Interferometric Synthetic Aperture Radar (InSAR) is used to measure deformation rates over whole continents to constrain tectonic processes. The resulting velocity measurements are only relative, due to unknown integer ambiguities introduced during propagation of the signal through the atmosphere. However, these ambiguities mostly cancel when using spectral diversity to estimate along-track motion, allowing measurements to be made with respect to a global reference frame. Here, we calculate along-track velocities for a partial global dataset of Sentinel-1 acquisitions and find good agreement with ITRF2014 model values. We include corrections for solid-earth tides and gradients of ionospheric total electron content. By combining data from ascending and descending orbits we are able to estimate north and east velocities with average precision of 4 and 20 mm/year, respectively. Although we have calculated these over large 250x250 km areas, such measurements can also be made at much higher resolution, albeit with lower accuracy. These “absolute” measurements can be particularly useful for global velocity and strain rate estimation, where GNSS measurements are sparse.

Detailed analysis of volcanic thermal and gas emissions over time can constrain subsurface processes throughout the pre- and post-eruption phases. Time series analyses are commonly applied to high temporal datasets like the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is the first study using the entire Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) twenty-plus year archive. The ASTER archive presents a unique opportunity to quantify volcanic precursors and processes. The spatial, spectral, and radiometric resolution of its thermal infrared (TIR) subsystem allows detection of very low-magnitude surface temperature anomalies and passively emitted small gas plumes. We developed a new statistical algorithm to automatically detect these subtle anomalies and applied it to five recently active volcanoes with well-documented eruptions: Taal (Philippians), Popocatépetl (Mexico), Mt. Etna (Italy), Fuego (Guatemala), and Kluichevskoi (Russia). More than 3,300 ASTER level-1 terrain corrected (L1T), registered, radiance-at-sensor images were downloaded from the NASA EARTHDATA website. These were screened for significant summit cloud coverage, which removed approximately 25% of scenes. The remaining were converted to brightness temperature and a median background temperature per scene was determined from an annulus around the active crater to produce the temperature above background. The algorithm creates a rejection criterion value defined by the median absolute deviation to identify the thermal anomalies. The size and intensity of these anomalies as well as the detection, composition, emission rate of small plumes are retrieved one year prior to the known eruptions for each volcano to identify all precursory signals. The results of this study have the dual purpose of constraining volcanological processes that lead to eruptions as well as providing training data for machine learning modeling. Machine learning is an effective and well-established technique that provides rapid classification of volcanic activity such as thermal anomalies that exceed a certain size and/or intensity. The comparison of these two approaches is documented in a companion abstract in this session.

Stream water temperature is considered a “master variable” in environmental processes and human activities. Existing process-based models have difficulties with defining true equation parameters, and sometimes simplifications like assuming constant values influence the accuracy of results. Machine learning models are a highly successful tool for simulating stream temperature, but it is challenging to learn about processes and dynamics from their success. Here we integrate process-based modeling (SNTEMP model) and machine learning by building on a recently developed framework for parameter learning. With this framework, we used a deep neural network to map raw information (like catchment attributes and meteorological forcings) to parameters, and then inspected and fed the results into SNTEMP equations which we implemented in a deep learning platform. We trained the deep neural network across many basins in the conterminous United States in order to maximize the capturing of physical relationships and avoid overfitting. The presented framework has the ability of providing dynamic parameters based on the response of basins to meteorological conditions. The goal of this framework is to minimize the differences between stream temperature observations and SNTEMP outputs in the new platform. Parameter learning allows us to learn model parameters on large scales, providing benefits in efficiency, performance, and generalizability through applying global constraints. This method has also been shown to provide more physically-sensible parameters due to applying a global constraint. This model improves our understanding of how to parameterize the physical processes related to water temperature.

