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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 leading intraseasonal mode of atmospheric and oceanic variability, the Madden–Julian oscillation (MJO), influences tropical and extratropical sea level pressure, temperature, divergent and rotational wind components, moisture, and deep convection. As a 40- to 50-day oscillation, the MJO is also known to influence tropical phenomena, including tropical cyclone (TC) activity in various TC basins. The links between the MJO and multiple measures of TC activity, including genesis, landfall, and an integrative accumulated cyclone energy (ACE) index, were quantified for multiple TC-formation basins across the Western Hemisphere, including the North Atlantic and northeast Pacific Ocean and subbasins, for the period 1978–2006. Using this relatively long (29 yr) TC dataset and employing an upper-tropospheric MJO diagnostic that is physically meaningful over the entire Western Hemisphere, this study extends existing research on the relationships between the MJO and TCs. The NOAA Climate Prediction Center’s operational MJO index, derived from 200-hPa velocity potential data, was divided into three phases. Relative frequencies of the MJO phases were compared with observed levels of TC activity using a binomial distribution hypothesis test. The MJO was found to statistically significantly modulate the frequency of TC genesis, intensification, and landfall in the nine TC basins studied. For example, when an MJO index was large and positive at 120°W, hurricanes and intense hurricanes were 4 times as likely to make landfall in the North Atlantic. This modulation of TC activity, including landfall patterns in the North Atlantic, was physically linked to the upper-atmospheric response to the eastward-propagating MJO and is evident as a dipole of TC activity between Pacific and Atlantic subbasins.
Abstract
The leading intraseasonal mode of atmospheric and oceanic variability, the Madden–Julian oscillation (MJO), influences tropical and extratropical sea level pressure, temperature, divergent and rotational wind components, moisture, and deep convection. As a 40- to 50-day oscillation, the MJO is also known to influence tropical phenomena, including tropical cyclone (TC) activity in various TC basins. The links between the MJO and multiple measures of TC activity, including genesis, landfall, and an integrative accumulated cyclone energy (ACE) index, were quantified for multiple TC-formation basins across the Western Hemisphere, including the North Atlantic and northeast Pacific Ocean and subbasins, for the period 1978–2006. Using this relatively long (29 yr) TC dataset and employing an upper-tropospheric MJO diagnostic that is physically meaningful over the entire Western Hemisphere, this study extends existing research on the relationships between the MJO and TCs. The NOAA Climate Prediction Center’s operational MJO index, derived from 200-hPa velocity potential data, was divided into three phases. Relative frequencies of the MJO phases were compared with observed levels of TC activity using a binomial distribution hypothesis test. The MJO was found to statistically significantly modulate the frequency of TC genesis, intensification, and landfall in the nine TC basins studied. For example, when an MJO index was large and positive at 120°W, hurricanes and intense hurricanes were 4 times as likely to make landfall in the North Atlantic. This modulation of TC activity, including landfall patterns in the North Atlantic, was physically linked to the upper-atmospheric response to the eastward-propagating MJO and is evident as a dipole of TC activity between Pacific and Atlantic subbasins.
Abstract
Over the years there have been a number of studies comparing the relative merits of semi-Lagrangian and Eulerian schemes. These studies, which continue to appear in the literature up to the present, almost invariably conclude that semi-Lagrangian schemes are superior in accuracy, and produce less noise, than Eulerian schemes. It is argued in this note that such conclusions are not justified because they have compared semi-Lagrangian and Eulerian schemes of different orders of accuracy. Typically, the semi-Lagrangian schemes tested have employed cubic spatial interpolation (and therefore are third order) in space, whereas the Eulerian schemes have usually been second order (and sometimes fourth order) in space. It is shown here that when semi-Lagrangian and Eulerian schemes of the same order are applied to the test case, namely, that of “warm bubble” convection, there are almost indiscernible differences between the simulations. The contention presented here, therefore, is that it is the order of the scheme that is of primary importance, not whether it is semi-Lagrangian or Eulerian.
