Solar transients impinging on Earth’s magnetosphere often present a larger velocity than the surrounding solar wind, leading to a different response of the bow shock-magnetosheath system. As such, we intend to provide a systematic study of the global effects of different solar wind Mach numbers in a pure quasi-perpendicular configuration with a realistic three-dimensional terrestrial-like curved bow shock. Simulations have been performed with the hybrid code LatHyS, which is based on the widely used CAM-CL scheme. In particular, we have studied the interaction with the terrestrial magnetosphere of a solar wind at different Alfvénic Mach numbers and low-beta (less than unity, ratio between the thermal to the magnetic pressures). One of most noteworthy outcome is the generation of an intense rippling phenomenon propagating along the bow shock surface as the incoming Mach number increases. A similar rippling has been observed in-situ with satellites, as well as studied with computer simulations. However, the latter have mainly addressed by adopting ad-hoc planar-shock initial configurations, which still leaves poor knowledge of the possible effects on a global three-dimensional curved interaction. Our analysis then is expected to provide further insights into both the macroscopic and kinetic effects of different incoming solar wind conditions on the overall planetary bow-shock and magnetosheath structure.

The PRISM Data Library (DL) is designed to optimize the display, analysis, and retrieval of multiple domains datasets. Originally created for climate data, we aggregated data from agriculture and hydrology domains, as well as non-traditional domains for the DL such as ecology, finance, power outage and space weather data. These datasets range from simple geospatial point observations, to spatially gridded data products, to high-resolution satellite measurements, to GIS representation of administrative or domain-specific geographic entities. These datasets are represented in a consistent multi-dimensional (most often spatial and temporal) framework. As a result, dimension-wise comparisons are easily enabled through selection or transformation. Gridded data can be averaged over discrete geometrical entities (e.g. Counties, Bird Conservation Regions). The DL can be used in a browser, by connecting to servers at San Diego Supercomputing Center (SDSC) over the internet. Data selection, processing, and analysis are performed by the SDSC DL servers, and the resulting images or data files are sent back to the client’s desktop. This model optimizes the use of internet bandwidth.

We perform 3D simulations of seismic wavefields to clarify effects of strong reductions of shallow velocities on long-period seismic waves. The simulations use a reference community velocity model of Southern California and a modified version with strong velocity reductions in the top 500 m of the Los Angeles basin. Differences between wavefields generated by ten earthquakes in the reference and perturbed models are analyzed. Velocity changes are estimated by measuring relative time shifts between reference and perturbed seismograms using wavelet cross-correlation spectra. The results indicate that strong localized temporal velocity drops near the surface, such as those observed during strong ground motions, may generate regional perturbations of wavefields at periods up to 20 s. These perturbations may be misinterpreted as generated by temporal changes at seismogenic depths. The results also have important implications for waveform tomography studies.

There are records of past Earth climates that were ice-free all the way to the poles (Barron 1983), which can be described as “hothouse” climates. These hothouse climates can be contrasted with an “all-tropics” planet, where the tropics are defined by the atmospheric dynamics, i.e. the Hadley Cell extent (Faulk et al. 2017). This classification is thus primarily dependent on a planet’s rotation, rather than its ice-free extent or surface temperatures. We investigate the parameter space between Earth and an all-tropics world using the open-source GCM Isca, developed by Vallis et al (2018). We take an Earth analog and perform a parameter sweep in two dimensions: global reservoir depth (10m, 1m, 1cm) and rotation period (8 days, 4 days, 1 day). The sweep will allow us to explore the effects of surface liquid coverage and large-scale atmospheric circulation on an Earth-like climate. To better represent the distribution of surface water, we utilize the surface hydrology scheme developed by Faulk et al. (2020) for the Titan Atmosphere Model. In this presentation we provide a status report and analysis of initial findings.

