Global storm-resolving model (GSRM) simulations (kilometer-scale horizontal resolution) of the atmosphere can capture the interaction between the scales of deep cumulus convection and the large-scale dynamics and thermodynamic properties of the atmosphere. Here, we assess the vertical structure of tropical temperature change in the GSRM X-SHiELD, developed by the Geophysical Fluid Dynamics Laboratory, perturbed by a uniform sea surface temperature (SST) warming and/or increased CO2 concentration. The simulated response to SST warming shows weakly amplified warming from the surface through the mid-troposphere before increasing to a factor of about 2.5 near the tropopause. This combination of muted warming in the mid-troposphere and amplified warming aloft is within the range of CMIP6 models at individual pressure levels but, taken together, is distinctive behavior. The response to CO2 increase with unchanged SST is an approximately vertically uniform warming, comparable to CMIP6 models, and is linearly additive with the SST-induced warming in X-SHiELD.

Mineral hazards are a common, yet often less-recognized group of features compared to other types of natural and man-made hazards. We define “mineral hazards” in part as minerals and elements that occur naturally in elevated, potentially harmful, concentrations in rocks, soils, and certain fluids. Also included are features from human activities related to extraction of mineral and energy resources. Along with its large human population, extensive development, and diverse natural environment, California is very complex geologically, thus it contains many areas of mineral hazards that make it appropriate for such a study. Although mineral hazards have been investigated over several decades by the California Geological Survey (CGS), no systematic statewide assessment had been accomplished until recently when, at the request of the California Department of Transportation (Caltrans), the CGS completed a preliminary assessment of potential mineral hazards over the entire state. This work focused on natural and man-made minerals-related features that might adversely affect construction, use, and maintenance of state and federal highways under Caltrans jurisdiction. The features evaluated include: 1) geologic units that may contain naturally-occurring asbestos (NOA), fibrous erionite, or elevated concentrations of regulated metals (Ag, Ba, Be, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Tl, V, Zn) and metalloids (As, Sb, Se); 2) faults, which can be sites of increased potential for certain types of mineralization; 3) mines and prospects, which can be sources of anomalous concentrations of metals and ore-processing chemicals; 4) oil and natural-gas seeps; 5) thermal springs and fumaroles; and 6) oil, natural-gas, and geothermal wells. The methods and products developed during the Caltrans study can be applied worldwide to many other uses besides highways where there are obligations to protect public health and safety and the environment. The products include maps that highlight types and locations of potential mineral hazards, digital data in GIS format, and accompanying reports that provide details and additional information. Although they do not indicate risk or probability, these products can be applied by users from many backgrounds as screening tools to assess potential for the presence of mineral hazards.

The decline in global emissions of carbon dioxide due to the COVID-19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID-related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO2), a quantity that is regularly measured. We find a unique fingerprint in global-scale ΔpCO2 that is attributable to COVID and potentially detectable in observations, but only with much larger emissions reductions than those that have been observed to date.

We present detailed 3-D images of whole mantle P-wave velocity structure beneath Southeast Asia and surrounding regions. The results are obtained by applying an updated global tomographic method to invert ~8 million P, pP, PP, PcP, and Pdiff arrival times from 23,587 earthquakes recorded at 14,136 stations distributed all over the world. Our tomographic model reveals a continuous, thin low-velocity (low-V) zone from the surface to the core-mantle boundary beneath the Hainan hotspot, which may reflect the Hainan plume that exists in the whole mantle. Beneath the Australian slab that has subducted into the lower mantle, a strong low-V anomaly is detected, which may reflect subslab hot mantle upwelling (SHMU) due to return flow of the slab subduction. Our model also shows the distinct shape of subducted slabs in the upper mantle and slab remnants in the lower mantle. In particular, a hole in the subducting Australian slab is revealed at depths of 280–430 km beneath eastern Java. The low-V anomaly in the mantle wedge above the Australian slab is connected with the SHMU through the slab hole, suggesting that mixture of the island arc magma and the SHMU may have caused huge eruptions of the Tambora and Rinjani volcanoes in eastern Java.

