A purely data‐driven and transformer‐based model with a novel self‐attention mechanism (3D‐Geoformer) is used to make predictions by adopting a rolling predictive manner similar to that in dynamical ...coupled models. The 3D‐Geoformer yields a successful prediction of the 2021 second‐year cooling conditions that followed the 2020 La Niña event, including covarying anomalies of surface wind stress and three‐dimensional (3D) upper‐ocean temperature, the reoccurrence of negative subsurface temperature anomalies in the eastern equatorial Pacific and a corresponding turning point of sea surface temperature (SST) evolution in mid‐2021. The reasons for the successful prediction with interpretability are explored comprehensively by performing sensitivity experiments with modulating effects on SST due to wind and subsurface thermal forcings being separately considered in the input predictors for prediction. A comparison is also conducted with physics‐based modeling, illustrating the suitability and effectiveness of 3D‐Geoformer as a new platform for El Niño and Southern Oscillation studies.
Plain Language Summary
The tropical Pacific experienced the prolonged cooling conditions during 2020–2022 (often called a triple La Niña), which exerted great impacts on the weather and climate globally. However, physics‐derived coupled models still have difficulty in accurately making long‐lead real‐time predictions for sea surface temperature (SST) evolution in the tropical Pacific. With the rapid development of deep learning‐based modeling, purely data‐driven models provide an innovative way for SST predictions. Here, a transformer‐based deep learning model is used to evaluate its performance in predicting the evolution of SST in the tropical Pacific during 2020–2022 and explore process representations that are important for SST evolution during 2021, including subsurface thermal effect and surface wind forcing on SST, the crucial factors determining the second‐year prolonged La Niña conditions and turning point of SST evolution. A comparison is made between the completely differently constructed physics‐derived dynamical coupled model and the pure‐data driven deep learning model, showing they both can be used for predictions of SST evolution in the 2021 second‐year cooling conditions. This indicates that it is necessary to adequately represent the thermocline feedback in predictive models, either in dynamical coupled models or purely data‐driven models, so that El Niño and Southern Oscillation predictions can be improved.
Key Points
A transformer‐based deep learning model is used for El Niño‐Southern Oscillation multivariate prediction in a rolling predictive manner
The purely data‐driven model successfully predicts the 2021 second‐year La Niña and turning point of temperature evolution in mid‐2021
Applications of purely data‐driven model for process representations and understanding are demonstrated as in dynamical coupled models
Severe COVID-19 disease caused by SARS-CoV-2 is frequently accompanied by dysfunction of the lungs and extrapulmonary organs. However, the organotropism of SARS-CoV-2 and the port of virus entry for ...systemic dissemination remain largely unknown. We profiled 26 COVID-19 autopsy cases from four cohorts in Wuhan, China, and determined the systemic distribution of SARS-CoV-2. SARS-CoV-2 was detected in the lungs and multiple extrapulmonary organs of critically ill COVID-19 patients up to 67 days after symptom onset. Based on organotropism and pathological features of the patients, COVID-19 was divided into viral intrapulmonary and systemic subtypes. In patients with systemic viral distribution, SARS-CoV-2 was detected in monocytes, macrophages, and vascular endothelia at blood-air barrier, blood-testis barrier, and filtration barrier. Critically ill patients with long disease duration showed decreased pulmonary cell proliferation, reduced viral RNA, and marked fibrosis in the lungs. Permanent SARS-CoV-2 presence and tissue injuries in the lungs and extrapulmonary organs suggest direct viral invasion as a mechanism of pathogenicity in critically ill patients. SARS-CoV-2 may hijack monocytes, macrophages, and vascular endothelia at physiological barriers as the ports of entry for systemic dissemination. Our study thus delineates systemic pathological features of SARS-CoV-2 infection, which sheds light on the development of novel COVID-19 treatment.
Model biases are substantial in ocean and coupled ocean‐atmosphere simulations in the tropical Pacific Ocean, including a too cold tongue and too diffuse thermocline. These biases can be partly ...attributed to vertical mixing parameterizations in which the background diffusivity depiction has great uncertainties. Here based on the fine‐scale parameterization, the Argo data are used to derive the spatially varying background diffusivity, with a magnitude of O(10−6 m2 s−1) in the most area of tropical Pacific. This new scheme is then employed into the version 5.1 of the Modular Ocean Model‐based ocean‐only and coupled models, resulting in substantial improvements in ocean simulations, including a more realistic cold tongue and equatorial thermocline. The improved simulations can be attributed to the reduced cooling effects induced by weakened equatorial upwelling. Additionally, the subsurface cooling effect is attributed to the reduced heat transfer from the upper layer to the subsurface layer and the convergence of the colder water from off the equator.
