Climate Trends and the Remarkable Sensitivity of Shelf Regions

Tidal motion of oceanic salt water through the ambient geomagnetic field induces periodic electromagnetic field signals. Amplitudes of the induced signals are sensitive to variations in electrical seawater conductivity and, consequently, to changes in oceanic temperature and salinity. In this paper, we computed and analyzed time series of global ocean tide‐induced magnetic field amplitudes. For this purpose, we combined data of global in situ observations of oceanic temperature and salinity fields from 1990–2016 with data of oceanic tidal flow, the geomagnetic field, mantle conductivity, and sediment conductance to derive ocean tide‐induced magnetic field amplitudes. The results were used to compare present day developments in the oceanic climate with two existing climate model scenarios, namely, global oceanic warming and Greenland glacial melting. Model fits of linear and quadratic long‐term trends of the derived magnetic field amplitudes show indications for both scenarios. Also, we find that magnetic field amplitude anomalies caused by oceanic seasonal variability and oceanic climate variations are 10 times larger in shallow ocean regions than in the open ocean. Consequently, changes in the oceanic and therefore the Earth's climate system will be observed first in shelf regions. In other words, climate variations of ocean tide‐induced magnetic field amplitudes are best observed in shallow ocean regions using targeted monitoring techniques

A data assimilation twin experiment

Satellite observations of the magnetic field induced by the general ocean circulation could provide new constraints on global oceanic water and heat transports. This opportunity is investigated in a model-based twin experiment by assimilating synthetic satellite observations of the ocean-induced magnetic field into a global ocean model. The general circulation of the world ocean is simulated over the period of 1 month. Idealized daily observations are generated from this simulation by calculating the ocean-induced magnetic field at 450 km altitude and disturbing these global fields with error estimates. Utilizing an ensemble Kalman filter, the observations are assimilated into the same ocean model with a different initial state and different atmospheric forcing. Compared to a reference simulation without data assimilation, the corrected ocean-induced magnetic field is improved throughout the whole simulation period and over large regions. The global RMS differences of the ocean-induced magnetic field are reduced by up to 17%. Local improvements show values up to 54%. RMS differences of the depth-integrated zonal and meridional ocean velocities are improved by up to 7% globally, and up to 50% locally. False corrections of the ocean model state are identified in the South Pacific Ocean and are linked to a deficient estimation of the ocean model error covariance matrices. Most Kalman filter induced changes in the ocean velocities extend from the sea surface down to the deep ocean. Allowing the Kalman filter to correct the wind stress forcing of the ocean model is essential for a successful assimilation

Electromagnetic characteristics of ENSO

The motion of electrically conducting sea water through Earth's magnetic field induces secondary electromagnetic fields. Due to its periodicity, the oceanic tidally induced magnetic field is easily distinguishable in magnetic field measurements and therefore detectable. These tidally induced signatures in the electromagnetic fields are also sensitive to changes in oceanic temperature and salinity distributions. We investigate the impact of oceanic heat and salinity changes related to the El Niño–Southern Oscillation (ENSO) on oceanic tidally induced magnetic fields. Synthetic hydrographic data containing characteristic ENSO dynamics have been derived from a coupled ocean–atmosphere simulation covering a period of 50 years. The corresponding tidally induced magnetic signals have been calculated with the 3-D induction solver x3dg. By means of the Oceanic Niño Index (ONI), based on sea surface temperature anomalies, and a corresponding Magnetic Niño Index (MaNI), based on anomalies in the oceanic tidally induced magnetic field at sea level, we demonstrate that evidence of developing ENSO events can be found in the oceanic magnetic fields statistically 4 months earlier than in sea surface temperatures. The analysis of the spatio-temporal progression of the oceanic magnetic field anomalies offers a deeper understanding on the underlying oceanic processes and is used to test and validate the initial findings

Tide-induced magnetic signals and their errors derived from CHAMP and Swarm satellite magnetometer observations

Satellite-measured tidal magnetic signals are of growing importance. These fields are mainly used to infer Earth’s mantle conductivity, but also to derive changes in the oceanic heat content. We present a new Kalman filter-based method to derive tidal magnetic fields from satellite magnetometers: KALMAG. The method’s advantage is that it allows to study a precisely estimated posterior error covariance matrix. We present the results of a simultaneous estimation of the magnetic signals of 8 major tides from 17 years of Swarm and CHAMP data. For the first time, robustly derived posterior error distributions are reported along with the reported tidal magnetic fields. The results are compared to other estimates that are either based on numerical forward models or on satellite inversions of the same data. For all comparisons, maximal differences and the corresponding globally averaged RMSE are reported. We found that the inter-product differences are comparable with the KALMAG-based errors only in a global mean sense. Here, all approaches give values of the same order, e.g., 0.09 nT-0.14 nT for M2. Locally, the KALMAG posterior errors are up to one order smaller than the inter-product differences, e.g., 0.12 nT vs. 0.96 nT for M2

On the detectability of the magnetic fields induced by ocean circulation in geomagnetic satellite observations

