The lower shoreface, a transitional subaqueous region extending from the seaward limit of the surf zone to beyond the closure depth, often serves as a sediment sink or source in sandy beach environments over annual to millennial time scales. Despite its important role in shoreline dynamics, however, the morphodynamics of the lower shoreface remain poorly understood. This knowledge deficit is partly due to the absence of sediment compositional data across the seabed and to the challenges inherent in measuring subtle bed changes (mm-cm/yr) over historical time scales. It is also unclear how diverse lithologies and long-term changes in wave climate influence shoreface morphodynamics as previous work often considers these steady-state systems in equilibrium. To better understand the controls on shoreface dynamics, we extend an existing energetics-based framework to model sediment transport across theoretical shoreface equilibrium profiles under various physical and geologic disequilibrium conditions. We further incorporate varying shoreline input flux scenarios (i.e., accretion, erosion) to investigate potential coastline inheritance controls on shoreface evolution. Equilibrium profile shapes and disequilibrium sediment transport rates are more sensitive to changes in sediment settling velocity than wave period, indicating that grain size provides a strong geologic control on shoreface morphodynamics. We find that at depths greater than 20 meters, shallow water wave assumptions predict larger sediment transport rates (~1-8 orders of magnitude) than linearly shoaled waves. Furthermore, for linear wave theory, we find an abrupt, discontinuous offshore transition where the bed response to changing wave climates becomes exceptionally slow. Our results provide insight into the sediment dynamics that drive the spatiotemporal evolution of the shoreface, improving our understanding of the interactions between onshore and nearshore processes and geological inheritance.

Effects of permafrost degradation on carbon (C) and nitrogen (N) cycling on the Qinghai-Tibetan Plateau (QTP) have rarely been analyzed. This study used a revised process-based biogeochemical model to quantify the effects in the region during the 21st century. We found that permafrost degradation would expose 0.98±0.49 (mean±SD) and 2.17±0.38 Pg C of soil organic carbon under the representative concentration pathway (RCP) 4.5 and the RCP 8.5, respectively. Among them about 60% will be decomposed, enhancing heterotrophic respiration by 9.54±5.20 (RCP 4.5) and 38.72±17.49 (RCP 8.5) Tg C/yr in 2099. Deep soil N supply due to thawing permafrost is not accessible to plants, providing limited benefits to plant growth and only stimulating net primary production by 6.95±5.28 (RCP 4.5) and 27.97±12.82 (RCP 8.5) Tg C/yr in 2099. As a result, permafrost degradation would weaken the regional C sink (net ecosystem production) by 303.55±254.80 (RCP 4.5) and 518.43±234.04 (RCP 8.5) Tg C cumulatively during 2020–2099. Permafrost degradation has a higher influence on C balance of alpine meadow than alpine steppe ecosystems on the QTP. The shallower active layer, higher soil C and N stocks, and wetter environment in alpine meadow are responsible for its stronger response of C balance to permafrost thaw. This study highlights that permafrost degradation could continue to release large amounts of C to the atmosphere irrespective of potentially more nitrogen available from deep soils.

We investigate numerically the elastic interaction between a dipole and an axisymmetrical vortex in inviscid isochoric two-dimensional flows satisfying Euler’s vorticity conservation equation. This work contributes to previous studies addressing inelastic vortex interactions. The dipole is a straight moving Lamb-Chaplygin (L-C) vortex, where the absolute value of either the positive or the negative amount of vorticity equals the amount of vorticity of the target vortex. The results show that, when the straight moving L-C dipole approaches the axisymmetrical vortex, the potential flows of both vortices interact, the dipole’s trajectory acquires curvature and the dipole’s vorticity poles separate. Once the L-C dipole moves away from the target vortex, the poles close and the dipole continues with a straight trajectory but along a direction different from the initial one. Though there is very small vorticity exchange between the dipole’s poles and a small vorticity leakage to the background field, the vortices preserve, to a large extent, their amount of vorticity and the resulting interaction may be practically qualified as an elastic interaction. This process is sensitive to the initial conditions and, depending on the initial position of the dipole as well as on small changes in the vorticity distribution of the axisymmetrical vortex, inelastic interactions may instead occur. Since the initial vorticity distributions are based on the eigenfunctions of the two-dimensional Laplacian operator in circular geometry these results are directly applicable to three-dimensional baroclinic geophysical flows under the quasi-geostrophic approximation.

