Abstract
Moored observations on a critical continental slope in the northeastern South China Sea (SCS) are presented to reveal along-slope bottom current and sediment resuspension and transport caused by internal tides. During spring tides, the bottom-intensified diurnal internal tides were observed, and their breaking generated an along-slope bottom current toward the southwest direction, with a clear ∼14-day spring–neap cycle and a maximum low-frequency velocity exceeding 0.25 m s−1. The diurnal internal tides and along-slope bottom currents showed the same seasonal variations. There were larger near-bottom along-slope velocities in winter (∼9.0 cm s−1) and summer (∼8.9 cm s−1) than in spring (6.3 cm s−1) and autumn (7.4 cm s−1). The previous theory based on radiation stress caused by wave breaking is used to reproduce the observed along-slope bottom current velocity. Strong bottom flows caused by internal tides on the critical slope inhibit deposition of fine-grained sediments and may erode bottom coarse-grained sediments, leading to the generation of near-bottom nepheloid layer with a thickness H > 130 m, as demonstrated by the fact that seafloor sediments on the critical slope are dominated by coarse-grained sediments (sands). The suspended sediments can be southwest transported by the persistent along-slope bottom current generated by internal tide breaking. It was estimated that approximately three million tons of sediments during the observation were carried along the slope to the south of the SCS. Our observations suggest that internal tide-induced sediment resuspension/deposition and along-slope transport could be important for seafloor sediment distribution of the SCS.
Abstract
Moored observations on a critical continental slope in the northeastern South China Sea (SCS) are presented to reveal along-slope bottom current and sediment resuspension and transport caused by internal tides. During spring tides, the bottom-intensified diurnal internal tides were observed, and their breaking generated an along-slope bottom current toward the southwest direction, with a clear ∼14-day spring–neap cycle and a maximum low-frequency velocity exceeding 0.25 m s−1. The diurnal internal tides and along-slope bottom currents showed the same seasonal variations. There were larger near-bottom along-slope velocities in winter (∼9.0 cm s−1) and summer (∼8.9 cm s−1) than in spring (6.3 cm s−1) and autumn (7.4 cm s−1). The previous theory based on radiation stress caused by wave breaking is used to reproduce the observed along-slope bottom current velocity. Strong bottom flows caused by internal tides on the critical slope inhibit deposition of fine-grained sediments and may erode bottom coarse-grained sediments, leading to the generation of near-bottom nepheloid layer with a thickness H > 130 m, as demonstrated by the fact that seafloor sediments on the critical slope are dominated by coarse-grained sediments (sands). The suspended sediments can be southwest transported by the persistent along-slope bottom current generated by internal tide breaking. It was estimated that approximately three million tons of sediments during the observation were carried along the slope to the south of the SCS. Our observations suggest that internal tide-induced sediment resuspension/deposition and along-slope transport could be important for seafloor sediment distribution of the SCS.
Abstract
The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper NADW (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014–20 observations from the transatlantic Overturning in the Subpolar North Atlantic Program (OSNAP) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre [−13.0 ± 0.5 Sv (1 Sv ≡ 106 m3 s−1)] mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intraseasonal changes across the region that result from a basinwide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation.
Abstract
The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper NADW (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014–20 observations from the transatlantic Overturning in the Subpolar North Atlantic Program (OSNAP) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre [−13.0 ± 0.5 Sv (1 Sv ≡ 106 m3 s−1)] mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intraseasonal changes across the region that result from a basinwide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation.
Abstract
Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
Abstract
Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
Abstract
It has been over 75 years since the concept of directly suppressing lightning by modifying thunderstorm cloud processes was first proposed as a technique for preventing wildfire ignitions. Subsequent decades produced a series of successful field campaigns that demonstrated several techniques for interrupting storm electrification, motivated in part by the prospect of protecting Apollo-era rocket launches from lightning strike. Despite the technical success of these research programs, funding and interest diminished until the final field experiment in 1975 marked the last large-scale activity toward developing lightning prevention technology. Having lost widespread awareness over the ensuing 50 years, these pioneering efforts in experimental cloud physics have largely been forgotten, and this approach for mitigating lightning hazards has fallen into obscurity. At the present day, risks from lightning-ignited wildfires to lives, property, and infrastructure are once again a major topic of concern. Similarly, the rapid development of an emerging commercial space sector is placing new demands on airspace management and launch scheduling. These modern challenges may potentially be addressed by a seemingly antiquated concept—lightning suppression—but considerations must be made to understand the consequences of deploying this technology. Nonetheless, the possible economic, environmental, and societal benefits of this approach merit a critical reevaluation of this hazard mitigation technology in the current era.
