Abstract
Convective variability is used to diagnose different pathways towards convective self-aggregation (CSA) in radiative-convective equilibrium simulations with two cloud-resolving models, SCALE and VVM. The results show that convection undergoes gradual growth in SCALE and fast transition in VVM, which is associated with different mechanisms between the two models. In SCALE, strong radiative cooling associated with a dry environment drives the circulation from the dry region, and the dry environment results from strong subsidence and insufficient surface flux supply. The circulation driven by the radiative cooling then pushes convection aggregating, which is the dry-radiation pathway. In VVM, CSA develops due to the rapid strengthening of circulation driven by convective systems in the moist region, which is the convection-upscaling pathway. The different pathways of CSA development can be attributed to the upscale process of convective structures identified by the cloud size spectrum. The upscaling of large-size convective systems can enhance circulation from the moist region in VVM. In SCALE, the infrequent appearance of large convective systems is insufficient to generate circulation, as compensating subsidence can occur within the moist region even in the absence of convective systems. This study shows that the convective variabilities between models can lead to different pathways of CSA, and mechanism-denial experiments also support our analyses.
Abstract
Convective variability is used to diagnose different pathways towards convective self-aggregation (CSA) in radiative-convective equilibrium simulations with two cloud-resolving models, SCALE and VVM. The results show that convection undergoes gradual growth in SCALE and fast transition in VVM, which is associated with different mechanisms between the two models. In SCALE, strong radiative cooling associated with a dry environment drives the circulation from the dry region, and the dry environment results from strong subsidence and insufficient surface flux supply. The circulation driven by the radiative cooling then pushes convection aggregating, which is the dry-radiation pathway. In VVM, CSA develops due to the rapid strengthening of circulation driven by convective systems in the moist region, which is the convection-upscaling pathway. The different pathways of CSA development can be attributed to the upscale process of convective structures identified by the cloud size spectrum. The upscaling of large-size convective systems can enhance circulation from the moist region in VVM. In SCALE, the infrequent appearance of large convective systems is insufficient to generate circulation, as compensating subsidence can occur within the moist region even in the absence of convective systems. This study shows that the convective variabilities between models can lead to different pathways of CSA, and mechanism-denial experiments also support our analyses.
Abstract
The export of Antarctic Bottom Water (AABW) supplies the bottom cell of the global overturning circulation and plays a key role in regulating climate. This AABW outflow must cross, and is therefore mediated by, the Antarctic Circumpolar Current (ACC). Previous studies present widely-varying conceptions of the role of the ACC in directing AABW across the Southern Ocean, suggesting either that AABW may be zonally recirculated by the ACC, or that AABW may flow northward within deep western boundary currents (DWBC) against bathymetry. In this study the authors investigate how the forcing and geometry of the ACC influences the transport and transformation of AABW using a suite of process-oriented model simulations. The model exhibits a strong dependence on the elevation of bathymetry relative to AABW layer thickness: higher meridional ridges suppress zonal AABW exchange, increase the strength of flow in the DWBC, and reduce the meridional variation in AABW density across the ACC. Furthermore, the transport and transformation vary with density within the AABW layer, with denser varieties of AABW being less efficiently transported between basins. These findings indicate that changes in the thickness of the AABW layer, for example due to changes in Antarctic shelf processes, and tectonic changes in the sea floor shape may alter the pathways and transformation of AABW across the ACC.
