Nuclear and coal power use in the United States are projected to decline over the coming decades. Here, we explore how simultaneous phase-outs of these energy sources could affect air pollution and distributional health risk with existing grid infrastructure. We develop an energy grid dispatch model to estimate the emissions of CO2, NOx and SO2 from each U.S. electricity generating unit. We couple the emissions from this model with a chemical transport model to calculate impacts on ground-level ozone and fine particulate matter (PM2.5). Our yearlong scenario removing nuclear power results in compensation by coal, gas and oil, leading to increased emissions that impact climate and air quality nationwide. We estimate that changes in PM2.5 and ozone lead to an additional 9,200 yearly mortalities, and that changes in CO2 emissions over that period lead to an order of magnitude higher mortalities throughout the 21st century. Together, air quality and climate impacts incur between \$80.7-\$126.1 billion of annual costs. In a scenario where nuclear and coal power are shut down simultaneously, air quality impacts due to PM2.5 are larger and those due to ozone are smaller, because of more reliance on high emitting gas and oil, and climate impacts are substantially smaller than that of nuclear power shutdowns. With current reliance on non-coal fossil fuels, closures of nuclear and coal plants shift the distribution of health risks, exemplifying the importance of multi-system analysis and unit-level regulations when making energy decisions.
This editorial aims to improve awareness of the current best practices in open research, and stimulate discussion on the practical implementation of AGU's data and software policy in key areas of space weather research. We also further aim to encourage authors to take additional steps to ensure clear credit to all contributors to the work, whether that is underlying data, key software, or direct contributions to the manuscript.
Drought alone and with associated abiotic stress such as heat and nutrient deficiency leads to significant agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. In this review, we discuss the modifications in plants while responding to drought giving special focus on roots as they are the primary sense organs in this context. Prospects of genomic crop improvement by pointing out the focus areas to engineer root system architecture and genomic regions involved in the related traits are also discussed. We have also listed instruments and software facilitating high throughput phenotyping of root system in field conditions as the phenotyping of root system architecture in the field is a challenge.
Seismologists working with fiber-optic sensing, commonly referred to as Distributed Acoustic Sensing (DAS), have yet to find an established way of automatically detecting signals of interest within its recordings. We propose a new research perspective within the field by examining the output of a DAS array as an image and processing the image to find signals of interest. In this manuscript, we show an example of such a method, where we automatically detect seismic events of interest within two different DAS datasets, finding, respectively 99 % and 96 % of the local earthquakes previously identified within the data by manual analysis. The method is based on simple image processing and computer vision techniques, which clean the image, and, ideally, leave nothing but the signal of interest. These simple image processing steps yield promising results, indicating that computer vision and image processing might have an immediate impact in geophysical applications of fiber-optic sensing.
Venus today is inhospitable at the surface, its average temperature of 750 K being incompatible to the existence of life as we know it. However, the potential for past surface habitability and upper atmosphere (cloud) habitability at the present day is hotly debated, as the ongoing discussion regarding a possible phosphine signature coming from the clouds shows. We review current understanding about the evolution of Venus with special attention to scenarios where the planet may have been capable of hosting microbial life. We compare the possibility of past habitability on Venus to the case of Earth by reviewing the various hypotheses put forth concerning the origin of habitable conditions and the emergence and evolution of plate tectonics on both planets. Life emerged on Earth during the Hadean when the planet was dominated by higher mantle temperatures (by about 200$^\circ$C), an uncertain tectonic regime that likely included squishy lid/plume-lid and plate tectonics, and proto continents. Despite the lack of well-preserved crust dating from the Hadean-Paleoarchean eons, we attempt to resume current understanding of the environmental conditions during this critical period based on zircon crystals and geochemical signatures from this period, as well as studies of younger, relatively well-preserved rocks from the Paleoarchean. For these early, primitive life forms, the tectonic regime was not critical but it became an important means of nutrient recycling, with possible consequences to the global environment on the long-term, that was essential to the continuation of habitability and the evolution of life. For early Venus, the question of stable surface water is closely related to tectonics. We discuss potential transitions between stagnant lid and (episodic) tectonics with crustal recycling, as well as consequences for volatile cycling between Venus’ interior and atmosphere. In particular, we review insights into Venus’ early climate and examine critical questions about early rotation speed, reflective clouds, and silicate weathering, and summarize implications for Venus’ long-term habitability. Finally, the state of knowledge of the venusian clouds and the proposed detection of phosphine is covered.
