Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku-Oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4\pm0.140 m/s and 4.11\pm0.152 m, respectively after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis.

Supervised deep learning models have become a popular choice for seismic phase arrival detection. However, they don’t always perform well on out-of-distribution data and require large training sets to aid generalization and prevent overfitting. This can present issues when using these models in new monitoring settings. In this work, we develop a deep learning model for automating phase arrival detection at Nabro volcano using a limited amount of training data (2498 event waveforms recorded over 35 days) through a process known as transfer learning. We use the feature extraction layers of an existing, extensively-trained seismic phase picking model to form the base of a new all-convolutional model, which we call U-GPD. We demonstrate that transfer learning reduces overfitting and model error relative to training the same model from scratch, particularly for small training sets (e.g., 500 waveforms). The new U-GPD model achieves greater classification accuracy and smaller arrival time residuals than off-the-shelf applications of two existing, extensively-trained baseline models for a test set of 800 event and noise waveforms from Nabro volcano. When applied to 14 months of continuous Nabro data, the new U-GPD model detects 31,387 events with at least four P-wave arrivals and one S-wave arrival, which is more than the original base model (26,808 events) and our existing manual catalogue (2,926 events), with smaller location errors. The new model is also more efficient when applied as a sliding window, processing 14 months of data from 7 stations in less than 4 hours on a single GPU.

The predicted Antarctic contribution to global-mean sea-level rise is one of the most uncertain among all major sources. Partly this is because of instability mechanisms of the ice flow over deep basins. Errors in bedrock topography can substantially impact the projected resilience of glaciers against such instabilities. Here we analyze the Pine Island Glacier topography to derive a statistical model representation. Our model allows for inhomogeneous and spatially dependent uncertainties and avoids unnecessary smoothing from spatial averaging or interpolation. A set of topography realizations is generated representing our best estimate of the topographic uncertainty in ice sheet model simulations. The bedrock uncertainty alone creates a 5% to 25% uncertainty in the predicted sea level rise contribution at year 2100, depending on friction law and climate forcing. Pine Island Glacier simulations on this new set are consistent with simulations on the BedMachine reference topography but diverge from Bedmap2 simulations.

Using template matching and GPS data, we investigate the evolution of seismicity and observable deformation in Central Apennines. Seismicity appears more persistent at the base of the seismogenic layer than in the shallower crust. Diffuse activity is reported on segments at depth, alternating along strike with apparent quiescence on segments that experienced one or more Mw6+ earthquakes in 1997, 2009 and 2016. Central Apennines are likely underlain by a sizeable shear zone with areas of diffuse seismicity bounding shallow normal faults where Mw6+ earthquakes occurred. The deformation observed at the surface seems to follow the seismicity variations at the base of seismogenic layer along the Apenninic chain. Principal and independent component analysis of GPS data exhibits a transient when the 2016 foreshock sequence starts. This transient propagated northward from the Campotosto fault up to the Alto Tiberina fault system and has likely loaded the Mw6+ 2016 earthquake sequence.

This article is composed of three independent commentaries about the state of ICON principles (Goldman et al., 2021a) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of FAIR research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi-scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

