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Self-organizing maps provide a powerful, non-linear technique of dimensionality reduction that can be used to identify clusters with similar attributes. Here, they were constructed from a 1000-year-long gridded palaeoclimatic dataset, namely the Old World Drought Atlas, to detect regions of homogeneous hydroclimatic variability across the European continent. A classification scheme of 10 regions was found to describe most efficiently the spatial properties of Europe’s hydroclimate. These regions were mainly divided into a northern and a southern subset, linked together with a northwest-to-southeast orientation. Further analysis of the classification scheme with complex networks confirmed the divergence between the northern and southern components of European hydroclimate, also revealing that is not strongly correlated to the Iberian Peninsula. On the contrary, the region covering the British Isles, France and Germany appeared to be linked to both branches, implying links of hydroclimate with atmospheric/oceanic circulation.

Flow and transport in porous media are driven by pore scale processes. Particle tracking in transparent porous media allows for the observation of these processes at the time scale of ms. We demonstrate an application of defocusing particle tracking using brightfield illumination and a CMOS camera sensor. The resulting images have relatively high noise levels. To address this challenge, we propose a new calibration for locating particles in the out-of-plane direction. The methodology relies on extracting features of particle images by fitting generalized Gaussian distributions to particle images. The resulting fitting parameters are then linked to the out-of-plane coordinates of particles using flexible machine learning tools. A workflow is presented which shows how to generate a training dataset of fitting parameters paired to known out-of-plane locations. Several regression models are tested on the resulting training dataset, of which a boosted regression tree ensemble produced the lowest cross-validation error. The efficacy of the proposed methodology is then examined in a laminar channel flow in a large measurement volume of 2048, 1152 and 3000 μm in length, width and depth respectively. The size of the test domain reflects the representative elementary volume of many fluid flow phenomena in porous media. Such large measurement depths require the collection of images at different focal levels. We acquired images at 21 focal levels 150 μm apart from each other. The error in predicting the out-of-plane location in a single slice of 240 μm thickness was found to be 7 μm, while in-plane locations were determined with sub-pixel resolution (below 0.8 μm). The mean relative error in the velocity measurement was obtained by comparing the experimental results to an analytic model of the flow. The estimated displacement errors in the axial direction of the flow were 0.21 pixel and 0.22 pixel at flows rates of 1.0 mL/h and 2.5 mL/h, respectively. These results demonstrate that it is possible to conduct three-dimensional particle tracking in a representative elementary volume based on a simple apparatus comprising a microscope with standard brightfield illumination and a camera with CMOS sensor.

Dislocation glide is an important contributor to the rheology of olivine under conditions of high stress and low to moderate temperature, such as occur in mantle wedges. Interactions between point defects and dislocation core may alter the Peierls stress, σp, and has been suggested that vacancy-related defects may selectively enhance glide on certain slip systems, changing the olivine deformation fabric. In this study, the Peierls-Nabarro model, parameterized by generalized stacking fault (GSF) energies calculated atomistically using empirical interatomic potentials, is used to determine the effect of bare Mg vacancies on the Peierls stresses of [1 0 0](0 1 0) and [0 0 1](0 1 0) dislocations in forsterite. Mg vacancies considerably reduce GSF energies and, consequently, σp for dislocations gliding on (0 1 0) in olivine. The magnitude of this decrease depends strongly on dislocation and the type of the lattice site, with vacant M2 sites producing the largest reduction of σp. The [0 0 1](0 1 0) slip system is found to be more sensitive than the [1 0 0](0 1 0) slip system to the presence of vacancies. Although, at ambient pressure, σp is lower for [1 0 0](0 1 0) than [0 0 1](0 1 0) edge dislocations, dσp/dP is greater for [1 0 0](0 1 0) dislocations, resulting in a change in the preferred slip system at 1.5 GPa. By preferentially lubricating [0 0 1](0 1 0) glide, Mg vacancies reduce the pressure at which this cross-over occurs. An M2 vacancy concentration at the glide plane of 0.125 defects/site is sufficient to reduce cross-over to 0.7 GPa. This may account for the existence of the B-type olivine deformation fabric in the corners of mantle wedges.

Changes in stress applied to mantle rocks, such as those imposed by earthquakes, induce a period of evolution in viscosity and microstructure. This transient creep is often modelled based on stress transfer among slip systems due to grain interactions. However, recent experiments have demonstrated that the intragranular accumulation of stresses among dislocations is the dominant cause of strain hardening in olivine at low temperatures, raising the question of whether the same process contributes to transient creep at higher temperatures. Here, we demonstrate that olivine samples deformed at 25{\deg}C or 1150 to 1250{\deg}C both contain stress heterogeneities of ~1 GPa that are imparted by dislocations and have correlation lengths of ~1 micrometre. The similar stress distributions formed in both temperature regimes indicate that accumulation of stresses among dislocations also provides a contribution to transient creep at high temperatures. The results motivate a new generation of models that capture these intragranular processes and may refine predictions of evolving mantle viscosity over the earthquake cycle., Comment: 17 pages, 5 figures, 2 tables

