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, such as the power dissipation index (PDI; Emanuel 2005a ) and the accumulated cyclone energy (ACE; Bell et al. 2000 ). Our focus here is on interannual–decadal variability of seasonal TC track density. The seasonal track density can be considered as a combination of seasonal TC counts, spatial distribution of TC genesis, and subsequent tracks, but it has not received enough attention as its three contributors. Numerous studies have shown that, in the NA, TC genesis and the associated seasonal TC
, such as the power dissipation index (PDI; Emanuel 2005a ) and the accumulated cyclone energy (ACE; Bell et al. 2000 ). Our focus here is on interannual–decadal variability of seasonal TC track density. The seasonal track density can be considered as a combination of seasonal TC counts, spatial distribution of TC genesis, and subsequent tracks, but it has not received enough attention as its three contributors. Numerous studies have shown that, in the NA, TC genesis and the associated seasonal TC
linked to variations in large-scale flow patterns. They attribute part of the decadal variability in TC track density to the PDO. On interannual time scales, owing to the influence of ENSO on the position of TC genesis and tracks, it is natural to look into its effect on TC track density. Indeed, Wang and Chan (2002) find that during strong El Niño years, TC track density almost doubles that in strong La Niña years. Recently, there is much debate about the two types of ENSO: the central Pacific (CP
linked to variations in large-scale flow patterns. They attribute part of the decadal variability in TC track density to the PDO. On interannual time scales, owing to the influence of ENSO on the position of TC genesis and tracks, it is natural to look into its effect on TC track density. Indeed, Wang and Chan (2002) find that during strong El Niño years, TC track density almost doubles that in strong La Niña years. Recently, there is much debate about the two types of ENSO: the central Pacific (CP
interannual-to-decadal Atlantic meridional mode (AMM) ( Vimont and Kossin 2007 ; Kossin and Vimont 2007 ), which describes the meridional gradient between northern and southern tropical Atlantic SST ( Chang et al. 1997 ; Servain et al. 1999 ; Chiang and Vimont 2004 ), and the Atlantic multidecadal oscillation (AMO) ( Landsea et al. 1999 ; Goldenberg et al. 2001 ; Vitart and Anderson 2001 ), which describes North Atlantic SST variability. Different phases of the AMO can dampen or amplify the effect of
interannual-to-decadal Atlantic meridional mode (AMM) ( Vimont and Kossin 2007 ; Kossin and Vimont 2007 ), which describes the meridional gradient between northern and southern tropical Atlantic SST ( Chang et al. 1997 ; Servain et al. 1999 ; Chiang and Vimont 2004 ), and the Atlantic multidecadal oscillation (AMO) ( Landsea et al. 1999 ; Goldenberg et al. 2001 ; Vitart and Anderson 2001 ), which describes North Atlantic SST variability. Different phases of the AMO can dampen or amplify the effect of
observations (11.7). In contrast, the climatology in the GISS model (6.2) is only about a half of the observations while the GFS model (22.0) has double the number in observations. The strength of the interannual variability in the GSFC and GFS models is comparable to observations and weaker in the other models and the MME. The linear trends in all models (~2 TCs decade −1 ) are weaker than in the observations (~4 TCs decade −1 ). AC is highest for the MME (0.86), followed by the GFDL (0.74) and GFS (0
observations (11.7). In contrast, the climatology in the GISS model (6.2) is only about a half of the observations while the GFS model (22.0) has double the number in observations. The strength of the interannual variability in the GSFC and GFS models is comparable to observations and weaker in the other models and the MME. The linear trends in all models (~2 TCs decade −1 ) are weaker than in the observations (~4 TCs decade −1 ). AC is highest for the MME (0.86), followed by the GFDL (0.74) and GFS (0
. Botzet , and M. Esch , 1996 : Will greenhouse gas-induced warming over the next 50 years lead to higher frequency and greater intensity of hurricanes? Tellus , 48A , 57 – 73 , https://doi.org/10.3402/tellusa.v48i1.11632 . 10.3402/tellusa.v48i1.11632 Burgman , R. J. , A. C. Clement , C. M. Mitas , J. Chen , and K. Esslinger , 2008 : Evidence for atmospheric variability over the Pacific on decadal timescales . Geophys. Res. Lett. , 35 , L01704 , https://doi.org/10
. Botzet , and M. Esch , 1996 : Will greenhouse gas-induced warming over the next 50 years lead to higher frequency and greater intensity of hurricanes? Tellus , 48A , 57 – 73 , https://doi.org/10.3402/tellusa.v48i1.11632 . 10.3402/tellusa.v48i1.11632 Burgman , R. J. , A. C. Clement , C. M. Mitas , J. Chen , and K. Esslinger , 2008 : Evidence for atmospheric variability over the Pacific on decadal timescales . Geophys. Res. Lett. , 35 , L01704 , https://doi.org/10
