Four transform faults and three intra-transform segments located at Equatorial Atlantic form the Saint Paul Transform System (SPTS), with a long-offset of 630 km. In the northern transform, the 200 km long and 30 km wide Atoba Ridge is a major topographic feature that reaches the sea level at the St. Peter and St. Paul Archipelago island (SPSPA, 00º 55 ‘0’ ‘N and 29º 20 ”43”W). The islets have an average uplift rate of approximately 1.5 mm/year. The southern and northern flanks of the Atobá Ridge are marked by a series of large thrust faults visible in the bathymetry and clearly imaged through seismics and correspond to an exceptionally serpentinized mantle. We have determined the hypocentral location of 62 minor-moderate earthquakes of SPTS, with magnitudes 1.9 ≥ M ≤ 5.3. The earthquakes occurred in 2013 and were recorded by a seismometer installed in SPSPA and three autonomous hydrophones deployed during the COLMEIA cruise. The HYPOCENTER software and Seismic Analysis Code (SAC) were used for data analysis and hypocenter location. The depth range is from 0.2 to 17.5 km and are concentrated in three different zones: the East Shear Zone (ESZ), the Atobá Ridge Zone (ARZ) and the Central Fracture Zone (CFZ). A seismogenic zone with a deep britle-ductile transition was identified in SPTS, with hypocenters reaching 18 km beneath the seafloor. We observed that this lithospheric structure presents relation with the offset age and controls the maximum hypocentral depths of oceanic transform faults. Besides, the earthquakes indicated the existence of a broad serpentinization depth reaching 18 km beneath the ARZ. This was interpreted as the effect of deep water percolation into the mantle in the SPTS, which caused a fluid-mantelic rocks interaction and allowed the expansion of faults into the mantle. Some hypocenters were located in the central fracture zone (CFZ) segment of SPTS and their depths reached 8.8 km beneath the seafloor. We interpreted this seismicity as reactivation of a weakness zone existent in CFZ due to the transpressive load-induced stress originated in Atobá Ridge.

From devastating monsoons to sea level rise, extreme weather is taking its toll across the globe. Surging Waters looks at flooding in the United States and demonstrates how science is supporting flood management, as well as furthering the solutions needed to mitigate flood impacts on people and property in the future. The report's authors highlight three types of flooding---flooding due to hurricanes, flooding in the central U.S., and coastal flooding---through local stories. In 2017, Houston, Texas, was hit by Hurricane Harvey, the second most damaging weather disaster in U.S. history, and is still recovering. The city of De Soto, Mo., is emblematic of many areas in the Midwest that have been plagued by recurrent flash flooding. The Hampton Roads area of coastal Virginia has fallen victim to sinking land and rising seas. Through these stories and others, and compelling flood data presented for regions across the United States, the report shows how scientific research and data collection are essential to finding modern-day and future solutions to mitigate flooding. Robust funding for science-related federal agencies drives the advancement of science and provides support that is critical for the most vulnerable communities and individuals. Surging Waters, produced by AGU, a global not-for-profit scientific society dedicated to advancing the Earth and space sciences for the benefit of humanity, recommends actions to build a more resilient and sustainable society. Reviewed by leading experts on the issues of flooding and extreme weather, this report aims to empower community members and leaders, scientists, federal agencies, and policymakers to work together to establish a stronger future for us all.

A number of interdependent conditions and processes contribute to ionospheric-origin energetic ion outflows. Due to these interdependences and the associated observational challenges, energetic ion outflows remain a poorly understood facet of atmosphere-ionosphere-magnetosphere coupling. Here we demonstrate the relationship between east-west magnetic field fluctuations ($\Delta B_{\textrm{EW}}$) and energetic outflows in the magnetosphere-ionosphere transition region. We use dayside cusp-region FAST satellite observations made at apogee ($\sim$4200-km altitude) near fall equinox and solstices in both hemispheres to derive statistical relationships between ion upflow and ($\Delta B_{\textrm{EW}}$) spectral power as a function of spacecraft-frame frequency bands between 0 and 4 Hz. Identification of ionospheric-origin energetic ion upflows is automated, and the spectral power $P_{EW}$ in each frequency band is obtained via integration of $\Delta B_{\textrm{EW}}$ power spectral density. Derived relationships are of the form $J_{\parallel,i} = J_{0,i} P_{EW}^\gamma$ for upward ion flux $J_{\parallel,i}$ at 130-km altitude. The highest correlation coefficients are obtained for spacecraft-frame frequencies $\sim$0.1–0.5 Hz. Summer solstice and fall equinox observations yield power law indices $\gamma \simeq$ 0.9–1.3 and correlation coefficients $r \geq 0.92$, while winter solstice observations yield $\gamma \simeq$ 0.4–0.8 with $r \gtrsim 0.8$. Mass spectrometer observations reveal that the oxygen/hydrogen ion composition ratio near summer solstice is much greater than the corresponding ratio near winter. These results thus reinforce the importance of ion composition in any outflow model. If observed $\Delta B_{\textrm{EW}}$ variations are purely spatial and not temporal, we show that spacecraft-frame frequencies $\sim$0.1–0.5 Hz correspond to perpendicular spatial scales of several to tens of kilometers.

