Tropical cyclone genesis over the western North Pacific simulated by Coupled Model Intercomparison Project Phase 6 models

Cong Gao Lei Zhou

Cong Gao, Lei Zhou. Tropical cyclone genesis over the western North Pacific simulated by Coupled Model Intercomparison Project Phase 6 models[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1860-9
Citation: Cong Gao, Lei Zhou. Tropical cyclone genesis over the western North Pacific simulated by Coupled Model Intercomparison Project Phase 6 models[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1860-9

doi: 10.1007/s13131-021-1860-9

Tropical cyclone genesis over the western North Pacific simulated by Coupled Model Intercomparison Project Phase 6 models

Funds: The National Natural Science Foundation of China under contract Nos 42076001, 41690121, and 41690120; the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. 311020004; the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University under contract No. SL2020PT205.
More Information
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  
  • Figure  1.  The observed and simulated climatological TC genesis during the period of 1980–2014 over the WNP. a. Results from IBTrACS. Black dots indicate TC genesis locations and colors represent TCGN in a regular grid with a horizontal resolution of 2.5°×2.5° (longitude×latitude). b. GPI derived using ERSSTv5 and NCEP/NCAR reanalysis. R represents the CPCC between the observation and simulation, which passes the two-tailed Student’s t-test at p = 0.01.

    Figure  2.  The observed and simulated annual cycle of TCGN in the period of 1980–2014 over the WNP. Red line represents the results from IBTrACS, and blue line represents the GPI derived using ERSSTv5 and NCEP/NCAR reanalysis. R is the Pearson linear correlation coefficient, which passes the two-tailed Student’s t-test at p = 0.01.

    Figure  3.  TC genesis during El Niño and La Niña years over the WNP. a. Results from IBTrACS. Red dots and blue dots indicate TC genesis locations in El Niño and La Niña years, respectively. b. Difference in the GPI (derived using ERSSTv5 and NCEP/NCAR reanalysis) between El Niño and La Niña years. Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. Zonal gray line at 20°N and meridional line at 145°E divide the WNP into four subregions.

    Figure  4.  Cumulative distribution function of the longitude (a) and latitude (b) of TC genesis locations over the WNP. Blue and red lines represent the results of La Niña (LN) and El Niño (EN) years, respectively. The observed TC genesis is shown in solid lines and the observed GPIs (derived using ERSSTv5 and NCEP/NCAR reanalysis) are shown in dashed lines.

    Figure  5.  The simulated climatological GPIs over the WNP in CMIP6 models. a. Multi-model ensemble (MME) of 25 CMIP6 models and b–z. simulations in each of 25 CMIP6 models. The skill score of each model is shown on the upper-right corner from a to z.

    Figure  6.  The GPI obtained with ERSSTv5 and NCEP/NCAR reanalysis.

    Figure  7.  The impacts of the $ {H}_{600} $ bias on the GPI. a. Ensemble mean impact of 25 CMIP6 models and b–z. Impacts for each of 25 CMIP6 models. The impacts shown by shaded areas is statistically significant at the 99% confidence level.

    Figure  8.  Annual cycle of the observed GPI (thick black line) and the simulated GPIs in 25 CMIP6 models. All GPIs are normalized by subtracting the corresponding climatological mean and dividing by the standard deviation. Numbers in the legend are the serial numbers corresponding to the 25 CMIP6 models in Table 1.

    Figure  9.  Differences in GPI between El Niño and La Niña years simulated in WMME (a) and PMME (b). Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. Contours indicate the difference in the observed GPI (Fig. 3b) between El Niño and La Niña years.

    Figure  10.  Difference in GPI composites during El Niño and La Niña years caused by varying each variable in Eq. (1). Rows 1–4 show the contribution of low-level vorticity, vertical wind shear, humidity in the mid-troposphere, and potential intensity, respectively. Left column shows the results obtained with ERSSTv5 and NCEP/NCAR reanalysis. Middle column shows the results from the multi-model mean of the well-simulated group. Right column shows the results from the multi-model mean of the poorly simulated group. Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. For each panel in the middle and right columns, the CPCCs between the panel and the corresponding observations (the panel on the left column) are shown (purple) in the upper-right corner. For example, the CPCC in b is the correlation coefficient between the pattern in b and the pattern in a.

