The loss of biodiversity and the associated decline of ecosystem services vital for sustaining human life demand a comprehensive monitoring of plant biodiversity. Measuring biodiversity in the field on large areas generates issues like the need of a robust sampling design, the high demand on human and monetary resources and different biases introduced by humans and environmental conditions. These circumstances have recently triggered an extended use of remote sensing data to quantify biodiversity in a cost- and time-efficient way. Remotely sensed datasets represent the Earth surface at a certain point in time. Yet, it is not well studied what the use of a single dataset in time implies for biodiversity estimates. The functional dimension of biodiversity, expressed through functional traits within or between species, varies according to the phenological cycle. Further in grasslands, mowing and grazing events lead to temporal variations in the remotely sensed diversity. We provide an approach in which we integrate the temporal dimension in the quantification of biodiversity from space. Functional diversity is partitioned into a spatial and a temporal component. In particular, Sentinel-2 satellite datasets are well suited for this purpose, providing a complete landscape picture with high revisit time. In our study case, the incorporation of the temporal dimension and the interaction between spatial and temporal diversity by employing multiple datasets improves the retrieval of functional diversity in differently managed alpine grasslands. In comparison to the use of a single dataset, our approach provides more reliable recommendations for conservation and restoration decision-making on a regional scale.

The Hikurangi Margin east of New Zealand’s North Island hosts an extensive gas hydrate province with numerous gas hydrate accumulations related to the faulted structure of the accretionary wedge. One such hydrate feature occurs in a small perched upper-slope basin known as Urutī Basin. We investigate this hydrate accumulation by combining a long-offset seismic line (10-km-long receiver array) with a grid of high-resolution seismic lines acquired with a 600-m-long hydrophone streamer. The long-offset data enable quantitative velocity analysis while the high-resolution data constrain the three-dimensional geometry of the hydrate accumulation. The sediments in Urutī Basin dip landward due to ongoing deformation of the accretionary wedge. These strata are clearly imaged in seismic data where they cross a distinct bottom simulating reflection (BSR) that dips, counterintuitively, in the opposite direction to the regional dip of the seafloor. BSR-derived heat flow estimates reveal a distinct heat flow anomaly that coincides spatially with the upper extent of a landward-verging thrust fault. We present a conceptual model of this gas hydrate system that highlights the roles of fault-controlled fluid flow at depth merging into strata-controlled fluid flow into the hydrate stability zone. The result is a layer-constrained accumulation of concentrated gas hydrate in the dipping strata. Our study provides new insight into the interplay between deep faulting, fluid flow and the shallow processes involved in gas hydrate formation.

Export of sinking particles from the surface ocean is critical for carbon sequestration and for providing energy to the deep-ocean biosphere. The magnitude and spatial patterns of this flux have been estimated in the past by satellite-based algorithms and ocean biogeochemical models; however, these estimates remain uncertain. Here, we present a novel analysis of a global compilation of \textit{in situ} ocean particle size spectra from Underwater Vision Profiler 5 (UVP5) measurements, from which we determine particulate carbon fluxes. Using a machine learning algorithm, we extrapolate sparse observations of particle abundance by size to the global ocean from oceanographic variables that are more commonly observed. We reconstruct global maps of particle size distribution parameters for large sinking particles (80 \textmu{}m to 2.6 cm), and combine them with empirical relationships to calculate the sinking carbon flux from the euphotic zone and the wintertime mixed layer depth. Our flux reconstructions are comparable to other estimates, but suggest a less variable seasonal cycle in the tropical ocean, and a more continuous export in the Southern Ocean than previously thought. Because our estimates are not bounded by a specific depth horizon, we reconstruct export at multiple depths, and find that export from the wintertime mixed layer globally exceeds that from the euphotic zone. Our estimates provide a baseline for more accurate understanding of particle cycles in the ocean, and open the way to fully three-dimensional global reconstructions of particle size spectra and fluxes in the ocean, supported by the growing database of UVP5 observations.

Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku-Oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4\pm0.140 m/s and 4.11\pm0.152 m, respectively after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis.

Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is sea surface salinity (SSS), which is an important proxy for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the effects of SSS on TC RI, we use a previously built statistical model consisting of a variety of machine learning (ML) methods. A calibrator was trained on top of the ML models to correct probability forecasts. The ML model performance is improved with the addition of SSS in the Eastern North Pacific and the Caribbean subregion of the North Atlantic. Limited improvement is found in the Western North Pacific. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

The process of scientific visualization often involves making design choices- colors being one one them, to effectively communicate and highlight features in the data (e.g. high/low temperatures). Using the techniques of registration and image tracking, which are widely used in Augmented Reality (AR) applications to anchor digital content to the real world, an iPad/iPhone application has been developed that visualizes hand colored earth science datasets. The application would scan a student’s hand-colored page of a rectangular image of some global dataset, obtain the colors used, and convert that to an AR interactive, 3D globe with the dataset in study, animated with the students’ colors. This exercise could also be used to educate students about different map projections and is a flexible, customizable, inexpensive tool for teachers to teach a variety of geoscience topics. This engaging interactive environment could help instill a sense of ownership of the data and encourage the student to be more engaged with the science being presented.

In recent years, many researchers and advocates have noted the potential of religious groups and institutions to leverage their significant influence in favor of addressing environmental challenges. However, in the United States, many scientists struggle to communicate the implications of their work on climate change with faith communities who may be skeptical of both climate science and scientists. Recent polls from the Pew Research Center show that white evangelical Protestants are the least likely to believe climate change is caused by human activity and the most likely to assert that there is no solid scientific support for a changing climate. However, the full picture is more nuanced than can be captured in a news headline or polling survey, and evangelical Christiantiy is a diverse movement that is also found at the forefront of enviornmental and climate science and action. Drawing on more than six years of experience working on climate science communication and climate action solutions among fellow evangelicals in the United States, this presentation highlights best practices for communicating climate science to faith communities. Showcasing examples of work advanced through the Evangelical Environmental Network, Young Evangelicals for Climate Action, and PBS Global Weirding series with Dr. Katharine Hayhoe, I present a hopeful view of efforts to communicate climate change in a way that intentionally and genuinely connects with people's values, and ultimately motivates action. Additionally, this presentation discusses the challenges of and opportunities for engaging communities of faith as scientists with a different or no faith affiliation.

Low-gradient coastal watersheds are susceptible to flooding caused by various flows such as rainfall-runoff, astronomical tides, storm surges, and riverine flows. Compound flooding occurs when at least one coastal flood driver occurs simultaneously or in close succession with a pluvial and/or fluvial flood driver, such as during a tropical cyclone event. This study presents a one-dimensional (1-D), reduced-order physics compound inundation model tested over an idealized coastal watershed transect under various forcing conditions (e.g., storm surge, astronomical tides, and rainfall) that varied in magnitude, time, and space. This study aims to evaluate each flooding mechanism and the associated hydrodynamic responses to identify generalized coastal transition zones and enhance the production of flood maps for varying regions in a coastal watershed. Compound inundation levels are affected by the magnitude and timing of each flooding mechanism. The desire is a more holistic compound inundation model that can be a critical tool for decision-makers, stakeholders, and authorities who provide evacuation planning to save human lives and enhance resilience.

The Parker Solar Probe (PSP) spacecraft completed its second Venus gravity assist maneuver (VGA2) on 26 December 2019. For a 20 minute interval surrounding closest approach, the PSP/FIELDS Radio Frequency Spectrometer (RFS) was set to ‘burst mode’, recording radio spectra from 1.3–19.2 MHz at sub-second cadence. We analyze this burst mode data, searching for signatures of radio frequency ‘sferic’ emission from lightning discharges. During the burst mode interval, only 4 spectra were observed with strong impulsive signals, and all 4 could be attributed to saturation of the RFS high gain stage by \emph{in situ} electrostatic plasma waves. These RFS measurements during VGA2 are consistent with previous non-detection of radio frequency lightning signals from Venus reported by Gurnett et al. (2001).

Earth System Models (ESMs) project that the terrestrial carbon sink will continue to grow as atmospheric CO$_2$ increases, but this projection is uncertain due to biases in the simulated climate and how ESMs represent ecosystem processes. In particular, the strength of the CO$_2$ fertilization effect, which is modulated by nutrient cycles, varies substantially across models. This study evaluates land carbon balance uncertainties for the Canadian Earth System Model (CanESM) by conducting simulations where the latest version of CanESM’s land surface component is driven offline with raw and bias-adjusted CanESM5 climate forcing data. To quantify the impact of nutrient limitation, we complete simulations where the nitrogen cycle is enabled or disabled. Results show that bias adjustment improves model performance across most ecosystem variables, primarily due to reduced biases in precipitation. Turning the nitrogen cycle on increases the global land carbon sink during the historical period (1995-2014) due to enhanced nitrogen deposition, placing it within the Global Carbon Budget uncertainty range. During the future period (2080-2099), the simulated land carbon sink increases in response to bias adjustment and decreases in response to the dynamic carbon-nitrogen interaction, leading to a net decrease when both factors are acting together. The dominating impact of the nitrogen cycle demonstrates the importance of representing nutrient limitation in ESMs. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement.

