Advanced Search
Article Contents

Quantifying the Contribution of Track Changes to Interannual Variations of North Atlantic Intense Hurricanes


doi: 10.1007/s00376-021-1116-9

  • Previous studies have linked interannual variability of tropical cyclone (TC) intensity in the North Atlantic basin (NA) to Sahelian rainfall, vertical shear of the environmental flow, and relative sea surface temperature (SST). In this study, the contribution of TC track changes to the interannual variations of intense hurricane activity in the North Atlantic basin is evaluated through numerical experiments. It is found that that observed interannual variations of the frequency of intense hurricanes during the period 1958–2017 are dynamically consistent with changes in the large-scale ocean/atmosphere environment. Track changes can account for ~50% of the interannual variability of intense hurricanes, while no significant difference is found for individual environmental parameters between active and inactive years. The only significant difference between active and inactive years is in the duration of TC intensification in the region east of 60°W. The duration increase is not due to the slow-down of TC translation. In active years, a southeastward shift of the formation location in the region east of 60°W causes TCs to take a westward prevailing track, which allows TCs to have a longer opportunity for intensification. On the other hand, most TCs in inactive years take a recurving track, leading to a shorter duration of intensification. This study suggests that the influence of track changes should be considered to understand the basin-wide intensity changes in the North Atlantic basin on the interannual time scale.
    摘要: 一般认为北大西洋热带气旋强度的年际变化与非洲萨赫勒地区降水量、海区环境风的垂直切变和相对海表面温度变化有关。本研究通过数值试验定量评估了飓风路径变化对北大西洋4、5级飓风活动年际变化的贡献,首先验证了1958-2017年期间强飓风频数年际变化与大尺度海洋/大气环境的变化动力学上的一致性,然后分别评估各个大尺度环境因子和热带气旋路径变化的贡献。模拟结果表明,仅仅环境参数引起的强飓风年际变化并不明显,而热带气旋路径的变化对强飓风活动年际变化有重要影响。路径的变化主要来自生成位置的变化,在西经60度以东地区,热带气旋生成位置的变化造成了盛行路径的变化,进而影响了飓风的强度。在活跃年份,热带气旋生成位置向东南偏移,使得盛行路径偏西,从而有了更多的增强时间;在不活跃的年份,热带气旋盛行远海转向的路径,增强时间相对较短。这项研究表明,热带气旋路径变化是影响海盆尺度热带气旋强度的一个重要因子。
  • 加载中
  • Figure 1.  The observed (red) and simulated (blue) time series of (a) the annual frequency of intense hurricanes (solid lines) and 5-yr running average (dotted lines) during 1958–2017, and (b) the interannual variations after removing the 5-yr running average. The correlation of the two time series is indicated in (a) and (b).

    Figure 2.  The location of the (a) observed and (b) simulated intense hurricanes that first reach category 4 intensity during 1958–2017. The rectangle indicates the region 10°–30°N, 40°–100°W. The total numbers of the observed and simulated intense hurricanes are indicated in (a) and (b).

    Figure 3.  The 6-h intensity change (m s−1) of the observed (a) and simulated (b) tropical cyclones averaged in 2.5° × 2.5° boxes during 1958–2017.

    Figure 4.  (a) Time series of the frequency of intense hurricanes (solid lines) and the 5-yr running average (dotted line) in E1 (red) and E2 (blue) during 1958–2017, and (b) the corresponding time series of interannual variations.

    Figure 5.  Comparisons of the time series of the frequency of intense hurricanes (left) and the corresponding time series of interannual variations (right) between E1 and each sensitivity experiment during 1958–2017. The correlation of the two time series in each panel is indicated.

    Figure 6.  Comparisons of the time series of the frequency of intense hurricanes (left) and the corresponding time series of interannual variations (right) between E2 and each sensitivity experiment during 1958–2017. The correlation of the two time series in each panel is indicated.

    Figure 7.  The difference of TC formation frequency (times a factor of 10) between the active and inactive years. Red (blue) dots indicate the formation location of intense hurricanes in the active (inactive) years. The unit for the TC formation frequency is the number per season (August–October) over a 2.5° × 2.5° box.

    Figure 8.  The frequency of TC occurrence (contours) for TCs in the east region and the locations (dots) where TCs first reach category 4. The arrowed curves schematically show the prevailing track for (a) active and (b) inactive years. The unit for the TC occurrence frequency is the number per season (August–October) over a 2.5° × 2.5° box.

    Figure 9.  The observed (a) and simulated (b) difference of occurrence frequency between the active and inactive years for TCs that formed in the east region, and (c) the effect of TC formation location changes in the east region. The unit for the TC occurrence frequency is the number per season (August–October) over a 2.5° × 2.5° box.

    Table 1.  Summary of intensity experiments.

    ExperimentsDescription
    E1Control experiment: Monthly environmental parameters and observed TC tracks are used during 1958–2017.
    E2Track effect: Same as E1, but the long-term (1958–2017) monthly mean environmental parameters are used.
    SE1MLD effect: Same as E1, but the long-term (1958–2017) monthly mean MLD is used.
    SE2SST effect: Same as E1, but the long-term (1958–2017) monthly mean SST is used.
    SE3OFT effect: Same as E1, but the long-term (1958–2017) monthly mean OFT is used.
    SE4VWS effect: Same as E1, but the long-term (1958–2017) monthly mean VWS is used.
    DownLoad: CSV

    Table 2.  Differences of the environmental parameters caused by track change (used in E2) between the active and inactive years in the east and west region. The differences at the 95% confidence level are in bold.