Remote sensing observations are our primary method of studying planetary surfaces, and in the inner solar system, in situ exploration quickly provided ground truth to these remote sensing observations. Our view of the surface appearance of worlds like the Moon, Mars, and even Venus has grown in tandem with our understanding of the large-scale structure from remote sensing. However, our knowledge of the icy worlds of the outer solar system is based solely on decades of remote sensing observations without any in situ surface data to help understand how geological processes are manifest on these worlds. The surfaces of icy worlds like Europa are likely to be truly alien in appearance, dominated by processes such as impact gardening, sputtering, sintering, and other types of physical and chemical weathering that act together in ways we have never yet observed in situ. Remote sensing has revealed that Europa’s surface consists of an icy layer, exposed to the vacuum of space at cryogenic temperatures. The airless rocky Moon may be the best landed analog for Europa’s surface, but the Moon is an old, battered world covered with impact craters, which have gardened the surface to a highly-mixed regolith depth of 5-15 meters overlying kilometers of broken-up megaregolith. Europa’s young surface, approximately tens of millions of years old, likely has a gardening depth on the scale of centimeters up to a meter (Costello et al., AGU Fall Meeting, 2019). The rocky Moon is also compositionally different from icy Europa, and the thermal and radiolytic processes that shape the texture of the uppermost surface of an icy body have no rocky analog. As study of icy worlds has continued on the basis of remote sensing data only, multiple competing models exist for the formation of various surface features. Follow-up flyby and orbital missions may not be able to resolve these situations even with higher-resolution remote sensing data and digital elevation models. Images taken by an in situ surface lander on an icy world such as Europa, coupled with ground truth compositional and other measurements, will be essential to our understanding of how geologic processes work on these worlds. A mission such as a Europa Lander is the necessary next step, and will revolutionize our ability to interpret remote sensing data from myriad other bodies in the outer solar system.

Accessibility of scientific research to underrepresented voices is a forefront issue. Intentionally-designed citizen science programs, such as the University of Alaska Fairbanks’ (UAF) citizen science and learning research project “Arctic Harvest-Public Participation in Scientific Research,” are poised to improve the participation and effectiveness of citizen science across diverse audiences while gathering rigorous data. We selected our research question after first listening to stories of how Alaska’s rural grocery store - the land and its bounty of berries - has become more variable. We investigate how shifts in climate affect the fate of subsistence berries and timing of berry loss from plants in fall and winter across Alaska. In our presentation, we outline the design elements and accommodations we made to enable a diverse group of 1,099 participants in 28 communities to collect phenology, berry abundance and condition, temperature, cloud cover, and snowpack data across ages, cultures, and learning environments. Over half of our volunteers were pre-K to 6th grade, while just under 10% were adults. Approximately 44% of our participants (479 of 1099 participants) were from groups historically underrepresented in STEM fields. We present learning outcomes evaluation, data collection approaches, and data quality per age group. To improve the success of a citizen science program, find the overlap between a topic of personal and cultural relevance to diverse participants and your university’s ongoing research programs.

Landslides cause billions of dollars in property damage and thousands of deaths every year worldwide. India has more than 15% of its land area prone to landslides, hence mapping of these areas for the presence of landslides is of utmost importance. Landslide susceptibility zonation maps give approximate information about the occurrence of landslides. There are various factors responsible for slope instability. In this work, 11 causative factors have been considered such as Aspect, Elevation, Geology, Distance from thrusts, Distance from streams, Plan curvature, Profile curvature, Slope, Stream power index, Tangential curvature, Topographic wetness index. Machine learning methods such as artificial neural network, support vector machine require a large amount of training data; however, the number of landslide occurrences are limited in a study area. The limited number of landslides leads to a small number of positive class pixels in the training data. On contrary, the number of non-landslide pixels (negative class pixels) are huge in numbers. This under-represented data and severe class distribution skew create a data imbalance for learning algorithms and sub-optimal models, which are biased towards the majority class (non-landslide pixels) and have low performance on the minority class (landslide pixels). Generally, the data is imbalanced when the class ratio is of the order of 100:1, 1000:1 and 10000:1 (i.e., one-class points are 100, 1000 or 10000 times more than that of another class points). In our work, class ratio is more than 300:1 (i.e. for each one landslide pixel, we have more than 300 non-landslide pixels). Thus, we can clearly say that our data is imbalanced. There are two major data balancing techniques, which are oversampling of a minority class and under-sampling of majority class. The minority oversampling cannot be applied, as it will create false landslide pixels. We have performed under-sampling of non-landslide pixels using various techniques. We will discuss landslide susceptibility zonation with and without using data imbalance technique and show major improvements in accuracy over imbalanced learning.

The Community Surface Dynamics Modeling System (CSDMS), an international organization of over 1700 members, has a mission to enable model use and development for research in earth surface processes. CSDMS strives to expand the use of quantitative modeling techniques, promotes best practices in coding, and advocates for the use of open-source software. As a service for its members, the CSDMS Integration Facility (IF) maintains a code repository for numerical models. The CSDMS Model Repository, initialized in 2009, currently holds over 300 open source models and tools. To submit code to the Repository, a community member completes an online form, providing metadata for their code and selecting an open source license. In return for the code contribution, CSDMS provides a home for the model on its publicly accessible site. The model page is initially populated with the metadata provided by the author, but it can be edited and expanded to include documentation, examples, references, and graphics. If the code is available on a public repository, such as GitHub, a link to it is provided from the Repository; otherwise, the code is added to the Repository’s GitHub repository. The version of the code submitted to the Repository is assigned a DOI, making it citable. A QR code, suitable for display on a conference poster, is also created. Finally, the CSDMS IF has devised a model h-index, which gives a measure of a model’s visibility through journal citations. By submitting code to the CSDMS Model Repository, a model developer gets visibility, findability, accessibility, storage, and preservation for their model code. CSDMS gets a library of open source models that can be used for research. This can help accelerate science, since it’s often easier to use or modify an existing model than it is to start from scratch. The Repository also helps prevent model codes from going “dark” and being forgotten. Above all, the Repository serves the ethos of community modeling promoted by CSDMS.