Abstract
It has been over 75 years since the concept of directly suppressing lightning by modifying thunderstorm cloud processes was first proposed as a technique for preventing wildfire ignitions. Subsequent decades produced a series of successful field campaigns that demonstrated several techniques for interrupting storm electrification, motivated in part by the prospect of protecting Apollo-era rocket launches from lightning strike. Despite the technical success of these research programs, funding and interest diminished until the final field experiment in 1975 marked the last large-scale activity toward developing lightning prevention technology. Having lost widespread awareness over the ensuing 50 years, these pioneering efforts in experimental cloud physics have largely been forgotten, and this approach for mitigating lightning hazards has fallen into obscurity. At the present day, risks from lightning-ignited wildfires to lives, property, and infrastructure are once again a major topic of concern. Similarly, the rapid development of an emerging commercial space sector is placing new demands on airspace management and launch scheduling. These modern challenges may potentially be addressed by a seemingly antiquated concept—lightning suppression—but considerations must be made to understand the consequences of deploying this technology. Nonetheless, the possible economic, environmental, and societal benefits of this approach merit a critical reevaluation of this hazard mitigation technology in the current era.
Abstract
We explore the skill in predicting Southwest United States (SWUS) October to March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest-southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño-like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic flow inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests re-examining high-latitude drivers of SWUS precipitation.
Abstract
We explore the skill in predicting Southwest United States (SWUS) October to March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest-southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño-like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic flow inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests re-examining high-latitude drivers of SWUS precipitation.
Abstract
Punctuality monitoring and analysis aim to optimize resource allocation for enhancing rail traffic performance and quality. Adverse weather conditions, particularly heavy precipitation events, are recognized as significant drivers of delays and reduced punctuality of the rail system. This study addresses two key research questions using high-speed rail (HSR) as an example—what is the impact of rainfall on HSR’s delay and to what extent are HSR vulnerable to rainstorms. The data for the study were collected from the HSR on the major lines of eastern China in the rainy season of 2015–17 which lasted from May to October. High-resolution precipitation data are integrated with nonspatial HSR operational data using GIS to create composite grids covering buffer zones around HSR lines. These grids match the spatial scale of historical hourly precipitation data and enable regression analyses to assess how precipitation affects HSR operations. The results indicate that extreme rainfall significantly contributes to delays and reduced punctuality, with varying impacts observed across different HSR lines. Specifically, daily areal precipitation significantly delays services on the Hangzhou–Shenzhen and Nanjing–Hangzhou HSR lines. Rainfall intensity has a more pronounced impact on delay services of the Beijing–Shanghai HSR, while extreme precipitation most frequently affects the Shanghai–Nanjing and Jinhua–Wenzhou HSR lines. The case analysis enhances understanding of HSR vulnerability to heavy rainfall conditions and recommends regional adaptation strategies to manage climate-related uncertainties.
Abstract
Punctuality monitoring and analysis aim to optimize resource allocation for enhancing rail traffic performance and quality. Adverse weather conditions, particularly heavy precipitation events, are recognized as significant drivers of delays and reduced punctuality of the rail system. This study addresses two key research questions using high-speed rail (HSR) as an example—what is the impact of rainfall on HSR’s delay and to what extent are HSR vulnerable to rainstorms. The data for the study were collected from the HSR on the major lines of eastern China in the rainy season of 2015–17 which lasted from May to October. High-resolution precipitation data are integrated with nonspatial HSR operational data using GIS to create composite grids covering buffer zones around HSR lines. These grids match the spatial scale of historical hourly precipitation data and enable regression analyses to assess how precipitation affects HSR operations. The results indicate that extreme rainfall significantly contributes to delays and reduced punctuality, with varying impacts observed across different HSR lines. Specifically, daily areal precipitation significantly delays services on the Hangzhou–Shenzhen and Nanjing–Hangzhou HSR lines. Rainfall intensity has a more pronounced impact on delay services of the Beijing–Shanghai HSR, while extreme precipitation most frequently affects the Shanghai–Nanjing and Jinhua–Wenzhou HSR lines. The case analysis enhances understanding of HSR vulnerability to heavy rainfall conditions and recommends regional adaptation strategies to manage climate-related uncertainties.
