Accurate fire emissions inventories are crucial to predict the impacts of wildland fires on air quality and atmospheric composition. Two traditional approaches are widely used to calculate fire emissions: a satellite-based top-down approach and a fuels-based bottom-up approach. However, these methods often considerably disagree on the amount of particulate mass emitted from fires. Previously available observational datasets tended to be sparse, and lacked the statistics needed to resolve these methodological discrepancies. Here, we leverage the extensive and comprehensive airborne in situ and remote sensing measurements of smoke plumes from the recent Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to statistically assess the skill of the two traditional approaches. We use detailed campaign observations to calculate and compare emission rates at an exceptionally high resolution using three separate approaches: top-down, bottom-up, and a novel approach based entirely on integrated airborne in situ measurements. We then compute the daily average of these high-resolution estimates and compare with estimates from lower resolution, global top-down and bottom-up inventories. We uncover strong, linear relationships between all of the high-resolution emission rate estimates in aggregate, however no single approach is capable of capturing the emission characteristics of every fire. Global inventory emission rate estimates exhibited weaker correlations with the high-resolution approaches and displayed evidence of systematic bias. The disparity between the low resolution global inventories and the high resolution approaches is likely caused by high levels of uncertainty in essential variables used in bottom-up inventories and imperfect assumptions in top-down inventories.

Purpose: The characterization of soil properties is an important part of many different types of agri-environmental research including inventory, comparison, and manipulation studies. Sediment source fingerprinting is a method that is increasingly being used to link sediment sources to downstream sediment. There is currently not a standard approach to characterizing sources and the different approaches to sampling have not been well assessed. Methods: Grid (n=49), transect (n=14), and likely to erode (n=8) sampling designs were used to characterize the geochemical, colour, grain size distribution, and soil organic matter content at two sites under contrasting land uses (agricultural and forested). The impact of the three sampling designs on characterization of fingerprint properties, the relationship between particle size and organic matter content on fingerprint properties, fingerprint selection, source discrimination, and mixing apportionment results were evaluated using a range of methods including 21 virtual mixtures. Results: The likely to erode design resulted in a unique fingerprint signature compared to the other two sampling designs. The correlation between particle size and organic matter on fingerprint properties varied between fingerprint, source, and sampling design. While the number and composition of the fingerprints selected varied between sampling designs there was strong (100%) discrimination between sources regardless of the sampling approach. The maximum absolute difference between the virtual mixtures and the modeled proportions was 7.7, 7.8, and 8.9% for the grid, transect, and likely to erode sampling designs, respectively. Conclusions: The likely to erode sampling design was not representative of the upslope areas as characterized by the

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA’s Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing datasets. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flaring activities over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. The region was impacted by the winter storm Uri which resulted in a significant reduction of flaring activities followed by a post-storm resumption. Our method is extendible to combustion events, such as biomass and waste burning, and can be scaled globally for transparent emission estimate reporting.

Understanding the origin of stress avalanche of fault gouges may offer deeper insights into many geophysical processes such as earthquakes. Microslips of sheared granular gouges were found to be precursors of large slip events, but the documented relation between local and global avalanches remains largely qualitative. We examine the stick-slip behavior of a slowly sheared granular system using discete element method simulations. The microslips, i.e., local avalanche events, are found to demonstrate significantly different statistical and spatial characteristics between the stick and slip states. We further investigate the correlation between the global stress fluctuations and the features extracted from microslips based on the machine learning (ML) approach. The data-driven model that incorporates the information of the spatial distribution of microslips can robustly predict the magnitude of stress fluctuation. A further feature importance analysis confirms that the spatial patterns of microslips manifest key information governing the global stress fluctuations.

Understanding the dynamic evolution of relativistic electrons in the Earth’s radiation belts during both storm and non-storm times is a challenging task. The U.S. National Science Foundation’s Geospace Environment Modeling (GEM) focus group “Quantitative Assessment of Radiation Belt Modeling” (QARBM) has selected two storm time and two non-storm time events that occurred during the second year of the Van Allen Probes mission for in-depth study. Here, we perform simulations for these GEM challenge events using the 3-Dimensional Versatile Electron Radiation Belt (VERB-3D) code. We set up the outer $L^*$ boundary using data from Geostationary Operational Environmental Satellites (GOES) and validate the simulation results against satellite observations from both the GOES and Van Allen Probe missions for 0.9 MeV electrons. Our results show that the position of the plasmapause plays a significant role in the dynamic evolution of relativistic electrons. The magnetopause shadowing effect is included by using last closed drift shell (LCDS), and it is shown to significantly contribute to the dropouts of relativistic electrons at high $L^*$.