Deep-marine volcanism drives Earth’s most energetic transfers of heat and mass between the crust and the oceans. While magmatic activity on the seafloor has been correlated with the occurrence of colossal enigmatic plumes of hydrothermal fluid known as megaplumes, little is known of the primary source and intensity of the energy release associated with seafloor volcanism. As a result, the specific origin of megaplumes remains ambiguous. By developing a mathematical model for the dispersal of submarine tephras, we show that the transport of pyroclasts requires an energy discharge that is sufficiently powerful (~1-2 TW) to form a hydrothermal plume with characteristics matching those of observed megaplumes in a matter of hours. Our results thereby directly link megaplume creation, active magma extrusion, and tephra dispersal. The energy flux at the plume source required to drive the dispersal is difficult to attain by purely volcanogenic means, and likely requires an additional input of heat, potentially from rapid evacuations of hot hydrothermal fluids triggered by dyke intrusion. In view of the ubiquity of submarine tephra deposits, our results demonstrate that intervals of rapid hydrothermal discharge are likely commonplace during deep-ocean volcanism., A novel model for submarine tephra dispersal by hydrothermal megaplumes is proposed. The energy flux inferred from our model aligns with megaplume observations, and suggests that the catastrophic release of hot crustal fluids, as opposed to lava heating, is responsible for megaplume generation.

SUMMARY We use data from the Cascadia Initiative (CI) amphibious array and the USArray Transportable Array to construct and compare Rayleigh wave isotropic and azimuthally anisotropic phase speed maps across the Juan de Fuca and Gorda Plates extending onto the continental northwestern United States. Results from both earthquakes (28–80 s) as well as ambient noise two- and three-station interferometry (10–40 s) are produced. Compared with two-station interferometry, three-station direct wave interferometry provides >50 per cent improvement in the signal-to-noise ratio and the number of dispersion measurements obtained, particularly in the noisier oceanic environment. Earthquake and ambient noise results are complementary in bandwidth and azimuthal coverage, and agree within about twice the estimated uncertainties of each method. We, therefore, combine measurements from the different methods to produce composite results that provide an improved data set in accuracy, resolution and spatial and azimuthal coverage over each individual method. A great variety of both isotropic and azimuthally anisotropic structures are resolved. Across the oceanic plate, fast directions of anisotropy with 180° periodicity (2ψ) generally align with palaeo-spreading directions while 2ψ amplitudes mostly increase with lithospheric age, both displaying substantial variations with depth and age. Strong (>3 per cent) apparent anisotropy with 360° periodicity (1ψ) is observed at long periods (>50 s) surrounding the Cascade Range, probably caused by backscattering from heterogeneous isotropic structures.

Hyperspectral imaging (HSI) is a non-destructive high-resolution sensor, which is currently under significant development to analyze geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that need to be separated to temporally and spatially describe the sample. Sediment sequences are composed of successive deposits (strata, homogenite, flood) that can be visible or not depending on sample properties. The classical methods to identify them are time-consuming, have a low spatial resolution (millimeter), and are generally based on a naked-eye counting. In this study, we propose to compare several supervised classification algorithms for the discrimination of sedimentological structures on lake sediments. Instantaneous events in lake sediments are generally linked to extreme geodynamical events (e.g., floods, earthquakes), their identification and counting are essential to understand long-term fluctuations and improve hazard assessments. This is done by reconstructing a chronicle of event layer occurrence, including estimation of deposit thicknesses. Here we applied two hyperspectral imaging sensors (Visible Near-Infrared VNIR, 60 μm, 400-1000 nm; Short Wave Infrared SWIR, 200 μm, 1000-2500 nm) on three sediment cores from different lake systems. We highlight that the SWIR sensor is the optimal one to create robust classification models with discriminant analyses. Indeed, the VNIR sensor is impacted by the surface reliefs and structures that are not in the learning set, which lead to miss-classification. These observations are also valid for the combined sensor (VNIR-SWIR). Several spatial and spectral pre-processing were also compared and allowed to highlight discriminant information specific to a sample and a sensor. These works show that the combined use of hyperspectral imaging and machine learning improves the characterization of sedimentary structures in laboratory conditions.

Propagation of a slip transient on a fault with rate-and-state dependent friction resembles a fracture which near tip region is characterized by large departure of the slip velocity and fault strength from the steady-state sliding. We develop a near tip solution to describe this unsteady dynamics, and obtain the fracture energy Gc, dissipated in overcoming strength-excursion away from steady-state, as a function of the rupture velocity vr. This opens a possibility to model slip transients on rate-state faults as singular cracks characterized by approximately steady-state frictional resistance in the fracture bulk, and by a stress singularity with the intensity defined by Gc(vr) at the crack tip. In pursuing this route, we develop and use an analytical equation of motion to study 1D slip driven by a combination of uniform background stress and a localized perturbation of the fault strength with the net Coulomb force ∆T. In the context of fluid injection, ∆T is a proxy for the injection volume Vinj. We then show that, for ongoing fluid injection, the maximum propagation speed of the transient induced on a frictionally-stable fault is bounded by a value proportional to Vinj(t), and, for stopped injection, the maximum slip run-out distance is proportional to the square of the total injected volume.

A deep spatial transformer based encoder-decoder model has been developed to autoregressively predict the time evolution of the upper layer’s stream function of a two-layered quasi-geostrophic (QG) system without any information about the lower layer’s stream function. The spatio-temporal complexity of QG flow is comparable to the complexity of 500hPa Geopotential Height (Z500) of fully coupled climate models or even the Z500 which is observed in the atmosphere, based on the instantaneous attractor dimension metric. The ability to predict autoregressively, the turbulent dynamics of QG is the first step towards building data-driven surrogates for more complex climate models. We show that the equivariance preserving properties of modern spatial transformers incorporated within a convolutional encoder-decoder module can predict up to 9 days in a QG system (outperforming a baseline persistence model and a standard convolutional encoder decoder with a custom loss function). The proposed data-driven model remains stable for multiple years thus promising us of a stable and physical data-driven climate model.

