Anomalous Patterns Research Articles

Fault Diagnosis in Power Line Inspection Using Normalized Multihierarchy Embedding Matching

For the maintenance of power lines, a core task is to diagnose the fault of different components from the aerial inspection images. Currently, deep-learning models trained on defect samples have achieved promising performances for automatic fault diagnosis. However, the slow accumulation process of fault data leads to a long-term challenge of data insufficiency in this field. In this article, a normalized multihierarchy embedding matching (NMHEM)-based anomaly detection method is proposed to inspect power line faults, which only utilizes defect-free samples during training. To impart the NMHEM with the ability to detect anomalous patterns in images, three main modules are introduced. First, the embedding generation module (EGM) is employed to extract deep hierarchical representations. Next, hierarchy-wise anomaly scores are calculated through the embedding matching module (EMM) to measure the anomalous degree, which can make the model more discriminative at different hierarchies. Finally, a normalizing module (NM) is developed and served as a credible scoring function indicating the probability of anomaly occurring, thus boosting the performance of the fault diagnosis. The proposed NMHEM adaptively aggregates local spatial and global semantic information which leverages the available nominal knowledge from normal data, achieving effective fault diagnosis of power lines. Experiments are conducted on the dataset that contains five key components. Results show that our method achieves 88.4% area under the curve (AUC) and 80.5% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score, which outperforms other supervised and semisupervised methods.

Anatomy and Variants of Hepatic Arteries in 120 Patients Studied with 128 Slice Volumetric Scan in Nepalese Population

Introduction: Modern advances in radiology, imaging, and novel surgical methods have greatly aided doctors in making both surgical and non-surgical decisions. During surgery, anatomical variances are frequently encountered by surgeons, making operation complex and occasionally challenging. The purpose of our study was to evaluate the hepatic artery and its variants, including the length and diameter of the right and left hepatic arteries. This information is vital for patients undergoing major hepatic surgeries and hepatic transplantation Methods: A total of 120 patients who underwent contrast-enhanced Triphasic CT of the abdomen were evaluated with the consent of the ethics committee at Chitwan medical college. Study subjects were selected on a random basis, who had no previous abdominal or hepatic surgeries, and without the known hepatic disease. Results: Out of 120 participants, 63 (52.5%) were male and the rest 57(47.5 %) were females. Normal anatomy was observed in 104 cases (86.7%) and the rest of the 16 cases showed an anomalous hepatic arterial pattern, which consisted of Michel’s type III in 6 cases (5%), Type IV in 1 case (0.8%) and Type X in 3 cases (2.5%); and 5 cases (4.2%). did not fall under Michel’s Classification. Supply to segment IV of the liver was observed from Left hepatic artery in 97 cases (80.8%) and from the right hepatic artery in 23 cases (19.2%). Conclusions: Many hepatic arterial variants which did not fall under Michel’s classification were observed in our population.

A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism

The analysis of transient coil current signals for anomaly detection in a control element drive mechanism (CEDM) involves identifying signature mismatch based on comparison to normal signal profiles. This paper presents an anomaly detection model for optimizing the health monitoring framework for CEDMs. Our approach is a machine learning clustering algorithm based on the multivariate Gaussian Mixture Model (GMM), for the detection of incipient abnormal transient signatures in CEDM coils. The underlying considerations for the choice of a GMM-based clustering approach are that (1) the transient coil current profiles at normal operational behavior are very similar, and (2) only very few data are usually available that describe anomalous CEDM behavior. Since available data is heavily skewed toward normal patterns, this methodology allows us to establish the norm for detecting anomalous patterns based on a preset detection threshold. The model evaluation result shows a very low false-negative rate for anomaly detection.

