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

Tidal transports from satellite observations of earth’s magnetic field

The tides are a major driver of global oceanic mixing. While global tidal elevations are very well observed by satellite altimetry, the global tidal transports are much less well known. For twenty years, magnetic signals induced by the ocean tides have been detectable in satellite magnetometer observations, such as Swarm or CHAMP. Here, we demonstrate how satellite magnetometer observations can be used to directly derive global ocean tidal transports. As an advantage over other tidal transport estimates, our tidal estimates base on very few and very loose constraints from numerical forward models

An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model

Glacial isostatic adjustment is largely governed by the rheological properties of the Earth's mantle. Large mass redistributions in the ocean–cryosphere system and the subsequent response of the viscoelastic Earth have led to dramatic sea level changes in the past. This process is ongoing, and in order to understand and predict current and future sea level changes, the knowledge of mantle properties such as viscosity is essential. In this study, we present a method to obtain estimates of mantle viscosities by the assimilation of relative sea level rates of change into a viscoelastic model of the lithosphere and mantle. We set up a particle filter with probabilistic resampling. In an identical twin experiment, we show that mantle viscosities can be recovered in a glacial isostatic adjustment model of a simple three-layer Earth structure consisting of an elastic lithosphere and two mantle layers of different viscosity. We investigate the ensemble behaviour on different parameters in the following three set-ups: (1) global observations data set since last glacial maximum with different ensemble initialisations and observation uncertainties, (2) regional observations from Fennoscandia or Laurentide/Greenland only, and (3) limiting the observation period to 10 ka until the present. We show that the recovery is successful in all cases if the target parameter values are properly sampled by the initial ensemble probability distribution. This even includes cases in which the target viscosity values are located far in the tail of the initial ensemble probability distribution. Experiments show that the method is successful if enough near-field observations are available. This makes it work best for a period after substantial deglaciation until the present when the number of sea level indicators is relatively high

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

Data assimilation for a visco-elastic Earth deformation model

We present a data assimilation algorithm for the time-domain spectral-finite element code VILMA. We consider a 1D earth structure and a prescribed glaciation history ICE5G for the external mass load forcing. We use the Parallel Data Assimilation Framework (PDAF) to assimilate sea level data into the model in order to obtain better estimates of the viscosity structure of mantle and lithosphere. For this purpose, we apply a particle filter in which an ensemble of models is propagated in time, starting shortly before the last glacial maximum. At epochs when observations are available, each particle's performance is estimated and they are resampled based on their performance to form a new ensemble that better resembles the true viscosity distribution. In a proof of concept we show that with this method it is possible to reconstruct a synthetic viscosity distribution from which synthetic data were constructed. In a second step, paleo sea level data are used to infer an optimised 1D viscosity distribution

An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model

Glacial isostatic adjustment is largely governed by the rheological properties of the Earth's mantle. Large mass redistributions in the ocean–cryosphere system and the subsequent response of the viscoelastic Earth have led to dramatic sea level changes in the past. This process is ongoing, and in order to understand and predict current and future sea level changes, the knowledge of mantle properties such as viscosity is essential. In this study, we present a method to obtain estimates of mantle viscosities by the assimilation of relative sea level rates of change into a viscoelastic model of the lithosphere and mantle. We set up a particle filter with probabilistic resampling. In an identical twin experiment, we show that mantle viscosities can be recovered in a glacial isostatic adjustment model of a simple three-layer Earth structure consisting of an elastic lithosphere and two mantle layers of different viscosity. We investigate the ensemble behaviour on different parameters in the following three set-ups: (1) global observations data set since last glacial maximum with different ensemble initialisations and observation uncertainties, (2) regional observations from Fennoscandia or Laurentide/Greenland only, and (3) limiting the observation period to 10 ka until the present. We show that the recovery is successful in all cases if the target parameter values are properly sampled by the initial ensemble probability distribution. This even includes cases in which the target viscosity values are located far in the tail of the initial ensemble probability distribution. Experiments show that the method is successful if enough near-field observations are available. This makes it work best for a period after substantial deglaciation until the present when the number of sea level indicators is relatively high

On the characterization of tidal ocean-dynamo signals in coastal magnetic observatories