Numerical wave models have been developed to reproduce the evolution of waves generated in all directions and over a wide range of wavelengths. The amount of wave energy in the different directions and wavelength is the result of a number of physical processes that are not well understood and that may not be represented in parameterizations. Models have generally been tuned to reproduce dominant wave properties: significant wave height, mean direction, dominant wavelengths. A recent update in wave dissipation parameterizations has shown that it can produce realistic energy levels and directional distribution for shorter waves too. Here we show that this new formulation of the wave energy sink can reproduce the variability of measured infrasound power below a frequency of 2 Hz, associated with a large energy level of waves propagating perpendicular to the wind, for waves with frequencies up to at least 1 Hz. The details are sensitive to the balance between the non-linear transfer of energy away from the wind direction, and the influence of dominant and relatively long waves on the dissipation of shorter waves in other directions.
Anthropogenic litter is omnipresent in terrestrial and freshwater systems, and can have major economic and ecological impacts. Monitoring and modelling of anthropogenic litter comes with large uncertainties due to the wide variety of litter characteristics, including size, mass, and item type. It is unclear as to what the effect of sample set size is on the reliability and representativeness of litter item statistics. Reliable item statistics are needed to (1) improve monitoring strategies, (2) parameterize litter in transport models, and (3) convert litter counts to mass for stock and flux calculations. In this paper we quantify sample set size requirement for riverbank litter characterization, using a database of more than 14,000 macrolitter items (>0.5 cm), sampled for one year at eight riverbank locations along the Dutch Rhine, IJssel and Meuse rivers. We use this database to perform a Monte Carlo based bootstrap analysis on the item statistics, to determine the relation between sample size and variability in the mean and median values. Based on this, we present sample set size requirements, corresponding to selected uncertainty and confidence levels. Optima between sampling effort and information gain is suggested (depending on the acceptable uncertainty level), which is a function of litter type heterogeneity. We found that the heterogeneity of the characteristics of litter items varies between different litter categories, and demonstrate that the minimum required sample set size depends on the heterogeneity of the litter category. More items of heterogeneous litter categories need to be sampled than of heterogeneous item categories to reach the same uncertainty level in item statistics. For example, to describe the mean mass the heterogeneous category soft fragments (>2.5cm) with 90% confidence, 990 items were needed, while only 39 items were needed for the uniform category metal bottle caps. Finally, we use the heterogeneity within litter categories to assess the sample size requirements for each river system. All data collected for this study are freely available, and may form the basis of an open access global database which can be used by scientists, practitioners, and policymakers to improve future monitoring strategies and modelling efforts.
The agricultural Sustainable Nitrogen Management Index (SNMI) is defined to provide a more comprehensive measurement of the environmental performance of the agricultural production. Here, the SNMI is defined based on two important efficiency terms in crop production, namely Nitrogen Use Efficiency and land use efficiency. As more data become available, the SNMI could be reviewed and improved by including more efficiency terms in crop production, such as water use efficiency.
This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, in 2022 23 million east Africans faced starvation, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamic models to predict droughts at eight-month lead-times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization (WMO) “Early Warning for All” plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance. The main messages of this commentary will be widely. Climate change is interacting with La Niña to produce extreme, but extremely predictable, Pacific sea surface temperature gradients. These gradients will affect the climate in many countries creating opportunities for prediction. Effective use of such predictions, however, will demand cross-silo collaboration.
Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, "littoral-cell"-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise, and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,350 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in-situ surveyed shorelines (e.g., Mean Sea Level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995 to 2015) provides a nearly equivalent model forecast accuracy during a validation period (2015 to 2020) compared to model calibration with monthly in-situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due projected wave and sea-level conditions while holding the model parameters fixed. By 2100, the model estimates that 25 to 70% of California's beaches may become completely eroded due to sea-level rise scenarios of 0.5 to 3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.