We perform statistical analysis of solar flare light curves and ribbon morphology to advance our understanding of flare impulsiveness, an important parameter to describe stellar flares. The Solar Dynamics Observatory Extreme Ultraviolet Variability Experiment (SDO/EVE) provides “Sun-as-a-star” data corresponding to the variability of the Sun’s irradiance in the XUV and EUV wavelengths (from 0.1 to 106 nm). Using EVE light curves in the 304 Angstrom line, we study 2049 solar flares from 30 April 2010 to 26 May 2014. We present an algorithm for fitting the flare light curves in the 304 Angstrom line, emitted by He II at around 50000 K from the chromosphere and transition region and therefore representative of the dominant source of radiation in a solar flare. We use this algorithm to identify particularly high signal-to-noise flare light curves within the database, with representatives from C, M, and X flare classes. The parameters of the model associated with each flare can be used to identify notable features such as the incidence of multiple peaks in the rise phase. Identification of the rise and decay phases for each flare allows us to compare rise phase duration and flare impulsiveness to geometrical and physics-based properties of each flare, an important step in advancing our understanding of flare energy release. Specifically, using SDO Atmospheric Imaging Assembly (SDO/AIA) instrument data in the 1600 Angstrom line, we analyze the flare morphology and energy release in the context of the “impulsiveness” classification scheme for a sub-sample of the flares. We also compare this index to several solar flare properties including duration, peak X-ray flux, reconnection rate, and quasi-periodic pulsation (QPP) period, among others.

The Eclipse Soundscapes Project originally launched to make the “Great American Eclipse” of 2017 accessible to everyone, with a special focus on users who are blind or have low vision (BLV). Its first project was a mobile application (app). The app includes illustrative audio descriptions of eclipses, as well as an interactive “rumble map” that allows users to conceptualize an eclipse through touch and sound. Development of the Eclipse Soundscapes (ES) mobile application is led by the Advanced Research in Inclusion and STEAM Accessibility (ARISA) Lab team with the support of NASA’s Heliophysics Education Activation Team. One of the lessons learned in the original 2017 ES mobile application (ES 1.0) development was a greater understanding of how language choices impact accessibility and inclusion. It is clear that sharing information in text, in audio, and in a tactile way not only makes information more accessible and inclusive but also more impactful for everyone! With GBH National Center Accessible Media (NCAM) and Subject Matter Experts (SME), scientific annular eclipse descriptions were transformed into accessible descriptions that focused on utilizing tactilely relevant vocabulary. In conjunction with the tactile elements of the ES mobile app, this more inclusive language created an opportunity for more members of the BLV community to experience the 2017 total solar eclipse autonomously. However, with this success, a new challenge was discovered. We created more linguistically accessible descriptions, but they read at a very high level - often university level. This created a new inclusion and accessibility challenge when trying to reach the general public. In this presentation we will discuss how we addressed this challenge as we worked on ES 2.0 via collaborations with SMEs, accessibility partners, AND educators to create scientifically accurate annular eclipse descriptions that are linguistically appropriate for the BLV community as well as at a readability level to support a more diverse audience. This work was supported by a subaward from NASA Heliophysics Education Activation Team, supported by NASA under cooperative agreement number NNH15ZDA004C.

To address discrepancies between bottom-up and top-down inventories of methane emissions, the detection and quantification of methane point source emissions is of critical concern. Multiple airborne imaging spectrometer campaigns have identified the heavy-tailed distribution of point source methane emissions. The quantification of point source plumes is a two-part problem requiring the detection and delineation of plumes, followed by an accurate accounting of their total methane enhancement. Plume detection and delineation has often relied on manual or statistical methods, but automated methods taking into account plume morphology are essential as the volume of imaging spectrometer data rapidly increases. Fully convolutional neural networks (FCNNs) represent a robust solution to this problem allowing for the detection and delineation of methane point source emissions with minimal analyst input. This work demonstrates the applicability of FCNNs for accurate quantification of methane point source emissions by training a model on data from a 2019 Permian Basin survey by the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG). FCNNs were trained using plumes that were manually interpreted from matched filter retrievals of methane enhancements. Our methodology was able to accurately detect and delineate methane plumes, and did so with fewer false positives than statistical methods. Given the anticipated satellite imaging spectrometer missions capable of global mapping of point sources, automated deep learning methods will be necessary to deal with methane plume detection in very large volumes of data.