In 2019 sprite observations appeared on You-Tube showing green emissions in the top of some sprites and after the main sprite. The emissions were named green ghosts. We present here similar color camera observations made on 24 and 25 May 2020 from Lamy, NM. The recordings were made at 30 fps. The green ghost increased in brightness for 3 to 4 frames after the main ‘jellyfish’ sprite and decayed thereafter. The delayed green emissions are likely from atomic oxygen at 557.7 nm, the auroral green line, which is a forbidden atomic oxygen emission with a radiative life time of 0.7 s. In the atmosphere the emissions are affected by quenching and at the altitude of the green ghost the decay time constant is substantially less. We suggest that the green ghost is generated by super thermal electrons exciting ambient atomic oxygen (only 4.1 eV required). The electrons are energized by the large-scale sprite electric field, which initially caused the sprite, and decays over some time after the sprite. We present a simple model based on this idea. The model assumes an exponentially decaying sprite E-field and reproduces the observed delayed peak in the green emissions and the subsequent decay of the emissions.

Within the AtMoDat project (Atmospheric Model Data, www.atmodat.de), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. Atmospheric model data form the basis to understand and predict natural events, including atmospheric circulation, local air quality patterns, and the planetary energy budget. Such data should be made available for evaluation and reuse by scientists, the public sector, and relevant stakeholders. Atmospheric modeling is ahead of other fields in many regards towards FAIR (Findable, Accessible, Interoperable, Reusable, see e.g. Wilkinson et al. (2016, doi:10.1101/418376)) data: many models write their output directly into netCDF or file formats that can be converted into netCDF. NetCDF is a non-proprietary, binary, and self-describing format, ensuring interoperability and facilitating reusability. Nevertheless, consistent human- and machine-readable standards for discipline-specific metadata are also necessary. While standardisation of file structure and metadata (e.g. the Climate and Forecast Conventions) is well established for some subdomains of the earth system modeling community (e.g. the Coupled Model Intercomparison Project, Juckes et al. (2020, https:doi.org/10.5194/gmd-13-201-2020)), other subdomains are still lacking such standardisation. For example, standardisation is not well advanced for obstacle-resolving atmospheric models (e.g. for urban-scale modeling). The ATMODAT standard, which will be presented here, includes concrete recommendations related to the maturity, publication, and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF), and the structure within files. Human- and machine-readable landing pages are a core element of this standard and should hold and present discipline-specific metadata on simulation and variable level.

Data maturity describes the degree of the formalisation/standardisation of a data object with respect to FAIRness and quality of the (meta-) data. Therefore, a high (meta-) data maturity increases the reusability of data. Moreover, it is an important topic in data management, which is reflected by a growing number of tools and theories trying to measure it, e.g. the FAIR testing tools assessed by RDA(1) or the NOAA maturity matrix(2). If the results of stewardship tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data is easy to reuse. For example, the DataCite Metadata Schema does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (3, Atmospheric Model Data) aims to improve the reusability of published atmospheric model data by scientists, the public sector, companies, and other stakeholders. These data are valuable because they form the basis to understand and predict natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data from other fields of research would also benefit from easily findable maturity information. Therefore, we developed a Maturity Indicator concept and propose to introduce it as a new property in the DataCite Metadata Schema. This indicator is generic and independent of any scientific discipline and data stewardship tool. Hence, it can be used in a variety of research fields. 1 https://doi.org/10.15497/RDA00034 2 Peng et al., 2015: https://doi.org/10.2481/dsj.14-049 3 www.atmodat.de

The role of waves in the propagation, scattering and energization of electrons in the solar wind has long been a topic of interest. Conversely, understanding the excitation of waves by energetic electrons can provide us with a diagnostic for the processes that accelerate the electrons. We will discuss two different processes: (1) the interaction of narrowband whistler-mode waves with solar wind electrons, and (2) how periodic Type III radio bursts yield clues to small-scale acceleration of energetic electrons in the solar corona. Waveform captures in the solar wind at 1 AU obtained by the STEREO revealed the existence of narrowband large amplitude whistler mode waves, propagating at highly oblique angles to the magnetic field. Similar waves are less commonly seen inside .2 AU by Parker Solar Probe. The differences provide clues for understanding electron propagation, scattering and energization. Type III radio bursts have long been used as remote probes of electron acceleration in the solar corona. The occurrence of periodic behavior in Type III bursts observed by Parker Solar Probe, Wind and STEREO when there are no observable flares provides a unique opportunity to diagnose small-scale acceleration of electrons in the corona. Periodicities of ~ 5 minutes in the Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA) Extreme Ultraviolet data in several areas of an active region are well correlated with the repetition rate of the Type III radio bursts. Similar periods occur in the Helioseismic and Magnetic Imager (HMI )data. These results provide evidence for acceleration by wave-modulated reconnection or small-scale size waves, such as kinetic Alfven waves, even during intervals with no observable flares. The possible connections between these two phenomena will be addressed.

