Classic surface wave imaging relies mainly on the dispersion of Rayleigh waves or Love waves. In addition to these types of waves, waveforms that arrive prior to the S wave also exhibit dispersion. These early dispersed waves are controlled mainly by the leaking modes and are rarely used for imaging. We applied the frequency-Bessel transform method to waveforms that arrive before the S wave and successfully extracted multiple leaking mode dispersion curves. The extracted “0th” mode shows continuous sensitivity to S-wave velocity (Vs) and mass density structure whereas the “higher” modes behave more like surface waves that are sensitive to P-wave velocity (Vp) structure in the crust. Thus, the “higher” modes can be used to invert the Vp structure, which can compensate for the fact that conventional surface wave imaging methods are mainly sensitive to Vs structure.

Large igneous provinces (LIPs) represent some of the largest volcanic events in Earth history with significant impacts on the ecosystem, including mass extinctions. However, there are some fundamental questions related to the eruption rate, eruption style, and vent locations for LIP lava flows that remain unanswered. In this review, we use the Cretaceous-Paleogene Deccan Traps as an archetype to address these questions since it is one of the best-preserved large continental flood basalt provinces. We describe the volcanological features of the Deccan flows and their potential temporal and regional variations as well as the spatial characteristics of potential feeder dikes. Along with estimates of eruption rates for Deccan lavas from paleomagnetism and Hg proxy records, the Deccan volcanic characteristics suggest a unified conceptual model for the eruption of voluminous (> 1000 km$^3$) LIP lavas with large spatial extent (> 40,000 km$^2$). We conclude the review by highlighting a few key open questions and challenges that can help improve our understanding of how Deccan, as well as LIP flows in general, erupt and flow over long distances.

Over the last nearly five decades, optical remote sensing has played a key role in monitoring and quantifying global change, plant diversity, and vegetation functioning across Earth’s terrestrial biomes. As a key tool for researchers, land managers, and policy makers, optical remote sensing facilitates scaling, mapping, and characterizing surface properties over large areas and through time. In addition, steady technological improvements have led to transformational changes in our ability to understand ecosystem state and change, particularly through the expansion of high spectral resolution (i.e. spectroscopic) remote sensing platforms. Point and imaging spectroscopy systems have been used across a range of scales, vegetation types, and biomes to infer plant diversity, leaf traits, and ecosystem functioning. However, despite the acknowledged utility of spectroscopic systems, data availability has been limited to smaller geographic regions given a number of technical challenges, including issues related to data volume and limited spatial coverage by previous Earth Observing (EO) missions (i.e. Hyperion). The NASA Surface Biology and Geology (SBG) mission is designed to fill this gap in ecosystem monitoring. As part of the Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) and Modeling end-to-end traceability (MEET) SBG efforts, we used field, unoccupied aerial system (UAS), and airborne imagery (from NASA’s AVIRIS-NG plafrom) to evaluate the impacts of proposed and theoretical sensor instrument properties on the retrieval of vegetation reflectance across tundra, shrub, and treeline ecosystems in Alaska. Existing observations and open-source tools are used for the simulation of surface reflectance under a range of atmospheric conditions, vegetation types, and different sensor properties. We find that retrieval uncertainty is reduced across all surface types with increasing detector signal-to-noise (SNR) but also key differences across different plant types. Results were also strongly tied to sun-sensor geometry and atmospheric state. Through this exercise we highlight key outcomes to consider for the SBG mission to optimize surface reflectance retrieval in high latitudes that will help to minimize errors in down-stream algorithms, such as functional trait retrievals.

Continuous seismograms contain a wealth of information with a large variety of signals with different origins. Identifying these signals is a crucial step in understanding physical geological objects. We propose a strategy to identify classes of seismic signals in continuous single-station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an in- dependent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two-day-long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics. At a low level in the cluster hierarchy, we obtain three clusters related to anthropogenic and ambient seismic noise and one cluster related to earthquake activity. At a high level in the cluster hierarchy, we identify a seismic crisis that includes more than 200 repeating events and high-frequent signals with correlated envelopes and an anthropogenic origin. The application shows that the cluster hierarchy helps to identify particular families of signals and to extract subclusters for further analysis. This is valuable when certain types of signals, such as earthquakes, are under-represented in the data. The proposed method may also successfully discover new types of signals since it is entirely data-driven.

