Traditionally, astronomers have separated giant planets from brown dwarfs based on the object’s mass. Objects more massive than 13 Jupiter masses but less massive than 80 Jupiter masses are considered to be brown dwarfs. However, in detail, the lower mass threshold is 11 to 16 Jupiter masses depending on the metallicity of the object. This betrays how arbitrary a purely mass-based distinction between planets and brown dwarfs is. Instead, we take a critical look at the population of brown dwarfs for which we have the most fundamental information: transiting brown dwarfs. Transiting brown dwarfs provide us their mass, radius, and sometimes age, which makes them useful for directly testing substellar evolutionary models. Through a better understanding of how well these models describe the population of transiting brown dwarfs, we will develop a better definition of what makes a brown dwarf different than a giant planet: its formation mechanism. It is certainly true that in the mass range spanning between giant planets and low-mass stars that the dominant formation mechanism must change in a significant way. If we can determine which mass or distribution of masses that this change occurs at, then we will have a more physical way to distinguish planets from brown dwarfs. This poster provides an overview of the substellar mass-radius diagram and highlights several of the recent discoveries facilitated by the TESS mission.

The current increase in temperature over Greenland and other glaciated regions allows for more surface melt, which poses the question of the impact of this extra amount of meltwater on ice dynamics. As subglacial hydrology models evolve they are now easier to apply to realistic scenarios to quantify the effect of an increase in melt on the dynamics of glaciers. However, a number of processes linking the surface melt to the water pressure at the base of glaciers are still overlooked in models due to a lack of knowledge or an excess of complexity. Here, we apply a subglacial hydrology model coupled to an ice dynamics model to a synthetic geometry to investigate the impact of moulins distribution on the dynamics of the glacier. Our results show that a sparser distribution of moulins leads to the faster development of the efficient drainage system and greatly slows down the glacier.

Recent observations of faults in the shallow regions of subduction zones have revealed slow slip events that nucleate up-dip of the locked zone. Clay-rich sediments are prevalent at shallow depths and a large body of experimental work has shown that these sediments have a tendency towards velocity-strengthening frictional behavior, although velocity-weakening behavior is observed as well. Models of deeper slow slip, down-dip of the locked zone, generally require velocity-weakening behavior for events to nucleate. Here I show that slow slip events can nucleate and propagate on shallow, velocity-strengthening thrust faults, in a numerical model of a thrust fault dipping in a homogeneous, elastic half-space. This behavior is due to the broken symmetry of the thrust fault geometry, and is similar to behavior previously reported on bi-material, and poro-elastic faults. The interaction of the fault with the free surface (i.e. the sea floor) creates a coupling between normal stress on the fault and fault slip. This coupling allows velocity-strengthening slow slip events to nucleate, and becomes stronger at shallower depths. Here I conduct a parameter analysis, and show how this behavior is limited to certain values of the frictional and elastic parameters on the fault.

Although iron and light are understood to regulate the Southern Ocean biological carbon pump, observations have also indicated a possible role for manganese. Low concentrations in Southern Ocean surface waters suggest manganese limitation is possible, but its spatial extent remains poorly constrained and direct manganese limitation of the marine carbon cycle has been neglected by ocean models. Here, using available observations, we develop a new global biogeochemical model and find that phytoplankton in over half of the Southern Ocean cannot attain maximal growth rates because of manganese deficiency. Manganese limitation is most extensive in austral spring and depends on phytoplankton traits related to the size of photosynthetic antennae and the inhibition of manganese uptake by high zinc in Antarctic waters. Importantly, manganese limitation expands under the increased iron supply of past glacial periods, reducing the response of the biological carbon pump. Overall, these model experiments describe a mosaic of controls on Southern Ocean productivity that emerge from the interplay of light, iron, manganese and zinc, shaping the evolution of Antarctic phytoplankton since the opening of the Drake Passage.

