The 15 January 2022 explosion of the Hunga Tonga-Hunga Ha’apai (HT-HH) volcano generated an extreme, quasi-instantaneous perturbation of the atmosphere. As part of its adjustment following the eruption, a rich spectrum of waves radiated away from HT-HH and achieved worldwide propagation. Among numerous platforms monitoring the event, two long-duration stratospheric balloons flying over the tropical Pacific provided unique observations of Lamb and infrasonic wave arrivals, detecting three revolutions of the Lamb wave and five of infrasound waves. Combined with ground measurements from the infrasound network of the International Monitoring System, such observations bring precious insights into the eruption process (chronology and altitude of energy release), and highlight previously unobserved long-range propagation of infrasound modes triggered by the eruption and their dispersion patterns. A comparison between ground- and balloon-based measurements emphasizes generally larger signal-to-noise ratios onboard the balloons and further demonstrates their potential for infrasound studies.

The spatiotemporal patterns of precipitation are critical for understanding the underlying mechanism of many hydrological and climate phenomena. Over the last decade, applications of the complex network theory as a data-driven technique has contributed significantly to study the intricate relationship between many variable in a compact way. In our work, we conduct a study to compare an extreme precipitation pattern in Ganga River Basin, by constructing the networks using two nonlinear methods - event synchronization (ES) and edit distance (ED). Event synchronization has been frequently used to measure the synchronicity between the climate extremes like extreme precipitation by calculating the number of synchronized events between two events like time series. Edit distance measures the similarity/dissimilarity between the events by reducing the number of operations required to convert one segment to another, that consider the events’ occurrence and amplitude. Here, we compare the extreme precipitation patterns obtained from both network construction methods based on different network’s characteristics. We used degree to understand network topology and identify important nodes in the networks. We also attempted to quantify the impact of precipitation seasonality and topography on extreme events. The study outcomes suggested that the degree is decreased in the southwest to the northwest direction and the timing of peak precipitation influences it. We also found an inverse relationship between elevation and timing of peak precipitation exists and the lower elevation greatly influences the connectivity of the stations. The study highlights that Edit distance better captures the network’s topology without getting affected by artificial boundaries.

Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian inference to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting parametric uncertainty and data discrepancy can drastically underestimate extreme flood events and risks. Precalibration improves prediction skill score over a stepwise line search. The Bayesian calibration improves the uncertainty characterization of model parameters and flood risk projections.

Warm, subsurface ocean waters that access ice shelves in the Amundsen Sea are likely to be a key driver of high meltrates and ice shelf thinning. Numerical models of the ocean circulation have been essential for gaining understanding of the mechanisms responsible for heat delivery and meltrate response, but a number of challenges remain for simulations that incorporate this region. Here, we develop a suite of numerical experiments to explore how sub ice shelf cavity circulation and meltrate patterns are impacted by parameterization schemes for (1) subgrid-scale ocean turbulence, and (2) ice-ocean interactions. To provide a realistic context, our experiments are developed to simulate the ocean circulation underneath the Pine Island ice shelf, and validated against mooring observations and satellite derived meltrate estimates. Each experiment is forced with data-informed open boundary conditions that bear the imprint of the gyre in Pine Island Bay. We find that even at a ~600 m grid resolution, flow aware ocean parameterizations for subgrid-scale momentum and tracer transfer are crucial for representing the circulation and meltrate pattern accurately. Our simulations show that enhanced meltwater diffusion near the ice-ocean interface intensifies near wall velocities via thermal wind, which subsequently increases meltrates near the grounding line. Incorporating a velocity dependent ice-ocean transfer coefficient together with a flow aware ocean turbulence parameterization therefore seems to be necessary for modelling the ocean circulation underneath ice shelves in the Amundsen Sea at this resolution.

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.

Increasing ice flux from glaciers retreating over deepening bed topography has been implicated in the recent acceleration of mass loss from the Greenland and Antarctic ice sheets. We show in observations that some glaciers have remained at peaks in bed topography without retreating despite enduring significant changes in climate. Observations also indicate that some glaciers which persist at bed peaks undergo sudden retreat years or decades after the onset of local ocean or atmospheric warming. Using model simulations, we show that glacier persistence may lead to two very different futures: one where glaciers persist at bed peaks indefinitely, and another where glaciers retreat from the bed peak suddenly without a concurrent climate forcing. However, it is difficult to distinguish which of these two futures will occur from current observations. We conclude that inferring glacier stability from observations of persistence obscures our true commitment to future sea-level rise under climate change.

The freshwater ecosystems around the world are degrading, such that maintaining environmental flow (EF) in river networks is critical to their preservation. The relationship between streamflow alterations and, respectively, EF violations, and freshwater biodiversity is well established at the scale of stream reaches or small basins (~<100 km²). However, it is unclear if this relationship is robust at larger scales even though there are large-scale initiatives to legalize the EF requirement. Moreover, EFs have been used in assessing a planetary boundary for freshwater. Therefore, this study intends to carry out an exploratory evaluation of the relationship between EF violation and freshwater biodiversity at globally aggregated scales and for freshwater ecoregions. Four EF violation indices (severity, frequency, the probability to shift to violated state, and probability to stay violated) and seven independent freshwater biodiversity indicators (calculated from observed biota data) were used for correlation analysis. No statistically significant negative relationship between EF violation and freshwater biodiversity was found at global or ecoregion scales. While our results thus suggest that streamflow and EF may not be an only determinant of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods (e.g., including water temperature, water quality, intermittency, connectivity etc.) or with other biodiversity data or metrics.