Delivering long-term, high quality environmental sensor data spanning the continent is a primary goal in the National Ecological Observatory Network’s (NEON) Instrumented Systems (IS) group. Some independent observations collected by NEON’s Observation System (OS) measure similar data at the same location and time as the in-situ sensors. Coinciding IS and OS measurements facilitate supplementary data quality assessments by vetting IS sensor data (e.g. aquatic pH probe) against corresponding OS data (e.g. water grab sample analyzed in a lab for pH). To assess whether IS data agree with OS measurements, we use uncertainty as a tool to understand data quality. The uncertainty between NEON IS and OS data follow analytical (e.g. summation in quadrature) or numerical (e.g. Monte Carlo) approaches depending on the complexity of the IS-OS comparison algorithms. NEON calculates the IS-OS uncertainties, and applies the expanded uncertainty as control limits for acceptable IS-OS data comparisons. IS-OS comparisons falling outside the uncertainty-based control limits help to (i) explore unaccounted uncertainty in the IS and OS data, and (ii) address issues in the data or sample collection process as ongoing continuous improvement strategies.

This study examines a scenario combining a prolonged solar minimum, like the historical Maunder Minimum, with the increased CO2 emissions characteristic of the post-industrial era. Two scenarios were developed in NCAR/CESM 2.0 implemented at LAMMOC/UFF to create simulations from 1850 to 2000, it contrasts differing radiative forcings from 1950 onward—one reflecting actual observed changes, including rising CO2 levels, and the other simulating a decrease in solar output like that during the Maunder Minimum but with continued CO2 growth. The results were validated against ERA5 data and 20th-century reanalysis. By calculating meridional averages at 30-degree latitude intervals, distinct regional impacts of the Maunder Minimum were identified. Notably the simulated Maunder Minimum reduced global warming by and even mitigated 70% in HS in the last decade of the 20th century. However, this attenuation was lower in the HN, especially in the 30-60N region where no attenuation was observed.

Atmospheric cold fronts are frequently occurring perturbations to the northern Gulf of Mexico coastal region. Given the low-lying elevations in this region and the connectivity between distributary channels and deltaic wetlands, the nonlinear interplay between the cold frontinduced storm surge and tidal oscillations are likely important hydrodynamic processes regulating circulation and sediment dynamics. This study uses the Delft3D Flexible Mesh numerical modeling suite to assess the water level fluctuations resulting from the non-linear interaction between cold front induced storm surge and tidal oscillations in Wax Lake Delta (WLD) between December 2022 and January 2023. The WLD is a small, dynamic sub-delta located in Louisiana, USA, known for its ongoing progradation. The primary focus of this study lies on analyzing two cold fronts that approach from the northwest direction. The results illustrate that within the shallow, vegetated wetland interiors with spatially-variable inundation, the water level fluctuations resulting from non-linear interaction can exceed four times the water depth, while the variations relative to the water depth in the relatively deep primary channels are insignificant. Analysis for two cold fronts from the northwest and further numerical experiments revealed that the water level variation response to the non-linear interaction between the cold front storm surge and tides is predominantly influenced by the intensity of the cold front and the magnitude of the tidal range. This study emphasizes the notable impact of the non-linear interaction between cold front and tide on water level variation, which, in turn, influences inundation extent, sediment transport, and ecological factors in the WLD.  AGU 2023, H51N-1280, San FranciscoLink: https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1293693

Lower-viscosity fluids are commonly believed to be able to create more complex fractures in hydraulic fracturing, however, the mechanism remains stubbornly unclear. We use a new grain-scale model with accurate coupling of hydrodynamic forces to simulate the propagation of fluid-driven fracturing. The results clarify that fracturing fluid with a lower viscosity does not always create more complex fractures. The heterogeneity in the rock exerts the principal control on systematic evolution of fracture complexity. In homogeneous rock, low viscosity fluids result in low breakdown pressure, but viscosity exerts little influence on fracture complexity. However, in heterogeneous rock, lower viscosity can lead to more complex network of fracturing. A regime map shows the dependence of fracture complexity on the degree of rock heterogeneity where low viscosity fracturing fluid more readily permeates weak defects and creates complex fracture networks.