Abstract
Over the years there have been a number of studies comparing the relative merits of semi-Lagrangian and Eulerian schemes. These studies, which continue to appear in the literature up to the present, almost invariably conclude that semi-Lagrangian schemes are superior in accuracy, and produce less noise, than Eulerian schemes. It is argued in this note that such conclusions are not justified because they have compared semi-Lagrangian and Eulerian schemes of different orders of accuracy. Typically, the semi-Lagrangian schemes tested have employed cubic spatial interpolation (and therefore are third order) in space, whereas the Eulerian schemes have usually been second order (and sometimes fourth order) in space. It is shown here that when semi-Lagrangian and Eulerian schemes of the same order are applied to the test case, namely, that of “warm bubble” convection, there are almost indiscernible differences between the simulations. The contention presented here, therefore, is that it is the order of the scheme that is of primary importance, not whether it is semi-Lagrangian or Eulerian.
Abstract
Through the use of the dimensional splitting “cascade” method of grid-to-grid interpolation, it is shown that consistently high-order-accurate semi-Lagrangian integration of a three-dimensional hydrostatic primitive equations model can be carried out using forward (downstream) trajectories instead of the backward (upstream) trajectory computations that are more commonly employed in semi-Lagrangian models. Apart from the efficiency resulting directly from the adoption of the cascade method, improved computational performance is achieved partly by the selective implicit treatment of only the deepest vertical gravity modes and partly by obviating the need to iterate the estimation of each trajectory's location. Perhaps the main distinction of our present semi-Lagrangian method is its inherent exact conservation of mass and passive tracers. This is achieved by adopting a simple variant of the cascade interpolation that incorporates mass (and tracer) conservation directly and at only a very modest additional cost. The conserving cascade, which is described in detail, is a generic algorithm that can be applied at arbitrary order of accuracy.
Tests of the new mass-conserving scheme in a regional forecast model show small but consistent improvements in accuracy at 48 h. It is suggested that the benefits to extended global forecasting and simulation should be much greater.
Abstract
Through the use of the dimensional splitting “cascade” method of grid-to-grid interpolation, it is shown that consistently high-order-accurate semi-Lagrangian integration of a three-dimensional hydrostatic primitive equations model can be carried out using forward (downstream) trajectories instead of the backward (upstream) trajectory computations that are more commonly employed in semi-Lagrangian models. Apart from the efficiency resulting directly from the adoption of the cascade method, improved computational performance is achieved partly by the selective implicit treatment of only the deepest vertical gravity modes and partly by obviating the need to iterate the estimation of each trajectory's location. Perhaps the main distinction of our present semi-Lagrangian method is its inherent exact conservation of mass and passive tracers. This is achieved by adopting a simple variant of the cascade interpolation that incorporates mass (and tracer) conservation directly and at only a very modest additional cost. The conserving cascade, which is described in detail, is a generic algorithm that can be applied at arbitrary order of accuracy.
Tests of the new mass-conserving scheme in a regional forecast model show small but consistent improvements in accuracy at 48 h. It is suggested that the benefits to extended global forecasting and simulation should be much greater.
Abstract
Explosive cyclogenesis occurs on average once a year over the coast of New South Wales (NSW), Australia. Known locally as east coast lows, these storms are characterized by very strong winds and heavy rain. Intensity, size, proximity to the coast, and speed of movement of the cyclone are important in their impact on coastal NSW, especially Sydney. Predicting the location of the system, the maximum sustained wind speeds, and the rainfall totals all are operational forecasting challenges. Warnings are issued when predictions exceed threshold values. For example, land gale forecasts are issued if sustained wind speeds are expected to reach or exceed 34 kt (about 17 m s−1). The east coast low of 30–31 August 1996 featured land gales over the greater Sydney area. No warnings were issued as the forecasters estimated that the wind strength would fall below gale force. In this study, uncertainty in the predictions is estimated and reduced by providing, in addition to the routine single operational numerical weather prediction, a Monte Carlo–based short-range ensemble (SREF) approach. The intention is to improve the forecasts and also to provide valuable statistical information such as sea level pressure probability ellipses and estimates of the variances in the wind and rainfall predictions. For this event, both the unperturbed and ensemble forecasts predicted sustained maximum wind speeds in excess of 40 kt (20 m s−1) at the official Sydney observation station. However, the SREF provided vital additional information, namely, that over 70% of the forecasts were within one standard deviation (plus or minus 5 kt) of the mean. The SREF guidance therefore strongly supported the prediction of land gales. Moreover, although the ensemble forecast mean slightly underpredicted the rainfall total at Sydney, the forecast spread encompassed the observed 24-h total of 127 mm.
