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, variability, and change with global warming. The main difference is that HiRAM2.2 incorporates a new land model [GFDL land model version 3 (LM3)]. The atmospheric dynamical core of the model was also updated to improve efficiency and stability. As a result of these changes, there are minor retunings of the atmospheric parameters in the cloud and surface boundary layer parameterizations necessary to achieve the top-of-atmosphere (TOA) radiative balance. This model is also the version of HiRAM used for the
, variability, and change with global warming. The main difference is that HiRAM2.2 incorporates a new land model [GFDL land model version 3 (LM3)]. The atmospheric dynamical core of the model was also updated to improve efficiency and stability. As a result of these changes, there are minor retunings of the atmospheric parameters in the cloud and surface boundary layer parameterizations necessary to achieve the top-of-atmosphere (TOA) radiative balance. This model is also the version of HiRAM used for the
for Global Environmental Risk (UPSCALE) project ran the Met Office Unified Model (MetUM), using a forced atmosphere–land configuration named Global Atmosphere 3.0 (GA3.0; Walters et al. 2011 ), on the Cray XE6 supercomputer Hermit at the High Performance Computing Center Stuttgart (HLRS) in Stuttgart, Germany. Using a hierarchy of models with midlatitude resolutions of N96 (130 km), N216 (60 km), and N512 (25 km) with consistent physics and dynamics settings, our goal was to investigate the
for Global Environmental Risk (UPSCALE) project ran the Met Office Unified Model (MetUM), using a forced atmosphere–land configuration named Global Atmosphere 3.0 (GA3.0; Walters et al. 2011 ), on the Cray XE6 supercomputer Hermit at the High Performance Computing Center Stuttgart (HLRS) in Stuttgart, Germany. Using a hierarchy of models with midlatitude resolutions of N96 (130 km), N216 (60 km), and N512 (25 km) with consistent physics and dynamics settings, our goal was to investigate the
1. Introduction The effects of anthropogenic warming on tropical cyclone (TC) activity are critical for estimating the future costs of climate-related socioeconomic impacts. Recently, many studies have attempted to address future changes in TC activity using high-resolution atmospheric general circulation models (AGCMs) (e.g., Zhao et al. 2009 ; Bender et al. 2010 ; Murakami et al. 2012b ; Knutson et al. 2013 ), atmosphere–ocean coupled general circulation models (CGCMs) (e.g., Yokoi et al
1. Introduction The effects of anthropogenic warming on tropical cyclone (TC) activity are critical for estimating the future costs of climate-related socioeconomic impacts. Recently, many studies have attempted to address future changes in TC activity using high-resolution atmospheric general circulation models (AGCMs) (e.g., Zhao et al. 2009 ; Bender et al. 2010 ; Murakami et al. 2012b ; Knutson et al. 2013 ), atmosphere–ocean coupled general circulation models (CGCMs) (e.g., Yokoi et al
approach uses relative thresholds that are adjusted model to model or basin to basin. This approach is motivated by the assumption that TCs represent the extreme tails of the distributions in relevant variables, and that the position of TCs in these distributions (in terms of standard deviations from the mean) will remain the same in different models, even if the distributions themselves are substantially different. By design, these schemes produce a fairly realistic present-day climatology in most
approach uses relative thresholds that are adjusted model to model or basin to basin. This approach is motivated by the assumption that TCs represent the extreme tails of the distributions in relevant variables, and that the position of TCs in these distributions (in terms of standard deviations from the mean) will remain the same in different models, even if the distributions themselves are substantially different. By design, these schemes produce a fairly realistic present-day climatology in most
(2010) , and Held and Zhao (2011) for studies of global hurricane climatology, variability, and change with global warming. It is a 50-km-resolution model using a cubed-sphere dynamical core and the Bretherton et al. (2004) convection scheme for both shallow and deep cumulus clouds. The main difference in the newer version is that it incorporates a new land model (GFDL LM3). The atmospheric dynamical core of the model was also updated to improve efficiency and stability. As a result of these
(2010) , and Held and Zhao (2011) for studies of global hurricane climatology, variability, and change with global warming. It is a 50-km-resolution model using a cubed-sphere dynamical core and the Bretherton et al. (2004) convection scheme for both shallow and deep cumulus clouds. The main difference in the newer version is that it incorporates a new land model (GFDL LM3). The atmospheric dynamical core of the model was also updated to improve efficiency and stability. As a result of these