The two most abundant minerals on Earth which together make up over 90% of the Earth’s lower mantle are (Mg,Fe)O-ferropericlase (Fp) and (Mg,Fe)SiO3-bridgmanite (Bm). Iron in Fp undergoes a high-spin to low-spin (HS-LS) crossover that influences density, viscosity, elasticity, thermal conductivity, and elemental partitioning, however, the predicted effects of this transition are not apparent in global 1D seismic velocity profiles. This discrepancy suggests that the predictions are inaccurate, seismic resolution is insufficient to resolve the effects, or a substantial portion of the mid-lower mantle is relatively SiO2-rich (hence Fp poor) compared to the shallow mantle. The melt-depleted mantle lithosphere of subducted oceanic slabs that sink into the lower mantle contains 22% Fp, and thus offers the best opportunity to prospect for a spin change in Fp. Here we reveal a loss in the abundance of fast seismic velocity anomalies in compressional (P-wave) tomography models at 1,400-2,000 km depth that is opposite to the trend in shear (S-wave) models. This can be explained by the decreasing temperature sensitivity of P-velocity expected for the mixed spin state of iron in Fp at corresponding pressures7. We also observe a similar but subtle signal for seismically slow regions below 1,800 km, consistent with a pressure increase and broadening of the Fp spin transition at higher temperatures. Seismic wave raypath distribution is similar for both P- and S-waves in this depth range, therefore this signature cannot be attributed to substantial differences in data coverage. Our identification of the spin transition signal in seismically fast and slow regions indicates that the spin crossover can identify the presence of Fp in the lower mantle. The absence of a Fp spin crossover signal in global seismic profiles supports the notion that the lower mantle is chemically heterogeneous at large scales and contains SiO2-rich regions that suppress the average signature of these pressure-induced electron spin pairing transitions.

The purpose of this study is to further investigate the assumption that geophysical flows can be described and simulated by direct sampling from a dynamically constrained probability distribution, with a reversible formulation of dissipation. This is done by making the probability of the flow decrease with the deviation of the Lagrangian with respect to local equilibrium, and by assuming that this deviation can be computed from the local structure of the macroscopic flow. It is shown that this formalism can reproduce a wide variety of linear dissipation behaviours by adjusting appropriately the spectrum of the stochastic fluctuations in the macroscopic flow, and that it can be easily extended to include nonlinear dissipation in the probability distribution. This reversible, stochastic and nonlinear formulation of dissipation is then applied to the simulation of turbulent geophysical flows, using the lowest-order nonlinear term, with constant parameters. Results obtained for the turbulent boundary layer (in canal flows) show that the main observed characteristics of the flow can be quite faithfully reproduced. In particular, far enough from the boundary conditions, the universal logarithmic velocity profile is an analytic solution of the governing equation, and the behaviour of the friction coefficient as a function of the Reynolds number is correctly simulated by this single model, across the viscous and turbulent regimes. Unlike linear dissipation, the nonlinear dissipation term is specific to the reversible formulation of the problem, and has no equivalent as a forward-in-time diffusion equation. On the other hand, results obtained with a shallow-water model suggest that the same approach can be applied to more complex flows, and that it might become applicable to produce a probabilistic forecast of geophysical flows. More generally, this study suggests that it is possible to construct a reversible probabilistic dynamics that connects observations across time, at a macroscopic level, without explicitly resolving the finest structures of the system.

The purpose of this study is to further investigate the problem of explicitly simulating uncertainties in geophysical models. Rather than introducing stochastic processes in forward-in-time partial differential equations, this is done here by reformulating the dynamical equations governing the time evolution of the flow as a probability distribution. Flows can then be drawn directly from the probability distribution using an efficient sampler. This approach is illustrated in the paper with the example of a two-dimensional shallow-water flow on the surface of a rotating sphere. With the sampler, the simulated flows evolve smoothly in time according to the advection constraint, while time-dependent stochastic fluctuations are produced according to the probability distribution describing the effect of the unresolved motions. This can be done without closing the dynamical equations, with constraints applied only to what is known, and using a larger time step, since there is no more numerical stability condition to verify. From a physical point of view, this probabilistic reformulation introduces two main differences with respect to forward-in-time partial differential equations. First, dissipation can only behave symmetrically with respect to the reversal of time. It is shown however that the formulation can be made equivalent to the classic diffusion equation in the particular case of an initial condition problem. Second, in this framework, information about the past of the system does not need to be provided in the form of a complete initial condition at a given time. A constraint on the past of the system can here be directly applied using a history of incomplete and imperfect observations, thus resolving the inverse and direct problems together at a similar numerical cost.

While climate change will alter the distribution in time and space of water, quantifications of drought risk in view of global warming remain little explored. Here, we show that in Europe drought damages could strongly increase with global warming and cause a strong regional imbalance in future drought impacts. In the absence of climate action (4°C in 2100 and no adaptation) annual drought losses in the EU + UK are projected to rise to more than 65 billion €/year compared to baseline 9 billion €/year, or two times larger when expressed relative to the size of the economy. Drought losses show the strongest rise in southern and western parts of Europe, where drought conditions at 4°C could reduce regional agriculture economic output by 10%. With high warming, drought impacts will become a fraction of present risk in northern and northeastern regions. Keeping global warming well below 2°C would avoid most impacts in the affected regions.

