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NASA NTRS2021

Importance of Quantifying the Full-depth Carbon Reservoir of Jamaica Bay Salt Marshes, New York

Constraining uncertainty in the global carbon cycle requires valid assessment of both surface and stored carbon in marine and coastal ecosystems (Blue Carbon) as well as terrestrial carbon (forests, peatlands, and soils) [Pendleton et al., 2012]. Quantifying the global carbon stock of coastal salt marshes, potentially the most efficient carbon-burying ecosystems in the world per area, is a key area of further research in both of these fields Pendleton et al., 2012. One of the largest challenges is that despite the fact that salt marshes often sequester carbon several meters deep, nearly all estimates of salt marsh carbon stocks consider only the upper 1 m of sediment (Windham-Myers et al., 2015) [54]. This is particularly concerning because coastal wetlands are increasingly at risk due to climate change, sea level rise, and anthropogenic disturbance and destruction (Deegan et al., 2012) [15]. Using full-depth measurements from marsh cores, we estimate the carbon stock of five salt marshes in the highly urbanized estuary of Jamaica Bay, New York and argue that partial-depth measurements can underestimate carbon stocks. These estimates use calculated carbon content and probe depth data of these marshes collected between 2000 and 2019, applying this data across the full area of the marsh obtained from satellite imagery. Carbon density measurements are then multiplied by the full-depth volume of the marshes to create an estimate of total carbon stock. In addition to calculating present-day estimates, we compare our carbon stock estimates to historical Jamaica Bay imagery to calculate historical carbon stocks and carbon loss. The carbon stock estimates presented here show a 95% carbon stock loss between 1885 and 2019 in Jamaica Bay and highlight the severe underestimation of carbon stocks without full-depth calculations. These findings have important implications for disappearing salt marshes with regard to the global carbon cycle and the incorporation of belowground carbon into global climate models. The findings are increasingly relevant for advocacy efforts aiming to conserve these marshes with sea level rise.

Tidal wetlands↗

NASA NTRS2022

An Investigation Into Some Important Aspects of Droplet Breakup/Vaporization Behavior Caused By a Gas Flow (Both Nonreacting & Detonative Combustion) in a Shock Tube

As a part of the rotating detonation engine (RDE) technology enablement project at NASA Glenn Research Center (GRC), an effort was undertaken to extend our current computational capabilities of OpenNCC in some important ways with the implementation of a modeling approach to account for the droplet breakup caused by a shock-induced gas motion, & a vaporization model valid over a wide range of pressure conditions encountered in multiphase detonation. With the modified code, a study was undertaken to investigate the individual droplet behavior followed by the passage of a shock front. The study is carried out by tracking a sparse group of droplets to gain some understanding of shock induced droplet behavior under various shock strengths & fuel injector conditions. The study also looks into the effect of randomization involved in determining the droplet breakup outcomes. Over a wide range of sub-critical conditions examined, larger droplets are observed to undergo significant changes in droplet behavior following their breakup. However, smaller droplets (10 µm or less ) remain unaffected by any shock induced breakup. In a follow-on work, we investigated the impact of shock and droplet interaction in a detonation study involving both gaseous as well as gas/liquid (droplet clouds) fuel/air stoichiometric mixtures in a simple 3D shock-tube configuration. The droplet clouds are made up of different initial droplet sizes of either 6, 10, or 30 µm. We also investigated the individual droplet behavior followed by the passage of a detonation front. The results represent conditions that lead to both overdriven and C-J (ChapmanJouguet) detonations. Under both test conditions, most of the droplet vaporization is completed within a short distance (duration) behind the detonation front & well within the region of complete combustion observed in a corresponding equivalent gas-phase fuel/air mixture. The impact of the shock-induced droplet breakup is found to be significant in the calculations involving the 30-µm droplets. Subsequent to the breakup, the drop sizes vary from 1 to 10 µm. Another factor that contributed to the observed rapid vaporization is the result of vaporization taking place under supercritical conditions. The overall detonation properties of various droplet clouds (made up of different initial sizes) are similar to those observed in a corresponding gaseous fuel/air mixture. In the calculation involving a gaseous fuel, the calculated C-J detonation velocity is 1822 m/s involving Jet-A/air and φ = 1. In the overdriven detonation, it is 2044 m/s. In the calculations involving droplet clouds, the corresponding detonation velocities are lower. The impact of increased droplet size is primarily seen in a higher reduction in the detonation velocity.