A machine learning approach to joint gravity and cosmic-ray muon inversion at Mt Usu, Japan

SUMMARY The ability to accurately and reliably obtain images of shallow subsurface anomalies within the Earth is important for hazard monitoring and a fundamental understanding of many geologic structures, such as volcanic edifices. In recent years, machine learning (ML) has gained increasing attention as a novel approach for addressing complex problems in the geosciences. Here we present an ML-based inversion method to integrate cosmic-ray muon and gravity data sets for shallow subsurface density imaging at a volcano. Starting with an ensemble of random density anomalies, we use physics-based forward calculations to find the corresponding set of expected gravity and muon attenuation observations. Given a large enough ensemble of synthetic density patterns and observations, the ML algorithm is trained to recognize the expected spatial relations within the synthetic input–output pairs, learning the inherent physical relationships between them. Once trained, the ML algorithm can then interpolate the best-fitting anomalous pattern given data that were not used in training, such as those obtained from field measurements. We test the validity of our ML algorithm using field data from the Showa-Shinzan lava dome (Mt Usu, Japan) and show that our model produces results consistent with those obtained using a more traditional Bayesian joint inversion. Our results are similar to the previously published inversion, and suggest that the Showa-Shinzan lava dome consists of a relatively high-density (2200–2400 km m–3) cylindrical anomaly, about 300 m in diameter. Adding noise to synthetic training and testing data sets shows that, as expected, the ML algorithm is most robust in areas of high sensitivity, as determined by the forward kernels. Overall, we discover that ML offers a viable alternate method to a Bayesian joint inversion when used with gravity and muon data sets for subsurface density imaging.

A two-stage anomaly detection framework: Towards low omission rate in industrial vision applications

In industrial manufacturing, there are many types of defective samples that are difficult to obtain. Practical industrial vision anomaly detection has proven to be a challenging task because techniques use only normal (non-defective) samples to train a model to detect anomalies. Currently, some reasonably effective models do not perform very well once differences between samples are large, and they ignore the fact that the cost of missing a defect is much higher than the cost of misidentifying a normal sample. To that end, in this paper, we propose a two-stage framework to construct an anomaly detector. We first train a classification network and then build a one-class classifier on learned representations using another pre-trained network. This paper innovatively proposes using the theoretical quantile as the discriminant threshold. We conduct experiments on the Nut and Motor Brush Holder datasets from real industrial production lines. The results show that our method greatly reduces missed detection of anomalous samples, achieving state-of-the-art AUROC scores of 99.3 % and 96.2 %. We also conduct experiments on the publicly available dataset Rd-MVTec AD, showing that our model has good generalizability and fast testing speed while maintaining high AUROC scores. Our model gives excellent results for nonaligned and defective data with diverse anomalous patterns, and it is easy to optimize. Therefore, not only does our technique handle industrial cold starts well, but it also meets the requirement of online updating, which indicates that our solution is highly suitable for industrial manufacturing scenarios.

Unusual Abundances from Planetary System Material Polluting the White Dwarf G238-44

Ultraviolet and optical spectra of the hydrogen-dominated atmosphere white dwarf star G238-44 obtained with FUSE, Keck/HIRES, HST/COS, and HST/STIS reveal 10 elements heavier than helium: C, N, O, Mg, Al, Si, P, S, Ca, and Fe. G238-44 is only the third white dwarf with nitrogen detected in its atmosphere from polluting planetary system material. Keck/HIRES data taken on 11 nights over 24 yr show no evidence for variation in the equivalent width of measured absorption lines, suggesting stable and continuous accretion from a circumstellar reservoir. From measured abundances and limits on other elements, we find an anomalous abundance pattern and evidence for the presence of metallic iron. If the pollution is from a single parent body, then it would have no known counterpart within the solar system. If we allow for two distinct parent bodies, then we can reproduce the observed abundances with a mix of iron-rich Mercury-like material and an analog of an icy Kuiper Belt object with a respective mass ratio of 1.7:1. Such compositionally disparate objects would provide chemical evidence for both rocky and icy bodies in an exoplanetary system and would be indicative of a planetary system so strongly perturbed that G238-44 is able to capture both asteroid and Kuiper Belt–analog bodies near-simultaneously within its <100 Myr cooling age.

