In 2015, at the United Nations Conference of the Parties in Paris, France, countries agreed to limit the global mean surface temperature (GMST) increase to 2°C above preindustrial levels, and to pursue efforts to limit it to 1.5°C. However, risks from sea level rise are not well encapsulated by temperature targets. Near term emissions will dictate long term sea level rise responses, but the tendency for policy and negotiations to concentrate on the year 2100 can limit our understanding of intergenerational justice concerns arising from this commitment. Here we present an analysis of the long term spatial variability of sea level rise, and an interdisciplinary review of associated justice considerations from across a wide range of literatures. We center the positioning of the Alliance of Small Island States (AOSIS) to show that AOSIS nations are disproportionately impacted by sea level rise, and that ice sheet instabilities, which could dominate the long term trend in sea level, are associated with feedbacks which can potentially exacerbate climate justice implications.

It is now understood that if life had ever erose on Mars, it might have been preserved in the simplest form. Therefore, studying the traces of simple life forms from the rock records of various Earth environments and climatic conditions perhaps helps to narrow down the region of interest while searching for life on the red planet. The Precambrian era covered almost 80% of Earth’s geologic history, witnessed the appearance of life on Earth and experienced prolonged extreme climatic events that delayed biological evolution. During this extreme period, primitive lifeforms such as microbial mats had a strong influence on sedimentation, and they facilitated the formation of a variety of mat-induced sedimentary structures (MISS) in siliciclastic and carbonate sedimentary environments. In the last two decades MISS have been identified from several Precambrian successions of India for example, Vindhyan, Marwar, Chhattisgarh and, Cuddapah Supergroup. In this study we tried to provide an updated catalogue based on the chronologic, stratigraphic and paleoenvironmental occurrences of MISS from the Indian Precambrian successions. We further explore their potential in understanding extreme habitability, searching biomarkers and biosignatures on Mars and propose a few potential sites for astrobiological research.

The discovery of exoplanets has altered our understanding of the universe. But, for the planets to show the possibility to harbour life in it or have biosignatures, it must have optimum physical, biological, geological and chemical conditions. There are two types of indicators of habitability: direct and indirect. The former indication is the presence of water and its stability on the surface of the planet. Thus, the reflection from the waterbody will lead to ‘glint’. Polarization of light is another alternative method to find water. The reflection, emission of radiation help us to characterize habitable zones. Indirect methods include the presence of CO2 and water vapour in the atmosphere, size of the planet and extent of axial tilt. The presence of magnetic fields and satellites revolving around the planet also play an important role. In this review article, we aim to provide a comprehensive explanation to the researches done till date to characterize habitable zones for exoplanets. The methods devised to retrieve results will also be discussed. Future prospects, the voids which could be amended are also elaborated. This could give cosmological research a new dimension, demonstrating that life is not limited to our planet.

This study’s objective was to evaluate the potential geospatial relationship between agricultural pesticide use and cancer incidences by using the concept of consolidated environmental burden indices. We conducted two sets of experiments, using pesticide data collected from the U.S. Geological Survey Pesticide National Synthesis Project database and using cancer data collected from the National Cancer Institute State Cancer Profiles and Cancer Data Registry of Idaho. In the first experiment, we analyzed the associations between several potentially carcinogenic pesticides and metals to the pediatric cancer incidence among the counties of Idaho. Principal component analysis was adopted to consolidate the environmental variables. This study identified that the principal components (PCs) were significantly associated with pediatric cancer at P-value < 0.01 and a t-statistic > 3. The PC’s improved model performance substantially, from NSE = 0.22 for the control case to NSE = 0.52 for the treatment case. In the second experiment, we evaluated the associations of pesticides to two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western U.S. at the state and county spatial scales. A multi-level model was developed using fumigant mass, fumigant mass tertiles as well as county and state boundaries, which strongly predicted total cancer incidence (R-squared = 0.95, NSE = 0.91, and SSR = 8.22). Further, this study also identified significant associations between total fumigant mass, total pesticide mass, medium and high fumigant class (tertiles > 0.33), and high pesticide class (tertile > 0.66), relative to total cancer incidence (P-value < 0.01) in the U.S. west. Fumigant application rate was shown to be significantly related to the incidence of total cancers and pediatric cancer. Moreover, this relationship was maintained regardless of the spatial resolution used in the analysis (county or state). Both studies reinforce inferences from previous studies that pesticides are related to an elevated risk for pediatric cancer. Similarly, among the pesticides, fumigants such as metam were shown to be important relative to the cancer incidences for both experiments, highlighting the significance of further analysis.

