The connections between Greenland’s tidewater glaciers and the continental shelf waters are modulated by fjords that are geometrically complex with numerous modes of variability. These fjords transport heat towards the glaciers and meltwater away, with a renewal determined by a range of factors including buoyancy forcing at the surface (surface melt) and at depth (subglacial discharge), wind stresses, shelf currents, bathymetry, and mixing processes. This study aims to classify the various regimes of flow and fjord renewal over the diverse parameter space of Greenland fjords with an emphasis on investigating the role of shelf-driven variability and mixing. We conduct idealized numerical simulations using MITgcm to explore how fjord renewal is controlled by elements of the shelf and fjord geometry, and by thermodynamic and mechanical forcings, with domains consisting of idealized bays and shelves representative of Greenland. Specifically, we investigate the sensitivity of the renewal to the following dynamic/thermodynamic influences: (a) bathymetric sills and their influence on mixing, (b) horizontal constrictions (peninsulas, islands, turns), (c) ice cover, and (d) subglacial meltwater discharge. To explain our numerical model results, we develop dynamical theories for the exchange flow as a function of the geometric and forcing parameters.

Volcanic eruptions are usually not easily predictable and this poses a significant hazard not only for exposures to the local population but due to possible presence of tephra also for airline traffic. The significant investments of the last years in new monitoring techniques and networks have improved our capabilities to sense volcano health, but the path to automatically recognize signs of potentially hazardous unrest is still long. On the other hand, machine learning is currently living a period of tumultuous growth and it is possible to find its applications practically in all the contexts where there is an overflow of data to be interpreted. Our aim is to exploit the capability of some established algorithms in machine learning to test their reliability in early detecting anomalous signals from the monitoring network before eruption events on Mt. Etna (Italy). In particular, we evaluate the effectiveness of using random forest approaches to learn from the measured signals the complex dynamics of the Etnean volcanic environment without any a-priori information on the data relationships. Such models are then tested against real eruptive cases to assess their performance.

The lateral size of sea ice floes is receiving increasing attention as an important variable for the polar climate system. We have developed a model for prognostic evolution of the floe size distribution, which emerges due to five key physical processes: new ice formation, welding of floes in freezing conditions, lateral growth and melt, and fracture of floes by ocean surface waves. As a result of the model’s foundation in the governing physics, free parameters occurring in the equations can be directly constrained by observations. Initial model experiments provided insight into the relative importance of various processes, showing floe freezing processes were particularly important for simulation of the floe size distribution. Previously, physical descriptions of lateral growth and welding together of floes had not been constrained by observations. This motivated an analysis of images obtained by drifting wave buoys (SWIFTs) deployed in the autumn Arctic Ocean to quantify these processes in-situ for the first time. We separated floe area growth due to welding from that due to lateral expansion, and compared observations to our physical descriptions of the individual processes. We also found a strong limitation on floe sizes imposed by the wave field. These results have been used to inform new physical descriptions of processes important for the sea ice floe size distribution.

Machine learning algorithms have become a powerful tool in different areas of seismology, such as phase picking/earthquake detection, earthquake early warning and focal mechanism determination. Previously convolutional neural networks (CNN) have been applied to continuous seismic waveform recordings to perform efficient phase picking and event detection with good accuracy [Zhu et al., 2018]. However, the off-line training of current CNN requires at least a few thousands of accurately picked seismic phases, which makes it difficult to be applied to regions without sufficient picked phases. In this work, we will validate the transfer learning among different geographic regions. Our tests show that the phase picker trained on manually-labeled data acquired from Sichuan, China following the 2008 M7.9 Wenchuan earthquake [Zhu et al., 2018] works equally well on the continuous waveform acquired from Oklahoma, US [Zhu et al., 2018]. Specifically, using the CNN trained on the Wenchuan dataset, together with 895 local/regional catalog events recorded in central Oklahoma, we refine part of the networks to pick the arrival times of the local seismicity in Oklahoma. The refined CNN results are compatible with the matched filter results using the same catalog events as templates. Our next step is to extend our test to waveforms from different tectonic regions to demonstrate the generality of CNN-based phase picker. We plan to further use a New Zealand seismic dataset that includes more than 20 GeoNet stations in the North Island, where the matched-filter detected results are available to be compared with (Yao et al., 2018). Alternatively dataset include a subset of events in the waveform relocated catalog in Southern California. Updated results will be presented at the meeting.

