Abstract
With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.
Abstract
With global warming, the behavior of extreme precipitation shifts toward nonstationarity. Here, we analyze the annual maxima of daily precipitation (AMP) all over the globe using projections of the latest phase of the Coupled Model Intercomparison Project (CMIP6) under four shared socioeconomic pathways (SSPs). The projections were bias corrected using a semiparametric quantile mapping, a novel technique extended to extreme precipitation. This analysis 1) explores the variability of future AMP globally and 2) investigates the performance of stationary and nonstationary models in describing future AMP with trends. The results show that global warming potentially intensifies AMP. For the nonparametric analysis, the 33-yr precipitation levels are increasing up to 33.2 mm compared to the historical period. The parametric analysis shows that the return period of 100-yr historical events will decrease approximately to 50 and 70 years in the Northern and Southern Hemispheres, respectively. Under the highest emission scenario, the projected 100-yr levels are expected to increase by 7.5%–21% over the historical levels. Using stationary models to estimate the 100-yr return level for AMP projections with trends leads to an underestimation of 3.4% on average. Extensive Monte Carlo experiments are implemented to explain this underestimation.
Abstract
Coupled climate models project robust wintertime wetting trend over midlatitude East Asia under global warming scenarios, but the projected change in precipitation shows large intermodel uncertainty over subtropical East Asia from southern China to southwestern Japan. Based on an ensemble of climate models participating in CMIP6, this study shows that the weakened southern branch westerly jet (SWJ) on the southern side of Tibetan Plateau (TP) plays a key role in suppressing subtropical East Asian precipitation. The SWJ is deflected into southwesterly wind on the southeastern side of TP, bringing ascent and precipitation to subtropical East Asia primarily through isentropic gliding. As a result of the poleward and upward shift of the planetary-scale westerly jet under global warming, the SWJ becomes weaker and it acts to suppress subtropical East Asian precipitation by weakening the southwesterly wind and ascent. The SWJ–precipitation linkage also exists on interannual time scales, but the sensitivity of precipitation to interannual SWJ variability is systematically underestimated by the models compared with observation. The combined effects of the change in SWJ strength and the sensitivity of precipitation to SWJ explain about 40% of the intermodel spread of the projected precipitation changes. Observational constraint on the SWJ–precipitation relationship amplifies the projected drying trend and narrows the intermodel spread. It shows that the regional-averaged precipitation over subtropical East Asia decreases by 3.3% per degree of warming, and the amplitude of precipitation reduction over subtropical East Asia (southern China) is about 1.4 (3.4) times the raw projection.
Abstract
Coupled climate models project robust wintertime wetting trend over midlatitude East Asia under global warming scenarios, but the projected change in precipitation shows large intermodel uncertainty over subtropical East Asia from southern China to southwestern Japan. Based on an ensemble of climate models participating in CMIP6, this study shows that the weakened southern branch westerly jet (SWJ) on the southern side of Tibetan Plateau (TP) plays a key role in suppressing subtropical East Asian precipitation. The SWJ is deflected into southwesterly wind on the southeastern side of TP, bringing ascent and precipitation to subtropical East Asia primarily through isentropic gliding. As a result of the poleward and upward shift of the planetary-scale westerly jet under global warming, the SWJ becomes weaker and it acts to suppress subtropical East Asian precipitation by weakening the southwesterly wind and ascent. The SWJ–precipitation linkage also exists on interannual time scales, but the sensitivity of precipitation to interannual SWJ variability is systematically underestimated by the models compared with observation. The combined effects of the change in SWJ strength and the sensitivity of precipitation to SWJ explain about 40% of the intermodel spread of the projected precipitation changes. Observational constraint on the SWJ–precipitation relationship amplifies the projected drying trend and narrows the intermodel spread. It shows that the regional-averaged precipitation over subtropical East Asia decreases by 3.3% per degree of warming, and the amplitude of precipitation reduction over subtropical East Asia (southern China) is about 1.4 (3.4) times the raw projection.