Mass loss from the Greenland Ice Sheet (GrIS) is a primary contributor to sea level rise, but substantial uncertainty exists in estimates of future ice sheet losses. Surface mass balance (SMB) models, the current leading approach to sea level rise projection, anticipate continued dominance of runoff as a mass loss pathway. Despite their preeminence, SMB models in vulnerable northern environments lack adequate field validation, particularly for error-sensitive runoff estimates. We have installed a cluster of high quality field instruments at the Minturn Elv, a proglacial river site in Inglefield Land, NW Greenland to provide discharge and weather datasets for the validation and refinement of climate/SMB runoff models. The instrument cluster has meteorological, hydrological, and time lapse camera instrumentation, including a vented water level stage recorder, single shot and scanning lidars, time lapse cameras, and in situ ADCP discharge and terrestrial scanning lidar measurements. The instrument suite provides novel flow and weather datasets with the opportunity to evaluate experimental approaches to stage measurement in adverse, high-latitude areas. Inglefield is a uniquely advantaged location because proglacial runoff is dominated by SMB processes operating on the ice surface without interference from subglacial hydrology. Overall, our hydrometeorological instrument cluster at Inglefield Land will provide one of the few validation datasets for regional climate models outside of Southwest Greenland.

InSAR measurements of ground displacement are relative, due to unknown integer ambiguities introduced during propagation of the signal through the atmosphere. However, these ambiguities mostly cancel when using spectral diversity to estimate along-track (azimuth) velocities allowing measurements to be made with respect to a terrestrial reference frame. Here, we calculate along-track velocities for a partial global dataset of Sentinel-1 acquisitions as processed by the COMET LiCSAR system, and find good agreement with model values from ITRF2014 plate motion model. We include corrections for solid-Earth tides and gradients of ionospheric total electron content based on a moderate resolution model IRI2016. Application of tidal corrections improves the average velocity precision from 23 to 11 mm/yr. Ionospheric corrections, however, have significant effect only in near-equatorial regions. The median difference between along-track velocities and values predicted by ITRF2014 is 3 mm/yr. A preliminary study using reprocessed precise orbit determination products in a limited dataset shows significant improvement in both precision and accuracy. By combining data from ascending and descending orbits we are able to estimate north-south (N-S) and east-west (E-W) velocities with an average precision of 3 and 16 mm/yr, respectively. Although we have calculated these estimates over large 250 x 250 km areas, such measurements can also be made at much higher resolution, albeit with lower precision. These “absolute” measurements can be particularly useful for global velocity and strain rate estimation, where GNSS measurements are sparse. We will also investigate large-scale averages of across-track (range) pixel offsets, which are most sensitive to E-W and vertical displacements, and perform a comparison to a GNSS network in selected areas.

Solar energy development is land intensive and recent studies have demonstrated the negative impacts of large-scale solar deployment on vegetation and soil. Co-locating vegetation with managed grazing on utility scale solar PV sites could provide a sustainable solution to meeting the growing food and energy demands, along with providing several co-benefits. However, the impacts of introducing grazing on soil properties at vegetated solar PV sites are not well understood. To address this knowledge gap, we investigated the impacts of episodic sheep grazing on soil properties (micro and macro nutrients, carbon storage, soil grain size distribution) at six commercial solar PV sites (MN, USA) and compared that to undisturbed control sites. Results indicate that implementing managed sheep grazing significantly increased total carbon storage (10-80%) and available nutrients, and the magnitude of change correlated with the grazing frequency (1-5 years) at the study sites. Furthermore, it was found that sites that experienced consecutive annual grazing treatments benefitted more than intermittently grazed sites. The findings will help in designing resource conserving integrated solar energy and food/fodder systems, along with increasing soil quality and carbon sequestration.

The largest city in New Zealand, Auckland is home to roughly 1.5 million people -- one third of New Zealand's population. Here we assemble a bottom-up inventory of Auckland's fossil fuel carbon dioxide emissions from a variety of data sources. We use these emissions estimates in combination with the UrbanVPRM land surface model to estimate the net carbon balance of the region. This work is part of the larger CarbonWatch NZ project, which aims to produce estimates of New Zealand's net carbon balance quickly enough to assess and refine ongoing national efforts to reach carbon neutrality.

