A document by Ron baiman. Click on the document to view its contents.

Body waves traversing the Earth’s interior from a seismic source to receivers on the surface carry rich information about its internal structures. Their travel time measurements have been widely used in seismology to constrain Earth’s interior at the global scale by mapping the time anomaly along their ray paths. However, picking the travel time of global seismic waves, suitable for studying Earth’s fine-scale structures, requires highly skilled personnel and is often fairly subjective. Here, we report the development of an automatic picker for PKIKP waves, traversing the Earth nearly along its diameters and through the inner core, based on the latest advances in supervised deep learning. A convolutional neural network (CNN) we developed automatically determines the PKIKP onset on vertical seismograms near its theoretical prediction of cataloged earthquakes. As high-quality manual onset picks of global seismic phases are limited, we employed a scheme to generate a synthetic supervised training dataset containing 300,000 waveforms. The PKIKP onsets picked by our trained CNN automatic picker exhibit a mean absolute error of ~0.5 s compared to 1,503 manual picks, comparable to the estimated human-picking error. In an integration test, the CNN automatic picks obtained from an extended waveform dataset yield a cylindrically anisotropic inner core model that agrees well with the models inferred from manual picks, which illustrates the success of this pilot model. This is a significant step closer to harvesting an unprecedented volume of travel time measurements for studying the inner core or other regions of the Earth’s deep interior.

The construction sector serves as a vital pillar from both social and economic standpoints, fulfilling essential housing needs and contributing significantly to economic growth. However, its environmental impact is notable, manifesting through the release of substantial greenhouse gas emissions, ultimately leading to detrimental environmental consequences, such as global warming and ozone depletion. The global construction industry is a significant contributor, accounting for a substantial 39% of the world's greenhouse gas emissions. Civil construction, particularly in developing countries like India, holds a pronounced economic role. To address environmental concerns, the adoption of sustainable construction practices becomes imperative, not only to curtail greenhouse gas emissions but also to conserve finite resources. Developing economies are compelled to embark on large-scale civil construction and energy production ventures, accentuating the criticality of transitioning to a low-carbon economy, though this poses challenges in the process. To identify potential mitigation opportunities, comprehending the contributions of the construction industry and related activities is vital. Thus, the present study seeks to quantify the overall carbon material footprint of the construction sector in India, with a focus on identifying key contributing supply chains, industries, and products. To achieve this, extensive economy-wide input-output analysis is conducted utilizing economic input-output transaction tables jointly by the Central Statistical Organization (CSO) and Planning Commission. This comprehensive approach enables detailed sector disaggregation refined analysis, and facilitates benchmarking. To assess GHG emissions, the study develops a model based on input-output and process-based life cycle assessment principles. The significance of this in-depth analysis is to provide critical insights for reducing construction-related greenhouse gas emissions in developing countries. The outcome will aid in prioritizing future emissions reduction endeavors, directing targeted solutions toward improving the overall environmental performance of the construction industries.

In this study, we compare two significant geomagnetic storms of the 21st century: the well-known Halloween geomagnetic storm of 2003 (Kp index 9) and a somewhat milder storm of September 2017 (Kp 8). Both events caused exceptionally high values of geomagnetically induced currents (GIC) and earned a place among the top ten with respect to the measured GIC in the Finnish natural gas pipeline.. We analyze solar wind and geomagnetic data as well as modeled geoelectric fields during these two events to better understand the drivers behind these strong GIC. We discover certain geographic locations that experienced stronger magnetic field time derivatives during the 2017 storm. This is interesting because in terms of magnetic indices, the 2017 storm was a weaker event. We use equivalent currents to get a view of the ionospheric and induced currents in the Fennoscandian region. We find that the interplay between different structures of ionospheric currents and the three-dimensional ground conductivity leads to a complex behaviour of the geoelectric field. This study improves knowledge in space weather preparedness by identifying location-specific risks for geoelectric hazards, which can create severe problems in the high-voltage power grid.

