Hollow glass microspheres (HGMs) are promising materials for arctic ice restoration. When spread in a thin layer on top of ice, these materials can increase the albedo of arctic ice, potentially slowing the rate of ice melt and enhancing the formation of multiyear ice in the polar region.[1] The effect of particle diameter and wall thickness of glass for backscatter efficiency in the visible and IR regions is evaluated by Mie scattering calculations and UV-Vis spectrophotometry of several candidate materials. The design of HGMs for highest backscatter efficiency and albedo enhancement is discussed. For use in arctic environments, both the fate of HGMs and their ecotoxicological impact must also be considered. HGMs with density <1 g/cc are preferred as they will float on the surface of melted ice pools. However, HGMs with very low density and thin walls may not be sufficiently robust to withstand significant weathering in the environment. Design considerations of tradeoff for density, reflectivity, and crush strength will be discussed. [1] Field, L., Ivanova, D., Bhattacharyya, S., Mlaker, V., Sholtz, A., Decca, R., et al. (2018). Increasing Arctic sea ice albedo using localized reversible geoengineering. Earth’s Future, 6, 882–901. https://doi.org/10.1029/2018EF000820

During the Arctic summer season, snow atop the sea ice melts and pools into low-lying areas on the surface. These melt ponds reduce surface albedo and increase solar absorption in the Arctic Ocean. Throughout the summer, melt ponds grow, drain, and connect, through a complex drainage system. Current melt pond schemes in sea ice models, such as the level-ice scheme in the Los Alamos Sea Ice Model (CICE), rely on a linear relationship between pond depth and fraction to predict the evolution of pond growth as the snow and sea ice melt. Although the inclusion of melt ponds in models has been shown to improve forecasts of end-of-summer sea ice extent, observations of melt pond depth and fraction guiding these models are from SHEBA, a spatially-limited field campaign which occurred over 20 years ago. Until recently, melt ponds characteristics have been difficult to resolve from spaceborne platforms due to their small size (10s - 100s m in diameter), and indistinguishable radiometric similarity to open water. Here we show that new, high-resolution laser altimetry measurements from ICESat-2 (IS2), combined with coincident high-resolution satellite imagery, provides a three-dimensional view of the melting sea ice cover. IS2, launched in September 2018, has now observed two summer melt seasons in the Arctic. IS2 operates at 532 nm, a wavelength that penetrates low turbidity water, and can therefore be used to capture the bathymetry of shallow water features. Building on previous work, we demonstrate IS2’s ability to detect and measure melt ponds on multiyear sea ice. We validate the existence of melt ponds with high resolution (10 m) visible imagery from the Sentinel-2 (S2) MultiSpectral Instrument. We apply the “density dimension algorithm – bifurcate” (DDA-bifurcate), an auto-adaptive algorithm utilizing data aggregation with the ability to track two surfaces, as well as a second algorithm that tracks melt pond surface and bottom, to derive melt pond depth for dozens of melt ponds in 2019 and 2020. Applying a sea ice surface classification algorithm to S2 imagery, we are able to determine melt pond fraction. We compare our findings of coincident melt pond fraction and depth with the melt pond parameterization used in the level-ice scheme in CICE. We discuss our results in the context of the existing literature on pond depth and volume.

Mount St. Helens, Washington State, USA, is characterized by a 2 x 3.5 km horseshoe-shaped and north-facing crater and hosts one of the last expanding glaciers in the Cascade Volcanic Arc, Crater Glacier. First observed in 2012, a new and extensive system of glaciovolcanic caves has developed around the 2004-2008 Lava Dome inside the Crater Glacier. Field studies have documented the cave characteristics via photography and tacheometric survey methods repeated from 2014-2019. Climatologic measurements reveal that sub-glacial fumarole activity leads to large-scale cave complexes, sometimes several hundred meters long. These allowed comparisons of passage extent and volume to be made and gave us a unique opportunity to observe their genesis over time. Ten distinct caves have been discovered in a circumferential pattern surrounding the 2004-2008 Lava Dome. The caves comprise a combined length of 2,232.3 m, the three most significant ones reaching more than 400 m each. Changes in the output of individual fumaroles which have melted the passages have also contributed to the appearance and disappearance of subglacial rooms and marginal dendritic passages which typically orient to entrances along the rock/ice interface. Over time the caves have demonstrated significant morphological changes. Multiple caves demonstrated increases in length and volume over the study period, along with changes to internal morphology. Caves located on the east and west arm of the glacier are additionally influenced by glacier movement and a fast flow rate which contribute much to morphology changes. Resurveys of cave passages over multiple years have revealed the dynamic nature of the systems, which are not necessarily in balance with the geothermal heat release. We expect that the overall growth of the cave systems will continue as long as an equilibrium of snow accumulation and ablation is reached or changes in volcanic activity occur. The newly formed glaciovolcanic cave systems on Mount St. Helens offer a rare view into the internal workings of a glacier and can lead to a better understanding of how glaciers and active volcanoes interact.