We consider the transient electrokinetic response of an aquifer-aquitard system to groundwater abstraction from the aquifer. The system was instrumented with 18 non-polarizable copper/copper sulphate electrodes installed at three different depths in the aquitard above the aquifer. The sensing electrodes were installed at depth of 1, 2, and 4 m below ground surface along three overlapping transects. The differential voltages relative to a single permanent electrode were measured with a Campbell Scientific CR1000 datalogger with a single multiplexer. Additionally, six piezometers screened in the top 1.5 m of the confined aquifer, were installed by direct-push. All the piezometers were instrumented with pressure transducers to measure directly the hydraulic response of the aquifer. The vertical variation of resistivity in the aquitard was measured on sediment cores recovered from one of the boreholes used to install the deepest piezometer. The resistivity distribution at antecedent sediment wetness (moisture content) was measured using the MC Miller resistivity boxes and meter, with wetness measured gravimetrically. Previous exploratory drilling and sampling activities at the site indicate that the aquifer is fractured greywacke sandstone overlain with clayey aquitard of semi-consolidated alluvial sediment, with the aquifer-aquitard contact at a depth of 10.3 m below the ground surface. We report the results of the site instrumentation, monitoring, characterization, hydraulic testing, and compare the results of parameter estimation using streaming potential and hydraulic data separately and jointly. We explore the effect of depth of installation of the electrodes in the aquitard on signal strength and quality and compare this to model predicted behavior using semi-analytical models from the literature. The results suggest the need for deep sensing of electrokinetic signals generated by groundwater flow to improve signal-to-noise ratios and the usefulness of self-potential data for hydrogeophysical characterization of aquifer-aquitard systems.

Basaltic melts are produced when convection adiabatically brings deep and hot mantle to lower pressures. Such primary melts were extracted from the mantle of Mars, crystallized near the surface and progressively built the Martian crust. This process displaced a large fraction of the heat producing elements from the mantle to the crust and created an insulating layer that slowed down further cooling of the mantle. The complex crust-mantle system controlled many aspects of the geologic history of Mars, including the development of an atmosphere and whether conditions favorable to life could have existed. Our knowledge of the mineralogy, chemical composition and physical properties of the crust of Mars is rapidly expanding. Global geodynamical models can be used to interpret the available data and constrain the processes of crust-mantle differentiation. However, existing models still treat melting in a simplified way. For example, the degree of melting is often assumed to increase linearly above the solidus temperature, while the density of the residue is assumed to decrease linearly. Calculating the density of the residual mantle more accurately is critical because the compositional buoyancy that develops during partial melting fundamentally modifies mantle dynamics. Here, we present an improved parametrization of partial melting of the Martian mantle, which will be combined with the convection code Gaia. We created a new empirical model of melting that calculates the composition of the extracted melts and, when combined to thermodynamic models (e.g., Perple_X), the density of the corresponding residual mantle. Another advantage of the new melting parametrization is that the major-element composition of partial melts can be tracked and used to constrain the petrogenesis of surface rocks. Preliminary results will be compared to available Martian rocks believed to represent primary mantle melts or melts affected by minor fractional crystallization.