Abstract
Research has found that people who know the least about a topic are often very overconfident of their knowledge, while those who know the most often underestimate their knowledge. This finding, known as the Dunning–Kruger effect (DKE) has recently been shown to occur in knowledge of severe weather as well. The current study investigated whether being overconfident in one’s knowledge might translate into a tendency to make poorer sheltering decisions when faced with severe weather. Participants took two severe weather quizzes, one of perceived knowledge and one of objective knowledge. Participants also predicted their performance on both quizzes. The participants then saw four wireless emergency tornado warning alerts on a simulated smartphone screen, along with a tornado scenario, and then made two protective action decisions: one about immediately sheltering in place and the other the likelihood they would drive away. The results revealed that the participants did exhibit the DKE: those with the lowest levels of knowledge exhibited the most overconfidence while those with the highest levels of knowledge underestimated their performance. Also, in comparison with individuals with the most knowledge, those with the least knowledge were the most likely to state that they would not shelter immediately and that they would get in their car and drive away. Although more education is needed, the findings suggest a conundrum: those who know the least about severe weather, thinking they know a lot, are probably those individuals least likely to seek out additional education on the topic.
Significance Statement
Tornadoes are common in many states, and the National Weather Service issues tornado warnings in the hopes that individuals will take protective action. Previous research has found that people with low levels of knowledge (such as knowledge of severe weather) are often overconfident of their knowledge. This study explores whether those with low (as compared with high) severe weather knowledge make poorer decisions to a tornado warning. The findings show that those with the lowest knowledge were indeed overconfident and that they were less likely to shelter and more likely to drive away than those with high knowledge. The findings highlight that more severe weather education, although a worthy goal, might be difficult to implement if knowledge confidence is already high.
Abstract
Research has found that people who know the least about a topic are often very overconfident of their knowledge, while those who know the most often underestimate their knowledge. This finding, known as the Dunning–Kruger effect (DKE) has recently been shown to occur in knowledge of severe weather as well. The current study investigated whether being overconfident in one’s knowledge might translate into a tendency to make poorer sheltering decisions when faced with severe weather. Participants took two severe weather quizzes, one of perceived knowledge and one of objective knowledge. Participants also predicted their performance on both quizzes. The participants then saw four wireless emergency tornado warning alerts on a simulated smartphone screen, along with a tornado scenario, and then made two protective action decisions: one about immediately sheltering in place and the other the likelihood they would drive away. The results revealed that the participants did exhibit the DKE: those with the lowest levels of knowledge exhibited the most overconfidence while those with the highest levels of knowledge underestimated their performance. Also, in comparison with individuals with the most knowledge, those with the least knowledge were the most likely to state that they would not shelter immediately and that they would get in their car and drive away. Although more education is needed, the findings suggest a conundrum: those who know the least about severe weather, thinking they know a lot, are probably those individuals least likely to seek out additional education on the topic.
Significance Statement
Tornadoes are common in many states, and the National Weather Service issues tornado warnings in the hopes that individuals will take protective action. Previous research has found that people with low levels of knowledge (such as knowledge of severe weather) are often overconfident of their knowledge. This study explores whether those with low (as compared with high) severe weather knowledge make poorer decisions to a tornado warning. The findings show that those with the lowest knowledge were indeed overconfident and that they were less likely to shelter and more likely to drive away than those with high knowledge. The findings highlight that more severe weather education, although a worthy goal, might be difficult to implement if knowledge confidence is already high.
Abstract
Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h−1), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate > 10 mm h−1, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (Z DR) and specific differential phase (K DP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The K DP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage.
Abstract
Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h−1), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate > 10 mm h−1, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (Z DR) and specific differential phase (K DP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The K DP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage.
