Geoscientists often spend significant research time identifying, downloading, and refining geospatial data before they can use it for analysis. Exploring interdisciplinary data is even more challenging because it may be difficult to evaluate data quality outside of one’s expertise. QGreenland, a newly funded EarthCube project, is designed to remove these barriers for interdisciplinary Greenland-focused research and analysis via an open data, open platform Greenland GIS tool. QGreenland will combine interdisciplinary data (e.g., glaciology, human health, geopolitics, hydrology, biology, etc.) curated by an international Editorial Board into a unified, all-in-one GIS environment for offline and online use. The package is designed for the open source GIS platform QGIS. QGreenland will include multiple levels of data use: 1) a fully downloadable base package ready for offline use, 2) additional disciplinary and/or high-resolution data extension packages for select download, and 3) online-access-only data to facilitate especially large datasets or updating time series. Software development has begun and we look forward to discussing techniques to create the best open access, reproducible methods for package creation and future sustainability. We also now have a beta version available for experimentation and feedback from interested users and the Editorial Board. The version 1 public release is slated for fall 2020, with two subsequent annual updates. As an interdisciplinary data package, QGreenland is designed to aid collaboration and discovery across fields. Along with discussing QGreenland development, we will also provide an example use case to demonstrate the potential utility of QGreenland for researchers, educators, planners, and communities.

Climate communication involves deep community engagement and multiple parallel behavior interventions for a wide array of audiences. Images include large amounts of culturally nuanced details that can improve the social uptake of community climate interventions. Image specificity grounds information within subgroups improving the relatability and accuracy of emerging regional interventions. We experimented with using ArcGIS to collect and sort regionally and topically sorted climate mitigation and resilience behaviors. We'll review how our team set up a feedback loop in Survey123 and ArcGIS and how this might be useful in future climate messaging.

We describe Ziggy, an infrastructure for pipelines that process large volumes of science data. Ziggy is based on the pipeline infrastructure software that was developed to process flight data for the Kepler and TESS exoplanet missions. In this latter capacity, multiple terabytes of data are processed every month. Ziggy provides execution control, logging, exception management, marshaling, and persistence, and data accountability record management for user-defined sequences of processing steps. Users define a pipeline via a set of XML files that specify the order in which processing algorithms are applied (including optional branching, in which one step is followed by multiple algorithms that run simultaneously), inputs, outputs, and any instrument models or control parameters that are required for each step. Ziggy supports heterogeneous pipelines: each processing algorithm can be in any supported language, and each step can run locally on a server or remotely on a supercomputer or cloud computing facility. Ziggy is sufficiently lightweight to run on a laptop and sufficiently robust to run on a supercomputer; builds on Mac OS X and Linux are supported. Ziggy is currently in use as the pipeline infrastructure tool for reprocessing the full data volume of the EO-1/Hyperion mission data and is a candidate for use in the upcoming Surface Biology and Geology (SBG) mission of the Earth System Observatory (ESO). Ziggy contains no proprietary or sensitive/controlled software or algorithms, and approval for its release as a NASA Open Source Software Project is underway.

Through the NESDIS Cloud Archive Project (NCAP), NESDIS is building on previous cloud pilot efforts and making significant progress on delivering an end-to-end cloud archive prototype in late 2021 and operational cloud archive capability in 2022. Critical to the overall design is a clear mapping between the features being designed and the functional requirements driven by National Archives and Records Administration (NARA), the Open Archival Information System Reference Model (OAIS-RM), and the NESDIS vision for a consolidated cloud archive workflow. The mapping between these drivers and the features being developed using Agile methodologies will be reviewed, and a technical summary of NESDIS’ plans to implement data archiving using commercial cloud services will be provided.

We report the interaction between non-spherical swimmers and a long-standing flow structure, Lagrangian coherent structures (LCSs), in a weakly turbulent two-dimensional flow. Using a hybrid experimental-numerical model, we show that rod-like swimmers have a much stronger and more robust preferential alignment with attracting LCSs than with repelling LCSs. Tracing the swimmers’ Lagrangian trajectories, we reveal that the preferential alignment is the consequence of the competition between the intrinsic mobility of the swimmers and the reorientation ability of the strain rate near the attracting LCSs. The strong preferential alignment with attracting LCSs further leads to a strong clustering near the attracting LCSs. Moreover, we show the self-similarity of this clustering, which reduces the intricate interaction to only one control parameter. Our results generically elucidate the interaction between active and non-spherical swimmers with LCSs and, thus, can be widely applied to many natural and engineered fluids including ocean flow.

The NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project provides an extensive data set of aircraft in-situ meteorological, aerosol, and total water heavy isotope ratio measurements in the southeast Atlantic (SEA) lower troposphere over the months of Sept. 2016, Aug. 2017, and Oct. 2018. These months are during southern hemisphere spring, which is the agricultural burning season in subtropical southern Africa. During this season, biomass burning aerosol (BBA) loaded air in the African planetary boundary layer is carried out over the SEA by lower troposphere easterly flow. The goal of ORACLES was to study the effects of aerosol loading in the lower troposphere and as they mix into the semi-permanent cloud deck over the SEA. During ORACLES, water isotope ratio measurements D/H and 18O/16O were taken using Picarro brand cavity ring-down spectroscopic analyzers integrated into our Water Isotope System for Precipitation and Entrainment Research (WISPER). With over 300 hrs (~140,000 linear km at the typical aircraft speed) of 1 Hz sampling between 22°S to the equator and from 70 m to 6 km, this is a large and vertically resolved isotope dataset. In this presentation, we first use the coupled measurements of total water concentration and its heavy isotope ratio D/H to distinguish BBA plumes which have experienced prior precipitation vs those which have not. The findings are supported with lagrangian back-trajectories, MERRA surface temperatures and moisture, and isoCAM surface isotope values, which are combined to constrain simple isotope models. We find that BBA air sampled in 2016 experienced almost no precipitation, while BBA air and moisture in 2017 follow a model of convective outflow and most of the moisture loss is due to precipitation. Finally, we show the strong agreement between D/H evidence of precipitation amount and an indicator of aerosol removal via precipitation: the ratio of black carbon to carbon monoxide. In contrast, no correlation between total water concentration and aerosol removal is found, demonstrating the additional information contained in the isotope ratio measurements. This also provides an example of isotope ratio measurements supplementing other variables during a multifaceted field project to address science questions.

Air containing ~100 ppm H2 gas is venting about 8 hours a day from multiple locations on the periphery of 550m-diameter barren-of-vegetation depression in the Sao Francisco Basin in Brazil. Atmospheric pressure tides modulate the hydrogen venting in accordion fashion and explain the daily cycle. Modeling indicates that for hydrogen concentrations to change as observed, a substantial, permeable, subsurface reservoir must be tapped by fractures of limited volume. The fractures must have 1/1000th the volume of the accessed gas reservoir. For the phase of H2 venting to match the phase of the atmospheric pressure changes, the subsurface reservoir must be terminated by barriers such that the volume decompressed is about 25% of that which could be decompressed. How simple models can probe observations to obtain these conclusions will be the subject of the presentation.

We present a study of the potential impact of deepening the Corpus Christi Ship Channel through Aransas Pass; in particular, we study the effect on the transport of red drum fish larvae due to the change in channel depth. The study is conducted by high resolution simulation of the circulation of the seawater entering and exiting the pass for the current and proposed Ship Channel depths. The computer model incorporates tides and meteorological forcing and includes the entire Gulf of Mexico and the North American Atlantic coast. The corresponding transport of larvae modeled as passive particles due to the sea water circulation is established by releasing particles in the nearshore region outside Aransas Pass and subsequently tracking their trajectories. Both models are implemented in supercomputers at the Texas Advanced Computing Center (TACC). Accuracy of the circulation model computations is validated through comparisons to data collected from tidal gauges in the fall of 2020. We compare the difference in the number of larvae that successfully reach appropriate nursery grounds inside Aransas Pass for four distinctive initial larvae positions in the nearshore region. Our results indicate that the change in channel depth does not significantly alter the number of red drum larvae that reach suitable see a net increase of 0.5%.

Accurate mapping of urban infrastructure, specifically buildings, and extent is a high priority in addressing environmental and socioeconomic problems. Importantly, such mapping must account for the three-dimensionality (3D) of the urban environment that has traditionally been lacking (e.g., land cover and land use change—LCLUC—analyses often utilize two-dimensional satellite imagery). Undoubtedly, considerable development and change in urban areas takes place in the vertical dimension. Light detection and ranging (lidar) data provide the ability to map in 3D, but systematic data coverage both spatially and temporally is direly insufficient globally. Previous works have successfully implemented spaceborne radar data such as 1-km QuikSCAT data in lieu of lidar data to assess 3D urban build-up. Higher spatial resolution is required, though, to examine urban characteristics in detail. In this study, we test whether Sentinel-1 C-band Synthetic Aperture Radar (SAR) data can monitor 3D urban build-up in high spatial resolution (i.e. 40-m) in Detroit, Michigan, USA, 2015. Findings confirm, through comparison with aggregated 1-m lidar data, the utility of high spatial resolution SAR data for examination of urban built-up volume. Correlation and regression results show a strong linear relationship between SAR backscatter and lidar volume citywide. Moreover, SAR data, unlike coarser resolution radar data, can detect 3D anomalies over time (e.g., new building construction, building demolitions, etc.) further attesting to their utility for comprehensive 3D urban analyses a lower cost and higher repeatability over a much more extensive coverage compared to lidar data.

Wave energy has received significant attention in both academic and industrial areas during the past few decades. Among all of Wave Energy Devices (WEC) devices, many researchers focus on modeling the point absorber since it can provide a large amount of power in a small simple device when compared with other technologies. In this present work, we developed an efficient Structured Adaptive Mesh Refinement (SAMR) framework to model the interactions between the wave and pointer absorber by directly solving the Naiver-Stokes equation in a conservative manner. In particular, the level set function is used to capture the air-water interface, and re-initialization across different levels is applied. The Discrete Immersed Boundary Method (DIBM) is applied to describe geometry of the pointer absorber and include its effects on the incoming wave, which is generated in the inlet and absorbed by using a sponger layer closed to the outlet. To save computational cost, meshes are only refined near the air-water interface and surface of point absorber. Specially, both non-subcycling, where a uniform time step is employed for all variables on composite levels, and subcycling, in which variables on different levels advance via different time steps, are embedded in the SAMR framework. Cases about wave generation and fluid-structure interaction are obtained to validate the above proposed algorithm. Results show that subcycling takes significantly less CPU hours than non-subcycling to model the wave and pointer absorber interaction. Heave motions under different wave inputs are compared with previous experiments as well as results from potential flow theory, which shows viscous effects are important in this process. Future work including coupling AMR with Turbulent Models (e.g. RANS or LES) are also listed as an extension.

The Dungeness crab (Metacarcinus magister) fishery is one of the highest value fisheries in the US Pacific Northwest, but its catch size fluctuates widely across years. Although the underlying causes of this variability are not well understood, the abundance of M. magister megalopae has been linked to recruitment into the adult fishery four years later. These pelagic megalopae are exposed to a range of ocean conditions during their dispersal period, which may drive their occurrence patterns. Environmental exposure history has been found to be important for some pelagic organisms, so we hypothesized that inclusion of environmental exposure history would improve our ability to predict M. magister megalopae occurrence patterns compared to using ‘in situ’ conditions alone. We combined local observations of M. magister megalopae and regional simulations of ocean conditions to model megalopae occurrence using a generalized linear model (GLM) framework. The modeled ocean conditions were extracted from J-SCOPE, a high-resolution coupled physical-biogeochemical model. The analysis included variables from J-SCOPE identified in the literature as important for larval crab occurrence: temperature, salinity, dissolved oxygen concentration, nitrate concentration, phytoplankton concentration, aragonite and calcite saturation state, and pH. GLMs were developed with either in situ ocean conditions or environmental exposure histories generated using particle tracking experiments. We found that inclusion of exposure history improved the ability of the GLMs to predict megalopae occurrence. Of the five swimming behaviors used to simulate megalopae dispersal, several behaviors generated GLMs with superior fits to the observations, so a biological ensemble of these models was constructed. Our results highlight the importance of including exposure history in larval occurrence modeling and help provide a method for predicting pelagic megalopae occurrence. This work is a step towards developing a forecast product to support management of the fishery.

Microbial eukaryotes (protists) are important contributors to marine biogeochemistry and play essential roles as both producers and consumers in marine ecosystems. Among protists, mixotrophs—those that use both heterotrophy and autotrophy to satisfy their energy requirements—are especially important to primary production in oligotrophic regions where nutrient availability is otherwise limiting. For instance, acantharians accomplish mixotrophy by hosting Phaeocystis spp. as endosymbionts. Despite their ecological importance, Acantharea-Phaeocystis symbioses are understudied due to host fragility and inability to survive in culture. We investigated the evolution and ecological functioning of these symbioses by sequencing single-cell transcriptomes from sixteen acantharians. Since hosts harbor multiple Phaeocystis species, we prepared transcriptomes for the two most common symbiont species available in culture—P. cordata and P. jahnii—and evaluated differential gene expression between symbiotic and free-living cells. Results indicate photosynthesis genes are upregulated in symbiosis for both symbiont species, suggesting symbionts are photosynthesizing at elevated rates within hosts. However, biosynthesis and metabolism of storage carbohydrates and lipids are downregulated in symbiosis, indicating that extra energy captured through elevated photosynthesis is not retained. Symbiont gene expression suggests symbionts relinquish fixed carbon as small organonitrogen compounds, such as amides and amino acids, while receiving host-supplied nitrogen as urea and ammonium. Importantly, genes associated with protein kinase signaling pathways that promote cell proliferation are deactivated in symbionts. Manipulation of these pathways may prevent symbionts from overgrowing hosts and therefore represents a key component of maintaining the symbiosis. This study illuminates mechanisms of host control and nutrient transfer in an important microbial symbiosis in oligotrophic waters.

