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- Author or Editor: Hamish A. Ramsay x
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Abstract
The effects of stratospheric cooling and sea surface warming on tropical cyclone (TC) potential intensity (PI) are explored using an axisymmetric cloud-resolving model run to radiative–convective equilibrium (RCE). Almost all observationally constrained datasets show that the tropical lower stratosphere has cooled over the past few decades. Such cooling may affect PI by modifying the storm's outflow temperature, which together with the sea surface temperature (SST) determines the thermal efficiency in PI theory. Results show that cooling near and above the model tropopause (∼90 hPa), with fixed SST, increases the PI at a rate of 1 m s−1 per degree of cooling. Most of this trend comes from a large increase in the thermal efficiency component of PI as the stratosphere cools. Sea surface warming (with fixed stratospheric temperature) increases the PI by roughly twice as much per degree, at a rate of about 2 m s−1 K−1. Under increasing SST, most of the PI trend comes from large changes in the air–sea thermodynamic disequilibrium. The predicted outflow temperature shows no trend in response to SST increase; however, the outflow height increases substantially. Under stratospheric cooling, the outflow temperature decreases and at the same rate as the imposed cooling. These results have considerable implications for global PI trends in response to climate change. Tropical oceans have warmed by about 0.15 K decade−1 since the 1970s, but the stratosphere has cooled anywhere from 0.3 to over 1 K decade−1, depending on the dataset. Therefore, global PI trends in recent decades appear to have been driven more by stratospheric cooling than by surface warming.
Abstract
The effects of stratospheric cooling and sea surface warming on tropical cyclone (TC) potential intensity (PI) are explored using an axisymmetric cloud-resolving model run to radiative–convective equilibrium (RCE). Almost all observationally constrained datasets show that the tropical lower stratosphere has cooled over the past few decades. Such cooling may affect PI by modifying the storm's outflow temperature, which together with the sea surface temperature (SST) determines the thermal efficiency in PI theory. Results show that cooling near and above the model tropopause (∼90 hPa), with fixed SST, increases the PI at a rate of 1 m s−1 per degree of cooling. Most of this trend comes from a large increase in the thermal efficiency component of PI as the stratosphere cools. Sea surface warming (with fixed stratospheric temperature) increases the PI by roughly twice as much per degree, at a rate of about 2 m s−1 K−1. Under increasing SST, most of the PI trend comes from large changes in the air–sea thermodynamic disequilibrium. The predicted outflow temperature shows no trend in response to SST increase; however, the outflow height increases substantially. Under stratospheric cooling, the outflow temperature decreases and at the same rate as the imposed cooling. These results have considerable implications for global PI trends in response to climate change. Tropical oceans have warmed by about 0.15 K decade−1 since the 1970s, but the stratosphere has cooled anywhere from 0.3 to over 1 K decade−1, depending on the dataset. Therefore, global PI trends in recent decades appear to have been driven more by stratospheric cooling than by surface warming.
Abstract
The interaction between complex terrain and a landfalling tropical cyclone (TC) over northeastern Australia is investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Severe TC Larry (in March 2006) made landfall over an area of steep coastal orography and caused extensive damage. The damage pattern suggested that the mountainous terrain had a large influence on the TC wind field, with highly variable damage across relatively small distances. The major aims in this study were to reproduce the observed features of TC Larry, including track, intensity, speed of movement, size, decay rate, and the three-dimensional wind field using realistic high-resolution terrain data and a nested grid with a horizontal spacing of 1 km for the finest domain (referred to as CTRL), and to assess how the above parameters change when the terrain height is set to zero (NOTOPOG). The TC track for CTRL, including the timing and location of landfall, was in close agreement with observation, with the model eye overlapping the location of the observed eye at landfall. Setting the terrain height to zero resulted in a more southerly track and a more intense storm at landfall. The orography in CTRL had a large impact on the TC’s 3D wind field, particularly in the boundary layer where locally very high wind speeds, up to 68 m s−1, coincided with topographic slopes and ridges. The orography also affected precipitation, with localized maxima in elevated regions matching observed rainfall rates. In contrast, the precipitation pattern for the NOTOPOG TC was more symmetric and rainfall totals decreased rapidly with distance from the storm’s center. Parameterized maximum surface wind gusts were located beneath strong boundary layer jets. Finally, small-scale banding features were evident in the surface wind field over land for the NOTOPOG TC, owing to the interaction between the TC boundary layer flow and land surface characteristics.