One of the unresolved and debated topics in climatic and atmospheric sciences is whether soil moisture (SM) anomalies can trigger extreme precipitation (P) events? Researchers have used coupled atmospheric model experiments, models based on simple water budget equations, and water vapour tracing studies; however, a consensus is lacking. Some studies reported that the excess SM anomalies trigger subsequent P; a few also postulate about a negative feedback loop. In the present study, we used a novel Event Coincidence Analysis to investigate this trigger relationship between SM and P. Using SM and P data from 2004-2020, we identified hotspots of SM-P coupling over India. A statistical significance test (α = 0.10) was carried out to ensure that the observed coincidences are not by chance. On increasing, the temporal window from one day to three-day the extent and severity of the hotspots increased significantly. The highest values of trigger coincidence rates are observed over central India (>70%). Our observed results agree with the widely regarded hypothesis of stronger SM-P coupling in transitional regions between wet and dry climates. The results obtained in our work has vast potential for atmospheric forecast purposes, including flood early warning systems for India.

Measured ice number concentrations are often much higher than the number concentrations of ice nucleating particles (INPs) in moderately cold mixed-phase clouds, suggesting the potential importance of secondary ice nucleation (SIP). However, the occurrence frequency and importance of SIP relative to primary ice nucleation for ice formation and mixed-phase cloud properties are largely unknown. Representing the SIP processes in weather and climate models is equally challenging. In this study, we present a process-level understanding of SIP in high-latitude mixed-phase clouds based on integrated model-observational analyses of the NSF SOCRATES aircraft and DOE ARM MARCUS ship-borne data. We run the Community Earth System Model version 2 (CESM2) nudged towards the MERRA2 Reanalysis and output the modeled clouds and aerosols along the aircraft flight and ship tracks for direct model-observation comparisons. We found that CESM2 with a physical representation of SIP processes (e.g., ice-ice collisional break-up, droplet shattering during rain freezing) better capture the observed ice crystal number concentrations (ICNCs) and cloud properties. SIP often dominants the ice formation in the moderately cold mixed-phase clouds, and transforms ~30% of pure liquid-phase clouds simulated in the model into mixed-phase clouds. We also compare modeled ice enhancement ratio due to SIP to ICNC and INP number concentrations observed during SOCRATES and MARCUS.

Radio science data collected from NASA’s Deep Space Networks (DSNs) are made available in various formats through NASA’s Planetary Data System (PDS). The majority of these data are packed in complex formats, making them inaccessible to users without specialized knowledge. In this paper, we present a Python-based tool that can preprocess the closed-loop archival tracking data files (ATDFs), produce Doppler and range observables, and write them in an ASCII table along with ancillary information. ATDFs are the earliest closed-loop radio science products with limited available documentation. Most data processing software (e.g., orbit determination software) cannot use them directly, thus limiting the utilization of these data. As such, the vast majority of historical closed-loop radio science data have not yet been processed with modern software and with our improved understanding of the solar system. The preprocessing tool presented in this paper makes it possible to revisit such historical data using modern techniques and software to conduct crucial radio science experiments.

We report on the extraction of deep ocean travel time variations from time-lapse cross-correlations between a hydrophone station and a three-component broadband seismometer. The signals we cross-correlate in this study result from repeated activity by the Monowai seamount, one of the most active submarine volcanoes of the Tonga-Kermadec ridge. In particular, we introduce a specific workflow to exploit repetitive hydroacoustic underwater source activity, which we detail to such extent that it serves as an example (or “cookbook”). For this reason, we have made the source code publicly available. The workflow proposed in this study (i) overcomes differences in instrument sensitivity and sample rates, (ii) involves the selection of eligible cross-correlations based on a source activity criterium as well as slowness analysis, and (iii) extracts the travel time variations in distinct frequency bands. In our case, the two frequency bands are 3-6 Hz and 6-12 Hz. We find that the estimated travel time variations in both frequency bands consist of a complex periodic pattern superimposed on a distinct long-term linear trend. This long-term linear trend is decreasing, which we attribute to increasing water temperatures along the propagation path of the hydroacoustic signals.

Geoscience education is, itself, a dynamical sub-system in the science, technology, engineering and mathematics (STEM) learning ecosystem. Both the larger STEM environment and the geoscience ecosystem are shaped by the ever-changing relationships among people, places, science content (and its communication), teaching practices, and shifting roles for each of these. In answer to calls in the literature for research on professional learning in which the faculty member is central to the process of change, the study reported here addressed the question: What is the nature of college science faculty readiness for change in instructional practices? The setting was a professional development experience in oceanography/marine science and paleoclimatology among 32 faculty from 2- and 4-year colleges. Ten of the 32 participated in interviews and all provided survey responses and documents used in analysis. Qualitative research methods resulted in three example cases to illustrate a new framework for exploring faculty readiness for professional change in teaching. This framework blends the Clarke and Hollingsworth (2002) model of a professional change environment with research from health sciences on readiness for behavioral change (Dalton & Gottlieb, 2003). From the first model came the multi-part foundation of personal, external, professional, and consequence domains of experience and from the second came how an instructor draws on those domains to: (a) see an instructional challenge as requiring intentional action to be resolved; (b) notice new significance (for the instructor) in some aspect of instructional practice; (c) feel able to manage instructional stressors/challenges; (d) have commitment to initiate/sustain change; (e) perceive adequate support in undertaking change. Three profiles of readiness for change are represented by three composite instructor cases named Lee, Pat, and Chris. In the case of Lee, factor c drove change efforts, for Pat, factors a and b were in the forefront, and for Chris it was factors d and e. Building a healthy learning ecosystem includes attention to faculty as learners. The three cases are valuable both as illustrations of the framework in use and as touchstones for future research and development related to post-secondary professional learning for teaching.

