January  2017, 11(2): 107-116. doi: 10.1007/s13351-017-6088-4

How the “Best” CMIP5 Models Project Relations of Asian–Pacific Oscillation to Circulation Backgrounds Favorable for Tropical Cyclone Genesis over the Western North Pacific

1. 

National Climate Center, China Meteorological Administration, Beijing 100081

2. 

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044

zhoubt@cma.gov.cn

Received  May 24, 2016 Published  February 2017

Based on the simulations of 32 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the present study assesses their capacity to simulate the relationship of the summer Asian–Pacific Oscillation (APO) with the vertical zonal wind shear, low-level atmospheric vorticity, mid-level humidity, atmospheric divergence in the lower and upper troposphere, and western Pacific subtropical high (WPSH) that are closely associated with the genesis of tropical cyclones over the western North Pacific. The results indicate that five models can simultaneously reproduce the observed pattern with the positive APO phase accompanied by weak vertical zonal wind shear, strengthened vorticity in the lower troposphere, increased mid-level humidity, intensified low-level convergence and high-level divergence, and a northward-located WPSH over the western North Pacific. These five models are further used to project their potential relationship under the RCP8.5 scenario during 2050–2099. Compared to 1950–1999, the relationship between the APO and the vertical zonal wind shear is projected to weaken by both the multi-model ensemble and the individual models. Its linkage to the low-level vorticity, mid-level humidity, atmospheric divergence in the lower and upper troposphere, and the northward–southward movement of the WPSH would also reduce slightly but still be significant. However, the individual models show relatively large differences in projecting the linkage between the APO and the mid-level humidity and low-level divergence.
Citation: Botao ZHOU, Ying XU. How the “Best” CMIP5 Models Project Relations of Asian–Pacific Oscillation to Circulation Backgrounds Favorable for Tropical Cyclone Genesis over the Western North Pacific. Inverse Problems & Imaging, 2017, 11 (2) : 107-116. doi: 10.1007/s13351-017-6088-4
References:
[1]

Chen, L. S., and Y. H. Ding, 1979: Summary of Tropical Cyclones over the Western North Pacific. Science Press, Beijing, 491 pp. (in Chinese) Google Scholar

[2]

Chen G., Interdecadal variation of tropical cyclone activity in association with summer monsoon, sea surface temperature over the western North Pacific, Chinese Sci. Bull., 54 (2009), 1417-1421.  doi: 10.1007/s11434-008-0564-2.  Google Scholar

[3]

Cui X.Zhou B. T. and Fan K., Linkage between Asian-Pacific oscillation and the large-scale atmospheric circulations related to the tropical cyclone frequency over the western North Pacific in Bergen climate model, Climatic Environ. Res., 15 (2010), 120-128.   Google Scholar

[4]

Ding Y. H. and Reiter E. R., Large-scale circulation influencing the typhoon formation over the western Pacific, Acta Oceanol. Sin., 5 (1983), 561-574.   Google Scholar

[5]

Fan K., New predictors and a new prediction model for the typhoon frequency over western North Pacific, Sci. China: Earth Sci., 50 (2007), 1417-1423.  doi: 10.1007/s11430-007-0105-x.  Google Scholar

[6]

Fan K. and Wang H. J., A new approach to forecasting typhoon frequency over the western North Pacific, Wea. Forecasting, 24 (2009), 974-986.  doi: 10.1175/2009WAF2222194.1.  Google Scholar

[7]

Gray W. M., Global view of the origin of tropical disturbances and storms, Mon. Wea. Rev., 96 (1968), 669-700.   Google Scholar

[8]

Ho C. H.Kim J. H. and Kim H. S., Possible influence of the Antarctic Oscillation on tropical cyclone activity in the western North Pacific, J. Geophys. Res., 110 (2005), D19104.  doi: 10.1029/2005JD005766.  Google Scholar

[9]

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., D. Qin, G. K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Google Scholar

[10]

Kalnay E.Kanamitsu M. and Kistler R., The NCEP/NCAR 40-yr reanalysis project, Bull. Amer. Meteor. Soc., 77 (1996), 437-471.   Google Scholar

[11]

Kidston J. and Gerber E. P., Intermodel variability of the poleward shift of the austral jet stream in the CMIP3 integrations linked to biases in 20th century climatology, Geophys. Res. Lett., 37 (2010), L09708.  doi: 10.1029/2010GL042873.  Google Scholar