Research data are a vital component of the scientific record. Discovering and assessing data for possible reuse in future research is challenging. The Belmont Forum has recently awarded funds to three international teams as part of a four-year Collaborative Research Action (CRA) on Science-driven e-Infrastructure Innovation (SEI) for the Enhancement of Transnational, Interdisciplinary and Transdisciplinary Data Use to improve data management practices that will increase data reuse. One of these awardees, PARSEC, comprises two interwoven strands, one focused on improving data practices for reuse and credit, and one for synthesis science. The data specialists work alongside synthesis science researchers as they determine the influence of natural protected areas on socioeconomic outcomes for local communities. They collaborate with the researchers to better understand their motivations and work practices, and to aid them in the data-related steps that need to be taken during the research lifecycle. This will ensure their data and code are FAIR-compliant and thus enhance the likelihood of their data being reused and their analyses reproducible. The PARSEC team is working with Research Data Alliance (RDA), Earth Science Information Partners (ESIP), DataCite and ORCID to build awareness of the elements required for data creators to receive credit and automated attribution for their data contributions, and the tools that will make it easier to observe usage. Credit for data is an important incentive for researchers to make their data reusable. When data are FAIR and cited, their related publications have higher visibility. We shall discuss various ways in which we are working across the science-data interface in our multi-country and multi-disciplinary working environment to improve data (and code) reuse through better management and crediting. Make your Data FAIR, Cite your Data, Get Credit, Increase Reuse and reap the rewards!

The alluvial wetlands are one of the most important ecosystems of the world and are in abundance in the vast Indo-Gangetic plains. The wetlands of this region are of variable sizes and characteristics but currently face similar problems of drying-out and fragmentation. It is empirical to understand the evolutionary pathways and hydrological connectivity of these wetlands for planning and execution of management and restoration for them. These pathways have been studied for a wetland namely, the Kaabar Tal, situated in the Kosi-Gandak interfan region of the eastern Gangetic plains. Its geomorphic evolutionary pathways have been established using satellite imageries, DEMs, toposheets, and high resolution aerial imagery obtained using unmanned aerial vehicle (UAV). Various geomorphic units characterized by an assemblage of geomorphic features have been mapped for the Kaabar Tal and its surroundings. Seasonal, annual, and decadal variability in the hydrological status of this wetland were estimated for a time-period of 1976-2017 using the historical Landsat datasets. Seasonal variability in hydrological connectivity structure of the wetland with its catchment for the time-period of 1989 to 2017 was estimated in a GIS framework. The structural connectivity was estimated using the technique of diffusion kernel interpolation. The dynamic connectivity was estimated using the Getis-Ord Gi* statistic and Mann-Kendall trend test using the concepts of space-time cubes. The detailed geomorphic mapping revealed that this wetland primarily originated through fluvial processes. A historical reconstruction of its hydrological status revealed that in the recent times the wetland is getting fragmented, and the connectivity potential of different areas of the catchment is a function of the prevalent land-use and land-cover (LULC) pattern and seasonality. Therefore, the heterogeneity and complexity of the geomorphic units of the wetland and the historical LULC patterns of the catchment should be considered in designing any management and restoration plan.

Data that are FAIR demonstrate specific characteristics including: ease of discovery, ability to access, community acceptable formats allowing interoperability, and information that supports the decision for reuse. The process used to determine data reuse is commonly called “fit for purpose” or “fit for use”. These criteria are defined using relevant factors established by the community for which the data was originally created, and also a “best effort” for criteria needed by other research communities. The FAIR Data Principles support robust documentation of datasets to include the necessary information for reuse. An important part of that documentation, or metadata, is clear documentation of the quality and uncertainty related to the data being considered. When this information is not complete, data has a higher tendency of being used incorrectly leading to inaccurate research, rejected papers, or even retracted papers. The importance of data creators to make their data FAIR – including uncertainty information – directly improves the transparency and integrity of our science today and into the future.