Identifying fluid flow maldistribution in planar geometries is a well-established problem in subsurface science/engineering. Of particular importance to the thermal performance of Engineered (or “Enhanced”) Geothermal Systems (EGS) is identifying the existence of non-uniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a Genetic Algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two-dimensional Hele-Shaw geometry (i.e., parallel-plates). The inverse problem is solved by minimizing the L2-norm between simulated Residence Time Distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C-Dot nanoparticle). Principal Component Analysis (PCA) of spatially-correlated permeability fields enabled reduction of the parameter space by more than a factor of ten and restricted the inverse search to reservoir-scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding-plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

Since 2001, the Mars Exploration Program Analysis Group (MEPAG) has maintained a document outlining community consensus priorities for scientific goals, objectives, and investigations for the robotic and human exploration of Mars [1]. This “Goals Document” is a living document that is revised regularly (~every few years) in light of new Mars science results. It is organized into a hierarchy of goals, objectives, and investigations. The four Goals are not prioritized and are organized around major areas of scientific knowledge: “Life”, “Climate”, “Geology”, and “Preparation for Human Exploration”. Don Banfield is the current MEPAG Goals Committee Chair, and he oversees 2-3 representatives per Goal [2]. The most recent round of revisions (2018) was prompted by discussion at the 6th International Mars Polar Science and Exploration Conference (held in 2016 in Reykjavik, Iceland [3]), which pointed out that current high-priority Polar Science and Present-Day Activity questions were not well represented in content or priorities within the 2015 Goals Document. Upon request from the MEPAG Executive and Goals Committees [2], specific areas of disconnect were highlighted by representatives of the Mars Polar Science community; these were evaluated by the Goals Committee who proposed changes at sub-objective and investigation levels within the Climate and Geology Goals. These proposed changes were open for comment by the larger Mars community for 6 weeks, and then finalized. The official MEPAG 2018 Goals Document will be presented at the meeting. Additionally, the presentation will describe plans for the next round of revisions, which are expected to primarily come out of the presentations and discussion at the 9th International Conference on Mars (to be held at Caltech, Pasadena, CA in July 2019 [4]), and which are expected to include reference to returned sample science. The 2019 MEPAG Goals Document will form an important input to the next Planetary Science Decadal Survey [5]. [1] https://mepag.jpl.nasa.gov/reports.cfm?expand=science [2] https://mepag.jpl.nasa.gov/about.cfm [3] https://www.hou.usra.edu/meetings/marspolar2016/ [4] https://www.hou.usra.edu/meetings/ninthmars2019/ [5] NASEM, 2017. CAPS: Getting Ready for the Next Planetary Science Decadal Survey. https://doi.org/10.17226/24843.

Numeric weather prediction is undergoing a revolution resulting from the continuous advances in scientific knowledge and technologies. With dozens of weather models emerging that all generate different predictions from each other, forecasts have been gradually shifting from a deterministic form to a probabilistic form which shows the increasing concerns of, not just the absolute prediction values, but the confidence of predictions and the uncertainty of models. As a computational problem, generating uncertainty information can be an expensive task. Conventionally, prediction models are initiated with slightly perturbed parameters and then the diversion of model results can be a measure of model uncertainty. However, the multi-simulation approach drastically increases the computational requirement so that it can potentially exceed the ability of the state-of-art high-performance computing platforms. Meanwhile, if spatial and temporal resolutions are of concern, this approach is far from being efficient and viable. The Parallel Ensemble Forecast system is designed to generate probabilistic weather forecasts by using the revolutionary numerical weather prediction technique, Analog Ensemble. It is a data-driven method that derives probability information of a deterministic prediction model using past forecasts and observations without multiple simulation runs. Integrated with high-performance platforms, the system distributes computational tasks among nodes and therefore further boosts the data simulation process.

Although mass and energy in Jupiter's magnetosphere mostly come from the innermost Galilean moon Io's volcanic activity, solar wind perturbations can play crucial roles in releasing the magnetospheric energy and powering aurorae in Jupiter's polar regions. The relative importance of solar wind and internal processes in driving Jupiter's auroras remains poorly understood. Recently, the contemporaneous measurements from NASA Juno mission and the Hubble Space Telescope provide an unprecedented opportunity to determine the magnetospheric drivers of auroral variations at Jupiter, and key evidence on how solar wind would affect the auroral brightening. In this presentation, we will discuss several important advances on several distinctive auroral morphologies at Jupiter, i.e., auroral dawn storm, the main auroral brightening and auroral injection processes. We find that magnetic reconnection and dipolarization play crucial roles in driving these auroras, and the auroral drivers for the Earth and Jupiter have more in common than ever expected.