Cloud tomography (CT) is a promising approach in passive remote sensing using space-based imaging sensors like MISR and MODIS. In contrast with current cloud property retrievals in the VNIR-SWIR, which are grounded in 1D radiative transfer (RT), CT embraces the 3D nature of convective clouds. Forster et al. (2020) defined the “veiled core” (VC) of such clouds as the optically deep region where detailed 3D structure of the cloud has little impact on the multi-angle/multi-spectral images as long as average VC extinction and any significant cloud-scale gradient are preserved. Quantitatively, the difference between radiance fields escaping clouds remains commensurate with sensor noise when said clouds differ only in the small-scale distribution of extinction inside their VC. An important corollary for the large and ill-posed CT inverse problem is that the only unknowns of interest for the whole VC are its mean and any cloud-scale vertical trend in the extinction coefficient. Another ramification for CT algorithms under development is that the forward 3D RT model driving the inversion may be vastly simplified in the VC to gain efficiency. We explore that possibility here, assuming radiative diffusion as the simplified RT for the VC. We also describe the relevant RT physics that unfold in the VC and in the outer shell (OS) where detailed spatial structure does matter for image formation. This includes control by the VC of the cloud-scale contrast between brightnesses of illuminated and shaded boundaries, as well as the gradual blurring of spatial structure via directional diffusion with increasing optical distance into the OS. “Transport” space is the merger of 3D (or 1D) physical space and 2D direction space. Cloud image formation involves radiative diffusion processes (i.e., random walks) in both of these spaces, depending on what transport regime prevails. Fortunately for the future of computed CT and of passive cloud remote sensing in general, there is a clear spatial separation: asymptotic limit of radiative diffusion in the VC, standard RT in the OS. A hybrid forward model for CT will make use of this fact. Reference: Forster, L., Davis, A. B., Diner, D. J., & Mayer, B. (2021). Toward Cloud Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in Opaque Convective Clouds, Journal of the Atmospheric Sciences, 78(1), 155-166.

The research data repository of the Environmental Data Initiative (EDI) is a signatory of the FAIR Data Principles. Building on over 30 years of data curation research and experience in the NSF-funded US Long-Term Ecological Research program (LTER), it provides mature functionalities, well established workflows, and support for ‘long-tail’ environmental data publication. High quality scientific metadata are enforced through automatic checks against community developed rules and the Ecological Metadata Language (EML) standard. Although the EDI repository is far along the continuum of making its data FAIR, representatives from EDI and the LTER Information Management community have recently been developing best practices for the edge cases in environmental data publishing. Here we discuss and seek feedback on how to best handle the publication of these ‘long-tail’ data when extensive additional data are available along with e.g., genomics data, physical specimens, or flux tower data. While these latter data are better handled in other discipline-specific repositories such as NCBI, iDigBio, and AmeriFlux, they are frequently associated with other data collected at the same time and location, or even from the same samples. This is particularly relevant across the LTER Network, where sites represent integrative research projects. Questions we address (and seek community input from) include: How to archive documents and images when they are data, e.g., field notebooks, or time-lapse photographs of plant phenology? How to deal with data from Unmanned Vehicle (e.g., drones and underwater gliders), acoustic data, or model outputs, which may be several terabytes in size? How should processing scripts or modeling code be associated with data? Overall, these best practices address issues of Findability and Accessibility of data as well as greater transparency of the research process.