    Figure  11.  Differences in SST (a, b), SAT (c, d) and CP (e and f) between El Niño and La Niña years simulated in WMME (a, c, e) and PMME (b, d, f). Shaded areas represent the 99% confidence level according to the two-tailed Student’s t-test. Contours indicate the differences in the SST from ERSSTv5, SAT from NCEP/NCAR reanalysis, and CP from ERA5 between El Niño and La Niña years. The CPCCs between the simulations and the observations are shown (purple) in the upper-right corner.

    Table  1.   List of the 25 CMIP6 models employed in this study

    No.ModelInstitute IDResolution (number of grids, lon×lat)
    AtmosphereOcean
    1ACCESS-CM2CSIRO-ARCCSS192×144360×300
    2ACCESS-ESM1-5CSIRO192×145360×300
    3BCC-CSM2-MRBCC320×160360×232
    4BCC-ESM1BCC128×64360×232
    5CAMS-CSM1-0CAMS320×160360×200
    6CanESM5CCCma128×64361×290
    7CAS-ESM2-0CAS256×128362×196
    8CESM2NCAR288×192320×384
    9CESM2-FV2NCAR144×96320×384
    10CESM2-WACCMNCAR288×192320×384
    11CESM2-WACCM-FV2NCAR144×96320×384
    12CMCC-CM2-SR5CMCC288×192362×292
    13FGOALS-g3CAS180×80360×218
    14FIO-ESM-2-0FIO-QLNM192×288320×384
    15GISS-E2-1-GNASA-GISS144×90360×180
    16GISS-E2-1-HNASA-GISS144×90360×180
    17MCM-UA-1-0UA96×80192×80
    18MIROC6MIROC256×128360×256
    19MPI-ESM1-2-HRMPI-M DWD DKRZ384×192802×404
    20MRI-ESM2-0MRI320×160360×364
    21NESM3NUIST192×96384×362
    22NorESM2-LMNCC144×96360×384
    23NorESM2-MMNCC288×192360×384
    24SAM0-UNICONSNU288×192320×384
    25TaiESM1AS-RCEC288×192320×384
    Note: CSIRO-ARCCSS, Commonwealth Scientific and Industrial Research Organisation-Australian Research Council Centre of Excellence for Climate System Science; CSIRO, Commonwealth Scientific and Industrial Research Organisation; BCC, Beijing Climate Center; CAMS: Chinese Academy of Meteorological Sciences; CCCma, Canadian Centre for Climate Modelling and Analysis; CAS, Chinese Academy of Sciences; NCAR, National Center for Atmospheric Research; NCAR, National Center for Atmospheric Research; NCAR, National Center for Atmospheric Research; NCAR, National Center for Atmospheric Research; CMCC: Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici; FIO-QLNM, First Institute of Oceanography-Ministry of Natural Resources, Qingdao National Laboratory for Marine Science and Technology; NASA-GISS, National Aeronautics and Space Administration-Goddard Institute for Space Studies; UA, University of Arizona-Department of Geosciences; MIROC, Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, RIKEN Center for Computational Science; MPI-M DWD DKRZ: Max Planck Institute for Meteorology, German Meteorological Service, German Climate Computing Center; MRI: Meteorological Research Institute; NUIST, Nanjing University of Information Science and Technology; NCC, Norwegian Climate Centre; SNU, Seoul National University; AS-RCEC: Academia Sinica-Research Center for Environmental Changes.
    下载: 导出CSV

    Table  2.   Centered pattern correlation coefficients (CPCCs) of 25 CMIP6 models with the observed GPIs

    EvaluationModelCPCC
    Well-simulatedTaiESM10.94
    ACCESS-CM20.93
    FIO-ESM-2-00.89
    BCC-CSM2-MR0.88
    CESM2-WACCM0.86
    CMCC-CM2-SR50.85
    NorESM2-LM0.85
    Moderately simulatedCESM20.83
    BCC-ESM10.83
    CAMS-CSM1-00.82
    FGOALS-g30.81
    NorESM2-MM0.80
    SAM0-UNICON0.79
    CESM2-WACCM-FV20.79
    CESM2-FV20.77
    ACCESS-ESM1-50.77
    MIROC60.75
    CAS-ESM2-00.72
    Poorly simulatedMRI-ESM2-00.71
    MPI-ESM1-2-HR0.71
    GISS-E2-1-H0.70
    GISS-E2-1-G0.69
    CanESM50.65
    MCM-UA-1-00.51
    NESM30.48
    Note: All CPCCs depict statistical significance based on a two-tailed Student’s t-test (>99%).
    下载: 导出CSV

    Table  3.   R2 difference among CPCCs of WMME, MME, and PMME.