Precisely classifying earthquake types is crucial for elucidating the relationship between volcanic earthquakes and volcanic activity. However, traditional methods rely on subjective human judgment, which requires considerable time and effort. To improve this, we developed a deep learning model using a transformer encoder for a more objective and efficient classification. Tested on Mount Asama’s diverse seismic activity, our model achieved high F1 scores (0.876 for tectonic, 0.964 for low-frequency earthquakes, and 0.995 for noise), equivalent to or better than other methods. According to the attention weight visualization, our model focuses on critical seismic signal features for classification, similar to expert analysis. However, it has been demonstrated that removing subjective elements and employing standardized labeling of the training data based on waveform features are necessary to enhance the interpretability of the model. Additionally, the analyses suggest that stations near the volcanic crater are essential for a highly interpretative and accurate classification.

Within the climate modeling community, the complex citation issue has been discussed for a decade in the context of research traceability and data citation/data impact. The traceability requires fine-granular information on individual datasets, whereas meaningful data impact analysis relies on data citations on large data collections of data belonging to and individual model run or to a model experiment. To this date, it is not yet possible to achieve both goals with one technical solution. Suggestions for combinations of DOIs on data collections and user-defined PID collections of data subsets across several DOIs have not been taken up (see Stockhause et al., 2013).The IPCC FAIR Guidelines introduced in the Sixth Assessment Report (AR6) aimed to enhance the transparency of the AR6 and its outcomes by documenting the figure creation process (Pirani et al., 2022). Many figures are based on large numbers of datasets hosted in various repositories. Citing every dataset in the captions is not feasible. User-defined data collections utilizing data provenance records could be included in a caption, but lack the information about the authors and funders of the individual objects required for data citation and data impact analysis.The exchange on complex citation difficulties intensified at the AGU 2020 within the Community of Practice and led to the establishment of the RDA Complex Citation Working Group (WG). The WG brings all stakeholders together. It aims to provide recommendations for citing a large number of existing objects in a way that allows to properly assign credit for individual objects.

A document by Martina Stockhause. Click on the document to view its contents.

Nitrogen-rich tropical/subtropical forest soil acts as a terrestrial source of nitrous oxide (N2O) emissions, a greenhouse gas commonly affected by soil nitrogen availability and soil moisture. However, in tropical and subtropical regions experiencing both elevated nitrogen deposition and altered precipitation regimes, it is unclear whether nitrogen deposition and precipitation regimes have interactive effects on forest soil N2O emissions and what roles N2O-associated nitrifiers/denitrifiers play in these interactions. We conducted a two-year field study in a subtropical evergreen broadleaf forest in southern China by applying four treatments: nitrogen addition (N), seasonal precipitation distribution change (P), both nitrogen addition and seasonal precipitation distribution change (NP) and a control (C). The Results showed that N2O efflux from the forest soil was significantly greater in the wet season than in the dry season, but was promoted by the NP treatment only in the dry season. Soil moisture and pH decreased in the P and N treatments, respectively. The abundances of the nitrifying gene AOA-amoA and denitrifying gene nosZ in the wet season and the abundance of the denitrifying gene nirK in the dry season differed significantly among the four treatments. A structural equation model showed that precipitation change was more important than nitrogen addition in affecting soil properties (e.g. moisture and pH) and N2O-associated nitrifiers/denitrifiers, while soil nirK- and nosZ-denitrifiers were the dominant functional microbes in regulating N2O emissions. The results support predictions of future nitrogen losses (N2O) in subtropical forests in the context of interactions between elevated nitrogen deposition and altered precipitation regimes.

