Lightning flashes result from charge separation caused by colliding ice particles. They are particularly prevalent in continental convection but also occur over oceans, where their frequency correlates with maritime shipping emissions. In 2020, the International Maritime Organization (IMO) restricted ship fuels to much lower sulfur concentrations. We use this abrupt perturbation to study the response of oceanic lightning to aerosol, cloud, and convective properties. In principle, a reduction in aerosols should lead to a decrease in lightning over shiptracks relative to unpolluted regions. However, we find only one of the two investigated shipping lanes displays this sensitivity. Using the spatiotemporal sensitivity of lightning flash rate to hydrometeor size, we estimate the sensitivity of cloud water path to cloud number concentration. The results offers insights into both the role of aerosols in generating oceanic lightning and the mechanisms of aerosol-cloud interactions in deep convection.

Around the world there is a consensus of the importance of knowladge the ice thickness distribution and total ice volume for estimating future glacier change for both glaciological and hydrological application, especially the Polar Regions for being important regulators of the global climate system and response rapidly to climate change. This work aimed to estimate the ice thickness distribution and total ice volume of Znosko glacier (King George Island, Antarctic Peninsula) using Glacier Bed Topography (Glabtop) model and ground data. Ground penetrating radar (GPR) measurements were collected in two field campaigns. GPR measurements were used to validate, calibrate (shape factor) and improve the accuracy of the ice thickness distribution model. The ice thickness (GPR) distribution is not uniform over the glacier, which varies from 0 to 155 m. The total stored ice volumen of Znosko glacier in 2020 is estimated to be 0.11 km3. Additionally, the model bounded areas in the bedrock topography below sea level, as a sign of future lake formation. Sensitivity analysis reveal that Glabtop model with a shape factor (f) = 0.5 gave best results when compared with ground penetrating radar (GPR) data measured on Znosko glacier and has an uncertainty range of ± 20.5%.

The remediation of chromium (Cr) contaminated sites presents a complex challenge due to the hazardous nature of Cr compounds and their adverse impacts on human health and the environment. In addressing this pressing concern, the present study endeavors to contribute to the understanding of technology suitability for effective Cr-contaminated site remediation through a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Drawing upon an exhaustive review of the existing literature, this research first identifies and evaluates the strengths of various technologies commonly employed in Cr-contaminated site remediation. These strengths encompass the capacity of technologies to efficiently reduce Cr concentrations, promote sustainable remediation practices, and ensure minimal collateral damage to surrounding ecosystems. Furthermore, the analysis delves into the weaknesses of these technologies, highlighting potential limitations that may impede their optimal performance. The study then explores the opportunities that arise from implementing these technologies, including advancements in remediation processes, novel materials, and cutting-edge monitoring and assessment tools. Additionally, the research critically assesses the threats posed by the application of these technologies, encompassing potential economic, technical, and environmental challenges that may hinder their widespread adoption. By synthesizing these key findings, the study aims to provide valuable insights into the selection and deployment of appropriate remediation technologies tailored to Cr-contaminated sites. Furthermore, the SWOT analysis offers a structured framework for decision-makers, environmental practitioners, and policymakers to make informed choices concerning the most suitable technology options for site-specific circumstances. In conclusion, this research illuminates the diverse landscape of technologies available for Cr-contaminated site remediation, accentuating their respective strengths, weaknesses, opportunities, and threats. As such, this SWOT analysis serves as a vital resource in the ongoing efforts to remediate and restore Cr-contaminated sites, thereby safeguarding public health and the environment for future generations. Article in AGU23 Website

Lessons from the strong global response to the coronavirus disease 2019 (COVID-19) pandemic and a renewed call for “One health” approach to health systems management in “The Lancet” parallel climate change emergencies. The weakened health - climate change nexus, perceived largely within public health need to engage how the Earth system (i.e. relationships between air, land, life and water on earth) in shaping the etiologies, incidences and transmission dynamics of diseases. The question “What are the drivers of the drivers of diseases?” using the context of diarrheal diseases is posed. Subsequently, we need to understand how (i) climatic risks drive biological health hazards, (ii) shifts in disease control services of ecosystems regulate diseases, (iii) climate change within Earth systems modify disease pathogens and species hosts relationships. Hence, safeguarding Earth system-related disease dynamics would inform pluralistic approaches beyond “One health”.