    (a) East region
    ParametersActive yearsInactive yearsDifference
    MLD (°C)37.3736.311.06
    SST (°C)27.5727.250.32
    OFT (°C)–73.06–72.88–0.18
    VWS (m s−1)9.209.120.08
    Translation speed (m s−1)6.276.000.28
    Mean duration (d)4.202.671.53
    (b) West region
    ParametersActive yearsInactive yearsDifference
    MLD (°C)42.0339.732.3
    SST (°C)28.327.970.21
    OFT (°C)–72.30–71.86–0.44
    VWS (m s−1)9.879.95–0.08
    Translation speed (m s−1)8.096.271.82
    Mean duration (d)2.172.55–0.38
    DownLoad: CSV
  • Balaguru, K., G. R. Foltz, and L. R. Leung, 2018: Increasing magnitude of hurricane rapid intensification in the central and eastern tropical Atlantic. Geophys. Res. Lett., 45, 4238−4247, https://doi.org/10.1029/2018GL077597.
    Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 1132−1161, https://doi.org/10.1002/qj.2063.
    Bender, M. A., and I. Ginis, 2000: Real-case simulations of hurricane–ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon. Wea. Rev., 128, 917−946, https://doi.org/10.1175/1520-0493(2000)128<0917:RCSOHO>2.0.CO;2.
    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, https://doi.org/10.1175/JCLI4282.1.
    Emanuel, K., 2015: Effect of upper-ocean evolution on projected trends in tropical cyclone activity. J. Climate, 28, 8165−8170, https://doi.org/10.1175/JCLI-D-15-0401.1.
    Emanuel, K., 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.
    Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: Results from downscaling IPCC AR4 simulations. Bull. Amer. Meteor. Soc., 89, 347−368, https://doi.org/10.1175/BAMS-89-3-347.
    Emanuel, K., S. Solomon, D. Folini, S. Davis, and C. Cagnazzo, 2013: Influence of tropical tropopause layer cooling on Atlantic hurricane activity. J. Climate, 26, 2288−2301, https://doi.org/10.1175/JCLI-D-12-00242.1.
    Emanuel, K. A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 483−485, https://doi.org/10.1038/326483a0.
    Fink, A. H., and J. M. Schrage, 2007: The non-stationary correlation between Sahel precipitation indices and Atlantic Hurricane activity. Preprints, 19th Conf. on Climate Variability and Change, San Antonio, TX, Amer. Meteor. Soc.
    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, https://doi.org/10.1175/1520-0442(1996)009<1169:PMFTAO>2.0.CO;2.
    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, https://doi.org/10.1175/1520-0493(1984)112<1649:ASHFPI>2.0.CO;2.
    Holland, G. J., 1983: Tropical cyclone motion: Environmental interaction plus a beta effect. J. Atmos. Sci., 40, 328−342, https://doi.org/10.1175/1520-0469(1983)040<0328:TCMEIP>2.0.CO;2.
    Holland, G. J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 2519−2541, https://doi.org/10.1175/1520-0469(1997)054<2519:TMPIOT>2.0.CO;2.
    Huang, B. Y., and Coauthors, 2017: Extended reconstructed sea surface temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179−8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
    Huang, P. Q., Y. Z. Lu, and S. Q. Zhou, 2018: An objective method for determining ocean mixed layer depth with applications to WOCE data. J. Atmos. Oceanic Technol., 35, 441−458, https://doi.org/10.1175/JTECH-D-17-0104.1.
    Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437−472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
    Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic Basin. Wea. Forecasting, 18, 1093−1108, https://doi.org/10.1175/1520-0434(2003)018<1093:LCORIT>2.0.CO;2.
    Kara, A. B., P. A. Rochford, and H. E. Hurlburt, 2000: An optimal definition for ocean mixed layer depth. J. Geophys. Res.: Oceans, 105, 16803−16821, https://doi.org/10.1029/2000JC900072.
    Kelly, K. A., and B. Qiu, 1995: Heat flux estimates for the western North Atlantic. Part I: Assimilation of satellite data into a mixed layer model. J. Phys. Oceanogr., 25, 2344−2360, https://doi.org/10.1175/1520-0485(1995)025<2344:HFEFTW>2.0.CO;2.
    Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nature Geoscience, 3, 157−163, https://doi.org/10.1038/NGEO779.
    Kossin, J. P., and D. J. Vimont, 2007: A more general framework for understanding Atlantic hurricane variability and trends. Bull. Amer. Meteor. Soc., 88, 1767−1782, https://doi.org/10.1175/BAMS-88-11-1767.
    Kossin, J. P., and S. J. Camargo, 2009: Hurricane track variability and secular potential intensity trends. Climatic Change, 97, 329−337, https://doi.org/10.1007/s10584-009-9748-2.
    Kossin, J. P., T. L. Olander, and K. R. Knapp, 2013: Trend analysis with a new global record of tropical cyclone intensity. J. Climate, 26, 9960−9976, https://doi.org/10.1175/JCLI-D-13-00262.1.
    Landsea, C. W., and W. M. Gray, 1992: The strong association between western Sahelian monsoon rainfall and intense Atlantic hurricanes. J. Climate, 5, 435−453, https://doi.org/10.1175/1520-0442(1992)005<0435:TSABWS>2.0.CO;2.
    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, https://doi.org/10.1175/MWR-D-12-00254.1.
    Landsea, C. W., B. A. Harper, K. Hoarau, and J. A. Knaff, 2006: Can we detect trends in extreme tropical cyclones. Science, 313, 452−454, https://doi.org/10.1126/science.1128448.
    Lin, I. I., C. C. Wu, I. F. Pun, and D. S. Ko, 2008: Upper-ocean thermal structure and the western North Pacific category 5 typhoons. Part I: Ocean features and the category 5 typhoons’ intensification. Mon. Wea. Rev., 136, 3288−3306, https://doi.org/10.1175/2008MWR2277.1.
    Lin, I. I., I. F. Pun, and C. C. Wu, 2009: Upper-ocean thermal structure and the western North Pacific category 5 typhoons. Part II: Dependence on translation speed. Mon. Wea. Rev., 137, 3744−3757, https://doi.org/10.1175/2009MWR2713.1.
    Malkus, J. S., and H. Riehl, 1960: On the dynamics and energy transformations in steady-state hurricanes. Tellus, 12, 1−20, https://doi.org/10.1111/j.2153-3490.1960.tb01279.x.
    Mei, W., S. P. Xie, F. Primeau, J. C. McWilliams, and C. Pasquero, 2015: Northwestern Pacific typhoon intensity controlled by changes in ocean temperatures. Science Advances, 1, e1500014, https://doi.org/10.1126/sciadv.1500014.
    Miller, B. I., 1958: On the maximum intensity of hurricanes. J. Atmos. Sci., 15, 184−195, https://doi.org/10.1175/1520-0469(1958)015<0184:OTMIOH>2.0.CO;2.
    Monterey, G., and S. Levitus, 1997: Seasonal Variability of Mixed Layer Depth for the World Ocean. NOAA Atlas NEDIS 14, Washington, D.C.
    Murakami, H., E. Levin, T. L. Delworth, R. Gudgel, and P. C. Hsu, 2018: Dominant effect of relative tropical Atlantic warming on major hurricane occurrence. Science, 362, 794−799, https://doi.org/10.1126/science.aat6711.
    Pielke, R. A. Jr., J. Gratz, C. W. Landsea, D. Collins, M. A. Saunders, and R. Musulin, 2008: Normalized hurricane damage in the United States: 1900–2005. Natural Hazards Review, 9, 29−42, https://doi.org/10.1061/(ASCE)1527-6988(2008)9:1(29).
    Price, J. F., 1981: Upper ocean response to a hurricane. J. Phys. Oceanogr., 11, 153−175, https://doi.org/10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2.
    Price, J. F., R. A. Weller, and R. Pinkel, 1986: Diurnal cycling: Observations and models of the upper ocean response to diurnal heating, cooling, and wind mixing. J. Geophys. Res.: Oceans, 91, 8411−8427, https://doi.org/10.1029/JC091iC07p08411.
    Pun, I. F., I. I. Lin, and M. H. Lo, 2013: Recent increase in high tropical cyclone heat potential area in the Western North Pacific Ocean. Geophys. Res. Lett., 40, 4680−4684, https://doi.org/10.1002/grl.50548.
    Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354−384, https://doi.org/10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.
    Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of a warm oceanic feature on Hurricane Opal. Mon. Wea. Rev., 128, 1366−1383, https://doi.org/10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2.
    Vecchi, G. A., and B. J. Soden, 2007: Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature, 450, 1066−1070, https://doi.org/10.1038/nature06423.