We present a book entitled “Machine Learning, Statistics, and Data Mining for Heliophysics,” an online and open source book available at helioml.org. This book includes a collection of interactive Jupyter notebooks, written in the programming language Python, that walks the reader through the process of applying machine learning, statistics, and data mining techniques on various kinds of solar and space physics data sets to reproduce published results. We consider this book to be a living document with frequent updates. Please contact us if you’d like to submit a chapter!

Using meteor radar, radiosonde and digisonde observations and MERRA-2 reanalysis data from 12 August to 31 October 2006, we report a dynamical coupling from the tropical lower atmosphere to the mesosphere and ionospheric F2 region through a quasi 27-day intraseasonal oscillation (ISO). It is interesting that the quasi 27-day ISO is active in the troposphere and stratopause and mesopause regions, exhibiting a three-layer structure. In the mesosphere and lower thermosphere (MLT), the amplitude in the zonal wind increases from about 4 ms at 90 km to 15 ms at 100 km, which is different from previous observations that ISOs generally have the amplitude peak at about 80-85 km, and then weakens with height. OLR and specific humidity data demonstrate that there is a quasi 27-day periodicity in convective activity in the tropics, which causes the ISO of the zonal wind and gravity wave (GW) activity in the troposphere. GW energy in the stratosphere also exhibits a sharp spectral speak at 27-day period, meaning that the convectively modulated GWs play a vital role in driving the oscillation in the MLT. The quasi 27-day variability arises clearly in the hmF2. Wavelet analysis shows that the dominant period and active time of the hmF2 oscillation are in good agreement with those in the zonal wind of the MLT and OLR rather than in the F10.7 and Kp index. Hence, tropical convective activity has an influence on the dynamics of the MLT and F2 region through modulated waves and ISOs.

VLF spectrograms registered at Kannuslehto ground station, after cleaning them from strong sferics, reveal VLF noise suppression by whistlers and whistler echo trains, which consists in significant reduction in the noise spectral power after a strong whistler event. We have found similar effect in the VLF data from Van Allen Probe B taken in the equatorial region on L-shell ~ 3. Detailed analysis of the data shows that the whistler echo train as well as the VLF noise have small wave normal angles. Based on this observation, we limit our analysis to parallel (ducted) whistler wave propagation. The persistence of whistler echo train as well as the VLF noise suggests that in the events under discussion, plasma is unstable in the frequency range corresponding to the observed VLF noise band. In an attempt to explain the effect of VLF noise suppression, we follow up the long-standing idea that relates this effect to the reduction of free energy in the unstable plasma distribution by whistler echo train. To develop this idea into qualitative model, we have studied the motion of energetic electrons, responsible for the noise generation, in the field of ducted whistler echo train. We show that energetic electrons that make the main contribution to the growth rate of VLF noise, during their bounce oscillations in the magnetosphere are subject to multiple resonant impacts from the whistler echo train. These lead to energetic electron diffusion in the phase space, and the corresponding reduction in free energy of the unstable distribution.

In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network-based models capture the large-scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non-existent, as occurs during geomagnetic storms, the performance of NNs significantly decreases, as networks inherently cannot learn from the limited number of examples. This limitation can be overcome by employing physics-based modeling during strong geomagnetic storms. Physics-based models show a stable performance during periods of disturbed geomagnetic activity, if they are correctly initialized and configured. In this study, we illustrate how to combine the neural network- and physics-based models of the plasmasphere in an optimal way by using the data assimilation Kalman filtering. The proposed approach utilizes advantages of both neural network- and physics-based modeling and produces global plasma density reconstructions for both quiet and disturbed geomagnetic activity, including extreme geomagnetic storms. We validate the models quantitatively by comparing their output to the in-situ density measurements from RBSP-A for an 18-month out-of-sample period from 30 June 2016 to 01 January 2018, and computing performance metrics. To validate the global density reconstructions qualitatively, we compare them to the IMAGE EUV images of the He+ particle distribution in the Earth’s plasmasphere for a number of events in the past, including the Halloween storm in 2003.