shock/droplet interaction↗

NASA NTRSDate not supplied

On the Importance of Producing and Characterizing Laboratory Analogs of Cosmic Grains, Planetary Atmospheric Aerosols, and Surface Materials

Carbonaceous dust and aerosols have been observed in interstellar/circumstellar and planetary environments. Dedicated laboratory experiments have been developed to produce analogs of these solid materials under different experimental conditions (molecular precursors, temperature, pressure, energy source…). These experimental studies are key to investigating the physical and chemical processes that drive the formation of solid particles from gas and solid phase molecular precursors in astrophysical and planetary environments. They also allow the characterization of the physical, optical, and chemical properties of these laboratory-generated analogs, hence providing critical information that can be used as input parameters in models for the analysis and interpretation of observational data. Here, as an example of these laboratory efforts, we will present various studies that combined (1) experiments performed with the NASA Ames COSmIC facility to produce analogs of Titan’s atmospheric aerosols and cosmic grains from gas phase molecular precursors, and (2) the characterization of these analogs with the NASA Ames Optical Constants Facility (OCF) to provide the real and imaginary parts of their refractive indices, n + ik, to the community, from the visible to the far-infrared (0.59–200 µm, 16,950–50 per cm). We will also discuss the importance of the wavelength and spectral range coverage of these optical constants, n and k, for their use in radiative transfer, atmospheric and reflectance models, and present a new effort to develop an Optical Constant database (OCdb) to provide this critical data to the scientific community.

Cosmic Grains↗

NASA NTRS2022

The Scientific Importance of Returning Airfall Dust as a Part of Mars Sample Return (MSR)

Dust transported in the martian atmosphere is of intrinsic scientific interest and has relevance for the planning of human missions in the future. The MSR Campaign, as currently designed, presents an important opportunity to return serendipitous, airfall dust. The tubes containing samples collected by the Perseverance rover would be placed in cache depots on the martian surface perhaps as early as 2023–24 for recovery by a subsequent mission no earlier than 2028–29, and possibly as late as 2030–31. Thus, the sample tube surfaces could passively collect dust for multiple years. This dust is deemed to be exceptionally valuable as it would inform our knowledge and understanding of Mars' global mineralogy, surface processes, surface-atmosphere interactions, and atmospheric circulation. Preliminary calculations suggest that the total mass of such dust on a full set of tubes could be as much as 100 mg and, therefore, sufficient for many types of laboratory analyses. Two planning steps would optimize our ability to take advantage of this opportunity: (1) the dust-covered sample tubes should be loaded into the Orbiting Sample container (OS) with minimal cleaning and (2) the capability to recover this dust early in the workflow within an MSR Sample Receiving Facility (SRF) would need to be established. A further opportunity to advance dust/atmospheric science using MSR, depending upon the design of the MSR Campaign elements, may lie with direct sampling and the return of airborne dust.

Monica M. Grady↗

NASA NTRS2022

Importance of Different Parameterization Changes for the Updated Dust Cycle Modeling in the Community Atmosphere Model (Version 6.1)