    WMMEMMEPMME
    WMME (0.93)00.030.35
    MME (0.91)−0.0300.31
    PMME (0.72)−0.35−0.310
    Note: Values in bold font were applied to statistically significant differences (two-tailed, p<0.01) based on the Hotelling’s T-squared test and Steiger’s Z-test.
    下载: 导出CSV
  • [1] Aiyyer A, Thorncroft C. 2011. Interannual-to-multidecadal variability of vertical shear and tropical cyclone activity. Journal of Climate, 24(12): 2949–2962. doi: 10.1175/2010JCLI3698.1
    [2] Bell R, Hodges K, Vidale P L, et al. 2014. Simulation of the global ENSO-tropical cyclone teleconnection by a high-resolution coupled general circulation model. Journal of Climate, 27(17): 6404–6422. doi: 10.1175/JCLI-D-13-00559.1
    [3] Bellenger H, Guilyardi E, Leloup J, et al. 2014. ENSO representation in climate models: from CMIP3 to CMIP5. Climate Dynamics, 42(7-8): 1999–2018. doi: 10.1007/s00382-013-1783-z
    [4] Bister M, Emanuel K A. 1998. Dissipative heating and hurricane intensity. Meteorology and Atmospheric Physics, 65(3-4): 233–240. doi: 10.1007/BF01030791
    [5] Bruyère C L, Holland G J, Towler E. 2012. Investigating the use of a genesis potential index for tropical cyclones in the North Atlantic Basin. Journal of Climate, 25(24): 8611–8626. doi: 10.1175/JCLI-D-11-00619.1
    [6] Camargo S J. 2013. Global and regional aspects of tropical cyclone activity in the CMIP5 models. Journal of Climate, 26(24): 9880–9902. doi: 10.1175/JCLI-D-12-00549.1
    [7] Camargo S J, Barnston A G, Zebiak S E. 2005. A statistical assessment of tropical cyclone activity in atmospheric general circulation models. Tellus A: Dynamic Meteorology and Oceanography, 57(4): 589–604. doi: 10.3402/tellusa.v57i4.14705
    [8] Camargo S J, Emanuel K A, Sobel A H. 2007a. Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. Journal of Climate, 20(19): 4819–4834. doi: 10.1175/JCLI4282.1
    [9] Camargo S J, Tippett M K, Sobel A H, et al. 2014. Testing the performance of tropical cyclone genesis indices in future climates using the HiRAM model. Journal of Climate, 27(24): 9171–9196. doi: 10.1175/JCLI-D-13-00505.1
    [10] Camargo S J, Sobel A H. 2005. Western North Pacific tropical cyclone intensity and ENSO. Journal of Climate, 18(15): 2996–3006. doi: 10.1175/JCLI3457.1
    [11] Camargo S, Sobel A H, Barnston A G, et al. 2007b. Tropical cyclone genesis potential index in climate models. Tellus A: Dynamic Meteorology and Oceanography, 59(4): 428–443. doi: 10.1111/j.1600-0870.2007.00238.x
    [12] Camargo S J, Wheeler M C, Sobel A H. 2009. Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. Journal of the Atmospheric Sciences, 66(10): 3061–3074. doi: 10.1175/2009JAS3101.1
    [13] Chan J C L. 1985. Tropical cyclone activity in the northwest Pacific in relation to the El Niño/Southern Oscillation phenomenon. Monthly Weather Review, 113(4): 599–606. doi: 10.1175/1520-0493(1985)113<0599:TCAITN>2.0.CO;2
    [14] Chan J C L, Liu K S. 2004. Global warming and western North Pacific typhoon activity from an observational perspective. Journal of Climate, 17(23): 4590–4602. doi: 10.1175/3240.1
    [15] Chen T C, Weng S P, Yamazaki N, et al. 1998. Interannual variation in the tropical cyclone formation over the western North Pacific. Monthly Weather Review, 126(4): 1080–1090. doi: 10.1175/1520-0493(1998)126<1080:IVITTC>2.0.CO;2
    [16] Chia H H, Ropelewski C F. 2002. The interannual variability in the genesis location of tropical cyclones in the northwest Pacific. Journal of Climate, 15(20): 2934–2944. doi: 10.1175/1520-0442(2002)015<2934:TIVITG>2.0.CO;2
    [17] Du Yan, Yang Lei, Xie Shangping. 2011. Tropical Indian Ocean influence on northwest Pacific tropical cyclones in summer following strong El Niño. Journal of Climate, 24(1): 315–322. doi: 10.1175/2010JCLI3890.1
