Recent research has demonstrated the existence of a new type of solar event, the “terminator”. Unlike the Sun’s signature events, flares and Coronal Mass Ejections, the terminator takes place in the solar interior. The terminator signals the end of a magnetic activity cycle at the Sun’s equator and the start of a sunspot cycle at mid latitudes. Observations indicate that the time difference between these events is very short, less than a solar rotation, in the context of the sunspot cycle. As the (definitive) start and end point of solar activity cycles the precise timing of terminators should permit new investigations into the meteorology of our star’s atmosphere. In this letter we use a standard method in signal processing, the Hilbert transform, to identify a mathematically robust signature of terminators in sunspot records and in radiative proxies. Using this technique we can achieve higher fidelity terminator timing than previous estimates have permitted. Further, this method presents a unique opportunity to project when the next terminator will occur, 2020.33(±0.16), and trigger the growth of sunspot cycle 25. We also will use this method to show why Cycle 23 was unusually long, why the Cycle 23-24 minimum was unusually quiet, and why neither of these occurrences will happen with the end of Cycle 24. Ignoring the wealth of observational evidence and viewing the solar activity cycle as merely the growth and decay of sunspot number is one “social constraint that hampers progress” to be overcome.

Jovian moist convection has been the study of both observational and modelling attempts for several decades. In the Pioneer and Voyager era, plumes of volatiles were observed to erupt from the deep atmosphere regularly, prompting the question of the strength of the internal heat flux within Jupiter’s atmosphere. Later, Galileo observed towering convective storms coinciding with the presence of lightning. Analysis of cloud formation on Jupiter considering the abundances of various condensible species revealed that the most likely source of these convective events was the deep water cloud which contains both the high density of volatiles and necessary convective potential to breach the upper cloud deck. In this study, we use the Explicit Planetary Isentropic Coordinate (EPIC) atmospheric 3-dimensional general circulation model (GCM) to study the formation of Jovian moist convective events, using an active cloud microphysics scheme. We focus on the region centered on the 24° N jet where plume formation has been observed several times. We initiate cloud formation assuming different initial deep abundance values of both water and ammonia to test the sensitivity on the strength of plume formation and the buildup of convective potential energy (CAPE). We find that convective activity is affected by the thermal properties of the environment – the jet and the North Equatorial Belt are conducive of convection while the North North Tropical Zone is not.

Miocene-Pliocene strata in the Pacific Northwest preserve a rich record of landscape evolution and coincident faunal shifts. Considerable research efforts in the past century have been aimed at understanding major drainage reorganization and its relation to tectonics, volcanism, climate change, and aquatic biota. Many studies have focused on fish fossils, which show that Miocene fish diversity, particularly salmonoids, displays great adaptive plasticity. However, the details and mechanisms for river reorganization are still debated. Here we present new and recent detrital zircon provenance results from modern and ancestral river sands collected throughout Oregon, Washington, and Idaho. We synthesize our new results and interpretations with existing paleontological evidence for basin isolation and drainage capture. Detrital zircons from the Columbia Basin (CB) consistently show populations derived from the Snake River Plain (SRP) throughout late Miocene-Pliocene time. However, comparisons of Miocene-Pliocene detrital zircons from the CB to modern major rivers and tributaries in the CB and SPR show that the upstream eastern SRP is a major contributor. CB strata do not require zircons sourced from the western SRP, where Pliocene Lake Idaho existed in a large, deep, and occasionally internally drained basin. Based on the age and provenance results, we suggest that the transiting Yellowstone Hotspot divided the modern SRP into two basins: the western basin was isolated and possibly closed, while the eastern basin drained northward into the modern Clark Fork and Columbia Rivers. This scenario is consistent with fish, mollusk, and rodent fossil evidence from the SRP and CB. In addition, the detrital zircon data indicate a Miocene confluence of the Columbia and Clearwater rivers south of the Saddle Mountains anticline, but north of the current Columbia-Snake River confluence. We also find that the Salmon River may have been captured by the Clearwater River sometime between 4.6 and 8.5 Ma. Prior to this time, the Salmon River likely drained into the SRP. Lastly, we find that faunal localities in southern Oregon suggested to contain evidence for fluvial connection between the western SRP and California are ~2.5 Ma, younger than the incision of Hells Canyon.