Securing electronic health records against insider-threats: A supervised machine learning approach

The introduction of electronic health records (EHR) has created new opportunities for efficient patient data management. For example, preventative medical practice, rather than reactive, is possible through the integration of machine learning to mine digital patient record datasets. Furthermore, within the wider smart cities’ infrastructure, EHR has considerable environmental and cost-saving benefits for healthcare providers. Yet, there are inherent dangers to digitising patient records. Considering the sensitive nature of the data, EHR is equally at risk of both external threats and insider attacks, but security applications are predominantly facing the outer boundary of the network. Therefore, in this work, the focus is on insider data misuse detection. The approach involves the use of supervised classification (decision tree, random forest and support vector machine) based off pre-labelled real-world data collated from a UK-based hospital for the detection of EHR data misuse. The results demonstrate that by employing a machine learning approach to analyse EHR data access, anomaly detection can be achieved with a 0.9896 accuracy from a test set and 0.9908 from the validation set using a support vector machine classifier. The emphasis of this research is on the detection of EHR data misuse, through the detection of anomalous behavioural patterns. Based on the results, the recommendation is to adopt an SVM for data misuse/insider threat detection.

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

Alternate modulations of ENSO and the Arctic Oscillation on winter extreme cold events in China

Station observations and global reanalysis datasets were applied to investigate the dominant modes of winter cold days (CDs) in China and their relationships with the Arctic Oscillation (AO) and El Niño–Southern Oscillation (ENSO). The dominant modes of the CDs exhibited an interdecadal shift around the 1980s. The first mode reflected a nearly coherent interannual variability with a maximum center over central-northeastern China during 1961–1985 (P1), whereas the maximum center shifted to central-southwestern China during 1993–2017 (P2). The second mode represented a north-south dipole variation, with maximum variations over southern China in the P1 but northeastern China in the P2. The first mode was significantly related to the AO in the P1 and ENSO in the P2, whereas the second mode was closely linked to ENSO in the P1 and the AO in the P2. During the P1, an anomalous quasi-barotropic cyclone over northern East Asia was induced by a negative AO, deepening the East Asian trough along the East Asian coast, which was favorable for the occurrence of CDs over central-northeastern China (i.e., the first mode). The El Niño excited an anomalous anticyclonic circulation over the Northwest Pacific, impeding cold air along the coast of southern China, which seemed to produce a pattern of the second mode. During the P2, the AO was weaker (smaller magnitude), and the AO-related anomalous cyclone shifted northwestward compared to that in the P1, limiting its effect to northeastern China and inducing a north-south dipole pattern. On the other hand, ENSO-related anomalous circulation shifted southwestward compared to the P1, modulating CDs over central-southwestern China and producing an anomalous pattern of CDs similar to the first mode.

Effects of anthropogenic forcing and atmospheric circulation on the record-breaking welt bulb heat event over southern China in September 2021

In September 2021, southern China witnessed an extreme high-temperature and high-humidity event. The average regional wet bulb globe temperature (WBGT) anomaly (relative to 1961–1990 mean) in 110.0°–120.0°E, 27.5°–32.5°N region was the highest on record at 3.28 °C and exceeded three times the observed standard deviation. To investigate the underlying causes, we examine the effects of anthropogenic forcings and anomalous circulation patterns on this event using the multi-model ensembles from the Coupled Model Intercomparison Project Phase 6. Results indicate that 2021-like events would happen extremely rarely without anthropogenic warming (would not occur in counterfactual world simulations) and have become a 1-in-16-year event in the factual world. For the threshold of the second most extreme year, the occurrence probability of extreme WBGT events increases approximately 50 times due to the impact of anthropogenic forcings. The effect of anthropogenic warming under similar atmosphere circulation increases the probability of extreme WBGT events by 13–60 times, and that of corresponding circulation patterns under the same anthropogenic warming increases the probability by 1.3–1.8 times.