The non-linear reliance of channel steepness on erosion rates can be reconciled by the stochastic-threshold incision model that incorporates river incision threshold and discharge probability distribution into erosion efficiency. Here, we explored the usage of the model in river longitudinal profile inversion, by assuming time-dependent tectonic forcing and a linear exponent that relates channel incision to slope. We developed an analytical solution to the model equation and an inverse scheme to retrieve relative uplift rate history, whose validity was based on the theoretical demonstration on knickpoint preservation. Application of the inverse scheme to the main trunks of the Dadu River basin in the eastern Tibetan Plateau produced a history with two-phase increases in the uplift/incision rates, which is similar to the results from low-temperature thermochronology. Thus, our analytical procedures provide new insights into the link of tectonic uplift and river profile evolution, when channel steepness depends on erosion rates non-linearly.
Mesoscale eddies are abundant in the global oceans and known to affect marine biogeochemistry. Understanding their cumulative impact on the air-sea carbon dioxide (CO2) flux is likely important for quantifying the ocean carbon sink. Here, observations and Lagrangian tracking are used to estimate the air-sea CO2 flux of 67 long lived (i.e. > 1 year) mesoscale eddies in the South Atlantic Ocean over a 16 year period. We find that anticyclonic eddies originating from the Agulhas retroflection and cyclonic eddies originating from the Benguela upwelling act as net CO2 sinks over their lifetimes. In combination, the eddies significantly enhanced the CO2 sink into the South Atlantic Ocean by 0.08 ± 0.01%. Although this modification appears small, long lived eddies account for just ~0.4% of global ocean eddies and eddy activity is increasing; therefore, explicitly resolving eddy processes within all models used to assess the ocean carbon sink would appear critical.
Texas A&M University recently completed a set of Automated Precision Phenotyping (APP) Greenhouses that incorporate robotic systems for automated collection of advanced sensor-based plant phenotypes. Transiting the length of a greenhouse is a gantry beam, on which a rolling truck provides a second axis of motion along the gantry. Attached to the truck is a 3.0-m long robotic arm that is controlled to position a sensor head at virtually any position relative to any plant in a greenhouse. The robotic arm can be programmed to operate quickly and safely in complicated scanning patterns to enable data collection on all plants in the greenhouse within a time window of a few hours, ensuring consistent conditions during data collection. The sensor head includes a high-speed multispectral camera and eventually a Raman spectrometer. Relative to phenotyping greenhouses at other institutions, the APP Greenhouses have the advantage of maximum flexibility in configuration of plants in the greenhouses, in positioning of sensors relative to the plants, and in the types of sensors used, making research capabilities in the APP Greenhouses truly unique. Preliminary data have been collected on sorghum and maize plants. Four-band multispectral images have been collected daily, scanning the side of each plant from top to bottom. Preliminary software development is directed at automated image stitching to create a full side-view image of each plant, from which consistent metrics can be automatically calculated, such as plant height, stalk diameter, leaf angle, etc.
Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements is crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the VallÃ©e de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.
Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.
The 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near-term climate change. Emission inventories generated through surface activity measurements are reported annually or bi-annually and evaluated periodically through a “Global Stocktake”. Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (smoothing error), as-well-as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state-of-the-art inventory (EDGAR v6.0) using satellite-based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite-based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, -11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25 to 1.3 Tg CH4/ yr / yr), primarily in the Indo-Pakistan region, sub-tropical Africa, and the Brazilian arc of deforestation; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.
As tsunamis propagate across open oceans, they remain largely unseen due to the lack of adequate sensors, hence limiting the scope of existing tsunami warnings. A potential alternative method relies on the Global Navigation Satellites Systems to monitor the ionosphere for Traveling Ionospheric Disturbances created by tsunami-induced internal gravity waves (IGWs). The approach has been applied to tsunamis generated by earthquakes but rarely by undersea volcanic eruptions injecting energy into both the ocean and the atmosphere. The large 2022 Hunga Tonga-Hunga Ha’apai volcanic eruption tsunami is thus a challenge for tsunami ionospheric imprint detection. Here, we show that in near-field regions (<1500km), despite the complex wavefield, we can isolate the tsunami imprint. We also highlight that the eruption-generated Lamb wave’s ionospheric imprints show an arrival time and an amplitude spatial pattern consistent with internal gravity wave origin.