Abstract
The interaction between complex terrain and a landfalling tropical cyclone (TC) over northeastern Australia is investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Severe TC Larry (in March 2006) made landfall over an area of steep coastal orography and caused extensive damage. The damage pattern suggested that the mountainous terrain had a large influence on the TC wind field, with highly variable damage across relatively small distances. The major aims in this study were to reproduce the observed features of TC Larry, including track, intensity, speed of movement, size, decay rate, and the three-dimensional wind field using realistic high-resolution terrain data and a nested grid with a horizontal spacing of 1 km for the finest domain (referred to as CTRL), and to assess how the above parameters change when the terrain height is set to zero (NOTOPOG). The TC track for CTRL, including the timing and location of landfall, was in close agreement with observation, with the model eye overlapping the location of the observed eye at landfall. Setting the terrain height to zero resulted in a more southerly track and a more intense storm at landfall. The orography in CTRL had a large impact on the TC’s 3D wind field, particularly in the boundary layer where locally very high wind speeds, up to 68 m s−1, coincided with topographic slopes and ridges. The orography also affected precipitation, with localized maxima in elevated regions matching observed rainfall rates. In contrast, the precipitation pattern for the NOTOPOG TC was more symmetric and rainfall totals decreased rapidly with distance from the storm’s center. Parameterized maximum surface wind gusts were located beneath strong boundary layer jets. Finally, small-scale banding features were evident in the surface wind field over land for the NOTOPOG TC, owing to the interaction between the TC boundary layer flow and land surface characteristics.
Abstract
The effects of relative and absolute sea surface temperature (SST) on tropical cyclone potential intensity are investigated using the Massachusetts Institute of Technology (MIT) single-column model. The model is run in two modes: (i) radiative–convective equilibrium (RCE) to represent the convective response to uniform warming of the ocean as in a homogeneous aqua planet, and (ii) weak temperature gradient (WTG) to represent the convective response to warming over a limited area of ocean while the SST outside that area remains unchanged. The WTG calculations are taken to represent the sensitivity of the atmospheric state to relative SST changes, while the RCE calculations are taken to represent the sensitivity to absolute SST changes occurring in the absence of relative SST changes. The potential intensity is computed using temperature and moisture profiles from the two sets of experiments for various values of SST. The computed potential intensity is more sensitive to relative SST than to absolute SST, with slopes of between about 7 and 8 m s−1 °C−1 (depending on choice of input parameters in the model’s convection scheme and other details of the model configuration) in the WTG calculations and about 1 m s−1 °C−1 in RCE. The sensitivity to relative SST obtained from these calculations is quantitatively similar to that obtained previously by G. Vecchi and B. J. Soden from global climate model output. The greater sensitivity of potential intensity to SST in the WTG simulations (relative to RCE) can be attributed primarily to larger changes in the air–sea thermodynamic disequilibrium in those calculations as SST changes, which results from the inability of the free troposphere to adjust to the SST in WTG as it does in RCE.
