Global Model Research Articles

The microbial phosphorus cycle in aquatic ecosystems.

Phosphorus is an essential element for life, and phosphorus cycling is crucial to planetary habitability. In aquatic environments, microorganisms are a major component of phosphorus cycling and rapidly transform the diverse chemical forms of phosphorus through various uptake, assimilation and release pathways. Recent discoveries have revealed a more dynamic and complex aquatic microbial phosphorus cycle than previously understood. Some microorganisms have been shown to use and produce new phosphorus compounds, including those in reduced forms. New findings have also raised numerous unanswered questions that warrant further investigation. There is an expanding influence of human activity on aquatic ecosystems. Advancements in understanding the phosphorus biogeochemistry of evolving aquatic environments offer a unique opportunity to comprehend, anticipate and mitigate the effect of human activities. In this Review, I discuss the wealth of new aquatic phosphorus cycle research, spanning disciplines from omics and physiology to global biogeochemical modelling, with a focus on the current comprehension of how aquatic microorganisms sense, transport, assimilate, store, produce and release phosphorus. Of note, I delve into cellular phosphorus allocation, an underexplored topic with wide-ranging implications for energy and element flux in aquatic ecosystems.

Age of air from in situ trace gas measurements: insights from a new technique

Abstract. The age of air is an important transport diagnostic that can be derived from trace gas measurements and compared to global chemistry climate model output. We describe a new technique to calculate the age of air, measuring transport times from the Earth's surface to any location in the atmosphere based on simultaneous in situ measurements of multiple key long-lived trace gases. The primary benefits of this new technique include (1) optimized ages of air consistent with simultaneously measured SF6 and CO2; (2) age of air from the upper troposphere through the stratosphere; (3) estimates of the second moment of age spectra that have not been well constrained from measurements; and (4) flexibility to be used with measurements across multiple instruments, platforms, and decades. We demonstrate the technique on aircraft and balloon measurements from the 1990s, the last period of extensive stratospheric in situ sampling, and several recent missions from the 2020s, and compare the results with previously published and modeled values.

Recent progress in atmospheric modeling over the Andes – part II: projected changes and modeling challenges

In the Andes, the complex topography and unique latitudinal extension of the cordillera are responsible for a wide diversity of climate gradients and contrasts. Part I of this series reviews the current modeling efforts in simulating key atmospheric-orographic processes for the weather and climate of the Andean region. Building on this foundation, Part II focuses on global and regional climate models challenging task of correctly simulating changes in surface-atmosphere interactions and hydroclimate processes to provide reliable future projections of hydroclimatic trajectories in the Andes Cordillera. We provide a review of recent advances in atmospheric modeling to identify and produce reliable hydroclimate information in the Andes. In particular, we summarize the most recent modeling research on projected changes by the end of the 21st century in terms of temperature and precipitation over the Andes, the mountain elevation-dependent warming signal, and land cover changes. Recent improvements made in atmospheric kilometer-scale model configurations (e.g., resolution, parameterizations and surface forcing data) are briefly reviewed, highlighting their impact on modeling results in the Andes for precipitation, atmospheric and surface-atmosphere interaction processes, as mentioned in recent studies. Finally, we discuss the challenges and perspectives of climate modeling, with a focus on the hydroclimate of the Andes.

Vessel Trajectory Prediction Based on AIS Data: Dual-Path Spatial–Temporal Attention Network with Multi-Attribute Information

With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages.

Federated Learning : A Paradigm Shift in Collaborative Machine Learning

Federated learning (FL) has emerged as an exceptionally promising method within the realm of machine learning, enabling multiple entities to jointly train a global model while maintaining decentralized data. This paper presents a comprehensive review of federated learning methodologies, applications, and challenges. We begin by elucidating the fundamental concepts underlying FL, including federated optimization algorithms, communication protocols, and privacy-preserving techniques. Subsequently, we delve into various domains where FL has found significant traction, examples include healthcare, finance, and the Internet of Things (IoT), showcasing successful deployments and innovative strategies. Furthermore, we discuss the inherent challenges associated with federated learning, such as communication overhead, heterogeneity of data sources, and privacy concerns, and explore state- of-the-art solutions proposed in literature. Finally, we outline future research directions in federated learning, including advancements in privacy-preserving techniques, scalability improvements, and extension of FL to emerging domains. This thorough examination provides a valuable asset for researchers, practitioners, and policymakers keen on grasping the panorama of federated learning and its ramifications for collaborative machine learning in dispersed settings.