Abstract
At 26.5°N in the North Atlantic, a continuous transbasin observational array has been established since 2004 to detect the strength of the Atlantic meridional overturning circulation. The observational record shows that the subtropical Atlantic meridional overturning circulation has weakened by 2.5 ± 1.5 Sv (as mean ± 95% interval; 1 Sv ≡ 106 m3 s−1) since 2008 compared to the initial 4-yr average. Strengthening of the upper southward geostrophic transport (with a 2.6 ± 1.6 Sv southward increase) derived from thermal wind dominates this Atlantic meridional overturning circulation decline. We decompose the geostrophic transport into its temperature and salinity components to compare their contributions to the transport variability. The contributions of temperature and salinity components to the southward geostrophic transport strengthening are 1.0 ± 2.5 and 1.6 ± 1.3 Sv, respectively. The variation of salinity component is significant at the 95% confidence level, while the temperature component’s variation is not. This result highlights the vital role that salinity plays in the subtropical Atlantic meridional overturning circulation variability, which has been overlooked in previous studies. We further analyze the geostrophic transport variations and their temperature and salinity components arising from different water masses, which shows that a warming signal in Labrador Sea Water and a freshening signal in Nordic Sea Water are two prominent sources of the geostrophic transport increase. Comparison of the temperature and salinity records of the 26.5°N array with the upstream records from repeated hydrographic sections across the Labrador Sea suggests that these thermohaline signals may be exported from the subpolar Atlantic via the deep western boundary current.
Abstract
At 26.5°N in the North Atlantic, a continuous transbasin observational array has been established since 2004 to detect the strength of the Atlantic meridional overturning circulation. The observational record shows that the subtropical Atlantic meridional overturning circulation has weakened by 2.5 ± 1.5 Sv (as mean ± 95% interval; 1 Sv ≡ 106 m3 s−1) since 2008 compared to the initial 4-yr average. Strengthening of the upper southward geostrophic transport (with a 2.6 ± 1.6 Sv southward increase) derived from thermal wind dominates this Atlantic meridional overturning circulation decline. We decompose the geostrophic transport into its temperature and salinity components to compare their contributions to the transport variability. The contributions of temperature and salinity components to the southward geostrophic transport strengthening are 1.0 ± 2.5 and 1.6 ± 1.3 Sv, respectively. The variation of salinity component is significant at the 95% confidence level, while the temperature component’s variation is not. This result highlights the vital role that salinity plays in the subtropical Atlantic meridional overturning circulation variability, which has been overlooked in previous studies. We further analyze the geostrophic transport variations and their temperature and salinity components arising from different water masses, which shows that a warming signal in Labrador Sea Water and a freshening signal in Nordic Sea Water are two prominent sources of the geostrophic transport increase. Comparison of the temperature and salinity records of the 26.5°N array with the upstream records from repeated hydrographic sections across the Labrador Sea suggests that these thermohaline signals may be exported from the subpolar Atlantic via the deep western boundary current.
Abstract
This paper presents an innovational way of assimilating observations of clouds into the icosahedral nonhydrostatic weather forecasting model for regional scale (ICON-D2), which is operated by the German Weather Service (Deutscher Wetterdienst) (DWD). A convolutional neural network (CNN) is trained to detect clouds in camera photographs. The network’s output is a grayscale picture, in which each pixel has a value between 0 and 1, describing the probability of the pixel belonging to a cloud (1) or not (0). By averaging over a certain box of the picture a value for the cloud cover of that region is obtained. A forward operator is built to map an ICON model state into the observation space. A three-dimensional grid in the space of the camera’s perspective is constructed and the ICON model variable cloud cover (CLC) is interpolated onto that grid. The maximum CLC along the rays that fabricate the camera grid, is taken as a model equivalent for each pixel. After superobbing, monitoring experiments have been conducted to compare the observations and model equivalents over a longer time period, yielding promising results. Further we show the performance of a single assimilation step as well as a longer assimilation experiment over a time period of 6 days, which also yields good results. These findings are proof of concept and further research has to be invested before these new innovational observations can be assimilated operationally in any numerical weather prediction (NWP) model.