We compare the long-term suprathermal heavy ion composition measured at three planets using functionally identical charge-energy-mass ion spectrometers, one on Geotail, orbiting Earth at ~9-30 Re, the other on Cassini, in interplanetary space, during Jupiter flyby, and then in orbit around Saturn. O+, a principal suprathermal (~80-220 keV/e) heavy ion in each magnetosphere, derives primarily from outflowing ionospheric O+ at Earth, but mostly from satellites and rings at Jupiter and Saturn. Comparable amounts of Iogenic O+ and S+ are present at Jupiter. Ions escaping the magnetospheres are: O+ and S+ at Jupiter; C+, N+, O+, H2O+, CO+(N2+), and O2+ at Saturn; and N+, O+, N2+, NO+, O2+, and Fe+ at Earth. Generally, escaped atomic ions (molecular ions, MI) at Earth and Saturn have similar (higher) ratios to O+ compared to their magnetospheric ratios; Saturn’s H2O+ and Fe+ ratios are lower. At Earth: after O+ and N+, ionospheric origin N2+, NO+, and O2+ (with proportions ~0.9:1.0:0.2) dominate magnetospheric heavy ions, consistent with recent high-altitude/latitude ionospheric measurements and models; average ion count rates correlate positively with geomagnetic and solar activity. At ~27-33 amu/e: Earth’s MIs dominate over lunar pickup ions (PUIs) in the magnetosphere; MIs are roughly comparable to lunar PUIs in the magnetosheath; and lunar PUIs dominate over MIs beyond Earth’s bow shock. Lunar PUIs are detected at ~39-48 amu/e in the lobe and possibly in the plasma sheet at very low levels.

Ground-clutter is a major cause of large detection and underestimation errors in satellite-based (e.g. Global Precipitation Measurement Dual Polarization Radar, GPM DPR) precipitation radar retrievals in complex terrain. Here, an Artificial Intelligence (AI) framework consisting of sequential precipitation detection and vertical structure prediction algorithms is proposed to mitigate these errors using machine learning techniques to uncover predictive associations among satellite- and ground-based measurements aided by Numerical Weather Prediction model analysis, specifically the High-Resolution Rapid Refresh (HRRR) model. The framework is implemented and tested for quantitative estimation of orographic precipitation in the Southern Appalachian Mountains (SAM). Precipitation detection relies on a Random Forest Classifier to identify rainfall based on GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and HRRR mixing ratios in the lower troposphere (~ 1.5 km above ground level). The vertical structure of precipitation prediction algorithm is a Convolution Neural Network trained to learn associations among GPM DPR Ku-band reflectivity profiles, GMI Tbs, and orographic precipitation regimes in the SAM including low level light rainfall, shallow rainfall with low-level enhancement, stratiform rainfall with bright band, and deep heavy rainfall with low- and mid-level enhancement. Vertical structure classes corresponding to the distinct orographic precipitation regimes were isolated through k-means clustering of ground-based Multi-Radar/Multi-Sensor radar reflectivity profiles. The AI framework is demonstrated for automatic retrieval of warm season precipitation in the SAM over a 3-year period (2016-2019) achieving large reductions in false alarms (77%) and missed detections (82%) relative to GPM Ku-PR precipitation products, and significant rain-rate corrections (up to one order of magnitude) by using a physically-based model to capture the microphysics of low-level enhancement (i.e. seeder-feeder interactions).