    [18] Emanuel K A. 1986. An air–sea interaction theory for tropical cyclones. Part I: steady-state maintenance. Journal of the Atmospheric Sciences, 43(6): 585–605. doi: 10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2
    [19] Emanuel K A. 2013. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proceedings of the National Academy of Sciences of the United States of America, 110(30): 12219–12224. doi: 10.1073/pnas.1301293110
    [20] Emanuel K, Nolan D S. 2004. Tropical cyclone activity and the global climate system. In: Proceedings of the 26th Conference on Hurricanes and Tropical Meteorolgy. Miami, FL: 240–241
    [21] Emanuel K, Sundararajan R, Williams J. 2008. Hurricanes and global warming: results from downscaling IPCC AR4 simulations. Bulletin of the American Meteorological Society, 89(3): 347–368. doi: 10.1175/BAMS-89-3-347
    [22] Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5): 1937–1958. doi: 10.5194/gmd-9-1937-2016
    [23] Frank W M, Ritchie E A. 2001. Effects of vertical wind shear on the intensity and structure of numerically simulated hurricanes. Monthly Weather Review, 129(9): 2249–2269. doi: 10.1175/1520-0493(2001)129<2249:EOVWSO>2.0.CO;2
    [24] Fu Dan, Chang Ping, Patricola C M. 2017. Intrabasin variability of East Pacific tropical cyclones during ENSO regulated by central American gap winds. Scientific Reports, 7: 1658. doi: 10.1038/s41598-017-01962-3
    [25] Gao Si, Zhu Langfeng, Zhang Wei, et al. 2020. Western North Pacific tropical cyclone activity in 2018: a season of extremes. Scientific Reports, 10(1): 5610. doi: 10.1038/s41598-020-62632-5
    [26] Gilford D M, Solomon S, Emanuel K A. 2017. On the seasonal cycles of tropical cyclone potential intensity. Journal of Climate, 30(16): 6085–6096. doi: 10.1175/JCL-D-16-0827.1
    [27] Gray W M. 1979. Hurricanes: their formation, structure and likely role in the tropical circulation. In: Shaw D B, ed. Supplement to Meteorology over the Tropical Oceans. Bracknell: James Glaisher House, 155–218
    [28] Hagedorn R, Doblas-Reyes F J, Palmer T N. 2005. The rationale behind the success of multi-model ensembles in seasonal forecasting - I. basic concept. Tellus A: Dynamic Meteorology and Oceanography, 57(3): 219–233. doi: 10.3402/tellusa.v57i3.14657
    [29] Hersbach H, Bell B, Berrisford P, et al. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999–2049. doi: 10.1002/qj.3803
    [30] Hoesly R M, Smith S J, Feng Leyang, et al. 2018. Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geoscientific Model Development, 11(1): 369–408. doi: 10.5194/gmd-11-369-2018
    [31] Hotelling H. 1940. The selection of variates for use in prediction with some comments on the general problem of nuisance parameters. Annals of Mathematical Statistics, 11(3): 271–283. doi: 10.1214/aoms/1177731867
    [32] Huang Ping, Chou C, Huang Ronghui. 2011. Seasonal modulation of tropical intraseasonal oscillations on tropical cyclone geneses in the western North Pacific. Journal of Climate, 24(24): 6339–6352. doi: 10.1175/2011JCLI4200.1
    [33] Huang Boyin, Thorne P W, Banzon V F, et al. 2017. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): upgrades, validations, and intercomparisons. Journal of Climate, 30(20): 8179–8205. doi: 10.1175/JCLI-D-16-0836.1
    [34] Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3): 437–472. doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
    [35] Kim H M, Webster P J, Curry J A. 2011. Modulation of North Pacific tropical cyclone activity by three phases of ENSO. Journal of Climate, 24(6): 1839–1849. doi: 10.1175/2010JCLI3939.1