Heterogeneous reactions occurring at the surface of atmospheric aerosol particles regulate the production and lifetime of a wide array of atmospheric gases. Aerosol surface area plays a critical role in setting the rate of heterogeneous reactions in atmospheric chemistry. Despite the central role for aerosol surface area, there are few assessments of the accuracy of aerosol surface area concentrations in regional and global models. In this study, we compare aerosol surface area concentrations in the EPA’s CMAQ (Community Multiscale Air Quality) model with commensurate observations from the 2011 NASA flight-based DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign. The study region for the 2011 DISCOVER-AQ campaign focused primarily on the Baltimore and Washington, DC region. Dry aerosol surface area was measured aboard the NASA P-3B aircraft using a combination of a Scanning Mobility Particle Sizer (SMPS), Aerodynamic Particle Sizer (APS), and Ultra-High Sensitivity Aerosol Spectrometer (UHSAS). In this study, we focus on the continuous 1s UHSAS measurements in size range of 60-1000nm, as it captures the majority of the dry surface area distribution. Over the course of 13 flight campaigns, we show strong agreement between measured and modeled aerosol number concentration (CMAQ Number/UHSAS Number= 0.88). In contrast, the total surface area showed a larger discrepancy (CMAQ surface area/UHSAS surface area = 0.44). We hypothesize that the emissions and chemistry in CMAQ relating to the production and loss of each moment play a large part in the model/measurement discrepancy. Despite the disagreement, this analysis suggests that modeled aerosol surface area is accurate to within a factor of two, highlighting that uncertainty in the rate of heterogeneous reactions is largely driven by uncertainty in the reactive uptake coefficients. We would like to thank the DISCOVER-AQ NASA Langley Aerosol Research Group Experiment (LARGE) research team, including Richard Moore, Bruce Anderson, Andreas Beyersdorf, Luke Ziemba, Lee Thornhill, and Edward Winstead for the use of their data in this study.

Hydrofluorocarbons (HFCs) have been increasingly replacing chlorofluorocarbons and hydrochlorofluorocarbons. Although their ozone-depleting potential is negligible, as potent greenhouse gases they indirectly influence stratospheric ozone recovery. Measurements and model projections must continue to evaluate HFC limitation measures and assess the long-term impact of HFCs on the atmospheric radiation budget and stratospheric ozone. In this study, we present multi-member ensemble simulations designed to estimate the impact of HFCs on stratospheric temperature, ozone and circulation changes at the end of the century. We compared simulations with and without HFCs for two three-dimensional chemistry-climate models that use the same chemistry module but different physical schemes. At low and mid-latitudes, temperature and ozone responses were comparable for both models and in general agreement with previous studies. HFCs induced a marked temperature increase up to 5 hPa and vertically alternating positive and negative ozone anomalies. We explained this pattern by competing effects of vertical motion (low and middle stratosphere) and temperature (upper stratosphere) anomalies. At northern high latitudes, there were strong discrepancies with previous studies and between the models themselves, attributed to differences in ozone anomalies caused by wave activity during winter. Quantitatively, we found a net positive, but small, HFC impact on total ozone amounts. Largest anomalies were less than 1% in the winter polar stratosphere. Our results indicate that increasing HFC amounts will likely have a limited impact on stratospheric ozone recovery within this century, with large uncertainty in the polar regions.

Despite their importance, wetland ecosystems protected through the Ramsar Convention on Wetlands are under pressure from climate change and human activities. These drivers are altering water availability in these wetlands, changing water levels or surface extent, in some cases, beyond historical variability. Attribution of the effects of human and climate activities is usually focused on changes within the wetlands or their upstream surface and groundwater inputs. However, the reliance of wetland water availability on upwind atmospheric moisture supply is less understood. Here, we assess the vulnerability of 40 Ramsar wetland basins to precipitation changes caused by land use and hydroclimatic changes occurring in their upwind moisture-supplying regions. We use moisture flows from a Lagrangian tracking model, atmospheric reanalysis data, and historical land use change data to assess and quantify these changes. Our analyses show that historical land use change decreased precipitation and terrestrial moisture recycling in most wetland hydrological basins, accompanied by decreasing surface water availability (precipitation minus evaporation) in some wetlands. The most substantial effects on wetland water availability occurred in the tropical and subtropical regions of Central Europe and Asia. Overall, we found wetlands in Asia and South America to be especially threatened by a combination of land use change-driven effects on runoff, high terrestrial precipitation recycling, and recently decreasing surface water availability. This study stresses the need to incorporate upwind effects of land use changes in the restoration, management and conservation of the world’s wetlands.

The holy grail of ExoPlanet research is finding and characterizing terrestrial planets orbiting in the habitable zones of a neighboring stars. The first question to be answered is: does the planet have an atmosphere, and what is it’s composition. Theory and observation confirm that the one universal atmospheric component should be CO_2. Hence a successful mission must determine it’s presence. The most direct path to detection is through the enormously strong 15 micron vibration-rotation feature of CO_2, hence the technology we’re developing. Because of the CO_2 in the earth’s atmosphere, this requires a space observatory, such as the JWST. Our technology can also be used to detect water vapor and ozone in exoplanet atmospheres, the latter of which indicates abundant life, probably indicative of a ‘Cambrian Explosion’ event on the exoplanet.