Large earthquakes transfer stress from the shallow lithosphere to the underlying viscoelastic lower crust and upper mantle, inducing transient creep during the postseismic interval. Recent experiments on olivine have provided a new rheological model for this transient creep based on accumulation and release of back stresses among dislocations. Here, we test whether natural rocks preserve dislocation-induced stress heterogeneity consistent with the back-stress hypothesis by mapping olivine from the palaeosubduction interface of the Oman-UAE ophiolite with high-angular resolution electron backscatter diffraction. The olivine preserves heterogeneous residual stresses that vary in magnitude by several hundred megapascals over length scales of a few micrometres. Large stresses are commonly spatially associated with elevated densities of geometrically necessary dislocations within subgrain interiors. These spatial relationships, along with characteristic probability distributions of the stresses, confirm that the stress heterogeneity is generated by the dislocations and records their long-range elastic interactions. Images of dislocations decorated by oxidation display bands of high and low dislocation density, suggesting that dislocation interactions contributed to organisation of the substructure. These results support the applicability of the back-stress model of transient creep to deformation in the mantle portion of plate-boundary shear zones. The model predicts that rapid stress changes, such as those imposed by large earthquakes, can induce order-of-magnitude changes in viscosity that depend nonlinearly on the stress change, consistent with inferences of mantle rheology from geodetic observations.

Understanding the dynamic evolution of relativistic electrons in the Earth’s radiation belts during both storm and non-storm times is a challenging task. The U.S. National Science Foundation’s Geospace Environment Modeling (GEM) focus group “Quantitative Assessment of Radiation Belt Modeling” (QARBM) has selected two storm time and two non-storm time events that occurred during the second year of the Van Allen Probes mission for in-depth study. Here, we perform simulations for these GEM challenge events using the 3-Dimensional Versatile Electron Radiation Belt (VERB-3D) code. We set up the outer $L^*$ boundary using data from Geostationary Operational Environmental Satellites (GOES) and validate the simulation results against satellite observations from both the GOES and Van Allen Probe missions for 0.9 MeV electrons. Our results show that the position of the plasmapause plays a significant role in the dynamic evolution of relativistic electrons. The magnetopause shadowing effect is included by using last closed drift shell (LCDS), and it is shown to significantly contribute to the dropouts of relativistic electrons at high $L^*$.

The solar corona, the outer atmosphere of the Sun, is heated to millions of degrees. This is several orders of magnitude hotter than the photosphere, the optical surface of the Sun, below, and a mystery that has baffled scientists for centuries. The answer to the question of how the solar corona is heated lies in the crucial magnetic connection through the atmosphere of the Sun. The magnetic field that threads the corona extends below the solar photosphere, where the convective motions drag the magnetic field footpoints, tangling and twisting them. The chromosphere is the atmospheric layer above the photosphere, below the corona, and the magnetic field provides an important connection between these layers. The exchange of mass and energy between the chromosphere and corona is an essential piece of this puzzle. The connection between the chromosphere and the corona is a challenging piece of the puzzle both observationally and computationally, as it is highly complex in space and time. We describe the history of the observations and theoretical understanding of the heating of the solar atmosphere, and end with future prospects of the coronal heating problem.

The ecosystem function of vegetation to attenuate riverine export of nutrients is of substantial importance for securing water quality. This ecosystem function is at risk of deterioration due to an increasing risk of large-scale forest dieback under climate change. The present study explores the response of the nitrogen (N) cycle of a forest catchment in the Bavarian Forest National Park, Germany, in the face of a severe bark beetle () outbreak and resulting large-scale forest dieback. Outbreaks of bark beetle killed the dominant tree species Norway spruce () in up to 55 % of the area. A Bayesian hierarchical model that predicts stream NO concentration (C) with discharge (Q) and water temperature (T) as predictors (C-Q-T relationship) was found as the best fitting model. This informed top-down development of a catchment model to explain the C-Q-T relationship so that the annually-varying model parameter estimates provide mechanistic interpretations of the catchment processes. NO concentration increased after the dieback because N was released from the decaying fine litter of trees beyond the capacity of the terrestrial vegetation and riparian zone to regulate the nutrient export. Within a decade after the dieback, the released N was flushed out and nutrient retention capacity was restored with the regrowth of the vegetation. Greater understanding of canopy mortality due to climate change and other anthropogenic impacts are required to mitigate the deterioration of nutrient retention and prevent nutrient loss.

The inevitable coarse pixels (~25 km) of satellite passive microwave images introduced large uncertainty to the surface melt area estimation on Antarctic ice margins. Our test showed that the melt index of the Austral year 2012-13 in the Antarctic Peninsula calculated from the high resolution product was 33% lower than the original Special Sensor Microwave/Imager (SSM/I) images. Therefore, by allowing for fractional melt estimation, a subpixel mapping method was adopted in this research to improve the accuracy and reliability of surface melt measurement from passive microwave images. This innovative method uses the least squares mixture analysis (LSMA) on the time series of daily passive microwave images by taking advantage of their high temporal resolution. The endmembers for the unmixing calculation were collected under the constraint of voronoi polygons. The fractional melt index of each pixel was calculated by multiplying its area with melt fraction. By using the high resolution passive microwave earth system data record (PMESDR) dataset as the reference, we found that compared with the original SSM/I images, the overestimation of surface melt was corrected by the unmixing analysis. A log-linear regression between melt fraction and elevation showed that the melt fraction is inversely correlated to the elevation, and the topography is the dominant factor for melt fraction distribution in high elevations. We recommend such a treatment of linear unmixing analysis on the passive microwave images to be used for future surface melt mapping in Antarctica and Greenland.