Abstract
The export of Antarctic Bottom Water (AABW) supplies the bottom cell of the global overturning circulation and plays a key role in regulating climate. This AABW outflow must cross, and is therefore mediated by, the Antarctic Circumpolar Current (ACC). Previous studies present widely-varying conceptions of the role of the ACC in directing AABW across the Southern Ocean, suggesting either that AABW may be zonally recirculated by the ACC, or that AABW may flow northward within deep western boundary currents (DWBC) against bathymetry. In this study the authors investigate how the forcing and geometry of the ACC influences the transport and transformation of AABW using a suite of process-oriented model simulations. The model exhibits a strong dependence on the elevation of bathymetry relative to AABW layer thickness: higher meridional ridges suppress zonal AABW exchange, increase the strength of flow in the DWBC, and reduce the meridional variation in AABW density across the ACC. Furthermore, the transport and transformation vary with density within the AABW layer, with denser varieties of AABW being less efficiently transported between basins. These findings indicate that changes in the thickness of the AABW layer, for example due to changes in Antarctic shelf processes, and tectonic changes in the sea floor shape may alter the pathways and transformation of AABW across the ACC.
Abstract
Drought is a recurrent natural phenomenon, but there is concern that climate change may increase the frequency or severity of drought in Alaska. Because most common drought indices were designed for lower latitudes, it is unclear how effectively they characterize drought in Alaska’s diverse, high-latitude climates. Here, we compare three commonly used meteorological drought indices [the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (scPDSI)] with each other and with streamflow across Alaska’s 13 climate divisions. All of the drought indices identify major droughts, but the severity of the drought varies depending on the index used. The SPI and the SPEI are more flexible and often better correlated with streamflow than the scPDSI, and we recommend using them. Although SPI and SPEI are very similar in energy-limited climates, the drought metrics do diverge in drier locations in recent years, and consideration of the impact of temperature on drought may grow more important in the coming decades. Hargreaves potential evapotranspiration (PET) estimates appeared more physically realistic than the more commonly used Thornthwaite equation and are equally easy to calculate, so we suggest using the Hargreaves equation when PET is estimated from temperature. This study, one of the first to evaluate drought indices for high-latitude regions, has the potential to improve drought monitoring and representation within the U.S. Drought Monitor, leading to more informed decision-making during drought in Alaska, and it improves our ability to track changes in drought driven by rising temperatures.
Significance Statement
Tracking drought at high latitudes is challenging because we have not adequately studied drought impacts in cold climates, and the primary meteorological drought indices were designed for lower latitudes and may not accurately estimate evaporative demand and the influence of snow. We investigate three common drought indices and recommend using the standardized precipitation index (SPI) or the standardized precipitation evapotranspiration index (SPEI) because they can track short and long droughts. The SPEI may be more useful because comparisons between the SPI and SPEI show that, in recent decades, temperature has made noticeable contributions to drought in drier parts of Alaska. If using the SPEI, we suggest the Hargreaves potential evapotranspiration rather than the Thornthwaite because it is more physically realistic.
Abstract
Drought is a recurrent natural phenomenon, but there is concern that climate change may increase the frequency or severity of drought in Alaska. Because most common drought indices were designed for lower latitudes, it is unclear how effectively they characterize drought in Alaska’s diverse, high-latitude climates. Here, we compare three commonly used meteorological drought indices [the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (scPDSI)] with each other and with streamflow across Alaska’s 13 climate divisions. All of the drought indices identify major droughts, but the severity of the drought varies depending on the index used. The SPI and the SPEI are more flexible and often better correlated with streamflow than the scPDSI, and we recommend using them. Although SPI and SPEI are very similar in energy-limited climates, the drought metrics do diverge in drier locations in recent years, and consideration of the impact of temperature on drought may grow more important in the coming decades. Hargreaves potential evapotranspiration (PET) estimates appeared more physically realistic than the more commonly used Thornthwaite equation and are equally easy to calculate, so we suggest using the Hargreaves equation when PET is estimated from temperature. This study, one of the first to evaluate drought indices for high-latitude regions, has the potential to improve drought monitoring and representation within the U.S. Drought Monitor, leading to more informed decision-making during drought in Alaska, and it improves our ability to track changes in drought driven by rising temperatures.