    Vecchi, G. A., and T. R. Knutson, 2008: On estimates of historical North Atlantic tropical cyclone activity. J. Climate, 21, 3580−3600, https://doi.org/10.1175/2008JCLI2178.1.
    Vimont, D. J., and J. P. Kossin, 2007: The Atlantic Meridional Mode and hurricane activity. Geophys. Res. Lett., 34, L07709, https://doi.org/10.1029/2007GL029683.
    Wang, B., and X. Zhou, 2008: Climate variation and prediction of rapid intensification in tropical cyclones in the western North Pacific. Meteorol. Atmos. Phys., 99, 1−16, https://doi.org/10.1007/s00703-006-0238-z.
    Wang, R. F., and L. G. Wu, 2019: Influence of track changes on the Poleward shift of LMI location of western North Pacific tropical cyclones. J. Climate, 32, 8437−8445, https://doi.org/10.1175/JCLI-D-18-0855.1.
    Webster, P. J., G. J. Holland, J. A. Curry, and H. R. Chang, 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844−1846, https://doi.org/10.1126/science.1116448.
    Wing, A. A., K. Emanuel, and S. Solomon, 2015: On the factors affecting trends and variability in tropical cyclone potential intensity. Geophys. Res. Lett., 42, 8669−8677, https://doi.org/10.1002/2015GL066145.
    Wu, L. G., 2007: Impact of Saharan air layer on hurricane peak intensity. Geophys. Res. Lett., 34, L09802, https://doi.org/10.1029/2007GL029564.
    Wu, L. G., and B. Wang, 2004: Assessing impacts of global warming on tropical cyclone tracks. J. Climate, 17, 1686−1698, https://doi.org/10.1175/1520-0442(2004)017<1686:AIOGWO>2.0.CO;2.
    Wu, L. G., and B. Wang, 2008: What has changed the proportion of intense hurricanes in the last 30 years. J. Climate, 21, 1432−1439, https://doi.org/10.1175/2007JCLI1715.1.
    Wu, L. G., and H. K. Zhao, 2012: Dynamically derived tropical cyclone intensity changes over the western North Pacific. J. Climate, 25, 89−98, https://doi.org/10.1175/2011JCLI4139.1.
    Wu, L. G., B. Wang, and S. A. Braun, 2005: Impacts of air–sea interaction on tropical cyclone track and intensity. Mon. Wea. Rev., 133, 3299−3314, https://doi.org/10.1175/MWR3030.1.
    Wu, L. G., R. F. Wang, and X. F. Feng, 2018: Dominant role of the ocean mixed layer depth in the increased proportion of intense typhoons during 1980–2015. Earth’s Future, 6, 1518−1527, https://doi.org/10.1029/2018EF000973.
    Zhan, R. F., and Y. Q. Wang, 2017: Weak tropical cyclones dominate the poleward migration of the annual mean location of lifetime maximum intensity of Northwest Pacific tropical cyclones since 1980. J. Climate, 30, 6873−6882, https://doi.org/10.1175/JCLI-D-17-0019.1.
    Zhang, G., and Z. Wang, 2013: Interannual variability of the Atlantic Hadley circulation in boreal summer and its impacts on tropical cyclone activity. J. Climate, 26, 8529−8544, https://doi.org/10.1175/JCLI-D-12-00802.1.
  • [1] LIN Zhongda, LU Riyu, 2005: Interannual Meridional Displacement of the East Asian Upper-tropospheric Jet Stream in Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 199-211.  doi: 10.1007/BF02918509
    [2] Lei Xiaoen, 1985: EFFECT OF WIND VERTICAL SHEAR ON DIFFUSION CHARACTERISTICS IN THE MESOSCALE RANGE, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 225-233.  doi: 10.1007/BF03179754
    [3] ZHAO Haikun, WU Liguang*, and WANG Ruifang, 2014: Decadal Variations of Intense Tropical Cyclones over the Western North Pacific during 19482010, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 57-65.  doi: 10.1007/s00376-013-3011-5
    [4] YU Jinhua, TAN Zhemin, Yuqing WANG, 2010: Effects of Vertical Wind Shear on Intensity and Rainfall Asymmetries of Strong Tropical Storm Bilis (2006), ADVANCES IN ATMOSPHERIC SCIENCES, 27, 552-561.  doi: 10.1007/s00376-009-9030-6
    [5] ZHOU Yushu, 2013: Effects of Vertical Wind Shear, Radiation and Ice Microphysics on Precipitation Efficiency during a Torrential Rainfall Event in China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1809-1820.  doi: 10.1007/s00376-013-3007-1
    [6] Lu Riyu, Huang Ronghui, Hee-Jeong Baek, Jai-Ho Oh, Baek-Jo Kim, 2001: Associations with the Interannual Variations of Onset and Withdrawal of the Changma, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1066-1080.  doi: 10.1007/s00376-001-0023-3
    [7] LI Xiangshu, GUO Xueliang, FU Danhong, 2013: TRMM-retrieved Cloud Structure and Evolution of MCSs over the Northern South China Sea and Impacts of CAPE and Vertical Wind Shear, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 77-88.  doi: 10.1007/s00376-012-2055-2
    [8] Zhu Weijun, Sun Zhaobo, Zhou Bing, 2001: The Impact of Pacific SSTA on the Interannual Variability of Northern Pacific Storm Track during Winter, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 1029-1042.
    [9] Xingyan ZHOU, Riyu LU, Guanghua CHEN, Liang WU, 2018: Interannual Variations in Synoptic-Scale Disturbances over the Western North Pacific, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 507-517.  doi: 10.1007/s00376-017-7143-x
    [10] He Youhai, Guan Cuihua, Gan Zijun, 1997: Interannual and Interdecadal Variations in Heat Content of the Upper Ocean of the South China Sea, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 271-276.  doi: 10.1007/s00376-997-0025-x
    [11] Hanying LI, Peng HU, Qiong ZHANG, Ashish SINHA, Hai CHENG, 2021: Understanding Interannual Variations of the Local Rainy Season over the Southwest Indian Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1852-1862.  doi: 10.1007/s00376-021-1065-3
    [12] WANG Chao, WU Liguang, 2015: Influence of Future Tropical Cyclone Track Changes on Their Basin-Wide Intensity over the Western North Pacific: Downscaled CMIP5 Projections, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 613-623.  doi: 10.1007/s00376-014-4105-4
    [13] Peilong YU, Minghao YANG, Chao ZHANG, Yi LI, Lifeng ZHANG, Shiyao CHEN, 2023: Response of the North Pacific Storm Track Activity in the Cold Season to Multi-scale Oceanic Variations of Kuroshio Extension System: A Statistical Assessment, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 514-530.  doi: 10.1007/s00376-022-2044-z
    [14] WANG Yuesi, HU Bo, LIU Guangren, 2005: A Primary Study of the Variations of Vertical Radiation with the Beijing 325-m Meteorological Tower, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 172-180.  doi: 10.1007/BF02918507
    [15] P. Ernest Raj, P.C.S. Devara, 1997: Seasonal Variations in the Vertical Structure of Water Vapor Optical Depth in the Lower Troposphere over a Tropical Station, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 425-432.  doi: 10.1007/s00376-997-0049-2
    [16] P. Ernest Raj, P.C.S. Devara, 1997: Seasonal Variations in the Vertical Structure of Water Vapor Optical Depth in the Lower Troposphere over a Tropical Station, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 103-110.  doi: 10.1007/s00376-997-0062-5
    [17] Xiaoxuan ZHAO, Riyu LU, 2020: Vertical Structure of Interannual Variability in Cross-Equatorial Flows over the Maritime Continent and Indian Ocean in Boreal Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 173-186.  doi: 10.1007/s00376-019-9103-0
    [18] Yu FU, Hong LIAO, Yang YANG, 2019: Interannual and Decadal Changes in Tropospheric Ozone in China and the Associated Chemistry-Climate Interactions: A Review, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 975-993.  doi: 10.1007/s00376-019-8216-9
    [19] Kaiming HU, Yingxue LIU, Gang HUANG, Zhuoqi HE, Shang-Min LONG, 2020: Contributions to the Interannual Summer Rainfall Variability in the Mountainous Area of Central China and Their Decadal Changes, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 259-268.  doi: 10.1007/s00376-019-9099-5
    [20] Xiaofei WU, Jiangyu MAO, 2019: Decadal Changes in Interannual Dependence of the Bay of Bengal Summer Monsoon Onset on ENSO Modulated by the Pacific Decadal Oscillation, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 1404-1416.  doi: 10.1007/s00376-019-9043-8

