Recent inversions of InSAR ground surface deformation in the Delaware Basin have revealed aseismic slip on conjugate, semi-optimally oriented normal faults. Slip, occurring over 3-5 years, extends approximately 1 km down-dip, over 10 km along strike, and reaches 23 cm. Several disposal wells are located nearby, suggesting that pressure changes from injection might have induced slip. We develop and calibrate 2D and pseudo-3D coupled pore pressure diffusion and rate-state models with velocity-strengthening friction. Pore pressure diffusion is limited to a high-permeability fault damage zone, and the net influx of fluid is adjusted to match the observed slip. A pressure increase of 1-2 MPa is required to initiate slip. Most aseismic slip occurs at approximately constant friction and ~5 MPa additional pressure increase is needed to reach ~20 cm slip. Our work suggests that models of the type developed here can be used to operationally manage injection-induced aseismic slip.

The Southwest Watershed Research Center (SWRC) of the United States Department of Agriculture-Agricultural Research Service has been conducting arid and semiarid (dryland) watershed research since 1953. This included establishment and continuous operation of the Walnut Gulch Experimental Watershed (WGEW) in southeast Arizona. The 149 km2 ephemeral watershed is one of the most densely instrumented dryland research catchments in the world with a drainage area greater than 10 km2. This instrumentation captures many aspects of the hydrological cycle including how precipitation is partitioned into soil moisture, runoff and evapotranspiration and its subsequent effects on sediment transport, vegetation productivity, carbon sequestration, and groundwater recharge. The long-term, high-resolution record of observations on the WGEW enables understanding of the mean and variability of the hydrological processes, not well characterized with shorter term records, that fail to capture the large variability common to dryland regions. This presentation will highlight trends in temperature, precipitation, and runoff over the WGEW observation period. Additional research findings made by the SWRC and collaborators on erosion; plant productivity and carbon sequestration; the facilitation of soil water redistribution by plant roots; and groundwater recharge will also be presented.

The International Monitoring System (IMS) infrasound network has been established to detect nuclear explosions and other signals of interest embedded in the station specific ambient noise. The ambient noise can be separated into coherent infrasound (e.g. real infrasonic signals) and incoherent noise (such as that caused by wind turbulence). Previous work statistically and systematically characterizing coherent infrasound recorded by the IMS. This paper expands on this analysis of the coherent ambient infrasound by including updated IMS datasets up to the end of 2020, for all 53 of the currently certified IMS infrasound stations using an updated configuration of the Progressive Multi-Channel Correlation (PMCC) method. This paper presents monthly station dependent reference curves for the back azimuth, apparent speed, and root-mean squared amplitude, which provide a means to determine the deviation from nominal monthly behaviour. In addition, a daily Ambient Noise Stationarity (ANS) factor based on deviations from the reference curves is determined for a quick reference to the data quality compared to the nominal situations. Newly presented histograms provide a higher resolution spectrum, including the observations of the microbarom peak, as well as additional peaks reflecting station dependent environmental noise. The aim of these reference curves is to identify periods of sub-optimal operation (e.g. non-operational sensor) or instances of strong abnormal signals of interest.

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.

As it is known, Space Weather (SWx) phenomena can have dramatic impact on satellite navigation and HF radio communication systems, being also responsible for increases on radiation levels at flight altitudes. For this reason, in recent years the International Civil Aviation Organization (ICAO) has been showing great interest in operational SWx services for aviation purposes in these three domains. Four global SWx centers have been then appointed since November 2019 by ICAO to provide real-time SWx advisories for aviation users. In particular, HF COM conditions are assessed by monitoring the F2-layer critical frequency foF2 or the MUF(3000) ionospheric characteristic (MUF = Maximum Usable Frequency), the latter representing the highest HF radio frequency that can be used for communications over a standard distance of 3000 km via F2-layer ionospheric reflection. As one of the designed SWx centers, several key operational 24/7 products for HF COM conditions assessment have been developed within PECASUS (Partnership for Excellence in Civil Aviation Space weather User Services). Nowcasting and forecasting (1-24hr) maps over Europe of MUF(3000) and its ratio with respect to a background level are then developed by INGV, as a PECASUS partner. The MUF(3000) nowcasting uses all the available real-time ionosonde measurements in different locations in order to upgrade IRI-CCIR-based background maps, and Ordinary Kriging method for spatial interpolation. The MUF(3000) modeling performance was assessed comparing predicted values to measured ones over two test stations during strong geomagnetic storm periods, obtaining an overall RMSE < 2 MHz at both stations. The MUF(3000) predicted 1-24 hours ahead depends on foF2 and M(3000) ionospheric parameters: EUROMAP forecasting model and IRI model are used for the former and the latter, respectively. The method has been applied to Europe where there are ionospheric stations with long (for some solar cycles) historical data and current real-time foF2 observations. A mapping procedure applied to the European stations provides MUF(3000) short-term prediction over the whole area. The application of these methods to storm events occurred after November 2019 is here presented, in order to study the ionospheric conditions they provide when HF COM advisories are expected to be issued.

