Abstract
Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.
Abstract
Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.
Abstract
North Atlantic sea surface temperature (SST) variability plays a critical role in modulating the climate system. However, characterizing patterns of North Atlantic SST variability and diagnosing the associated mechanisms is challenging because they involve coupled atmosphere–ocean interactions with complex spatiotemporal relationships. Here we address these challenges by applying a time-evolving self-organizing map approach to a long preindustrial coupled control simulation and identify a variety of 10-yr spatiotemporal evolutions of winter SST anomalies, including but not limited to those associated with the North Atlantic Oscillation–Atlantic multidecadal variability (NAO–AMV)-like interactions. To assess mechanisms and atmospheric responses associated with various SST spatiotemporal evolutions, composites of atmospheric and oceanic variables associated with these evolutions are investigated. Results show that transient-eddy activities and atmospheric circulation responses exist in almost all the evolutions that are closely correlated to the details of the SST pattern. In terms of the mechanisms responsible for generating various SST evolutions, composites of ocean heat budget terms demonstrate that contributions to upper-ocean temperature tendency from resolved ocean advection and surface heat fluxes rarely oppose each other over 10-yr periods in the subpolar North Atlantic. We further explore the potential for predictability for some of these 10-yr SST evolutions that start with similar states but end with different states. However, we find that these are associated with abrupt changes in atmospheric variability and are unlikely to be predictable. In summary, this study broadly investigates the atmospheric responses to and the mechanisms governing the North Atlantic SST evolutions over 10-yr periods.
Significance Statement
Climate variability in the North Atlantic Ocean has wide-ranging impacts on global and regional climate. However, the processes involved include interactions between the ocean and atmosphere that vary across both space and time, making it challenging to characterize and predict. Using a novel machine learning approach, this study identifies various time evolutions of North Atlantic sea surface temperature patterns over 10-yr periods. This includes evolutions with similar start states but different trajectories, which have important implications for predictability. Furthermore, we investigate the mechanisms responsible for these evolutions and how different sea surface temperature patterns affect atmospheric circulation through small-scale atmospheric disturbances. These new insights into the complex ocean–atmosphere interactions over time are critical for improving decadal prediction skill.
Abstract
North Atlantic sea surface temperature (SST) variability plays a critical role in modulating the climate system. However, characterizing patterns of North Atlantic SST variability and diagnosing the associated mechanisms is challenging because they involve coupled atmosphere–ocean interactions with complex spatiotemporal relationships. Here we address these challenges by applying a time-evolving self-organizing map approach to a long preindustrial coupled control simulation and identify a variety of 10-yr spatiotemporal evolutions of winter SST anomalies, including but not limited to those associated with the North Atlantic Oscillation–Atlantic multidecadal variability (NAO–AMV)-like interactions. To assess mechanisms and atmospheric responses associated with various SST spatiotemporal evolutions, composites of atmospheric and oceanic variables associated with these evolutions are investigated. Results show that transient-eddy activities and atmospheric circulation responses exist in almost all the evolutions that are closely correlated to the details of the SST pattern. In terms of the mechanisms responsible for generating various SST evolutions, composites of ocean heat budget terms demonstrate that contributions to upper-ocean temperature tendency from resolved ocean advection and surface heat fluxes rarely oppose each other over 10-yr periods in the subpolar North Atlantic. We further explore the potential for predictability for some of these 10-yr SST evolutions that start with similar states but end with different states. However, we find that these are associated with abrupt changes in atmospheric variability and are unlikely to be predictable. In summary, this study broadly investigates the atmospheric responses to and the mechanisms governing the North Atlantic SST evolutions over 10-yr periods.
