lbl.gov

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

Save
Cover Journal of Climate
Journal of Climate

  • Bacmeister, J. T., M. F. Wehner, R. B. Neale, A. Gettelman, C. Hannay, P. H. Lauritzen, J. M. Caron, and J. E. Truesdale, 2014: Exploratory high-resolution climate simulations using the Community Atmosphere Model (CAM). J. Climate, 27, 3073–3099, doi:10.1175/JCLI-D-13-00387.1.

    • Search Google Scholar
    • Export Citation

  • Bister, M., and K. A. Emanuel, 1998: Dissipative heating and hurricane intensity. Meteor. Atmos. Phys., 65, 233–240, doi:10.1007/BF01030791.

    • Search Google Scholar
    • Export Citation

  • Bister, M., and K. A. Emanuel, 2002a: Low frequency variability of tropical cyclone potential intensity. 1. Interannual to interdecadal variability. J. Geophys. Res., 107, 4801, doi:10.1029/2001JD000776.

    • Search Google Scholar
    • Export Citation

  • Bister, M., and K. A. Emanuel, 2002b: Low frequency variability of tropical cyclone potential intensity. 2. Climatology for 1982–1995. J. Geophys. Res., 107, 4621, doi:10.1029/2001JD000780.

    • 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., K. A. Emanuel, and A. B. 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

  • Christensen, J. H., and Coauthors, 2013: 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.

    • Search Google Scholar
    • Export Citation

  • Daloz, A. S., and Coauthors, 2015: Cluster analysis of downscaled and explicitly simulated North Atlantic tropical cyclone tracks. J. Climate,28, 1333–1361, doi:10.1175/JCLI-D-13-00646.1.

  • Done, J., G. Holland, C. Bruyère, L. Leung, and A. Suzuki-Parker, 2015: Modeling high-impact weather and climate: Lessons from a tropical cyclone perspective. Climatic Change, doi:10.1007/s10584-013-0954-6, in press.

    • Search Google Scholar
    • Export Citation

  • Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585–604, doi:10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Emanuel, K. A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 483–485, doi:10.1038/326483a0.

  • Emanuel, K. A., 1995: Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics. J. Atmos. Sci., 52, 3969–3976, doi:10.1175/1520-0469(1995)052<3969:SOTCTS>2.0.CO;2.

    • 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

  • Emanuel, K. A., and D. S. Nolan, 2004: Tropical cyclone activity and global climate. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 240–241.

  • Held, I. M., and M. Zhao, 2011: The response of tropical cyclone statistics to an increase in CO2 with fixed sea surface temperatures. J. Climate, 24, 5353–5364, doi:10.1175/JCLI-D-11-00050.1.

    • Search Google Scholar
    • Export Citation

  • Hodges, K. I., 1996: Spherical nonparametric estimators applied to the UGAMP model integration for AMIP. Mon. Wea. Rev., 124, 2914–2932, doi:10.1175/1520-0493(1996)124<2914:SNEATT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Holland, G., and C. L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dyn., 42, 617–627, doi:10.1007/s00382-013-1713-0.

    • Search Google Scholar
    • Export Citation

  • Horn, M., and Coauthors, 2014: Tracking scheme dependence of simulated tropical cyclone response to idealized climate simulations. J. Climate,27, 9197–9213, doi:10.1175/JCLI-D-14-00200.1.

  • Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, Eds., 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp. [See appendix 12.3 therein for a discussion of pattern correlation.]

  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bull. Amer. Meteor. Soc., 91, 363–376, doi:10.1175/2009BAMS2755.1.

    • Search Google Scholar
    • Export Citation

  • Knutson, T. R., R. E. Tuleya, W. Shen, and I. Ginis, 2001: Impact of CO2-induced warming on hurricane intensities as simulated in a hurricane model with ocean coupling. J. Climate, 14, 2458–2468, doi:10.1175/1520-0442(2001)014<2458:IOCIWO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Knutson, T. R., J. J. Sirutis, S. T. Garner, I. Held, and R. E. Tuleya, 2007: Simulation of the recent multidecadal increase of Atlantic hurricane activity using an 18-km-grid regional model. Bull. Amer. Meteor. Soc., 88, 1549–1565, doi:10.1175/BAMS-88-10-1549.

    • Search Google Scholar
    • Export Citation

  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157–163, doi:10.1038/ngeo779.

  • 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

  • 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

  • Li, F., W. D. Collins, M. F. Wehner, and R. L. Leung, 2013: Hurricanes in an aquaplanet world: Implications of the impacts of external forcing and model horizontal resolution. J. Adv. Model. Earth Syst., 5, 134–145, doi:10.1002/jame.20020.

    • Search Google Scholar
    • Export Citation

  • Lin, I., and Coauthors, 2013: An ocean coupling potential intensity index for tropical cyclones. Geophys. Res. Lett., 40, 1878–1882, doi:10.1002/grl.50091.

    • Search Google Scholar
    • Export Citation

  • Murakami, H., and Coauthors, 2012: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Climate, 25, 3237–3260, doi:10.1175/JCLI-D-11-00415.1.

    • Search Google Scholar
    • Export Citation

  • Neale, R. B., and Coauthors, 2010: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 282 pp.

  • Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S. Kusonoki, and A. Noda, 2006: Tropical cyclone climatology in a global-warming climate as simulated in a 20-km mesh global atmospheric model: Frequency and wind intensity analyses. J. Meteor. Soc. Japan, 84, 259–276, doi:10.2151/jmsj.84.259.

    • Search Google Scholar
    • Export Citation

  • Prabhat, O.Rübel, S. Byna, K. Wu, F. Li, M. Wehner, and W. Bethel, 2012: TECA: A Parallel Toolkit for Extreme Climate Analysis. Procedia Comput. Sci., 9. 866–876, doi:10.1016/j.procs.2012.04.093.

  • Reed, K. A., and C. Jablonowski, 2011a: An analytic vortex initialization technique for idealized tropical cyclone studies in AGCMs. Mon. Wea. Rev., 139, 689–710, doi:10.1175/2010MWR3488.1.

    • Search Google Scholar
    • Export Citation

  • Reed, K. A., and C. Jablonowski, 2011b: Assessing the uncertainty of tropical cyclone simulations in NCAR’s Community Atmosphere Model. J. Adv. Model. Earth Syst., 3, M08002, doi:10.1029/2011MS000076.

    • Search Google Scholar
    • Export Citation

  • Reed, K. A., C. Jablonowski, and M. A. Taylor, 2012: Tropical cyclones in the spectral element configuration of the Community Atmosphere Model. Atmos. Sci. Lett., 13, 303–310, doi:10.1002/asl.399.

    • Search Google Scholar
    • Export Citation

  • Schade, L. R., and K. E. Emanuel, 1999: The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere–ocean model. J. Atmos. Sci., 56, 642–651, doi:10.1175/1520-0469(1999)056<0642:TOSEOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Shaevitz, D. A., and Coauthors, 2014: Characteristics of tropical cyclones in high-resolution models of the present climate. J. Adv. Model. Earth Syst., 6, 1154–1172, doi:10.1002/2014MS000372.

  • Strachan, J., P. L. Vidale, K. Hodges, M. Roberts, and M.-E. Demory, 2013: Investigating global tropical cyclone activity with a hierarchy of AGCMs: The role of model resolution. J. Climate,26, 133–152, doi:10.1175/JCLI-D-12-00012.1.

  • Sugi, M., H. Murakami, and J. Yoshimura, 2009: A reduction in global tropical cyclone frequency due to global warming. SOLA, 5, 164–167.

    • Search Google Scholar
    • Export Citation

  • Sugi, M., H. Murakami, and J. Yoshimura, 2012: On the mechanism of tropical cyclone frequency changes due to global warming. J. Meteor. Soc. Japan, 90A, 397–408, doi:10.2151/jmsj.2012-A24.

    • Search Google Scholar
    • Export Citation

  • Villarini, G., D. A. Lavers, E. Scoccimarro, M. Zhao, M. F. Wehner, G. A. Vecchi, T. R. Knutson, and K. A. Reed, 2014: Sensitivity of tropical cyclone rainfall to idealized global-scale forcings. J. Climate,27, 4622–4641, doi:10.1175/JCLI-D-13-00780.1.

  • Walsh, K. J. E., M. Fiorino, C. W. Landsea, and K. L. McInnes, 2007: Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J. Climate, 20, 2307–2314, doi:10.1175/JCLI4074.1.

    • Search Google Scholar
    • Export Citation

  • Walsh, K. J. E., and Coauthors, 2015: Hurricanes and climate: The U.S. CLIVAR working group on hurricanes. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-13-00242.1, in press.

  • Wehner, M. F., and Coauthors, 2014: The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. J. Adv. Model. Earth Syst.,6, 980–997, doi:10.1002/2013MS000276.

  • Yamada, Y., K. Oouchi, M. Satoh, H. Tomita, and W. Yanase, 2010: Projection of changes in tropical cyclone activity and cloud height due to greenhouse warming: Global cloud-system-resolving approach. Geophys. Res. Lett., 37, L07709, doi:10.1029/2010GL042518.

    • Search Google Scholar
    • Export Citation

  • Yoshimura, J., and M. Sugi, 2005: Tropical cyclone climatology in a high-resolution AGCM—Impacts of SST warming and CO2 increase. SOLA, 1, 133–136.

    • Search Google Scholar
    • Export Citation

  • Zhao, M., and I. M. Held, 2012: TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late-twenty-first century. J. Climate, 25, 2995–3009, doi:10.1175/JCLI-D-11-00313.1.

    • 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

  • Zhao, M., I. M. Held, and S.-J. Lin, 2012: Some counterintuitive dependencies of tropical cyclone frequency on parameters in a GCM. J. Atmos. Sci., 69, 2272–2283, doi:10.1175/JAS-D-11-0238.1.

    • Search Google Scholar
    • Export Citation

  • Zhao, M., and Coauthors, 2013: Robust direct effect of increasing atmospheric CO2 concentration on global tropical cyclone frequency—A multi-model inter-comparison. U.S. CLIVAR Variations, No. 11 (3), U.S. CLIVAR Project Office, Washington, DC, 17–23.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1502 679 35
PDF Downloads 667 184 11