Deterministic Numerical Weather Prediction (NWP) models are the state-of-the-science models to provide reliable weather forecasts that provide indispensable actionable data to society. However, each NWP model run carry uncertainty caused by errors in initial conditions and assumptions in the model. As a result, probabilistic forecasts can be used to quantify and even correct the model bias. The Analog Ensemble (AnEn) technique uses a historical dataset of past deterministic forecasts and their associated observations to generate an ensemble of future outcomes. One of the main advantages of the AnEn technique, along with other related statistical ensemble techniques, is that it is not necessary to run multiple NWP runs by varying initial conditions or model settings. However, all these techniques require access to the entire historical dataset to generate analogs. Moreover, these techniques require reading the dataset for every forecast which is computationally expensive. In this work, the whole historical dataset is replaced by a model that has the capability to learn the Probability Density Function (PDF) of that dataset. Specifically, we utilize a Conditional Variational Autoencoder (CVAE) deep generative machine learning model in order to correct the wind speed forecasts of North American Mesoscale (NAM) forecasting system. As a result, we feed the values forecasted by the NWP model as a condition to our CVAE and generate an ensemble used to correct the forecasted value in constant time and with small memory usage. Initial results show that CVAE probabilistic performance is comparable to AnEn while CVAE can be up to 25 and 2 times smaller in memory and runtime, respectively, for 5 years of historical data.

Local base-level change drives the transition of river morphology from one steady state to the new one. Although extracting tectonic information from river steepness assumes steady state, presence of knickpoints often complicates the stream profile analysis. This study aims to investigate the transient process of channel adjustment to tectonic perturbation using channel width, width to depth ratio, and normalized steepness index (θref=0.45). I focus on rivers along a normal fault in Iwaki, Fukushima, the eastern part of Japan. This fault ruptured on 11 April 2011, and the maximum vertical displacement was ~0.8 m. Paleoseismic trenches indicated that three surface-rupturing observed three types of rivers: one which is away from the fault and at old steady state, one which flows across the fault and at transient state characterized by a slope-break knickpoint, and one which flows across the fault and does not include any prominent knickpoints thus at new steady state. For a river at old steady state, channel width (W) increases with drainage area (A) (W~3.51A0.44), and width to depth ratio and steepness index are almost constant over the reach. For a river at transient state, width and width to depth ratio decrease at downstream of a knickpoint, and steepness index increases by a factor of two to four. For a river at new steady state, width, width to depth ratio, and steepness index show no discernible changes. Preliminary interpretation is that channel narrowing occurs after a channel starts steepening, and that channel width to depth ratio drops when a river is subjected to active deformation and becomes constant over an entire reach. Combining with ongoing field measurements, I will discuss the transient response of a river to tectonics.

At regional scales, electrical resistivity illuminates Earth processes involving fluid evolution and transport, temperature contrasts, and fault characteristics and behavior. It also clarifies continental terrane assembly and event sequencing through electronic mineral markers. Magnetotellurics (MT) is sensitive over such scales, but faces high property contrasts, small signals, 3-D complexity, discontinuous fields, and ill-posed inversion. So-called wideband (~0.003 – 500 s wave period) MT recording constrains crustal structure, and high fidelity through its central dead band is routinely achieved now via distant remote referencing, continuous streaming, and outlier removal. To resolve across the upper mantle, long period data must be of high quality through 10,000 s. Electronics modifications now permit good quality MT data over polar ice-covered regions, and non plane wave outliers appear largely avoidable. Regularized 3D non-linear inversion using simulation equations that recognize a spatially discontinuous electric field has become common practice and lends essential credibility to interpretations. However, resistivity model non-uniqueness is seldom tested enough, and assuming isotropic resistivity can lead to artifacts. Fluids interpreted to cause low resistivity in ductile deep crust should be at lithostatic pressures and have compositions compatible with ambient temperature and metamorphic grade. Vertical current channeling enhances resolution of large-scale fault zones connecting deep and shallow structures. Stabilized terranes can exhibit strong, quasi-linear conductors marking belts of graphite or sulfides deposited in sediment-starved foredeeps or rift margin basins, with a particular concentration in the Proterozoic corresponding to atmospheric oxygenation events. An exciting recent avenue is estimating H2O content of nominally anhydrous minerals (NAMs) in the upper mantle, which strongly affects electrical conductivity but not seismic velocity. The large bandwidth of MT data affords a broad-scale, unified view of Earth processes from mantle level sources through crustal storage and evolution to near-surface deposition. Support has been from U.S. Dept of Energy contract DE-0006732 and National Science Foundation grant OPP-1443532, and numerous prior.