. Landfalls Besides specific intensity, frequency and duration characteristics, Kossin et al. (2010) showed that observed clusters present different landfall properties. To obtain the points of landfall for each model, we first used a very high-resolution land–sea mask based on observational data in order to include all small islands. Then we interpolated each track from 6-hourly positions to 15-min increments using cubic splines. Finally, for each interpolated track, whenever the storm location is over
. Landfalls Besides specific intensity, frequency and duration characteristics, Kossin et al. (2010) showed that observed clusters present different landfall properties. To obtain the points of landfall for each model, we first used a very high-resolution land–sea mask based on observational data in order to include all small islands. Then we interpolated each track from 6-hourly positions to 15-min increments using cubic splines. Finally, for each interpolated track, whenever the storm location is over
1. Introduction The western North Pacific (WNP) is the basin where tropical cyclones (TCs) are most active. On average it witnesses more than one-third of global TCs, some being the strongest TCs in individual years. These, together with the large and dense population in East and Southeast Asia, have motivated numerous efforts to understand the variability of WNP TCs (e.g., Chan 1985 ; Lander 1994 ; Wang and Chan 2002 ; Chia and Ropelewski 2002 ; Elsner and Liu 2003 ; Wu et al. 2004
1. Introduction The western North Pacific (WNP) is the basin where tropical cyclones (TCs) are most active. On average it witnesses more than one-third of global TCs, some being the strongest TCs in individual years. These, together with the large and dense population in East and Southeast Asia, have motivated numerous efforts to understand the variability of WNP TCs (e.g., Chan 1985 ; Lander 1994 ; Wang and Chan 2002 ; Chia and Ropelewski 2002 ; Elsner and Liu 2003 ; Wu et al. 2004
used: the Kain–Fritsch cumulus, Lin et al. microphysics, the Rapid Radiative Transfer Model for general circulation models (RRTMG) longwave radiation, Goddard shortwave radiation, Yonsei University (YSU) planetary boundary layer using a Monin–Obukhov surface scheme, and the Noah land surface model. WRF is configured with a horizontal resolution of 27 km and 28 levels in the vertical reaching to 50 hPa on a domain ( Fig. 1 ) covering the Atlantic sector. The model time step is 90 s, and output is
used: the Kain–Fritsch cumulus, Lin et al. microphysics, the Rapid Radiative Transfer Model for general circulation models (RRTMG) longwave radiation, Goddard shortwave radiation, Yonsei University (YSU) planetary boundary layer using a Monin–Obukhov surface scheme, and the Noah land surface model. WRF is configured with a horizontal resolution of 27 km and 28 levels in the vertical reaching to 50 hPa on a domain ( Fig. 1 ) covering the Atlantic sector. The model time step is 90 s, and output is
1. Introduction The distinct modulation of global tropical cyclone (TC) activity by the El Niño–Southern Oscillation (ENSO) has received much attention in the past three decades. For example, in the western North Pacific (WNP), with the highest fraction of the global annual mean number of TCs ( Camargo et al. 2005 ), the phase of ENSO is one of the most important climate factors affecting the genesis, tracks, durations, landfall numbers, and intensities of TCs (e.g., Lander 1994 ; Chan 2000
1. Introduction The distinct modulation of global tropical cyclone (TC) activity by the El Niño–Southern Oscillation (ENSO) has received much attention in the past three decades. For example, in the western North Pacific (WNP), with the highest fraction of the global annual mean number of TCs ( Camargo et al. 2005 ), the phase of ENSO is one of the most important climate factors affecting the genesis, tracks, durations, landfall numbers, and intensities of TCs (e.g., Lander 1994 ; Chan 2000
wind, SST, and precipitation data of the three experiments [historical and representative concentration pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) experiments] from CMIP5 models are used in this study. The historical experiment is forced by observed conditions, including changes in atmospheric composition, solar forcing, natural or anthropogenic aerosols, and so on. The radiative forcing in the RCP4.5 (RCP8.5) experiments stabilizes at 4.5 (8.5) W m −2 in 2100 ( Taylor et al. 2012 ). The output of
wind, SST, and precipitation data of the three experiments [historical and representative concentration pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) experiments] from CMIP5 models are used in this study. The historical experiment is forced by observed conditions, including changes in atmospheric composition, solar forcing, natural or anthropogenic aerosols, and so on. The radiative forcing in the RCP4.5 (RCP8.5) experiments stabilizes at 4.5 (8.5) W m −2 in 2100 ( Taylor et al. 2012 ). The output of
