Data-processing methods only considering the turn-off ramp in transmitting waveforms are well established for land-based transient electromagnetic (TEM) methods. The turn-on ramp effects on late-time responses are generally neglected by data-processing methods. Our forward-modeling results of homogeneous half-space models show that the full-waveform effect which includes both effects caused by turn-on and turn-off stages is common for a wide range of conductivities. The inversion results for synthetic and field examples illustrate that the inversion algorithm which does not consider the full-waveform effects can lead to a higher resistivity in deeper parts of recovered models compared with the true model. Therefore, it is of vital importance to account for the full waveform effects in both forward modeling and inversion algorithms. We use synthetic examples to show how our inversion algorithms can improve the recovered model, and use real-data examples to show the importance of incorporating full transmitting waveforms in data-processing procedures.

Topographic flexure in response to vertical loads reveals key lithospheric properties, including elastic thickness and the heat flow from the interior. Flexural stresses may also control volcano morphology. One previous study predicted that steep-sided domes on Venus usually form where the elastic thickness is ~15-40 km. We surveyed flexural signatures around steep-sided domes and confirmed this hypothesis. We determined elastic thickness from topographic profiles with a curve-fitting algorithm and a plate bending model in Cartesian and axisymmetric geometry. We used a yield stress envelope to convert elastic thickness and plate curvature into mechanical thickness and surface heat flow. The average elastic thickness for domes not near coronae is ~30 km, corresponding to a heat flow of ~60 mW/m2. Coronae on Venus are typically associated with elastic thicknesses of <10-15 km. Domes near coronae yielded elastic thicknesses in this range, and higher heat flows than domes not near coronae.

The long-existing double-ITCZ problem in Global Climate Models (GCMs) hampers accurate climate simulation. Using a regional climate model (RCM) over the tropical and sub-tropical Atlantic with a horizontal resolution of 12 km and explicit convection, we develop a bias-correction downscaling methodology to remove GCM biases. The methodology is adapted from the pseudo-global warming (PGW) approach, typically used to exert the climate-change signal to a reanalysis-driven RCM simulation. We show that the double ITCZ problem persists with conventional dynamical downscaling, but with our bias-corrected downscaling, the double ITCZ problem is removed. Detailed analysis attributes the main cause of the double ITCZ problem of the selected GCM to the sea surface temperature (SST) bias. Compared to the GCM’s AMIP simulations, RCMs with higher resolution allow explicit deep convection and enable a better simulation of tropical convection and clouds. The developed methodology is promising for constraining climate sensitivity by removing double-ITCZ biases.

The Weddell Gyre is a dominant feature of the Southern Ocean and an important component of the climate system; it regulates air-sea exchanges, controls the formation of deep and bottom water, and hosts upwelling of relatively warm subsurface waters. It is characterized by extremely low sea surface temperatures, active sea ice formation, and widespread salt stratification that stabilizes the water column. Studying the Weddell Gyre is difficult, as it is extremely remote and largely covered with sea ice; at present, it is one of the most poorly-sampled regions of the global ocean, highlighting the need to extract as much value as possible from existing observations. Thanks to recent efforts of the EU SO-CHIC project, much of the existing Weddell Gyre data, including ship-based CTD, seal tag, and Argo float profiles, has been assembled into a coherent framework, enabling new comprehensive studies. Here, we apply unsupervised classification techniques (e.g. Gaussian Mixture Modeling) to the new comprehensive Weddell Gyre dataset to look for coherent regimes in temperature and salinity. We find that, despite not being given any latitude or longitude information, unsupervised classification algorithms identify spatially coherent thermohaline domains. The highlighted features include the Antarctic Circumpolar Current, the central Weddell Gyre, and the Antarctic Slope current; we also find potential signatures of the inflow of Weddell Deep Water and export pathways of Antarctic Bottom Water. We show how varying the statistical, machine learning derived representations of the data can reveal different physical structures and circulation pathways that are relevant to the delivery of relatively warm waters to the higher-latitude seas and their associated ice shelves.