In every tight formation reservoir, natural fractures play an important role for mass and energy transport and stress distribution. Enhanced Geothermal Systems (EGS) make no exception and stimulation aims at increasing the reservoir permeability to enhance fluid circulation and heat transport. EGS development relies upon the complex task of predicting accurate hydraulic fracture propagation pathway by taking into account reservoir heterogeneities and natural or pre-existing fractures. In this contribution, we employ the variational phase-field method which handles hydraulic fracture initiation, propagation and interaction with natural fractures and is tested under varying conditions of rock mechanical properties and natural fractures distributions. We run bi-dimensional finite element simulations employing the open-source software OpenGeoSys and apply the model to simulate realistic stimulation scenarios, each one built from field data and considering complex natural fracture geometries in the order of a thousand of fractures. Key mechanical properties are derived from laboratory measurements on samples obtained in the field. Simulations results confirm the fundamental role played by natural fractures in stimulation’s predictions, which is essential for developing successful EGS projects.

Rice fields are important contributors to the global methane budget. While rice production must increase to meet the demand from a growing population, there is a need to mitigate methane emissions through the employment of sustainable management strategies. A popular one consists of a short drainage that shortens the period of time spent in anaerobic conditions, but it is still unclear how drainage timing affects its overall effectiveness. Here we introduce a mechanistic model of methane emissions, coupled to the dynamics of redox potential and rice growth, to analyze the effect of a single drainage and the timing of its application on the temporal evolution of methane emissions. In particular, we identify the drainage timing that maximizes the mitigation efficiency of the drainage, defined as the reduction in methane emission relative to the emissions in continuous flooding. We also explore the role of organic amendment and show how it changes the optimal drainage timing. Application of this framework to a set of experiments demonstrates that emissions can be reduced by more than 60% if drainage is applied at optimal timing. The high efficiency and the limited negative effects on rice yields and N0 emissions place this water management among the most sustainable ones.

The Atlantic’s Meridional Overturning Circulation (AMOC) is weak when the northern summer insolation is strong and vice-versa in the 23-kyr precessional frequency band. The mechanism behind this response is unknown and does not conform in any obvious way to Milankovitch theory. The link between the AMOC and precession is attributed here to the trade winds that blow out from Africa across the Atlantic. The trade winds, like the AMOC itself, are weak when the northern summer insolation is strong. The trade winds are shown here to have a direct impact on the AMOC’s return flow. A return flow altered this way should warm and cool the Earth in a way that has not been previously considered.

Tephra fallout hazard assessment is commonly undertaken with the development of probabilistic maps that rely on numerical models. Among the steps for map production, the definition of input parameters of the model (including atmospheric conditions), the physical approximations of the numerical simulations, and the probabilities of occurrence of different eruption types in specific time frames are among the most critical sources of uncertainty. In this paper, we present a tephra fallout hazard assessment study for two volcanoes (Cotopaxi and Guagua Pichincha) in Ecuador. We utilize the coupled PLUME-MoM/HYSPLIT models, and we develop a procedure for uncertainty quantification where: i) we quantify the uncertainty on eruptive source parameters and eruption type occurrence through expert elicitation; ii) we implement a new procedure for correlations between the different parameters, and iii) we quantify the uncertainty of the numerical model by testing it with past eruptions and by deriving coefficients of mean model overestimation/underestimation. Probability maps of exceedance, given a deposit thickness threshold, and thickness maps, given a probability of exceedence, are produced for eruption of sub-Plinian and Plinian types, which are then merged into single maps concerning the next eruption. These are described according to the uncertainty distribution of eruption type occurrence probabilities, in terms of their 5th percentile, mean and 95th percentile values. We finally present hazard curves describing exceeding probabilities in 10 sensitive sites within the city of Quito. Additional information includes the areal extent and the people potentially affected by different isolines of tephra accumulation.