Abstract
This paper presents an innovational way of assimilating observations of clouds into the icosahedral nonhydrostatic weather forecasting model for regional scale (ICON-D2), which is operated by the German Weather Service (Deutscher Wetterdienst) (DWD). A convolutional neural network (CNN) is trained to detect clouds in camera photographs. The network’s output is a grayscale picture, in which each pixel has a value between 0 and 1, describing the probability of the pixel belonging to a cloud (1) or not (0). By averaging over a certain box of the picture a value for the cloud cover of that region is obtained. A forward operator is built to map an ICON model state into the observation space. A three-dimensional grid in the space of the camera’s perspective is constructed and the ICON model variable cloud cover (CLC) is interpolated onto that grid. The maximum CLC along the rays that fabricate the camera grid, is taken as a model equivalent for each pixel. After superobbing, monitoring experiments have been conducted to compare the observations and model equivalents over a longer time period, yielding promising results. Further we show the performance of a single assimilation step as well as a longer assimilation experiment over a time period of 6 days, which also yields good results. These findings are proof of concept and further research has to be invested before these new innovational observations can be assimilated operationally in any numerical weather prediction (NWP) model.
Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.
Abstract
Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.
Abstract
The second part of this series presents results from verifying a precipitation forecast calibration method discussed in part 1, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12) reforecasts were used in this study. The method was validated with pre-operational GEFSv12 forecasts from the between December 2017 and November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models.
Part 1 described adaptations to the methodology to leverage the ~ 20-year GEFSv12 reforecast data. As shown in this part 2, when compared to probabilistic quantitative precipitation forecasts (PQPFs) from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., < 5 days) and for forecasts of events from light precipitation to > 10 mm 6 h−1. Cool-season events in the western US were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Abstract
The second part of this series presents results from verifying a precipitation forecast calibration method discussed in part 1, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12) reforecasts were used in this study. The method was validated with pre-operational GEFSv12 forecasts from the between December 2017 and November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models.
Part 1 described adaptations to the methodology to leverage the ~ 20-year GEFSv12 reforecast data. As shown in this part 2, when compared to probabilistic quantitative precipitation forecasts (PQPFs) from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., < 5 days) and for forecasts of events from light precipitation to > 10 mm 6 h−1. Cool-season events in the western US were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.
Abstract
Data from a dual-polarized, solid-state X-band radar and an operational C-band weather radar are used for high-resolution analyses of two hailstorms in the Vienna, Austria, region. The combination of both radars provides rapid-update (1 min) polarimetric data paired with wind field data of a dual-Doppler analysis. This is the first time that such an advanced setup is used to examine severe storm dynamics at the eastern Alpine fringe, where the influence of local topography is particularly challenging for thunderstorm prediction. We investigate two storms transitioning from the pre-Alps into the Vienna basin with different characteristics: 1) A rapidly evolving multicell storm producing large hail (5 cm), with observations of an intense Z DR column preceding hail formation and the rapid development of multiple pulses of hail; and 2) a cold pool–driven squall line with small hail, for which we find that the updraft location inhibited the formation of larger hailstones. For both cases, we analyzed the evolution of different Z DR column metrics as well as updraft speed and size and found that (i) the 90th percentile of Z DR within the Z DR column was highest for the cell later producing large hail, (ii) the peak 90th percentile of Z DR preceded large hailfall by 20 min and highest updraft size and speed by 10 min, and (iii) sudden drops of the 90th percentile of ZH within the Z DR column indicated imminent hailfall.
Significance Statement
Thunderstorm evolution on the transition from complex terrain into the Vienna basin in northeastern Austria varies strongly. In some instances, thunderstorm cells intensify once they reach flat terrain, while in most cases there is a weakening tendency. To improve our process understanding and short-term forecasting methods, we analyze two representative cases of hail-bearing storms transitioning into the Vienna basin. We mainly build our study on data from a new, cost-efficient weather radar, complemented by an operational radar, lightning observations, and ground reports. Our results show which radar variables could be well suited for early detection of intensification, and how they relate to thunderstorm updraft speeds and lightning activity.