Satellite observations are widely used to investigate, monitor, and forecast volcanic activity. Spaceborne thermal infrared (TIR) measurements of high-temperature volcanic features improve our understanding of the underlying processes and our ability to identify reactivation of activity, forecast eruptions, and assess hazards. In particular, thermal changes, indicative of subtle pre-eruptive volcanic thermal activity, have been observed. Over the last several decades, different approaches have been explored to detect and estimate the temperature above background of these thermal anomalies. The most common approach relies on a spatial statistical analysis based on a scene by scene choice of the background temperature region. Satisfactory results have also been shown by using time series anomaly detection algorithms based on statistical profiling approach. Artificial intelligence (AI) is growing sharply in different remote sensing fields because of its capability to automatically learn patterns from the data. Here, we develop an AI approach to automatically detect volcanic thermal features by using spatiotemporal information from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), LANDSAT-8 Thermal Infrared Sensor (TIRS) and MODerate-resolution Imaging Spectroradiometer (MODIS) data acquired over several decades. Our goal is to exploit AI techniques using a combination of high temporal and high spatial resolution satellite data to improve thermal volcanic monitoring and detect very low-level anomalies caused by pre-eruptive activity. We use both low-spatial, high-temporal resolution MODIS data to detect hotter thermal features at short time scales; as well as high-spatial low-temporal resolution ASTER TIR and TIRS data to detect more subtle thermal changes otherwise missed by MODIS. Our analysis is conducted in Google Earth Engine (GEE), a cloud computing platform with fast access and processing of satellite data. The comparison with a new statistical algorithm is documented in a companion abstract in this session.

Modern research methods enable unfolding the structure of the water column with higher resolution than ever revealing the importance of submesoscale. Submesoscale processes have intermediate space and time scales of <5 km and a few days in the Baltic Sea. A glider mission was conducted in the Gulf of Finland in May 2018. During the formation of the seasonal thermocline characterized by the increase of the maximum temperature gradient from 0.5 to 3.1 °C dbar-1, the structure of the water column changed constantly. A horizontal buoyancy gradient was captured and the vertical flows at a lateral scale of a km were apparent in isotherms intersecting with isopycnals. We suggest that frontal submesoscale processes manifested as smaller-scale tracer patterns on the mesoscale background demonstrate an ageostrophic secondary circulation. While the front appeared because of the persisting NE–E winds, the decrease in wind stress promoted the non-forced dynamics that, in turn, appeared to enhance the development of the thermocline. The spatial spectra of isopycnal tracer variance revealed the slopes a bit gentler than -2 between the lateral scales 2–10 km in the upper part of the water column. The discrepancy from the deeper layers where the slope closed to -1 suggests the contribution of the ageostrophic frontal effects in the energy cascade.

Discrete aurora at Mars, characterized by their small spatial scale and tendency to form near strong crustal magnetic fields, are emissions produced by particle precipitation into the Martian upper atmosphere. Since 2014, Mars Atmosphere and Volatile EvolutioN’s (MAVEN’s) Imaging Ultraviolet Spectrograph (IUVS) has obtained a large collection of nightside UV discrete aurora observations. Initial analysis of these observations has shown that, near the strong crustal field region (SCFR) in the southern hemisphere, the aurora detection frequency is highly sensitive to the interplanetary magnetic field (IMF) clock angle. However, the role of other solar wind properties in controlling the aurora detection frequency has not yet been determined. In this work, we use IUVS discrete aurora observations, and MAVEN solar wind observations, to determine how the discrete aurora detection frequency varies with solar wind dynamic pressure, IMF strength, and IMF cone angle. We find that, outside of the SCFR, the detection frequency is relatively insensitive to the IMF orientation, but significantly increases with solar wind dynamic pressure and moderately increases with IMF strength. Interestingly, the auroral emission brightness outside the SCFR is insensitive to the dynamic pressure. Inside the SCFR, the detection frequency is moderately dependent on the dynamic pressure and is much more sensitive to the IMF clock and cone angles. In the SCFR, aurora are unlikely to occur when the IMF points near the radial or anti-radial directions. Together, these results provide the first comprehensive characterization of how upstream solar wind conditions affect the formation of discrete aurora at Mars.

