Over the past three decades, large tracts of tropical forests have been converted to crop and pasturelands across southern Amazonia, largely to meet the increasing worldwide demand for protein. As the world’s population continue to grow and consume more protein per capita, forest conversion to grow more crops could be a potential solution to meet such demand. However, widespread deforestation is expected to negatively affect crop productivity via multiple pathways (e.g., thermal regulation, rainfall, local moisture, pest control, among others). To quantify how deforestation affects crop productivity, we modeled the relationship between forest amount and enhanced vegetation index (EVI—a proxy for crop productivity) during the soybean planting season across southern Amazonia. Our hypothesis that forest amount causes increased crop productivity received strong support. We found that the maximum MODIS-based EVI in soybean fields increased as a function of forest amount across three spatial-scales, 0.5 km, 1 km, 2 km, 5 km, 10 km, 15 km and 20 km. However, the strength of this relationship varied across years and with precipitation, but only at the local scale (e.g., 500 meters and 1 km radius). Our results highlight the importance of considering forests to design sustainable landscapes.

Hydrological distributed modeling is a key point for a comprehensive assessment of the feedback between the dynamics of the hydrological cycle, climate conditions, and land use. Such modeling results are markedly relevant in the fields of water resources management. Here TopModel (TOPography based hydrological MODEL) is employed for the hydrological modeling of an area in the Middle Magdalena Valley (MMV), a tropical basin located in Colombia. This study is located in the intertropical convergence zone and is characterized by special meteorological conditions, with fast water fluxes over the year. It has been subject to significant land use changes, as a result of intense economic activities, i.e., agriculture, energy and oil & gas production. The model employees a record of 12 years of: • Daily precipitation database from observed gauges • Daily evapotranspiration database from temperature data • Streamflow database as observed data from calibration Calibration is performed using data from 2000 to 2008, and validation is performed with data from 2009 to 2012. The Nash-Sutcliffe coefficient is used to assess the robustness of our calibration process.(values of this metric being 0.62 and 0.53, respectively for model calibration and validation). The results reveal high water storage capacity in the soil, and a marked subsurface runoff, consistent with the characteristics of the soil types in the regions. The calibrated model provides relevant indications about recharge in the region, which is important to quantify the interaction between surface water and groundwater, especially during the dry season, which is more relevant in climate-change and climate-variability scenarios.

Microbial transformation and decomposition of organic matter in sediments constitutes one of the largest fluxes of carbon in marine environments. Mineralization of sedimentary organic matter by microorganisms results in selective degradation such that bioavailable or accessible compounds are rapidly metabolized while more recalcitrant, complex compounds are preserved and buried in sediment (Mahmoudi et al., 2017). Recent studies have found that the ability to use different carbon sources appears to vary among microorganisms, suggesting that the availability of certain pools of carbon can be specific to the taxa that utilize the pool. This implies that organic matter mineralization in marine environments may depend on the metabolic potential of the microbial populations that are present and active. The goal of our study was to investigate the extent to which organic matter availability and transformation may be species-specific using sediment from Guaymas Basin (Gulf of California). We carried out time-series incubations using bacterial isolates and sterilized sediment in the IsoCaRB system (Beaupre et al., 2016) which allowed us to measure the production rates and natural isotopic signatures (δ13C and Δ14C) of microbially-respired CO2. Separate incubations using two different marine bacterial isolates (Vibrio sp. and Pseudoalteromonas sp.) and sterilized Guaymas Basin sediment under oxic conditions showed that the rate and total quantity of organic matter metabolized by these two species differs. Approximately twice as much CO2 was collected during the Vibrio sp. incubation compared to the Pseudoalteromonas sp. incubation. Moreover, the rate at which organic matter was metabolized by the Vibrio sp. was much higher than the Pseudoalteromonas sp. indicating the intrinsic availability of organic matter in sediments may depend on the species that is present and active. Isotopic analyses of microbially respired CO2 will be used to constrain the type and age of organic matter that is accessible to each species. Moreover, molecular analysis of subsamples collected from each incubation will link carbon utilization with the underlying gene expression. Our study sheds light on the degree to which the metabolic capacities of microorganisms affect carbon transformation in sedimentary environments.

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.