Global Spatial Projections of Forest Soil Respiration and Associated Uncertainties

The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections.

Assessing Radiative Feedbacks and Their Contribution to the Arctic Amplification Measured by Various Metrics

AbstractArctic amplification (AA), characterized by a more rapid surface air temperature (SAT) warming in the Arctic than the global average, is a major feature of global climate warming. Various metrics have been used to quantify AA based on SAT anomalies, trends, or variability, and they can yield quite different conclusions regarding the magnitude and temporal patterns of AA. This study examines and compares various AA metrics for their temporal consistency in the region north of 70°N from the early twentieth to the early 21st century using observational data and reanalysis products. We also quantify contributions of different radiative feedback mechanisms to AA based on short‐term climate variability in reanalysis and model data using the Kernel‐Gregory approach. Albedo and lapse rate feedbacks are positive and comparable, with albedo feedback being the leading contributor for all AA metrics. The net cloud feedback, which has large uncertainties, depends strongly on the data sets and AA metrics used. By quantifying the influence of internal variability on AA and related feedbacks based on global climate model ensemble simulations, we find that water vapor and cloud feedbacks are most heavily affected by internal variability.

Characteristic analysis method for integrated multi-parameter hydro-viscous speed control system

Hydro-viscous clutch has become an inevitable choice for special vehicle transmissions. As a nonlinear dynamic system with large lagging link, its timing performance is affected by input rotational speed, lubricating oil temperature and pressure and other factors. However, from the control perspective, the speed regulation law, formation mechanism control characteristics, global model of hydro-viscous speed control system (HSCS) are unclear. To solve these problems, this paper presents a comprehensive analysis of the Hydro-viscous Speed Control System (HSCS), focusing on its steady-state and dynamic speed control characteristics. A data-driven model is established to describe the relationship between input rotational speed, output speed, control oil pressure, and lubricating oil temperature. The findings provide a foundation for optimizing HSCS structure and parameters, enhancing the performance and reliability of such systems in special vehicle transmissions and establishing a temperature-speed control system for special vehicle transmissions.

Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.

Population exposure to flooding in Small Island Developing States under climate change

Abstract Estimates of current and future population exposure to both coastal and inland flooding do not exist consistently in all Small Island Developing States (SIDS), despite these being some of the places most at risk to climate change. This has primarily been due to a lack of suitable or complete data. In this paper, we utilise a ∼30 m global hydrodynamic flood model to estimate population exposure to coastal and inland flood hazard in all SIDS under present day, as well as under low, intermediate, and very high emissions climate change scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Our analysis shows that present day population exposure to flooding in SIDS is high (19.5% total population: 100 year flood hazard), varies widely depending on the location (3%–66%), and increases under all three climate scenarios—even if global temperatures remain below 2 °C warming (range in percentage change between present day and SSP1-2.6: −4.5%–44%). We find that levels of flood hazard and population exposure are not strongly linked, and that indirect measures of exposure in common vulnerability or risk indicators do not adequately capture the complex drivers of flood hazard and population exposure in SIDS. The most exposed places under the lowest climate change scenario (SSP1-2.6) continue to be the most exposed under the highest climate change scenario (SSP5-8.5), meaning investment in adaptation in these locations is likely robust to climate scenario uncertainty.

Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region

The Arctic-Boreal region is projected to experience spatially divergent trends in snow depth following climate change. However, the impact of these spatial trends has remained largely unexplored, despite potentially large consequences for the carbon cycle. To address this knowledge gap, we forced a customised arctic version of the dynamic vegetation model LPJ-GUESS with daily CMIP6 outputs from a global climate model (MRI-ESM2-0) under three climate scenarios. We find that snow depths increased the most in the coldest, northernmost regions, insulating the soil, which led to increased heterotrophic respiration and reduced carbon residence times. We emphasise the need for improved projections of future snow depth - in particular diverging trends across landscapes - to more accurately simulate the strength of Arctic-Boreal carbon feedbacks and their impact on global climate.

Correcting Multivariate Biases in Regional Climate Model Boundaries: How Are Synoptic Systems Impacted Over the Australian Region?