Key Points
Overestimated diapycnal mixing in OGCMs is partly responsible for model biases in the tropical Pacific
Cold tongue and equatorial thermocline biases are reduced by ~25% by employing the Argo‐derived, spatially varying background diffusivity
The improved simulations can be attributed to the regulation of the currents system, including an intensified subtropical cell and a weakened equatorial upwelling
Organic electroactive compounds hold great potential to act as cathode material for organic sodium‐ion batteries (OSIBs) because of their environmental friendliness, sustainability, and high ...theoretical capacity. Although some organic electrodes have been developed with good performance, their practical application is still obstructed by some inherent drawbacks such as low conductivity and solubility in organic electrolytes. In addition, research on OSIBs has been mainly focused on the performance of electrodes on the material level and neglected the trade‐off relationship between the high redox potentials and specific capacities. Almost all organic cathodes used in OSIBs lack the ability to be charged first in half‐cells because of the absence of detachable sodium ions, resulting in low attractiveness when assembling full cells with hard carbon as anode. Here, this review presents several existing reaction mechanisms in OSIBs and designs of organic cathode materials. Furthermore, strategies are proposed in order to provide guidelines for improving their performance according to some critical parameters (output voltage, specific capacity, and cycle life) in potential practical OSIBs, and some accounts of organic materials assembled in full cells are summarized. Finally, the challenges and prospects of organic electrodes for OSIBs are also discussed in this review.
A comprehensive summary on how to improve the electronic performance of organic cathode materials for the potential commercial application of organic sodium‐ion batteries is presented.
Abstract
Available satellite data reveal a decreasing trend in surface chlorophyll (SChl) over the entire tropical ocean until 2020. Where contributions by internal variability and external forcing ...remain unclear. Here, state-of-the-art climate model simulations are analyzed to show that external forcing significantly contributes to the decreasing SChl trend. In contrast, internal variability plays a weak or even offsetting role. As for the underlying processes, anthropogenic greenhouse emissions lead to a remarkable reduction in SChl over the tropical oceans, whereas industrial aerosol load facilitates a considerable increase in SChl in the western tropical Pacific. In addition, the negative phase of the interdecadal Pacific variability during 1998–2020 contributes to an increase in SChl, while the impact from the Atlantic multidecadal variability is relatively weak in facilitating a decrease in SChl. Overall, these results imply that the impact of anthropogenic forcing has emerged as indicated in the tropical marine ecosystem.
El Niño-Southern Oscillation (ENSO) can be currently predicted reasonably well six months and longer, but large biases and uncertainties remain in its real-time prediction. Various approaches have ...been taken to improve understanding of ENSO processes, and different models for ENSO predictions have been developed, including linear statistical models based on principal oscillation pattern (POP) analyses, convolutional neural networks (CNNs), and so on. Here, we develop a novel hybrid model, named as POP-Net, by combining the POP analysis procedure with CNN-long short-term memory (LSTM) algorithm to predict the Niño-3.4 sea surface temperature (SST) index. ENSO predictions are compared with each other from the corresponding three models: POP model, CNN-LSTM model, and POP-Net, respectively. The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise. Consequently, an improved prediction is achieved in the POP-Net relative to others. The POP-Net shows a high-correlation skill for 17-month lead time prediction (correlation coefficients exceeding 0.5) during the 1994–2020 validation period. The POP-Net also alleviates the spring predictability barrier (SPB). It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.
Abstract
Climate models suffer from significant biases over the tropical Pacific Ocean, including a too-cold cold tongue and too-warm temperature at the depth of the thermocline. The emergence of ...model biases can be partly attributed to vertical mixing parameterizations, in which there are great uncertainties in selections of functional forms and empirical parameters. In this paper, the impacts of two different vertical mixing schemes on the tropical Pacific temperature simulations are investigated using version 5 of the Modular Ocean Model (MOM5). One vertical mixing scheme is the widely used
K
-profile parameterization (KPP) scheme, and the other is a hybrid mixing scheme (the Chen scheme) by combining a Kraus–Turner-type bulk mixed layer (ML) model with Peters et al.’s shear instability mixing model (PGT model). It is shown that the Chen scheme works better than the KPP scheme for SST simulation but produces an exaggerated subsurface warm bias simultaneously. The better SST simulation can be attributed to the employment of the PGT model, which produces lower levels of shear instability mixing than its counterpart in the KPP scheme. Furthermore, a modified KPP scheme is presented in which its shear instability mixing model and constant background diffusivity are replaced by the PGT model and the Argo-derived background diffusivity, respectively. This new scheme is then employed into MOM5-based ocean-only and coupled simulations, demonstrating substantial improvements in temperature simulations over the tropical Pacific. The modified KPP scheme can be easily employed into other ocean models, offering an effective way to improve ocean simulations.
