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Both historical observations and recent modeling studies reveal a faster warming in the Arctic compared to the Antarctic. To understand the role of the Antarctic Circumpolar Circulation (ACC) in this warming asymmetry, we simulate the climate mean state and climate response to doubled CO2 under different climate mean ACC states by closing or opening the Drake Passage (DP) with the Community Earth System Model. From closed to open DP, a stronger climate mean ACC leads to a stronger climate mean Atlantic Meridional Overturning Circulation (AMOC), as well as a colder Antarctic but a warmer Arctic in the climate mean state. The less climate mean sea ice coverage in a warmer Arctic implies less extensive sea ice melting under global warming. This causes a reduced asymmetry in warming between the two poles in response to the doubled CO2. Plain Language Summary: To understand the role of the Antarctic Circumpolar Circulation (ACC) in the asymmetric Arctic and Antarctic warming, we conduct two numerical model experiments. We consider two different climate states by closing or opening the Drake Passage (DP). The former yields a weak ACC, while the latter results in a strong ACC. A stronger climate mean ACC leads to a colder Antarctic but a warmer Arctic, due primarily to a concurrent stronger Atlantic Meridional Overturning Circulation (AMOC). The diminished coverage of sea ice in the warmer Arctic signifies a reduction in sea ice melting under global warming. Consequently, this leads to a reduction in the asymmetry in warming between the two poles in response to doubled CO2. These findings highlight a significant impact of the ACC on the global climate mean state and polar warming asymmetry, and shed light on the uncertainties in the coordinated interaction between major ocean circulation systems (e.g., AMOC and ACC) and on those in inter‐hemisphere interactions. Key Points: A stronger climate mean Antarctic Circumpolar Circulation (ACC) is accompanied by a stronger mean Atlantic Meridional Oceanic Circulation (AMOC)A stronger climate mean AMOC results in a warmer Arctic with less climate mean sea ice coverageThe less asymmetric polar warming in the presence of stronger climate mean ACC and AMOC is largely due to less sea ice melting in Arctic [ABSTRACT FROM AUTHOR]

Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations. Plain Language Summary: Numerical weather prediction models depend on certain empirical formulations known as parameterizations to estimate precipitation. However, these methods often fall short due to the intricate dynamics of rainfall, which involves numerous small‐scale interactions that these models are unable to fully capture. To counteract these limitations, our study deploys a form of machine learning known as deep learning (DL) to predict precipitation. This DL model utilizes fundamental weather variables derived from NWP models to make its estimations, serving as a remedy for the inherent weaknesses of traditional models caused by the uncertainties in their parameterization schemes. The implementation of our DL model resulted in a significant enhancement in rainfall prediction accuracy, particularly in the case of extreme precipitation events. This suggests that the application of machine learning strategies could be a promising approach to improve the reliability of rainfall forecasts, a crucial element for effective weather prediction and water resource management. Key Points: We employ a transformer based deep learning model to improve the accuracy of precipitation estimation in numerical weather prediction modelsVarious training strategies were implemented to manage the highly skewed precipitation data leading to improvements in heavy rainfall eventsEvaluation conducted with multiple metrics including skill score, quantile and spatial distribution as well as two case studies [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients. Methods: A total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1‐weighted images (T1WI) and FS‐T2WI (fat‐suppressed T2‐weighted images), group II consisted of patients with T1WI, FS‐T2WI, and contrast enhanced MRI (CE‐MRI), group III consisted of patients with T1WI, FS‐T2WI, and T2‐weighted images (T2WI), group IV consisted of patients with T1WI, FS‐T2WI, CE‐MRI, and T2WI, group V consisted of patients with T1WI, FS‐T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS‐T2WI, CE‐MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group. Results: The machine learning model in group IV including T1WI, FS‐T2WI, T2WI, and CE‐MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE‐MRI performed better than the models without CE‐MRI(I vs. II, III vs. IV, V vs. VI). Conclusions: The radiomics machine learning models based on CE‐MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients. [ABSTRACT FROM AUTHOR]

The prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real‐world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical‐structured context‐aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off‐road predictions. Empirical results on the Lyft l5kit data set demonstrate the state‐of‐the‐art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real‐world rules in autonomous driving. [ABSTRACT FROM AUTHOR]