AbstractSynoptic climatology, which connects atmospheric circulation with regional environmental conditions, is pivotal to understanding climate dynamics. While regional climate models (RCMs) can reproduce key mesoscale precipitation patterns, biases related to synoptic circulation from the driving model, typically global climate models (GCMs), often remain unaddressed. This study examines the influence of correcting systematic bias in RCM boundaries on the representation of Australian synoptic systems. We utilize a structural self‐organizing map to evaluate the frequency, persistence, and transitions of daily synoptic systems. Our findings reveal that an RCM with multivariate bias‐corrected boundaries improves the representation of synoptic systems compared to the driving GCM, or an RCM with uncorrected or simply bias‐corrected boundaries, particularly in reference to the frequency of systems identified. This demonstrates that appropriately correcting RCM boundary conditions helps correct many of the circulation errors inherited from the driving GCM but not all.

A machine learning assisted preliminary design methodology for bolted composite joints in large structures

Abstract Damage initiation hotspots around features, such as bolts and ply drops, must be investigated during the preliminary design phase of large composite structures, such as composite airframes. A global-local modelling approach is commonly employed to perform this investigation, whereby a global low-fidelity model is used to drive high-fidelity local models around the features of interest. However, this methodology is slow, repetitive and expert-dependent. In this investigation, we address these issues by applying machine learning techniques to this global-local modelling framework and demonstrate the time-saving benefit when predicting damage initiation of bolted composite joints. Feature engineering of model inputs and outputs, and appropriate customisation of machine learning methods enables damage initiation prediction. Special consideration is given to the boundary conditions that must be varied to simulate the response of the bolted composite joints. Results show over three orders of magnitude time-saving benefit and satisfactory accuracy of the proposed methodology. This indicates its potential to be developed further into a rapid design and optimisation tool.

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

A Stepwise‐Clustered Precipitation Downscaling Method for Ensemble Climatic Projections in the Mediterranean Region

ABSTRACTPrecipitation changes dynamically in the Mediterranean region. Therefore, the projection of future precipitation and its historical distribution mechanism is essential for climate mitigation and adaptation. In this study, a stepwise clustered precipitation downscaling method (SCPD) was developed and adopted in the Mediterranean region to reveal the inherent variation rules and trends over the future 100 years under two SSP scenarios. A cutting and merging multivariate process is introduced to build a cluster tree for supporting further downscaling and projecting steps. The ensemble average from the global climate model (GCM) dataset is used for precipitation projections. The precipitation performance of SCPD, evaluated by R2, is fairly decent. The precipitation projections vary with the original rainfall patterns over the gauge stations. Dry places tend to become comparably drier in the future. Precipitation in the northern Mediterranean region shows a drier winter–spring and wetter summer–autumn. Opposite trends emerged in the southern part, with increasing winter precipitation and decreasing summer rainfall. The rising carbon dioxide concentration will further intensify the decrease in rainfall. However, the centres of these two EOFs are not identical. The contributions of NAO (positive) and Niño 3.4 (negative) to PC1 are relatively high. Accordingly, the strongest positive correlation with PC2 is SCAND, as well as negative correlations with AO, NAO and EAWR. Positive anomaly precipitation is attributed to PC1, whereas PC2 is responsible for most of the negative variance precipitation.

Federated learning with tensor networks: a quantum AI framework for healthcare

Abstract The healthcare industries frequently handle sensitive and proprietary data, and due to strict
privacy regulations, they are often reluctant to share data directly. In today’s context, Federated
Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine
learning while effectively managing critical concerns regarding data privacy and governance.
The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary
approach with immense potential to revolutionize various industries, from healthcare to
finance. In this work, we propose a federated learning framework based on quantum tensor networks
that takes advantage of the principles of many-body quantum physics. Currently, there are
no known classical tensor networks implemented in federated settings. Furthermore, we investigated
the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis
to ensure the security of sensitive data across healthcare institutions. Experiments on popular
medical image datasets show that the federated quantum tensor network model achieved a mean
receiver-operator characteristic area under the curve (ROC-AUC) of 91-98%, outperforming several
state-of-the-art federated learning methods while using fewer parameters. Experimental results
demonstrate that the quantum federated global model, consisting of highly entangled tensor network
structures, showed better generalization and robustness and achieved higher testing accuracy,
surpassing the performance of locally trained clients under unbalanced data distributions among
healthcare institutions.