Significance Statement
Tracking drought at high latitudes is challenging because we have not adequately studied drought impacts in cold climates, and the primary meteorological drought indices were designed for lower latitudes and may not accurately estimate evaporative demand and the influence of snow. We investigate three common drought indices and recommend using the standardized precipitation index (SPI) or the standardized precipitation evapotranspiration index (SPEI) because they can track short and long droughts. The SPEI may be more useful because comparisons between the SPI and SPEI show that, in recent decades, temperature has made noticeable contributions to drought in drier parts of Alaska. If using the SPEI, we suggest the Hargreaves potential evapotranspiration rather than the Thornthwaite because it is more physically realistic.
Abstract
Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10.
Significance Statement
The latest U.S. operational weather prediction model—Global Ensemble Forecast System version 12—is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended-range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.
Abstract
Three levels of process-oriented model diagnostics are applied to evaluate the Global Ensemble Forecast System version 12 (GEFSv12) reforecasts. The level-1 diagnostics are focused on model systematic errors, which reveals that precipitation onset over tropical oceans occurs too early in terms of column water vapor accumulation. Since precipitation acts to deplete water vapor, this results in prevailing negative biases of precipitable water in the tropics. It is also associated with overtransport of moisture into the mid- and upper troposphere, leading to a dry bias in the lower troposphere and a wet bias in the mid–upper troposphere. The level-2 diagnostics evaluate some major predictability sources on the extended-range time scale: the Madden–Julian oscillation (MJO) and North American weather regimes. It is found that the GEFSv12 can skillfully forecast the MJO up to 16 days ahead in terms of the Real-time Multivariate MJO indices (bivariate correlation ≥ 0.6) and can reasonably represent the MJO propagation across the Maritime Continent. The weakened and less coherent MJO signals with increasing forecast lead times may be attributed to humidity biases over the Indo-Pacific warm pool region. It is also found that the weather regimes can be skillfully predicted up to 12 days ahead with persistence comparable to the observation. In the level-3 diagnostics, we examined some high-impact weather systems. The GEFSv12 shows reduced mean biases in tropical cyclone genesis distribution and improved performance in capturing tropical cyclone interannual variability, and midlatitude blocking climatology in the GEFSv12 also shows a better agreement with the observations than in the GEFSv10.
Significance Statement
The latest U.S. operational weather prediction model—Global Ensemble Forecast System version 12—is evaluated using a suite of physics-based diagnostic metrics from a climatic perspective. The foci of our study consist of three levels: 1) systematic biases in physical processes, 2) tropical and extratropical extended-range predictability sources, and 3) high-impact weather systems like hurricanes and blockings. Such process-oriented diagnostics help us link the model performance to the deficiencies of physics parameterization and thus provide useful information on future model improvement.
Abstract
This study investigates whether and how energy consumers respond to public appeals for voluntary conservation during an extended and extreme winter energy emergency. Public appeals are an increasingly important tool for managing demand when grid disruptions are anticipated, especially given the increase in severe-weather events. We add to the few studies on winter energy crises by investigating a case in which there were repeated public appeals during an extended event. Using a survey implemented via social media immediately after the February 2021 winter storm, we asked residents of Norman, Oklahoma, a series of questions about their responses to the public appeals distributed by the utility company, including whether they followed the actions suggested in the messages as well as where they got information and their level of concern about the storm impacts. We compare mean responses across a range of categorical answers using standard independent t tests, one-way ANOVA tests, and chi-squared tests. Among the 296 respondents, there was a high degree of reported compliance, including setting the thermostat to 68°F (20°C) or lower (72%), avoiding using major appliances (86%), and turning off nonessential appliances, lights, and equipment (89%). Our findings suggest a high degree of willingness to voluntarily reduce energy consumption during an energy emergency. This is encouraging for energy managers: public appeals can be disseminated via social media at a low cost and in real time during an extended emergency event.
Significance Statement
The purpose of this study is to better understand whether and how energy consumers respond to public appeals for voluntary conservation during a winter energy emergency event. This is important because voluntary conservation can help utility managers minimize grid disruptions, particularly if consumers respond to evolving conditions. Our survey results suggest that individuals are willing to voluntarily conserve energy and follow conservation recommendations provided by utility managers during a severe winter event.