Abstract
The effects of relative and absolute sea surface temperature (SST) on tropical cyclone potential intensity are investigated using the Massachusetts Institute of Technology (MIT) single-column model. The model is run in two modes: (i) radiative–convective equilibrium (RCE) to represent the convective response to uniform warming of the ocean as in a homogeneous aqua planet, and (ii) weak temperature gradient (WTG) to represent the convective response to warming over a limited area of ocean while the SST outside that area remains unchanged. The WTG calculations are taken to represent the sensitivity of the atmospheric state to relative SST changes, while the RCE calculations are taken to represent the sensitivity to absolute SST changes occurring in the absence of relative SST changes. The potential intensity is computed using temperature and moisture profiles from the two sets of experiments for various values of SST. The computed potential intensity is more sensitive to relative SST than to absolute SST, with slopes of between about 7 and 8 m s−1 °C−1 (depending on choice of input parameters in the model’s convection scheme and other details of the model configuration) in the WTG calculations and about 1 m s−1 °C−1 in RCE. The sensitivity to relative SST obtained from these calculations is quantitatively similar to that obtained previously by G. Vecchi and B. J. Soden from global climate model output. The greater sensitivity of potential intensity to SST in the WTG simulations (relative to RCE) can be attributed primarily to larger changes in the air–sea thermodynamic disequilibrium in those calculations as SST changes, which results from the inability of the free troposphere to adjust to the SST in WTG as it does in RCE.
Abstract
Four supercell motion forecast algorithms are investigated with respect to their hodograph-analysis parameters. Another method derived from the data presented herein, the so-called offset method, is used to develop a baseline standard for the aforementioned schemes, using the observed storm motions and the mean wind. It is not a forecast scheme, as it is based on knowing the observed storm motions. This work explores the sensitivity of these algorithms to their arbitrary parameters by systematically varying those parameters, using a dataset of 394 right-moving supercells, and associated proximity soundings. The parameters used in these algorithms to define the layer depths for advection and/or propagation of supercells have not been shown to be optimum for this purpose. These arbitrary parameters compose the top and bottom levels of the mean wind layer, and a deviation vector from the mean wind defined through that layer. Two of the most recently developed algorithms have also implemented the vertical wind shear vector over an arbitrary layer depth. It has been found that, among other results, the scheme using both mean wind and vertical wind shear is more sensitive to the depth of the mean wind layer than it is to the depth of the vertical wind shear layer. It has also been shown that, when using the simplest schemes, the most accurate forecasts, on average, are obtained by using deep mean wind layers (i.e., greater than 0–10 km). Indeed, all the forecast schemes show a strong tendency for the u component of the predicted storm motion to be regulated by the depth of the mean wind layer. The υ component of the prediction storm motion, on the other hand, appears to be controlled by the deviation vector from the layer-mean wind. Although the schemes using vertical shear are shown to perform somewhat better on average than schemes based on the mean wind alone, there are times in which they also result in large forecast errors. The results demonstrate the inherent difficulty in using an observed hodograph to predict supercell motion.
Abstract
Four supercell motion forecast algorithms are investigated with respect to their hodograph-analysis parameters. Another method derived from the data presented herein, the so-called offset method, is used to develop a baseline standard for the aforementioned schemes, using the observed storm motions and the mean wind. It is not a forecast scheme, as it is based on knowing the observed storm motions. This work explores the sensitivity of these algorithms to their arbitrary parameters by systematically varying those parameters, using a dataset of 394 right-moving supercells, and associated proximity soundings. The parameters used in these algorithms to define the layer depths for advection and/or propagation of supercells have not been shown to be optimum for this purpose. These arbitrary parameters compose the top and bottom levels of the mean wind layer, and a deviation vector from the mean wind defined through that layer. Two of the most recently developed algorithms have also implemented the vertical wind shear vector over an arbitrary layer depth. It has been found that, among other results, the scheme using both mean wind and vertical wind shear is more sensitive to the depth of the mean wind layer than it is to the depth of the vertical wind shear layer. It has also been shown that, when using the simplest schemes, the most accurate forecasts, on average, are obtained by using deep mean wind layers (i.e., greater than 0–10 km). Indeed, all the forecast schemes show a strong tendency for the u component of the predicted storm motion to be regulated by the depth of the mean wind layer. The υ component of the prediction storm motion, on the other hand, appears to be controlled by the deviation vector from the layer-mean wind. Although the schemes using vertical shear are shown to perform somewhat better on average than schemes based on the mean wind alone, there are times in which they also result in large forecast errors. The results demonstrate the inherent difficulty in using an observed hodograph to predict supercell motion.
