Estimating ocean tide model uncertainties for electromagnetic inversion studies

Over a decade ago the semidiurnal lunar M2 ocean tide was identified in CHAMP satellite magnetometer data. Since then and especially since the launch of the satellite mission Swarm, electromagnetic tidal observations from satellites are increasingly used to infer electric properties of the upper mantle. In most of these inversions, ocean tidal models are used to generate oceanic tidal electromagnetic signals via electromagnetic induction. The modeled signals are subsequently compared to the satellite observations. During the inversion, since the tidal models are considered error free, discrepancies between forward models and observations are projected only onto the induction part of the modeling, e.g., Earth's conductivity distribution. Our study analyzes uncertainties in oceanic tidal models from an electromagnetic point of view. Velocities from hydrodynamic and assimilative tidal models are converted into tidal electromagnetic signals and compared. Respective uncertainties are estimated. The studies main goal is to provide errors for electromagnetic inversion studies. At satellite height, the differences between the hydrodynamic tidal models are found to reach up to 2&thinsp;nT, i.e., over 100&thinsp;% of the local M2 signal. Assimilative tidal models show smaller differences of up to 0.1&thinsp;nT, which in some locations still corresponds to over 30&thinsp;% of the M2 signal.</p

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

Mortality and Cardiovascular Disease among Older Live Kidney Donors

Over the past two decades, live kidney donation by older individuals (≥55 years) has become more common. Given the strong associations of older age with cardiovascular disease (CVD), nephrectomy could make older donors vulnerable to death and cardiovascular events. We performed a cohort study among older live kidney donors who were matched to healthy older individuals in the Health and Retirement Study. The primary outcome was mortality ascertained through national death registries. Secondary outcomes ascertained among pairs with Medicare coverage included death or CVD ascertained through Medicare claims data. During the period from 1996 to 2006, there were 5717 older donors in the United States. We matched 3368 donors 1:1 to older healthy nondonors. Among donors and matched pairs, the mean age was 59 years; 41% were male and 7% were black race. In median follow-up of 7.8 years, mortality was not different between donors and matched pairs (p = 0.21). Among donors with Medicare, the combined outcome of death/CVD (p = 0.70) was also not different between donors and nondonors. In summary, carefully selected older kidney donors do not face a higher risk of death or CVD. These findings should be provided to older individuals considering live kidney donation

Impact of variable seawater conductivity on motional induction simulated with an ocean general circulation model

Carrying high concentrations of dissolved salt, ocean water is a good electrical conductor. As seawater flows through the Earth's ambient geomagnetic field, electric fields are generated, which in turn induce secondary magnetic fields. In current models for ocean-induced magnetic fields, a realistic consideration of seawater conductivity is often neglected and the effect on the variability of the ocean-induced magnetic field unknown. To model magnetic fields that are induced by non-tidal global ocean currents, an electromagnetic induction model is implemented into the Ocean Model for Circulation and Tides (OMCT). This provides the opportunity to not only model ocean-induced magnetic signals but also to assess the impact of oceanographic phenomena on the induction process. In this paper, the sensitivity of the induction process due to spatial and temporal variations in seawater conductivity is investigated. It is shown that assuming an ocean-wide uniform conductivity is insufficient to accurately capture the temporal variability of the magnetic signal. Using instead a realistic global seawater conductivity distribution increases the temporal variability of the magnetic field up to 45 %. Especially vertical gradients in seawater conductivity prove to be a key factor for the variability of the ocean-induced magnetic field. However, temporal variations of seawater conductivity only marginally affect the magnetic signal

Depth of origin of ocean-circulation-induced magnetic signals

As the world ocean moves through the ambient geomagnetic core field, electric currents are generated in the entire ocean basin. These oceanic electric currents induce weak magnetic signals that are principally observable outside of the ocean and allow inferences about large-scale oceanic transports of water, heat, and salinity. The ocean-induced magnetic field is an integral quantity and, to first order, it is proportional to depth-integrated and conductivity-weighted ocean currents. However, the specific contribution of oceanic transports at different depths to the motional induction process remains unclear and is examined in this study. We show that large-scale motional induction due to the general ocean circulation is dominantly generated by ocean currents in the upper 2000 m of the ocean basin. In particular, our findings allow relating regional patterns of the oceanic magnetic field to corresponding oceanic transports at different depths. Ocean currents below 3000 m, in contrast, only contribute a small fraction to the ocean-induced magnetic signal strength with values up to 0.2 nT at sea surface and less than 0.1 nT at the Swarm satellite altitude. Thereby, potential satellite observations of ocean-circulation-induced magnetic signals are found to be likely insensitive to deep ocean currents. Furthermore, it is shown that annual temporal variations of the ocean-induced magnetic field in the region of the Antarctic Circumpolar Current contain information about sub-surface ocean currents below 1000 m with intra-annual periods. Specifically, ocean currents with sub-monthly periods dominate the annual temporal variability of the ocean-induced magnetic field

Improving Atmospheric Angular Momentum Forecasts by Machine Learning

Earth angular momentum forecasts are naturally accompanied by forecast errors that typically grow with increasing forecast length. In contrast to this behavior, we have detected large quasi‐periodic deviations between atmospheric angular momentum wind term forecasts and their subsequently available analysis. The respective errors are not random and have some hard to define yet clearly visible characteristics which may help to separate them from the true forecast information. These kinds of problems, which should be automated but involve some adaptation and decision‐making in the process, are most suitable for machine learning methods. Consequently, we propose and apply a neural network to the task of removing the detected artificial forecast errors. We found that a cascading forward neural network model performed best in this problem. A total error reduction with respect to the unaltered forecasts amounts to about 30% integrated over a 6‐days forecast period. Integrated over the initial 3‐days forecast period, in which the largest artificial errors are present, the improvements amount to about 50%. After the application of the neural network, the remaining error distribution shows the expected growth with forecast length. However, a 24‐hourly modulation and an initial baseline error of 2 × 10−8 became evident that were hidden before under the larger forecast error.Plain Language Summary: Variations in Earth rotation can be described by changes in Earth angular momentum. Angular momentum functions are calculated from mass redistributions, for example, given by atmospheric models. Typically, atmospheric model forecasts are naturally accompanied by forecast errors that grow with increasing forecast length. In contrast to this behavior, atmospheric angular momentum wind term forecasts show large quasi‐periodic deviations when compared to their subsequently available model analysis data. The detected errors are not random and have some hard to define yet clearly visible characteristics. A postprocessing step using machine learning methods was established to remove the detected artificial forecast errors. A cascading forward neural network approach was able to reduce the forecast error by about 50% for the first forecast days and about 30% for a 6‐day forecast horizon. Moreover, the remaining error distribution shows the expected growth with forecast length. This postprocessing step improves atmospheric angular momentum forecasts without touching the numerical weather prediction model itself. Improved angular momentum forecasts should help to further decrease Earth rotation predictions errors.Key Points: Motion terms of atmospheric angular momentum forecasts contain systematic errors. Machine learning is used to learn and reduce these errors. Remaining stochastic errors show modulations with a 24‐hr period.http://esmdata.gfz-potsdam.de:8080/repositor