Recent work using CMIP5 models under RCP8.5 suggests that individual multimodel-mean changes in precipitation and wind variability associated with the Madden-Julian oscillation (MJO) are not detectable until the end of the 21st century. However, a decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detectable as early as 2021-2040, consistent with an increase in dry static stability as predicted by weak-temperature-gradient balance. Here, we examine MJO activity in multiple reanalyses (ERA5,MERRA-2, and ERA-20C) and find that MJO wind and precipitation anomaly amplitudes have a complicated time evolution over the record. However, a decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detected over the observational period, consistent with the change in dry static stability. These results suggest that weak-temperature-gradient theory may be able to help explain changes in MJO activity in recent decades.

From devastating monsoons to sea level rise, extreme weather is taking its toll across the globe. Surging Waters looks at flooding in the United States and demonstrates how science is supporting flood management, as well as furthering the solutions needed to mitigate flood impacts on people and property in the future. The report's authors highlight three types of flooding---flooding due to hurricanes, flooding in the central U.S., and coastal flooding---through local stories. In 2017, Houston, Texas, was hit by Hurricane Harvey, the second most damaging weather disaster in U.S. history, and is still recovering. The city of De Soto, Mo., is emblematic of many areas in the Midwest that have been plagued by recurrent flash flooding. The Hampton Roads area of coastal Virginia has fallen victim to sinking land and rising seas. Through these stories and others, and compelling flood data presented for regions across the United States, the report shows how scientific research and data collection are essential to finding modern-day and future solutions to mitigate flooding. Robust funding for science-related federal agencies drives the advancement of science and provides support that is critical for the most vulnerable communities and individuals. Surging Waters, produced by AGU, a global not-for-profit scientific society dedicated to advancing the Earth and space sciences for the benefit of humanity, recommends actions to build a more resilient and sustainable society. Reviewed by leading experts on the issues of flooding and extreme weather, this report aims to empower community members and leaders, scientists, federal agencies, and policymakers to work together to establish a stronger future for us all.

We have used the LOw-Frequency ARray (LOFAR) to image a few lightning flashes during a particularly severe thunderstorm. The images show an exceptional amount of VHF activity at altitudes above 10 km. Much of this is in the form of small-scale discharges occurring seemingly randomly around the centers of active storm cells. Because of their small and incidental structure we refer to these as ‘speckles’. A detailed investigation shows strong evidence that these speckles are indicative of positive leader channels and that they are equivalent to the needle activity seen around positive leader tracks at lower altitudes.

Forests are under major pressures due to contemporary land-use, which creates mosaics of stand-stage development that follow different successional paths, that imply ecosystem complexity. The interplay of carbon and water dynamics across succession involves physical and biological interactions that shape net ecosystem production (NEP) and water use efficiency. Here we present 13 years-site of eddy covariance data (2016-2020) from a seasonally dry tropical forest in Northwestern Mexico to elucidate the environmental controls on ecosystem fluxes and explore the interactions with changes in resource availability. Across a successional gradient, an early (9 years since abandonment) and a mid-successional (about 45 years with natural recruitment and regrowth) sites were net carbon sinks (in the order of 100 to 500 g C m-2 y-1) while an old- growth forest was a chronic net source over the 5 years studied (losing between 100 and 300 g C m-2 y-1). In contrast evapotranspiration was alike at sites and close to the precipitation input. Ecosystem water use efficiency tended to be higher at the old-growth forest site (ca. 3.0 g C m-2 /mm H2O vs. ca. 2.0 g C m-2 / mm H2O at the secondary sites). Water availability and radiation where clearly dominant environmental controls across sites, but notably vapor pressure deficit was not a controlling factor for gas exchange at the old-growth forest. Surface characteristics, canopy structure and species composition may explain differences in NEP across succession in TDF at its northernmost extent.

Machine learning has the potential to automate the analysis of vast amounts of raw geophysical data, allowing scientists to monitor changes in key aspects of our climate such as cloud cover in real-time and at fine spatiotemporal scales. However, the lack of large labeled training datasets poses a significant barrier for effectively applying machine learning to these applications. Transfer learning, which involves first pretraining a neural network on an auxiliary “source” dataset and then finetuning on the “target” dataset, has been shown to improve accuracy for machine learning models trained on small datasets. Across prior work on machine learning for geophysical imaging, different choices are made about what data to pretrain on, and the impact of these choices on model performance is unclear. To address this, we systematically explore various settings of transfer learning for cloud classification, cloud segmentation, and aurora classification. We pretrain on different source datasets, including the large ImageNet dataset as well as smaller geophysical datasets that are more similar to the target datasets. We also experiment with multiple transfer learning steps where we pretrain on more than one source dataset. Despite the smaller source datasets’ similarity to the target datasets, we find that pretraining on the large, general-purpose ImageNet dataset yields significantly better results across all of our experiments. Transfer learning is especially effective for smaller target datasets, and in these cases, using multiple source datasets can give a marginal added benefit.