Clouds and their associated radiative effects are a large source of uncertainty in global climate models. One region with particularly large model biases in cloud radiative effects (CRE) is the Southern Ocean. Previous research has shown that there are many dynamic “cloud controlling factors” that influence shortwave CRE, and that three important cloud controlling factors over the Southern Ocean are mid-tropospheric vertical velocity, surface thermal advection, and Estimated Inversion Strength (EIS), which have been shown to influence shortwave CRE on monthly timescales. Model errors may thus arise from biases in representing cloud controlling factors (atmospheric dynamics), in representing how clouds respond to those cloud controlling factors (cloud parameterizations), or some combination thereof. This study extends previous work by examining cloud controlling factors over the Southern Ocean on daily timescales in both observations and global climate models. This allows the cloud controlling factors to be examined in the context of transient weather systems, such as extratropical cyclones, and in the context of high pressure quiescent scenes. Composites of EIS and mid-tropospheric vertical velocity are constructed around extratropical cyclones to examine how the different dynamic cloud controlling factors influence shortwave CRE around the cyclone and how similar the model cyclones are to observations. On average, models tend to produce a realistic cyclone, when compared to observations, in terms of the dynamic cloud controlling factors. The difference between observations and models instead lies in how the models’ shortwave CRE responds to the cyclone dynamics. In particular, the models’ cloud radiative effects are too sensitive to perturbations in mid-tropospheric vertical velocity and, as a result, they tend to produce clouds that are too bright in the cold frontal region of the cyclone and too dim in the center of high pressure systems.

Rapid Arctic warming and sea ice decline in recent decades might have profound impacts on midlatitude weather and climate. This study uses a high-top atmospheric general circulation model - Whole Atmosphere Community Climate Model version 6 (WACCM6) - to investigate the atmospheric responses to the Arctic sea ice reduction, including impacts on mid-latitude blocking and cyclones. The high-top configuration (top layer at about 100 km or 0.001 hPa) together with the finer vertical resolution (70 layers) in WACCM6 allow more realistic representation of stratosphere-troposphere interactions. The sea ice impact during 1979-2015 is obtained by comparing ensemble simulations forced by daily observational sea ice concentration (SIC) and sea surface temperature (SST) to simulations where the time-varying SIC is replaced by a daily SIC climatology. A series of SIC-SST adjustments are performed to minimize unrealistic SIC and SST forcings used in the simulations. Each ensemble consists of 30 members with slightly different atmospheric initial conditions in order to separate forced atmospheric responses from internal model spread.

Heat has historically resulted in more mortality than any other severe weather-related phenomenon in the United States, with the U.S. Centers for Disease Control attributing more than six hundred deaths to extreme heat each year. Agricultural workers are particularly vulnerable to environmental heat exposure: mortality rates due to heat amongst crop workers are twenty times higher than in the general worker population. The vulnerability of this group of workers to heat is exacerbated by factors such as economic vulnerability, immigration status, language barriers, access to health care, and the absence of worker heat standards in all but three of the fifty United States. Climate change threatens to increase the risk of heat-related illness in agricultural workers, but so far there has been no nation-wide evaluation of the expected change in heat exposure in a warmer climate for this group of workers. Here, we estimate baseline and future exposure of agricultural workers in the main growing regions of the continental U.S. to extreme heat. Given the diverse health impacts of different types of heat events, we compare metrics for both single day extremes and multi-day heat waves, and calculate how these statistics will change at the county level with 2 and 4 degrees Celsius of global warming. Our findings serve as a guideline for determining regionally appropriate heat standards, and underscore the need for agencies in all agricultural states to make preparations for a warmer climate.