Abstract
Advances in observations, theory, and modeling have revealed that inner-core asymmetries are a common feature of tropical cyclones (TCs). In this study, the inner-core asymmetries of a severe Southern Hemisphere tropical cyclone, TC Larry (2006), are investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Kepert–Wang boundary layer model. The MM5-simulated TC exhibited significant asymmetries in the inner-core region, including rainfall distribution, surface convergence, and low-level vertical motion. The near-core environment was characterized by very low environmental vertical shear and consequently the TC vortex had almost no vertical tilt. It was found that, prior to landfall, the rainfall asymmetry was very pronounced with precipitation maxima consistently to the right of the westward direction of motion. Persistent maxima in low-level convergence and vertical motion formed ahead of the translating TC, resulting in deep convection and associated hydrometeor maxima at about 500 hPa. The asymmetry in frictional convergence was mainly due to the storm motion at the eyewall, but was dominated by the proximity to land at larger radii. The displacement of about 30°–120° of azimuth between the surface and midlevel hydrometeor maxima is explained by the rapid cyclonic advection of hydrometeors by the tangential winds in the TC core. These results for TC Larry support earlier studies that show that frictional convergence in the boundary layer can play a significant role in determining the asymmetrical structures, particularly when the environmental vertical shear is weak or absent.
Abstract
Advances in observations, theory, and modeling have revealed that inner-core asymmetries are a common feature of tropical cyclones (TCs). In this study, the inner-core asymmetries of a severe Southern Hemisphere tropical cyclone, TC Larry (2006), are investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Kepert–Wang boundary layer model. The MM5-simulated TC exhibited significant asymmetries in the inner-core region, including rainfall distribution, surface convergence, and low-level vertical motion. The near-core environment was characterized by very low environmental vertical shear and consequently the TC vortex had almost no vertical tilt. It was found that, prior to landfall, the rainfall asymmetry was very pronounced with precipitation maxima consistently to the right of the westward direction of motion. Persistent maxima in low-level convergence and vertical motion formed ahead of the translating TC, resulting in deep convection and associated hydrometeor maxima at about 500 hPa. The asymmetry in frictional convergence was mainly due to the storm motion at the eyewall, but was dominated by the proximity to land at larger radii. The displacement of about 30°–120° of azimuth between the surface and midlevel hydrometeor maxima is explained by the rapid cyclonic advection of hydrometeors by the tangential winds in the TC core. These results for TC Larry support earlier studies that show that frictional convergence in the boundary layer can play a significant role in determining the asymmetrical structures, particularly when the environmental vertical shear is weak or absent.
Abstract
Seasonal predictions of tropical cyclone (TC) landfalls are challenging because seasonal landfall count not only depends on the number and spatial distribution of TC genesis, but also whether those TCs are steered toward land or not. Past studies have separately examined genesis and landfall as a function of large-scale ocean and atmospheric environmental conditions. Here, we introduce a practical statistical framework for estimating the seasonal count of TC landfalls as the product of a Poisson model for seasonal TC genesis and a logistic model for landfall probability. We compute spatial variations in TC landfall and genesis by decomposing TC activity in the western North Pacific (WNP) basin into 10° × 10° bins, then identify coherent regions where El Niño–Southern Oscillation (ENSO) and the western extent of the Pacific subtropical high (WPSH) have significant influences on seasonal landfall count. Our framework shows that ENSO and the WPSH are weakly related to basinwide landfalls but strongly related to regional genesis and landfall probability. ENSO modulates the zonal distribution of TC genesis, consistent with past work, whereas the WPSH modulates the meridional distribution of landfall probability due to variations in steering flow associated with the Pacific subtropical high. These spatial patterns result in four coherent subregions of the WNP basin that define seasonal landfall variations: landfall count increases in the southwestern WNP during a positive WPSH and La Niña, the south-central WNP during a positive WPSH and El Niño, the eastern WNP during a negative WPSH and El Niño, and the northern WNP during a negative WPSH and La Niña.