Seismic wave propagation in porous rocks that are saturated with a liquid exhibits significant dispersion and attenuation due to fluid flow at the pore scale, so-called squirt flow. This phenomenon takes place in compliant flat pores such as microcracks and grain contacts that are connected to stiffer isometric pores. Accurate quantitative description is crucial for inverting rock and fluid properties from seismic attributes such as attenuation. Up to now, many analytical models for squirt flow were proposed based on simplified geometries of the pore space. These models were either not compared with a numerical solution or showed poor accuracy. We present a new analytical model for squirt flow which is validated against a three-dimensional numerical solution for a simple pore geometry that has been classically used to explain squirt flow; that is why we refer to it as classical geometry. The pore space is represented by a flat cylindrical (penny-shaped) pore whose curved edge is fully connected to a toroidal (stiff) pore. Compared with correct numerical solutions, our analytical model provides very accurate predictions for the attenuation and dispersion across the whole frequency range. This includes correct low-and high-frequency limits of the stiffness modulus, the characteristic frequency, and the shape of the dispersion and attenuation curves. In a companion paper (Part 2), we extend our analytical model to more complex pore geometries. We provide as supplementary material Matlab and symbolic Maple routines to reproduce our main results.

Antarctica has been proposed as the dominant source of the meltwater that entered the oceans during Meltwater Pulse 1b (MWP1b) that occurred approximately 11,500 years ago. The deglaciation of heavily glaciated fjords off the coast of Antarctica at approximately this time has provided support for this hypothesis. Further support for this scenario was provided by the fact that the highly non-monotonic relative sea level histories recorded at sites on the coast of Scotland, which had been heavily glaciated at last glacial maximum, could be explained by the inter-hemispheric sea level teleconnection associated with a significant deglaciation of Antarctic ice sheets at this time. That the magnitude of grounded ice loss from Antarctica at MWP1b time was adequate to provide the necessary RSL rise along the coast of Scotland has not been demonstrated. Furthermore, there exist implicit suggestions to the effect that a significant contribution to MWP1b must have also been delivered to the oceans by the abrupt northern hemisphere warming that occurred at the end of the Younger Dryas (YD) cold reversal, which also occurred approximately 11,500 years ago. This warming event occurred due to the rapid intensification of the Atlantic Meridional Overturning Circulation (AMOC) when it recovered after the YD. We present a fingerprinting analysis of the contribution of all major ice sheets to MWP1b using the ICE7G_NA (VM7) model of ice loading history and find that the best agreement between calculated sea level curves and observations is obtained with a minimal Antarctic contribution.

Inland waters are an important component of the global carbon budget, emitting CO2 to the atmosphere. However, our ability to predict carbon fluxes from stream systems remains uncertain as small scales of pCO2 variability within streams (100-102 m), which makes efforts relying on monitoring data uncertain. We incorporate CO2 input and output fluxes into a stream network advection-reaction model, representing the first process-based representation of stream CO2 dynamics at watershed scales. This model includes groundwater (GW) CO2 inputs, water column (WC), and benthic hyporheic zone (BHZ) respiration, downstream advection, and atmospheric exchange. We evaluate this model against existing statistical methods including upscaling and multiple linear regressions through comparisons to high-resolution stream pCO2 data collected across the East River Watershed in the Colorado Rocky Mountains (USA). The stream network model accurately captures topography-driven pCO2 variability and significantly outperforms multiple linear regressions for predicting pCO2. Further, the model provides estimates of CO2 contributions from internal versus external sources suggesting that streams transition from GW- to BHZ-dominated sources between 3rd and 4th Strahler orders, with GW, BHZ, and WC accounting for 49.3, 50.6, and 0.1% of CO2 fluxes from the watershed, respectively. Lastly, stream network model CO2 fluxes are 4-12x times smaller than upscaling technique predictions, largely due to inverse correlations between stream pCO2 and atmosphere exchange velocities. Taken together, this stream network model improves our ability to predict stream CO2 dynamics and efflux. Furthermore, future applications to regional and global scales may result in a significant downward revision of global flux estimates.