Observations from the LADEE, Apollo and Surveyor missions have provided evidence of an active lunar dust environment where dust is ejected from the surface into the exosphere by natural physical mechanisms (e.g., meteoroid impacts and possibly electrostatic lofting). Characterizing the spatial and temporal distribution of different exospheric dust populations, and how they are coupled to other components of the dynamic lunar environment, is important to understanding the surface evolution of the Moon. Additionally, anthropogenic dust ejected from the surface by human/robotic exploration activities can be a significant source of exospheric dust. Experience from the Apollo missions showed that dust has the potential to be hazardous as it can interfere with the operation of mechanical, thermal and optical systems. Therefore, it is vitally important to be able to monitor the dust environment to ensure mission safety for future exploration, especially in the Artemis era. An effective method for observing any dust populations in the lunar exosphere is to measure the intensity of sunlight scattered by the dust. This can reveal the dust abundance and spatial distribution, as well as constrain the average grain size by measuring the angular width of the forward scattering lobe. The largest uncertainties in such measurements lies in the grain scattering properties, namely: scattering coefficient, phase function shape and polarization. All of these become more important at larger scattering angles, where diffraction no longer dominates. Typically, the dust size distribution cannot be measured uniquely, and must be constrained by a set of forward simulations at multiple wavelengths and scattering angles. We present a precomputed grid of light scattering properties for irregularly shaped grains (Richard et al., 2011), which are a more realistic representation of lunar dust than the Mie models commonly employed. The grid spans UV to near-IR wavelengths and encompasses a wide range of grain size. Scattering from smaller grains is computed using the Discrete Dipole (DDA) method (DDSCAT) and at larger sizes using the Hapke-Equivalent Slab with Allen diffraction (this will soon be replaced by ray tracing with diffraction). Applications of this grid include observation interpretation and modelling of sunlight scattered by meteoroid impact plumes, lunar horizon glow, and exploration activities.

We measured during the MOSAiC field experiment the snow microstructure on sea-ice from early winter to the melt season, and continued measurements with the surface scattering layer. We could measure every week 1-2 full snow profiles between 0.1-0.3 m. We extracted, mostly in-situ, cores of 48-78 mm diameter and of about 0.1 m length, and scanned with 18-28 µm resolution. The goal of these measurements is to understand the formation and metamorphism of the snowpack in detail, and to derive detailed geometrical and physical properties from the samples. We will present first examples and and overview of characteristic snow profiles from leg 1 - 4, spanning the winter, spring and melt season.

The Permian Basin has become the United States’ largest producer of oil over the past decade. Along with the rise in production, there has been an increase in the rate of low magnitude earthquakes, some of which have been associated with hydrocarbon extraction and wastewater injection. A detailed knowledge of changes to the subsurface can aid in understanding the causes of seismicity, and these changes can be inferred from InSAR surface deformation measurements. In this study, we show that both cm-level cumulative deformation, as well as mm-level coseismic deformation signals, are detectable in West Texas. In a region west of Mentone, TX, we reconstructed the subtle coseismic deformation signal on the order of ~5 mm associated with the recent M4.9 earthquake. Over ~100,000 km2 of the Permian Basin, we created annual cumulative LOS deformation maps, decomposing into vertical and eastward components where overlapping data are available. These maps contain numerous subsidence and uplift features near active production and disposal wells. The most important deformation signatures are linear streaks that extend tens of kilometers near Pecos, TX, where a cluster of increased seismic events was cataloged by TexNet. As validated by independent GPS data, our InSAR processing strategy achieved millimeter-level accuracy. A careful treatment of the InSAR tropospheric noise, which can be as large as 15 cm in West Texas, is required to detect surface deformation signals with such low signal-to-noise ratio. We developed an outlier removal technique based on robust statistics to detect the presence of strong, non-Gaussian noise. We compared the surface deformation solutions of multiple InSAR time series methods, and all of them produced more accurate and consistent deformation trends after removing outlier InSAR measurements. We are exploring a Bayesian generalization of SBAS velocity estimation by including probabilistic data rejection to determine which pixels should be excluded from the model fitting. This technique provides a full posterior distribution of the model parameters along with the best-fit surface velocity.