Despite significant developments in global modeling, the determination of ionospheric conductance in the auroral region remains a challenge in the space science community. With advances in adiabatic kinetic theory and numerical couplings between global magnetohydrodynamic models and ring current models, the dynamic prediction of individual sources of auroral conductance have improved significantly. However, the individual impact of these sources on the total conductance and ionospheric electrodynamics remains understudied. In this study, we have investigated individual contributions from four types of auroral precipitation - electron & ion diffuse, monoenergetic & Alfven wave-driven - on ionospheric electrodynamics using a novel modeling setup. The setup encompasses recent developments within the University of Michigan’s Space Weather Modeling Framework (SWMF), specifically through the use of the MAGNetosphere - Ionosphere - Thermosphere auroral precipitation model and dynamic two-way coupling with the Global Ionosphere-Thermosphere Model. This modeling setup replaces the empirical idealizations traditionally used to estimate conductance in SWMF, with a physics-based approach capable of resolving 3-D high-resolution mesoscale features in the ionosphere-thermosphere system. Using this setup, we have simulated an idealized case of southward Bz 5nT & the April 5-7 “Galaxy15” Event. Contributions from each source of precipitation are compared against the OVATION Prime Model, while auroral patterns and hemispheric power during Galaxy15 are compared against observations from DMSP SSUSI and the AE-based FTA model. Additionally, comparison of field aligned currents (FACs) and potential patterns are also conducted against AMPERE, SuperDARN & AMIE estimations. Progressively applying conductance sources, we find that diffuse contributions from ions and electrons provide ~75% of the total energy flux and Hall conductance in the auroral region. Despite this, we find that Region 2 FACs increase by ~11% & cross-polar potential reduces by ~8.5% with the addition of monoenergetic and broadband sources, compared to <1% change in potential for diffuse additions to the conductance. Results also indicate a dominant impact of ring current on the strength and morphology of the precipitation pattern.

We report on an extreme ionospheric plasma density enhancement and Global Positioning System (GPS) scintillation at dawn, observed within the expanding equatorial ionization anomaly (EIA). The total electron content (TEC) in central America reached 50~TECu at sunrise, the value almost twice as high as the normal afternoon peak. The enhanced EIA expanded poleward and westward from just below 20$^\circ$ magnetic latitude (MLAT) to beyond 30$^\circ$ MLAT at sunrise. The chief ramification of the enhanced EIA was strong GPS scintillation which was observed poleward of 30$^\circ$ northern MLAT and lasted until 8:00 local time. In total, the scintillation lasted for $\sim$5~hours at latitudes north of 20$^\circ$MLAT in central America.

Pharmaceuticals have been recognized as contaminants of emerging concern because of their ubiquitous presence in the environment and the potential they possess in causing undesirable ecological effects. Many long term ill effects of pharmaceuticals on humans are still unknown. Pharmaceuticals are recalcitrant to wastewater treatment facilities and their occurrence has been documented in almost all environmental matrices around the globe. Every third pill consumed by the world is synthesized in India, and despite that, much less attention and concern is noticed in documenting pharmaceuticals occurrence report in the Indian context. In Indian environment, pharmaceuticals have been detected in influent and effluent of wastewater treatment plants, surface waters and groundwater systems. Concentration of few pharmaceuticals such as ciprofloxacin (31 mg/L) and fluconazole (236 mg/L) reported in wastewaters of Hyderabad city in India show the highest values compared to pharmaceuticals concentration reported elsewhere in the world. Other pharmaceutical compounds that are detected in Indian matrix in mg/L range include cetirizine, losartan, and voriconazole. Metoprolol (950 µg/L), enrofloxacin (900 µg/L), citalopram (840 µg/L), moxifloxacin (694 µg/L), norfloxacin (420 µg/L), azithromycin (300 µg/L), atenolol (300 µg/L), levofloxacin (200 µg/L) are amongst other pharmaceuticals that have been detected at high concentration. Only a few research groups have paid attention on detecting pharmaceuticals in India. Disparity exists in occurrence data with most of the studies concentrated in southern India. India is amongst the top manufacturer and consumer of the pharmaceutical compounds and with pharmaceuticals occurring at alarmingly high concentration in environmental waters, much needed attention and emphasis is required to conduct nation-wide occurrence study program to fully assess the ecological risks possessed by the pharmaceuticals.