We perform Observation System Simulation Experiments (OSSEs) with the GEOS-Chem adjoint model to test how well methane emissions over North America can be resolved using measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and similar high-resolution satellite sensors. We focus analysis on the impacts of i) spatial errors in the prior emissions, and ii) model transport errors. Along with a standard scale-factor (SF) optimization we conduct a set of inversions using alternative formalisms that aim to overcome limitations in the SF-based approach that arise for missing sources. We show that 4D-Var analysis of the TROPOMI data can improve monthly emission estimates at 25 km even with a spatially biased prior or model transport errors (42–93% domain-wide bias reduction; R increases from 0.51 up to 0.73). However, when both errors are present, no single inversion framework can successfully improve both the overall bias and spatial distribution of fluxes relative to the prior on the 25 km model grid. In that case, the ensemble-mean optimized fluxes have a domain-wide bias of 77 Gg/d (comparable to that in the prior), with spurious source adjustments compensating for the transport errors. Increasing observational coverage through longer-timeframe inversions does not significantly change this picture. An inversion formalism that optimizes emission enhancements rather than scale factors exhibits the best performance for identifying missing sources, while an approach combining a uniform background emission with the prior inventory yields the best performance in terms of overall spatial fidelity—even in the presence of model transport errors. However, the standard SF optimization outperforms both of these for the magnitude of the domain-wide flux. For the common scenario in which prior errors are non-random, approximate posterior error reduction calculations for the inversions reflect the sensitivity to observations but have no spatial correlation with the actual emission improvements. This demonstrates that such information content analysis can be used for general observing system characterization but does not describe the spatial accuracy of the posterior emissions or of the actual emission improvements. Findings here highlight the need for careful evaluation of potential missing sources in prior emission datasets and for robust accounting of model transport errors in inverse analyses of the methane budget.

Modeling of the electrical conductivity (EC) of icy moon oceans has previously assumed that chloride, sulfate, and other ions released from rock leaching are the main solutes and carriers of electrical conductivity. Here, we show that accreted volatiles, such as carbon dioxide and ammonia, can add a significant fraction of solutes in bodies whose volatile content was in part supplied from cometary materials. These volatiles can increase the EC of aqueous solutions above 1 S/m. Our salinity and EC estimates can serve as a basis for planning future magnetometer investigations at icy moons and dwarf planets. In particular, oceans expected in some of the Uranian satellites and Neptune’s satellite Triton could have EC above 3 S/m as a result of accretion of large abundances of carbon dioxide and ammonia, even if rock leaching during water-rock separation was limited, and chlorine and sulfur abundances may be at CI carbonaceous chondritic levels.

A new Aitken mode aerosol scheme is developed for a large eddy simulation (LES) model in order to better investigate cloud-aerosol interactions in the marine boundary layer and to study the Aitken buffering hypothesis of McCoy et al. (2021). This scheme extends the single-mode two-moment prognostic aerosol scheme of Berner et al. (2013). Nine prognostic variables represent accumulation and Aitken log-normal aerosol modes in air and droplets as well as 3 gas species. Scavenging of interstitial aerosol by cloud and rain drops and coagulation of dry aerosol are treated using the scheme described in B13. The scheme includes a simple chemistry model with gas phase H2SO4, SO2, and DMS as prognostic variables to capture basic influences of sulfur chemistry on the model aerosols. Primary nucleation of H2SO4 aerosol particles from gas-phase H2SO4 is neglected. A deep, precipitating stratocumulus case (VOCALS RF06) is used to test the new scheme. The presence of the Aitken mode aerosol increases the cloud droplet concentration through activation of the larger Aitken particles and delays the creation of an ultraclean, strongly precipitating cumulus state. Scavenging of dry accumulation and Aitken particles by cloud and precipitation droplets accelerates the collapse. Increasing either the above-inversion Aitken concentration or the surface Aitken flux increases the Aitken population in the boundary layer and prevents the transition to an ultraclean state.

Southeast Asian biomass burning is a major pollutant source that contributes to poor air quality throughout the region. Thus, understanding these emissions is critical for predicting and mitigating their health impacts. While many studies have reported ground-based and satellite measurements, airborne measurements at a regional scale capable of tying the two together have not been common. The 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field project examined Southeast Asian regional sources and their effects on aerosol/cloud interactions using a combination of airborne, shipboard, and ground-based measurements. These flights sampled a variety of airmass sources over the Philippine, South China, and Sulu seas during both the southwest monsoon and monsoon transition periods. Measurements during CAMP2Ex provide a unique opportunity to investigate how these transported and local emissions affected air quality trends and airmass chemical composition. We present correlated airborne in situ enhancement ratios of CH4 to CO, using them to identify source regimes of either high urban or biomass burning influence as well as urban regimes with different emission factors. Combined with backtrajectory analysis using HYSPLIT, source regimes were examined for differences in ozone, reactive nitrogen, and aerosol chemical composition. While observed O3/CO enhancement ratios remain constant for differing urban source regimes, NOy/CO ratios varied across these regimes. For biomass burning sources, O3/CO enhancement ratios are observed to be lower than previously reported by measurements in the region.