The Array of Things (AoT) is a collaborative effort among leading scientists, universities, local government and communities in Chicago to collect real-time data on the city’s environment, infrastructure, and activity for research and public use. The AoT is composed of nodes that will measure and sense the urban environment of Chicago and provide openly accessible data in near real time. One component of each node is the ChemSense board, which uses chemical sensors to measure five gas-phase species: ozone, nitrogen dioxide, carbon monoxide, sulfur dioxide and hydrogen sulfide. In addition, the ChemSense board provides information on total reducing gases and total oxidizing gases. The nodes also include meteorological information and cameras that will provide pedestrian and traffic counts using computer vision algorithms. Because the ChemSense boards rely on low-cost sensors, characterizing the sensor responses is critical to understanding the applicability of the AoT for urban air quality issues. As a first step, a node with the ChemSense board was installed at an EPA air quality monitoring site within the City of Chicago, which is run by the Cook County Department of Environment and Sustainability. The EPA site has Federal Reference Method monitors for ozone, nitrogen dioxide and sulfur dioxide. After collecting collocation data for seven months, the results are promising for ozone, but much less so for sulfur dioxide. For nitrogen dioxide, unexplained spikes not observed in the EPA data drive a poor fit. Results from the collocation project will be used to consider larger issues for characterizing the air quality component of the AoT.

Terrestrial hot springs have existed throughout Earth’s history and house some of the most ancient evidence of life on our planet. These settings are known for their high habitability and preservation potential, and are extensively studied as analog environments since hot spring deposits are thought to exist on the surface of Mars. Hot spring water commonly precipitates silica that coats microbial life dwelling in the hot spring outflow streams. This process can entomb microorganisms and preserve microbial remains over long timescales and with high morphological fidelity. Here we present research carried out on modern and sub-recent remains of microbial filaments from amorphous (unaltered) silica deposits in Yellowstone National Park. This work suggests that various elements sequestered by hot spring-dwelling organisms during life are preserved in microbial remains and persist over > 10,000 years. We also present findings from microfossils preserved in mid-Paleozoic terrestrial hot spring deposits which also show sequestrations of select elements in microfossil remains, suggesting that certain elements may persist even after several hundred million years and substantial host rock alteration. These elemental concentrations may be indicative of metabolic functioning during life and have application as biosignatures. Recent developments in analytical instrumentation now allow for even extremely low trace elemental abundances to be detected and mapped, regardless of sample complexity. This work is especially relevant to the search for life on Mars, as evidence of impact-induced hydrothermal activity may exist near the rim of Jezero Crater and may be sampled by the Perseverance rover. As a primary objective of the Mars 2020 mission is to search for evidence of past life on Mars, we suggest the application of this analytical technique to be valuable for potential samples returned to Earth by future Mars Sample Return missions. Distribution Statement A. Approved for public release: distribution unlimited.

The 13 km SHiELD (System for High-resolution prediction on Earth-to-Local Domains) global model that is under development at the Geophysical Fluid Dynamics Laboratory (GFDL) and runs in near real time, produced outstanding tropical cyclone track forecasts during the 2021 Atlantic hurricane season, compared to both the upgraded National Weather Service Global Forecast System (GFSv16), the Hurricane Weather Research and Forecasting (HWRF) model and the European Centre for Medium-Range Weather Forecast Integrated Forecasting System (IFS). SHiELD’s average track forecast errors were 10% and 15% less than the GFSv16 and HWRF, respectively, for the 3-5 day forecast lead times. SHiELD’s track forecast skill was comparable to the National Hurricane Center’s official forecast at several forecast lead times, and approached 70% skill relative to the Climatology and Persistence Model (CLIPER) at 3 and 4 days. Similar improvements were found in the western Pacific basin in 2021, with improvements also seen in the eastern Pacific at days 4 and 5. Improved performance was also found in the 2019 Atlantic hurricane season, with neutral performance in 2020, when SHiELD was run retrospectively from the GFSv16 initial conditions. Distribution of the spatial errors and biases showed that in both the 2021 Atlantic hurricane season and the previous two years, the largest track forecast errors from both SHiELD and GFSv16 occurred in the subtropical eastern Atlantic, associated with a distinct northeast bias. Analysis indicated that some of the excessive north bias in the GFSv16 is associated with lower geopotential height fields compared to those in SHiELD.