Publicly accessible data has been used to construct a county-scale supply chain model of United States gasoline consumption and quantify the scope 3 CO2; emissions from gasoline consumption. Our model tracks the movement of refined fuels from county of refinement to county of blending and eventually to county of consumption via multiple infrastructure networks – pipelines, tankers, trains, and trucks. Where quantities of the fuel moved across different linkages and different transportation modes are known, they are used as is. However, for the vast majority of the country, the exact quantities of fuel moved between county of refining and county of blending or county of blending and county of consumption, as well as the mode of transportation, is not known with certainty. Linear optimization is used to model those links with constraints related to total supply and demand at lower spatial resolutions (State-level and Petroleum Administration for Defense (PAD) Districts). This is the first real attempt at a spatially-resolved scope 3 style CO2 emissions data product specific to United States gasoline consumption. This model can improve understanding of the complex liquid fuel supply chain, and has significant implications for local policy. With a complete model of scope 3 CO2 emissions, it is also possible to analyze how the differences between scope 1 and scope 3 emissions vary across the country. Finally, this model lays the foundation to model the evolution of the U.S. gasoline supply chain – its dependencies, critical linkages, and pinch points – and the evolution of scope 1 and scope 3 CO2 emissions using the full extent of available public data.

Nitrous oxide (N2O), a potent greenhouse gas and ozone depleting substance, plays a crucial role in the atmosphere. Anthropogenic emissions from agriculture contribute to a rising trend in global N2O emissions and atmospheric concentrations. However, due to insufficient direct observations, regional N2O emissions derived in bottom-up and top-down studies are highly uncertain. The U.S. Midwest is one of the most intensive agriculture areas worldwide and hence may contribute significantly to the observed trend. Recent top-down studies suggest that bottom-up estimates underestimate agricultural emissions in that area by up to an order of magnitude. Here we quantify nitrous oxide emissions in the Midwest in October 2017 and June-July 2019 with a top-down approach. Unique continuous aircraft-based measurements of N2O conducted during the ACT-America campaign together with forward WRF-Chem model simulations are used to scale the EDGAR inventory thus quantifying emissions. On average we had to upscale October 2017 and June-July 2019 agricultural EDGAR 4.3.2/5.0 emissions by a factor of 6.3/3.5 and 11.4/9.9, resulting in 0.42 nmol m-2 s-1 and 1.06 nmol m-2 s-1 emissions in the Midwest, respectively. Finally, calculations of direct soil N2O emissions from the DayCent biogeochemical model are compared to our estimates.

Many communities along the East and Gulf coasts of the United States are experiencing moderate to severe erosion. Decadal shoreline erosion rates determined by the North Carolina Division of Coastal Management indicate that >65% of the NC shoreline is eroding, with 20% losing shore at ~1 m per year. Chronic long-term erosion and episodic rapid impacts from storms have encouraged most coastal NC communities to conduct beach nourishments for mitigation. One concern is that there is limited knowledge of how offshore sand shoals – often sources for nourishments – are evolving in response to storm conditions and dredging activity. In September 2018, Hurricane Florence impacted southern NC and northern SC, and many communities experienced enhanced erosion. Future offshore sand removal for beach nourishment is inevitable here and elsewhere along the East Coast. A borrow area on the continental shelf seaward of Bogue Inlet (NC) offers an opportunity to see how the seabed is changing as a result of anthropogenic and oceanographic processes. Hurricane Florence made landfall as a Category 1 near Wilmington, NC on the morning of September 14, 2018. Prior to making landfall the storm was a powerful Category 2 hurricane, down from Category 4 status days prior. Hurricane-force winds were experienced over a large region, yielding powerful storm surge and waves over 8 m near the study area. Given the water depths of the ODMDS, seabed reworking was anticipated. The project collected geophysical data and sediment samples over a portion of the ODMDS offshore of Bogue Banks, NC in February 2019. Multibeam bathymetry, backscatter and seismic reflection data were obtained along with 24 sediment samples. These data add to previously collected data over the same area in 2013 and 2018. Grain-size analysis has been completed on all collected samples. Preliminary analysis of the post-hurricane data in comparison to earlier results indicate that the seabed has been reshaped since the last survey in March 2018, likely in response to Hurricane Florence. The broad dome-shape of the ODMDS remains, however, there is conspicuous morphological change. In shallower areas large sand waves (crests oriented roughly N-S, wavelength 20-30 m) differ from earlier mapping. Despite notable reworking, the anthropic signature of dredging in 2013 remains on the seascape.