    [36] Klotzbach P J. 2014. The Madden-Julian Oscillation's impacts on worldwide tropical cyclone activity. Journal of Climate, 27(6): 2317–2330. doi: 10.1175/JCLI-D-13-00483.1
    [37] Klotzbach P J, Landsea C W. 2015. Extremely intense hurricanes: revisiting Webster et al. (2005) after 10 Years. Journal of Climate, 28(19): 7621–7629. doi: 10.1175/JCLI-D-15-0188.1
    [38] Knapp K R, Kruk M C, Levinson D H, et al. 2010. The International Best Track Archive for Climate Stewardship (IBTrACS): unifying tropical cyclone data. Bulletin of the American Meteorological Society, 91(3): 363–376. doi: 10.1175/2009bams2755.1
    [39] Knutson T R, McBride J L, Chan J, et al. 2010. Tropical cyclones and climate change. Nature Geoscience, 3(3): 157–163. doi: 10.1038/ngeo779
    [40] Knutson T R, Sirutis J J, Garner S T, et al. 2008. Simulated reduction in Atlantic hurricane frequency under twenty-first-century warming conditions. Nature Geoscience, 1(6): 359–364. doi: 10.1038/ngeo202
    [41] Knutson T R, Sirutis J J, Vecchi G A, et al. 2013. Dynamical downscaling projections of twenty-first-century Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. Journal of Climate, 26(17): 6591–6617. doi: 10.1175/JCLI-D-12-00539.1
    [42] Kowaleski A M, Evans J L. 2015. Thermodynamic observations and flux calculations of the tropical cyclone surface layer within the context of potential intensity. Weather and Forecasting, 30(5): 1303–1320. doi: 10.1175/WAF-D-14-00162.1
    [43] Li Chunxiang, Wang Chunzai. 2014. Simulated impacts of two types of ENSO events on tropical cyclone activity in the western North Pacific: large-scale atmospheric response. Climate Dynamics, 42(9-10): 2727–2743. doi: 10.1007/s00382-013-1999-y
    [44] Li R C Y, Zhou Wen. 2013. Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: genesis and intensity. Journal of Climate, 26(9): 2904–2918. doi: 10.1175/JCLI-D-12-00210.1
    [45] Lin Jialin. 2007. The double-ITCZ problem in IPCC AR4 coupled GCMs: ocean-atmosphere feedback analysis. Journal of Climate, 20(18): 4497–4525. doi: 10.1175/JCLI4272.1
    [46] Lin I I, Chan J C L. 2015. Recent decrease in typhoon destructive potential and global warming implications. Nature Communications, 6: 7182. doi: 10.1038/ncomms8182
    [47] Liu K S, Chan J C L. 2008. Interdecadal variability of western North Pacific tropical cyclone tracks. Journal of Climate, 21(17): 4464–4476. doi: 10.1175/2008JCLI2207.1
    [48] Makarieva A M, Gorshkov V G, Nefiodov A V, et al. 2017. Fuel for cyclones: the water vapor budget of a hurricane as dependent on its movement. Atmospheric Research, 193: 216–230. doi: 10.1016/j.atmosres.2017.04.006
    [49] Maloney E D, Hartmann D L. 2001. The Madden-Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: observations. Journal of the Atmospheric Sciences, 58(17): 2545–2558. doi: 10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2
    [50] McPhaden M J, Zebiak S E, Glantz M H. 2006. ENSO as an integrating concept in earth science. Science, 314(5806): 1740–1745. doi: 10.1126/science.1132588
    [51] Meinshausen M, Vogel E, Nauels A, et al. 2017. Historical greenhouse gas concentrations for climate modelling (CMIP6). Geoscientific Model Development, 10(5): 2057–2116. doi: 10.5194/gmd-10-2057-2017
    [52] Menkes C E, Lengaigne M, Marchesiello P, et al. 2012. Comparison of tropical cyclogenesis indices on seasonal to interannual timescales. Climate Dynamics, 38(1-2): 301–321. doi: 10.1007/s00382-011-1126-x
    [53] Merlis T M, Zhao Ming, Held I M. 2013. The sensitivity of hurricane frequency to ITCZ changes and radiatively forced warming in aquaplanet simulations. Geophysical Research Letters, 40(15): 4109–4114. doi: 10.1002/grl.50680