[12]

Lander M. A., Specific tropical cyclone track types and unusual tropical cyclone motions associated with a reverse-oriented monsoon trough in the western North Pacific, Wea. Forecasting, 11 (1996), 170-186.   Google Scholar

[13]

Liebmann B.Hendon H. H. and Glick J. D., The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation, J. Meteor. Soc. Japan, 72 (1994), 401-412.   Google Scholar

[14]

Moss R. H.Edmonds J. A. and Hibbard K. A., The next generation of scenarios for climate change research and assessment, Nature, 463 (2010), 747-756.  doi: 10.1038/nature08823.  Google Scholar

[15]

Sun J. Q. and Chen H. P., Predictability of western North Pacific typhoon activity and its factors using DEMETER coupled models, Chinese Sci. Bull., 56 (2011), 3474-3479.  doi: 10.1007/s11434-011-4640-7.  Google Scholar

[16]

Taylor K. E.Stouffer B. J. and Meehl G. A., An overview of CMIP5 and the experiment design, Bull. Amer. Meteor. Soc., 93 (2012), 485-498.  doi: 10.1175/BAMS-D-11-00094.1.  Google Scholar

[17]

Wang H. J. and Fan K., Relationship between the Antarctic Oscillation in the western North Pacific and typhoon frequency, Chinese Sci. Bull., 52 (2007), 561-565.  doi: 10.1007/s11434-007-0040-4.  Google Scholar

[18]

Wang H. J.Sun J. Q. and Fan K., Relationships between the North Pacific Oscillation and the typhoon/hurricane frequencies, Sci. China: Earth Sci., 50 (2007), 1409-1416.  doi: 10.1007/s11430-007-0097-6.  Google Scholar

[19]

Zhang Q. Y. and Peng J. B., The interannual and interdecadal variations of East Asian summer circulation and its impact on the landing typhoon frequency over China during summer, Chinese J. Atmos. Sci., 27 (2003), 97-106.   Google Scholar

[20]

Zhang Q. Y.Tao S. Y. and Chen L. T., The inter-annual variability of East Asian summer monsoon indices and its association with the pattern of general circulation over East Asia, Acta Meteor. Sinica, 61 (2003), 559-568.   Google Scholar

[21]

Zhao P.Zhu Y. N. and Zhang R. H., An Asian–Pacific teleconnection in summer tropospheric temperature and associated Asian climate variability, Climate Dyn., 29 (2007), 293-303.  doi: 10.1007/s00382-007-0236-y.  Google Scholar

[22]

Zhou B. T., The Asian–Pacific Oscillation pattern in CMIP5 simulations of historical and future climate, Int. J. Climatol., 36 (2016), 4778-4789.  doi: 10.1002/joc.4668.  Google Scholar

[23]

Zhou B. T. and Cui X., Hadley circulation signal in the tropical cyclone frequency over the western North Pacific, J. Geophys. Res., 113 (2008), D16107.  doi: 10.1029/2007JD009156.  Google Scholar

[24]

Zhou B. T. and Cui X., Interdecadal change of the linkage between the North Atlantic Oscillation and the tropical cyclone frequency over the western North Pacific, Sci. China: Earth Sci., 57 (2014), 2148-2155.  doi: 10.1007/s11430-014-4862-z.  Google Scholar

[25]

Zhou B. T.Cui X. and Zhao P., Relationship between the Asian–Pacific Oscillation and the tropical cyclone frequency in the western North Pacific, Sci. China: Earth Sci., 51 (2008), 380-385.  doi: 10.1007/s11430-008-0014-7.  Google Scholar

show all references

References:
[1]

Chen, L. S., and Y. H. Ding, 1979: Summary of Tropical Cyclones over the Western North Pacific. Science Press, Beijing, 491 pp. (in Chinese) Google Scholar

[2]

Chen G., Interdecadal variation of tropical cyclone activity in association with summer monsoon, sea surface temperature over the western North Pacific, Chinese Sci. Bull., 54 (2009), 1417-1421.  doi: 10.1007/s11434-008-0564-2.  Google Scholar

[3]

Cui X.Zhou B. T. and Fan K., Linkage between Asian-Pacific oscillation and the large-scale atmospheric circulations related to the tropical cyclone frequency over the western North Pacific in Bergen climate model, Climatic Environ. Res., 15 (2010), 120-128.   Google Scholar