Habitable regions of Europa may include a subsurface ocean and transient liquid environments within its icy shell. Ocean-surface communication may occur on 1–2 million-year (My) timescales or even more rapidly in chaos regions. Any ice-entombed organisms could remain viable at near-surface depths (10–100 cm) over 1–10 ky. The proposed Europa Lander will target samples at depths >10 cm, potentially enabling recovery of viable organisms if sampling conditions are ideal. Any life there would likely represent a separate genesis event from Earth life. Life detection approaches should therefore not only target life as we know it (contamination, common physicochemistry), but also as we don’t know it, to lower the risk of false negatives. We propose to target prebiotic, ancient, or extant life using a novel fully-electronic single-molecule detection strategy. Now in early development (PICASSO), the Electronic Life-detection Instrument for Enceladus/Europa (ELIE) instrument will utilize quantum electron tunneling between nanogap electrodes to interrogate the electronic structure of single molecules. Nanogaps are formed by breaking a gold nanowire embedded on a silicon chip. Bending is then used to control the gap size in the sub-nanometer regime. A molecule can be identified by its characteristic conductance and interaction time as a function of gap size. This technology can detect and distinguish among amino acids, and detect RNA and DNA bases and short base sequences. The extrapolated limit of detection for single amino acids is ~200 ppt after 5 min of sampling (~1 pMol/g). Integrating upfront separation methods will enhance specificity and sensitivity. Our lab-bench prototype integrates a nanogap chip, low-noise amplifier, and a laptop for data processing. We target a ~1 kg flight instrument mass. ELIE will be able to measure two key biosignatures: the amino acid complexity distribution, and charged informational polymers, through to be universal for aqueous based life.

Cloud microphysics plays important role on the storm dynamics. To investigate the impact of advanced microphysics schemes, using single and double moment (WSM6/WDM6) schemes, numerical simulations are conducted for a severe convective system that formed over the Korean Peninsula. Spatial rainfall distribution and pattern correlation associated with the convective system are improved in WDM6. During developing stage of the system, the distribution of total hydrometeors is larger in WDM6 compared to WSM6. Along with mixing ratio of hydrometeors (cloud, rain, graupel, snow and ice),number concentration of cloud and rainwater are also predicted in WDM6. To understand the differences in vertical representation of cloud hydrometeors between the schemes, rain number concentration (Nr) from WSM6 is also computed using particle density to compare with Nr readily available in WDM6.Varied vertical distribution, and large differences in rain number concentration, rain particle mass are evident between the schemes. Inclusion of number concentration of rain and cloud, CCN along with mixing ratio of different hydrometers have improved the storm morphology in WDM6. Inorder to investigate the cloud-aerosol interactions, numerical simulation has been conducted using an increase in CCN(aerosol) in WDM6 which has shown an improved rainfall distribution with intense hydrometer distribution. The latent heating (LH) rates of different phase change processes (condensation, evaporation, freezing, melting, sublimation and deposition) are also computed using various transformation rate terms in the microphysics modules. It is inferred that the change in aerosol has increased the LH of evaporation and freezing and affected the warming and cooling processes, cloud vertical distribution and subsequent rainfall.

Interoperability between datasets in Heliophysics and Planetary archives is increasingly important to address complex science questions about space weather and planetary plasma environments. Yet for cross-disciplinary studies, data ingestion is often a tedious, time-consuming process. We have developed the Heliophysics Application Programmer’s Interface (HAPI), a standard specification that captures a lowest common denominator method for accessing time series data. HAPI offers the ability to request data from multiple sources using a single interface, coupled with the ability to get identically formatted data from each source. HAPI has been recognized as a standard by the Committee on Space Research (COSPAR) and has gained adoption at multiple institutions in the US, including Goddard Space Flight Center’s Coordinated Data Analysis Web (GSFC/CDAWeb), the Planetary Data System Planetary Plasma Interactions Node (PDS/PPI), and the Laboratory for Atmospheric and Space Physics (LASP) Interactive Solar Irradiance Data Center (LISIRD). European plasma data centers such as the French Plasma Physics Data Centre (CDPP) and European Space Astronomy Centre (ESAC) are also in the process of adopting HAPI. We present an overview of the HAPI specification and describe how data centers can add HAPI access to their content. We also present how scientists can plot or download HAPI data using Python or using existing analysis tools such as Autoplot (Faden, 2010) and Space Physics Environment Data Analysis Software (SPEDAS) (Angelopoulos, 2019). Faden, J.B., Weigel, R.S., Merka, J. et al. Autoplot: a browser for scientific data on the web. Earth Sci Inform 3, 41–49 (2010). https://doi.org/10.1007/s12145-010-0049-0 Angelopoulos V, Cruce P, Drozdov A, et al. The Space Physics Environment Data Analysis System (SPEDAS). Space Sci Rev. 2019;215(1):9. doi:10.1007/s11214-018-0576-4