Significance Statement
Climate variability in the North Atlantic Ocean has wide-ranging impacts on global and regional climate. However, the processes involved include interactions between the ocean and atmosphere that vary across both space and time, making it challenging to characterize and predict. Using a novel machine learning approach, this study identifies various time evolutions of North Atlantic sea surface temperature patterns over 10-yr periods. This includes evolutions with similar start states but different trajectories, which have important implications for predictability. Furthermore, we investigate the mechanisms responsible for these evolutions and how different sea surface temperature patterns affect atmospheric circulation through small-scale atmospheric disturbances. These new insights into the complex ocean–atmosphere interactions over time are critical for improving decadal prediction skill.
Abstract
Previous studies have indicated that boreal winter-to-spring sea surface temperature anomalies (SSTA) over the tropical Atlantic or Indian Ocean can trigger the central-Pacific (CP) type of ENSO in the following winter due to winds over the western Pacific. Here, with the aid of observational data and CMIP5 model simulations, we demonstrate that the ability of the winter-to-spring north tropical Atlantic (NTA) SSTA or Indian Ocean Basin (IOB) mode to initiate CP ENSO events in the following winter may strongly depend on each other. Most warming events of the IOB and NTA, which are followed by CP La Niña events, are concomitant. The synergistic effect of the IOB and NTA SSTA may produce greater CP ENSO events in the subsequent winter via Walker circulation adjustments. The impacts between warming and cooling events of the IOB and NTA SSTA are asymmetric. IOB and NTA warmings appear to contribute to the subsequent CP La Niña development, which is much greater than IOB and NTA cooling contributing to CP El Niño. Overall, a combination of the IOB and NTA SSTA precursors may improve predictions of La Niña events.
Significance Statement
Although boreal winter-to-spring sea surface temperature anomalies over the tropical Atlantic or Indian Ocean can trigger central-Pacific (CP) ENSO in the following winter, it is not yet clear whether the effects of these two basins are independent. The purpose of this study is to better understand the joint effect of these two basins on CP ENSO events. We demonstrate that the ability of the north tropical Atlantic (NTA) SSTA to initiate CP ENSO events in the following winter may strongly depend on the state of the Indian Ocean Basin mode (IOB). The synergistic impact of these two basins may produce stronger CP ENSO events. These results highlight the role of three-ocean interactions in ENSO diversity and prediction.
Abstract
Previous studies have indicated that boreal winter-to-spring sea surface temperature anomalies (SSTA) over the tropical Atlantic or Indian Ocean can trigger the central-Pacific (CP) type of ENSO in the following winter due to winds over the western Pacific. Here, with the aid of observational data and CMIP5 model simulations, we demonstrate that the ability of the winter-to-spring north tropical Atlantic (NTA) SSTA or Indian Ocean Basin (IOB) mode to initiate CP ENSO events in the following winter may strongly depend on each other. Most warming events of the IOB and NTA, which are followed by CP La Niña events, are concomitant. The synergistic effect of the IOB and NTA SSTA may produce greater CP ENSO events in the subsequent winter via Walker circulation adjustments. The impacts between warming and cooling events of the IOB and NTA SSTA are asymmetric. IOB and NTA warmings appear to contribute to the subsequent CP La Niña development, which is much greater than IOB and NTA cooling contributing to CP El Niño. Overall, a combination of the IOB and NTA SSTA precursors may improve predictions of La Niña events.
Significance Statement
Although boreal winter-to-spring sea surface temperature anomalies over the tropical Atlantic or Indian Ocean can trigger central-Pacific (CP) ENSO in the following winter, it is not yet clear whether the effects of these two basins are independent. The purpose of this study is to better understand the joint effect of these two basins on CP ENSO events. We demonstrate that the ability of the north tropical Atlantic (NTA) SSTA to initiate CP ENSO events in the following winter may strongly depend on the state of the Indian Ocean Basin mode (IOB). The synergistic impact of these two basins may produce stronger CP ENSO events. These results highlight the role of three-ocean interactions in ENSO diversity and prediction.
Abstract
This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices.
Significance Statement
Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.
Abstract
This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices.
Significance Statement
Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.