To better understand the role projected land-use changes (LUC) may play in future regional climate projections, we assess the combined effects of greenhouse-gas (GHG)-forced climate change and LUCs in regional climate model (RCM) simulations. To do so, we produced RCM simulations that are complementary to the North-American Coordinated Regional Downscaling Experiment (NA-CORDEX) simulations, but with future LUCs that are consistent with particular Shared Socioeconomic Pathways (SSPs) and related to a specific Representative Concentration Pathway (RCP). We examine the state of the climate at the end of the 21st Century with and without two urban and agricultural LUC scenarios that follow SSP3 and SSP5 using the Weather Research and Forecasting model (WRF) forced by one global climate model, the MPI-ESM, under the RCP8.5 scenario. We find that LUCs following different societal trends under the SSPs can significantly affect climate projections in different ways. In regions of significant cropland expansion over previously forested area, projected annual mean temperature increases are diminished by around 0.5-1.0℃. Across all seasons, where urbanization is high, projected temperature increases are magnified. In particular, summer mean temperature projections are up to 4-5℃ greater and minimum and maximum temperature projections are increased by 2.5-6℃, amounts that are on par with the warming due to GHG-forced climate change. Warming is also enhanced in the urban surroundings. Future urbanization also has a large influence on precipitation projections during summer, increasing storm intensity, event length, and the overall amount over urbanized areas, and decreasing precipitation in surrounding areas.

Enhanced water management systems depend on accurate estimation of hydraulic properties of subsurface formations. This is while hydraulic conductivity of geologic formations could vary significantly. Therefore, using information only from widely spaced boreholes will be insufficient in characterizing subsurface aquifer properties. Hence, there is a need for other sources of information to complement our hydro-geophysics understanding of a region of interest. This study presents a numerical framework where information from different measurement sources is combined to characterize the 3-dimensional random field representing the hydraulic conductivity of a watershed in a Multi-Fidelity estimation model. Coupled with this model, a Bayesian experimental design will also be presented that is used to select the best future sampling locations. This work draws upon unique capabilities of electrical resistivity tests as well as statistical inversion. It presents a Multi-Fidelity Gaussian Processes (Kriging) model to estimate the geological properties in Upper Sangamon Watershed in east central Illinois, using multi-source observation data, obtained from electrical resistivity and pumping tests. We demonstrate the accuracy of Co-Kriging that is dependent on the locations and the distribution of both the high- and low-fidelity data, and also discuss its comparison with Single-High-Fidelity Kriging results. The uncertainties and confidence in the measurements and parameter estimates are then quantified and are in turn used to design future cycles of data collection to further improve the confidence intervals.

This study aims to understand and document the occurrence and variability of cloud cover in West Africa (WA) essential for assessing feasibility of planned large-scale solar energy projects. Investigations are carried out with a 10-year hourly record of two cloud cover data products: Clouds and the Earth Radiant Energy System passive satellite observations (CERES SYN1deg) and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5). The seasonal evolution of high-level, mid-level and low-level clouds (HCC, MCC and LCC) as well as the total cloud cover (TCC) over the region is examined. The results show that both products agree on the average seasonal and spatial distribution of cloud cover over the region, although the CERES SYN1deg product presents lower cloud fractions than ERA5. This is partly attributed to the inability of the satellite sensor to detect optically thin clouds in the atmosphere. Southern WA is found to be cloudier than other parts of the region in all seasons. During the monsoon season (June, July, August and September) cloudiness increases over southern WA (mean cloud fraction up to 70% in CERES SYN1deg and 80% in ERA5) but also extends northwards and strengthens over the Sahelian region. The intense cloud coverage over southern WA during the monsoon season leads to a large regional mean reflectance of incoming solar radiation of about 55%. In all seasons, the presence of LCC over large areas of the Sahel/Sahara region is highlighted in the CERES SYN1deg product. This could be due to a potential misinterpretation of Saharan dust as low clouds by the satellite sensor which may have overestimated their occurrences and fractions and may lead to errors in the estimation of cloud radiative effects over the region. Northern WA is associated with higher frequencies of no cloud occurrence events unlike the south where cloudless skies are rare. Furthermore, in southern WA, overcast conditions of LCC which significantly reduce incoming solar radiation are observed for a significant number of times (up to 10% of the time in CERES SYN1deg and 20% in ERA5).