Soil-bentonite slurry cutoff walls are used to prevent the movement of contaminated groundwater. Defects which reduce the overall efficacy of the wall may become present during construction or the aging of the wall. In this research, we use ground penetrating radar (GPR) and electrical resistivity (ER) as non-invasive and minimally invasive geophysical techniques, respectively, in an attempt to characterize the shallow portions of a soil bentonite slurry cutoff wall and detect defects within the wall. Sandbags and a limestone block were placed within the northernmost section of an experimental soil-bentonite slurry cutoff wall in Montandon, PA to represent defects that could lead to high hydraulic conductivity within the wall. GPR surveys were collected along the wall and surrounding area to investigate the unsaturated zone and determine if GPR could reliably observe the elevation difference of the water table across the wall. GPR estimates of the soil water content (SWC) differences were also evaluated as a possible way to determine the location of more permeable zones (e.g., fractures) within the unsaturated zone of the cutoff wall. The apparent resistivity of both defect and non-defect locations in the wall were collected using long point-source electrodes in Wenner and dipole-dipole arrays at various depths, as well as non-traditional geometries. The point electrodes inserted into the wall at a 1 meter a-spacing indicate a higher apparent resistivity associated at defect locations relative to at the non-defect locations. Additionally, the mise-a-la-masse method with in-wall electrodes and surface electrodes was used to measure the change in voltage along the wall and the surrounding areas in both defect and non-defect locations. We will discuss how data from the individual or concurrent use of these two methods may help locate potential defects within soil-bentonite slurry cutoff walls. The ability to reliably detect defects within slurry walls using non-invasive or minimally invasive techniques will help retain the integrity of the wall, allow for quality assurance for construction companies, and monitor walls throughout their life at a low cost.

During geomagnetic storms, charge exchange between neutral hydrogen (H) atoms in the terrestrial exosphere and H+ and O+ ions in the ring current serves to dissipate magnetospheric energy through the generation of energetic neutral atoms (ENAs), which escape Earth’s gravity on ballistic trajectories. Imaging of the resulting ENA flux is a well-known technique to infer the ring current ion distribution, but its accuracy depends critically on the specification of the exospheric H density distribution. Although measurements of H airglow emission exhibit storm-time variations, the H density distributions used in ENA image inversion are typically assumed to be static, and the current lack of knowledge regarding global exospheric evolution during storms represents an important source of error in investigations of ring current dynamics. In this work, we present a new technique to reconstruct the global, 3D, and time-dependent H density distribution from observations of its optically thin emission at 121.6 nm (Lyman-α) acquired from the Lyman-alpha detectors onboard the NASA TWINS satellites. The technique is based on our recent development of a robust tomographic inversion algorithm, which is modified to incorporate the temporal dimension via Kalman filtering. We present the first time-dependent reconstructions of exospheric structure during geomagnetic storms, which exhibit pronounced dayside density enhancements and a strong anti-correlation with the DST index.

Hydrologic persistence is a unique property that explains the temporal clustering of extreme events such as floods and droughts. It influences the inter-arrival times of these extreme events and helps in understanding the mechanism of their return periods. In this work, the influence of rainfall amount and changing patterns of dry and wet spells on persistence of their joint behaviour is investigated at multiple time scales through estimation of Hurst Exponent (H) using Detrended Fluctuation Analysis (DFA) and Detrended Cross-Correlation Analysis (DCCA) at MOPEX (Model Parameter Estimation Project) catchments in United States. The effect of catchment size on the persistence of streamflow at multiple temporal scales is also analyzed. The results of the analyses suggest that the state of persistence of joint behavior of streamflow and rainfall is neither affected by rainfall amount nor by the changing patterns of the dry and wet spells. However, the state of persistence of rainfall is affected by the patterns of dry and wet spells of observed rainfall. The results also suggest that catchment area has a significant effect on the persistence of rainfall as there exists a statistically significant correlation between catchment area and H estimated at multiple time scales. Overall, the study finds that catchment acts as a filter that transforms rainfall with low or no persistence to streamflow with higher persistence manifested by different catchment characteristics at multiple spatio-temporal scales.

