Reliability of future global warming projections depends on how well climate models reproduce the observed climate change over the twentieth century. In this regard, deviations of the model simulated climate change from observations, such as a recent “pause” in global warming, have received considerable attention. Such decadal mismatches between model simulated and observed climate trends are common throughout the twentieth century, and their causes are still poorly understood. Here we show that the discrepancies between the observed and simulated climate variability on decadal and longer time scale have a coherent structure suggestive of a pronounced global multidecadal oscillation. Surface temperature anomalies associated with this variability originate in the North Atlantic and spread out to the Pacific and Southern oceans and Antarctica, with Arctic following suit in about 25–35 years. While climate models exhibit various levels of decadal climate variability and some regional similarities to observations, noneof the model simulations considered match the observed signal in terms of its magnitude, spatial patterns and their sequential time development. These results highlight a substantial degree of uncertainty in our interpretation of the observed climate change using current generation of climate models.

River corridors, the spatial domains around rivers in which river water interacts with surrounding sediment and rock, are important components of watersheds. They comprise extremely complex ecosystems: heterogeneous at all spatial scales with strong temporal dynamics, coupled biological, geochemical, and hydrologic processes, and ubiquitous human impacts. We present several ways that our project, focused around the 75 km Hanford Reach of the Columbia River but with multiple connections to other systems, is addressing this challenge. These include 1) deployment of intensive, automated sensor networks supplemented by data from the Hanford Environmental Information System (HEIS) for hyporheic zone monitoring 2) data assimilation of these and other data into models using joint hydrologic and geophysical inversion, 3) integrating MASS2 model outputs and bathymetry data using machine learning to classify hydromorphologic features, 4) a community-based effort to develop broad understanding of organic carbon biogeochemistry and microbiomes in diverse river systems, and 5) use of multi-‘omics data to develop new biogeochemical reaction networks. These underpin the incorporation of process understanding and diverse data into high-resolution mechanistic models, and employment of those models to develop reduced-order models that can be applied at large scales while retaining the effects of local features and processes. In so doing we are contributing to reduction of uncertainties associated with major Earth system biogeochemical fluxes, thus improving predictions of environmental and human impacts on water quality and riverine ecosystems and supporting environmentally responsible management of linked energy-water systems.

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.

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.

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.

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.

The Sample Analysis at Mars (SAM) instrument is a suite of instruments aboard the Mars Science Laboratory that landed on Mars in 2012. Recent measurements of SAM inlet cover actuator temperatures during the 2018 Mars Global Dust Storm have shown less extreme, more benign effects that are beneficial to mechanism performance. These in-situ measurements and models developed from the current study can guide development of actuators and mechanisms on future robotic and manned mission to Mars. Deck-mounted actuators saw drastic, factor of two reduction in diurnal temperature range from 70C to 35C. Maximum temperatures were reduced from +10C to -10C due lower daytime air temperature and attenuation of solar flux absorbed by the actuator body due to increased opacity. Minimum temperatures increased from -60C to -45C due to warmer night-time air temperatures and an enhanced downwelling atmospheric radiation at the surface also caused by dust in the air. Another demonstration of the effects of the dust storm on inlet cover actuator temperature is the linear relation of optical depth plotted against logarithmic diurnal temperature range. Air-fall dust deposition on the white rover deck during the dust storm is indicated by scatter on this linear trend. Other constantly-monitored SAM temperatures include sensors on a second actuator that also shows the effects discussed above and two sensors mounted internally to SAM with less pronounced effects. In this work we will present an overview of the dust storm effects superimposed on the seasonal variation of actuator and other SAM temperatures.

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.

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.