Most machine learning applications in Earth system modeling currently rely on gradient-based supervised learning. This imposes stringent constraints on the nature of the data used for training (typically, residual time tendencies are needed), and it complicates learning about the interactions between machine-learned parameterizations and other components of an Earth system model. Approaching learning about process-based parameterizations as an inverse problem resolves many of these issues, since it allows parameterizations to be trained with partial observations or statistics that directly relate to quantities of interest in long-term climate projections. Here we demonstrate the effectiveness of Kalman inversion methods in treating learning about parameterizations as an inverse problem. We consider two different algorithms: unscented and ensemble Kalman inversion. Both methods involve highly parallelizable forward model evaluations, converge exponentially fast, and do not require gradient computations. In addition, unscented Kalman inversion provides a measure of parameter uncertainty. We illustrate how training parameterizations can be posed as a regularized inverse problem and solved by ensemble Kalman methods through the calibration of an eddy-diffusivity mass-flux scheme for subgrid-scale turbulence and convection, using data generated by large-eddy simulations. We find the algorithms amenable to batching strategies, robust to noise and model failures, and efficient in the calibration of hybrid parameterizations that can include empirical closures and neural networks.

A linear theory of the generalized Rayleigh-Taylor instability (GRTI) is derived which includes ion inertia and acceleration forces, as well as E region drivers: the zonal neutral wind and plasma drift. This is in contrast to the F region drivers aside from gravity: the meridional neutral wind and the meridional/vertical plasma drifts. Both a local theory and a flux-tube integrated theory are presented with application to the onset of ionosphere irregularities associated with equatorial spread F. Inertia and acceleration forces do not affect the growth rate of the GRTI for nominal ionospheric conditions, but the E region zonal drifts can significantly increase or decrease the growth rate of the GRTI in the equatorial and mid-latitude ionosphere depending on their direction.

Predicting Pacific Decadal Oscillation (PDO) transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability, the PDO is associated with both large-scale climate shifts and regional climate predictability. We show that artificial neural networks (ANNs) predict PDO persistence and transitions from 12 months onward. Using layer-wise relevance propagation to investigate the ANN predictions, we demonstrate that the ANNs utilize oceanic patterns that have been previously linked to predictable PDO behavior. For PDO transitions, ANNs recognize a build-up of ocean heat content in the off-equatorial western Pacific 12-27 months before a transition occurs. The results support the continued use of ANNs in climate studies where explainability tools can assist in mechanistic understanding of the climate system.

Modelling of the land surface water-, energy-, and carbon balance provides insight into the behaviour of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large-scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyse the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS-1D was simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in-depth analysis of the soil SHPs and derived soil characteristics was performed to analyse why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time-integrated behaviour of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby et al. (1984) for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter-comparison studies to avoid artefacts originating from the choice of PTF rather from different model structures.

We explore the use of three advanced statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of three tropical rain types (deep convective, stratiform, and shallow convective) observed by the radar onboard the GPM satellite over the West Pacific at three-hourly, 0.5-degree resolution. Temperature and moisture profiles from MERRA-2 were used as predictors. All three methods perform reasonably well at predicting the occurrence and rain rate distribution of each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models.

Terrestrial ecosystems of Canada store a large amount of organic carbon (C) in soils, peats and plant materials, yet little is known about the C stock size and distributions, both spatially and in various C pools. As temperature rises, C is becoming available for disturbance, decomposition and eventual release into the atmosphere, which makes the quantification of C stocks in terrestrial ecosystems of Canada of high interest for the assessment of climate change impacts and conservation efforts. We used a large number of field measurements, multisource satellite, climate and topographic data and a machine learning algorithm to produce the first wall-to-wall estimates of C stocks and uncertainties in plants and soils of Canada at 250 m spatial resolution. Our findings show that above and belowground live biomass and detritus store a total of 21.1 Pg C. Whereas the Canadian soils store 384 (±214, 90% confidence interval) Pg organic C in the top 1 m, 92 Pg C of which are stored in peatlands, confirming that the soil organic C dominates terrestrial carbon stocks in Canada. We also find previously under-reported large soil organic C stocks in forested peatlands on the boreal shields of Canada. Given that Canada is warming twice the global average rate and Canadian soils store approximately 25% of world soil C stocks in top 1 m, initiatives to understand their vulnerabilities to climate change and disturbance are indispensable not only for Canada but also for the global C budget and cycle.

Changes in the circulation of the Southern Ocean are known to have impacted global nutrient, heat, and carbon cycles during the glacial and interglacial periods of the late Pleistocene. Proxy-based records of these changes deserve continued scrutiny as the implications may be important for constraining future change. A record of authigenic uranium from the South Atlantic has been used to infer changes in deep-sea oxygenation and organic matter export over the past 0.5 million years. Since sedimentary uranium has the possible complication of remobilization, it is prudent to investigate the behavior of other redox-sensitive trace metals to confidently interpret temporal changes in oxygenation. Focusing here on the exceptionally long interglacial warm period, Marine Isotope Stage (MIS) 11, we found concurrent authigenic enrichments of uranium and rhenium throughout MIS 12 to 10, overall supporting prior interpretations of low-oxygen periods. However, there are differential responses of Re and U to oxygen changes and some evidence of small-scale Re remobilization, which may involve differences in molecular-level reduction mechanisms. Peaks in authigenic manganese intervening with peaks in Re and U indicate increases in porewater oxygenation which likely relate to increased Antarctic Bottom Water circulation at the onset of MIS11c and during the peak warmth of the interglacial around 400 ka.

Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Ionospheric measurements of Co-Seismic Disturbances (CIDs) offer a unique solution to characterize an earthquake’s tsunami potential in Near-Real-Time (NRT) since CIDs can be detected within 15 min of a seismic event. However, the detection of CIDs relies on human experts, which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we designed a machine-learning based framework to (1) classify ionospheric waveforms into CIDs and noise, (2) pick CID arrival times, and (3) associate arrivals across a satellite network in NRT. Machine-learning models (random forests) trained over an extensive ionospheric waveform dataset show excellent classification and arrival-time picking performances compared to existing detection procedures, which paves the way for the NRT imaging of surface displacements from the ionosphere.

The Mexican Array Radio Telescope (MEXART) is a transit instrument mainly dedicated to performing Interplanetary Scintillation (IPS) observations with a central operating frequency of 139.65 MHz. The main scientific objective is to perform studies of solar wind properties and space weather effects. MEXART initially operated with an analog beamformer (16x16 Butler matrix), which produced 16 fixed latitudinal beams. MEXART began operations and reported the first measurements of IPS sources. MEXART’s beamforming system had several problems, however. The North-South beams had poor directivity, with large side lobes, and the instrument did not achieve the expected performance. Therefore, we commissioned the design and construction of a digital back-end. The digital system solved the problems with the beamforming, increased the bandwidth, and improved significantly the instrument’s sensitivity. In this paper, we present the first light of MEXART’s digital system. We describe the new technical capabilities of the instrument, and we show some preliminary results: an estimation of the radio telescope’s sensitivity ($\Delta S_{min} = 2.28 \pm 0.23$ Jy), the transit of the Galaxy at 140 MHz with the simultaneous tracking of 62 latitudinal beams, and an example of an IPS observation and the single-station methodology to calculate the solar wind speed. The new technical capabilities of the radio telescope will provide the potential to participate in several scientific studies. These include solar wind properties, space weather forecasting, ionospheric perturbations, and astrophysical aims such as monitoring of repeating Fast Radio Bursts (FRBs), and pulsars’ observations.

Recent experiments systematically explore rock friction under crustal earthquake conditions revealing that faults undergo abrupt dynamic weakening. Processes related to heating and weakening of fault surface have been invoked to explain pronounced velocity weakening. Both contact asperity temperature $T_a$ and background temperature $T$ of the slip zone evolve significantly during high velocity slip due to heat sources (frictional work), heat sinks (e.g. latent heat of decomposition processes) and diffusion. Using carefully calibrated High Velocity Rotary Friction experiments, we test the compatibility of thermal weakening models: (1) a model of friction based only on $T$ in an extremely simplified, Arrhenius-like thermal dependence; (2) a flash heating model which accounts for evolution of both $V$ and $T$; (3) same but including heat sinks in the thermal balance; (4) same but including the thermal dependence of diffusivity and heat capacity. All models reflect the experimental results but model (1) results in unrealistically low temperatures and models (2) reproduces the restrengthening phase only by modifying the parameters for each experimental condition. The presence of dissipative heat sinks in (3) significantly affects $T$ and reflects on the friction, allowing a better joint fit of the initial weakening and final strength recovery across a range of experiments. Temperature is significantly altered by thermal dependence of (4). However, similar results can be obtained by (3) and (4) by adjusting the energy sinks. To compute temperature in this type of problem we compare the efficiency of three different numerical solutions (Finite differences, wavenumber summation, and discrete integral).

Excess nitrogen (N) from anthropogenic sources deteriorates freshwater resources. Actions taken to reduce N inputs to the biosphere often show no or only delayed effects in receiving surface waters hinting at large legacy N stores built up in the catchments soils and groundwater. Here, we quantify transport and retention of N in 238 Western European catchments by analyzing a unique data set of long-term N input and output time series. We find that half of the catchments exhibited peak transport times larger than five years with longer times being evident in catchments with high potential evapotranspiration and low precipitation seasonality. On average the catchments retained 72% of the N from diffuse sources with retention efficiency being specifically high in catchments with low discharge and thick, unconsolidated aquifers. The estimated transport time scales do not explain the observed N retention, suggesting a dominant role of biogeochemical legacy in the catchments’ soils rather than a legacy store in the groundwater. Future water quality management should account for the accumulated biogeochemical N legacy to avoid long-term leaching and water quality deteriorations for decades to come.