Neural Operator for Planetary Remote Sensing Super-Resolution with Spectral Learning

High-resolution planetary remote sensing imagery provides detailed information for geomorphological and topographic analyses. However, acquiring such imagery is constrained by limited deep-space communication bandwidth and challenging imaging environments. Conventional super-resolution methods typically employ separate models for different scales, treating them as independent tasks. This approach limits deployment and real-time applications in planetary remote sensing. Moreover, capturing global context is crucial in planetary remote sensing images due to their contextual similarities. To address these limitations, we propose Discrete Cosine Transform (DCT)–Global Super Resolution Neural Operator (DG-SRNO), a global context-aware arbitrary-scale super-resolution model. DG-SRNO achieves super-resolution at any scale using a single framework by learning the mapping between low-resolution (LR) and high-resolution (HR) function spaces. We mathematically prove the global receptive field of DG-SRNO. To evaluate DG-SRNO’s performance in planetary remote sensing tasks, we introduce the Ceres 800 dataset, a planetary remote sensing super-resolution dataset. Extensive quantitative and qualitative experiments demonstrate DG-SRNO’s impressive reconstruction capabilities.

Substantial Global Radial Variations of Basalt Content Near the 660‐km Discontinuity

AbstractMid‐ocean ridges generate basalt and harzburgite, which are introduced into the mantle through subduction as a mechanical mixture, contributing to both lateral and radial compositional heterogeneity. The possible accumulation of basalt in the mantle transition zone has been examined, but details of the mantle composition below the 660‐km discontinuity (hereafter d660) remain poorly constrained. In this study, we utilize the subtle waveform details of S660S, the underside shear‐wave reflection off the d660, to interpret the seismic velocity, density, and compositional structure near, and particularly below, the d660. We identify a significant difference in S660S waveform shape in subduction zones compared to other regions. The inversion results reveal globally enriched basalt at the d660, with a notably higher content in subduction zones, consistent with the smaller impedance jump and S660S peak amplitude. The basalt fraction decreases significantly to less than 10% near 800‐km depth, forming a global harzburgite‐enriched layer and resulting in a steep seismic velocity gradient just below the d660, in agreement with 1D global reference models. The striking compositional radial variations near the d660 verify geodynamic predictions and challenge the applicability of homogeneous radial compositional models in the mantle. These variations may also affect the viscosity profile and, consequently, the dynamics at the boundary between the upper and lower mantle.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Prognostic Implications and Reversibility of Pulmonary Vascular Resistance Derived by Echocardiography in Patients Undergoing Tricuspid Annuloplasty.

Pulmonary vascular resistance (PVR) intimately correlates with right ventricular afterload and the development of secondary tricuspid regurgitation (sTR). We sought to investigate the prognostic roles of PVR derived by echocardiography in patients with sTR undergoing tricuspid annuloplasty (TA). Data from 322 TA patients [median age (interquartile range): 65.0 (59.0-70.0) years; 35.7% males] were obtained from a prospective registry to determine the impact of PVR on the composite outcome (including all-cause mortality and heart failure hospitalization). PVR was calculated by dividing the peak TR velocity by time-velocity integral of the right ventricular outflow tract followed by adding 0.16. During a median follow-up of 5.2 years, 108 adverse events occurred including 48 deaths and 60 heart failure readmissions. Baseline PVR ≥2 WU was independently associated with a higher risk of composite outcome (HR:1.674, 95% CI: 1.028-2.726, P=0.038). Baseline PVR outperforms both pulmonary artery systolic pressure (PASP) and the ratio of tricuspid annulus plane systolic excursion to PASP in terms of outcome prediction, with pronounced improvement of global model fit, reclassification, and discrimination. In 150 patients who received short-term echocardiograms after surgery, the presence of postoperative PVR ≥2 WU (n=20, 13.3%) was independently associated with composite outcome (HR: 2.621, 95% CI: 1.292-5.319, P=0.008). PVR derived by echocardiography is an independent determinant of outcomes in patients undergoing TA for sTR. The inclusion of noninvasive PVR may provide valuable information to improve patient selection and postoperative management in this population.