While physics-informed neural networks (PINNs) can solve the problem pertaining to the absence of boundary conditions in soil water systems, their results exhibit low accuracy primarily due to insufficient utilization of the available prior knowledge regarding soil hydraulic parameters. In this research, an improved PINNs framework is proposed, which introduces an optimizable saturated hydraulic conductivity into the activation function, and an advanced optimization strategy is developed to identify the optimal superparameters for the multi-objective loss function. The PINNs was trained using synthetic volumetric soil water content (VSWC) and soil matric potential (SMP) data generated by a numerical solution of the Richardson-Richards equation (RRE) for three soil types (silt loam, loam and sandy loam). The results show that the proposed framework increases the accuracy of SMP estimations in the unsaturated soil system. The results reveal that the relative error achieved by the proposed framework in loam or silt loam has been reduced by two orders of magnitude in comparison with that achieved by the framework introduced by Bandai and Ghezzehei (2020), indicating a significant improvement. While there is a slight reduction in the accuracy of volumetric soil water content estimation, this minor reduction has minimal practical significance. Both the soil water retention curve and the soil hydraulic conductivity exhibit superior performance at the near-saturation scale. For unsaturated flow in homogeneous soil, the proposed PINNs framework provides accurate estimations of soil hydraulic parameters and holds significant potential for the practical application and widespread adoption of PINNs in the realm of soil hydrodynamics.

Magnetic reconnection in the Earth’s magnetosphere is usually manifested as a turbulent state in which the large amplitude fluctuations disrupt the main reconnection layer, while it occasionally shows a clear structured reconnection layer with weak fluctuations, i.e., a laminar state. To understand why the fluctuation strength varies significantly among reconnection in the Earth’s magnetotail, we have examined tens of reconnection events in the Earth’s magnetotail observed by the Magnetospheric Multi-Scale (MMS) mission. We primarily examine the correlation between fluctuation strength in reconnection, quantified by dBrec and dErec, and reconnection inflow conditions and upstream solar wind conditions. The fluctuation strength (dBrec, dErec) for these reconnections ranges from 0.7 to 10 nT and 0.8 to 30 mV/m, respectively. Our analysis unveils significant correlations between inflow conditions including Alfven speed VA,in, , magnetic disturbances dBin and electric field disturbances dEin with (dBrec, dErec). Fluctuation strength also shows good correlations with interplanetary magnetic field (IMF) cone angle and solar wind dynamic pressure, whereas it has an unclear relationship with substorm and storm activities. We suggest that inflow reconnection conditions act as the principal catalysts for turbulence during reconnection.

Short ocean surface waves are important for remote sensing, air-sea exchange, and underwater acoustics. The energy spectrum at scales much shorter than the dominant waves are azimuthally bimodal. However, widely used wave models fail to reproduce the bimodality of the short gravity waves. Recent studies have shown that an azimuthally narrow dissipation due to breaking can significantly improve model performance. Thus, highlighting the importance of the directional energy balance of wave models. We utilized stereo visible imagery to quantify the directional wave-breaking kinematics and compare them against the energy spectrum and different dissipation parameterizations and model solutions. The results show that wave-breaking is azimuthally unimodal and narrower than the bimodal energy spectrum, suggesting that wave-breaking dissipation combines with the nonlinear energy fluxes due to wave-wave interactions to yield enhanced bimodality. The findings are useful for constraining energy dissipation parameterizations for spectral wave models and improved understanding of air-sea fluxes.

A regional ocean model was used to study the influence of the Madagascar Ridge on the circulation and eddy variability in the Agulhas Current system. In the control experiment, the model was run with a realistic bathymetry, whereas in the idealized run the bathymetry was modified by flattening Madagascar Ridge. When the Ridge was suppressed, no obvious changes were observed in the large-scale circulation. However, integrated transports revealed an excess of about 10 Sv (1 Sv = 106 m3s-1) in the recirculation of the greater Agulhas Current system. Dynamic and statistically (p < 0.05) significant changes were observed at the mesoscale variability. Composite analysis of the radial distribution of the eddy azimuthal velocity, surface height, and relative vorticity within the Ridge domain in the experiments revealed that the presence of the Madagascar Ridge determines the emergence of a secondary class of large anticyclonic eddy types in the region, the “Madagascar rings”.