Changes in the atmospheric composition alter the magnitude and partitioning between the downward propagating solar and atmospheric longwave radiative fluxes heating the Earth's surface. These changes are computed by radiative transfer codes in Global Climate Models, and measured with high precision at surface observation networks. Changes in radiative heating signify changes in the global surface temperature and hydrologic cycle. Here, we develop a conceptual framework using an Energy Balance Model to show that first order changes in the hydrologic cycle are mainly associated with changes in solar radiation, while that in surface temperature are mainly associated with changes in atmospheric longwave radiation. These insights are used to explain a range of phenomena including observed historical trends, biases in climate model output, and the inter-model spread in climate change projections. These results may help identify biases in future generations of climate models., 8 pages, 3 figures, 2 tables

Advanced Land Observing Satellite-2 (ALOS-2) is designed to routinely acquire both scanning synthetic aperture radar (ScanSAR) and stripmap data. In this paper, we present a special multiband bandpass filter (MBF) to remove azimuth nonoverlap spectra for the interferometric processing of ALOS-2 full-aperture ScanSAR product. As required by the MBF, we estimate the important ScanSAR system parameters and the start times of raw bursts using ALOS-2 full-aperture ScanSAR image. The resulting MBF can remove the nonoverlap spectra caused by both Doppler centroid frequency difference and burst misalignment. It can be used in ScanSAR-ScanSAR interferometry, as well as ScanSAR-stripmap interferometry. Based on the MBF, we propose a single processing workflow that is able to implement both ScanSAR-ScanSAR interferometry and ScanSAR-stripmap interferometry. Finally, we present example interferograms of the 2015 Gorkha earthquake in Nepal processed using the proposed processing workflow. The interferograms are greatly improved after applying the MBF to remove the significant amount of nonoverlap spectra in the data.

Estimating azimuth offset with double-difference interferometric (DDI) phase, which is called multiple-aperture interferometric synthetic aperture radar (InSAR) or spectral diversity, is increasingly used in recent years to measure azimuth deformation or to accurately coregister a pair of InSAR images. We analyze the effect of frequency modulation (FM) rate error in focusing on the DDI phase with an emphasis on the azimuth direction. We first comprehensively analyze the errors in various focusing results caused by the FM rate error. We then derive the DDI phase error considering different acquisition modes including stripmap, ScanSAR, and TOPS modes. For stripmap mode, typical DDI phase error is a range ramp, while for burst modes including ScanSAR and TOPS modes it is an azimuth ramp within a burst. The correction methods for the DDI phase error are suggested for different acquisition modes.

We identify a distinct two-phase flow invasion pattern in a mixed-wet porous medium. Time-resolved high-resolution synchrotron X--ray imaging is used to study the invasion of water through a small rock sample filled with oil, characterized by a wide non-uniform distribution of local contact angles both above and below $90^{\circ}$. The water advances in a connected front, but throats are not invaded in decreasing order of size, as predicted by invasion percolation theory for uniformly hydrophobic systems. Instead, we observe pinning of the three-phase contact between the fluids and the solid, manifested as contact angle hysteresis, which prevents snap-off and interface retraction. In the absence of viscous dissipation, we use an energy balance to find an effective, thermodynamic, contact angle for displacement and show that this angle increases during the displacement. Displacement occurs when the local contact angles overcome the advancing contact angles at a pinned interface: it is wettability which controls the filling sequence. The product of the interfacial curvatures, the Gaussian curvature, is negative, implying well-connected phases which is consistent with pinning at the contact line while providing a topological explanation for the high displacement efficiencies in mixed-wet media.

Solar flares, energetic particles and Earth-impacting coronal mass ejections enhance ionization in the lower ionosphere, inhibiting radio wave propagation in the Earth-ionosphere waveguide (EIWG). This enhanced ionization is observed locally by ionosondes and GPS/GNSS receivers, but spatial coverage of these observations is limited by receiver location. VLF propagation studies have previously been performed to assess the impact of space weather on the EIWG; however, these studies are typically limited by small numbers of fixed VLF transmitters and receivers, and observe only the region of the EIWG along propagation paths between transmitters and receivers. Here, we use global lightning as a VLF source, and an existing lightning detection network as a receiver. By mapping sferic propagation paths between lightning strokes and numerous network stations, and considering how this distribution of paths changes during solar events, we can identify attenuation regions in the EIWG caused by space weather. We describe the VLF response in the EIWG to two X-class solar flares, and compare mapped attenuation regions with those provided by the NOAA D-Region Absorption Prediction (D-RAP) model. The identified attenuation regions associated with these flares match the D-RAP-predicted regions well in both spatial extent and onset timing. Measurements of VLF attenuation caused by solar flares can provide ground-truth confirmation of modeled attenuation, and can inform the detection efficiency of lightning location networks. This analysis also paves the way for real-time VLF attenuation mapping in the EIWG.