[4]

Ding Y. H. and Reiter E. R., Large-scale circulation influencing the typhoon formation over the western Pacific, Acta Oceanol. Sin., 5 (1983), 561-574.   Google Scholar

[5]

Fan K., New predictors and a new prediction model for the typhoon frequency over western North Pacific, Sci. China: Earth Sci., 50 (2007), 1417-1423.  doi: 10.1007/s11430-007-0105-x.  Google Scholar

[6]

Fan K. and Wang H. J., A new approach to forecasting typhoon frequency over the western North Pacific, Wea. Forecasting, 24 (2009), 974-986.  doi: 10.1175/2009WAF2222194.1.  Google Scholar

[7]

Gray W. M., Global view of the origin of tropical disturbances and storms, Mon. Wea. Rev., 96 (1968), 669-700.   Google Scholar

[8]

Ho C. H.Kim J. H. and Kim H. S., Possible influence of the Antarctic Oscillation on tropical cyclone activity in the western North Pacific, J. Geophys. Res., 110 (2005), D19104.  doi: 10.1029/2005JD005766.  Google Scholar

[9]

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., D. Qin, G. K. Plattner, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. Google Scholar

[10]

Kalnay E.Kanamitsu M. and Kistler R., The NCEP/NCAR 40-yr reanalysis project, Bull. Amer. Meteor. Soc., 77 (1996), 437-471.   Google Scholar

[11]

Kidston J. and Gerber E. P., Intermodel variability of the poleward shift of the austral jet stream in the CMIP3 integrations linked to biases in 20th century climatology, Geophys. Res. Lett., 37 (2010), L09708.  doi: 10.1029/2010GL042873.  Google Scholar

[12]

Lander M. A., Specific tropical cyclone track types and unusual tropical cyclone motions associated with a reverse-oriented monsoon trough in the western North Pacific, Wea. Forecasting, 11 (1996), 170-186.   Google Scholar

[13]

Liebmann B.Hendon H. H. and Glick J. D., The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation, J. Meteor. Soc. Japan, 72 (1994), 401-412.   Google Scholar

[14]

Moss R. H.Edmonds J. A. and Hibbard K. A., The next generation of scenarios for climate change research and assessment, Nature, 463 (2010), 747-756.  doi: 10.1038/nature08823.  Google Scholar

[15]

Sun J. Q. and Chen H. P., Predictability of western North Pacific typhoon activity and its factors using DEMETER coupled models, Chinese Sci. Bull., 56 (2011), 3474-3479.  doi: 10.1007/s11434-011-4640-7.  Google Scholar

[16]

Taylor K. E.Stouffer B. J. and Meehl G. A., An overview of CMIP5 and the experiment design, Bull. Amer. Meteor. Soc., 93 (2012), 485-498.  doi: 10.1175/BAMS-D-11-00094.1.  Google Scholar

[17]

Wang H. J. and Fan K., Relationship between the Antarctic Oscillation in the western North Pacific and typhoon frequency, Chinese Sci. Bull., 52 (2007), 561-565.  doi: 10.1007/s11434-007-0040-4.  Google Scholar

[18]

Wang H. J.Sun J. Q. and Fan K., Relationships between the North Pacific Oscillation and the typhoon/hurricane frequencies, Sci. China: Earth Sci., 50 (2007), 1409-1416.  doi: 10.1007/s11430-007-0097-6.  Google Scholar

[19]

Zhang Q. Y. and Peng J. B., The interannual and interdecadal variations of East Asian summer circulation and its impact on the landing typhoon frequency over China during summer, Chinese J. Atmos. Sci., 27 (2003), 97-106.   Google Scholar

[20]

Zhang Q. Y.Tao S. Y. and Chen L. T., The inter-annual variability of East Asian summer monsoon indices and its association with the pattern of general circulation over East Asia, Acta Meteor. Sinica, 61 (2003), 559-568.   Google Scholar

[21]

Zhao P.Zhu Y. N. and Zhang R. H., An Asian–Pacific teleconnection in summer tropospheric temperature and associated Asian climate variability, Climate Dyn., 29 (2007), 293-303.  doi: 10.1007/s00382-007-0236-y.  Google Scholar

[22]