    [54] Mori M, Kimoto M, Ishii M, et al. 2013. Hindcast prediction and near-future projection of tropical cyclone activity over the western North Pacific using CMIP5 near-term experiments with MIROC. Journal of the Meteorological Society of Japan, 91(4): 431–452. doi: 10.2151/jmsj.2013-402
    [55] Murphy A H. 1988. Skill scores based on the mean square error and their relationships to the correlation coefficient. Monthly Weather Review, 116(12): 2417–2424. doi: 10.1175/1520-0493(1988)116<2417:SSBOTM>2.0.CO;2
    [56] Ooyama K V. 1982. Conceptual evolution of the theory and modeling of the tropical cyclone. Journal of the Meteorological Society of Japan, 60(1): 369–380. doi: 10.2151/jmsj1965.60.1_369
    [57] Shen Yixuan, Sun Yuan, Zhong Zhong, et al. 2020. A possible cause of tropical cyclone eastward genesis location bias study using CAM5 model in western North Pacific. Earth and Space Science, 7(1): e2019EA000955. doi: 10.1029/2019EA000955
    [58] Shultz J M, Russell J, Espinel Z. 2005. Epidemiology of tropical cyclones: the dynamics of disaster, disease, and development. Epidemiologic Reviews, 27(1): 21–35. doi: 10.1093/epirev/mxi011
    [59] Song Yajuan, Wang Lei, Lei Xiaoyan, et al. 2015. Tropical cyclone genesis potential index over the western North Pacific simulated by CMIP5 models. Advances in Atmospheric Sciences, 32(11): 1539–1550. doi: 10.1007/s00376-015-4162-3
    [60] Steiger J H. 1980. Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2): 245–251. doi: 10.1037/0033-2909.87.2.245
    [61] Tan Kexin, Huang Ping, Liu Fei, et al. 2019. Simulated ENSO's impact on tropical cyclone genesis over the western North Pacific in CMIP5 models and its changes under global warming. International Journal of Climatology, 39(8): 3668–3678. doi: 10.1002/joc.6031
    [62] Tian Baijun, Dong Xinyu. 2020. The double-ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation. Geophysical Research Letters, 47(8): e2020GL087232. doi: 10.1029/2020GL087232
    [63] Tian Fangxing, Zhou Tianjun, Zhang Lixia. 2013. Tropical cyclone genesis potential index over the western North Pacific simulated by LASG/IAP AGCM. Acta Meteorologica Sinica, 27(1): 50–62. doi: 10.1007/s13351-013-0106-y
    [64] Tippett M K, Camargo S J, Sobel A H. 2011. A Poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis. Journal of Climate, 24(9): 2335–2357. doi: 10.1175/2010JCLI3811.1
    [65] Walsh K, Lavender S, Scoccimarro E, et al. 2013. Resolution dependence of tropical cyclone formation in CMIP3 and finer resolution models. Climate Dynamics, 40(3-4): 585–599. doi: 10.1007/s00382-012-1298-z
    [66] Wang Shuguang, Camargo S J, Sobel A H, et al. 2014. Impact of the tropopause temperature on the intensity of tropical cyclones: an idealized study using a mesoscale model. Journal of the Atmospheric Sciences, 71(11): 4333–4348. doi: 10.1175/JAS-D-14-0029.1
    [67] Wang Bin, Chan J C L. 2002. How strong ENSO events affect tropical storm activity over the western North Pacific. Journal of Climate, 15(13): 1643–1658. doi: 10.1175/1520-0442(2002)015<1643:HSEEAT>2.0.CO;2
    [68] Wang Chunzai, Li Chunxiang, Mu Mu, et al. 2013. Seasonal modulations of different impacts of two types of ENSO events on tropical cyclone activity in the western North Pacific. Climate Dynamics, 40(11-12): 2887–2902. doi: 10.1007/s00382-012-1434-9
    [69] Wang Xidong, Liu Hailong. 2016. PDO modulation of ENSO effect on tropical cyclone rapid intensification in the western North Pacific. Climate Dynamics, 46(1-2): 15–28. doi: 10.1007/s00382-015-2563-8
    [70] Wang Chunzai, Wang Xin. 2013. Classifying El Niño Modoki I and II by different impacts on rainfall in southern china and typhoon tracks. Journal of Climate, 26(4): 1322–1338. doi: 10.1175/JCLI-D-12-00107.1