Traumatic brain injury (TBI) is a major health and socioeconomic problem throughout the world. It is a complicated pathological process that consists of primary insults and a secondary insult ...characterized by a set of biochemical cascades. The imbalance between a higher energy demand for repair of cell damage and decreased energy production led by mitochondrial dysfunction aggravates cell damage. At the cellular level, the main cause of the secondary deleterious cascades is cell damage that is centred in the mitochondria. Excitotoxicity, Ca2+ overload, reactive oxygen species (ROS), Bcl‐2 family, caspases and apoptosis inducing factor (AIF) are the main participants in mitochondria‐centred cell damage following TBI. Some preclinical and clinical results of mitochondria‐targeted therapy show promise. Mitochondria‐ targeted multipotential therapeutic strategies offer new hope for the successful treatment of TBI and other acute brain injuries.
Substantial model biases are still prominent even in the latest CMIP6 simulations; attributing their causes is defined as one of the three main scientific questions addressed in CMIP6. In this paper, ...cold temperature biases in the North Pacific subtropics are investigated using simulations from the newly released CMIP6 models, together with other related modeling products. In addition, ocean-only sensitivity experiments are performed to characterize the biases, with a focus on the role of oceanic vertical mixing schemes. Based on the Argo-derived diffusivity, idealized vertical diffusivity fields are designed to mimic the seasonality of vertical mixing in this region, and are employed in ocean-only simulations to test the sensitivity of this cold bias to oceanic vertical mixing. It is demonstrated that the cold temperature biases can be reduced when the mixing strength is enhanced within and beneath the surface boundary layer. Additionally, the temperature simulations are rather sensitive to the parameterization of static instability, and the cold biases can be reduced when the vertical diffusivity for convection is increased. These indicate that the cold temperature biases in the North Pacific can be largely attributed to biases in oceanic vertical mixing within ocean-only simulations, which likely contribute to the even larger biases seen in coupled simulations. This study therefore highlights the need for improved oceanic vertical mixing in order to reduce these persistent cold temperature biases seen across several CMIP models.
Recent studies have demonstrated great values of deep‐learning (DL) methods for improving El Niño‐Southern Oscillation (ENSO) predictions. However, the black‐box nature of DL makes it challenging to ...physically interpret mechanisms responsible for successful ENSO predictions. Here, we demonstrate an interpretable method by performing perturbation experiments to predictors and quantifying input‐output relationships in predictions by using a transformer‐based model; ENSO‐related thermal precursors serving as initial conditions during multi‐month time intervals (TIs) are identified in the equatorial‐northern Pacific, acting to precondition input predictors to provide for long‐lead ENSO predictability. Results reveal the existence of upper‐ocean temperature anomaly pathways and consistent phase propagations of thermal precursors around the tropical Pacific. It is illustrated that three‐dimensional thermal fields and their basinwide evolution during long TIs act to enhance long‐lead prediction skills of ENSO. These physically explainable results indicate that neural networks can adequately represent predictable precursors in the input predictors for successful ENSO predictions.
Plain Language Summary
Deep learning (DL) methods have emerged as a powerful tool for improving El Niño‐Southern Oscillation (ENSO) predictions. But DL‐based modeling looks like “black boxes” without effectively telling why good predictions can be made. In this study, we conduct interpretable analyses to uncover the key physical processes responsible for successful ENSO predictions using a DL‐based prediction model. Results identify ENSO‐related thermal precursors in the equatorial‐northern Pacific region, which precondition ENSO evolution months ahead of time. Specifically, interannual thermal precursors are illustrated to have consistent and coherent phase propagations in the tropical Pacific basin: eastward along the equator, westward across the off‐equatorial tropical North Pacific, and apparent meridional phase connections both in the western and eastern boundaries. From the prediction perspective, the demonstrated existence of upper‐ocean temperature anomaly pathways acts to enhance long‐lead ENSO predictability in the purely data‐driven DL framework. These physically explainable results indicate that the neural networks, despite their absence of explicit physical constraints, are capable of representing predictable precursors whose information is included in the input predictors, being able to make convincing and successful ENSO predictions.
Key Points
A deep learning (DL) model is used to conduct El Niño‐Southern Oscillation (ENSO) predictability studies for physical interpretability
DL model experiments are made to identify ENSO‐related thermal precursors along a counterclockwise pathway encircling the tropical Pacific
The existence of upper‐ocean thermal anomaly pathways is demonstrated to enhance long‐lead ENSO predictability
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