Team collaboration theories or the role that organizational design plays in promoting long-term research success are often secondary considerations within ambitious community-wide research initiatives. However, for research project managers or community managers called in to consult on the formation of new projects, research proposals, or collaborative partnerships, understanding and applying this body of knowledge in the early initiating or planning phases of new research can strongly promote innovative, collegial, and productive outcomes. In 2018, the Arctic Research Consortium of the U.S. undertook a strategic planning initiative to identify the strategic goals and objectives that will guide our research community support efforts over the next five years. As part of this effort, we determined ARCUS would be doing more to directly evaluate and understand the Arctic research community and its support needs. We also confirmed that we will be taking part in efforts to build out and new initiatives such as the Arctic System Science Collaboratory and UArctic Analytics Institute. As a first step toward achieving both of these objectives, ARCUS staff undertook an internal study to identify the common organizational design structures of current Arctic research programs and what recommendations we could make to improve on these designs within the new and emerging programs we support. Using document analysis and key informant interviews, we created a typology of the collaborative research program structures supported by ARCUS as well as other Arctic research programs funded through the National Science Foundation. The pros and cons of these support structures were then explored and evaluated through the lens of team collaboration and organizational design theories. Our findings highlight the important role that research project and community managers play in promoting collaborative institutional structures and cultures and that without intentional and without ongoing efforts to build and improve them, the productivity and longevity of research communities is likely to decline.

Understanding the falling velocity of cloud droplets and ice crystals in a nonuniform flow is fundamental to cloud physics and precipitation. In this study, the mean settling velocity of a hydrometeor falling into a non-stationary fluid is examined. To investigate the possible particle-fluid interaction, a homogeneous and isotropic turbulent flow fields is considered. The study covers a wide range of flow velocities and hydrometeor density and size. The results show initially reduction of hydrometeor settling velocity and then enhancement in strong turbulence. The mean settling velocity depends on a) mean flow and turbulent intensity i.e., standard deviation of flow and b) particle terminal velocity i.e., the shape and size of hydrometeor, its density, the dynamic viscosity and the density of the fluid, and the gravitational acceleration. The non-dimensional parameters that are important in characterization of hydrometeor settling velocities are estimated using dimensional analysis. These non-dimensional parameters are then used to formulate the mean settling velocity.

Water erosion triggers the spending of billions of dollars a year to Brazil, as well as social and environmental issues. One of the strategies to minimise soil erosion and to foster economic development is adopting techniques for the integration of agricultural systems. Nevertheless, studies on long-term experimental fields under integrated systems are scarce regarding the effect of the integrated crop, livestock, and forestry system on soil and water loss. As integrated agricultural systems are popularising, especially in the Brazilian Cerrado, it is key to understand their impacts on the erosive processes. As experimental data are fundamental to develop and assess mathematical models, the lack of such information halts the advance of technologies that support agricultural development. Here, we assess the influence of integrated crop, livestock, and forestry systems on interrill erosion. For that, we will run tests using a portable plot-scale (0.7 m2) rainfall simulator on Dystrophic Red Latosol using a rainfall intensity of 60 ± 5 mm h-1. We will study the following management systems: livestock loading in rotation Panicum Maximum cv. Mombaça pasture; continuous Brachiaria Decumbens pasture; soybean crop during summer and crop rotation during fall-winter; and integrated crop-livestock-forestry system alternating between a four-year Panicum Maximum cv. Massai pasture and a four-year soybean crop both associated with Eucalyptus production. The soil management systems have been established since 1993 to evaluate economic and agriculture efficiencies. Thereby, we look forward to providing not only economic advantages but also new reference values of soil erosion under integrated agricultural systems, helping to build resilience for food security. Our findings will also contribute to adequate land use, therefore, promoting soil and water conservation.