Extreme Positive Indian Ocean Dipole in 2019 and Its Impact on Indonesia

The evolution of an extreme positive Indian Ocean Dipole (pIOD) that took place in the tropical Indian Ocean during the late boreal summer to early winter of 2019 is examined in terms of coupled ocean–atmosphere dynamics. The patterns of anomalous sea-surface temperature (SST) revealed a typical pIOD characteristic: cooling (warming) in the southeastern (western) tropical Indian Ocean. Based on the Dipole Mode Index (DMI), the evolution of the event started in mid-July and gradually strengthened with an abrupt weakening in early September before coming to its peak in late October/early November. It quickly weakened in November, and then it terminated in mid-December. During the peak phase of the event, the SST anomaly in the southeastern (western) tropical Indian Ocean reached about −2 °C (+1 °C). The pattern of anomalous SST was followed by an anomalous pattern in precipitation, in which deficit precipitation was observed over the eastern Indian Ocean, particularly over the Indonesia region. Earlier study has shown that dry conditions associated with the pIOD event created a favorable condition for a forest-peat fire in southern Sumatra. The number of fire hotspots has increased significantly during the peak phase of the 2019 pIOD event. In addition, anomalously strong upwelling forced by strong southeasterly wind anomalies along the southern coast of Java and Sumatra had induced a surface chlorophyll-a (Chl-a) bloom in this region. High surface Chl-a concentration was collocated with the negative SST anomalies observed off the southwest Sumatra coast and south Java.

A Machine Learning-Based Anomaly Prediction Service for Software-Defined Networks

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems' impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively.

Thermoluminescence characteristics and kinetic analyses of europium doped strontium gadolinium oxide phosphor

In this work, SrGd2O4 phosphors incorporated with Eu3+ at various dopant concentrations are synthesized via solid state reaction method. An X-ray diffraction (XRD) and thermoluminescence (TL) technique were used to examine the structural and thermoluminescent properties of as-prepared phosphors. Orthorhombic phase formation of Eu3+ doped samples was confirmed by XRD. The influence of impurity concentration and heating rates on the glow curves was also investigated. Anomalous heating rate pattern was observed in the sample with Eu3+ additive. A model of semi-localized transition was used to explain this behaviour. The TL glow curves of beta irradiated SrGd2O4:Eu3+ (0.25 mass %) reveal three well-resolved peaks at 105, 189, and 245 °C. Various heating rates, TM–Tstop, initial rise, and computerized glow curve deconvolution techniques were employed to detect the overlapping peak numbers and establish the kinetic parameters of SrGd2O4:Eu3+ (0.25 mass %). When the trap numbers and comparable energy values are considered, the findings of the approaches are very similar. For the dose ranges between 0.1 and 8 Gy, SrGd2O4:Eu3+ (0.25 mass %) samples exhibited linear behaviour, and high reproducibility, indicating their applicability for TL dosimetry applications.

A Spatio-Temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data

Tracking multiple moving objects in real-time in a dynamic threat environment is an important element in national security and surveillance system. It helps pinpoint and distinguish potential candidates posing threats from other normal objects and monitor the anomalous trajectories until intervention. To locate the anomalous pattern of movements, one needs to have an accurate data association algorithm that can associate the sequential observations of locations and motion with the underlying moving objects, and therefore, build the trajectories of the objects as the objects are moving. In this work, we develop a spatio-temporal approach for tracking maritime vessels as the vessel’s location and motion observations are collected by an Automatic Identification System. The proposed approach is developed as an effort to address a data association challenge in which the number of vessels as well as the vessel identification are purposely withheld and time gaps are created in the datasets to mimic the real-life operational complexities under a threat environment. Three training datasets and five test sets are provided in the challenge and a set of quantitative performance metrics is devised by the data challenge organizer for evaluating and comparing resulting methods developed by participants. When our proposed track association algorithm is applied to the five test sets, the algorithm scores a very competitive performance.