Birds are some of the most diverse organisms on Earth, with species inhabiting nearly every conceivable niche in every major biome. As such, birds are vital to our understanding of modern ecosystems. Unfortunately, this is hampered by knowledge gaps relating to the origin of this modern diversity and its role in ecosystems. A crucial part of addressing these shortcomings is improving our understanding of the earliest birds, the non-avian avialans i.e. non-crown birds. The diet of non-avian avialans has been a matter of substantial debate, partly related to some of the ambiguous qualitative approaches that have been used to reconstruct it. Here we review the methods of determining diet in both modern avians and fossil avian and non-avian theropods, and comment on their usefulness when applied to non-avian avialans. We use this to propose a set of comparable, quantitative approaches to ascertain fossil bird diet and on this basis provide a consensus of what we currently know about fossil bird diet. While no single approach can precisely predict diet in birds, each can exclude some diets and narrow the dietary possibilities. We recommend combining [1] dental microwear, [2] landmark-based muscular reconstruction, [3] stable isotope geochemistry, [4] body mass estimations, [5] traditional and/or geometric morphometric analysis, and [6] finite element analysis to accurately reconstruct fossil bird diet. Our review provides specific methodologies to implement each approach and discusses complications future researchers should keep in mind. On this basis we report the current state of knowledge of non-avian avialan diet which remains very incomplete. The ancestral dietary condition in non-avian avialans remains unclear due to a scarcity of data and contradictory evidence in Archaeopteryx. Among early non-avian pygostylians, Confuciusornis has finite element analysis and mechanical advantage evidence pointing to herbivory, whilst Sapeornis only has mechanical advantage evidence indicating granivory, which agrees with fossilised ingested material known for this taxon. The enantiornithine ornithothoracine Shenqiornis has mechanical advantage and pedal morphometric evidence pointing to carnivory. In the hongshanornithid ornithuromorph Hongshanornis, only mechanical advantage evidence indicates granivory, but this is congruent with evidence of fossilised ingested material in this taxon. The same is true for the songlingornithid ornithuromorph Yanornis and its inferred carnivorous diet. Due to the sparsity of robust dietary assignments, no clear trends in non-avian avialan dietary evolution have yet emerged. Dietary diversity may seem to increase through time, but this is a preservational bias associated with a predominance of data from the Early Cretaceous Jehol Lagerstatte. With this new framework and our current synthesis of current knowledge of non-avian non-avialan diet, we expect dietary knowledge and evolutionary trends to become much clearer[…]

A realistic numerical model is used to study the circulation and mixing of the Salish Sea, a large, complex estuarine system on the United States and Canadian west coast. The Salish Sea is biologically productive and supports many important fisheries but is threatened by recurrent hypoxia and ocean acidification, so a clear understanding of its circulation patterns and residence times is of value. The estuarine exchange flow is quantified at 39 sections over three years (2017-2019) using the Total Exchange Flow method. Vertical mixing in the 37 segments between sections is quantified as opposing vertical transports: the efflux and reflux. Efflux refers the rate at which deep, landward-flowing water is mixed up to become part of the shallow, seaward-flowing layer. Similarly, reflux refers to the rate at which upper layer water is mixed down to form part of the landward inflow. These horizontal and vertical transports are used to create a box model to explore residence times in a number of different sub-volumes, seasons, and years. Residence times from the box model are generally found to be longer than those based on simpler calculations of flushing time. The longer residence times are partly due to reflux, and partly due to incomplete tracer homogenization in sub-volumes. The methods presented here are broadly applicable to other estuaries.

Mars has a magnetic field originating in its strongly magnetised crust that holds clues to the planet’s interior. The relation between magnetic anomalies and the underlying crustal magnetisation is complex because of magnetic structures that produce no observable field. We use a recently-developed method to isolate these “invisible’ structures to explore explanations for the observations. The strong magnetisation suggested by ground observations from InSight can be obtained simply by adding a suitable invisible magnetisation to that required to explain the data. A thin Northern Hemisphere and thick Southern Hemisphere crust produces magnetic anomalies confined around the equator, not the Southern Hemisphere. Variations in crustal thickness produce differences with the satellite field, most notably strong anomalies associated with the impact craters that are not in the data. Magnetisation may be confined to depths greater than that of the craters, or anomalies from shallower material are not observable at satellite altitude.