The Community Atmosphere Model (CAM6.1), the atmospheric component of the Community Earth System Model (CESM; version 2.1), simulates the life cycle (emission, transport, and deposition) of mineral dust and its interactions with physio-chemical components to quantify the impacts of dust on climate and the Earth system. The accuracy of such quantifications relies on how well dust-related processes are represented in the model. Here we update the parameterizations for the dust module, including those on the dust emission scheme, the aerosol dry deposition scheme, the size distribution of transported dust, and the treatment of dust particle shape. Multiple simulations were undertaken to evaluate the model performance against diverse observations, and to understand how each update alters the modeled dust cycle and the simulated dust direct radiative effect. The model–observation comparisons suggest that substantially improved model representations of the dust cycle are achieved primarily through the new more physically-based dust emission scheme. In comparison, the other modifications induced small changes to the modeled dust cycle and model–observation comparisons, except the size distribution of dust in the coarse mode, which can be even more influential than that of replacing the dust emission scheme. We highlight which changes introduced here are important for which regions, shedding light on further dust model developments required for more accurately estimating interactions between dust and climate.

mineral dust parameterizations↗

NASA NTRS2022

The Importance of Policies: It’s Not Just A Pipeline Problem

For decades, a leaky pipeline analogy has been used when discussing diversity issues in STEM fields. However, this imagery is overly simplistic and does not capture critical issues that contribute to people leaving the field. It puts distance between structural issues, our actions, and why people leave the field. When we view our research structure as something more complex, we can start taking ownership and frame more impactful solutions instead of misidentifying important issues and providing ineffective short-term solutions. Many of the issues discussed in the "Cultivating a culture of inclusivity in Heliophysics" position paper have counterparts within our policies and our institutions. To fully address and mitigate the current issues within our field, we have identified a need to cultivate a positive, safe, inclusive, and effective environment. However, we need both cultural and programmatic changes. We will try to identify systemic issues that inhibit many from fully participating and potential solutions, as well as groups and fields producing best practices for creating and enabling effective environments where innovation can occur.

A.J. Halford↗

NASA NTRS2023

Importance of Ice Nucleation and Precipitation on Climate With the Parameterization of Unified Microphysics Across Scales Version 1 (PUMASv1)

Cloud microphysics is critical for weather and climate prediction. In this work, we document updates and corrections to the cloud microphysical scheme used in the Community Earth System Model (CESM) and other models. These updates include a new nomenclature for the scheme, now called Parameterization of Unified Microphysics Across Scales (PUMAS), and the ability to run the scheme on graphics processing units (GPUs). The main science changes include refactoring an ice number limiter and associated changes to ice nucleation, adding vapor deposition onto snow, and introducing an implicit numerical treatment for sedimentation. We also detail the improvements in computational performance that can be achieved with GPU acceleration. We then show the impact of these scheme changes on the (a) mean state climate, (b) cloud feedback response to warming, and (c) aerosol forcing. We find that corrections are needed to the immersion freezing parameterization and that ice nucleation has important impacts on climate. We also find that the revised scheme produces less cloud liquid and ice but that this can be adjusted by changing the loss process for cloud liquid (autoconversion). Furthermore, there are few discernible effects of the PUMAS changes on cloud feedbacks but some reductions in the magnitude of aerosol–cloud interactions (ACIs). Small cloud feedback changes appear to be related to the implicit sedimentation scheme, with a number of factors affecting ACIs.

Clouds↗

NASA NTRS2022

On the Importance of Neutral Composition and Temperature Measurements in the 100–200 km Altitude Region

Currently, thermospheric species densities and temperatures between ∼100 and 200 km are not known to the accuracy needed to fully characterize how the thermosphere transitions from a well-mixed atmosphere to a diffusively separated atmosphere with zero temperature gradient. This greatly inhibits scientific discovery attainable from either models or observations in this region, especially the understanding of mechanisms that drive thermosphere and ionospheric variability from space weather to climatological time scales. The purpose of this paper is to highlight the importance and critical need for new, global, height-resolved neutral composition (O, O2, N2) and temperature measurements in the new ignorosphere: the 100–200 km region of the thermosphere. We conclude with observation recommendations and requirements for new comprehensive composition and temperature measurements in the 100–200 km altitude region that would lead to significant advances in thermosphere-ionosphere science, space weather, and space climate.