Causes and processes of two opposite climatic years in the tropical Atlantic warm pools

A numerical investigation of the interannual Southwestern and Equatorial Atlantic warm pools (WPs) (SST>28 °C) was conducted for the 2000–2014 period, focusing on two major opposite events over the past 39 years (1982–2020): an intense warming event in 2010 (sizeable warm pool) and a moderate-to-low event in 2012 (small warm pool). Some modes of climate variability (remote and local) were confronted with the WP area. Significant remote connections were found for the North Atlantic Oscillation (NAO) and the Atlantic Meridional Mode (AMM). Whereas, the Tropical Northern Atlantic (TNA) and Tropical Southern Atlantic (TSA) indices were locally related to anomalies over the WP area. On the months that preceded the 2010 WP event, an anomalous atmospheric circulation in the tropical North Atlantic basin occurred (extremely negative NAO and positive AMM), causing a major weakening of winds, shallower mixed layer, and higher SST. Consequently, the WP boundaries, which usually developed southward along the equator in the austral fall, were pushed northward as warm waters spread out into the north tropical basin. Locally, both basins (i.e., tropical north and south) were warmer than climatologically expected (positive TNA and TSA), leading to a significant increase in the WP area recorded in this period. On the other hand, during the months that preceded the 2012 WP, the trade winds were faster (positive NAO and positive AMM), leading to a condition close to the climatologically expected. The approximation of the center of the subtropical high in the southern ocean basin practically extinguished the portion of warm waters that usually spreads southwest along the basin. While the TNA was weakly positive, the TSA presented negative anomalies that ensured a significant reduction in the WP area in the southern basin during this time. Numerical results from the 2000–2014 run were contrasted with PIRATA buoys and satellite data to assess the model's performance. Investigations of the mixed layer temperature terms disclosed the predominance of the atmosphere over the oceanic terms during the warm pool area increase/decrease, showing a significantly different composition within the oceanic terms. The persistence of the same sign anomalies during the three months preceding the WP's seasonal development appeared to be a pivotal factor in understanding how different the events in 2010 and 2012 were. Net heat flux anomalies, mainly controlled by anomalous wind patterns, were primarily responsible for the extreme events observed in 2010 and 2012.

When the levee breaks: Visualization of a deepwater crevasse splay in Late Cretaceous in Exmouth Basin Offshore Western Australia

“The present is the key to the past” is a foundational geologic concept that helps us contextualize buried subsurface features in current geologic analogs. As seismic interpreters, the generation of the geologic model should be unbiased, yet as humans our unconscious biases are expected, and we sometimes overlook anomalous reflection patterns in our seismic data that do not fit the model. As a result, we often disservice ourselves when we overlook these characteristics, potentially ignoring additional geologic context. These anomalous geoforms or funny-looking things (FLTs) may provide further geologic context and aid us in solving the geologic model if included. Crevasse splay on a continental slope marine environment is described and analyzed using attributes, seismic inversion, and voxel-based classification. We discuss possible causes that may have triggered the break of the levee on the Exmouth Basin during the Early Eocene and why a crevasse splay on a steep slope is an FLT. A possible explanation is that the presence of preexisting faults beneath said feature is the likely culprit for a levee break that created the crevasses splay. Thus, in contrast with the Led Zeppelin song about why the levee breaks, it is equally important to understand the preexisting faults when analyzing sediment supply. In addition, this highlights the importance of integral stratigraphic sequence interpretation — from deep to shallow — to understand geology in a full context. Geologic feature: Crevasse splays and distributary channels Seismic appearance: Mounded high-amplitude reflection with hummocky low-amplitude internal reflection on vertical section, subparallel overlapping sinuous features on seismic attribute horizon slice Alternative interpretation: Mass-transport deposit, avulsion node; channel overbank Formation: The Wilcox Formation Age: Early Eocene-Late Paleocene Location: Offshore Western Australia Seismic data: Stybarrow 2008 M4D MSS obtained by the Geoscience Department of Australia Analysis tools: Coherence, multispectral coherency, principal curvature (K1 and K2), acoustic seismic inversion, flatness and curvedness, CNN automatic fault interpretation, and spectral decomposition

Real-Time Detection and Classification of Power Quality Disturbances.

This paper considers the problem of real-time detection and classification of power quality disturbances in power delivery systems. We propose a sequential and multivariate disturbance detection method (aiming for quick and accurate detection). Our proposed detector follows a non-parametric and supervised approach, i.e., it learns nominal and anomalous patterns from training data involving clean and disturbance signals. The multivariate nature of the method enables joint processing of data from multiple meters, facilitating quicker detection as a result of the cooperative analysis. We further extend our supervised sequential detection method to a multi-hypothesis setting, which aims to classify the disturbance events as quickly and accurately as possible in a real-time manner. The multi-hypothesis method requires a training dataset per hypothesis, i.e., per each disturbance type as well as the ’no disturbance’ case. The proposed classification method is demonstrated to quickly and accurately detect and classify power disturbances.