The initial list of Essential Water Variables (EWVs) evolved from wide meta-surveys of water data needs for research and applications that were carried out in 2010 to support GEO Societal Benefit Areas (SBAs). These EWVs were formalized in the Group on Earth Observations System of Systems (GEOSS) Water Strategy Report (WSR) “From Observations to Decisions”, released in 2014. Subsequently, discussions with additional user communities have augmented the list, for example with Surface Water Extent. Besides “primary” EWVs that identify key water variables, including precipitation, soil moisture, and water quality, a set of “supplementary” EWVs is also needed to complete the information that the formal list of primary EWVs should provide, such as Digital Elevation Models. It is clear that all available observing systems, employing both remote sensing and in situ observing instruments and networks are required to address the range of space/time resolutions, accuracies, and data latencies that the end-user applications require. In fact, there are still gaps in our ability to deliver all variables as required. In some cases this is a technical challenge, such as remote sensing capabilities for some water quality variables, while in many other cases it is a matter of administrative and resource challenges. This paper summarizes EWVs as currently defined and required by key end-user research and applications sectors. As a follow up to the WSR, we highlight the relevance of EWVs to the indicator monitoring objectives of the UN Sustainable Development Goals (SDGs), various international Conventions and Frameworks, and the GEO Global Water Sustainability (GEOGloWS) priority thematic communities.

Xavier University of Louisiana, in collaboration with the Institute of Earth Science Research and Education (IESRE) run the ECoSTEM project. This project aims to improve the quality of undergraduate STEM education by introducing a computational, community-oriented component into STEM programs at Xavier University. Specifically, undergraduate students develop microcontroller-based systems for collecting airborne particulate data. They work with public school teachers (and their students) and Louisiana Department of Environmental Quality (LDEQ) officers to deploy the sensors at locations around New Orleans and to analyze the resulting data. Airborne particulates are often the pollutant of greatest concern in minority and other underserved communities. This project provide undergraduates at Xavier and the local teachers (and their students) with learning experiences that are relevant to them and their communities. ECoSTEM uses a tiered mentoring system in which the project team members mentor undergraduates, who in turn work with public school teachers and mentor their students. Research has shown positive effects from student involvement in such real-world, personally relevant STEM projects, including increases in student interest in STEM-related careers after graduation. As a result, the project has the potential to broaden student interest in STEM and increase pursuit of STEM-related majors.

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.

Lahars, or volcanic mudflows, are one of the most devastating natural, volcanic hazards. We present an analysis of several rain-triggered lahar events at Volcán Fuego in Guatemala with an infrasound array and time-lapse camera network deployed along the Las Lajas river channel. While infrasound detects the passage of low frequency sound waves associated with these mudflows, the time-lapse cameras can be used to verify lahar passages and channel responses as these flows move downstream. Twenty-three infrasound microphones and five time-lapse cameras were deployed during the 2021 rainy season (May-October) in the Las Lajas drainage on the southeasterly side of Volcán de Fuego. With the data collected over this field campaign, we hope to quantify flow parameters such as volumes, velocities, and the frequency of these rain-triggered mudflows, as well as characterize flow behaviors in our infrasound signals by correlating data to the time-lapse imagery. This study allows us to identify the occurrence of several lahars, quantify important characteristics, and use a multi-faceted approach to verify and delineate important flow behaviors related to these volcanic hazards.

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.

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.

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.

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.