Topographic maps represent a three-dimensional (3D) surface using a system of symbols in two-dimensions (2D). To facilitate students’ understanding of topographic maps, the Augmented Reality (AR) Sandbox reads the elevation of actual sand and projects the topographic map lines onto the surface of the sand. Although over 600 institutions have built AR Sandboxes to help people interpret topographic maps, classroom studies using the AR Sandbox have not found significant gains on topographic map assessments. The present study is a 2 by 2 design testing the affordances of the AR Sandbox in a laboratory setting. In the first level of the study, participants interacted with the AR sandbox (3D) or a regular computer monitor (2D) to give them feedback on five landforms they constructed in the sand. Participants initially constructed the landforms in the sand with the feedback off (i.e., sand without the overlaid projection or monitor). The feedback was then turned on and participants compared and contrasted their landform to the target topographic map. Participants were then asked to modify their landform with the feedback on (continuous), or the feedback was turned off (discrete) during modification. A mixed-ANOVA revealed significant gains on a modified version of the Topographic Map Assessment (TMA-B) from pre- to post-intervention (F(1, 74) = 80.34, p < .001). A significant interaction revealed that participants in the 2D condition had greater gains (M = 2.91, SD = 2.48) than those in the 3D (M = 1.64, SD = 2.07) condition (F(1, 74) = 6.38, p = .014), although both conditions had significant pre- to post-intervention improvement (2D: t(37) = 7.24, p < .001, d = 1.02; 3D: t(39) = 5.01, p < .001, d = 0.64). On average, the discrete feedback groups spent significantly less intervention time (M = 48.3, SD = 16.9) compared to the continuous groups (M = 58.2, SD = 18.1) (F(1, 76) = 6.20, p = .015). The findings suggest that the AR Sandbox does improve topographic map skill for individual students using our approach, and that the most efficient technique engages students in discrete episodes of feedback using the 2D computer monitor.

Understanding, predicting, and responding to a rapidly changing Arctic requires sustained observations that capture variability and transformative change of the Arctic systems with all its major components. A key challenge for researchers, Arctic communities, and others tasked with effective responses to such change is to achieve structured coordination of numerous individual observing activities and networks. These have different regional and thematic foci. Many are driven from the bottom-up by research interests, while others are mission-oriented operational networks. The Arctic Observing Summit (AOS) is an effort that seeks to help coordinate such disparate activities and support efforts such as the Sustaining Arctic Observing Networks (SAON) initiative. We report on progress as part of an AOS 2018 working group focused on implementation and optimization of sustained observations. Drawing on the Framework on Ocean Observations, our group identified effective approaches and barriers to integration of different observation requirements and activities/platforms into a coherent observing framework. Case studies for benthic communities, sea ice prediction, and permafrost highlighted the importance of allowing for independently driven activities to coalesce into a uniform framework. This in turn requires clearly defined requirements that ideally serve multiple societal benefits. Such clear definitions also aid private-public partnerships and the development of new observing system business models. Prerequisite to better coordination is a comprehensive, international assessment that describes the current set of systems, community-based networks, sensors, networks, and surveys that are used to observe the Arctic today. Pieces of such an endeavor are starting to emerge, and SAON may serve as a home for integrating and building upon these pieces. Essential to this goal is the development of a knowledge map that collates and connects observing resources to societal benefits, helps identify and prioritize essential variables, data management needs, and critical products and services. The AOS 2018 calls for the launch of an optimization and implementation team of experts that would conduct such an effort under the auspices of SAON. We explore different elements of such a team’s portfolio of tasks.