Scientific research is increasingly collaborative and globally distributed; research groups now rely on web- based scientific tools and data management systems to simplify their day-to-day collaborative workflows. However, such tools often lack seamless interfaces, requiring researchers to contend with manual data transfers, annotation and sharing. MyGeoHub is a web platform that supports out-of-the-box, seamless workflows involving data ingestion, metadata extraction, analysis, sharing and publication. MyGeoHub is built on the HUBzero cyberinfrastructure platform and adds general-purpose software building blocks (GABBs), for geospatial data management, visualization and analysis. A data management building block iData, processes geospatial files, extracting metadata for keyword and map-based search while enabling quick previews. iData is pervasive, allowing access through a web interface, scientific tools on MyGeoHub or even mobile field devices via a data service API. GABBs includes a Python map library as well as map widgets that in a few lines of code, generate complete geospatial visualization web interfaces for scientific tools. GABBs also includes powerful tools that can be used with no programming effort. The GeoBuilder tool provides an intuitive wizard for importing multi-variable, geo-located time series data (typical of sensor readings, GPS trackers) to build visualizations supporting data filtering and plotting. MyGeoHub has been used in tutorials at scientific conferences and educational activities for K-12 students. MyGeoHub is also constantly evolving; the recent addition of Jupyter and R Shiny notebook environments enable reproducible, richly interactive geospatial analyses and applications ranging from simple pre- processing to published tools. MyGeoHub is not a monolithic geospatial science gateway, instead it supports diverse needs ranging from just a feature-rich data management system, to complex scientific tools and workflows.

Accurately estimating the net seasonal snow accumulation (or “winter balance”) on glaciers is central to assessing glacier health and predicting glacier runoff. However, measuring and modeling snow distribution is inherently difficult in mountainous terrain, resulting in high uncertainties in estimates of winter balance. Our work focuses on uncertainty attribution within the process of converting direct measurements of snow depth and density to estimates of winter balance. We collected more than 9000 direct measurements of snow depth across three glaciers in the St. Elias Mountains, Yukon, Canada in May 2016. Linear regression (LR) and simple kriging (SK), combined with cross correlation and Bayesian model averaging, are used to interpolate estimates of snow water equivalent (SWE) from snow depth and density measurements. Snow distribution patterns are found to differ considerably between glaciers, highlighting strong inter- and intra-basin variability. Elevation is found to be the dominant control of the spatial distribution of SWE, but the relationship varies considerably between glaciers. A simple parameterization of wind redistribution is also a small but statistically significant predictor of SWE. The SWE estimated for one study glacier has a short range parameter (90 m) and both LR and SK estimate a winter balance of ~0.6 m w.e. but are poor predictors of SWE at measurement locations. The other two glaciers have longer SWE range parameters (~450 m) and due to differences in extrapolation, SK estimates are more than 0.1 m w.e. (up to 40%) lower than LR estimates. By using a Monte Carlo method to quantify the effects of various sources of uncertainty, we find that the interpolation of estimated values of SWE is a larger source of uncertainty than the assignment of snow density or than the representation of the SWE value within a terrain model grid cell. For our study glaciers, the total winter balance uncertainty ranges from 0.03 (8%) to 0.15 (54%) m w.e. depending primarily on the interpolation method. Despite the challenges associated with accurately and precisely estimating winter balance, our results are consistent with the previously reported regional accumulation gradient.

The Weather Research and Forecasting with Chemistry (WRF-Chem) community model have been widely used for the study of pollutants transport, formation of secondary pollutants, as well as for the assessment of air quality policies implementation. A key factor to improve the WRF-Chem air quality simulations over urban areas is the representation of anthropogenic emission sources. There are several tools that are available to assist users in creating their own emissions based on global emissions information (e.g. anthro_emiss, prep_chem_src); however, there is no single tool that will construct local emissions input datasets for any particular domain at this time. Because the official emissions pre-processor (emiss_v03) is designed to work with domains located over North America, this work presents the Another Assimilation System for WRF-Chem (AAS4WRF), a ncl based mass-conserving emissions pre-processor designed to create WRF-Chem ready emissions files from local inventories on a lat/lon projection. AAS4WRF is appropriate to scale emission rates from both surface and elevated sources, providing the users an alternative way to assimilate their emissions to WRF-Chem. Since it was successfully tested for the first time for the city of Lima, Peru in 2014 (managed by SENAMHI, the National Weather Service of the country), several studies on air quality modelling have applied this utility to convert their emissions to those required for WRF-Chem. Two case studies performed in the metropolitan areas of Sao Paulo and Manizales in Brazil and Colombia, respectively, are here presented in order to analyse the influence of using local or global emission inventories in the representation of regulated air pollutants such as O3 and PM2.5. Although AAS4WRF works with local emissions information at the moment, further work is being conducted to make it compatible with global/regional emissions data file format. The tool is freely available upon request to the corresponding author.