The 15 January 2022 eruption of the Hunga volcano provides a unique opportunity to study the reaction of the ionosphere to large explosive events. In particular, this event allows us to study the global propagation of travelling ionospheric disturbances using various instruments. We focus on the detection of the ionospheric disturbances caused by this eruption over Europe, where dense networks of both ionosondes and GNSS receivers are available. This event took place on the day of a geomagnetic storm. We show how data from different instruments and from different observatories can be combined to clearly distinguish the TIDs produced by the eruption from those caused by concurrent geomagnetic activity. By comparing observations obtained from multiple types of instruments, we also show that TIDs produced by various mechanisms are present simultaneously, with different types of waves affecting different physical quantities.

Systemic modification of coastal systems in the northern Gulf of Mexico is generated by rapid geomorphic change due to storms, relative sea level rise, significant reduction in sediment supply, and anthropogenic alteration. Policy makers, engineers, and scientists must understand the overall geologic evolution as well as small scale processes associated with past sea level cycles to make informed decisions when addressing current and future sea level rise. After the Last Glacial Maximum, sea level rose rapidly during marine isotope stage (MIS) 2 (approximately 29-14 ka) leading to a transgressive reworking of lithosomes. As sea level continued to rise, Holocene sediments underwent significant reworking and backstepping resulting in drowned paleovalley architecture. Coastal geomorphic evolution is partially preserved within the geologic record specifically within incised valleys and shelf deposits. This study synthesizes ~700 km of boomer geophysical data collected in 2021, 19 sediment cores, microfossil analyses, and radiocarbon dates to create a geomorphic evolutionary framework of the Pascagoula-Biloxi paleovalley and associated fill along the innershelf of the northern Gulf of Mexico. Sediment cores described within the footprint of the Pascagoula-Biloxi paleovalley consist of muddy bedding overlying muddy sand and sandy mud with Pleistocene clay around 450-500 cm downcore. One such core contained large wood chunks dated to ~11 ka cal yr BP resting on a Pleistocene clay basal facies. Preserved wood indicates either rapid burial or an anoxic system, in this case - likely a swamp. Along the edge of the Pascagoula-Biloxi paleovalley, a sediment core exhibits well preserved interbedded clay and peat layers also dated to ~11 ka cal yr BP. These similar ages indicate terrestrial/shoreline deposition, and these data provide constraints to reconstruct the immature paleo shoreline and associated features of the early Holocene.

Metal compounds abundant in Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals, for example, are proposed to have served as primitive metalloenzyme cofactors for their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis and astrobiology.

Carbon monoxide (CO) is an ozone precursor, oxidant sink, and widely-used pollution tracer. The importance of anthropogenic versus other CO sources in the US is uncertain. Here we interpret extensive airborne measurements with an atmospheric model to constrain US fossil and non-fossil CO sources. Measurements reveal a low bias in the simulated CO background and a 30% overestimate of US fossil CO emissions in the 2016 National Emissions Inventory. After optimization we apply the model for source partitioning. During summer, regional fossil sources account for just 9-16% of the sampled boundary layer CO, and 32-38% of the North American enhancement-complicating use of CO as a fossil fuel tracer. The remainder predominantly reflects biogenic hydrocarbon oxidation plus fires. Fossil sources account for less domain-wide spatial variability at this time than non-fossil and background contributions. The regional fossil contribution rises in other seasons, and drives ambient variability downwind of urban areas.