2022: An Unprecedentedly Rainy Early Summer in Northeast China

In the early summer (June) of 2022, the spatial mean precipitation in northeast China (NEC) was 62% higher than normal and broke the historical record since 1951. Based on the precipitation data of 245 meteorological stations in NEC and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, this paper analyzes the role of large-scale circulation and sea-surface temperature (SST) associated with anomalous precipitation over NEC in June using singular value decomposition (SVD), correlation analysis, regression analysis, and composite analysis methods, and further investigates the possible cause of the abnormal precipitation in June 2022. Results show that the northeast China cold vortex (NCCV) accompanying the blocking high in the Okhotsk Sea (BHOS) has been the primary mid-to-high latitude atmospheric circulation pattern affecting NEC precipitation in June since 2001. This circulation pattern is closely related to the tripole SST pattern over the North Atlantic (NAT) in March. In June 2022, the NAT SST anomaly in March stimulates eastward-propagating wave energy, resulting in the downstream anomalous circulation pattern in which the NCCV cooperates with the BHOS in the mid-high latitudes of East Asia. Under this background atmospheric circulation favorable for precipitation, the Kuroshio region SST anomaly in June led to a more northward and stronger anomalous anticyclone in the northwestern Pacific through local air–sea interaction, which provides more sufficient water vapor for NEC, resulting in unprecedented precipitation in June 2022.

Potential SST drivers for Chlorophyll-a variability in the Alboran Sea: A source for seasonal predictability?

This study investigates the link between large-scale variability modes of the sea surface temperature (SST) and the surface chlorophyll-a (Chl-a) concentration in spring along the northern flank of the Alboran Sea. To this aim, surface satellite-derived products of SST and Chl-a, together with atmospheric satellite variables, are used. Our results indicate that both the tropical North Atlantic and El Niño Southern Oscillation (ENSO) could trigger the development of anomalous distribution patterns of Chl-a in spring in northern Alboran. This anomalous feature of Chl-a is, in turn, associated with the alteration of the usual upwelling taking place in northern Alboran at that time of the year. The skill of the related SST signals, over the tropical North Atlantic and the tropical Pacific, as predictors of the aforementioned Chl-a response in Alboran, has also been assessed through a statistical prediction model with leave-one-out cross-validation. Our results confirm the predictive skill of ENSO to realistically estimate the coastal Chl-a concentration in spring in northern Alboran. In particular, during the El Niño/La Niña years, this Chl-a response can be robustly predicted with 4 months in advance. On the other hand, the tropical North Atlantic SSTs allow to significantly predict, up to 7 months in advance, the Chl-a concentration in spring offshore, in particular by the north of the Western and the Eastern Alboran gyres. The results presented here could contribute to develop a future seasonal forecasting tool of upwelling variability and living marine resources in northern Alboran.

Factors affecting the subsurface aragonite undersaturation layer in the Pacific Arctic region.

This study evaluated interannual variation in the subsurface aragonite undersaturation zone (ΩAr<1 layer) in the Pacific Arctic Ocean, using data from the 2016-2019 period. The upper boundary (DEPΩ<1UB) of the ΩAr<1 layer generally formed at a depth where the contribution of corrosive Pacific water was approximately 98%. The intensity of the Beaufort Gyre associated with freshwater accumulation mainly determined interannual variation in DEPΩ<1UB, but the direction of its effect was opposite west and east of ~166°W. The lower boundary (DEPΩ<1LB) of the ΩAr<1 layer was generally found at a depth range where equal contributions of Pacific and Atlantic water were expected. An Atlantic-origin cold saline water intrusion event in 2017 caused by an anomalous atmospheric circulation pattern significantly lifted the DEPΩ<1LB, thus the thickness of the ΩAr<1 layer decreased.