Oceans play a major role in Earths climate by storing and transporting heat via turbulent currents called mesoscale eddies. However, direct monitoring of eddy-driven heat fluxes is currently impossible because it requires simultaneous surface and subsurface observations of velocity and heat content, while only surface properties of mesoscale eddies can be comprehensively measured by satellites in the form of sea surface height (SSH) anomalies. Nonetheless, surface and subsurface expressions of eddies are dynamically linked, suggesting that surface observations may contain at least partial information about subsurface flows and thus heat transport. Here we used deep convolutional neural networks (CNNs) to demonstrate that SSH expressions of mesoscale turbulence contain sufficient information to predict about 64\% of eddy heat flux variance, with CNNs significantly outperforming other conventional data-driven techniques. Our results suggest that deep CNNs could provide an effective pathway towards an operational monitoring of eddy heat fluxes using satellite altimetry.

Pyroclastic density currents (PDCs) are a life-threatening volcanic hazard. Our understanding and hazard assessments of these flows rely on interpretations of their deposits. The occurrence of stratified layers, cross-stratification, and bedforms in these deposits has been assumed as indicative of dilute, turbulent, supercritical flows causing traction-dominated deposition. Here we show, through analogue experiments, that a variety of bedforms can be produced by denser, aerated, granular currents, including backset bedforms that are formed in waning flows by an upstream-propagating granular bore. We are able to, for the first time, define phase fields for the formation of bedforms in PDC deposits. We examine how our findings impact the understanding of bedform features in outcrop, using the example of the Pozzolane Rosse ignimbrite of the Colli Albani volcano, Italy, and thus highlight that interpretations of the formative mechanisms of these features observed in the field must be reconsidered., In this study, Smith and colleagues employ analogue experiments to show the controlling parameters on sediment bedforms in pyroclastic density current deposits. The findings are applied and validated on natural deposits.

We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co-recurrence ratio (α), which elucidates the dependence structure between maps by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA-Interim reanalysis over the 1979-2018 period. The analysis focuses on concurrent (i.e. same-day) wet (total precipitation) and windy (10m wind gusts) extremes in Europe and concurrent cold (2m temperature) extremes in Eastern North America and wet extremes in Europe. Results for wet and windy extremes in Europe, which we use as a test-bed for our methodology, show that α peaks during boreal winter. High αvalues correspond to wet and windy extremes in north-western Europe, and to large-scale conditions resembling the positive phase of the North Atlantic Oscillation (NAO). This confirms earlier findings which link the positive NAO to a heightened frequency of extra-tropical cyclones impacting north-western Europe, resulting in frequent wet and windy extremes. For the Eastern North America-Europe case, α extremes once again reflect concurrent climate extremes -- in this case cold extremes over North America and wet extremes over Europe. Our analysis provides detailed spatial information on regional hotspots for these compound extreme occurrences, and encapsulates information on their spatial footprint which is typically not included in a conventional co-occurrence analysis. We conclude that α successfully characterises compound extremes by reflecting the evolution of the associated meteorological maps. This approach is entirely general, and may be applied to different types of compound extremes and geographical regions.

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale ($X$), intermediate ($Y$), and fast/small-scale ($Z$) processes. For training or testing, only $X$ is available; $Y$ and $Z$ are never known or used. We show that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps, equivalent to several Lyapunov timescales. The RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN. Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails. The PDF of the data predicted using ANN, however, deviates from the true PDF. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems such as weather/climate are discussed., Some changes, in Figures, addition of an appendix etc has been done

That is a preprint of an article suubmitted at Applied Greography (20tf of March 19). Climate change studies in the last decades have been based on Global Climate Models (GCM), and the distribution of climatic regions over time extracted from these models can be represented using the Köppen climatic classification. The Köppen approach predicts the distribution of biomes worldwide on the basis of monthly precipitation and average temperature. This study aims to use the Köppen classification to evaluate the impact of the Greenland and Antarctic ice sheets melting on GCM simulation results at the regional and global scale. To assess the impact of accelerated ice-sheet melting, our approach is based on numerical simulations from the IPSL-CM5A-LR GCM with the introduction of freshwater near the ice sheets superimposed on the RCP8.5 scenario, leading to a global warming of +5°C. Static mapping of distribution changes in Köppen climatic regions under various scenarios (historical run from observations, RCP 8.5, and various cases of polar sheets melting) and comparisons between them reveal that major changes occur at the global scale for the period 2041-2060. A first level of analysis revealed that, when the input of freshwater originates from Greenland or Antarctica, the inter-tropical belt undergoes greater change than under the RCP8.5 scenario. A second level of analysis showed that changes in precipitation have major impacts on the southern hemisphere, with more drastic changes if the freshwater came from the Greenland ice sheet than if it came from Antarctica or from the combination of both. Changes in temperature, however, strongly impact the northern hemisphere, and are significantly affected by the melting of the Greenland ice sheet. This study highlights the importance of considering ice sheets melting in the modelling of future global climate.

Two thirds of the surface of our planet are covered by water and are still poorly instrumented, which has prevented the earth science community from addressing numerous key scientific questions. The potential to leverage the existing fiber optic seafloor telecom cables that criss-cross the oceans, by using them as dense arrays of seismo-acoustic sensors, remains to be evaluated. Here, we report Distributed Acoustic Sensing measurements on a 41.5 km-long telecom cable that is deployed offshore Toulon, France. Our observations demonstrate the capability to monitor with unprecedented details the ocean-solid earth interactions from the coast to the abyssal plain, in addition to regional seismicity (e.g., a magnitude 1.9 micro-earthquake located 100 km away) with signal characteristics comparable to those of a coastal seismic station., Existing fibers beneath the world’s oceans can in principle be used as seismic sensors, but the full potential of this possibility has yet to be explored. Here, the authors demonstrate the feasibility of distributed acoustic sensing in a coastal fiber as a sensor for earthquakes and wave phenomena.