Mesoscale currents account for 80% of the ocean’s kinetic energy, whereas submesoscale currents capture 50% of the vertical velocity variance. SWOT’s first sea surface height (SSH) observations have a spatial resolution an order of magnitude greater than traditional nadir-looking altimeters and capture mesoscale and submesoscale features. This enables the derivation of submesoscale vertical velocities, crucial for the vertical transport of heat, carbon and nutrients between the ocean interior and the surface. This work focuses on a mesoscale energetic region south of Tasmania using a coupled ocean-atmosphere simulation at km-scale resolution and preliminary SWOT SSH observations. Vertical velocities (w), temperature anomalies and vertical heat fluxes (VHF) from the surface down to 1000 m are reconstructed using effective surface Quasi-Geostrophic (sQG) theory. An independent method for reconstructing temperature anomalies, mimicking an operational gridded product, is also developed. Results show that sQG reconstructs 90% of the total \textit{w} and VHF rms at scales down to 30 km just below the mixed layer and 50-70% of the rms for scales larger than 70 km at greater depth, with a spatial correlation of ~0.6. The reconstruction is spectrally coherent (>0.65) for scales larger than 30-40 km at the surface, slightly degrading (~0.55) at depth. The two temperature anomalies datasets yield similar results, indicating the dominance of w on VHF. RMS of sQG w and VHF derived from SWOT are twice as large as those derived from conventional altimetry, highlighting the potential of SWOT for reconstructing energetic meso and submesoscale dynamics in the ocean interior.

Deep-marine brine seeps in the modern ocean are considered analogues for settings that favoured the formation of sedimentary-exhalative zinc and lead deposits in deep time. Microbial activity plays an important role in the accumulation of ore minerals, meaning that the extent of mineralization is at least indirectly dependent on nutrient fluxes. Here, we investigated the biogeochemical nitrogen cycle in shallow (15-50 cm) sediment cores from the Orca Basin brine pool and surrounding sites, as well as from an active brine seep area near Dead Crab Lake in the Gulf of Mexico, with the aim of constraining the effect of brine seepage on this bio-essential element. We find high porewater ammonium concentrations in the millimolar range, paired with elevated ratios of organic carbon to nitrogen in sediments, which confirm previous hypotheses that the brine recycles ammonium from sedimentary strata back into the water column. Within Orca Basin, we note tentative evidence of microbial ammonium utilization. At the active seep, ammonium is mixed into the overlying water column and likely undergoes oxidation. Isotopic data from sediments and dissolved ammonium, paired with previously published genomic data, suggest the presence of dissimilatory nitrate reduction to ammonium (DNRA) at the brine-seawater interface. We conclude that brine seeps can stimulate biological nitrogen metabolisms in multiple ways. Our results may help calibrate studies of biogeochemical cycles around brine seeps that are archived in the rock record.

A series of transient global warming events (“hyperthermals”) in the early Eocene is marked by massive environmental and carbon cycle change. Among these events, the impacts of the Paleocene Eocene Thermal Maximum (~56 Ma), Eocene Thermal Maximum 2 (~54 Ma) and Eocene Thermal Maximum 3 (~53 Ma) are relatively well documented, but much less is known on the many later hyperthermals that apparently occurred on orbital eccentricity maxima until at least the end of the Early Eocene Climatic Optimum (EECO; ~53­–49 Ma). Here, at Ocean Drilling Program (ODP) Site 959 (Equatorial Atlantic Ocean), we report a large negative carbon isotope excursion (CIE) in both organic and carbonate substrates that we correlate to the “V” event sensu Lauretano et al. (2016) (or C22nH1 sensu Sexton et al. (2011)) at ~49.7 Ma, following combined bio- and chemostratigraphic constraints. Through TEX86 paleothermometry, we reconstruct a sea surface temperature rise of 1.1–1.9 ºC associated with this CIE, which, combined with evidence for warming from the deep sea, implies that this event indeed represents a transient global warming episode like the earlier hyperthermals. Organic walled dinoflagellate cyst assemblages indicate a productive paleoceanographic background setting, likely through regional upwelling, which alternated with episodes of stratification. Warming reconstructed across V at Site 959 is relatively similar to the higher-latitude-derived deep ocean reconstructions. However, the presence of upwelling and its variable intensity across the event compromises the use of the reconstructed warming as an estimate for the complete tropical band.