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 20 March 2021
Manuscript revised: 31 July 2021
Manuscript accepted: 12 August 2021
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Quantifying the Contribution of Track Changes to Interannual Variations of North Atlantic Intense Hurricanes

    Corresponding author: Liguang WU, liguangwu@fudan.edu.cn
  • 1. Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Joint Center for Data Assimilation Research and Applications, Nanjing University of Information Science and Technology, Nanjing 210031, China
  • 2. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China
  • 3. Innovation Center of Ocean and Atmosphere System, Zhuhai Fudan Innovation Research Institute, Zhuhai 518057, China

Abstract: Previous studies have linked interannual variability of tropical cyclone (TC) intensity in the North Atlantic basin (NA) to Sahelian rainfall, vertical shear of the environmental flow, and relative sea surface temperature (SST). In this study, the contribution of TC track changes to the interannual variations of intense hurricane activity in the North Atlantic basin is evaluated through numerical experiments. It is found that that observed interannual variations of the frequency of intense hurricanes during the period 1958–2017 are dynamically consistent with changes in the large-scale ocean/atmosphere environment. Track changes can account for ~50% of the interannual variability of intense hurricanes, while no significant difference is found for individual environmental parameters between active and inactive years. The only significant difference between active and inactive years is in the duration of TC intensification in the region east of 60°W. The duration increase is not due to the slow-down of TC translation. In active years, a southeastward shift of the formation location in the region east of 60°W causes TCs to take a westward prevailing track, which allows TCs to have a longer opportunity for intensification. On the other hand, most TCs in inactive years take a recurving track, leading to a shorter duration of intensification. This study suggests that the influence of track changes should be considered to understand the basin-wide intensity changes in the North Atlantic basin on the interannual time scale.

摘要: 一般认为北大西洋热带气旋强度的年际变化与非洲萨赫勒地区降水量、海区环境风的垂直切变和相对海表面温度变化有关。本研究通过数值试验定量评估了飓风路径变化对北大西洋4、5级飓风活动年际变化的贡献,首先验证了1958-2017年期间强飓风频数年际变化与大尺度海洋/大气环境的变化动力学上的一致性,然后分别评估各个大尺度环境因子和热带气旋路径变化的贡献。模拟结果表明,仅仅环境参数引起的强飓风年际变化并不明显,而热带气旋路径的变化对强飓风活动年际变化有重要影响。路径的变化主要来自生成位置的变化,在西经60度以东地区,热带气旋生成位置的变化造成了盛行路径的变化,进而影响了飓风的强度。在活跃年份,热带气旋生成位置向东南偏移,使得盛行路径偏西,从而有了更多的增强时间;在不活跃的年份,热带气旋盛行远海转向的路径,增强时间相对较短。这项研究表明,热带气旋路径变化是影响海盆尺度热带气旋强度的一个重要因子。

    • Hurricane damage in the United States is closely related to hurricane intensity. Pielke et al. (2008) found that about 85% of all hurricane damage is caused by major hurricanes (Saffir-Simpson Categories 3, 4, and 5). In the North Atlantic (NA), interannual variability of the frequency of major hurricanes has been linked to physical factors such as Sahelian rainfall, vertical shear of the environmental flow, and sea surface temperature (SST) (e.g., Landsea and Gray, 1992; Goldenberg and Shapiro, 1996; Murakami et al., 2018). However, mechanisms by which physical factors affect tropical cyclone (TC) intensity in the NA basin and their relative importance have not been well understood.

      The interannual variability of TC activity in the North Atlantic basin has been investigated by focusing on the changes in vertical wind shear (VWS) associated with the influence of the El Niño/Sothern Oscillation (ENSO). The pioneering study of Gray (1984) noted that the development of El Niño conditions suppressed TC occurrence, while La Niña years enhanced TC activity. The reduction in TC activity during El Niño years was attributed to the increased VWS associated with anomalous upper-level westerly winds driven by an eastward-shifted and weaker Walker Circulation. Goldenberg and Shapiro (1996) further found that the VWS changes associated with ENSO were one of the most important factors affecting interannual variations of major hurricanes.

      Along with studies related to ENSO, the Atlantic Meridional Mode (AMM) has been considered in the analyses of Atlantic hurricane variations. Vimont and Kossin (2007) found that seasonal hurricane activity in the Atlantic is strongly related to the AMM on interannual time scales. Kossin and Vimont (2007) also related the frequency and distribution of major hurricanes to the AMM phase. During strong negative phases, very few tropical storms form and only a small percentage intensify into major hurricanes, while during strong positive phases, more storms form and many become major hurricanes. They found that the composite differences of VWS and SST provide causal evidence for the observed change; anomalously low (high) VWS and high (low) SST are found during positive (negative) AMM phases, and various local mechanisms were proposed to explain the changes in VWS.