Abstract
Data from a dual-polarized, solid-state X-band radar and an operational C-band weather radar are used for high-resolution analyses of two hailstorms in the Vienna, Austria, region. The combination of both radars provides rapid-update (1 min) polarimetric data paired with wind field data of a dual-Doppler analysis. This is the first time that such an advanced setup is used to examine severe storm dynamics at the eastern Alpine fringe, where the influence of local topography is particularly challenging for thunderstorm prediction. We investigate two storms transitioning from the pre-Alps into the Vienna basin with different characteristics: 1) A rapidly evolving multicell storm producing large hail (5 cm), with observations of an intense Z DR column preceding hail formation and the rapid development of multiple pulses of hail; and 2) a cold pool–driven squall line with small hail, for which we find that the updraft location inhibited the formation of larger hailstones. For both cases, we analyzed the evolution of different Z DR column metrics as well as updraft speed and size and found that (i) the 90th percentile of Z DR within the Z DR column was highest for the cell later producing large hail, (ii) the peak 90th percentile of Z DR preceded large hailfall by 20 min and highest updraft size and speed by 10 min, and (iii) sudden drops of the 90th percentile of ZH within the Z DR column indicated imminent hailfall.
Significance Statement
Thunderstorm evolution on the transition from complex terrain into the Vienna basin in northeastern Austria varies strongly. In some instances, thunderstorm cells intensify once they reach flat terrain, while in most cases there is a weakening tendency. To improve our process understanding and short-term forecasting methods, we analyze two representative cases of hail-bearing storms transitioning into the Vienna basin. We mainly build our study on data from a new, cost-efficient weather radar, complemented by an operational radar, lightning observations, and ground reports. Our results show which radar variables could be well suited for early detection of intensification, and how they relate to thunderstorm updraft speeds and lightning activity.
Abstract
This study revisits the issue of why tropical cyclones (TCs) develop more rapidly at lower latitudes, using ensemble axisymmetric numerical simulations and energy diagnostics based on the isentropic analysis, with the focus on the relative importance of the outflow-layer and boundary-layer inertial stabilities to TC intensification and energy cycle. Results show that although lowering the outflow-layer Coriolis parameter and thus inertial stability can slightly strengthen the outflow, it does not affect the simulated TC development, whereas lowering the boundary-layer Coriolis parameter largely enhances the secondary circulation and TC intensification as in the experiment with a reduced Coriolis parameter throughout the model atmosphere. This suggests that TC outflow is more likely a passive result of the convergent inflow in the boundary layer and convective updraft in the eyewall.
The boundary-layer inertial stability is found to control the convergent inflow in the boundary layer and depth of convection in the eyewall and thus the temperature of energy sink in the TC heat engine, which determines the efficiency and overall mechanical output of heat engine and thus TC intensification. It is also shown that the hypothesized isothermal and adiabatic compression legs at the downstream end of the outflow in the classical Carnot cycle is not supported in the thermodynamic cycle of the simulated TCs, implying that the assumed TC Carnot cycle is not closed. It is the theoretical maximum work of heat engine, not the energy expenditure following the outflow downstream, that determines the mechanical work used to intensify a TC.
Abstract
This study revisits the issue of why tropical cyclones (TCs) develop more rapidly at lower latitudes, using ensemble axisymmetric numerical simulations and energy diagnostics based on the isentropic analysis, with the focus on the relative importance of the outflow-layer and boundary-layer inertial stabilities to TC intensification and energy cycle. Results show that although lowering the outflow-layer Coriolis parameter and thus inertial stability can slightly strengthen the outflow, it does not affect the simulated TC development, whereas lowering the boundary-layer Coriolis parameter largely enhances the secondary circulation and TC intensification as in the experiment with a reduced Coriolis parameter throughout the model atmosphere. This suggests that TC outflow is more likely a passive result of the convergent inflow in the boundary layer and convective updraft in the eyewall.
The boundary-layer inertial stability is found to control the convergent inflow in the boundary layer and depth of convection in the eyewall and thus the temperature of energy sink in the TC heat engine, which determines the efficiency and overall mechanical output of heat engine and thus TC intensification. It is also shown that the hypothesized isothermal and adiabatic compression legs at the downstream end of the outflow in the classical Carnot cycle is not supported in the thermodynamic cycle of the simulated TCs, implying that the assumed TC Carnot cycle is not closed. It is the theoretical maximum work of heat engine, not the energy expenditure following the outflow downstream, that determines the mechanical work used to intensify a TC.