Abstract
This study investigates whether and how energy consumers respond to public appeals for voluntary conservation during an extended and extreme winter energy emergency. Public appeals are an increasingly important tool for managing demand when grid disruptions are anticipated, especially given the increase in severe-weather events. We add to the few studies on winter energy crises by investigating a case in which there were repeated public appeals during an extended event. Using a survey implemented via social media immediately after the February 2021 winter storm, we asked residents of Norman, Oklahoma, a series of questions about their responses to the public appeals distributed by the utility company, including whether they followed the actions suggested in the messages as well as where they got information and their level of concern about the storm impacts. We compare mean responses across a range of categorical answers using standard independent t tests, one-way ANOVA tests, and chi-squared tests. Among the 296 respondents, there was a high degree of reported compliance, including setting the thermostat to 68°F (20°C) or lower (72%), avoiding using major appliances (86%), and turning off nonessential appliances, lights, and equipment (89%). Our findings suggest a high degree of willingness to voluntarily reduce energy consumption during an energy emergency. This is encouraging for energy managers: public appeals can be disseminated via social media at a low cost and in real time during an extended emergency event.
Significance Statement
The purpose of this study is to better understand whether and how energy consumers respond to public appeals for voluntary conservation during a winter energy emergency event. This is important because voluntary conservation can help utility managers minimize grid disruptions, particularly if consumers respond to evolving conditions. Our survey results suggest that individuals are willing to voluntarily conserve energy and follow conservation recommendations provided by utility managers during a severe winter event.
Abstract
We present an overviewof recentwork on using artificial intelligence/machine learning techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact phenomena globally cause both massive property damage and loss of life yet they are quite challenging to forecast. Given the recent explosion in developing machine learning techniques across the weather spectrum and the fact that the skillful prediction of convective weather has immediate societal benefits, we present a thorough review of the current state of the art in artificial intelligence and machine learning techniques for convective hazards. Our review includes both traditional approaches, including support vector machines and decision trees as well as deep learning approaches. We highlight the challenges in developing machine learning approaches to forecast these phenomena across a variety of spatial and temporal scales. We end with a discussion of promising areas of future work for ML for convective weather, including a discussion of the need to create trustworthy AI forecasts that can be used for forecasters in real-time and the need for active cross-sector collaboration on testbeds to validate machine learning methods in operational situations.
Abstract
We present an overviewof recentwork on using artificial intelligence/machine learning techniques for forecasting convective weather and its associated hazards, including tornadoes, hail, wind, and lightning. These high-impact phenomena globally cause both massive property damage and loss of life yet they are quite challenging to forecast. Given the recent explosion in developing machine learning techniques across the weather spectrum and the fact that the skillful prediction of convective weather has immediate societal benefits, we present a thorough review of the current state of the art in artificial intelligence and machine learning techniques for convective hazards. Our review includes both traditional approaches, including support vector machines and decision trees as well as deep learning approaches. We highlight the challenges in developing machine learning approaches to forecast these phenomena across a variety of spatial and temporal scales. We end with a discussion of promising areas of future work for ML for convective weather, including a discussion of the need to create trustworthy AI forecasts that can be used for forecasters in real-time and the need for active cross-sector collaboration on testbeds to validate machine learning methods in operational situations.
Abstract
Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the midlatitude jet stream’s latitudinal position, often referred to as a “tug-of-war.” Studies that investigate the jet’s response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation’s response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuation–dissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could be useful for early-stage experiment design.
Abstract
Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the midlatitude jet stream’s latitudinal position, often referred to as a “tug-of-war.” Studies that investigate the jet’s response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation’s response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuation–dissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could be useful for early-stage experiment design.