Aerosol-cloud interactions are the most uncertain component of the Earth system, due to their major influence on cloud properties, and as a result, Earth’s energy budget. We need to better characterize these interactions, which requires constraining the cloud condensation nuclei (CCN) budget and disentangling the influences of aerosol microphysics from meteorology. Observational data are essential for evaluating and improving climate models, but airborne field campaigns have, until recently, been limited to a few (mostly continental) regions worldwide. CCN measurements over the remote ocean are scarce and only occur during extensive field missions involving airborne or ship-based measurements of limited spatial and temporal extent. Polar-orbiting satellite observations hold great promise for expanding the spatial coverage of observations to remote regions, however, it is currently not well understood to what extent these active and passive remote sensing observations can be considered adequate proxies for CCN. Recent literature make use of column integrated retrievals, such as aerosol optical depth or aerosol index, to characterize aerosol concentration and CCN, and the utility of vertically resolved optical properties from active sensors is only now becoming more fully understood. The NASA ACTIVATE, NAAMES, CAMP2EX and ORACLES field campaigns are particularly well suited for evaluating the skill of advanced satellite aerosol and cloud microphysical retrievals, given the comprehensive suite of airborne aerosol, cloud, and trace gas measurements, combined with airborne High Spectral Resolution Lidar (HSRL) and polarimetric imaging instruments that will be the basis for the next generation of space-based remote sensors. Here, we characterize the properties of aerosol and CCN from these NASA field campaigns and critically assess methods for deriving CCN and CCN proxies using visible and infrared satellite remote sensing retrievals.

Stream temperature is a critical ecosystem indicator and plays an important role in river ice formation and breakup. Typically, stream temperature is simulated either using statistical models or discrete Eulerian energy balance models. These physically-based models often rely upon a grid-based finite difference or finite volume approach for simulating the advection-dominated in-stream energy balance. Such methods are conditionally stable and thus introduce constraints upon time and space step (e.g., Courant and Peclet constraints). They are also not conceptually consistent with commonly applied convolution-based hydrologic routing approaches, which don’t require reach discretization. Here, a novel semi-analytical technique for simulating advection, latent and sensible heat transfer, heat generation from friction, and hyporheic exchange with groundwater is introduced. It assumes a discrete convolution (i.e., transfer function or unit hydrograph) method is used for routing flows through the reach. The energy balance is applied to discrete parcels of water that travel along the reach exchanging energy with their surroundings; the parcel-based energy balance is solved exactly. The method is unconditionally stable, runs at the native time step of the hydrological model, and requires no spatial discretization. It can also easily simulate edge cases that are exceedingly difficult using discrete methods, such as a near-infinite convective exchange coefficient. The only approximation errors are associated with the model time step used to represent the inflow time series, the linearization of the Stefan-Boltzmann equation for longwave radiation flux, and the full mixing assumed at nodes of the stream network. The stream temperature model, as implemented within the Raven hydrological modelling framework, is demonstrated at several test catchments in the North American Rocky Mountains.

The Plankton, Aerosol, Cloud, and Ocean Ecosystem (PACE) mission is NASA’s next great investment in Earth Science, continuing NASA’s legacy of over forty years of satellite ocean color measurements. PACE, expected to launch in 2023, will advance our Earth-observing and monitoring capabilities through hyperspectral imaging and multi-angle polarimetric observations of ocean, atmosphere, and land ecosystems. PACE will give us an unprecedented view of our home planet and will support user-driven environmental applications through research and applied science to address societal challenges and inform decision-making. The PACE Applications program seeks to strengthen the ties between science and decision-making through programming such as the Early Adopter initiative, workshops and focus sessions, and information-sharing and co-production activities. Practical applications of PACE data include waterborne pathogen monitoring and prediction, harmful algal bloom species discrimination and early-detection, support for best-management practices for estuary and watershed health, as well as sustainable fishery and aquaculture practices. Hyperspectral and polarimetric PACE data will provide water resource managers and decision-makers with highly accurate ocean color and atmospheric observations that will directly improve their operational products and applied technologies, ultimately fulfilling real-world needs. This presentation will highlight PACE’s novel hyperspectral and polarimetric capabilities, introduce PACE’s standard and exploratory data products, outline the PACE Applications Program, share real-world coastal applications of PACE data, and summarize the results of the 2021 PACE Applications Water Quality Focus Session and the 2021 PACE Applications Workshop as they relate to community engagement and capacity development. Finally, we will share the revamped PACE Applications Outreach Strategy and how it will be used to promote partnerships within the existing user communities of practice and new user communities of potential for greater societal benefit.