Management of used nuclear fuel is a major technical challenge faced by nuclear energy producing nations worldwide. In Canada, the Nuclear Waste Management Organization is responsible for the design and implementation of a deep geological repository (DGR), which will be placed ~500 m below ground surface in a stable host rock to safely contain and isolate used fuel. Within a DGR, used nuclear fuel will be placed in used fuel containers (UFCs) that are encased in copper because of copper’s corrosion resistance. The UFCs will be surrounded by highly compacted bentonite to suppress the transport of corrosive agents to the UFC and limit the movement of radionuclides out of a DGR in the unlikely case of a breach. Over the design lifespan of one million years for the DGR, it is possible that sulfate-reducing bacteria near the bentonite-host rock interface can produce bisulfide (HS-) that can be transported to the UFC surface and potentially corrode the outer copper barrier. Therefore, it is crucial to understand HS- transport mechanisms through bentonite to assess the long-term performance of a DGR. This study aims to quantify HS- transport through bentonite using through-diffusion experiments under a range of anticipated DGR and bentonite conditions (e.g., temperature, ionic concentration, bentonite densities). In addition, as geochemical reactions/sorption are expected to affect HS- transport, batch experiments are being conducted to understand these processes independent of transport. The preliminary batch of experimental results show that ~85% of HS- was partitioned from the aqueous to solid phase within first hour and ~97% after 24 hours. However, this partitioning efficiency decreased with increased liquid-to-solid ratio because of the reduction in available reaction/sorption sites on the bentonite. This presentation shows the results from both transport and batch experiments performed on the bentonite under relevant DGR conditions.

We present improvements in the modeling of the vertical wavenumber spectrum of the internal gravity wave continuum in high-resolution regional ocean simulations. We focus on model sensitivities to mixing parameters and comparisons to McLane moored profiler observations in a Pacific region near the Hawaiian Ridge, which features strong semidiurnal tidal beams. In these simulations, the modeled continuum exhibits high sensitivity to the background mixing components of the K-Profile Parameterization (KPP) vertical mixing scheme. Without the KPP background mixing, stronger vertical gradients in velocity are sustained in the simulations and the modeled kinetic energy and shear spectral slopes are significantly closer to the observations. The improved representation of internal wave dynamics in these simulations makes them suitable for improving ocean mixing estimates and for the interpretation of satellite missions such as the Surface Water and Ocean Topography (SWOT) mission.

Macroalgae are foundational to the health of many Indigenous social-ecological systems, and their production of dissolved organic carbon (DOC) serves various biogeochemical roles. Improved understanding of seasonal variations in DOC release as an ecophysiological response could therefore help Indigenous stewards balance these implications. However, multi-year seasonal studies of macroalgal DOC release are few and the underlying roles of passive and active DOC diffusion need clarifying. This study focuses on the kelp Saccharina japonica var. religiosa (class Phaeophyceae) from Oshoro Bay, Ainu Mosir (Hokkaido). The conclusions are supported by three years (2020–2022) of data, including 1091 DOC samples from 16 incubation experiments (t = 4–9 days) comparing individual kelp (n = 88) to in situ seawater control tanks (n = 31) under different photosynthetically active radiation (PAR) treatments (200, 400, 1200, or 1500 µmol photons · m−2 · s−1). Differences in PAR, dry weight biomass, sea surface temperature, or salinity could not explain DOC release rate variability, which was high between individual kelp. Instead, there were significant intra-annual differences, with mean DOC release rates (mg C · g DW−1 · d−1) (± standard error between n kelp) higher (p < 0.05) during the autumn “late decay” period (0.82 ± 0.12, n = 27) compared to the winter “early growth” period (0.20 ± 0.028, n = 10) and summer “early decay” period (0.34 ± 0.066, n = 24). Monitoring this relationship between seasonal decay and macroalgal DOC release may therefore help inform Indigenous stewardship strategies.

