Description: 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. Graphical Abstract

Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659

Description: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZ (4217)

Description: https://ionocovar.agnld.uni-potsdam.de/Kalmag/

Description: https://www.gfz-potsdam.de/en/section/geomagnetism/infrastructure/

Description: ftp://ftp.gfz-potsdam.de/pub/home/obs/kp-ap/

Description: 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.

Description: 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.

Description: The Wester Boundary Undercurrent (WBUC) flows around the Southern tip of Greenland and is a major contributor to the lower branch of the Atlantic Meridional Overturning Circulation (AMOC). It is mainly driven by deep water producing convection processes in the Nordic Seas and thus sensitive to atmospheric changes on a broad spectrum of time scales. A major presentconcern is the impact of long term climate change on both strength and flow path of the current. However, such time scales also include tectonic modifications as a contribution to alterations of the flow. The investigation of the state of the circulation in past geological epochs is feasible by means of sediment drift bodies which are plastered by the deep current along the lower shelf slope, e.g. the Eirik Drift on the Southern slope of Greenland. Seismic profiles (e.g., Müller-Michaels and Uenzelmann-Neben (2014, 2015)) and drill cores (Expedition 303 Scientists (2006), Shipboard Scientific Party (1987)) allow the determination of sedimentation rates and grain sizes since the late Miocene and the Pliocene. Such studies showed pronounced modifications during both the Miocene and the Pliocene which are especially geological epochs of interest to the climatological community due their resemblance to possible future anthropogenically modified climate states (Salzmann et al. (2009)). Several numerical climate and ocean studies have linked local temperature and precipitation proxies to global climate changes during the late Miocene and the Pliocene. In the project TRANSPORTED we aim to link tectonic events and climate change to alterations of the strength and flow paths of the WBUC and, hence, to sedimentation rates and grain sizes recorded in the cores from Sites 646 and U1305-1307 in the Eirik Drift.Density-driven deep currents in geostrophic balance show distinct features such as sustained widening while reducing velocity and pronounced narrow eddies at the bottom. These currents therefore produce an equally distinct sediment structure: a strong erosion channel and enhanced deposition downstream, as has been described by Rebesco et al. (2014)). We were able to reconstruct such currents successfully at a very high horizontal and vertical resolution along the southern slope of Greenland enabling us to drive our sediment module at most realistic conditions. Extracting grain sizes and densities of the main material deposited in the area of investigation from the previous sedimentological studies, we were able to conduct the first sensitivity studies altering tectonic and atmospheric parameters. These studies show a significant but moderate response to the modifications under present conditions but changes may be more pronounced for the conditions of past geological epochs when local deposition rates were peaking (Müller-Michaelis, Uenzelmann-Neben (2014)). Therefore, our next aim is to conduct paleological simulationsunder past climate forcing and crucial tectonic changes in order compare numerical results and sedimentological output

Description: Ocean sediment drifts contain important information about past bottom currents but a direct link from the study of sedimentary archives to ocean dynamics is not always possible. To close this gap for the North Atlantic, we set up a new coupled Ice-Ocean-Sediment Model of the entire Pan-Arctic region. In order to evaluate the potential dynamics of the model, we conducted decadal sensitivity experiments. In our model contouritic sedimentation shows a significant sensitivity towards climate variability for most of the contourite drift locations in the model domain. We observe a general decrease of sedimentation rates during warm conditions with decreasing atmospheric and oceanic gradients and an extensive increase of sedimentation rates during cold conditions with respective increased gradients. We can relate these results to changes in the dominant bottom circulation supplying deep water masses to the contourite sites under different climate conditions. A better understanding of northern deep water pathways in the Atlantic Meridional Overturning Circulation (AMOC) is crucial for evaluating possible consequences of climate change in the ocean.

Description: Ocean sediment drifts contain important information about past bottom currents but a direct link between the study of sedimentary archives and ocean dynamics is not always possible. To close this gap for the North Atlantic, we set up a new coupled Ice-Ocean-Sediment Model of the N. Atlantic - Arctic region. In order to evaluate the potential dynamics of the model, we conducted decadal sensitivity experiments. In our model contouritic sedimentation shows a significant sensitivity towards climate variability for most of the contourite drift locations in the model domain. We observe a general decrease of sedimentation rates during warm conditions with decreasing atmospheric and oceanic gradients and an extensive increase of sedimentation rates during cold conditions with respective increased gradients. We can relate these results to changes in the dominant bottom circulation supplying deep water masses to the contourite sites under different climate conditions. A better understanding of northern deep water pathways in the Atlantic Meridional Overturning Circulation (AMOC) is crucial for evaluating possible consequences of climate change in the ocean.

Description: 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.

Description: 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.

Description: 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.

Description: 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.

Description: 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.

Description: 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.

Description: Helmholtz Association http://dx.doi.org/10.13039/501100001656

Description: Initiative and Networking Fund of the Helmholtz Association