Like most NSF-funded Research Experience for Undergraduates (REU) programs, the REU on Sustainable Land and Water Resources (REU SLAWR) had to choose between a virtual experience in summer 2020, or cancellation of the program due to the Covid19 pandemic. The REU SLAWR was restructured into a modular online program designed to meet the same program goals that have shaped the REU SLAWR over the past 11 years. Using program evaluations from 2011-2019, the authors will compare the results from 2020 to build knowledge on how the REU experience in 2020 was differently structured to meet the need for a virtual program, the impact this had on participant and mentor outcomes, and what can be learned for future REU programs. This provides valuable information for creating accessibility to the REU experience. The REU usually takes place at three locations (Salish Kootenai College, MT; at the Univ. of Minnesota in Mpls. and Duluth, MN). The program is centered on tribally-focused Community-based Participatory Research (CBPR), and is a place-based REU. The REU SLAWR has always incorporated a virtual experience designed to create cross-team socialization, community-building, and widen participants’ interest and knowledge about projects incorporating tribal CBPR. Summer 2020 immersed students, mentors, and tribal partners in a virtual learning environment. The PIs explored new methods for running an REU with virtual technology that will be incorporated in future programs for richer cross-team collaboration. A focus of the REU SLAWR has been to increase participants’ abilities to work on diverse teams. Collaborating virtually across distances is a skill all researchers need. Training in this can benefit next-generation researchers and STEM workers. One aspect of concern and interest is the impact of redesigning the research projects to make them possible to conduct in a virtual space. The projects were fundamentally different than previous years, with many focusing on using pre-existing data. While there were negative impacts in some aspects of building research skills (i.e., little exposure to field or lab work), other aspects (i.e., computational modeling, communicating science) showed gains. The authors explore both the limits and possibilities inherent in virtual collaboration in research for undergraduate students.

A fire-spotting parameterization was developed for the WRF-Fire component of the WRF model version 4.0.1. The parameterization uses a Lagrangian particle transport framework and is coupled to the fire component of the WRF-ARW model as an independent Fortran module. When fires are active, the fire-spotting module identifies areas at risk of fire spotting by modeling transport and physical processes of individual firebrands released from fire locations. Firebrands are released at varying heights, from locations with higher emission potential, defined as a function of fire rate of spread and fuel load. Firebrands are transported with the atmospheric flow, and physical properties (temperature, mass, and terminal velocity) are updated at the default model timestep. The particles may either burnout before settling or deposit at a grid point when carried below a specified height threshold. The number and spatial distribution of deposited firebrands correspond to the flow-dependent risk component of new fire ignitions due to fire spotting. The flow-dependent component is then combined with the risk associated with local fuel properties (load and moisture) to yield the fire spotting spatial likelihood. In this presentation, the fire-spotting parameterization is assessed through a qualitative analysis of wildfires in Colorado. Uncertainties in fire ignition observations, used to initialize fires in the WRF-Fire model, often limit the ability to accurately model fire area, which in turn controls the firebrands’ emission location. Limited spotting observations are also a challenge to an objective verification of the module skill. We expect that the most recent remote sensing products will improve the representation of surface properties and accuracy of ignition parameters for WRF-Fire, which will directly transfer to the fire-spotting module capability. Direct enhancements to the parameterization may be incorporated into the module as laboratory experiments and field campaigns provide data to improve our ability to model firebrands’ initial properties (e.g. firebrand size and ejection height) and physical processes (burnout and terminal velocity).