cyclone frequency over the western North Pacific . Geophys. Res. Lett. , 37 , L01803 , doi: 10.1029/2009GL041708 . Chen , J.-H. , and S.-J. Lin , 2011 : The remarkable predictability of inter-annual variability of Atlantic hurricanes during the past decade . Geophys. Res. Lett. , 38 , L11804 , doi: 10.1029/2011GL047629 . Chen , J.-H. , and S.-J. Lin , 2013 : Seasonal predictions of tropical cyclones using a 25-km-resolution general circulation model . J. Climate , 26 , 380 – 398
cyclone frequency over the western North Pacific . Geophys. Res. Lett. , 37 , L01803 , doi: 10.1029/2009GL041708 . Chen , J.-H. , and S.-J. Lin , 2011 : The remarkable predictability of inter-annual variability of Atlantic hurricanes during the past decade . Geophys. Res. Lett. , 38 , L11804 , doi: 10.1029/2011GL047629 . Chen , J.-H. , and S.-J. Lin , 2013 : Seasonal predictions of tropical cyclones using a 25-km-resolution general circulation model . J. Climate , 26 , 380 – 398
. Takayabu , 2013 : Attribution of decadal variability in tropical cyclone passage frequency over the western North Pacific: A new approach emphasizing the genesis location of cyclones . J. Climate , 26 , 973 – 987 . Yokoi , S. , C. Takahashi , K. Yasunaga , and R. Shirooka , 2012 : Multi-model projection of tropical cyclone genesis frequency over the western North Pacific: CMIP5 results . SOLA , 8 , 137 – 140 . Yukimoto , S. , and Coauthors , 2011 : Meteorological Research
. Takayabu , 2013 : Attribution of decadal variability in tropical cyclone passage frequency over the western North Pacific: A new approach emphasizing the genesis location of cyclones . J. Climate , 26 , 973 – 987 . Yokoi , S. , C. Takahashi , K. Yasunaga , and R. Shirooka , 2012 : Multi-model projection of tropical cyclone genesis frequency over the western North Pacific: CMIP5 results . SOLA , 8 , 137 – 140 . Yukimoto , S. , and Coauthors , 2011 : Meteorological Research
1. Introduction Every year, tropical cyclones (TCs) are responsible for a large number of fatalities and vast economic damages (e.g., Rappaport 2000 ; Pielke et al. 2008 ; Zhang et al. 2009 ; Jonkman et al. 2009 ; Czajkowski et al. 2011 , 2013 ; Mendelsohn et al. 2012 ; Peduzzi et al. 2012 ). To compound these TC hazards, over the last few decades there has been an increase in population and infrastructure in global coastal regions resulting in increased TC-related damages (e
1. Introduction Every year, tropical cyclones (TCs) are responsible for a large number of fatalities and vast economic damages (e.g., Rappaport 2000 ; Pielke et al. 2008 ; Zhang et al. 2009 ; Jonkman et al. 2009 ; Czajkowski et al. 2011 , 2013 ; Mendelsohn et al. 2012 ; Peduzzi et al. 2012 ). To compound these TC hazards, over the last few decades there has been an increase in population and infrastructure in global coastal regions resulting in increased TC-related damages (e
.5 scenario used by the D5 models). For CMIP3, we study the response in individual A1B models (CCCma CGCM3.1, ECHAM5, GFDL CM2.1, GFDL CM2.0, HadCM3, HadGEM1, MIROC3.2, and MRI-CGCM2.3.2) as well as the response to the multimodel mean SST increase. Because each set of simulations includes different models and different forcing scenarios, a direct comparison of the results incorporates variability arising from different projections of twenty-first-century climate. We refer to HiRAM forced with the CMIP5
.5 scenario used by the D5 models). For CMIP3, we study the response in individual A1B models (CCCma CGCM3.1, ECHAM5, GFDL CM2.1, GFDL CM2.0, HadCM3, HadGEM1, MIROC3.2, and MRI-CGCM2.3.2) as well as the response to the multimodel mean SST increase. Because each set of simulations includes different models and different forcing scenarios, a direct comparison of the results incorporates variability arising from different projections of twenty-first-century climate. We refer to HiRAM forced with the CMIP5
1. Introduction There has been a concerted effort over the last decade or so to understand the response of tropical cyclones (TCs) to a warming climate (e.g., Knutson et al. 2010 ; Walsh et al. 2016 ). TCs are a particularly hazardous natural phenomenon because of their extreme winds, flooding from torrential rainfall, and coastal inundation from storm surges. The Southern Hemisphere is home to about 30% of the roughly 80 TCs that form around the globe each year, with genesis locations and
1. Introduction There has been a concerted effort over the last decade or so to understand the response of tropical cyclones (TCs) to a warming climate (e.g., Knutson et al. 2010 ; Walsh et al. 2016 ). TCs are a particularly hazardous natural phenomenon because of their extreme winds, flooding from torrential rainfall, and coastal inundation from storm surges. The Southern Hemisphere is home to about 30% of the roughly 80 TCs that form around the globe each year, with genesis locations and