Cotton production in the United States has reduced since 1950. The U.S. cotton industry is committed to sustainable cotton production practices that reduce water, land, and energy usage and soil loss while improving soil health and cotton yield. Various climate-smart agriculture strategies have been planned that boost yields and may lower operating costs. Cultivars, soil types, management strategies, pests and diseases, climate, and weather patterns impact crops in complex and nonlinear ways and make crop yield prediction difficult. This is where machine learning (ML) comes in. In this work (Fig.1), we aim to accurately predict cotton yield using an ML method incorporating the effects of climate change, soil variety, cultivars, and the nitrogen from NH4, NO3, and Urea. Two types of cotton yield data—field data and synthetic data—were used. The field data was collected in the 1980s and early 1990s across the southern cotton belt. The dataset consisted of different soil types, cultivars, and amounts of nitrogen. However, these data do not reflect the most recent effects of climate change over the past few years. To address this issue, six years of cotton yield data were generated using the process-based cotton model, GOSSYM. This dataset helps train the ML algorithm with the climate change effect and more precisely predict cotton yield. We concentrated on three southern states: Texas, Mississippi, and Georgia. For each state, three different locations in the cotton-producing counties were chosen. Weather data from 2017 to 2022 for each location were generated using the POWER Data Access Viewer web interface. The same planting and harvest dates were selected for all cases. For each case, the accumulated heat unit (AHU) was calculated from the weather data and used as one of the inputs to the ML model. Instead of applying time series weather data, calculating AHU simplified the scenario, and we were able to reduce the number of computations. Soil types, cultivars, and amounts of nitrogen were varied to create combinations of inputs likely to correspond to the range of farm input factors currently being experienced in these locations. We then developed a Random Forest Regressor to predict the yield. The results show the use of this method is highly accurate (~89%), with R2 averaging around 0.82 and a root mean square error of 117.03 kg/ha.

High-resolution underwater glider data collected in the Gulf of Oman (2015-16), combined with reanalysis datasets, describe the spatial and temporal variability of the mixed layer during winter and spring. We assess the effect of surface forcing and submesoscale processes on upper ocean buoyancy and their effects on mixed layer stratification. Episodic strong and dry wind events from the northwest (Shamals) drive rapid latent heat loss events which lead to intraseasonal deepening of the mixed layer. Comparatively, the prevailing southeasterly winds in the region are more humid, and do not lead to significant heat loss, thereby reducing intraseasonal upper ocean variability in stratification. We use this unique dataset to investigate the presence and strength of submesoscale flows, particularly in winter, during deep mixed layers. These submesoscale instabilities act mainly to restratify the upper ocean during winter through mixed layer eddies. The timing of the spring restratification differs by three weeks between 2015 and 2016 and matches the sign change of the net heat flux entering the ocean and the presence of restratifying submesoscale fluxes. These findings describe key high temporal and spatial resolution drivers of upper ocean variability, with downstream effects on phytoplankton bloom dynamics and ventilation of the oxygen minimum zone.

All material that is collected from Mars (gases, dust, rock, regolith) will need to be carefully handled, stored, and analyzed following Earth return to minimize the alteration or contamination that could occur, and to maximize the scientific information that can be extracted from the samples, now and into the future. A Sample Receiving Facility (SRF) would be where the Earth Entry System is opened, and the sample tubes opened and processed after they land on Earth. The Mars Sample Return (MSR) Science Planning Group Phase 2 (MSPG2) was tasked with identifying the steps that encompass the curation activities that would happen within an MSR SRF and any anticipated curation-related requirements. To make the samples accessible for scientific investigation, a series of observations and preliminary analytical measurements would need to be completed to produce a sample catalog for the scientific community. The sample catalog would provide data to make informed requests for samples for scientific investigations and for the approval of allocations of appropriate samples to satisfy these requests. The catalog would include data and information generated during all phases of activity, including data derived from the landed Mars 2020 mission, during sample retrieval and transport to Earth, and upon receipt within the SRF, as well as through the initial sample characterization process, sterilization- and time-sensitive and science investigations. The Initial sample characterization process can be divided into three phases, with increasing complexity and invasiveness: Pre-Basic Characterization (Pre-BC), Basic Characterization (BC), and Preliminary Examination (PE). A significant portion of the Curation Focus Group’s efforts was determining which analyzes and thus instrumentation would be required to produce the sample catalog and how and when certain instrumentation should be used. The goal is to provide enough information for the PIs to request material for their studies but to avoid doing targeted scientific research better left to peer-reviewed competitive processes. Disclaimer: The decision to implement Mars Sample Return will not be finalized until NASA’s completion of the National Environmental Policy Act (NEPA) process. This document is being made available for planning and information purposes only.