      In addition to VWS, Landsea and Gray (1992) found that the annual frequency of major hurricanes was strongly related to concurrent western Sahelian monsoon rainfall. That is, there are more (fewer) major hurricanes during wet (dry) years. They proposed two possible physical mechanisms. The first possible physical mechanism is associated with a difference in VWS. Stronger upper tropospheric westerly winds lead to more VWS during drought years. The other possible physical mechanism is related to the influence of Sahelian monsoon rainfall on the intensity of easterly waves, since over 90% of all major hurricanes form from easterly waves in wet years. In wet western Sahelian years, easterly waves emanating from Africa have strong amplitudes with more concentrated, persistent deep convection. However, Landsea and Gray (1992) provided little evidence for the linkage between easterly waves with strong amplitudes and TC intensity. Recently, the effect of western Sahelian rainfall has been questioned. Fink and Schrage (2007) showed the degradation of the relationship between hurricane intensity and Sahel rainfall in terms of the relationship between accumulated cyclone energy (ACE) and the western Sahel rainfall index. Recent Colorado State University (CSU) seasonal forecasts do not consider the western Sahelian monsoon as a predictability source.

      Studies have also suggested other environmental parameters that can affect TC intensity. Based on maximum potential intensity (MPI) theory (Miller, 1958; Malkus and Reihl, 1960; Emanuel, 1987; Holland, 1997), SST and outflow temperature theoretically establish an upper limit for TC intensity, or MPI. Note that SST can indirectly affect intense hurricane activity. It is suggested that relative SST change (the SST change in the tropical main development region relative to the tropical mean SST) plays a more important role in causing potential intensity changes than local SST (Vecchi and Soden, 2007; Vecchi and Knutson, 2008; Murakami et al., 2018). Murakami et al. (2018) explored the factors linked to enhanced major hurricane activity in the NA basin during 2017. Using a suite of high-resolution model experiments, they suggested that the key factor controlling Atlantic major hurricane activity appears to be the degree to which the tropical Atlantic warms relative to the rest of the global ocean.

      Wing et al. (2015) indicated that outflow temperature (OFT) affects interannual variations of TC potential intensity. Emanuel et al. (2013) found that the recent stratospheric cooling trend near the tropopause contributed significantly to the upward trend in potential intensity. Moreover, TC-induced vertical mixing and upwelling of cooler subsurface ocean waters reduce the underlying SST (Price, 1981). The shallower the ocean mixed layer depth (MLD), the stronger the resulting sea surface cooling (Bender and Ginis, 2000; Shay et al., 2000; Wu et al., 2005; Lin et al., 2008, 2009; Pun et al., 2013). The influence of the ocean MLD on TC intensity has recently been confirmed (Emanuel, 2015; Mei et al., 2015). Balaguru et al. (2018) found that the increasing magnitude of hurricane rapid intensification in the central and eastern tropical Atlantic is mainly caused by the increase in mixed layer depth, which can be linked to the positive phase of the Atlantic Multidecadal Oscillation (AMO).

      Unlike individual TCs, the basin-wide changes of TC intensity can result from changes in prevailing TC tracks because of the spatial variations of large-scale environmental parameters (e.g., Wu and Wang, 2008; Kossin and Camargo, 2009; Zhan and Wang, 2017; Wu et al., 2018). Wu et al. (2018) integrated an axisymmetric intensity model coupled with a simple one-dimensional ocean model along the observed tracks of TCs in the western North Pacific during the period 1980–2015. They found that changes in prevailing TC tracks can account for more than half of the basin-wide intensity trend during the period 1980–2015.

      Although previous studies have identified the relationship of TC intensity in the NA basin with environmental parameters, the relative importance of these parameters and the possible impact of track changes (including formation location and subsequent movement) are still unknown. The objective of this study is to quantify the relative contributions of various factors to interannual variations of TC intensity in the NA basin during the period 1958–2017. Based on numerical experiments conducted with an intensity model (Emanuel et al., 2008; Wu and Zhao, 2012; Wu et al., 2018; Wang and Wu, 2019), two specific issues are addressed: (1) whether the observed interannual variations of TC intensity are consistent with changes in the atmosphere/ocean environment and prevailing tracks, and (2) which factor plays the dominant role in the interannual variations of intense hurricanes.

    2.   Data and methodology
    • Considering the uncertainty in the historical records of TC intensity (Landsea et al., 2006; Knutson et al., 2010; Kossin et al., 2013), the basin-wide intensity is measured by the annual count of the most intense hurricanes (categories 4 and 5 on the Saffir-Simpson scale) (Webster et al., 2005; Wu, 2007; Wu and Wang, 2008). For convenience, the most intense hurricanes (categories 4 and 5) are called intense hurricanes in this study. Wu and Zhao (2012) suggested that the frequency of intense hurricanes is more sensitive to changes in large-scale parameters than other intensity indices. Since the annual counts of intense hurricanes depend on the annual tropical cyclone formation frequency, the proportion of intense hurricanes is also used in this study. The Atlantic TC track data are from the second-generation Atlantic hurricane database (HURDAT2) maintained by the National Oceanic and Atmospheric Administration (NOAA) National Hurricane Center (Landsea and Franklin, 2013).

      The large-scale atmosphere/ocean environmental parameters are from the following datasets. The SST data are from the NOAA extended reconstructed SST (ERSST version 5) data with 2° latitude by 2° longitude resolution (Huang et al., 2017). The ocean MLD data are derived from the ocean reanalysis data from the ECMWF Ocean Reanalysis System 4 (ORAS4) with a resolution of 1° by 1° (Balmaseda et al., 2013). Effort has been made to establish reliable MLD calculation methods (e.g., Kara et al., 2000; Huang et al., 2018). In this study, we use the definition of the depth where the temperature is 0.5°C less than the SST (Price et al., 1986; Kelly and Qiu, 1995; Monterey and Levitus, 1997). The OFT is represented by the tropopause temperature because the tropopause can be taken as the cloud top. The tropopause is defined by where the temperature lapse rate becomes greater than 2 K km−1, and the tropopause temperature is obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR) with a resolution of 2.5° latitude by 2.5° longitude (Kalnay et al., 1996). The VWS, which is defined as the magnitude of the vector difference of the horizontal winds between 850 hPa and 200 hPa, is derived from the NCEP/NCAR reanalysis. The Niño-3.4 index is obtained from the National Center for Atmospheric Research/University Corporation for Atmospheric Research Reanalysis (NCAR/UCAR; Rasmusson and Carpenter, 1982).

