Search Results
). Parameterized deep convection, including the subgrid-scale cloud representation, is restricted by increasing λ 0 ( Held et al. 2007 ; Kang et al. 2008 ), while the fraction of large-scale condensation by nonconvective clouds increases through the large-scale cloud/condensation module ( Tiedtke 1993 ) as opposed to the convection module ( Kang et al. 2008 ). Wang (2014) found that this nondeep convection plays a role in moistening the lower to middle troposphere whereas deep convection moistens the
). Parameterized deep convection, including the subgrid-scale cloud representation, is restricted by increasing λ 0 ( Held et al. 2007 ; Kang et al. 2008 ), while the fraction of large-scale condensation by nonconvective clouds increases through the large-scale cloud/condensation module ( Tiedtke 1993 ) as opposed to the convection module ( Kang et al. 2008 ). Wang (2014) found that this nondeep convection plays a role in moistening the lower to middle troposphere whereas deep convection moistens the
TC rainfall structure and magnitude in six TC genesis basins under three warming scenarios: 1) a doubling of CO 2 in the atmosphere with no change in SST, 2) a uniform 2-K rise in global SST, and 3) a doubling of CO 2 plus a uniform 2-K rise in SST. The expectation prior to performing the analyses is for a decrease in TC rainfall under the CO 2 doubling experiment and for an increase associated with the global increase in SST. It is more difficult to know a priori what to expect from a
TC rainfall structure and magnitude in six TC genesis basins under three warming scenarios: 1) a doubling of CO 2 in the atmosphere with no change in SST, 2) a uniform 2-K rise in global SST, and 3) a doubling of CO 2 plus a uniform 2-K rise in SST. The expectation prior to performing the analyses is for a decrease in TC rainfall under the CO 2 doubling experiment and for an increase associated with the global increase in SST. It is more difficult to know a priori what to expect from a
lines are above the 99% significance level estimated by the Monte Carlo test. Figure 2 shows the composite of the observed TC track density (upper row) and its anomaly (middle row) associated with the three ENSO types. Compared to the composites of the two types of El Niño ( Figs. 2a,b ), the TC track density in the La Niña years ( Fig. 2c ) has a smaller maximum, and its centroid is located farther westward and northward, which is consistent with previous studies (e.g., Wang and Chan 2002
lines are above the 99% significance level estimated by the Monte Carlo test. Figure 2 shows the composite of the observed TC track density (upper row) and its anomaly (middle row) associated with the three ENSO types. Compared to the composites of the two types of El Niño ( Figs. 2a,b ), the TC track density in the La Niña years ( Fig. 2c ) has a smaller maximum, and its centroid is located farther westward and northward, which is consistent with previous studies (e.g., Wang and Chan 2002
, 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
to depend on the details of the spatial pattern of forcing changes. As the U.S. CLIVAR HWG forcing changes are spatially uniform, detailed basin analyses are deferred until more realistic simulations of a future climate are performed. The motivation for this particular set of simulation experiments is to understand the effects of increased available ocean heat energy on tropical cyclogenesis and development, and the potential competing effect of the vertical stabilization of the atmosphere by
to depend on the details of the spatial pattern of forcing changes. As the U.S. CLIVAR HWG forcing changes are spatially uniform, detailed basin analyses are deferred until more realistic simulations of a future climate are performed. The motivation for this particular set of simulation experiments is to understand the effects of increased available ocean heat energy on tropical cyclogenesis and development, and the potential competing effect of the vertical stabilization of the atmosphere by
, 1995, 1998, 1999, 2005, and 2007). Figure 1 shows the composite of ASO seasonal mean SST anomalies for EP El Niño, CP El Niño, and La Niña, respectively. Compared to EP El Niño ( Fig. 1a ), the SST anomalies in CP El Niño ( Fig. 1b ) shift toward the west. This may lead to significant differences in tropical heating for the atmosphere between the two types of El Niño. The amplitude of the CP El Niño SST anomalies (~1 K) is also smaller than the EP El Niño (~1.5 K), but comparable to the La Niña
, 1995, 1998, 1999, 2005, and 2007). Figure 1 shows the composite of ASO seasonal mean SST anomalies for EP El Niño, CP El Niño, and La Niña, respectively. Compared to EP El Niño ( Fig. 1a ), the SST anomalies in CP El Niño ( Fig. 1b ) shift toward the west. This may lead to significant differences in tropical heating for the atmosphere between the two types of El Niño. The amplitude of the CP El Niño SST anomalies (~1 K) is also smaller than the EP El Niño (~1.5 K), but comparable to the La Niña
future experiment corresponds to the climatological SST with 2 K added globally, “plus 2K” (p2K). The second future experiment, named “double CO 2 ” (2CO2), is forced with the same climatological SST, but the CO 2 concentration is doubled in the atmosphere. In the third future experiment, “plus 2K and double CO 2 ” (p2K2CO2) is the combination of the last two scenarios: that is, the models are forced with climatological SST with 2 K added globally and a doubling of the CO 2 concentration. Fig . 2
future experiment corresponds to the climatological SST with 2 K added globally, “plus 2K” (p2K). The second future experiment, named “double CO 2 ” (2CO2), is forced with the same climatological SST, but the CO 2 concentration is doubled in the atmosphere. In the third future experiment, “plus 2K and double CO 2 ” (p2K2CO2) is the combination of the last two scenarios: that is, the models are forced with climatological SST with 2 K added globally and a doubling of the CO 2 concentration. Fig . 2
customize it to HiRAM (or any other model or reanalysis dataset). An index derived from TC observations and reanalysis fields will not perform well when used with TCs and environmental variables from a model, since the relationships between environment and TCs in the model may differ from those in the real atmosphere. To address this problem, we can simply rederive our index using both TCs and large-scale fields from the model itself. In this case, we know that the resulting index will be faithful to
customize it to HiRAM (or any other model or reanalysis dataset). An index derived from TC observations and reanalysis fields will not perform well when used with TCs and environmental variables from a model, since the relationships between environment and TCs in the model may differ from those in the real atmosphere. To address this problem, we can simply rederive our index using both TCs and large-scale fields from the model itself. In this case, we know that the resulting index will be faithful to
monthly-average sea surface temperature (SST) for the periods ranging between 5 and 20 years, depending on model ( Table 1 ). The three highly idealized warming climate scenarios included experiments corresponding to a doubling of carbon dioxide concentration in the atmosphere (2CO2), a uniform 2-K increase of SST (p2K), and the combination of these two scenarios (p2K2CO2). Table 1. List of HWG-participating modeling centers and models, along with model horizontal resolution, number of years of
monthly-average sea surface temperature (SST) for the periods ranging between 5 and 20 years, depending on model ( Table 1 ). The three highly idealized warming climate scenarios included experiments corresponding to a doubling of carbon dioxide concentration in the atmosphere (2CO2), a uniform 2-K increase of SST (p2K), and the combination of these two scenarios (p2K2CO2). Table 1. List of HWG-participating modeling centers and models, along with model horizontal resolution, number of years of