We observed features of pore scale fluid distributions during oil-brine displacement in a mixed-wet sandstone rock sample. High-resolution X-ray imaging was used in combination with differential pressure measurements to measure relative permeability and capillary pressure simultaneously during a steady-state waterflood experiment on a sample of Bentheimer sandstone 51.6 mm long and 6.1 mm in diameter. After prolonged contact with crude oil to alter the surface wettability, a refined oil and formation brine were injected through the sample at a fixed total flow rate but in a sequence of increasing brine fractional flows. When the pressure across the system stabilized, X-ray tomographic images were taken. The images were used to compute saturation, interfacial area, curvature and contact angle. From this information relative permeability and capillary pressure were determined as functions of saturation. We compare our results with a previously published experiment with strongly water-wet conditions. The oil relative permeability was lower than in the water-wet case, although a smaller residual oil saturation, of approximately 0.11, was obtained, since the oil remained connected in layers in the altered wettability rock. The capillary pressure was slightly negative and ten times smaller in magnitude than a similar water-wet rock, and approximately constant over a wide range of intermediate saturation. The oil-brine interfacial area was also largely constant in this saturation range. The measured static contact angles had an average of $80^{\circ}$ with a standard deviation of $17^{\circ}$. We observed that the oil-brine interfaces were not flat, as may be expected for a very low mean curvature, but had two approximately equal, but opposite, curvatures in orthogonal directions. These interfaces were approximately minimal surfaces which allow efficient displacement and imply well-connected phases. Saddle-shaped menisci swept through the pore space at a constant capillary pressure and with an almost fixed area, removing most of the oil.

Faults, landslides and subglacial till often contain accumulations of granular debris. Their macroscopic motion is at least to some extent determined by the processes operating in this sheared granular material. A valid question in these environments is how the local behaviour at the individual granular contacts actually sums up to influence macroscopic sliding. Laboratory experiments and numerical modelling can potentially help elucidate this. Observations of jamming and unjamming as well as concentrated shear bands that appear to scale with grain size, suggest that a simple continuum description may be insufficient to capture important elements of the behaviour. We therefore seek a measure of the organisation of the granular fabric and the structure of the load bearing skeleton that shows how the individual grain interactions are summing up to influence the macroscopic sliding behaviour we observe. Contact force networks are an expression of this. We choose to investigate variability of the ‘important’ or most connected system spanning strong force networks produced in 3D discrete element models of granular layers under shear. We use percolation measures to identify, compare and track the evolution of these system spanning contact force networks. We show that the geometries and interactions of these load bearing structures, reflected as specific topological measures, are sensitive to size (and likely shape) distribution of the granular material. Importantly, distinct (measurable) changes in the topological structural characteristics of strong force networks with accumulated strain are directly correlated to fluctuations in macroscopic shearing resistance. This illustrates the sensitivity of 3D force networks to details of granular materials (such as size and shape distribution) and also the important bridging role they play between individual grain scale processes and macroscopic sliding behaviour.

We demonstrate levels of skill for forecasts of seasonal-mean wind speed and solar irradiance in Europe, using seasonal forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that, where there is skill, a simple linear regression-based method using the hindcast and forecast ensemble means provides a straightforward approach for producing calibrated probabilistic seasonal forecasts. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, and we show that this provides opportunities to improve the skill in some cases. \ud \ud We further demonstrate that, on seasonal-average and regional (e.g. national) average scales, wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance). The detailed non-linear transformations from meteorological quantities to energy quantities, which are essential for detailed simulation of power system operations, are usually not necessary when forecasting gross wind or solar generation potential at seasonal-mean regional-mean scales.\ud \ud Together, our results demonstrate that where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, skilful forecasts of seasonal mean wind and solar power generation can be made based on the climate variable alone, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

Slow slip events on tectonic faults, sliding instabilities that never accelerate to inertially limited ruptures or earthquakes, are one of the most enigmatic phenomena in frictional sliding. While observations of slow slip events continue to mount, a plausible mechanism that permits instability while simultaneously limiting slip speed remains elusive. Rate-and-state friction has been successful in describing most aspects of rock friction, faulting, and earthquakes; current explanations of slow slip events appeal to rate-weakening friction to induce instabilities, which are then stalled by additional stabilizing processes like dilatancy or a transition to rate-strengthening friction at high slip rates. However, the temperatures and/or clay-rich compositions at slow slip locations are almost ubiquitously associated with rate-strengthening friction. In this study, we propose a fundamentally different instability mechanism that may reconcile this contradiction, demonstrating how slow slip events can nucleate with mildly rate-strengthening friction. We identify two destabilizing mechanisms, both reducing frictional shear strength through reductions in effective normal stress, that counteract the stabilizing effects of rate-strengthening friction. The instability develops into slow slip pulses. We quantify parameter controls on pulse length, propagation speed, and other characteristics, and demonstrate broad consistency with observations of tectonic slow slip events as well as laboratory tribology experiments.