In this work, the characteristics of the diurnal cycle around Sumatra are examined with unprecedented detail using high-resolution satellite-derived cloud properties from the Himawari-8 Advanced Himawari Imager (AHI) data. Offshore propagation is resolved into multiple local propagation directions of cloud cells, and the interaction between propagations is shown to result in forced convection over the Strait of Malacca, Java Sea, and the Indian Ocean. The diurnal cycle of rainfall and deep convection over Sumatra show complex interactions between the land-sea-breeze system, the seasonal background wind, the local topography, and the influence of surrounding islands. We used high-resolution satellite-derived products from Himawari-8 AHI, the Geostationary Cloud Algorithm Testbed Geocat, and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) to investigate the cloud properties of deep convection and the signatures of the diurnal cycle of rainfall and cloudiness over Sumatra. Previous studies have shown evidence of the variability of diurnally forced convection in the Maritime Continent, including the diurnal signal over land in the late afternoon and the offshore propagation of Sumatra at night (Yang and Slingo 2001). The role of gravity waves has explained the night-time propagation (e.g., Mapes 2003; Love et al. 2011; Vincent and Lane 2016; Sakaeda et al. 2020). This propagation can be modified by the influence of small islands and the interconnection of diurnal cycles between Sumatra, Malay Peninsula, Java, and Borneo (Ruppert and Zhang 2019; Ruppert and Chen 2020). In this work, we present evidence of the cloudiness and rainfall patterns propagating offshore/onshore Sumatra during five austral summers from 2016 to 2020, employing cloud properties from Himawari-8 and IMERG collections. By combining detailed satellite-based cloud properties and rainfall estimates, we highlight the strong dependency of the diurnal cycle on local modulators.

Martian ice likely holds the key to interpreting Mars’ past climate, but much is still unknown regarding the distribution and properties of Mars’ ice deposits. It is well known that Mars has extensive polar ice caps the size of Greenland. Included in these large polar caps are the north and south polar layered deposits (NPLD and SPLD, respectively), that are composed of kilometers-thick deposits of water ice. In addition, surveys by Conway et al. (2012) and Sori et al. (2019) identified craters in the surrounding terrains that contain “outlying” deposits of ice, which may or may not have formed concurrently with the polar caps. There are many differences between the NPLD and SPLD, including higher dust content and sequestered CO2 within the SPLD, a younger surface age of the NPLD, and properties in some locations of the SPLD that causes a ‘fog-like’ scattering in radar observations. These differences between the NPLD and SPLD may or may not be shared in these outlying deposits, and may provide clues to the climate conditions under which the PLDs and outlier deposits formed. We have analyzed a total of 517 SHARAD radar tracks across 24 ice deposits within craters. For the northern population, we detected subsurface layers in 3 out of 4 crater deposits. In the southern population, we detected subsurface layers in 4 of the 20 crater deposits. Of the 4 southern crater deposits that exhibited subsurface layers, 3 were contiguous with the SPLD. We also found deposits with radargrams that contain fog, and one that contains a low reflectance zone. After examining the subsurface radar observations, we determined that the northern outlier deposits share many common characteristics with the NPLD, and thus may have a shared depositional history or at least were emplaced under similar environmental conditions. The southern outlying crater deposits exhibit a variety of subsurface characteristics, and likely represent 2 or more populations that may have differing depositional histories.