The system of total water use control index (TWUCI) has been established in China since 2011. However, current TWUCI is static all year round, making a poor adaptation to dynamic inflow and a deviation between water plans and actuals. To this end, an innovative methodology, namely “forecast-decision-implementation-renewal” (FDIR), is proposed to carry out the real-time determination of the TWUCI. Streamflow prediction is first used to determine adaptive TWUCI, and then the optimal water allocation model considering both the TWUCI and ecological demand is developed to make a decision-making of water supply system. The updated information at next stage will be fed back to the model, obtaining new water supply plan. By applying the method by scrolling over periods, real-time decisions for water transfer and water release could be derived. An application with the FDIR is successfully implemented in the Fuhe River Basin of eastern Jiangxi Province, China. The results show that the TWUCI in the remaining period increases/decreases when the inflow scenario becomes drier/wetter. With dynamic operating decisions, the water shortage ratio in the water-use area declines by 3.8%~25.3% when inflow scenario(IS) changes from wet to dry, while the actual water supply is reduced to the TWUCI when dry IS turns to wet. In this way, The FDIR provides a dynamic operating decision that guarantee the water consumption in the remaining period always within the TWUCI, and realizes flexible management for water use. It is expected to be useful for socio-water adaptation to future climate changes towards a sustainable regional development.

Energetic ash-producing volcanic eruptions are driven by the diffusive and decompressive growth of bubbles (mostly water) during ascent in a magma conduit. The spatial distribution of bubble nucleation sites is one of the primary controls on ash-forming fragmentation. However, the initial formation of bubbles in a supersaturated magma is problematical, especially for homogeneous nucleation. Excessive surface tension pressure should preclude the existence of small bubbles, because exsolved water is driven back into the melt. This is the “tiny bubble paradox.” We suggest that—under special circumstances—the tiny bubble paradox may be circumvented by spinodal decomposition, a process in which uphill diffusion enables spontaneous unmixing of phases to reduce the free energy of the system. As spinodal decomposition progresses, three dimensional, quasi-spherical, zones of water-rich magma develop. These zones are characterized by an increasingly high concentration of dissolved water at the centers and reducing concentration at the margins. Bubbles are born when the concentration of water in the interior of the water-rich zones goes to 100% and the concentration of melt goes to zero. The small, nascent, bubbles that emerge will be buffered from melt by water-rich shells with increasing melt concentration away from newly formed bubbles. This diffuse concentration gradient of water means that there is no surface, per se, for surface tension to arise. This is the crux of the solution of the tiny bubble paradox. Particle morphology may be used to distinguish ash with spinodal origins from ash associated with typical (metastable) bubble nucleation. Spinodal decomposition occurs at a wavelength determined by the pressure, temperature, and viscosity of the magmatic system. This wavelength should create bubbles of uniform size and bubble walls of equal strength in a fragmenting magmatic foam, leading to sharply mono-modal vesicle and ash particle size distributions. Classical bubble nucleation should create more-variable bubble sizes and bubble wall strengths, leading to a broader particle size distribution. Better understanding the mechanism of bubble formation in magmatic systems will, in turn, enable better understanding of hazardous, explosive, eruptions.