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.

The question of how many satellites it would take to accurately map the spatial distribution of ionospheric outflow is addressed in this study. Given an outflow spatial map, this image is then reconstructed from a limited number virtual satellite pass extractions from the original values. An assessment is conducted of the goodness of fit as a function of number of satellites in the reconstruction, placement of the satellite trajectories relative to the polar cap and auroral oval, season and universal time (i.e., dipole tilt relative to the Sun), geomagnetic activity level, and interpolation technique. It is found that the accuracy of the reconstructions increases sharply from one to a few satellites, but then improves only marginally with additional spacecraft beyond ~4. Increased dwell time of the satellite trajectories in the auroral zone improves the reconstruction, therefore a high-but-not-exactly-polar orbit is most effective for this task. Local time coverage is also an important factor, shifting the auroral zone to different locations relative to the virtual satellite orbit paths. The expansion and contraction of the polar cap and auroral zone with geomagnetic activity influences the coverage of the key outflow regions, with different optimal orbit configurations for each level of activity. Finally, it is found that reconstructing each magnetic latitude band individually produces a better fit to the original image than 2-D image reconstruction method (e.g., triangulation). A high-latitude, high-altitude constellation mission concept is presented that achieves acceptably accurate outflow reconstructions.

Baker et al (2015) developed the Community Earth System Model Ensemble Consistency Test (CESM-ECT) to provide a metric for software quality assurance by determining statistical consistency between an ensemble of CESM outputs and new test runs. The test has proved useful for detecting statistical difference caused by compiler bugs and errors in physical modules. However, detection is only the necessary first step in finding the causes of statistical difference. The CESM is a vastly complex model comprised of millions of lines of code which is developed and maintained by a large community of software engineers and scientists. Any root cause analysis is correspondingly challenging. We propose a new capability for CESM-ECT: identifying the sections of code that cause statistical distinguishability. The first step is to discover CESM variables that cause CESM-ECT to classify new runs as statistically distinct, which we achieve via Randomized Logistic Regression. Next we use a tool developed to identify CESM components that define or compute the variables found in the first step. Finally, we employ the application Kernel GENerator (KGEN) created in Kim et al (2016) to detect fine-grained floating point differences. We demonstrate an example of the procedure and advance a plan to automate this process in our future work.

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.

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.

Maar-diatremes are inverted conical structures formed by subterranean excavation and remobilization of country rocks during explosive volcanism and common in mafic volcanic fields. We focus on impacts of excavation and filling of maar-diatremes on the local state of stress, and its subsequent influence on underlying feeder dikes, which are critical for understanding the development of intrusive networks that feed surface eruptions. We address this issue using finite element models in COMSOL Multiphysics®. Inverted conical structures of varying sizes are excavated in a gravitationally loaded elastic half-space, and then progressively filled with volcaniclastic material, resulting in changes in the orientations and magnitudes of stresses generated within surrounding rocks and within the filling portion of the maar-diatreme. Our results show that rapid unloading during maar-diatreme excavation generates a horizontal compressive stress state beneath diatremes. These stresses allow magma to divert laterally as saucer-shaped sills and circumferential dikes at varying depths in the shallow feeder system, and produce intrusion geometries consistent with both field observations from exhumed volcanic fields and conceptual models of diatreme growth. Stresses generated in these models also provide an explanation for the evolving locations of fragmentation zones over the course of diatreme’s filling. In particular, results from this study suggest that: (1) extensional stresses at the base of the diatreme fill favor magma ascent in the lower half of the structure, and possibly promote volatile exsolution and magma fragmentation; and (2) increased filling of diatremes creates a shallow compressive stress state that can inhibit magma ascent to the surface, promoting widespread intra-diatreme explosions, efficient mixing of host rock, and upward widening of the diatreme structure.

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.

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.