Motivated by previous measurements of very low tropospheric ozone concentrations in the Tropical West Pacific (TWP) and the implied low oxidizing capacity of this key region for transport into the stratosphere (e.g. Rex et al. 2014), we set up an atmospheric research station in Palau (7° N 134° E). Our analysis of regular balloon-borne tropospheric ozone observations at Palau from 01/2016-10/2019 confirms the year-round dominance of a low ozone background in the mid-troposphere. Layers of enhanced ozone are often anti-correlated with water vapor and occur frequently. Moreover, the occurrence of respective layers shows a strong seasonality. Dry and ozone-rich air masses between 5 and 10 km altitude were observed in 71 % of the profiles from February until April compared to 25 % from August until October. By defining monthly atmospheric background profiles for ozone and relative humidity based on observed statistics, we found that the deviations from this background reveal a bimodal distribution of RH anomalies. A previously proposed universal bimodal structure of free tropospheric ozone in the TWP could not be verified (Pan et al. 2015). Back trajectory calculations confirm that throughout the year the mid-tropospheric background is controlled by local convective processes and the origin of air masses is thus close to or East of Palau in the Pacific Ocean. Dry and ozone-rich air originates in tropical Asia and reaches Palau in anticyclonic conditions over an area stretching from India to the Philippines. This supports the hypothesis of several studies which attribute ozone enhancement against the low ozone background to remote pollution events on the ground such as biomass burning (e.g. Andersen et al. 2016). A potential vorticity analysis revealed no stratospheric influence and we thus propose large-scale descent within the tropical troposphere as responsible for dehydration of air masses on their way to Palau.

We find evidence for multi-centennial climate cycles within the ages commonly described as the Medieval Warming Period and the Little Ice Age. We compare six proxy temperature records and find evidence for synchronicity of ~200-year cycles in the northern hemisphere. The first two data sets are (1) a reconstruction from an ice core in the Colle Gnifetti (CG) glacier on the Swiss-Italian border (Bohleber et al 2018), and (2) length records of the Great Aletsch glacier (GAG), Switzerland (Holzhauser, 2009). A third is a proxy (3) by Cabedo-Sanz et al (2016) using the biomarker IP25 in sea sediments north of Iceland, which serves as a proxy for drift ice and hence arguably for Arctic ice areal coverage. Further temperature proxies for (4) all-China (Ge et al, 2017), (5) north China and (6) central China (Wang et al, 2018) provide a distribution of coverage over the non-tropical northern hemisphere. These six regional temperature proxy data sets are also compared with the G7 global temperature reconstruction (Ludecke and Weiss, 2017). Prominent minima in temperature proxy data occur circa 1350CE (all 6 proxies), 1480-1520CE (4 proxies), 1650-1700CE (all 6 proxies) and 1800-1860CE (all 6 proxies). The last three of these are also visible in the G7 global temperature reconstruction. Over the period 1550-1949 CE the Pearson correlation coefficients for the IP25 (Arctic ice) data with the GAG and the all-China temperature proxy are 0.69 and 0.70 respectively; these high correlations in data sets from opposite sides of the globe suggest a global cause rather than regional internal variability. Since 1600CE we note that IP25 (Arctic ice) and the CG temperature proxy lag the glacier record by 17 and 10 years respectively; that lag is counterintuitive and may reflect precipitation variations in the Alps preceding the temperature drop, or it may be attributable to uncertainties in age dating between the data sets. Power spectral analysis shows the dominant centennial periods in the data sets are centered at 180, 240 and 500 which (within dating uncertainties) may relate to the ~160 year Jose cycle, the 208-year de Vries cycle and possible 350 and 500 year cycles previously recognized in solar activity via study of terrestrial cosmogenic isotopes. The consistency between the spectral maxima of temperature proxies studied here and spectral maxima of cosmogenic isotopes, supports the hypothesis of some association of these cycles with an “astronomical clock”, although the mechanism of possible forcing remains a subject for further study.