The digital zenith camera VESTA (VErtical by STArs) was designed by the Institute of Geodesy and Geoinformatics (GGI) of the University of Latvia and completed in 2016. Since that more than 300 terrestrial vertical deflection measurements were observed in the territory of Latvia. These observations were post-processed by the GGI developed software and the accuracy was evaluated at 0.1 arc seconds. Terrestrial observations were compared with global geopotential models, e.g. GGM+ and EGM2008. The results show a better correspondence with GGM+ model by evaluating the standard deviation: 0.314 and 0.307 arcseconds for ξ and η components respectively in comparison to 0.346 and 0.358 arcseconds for ξ and η components for EGM2008 model. The comparisons of average and minimum/maximum differences are introduced in this study for better evaluation of the results. Moreover, vertical deflections have been used as additional terrestrial data in DFHRS (Digital Finite-element Height Reference Surface) software v. 4.3 in combination with GNSS/levelling data (B, L, h|H) and global geopotential model EGM2008 for gravity field and quasi-geoid improvement (www.dfhbf.de). The results of the computed quasi-geoid models using different types of data are introduced in this research, representing several solutions, as well as these solutions are compared with the national quasi-geoid model LV’14. In the middle of 2019, the new upgraded version of digital zenith camera was developed by the scientific staff and the accuracy of the measurements of improved camera was evaluated at 0.05 arcseconds, which is two times better than previous one. The improvements of new digital zenith camera are also discussed in this research. It is important to point out that according to our observations the application of digital zenith camera reveals a new capabilities for studies of mass distribution beneath earth.

The generalized solution for the radiance equation is expanded by exploiting multiple hyperspectral image scans acquired by aerial platforms at different viewing angles. A machine learning solution based on convolutional neural networks is used to learn the relationships between the total radiance observed at the sensor, and different atmospheric components of the radiance equation. The goal is to precisely characterize the atmosphere, in order to properly solve the radiance equation, in which atmospheric components constitute important input. Traditionally, these atmospheric components are only estimated from averages of pixels, or assigned using heuristics tables. Compared to traditional image spectroscopy, this expanded radiance equation and machine learning solution integrates quantitative mathematical modeling, multiple scanned hyperspectral images and artificial intelligence. The solution is able to model and predict the transmittance, downwelling and upwelling components of the radiance equation with increased spatial and temporal dimensionality. It’s promising to use different combinations of the multiple scans to parameterize the radiance equation and improve the target detection in varying atmospheric conditions, where current solutions based on a single hyperspectral image normally fail. This works presents initial results of an expanded mathematical solution, along with the results from the convolution networks. Synthetic data were generated using the MODTRAN atmospheric software to simulate different vintage points, atmospheric models, time of the day and year, for an array of specific targets with varying reflectances. More specifically, MODTRAN was used to simulate Longwave Infrared Red between 7.5 and 12 microns with a 17.5 nanometers spectral sensitivity, which correspond to the range and resolution of the Blue Heron Longwave Hyperspectral sensor. Results from the convolutional neural network indicate our machine learning solution is computationally faster than the traditional radiative transfer (RT) model and is able to characterize the impact of varying atmospheric conditions on the at-sensor radiance components.

The present space-based solar observing facilities, such as the Helioseismic and Magnetic Imager (HMI) instrument aboard the Solar Dynamics Observatory (SDO) satellite, can obtain time series photospheric vector magnetograms of solar active regions with high spatial and temporal resolution. The time evolution of the photospheric vector magnetic fields can be traced continuously for nearly all active regions appearing on the solar disk except for the marginal area near the solar limb. This facilitating condition is a huge benefit to the solar flare forecasting. By numerical modeling of the coronal magnetic fields based on the sophisticated nonlinear force-free field model from the observed photospheric vector magnetograms, the time series data of the coronal magnetic fields corresponding to the time series photospheric vector magnetograms can also be obtained. Then, the numerical analysis can be performed on the coronal magnetic field data, and the time evolution of the internal coronal magnetic structures as well as the evolution of the nonpotentiality in solar active regions can be revealed. The deduced quantitative measures of the coronal magnetic fields from the numerical analysis, such as the electric current density, the force-free factor, and the magnetic energy density, can be utilized in the solar flare forecasting and for establishing the solar flare prediction model. Samples of electric current analysis are presented. Through the numerical analysis of the time series magnetic fields in solar active regions, it is expected to predict flare locations, flare classes, and timing of flares.

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. (Hodyss et al 2013, 2014) demonstrated that a correction term to various deterministic numerical schemes, known in stochastic analysis as the Itô correction, can help improve solution accuracy and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. It is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical simulation.

Phosphorus is a critical nutrient in sustaining food production. In agricultural systems, application of P fertilizers has significantly increased since the green revolution to become common practice globally, contributing to increased productivity. However, excess use of P fertilizer does not only pose a cost to farmers, but costs for society in the form of water quality problems and environmental degradation. Furthermore, rock phosphates from which these fertilizers are derived are a finite resource, which brings into question the long-term sustainability of this resource and the food production it supports. Soils play a critical role in hosting the P cycle, and organic forms of P (monoesters, diesters) often represent a significant portion of soil P stocks, that are so often overlooked. The mineralization of organic P by phosphatase enzymes is recognized as a key mechanism for converting organic to inorganic forms, which can then be potentially used for P uptake by plants. However, quantification of their contribution still remains a significant challenge. In order to sustainably meet growing food production demands over the next century and reduce the impacts of P fertilizers on waterways, there is a need to understand the extent that soil organic P is available, or can be made available for plants. Here, we present recent findings from a soil-plant biogeochemical model of integrated carbon-nitrogen-phosphorus cycling in agricultural environments. Comparison of observational yield data taken from various long-term experimental sites with model simulations indicate a gap in current scientific understanding of P sources. Whilst yields in the experiments are maintained under low P addition conditions, the model indicates yield declines due to exhaustion of available P resources. We use the model to explore the missing links: potential P inputs, processes and pathways. Finally, we discuss the need for additional empirical evidence to support understanding of organic P cycling, and development of models to include these processes to inform future land management and ensure long-term food security and sustainable water resources.

During recent droughts in North Carolina, decision makers have articulated needs for drought information, including current or anticipated impacts, geographic extent and timing, and how and who monitors drought in the state. This is despite the abundance of online information and data that can answer these questions. Feedback from sectors heavily impacted by drought in the state - namely, agriculture, forestry, and water resources - suggests that information users perceive existing drought information as unavailable, inaccessible, or insufficient to meet their needs. We are collaborating on a project to increase the accessibility of existing information through the development of resources that resonate with water resource managers, extension agents, and other decision makers in North Carolina, are discoverable through their preferred communication channels, and help them and their constituents better understand and respond to drought events. We identified and prioritized new information resource ideas through an initial needs assessment conducted via surveys and webinars with stakeholders in target sectors. We then developed and refined prototypes (e.g., infographics, factsheets, story maps) through multiple rounds of feedback, which included focus group discussions and eye tracking studies. Key findings of the project include (1) scientific information must be translated into less-technical terms to be useful, but users must be able to connect to the original source of the information; (2) decision makers want current conditions placed into a longer temporal context that includes the recent past (trends over past weeks to months) and anticipated changes to conditions in the future (forecasts with lead times of days to months); and (3) despite the increasing popularity of social media as a way to share information, more traditional formats (e.g., local broadcast and news media, websites) are still valuable to decision makers. In this presentation, we will describe project outcomes, including our process for engaging with decision makers to co-produce new drought information resources and strategies for creating and disseminating these to ensure that they will last beyond the project’s end date.

We apply deep learning to a synthetic near-surface hydrological response dataset of 4.4 million infiltration scenarios to determine conditions for the onset of positive pore-water pressures. This provides a rapid assessment of hydrologic conditions of potentially hazardous hillslopes where mass wasting is prevalent, and sidesteps the computationally expensive process of solving complex, highly non-linear equations. Each scenario considers antecedent soil moisture and storm depth with varying soil properties based on those measured at a USGS site in the East Bay Hills, CA, USA. Our model combines antecedent soil wetness and storm conditions with soil-hydraulic properties and predicts a binary output of whether or not positive pore pressures were generated. After parameterization, pore-water pressure conditions can be returned for any combination of antecedent soil moisture content and storm depth values. Similar to previous work, a deep learning model reduces computational cost: processing time is decreased by more than an order of magnitude for 1D simulated infiltration scenarios while maintaining high levels of accuracy. While the physical relevance and utility behind process-based numerical modeling cannot be replaced, the comparatively reduced computational cost of deep learning allows for rapid modeling of pore-water pressure conditions where solving complex, highly non-linear equations would otherwise be required. Furthermore, comparing the solution of a deep learning model with a hydrological model exemplifies how similar results can be produced through highly divergent mathematical relationships. This provides a unique opportunity to understand which variables are most relevant for the prediction of positive pore-water pressures on hillslopes, and can represent landslide-relevant hydrologic conditions for hillslopes where rapid analysis is imperative for informing potential hazard mitigation efforts. Ultimately, a calibrated deep learning model may reduce the need for computationally expensive physics-based modeling, which are often time and resource intensive, while providing critical statistical insight for the onset of hazardous conditions in landslide-prone areas.

Many studies have shown that the characteristics of lightning such as size and peak current differ between ocean and continental thunderstorms. In general, the lightning in oceanic thunderstorms are larger and have higher peak current than in continental thunderstorms. The reason for these differences have been stipulated to be related to differences in thermal properties and aerosol concentration, however, there is still disagreement over which is the dominant mechanism. In this study, we focus on how thunderstorm trends, as opposed to lightning trends (i.e., flash density), are affected by these mechanisms. We develop a lightning clustering algorithm that takes individual lightning strokes and creates thunderstorms based on their spatiotemporal proximity. We use lightning data from the Earth Networks Total Lightning Network and compare storms from the Eastern U.S.A. to storms off that coast, where the detection efficiency of the network is still good. Once these thunderstorms are obtained, we can split them into ocean and land thunderstorms and compare various characteristics (size, duration, flash rate, polarity and IC/CG ratio, etc.) to determine if any differences stand out. In this presentation, we will discuss the clustering algorithm used, analyze the results of the study, and discuss implications.

Bayesian methods play a prominent role in parameter estimation and uncertainty quantification. In a typical application of Bayes theorem, a prior distribution over the parameters is updated through a likelihood function to obtain the posterior distribution. In the absence of any prior knowledge, a non-informative prior is chosen to express lack of any preference by assigning a uniform distribution over the possible ranges of parameters. However, the validity of uniform priors as being truly non-informative is seldom questioned. The objective of this study is to test this assumption while estimating soil saturated hydraulic conductivity using data from infiltration experiments. The concept of a non-informative prior using an information theoretic approach is pursued for this application, and the results compared to those obtained from assignment of a uniform prior. Non-informative priors obtained by the information theoretic approach are different from a uniform prior, and estimates of the posterior distribution are influenced by the choice of the prior, especially when data are limited. Examples from both hypothetical and real data are utilized to highlight the importance of selecting truly non-informative priors.

The Dobi graben is a NW-trending, Quaternary continental rift found within the East-Central Block (ECB) of the Afar Depression (AD) in northeastern Ethiopia. Extension occurs on steeply dipping faults, where the ratio of maximum displacement to traced fault length extends to four orders of magnitude. We conducted fault population analysis in the Dobi graben using a 30 m resolution Shuttle Rader Topography Mission (SRTM) Digital Elevation Model (DEM). We traced a total of 953 faults. We used the fault displacement length profiles’ tapering directions and the different types of fault propagations’ termination styles to characterize the fault kinematics pattern. Our population analysis results show that ~45% of the normal faults in the Dobi graben are tapering towards the southeast in a manner similar to the Red Sea Rift (RSR) regional strain gradient. On the other hand, our analysis showed that ~40% of the faults in the Dobi graben are tapering towards the northwest direction in a manner similar to the Gulf of Aden Rift (GAR) regional strain gradient. We found the statistical regression correlation coefficients (R-square value) for both the southeast and the northwest tapering faults to be ~0.7. Therefore, we suggest that ~85% of the lateral propagation of the 953 faults in the Dobi graben is highly influenced by the regional strain transfer of the RSR and GAR. Additionally, based on the fault propagation termination styles, our faults population analysis shows that faults which exhibit the half-restricted termination style account for 85% of the 953 faults. The maximum displacement over the maximum length (Dmax/Lmax) ratio of these faults is 0.03, which is in accord with the constant displacement length fault growth model.