Inverse problems are ubiquitous in hydrological modelling for parameter estimation, system understanding, sustainable water resources management, and the operation of digital twins. While statistical inversion is especially popular, its sampling-based nature often inhibits its application to computationally costly models, which has compromised the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, e.g., for spatially distributed (partial) differential equation based models. In this study we introduce multilevel GLUE (MLGLUE), which alleviates the computational burden of statistical inversion by utilizing a hierarchy of model resolutions. Inspired by multilevel Monte Carlo, most parameter samples are evaluated on lower levels with computationally cheap low-resolution models and only samples associated with a likelihood above a certain threshold are subsequently passed to higher levels with costly high-resolution models for evaluation. Inferences are made at the level of the highest-resolution model but substantial computational savings are achieved by discarding samples with low likelihood already on levels with low resolution and low computational cost. Two example inverse problems, using a rainfall-runoff model and groundwater flow model, demonstrate the substantially increased computational efficiency of MLGLUE compared to GLUE as well as the similarity of inversion results. Findings are furthermore compared to inversion results from Markov-chain Monte Carlo (MCMC) and multilevel delayed acceptance MCMC, a corresponding multilevel variant, to compare the effects of the multilevel extension. All examples demonstrate the wide-range suitability of the approach and include guidelines for practical applications.

Recent findings by the Mars Science Laboratory (MSL) have confirmed the presence of nitrates near Gale Crater on Mars. In this work, we consider the formation and deposition of HNOx species in cold early Mars climates. We find that solar energetic particles could facilitate nitrogen fixation by photochemically generating pernitric and nitric acid, which then deposit onto icy particles that settle onto Mars’ surface. This study demonstrates that such deposition would be more efficient under higher atmospheric pressures, consistent with Mars’ ancient atmosphere, and could account for the nitrate levels detected by the MSL. We find a more rapid deposition rate for pernitric acid over nitric acid (in agreement with Smith et al., 2014), and a significant enhancement of deposition rates through consideration of deposition onto icy particles. This distinction could be crucial for interpreting the MSL data.

We examined the nitrogen (N) biogeochemistry of adjacent cyclonic and anticyclonic eddies near Hawai’i in the North Pacific Subtropical Gyre (NPSG) and explored mechanisms that may sustain productivity in the cyclone after the initial intensification stage. The top of the nutricline was uplifted into the euphotic zone in the cyclone and depressed in the anticyclone. Subsurface nutrient concentrations and apparent oxygen utilization at the cyclone’s inner periphery were higher than expected from isopycnal displacement, suggesting that shallow remineralization of organic material generated excess nutrients in the subsurface. The excess nutrients may provide a supply of subsurface nutrients to sustain productivity in maturing eddies. The shallow remineralization also raises questions regarding the extent to which cyclonic eddies promote deep carbon sequestration in subtropical gyres such as the NPSG. An upward increase in nitrate 15N/14N isotope ratios below the euphotic zone, indicative of partial nitrate assimilation, coincided with negative preformed nutrients – potentially signaling heterotrophic bacterial consumption of carbon-rich (nitrogen-poor) organic material. The 15N/14N of material collected in shallow sediment traps was significantly higher in the cyclone than the anticyclone and showed correspondence to the 15N/14N ratio of the nitrate supply, which is acutely sensitive to sea level anomaly in the region. A number of approaches were applied to estimate the contribution of N2 fixation to export production; results among approaches were inconsistent, which we attribute to non-steady state conditions during our observation period.