      The numerical experiments in the study are conducted with the TC intensity model, which is adopted from Emanuel et al. (2008). It is an axisymmetric numerical atmospheric model, coupled with a simple one-dimensional ocean model. The model is run along the observed track for each TC. In addition to the observed track, the model input includes four large-scale environmental parameters: SST, OFT, MLD, and VWS. The model is initialized with a warm-core cyclonic vortex with a maximum wind speed of 21 m s−1 because the model vortex weakens at the beginning of the simulation (Wu et al., 2018). The currently available water vapor dataset is of low confidence, so the effect of water vapor change on TC intensity is not considered, and the environmental relative humidity in the middle troposphere and boundary layer are constant (45% and 80%, respectively). The other model parameters are the same as those in Emanuel et al. (2008).

      The numerical experiments are carried out for the peak season (August, September, and October). Table 1 lists all the intensity experiments in this study. E1 is designed to examine the capability of the intensity model. The monthly mean environmental parameters are used for each year. E2 is designed to examine the influence of the track changes. In E2, there are no temporary changes in the four environmental parameters, which are averaged for each month over the 60-year period (1958–2017). Four sensitivity experiments are designed to examine the contributions of the four large-scale environmental parameters. In these experiments, we hold one parameter constant averaged for each month over the 60-year period (1958–2017), while the other parameters are the same as in E1. Specifically, the MLD (SST, OFT, and VWS) is fixed in SE1 (SE2, SE3, and SE4).

      ExperimentsDescription
      E1Control experiment: Monthly environmental parameters and observed TC tracks are used during 1958–2017.
      E2Track effect: Same as E1, but the long-term (1958–2017) monthly mean environmental parameters are used.
      SE1MLD effect: Same as E1, but the long-term (1958–2017) monthly mean MLD is used.
      SE2SST effect: Same as E1, but the long-term (1958–2017) monthly mean SST is used.
      SE3OFT effect: Same as E1, but the long-term (1958–2017) monthly mean OFT is used.
      SE4VWS effect: Same as E1, but the long-term (1958–2017) monthly mean VWS is used.

      Table 1.  Summary of intensity experiments.

      For comparison, we categorize intense hurricane activity over the period 1958–2017 as the active and inactive years. The interannual variations are first obtained by removing the 5-year running average. Active (inactive) years are defined by when the amplitude of the interannual variations of the frequency of intense hurricanes is larger (smaller) than 0.5 (–0.5) standard deviation. The 18 active years are 1961, 1964, 1971, 1974, 1978, 1979, 1985, 1988, 1989, 1992, 1995, 1996, 1999, 2004, 2005, 2008, 2010, and 2011, and the 21 inactive years are 1960, 1962, 1963, 1965, 1968, 1973, 1976, 1983, 1986, 1987, 1990, 1993, 1994, 1997, 2001, 2002, 2006, 2009, 2012, 2013, and 2015. There are 46 (7) intense hurricanes in the selected active (inactive) years.

      Note that the inactive years include El Niño years such as 1965, 1983, 1987, 1997, 2002, 2009, and 2015, while the active years include La Niña years such as 1988, 2008, and 2010. However, there are exceptions. The inactive year of 1973 corresponds to a La Niña year, and the active year of 1992 corresponds to a typical El Niño year. In fact, the correlation between the frequency of intense hurricanes and the Niño-3.4 index is –0.51. Murakami et al. (2018) found that the correlation coefficient between the Niño-3.4 index and the observed major hurricane frequency for the period 1979–2017 is −0.45. Using a suite of high-resolution model experiments, they found that the high number of 2017 major hurricanes was not primarily caused by La Niña conditions in the Pacific Ocean.

    3.   Verification of the intensity model
    • Wu et al. (2018) demonstrated the capability of the intensity model in the Western North Pacific basin, but its performance in the NA basin has not been evaluated. The performance of the intensity model can be evaluated by comparing the annual frequency of intense hurricanes simulated in E1 with the observations (Fig. 1). During the 60-year period, there were 82 intense hurricanes in the observations. Although the monthly environmental parameters are used, the model simulates 79 intense hurricanes, comparable to the observations. The annual frequency of the simulated intense hurricanes is also significantly correlated with the observations (0.69). As shown in Fig. 1a, the model also successfully simulates the variations on the interdecadal timescale, with a relatively inactive period during 1967–94. Close examination indicates that the model shows relatively poor performance during a few years. For example, the model simulates two fewer intense hurricanes in 1961, 1978, and 1988, but three and four more intense hurricanes in 2010 and 2011, respectively.

      Figure 1.  The observed (red) and simulated (blue) time series of (a) the annual frequency of intense hurricanes (solid lines) and 5-yr running average (dotted lines) during 1958–2017, and (b) the interannual variations after removing the 5-yr running average. The correlation of the two time series is indicated in (a) and (b).

      The TC intensity model can also simulate the average intensification rate well. This can be examined by comparing where TCs first reach category 4 intensity and the averaged 6-h intensification rate. Figure 2 shows the observed and simulated locations where TCs first reach category 4 intensity. In the observations, the TCs reached category 4 intensity mainly in the region 10°–30°N, 40°–100°W. This feature is captured well in E1. Since the intensity model is integrated along the observed track, this suggests that the averaged intensification rate is also simulated well. To demonstrate this, Fig. 3 shows the observed and simulated mean 6-h intensification rates during 1958–2017. The observed and simulated rates show that TCs generally intensify in the region south of 30°N. In addition, the model also simulated the enhanced intensification along the west coast of the Gulf of Mexico and in the region south of 15°N. Note that rapid intensification, which is common for intense hurricanes (Kaplan and DeMaria, 2003; Wang and Zhou, 2008), cannot be realistically simulated with the monthly environmental parameters.

      Figure 2.  The location of the (a) observed and (b) simulated intense hurricanes that first reach category 4 intensity during 1958–2017. The rectangle indicates the region 10°–30°N, 40°–100°W. The total numbers of the observed and simulated intense hurricanes are indicated in (a) and (b).

      Figure 3.  The 6-h intensity change (m s−1) of the observed (a) and simulated (b) tropical cyclones averaged in 2.5° × 2.5° boxes during 1958–2017.

      Figure 1b shows the time series of the frequency of intense hurricanes on the interannual time scale. The correlation between the simulation and observation time series is 0.65. The two time series have the same standard deviations of interannual variations (1.15). In summary, the intensity model can simulate the activity of intense hurricanes in the NA basin well in terms of the climatology and interannual variability. The successful simulation makes it possible to evaluate the contributions of individual environmental parameters and track changes to intense hurricane activity.

    4.   Results of the sensitivity experiments
    • In the last section, we have shown that the interannual variability of intense hurricanes can be simulated well with the observed tracks and monthly-averaged environmental parameters. Based on the sensitivity experiments, the influence of track changes is first examined in this section. As indicated in Table 1, all the environmental parameters in E2 are replaced by the monthly means averaged over the period 1958–2017. That is, the influences of the temporary variations in the environmental parameters are removed in E2. The correlation of the frequencies of intense hurricanes for the observations and the simulation in E2 is 0.67 over the period 1958–2017.