Abstract
Seasonal predictions of tropical cyclone (TC) landfalls are challenging because seasonal landfall count not only depends on the number and spatial distribution of TC genesis, but also whether those TCs are steered toward land or not. Past studies have separately examined genesis and landfall as a function of large-scale ocean and atmospheric environmental conditions. Here, we introduce a practical statistical framework for estimating the seasonal count of TC landfalls as the product of a Poisson model for seasonal TC genesis and a logistic model for landfall probability. We compute spatial variations in TC landfall and genesis by decomposing TC activity in the western North Pacific (WNP) basin into 10° × 10° bins, then identify coherent regions where El Niño–Southern Oscillation (ENSO) and the western extent of the Pacific subtropical high (WPSH) have significant influences on seasonal landfall count. Our framework shows that ENSO and the WPSH are weakly related to basinwide landfalls but strongly related to regional genesis and landfall probability. ENSO modulates the zonal distribution of TC genesis, consistent with past work, whereas the WPSH modulates the meridional distribution of landfall probability due to variations in steering flow associated with the Pacific subtropical high. These spatial patterns result in four coherent subregions of the WNP basin that define seasonal landfall variations: landfall count increases in the southwestern WNP during a positive WPSH and La Niña, the south-central WNP during a positive WPSH and El Niño, the eastern WNP during a negative WPSH and El Niño, and the northern WNP during a negative WPSH and La Niña.
Abstract
Reliable projections of future changes in tropical cyclone (TC) characteristics are highly dependent on the ability of global climate models (GCMs) to simulate the observed characteristics of TCs (i.e., their frequency, genesis locations, movement, and intensity). Here, we investigate the performance of a suite of GCMs from the U.S. CLIVAR Working Group on Hurricanes in simulating observed climatological features of TCs in the Southern Hemisphere. A subset of these GCMs is also explored under three idealized warming scenarios. Two types of simulated TC tracks are evaluated on the basis of a commonly applied cluster analysis: 1) explicitly simulated tracks, and 2) downscaled tracks, derived from a statistical–dynamical technique that depends on the models’ large-scale environmental fields. Climatological TC properties such as genesis locations, annual frequency, lifetime maximum intensity (LMI), and seasonality are evaluated for both track types. Future changes to annual frequency, LMI, and the latitude of LMI are evaluated using the downscaled tracks where large sample sizes allow for statistically robust results. An ensemble approach is used to assess future changes of explicit tracks owing to their small number of realizations. We show that the downscaled tracks generally outperform the explicit tracks in relation to many of the climatological features of Southern Hemisphere TCs, despite a few notable biases. Future changes to the frequency and intensity of TCs in the downscaled simulations are found to be highly dependent on the warming scenario and model, with the most robust result being an increase in the LMI under a uniform 2°C surface warming.
Abstract
Reliable projections of future changes in tropical cyclone (TC) characteristics are highly dependent on the ability of global climate models (GCMs) to simulate the observed characteristics of TCs (i.e., their frequency, genesis locations, movement, and intensity). Here, we investigate the performance of a suite of GCMs from the U.S. CLIVAR Working Group on Hurricanes in simulating observed climatological features of TCs in the Southern Hemisphere. A subset of these GCMs is also explored under three idealized warming scenarios. Two types of simulated TC tracks are evaluated on the basis of a commonly applied cluster analysis: 1) explicitly simulated tracks, and 2) downscaled tracks, derived from a statistical–dynamical technique that depends on the models’ large-scale environmental fields. Climatological TC properties such as genesis locations, annual frequency, lifetime maximum intensity (LMI), and seasonality are evaluated for both track types. Future changes to annual frequency, LMI, and the latitude of LMI are evaluated using the downscaled tracks where large sample sizes allow for statistically robust results. An ensemble approach is used to assess future changes of explicit tracks owing to their small number of realizations. We show that the downscaled tracks generally outperform the explicit tracks in relation to many of the climatological features of Southern Hemisphere TCs, despite a few notable biases. Future changes to the frequency and intensity of TCs in the downscaled simulations are found to be highly dependent on the warming scenario and model, with the most robust result being an increase in the LMI under a uniform 2°C surface warming.