Abstract
This study identified that the Silk Road pattern (SRP), which is a teleconnection pattern along the Asian upper-tropospheric westerly jet, becomes significantly weakened in August after the mid-1990s. The SRP in August dominates the upper-tropospheric meridional wind variability over the Eurasian continent before the mid-1990s but does not afterward. Further results suggested that the summer North Atlantic Oscillation (SNAO) and the South Asian rainfall play a role in inducing this decadal weakening of SRP. Before the mid-1990s, the SNAO is stronger and its southern pole is located over northwestern Europe but is weakened and its southern pole shifts southwestward afterward, resulting in the decadal weakening of its contribution to the SRP. In addition, the relationship between the SRP and South Asian rainfall is substantially weakened after the mid-1990s, which also contributes to the weakening of SRP.
Abstract
This study identified that the Silk Road pattern (SRP), which is a teleconnection pattern along the Asian upper-tropospheric westerly jet, becomes significantly weakened in August after the mid-1990s. The SRP in August dominates the upper-tropospheric meridional wind variability over the Eurasian continent before the mid-1990s but does not afterward. Further results suggested that the summer North Atlantic Oscillation (SNAO) and the South Asian rainfall play a role in inducing this decadal weakening of SRP. Before the mid-1990s, the SNAO is stronger and its southern pole is located over northwestern Europe but is weakened and its southern pole shifts southwestward afterward, resulting in the decadal weakening of its contribution to the SRP. In addition, the relationship between the SRP and South Asian rainfall is substantially weakened after the mid-1990s, which also contributes to the weakening of SRP.
Abstract
The National Weather Service plays a critical role in alerting the public when dangerous weather occurs. Tornado warnings are one of the most publicly visible products the NWS issues given the large societal impacts tornadoes can have. Understanding the performance of these warnings is crucial for providing adequate warning during tornadic events and improving overall warning performance. This study aims to understand warning performance during the lifetimes of individual storms (specifically in terms of probability of detection and lead time). For example, does probability of detection vary based on if the tornado was the first produced by the storm, or the last? We use tornado outbreak data from 2008 to 2014, archived NEXRAD radar data, and the NWS verification database to associate each tornado report with a storm object. This approach allows for an analysis of warning performance based on the chronological order of tornado occurrence within each storm. Results show that the probability of detection and lead time increase with later tornadoes in the storm; the first tornadoes of each storm are less likely to be warned and on average have less lead time. Probability of detection also decreases overnight, especially for first tornadoes and storms that only produce one tornado. These results are important for understanding how tornado warning performance varies during individual storm life cycles and how upstream forecast products (e.g., Storm Prediction Center tornado watches, mesoscale discussions, etc.) may increase warning confidence for the first tornado produced by each storm.
Significance Statement
In this study, we focus on better understanding real-time tornado warning performance on a storm-by-storm basis. This approach allows us to examine how warning performance can change based on the order of each tornado within its parent storm. Using tornado reports, warning products, and radar data during tornado outbreaks from 2008 to 2014, we find that probability of detection and lead time increase with later tornadoes produced by the same storm. In other words, for storms that produce multiple tornadoes, the first tornado is generally the least likely to be warned in advance; when it is warned in advance, it generally contains less lead time than subsequent tornadoes. These findings provide important new analyses of tornado warning performance, particularly for the first tornado of each storm, and will help inform strategies for improving warning performance.
Abstract
The National Weather Service plays a critical role in alerting the public when dangerous weather occurs. Tornado warnings are one of the most publicly visible products the NWS issues given the large societal impacts tornadoes can have. Understanding the performance of these warnings is crucial for providing adequate warning during tornadic events and improving overall warning performance. This study aims to understand warning performance during the lifetimes of individual storms (specifically in terms of probability of detection and lead time). For example, does probability of detection vary based on if the tornado was the first produced by the storm, or the last? We use tornado outbreak data from 2008 to 2014, archived NEXRAD radar data, and the NWS verification database to associate each tornado report with a storm object. This approach allows for an analysis of warning performance based on the chronological order of tornado occurrence within each storm. Results show that the probability of detection and lead time increase with later tornadoes in the storm; the first tornadoes of each storm are less likely to be warned and on average have less lead time. Probability of detection also decreases overnight, especially for first tornadoes and storms that only produce one tornado. These results are important for understanding how tornado warning performance varies during individual storm life cycles and how upstream forecast products (e.g., Storm Prediction Center tornado watches, mesoscale discussions, etc.) may increase warning confidence for the first tornado produced by each storm.