Periodic tidal ocean currents induce electric currents and, therefore, magnetic field signals that are observable using spaceborne and ground-based observation techniques. In theory, the signals can be used to monitor oceanic temperature and salinity variations. Tidal magnetic field amplitudes and phases have been extracted from magnetometer measurements in the past. However, due to uncertainties caused by a plentitude of influencing factors, the shape and temporal variation of these signals are only known to a limited extent. This study uses past extraction methods to characterize seasonal variations and long-term trends in the ten year magnetometer time series of three coastal island observatories. First, we assess data processing procedures used to prepare ground-based magnetometer observations for tidal ocean dynamo signal extraction to demonstrate that existing approaches, i.e., subtraction of core field models or first-order differencing, are unable to reliably remove low-frequency contributions. We hence propose low-frequency filtering using smoothing splines and demonstrate the advantages over the existing approaches. Second, we determine signal and side peak magnitudes of the M2 tide induced magnetic field signal by spectral analysis of the processed data. We find evidence for seasonal magnetic field signal variations of up to 25% from the annual mean. Third, to characterize the long-term behavior of tidal ocean dynamo signal amplitudes and phases, we apply different signal extraction techniques to identify tidal ocean-dynamo signal amplitudes and phases in sub-series of the ten-year time series with incrementally increasing lengths. The analyses support three main findings: (1) trends cause signal amplitude changes of up to ~1 nT and phase changes are in the order of O(10°) within the observation period; (2) at least four years of data are needed to obtain reliable amplitude and phase values with the extraction methods used and (3) signal phases are a less dependent on the chosen extraction method than signal amplitudes

Machine Learning‐Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses

The characterization of uncertainties in geophysical quantities is an important task with widespread applications for time series prediction, numerical modeling, and data assimilation. In this context, machine learning is a powerful tool for estimating complex patterns and their evolution through time. Here, we utilize a supervised machine learning approach to dynamically predict the spatiotemporal uncertainty of near‐surface wind velocities over the ocean. A recurrent neural network (RNN) is trained with reanalyzed 10 m wind velocities and corresponding precalculated uncertainty estimates during the 2012–2016 time period. Afterward, the neural network's performance is examined by analyzing its prediction for the subsequent year 2017. Our experiments show that a recurrent neural network can capture the globally prevalent wind regimes without prior knowledge about underlying physics and learn to derive wind velocity uncertainty estimates that are only based on wind velocity trajectories. At single training locations, the RNN‐based wind uncertainties closely match with the true reference values, and the corresponding intra‐annual variations are reproduced with high accuracy. Moreover, the neural network can predict global lateral distribution of uncertainties with small mismatch values after being trained only at a few isolated locations in different dynamic regimes. The presented approach can be combined with numerical models for a cost‐efficient generation of ensemble simulations or with ensemble‐based data assimilation to sample and predict dynamically consistent error covariance information of atmospheric boundary forcings.Plain Language Summary: Machine learning is increasingly used for a wide range of applications in geosciences. In this study, we use an artificial neural network in the context of time series prediction. In particular, the goal is to use a neural network for learning spatial and temporal uncertainties that are associated with globally estimated wind velocities. Three well‐known wind velocity products are used for the time period 2012–2016 in different training, validation, and prediction scenarios. Our experiments show that a neural network can learn the prevailing global wind regimes and associate these with corresponding uncertainty estimates. Such a trained neural network can be used for different applications, for example, a cost‐efficient generation of ensemble simulations or for improving traditional data assimilation schemes.Key Points: A recurrent neural network is set up to predict spatiotemporal uncertainties in wind velocity reanalyses. Global uncertainty maps can be derived from only few individual training locations. This method has benefits for time series prediction, ensemble simulations, and data assimilation

Self‐Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements

Quantifying and monitoring terrestrial water storage (TWS) is an essential task for understanding the Earth's hydrosphere cycle, its susceptibility to climate change, and concurrent impacts for ecosystems, agriculture, and water management. Changes in TWS manifest as anomalies in the Earth's gravity field, which are routinely observed from space. However, the complex underlying distribution of water masses in rivers, lakes, or groundwater basins remains elusive. We combine machine learning, numerical modeling, and satellite altimetry to build a downscaling neural network that recovers simulated TWS from synthetic space‐borne gravity observations. A novel constrained training is introduced, allowing the neural network to validate its training progress with independent satellite altimetry records. We show that the neural network can accurately derive the TWS in 2019 after being trained over the years 2003 to 2018. Further, we demonstrate that the constrained neural network can outperform the numerical model in validated regions.Plain Language Summary: Continuous monitoring of the distribution and movement of continental water masses is essential for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. Changes of continental water masses are encoded as coarse blob‐like patterns in satellite observations of the Earth's gravity field. Focusing on the South American continent, we introduce a self‐validating artificial neural network to recover detailed and accurate spatiotemporal information of continental water masses from such gravity field observations.Key Points: South American terrestrial water storage (TWS) is derived from satellite gravity observations with deep learning. A neural network accurately predicts multiscale monthly TWS anomalies in 2019 based on training data from 2003 to 2018. A data assimilation‐like training is introduced, allowing the neural network to validate itself with independent altimetry records.Helmholtz Association http://dx.doi.org/10.13039/501100001656Initiative and Networking Fund of the Helmholtz Associatio