thermosphere↗

NASA NTRSDate not supplied

Importance of Radiative Transfer Models in Atmospheric Remote Sensing

Radiative transfer models (RTMs) play a significant role in the development of satellite instruments for remote sensing applications. These models simulate electromagnetic radiation's propagation through the atmosphere, providing valuable insights into atmosphere-radiation interactions. RTMs facilitate the optimization of satellite instrument designs, ensuring their ability to measure targeted atmospheric and surface properties accurately. Moreover, they aid in simulating instrument’s measurements under various atmospheric conditions, enabling calibration and validation processes to enhance data quality and reliability. RTMs are extensively used in the Observing System Simulation Experiments (OSSE), to generate synthetic observations. By incorporating RTMs into OSSE, we can assess the potential impact of future satellite missions, sensor configurations, and data assimilation techniques. This approach allows for the optimization of satellite instruments and constellations and the evaluation of their influence on weather forecasting, climate monitoring, and other Earth science applications. Another crucial application area of RT models is data assimilation, where they play a fundamental role in combining satellite observations with numerical models to improve atmospheric and environmental predictions. RTMs provide the link between observed radiances and atmospheric parameters, enhancing the accuracy of numerical models and generating more reliable forecasts for weather events, air quality assessments, and climate projections. Moreover, adapting RT models to capture the intricate radiation interactions within the Planetary Boundary Layer will significantly contribute to improving weather forecasting and climate change projections. Current community radiative transfer (RT) models are primarily developed and optimized for operational data assimilation of satellite observations. These models excel at assimilating satellite data into numerical weather prediction models to improve forecast accuracy. However, their focus on data assimilation limits their suitability for other important applications, such as satellite instrument development, OSSE, and Planetary Boundary Layer (PBL) studies. Moreover, for PBL studies, RT models need to be adapted to capture the intricate radiation interactions within this crucial atmospheric layer. Developing RT models that can represent the PBL's unique characteristics, such as surface interactions, will contribute significantly to understanding and predicting weather phenomena, air quality, and climate dynamics. This abstract provides a comprehensive overview of the current status of RT models and highlights their limitations concerning satellite instrument development, OSSE, and PBL studies. Addressing these shortcomings requires concerted efforts to enhance RT models' capabilities and expand their applications beyond data assimilation. By investing in research and development to improve these

Isaac Moradi↗

NASA NTRSDate not supplied

Thermal Non-Equilibrium: as Fascinating as It Is Important

Thermal non-equilibrium (TNE) is the likely cause of most coronal condensations, including prominences and non-flaring coronal rain. It is therefore extremely important. It is also highly intriguing, being in some respects counter intuitive. In this talk I will review the basic properties of TNE, summarize the history of TNE research, and point to some of the outstanding remaining issues.

James A. Klimchuk↗

NASA NTRS2024

Interpretable Machine Learning for Molecular Biosignatures: a Novel Single-Sample Feature Importance Method That Is Sensitive To Statistical Interactions

Isotope ratio mass spectrometry (IRMS) of volatiles (e.g., CO 2 ) promises to be a powerful tool for potential biosignature detection for future missions to ocean worlds (OW) such as Europa and Enceladus. Machine learning (ML) methods for IRMS data could enable science autonomy by onboard prediction of seawater chemistry and biosignature presence. However, ML models are likely to be complex and involve statistical interactions between features (variables), which can make predictions seem opaque and enigmatic. For ML predictions as significant as extraterrestrial biosignatures, we must place extraordinary confidence in models. It is therefore essential that these models make interpretable predictions (i.e., human-understandable) and include false-prediction diagnostics. We achieve high accuracy and interpretability in ML biosignature and seawater chemistry models for OW through a nearest-neighbors feature selection tool that detects statistical interactions between predictors, constructs interaction networks for visualization of selected features working together to make a prediction, and reports single-sample feature importance scores for false-detection diagnostics. Here we develop a novel single-sample nearest-neighbors projected distance regression(ssNPDR) feature selection method that improves upon existing single-sample algorithms through the inclusion of statistical interactions while providing false-prediction diagnostics for ML models.

geochemistry↗

NASA NTRS2001