Solar wind models are highly dependent on global magnetic fields at the solar surface as their inner boundary condition, and the lack of global field data is a significant problem plaguing solar wind modeling. Currently, only direct observations of the near-side magnetic field exist and far-side approximations are incapable of predicting growth of existing active regions or new magnetic flux emergence. To fill this data gap, we develop a method that calibrates far-side helioseismic images, which are calculated using near-side Doppler observations, to far-side magnetic flux maps. The calibration employs multiple machine-learning methods that use EUV 304 Å data as a bridge. These algorithms determine a relation 1) between the near-side AIA 304 Å data and HMI magnetic field data, and 2) between STEREO 304 Å data and far-side helioseismic images obtained from a newly developed time-distance helioseismic far-side imaging method. The resulting magnetic flux maps have been further calibrated using maps produced by a flux transport model. The various data products from this work — far-side acoustic maps, far-side STEREO EUV-derived magnetic flux maps, and near-real-time acoustically-driven far-side magnetic flux maps, along with maps of the associated uncertainties — are being made available to enable a synchronic global magnetic flux input into coronal and solar wind models.

The parameterization of surface exchange coefficients (Ch) representing land–atmosphere coupling strength plays a key role in land surface modeling. Previous studies have found that land–atmosphere coupling in land surface models (LSMs) is overestimated, which affects the predictability of weather and climate evolution. To improve the representation of land–atmosphere interactions in LSMs, this study investigated the dynamic canopy-height-dependent coupling strength in the offline Noah LSM with multiparameterization options (Noah-MP) when applied to China. Comparison with the default Noah-MP LSM showed the dynamic scheme significantly improved the Ch calculations and realistically reduced the biases of simulated surface energy and water components against observations. It is noteworthy that the improvements brought by the dynamic scheme differed across land cover types. The scheme was found superior in reproducing the observed Ch as well as surface energy and water variables for short vegetation (grass, crop, and shrub), while the improvement for tall canopy (forest) was found not significant, although the estimations were reasonable. The improved version benefits from the treatment of the roughness length for heat. Overall, the dynamic coupling scheme markedly affects the simulation of land–atmosphere interactions, and altering the dynamics of surface coupling has potential for improving the representation of land–atmosphere interactions and thus furthering LSM development.

Understanding the hydrologic response to rainfall events is vital for flood forecasting and design for peak flows. The Time to Peak (Tp) is used to characterize the speed of catchment response, as the time from the start of a rainfall event to the time the peak flow is reached in a stream. Advancing our understanding of a catchment’s temporal response to rainfall is key to our overall understanding of hydrologic processes. In this study, more than 1400 storm hydrographs were isolated and utilized to calculate the Tp value for decades of storms spanning Great Britain. Previous works into understanding Tp have been static, with no variability due to storm magnitude or antecedent conditions, providing a single static value for each catchment. Using this data and machine learning techniques, dynamic Tp values were predicted for each storm within the hundreds of catchments, to allow for fuller understanding of the catchment response. Artificial Neural Networks are utilized in this study to create models which account for antecedent conditions of the catchment, and the storm size, to predict the storm-specific, dynamic Tp value.

Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (BC, Canada and WA, US) using locally-derived data, national statistics and standard parameters. Feed imports for dairy (mainly in the US) and poultry (mainly in Canada) accounted for 30 and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, slightly lower than atmospheric deposition (10%). Returning salmon represented <0.06% of total N input but was an important ecological flux by importing marine-derived nutrients. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the US and poultry products in Canada. Riverine export of N was estimated 28% of total N export. The commonly used crop nitrogen use efficiency (crop NUE) alone did not provide sufficient information on farming activities and should be combined with other criteria such as farm-gate NUE to understand management efficiency. Agriculture was the primary driver of N inputs to the environment despite widespread adoption of conservation practices, illustrating a need to optimize management to minimize hydrologic and volatilization losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.