Abstract
This study examines combining ENSO sea surface temperature (SST) regions for seasonal prediction of Coral Sea tropical cyclone (TC) frequency. The Coral Sea averages ~4 TCs per season, but is characterized by strong interannual variability, with 1–9 TCs per season, over the period 1977–2012. A wavelet analysis confirms that ENSO is a key contributor to Coral Sea TC count (TCC) variability. Motivated by the impact of El Niño Modoki on regional climate anomalies, a suite of 38 linear models is constructed and assessed on its ability to predict Coral Sea seasonal TCC. Seasonal predictions of TCC are generated by a leave-one-out cross validation (LOOCV). An important finding is that models made up of multiple tropical Pacific SST regions, such as those that comprise the El Niño Modoki Index (EMI) or the Trans-Niño Index (TNI), perform considerably better than models comprising only single regions, such as Niño-3.4 or Niño-4. Moreover, enhanced (suppressed) TC activity is expected in the Coral Sea when the central Pacific is anomalously cool (warm) and the eastern and western Pacific are anomalously warm (cool) during austral winter. The best cross-validated model has persistent and statistically significantly high correlations with TCC (r > 0.5) at lead times of ~6 months prior to the mean onset of the Coral Sea TC season, whereas correlations based heavily on the widely used Niño-3.4 region are not statistically significant or meaningful (r = 0.09) for the same lead times. Of the 38 models assessed, several optimized forms of the EMI and of the TNI perform best.
Abstract
This study examines combining ENSO sea surface temperature (SST) regions for seasonal prediction of Coral Sea tropical cyclone (TC) frequency. The Coral Sea averages ~4 TCs per season, but is characterized by strong interannual variability, with 1–9 TCs per season, over the period 1977–2012. A wavelet analysis confirms that ENSO is a key contributor to Coral Sea TC count (TCC) variability. Motivated by the impact of El Niño Modoki on regional climate anomalies, a suite of 38 linear models is constructed and assessed on its ability to predict Coral Sea seasonal TCC. Seasonal predictions of TCC are generated by a leave-one-out cross validation (LOOCV). An important finding is that models made up of multiple tropical Pacific SST regions, such as those that comprise the El Niño Modoki Index (EMI) or the Trans-Niño Index (TNI), perform considerably better than models comprising only single regions, such as Niño-3.4 or Niño-4. Moreover, enhanced (suppressed) TC activity is expected in the Coral Sea when the central Pacific is anomalously cool (warm) and the eastern and western Pacific are anomalously warm (cool) during austral winter. The best cross-validated model has persistent and statistically significantly high correlations with TCC (r > 0.5) at lead times of ~6 months prior to the mean onset of the Coral Sea TC season, whereas correlations based heavily on the widely used Niño-3.4 region are not statistically significant or meaningful (r = 0.09) for the same lead times. Of the 38 models assessed, several optimized forms of the EMI and of the TNI perform best.
Abstract
It has previously been suggested, based on limited observations, that vertical wind shear in the upper troposphere is a key control on supercell morphology, with the low-precipitation, high-precipitation, and classic archetypes favored under strong, weak, and moderate shear, respectively. The idea is that, with increasing upper-level shear (ULS), hydrometeors are transported farther from the updraft by stronger storm-relative anvil-level winds, limiting their growth and thereby reducing precipitation intensity. The present study represents the first attempt to test this hypothesis, using idealized simulations of supercells performed across a range of 6–12-km shear profiles.