We present a method for predicting wave dissipation by sea ice that is based on the dimensional analysis of data with a scaling defined by ice thickness. Applying the method to an extensive dataset from the measurements during the “Polynyas, Ice Production, and seasonal Evolution in the Ross Sea” (PIPERS) cruise in 2017, we derive a new model of wave dissipation which not only describes a nonlinear dependence on ice thickness but also reveals its relation with the dependence on frequency. This nonlinear dependence on ice thickness can have more implications on predicting low-frequency waves. The root-mean-square error of the prediction is significantly reduced using the new model, compared with other existing parametric models that are also calibrated for the PIPERS dataset. The new model also explicitly describes a condition of similarity between large- and small-scale observations, which is shown to exist when various laboratory datasets collapse onto the prediction.

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 highly 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.

Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.

An accurate estimation of the shale permeability is essential to understand heterogeneous organic-rich shale reservoir rocks and predict the complexity of pore fluid transport in the rocks. However, predicting the matrix permeability by traditional models is still challenging because they require information often measured from core measurements. First, Kozeny’s equation (Kozeny, 1927) uses porosity and specific surface area of solid grains. However, it is difficult to characterize the specific surface area values or grain sizes from the logs. Second, Herron’s method (Herron, 1987) has been used for predicting permeability based on the mineral contents provided by well log data in conventional sandstone reservoirs. However, the predictive accuracy is low due to the different pore network structures of the shales. In this study, we estimate shale matrix permeability by a combined exploratory data analysis (EDA) and nonlinear regression estimation from the wireline logs. First, we conduct a bivariate correlation analysis for permeability and rock properties in core measurements. According to the correlation and Shapley value sensitivity test, we find that permeability change has a significant effect on the variation in porosity. Also, we investigate a nonlinear behavior between porosity and permeability. Second, we derive a nonlinear polylogarithmic estimation function of porosity to permeability, comparing it to the multivariate linear regression of porosity and clay volume fraction. As a result, a cubic logarithmic function of porosity significantly improves the fitting performance of the permeability values, better than the traditional methods. Moreover, we generate the permeability logs from the calibrated porosity logs, and they imply better shale permeability prediction as well. Since we can invert the porosity distribution from seismic data, this approach can provide a more accurate permeability estimation and reliable fluid flow modeling for shale and mudrock.

The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.

Mars has a magnetic field originating in its strongly magnetised crust that holds clues to the planet’s interior. We apply vector spherical harmonic decomposition to simple candidate magnetic structures to separate the parts responsible for the anomalies from those that remain invisible. A uniform magnetic layer produces no anomalies: spatial variations are essential although secondary magnetisation does produce a weak field that might reflect the primordial dynamo field. A hemispheric layer produces anomalies confined to the equator rather than the observed hemispheric difference. A uniformly magnetised crust with variable thickness determined from gravity and topography produces a crustal field with large anomalies at the major impact crater sites that are not observed. These anomalies are not present if the magnetic layer lies deeper than the crater floor. We conclude that decomposing magnetisations in this way is a useful tool in the interpretation of Martian magnetic anomalies.

Numerical geodynamo simulations capture several features of the spatial and temporal geomagnetic field variability on historical and Holocene timescales. However, a recent analysis questioned the ability of these numerical models to comply with long-term palaeomagnetic field behaviour. Analysing a suite of 50 geodynamo models, we present here the first numerical simulations known to reproduce the salient aspects of the palaeosecular variation and time-averaged field behaviour since 10 Ma. We find that the simulated field characteristics covary with the relative dipole field strength at the core-mantle boundary (dipolarity). Only models dominantly driven by compositional convection, with an Ekman number (ratio of viscous to Coriolis forces) lower than $10^{-3}$ and a dipolarity in the range $0.34-0.56$ can capture the observed palaeomagnetic field behaviour. This dipolarity range agrees well with state-of-the-art statistical field models and represent a testable prediction for next generation global palaeomagnetic field model reconstructions.

Foreshock analysis promises new insights into the earthquake nucleation process and could potentially improve earthquake forecasting. Well-performing clustering models like the Epidemic-Type Aftershock Sequence (ETAS) model assume that foreshocks and general seismicity are generated by the same physical process, implying that foreshocks can be identified only in retrospect. However, several studies have recently found higher foreshock activity than predicted by ETAS. Here, we revisit the foreshock activity in southern California using different statistical methods and find anomalous foreshock sequences, i.e., those unexplained by ETAS, mostly for mainshock magnitudes below 5.5. The spatial distribution of these anomalies reveals a preferential occurrence in zones of high heat flow, which are known to host swarm-like seismicity. Outside these regions, the foreshocks generally behave as expected by ETAS. These findings show that anomalous foreshock sequences in southern California do not indicate a pre-slip nucleation process, but swarm-like behavior driven by heat flow.