One of the key questions in fault mechanics is how do earthquakes begin ? This is central to our understanding of earthquakes, including the long controversial issue of their predictability. Earthquakes preceding large events are commonly referred to as foreshocks. They are often considered as precursory phenomena reflecting a nucleation process of the main rupture potentially driven by an underlying slow pre-slip. On the other hand, some studies suggest that foreshock sequences may only be explained by cascades of triggered events. In this work, we choose to test the cascading hypothesis against the foreshock seismicity observed during a previously reported slow slip event preceding the 2017 Mw=6.9 Valparaiso earthquake. We build a very complete 5 years long earthquake catalog of the sequence using refine detection and location algorithms. We test the detected seismicity against the Epidemic Type Aftershock Sequences model. We identify time windows with anomalously high seismic activity compared to what is expected by the typical earthquake interactions. We analyze statistically these anomalies over 5 years to understand if the Mw=6.9 foreshock sequence presents a specific anomalous activity. In addition, using a hierarchical clustering method, we identify earthquakes with similar waveforms to evidence any repeating ruptures. We analyze the time distribution of these clusters over 5 years to understand if unusual rates of repeating events emerge during foreshock time ranges. The conjoint analysis of seismicity rate anomalies and repeating events along with the results of previous pre-slip studies allows to accurately describe the nucleation process and evolution of the 2017 Valparaiso earthquake sequence.

Leveraging aerosol data from multiple airborne and surface-based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate-sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154 – 0.0296; R = 0.76; N = 2948). Ground-based measurements of the oxalate-sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio towards higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing.

The ILRS completed the reanalysis of the entire period of useful data from geodetic spherical targets spanning 1983 to end of 2020. The combined products were submitted to ITRS for the development of the ITRF2020. The main period that is supported by all four target satellites (LAGEOS, LAGEOS-2 and the two Etalons) was already used in a preliminary release of an ITRF2020 model in May 2021. For the period 1983-1993 with only LAGEOS data the reduction was in of 15-day arcs with 3-day averaged EOP. Due to the lack of a priori estimated mean biases for the 1983-1993 period, we adjusted a 15-day average bias at all stations to accommodate systematic and target signature errors. Otherwise the reanalysis used the same improved modeling of the 1993-2020 data. The adoption of predetermined mean biases ensured the results are minimally affected by systematic errors in the data. The 2021 ILRS contribution to ITRF2020 minimized the scale difference between SLR and VLBI below 2 mm (ITRF2014 ~9 mm). The reanalysis incorporates an improved “target signature” model (CoG) for better separation of true systematic errors from errors in describing the target’s signature. The ILRS Analysis Standing Committee—ASC devoted its efforts on developing the new analysis approach over the past 5 years. The robust estimation of persistent systematic errors at the millimeter level, while still considering information provided by the stations, permitted the adoption of a consistent series of long-term mean corrections for each station for the period 1993-2020, that are now pre-applied. The use of this approach for this reanalysis led to improved results, reflected in the new time series of the TRF origin and especially in its scale. Seven official ILRS Analysis Centers contributed to the weekly time series and six ACs contributed to the 15-day series for 1983-1993, all computed according to the ILRS ASC guidelines. The series were combined by the ILRS Combination Center to obtain the official ILRS product contribution to ITRF2020 spanning a 38-year period. The presentation will provide an overview of the analysis procedures and models, and it will demonstrate the level of improvement with respect to the previous ILRS product series; the stability and consistency of the solution are discussed for the individual AC contributions and the combined SLR time series.

The five divisions of NASA’s Science Mission Directorate (SMD) represent a very broad spectrum of academic disciplines, ranging from Astronomy, to Planetary science, to Heliophysics, Earth science, Biology and Physical science with measurement scales ranging from components of atoms to the structure of the entire universe. In addition, the systems that support access to these data range from systems based on formal and broadly accepted OWL ontologies, to those based on current and historical disciplinary metadata standards, to ad-hoc or bespoke systems dating back to NASA’s very earliest missions; all generally developed to support the mission or, more recently, discipline focussed data users. Consequently the access mechanisms, data structures, vocabularies, terms in use, etc. vary widely across the divisions making cross-disciplinary research at best difficult if not impossible. Currently NASA SMD is working to improve support for cross-disciplinary/transdisciplinary research by developing a system that supports discovery across all of SMD’s data products, a model that can be extended to all forms of scientific output including software, tools, models, publications, etc. The core underpinnings of such a system is an information model being developed using the methodology developed by Dr. Peter Fox and Dr. Deborah McGuinness. Here we discuss the model (a knowledge graph), lessons learned along the way, and key findings for other systems attempting to bridge across broad disciplinary challenges.