High resolution in-situ measurements of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor (H2O) were made onboard the NASA HU-25 aircraft during the ACTIVATE (Aerosol Cloud meteorology Interactions oVer the western Atlantic Experiment) campaign during 2020 and 2021 in different seasons (winter through summer) over the mid-latitude western Atlantic Ocean. As most of the flights focused on the marine boundary layer (MBL) during the campaign, these trace gas observations are an excellent data set to examine seasonal variability of trace gas background values in the MBL without the influence of localized point sources. We will describe the variability of these trace gases in the MBL background by filtering out concentrated point sources using back trajectory analysis along with trace gas ratios. Additionally, the ocean is a significant sink of anthropogenic CO2 capturing about one quarter of total anthropogenic carbon. By looking at the MBL CO2 variation as a function of season, we discuss observed changes in CO2 uptake over the ocean. These high accuracy observations of trace gas backgrounds in the MBL along with characterizing seasonal effects on oceanic sequestering of anthropogenic CO2 will improve the understanding of seasonal variations and change in climate and inverse modelling over the ocean.

Connecting the sources of reactive nitrogen (Nr) emissions to downstream environmental impacts is challenging, since Nr “cascades” through sequential ecosystems. It is therefore important to examine a jurisdiction’s overall Nr emissions to gain perspective on whether total levels and specific forms of Nr emissions are being reduced. We study subnational (provincial) trends and variations in Nr emissions in Canada over 30 years (1990-2017) to examine the effects of key policy, socioeconomic, and technological changes on Nr emissions. We use data from national Nr emissions inventories, agricultural nutrient models, and wastewater treatment reports to estimate specific (N2O, NOx, NH3, and NO3), total (Gg N) and per capita (Kg N / capita) Nr emissions by province. We divide the initial sources of Nr emissions into 1) agricultural-related emissions, 2) fossil-fuel emissions, and 3) waste management emissions. Preliminary results show that annual total Nr emissions in Canada increased between 1990-2000 (1250 Gg to 1490 Gg), and have since decreased (to 1180 Gg). There was a redistribution of the main species of Nr, with decreases of NOx from fossil fuels (from 660 to 488 Gg Nr / year) and increases in agricultural Nr emissions (from 452 Gg to 537 Gg Nr / year). Provincial trends vary. Overall NOx emissions decreased across all provinces due to more stringent vehicle regulations, except in Alberta, where NOx emissions from export-oriented oil production increased. The increase in national agricultural emissions comes primarily from Saskatchewan whose combined agricultural emissions increased from 43 Gg to 97 Gg Nr year. Improving agricultural nitrogen-use efficiency, especially in these provinces, would be a key area for reducing Canada’s Nr emissions. However, given that diesel and gasoline vehicles still contribute 121 Gg Nr / year, electrifying vehicle fleets would also have considerable potential for reducing Nr emissions. Our study demonstrates the variation in and reconfiguration of drivers of Nr emissions at the sub-national scale in Canada, emphasizing the need to consider local contexts and relative contributions of different economic sectors when examining national Nr emissions.

Isolated breakdown (IBD) process is a lightning phenomenon that was rarely reported in the past. It is characterized by radiowave pulses typical for preliminary breakdown before negative cloud-to-ground flashes, which fail to evolve into return strokes. We identified 128 IBD pulse trains in measurements collected in the Mediterranean by a broadband receiver (0.005 – 37 MHz) in 2015 and 2018. By combining these records with concurrent Lightning Mapping Array measurements of very high frequency radiation (60 – 66 MHz) emitted by in-cloud discharges we investigate the development of each discharge. We identify two scenarios: either the discharges continue to propagate almost horizontally for more than 150 ms (73%), or they disappear sooner, typically within several tens of milliseconds (27%). Using numerical modeling, we verify that a potential barrier inside the thundercloud caused by a strong lower positive charge center could indeed block further propagation of lightning leaders toward the ground.