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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 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.

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.

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.

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.

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.