Modeling atmospheric chemistry across scales with the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) leverages a new regional refinement capability within the NCAR Community Earth System Model (CESM). This framework allows for simultaneous simulation at human-relevant and policy-relevant scales at the same time as hemispheric and global-scales. Here, we present the development of a regional refined grid over the Australian region and assess initial simulations for the 2019/2020 wildfire season. The Australian wildfire events of 2019/2020 saw large local emissions of pollution with wide-scale impacts across the southeast of the continent, where smoke and haze degraded air quality for many days for millions of people. Hemispheric transport of pollution at low and lofted altitudes also occurred, creating an atmospheric signature over New Zealand and South America, and had a direct influence on climate. These multiple-scale impacts from wildfire in the Australian region make it ideal for testing the new capability of multi-scale modeling.

Lava flows are one of the main hazards related to effusive basaltic volcanism. To minimize their impact during emplacement, we use lava flow potential distance-to-run predicted by propagation models. These models are partly based on infrared (IR) measurements of lava radiative heat fluxes by remote sensing (RS) methods (ground-based or satellite-based detectors) [1]. These results are however subjected to important errors related to the poor knowledge of spectral emissivity (ε), commonly considered constant by these well-established techniques[2, 3]. This oversimplification is an important source of uncertainties in derived temperatures, which restrain our capacity to accurately model active lava flows. In this study, we developed new algorithms that take into account the effect of spectral emissivity for calculating radiative heat fluxes. We describe the temperature-emissivity relationship with equations established at two wavelengths of interest for RS (10.9 μm and 1.6 μm) that are retrieved from in situ measurements of spectral emissivity for basaltic magma from the 2014–2015 Holuhraun eruption. Spectral emissivity data were systematically acquired over a wide spectral range (400–8000 cm−1) covering TIR, MIR and SWIR, and up to 1473 K [4]. Our results show that spectral emissivity varies linearly with temperature in TIR (10.9 μm), and nonlinearly in SWIR (1.6 μm). We confronted our lab-based results to the field IR data retrieved by [5] and found that temperature precision increases compared to data using constant emissivity value. These new insights will ultimately improve the thermo-rheological models of lava flows [6] in order to support hazard assessment in volcanic systems. References: [1] Kolzenburg et al. 2017. Bull. Volc. 79:45. [2] Harris, A. 2013: Cambridge University press. 728. [3] Rogic et al. 2019 Remote Sens., 11, 662 [4] De Sousa Meneses et al. 2015. Infrared Physics & Technology 69. [5] Aufaristama et al. 2018, Remote Sens, 10,151. [6] Thompson and Ramsey, 2021, Bulletin of Volcanology, 83:41. Keywords: Spectral emissivity, temperature, IR spectroscopy, remote sensing, basalt

Data-model comparisons are common when addressing (paleo)climate questions. Many applications require deriving continuous surface fields of scalar variables from a set of irregularly distributed data points, typically for model validation against data or data-derived model input as initial or boundary conditions. While various interpolation techniques and interfaces exist, few can simultaneously: (1) interpolate across local to global spatial scales, (2) perform anisotropic interpolation using the spatial structure derived from the data instead of an assumed one, and (3) explicitly derive uncertainty in the interpolated fields due to both data density and measurement error. We present a standalone interpolation toolbox including a graphical user interface (GUI), which is aimed at the general earth science community. It uses a kriging algorithm whose distance metric is the geodesic on an oblate spheroid, be it the WGS-84 reference ellipsoid for applications on the surface of the Earth, or an equivalent ellipsoid with varying radii for interpolation on vertical levels above the surface. While kriging algorithms exist that perform interpolation on such non-Euclidean distances, they do not provide a check for conditionally negative semi-definiteness (CNSD) of the variogram matrix, which is a requisite for the kriging method. Since mathematical theory of kriging on spheroids or ellipsoids has not yet provided a set of authorized variance-distance functions, we incorporated a numerical check for CNSD condition for each data realization and variance-distance modeling scheme. The GUI will allow the user a high degree of customization. Preliminary results are promising, with robust results for isotropic interpolation. The derivation of CNSD variogram matrices for anisotropic interpolation remains the major challenge of the project. When completed, ClimAG-Krigger will provide the community with an easy-to-use, robust tool for anisotropic global kriging that will be specifically tailored for (paleo)climate applications.