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

Course Based Undergraduate Research Experiences (CUREs) are an effective method of teaching students not only content, but also critical thinking, scientific practice, and other skills beneficial to their education and success. They lower the barrier to participation in undergraduate research, thereby increasing access to entry. Thus, CUREs are especially valuable to under-performing students as they are an effective means of bridging the achievement gap. Due to the value and effectiveness of CUREs in student development, Lynn University has implemented a means by which students are exposed to CUREs and skills necessary to complete a CURE throughout the Environmental Studies major curriculum. This presentation will give a description of the curriculum and how CUREs and CURE-required skills are taught throughout the curriculum culminating in a fully independent capstone research project.

The 1601 Common Era earthquake (Mw ca. 5.9) in “Unterwalden”, Central Switzerland triggered multiple subaqueous mass movements and a subaerial rockfall that generated tsunami waves with devastating run-up heights of up to 4 m and several hundred meters of inundation along the coastal lowland plain of Lake Lucerne. In the shallow Lucerne Bay at the outlet of the perialpine lake, historical chronicles reported a seiche with an initial amplitude of ~1–2 m and a period of 10 min that decreased with time but persisted for several days after the event. The impact and erosion potential of the tsunami wave on the Lucerne Bay is assessed with sediment core analysis and numerical simulation of wave propagation. A 60 cm thick offshore event deposit was recovered and radiocarbon dated along a sediment-core transect. The event deposit has a sharp basal contact with carbonate shell fragments and a normal graded succession of siliciclastic sand to silt with high amount of terrestrial-derived horizontally bedded wooden particles. The simulated tsunami waves have a water-surface displacement of up to 1.5 m and bed shear-stresses that are likely capable of remobilizing large amounts of sediment in the Lucerne Bay area. Our study thus successfully links the sedimentology of event deposits with physical principles of sediment mobilization derived from numerical wave modeling, providing a tool to improve the identification and interpretation of potential tsunami deposits.

A common approach to assessing how polar amplification affects lower latitude climate is to perform coupled ocean-atmosphere experiments in which sea ice is perturbed to a future state. A recent critique by M. England and others uses a simple 1-dimensional energy balance model (EBM) to show that sea ice perturbation experiments add artificial heat to the climate system. We explore this effect in a broader range of models and suggest a technique to correct for the artificial heat post-hoc. Our technique successfully corrects for artificial heat in the EBM and a possible generalization of this approach is developed to correct for artificial heat in an albedo modification experiment in a comprehensive earth system model. However, this technique can not be directly generalized to sea ice perturbation methodologies that employ a "ghost flux" seen only by the sea ice model. Applying the correction to the comprehensive albedo modification experiment, we find stronger artificial warming than in the EBM. Failing to account for the artificial heat also leads to overestimation of the climate response to sea ice loss, and can suggest false or artificially strong "tugs-of-war" between 19 low latitude warming and sea ice loss over some fields, for example Arctic surface temperature and 20 zonal wind.

Recent advances in imaging techniques and related modeling have made it possible to study micron and sub-micron scales in unprecedented detail. Currently, performing direct simulations at a representative scale has proven computationally prohibitive, hence, the ability to simulate flow cannot keep up with the size of images available (e.g. x-ray micro-tomography or large area scanning electron microscopy). To overcome this issue, we propose to train a neural network architecture to understand relationships between pore-scale morphology and the simulation outputs. With this, we improve our portability and prediction time. Convolutional neural networks are attractive for this task because they support flexible input size, they are able to capture local interactions, and they can find places that present similar patterns. Generally, deeper networks have better prediction performance, but they are very difficult to train due to the vanishing gradient problem. Also, information from different scales is commonly lost along the network.