Cross-correlations of ambient seismic noise are widely used for seismic velocity imaging, monitoring, and ground motion analyses. A typical step in analyzing Noise Cross-correlation Functions (NCFs) is stacking short-term NCFs over longer time periods to increase the signal quality. Spurious NCFs could contaminate the stack, degrade its quality, and limit its use. Many methods have been developed to improve the stacking of coherent waveforms, including earthquake waveforms, receiver functions, and NCFs. This study systematically evaluates and compares the performance of eight stacking methods, including arithmetic mean or linear stacking, robust stacking, selective stacking, cluster stacking, phase-weighted stacking, time-frequency phase-weighted stacking, $N^{th}$-root stacking, and averaging after applying an adaptive covariance filter. Our results demonstrate that, in most cases, all methods can retrieve clear ballistic or first arrivals. However, they yield significant differences in preserving the phase and amplitude information. This study provides a practical guide for choosing the optimal stacking method for specific research applications in ambient noise seismology. We evaluate the performance using multiple onshore and offshore seismic arrays in the Pacific Northwest region. We compare these stacking methods for NCFs calculated from raw ambient noise (referred to as Raw NCFs) and from ambient noise normalized using a one-bit clipping time normalization method (referred to as One-bit NCFs). We evaluate six metrics, including signal-to-noise ratios, phase dispersion images, convergence rate, temporal changes in the ballistic and coda waves, relative amplitude decays with distance, and computational time. We show that robust stacking is the best choice for all applications (velocity tomography, monitoring, and attenuation studies) using Raw NCFs. For applications using One-bit NCFs, all methods but phase-weighted, time-frequency phase-weighted, and $N^{th}$-root stacking are good choices for seismic velocity tomography. Linear, robust, and selective stacking methods are all equally appropriate choices when using One-bit NCFs for monitoring applications. For applications relying on accurate relative amplitudes, both the robust and cluster stacking methods perform well with One-bit NCFs. The evaluations in this study can be generalized to a broad range of time-series analysis that utilizes data coherence to perform ensemble stacking. Another contribution of this study is the accompanying open-source software, which can be used for general purposes in time-series stacking.

Monsoon rainfall proxy records show clear millennial variations corresponding to abrupt climate events in Greenland ice cores during Marine Isotope Stage 3 (MIS3). The occurrence of these abrupt climate changes is associated with Atlantic Meridio-nal Overturning Circulation (AMOC) strength variations which greatly impact the global oceanic energy transport. Hence, the AMOC most likely plays a key role in modulating the global monsoon rainfall at millennial time scale. No modeling work has hitherto investigated the global monsoon system response to AMOC changes under a MIS3 background climate. Using a coupled climate model CCSM3, we simu-lated MIS3 climate using true 38 ka before present boundary conditions and per-formed a set of freshwater hosing/extraction experiments. We show not only agreement between modeling results and proxies of monsoon rainfall within global monsoon domain but also highlights a nonlinear relationship between AMOC strength and annual mean global monsoon precipitation related to oceanic heat transport constraints. During MIS3, a weakened AMOC could lead to an increase of annual mean global monsoon rainfall dominated by the southern hemisphere, whereas northern hemisphere monsoon rainfall decreases. Above about 16 Sverdrups a further strengthening of the AMOC has no significant impact on hemi-spheric and global monsoon domain annual mean rainfall. The seasonal monsoon rainfall showed same asymmetric response like annual mean both hemispherical and globally.

We present a new spectral analysis method for the identification of periodic signals in geophysical time series. We evaluate the power spectral density with the adaptive multitaper method, a non-parametric spectral analysis technique suitable for time series characterized by colored power spectral density. Our method provides a maximum likelihood estimation of the power spectral density background according to four different models. It includes the option for the models to be fitted on four smoothed versions of the power spectral density when there is a need to reduce the influence of power enhancements due to periodic signals. We use a statistical criterion to select the best background representation among the different smoothing+model pairs. Then, we define the confidence thresholds to identify the power spectral density enhancements related to the occurrence of periodic fluctuations (γ test). We combine the results with those obtained with the multitaper harmonic F test, an additional complex-valued regression analysis from which it is possible to estimate the amplitude and phase of the signals. We demonstrate the algorithm on Monte Carlo simulations of synthetic time series and a case study of magnetospheric field fluctuations directly driven by periodic density structures in the solar wind. The method is robust and flexible. Our procedure is freely available as a stand-alone IDL code at https://zenodo.org/record/3703168. The modular structure of our methodology allows the introduction of new smoothing methods and models to cover additional types of time series. The flexibility and extensibility of the technique makes it broadly suitable to any discipline.