The Maritime Continent (MC) is regarded as an important heat source region and the impact of MC heating on the West Antarctic climate which is largely controlled by the Amundsen Sea Low (ASL) is unknown. This study is focused on the impact of intensified convective heating over the MC on the variation of the ASL in the austral fall. It is observed that enhanced MC heating is linked to deepening of the eastern ASL, a feature reproduced successfully by a model experiment in which additional heating is introduced to the MC. The intensified MC heating generates anticyclonic anomalies that result in a Rossby wave source in the upper troposphere to the south of MC. Then the Rossby wave originated from the new source propagates southeastward and deepens the eastern ASL. Additional model experiments prove that the response of ASL to MC heating is stronger than that to central Pacific cooling. Plain Language Summary: West Antarctica has experienced the strongest atmospheric warming in the Southern Hemisphere and the atmospheric circulation here is largely controlled by the Amundsen Sea Low (ASL) that resides along most of the western Antarctic Peninsula coast. Tropical heating is important for modulating the variations of ASL but the complicated mechanism between the tropics and the Antarctica is still unclear. Recent studies found that the atmospheric circulation over West Antarctica is affected by anomalous heating over the central Pacific (CP) that usually occurs simultaneously with the heating over the Maritime Continent (MC), and the impact of the MC heating on West Antarctic climate is unknown. This study is focused on the impact of intensified heating over the MC on the variation of ASL in austral fall. Both observations and model experiments prove that MC heating influences the ASL through Rossby wave trains while the CP cooling exerts an impact on the ASL through the westerly jet stream. The response of ASL to MC heating is stronger than that to CP cooling in austral fall in model simulations. This study enhances our understanding of how tropical heatings in different locations influence the West Antarctic climate. Key Points: Maritime Continent (MC) variability replaces the dominant role of central Pacific (CP) variability in modulating the variations of Amundsen Sea Low (ASL) in March‐April‐MayMC heating influences the ASL through Rossby wave trains while the CP cooling exerts an impact on the ASL through the westerly jet streamThe response of ASL to MC heating is stronger than that to CP cooling [ABSTRACT FROM AUTHOR]

Capturing TC intensity change remains a great challenge for most state‐of‐the‐art operational forecasting systems. Recent studies found TC intensity forecasts are sensitive to three‐dimensional ocean dynamics and air‐sea interface processes beneath extreme winds. By performing a series of numerical simulations based on hierarchical atmosphere‐wave‐ocean (AWO) coupling configurations, we showed how atmosphere‐ocean and atmosphere‐sea wave coupling can affect the intensity of super typhoon Mangkhut (2018). The AWO coupled model can simulate TC‐related strong winds, oceanic cold wake, and wind waves with high fidelity. With atmosphere‐ocean (AO) coupling implemented, the simulated maximum surface wind speed is reduced by 7 m s−1 compared to the atmosphere‐only run, due to TC‐induced oceanic cold wakes in the former experiment. In the fully coupled AWO simulations, the wind speed deficit can be completely compensated by the wave‐air coupling effect. Further analyses showed that, in the AWO experiment, two mechanisms contribute to the improvement of TC intensity. First, in the high wind scenario (>28 m s−1), the surface drag coefficient reaches an asymptotic level, assisting extreme wind speed to be maintained within the eyewall. Second, the wind speed distribution is modulated and becomes broader; higher wind within the TC area helps to offset the negative effect due to leveling off of the heat exchange coefficient as wind speed increases. Overall, the simulated TC in the AWO run can extract 8–9% more total heat energy from the ocean to maintain its strength, compared to that from the AO experiment. Key Points: Regional atmosphere‐wave‐ocean (AWO) coupled models have been customized to study the super typhoon Mangkhut (2018)Atmosphere‐ocean (AO) coupling reduces TC's surface maximum wind, but AWO coupling can completely compensate that deficitSurface drag coefficient leveling off and broader wind speed distribution function help to maintain the TC intensity [ABSTRACT FROM AUTHOR]