The sensitivity of seismic compressional and shear waves and their velocity ratios to rock lithology, pore fluids, and high-temperature materials makes these parameters very useful for constraining the physical state of the crust. In this study, we develop a joint inversion approach utilizing both radial and vertical components’ autocorrelations of tele seismic P-wave coda for imaging the crust by simultaneously characterizing the crustal Vp, Vs and Vp/Vs ratio. Autocorrelations of the radial and vertical components contain P and S waves that are reflected from the subsurface. Therefore, joint inversion of them can account for the variations of both Vp and Vs, and consequently, the Vp/Vs ratio. Synthetic inversions show significant improvement in the estimation of these parameters com pared to those from the inversion of either, receiver functions or the autocorrelation of the vertical component. The velocity models inferred from the application of the approach to teleseismic data recorded along a north-south passive seismic profile (BILBY experiment) in central Australia reveal a distinct pattern of the Moho and the Vp/Vs variations across the crustal blocks/domain. The general trend of the Moho structure corresponds well with the change of the reflectivity that can normally be seen at the base of the crust and also with the Moho estimated from the previous studies including the deep seismic reflection profiling method. The Vp/Vs structure at depths greater than 10 km shows dominant high values beneath locations where the crustal domains interact (e.g., at transition from one domain to another).

We present a reduced magnetohydrodynamic (MHD) mathematical model describing the dynamical behavior of highly conducting plasmas with frozen-in magnetic fields, constrained by the assumption that, there exists a frame of reference, where the magnetic field vector, B, is aligned with the plasma velocity vector, u, at each point. We call this solution “stream-aligned MHD” (SA-MHD). Within the framework of this model, the electric field, E = −u x B ≡ 0, in the induction equation vanishes identically and so does the electromagnetic energy flux (Poynting flux), E x B ≡ 0, in the energy equation. At the same time, the force effect from the magnetic field on the plasma motion (the Ampere force) is fully taken into account in the momentum equation. Any steady-state solution of the proposed model is a legitimate solution of the full MHD system of equations. However, the converse statement is not true: in an arbitrary steady-state magnetic field the electric field does not have to vanish identically (its curl has to, though). Specifically, realistic tree-dimensional solutions for the steady-state (ambient) solar atmosphere in the form of so-called Parker (1958) spirals, can be efficiently generated within the stream-aligned MHD (SA-MHD) with no loss in generality.

The isotopic composition of dissolved oxygen offers a family of potentially unique tracers of respiration and transport in the subsurface ocean. Uncertainties in transport parameters and isotopic fractionation factors, however, have limited the strength of the constraints offered by 18O/16O and 17O/16O ratios in dissolved oxygen. In particular, puzzlingly low 17O/16O ratios observed for some low-oxygen samples have been difficult to explain. To improve our understanding of oxygen cycling in the ocean’s interior, we investigated the systematics of oxygen isotopologues in the subsurface Pacific using new data and a 2-D isotopologue-enabled isopycnal reaction-transport model. We measured 18O/16O and 17O/16O ratios, as well as the “clumped” 18O18O isotopologue in the northeast Pacific, and compared the results to previously published data. We find that transport and respiration rates constrained by O2 concentrations in the oligotrophic Pacific yield good measurement-model agreement across all O2 isotopologues only when using a recently reported set of respiratory isotopologue fractionation factors that differ from those most often used for oxygen cycling in the ocean. These fractionation factors imply that an elevated proportion of 17O compared to 18O in dissolved oxygen―i.e., its triple-oxygen isotope composition―does not uniquely reflect gross primary productivity and mixing. For all oxygen isotopologues, transport, respiration, and photosynthesis comprise important parts of their respective budgets. Mechanisms of oxygen removal in the subsurface ocean are discussed.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.

Harsh subsurface environment limits robust workability of on-site instrumentation to be leveraged to track solid Earth’s dynamics. Distributed fiber-optic sensing technology (DFOS) allows long-period in-situ real-time detection of crustal geoenergy exploration-induced underground motions. Here, we first deployed 300-m-long fiber-optic cables behind casing of an actual injection well via single-ended, hybrid Brillouin-Rayleigh backscatterings interrogator to distributed monitor water injection test between two adjacent wells in onshore Mobara, Japan. Detailed DFOS recordings over the entire borehole visualized clear-cut spatiotemporal strain responses from one water injection. Potential injected water-transport footprint and impacted zone reasonably coincided with those of analogy-based strain fronts. Our study thus further uncovered that injection volume and injection pressure significantly dominated water injection-driven strain magnitude and coverage.