Contrary to expectations, there is a significant increase in surface precipitation and an associated strengthening of outflow winds as ULS magnitude is increased from 0 to 20 m s−1. These changes result from an increase in storm motion, which drives stronger low-level inflow, a wider updraft, and enhanced condensation. A further increase in ULS magnitude to 30 m s−1 promotes a slight reduction in storm intensity associated with surging rear-flank outflow. However, this transition in behavior is found to be sensitive to other factors that influence cold-pool strength, such as mixed-layer depth and model microphysics. Variations in the vertical distribution and direction of ULS are also considered, but are found to have a much smaller impact on storm intensity than variations in ULS magnitude.
Suggestions for the disparity between the current results and the aforementioned observations are offered and the need for further research on supercell morphology—in particular, simulations in drier environments—is emphasized.
Abstract
It has previously been suggested, based on limited observations, that vertical wind shear in the upper troposphere is a key control on supercell morphology, with the low-precipitation, high-precipitation, and classic archetypes favored under strong, weak, and moderate shear, respectively. The idea is that, with increasing upper-level shear (ULS), hydrometeors are transported farther from the updraft by stronger storm-relative anvil-level winds, limiting their growth and thereby reducing precipitation intensity. The present study represents the first attempt to test this hypothesis, using idealized simulations of supercells performed across a range of 6–12-km shear profiles.
Contrary to expectations, there is a significant increase in surface precipitation and an associated strengthening of outflow winds as ULS magnitude is increased from 0 to 20 m s−1. These changes result from an increase in storm motion, which drives stronger low-level inflow, a wider updraft, and enhanced condensation. A further increase in ULS magnitude to 30 m s−1 promotes a slight reduction in storm intensity associated with surging rear-flank outflow. However, this transition in behavior is found to be sensitive to other factors that influence cold-pool strength, such as mixed-layer depth and model microphysics. Variations in the vertical distribution and direction of ULS are also considered, but are found to have a much smaller impact on storm intensity than variations in ULS magnitude.
Suggestions for the disparity between the current results and the aforementioned observations are offered and the need for further research on supercell morphology—in particular, simulations in drier environments—is emphasized.
Abstract
This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.
Abstract
This study investigates the role of large-scale environmental factors, notably sea surface temperature (SST), low-level relative vorticity, and deep-tropospheric vertical wind shear, in the interannual variability of November–April tropical cyclone (TC) activity in the Australian region. Extensive correlation analyses were carried out between TC frequency and intensity and the aforementioned large-scale parameters, using TC data for 1970–2006 from the official Australian TC dataset. Large correlations were found between the seasonal number of TCs and SST in the Niño-3.4 and Niño-4 regions. These correlations were greatest (−0.73) during August–October, immediately preceding the Australian TC season. The correlations remain almost unchanged for the July–September period and therefore can be viewed as potential seasonal predictors of the forthcoming TC season. In contrast, only weak correlations (<+0.37) were found with the local SST in the region north of Australia where many TCs originate; these were reduced almost to zero when the ENSO component of the SST was removed by partial correlation analysis. The annual frequency of TCs was found to be strongly correlated with 850-hPa relative vorticity and vertical shear of the zonal wind over the main genesis areas of the Australian region. Furthermore, correlations between the Niño SST and these two atmospheric parameters exhibited a strong link between the Australian region and the Niño-3.4 SST. A principal component analysis of the SST dataset revealed two main modes of Pacific Ocean SST variability that match very closely with the basinwide patterns of correlations between SST and TC frequencies. Finally, it is shown that the correlations can be increased markedly (e.g., from −0.73 to −0.80 for the August–October period) by a weighted combination of SST time series from weakly correlated regions.