Abstract
Explosive cyclogenesis occurs on average once a year over the coast of New South Wales (NSW), Australia. Known locally as east coast lows, these storms are characterized by very strong winds and heavy rain. Intensity, size, proximity to the coast, and speed of movement of the cyclone are important in their impact on coastal NSW, especially Sydney. Predicting the location of the system, the maximum sustained wind speeds, and the rainfall totals all are operational forecasting challenges. Warnings are issued when predictions exceed threshold values. For example, land gale forecasts are issued if sustained wind speeds are expected to reach or exceed 34 kt (about 17 m s−1). The east coast low of 30–31 August 1996 featured land gales over the greater Sydney area. No warnings were issued as the forecasters estimated that the wind strength would fall below gale force. In this study, uncertainty in the predictions is estimated and reduced by providing, in addition to the routine single operational numerical weather prediction, a Monte Carlo–based short-range ensemble (SREF) approach. The intention is to improve the forecasts and also to provide valuable statistical information such as sea level pressure probability ellipses and estimates of the variances in the wind and rainfall predictions. For this event, both the unperturbed and ensemble forecasts predicted sustained maximum wind speeds in excess of 40 kt (20 m s−1) at the official Sydney observation station. However, the SREF provided vital additional information, namely, that over 70% of the forecasts were within one standard deviation (plus or minus 5 kt) of the mean. The SREF guidance therefore strongly supported the prediction of land gales. Moreover, although the ensemble forecast mean slightly underpredicted the rainfall total at Sydney, the forecast spread encompassed the observed 24-h total of 127 mm.
Abstract
Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958–2010 period in the wet months of November–April were primarily associated with El Niño–Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May–October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May–October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001–10 decade, which is consistent with the observed weaker drying trend during this period.
Abstract
Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958–2010 period in the wet months of November–April were primarily associated with El Niño–Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May–October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May–October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001–10 decade, which is consistent with the observed weaker drying trend during this period.
Abstract
Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958–2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November–April) there is a positive relationship with the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May–October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS.
A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.
Abstract
Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958–2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November–April) there is a positive relationship with the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May–October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS.
A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.
Abstract
Factors affecting aviation fuel efficiency are thermal and propulsive efficiencies, and overall drag on aircraft. An along-the-route integration is made for all direct flights in a baseline year, 2010, under current and future atmospheric conditions obtained from 26 climate models under the representative concentration pathway (RCP) 8.5 scenario. Thermal efficiency and propulsive efficiency are affected differently, with the former decreasing by 0.38% and the latter increasing by 0.35%. Consequently, the overall engine efficiency decrease is merely <0.02%. Over the same period, the skin frictional drag increases ~3.5% from the increased air viscosity. This component is only 5.7% of the total drag, and the ~3.5% increase in air viscosity accounts for a 0.2% inefficiency in fuel consumption. A t test is performed for the multiple-model ensemble mean time series of fuel efficiency decrease for two 20-yr periods centered on years 2010 and 2090, respectively. The trend is found to be statistically significant (p value = 0.0017). The total decrease in aircraft fuel efficiency is equivalent to ~0.68 billion gallons of additional fuel annually, a qualitatively robust conclusion, but quantitatively there is a large interclimate model spread.
Abstract
Factors affecting aviation fuel efficiency are thermal and propulsive efficiencies, and overall drag on aircraft. An along-the-route integration is made for all direct flights in a baseline year, 2010, under current and future atmospheric conditions obtained from 26 climate models under the representative concentration pathway (RCP) 8.5 scenario. Thermal efficiency and propulsive efficiency are affected differently, with the former decreasing by 0.38% and the latter increasing by 0.35%. Consequently, the overall engine efficiency decrease is merely <0.02%. Over the same period, the skin frictional drag increases ~3.5% from the increased air viscosity. This component is only 5.7% of the total drag, and the ~3.5% increase in air viscosity accounts for a 0.2% inefficiency in fuel consumption. A t test is performed for the multiple-model ensemble mean time series of fuel efficiency decrease for two 20-yr periods centered on years 2010 and 2090, respectively. The trend is found to be statistically significant (p value = 0.0017). The total decrease in aircraft fuel efficiency is equivalent to ~0.68 billion gallons of additional fuel annually, a qualitatively robust conclusion, but quantitatively there is a large interclimate model spread.