Although the Brown mathematical model is the standard model for waveform retracking over open oceans, coastal waveforms usually deviate from open ocean waveform shapes due to inhomogeneous surface reflections within altimeter footprints, and thus cannot be directly interpreted by the Brown model. Generally, the two primary sources of heterogeneous surface reflections are land surfaces and bright targets such as calm surface water. The former reduces echo power, while the latter often produces particularly strong echoes. In previous studies, sub-waveform retrackers, which use waveform samples collected from around leading edges in order to avoid trailing edge noise, have been recommended for coastal waveform retracking. In the present study, the peaky-type noise caused by fixed-point bright targets is explicitly detected and masked using the parabolic signature in the sequential along-track waveforms (or, azimuth-range echograms). Moreover, the power deficit of waveform trailing edges caused by weak land reflections is compensated for by estimating the ratio of sea surface area within each annular footprint in order to produce pseudo-homogeneous reflected waveforms suitable for the Brown model. Using this method, Jason-2 altimeter waveforms are retracked in several coastal areas. Our results show that both the correlation coefficient and root mean square difference between the derived sea surface height anomalies and tide gauge records retain similar values at the open ocean (0.9 and 20 cm) level, even in areas approaching 3 km from coastlines, which is considerably improved from the 10 km correlation coefficient limit of the conventional MLE4 retracker and the 7 km sub-waveform ALES retracker limit. These values, however, depend on the coastal topography of the study areas because the approach distance limit increases (decreases) in areas with complicated (straight) coastlines.

Consisting of charged particles originating from the Sun, the solar wind carries the Sun’s energy and magnetic field outward through interplanetary space. The solar wind is the predominant source of space weather events, and modeling the solar wind propagation to Earth is a critical component of space weather research. Solar wind models are typically separated into coronal and heliospheric parts to account for the different physical processes and scales characterizing each region. Coronal models are often coupled with heliospheric models to propagate the solar wind out to Earth’s orbit and beyond. The Wang-Sheeley-Arge (WSA) model is a semi-empirical coronal model consisting of a potential field source surface model and a current sheet model that takes synoptic magnetograms as input to estimate the magnetic field and solar wind speed at any distance above the coronal region. The current version of the WSA model takes the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model as input to provide improved time-varying solutions for the ambient solar wind structure. When heliospheric MHD models are coupled with the WSA model, density and temperature at the inner boundary are treated as free parameters that are tuned to optimal values. For example, the WSA-ENLIL model prescribes density and temperature assuming momentum flux and thermal pressure balance across the inner boundary of the ENLIL heliospheric MHD model. We consider an alternative approach of prescribing density and temperature using empirical correlations derived from Ulysses and OMNI data. We use our own modeling software (Multi-scale Fluid-kinetic Simulation Suite) to drive a heliospheric MHD model with ADAPT-WSA input. The modeling results using the two different approaches of density and temperature prescription suggest that the use of empirical correlations may be a more straightforward, consistent method.

Our increasingly robust history of ancient climates indicates that high latitude glaciation is the ultimate product of an episodic cooling trend that began about 100-million years ago rather than a result of a yet-to-be identified modal change. Antarctic geography (continent surrounded by ocean) allowed ice to develop prior to significant glaciation in the Northern Hemisphere (ocean surrounded by land), but global ice volume generally increased as Earth cooled. The question of what caused the Ice Ages should be reframed as to “What caused the Cenozoic Cooling?” Records tell us that changes in temperature and CO2 levels rise and fall together, however it is not clear when CO2 acts as a driver versus when it is primarily an indicator of temperature change. The episodic nature of the cooling trend suggests other more dynamic phenomena are involved. It is proposed that oceanic meridional overturning circulation (MOC) plays a significant role in regulating Earth’s surface temperature. Robust MOC has a cooling effect which results from its sequestration of cold waters (together with their increased heat-absorbing potential) below the surface. Unable to better absorb equatorial insolation for great lengths of time, oceanic deep waters are not able to fully compensate for the heat lost by warm-water transport to Polar Regions. A lag-time between cooling and subsequent warming yields lower operating temperatures commensurate with the strength of global MOC. The long-term decline in global temperatures is largely explained by the tectonic reshaping of ocean basins and the connections between them such that MOC has generally, but not uniformly, increased. Geophysically Influenced MOC (GIMOC) has caused a significant proportion of the lowering of global temperatures in the Cenozoic Era. Short-term disruptions in MOC (and subsequent impacts on global temperatures) were likely involved in Late Pleistocene glacial termination events and may already be compounding present anthropogenic CO2 induced warming. The immediate impacts of AMOC strength on North Atlantic and European temperatures coupled with the delayed and opposing effects on global temperatures offer explanations for phenomena including: Younger Dryas, Little Ice Age and the bipolar seesaw. (Please see supplemental file: Oceanic Geophysics & History of Climate(ver #02).pdf listed below.)