As part of the (AC)3 Arctic Amplification project, we are studying the influence of specific sea ice conditions like the presence of leads or polynyas on micro- and macrophysical cloud properties such as cloud fraction, altitude, thickness, thermodynamic phase, and their coupling state with respect to the underlying surface during the MOSAiC expedition’s legs 1 to 3. Micro- and macrophysical properties of surface-coupled clouds are analyzed as a function of sea ice concentration (SIC) in the vicinity of the ground-based atmospheric remote-sensing observations onboard the RV Polarstern. Only situations are analyzed where wind favored the transportation of air from location where open sea ice is detected. Cloud microphysical properties are obtained from the CloudNet cloud target classification algorithm which uses the atmospheric remote-sensing instrumentation suite on board of RV Polarstern provided by the US Atmospheric Radiation Measurement (ARM) mobile facility, the TROPOS ship-borne Atmosphere observation suite (OCEANET) and liquid water path retrievals by the University of Cologne. Primarily, the classical Matlab-based CloudNet classifications retrieved by TROPOS are used. Furthermore, the recently released ARM “evaluation” Active Remote Sensing Clouds (ARSCL) data product for the KA-band cloud radar is also evaluated by the new Python CloudNet version developed at the Finish Meteorological Institute. Discrepancies between those two CloudNet versions will be evaluated and reported as feedback for the ARM evaluation data set. High resolution (1-km) merged AMSR2-MODIS satellite retrievals of Sea Ice Concentration by the University of Bremen are used as information for sea ice monitoring. The present contribution only exploits SIC data, however future studies will focus on MOSAiC specific products for the classification of leads. Statistics for the cloud properties as a function of SIC will be presented as first approach to investigate the influence of sea ice conditions to central Arctic clouds.

Recent advances in modeling Rayleigh-Benard convection have demonstrated the existence of turbulent superstructures, whose life and morphology largely varies with Rayleigh (Ra) and Prandtl (Pr) numbers. These structures appear as a two scale phenomena, where small scale rolls organize in larger convection cells, and can be modelled only in 3D on a simulation box characterized by a very large (>10) width/height (W/L) ratio, and sufficiently refined to resolve the boundary layer up to Ra = 108 (>100 divisions in height) and to Ra = 1010 (>200 divisions). To achieve this goal, we use our own 3D Parallel Python implementation of the Lattice Boltzmann Method, tested to run with linear efficiency on thousands of cores. We show the dependency of horizontal fluctuations of RMS of temperature and vertical velocity in the middle of the box and illustrate how the superstructures emerge for W/L ratios of Terrestrial Planets and Super Earths, and quantify the duration of these superstructures and the likely implications for the evolution of their surface features. The effect of the P/T dependent viscosity and thermal conductivity is finally discussed.

Long-term measurements at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiagvik, Alaska and from the Multidisciplinary drifting Observatory for the study of the Arctic Climate (MOSAiC) expedition are being used to study the climatology of clouds containing supercooled liquid (SCL) in the Western and Central Arctic. Classification of cloud hydrometeors in the liquid, ice or mixed phase of the cloud is determined by using the Cloudnet algorithm developed by the Finish Meteorological Institute. We apply the Cloudnet processing chain to a set of ground-based remote sensing measurements from the NSA site and the ARM mobile facility and the TROPOS shipborne atmosphere observation suite (OCEANET) on board of the RV Polarstern research vessel during MOSAiC. In order to accurately detect cloud droplets and SCL layers within mixed-phase clouds, Cloudnet relies on lidar observations. Lidars however suffer from total signal attenuation at a penetration optical depth of about three. Conversely cloud radars with their capability to penetrate multiple liquid layers can be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height by using information of the cloud radar Doppler spectrum. The Leipzig Institute for Meteorology (LIM) recently developed a deep learning approach for reVealing supercOOled liquiD beyOnd lidar attenuatiOn (VOODOO) which benefits from the morphological features in cloud radar Doppler spectra to extract further information related to the existence of SCL using Cloudnet’s target classification as supervisor. The current contribution presents a SCL climatology obtained using Cloudnet for the NSA site along with case-studies for MOSAiC where VOODOO results are contrasted with the standard Cloudnet outputs. Advantages and limitations of both methods will be presented to the scientific community.