      By comparing the results in E1 and E2, we can evaluate the contribution from the TC track changes relative to the collective contribution from the environmental parameters. Figure 4 shows the time series of the simulated intense hurricanes in E1 and E2. The correlation of the two time series shown in Fig. 4a is 0.78. For the interannual time series shown in Fig. 4b, the correlation is 0.70. This suggests that track changes account for ~50% of the variance of the interannual variability of intense hurricanes simulated in E1. In other words, the collective influence of the environmental parameters also explains ~50% of the variance of the interannual variability of intense hurricanes simulated in E1. This indicates that track changes play an important role in intense hurricane activity on the interannual time scale.

      Figure 4.  (a) Time series of the frequency of intense hurricanes (solid lines) and the 5-yr running average (dotted line) in E1 (red) and E2 (blue) during 1958–2017, and (b) the corresponding time series of interannual variations.

      We further examine the relative contributions from the individual environmental parameters by comparing the results of SE1, SE2, SE3, and SE4 with those of E1. In each of the sensitivity experiments, the influence of the temporary variations of one environmental parameter is removed. In particular, MLD, SST, OFT, and VWS are fixed in SE1, SE2, SE3, and SE4, respectively. In comparison with E1, Fig. 5 shows the time series of the simulated intense hurricanes in these experiments. On the interannual time scale, the correlations of the simulated time series in E1 with those in SE1, SE2, SE3, and SE4 are 0.95, 0.92, 0.98, and 0.84, respectively. Note that a higher correlation means a lesser influence of the specific parameter. This suggests that VWS is the most important of all the environmental parameters, consistent with the findings of Goldenberg and Shapiro (1996).

      Figure 5.  Comparisons of the time series of the frequency of intense hurricanes (left) and the corresponding time series of interannual variations (right) between E1 and each sensitivity experiment during 1958–2017. The correlation of the two time series in each panel is indicated.

      Since the combined effect of the environmental parameters is removed in E2, we can also examine the relative contributions from the individual environmental parameters by comparing the results of SE1, SE2, SE3, and SE4 with those of E2. Figure 6 shows the comparisons of the time series of the simulated intense hurricanes in these experiments with the time series in E2. The correlations of the simulated interannual variations in E2 with those in SE1, SE2, SE3, and SE4 are 0.75, 0.80, 0.75, and 0.92, respectively. Note that a higher correlation means a greater influence of the specific parameter. Consistent with the comparisons with E1, VWS is again the most important of the four environmental parameters. Also consistent with the comparisons with E1, the SST influence is less than the VWS influence but greater than the MLD and OFT influences.

      Figure 6.  Comparisons of the time series of the frequency of intense hurricanes (left) and the corresponding time series of interannual variations (right) between E2 and each sensitivity experiment during 1958–2017. The correlation of the two time series in each panel is indicated.

      The above analysis indicates that track changes play an important role in the interannual variations of intense hurricane activity in the NA basin, while VWS plays a secondary role. On the interannual time scale, the direct influences of changes in MLD, OFT, and SST are relatively small.

    5.   Intense hurricane activity in the east and west regions
    • A TC track consists of a starting point (formation location) and subsequent movement. While movement is mainly controlled by large-scale steering and β-drift (Holland, 1983; Wu and Wang, 2004), TC tracks are also influenced by their own formation location, which can lead to changes in the duration of TC intensification and environmental parameters experienced by TCs. In this study, the duration of TC intensification is defined as the period between the formation and the time when a TC reaches its lifetime maximum intensity. Figure 7 shows the formation locations of intense hurricanes during active and inactive years. The formation locations for intense hurricanes can be roughly divided by the longitude of 60°W. The east region covers the tropical NA, while the west region mainly includes the Gulf of Mexico and Caribbean Sea. A total of 53 intense hurricanes formed in the east region, accounting for 65% of the total intense hurricanes in the NA basin. Figure 7 also shows the difference of TC formation frequency (contours) between the active and inactive years, which is counted in each 2.5° × 2.5° box. A positive difference indicates enhanced TC formation during active years. We can see that the east and west regions correspond to two regions with enhanced TC formation, respectively.

      Figure 7.  The difference of TC formation frequency (times a factor of 10) between the active and inactive years. Red (blue) dots indicate the formation location of intense hurricanes in the active (inactive) years. The unit for the TC formation frequency is the number per season (August–October) over a 2.5° × 2.5° box.

      Despite enhanced TC formation, especially in the east region, it should be noted that the active years are mainly signified by the relatively high proportion of intense hurricanes compared to all TCs. In the active (inactive) years, 23.6% (4.6%) of TCs intensified into intense hurricanes. This is also indicated by the correlation between the annual frequency and the proportion of intense hurricanes (Fig. 1), which is 0.92. This suggests that the variations of the proportion of intense hurricanes can explain 85% of the variance of the frequency of intense hurricanes. In the east region, 34.0% (4.8%) of TCs intensified into intense hurricanes in the active (inactive) years. In the west region, 15.0% (4.3%) of TCs intensified into intense hurricanes in the active (inactive) years. The correlations between the annual frequency and the proportion of intense hurricanes are 0.84 and 0.81 for the west and east regions, respectively. We can see that TCs have a much greater chance of becoming intense hurricanes during active years. This strongly suggests that the enhanced activity of intense hurricanes results from factors other than the annual formation frequency in the NA.

      As discussed in the last section, the importance of track changes relative to the collective contribution from environmental parameters can be evaluated by comparing the results in E1 and E2. In the east region, the correlation of the frequency of intense hurricanes between E1 and E2 is 0.81, indicating that track changes can account for 66% of the variance of the interannual variability of intense hurricanes. In the west region, the corresponding correlation is 0.68, and track changes can explain 46% of the variance of the interannual variability of intense hurricanes. Consistent with the discussion in section 4, TC track changes play an important role in regulating the interannual variations of intense hurricane activity in the west and east regions.

      Track changes include changes in translation speed, duration of intensification, and environmental parameters. These environmental parameters in E2 caused by TC tracks are calculated for all TCs and averaged for the active and inactive years (Table 2), respectively. By comparing these parameters in E2, we can further understand how track changes affect intense hurricane activity. In the west region, track changes do not lead to significant changes in environmental parameters, although the increases in MLD (2.3 m) and SST (0.21°C) and the decreases in OFT (−0.44°C) and VWS (−0.08 m s−1) are favorable for TC intensification. On the other hand, during active years, TCs move faster, and the mean duration is shorter. The decreasing duration of intensification is unfavorable for TC intensification. The decreasing duration of intensification is mainly due to the westward shift of the mean formation longitude, which significantly decreases by 3.79° during active years.