Gas exchange is a critical component of any biogeochemical mass balance model of dissolved gases in aquatic systems, yet the magnitude and drivers of spatial and temporal variations of air-water exchange rates in shallow streams are poorly understood. We investigated the relationships between gas exchange velocity of carbon dioxide and methane and flow hydraulics at different sections along a third order stream in Southwest Germany. To cover a wide range of different flow conditions, the sections were selected based on visual categorization of the dominant surface flow type. We found that in smooth and rippled flows, gas exchange velocities followed a universal dependence on turbulent dissipation rates predicted by the small-eddy and surface renewal models. For these surface flow types, the scaling applied to both, bulk-scale dissipation rates estimated from flow geometry and dissipation rates estimated from turbulence measurements. Turbulence was strongly anisotropic under rough flow conditions and gas exchange velocities were lower than predicted from measured dissipation rates. Nevertheless, near-surface turbulence and gas exchange velocities differed among surface flow type categories, indicating that quantitative assessment and mapping of surface flow type may facilitate improved parameterizations of gas exchange velocities at larger spatial scales. We further describe a novel instrument facilitating an objective assessment of surface flow by measuring the acceleration of a small floating sphere drifting freely on the water surface. In combination, our findings may open a new road for understanding, measuring and predicting spatial and temporal variability of gas exchange in streams.

(IF 5.59 [2018]; Q1); International audience; In the case of environmental samples, the use of a chemometrics-based prediction model is highly challenging because of the difficulty in experimentally creating a well-ranged reference sample set. In this study, we present a methodology using short wave infrared hyperspectral imaging to create a partial least squares regression model on a cored sediment sample. It was applied to a sediment core of the well-known Lake Bourget (Western Alps, France) to develop and validate a model for downcore high resolution LOI550 measurements used as a proxy of the organic matter. In lake and marine sediment, the organic matter content is widely used, for example, to reconstruct carbon flux variations through time. Organic matter analysis through routine analysis methods is time- and material-consuming, as well as not spatially resolved. A new instrument based on hyperspectral imaging allows high spatial and spectral resolutions to be acquired all along a sediment core. In this study, we obtain a model characterized by a 0.95 r prediction, with 0.77 wt% of model uncertainty based on 27 relevant wavelengths. The concentration map shows the variation inside each laminae and flood deposit. LOI550 reference values obtained with the loss on ignition are highly correlated to the inc/coh ratio used as a proxy of the organic matter in X-ray fluorescence with a correlation coefficient of 0.81. This ratio is also correlated with the averaged subsampled hyperspectral prediction with a r of 0.65.

Hypothesis: Capillary-dominated multiphase flow in porous materials is strongly affected by the pore walls' wettability. Recent micro-computed tomography (mCT) studies found unexpectedly wide contact angle distributions measured on static fluid distributions inside the pores. We hypothesize that analysis on time-resolved mCT data of fluid invasion events may be more directly relevant to the fluid dynamics. Experiment: We approximated receding contact angles locally in time and space on time-resolved mCT datasets of drainage in a glass bead pack and a limestone. Whenever a meniscus suddenly entered one or more pores, geometric and thermodynamically consistent contact angles in the surrounding pores were measured in the time step just prior to the displacement event. We introduced a new force-based contact angle, defined to recover the measured capillary pressure in the invaded pore throat prior to interface movement. Findings: Unlike the classical method, the new geometric and force-based contact angles followed plausible, narrower distributions and were mutually consistent. We were unable to obtain credible results with the thermodynamically consistent method, likely because of sensitivity to common imaging artifacts and neglecting dissipation. Time-resolved mCT analysis can yield a more appropriate wettability characterization for pore scale models, despite the need to further reduce image analysis uncertainties. (C) 2020 The Authors. Published by Elsevier Inc.

The published version of this article is available at https://advances.sciencemag.org/content/5/9/eaaw5531. Compound flooding (CF) is an extreme event taking place in low-lying coastal areas as a result of co-occurring high sea level and large amounts of runoff, caused by precipitation. The impact from the two hazards occurring individually can be significantly lower than the result of their interaction. Both the risk of storm surges and heavy precipitation, as well as their interplay is likely to change in response to anthropogenic global warming. Despite CF relevance, a comprehensive risk assessment beyond individual locations at the country scale is missing. In particular, no studies have examined possible future CF risk. Here we estimate the potential CF risk along the European coasts both for present and future climate according to the business-as-usual (RCP8.5) scenario. Under current climate conditions, the locations experiencing the highest risk are mostly located along the Mediterranean Sea. However, future climate projections show emerging risk along parts of the Atlantic coast and the North Sea. The increase of the risk is mostly driven by an intensification of precipitation extremes. In several European regions, increasing CF risk should be considered as a potential hazard aggravating the risk caused by the mean sea level rise (SLR).

Monitoring the migration of pore pressure, deformation, and saturation plumes with effective tools is important for the storage and utilization of fluids in underground reservoirs, such as geological stores of carbon dioxide (CO2) and natural gas. Such tools would also verify the security of the fluid contained reservoir–caprock system. Utilizing the swelling strain attributed to pressure build‐up and the adsorption of supercritical CO2 on clay minerals, we tracked the fluid plume in a natural clay‐rich Tako sandstone at the laboratory core‐scale. The strain was measured by a high‐resolution distributed fiber optic strain sensing (DFOSS) tool. The strain changes induced by CO2 adsorptions on clay minerals were significantly greater than those caused by pore pressure alone. The distribution of the swelling‐strain signals effectively captured the dynamic breakthrough of the CO2 plume from the high‐ to low‐ permeability regions in the Tako sandstone. Besides revealing the in situ deformation state, the measured strain changes can track the movement of the CO2 plume as it enters the clay‐rich critical regions in the reservoir–caprock system. The present findings and potential future applications of DFOSS in the field are expected to enhance the monitoring and management of underground fluid reservoirs.