Severe weather forecasting is an important tool for mitigating damages brought by intense lightning, large hail, heavy precipitation, strong winds, or tornadoes during thunderstorms, yet the reliability of such forecasts suffers from our limited understanding of the severe weather generative processes inside thunderclouds. With an increasing knowledge of the occurrence context of distinct types of lightning within storms, lightning remote sensing may elucidate the kinematic and microphysical environment where severe weather initiates. In particular, distinct energetic intra-cloud (IC) lightning discharges, compact intra-cloud lightning discharges [CID; e.g., Nag and Rakov, 2010] and energetic intra-cloud pulses [EIP; e.g., Lyu et al., 2015], have been shown to have different occurrence contexts, making them strong candidates for thunderstorm remote sensing research. In this study, observations from the RELAMPAGO field campaign in Argentina (November 1 to December 12th 2018) are used to determine lightning flash rates and the prevalence of different energetic lightning types in RELAMPAGO storms, enabling further research on the link between lightning activity and severe weather production inside thunderstorms.Lightning events during RELAMPAGO were observed by a deployed array of four Low-Frequency (LF, ~1-400 kHz) radio receivers. Using time of arrival, magnetic direction finding, and peak amplitude for each observed event at different stations, lightning source locations are estimated using a statistical least squares filter, along with clock and site errors associated with the receivers. Return stroke peak current for each event is also estimated in the filter, using an atmospheric attenuation observation model. The energetic lightning events in the campaign are then classified automatically between cloud-to-ground (CG), IC, CID or EIP, following an improved parametrization scheme originally proposed by Lyu et al. [2015]. In this paper we present the geolocation and classification of RELAMPAGO lightning events, and we also provide an analysis of lightning flash rates during the campaign. A few individual thunderstorm case studies are also discussed, which are augmented by other meteorological data from dual-polarimetric radar, hailpads, and other sources.

Airborne dust, including Dust storms and weaker dust traces, can have deleterious and hazardous effects on human health, agriculture, solar power generation, and aviation. Although earth observing satellites are extremely useful in monitoring dust using visible and infrared imagery, dust is often difficult to visually identify in single band imagery due to its similarities to clouds, smoke, and underlying surfaces. Furthermore, night-time dust detection is a particularly difficult problem, since radiative properties of dust mimic those of the cooling, underlying surface. The creation of false-color red-green-blue (RGB) composite imagery, specifically the EUMETSAT Dust RGB, was designed to enhance dust detection through the combination of single bands and band differences into a single composite image. However, dust is still often difficult to identify in night-time imagery even by experts. We developed a Deep Learning, UNET image segmentation model to identify airborne dust at night leveraging six GOES-16 infrared bands, with a focus on infrared and water vapor bands.The UNET model architecture is an encoder-decoder Convolutional Neural Network that does not require large amounts of training data, localizes and contextualized image data for precise segmentation, and provides fast training time for high accuracy pixel level prediction. This presentation highlights collection of the training database, development of the model, and preliminary model validation. With further model development, validation, and testing in a real-time context, probability-based dust prediction could alert weather forecasters, emergency managers, and citizens to the location and extent of impending dust storms.

Increasing agricultural demand for fresh water resources in the face of a changing climate requires improved irrigation management solutions to maximize resource efficiency while maintaining crop yield and quality. Soil water deficits can significantly reduce plant growth and development, dictating the quantity and quality of the crop. While plant-based measures of water deficits are considered to be the best measures of water stress, current methods for achieving precise stress measurements are time-consuming and inefficient. Dendrometers are one plant-based tool that have shown potential to improve irrigation management in high-value woody perennial crops. High-precision dendrometers continuously measure small fluctuations (± 1 micron) in stem diameter throughout the day, which directly correlates to water stress. However, currently available dendrometers are expensive, have mechanical hysteresis, and are subject to mechanical and environmental issues such as material expansion; weather and animal disruptions; and bulky, invasive design. The dendrometer created at the OPEnS Lab - tailored for grapevines - alleviates these key failure points through the use of zero-thermal expansion carbon fiber, spring tension, and a linear magnetic encoder. The design is also significantly less expensive than that of the competition, costing around $200 as opposed to $1000. Mass deployment of these automated dendrometers has the potential to provide a continuous picture of vineyard water stress at the whole-block level, thus providing valuable decision support for vineyard irrigation management. Follow the project at open-sensing.org/projects.