Zhou B. T., The Asian–Pacific Oscillation pattern in CMIP5 simulations of historical and future climate, Int. J. Climatol., 36 (2016), 4778-4789.  doi: 10.1002/joc.4668.  Google Scholar

[23]

Zhou B. T. and Cui X., Hadley circulation signal in the tropical cyclone frequency over the western North Pacific, J. Geophys. Res., 113 (2008), D16107.  doi: 10.1029/2007JD009156.  Google Scholar

[24]

Zhou B. T. and Cui X., Interdecadal change of the linkage between the North Atlantic Oscillation and the tropical cyclone frequency over the western North Pacific, Sci. China: Earth Sci., 57 (2014), 2148-2155.  doi: 10.1007/s11430-014-4862-z.  Google Scholar

[25]

Zhou B. T.Cui X. and Zhao P., Relationship between the Asian–Pacific Oscillation and the tropical cyclone frequency in the western North Pacific, Sci. China: Earth Sci., 51 (2008), 380-385.  doi: 10.1007/s11430-008-0014-7.  Google Scholar

Figure 1.  Observed correlations of vertical zonal wind shear with (a) tropical cyclone frequency over the western North Pacific and (b) Asian–Pacific Oscillation. Heavy (Light) shading indicates areas above the 95% (90%) confidence level. The dashed rectangle represents the key regions selected.
Figure 2.  Observed correlations of (a, b) 850-hPa vorticity and (c, d) 700–500-hPa averaged specific humidity with tropical cyclone frequency over the (a, c) western North Pacific and (b, d) Asian–Pacific Oscillation. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 3.  Observed correlations of atmospheric divergence at (a, b) 1000 hPa and (c, d) 150 hPa with TC frequency over the (a, c) western North Pacific and (b, d) Asian–Pacific Oscillation. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 4.  Observed correlations of 850-hPa horizontal winds with (a) tropical cyclone frequency over the western North Pacific and (b) Asian–Pacific Oscillation, which are shown as arrows. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 5.  Correlation coefficients of the Asian–Pacific Oscillation with (a) VWS, (b) VOR850, (c) HUM, (d) DIV1000, (e) DIV150, and (f) UV850 (see Section 3, paragraph 3, of the main text for definitions of these indices) in the historical simulation and observation. Correlations of tropical cyclone frequency over the western North Pacific with the six indices are also shown.
Figure 6.  MME simulated correlations between the Asian–Pacific Oscillation and vertical zonal wind shear in the (a) historical and (b) RCP8.5 simulations. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 7.  MME simulated correlations between the Asian–Pacific Oscillation and (a, b) vorticity at 850 hPa and (c, d) specific humidity averaged from 700 to 500 hPa, in the (a, c) historical and (b, d) RCP8.5 simulations. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 8.  MME simulated correlations between the Asian–Pacific Oscillation and atmospheric divergence at (a, b) 1000 and (c, d) 150 hPa, in the (a, c) historical and (b, d) RCP8.5 simulations. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 9.  MME simulated correlation between the Asian–Pacific Oscillation and 850-hPa horizontal winds (shown as arrows) in the (a) historical and (b) RCP8.5 simulations. Heavy (light) shading indicates areas above the 95% (90%) confidence level.
Figure 10.  Correlation coefficients of the Asian–Pacific Oscillation with (a) VWS, (b) VOR850, (c) HUM, (d) DIV1000, (e) DIV150, and (f) UV850 (see Section 3, paragraph 3, of the main text for definitions of these indices). Red bars represent 2050–2099 under RCP8.5 and blue bars represent 1950–1999 in the historical simulation.
Table 1.  Basic information on the 32 CMIP5 models used in this study
Model Modeling group Atmospheric resolution (lon. × lat.)
ACCESS1.0 Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BoM), Australia 192 × 145
ACCESS1.3 CSIRO and BoM, Australia 192 × 145
BCC_CSM1.1 Beijing Climate Center (BCC), China Meteorological Administration (CMA), China 128 × 64
BCC_CSM1.1(m) BCC, CMA, China 320 × 160