Classification of lightning produced VLF/LF signals plays crucial role in the detection and location of lightning flashes. The machine learning method has potential in the VLF/LF lightning signal classification. Traditional machine learning methods are data-driven and work in a black-box fashion, making the classification accuracy highly dependent on the size and quality of dataset. In this paper, an interpretable convolutional neural network model is proposed for VLF/LF lightning electric field waveform classification. Multi-scale convolutional kernels and shortcut connections are adopted in this model to enhance the ability to capture local waveform features. The CAM method is embedded in our model to open the black-box by visualizing the weight of different waveform features on the classification results. Based on the measured data from five different provinces in China, an accuracy of 98.5% is achieved in a four-type classification task including RS, active stage of IC, PB and NB. The correlation between the weight values of different waveform features and corresponding lightning discharge process are analyzed. It is found that the proposed model can extract decisive features of VLF/LF lightning signals closely related to the physical process of lightning discharges, which is similar to the human expert’s behavior. The proposed model is validated by using an open-source dataset from Argentina. It is indicated that the proposed model can resist the impact of unexpected waveform oscillation and achieve a higher accuracy of 98.39% than that of the support vector method. It is demonstrated that our model is less dependent on the training dataset.

Satellite remote sensing is commonly used to observe the hydrologic cycle at spatial scales ranging from river basins to the globe. Yet it remains difficult to obtain a balanced water budget using remote sensing data, which highlights the errors and uncertainties in earth observation (EO) data. Various methods have been proposed to correct EO datasets to make them more coherent, so that they result in a more balanced water budget. This study aimed to improve estimates of water budget components (precipitation, evapotranspiration, runoff, and total water storage change) at the global scale using the methods of optimal interpolation (OI) and neural network (NN) modeling. We trained a set of NNs on a set of 1,358 river basins and validated them on an independent set of 340 basins and in-situ observations of evapotranspiration and river discharge. We extended the models to make pixel-scale predictions in 0.5° grid cells for near-global coverage. Calibrated datasets result in lower water budget residuals in validation basins: the mean and standard deviation of the imbalance is 11 ± 44 mm/mo when calculated with uncorrected EO data and 0.03 ± 24 mm/mo after calibration by the NN models. This study suggests to data producers where corrections should be made to the EO datasets, and demonstrates the benefits of physically-driven NN models for studying the hydrologic cycle at the global scale.

An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlight the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (atmosphere-wave-ocean).

The launches of the Sentinel-1 synthetic aperture radar satellites in 2014 and 2016 started a new era of high-resolution velocity and strain rate mapping for the continents. However, multiple challenges exist in tying independently processed velocity data sets to a common reference frame and producing high-resolution strain rate fields. We analyse Sentinel-1 data acquired between 2014 and 2019 over the northeast Tibetan Plateau, and develop new methods to derive east and vertical velocities with ~100 m resolution and ~1 mm/yr accuracy across an area of 440,000 km^2. By implementing a new method of combining horizontal gradients of filtered east and interpolated north velocities, we derive the first ~1 km resolution strain rate field for this tectonically active region. The strain rate fields show concentrated shear strain along the Haiyuan and East Kunlun Faults, and local contractional strain on fault junctions, within the Qilianshan thrusts, and around the Longyangxia Reservoir. The Laohushan-Jingtai creeping section of the Haiyuan Fault is highlighted in our data set by extremely rapid strain rates. Strain across unknown portions of the Haiyuan Fault system, including shear on the eastern extension of the Dabanshan Fault and contraction at the western flank of the Quwushan, highlight unmapped tectonic structures. In addition to the uplift across most of the lowlands, the vertical velocities also contain climatic, hydrological or anthropogenic-related deformation signals. We demonstrate the enhanced view of large-scale active tectonic processes provided by high-resolution velocities and strain rates derived from Sentinel-1 data and highlight associated wide-ranging research applications.

The El Niño-Southern Oscillation (ENSO) is a natural climate phenomenon that alters the biogeochemical and physical dynamics of the Eastern Tropical Pacific Ocean. Its two phases, El Niño and La Niña, are characterized by decreased and increased coastal upwelling, respectively, which have cascading effects on primary productivity, organic matter supply, and ocean-atmosphere interactions. The Eastern Tropical South Pacific (ETSP) oxygen minimum zone (OMZ) is a source of nitrous oxide (N2O), a potent greenhouse gas, to the atmosphere. While nitrogen cycling in the ETSP OMZ has been shown to be sensitive to ENSO, we present the first study to directly compare N2O distributions during both ENSO phases using N2O isotopocule analyses. Our data show that during La Niña, N2O accumulation increased six-fold in the upper 100 m of the water column, and N2O fluxes to the atmosphere increased up to 100-fold. N2O isotopocule data demonstrated substantial increases in δ18O up to 60.5‰ and decreases in δ15Nβ down to -10.3‰, signaling a shift in N2O cycling during La Niña in the oxycline compared to El Niño. N2O production via the hybrid pathway and incomplete denitrification with overprinting of N2O consumption are likely co-occurring to maintain the high site preference (SP) values (17‰ – 26.7‰), corroborating previous hypotheses. Ultimately, our results illustrate a strong connection between upwelling intensity, biogeochemistry, and N2O flux to the atmosphere, and highlight the importance of repeat measurements in the same region to constrain N2O interannual variability and cycling dynamics under different climate scenarios.