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This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

© 2019, Springer Nature B.V. Globally, harmful algal blooms (HABs) caused by marine phytoplankton pose a significant threat to human health and result in enormous economic loss. Many strategies have been implemented for their detection, management, and control. One such instrument is the red-tide acoustic sensing system (RASS) which utilizes a backscattered acoustic signal to detect microalgal cells in the water column, proving to be a cheap and easy tool for the HABs detection. In the current study, five RASSs were deployed at fish farms located in HAB-occurring areas of South Korea during the summer of 2018, and their performance examined for the HABs detection. From 26 July to 6 August 2018, the dinoflagellate Cochlodinium polykrikoides was the most abundant HAB species across all locations. A standard curve between the relative received level (RRL) of acoustic intensity as measured by RASS and cell densities of HAB species was constructed (R2 = 0.846) and was successfully applied to quantify the HAB cell density. The RRL values noticeably increased from July 25, 2018 and decreased from August 1. The RRL values corresponding to warning level of HABs (≥ 100 cells mL−1) were not measured after August 5. The highest 22 RRL was recorded on July 29, which corresponded to 2,782 cells mL−1. To evaluate the efficacy and accuracy of the RASS, a significant correlation (R2 = 0.997) between HAB cell abundance and backscattered acoustic signals was confirmed. Results from our study show that the RASS can produce accurate, real-time bloom data, providing significant advantages for the warning of HABs.

© 2019, Springer Nature B.V. Globally, harmful algal blooms (HABs) caused by marine phytoplankton pose a significant threat to human health and result in enormous economic loss. Many strategies have been implemented for their detection, management, and control. One such instrument is the red-tide acoustic sensing system (RASS) which utilizes a backscattered acoustic signal to detect microalgal cells in the water column, proving to be a cheap and easy tool for the HABs detection. In the current study, five RASSs were deployed at fish farms located in HAB-occurring areas of South Korea during the summer of 2018, and their performance examined for the HABs detection. From 26 July to 6 August 2018, the dinoflagellate Cochlodinium polykrikoides was the most abundant HAB species across all locations. A standard curve between the relative received level (RRL) of acoustic intensity as measured by RASS and cell densities of HAB species was constructed (R2 = 0.846) and was successfully applied to quantify the HAB cell density. The RRL values noticeably increased from July 25, 2018 and decreased from August 1. The RRL values corresponding to warning level of HABs (≥ 100 cells mL−1) were not measured after August 5. The highest 22 RRL was recorded on July 29, which corresponded to 2,782 cells mL−1. To evaluate the efficacy and accuracy of the RASS, a significant correlation (R2 = 0.997) between HAB cell abundance and backscattered acoustic signals was confirmed. Results from our study show that the RASS can produce accurate, real-time bloom data, providing significant advantages for the warning of HABs.

The relationship between leading atmospheric teleconnection patterns and Greenland Ice Sheet (GrIS) temperature, precipitation, and surface mass balance (SMB) are investigated for the last 36 summers (1979-2014) based on Modern-Era Retrospective analysis for Research and Applications version 2 reanalyses. The results indicate that the negative phase of both the North Atlantic Oscillation (NAO) and Arctic Oscillation, associated with warm and dry conditions for the GrIS, lead to SMB decreases within 0-1 months. Furthermore, the positive phase of the East Atlantic (EA) pattern often lags the negative NAO, reflecting a dynamical linkage between these modes that acts to further enhance the warm and dry conditions over the GrIS, leading to a favorable environment for enhanced surface mass loss. The development of a strong negative NAO in combination with a strong positive EA in recent years leads to significantly larger GrIS warming compared to when the negative NAO occurs in combination with a negative or weak positive EA (0.69 K versus 0.13 K anomaly). During 2009 and 2011, weakened (as compared to conditions during the severe surface melt cases of 2010 and 2012) local high pressure blocking produced colder northerly flow over the GrIS inhibiting warming despite the occurrence of a strong negative NAO, reflecting an important role for the EA during those years. In particular, the EA acts with the NAO to enhance warming in 2010 and 2012, and weaken high pressure blocking in 2009 and 2011. In general, high pressure blocking primarily impacts the western areas of the GrIS via advective temperature increases, while changes in net surface radiative fluxes account for both western and eastern GrIS temperature changes.