Session Number and Title: GC41B: Connecting Cause and Effect in Analyses of Coupled Human and Geophysical Systems: The Early to Modern Anthropocene II Online Poster Discussion Abstract ID and Title: 1190904: Sustainability Analysis of the Indian Fashion Design Industry Final Paper Number: GC41B-02 Session Date and Time: Thursday, 15 December 2022; 08:00 - 09:00 VIEW POSTER

We conduct a series of eight 45-day experiments with a global storm-resolving model (GSRM) to test the sensitivity of relative humidity R in the tropics to changes in model resolution and parameterizations. These changes include changes in horizontal and vertical grid spacing as well as in the parameterizations of microphysics and turbulence, and are chosen to capture currently existing differences among GSRMs. To link the R distribution in the tropical free troposphere with processes in the deep convective regions, we adopt a trajectory-based assessment of the last-saturation paradigm. The perturbations we apply to the model result in tropical mean R changes ranging from 0.5% to 8% (absolute) in the mid troposphere. The generated R spread is similar to that in a multi-model ensemble of GSRMs and smaller than the spread across conventional general circulation models, supporting that an explicit representation of deep convection reduces the uncertainty in tropical R. The largest R changes result from changes in parameterizations, suggesting that model physics represent a major source of humidity spread across GSRMs. The R in the moist tropical regions is disproportionately sensitive to vertical mixing processes within the tropics, which impact R through their effect on the last-saturation temperature rather than their effect on the evolution of the humidity since last-saturation. In our analysis the R of the dry tropical regions strongly depends on the exchange with the extra-tropics. The interaction between tropics and extratropics could change with warming and presage changes in the radiatively sensitive dry regions.

In recent decades, climate change has lengthened wildfire seasons globally and doubled the annual area burned. Thus, capturing fire dynamics is critical for projecting Earth system processes in warmer, drier, more fire prone future. Recent advances in fire regime modeling have linked land surface and Earth system models with fire behavior models. Such models often rely on fine surface fuels to drive fire spread, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel succession), fuel loading estimates remain highly uncertain. Uncertainties are amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel succession as a key uncertainty in model systems. We review the current understanding of mechanisms controlling fine fuel succession (with an emphasis on decomposition), describe how these mechanisms are incorporated into models, and evaluate the strengths and uncertainties associated with different approaches. We also use three state-of-the-art fire regime models to demonstrate the sensitivity of decomposition projections to both parameter and model structure uncertainty and show that sensitivity increases dramatically under future climate warming. Given that many of the governing decomposition equations are hard-coded in models and often based on individual case studies, substantial uncertainties are currently ignored. To understand future climate-fuel-fire feedbacks, it is essential to be transparent about model choices and uncertainty. This is particularly critical as the domain of Earth system models is expanded to include evaluation of future wildfire regimes.

Outdoor workers perform critical societal functions, often despite higher-than-average on-the-job risks and below-average pay. Climate change is expected to increase the frequency of days when it is too hot to safely work outdoors, compounding risks to workers and placing new stressors on the personal, local, state, and federal economies that depend on them. After quantifying the number of outdoor workers in the contiguous United States and their median earnings, we couple heat-based work reduction recommendations from the US Centers for Disease Control and Prevention with an analysis of hourly weather station data to develop novel algorithms for calculating the annual number of unsafe workdays due to extreme heat. We apply these algorithms to projections of the frequency of extreme heat days to quantify the exposure of the outdoor workforce to extreme heat and the associated earnings at risk under different greenhouse gas emissions mitigation scenarios and, for the first time, different adaptation measures. With a trajectory of modest greenhouse gas emissions reductions (RCP4.5), outdoor worker exposure to extreme heat would triple that of the late 20th century baseline by midcentury, and earnings at risk would reach an estimated $39.3 billion annually. By late century with that same trajectory, exposure would increase four-fold compared to the baseline with an estimated $49.2 billion in annual earnings at risk. Losses are considerably higher with a limited-mitigation trajectory (RCP8.5). While universal adoption of two specific adaptation measures in conjunction could reduce future economic risks by roughly 90%, practical limitations to their adoption suggest that emissions mitigation policies will be critical for ensuring the wellbeing and livelihoods of outdoor workers in a warming climate.