Significance Statement
In this study, we focus on better understanding real-time tornado warning performance on a storm-by-storm basis. This approach allows us to examine how warning performance can change based on the order of each tornado within its parent storm. Using tornado reports, warning products, and radar data during tornado outbreaks from 2008 to 2014, we find that probability of detection and lead time increase with later tornadoes produced by the same storm. In other words, for storms that produce multiple tornadoes, the first tornado is generally the least likely to be warned in advance; when it is warned in advance, it generally contains less lead time than subsequent tornadoes. These findings provide important new analyses of tornado warning performance, particularly for the first tornado of each storm, and will help inform strategies for improving warning performance.
Abstract
With climate change causing rising sea-levels around the globe, multiple recent efforts in the United States have focused on the prediction of various meteorological factors that can lead to periods of anomalously high-tides despite seemingly benign atmospheric conditions. As part of these efforts, this research explores monthly-scale relationships between sea-level variability and atmospheric circulation patterns, and demonstrates two options for sub-seasonal to seasonal (S2S) predictions of anomalous sea-levels using these patterns as inputs to artificial neural network (ANN) models. Results on the monthly scale are similar to previous research on the daily scale, with above-average sea-levels and an increased risk of high-water events on days with anomalously low atmospheric pressure patterns and wind patterns leading to on-shore or downwelling-producing wind stress. Some wind patterns show risks of high-water events to be over 6-times higher than baseline risk, and exhibit an average water level anomaly of +94mm above normal. In terms of forecasting, nonlinear autoregressive ANN models with exogenous input (NARX models) and pattern-based lagged ANN (PLANN) models show skill over post-processed numerical forecast model output, and simple climatology. Damped-persistence forecasts and PLANN models show nearly the same skill in terms of predicting anomalous sea-levels out to 9 months of lead time, with a slight edge to PLANN models, especially with regard to error statistics. This perspective on forecasting – using predefined circulation patterns along with ANN models – should aid in the real-time prediction of coastal flooding events, among other applications.
Abstract
With climate change causing rising sea-levels around the globe, multiple recent efforts in the United States have focused on the prediction of various meteorological factors that can lead to periods of anomalously high-tides despite seemingly benign atmospheric conditions. As part of these efforts, this research explores monthly-scale relationships between sea-level variability and atmospheric circulation patterns, and demonstrates two options for sub-seasonal to seasonal (S2S) predictions of anomalous sea-levels using these patterns as inputs to artificial neural network (ANN) models. Results on the monthly scale are similar to previous research on the daily scale, with above-average sea-levels and an increased risk of high-water events on days with anomalously low atmospheric pressure patterns and wind patterns leading to on-shore or downwelling-producing wind stress. Some wind patterns show risks of high-water events to be over 6-times higher than baseline risk, and exhibit an average water level anomaly of +94mm above normal. In terms of forecasting, nonlinear autoregressive ANN models with exogenous input (NARX models) and pattern-based lagged ANN (PLANN) models show skill over post-processed numerical forecast model output, and simple climatology. Damped-persistence forecasts and PLANN models show nearly the same skill in terms of predicting anomalous sea-levels out to 9 months of lead time, with a slight edge to PLANN models, especially with regard to error statistics. This perspective on forecasting – using predefined circulation patterns along with ANN models – should aid in the real-time prediction of coastal flooding events, among other applications.