The relationship between leading atmospheric teleconnection patterns and Greenland Ice Sheet (GrIS) temperature, precipitation, and surface mass balance (SMB) are investigated for the last 36 summers (1979-2014) based on Modern-Era Retrospective analysis for Research and Applications version 2 reanalyses. The results indicate that the negative phase of both the North Atlantic Oscillation (NAO) and Arctic Oscillation, associated with warm and dry conditions for the GrIS, lead to SMB decreases within 0-1 months. Furthermore, the positive phase of the East Atlantic (EA) pattern often lags the negative NAO, reflecting a dynamical linkage between these modes that acts to further enhance the warm and dry conditions over the GrIS, leading to a favorable environment for enhanced surface mass loss. The development of a strong negative NAO in combination with a strong positive EA in recent years leads to significantly larger GrIS warming compared to when the negative NAO occurs in combination with a negative or weak positive EA (0.69 K versus 0.13 K anomaly). During 2009 and 2011, weakened (as compared to conditions during the severe surface melt cases of 2010 and 2012) local high pressure blocking produced colder northerly flow over the GrIS inhibiting warming despite the occurrence of a strong negative NAO, reflecting an important role for the EA during those years. In particular, the EA acts with the NAO to enhance warming in 2010 and 2012, and weaken high pressure blocking in 2009 and 2011. In general, high pressure blocking primarily impacts the western areas of the GrIS via advective temperature increases, while changes in net surface radiative fluxes account for both western and eastern GrIS temperature changes.

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models withmoderate to high horizontal resolution (1°-0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. For both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks' seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors' results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Finally, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.

Although a theory of the climatology of tropical cyclone formation remains elusive, high-resolution climate models can now simulate many aspects of tropical cyclone climate.

Although a theory of the climatology of tropical cyclone formation remains elusive, high-resolution climate models can now simulate many aspects of tropical cyclone climate.

A realistic representation of the North Atlantic tropical cyclone tracks is crucial as it allows, for example, explaining potential changes in U.S. landfalling systems. Here, the authors present a tentative study that examines the ability of recent climate models to represent North Atlantic tropical cyclone tracks. Tracks from two types of climate models are evaluated: explicit tracks are obtained from tropical cyclones simulated in regional or global climate models withmoderate to high horizontal resolution (1°-0.25°), and downscaled tracks are obtained using a downscaling technique with large-scale environmental fields from a subset of these models. For both configurations, tracks are objectively separated into four groups using a cluster technique, leading to a zonal and a meridional separation of the tracks. The meridional separation largely captures the separation between deep tropical and subtropical, hybrid or baroclinic cyclones, while the zonal separation segregates Gulf of Mexico and Cape Verde storms. The properties of the tracks' seasonality, intensity, and power dissipation index in each cluster are documented for both configurations. The authors' results show that, except for the seasonality, the downscaled tracks better capture the observed characteristics of the clusters. The authors also use three different idealized scenarios to examine the possible future changes of tropical cyclone tracks under 1) warming sea surface temperature, 2) increasing carbon dioxide, and 3) a combination of the two. The response to each scenario is highly variable depending on the simulation considered. Finally, the authors examine the role of each cluster in these future changes and find no preponderant contribution of any single cluster over the others.

The global characteristics of tropical cyclones (TCs) simulated by several climate models are analyzed and compared with observations. The global climate models were forced by the same sea surface temperature (SST) fields in two types of experiments, using climatological SST and interannually varying SST. TC tracks and intensities are derived from each model's output fields by the group who ran that model, using their own preferred tracking scheme; the study considers the combination of model and tracking scheme as a single modeling system, and compares the properties derived from the different systems. Overall, the observed geographic distribution of global TC frequency was reasonably well reproduced. As expected, with the exception of one model, intensities of the simulated TC were lower than in observations, to a degree that varies considerably across models. Key Points: Multimodel comparison of tropical cyclone activity in global climate models Geographic distribution of the TC activity is similar to observed Most models produce tropical cyclones weaker than observed

The global characteristics of tropical cyclones (TCs) simulated by several climate models are analyzed and compared with observations. The global climate models were forced by the same sea surface temperature (SST) fields in two types of experiments, using climatological SST and interannually varying SST. TC tracks and intensities are derived from each model's output fields by the group who ran that model, using their own preferred tracking scheme; the study considers the combination of model and tracking scheme as a single modeling system, and compares the properties derived from the different systems. Overall, the observed geographic distribution of global TC frequency was reasonably well reproduced. As expected, with the exception of one model, intensities of the simulated TC were lower than in observations, to a degree that varies considerably across models. Key Points: Multimodel comparison of tropical cyclone activity in global climate models Geographic distribution of the TC activity is similar to observed Most models produce tropical cyclones weaker than observed