Foredune growth results predominantly from sand that is blown from the beach and backshore. Predictions of multi-year potential sand supply that are based on time series of wind speed and direction measured at a regional (offshore or coastal) meteorological station, however, often grossly overpredict measured deposition volumes on the foredune. This is commonly ascribed to supply limiting factors, such as beach surface moisture or shell deposits, or to fetch limitations. Here we show that differences between regional and local (i.e., on the beach) wind characteristics can also contribute substantially to this overprediction. Using wind data collected during a five-week field experiment on a Dutch beach backed by a 20-m high, steep (1:2) foredune we found that the wind speed on the beach is lower and that the wind direction on the backshore is more alongshore than expected from the regional wind data. Both the difference in speed and direction were a function of the regional wind direction, with the largest speed reduction (to about 70% of the regional value) for shore-normal winds and the largest alongshore deflection (about 15 degrees) for shore-oblique winds. When these functional dependencies are applied to a 10-year series of regional wind data, we found that the potential annual onshore sand transport at our site, predicted with the aeolian sand transport equation of Hsu (1971), reduces from 86 to 24 m3/m. The latter is now comparable, although still somewhat higher than the measured annual deposition volume of 10 to 15 m3/m. Further analysis of the computations shows that most of this reduction is due to the difference between regional and local wind speed. In future work we will explore how much of the remaining overprediction is due to surface moisture and fetch limitations.

Over the last decade, the opportunistic use of broadband Ocean Bottom Seismometer (OBS) recordings has been extremely beneficial for baleen whales studies as they regularly record their stereotyped calls ([10-40] Hz). Often deployed in remote areas, OBSs offer additional and often unprecedented locations for acoustic data collection. The long-term, widespread networks are then ideal for passive acoustic monitoring. Therefore OBS-recorded data are crucial to conservation purposes from monitoring their post-exploitation recovery to improving our understanding of these endangered species and threats. As an example of the potential of OBSs for baleen whale studies, this work exploits data collected by the RHUM-RUM (Réunion Hotspot and Upper Mantle - Réunions Unterer Mantel) seismic network. It aimed initially at imaging the mantle structure beneath the Western Indian Ocean and the dynamics of La Réunion volcanic hotspot. To that extent, 57 OBSs were deployed on the ocean floor, covering an area of 2000x2000 km2 (Lat. 16-34°S, Long. 048-070°E), from October 2012 to November 2013. The RHUM-RUM data provides the second opportunity to record species-specific or regionally-distinct signals of Blue Whales (BWs; Antarctic and Madagascar Pygmy BW) within the Western Indian Ocean Sanctuary. BW calls can propagate from tens to hundreds of kilometers. Consequently, results show that recordings from a single OBS are sufficient to determine the acoustic presence of a species and estimate the range of a calling individual. However, the data analysis benefits from the use of automatic and reliable detection processes. Additional information arises from increasing the number of sensors: a small array of the RHUM-RUM network (20km inter-sensor distance) is used for localization and tracking, essential for animal counting. Besides, the broader configuration showed repartition and migration patterns of the studied species. These recordings from the bottom of the ocean shed new light on the study of BWs and, more generally, low-frequency sounds in this area.

The deeper subsurface layers beyond the lunar regolith are not well-constrained. Using in-situ lunar penetrating radar data (d) from Change’E-3 Yutu rover, we invert for its subsurface relative dielectric permittivity (εr) model. We use a hybrid physics informed convolutional neural network based deep learning architecture to simultaneously regress the relationship between εr – d; and using radar forward modeling to reconstruct radargrams to match the field data d. This dual pronged inversion approach will ensure predicted radargrams to closely match the field data and providing reasonable εr estimates. Our training dataset will incorporate a priori knowledge of existing εr ranges [1-10] from previous lunar regolith studies. We expect to predict accurate εr profiles of the lunar subsurface (~200 meters) and generating predicted data that closely matches the field data.

As a preliminary step to install the First Magnetic Observatory of Honduras, the local geomagnetic components are being computed in Tegucigalpa, Honduras (TEG), for the temporal interval 2010 –2013, using the Tsyganenko and Sitnov 2005 (J. Geophys. Res. 110, A03208, 2005: TS05) semi-empirical magnetospheric model. The diurnal variation of H (Sq) is obtained from the Chiripa Observatory (Costa Rica) data, using the International Quiet Days and the Normal Orthogonal Component Analysis. Pearson’s correlation coefficients are calculated between the H-component computed with the TS05 model in the Tegucigalpa coordinates and the Chiripa Observatory (CRP) data. The best correlations are obtained in days with Kp < 4, when the Sq and the quiet daily variation are removed from the CRP-H data and the TEG-H-component respectively. Seven TS05 magnetospheric currents are obtained in order to evaluate their contribution to the Sq. Using the Normal Orthogonal Component Analysis, the Chapman-Ferraro current fits better in the 0.00–12.00 local time interval, and the Birkeland currents fit better in the 12.00–24.00 local time interval.