Southern California has seen a resurgence of winegrowing regions in the past few decades, however the future of winegrape climatic suitability in the area has not been exhaustively explored. This study evaluated the future climate suitability for the cultivation of winegrape and potential global warming impacts on southern California’s winegrowing regions through a series of high-resolution surface air temperature and precipitation projections obtained with the WRF-SSIB regional climate model. Results reveal that by mid-21st-century the surface air temperature will increase by approximately 1.2 °C, while average precipitation will decrease by as much as 11% in the southern winegrowing areas under the Intergovernmental Panel on Climate Change high greenhouse-gas emissions scenario. Evaluation of bioclimatic suitability indices indicate increases in heat accumulation for all major winegrowing areas; including an increase of about 10% in growing-degree day, while morning low temperatures in September may experience increases of approximately 11% in the future, thus impacting negatively the ripening stage of grapevines and leading to changes in wine composition and quality. Additionally, the extent of areas classified under the cool to warm climate suitability categories could decrease by nearly 42% in the study area by 2050. Conditions in southern California are already warm and dry for viticulture and continuing heat accumulation increase, along with rainfall reduction, could potentially place additional stress to winegrape crop in the area, including advanced phenological timing and moisture deficit stress that could lead to decreases in yield. The projected decline in viticulture suitability highlights the need for adaptive capacity within this sector to mitigate the impacts of global warming. Possible mitigating strategies include planting hotter climate grape varieties, moving vineyards to regions that are more suitable in the future, and adopting dry-farming techniques.

Bottomland hardwood forests are a historically dominant ecosystem in the Southeastern United States, with 5.3 million-acres remaining in the Lower Mississippi Alluvial Valley. Despite the importance of forested wetlands in the global carbon cycle, not much is known about CO2 exchanges in such periodically flooded ecosystems. To address this, CO2 fluxes were measured over a mature, predominantly oak bottomland hardwood forest canopy in Northeast Louisiana. Measurements were carried out from a 37m tower, between August 2013 to October 2018, using the eddy covariance technique. Like other deciduous broadleaf forests, the site exhibited distinct diurnal and seasonal variability and was a net carbon sink during the spring and summer months, but unlike the majority of such forests and inland wetlands, the site was a net source of carbon annually. The cumulative annual net ecosystem exchange (NEE) ranged from 95.99 g C m-2 in 2014, to 267.91 g C m-2 in 2017, with an average annual NEE of 127.75 g C m-2. To better understand the variability, the relationship between the environmental factors and the components of NEE were observed. Diurnally, GPP increased with increasing global radiation and exceeded Reco from around 830 till around 1800 hrs. Seasonally, nighttime Reco increased with temperature. GPP increased and exceeded Reco from late March to early August, with a corresponding increase in the length of daylight, temperature, and leaf area index. GPP values showed more variability than Reco on a daily scale, thereby having a more distinct effect on the NEE. Research is on-going to better understand the effects of the characteristic flooding on carbon fluxes in this unique ecosystem.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov), as the non-solar NASA Heliophysics active final archive, works with current operating missions and the heliophysics community to ingest, preserve and serve a wide range of past and current public science-quality data from the mesosphere into the furthest reach of deep-space exploration. SPDF facilitates scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of the many missions. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Parker Solar Probe, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, IBEX, Voyager, Geotail, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions, and is expecting Solar Orbiter and ICON data. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and OMNIWeb and COHOWeb for near-Earth and deep-space solar wind plasma, magnetic field, and energetic particle database, respectively.

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.

Policies to regulate severe surface ozone pollution in cities in India are challenging to develop, due to the complex dependence on precursor emissions of volatile organic compounds (VOCs) and nitrogen oxides (NOx), non-linear chemistry leading to ozone formation, and very limited spatial and temporal surface air quality monitoring. Ratios of space-based observations of formaldehyde (HCHO), an intermediate oxidation product of VOCs, and nitrogen dioxide (NO2) have been used to characterize the sensitivity of surface ozone production to precursor emissions of VOCs and NOx, but interpretation of these depends on the local oxidation regime. Here we develop an improved approach in which we discretize the data into background HCHO due to methane and other long-lived VOCs (regression intercept) and the local relationship (regression slope) between HCHO associated with reactive VOCs and NO2. We apply this to TROPOMI HCHO and NO2 tropospheric columns oversampled to higher spatial resolution than the native pixel resolution of the instrument over the ten most populous cities in India. We use GEOS-Chem to characterize the ozone production regimes and then apply this updated interpretation of the relationship between HCHO and NO2 to the oversampled TROPOMI columns to identify the most effective strategies for regulating ozone and whether these should vary seasonally and spatially.