The shoreline development index – the ratio of a lake’s shore length to the circumference of a circle with the lake’s area – is a core metric of lake morphometry used in Earth and planetary sciences. In this paper, we demonstrate that the shoreline development index is scale-dependent and cannot be used to compare lakes with different areas. We show that large lakes will have higher shoreline development index measurements than smaller lakes of the same characteristic shape, even when mapped at the same scale. Specifically, the shoreline development index increases by about 14% for each doubling of lake area. These results call into question previously reported patterns of lake shape. We provide several suggestions to improve the application of this index, including a bias-corrected formulation for comparing lakes with different surface areas.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010-2019 decade, sometimes by more than +/-0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI dataset excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original dataset. SSS CCI data are freely available and will be updated and extended as more satellite data become available.

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their topographic structure is poorly mapped due to a reliance upon expensive and time intensive methods. Space-borne bathymetric mapping has helped address these issues, but has remained dependent upon in situ measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for fully space-borne estimates of nearshore bathymetry in shallow, clear water environments.

With plate tectonics operating on Earth, the preservation potential for mantle reservoirs from the Hadean Eon (>4.0 Ga) has been regarded as very small. The quest for such early remnants has been spurred by the observation that many Archean rocks exhibit excesses of 182W, the decay product of short-lived 182Hf. However, it remains speculative, if Archean 182W anomalies and also 182W deficits found in many young ocean island basalts (OIBs) mirror primordial Hadean mantle differentiation or just variable contributions from older meteorite building blocks delivered to the growing Earth. Here, we present a high-precision 182W isotope dataset for 3.22-3.55 Ga old rocks from the Kaapvaal Craton, southern Africa. In expanding previous work, our study reveals widespread 182W deficits in different rock units from the Kaapvaal Craton and also the very first discovery of a negative co-variation between short-lived 182W and long-lived 176Hf-143Nd-138Ce patterns, a trend of global significance. Amongst different models, these distinct patterns can be best explained by the presence of recycled mafic restites from Hadean protocrust in the ancient mantle beneath the Kaapvaal Craton. Further, the data provide unambiguous evidence for the operation of silicate differentiation processes on Earth during the lifetime of 182Hf, i.e., the first 60 million years after solar system formation. The striking isotopic similarity between recycled protocrust and the low 182W endmember of modern OIBs might also constitute the missing link bridging 182W isotope systematics in Archean and young mantle-derived rocks.

Free alternate bars are large-scale, downstream-migrating bedforms characterized by an alternating sequence of three-dimensional depositional fronts and scour holes that frequently develop in rivers as the result of an intrinsic instability of the erodible bed. Theoretical models based on two-dimensional shallow water and Exner equations have been successfully employed to capture the bar instability phenomenon, and to estimate bar properties such as height, wavelength and migration rate. However, the mathematical complexity of the problem hampered the understanding of the key physical mechanisms that sustain bar formation. To fill this gap, we considered a simplified version of the equations, based on neglecting the deformation of the free surface, which allows us to: (i) provide the first complete explanation of the bar formation mechanism as the result of a simple bond between variations of the water weight and flow acceleration; (ii) derive a simplified, physically based formula for predicting bar formation in a river reach, depending on channel width-to-depth ratio, Shields number and relative submergence. Comparison with an unprecedented large set of laboratory experiments reveals that our simplified formula appropriately predicts alternate bar formation in a wide range of conditions. Noteworthy, the hypothesis of negligible free surface effect also implies that bar formation is fully independent of the Froude number. We show that this intriguing property is intimately related to the three-dimensional nature of river bars, which allows for a gentle lateral deviation of the flow without significant deformation of the water surface.