Solar wind electrons play an important role in the energy balance of the solar wind acceleration by carrying energy into interplanetary space in the form of electron heat flux. The heat flux is stored in the complex electron velocity distribution functions (VDFs) shaped by expansion, Coulomb collisions, and field-particle interactions. We investigate how the suprathermal electron deficit in the anti-strahl direction, which was recently discovered in the near-Sun solar wind, drives a kinetic instability and creates whistler waves with wave vectors that are quasi-parallel to the direction of the background magnetic field. We combined high-cadence measurements of electron pitch-angle distribution functions and electromagnetic waves provided by Solar Orbiter during its first orbit. Our case study is based on a burst-mode data interval from the Electrostatic Analyser System (SWA-EAS) at a distance of 112 RS (0.52 au) from the Sun, during which several whistler wave packets were detected by Solar Orbiter’s Radio and Plasma Waves (RPW) instrument. The sunward deficit creates kinetic conditions under which the quasi-parallel whistler wave becomes unstable. We directly test our predictions for the existence of these waves through solar wind observations. We find whistler waves that are quasi-parallel and almost circularly polarised, propagating away from the Sun, coinciding with a pronounced sunward deficit in the electron VDF. The cyclotron-resonance condition is fulfilled for electrons moving in the direction opposite to the direction of wave propagation, with energies corresponding to those associated with the sunward deficit. The quasilinear diffusion of the resonant electrons tends to fill the deficit, leading to a reduction in the total electron heat flux.

Meter-scale AUV mapping of 85-km of the summit and rift zones of Axial Seamount shows systematic variation in morphology of the lava flows with depth and distance from the caldera. ROV sampling reveals flow age and chemistry variations. Each rift zone has a steady downward slope of ~2° outside the caldera. In the caldera and first few km down the rift zones, flows are predominantly channelized sheet flows with collapses along the channels. Mid-rift, drained inflated hummocky flows consisting of complex mounds with tumuli and lava lakes, and narrow ridges of hummocky mounds become common. On the axis of the south rift, the first cones with craters occur 3.2 km from the caldera at 1600 m depth, and broad inflated hummocky flows emplaced through complex lava tubes first appear 6 km down-rift at 1715 m. On the north rift, similar cones and complex flows appear at 10.5 km down-rift at 1725 m depth. The historical flows exemplify this variation: on the upper south rift as channelized sheet flows in 1998 and 2011 erupted from fissures that extended 6.5 km down-rift; on the middle north rift, inflated hummocky flows up to 126 m thick erupted in 2015 from fissures 17.5 km from the caldera; and on the distal south rift, a narrow ridge of coalesced hummocks 160 m tall formed during the 2011 eruption. Older and older lavas remain exposed at greater distances from the more active summit and upper rift zones. Deep on the rift zones, stellate and steep cones with smooth talus slopes occur that did not feed expansive flows, despite being constructed of hotter, less viscous, near-primary magmas. These cones are first observed on both rift zones at 1800 m depth and 18 km from the caldera. Deeper still, emanating from both distal rift axes beginning ~30 km from the caldera, lie voluminous inflated sheet and inflated hummocky flows 30 to 135 m thick with combined area of over 150 km2. The plagioclase-phyric voluminous flows on the south rift erupted ~1250 years ago, and the aphyric ones on the north rift ~13,000 years ago. Eruption rate is the most likely cause of the flow morphology changes since estimated magma viscosity does not correlate with flow morphology. Lateral transport through long dikes would slow magma delivery unless dike widths are large. The near-primary magmas may have risen through narrow conduits from the mantle to the distal rifts.