Transport of water, nutrients or energy fluxes in many natural or coupled human-natural systems occurs along different pathways that often have a wide range of transport timescales and might exchange fluxes with each other dynamically (e.g., surface-subsurface). Understanding this type of transport is key to predicting how landscapes will change under changing forcing. Here, we present a general framework for studying transport on a multi-scale coupled-connectivity system, via a multilayer network, which conceptualizes the system as a set of interacting networks, each arranged in a separate layer, and with interactions across layers acknowledged by interlayer links. We illustrate this framework by examining transport in river deltas as a dynamic interaction of flow within river channels and overland flow in the islands, when it is controlled by the flooding level. We show the potential of the framework to answer quantitatively questions related to the characteristic timescale of response in the system.

Studies on the impacts of land-use and land-cover change on stream hydrochemistry in active deforestation zones of the Amazon agricultural frontier are limited and have often used low-temporal-resolution datasets. Moreover, these impacts are not concurrently assessed in well-established agricultural areas and new deforestations hotspots. We aimed to identify these impacts using an experimental setup to collect high-temporal-resolution hydrological and hydrochemical data in two pairs of low-order streams in catchments under contrasting land use and land cover (native vegetation vs. pasture) in the Amazon and Cerrado biomes. Our results indicate that the conversion of natural landscapes to pastures increases carbon and nutrient fluxes via streamflow in both biomes. These changes were the greatest in total inorganic carbon in the Amazon and in potassium in the Cerrado, representing a 5.0- and 5.5-fold increase in the fluxes of each biome, respectively. We found that stormflow, which is often neglected in studies on stream hydrochemistry in the tropics, plays a substantial role in the carbon and nutrient fluxes, especially in the Amazon biome, as its contributions to hydrochemical fluxes are mostly greater than the volumetric contribution to the total streamflow. These findings demonstrate that assessments of the impacts of deforestation in the Amazon and Cerrado biomes should also take into account rapid hydrological pathways; however, this can only be achieved through collection of high-temporal-resolution data.

Modeling and representing moist convection in coarse-scale climate models remains one of the main bottlenecks of current climate simulations. Many of the biases present with parameterized convection are strongly reduced when convection is explicitly resolved (in cloud resolving models at high spatial resolution ~ a kilometer or so). We here present a novel approach to convective parameterization based on machine learning over an aquaplanet with prescribed sea surface temperatures. The machine learning is trained over a superparameterized version of a climate model in which convection is resolved by an embedded 2D cloud resolving models. The machine learning representation of convection, called Cloud Brain (CBRAIN) replicates many of the convective features of the superparameterized climate model, yet reduces its inherent stochasticity. The approach presented here opens up a new possibility and a first step towards better representing convection in climate models and reducing uncertainties in climate predictions.

The energy balance of a partially molten rocky planet can be expressed as a non-linear diffusion equation using mixing length theory to quantify heat transport by both convection and mixing of the melt and solid phases. Crucially, in this formulation the effective or eddy diffusivity depends on the entropy gradient, dS/dr, as well as entropy itself. First we present a simplified model with semianalytical solutions that highlights the large dynamic range of dS/dr---around 12 orders of magnitude---for physically-relevant parameters. It also elucidates the thermal structure of a magma ocean during the earliest stage of crystal formation. This motivates the development of a simple yet stable numerical scheme able to capture the large dynamic range of dS/dr and hence provide a flexible and robust method for time-integrating the energy equation.Using insight gained from the simplified model, we consider a full model, which includes energy fluxes associated with convection, mixing, gravitational separation, and conduction that all depend on the thermophysical properties of the melt and solid phases. This model is discretised and evolved by applying the finite volume method (FVM), allowing for extended precision calculations and using dS/dr as the solution variable. The FVM is well-suited to this problem since it is naturally energy conserving, flexible, and intuitive to incorporate arbitrary non-linear fluxes that rely on lookup data. Special attention is given to the numerically challenging scenario in which crystals first form in the centre of a magma ocean.The computational framework we devise is immediately applicable to modelling high melt fraction phenomena in Earth and planetary science research. Furthermore, it provides a template for solving similar non-linear diffusion equations that arise in other science and engineering disciplines, particularly for non-linear functional forms of the diffusion coefficient.

Meandering channels host geophysical flows that form the most extensive sediment transport systems on Earth (i.e. rivers and submarine channels). Measurements of helical flow structures in bends have been key to understanding sediment transport in rivers. Turbidity currents differ from rivers in both density and velocity profiles. These differences, and the lack of field measurements of turbidity currents, have led to multiple models for their helical flow. Here we present the first measurements of helical flows from turbidity currents in the ocean. These ten flows lasted between one and ten days, had up to ~80-metre thickness, and all displayed the same helical structure. This structure comprised two vertically-stacked cells, with the bottom cell rotating with the opposite direction to helical flow in rivers. Furthermore, we propose a general model that predicts the range of helical flow structures observed in rivers, estuaries and turbidity currents based on their density stratification.