BNU-ESM Beijing Normal University, China 128 × 64
CanESM2 Canadian Centre for Climate Modeling and Analysis, Canada 128 × 64
CCSM4 National Center for Atmosphere Research, United States 288 × 192
CMCC-CM Euro-Mediterranean Center on Climate Change (CMCC), Italy 480 × 240
CMCC-CMS CMCC, Italy 192 × 96
CNRM-CM5 Centre National de Recherches Météorologiques–Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, France 256 × 128
CSIRO Mk3.6.0 Queensland Climate Change Centre of Excellence and CSIRO, Australia 192 × 96
FGOALS-g2 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, China 128 × 60
FIO-ESM First Institute of Oceanography, China 128 × 64
GFDL CM3 NOAA Geophysical Fluid Dynamics Laboratory (GFDL), United States 144 × 90
GFDL-ESM2G NOAA GFDL, United States 144 × 90
GFDL-ESM2M NOAA GFDL, United States 144 × 90
GISS-E2-H NASA Goddard Institute for Space Studies (GISS), United States 144 × 90
GISS-E2-R NASA GISS, United States 144 × 90
HadGEM2-AO UK Met Office (UKMO) Hadley Centre, United Kingdom 192 × 144
HadGEM2-CC UKMO Hadley Centre, United Kingdom 192 × 144
HadGEM2-ES UKMO Hadley Centre, United Kingdom 192 × 144
INM-CM4.0 Institute for Numerical Mathematics, Russia 180 × 120
IPSL-CM5A-LR Institute Pierre-Simon Laplace (IPSL), France 96 × 96
IPSL-CM5A-MR IPSL, France 144 × 143
IPSL-CM5B-LR IPSL, France 96 × 96
MIROC5 Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 256 × 128
MIROC-ESM Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 128 × 64
MIROC-ESM-CHEM Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 128 × 64
MPI-ESM-LR Max Planck Institute for Meteorology, Germany 192 × 96
MRI-CGCM3 Meteorological Research Institute, Japan 320 × 160
NorESM1-M Norwegian Climate Centre, Norway 144 × 96
NorESM1-ME Norwegian Climate Centre, Norway 144 × 96
Model Modeling group Atmospheric resolution (lon. × lat.)
ACCESS1.0 Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BoM), Australia 192 × 145
ACCESS1.3 CSIRO and BoM, Australia 192 × 145
BCC_CSM1.1 Beijing Climate Center (BCC), China Meteorological Administration (CMA), China 128 × 64
BCC_CSM1.1(m) BCC, CMA, China 320 × 160
BNU-ESM Beijing Normal University, China 128 × 64
CanESM2 Canadian Centre for Climate Modeling and Analysis, Canada 128 × 64
CCSM4 National Center for Atmosphere Research, United States 288 × 192
CMCC-CM Euro-Mediterranean Center on Climate Change (CMCC), Italy 480 × 240
CMCC-CMS CMCC, Italy 192 × 96
CNRM-CM5 Centre National de Recherches Météorologiques–Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, France 256 × 128
CSIRO Mk3.6.0 Queensland Climate Change Centre of Excellence and CSIRO, Australia 192 × 96
FGOALS-g2 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, China 128 × 60
FIO-ESM First Institute of Oceanography, China 128 × 64
GFDL CM3 NOAA Geophysical Fluid Dynamics Laboratory (GFDL), United States 144 × 90
GFDL-ESM2G NOAA GFDL, United States 144 × 90
GFDL-ESM2M NOAA GFDL, United States 144 × 90
GISS-E2-H NASA Goddard Institute for Space Studies (GISS), United States 144 × 90
GISS-E2-R NASA GISS, United States 144 × 90
HadGEM2-AO UK Met Office (UKMO) Hadley Centre, United Kingdom 192 × 144
HadGEM2-CC UKMO Hadley Centre, United Kingdom 192 × 144
HadGEM2-ES UKMO Hadley Centre, United Kingdom 192 × 144
INM-CM4.0 Institute for Numerical Mathematics, Russia 180 × 120
IPSL-CM5A-LR Institute Pierre-Simon Laplace (IPSL), France 96 × 96
IPSL-CM5A-MR IPSL, France 144 × 143
IPSL-CM5B-LR IPSL, France 96 × 96
MIROC5 Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 256 × 128
MIROC-ESM Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 128 × 64
MIROC-ESM-CHEM Atmosphere and Ocean Research Institute (University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan 128 × 64
MPI-ESM-LR Max Planck Institute for Meteorology, Germany 192 × 96
MRI-CGCM3 Meteorological Research Institute, Japan 320 × 160
NorESM1-M Norwegian Climate Centre, Norway 144 × 96
NorESM1-ME Norwegian Climate Centre, Norway 144 × 96
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