    [71] Wang Chao, Wang Bin. 2019. Tropical cyclone predictability shaped by western Pacific subtropical high: integration of trans-basin sea surface temperature effects. Climate Dynamics, 53(5-6): 2697–2714. doi: 10.1007/s00382-019-04651-1
    [72] Wang Chunzai, Weisberg R H, Virmani J I. 1999. Western Pacific interannual variability associated with the El Niño-Southern Oscillation. Journal of Geophysical Research: Oceans, 104(C3): 5131–5149. doi: 10.1029/1998JC900090
    [73] Wang Bin, Yang Yuxing, Ding Qinghua, et al. 2010. Climate control of the global tropical storm days (1965–2008). Geophysical Research Letters, 37(7): L07704. doi: 10.1029/2010GL042487
    [74] Watterson I G, Evans J L, Ryan B F. 1995. Seasonal and interannual variability of tropical cyclogenesis: diagnostics from large-scale fields. Journal of Climate, 8(12): 3052–3066. doi: 10.1175/1520-0442(1995)008<3052:SAIVOT>2.0.CO;2
    [75] Wengel C, Dommenget D, Latif M, et al. 2018. What controls ENSO-amplitude diversity in climate models?. Geophysical Research Letters, 45(4): 1989–1996. doi: 10.1002/2017GL076849
    [76] Wing A A, Emanuel K, Solomon S. 2015. On the factors affecting trends and variability in tropical cyclone potential intensity. Geophysical Research Letters, 42(20): 8669–8677. doi: 10.1002/2015GL066145
    [77] Wu Qiong, Zhao Jiuwei, Zhan Ruifen, et al. 2021. Revisiting the interannual impact of the Pacific Meridional Mode on tropical cyclone genesis frequency in the western North Pacific. Climate Dynamics, 56(3-4): 1003–1015. doi: 10.1007/s00382-020-05515-9
    [78] Yamasaki M. 2007. A view on tropical cyclones as CISK. Journal of the Meteorological Society of Japan, 85B: 145–164. doi: 10.2151/jmsj.85B.145
    [79] Yan Qing, Korty R, Zhang Zhongshi, et al. 2019. Evolution of tropical cyclone genesis regions during the Cenozoic era. Nature Communications, 10(1): 3076. doi: 10.1038/s41467-019-11110-2
    [80] Yang Lei, Chen Sheng, Wang Chunzai, et al. 2018a. Potential impact of the Pacific Decadal Oscillation and sea surface temperature in the tropical Indian Ocean-Western Pacific on the variability of typhoon landfall on the China coast. Climate Dynamics, 51(7-8): 2695–2705. doi: 10.1007/s00382-017-4037-7
    [81] Yang Mengmiao, Zhang G J, Sun Dezheng. 2018b. Precipitation and moisture in four leading CMIP5 models: biases across large-scale circulation regimes and their attribution to dynamic and thermodynamic factors. Journal of Climate, 31(13): 5089–5106. doi: 10.1175/JCLI-D-17-0718.1
    [82] Yokoi S, Takayabu Y N, Chan J C L. 2009. Tropical cyclone genesis frequency over the western North Pacific simulated in medium-resolution coupled general circulation models. Climate Dynamics, 33(5): 665–683. doi: 10.1007/s00382-009-0593-9
    [83] Yonekura E, Hall T M. 2011. A statistical model of tropical cyclone tracks in the western North Pacific with ENSO-dependent cyclogenesis. Journal of Applied Meteorology and Climatology, 50(8): 1725–1739. doi: 10.1175/2011JAMC2617.1
    [84] Yonekura E, Hall T M. 2014. ENSO effect on East Asian tropical cyclone landfall via changes in tracks and genesis in a statistical model. Journal of Applied Meteorology and Climatology, 53(2): 406–420. doi: 10.1175/JAMC-D-12-0240.1
    [85] Zhang G J, Song Xiaoliang, Wang Yong. 2019. The double ITCZ syndrome in GCMs: a coupled feedback problem among convection, clouds, atmospheric and ocean circulations. Atmospheric Research, 229: 255–268. doi: 10.1016/j.atmosres.2019.06.023
  • 加载中
图(11) / 表(3)
计量
  • 文章访问数:  228
  • HTML全文浏览量:  88
  • PDF下载量:  16
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-03-05
  • 录用日期:  2021-05-08
  • 网络出版日期:  2022-01-17

目录

    /

    返回文章
    返回