Abstract
Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multi-model ensemble, to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte-Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Finally, we compare these scaling rates to those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.
Abstract
Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multi-model ensemble, to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte-Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Finally, we compare these scaling rates to those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.
Abstract
The Targeted Observation by Radars and UAS of Supercells (TORUS) field project observed two supercells on 8 June 2019 in northwestern Kansas and far eastern Colorado. Although these storms occurred in close spatial and temporal proximity, their evolutions were markedly different. The first storm struggled to maintain itself and eventually dissipated. Meanwhile, the second supercell developed just after and slightly to the south of where the first storm dissipated, and then tracked over almost the same location before rapidly intensifying and going on to produce several tornadoes. The objective of this study is to determine why the first storm struggled to survive and failed to produce mesocyclonic tornadoes while the second storm thrived and was cyclically tornadic. Analysis relies on observations collected by the TORUS project—including unoccupied aircraft system (UAS) transects and profiles, mobile soundings, surface mobile mesonet transects, and dual-Doppler wind syntheses from the NOAA P-3 tail Doppler radars. Our results indicate that rapid changes in the low-level wind profile, the second supercell’s interaction with two mesoscale boundaries, an interaction with a rapidly intensifying new updraft just to its west, and the influence of a strong outflow surge likely account for much of the second supercell’s increased strength and tornado production. The rapid evolution of the low-level wind profile may have been most important in raising the probability of the second supercell becoming tornadic, with the new updraft and the outflow surge leading to a favorable storm-scale evolution that increased this probability further.
Abstract
The Targeted Observation by Radars and UAS of Supercells (TORUS) field project observed two supercells on 8 June 2019 in northwestern Kansas and far eastern Colorado. Although these storms occurred in close spatial and temporal proximity, their evolutions were markedly different. The first storm struggled to maintain itself and eventually dissipated. Meanwhile, the second supercell developed just after and slightly to the south of where the first storm dissipated, and then tracked over almost the same location before rapidly intensifying and going on to produce several tornadoes. The objective of this study is to determine why the first storm struggled to survive and failed to produce mesocyclonic tornadoes while the second storm thrived and was cyclically tornadic. Analysis relies on observations collected by the TORUS project—including unoccupied aircraft system (UAS) transects and profiles, mobile soundings, surface mobile mesonet transects, and dual-Doppler wind syntheses from the NOAA P-3 tail Doppler radars. Our results indicate that rapid changes in the low-level wind profile, the second supercell’s interaction with two mesoscale boundaries, an interaction with a rapidly intensifying new updraft just to its west, and the influence of a strong outflow surge likely account for much of the second supercell’s increased strength and tornado production. The rapid evolution of the low-level wind profile may have been most important in raising the probability of the second supercell becoming tornadic, with the new updraft and the outflow surge leading to a favorable storm-scale evolution that increased this probability further.
Abstract
Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
Significance Statement
The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms.
Abstract
Road surface temperatures are a critical factor in determining driving conditions, especially during winter storms. Road temperature observations across the United States are sparse and located mainly along major highways. A machine learning–based system for nowcasting the probability of subfreezing road surface temperatures was developed at NSSL to allow for widespread monitoring of road conditions in real time. In this article, these products were evaluated over two winter seasons. Strengths and weaknesses in the nowcast system were identified by stratifying the evaluation metrics into various subsets. These results show that the current system performed well in general, but significantly underpredicted the probability of subfreezing roads during frozen precipitation events. Machine learning experiments were performed to attempt to address these issues. Evaluations of these experiments indicate reduction in errors when precipitation phase was included as a predictor and precipitating cases were more substantially represented in the training data for the machine learning system.
Significance Statement
The purpose of this study is to better understand the strengths and weaknesses of a system that predicts the probability of subfreezing road surface temperatures. We found that the system performed well in general, but underpredicted the probabilities when frozen precipitation was predicted to reach the surface. These biases were substantially improved by modifying the system to increase its focus on situations with falling precipitation. The updated system should allow for improved monitoring and forecasting of potentially hazardous conditions during winter storms.