Recent observational and theoretical studies have greatly revealed the dynamical nature of the Oort Cloud and its evolutionary history. However, many issues are yet to be known. Our goal is to understand current structure of this cloud as well as its dynamical origin. For estimating the current structure of the Oort Cloud, key information lies in the original orbit of the Oort Cloud new comets (OCNCs) that are defined at a distance where these objects do not receive gravitational perturbation from major planets (such as at r = 250 au from the Sun before comets enter into the planetary region). There have been several attempts to obtain OCNC’s original orbits, but it never has been an easy task. This requires numerical orbit propagation of the observed comets with high accuracy including perturbation from major disturbing bodies. In addition, non-gravitational forces often play significant roles here. First and foremost, the orbit determination of OCNC includes substantially large uncertainty because of limited number of observational arcs and very large eccentricity of the comets (~1). Here I show our result of comparison of various catalogues of OCNCs’ original orbital elements at r = 250 au: So-called the Warsaw catalogue by Krolikowska, the ephemeris given by MPC (Minor Planet Center), and that given by Horizons/JPL. In particular, I pay attention to the difference of the original semimajor axis among the several different solutions that the Warsaw catalogue and the MPC ephemeris have in comparison with the solutions given by Horizons/JPL - such as the difference between the solution 1 in the Warsaw catalogue and the solution from Horizons/JPL, the solutions 1 and 2 in the Warsaw catalogue, the solutions 2 and 3 in the MPC ephemeris and so forth. The resulting orbits that these solutions yield look overall similar, but sometimes they show stark difference for some reason.

There is no quantifiable definition of what comprises a peer reviewer, leading to a lack of trust in the outcomes of peer review by the public and some government officials. Using contextual content analysis, this study uncovered concepts from federal science policy and agency peer review guidance documents to begin the creation of such a definition. The first stage determined which term is most often used for a peer reviewer as seen in the U.S. Office of Management and Budget’s (OMB) Final Information Quality Bulletin for Peer Review 2005. The second stage analyzed the U.S. Department of Energy, Energy Efficiency & Renewable Energy, the U.S. Department of Interior, Bureau of Safety and Environmental Enforcement and the U.S. Department of Justice, Office on Violence Against Women peer review guidelines to determine the number of mentions of each concept (knowledge, skills, experience), if a definition of a merit peer reviewer is provided, and descriptors associated with qualities, or sub-concepts pertaining to peer reviews including discipline, teaching, education, postdoctoral positions, certifications, grant funding, publications, presentations, awards and consulting work. Stage one results found that the term reviewer is the most used in describing a peer reviewer for all three federal agencies and the OMB bulletin. Results of stage two show that both knowledge (M=9) and experience (M=14.33) categories were the largest reported with few mentions of skills (M=3.33). Each agency provided a definition of reviewer and qualifications they must meet. This analysis of three federal peer review guidance documents shows that the federal guidance on expertise, if measured by both experience as well as knowledge, is being followed. It would be a worthy effort to do an analysis of the publications, experience and knowledge of those selected for peer review and compare it to each agencies criteria to build a profile of reviewer characteristics. In addition to contributing to the scant literature on peer review, these findings will help narrow the qualities merit peer reviewers need and will assist with future research exploring all U.S. Federal Agency peer review manuals in which qualities in both knowledge and experience could be quantified, helping to defining an accurate measurement of goodness of fit for reviewers.

The position of the grounding line of marine terminating glaciers, the boundary where glacier ice is no longer supported by the ground and starts floating, is a key parameter for a better understanding of glacier dynamics and better quantification of glacier mass balance. Grounding lines have so far been delineated by human interpreters from Differential Interferometric Synthetic Aperture Radar (D-InSAR) products. This is an arduous and time-consuming process that is not scalable for large-scale delineation from the ever-larger amount of remote-sensing data becoming available, which is necessary for a better understanding of glaciological processes. In order to solve this issue, we present a deep learning approach using a convolutional neural network with parallel atrous convolutions and an asymmetric encoding/decoding structure to successfully delineate thousands of grounding lines rapidly and accurately. Furthermore, the neural network outputs uncertainty estimates, which have so far been missing from grounding line delineations. Over the Getz Ice Shelf in West Antarctica, we find a mean difference of 232 meters, or 2.3 pixels, between automatic delineations and manual delineations on test data not used during training. The spread of differences is given by a median absolute deviation of 101 meters. The performance of the neural network is comparable to that of human interpreters, with manual delineations falling within the uncertainty range of automatic delineations. Similar differences exist between multiple manual delineations, with a slightly higher mean difference (268 meters) and a lower spread (median absolute deviation of 52 meters). We show our deep learning pipeline is easily generalizable and scalable to the entire ice sheet, revolutionizing the availability of grounding line delineations for glaciological studies.