Bedforms (ripples, dunes, sandwaves) are ubiquitous features in many sandy subaqueous settings. They have been observed in a wide variety of flows, including rivers, the surf zone, estuaries, tidal inlets, shallow seas, and deep waters. Bedforms exert a major influence on a range of processes, from small-scale turbulence and sediment transport to large-scale coastal geomorphology. Therefore, knowledge on the dimensions, morphological characteristics and dynamics of large bedforms is relevant for a range of fundamental and applied research. Several methods have been developed over the years to characterise bedform dimensions from bathymetric data. Each method has been created for a specific purpose (e.g. discriminate bedform scale, calculate bedform size and/or shape, detect crestlines) and environment (unidirectional, constrained tidal or open marine) and with a certain accuracy (precise time-consuming detection or coarse rapid detection). Although some of these methods are freely available, it may be difficult for scientists to use them due to the specificity of their design. A unique toolbox which combines the available methods into one easy-to-use software would help the bedform community advance knowledge on bedform research by facilitating the analysis of bedform characteristics. This should also include recommendations of which method should be used for which purpose. The present project aims at creating a Bedform Analysis Toolbox which combines several methods already available. The toolbox will be made open source and freely available. Feedback on the need of the community or required design and specificity would help us create a toolbox which is useful to many scientists.

The NASA Mars 2020 Perseverance rover mission will collect a suite of scientifically compelling samples for return to Earth. On the basis of orbital data, the Mars 2020 science team* identified two notional sample caches to study (1) the geology of Jezero crater, collected during the prime mission and (2) the ancient crust outside of Jezero crater, collected during a possible extended mission. Jezero crater geology consists of well-preserved, Early Hesperian to Late Noachian deltaic and lacustrine deposits sourced from a river system that drained Noachian terrain. The crater floor comprises at least two distinct units of sedimentary or volcanic origin whose relationship to the deltaic deposits is presently unclear. Remotely-sensed data reveal signatures of carbonate+olivine and clay minerals within crater floor and crater margin units. Samples from within Jezero that comprise the prime mission notional sample collection thus include: crater floor units; fine- and coarse-grained delta facies, the former with potential to preserve organic matter and/or biosignatures, the latter to possibly constrain the type and timing of sediment deposition; chemical sediments with the potential to preserve biosignatures; a sample of crater rim bedrock; and at least one sample of regolith. The region of southern Nili Planum, directly outside the western rim of Jezero crater, is geologically distinct from Jezero crater and contains diverse Early or even Pre-Noachian lithologies, that may contain records of early planetary differentiation, magnetism, paleoclimate and habitability. The notional sample collection from this region will include: layered and other basement rocks; megabreccias, which may represent blocks of (pre-)Noachian crust; basement-hosted hydrothermal fracture fill; olivine+carbonate rocks that are regionally significant and may be related to units within Jezero crater; and mafic cap unit rocks. The samples described are notional and may change with ongoing surface investigations. However, the samples we anticipate collecting align well with community priorities for Mars exploration, addressing geologic diversity, potential ancient biologic activity on Mars, planetary evolution, volatiles, and human health hazards. *Many other Mars 2020 team members were involved in this planning