Coupled climate simulations that span several hundred years cannot be run at a high-enough spatial resolution to resolve mesoscale ocean dynamics. These mesoscale dynamics backscatter to macroscales. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from idealized simulations. In this manuscript, we present a stochastic Deep Learning parameterization that is trained on data generated by CM2.6, a high-resolution state-of-the-art coupled climate model with nominal resolution 1/10° . We train a Convolutional Neural Network for the subgrid momentum forcing using macroscale surface velocities from a few selected subdomains. At each location and each time step of the coarse grid, rather than predicting a single number, we predict the mean and standard deviation of a Gaussian probability distribution. This approach requires training our neural network to minimize a negative log-likelihood loss function rather than the Mean Square Error, which has been the standard in applications of Deep Learning to the problem of parameterizations. Each prediction of the mean subgrid forcing can be associated with an uncertainty estimate and can form the basis for a stochastic subgrid parameterization. Offline tests show that our parameterization generalizes well to the global oceans, and a climate with increased CO2 levels, without further training. We test our stochastic parameterization in an idealized shallow water model. The implementation is stable and improves some statistics of the flow. Our work demonstrates the potential of combining Deep Learning tools with a probabilistic approach in parameterizing unresolved ocean dynamics.

The Moho discontinuity plays an important role in crustal growth and evolution. In this study, we delineate the Moho geometry in southern California by jointly using local Moho-reflected waves PmP and teleseismic Moho-converted waves Ps. To well constrain the Moho geometry, we have developed a two-stage process to pick PmP waves and have created a reliable PmP travel time data set with a total of 10,192 picks. We have also extracted 38,648 high-quality P-wave receiver functions (RFs). The Moho depth is initially estimated via the common conversion point (CCP) stacking of RFs and then refined by inverting the PmP travel time data in a community velocity model (CVM-H, version 15.1.1). The newly built Moho geometry is generally consistent with the California Moho Model version 1.0 (CMM-1.0), that is, a shallow Moho beneath the Salton Trough (23 km), a uniformly shallow Moho beneath the Mojave Desert and the Basin and Range (<29 km), and a sliver of deep Moho under the western Peninsular Ranges, the eastern Transverse Ranges, and the western Sierra Nevada (>34 km). However, our Moho model reveals some new features different from the CMM–1.0, such as a deep Moho (∼34 km) beneath the northern end of the central and western Transverse Ranges, consistent with the observation of deep seismicities due to a thick brittle crust there. We also find a gradual transition from the lower crust to the uppermost mantle beneath the western Peninsular Ranges, leading to the rareness of pickable PmP waves as well as weak Moho-converted signals there.

The Monsoon Asia region is home to ten of the world’s biggest rivers, supporting the lives of 1.7 billion people who rely on streamflow for water, energy, and food. Yet, a synthesized understanding of multi-centennial streamflow variability for this region is lacking. To fill this gap, we produce the first large scale streamflow reconstruction over Monsoon Asia (62 stations in 16 countries, 813 years of mean annual flow). In making this reconstruction, we develop a novel, automated, climate-informed, and dynamic reconstruction framework that is skillful over most of the region. We show that streamflow in Monsoon Asia is spatially coherent, owing to common drivers from the Pacific, Indian, and Atlantic Oceans. We also show how these oceanic teleconnections change over space and time. By characterizing past and present hydroclimatic variability, we provide a platform for assessing the impact of future climatic changes and informing water management decisions.

We compute stress drops for earthquakes in Northern Chile recorded between 2007to 2021. By applying two different analysis techniques, (1) the spectral ratio (SR) method and (2) the spectral decomposition (SDC) method, a stress drop map for the subduction zone consisting of 51,510 stress drop values is produced. We build an extended set of empirical Green’s function (EGF) events for the SR method by systematic template matching. Outputs are used to compare with results from the SDC approach, where we apply cell-wise obtained global EGF’s to compensate for the structural heterogeneity of the subduction zone. We find a good consistency of results of the two methods. The increased spatial coverage and quantity of stress drop estimates from the SDC method facilitate a consistent stress drop mapping of the different seismo-tectonic domains. Albeit differences of median stress drops are relatively small, strike-perpendicular depth sections clearly reveal systematic variations, with earthquakes within the upper plate, along the interface and at intermediate depth exhibiting distinct values. In particular, interface seismicity is characterized by the lowest observed median value, whereas upper plate earthquakes show noticeably higher stress drop values. Intermediate depth earthquakes show comparatively high average stress drop and a rather strong depth-dependent in-crease of median stress drop.  Additionally, we observe spatio-temporal variability of stress drops related to the occurrence of the two megathrust earthquakes in the study region. The here presented study is the first coherent large scale 3D stress drop mapping of the Northern Chilean subduction zone. It provides an important component for further detailed analysis of the physics of earthquake ruptures.