Abstract
A critical issue is determining the factors that control the year-to-year variability in precipitation over southern Asia. In this study, we employ a cyclostationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from El Niño–Southern Oscillation (ENSO) enhances the variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms.
Abstract
A critical issue is determining the factors that control the year-to-year variability in precipitation over southern Asia. In this study, we employ a cyclostationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from El Niño–Southern Oscillation (ENSO) enhances the variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms.
Abstract
In February 2022, an extreme wet and cold event hits South China, with the regional precipitation ranking the second highest on record, while the temperature ranked the third lowest since 1979. In this study, the physical mechanisms of this extreme event are investigated from the perspective of multiple time-scale interactions. Results show that the strong confrontation between the warm and moist air advection by the India–Burma trough (IBT) and the invasion of cold air activity related to the strengthening of the East Asian winter monsoon (EAWM) is the key to trigger this extreme event. Further analyses show that the multitime-scale coupling of the South Asian jet wave train and Eurasian (EU) teleconnection is the main reason for the strong cold and warm–moist airflow. The EU teleconnection on both intraseasonal and synoptic time scales plays a key role in triggering this extreme event by strengthening the EAWM. On the synoptic time scale, not only the EU teleconnection but also the South Asian jet wave train plays a key role. They show a stronger intensity on this time scale, and their coupling is obvious. The South Asian jet wave train enhances the moisture supply by deepening the IBT, which further conflicts with the strong EAWM modulated by the EU teleconnection over South China, leading to this extreme wet–cold event. The forecast skills in the Subseasonal to Seasonal (S2S) Prediction project models of this event are evaluated in this paper, and results show that the ECMWF model can successfully predict the extreme precipitation by capturing the coupling of the two wave trains with a 5-day lead time.
Abstract
In February 2022, an extreme wet and cold event hits South China, with the regional precipitation ranking the second highest on record, while the temperature ranked the third lowest since 1979. In this study, the physical mechanisms of this extreme event are investigated from the perspective of multiple time-scale interactions. Results show that the strong confrontation between the warm and moist air advection by the India–Burma trough (IBT) and the invasion of cold air activity related to the strengthening of the East Asian winter monsoon (EAWM) is the key to trigger this extreme event. Further analyses show that the multitime-scale coupling of the South Asian jet wave train and Eurasian (EU) teleconnection is the main reason for the strong cold and warm–moist airflow. The EU teleconnection on both intraseasonal and synoptic time scales plays a key role in triggering this extreme event by strengthening the EAWM. On the synoptic time scale, not only the EU teleconnection but also the South Asian jet wave train plays a key role. They show a stronger intensity on this time scale, and their coupling is obvious. The South Asian jet wave train enhances the moisture supply by deepening the IBT, which further conflicts with the strong EAWM modulated by the EU teleconnection over South China, leading to this extreme wet–cold event. The forecast skills in the Subseasonal to Seasonal (S2S) Prediction project models of this event are evaluated in this paper, and results show that the ECMWF model can successfully predict the extreme precipitation by capturing the coupling of the two wave trains with a 5-day lead time.
Abstract
A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying Principal Component Analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three Empirical Orthogonal Functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2) and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability (i.e., Principal Component [PC] groups) with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low and high intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles, and deep, stratiform snowfall events with large aggregates, respectively.
Abstract
A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying Principal Component Analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three Empirical Orthogonal Functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2) and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability (i.e., Principal Component [PC] groups) with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low and high intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles, and deep, stratiform snowfall events with large aggregates, respectively.