      (a) East region
      ParametersActive yearsInactive yearsDifference
      MLD (°C)37.3736.311.06
      SST (°C)27.5727.250.32
      OFT (°C)–73.06–72.88–0.18
      VWS (m s−1)9.209.120.08
      Translation speed (m s−1)6.276.000.28
      Mean duration (d)4.202.671.53
      (b) West region
      ParametersActive yearsInactive yearsDifference
      MLD (°C)42.0339.732.3
      SST (°C)28.327.970.21
      OFT (°C)–72.30–71.86–0.44
      VWS (m s−1)9.879.95–0.08
      Translation speed (m s−1)8.096.271.82
      Mean duration (d)2.172.55–0.38

      Table 2.  Differences of the environmental parameters caused by track change (used in E2) between the active and inactive years in the east and west region. The differences at the 95% confidence level are in bold.

      In the east region, almost all changes of environmental parameters are favorable for TC intensification, but the changes cannot pass the significant test at the 95% confidence level. As indicated in Table 2, the only significant difference occurs for the mean duration time, which increases by 1.53 days, or 36.7 hours. Note that the elongated duration is not due to the slow-down of TC translation. In fact, the mean translation speed is faster during active years. We examined the mean distance between the formation location and the location of maximum intensity for all TCs in the east region. The TCs travel 2048 (1310) km to reach their maximum intensity during active (inactive) years. The difference of 738 km is statistically significant. This suggests that TCs during active years travel longer distances and have longer chances for intensification.

      What makes TCs during active years travel longer distances in the east region? Figure 8 shows the frequency of TC occurrence for TCs formed in the east region and the prevailing track indicated by the frequency maximum. During active years, the TCs take a westward prevailing track, which allows them to have longer chances for intensification. As shown in Fig. 3, TCs generally intensify south of 30°N. On the other hand, the TCs during inactive years take a recurving prevailing track, leading to a shorter duration of intensification.

      Figure 8.  The frequency of TC occurrence (contours) for TCs in the east region and the locations (dots) where TCs first reach category 4. The arrowed curves schematically show the prevailing track for (a) active and (b) inactive years. The unit for the TC occurrence frequency is the number per season (August–October) over a 2.5° × 2.5° box.

      We can further demonstrate that the different prevailing tracks mainly result from the difference of formation locations between the active and inactive years. The prevailing track formation location shifts southeastward by 1.54° in longitude and 0.55° in latitude for active years, compared to inactive years. Although it is not statistically significant at the 95% confidence level, the southeastward shift in prevailing track formation location can allow TCs to travel at lower latitudes and have more time to develop. To demonstrate this, we use the TC track model developed by Wu and Wang (2004). In the track model, a TC is taken as a point vortex, and its track is a function of the translation speed and formation location. The input of the model includes the formation locations and translation speeds of TCs. We can use the track model to examine the contributions of changes in the formation locations and translation speeds, respectively. The large-scale steering flow is defined as the mean flow between 850 hPa and 300 hPa. Following Wu and Wang (2004) and Wu et al. (2005), the climatological beta drift is used in this study. In the control run, the tracks are simulated with the monthly mean steering flow and the observed formation locations for each TC. Compared with the observations (Fig. 9a), Fig. 9b shows the difference of the frequency of TC occurrence between the active and inactive years in the control run. The simulated difference pattern is very similar to the observations. Two sensitivity experiments are conducted with the track model. In the two experiments, we use the same steering flow averaged for both the active and inactive years, while the formation locations are from the active and inactive years, respectively. Figure 9c shows the difference of the frequency of TC occurrence between the active and inactive years in the sensitivity experiments. This suggests that the track differences between the active and inactive years in the east region are mainly due to the formation location differences.

      Figure 9.  The observed (a) and simulated (b) difference of occurrence frequency between the active and inactive years for TCs that formed in the east region, and (c) the effect of TC formation location changes in the east region. The unit for the TC occurrence frequency is the number per season (August–October) over a 2.5° × 2.5° box.

    6.   Conclusions
    • Previous studies have linked interannual variability of TC intensity in the NA basin to Sahelian rainfall, vertical shear of the environmental flow, and relative SST (Landsea and Gray, 1992; Goldenberg and Shapiro, 1996; Murakami et al., 2018). In this study, using the TC intensity model, we evaluate the contribution of TC track changes to the interannual variations of intense hurricane activity in the NA basin. It is found that interannual variations of the frequency of intense hurricanes can be simulated well with the intensity model, which is run along the observed tracks with the monthly mean environmental parameters (MLD, SST, OFT, and VWS). Our simulation indicates that interannual variations of the frequency of observed intense hurricanes during the period 1958–2017 are dynamically consistent with changes in the large-scale ocean/atmosphere environment.

      The contributions of track changes relative to environmental parameters are evaluated by removing the temporary change of the environmental parameters. This suggests that track changes account for ~50% of the interannual variability of intense hurricanes, while the collective influence of the environmental parameters explains ~50% of the interannual variability of simulated intense hurricanes. In particular, track changes can account for 66% (46%) of the interannual variability of intense hurricanes in the region east (west) of 60°W. In conclusion, TC track changes play an important role in regulating the interannual variations of intense hurricane activity in the NA basin.

      No significant differences in the environmental parameters that are used in the intensity model are found between the active and inactive years. The only significant difference between the active and inactive years occurs for the duration of TC intensification in the region east of 60°W. The increase of 36.7 hours in the duration of intensification is not due to the slow-down of TC translation. During active years, the southeastward shift of the prevailing track formation location in the region east of 60°W causes TCs to take a westward prevailing track, which allows TCs to have longer chances for intensification. On the other hand, the TCs during inactive years take a recurving prevailing track, leading to a shorter duration of intensification.

      The track differences between the active and inactive years in the east region are mainly due to the formation location differences. A genesis potential index (GPI) developed by Emanuel and Nolan (2004) was used to discuss the reason for the formation location shift. The calculated climatologic GPI is consistent with Camargo et al. (2007). However, the GPI cannot account for the climatologic mean and variations of the TC formation frequency in the east region. This means that changes in TC formation frequency in the east region cannot be explained by large-scale environmental flows that are used in the GPI. The cause of the TC formation location shift in the east region requires further study.

      As mentioned in the introduction, Landsea and Gray (1992) suggested two possible mechanisms for the strong relationship of the annual frequency of major hurricanes with concurrent western Sahelian monsoon rainfall. They argued that easterly waves became stronger in wet years than in dry years. Zhang and Wang (2013) mentioned the role of the Atlantic regional Hadley circulation in modulating Atlantic TC genesis locations, and they found that the easterly waves are more active when the strength of the Atlantic Hadley circulation is stronger. This study suggests that the stronger easterly waves may cause the formation location to shift southeastward.

      Acknowledgements. This research was jointly supported by the National Natural Science Foundation of China (Grant Nos. 41730961, 41675051, and 41922033).

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return