Abstract
A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas, while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers determine cooling center locations in response to extreme heat.
Abstract
A cool environment is critical for protecting vulnerable populations from the adverse health effects associated with exposure to extreme heat. Although cooling centers are commonly established to provide temporary heat relief to the public, there is limited research exploring the spatial distributions and accessibility of cooling centers across cities in Texas. The intent of this study was to examine the spatial characteristics of cooling center locations throughout the Texas Triangle megaregion and evaluate the proximity of cooling centers to vulnerable populations. Specifically, spatial clustering analysis was used to quantitatively characterize the spatial distributions of cooling centers in San Antonio, Houston, and Dallas, while spatial lag regression was conducted to evaluate the relationships between indicators of socioeconomic vulnerability and proximity to cooling centers. The findings indicated that cooling centers exhibited clustering at short distances, which suggested there were potential spatial redundancies. The distributions of the cooling centers also illustrated possible accessibility issues due to the concentration of the locations in urban cores. The spatial lag regression models highlighted several problematic relationships, as elderly and disabled populations were located at significantly greater distances from cooling centers in San Antonio and Dallas, respectively. However, numerous insignificant relationships were also observed, which suggested that cooling center locations did not consistently marginalize or favor vulnerable populations. Therefore, a higher degree of intentionality that explicitly considers cooling center proximity to the vulnerable populations they aim to serve might be beneficial as planners and emergency managers determine cooling center locations in response to extreme heat.
Abstract
This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.
Abstract
This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.
Abstract
The distinction between eddy-driven and subtropical jets is conceptually important and well-founded based on different driving mechanisms and dominant types of variability. This climatological perspective may be augmented by considering instantaneous maxima in the wind field and linking these to the time-mean jets. Inspired by EOF and cluster analyses to explore the variability in jet occurrences, we propose a straightforward framework that naturally distinguishes subtropical from eddy-driven jets in instantaneous data. We document that for most ocean basins, there is a clear bimodality in instantaneous jet occurrences in potential temperature–wind speed space. The two types of jets in this phase space align well with the conceptual expectations for subtropical and eddy-driven jets regarding their vertical structure as well as their regional occurrence. Interestingly, the bimodality in phase space is most pronounced in the western North Pacific during winter. The climatological jet in this region is typically regarded as “merged,” resulting from a mixture of thermal driving and eddy driving. Our results clarify that the strongest instantaneous jets in this region are classified as subtropical, with eddy-driven jets occurring in close proximity to the climatological mean jet, though weaker and slightly more poleward. We also show that the regions of climatological transition from predominantly subtropical to predominantly eddy-driven jets are just downstream of the strongest climatological jets.
Abstract
The distinction between eddy-driven and subtropical jets is conceptually important and well-founded based on different driving mechanisms and dominant types of variability. This climatological perspective may be augmented by considering instantaneous maxima in the wind field and linking these to the time-mean jets. Inspired by EOF and cluster analyses to explore the variability in jet occurrences, we propose a straightforward framework that naturally distinguishes subtropical from eddy-driven jets in instantaneous data. We document that for most ocean basins, there is a clear bimodality in instantaneous jet occurrences in potential temperature–wind speed space. The two types of jets in this phase space align well with the conceptual expectations for subtropical and eddy-driven jets regarding their vertical structure as well as their regional occurrence. Interestingly, the bimodality in phase space is most pronounced in the western North Pacific during winter. The climatological jet in this region is typically regarded as “merged,” resulting from a mixture of thermal driving and eddy driving. Our results clarify that the strongest instantaneous jets in this region are classified as subtropical, with eddy-driven jets occurring in close proximity to the climatological mean jet, though weaker and slightly more poleward. We also show that the regions of climatological transition from predominantly subtropical to predominantly eddy-driven jets are just downstream of the strongest climatological jets.