Delivering long-term, high quality environmental sensor data spanning the continent is a primary goal in the National Ecological Observatory Network’s (NEON) Instrumented Systems (IS) group. Some independent observations collected by NEON’s Observation System (OS) measure similar data at the same location and time as the in-situ sensors. Coinciding IS and OS measurements facilitate supplementary data quality assessments by vetting IS sensor data (e.g. aquatic pH probe) against corresponding OS data (e.g. water grab sample analyzed in a lab for pH). To assess whether IS data agree with OS measurements, we use uncertainty as a tool to understand data quality. The uncertainty between NEON IS and OS data follow analytical (e.g. summation in quadrature) or numerical (e.g. Monte Carlo) approaches depending on the complexity of the IS-OS comparison algorithms. NEON calculates the IS-OS uncertainties, and applies the expanded uncertainty as control limits for acceptable IS-OS data comparisons. IS-OS comparisons falling outside the uncertainty-based control limits help to (i) explore unaccounted uncertainty in the IS and OS data, and (ii) address issues in the data or sample collection process as ongoing continuous improvement strategies.

The Bureau of Reclamation serves water to 31 million people and 20 percent of irrigators in the western United States. Reclamation generates large amounts of data and information describing reservoir operations, hydropower generation, environmental compliance, invasive mussels, infrastructure, and other aspects of Reclamation’s mission activities. Much of these data are currently accessed on a program-specific basis via legacy information management & technology (IMT) systems, often without modern features such as machine-readable data formats and web-services. The Department of the Interior’s Open Water Data Initiative addresses these issues, and Reclamation is striving to improve its data-publishing efforts, making its data more easily found, accessed, and applied to support public and private sector activities. This presentation provides an overview of two IMT systems that Reclamation has developed under OWDI: the Reclamation Water Information System (RWIS, water.usbr.gov) and the Reclamation Information Sharing Environment (RISE). RWIS was developed first as a pilot, demonstrating how multiple programs’ water data could be combined into a modernized data-publishing system. It provides free access to reservoir and water time series data from Reclamation’s five regions, includes map and query interfaces for data discovery and access, and web-services for automated retrieval. Based on the success of the RWIS pilot, Reclamation has more recently focused on developing RISE, which will envelop RWIS and feature a much larger collection of Reclamation data, including water quality as it pertains to Reclamation water management activities, invasive mussels, hydropower, and infrastructure, and more. RISE will also serve a mix of data types, going beyond RWIS’ scope of time series data to also include geospatial and documents data. This presentation summarizes RISE development approach, architecture and previews the upcoming release in Winter 2019.

The ongoing collision, convergence and northward underthrusting of the Indian plate beneath the Eurasian plate resulted in large lateral variations in crustal thickness and composition beneath the Himalaya.The Kumaon Himalaya lies in the central part of the Himalayan orogeny and provides unique opportunity to study typical characteristics of the underlying crustal structure of the Himalayan fold-thrust-belt system. In the present study, crustal thickness and Poisson’s ratios were estimated at 13 broadband seismological stations established in a profile along the Kali river valley, Kumaon (Central) Himalaya. The seismological profile extends from the Indo-Gangetic plain (IGP) in the south to the Higher Himalaya in the north, passing through the Sub and Lesser Himalaya. The Receiver Function (RF) method has been adopted to investigate the crustal structure beneath the profile. Time domain iterative deconvolution method has been adopted for RF computation. The H-k stacking method has been adopted to analyze the RFs for obtaining average crustal thickness and Poisson’s ratio (s) of the crust beneath each station. The study reveals that the crustal thickness beneath the IGP is ~38 km which gradually increases up to ~41 km at the northernmost station located in the Higher Himalaya. The value of s varies within the range 0.23 – 0.28. Low values of s are observed in the Sub-Himalaya and outer Lesser Himalaya suggesting more of a felsic composition of crust in the region. Significantly high value of s (~0.28) is observed in the Dharchula region. Such high Poisson’s ratio cannot be explained by the presence of solely dry crustal rocks. Presence of mid-crustal fluid/partial melts beneath the region can be the possible cause of high Poisson’s ratio. The recent seismicity suggests a large number of micro-to-moderate magnitude earthquakes forming a cluster at shallow down to mid-crustal depths beneath the Dharchula region. Presence of fluids influences the rheological property and controls the mechanical and shear strength of crustal rocks producing the cluster of seismicity observed beneath the Dharchula region. Keywords: Receiver function, Crustal Thickness, Poisson’s Ratio, Kumaon Himalaya.