Due to their sensitivity to conductivity and oceanic transport, magnetic signals caused by the movement of the ocean are a beneficial source of information. Satellite observed tidal-induced magnetic fields have already proven to be helpful to derive Earth’s conductivity or ocean heat content. However, magnetic signals caused by ocean circulation are still unobserved in satellite magnetometer data. We present a novel method to detect these magnetic signals from ocean circulation using an observing system simulation experiment. The introduced approach relies on the assimilation of satellite magnetometer data based on a Kalman filter algorithm. The separation from other magnetic contributions is attained by predicting the temporal behavior of the ocean-induced magnetic field through presumed proxies. We evaluate the proposed method in different test case scenarios. The results demonstrate a possible detectability of the magnetic signal in large parts of the ocean. Furthermore, we point out the crucial dependence on the magnetic signal’s variability and show that our approach is robust to slight spatial and temporal deviations of the presumed proxies. Additionally, we showed that including simple prior spatial constraints could further improve the assimilation results. Our findings indicate an appropriate sensitivity of the detection method for an application outside the presented observing system simulation experiment. Therefore, we finally discussed potential issues and required advances toward the method’s application on original geomagnetic satellite observations

A statistical method to validate reconstructions of late-glacial relative sea level – Application to shallow water shells rated as low-grade sea-level indicators

In this study, we propose a statistical method to validate sea-level reconstructions using geological records known as sea-level indicators (SLIs). SLIs are often the only available data to retrace late-glacial relative sea level (RSL). Determining the RSL from SLI height is not straight forward, the elevation at which an SLI was found usually does not represent the past RSL. In contrast, it has to be related to past RSL by investigating sample’s type, habitat and deposition conditions. For instance, water distribution at which a specific specimen is found today can be related to the indicator's depositional height range. Furthermore, the precision of dating varies between geological samples, and, in case of radiocarbon dating, the age has to be calibrated using a non-linear calibration curve. To avoid an a-priori assumption like normal-distributed uncertainties, we define likelihood functions which take into account the indicative meaning’s available error information and calibration statistics represented by joint probabilities. For this conceptional study, we restrict ourselves to one type of indicators, shallow-water shells, which are usually considered as low-grade samples giving only a lower limit of former sea level, as the depth range in which they live spreads over several tens of meters, and does not follow a normal distribution. The presented method is aimed to serve as a strategy for glacial isostatic adjustment reconstructions, in this case for the German Paleo-Climate Modelling Initiative PalMod (https://www.palmod.de/en) and by extending it to other SLI types

Anwendungsbereiche von künstlicher Intelligenz im Kontext von One Health mit Fokus auf antimikrobielle Resistenzen

Die Gesundheit der Menschen steht vor einer Reihe neuer Herausforderungen, die maßgeblich durch den fortschreitenden Klimawandel, den demografischen Wandel und die Globalisierung angetrieben werden. Der One-Health-Ansatz basiert auf dem Verständnis, dass die Gesundheit von Menschen, Tieren und Umwelt eng verknüpft ist. Bei der Umsetzung von One Health in die Praxis ergibt sich die Notwendigkeit, in der Forschung diverse und heterogene Datenströme und -typen aus den verschiedenen Sektoren zu kombinieren und zu analysieren. Verfahren der künstlichen Intelligenz (KI) bieten dabei neue Möglichkeiten zur sektorübergreifenden Beurteilung von heutigen und zukünftigen Gesundheitsgefahren. Dieser Beitrag gibt einen Überblick über verschiedene Anwendungsbereiche von KI-Verfahren im Zusammenhang mit One Health und zeigt Herausforderungen auf. Am Beispiel der Ausbreitung antimikrobieller Resistenzen (AMR), die eine zunehmende globale Gefahr im One-Health-Kontext darstellt, werden bestehende und zukünftige KI-basierte Lösungsansätze zur Eindämmung und Prävention beschrieben. Diese reichen von neuartiger Arzneientwicklung und personalisierter Therapie über gezieltes Monitoring der Antibiotikanutzung in Tierhaltung und Landwirtschaft bis hin zu einer umfassenden Umwelt-Surveillance für zukünftige AMR-Risikobewertungen.Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques. This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.Peer Reviewe

Clinical outcomes after anterior cruciate ligament injury: panther symposium ACL injury clinical outcomes consensus group

© 2020, The Author(s). Purpose: A stringent outcome assessment is a key aspect for establishing evidence-based clinical guidelines for anterior cruciate ligament (ACL) injury treatment. The aim of this consensus statement was to establish what data should be reported when conducting an ACL outcome study, what specific outcome measurements should be used and at what follow-up time those outcomes should be assessed. Methods: To establish a standardized approach to assessment of clinical outcome after ACL treatment, a consensus meeting including a multidisciplinary group of ACL experts was held at the ACL Consensus Meeting Panther Symposium, Pittsburgh, PA; USA, in June 2019. The group reached consensus on nine statements by using a modified Delphi method. Results: In general, outcomes after ACL treatment can be divided into four robust categories—early adverse events, patient-reported outcomes, ACL graft failure/recurrent ligament disruption and clinical measures of knee function and structure. A comprehensive assessment following ACL treatment should aim to provide a complete overview of the treatment result, optimally including the various aspects of outcome categories. For most research questions, a minimum follow-up of 2 years with an optimal follow-up rate of 80% is necessary to achieve a comprehensive assessment. This should include clinical examination, any sustained re-injuries, validated knee-specific PROs and Health-Related Quality of Life questionnaires. In the mid- to long-term follow-up, the presence of osteoarthritis should be evaluated. Conclusion: This consensus paper provides practical guidelines for how the aforementioned entities of outcomes should be reported and suggests the preferred tools for a reliable and valid assessment of outcome after ACL treatment. Level of evidence: V

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo