Shoreline change along the Eastern Atlantic shore of Virginia has been studied for the individual barrier islands but not as an integrated system. This study combines the Atlantic shoreline locations for eleven barrier islands obtained from LANDSAT 5, 7, and 8 images. Approximately 250 shoreline locations over a 24-year period from Jan 1990 to Dec 2014 were extracted from the digitized shoreline data at 338 transects. The resulting 338 by 250 matrix was analyzed by the empirical orthogonal function (EOF) technique. The first four principal components (PC) explained 86 percent of the sample variance. Since the data was not detrended, the first PC was the overall trend of the data with a discontinuity in 2004-2005. The 2004-2005 years included storm events and large shoreline changes. PCs 2 to 4 reflect the effects of El Nino events and tropical and non-tropical storms. Eigenvectors 1 to 4 all show the effects of the nine inlets in the island group. Eigenvector (EV) 1 explains 59 percent of the shoreline spatial variance and shows the largest changes at the northern and southern island ends. EVs 2 to 4 reflect the pattern of EV1 but at sequentially smaller percentages of the spatial variance. As a group, the eleven islands are losing ocean side shoreline. The lone exception is Hog Island. Sea level had the strongest correlation with the shoreline loss trend of PC1. The coefficient of determination was 0.41. The NAO and MEI also correlated with PC1 with correlations of determination of 0.05 and 0.12 respectively. These confidence level for the three factors was better than 99 percent. Sea level also correlated with PC3 and PC4. The PCs as a group show that the year intervals 2004-2005 and 2009-2010 had large effects on the shoreline change pattern for the island group. EVs 1 to 4 had the highest range of shoreline change at the island ends indicating the effect the changes of the inlets have on the adjacent islands. The smaller islands as a group had a higher level of eigenvector variance than the other eight islands. Sea level change is the major factor affecting the ocean shorelines of these islands. Continued sea level increase will facilitate loss of ocean shoreline for the barrier islands as a group.

Structural imaging using body-wave energy present in ambient seismic data remains a challenging task, largely because these wave modes are commonly much weaker than surface wave energy. In a number of situations body-wave energy has been extracted successfully; however, (nearly) all successful body-wave extraction and imaging approaches have focused on cross-correlation processing. While this is useful for interferometric purposes, it can also lead to the inclusion of unwanted noise events that dominate the resulting stack, leaving body-wave energy overpowered by the coherent noise. Conversely, wave-equation imaging can be applied directly on non-correlated ambient data that has been preprocessed to mitigate unwanted energy (i.e., surface waves, burst-like and electromechanical noise) to enhance body-wave arrivals. Following this approach, though, requires a significant preprocessing effort on often Terabytes of ambient seismic data, which is expensive and requires automation to be a feasible approach. In this work we outline an automated processing workflow designed to optimize body wave energy from an ambient seismic data set acquired on a large-N array at a mine site near Lalor Lake, Manitoba, Canada. We show that processing ambient seismic data in the recording domain, rather than the cross-correlation domain, allows us to mitigate energy that is inappropriate for body-wave imaging. We first develop a method for window selection that automatically identifies and removes data contaminated by coherent high-energy bursts. We then apply time- and frequency-domain debursting techniques to mitigate the effects of remaining strong amplitude and/or monochromatic energy without severely degrading the overall waveforms. After each processing step we implement a QC check to investigate improvements in the convergence rates - and the emergence of reflection events - in the cross-correlation plus stack waveforms over hour-long windows. Overall, the QC analyses suggest that automated preprocessing of ambient seismic recordings in the recording domain successfully mitigates unwanted coherent noise events in both the time and frequency domain. Accordingly, we assert that this method is beneficial for direct wave-equation imaging with ambient seismic recordings.

Amongst the numerous existing methods to characterize fractured media, previous work has demonstrated that geoelectrical measurements, acquired either along the Earth's surface or in boreholes, may provide important information regarding fracture network properties. However, the lack of numerical approaches adapted to the strong contrast in geometrical and electrical properties between the fractures and the rock matrix prevents us from systematically exploring the links between geoelectrical measurements and fractured rock properties. To address this issue, we present a highly computationally efficient methodology for the numerical simulation of geoelectrical data in 2.5-D complex fractured domains. Our approach is based upon a discrete-dual-porosity formulation, whereby the fractures and rock matrix are treated separately and coupled through the exchange of electric current between them. Our methodology is validated against standard analytical and finite-element solutions and used to simulate geoelectrical data for a variety of different fracture configurations. This results in demonstrating the sensitivity of these data to important parameters such as the fracture density, depth, and orientation, and in opening new perspectives in terms of the inversion of geoelectrical data in order to characterize fractured rocks.

Since many years precession is regarded as an alternative flow driving mechanism that may account, e.g., for remarkable features of the ancient lunar magnetic field [Dwyer 2011; Noir 2013; Weiss 2014] or as a complementary power source for the geodynamo [Malkus 1968; Vanyo 1991]. Precessional forcing is also of great interest from the experimental point of view because it represents a natural forcing mechanism that allows an efficient driving of conducting fluid flows on the laboratory scale without making use of propellers or pumps. Within the project DRESDYN (DREsden Sodium facility for DYNamo and thermohydraulic studies) a dynamo experiment is under development at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in which a precession driven flow of liquid sodium with a magnetic Reynolds number of up to Rm=700 will be used to drive dynamo action. Our present study addresses preparative numerical simulations and flow measurements at a small model experiment running with water. The resulting flow pattern and amplitude provide the essential ingredients for kinematic dynamo models that are used to estimate whether the particular flow is able to drive a dynamo. In the strongly non-linear regime the flow essentially consists of standing inertial waves. Most remarkable feature is the occurrence of a resonant-like axisymmetric mode which emerges around a precession ratio of Ωp/Ωc = 0.1 on top of the directly forced re-circulation flow. The combination of this axisymmetric mode and the forced m=1 Kelvin mode is indeed capable of driving a dynamo at a critical magnetic Reynolds number of Rmc=430 which is well within the range achievable in the experiment. However, the occurrence of the axisymmetric mode slightly depends on the absolute rotation rate of the cylinder and future experiments are required to indicate whether it persists at the extremely large Re that will be obtained in the large scale sodium experiment.

Climate data records typically exhibit considerable variation over short time scales both from natural variability and from instrumentation issues. The use of linear least squares regression can provide overall trend information from noisy data, however assessing intermediate time periods can also provide useful information unavailable from basic trend calculations. Extracting the short term information in these data for assessing changes to climate or for comparison of data series from different sources requires the application of filters to separate short period variations from longer period trends. A common method used to smooth data is the moving average, which is a simple digital filter that can distort the resulting series due to the aliasing of the sampling period into the output series. We utilized Hamming filters to compare MSU/AMSU satellite time series developed by three research groups (UAH, RSS and NOAA STAR), the results published in January 2017 (Swanson 2017).

Seasonal African Dust (AD) transports soluble iron to oligotrophic Caribbean waters, and when bioavailable, it could increase marine primary productivity (PP). Recently, the region has experienced the proliferation of unusually high quantities of Sargassum, an iron-absorbing macroalgae inhabiting the air-sea interface, which possess ecological and economic challenges and whose driving factors are still uncertain. AD events reach Puerto Rico (PR) mostly during boreal summer months. This is also the season when chlorophyll-α (CHL) concentrations are highest, when the algae starts to bloom, and when sediment plumes from the Orinoco River (ORP), also reach nutrient discharge maxima. This study seeks to better understand the temporal relationships between increases in chlorophyll-α (CHL) and the presence of AD events in the region. Aerosol data collected at the Cabezas de San Juan Atmospheric Observatory was used to identify AD events between January 2005 and December 2015. Light scattering coefficients were measured with a integrating Nephelometer, while light absorption coefficients were obtained from either the Particle Soot/Absorption Photometer (PSAP) or the Continuous Light Absorption Photometer (CLAP). Spectral properties suggesting AD events were cross-referenced with surface dust concentration image models and source-attributed air masses corresponding to dusty periods using Hybrid Single-Particle Lagrangian Integrated Trajectories (HYSPLIT). For all years with spectral data, modeled monthly wet dust deposition was correlated (R= 0.64) with mean CHL concentrations from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Daily dust mass column densities from NASA’s MERRA-2 model were also correlated (R^2= 0.53) to sea surface iron concentrations from NASA’s Ocean Biogeochemical Model. We present the 2010 case study, which coincides with the start of the Sargassum bloom and shows CHL peaks occurring a month before ORPs but during the AD season, suggesting the AD role in enhancing PP. Other possible influencing climatic and oceanographic variables could be associated to these observations. Further efforts include spatially linking the Floating Algae Index in satellite imagery to AD concentrations, to better predict harmful algal blooms and inform management.

Tikhonov’s regularization method is the standard technique applied to obtain models of the subsurface conductivity dis- tribution from electric or electromagnetic measurements by solving UT (m) = ||F(m) - d||^2 + P(m): The second term correspond to the stabilizing functional, with P(m) = ||m||^2 the usual approach, and the regularization parameter. Due to the roughness penalizer inclusion, the model developed by Tikhonov’s algorithm tends to smear discontinuities, a feature that may be undesirable. An important requirement for the regularizer is to allow the recovery of edges, and smooth the homogeneous parts. As is well known, Total Variation (TV) is now the standard approach to meet this requirement. Recently, Wang et.al. proved convergence for alternating direction method of multipliers in nonconvex, nonsmooth optimization. In this work we present a study of several algorithms for model recovering of Geosounding data based on Inmal Convolution, and also on hybrid, TV and second order TV and nonsmooth, nonconvex regularizers, observing their performance on synthetic and real data. The algorithms are based on Bregman iteration and Split Bregman method, and the geosounding method is the low-induction numbers magnetic dipoles. Non-smooth regularizers are considered using the Legendre-Fenchel transform.

The American Geophysical Union (AGU) is committed to inspiring and educating present and future generations of diverse, innovative, and creative Earth and space scientists. To meet our commitment, AGU provides career and educational resources, webinars, mentoring, and support for students and professionals at each level of development to reduce barriers to achievement and to promote professional advancement. AGU is also working with other organizations and educational institutions to collaborate on projects benefiting the greater geoscience community. The presentation will include an overview of current Pathfinder efforts, collaborative efforts, and an appeal for additional partnerships.

The natural sound generated by an unmanned aerial vehicle is used in conjunction with tomography to remotely sense atmospheric temperature and wind profiles simultaneously. Sound fields recorded onboard the aircraft and by an array of microphones on the ground are compared and converted to sound speed estimates for the ray paths intersecting the intervening medium. Tomographic inversion is then used to transform these sound speed values into vertical cross-sections and 3D volumes of virtual temperature and wind vectors, which enables the atmosphere to be visualised and monitored over time up to altitudes of 1,200m and over baselines of up to 600m. This paper reports on results from two short campaigns during which 2D and 3D profiles of wind and temperature obtained in this way were compared to: measurements taken by co-located mid-range Doppler SODAR and LIDAR; and temperature measurements made by instruments carried by unmanned aircraft flying through the intervening atmosphere. Large eddy simulation of daytime atmospheric boundary layers were also used to examine the anticipated performance of the instruments and the nature of any errors. The observations obtained using all systems are shown to correspond closely.

Nonmigrating tidal diagnostics of SABER temperature observations in the ionospheric dynamo region reveal a large amount of variability on time-scales of a few days to weeks. In this paper, we discuss the physical reasons for the observed short-term tidal variability using a novel approach based on Information theory and Bayesian statistics. We diagnose short-term tidal variability as a function of season, QBO, ENSO, and solar cycle and other drivers using time dependent probability density functions, Shannon entropy and Kullback-Leibler divergence. The statistical significance of the approach and its predictive capability is exemplified using SABER tidal diagnostics with emphasis on the responses to the QBO and solar cycle. Implications for F-region plasma density will be discussed.

Soil samples (n = 45) and water samples (n = 111) were collected in the coastal zone of SW Bangladesh in wet (November) and dry (May) seasons in 2016 to identify the factors influencing soil arsenic concentrations. Soils are entisols formed from recently deposited, predominantly silt-sized sediments with low carbon concentrations typical of the local mangrove forests. Arsenic concentrations in bulk soil are higher in November than in May and vary little between sites. Arsenic concentrations in deionized H2O extracts are ~2 orders of magnitude lower, indicating only ~1% of As is soluble. Water samples show that As concentrations are highest in groundwater from tubewells. Bulk soil As is positively correlated with As concentration in irrigation water, suggesting that As from irrigation water is added to the soil. Unlike other water types, As in rice paddy water is much higher in the wet season, consistent with some fields being irrigated with tubewell water. Arsenic in rice paddy water increases by soil sulfide dissolution, and decreases by dilution during the monsoon. Water soluble As in rice paddy soils is positively correlated with S and DOC concentrations in rice paddy soil extracts due to sulfide dissolution and complexation with DOC. Thus, waterlogging of rice paddy soils leads to reducing conditions, the absence of ferric oxyhydroxides that could sorb As, and the presence of sulfides that incorporate As. As soil pH increases from 7 to 8, KD(soil/extract) increases, consistent with the observed positive correlation between irrigation water pH and bulk soil As concentration. Arsenic bioavailability could be decreased through soil aeration (draining and tilling) and by avoiding the use of groundwater for rice paddy irrigation.

In this work we extend previous studies by exploring the potential impact of spectroscopic knowledge along with atmospheric state knowledge on retrievals of carbon dioxide (XCO2) and methane (XCH4) column amounts from laser differential absorption spectrometer (LAS) measurements. This has been done for multiple CO2 absorption lines in the 1.57 and 2.05 μm regions, and for CH4 in the 1.65 μm region. One such potential source of error in performing XCO2 retrievals is modeled surface pressure. Since it has been proposed to derive surface pressure from LAS-based O2 measurements in lieu of modeled surface pressure for use in XCO2 retrievals as a means of error reduction, our past work has also attempted to characterize and quantify potential improvements in XCO2 retrieval error associated with O2-derived surface pressure for a set of CO2 and O2 absorption line combinations. All of our previous analyses have relied on a radiative-transfer-based simulation framework utilizing the Line-by-Line Radiative Transfer Model (LBLRTM), version 12.2 (release date November, 2012). LBLRTM has undergone several upgrades since version 12.2, to include updates to its line parameter database, updates to its continuum model, and bug fixes. Our current work revisits our prior assessments using the latest version of LBLRTM (version 12.8) and comparisons are provided and discussed.

During enhanced magnetic activities, large ejections of energetic electrons from radiation belts are deposited in the upper polar atmosphere where they play important roles in its physical and chemical processes, including VLF signals subionospheric propagation. Electron deposition can affect D-Region ionization, which are estimated based on ionization rates derived from energy depositions. We present a model of D-region ion production caused by an arbitrary (in energy and pitch angle) distribution of fast (10 keV - 1 MeV) electrons. The model relies on a set of pre-calculated results obtained using a general Monte Carlo approach with the latest version of the MCNP6 (Monte Carlo N-Particle) code for the explicit electron tracking in magnetic fields. By expressing those results using the ionization yield functions, the pre-calculated results are extended to cover arbitrary magnetic field inclinations and atmospheric density profiles, allowing ionization rate altitude profile computations in the range of 20 and 200 km at any geographic point of interest and date/time by adopting results from an external atmospheric density model (e.g. NRLMSISE-00). The pre-calculated MCNP6 results are stored in a CDF (Common Data Format) file, and IDL routines library is written to provide an end-user interface to the model.

• Nitrogen is the main component of the atmosphere, a key nutrient for organisms, and exerts con- trol over climate via direct warming, as N2O, and indirect effects, including pressure-broadening • Previous models linked N to C, and typically only considered biologic or geologic fluxes in detail, focusing on sedimentary rocks. These studies suggest atmospheric mass steady over Earth history, which does not match new geochemical and physical paleobarometers • New approach is to more completely include biologic and geologic fluxes, and link N behavior to PO4 and O2 abundance through time • This approach is consistent with large-scale changes in atmospheric mass through time, up to 3 present atmospheric masses of nitrogen (1 PAN = 4 × 10^18 kg N) supporting geochemical proxies that indicate atmospheric drawdown through time

Inclusive, multi-stakeholder dialogue, participatory planning and actionable science are necessary for just and effective climate resilience outcomes. How can we support that in practice? The Resilience Dialogues launched a public Beta in 2016-2017 to allow scientists and resilience practitioners to engage with local leaders from 10 communities around the US through a series of facilitated, online dialogues. We developed two, one-week dialogues for each community: one to consider ways to respond to observed and anticipated climate impacts through a resilience lens, and one to identify next steps and resources to advance key priorities. We divided the communities into three cohorts and refined the structure and facilitation strategy for these dialogues from one to the next based on participant feedback. This adaptive method helped participants engage in the dialogues more effectively and develop useful results. We distributed a survey to all participants following each cohort to capture feedback on the use and utility of the dialogues. While there was room for improvement in the program’s technical interface, survey participants valued the dialogues and the opportunity to engage as equals. Local leaders said the dialogues helped identify new local pathways to approach resilience priorities. They felt they benefited from focused conversation and personalized introductions to best-matched resources. Practitioners learned how local leaders seek to apply climate science, and how to effectively communicate their expertise to community leaders in support of local planning efforts. We learned there is demand for specialized dialogues on issues like communication, financing and extreme weather. Overall, the desire of participants to continue to engage through this program, and others to enter, indicates that facilitated, open conversations between experts and local leaders can break down communication and access barriers between climate services providers and end-users. This presentation will share lessons learned from this process and methods that we found most effective.

Climate change is a problem that involves science, economics, and politics. Particularly in the United States, political resistance to addressing climate change has been exacerbated by a concerted misinformation campaign against the basic science, a negative response to how the proposed solutions to climate change intersect with values. Scientists often propose more climate science education as a solution to the problem, but preliminary studies indicate that more science education does not necessarily reduce polarization on the topic (Kahan et al. 2012). Is there a way that we can better engage non-science students in topics related to climate change that improve their comprehension of the problem and its implications, overcoming polarization? In an existing political science course, “Do You Want to Build a Nation?”, we are testing a new digital world-building model based on resource development and consequent environmental and societal impacts. Students spend half the class building their nations based on their assigned ideology (i.e., socialist, absolute monarchy, libertarian) and the second half of the class negotiating with other nations to resolve global issues while remaining true to their ideologies. The course instructor, co-author Lennon, and ASU’s Center for Education Through eXploration have collaborated to design a digital world model based on resources linked to an adaptive decision-making environment that translates student policies into modifications to the digital world. The model tracks students’ exploration and justification of their nation’s policy choices. In the Fall 2017 offering of the course, we will investigate how this digital world model and scenarios built around it affect student learning outcomes. Specifically, we anticipate improved understanding of the policy trade-offs related to energy development, better understanding of the ways that different ideologies approach solutions to climate change, and that both will result in more realistic diplomatic negotiations in the latter half of the course. We will report on the technical details of how the digital world model and scenarios are constructed as well as how students responded to the scenario.