For decades the scientific community has been trying to reconcile abundant evidence for fluvial activity on Noachian and early Hesperian Mars with the faint young Sun and reasonable constraints on ancient atmospheric pressure and composition. Recently, the investigation of H2-CO2 collision induced absorption has opened up a new avenue to warm Noachian Mars. We use the ROCKE-3D global climate model to simulate plausible states of the ancient Martian climate with this absorptive warming and reasonable constraints on surface paleopressure. We find that 1.5-2 bar CO2-dominated atmospheres with 3% H2 can produce global mean surface temperatures above freezing, while also providing sufficient warming to avoid surface atmospheric CO2 condensation at 0°-45° obliquity. Simulations conducted with both modern topography and a paleotopography, before Tharsis formed, highlight the importance of Tharsis as a cold trap for water on the planet. Additionally, we find that low obliquity (modern and 0°) is more conducive to rainfall over valley network locations than high (45°) obliquity.

Seafloor spreading at slow rates can be accommodated on large-offset oceanic detachment faults (ODFs), that exhume lower crustal and mantle rocks in footwall domes termed oceanic core complexes (OCCs). Footwall rock experiences large rotation during exhumation, yet important aspects of the kinematics - particularly the relative roles of rigid block rotation and flexure - are not clearly understood. Using a high-resolution numerical model, we explore the exhumation kinematics in the footwall beneath an emergent ODF/OCC. A key feature of the models is that footwall motion is dominated by solid rotation, accommodated by the concave-down ODF. This is attributed to a system behaviour in which the accumulation of distributed plastic strain is minimized. A consequence of these kinematics is that curvature measured along the ODF is representative of a neutral stress configuration, rather than a ‘bent’ one. Instead, it is in the subsequent process of ‘apparent unbending’ that significant flexural stresses are developed in the model footwall. The brittle strain associated with apparent unbending is produced dominantly in extension, beneath the OCC, consistent with earthquake clustering observed in the Trans-Atlantic Geotraverse at the Mid-Atlantic Ridge.

Planetary scientists can face challenges in effectively interacting with audiences. To help address their needs, the Lunar and Planetary Institute (LPI) conducts professional development for planetary scientists through seminars and workshops. LPI also enables scientists to participate in public events, and connects them to requests for speakers. LPI’s Sharing Planetary Science seminars include current educational research and incorporate the participants’ own knowledge and experiences in engaging public audiences. Participants attend in-person or online. Post-session surveys indicate that all participants (100%) have found at least one aspect of each seminar valuable. Resulting resources are available online. www.lpi.usra.edu/education/scientist-engagement. In March 2019, working with the Volatiles, Regolith and Thermal Investigations Consortium for Exploration and Science (VORTICES) program, LPI conducted the first Planetary Scientist Engagement Institute. This 2-day training was attended by 23 planetary scientists from across the U.S. who learned about audience needs, common solar system misconceptions, resources for engaging audiences, addressing controversial topics, partnering to reach diverse audiences, and more. In the follow-up evaluation, all (100%) of participants found that the program met their goals and was useful. LPI has conducted a variety of scientist engagement activities at conferences, including sessions and workshops on communicating with audiences, using social media to share their science, and communicating with news media. All have proven popular. LPI hosts a variety of public events, and enables planetary scientists to participate as speakers and as activity facilitators. LPI connects scientists with requests for presentations, both locally and virtually. Future plans include developing a mechanism for evaluating the process and the perceived impact by the scientists. This poster will include further details of these professional development sessions, their resulting resources, the evaluations, and future plans.

