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Title: Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks

Abstract

A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models with moderate to high horizontal resolution (1°–0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. Here, for both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks’ seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors’ results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possiblemore » future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Lastly, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [2];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [16]
  1. Univ. of Wisconsin, Madison, WI (United States). Space Science and Engineering Center
  2. Columbia Univ., Palisades, NY (United States). Lamont-Doherty Earth Observatory
  3. National Oceanic and Atmospheric Administration (NOAA), Asheville, NC (United States). National Climatic Data Center (NCDC)
  4. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  5. Univ. of Melbourne (Australia). School of Earth
  6. Columbia Univ., New York, NY (United States). Center for Climate Systems; NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States). Global Modeling and Assimilation Office, and Goddard Earth Sciences Technology and Research/I.M. Systems Group
  7. Florida State Univ., Tallahassee, FL (United States)
  8. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States). Global Modeling and Assimilation Office, and Goddard Earth Sciences Technology and Research/I.M. Systems Group
  9. Texas A & M Univ., College Station, TX (United States)
  10. Met Office Hadley Centre, Devon (United Kingdom)
  11. Istituto Nazionale di Geofisica e Vulcanologia, Bologna (Italy); Centro Euro-Mediterraneo sui Cambiamenti Climatici, Lecce (Italy)
  12. Columbia Univ., New York, NY (United States). Dept. of Applied Physics and Applied Mathematics
  13. Univ. of Reading, Reading (United Kingdom). National Centre for Atmospheric Science, Dept. of Meteorology
  14. National Oceanic and Atmospheric Administration (NOAA), College Park, MD (United States). National Weather Service (NWS), National Centers for Environmental Prediction (NCEP), Climate Prediction Center
  15. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
  16. National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Lab. (GFDL)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Aeronautic and Space Administration (NASA); National Science Foundation (NSF); National Oceanic and Atmospheric Administration (NOAA)
OSTI Identifier:
1407300
Grant/Contract Number:
AC02-05CH11231; NA11OAR4310093; AGS1143959; NNX09AK34G
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 28; Journal Issue: 4; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Tropical cyclones; Climatology; Climate models; Model comparison

Citation Formats

Daloz, Anne S., Camargo, S. J., Kossin, J. P., Emanuel, K., Horn, M., Jonas, J. A., Kim, D., LaRow, T., Lim, Y. -K., Patricola, C. M., Roberts, M., Scoccimarro, E., Shaevitz, D., Vidale, P. L., Wang, H., Wehner, M., and Zhao, M.. Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks. United States: N. p., 2015. Web. doi:10.1175/JCLI-D-13-00646.1.
Daloz, Anne S., Camargo, S. J., Kossin, J. P., Emanuel, K., Horn, M., Jonas, J. A., Kim, D., LaRow, T., Lim, Y. -K., Patricola, C. M., Roberts, M., Scoccimarro, E., Shaevitz, D., Vidale, P. L., Wang, H., Wehner, M., & Zhao, M.. Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks. United States. doi:10.1175/JCLI-D-13-00646.1.
Daloz, Anne S., Camargo, S. J., Kossin, J. P., Emanuel, K., Horn, M., Jonas, J. A., Kim, D., LaRow, T., Lim, Y. -K., Patricola, C. M., Roberts, M., Scoccimarro, E., Shaevitz, D., Vidale, P. L., Wang, H., Wehner, M., and Zhao, M.. Wed . "Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks". United States. doi:10.1175/JCLI-D-13-00646.1. https://www.osti.gov/servlets/purl/1407300.
@article{osti_1407300,
title = {Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks},
author = {Daloz, Anne S. and Camargo, S. J. and Kossin, J. P. and Emanuel, K. and Horn, M. and Jonas, J. A. and Kim, D. and LaRow, T. and Lim, Y. -K. and Patricola, C. M. and Roberts, M. and Scoccimarro, E. and Shaevitz, D. and Vidale, P. L. and Wang, H. and Wehner, M. and Zhao, M.},
abstractNote = {A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models with moderate to high horizontal resolution (1°–0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. Here, for both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks’ seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors’ results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Lastly, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.},
doi = {10.1175/JCLI-D-13-00646.1},
journal = {Journal of Climate},
number = 4,
volume = 28,
place = {United States},
year = {Wed Feb 11 00:00:00 EST 2015},
month = {Wed Feb 11 00:00:00 EST 2015}
}

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