In atmospheric models with kilometer-scale grids the resolution approaches the scale of convection. As a consequence the most energetic eddies in the atmosphere are partially resolved and partially unresolved. The modeling challenge to represent convection partially explicitly and partially as a subgrid process is called the convective gray zone problem. The gray zone issue has previously been discussed in the context of regional models, but the evolution in regional models is constrained by the lateral boundary conditions. Here we explore the convective gray zone starting from a defined global configuration of the Met Office Unified Model using initialized forecasts and comparing different model formulations to observations. The focus is on convection and turbulence, but some aspects of the model dynamics are also considered. The global model is run at nominal 5km resolution and thus contributions from both resolved and subgrid turbulent and convective fluxes are non-negligible. The main conclusion is that in the present assessment, the configurations which include scale-aware turbulence and a carefully reduced and simplified mass-flux convection scheme outperform both the configuration with fully parameterized convection as well as a configuration in which the subgrid convection parameterization is switched off completely. The results are more conclusive with regard to convective organization and tropical variability than extratropical predictability. The present study thus endorses the strategy to further develop scale-aware physics schemes and to pursue an operational implementation of the global 5km-resolution model to be used alongside other ensemble forecasts to allow researchers and forecasters to further assess these simulations.

Evaluating the subsurface characteristics of reservoirs is an important part of hydrocarbon exploration and production in sedimentary basins. This process combines geological, geophysical, and engineering data to comprehend the subsurface geology and fluid distribution, determine reserves, and predict the fluid movement in the reservoir. The primary goal of reservoir characterization is to create a precise and dependable model of the reservoir to maximize the production procedure and reduce the associated risks of hydrocarbon exploration and production. This review examines different approach utilized for reservoir characterization in sedimentary basins, including geological, geophysical, and engineering methods. Each method's advantages and disadvantages are discussed, alongside their uses in different reservoir contexts. The importance of combining multiple lines of evidence to enhance the accuracy of the reservoir models is also examined.

Carbon export from the ocean surface to depth is an important component of the biological carbon pump, a key regulator of the world’s climate. The Antarctic Marginal Ice Zone accounts for 15% of Southern Ocean primary production, however, limited observations mean that the variability and drivers of primary production and its link to export are poorly constrained. Using a combination of gliders, biogeochemical argo floats and satellite observations, we show that sea-ice impacts both primary production and export through its influence on the upper ocean vertical density structure, light availability, and nutrient supply. Resultant changes in community composition, coupled with variations in vertical stratification, appear to be important determinants of carbon transfer to depth. The response of primary production and carbon export to sea-ice indicates that the biological carbon pump in this region is sensitive to ongoing climate change and predictions of reduced sea-ice cover in the future.

The diurnal cycle is often poorly reproduced in global climate model (GCM) simulations, particularly in terms of rainfall frequency and amplitude. While improvements in the regional climate model (RCM) with bias-corrected boundaries have been reported in previous studies, they assumed that diurnal patterns are simulated correctly by the GCM, potentially leading to inaccuracies in the maximum rainfall timing and magnitude within the RCM domain. Here we provide the first examination of improvements to the diurnal cycle, within a RCM domain, achieved through the use of sophisticated bias-corrected lateral and lower boundary conditions. Results show that the RCMs with bias-corrected boundaries generally present improvement in capturing both rainfall timing and magnitude, particularly in northern Australia, where a strong diurnal pattern in rainfall is prevalent. We show that correcting systematic sub-daily multivariate bias in RCM boundaries improves the diurnal rainfall cycle, which is particularly important in regions where short-term intense precipitation occurs.

The environmental justice literature demonstrates consistently that low-income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin. We identified spatial hot spots in Milwaukee County both individually and through clusters across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The most sensitive cluster within the urban area was largely characterized by low socioeconomic status (SES) and an overrepresentation of the Non-Hispanic Black (NHB) population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county-level blood lead levels, 67th percentile in county-level NO2, 79th percentile in county-level CO, and 78th percentile in county-level air toxics while simultaneously having an average equivalent to the 62nd percentile in county-level unemployment, 70th percentile in county-level population rate lacking a high school diploma, 73rd percentile in county-level poverty rate, and 28th percentile in county-level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.