As the most severe drought over the Northeastern United States (NEUS) in the past century, the 1960s drought had pronounced socioeconomic and natural impacts. Although it was followed by a persisting wet period, the conditions leading to the 1960s extreme drought could return in the future, along with its challenges to water management. To project the characteristics and potential consequences of such a future drought, pseudo-global warming simulations using the Weather Research and Forecasting Model are performed to simulate the dynamical conditions of the historical 1960s drought, but with modified thermodynamic conditions under the RCP8.5 scenario in the early (2021-2027), middle (2041-2047) and late (2091-2097) 21st century. Our analysis focuses on essential hydroclimatic variables including temperature, precipitation, evapotranspiration, soil moisture, snowpack and surface runoff. In contrast to the historical 1960s drought, similar dynamical conditions will generally produce more precipitation, increased soil moisture and evapotranspiration, and reduced snowpack. However, we also find that although wet months get much wetter, dry months may become drier, meaning that wetting trends are most significant in wet months but are essentially negligible for extremely dry months with negative monthly mean net precipitation. For these months, the trend towards wetting conditions provides little relief from the effects of extreme dry months. These conditions may even aggravate water shortages due to an increasingly rapid transition from wet to dry conditions. Other challenges emerge for residents and stakeholders in this region, including more extreme hot days, record-low snow pack, frozen ground degradation and subsequent decreases in surface runoff.

Plant growth is the product of gene by environment (GxE) interactions during plant development. Effective characterization of plant growth under various conditions provides insight into genetic components of plant development and mechanisms of stress resilience. While the emergence of high-through phenotyping facilities provides new avenues to further understand plant development and stress responses, the large costs of such facilities are hindering the study of dynamic growth processes. To democratize high-throughput plant phenotyping, we developed three sets of image-based phenotyping devices utilizing Raspberry Pi computers and low-weight/low-cost materials to continuously monitor shoot and root growth. The process is further automated by our workflows including data collection and statistical analysis. Our devices and workflows are customizable to image a wide variety of plants and tissues. To validate our system, we measured growth of Arabidopsis rosettes, tomato roots, and characterized the relationship between cowpea growth and evapotranspiration. These results demonstrate the variety of applications for Raspberry Pi based phenotyping. Importantly, this low-cost system is ideal for studying the genetics of plant growth and identifying new components of abiotic stress tolerance in a wide variety of species.

The objectives of this paper are to investigate the tradeoffs between a physically constrained neural network and a deep, convolutional neural network and to design a combined ML approach (“VarioCNN”). Our solution is provided in the framework of a cyberinfrastructure that includes a newly designed ML software, GEOCLASS-image, modern high-resolution satellite image datasets (Maxar WorldView data) and instructions/descriptions that may facilitate solving similar spatial classification problems. Combining the advantages of the physically-driven connectionist-geostatistical classification method with those of an efficient CNN, VarioCNN, provides a means for rapid and efficient extraction of complex geophysical information from submeter resolution satellite imagery. A retraining loop overcomes the difficulties of creating a labeled training data set.Computational analyses and developments are centered on a specific, but generalizable, geophysical problem: The classification of crevasse types that form during the surge of a glacier system. A surge is a glacial catastrophe, an acceleration of a glacier to typically 100-200 times its normal velocity, which for a marine-terminating glacier leads to sudden and substantial mass transfer from the cryosphere to the oceans, contributing significantly to sea-level-rise. The sudden and rapid acceleration characteristic of a surge results in formation of crevasses, whose spatial characteristics provide informants on the ice-dynamic processes that occur during the surge. GEOCLASS-image is applied to study the current (2016-2024) surge in the Negribreen Glacier System, Svalbard. The geophysical result is a description of the structural evolution and expansion of the surge, based on crevasse types that capture ice deformation in 6 simplified classes.

Floods inflict significant damage even outside the 100-year floodplain. Thus, restricting flood risk analysis to the 100-year floodplain (special flood hazard area (SFHA) in the U.S.A.) is misleading. Flood risk outside the SFHA is often underestimated because of minimal flood-related insurance requirements and regulations and sparse flood depth data. This study proposes a systematic approach to predict flood risk for a single-family home using average annual loss (AAL) in the shaded X Zone – the area immediately outside the SFHA (i.e., the 500-year floodplain), which lies between the 1.0- and 0.2-percent annual flood probability. To further inform flood mitigation strategy, annual flood risk reduction with additional elevation above an initial first-floor height () is estimated. The proposed approach generates synthetic flood parameters, quantifies AAL for a hypothetical slab-on–grade, single-family home with varying attributes and scenarios above the slab-on-grade elevation, and compares flood risk for two areas using the synthetic flood parameters vs. an existing spatial interpolation-estimated flood parameters. Results reveal a median AAL in the shaded X Zone of 0.13 and 0.17 percent of replacement cost value () for a one-story, single-family home without and with basement, respectively, at and 500-year flood depth < 1 foot. Elevating homes one and four feet above substantially mitigates this risk, generating savings of 0.07–0.18 and 0.09–0.23 percent of for a one-story, single-family home without and with basement, respectively. These results enhance understanding of flood risk and the benefits of elevating homes above in the shaded X Zone.