Due to EC’s easy recordability and the existence of a strong correlation between EC and discharge in certain catchments, EC is a potential predictor of discharge. This potential has yet to be widely addressed. In this paper, we investigate the feasibility of using EC as a proxy for long-term discharge monitoring in a small karst catchment where EC always shows a negative correlation with the spring’s discharge. Given their complex relationship, a special machine learning architecture, LSTM (Long Short Term Memory), was used to handle the mapping from EC to discharge. The results indicate, based on LSTM, that the spring’s discharge can be predicted well with EC, particularly in storms when the dilution dominates the EC dynamic; however, the prediction may have relatively large uncertainties in the small or middle recharge events. A small number of discharge observations are sufficient to obtain a robust LSTM for the long-term discharge prediction from EC, indicating the practicality of recording EC in ungauged catchments for indirect discharge monitoring. Our study also highlights that the random or fixed-interval discharge measurement strategy, which covers various climate conditions, is more informative for LSTM to give robust predictions. While our study is implemented in a karst catchment, the method is also suitable for non-karst catchments where there is a strong correlation between EC and discharge.

We investigate the representation of individual supercells and intriguing tornado-like vortices in a simplified, locally-refined global atmosphere model. The model, featuring grid stretching, can locally enhance the model resolution and economically reach the cloud-resolving scale. Given an unstable sheared environment, the model can simulate supercells realistically, with a near-ground vortex and funnel cloud at the center of a rotating updraft reminiscent of a tornado. An analysis of the vorticity budget suggests that the updraft core of the supercell tilts environmental horizontal vorticity into the tornado-like vortex. The updraft also acts to amplify the vortex through vertical stretching. Results suggest that the simulated vortex is dynamically similar to observed tornadoes and modeling studies at much higher horizontal resolution.

A lightning nitrogen oxides (LNOx) emissions model using satellite-observed lightning optical energy is introduced for utilization in Air Quality modeling systems. The effort supports assessments of air-quality/climate coupling as related to the influence of LNOx on atmospheric chemistry. The Geostationary Lightning Mapper (GLM), International Space Station Lightning Imaging Sensor (ISS-LIS), and the Tropical Rainfall Measuring Mission (TRMM) LIS data are used to examine the efficacy of the method, extend the previously derived LNOx record, and demonstrate a path for using ISS-LIS observations to cross-calibrate regional LNOx estimates from the future global constellation of geostationary lightning observations. A detailed evaluation of the GLM dataset is provided to establish the robustness of observations for LNOx estimates and to make preliminary assessments of the LNOx emissions model. Seasonal and geographical variation, land/ocean contrast, and annual fluctuation in the GLM observed lightning activity and flash optical energy are provided. GLM detection substantially degrades with the increase in the field of view, resulting in 44% more flashes and 40% less optical energy observation by GLM-16 (compared to GLM-17) to the east of the middle-longitude between the two mappers (106.2°W). Regular horizontal striations are found in the optical energy product. On average, GLM flashes matched to the cloud-to-ground flashes have ~30% longer duration, 50-70% more extension, and ≥ 100% higher optical energy compared to the unmatched flashes (assumed to be intra-cloud). The results from summer-long chemical transport simulations using LNOx generated from the emission model agrees with previous studies and shows consistency across the GLM/LIS datasets.

In spite of clear fan-shaped magnetic anomalies in the Eastern Algerian Basin (EAB), the way how and the time when seafloor spreading occurred are still debated. In this work, a new seismo-stratigraphic interpretation based on deep-penetration reflection seismic data correlated to reduced-to-the-pole magnetic anomalies and to onshore-offshore litho-stratigraphic correlation of Pre-Messinian units bring new constraints on its age and mode of opening. Our results reveal that the seafloor spreading of EAB occurred with a intermediate to fast half-spreading rate of 3.7±0.5 cm/yr during 2.45±0.18 Myr in Langhian-Serravalian times, i.e. after the Corsica-Sardinia block rotation and the collision of Lesser Kabylia with Africa. We revise the kinematics of the Algero-Balearic domain into three stages: (1) birth of a highly stretched continental basin accommodating the southern drift of the Kabylies driven by Tethyan slab rollback between ~23 and ~15 Ma, (2) fast opening of a new basin (EAB) between 15.2 and 12.7 Ma by clockwise rotation of a Greater Alboran Block (GALB), and (3) continuation of westward translation of the GALB. The last stages match both the late formation of Subduction-Transform Edge Propagator (STEP) faults at the toes of the Algero-Balearic margins and the post-collisional volcanic migration along the Algerian margin interpreted as related to slab break-off. This new scheme invalidates most previous opening models of the Algero-Balearic basin and favors a significant stretching and splitting of the GALB into several continental fragments resulting from the westward propagation of the arcuate subduction front by lateral tearing of a narrow slab.

Many modern sea ice models used in global climate models represent the subgrid-scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea-ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard five ice thickness categories and fifteen ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year-round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice. The spatial resolution of the ICESat-2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat-2; 2018-2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.