Changes in the tropical atmospheric overturning circulation can strongly influence the global weather pattern via affecting the location, extent, and strength of tropical convective heating. However, due to limitations in obtaining reliable global data before the satellite era, there are still uncertainties on long‐term changes in the tropical overturning features, such as the Walker Circulation. By analysing the 20th century reanalysis products, ship‐based observations and gauge‐based land precipitation data, robust intensification of convection is found over the western equatorial Pacific and Maritime Continent (WEP‐MC; 90°E–150°E/15°S–15°N), where rainfall and total cloud cover increased by 15–20% in boreal spring during 1901–2010. The signal is noticeably seasonally dependent, and both rainfall and cloud cover show consistent and significant increasing trends only in the boreal spring season. General circulation model (GCM) experiments were conducted using two different reconstructed SST data sets as forcing; results from both runs indicate springtime intensification of WEP‐MC convection, even with relatively uniform SST warming. Further, numerical experiments using a simplified model show that the Pacific trade wind can be accelerated by intensifying cumulus heating; however, this can be sensitive to the location of convection, and only occurs if the latter is within ±5° in latitude along the equator. Our findings suggest that the secular change in the Walker Circulation is potentially discovered from the current reanalysis and observational data sets if "regional‐up" approaches are applied. [ABSTRACT FROM AUTHOR]

The objective of this study was to fabricate an acellular sheep periosteum and explore its potential application in guided bone regeneration. Sheep periosteum was collected and decellularized by a modified decellularization protocol. The effectiveness of cell removal was proved by hematoxylin and eosin and 4′,6‐diamidino‐2‐phenylindole staining, DNA quantitative test, and agarose gel electrophoresis. After decellularization, its microstructure was found to become more porous while the integrality of collagen fibers remained undamaged, and the contents of collagen and glycosaminoglycan were not decreased significantly. Biomechanical analysis showed that the elastic modulus was significantly declined, while the yield stress was not affected, probably due to the collagen integrality. In vitro study of CCK‐8 assay demonstrated that the acellular periosteum not only had no toxic effect to the MC3T3‐E1 cells, but benefited the cell proliferation to some degree. In vivo experiment of guided bone regeneration was performed using a rabbit cranial model. Micro‐CT and histological results revealed that the acellular periosteum not only effectively prevented the ingrowth of fibrous connective tissues, but also potentially facilitated bone regeneration. In conclusion, acellular sheep periostea, with wider sources, less costs, and more convenient fabrication process, would have great potential in the employment for guided bone regeneration. [ABSTRACT FROM AUTHOR]

In January of 2016, the Ross Sea sector of the West Antarctica ice sheet experienced a 3‐week‐long melting episode. Here we quantify the association of the large‐extent and long‐lasting melting event with the enhancement of the downward longwave (LW) radiative fluxes at surface due to water vapor, cloud, and atmospheric dynamic feedbacks using the ERA‐Interim data set. The abnormally long‐lasting temporal surges of atmospheric moisture, warm air, and low‐level clouds increase the downward LW radiative energy flux at the surface during the massive ice‐melting period. The concurrent timing and spatial overlap between poleward wind anomalies and positive downward LW radiative surface energy flux anomalies over West Antarctica due to warmer air temperature and increases in atmospheric water vapor and cloud coverage provide direct evidence that warm and moist air advection from lower latitudes to West Antarctica causes the rapid long‐lasting warming and vast ice mass loss in January of 2016. Plain Language Summary: The melting of the fringing ice shelves from below by warm ocean water is thought to be the leading factor responsible for the retreat of the West Antarctica ice sheet in the past several decades. The analysis presented here shows that atmospheric processes through the long‐range transport of heat and moisture from lower latitudes to West Antarctica by the atmospheric circulation can lead to temporal surges of downward longwave radiative fluxes at the surface, substantially warming the surface. Atmospheric poleward heat transport plays a key role for individual ice sheet melting events in West Antarctica, such as the ice sheet melting episode over Ross Sea sector of the West Antarctica Ice Sheet in January 2016. Key Points: Pronounced enhancement of downward longwave radiative energy fluxes results in massive melting of the West Antarctica Ice Sheet in January 2016Contributions to the enhancement of downward longwave radiative fluxes are quantified via a linearized radiative transfer modelAtmospheric poleward heat transport plays a key role for the enhancement of downward longwave radiative surface energy fluxes [ABSTRACT FROM AUTHOR]