The assessment of planktonic organisms is a prevailing task in marine ecology and oceanography as planktons form the principal food source for consumers at higher trophic levels. Reliable estimates on the production at the lowermost trophic levels are thus an integral part for the management of marine ecosystems. Traditional plankton sampling and analysis are limited in their spatial and temporal context of the organisms’ environment, which are often critical clues to a biologist for its habitation. In addition, ship-based sampling as described leads to a high level of uncertainty in the estimation, since point measurements that are intermittent in space and time are used (Reid et al 2003; Lermusiaux 2006; Vannier 2018). A disruptive change in approach to tackle this problem is currently taking place, enabled by the use of autonomous robots (Henthorn et al 2006) and augmented by visual sensing for real-time analysis (Ohman et al 2019; AILARON 2019). Approaches providing taxonomy estimates from time-series image analysis (Sosik and Olson 2008) or via computer simulations (Roberts and Jaffe 2007), with the recent advances in deep learning, enabled by the computational power of multicore CPUs and GPUs, made possible processing and classification of large datasets while learning higher level representations. Enhanced traditional machine learning methods are driven by multiple kernel learning, where general features are combined with robust features and new types from multiple views are defined in order to generate multiple classifiers (Py et al 2016; Dai et al 2016; Lee et al 2016; Moniruzzaman et al 2017). In this paper, we present recent DL methods for microscopic organisms’ identification and classification. A proposed DL architecture (cf. figure 2) reported an accuracy of 95% as opposed to (90% - 93% cf. table 2) achieved by the state-of-the-art networks: ZooplanktoNet, VGGNet, AlexNet, ResNet, and GoogleNet, while training over a labeled dataset of extracted objects from images of plankton organisms captured in-situ (cf. table 1). COAPNet is embedded into a light-weight autonomous vehicle (LAUV) for real-time in-situ plankton taxa identification and classification. The LAUV in turn utilizes the feedback from the image analysis to constantly update a probability density map that further enables an adaptive sampling process.

To address climate change, social issues need to be integrated in geoscience-related STEM research and curriculum. However, social issues are often ignored in STEM fields, even though STEM practices are usually implicitly rooted in inequitable values and practices. A previous study showed that some students do not see the relevance of diversity, equity, and inclusion (DEI) activities when they are presented as separate from technical content, and some students argued that social content was irrelevant or inappropriate in a technical STEM course. Some engineering faculty have identified the lack of curriculum as a barrier to integrating the socially embedded nature of STEM into their introductory courses. Therefore, we developed a case that addresses social issues such as race, class, and gender in relationship to the social and environmental characteristics of New Orleans and its built environment. The case shows how marginalized people, who have been historically and systemic discriminated against, suffered the greatest losses under the impacts of climate-change driven Hurricane Katrina. The case uses student-centered learning strategies, is applicable for use in a range of courses, including many geoscience courses, and will be publicly available. In Fall 2019 the case will be taught in two first year undergraduate civil and environmental engineering courses (n ≈120) at a R1 university. Coursework from the case will be analyzed using Qualitative Content Analysis, which distills responses into categories and themes that characterize both commonalities and differences. While we do not yet have results, results will be presented at AGU. Our results will provide insight into the effectiveness of our case study as an intervention as well as challenges and successes in implementation, which may help others implement this or other similar activities. More broadly, our intervention demonstrates one way of integrating content more commonly found in interdisciplinary courses into technical, disciplinary-focuses courses. Our study will provide insight into how students respond when DEI and technical content are interconnected, helping further our ability to prepare students in a range of geoscience-related courses to respond to the challenges of climate change through an equity and justice-oriented lens.