Marine sediments from the Western and Eastern South Atlantic continental margins are used to reconstruct mercury (Hg) accumulation over the last glacial/interglacial cycle. Sediment core GL-1248, collected from the continental slope off northeastern Brazil, and sediment core ODP1077, retrieved from the Congo deep-sea fan area, both dated to the last 128 ka and 130 ka respectively. Mercury concentrations in GL-1248 ranged between 14.95 and 69.43ng/g, and varied with periodicities of 56 ka and 900 yr suggesting the presence of glacial-interglacial changes and millennial-scale variability respectively. Parallel trends of Hg and XRF-Fe plots suggest that following atmospheric Hg deposition onto the continent, Hg is incorporated with iron (Fe) minerals before transportation and eventual immobilization at the NE Brazil continental slope. Mercury concentrations in ODP1077 varied significantly, having concentrations between 23.12 ng/g and 256 ng/g, and its plot exhibits an anti-phase pattern with the Fe/Ca ratio plot, that distinguishes between periods of increased and decreased terrigenous material delivery. This inverse trend in the plots of mercury concentration and Fe/Ca ratio shows that during periods of increased (decreased) terrigenous material delivery, less (more) mercury accumulates in the marine sediment. Although Hg concentration is poorly correlated with total organic carbon (TOC), it correlates positively with XRF-Ca implying that marine organic matter played a significant role in mercury distribution and accumulation in the ODP1077 marine sediment core. Despite the fact that both marine sediment cores were retrieved from the tropics and cover the same glacial/interglacial periods, their mercury variations and the main drivers of mercury accumulations are dissimilar. Accordingly, we identified two different pathways by which mercury is incorporated into marine sediments for prolonged storage and inclusion in the global mercury biogeochemical cycle. The outcome of this study suggests that regional climate processes and geochemical conditions are essential to Hg variations in environmental archives. Another obvious finding is that the source of sedimentary organic carbon is a key determinant of their affinity for mercury.

Climate change and disruptions to disturbance regimes contribute to uncertainty about the best ways to manage forests and promote individual tree health. Indicators of tree health include annual growth, formation of defensive structures (resin ducts), and ability to produce defensive compounds (resin). Higher growth and resin duct area are associated with a lower chance of mortality from disturbances (Ferrenberg et al. 2014). Past research suggests that prescribed burning and thinning treatments can have positive impacts on growth and resin duct metrics (Kolb et al. 1998; Hood et al. 2016), indicating that management activities have the potential to help increase and restore tree health. However, there is limited research comparing different disturbances and management activities in terms of impacts on tree growth, resin ducts, and resin flow. In this research we explore the questions: 1) What factors most influence resin flow in ponderosa pine (Pinus ponderosa)? 2) What is the impact of fire or timber harvest on tree growth, resin flow, and resin ducts? 3) Are there changes in the relationships between tree growth and resin duct metrics that occur after disturbance, which may indicate shifts in tree resource allocation to growth vs. defenses?

Paleoclimate studies on speleothems commonly use oxygen isotopes as a record of precipitation variability and carbon isotopes to document soil, vegetation, and atmospheric processes. Magnetic minerals in speleothems record complementary paleoclimate information but need to be interpreted within the context of the particular geographic and geologic setting in which a karst environment occurs. This study surveys 23 caves in South America (7°N to 25°S latitude). The present-day climate is dominated by a monsoon regime, with variable precipitation between 50 to 800 mm/month covering different biomes, therefore making South America a good candidate to explore the properties of magnetic minerals at the tropical/subtropical climate. We share a database of magnetic properties from 23 stalagmites samples (90 specimens), 4 soil samples (34 specimens) and 2 limestone samples (15 specimens). Measured rock magnetic parameters include magnetic susceptibility, natural, anhysteretic, and isothermal remanent magnetization (NRM, ARM, IRM), as well as low-temperature magnetometry and first-order reversal curves. These data help constrain the types and granulometry of the magnetic mineralogy that commonly occur in South American speleothems, their host carbonates, and their overlying soils. We show that concentration-dependent parameters in soils overlying the caves are two to three orders of magnitude higher than those in stalagmite and limestones. Despite these differences, unmixed coercivities between soil (median value of 19 mT) and stalagmites (median value 20 mT) and substantially different from those of host limestones (median 39 mT). Our results suggest that much of the magnetite in South American speleothems is pedogenic in origin, and may allow magnetic measurements to capture changing soil and vegetation dynamics in the epikarst through time.

River flow to the Arctic Ocean plays a significant role in the oceanic freshwater budget accounting for about 2/3 of the total freshwater flux to the Arctic Ocean. Ocean salinity and sea ice formation are critically affected by river input and changes in the freshwater and heat fluxes to the ocean can exert significant control over global ocean circulation via the North Atlantic deep water formation. There are mounting evidences that hydrological regime across the pan-Arctic is experiencing an unprecedented degree of change. However, the exact causes of such change are not immediately apparent, as they constitute a complex interplay between climate- and human-induced drivers. We used a new version of University of New Hampshire Water Balance Model (WBM) to quantify major sources of changes in river flux to the Arctic Ocean from Eurasian drainage basin. WBM is a grid based model which simulates the vertical water exchange between the land surface and the atmosphere and the horizontal water transport along a prescribed river networks for both natural and anthropogenic systems. The model accounts for sub-pixel land cover types, glacier and snow-pack accumulation/melt across sub-pixel elevation bands, permafrost dynamics, anthropogenic water use (e.g. domestic and industrial consumption, and irrigation for most of existing crop types), hydro-infrastructure for inter-basin water transfer and reservoir/dam regulations. To identify and quantify contributions of individual drivers/factors to river flow we used a new water source tracking capability recently developed in WBM. It allows “fingerprinting” of water at all steps in the water cycle such as in soils, groundwater pools, lakes, reservoirs, and fluxes such as originated from snowmelt, rain, glacier runoff, and baseflow. By comparing the difference in water component fractions in water storages and fluxes resulting from multiple historical runs we were able to quantify responses of each water source to the changes in climatic drivers and to characterize causes of observed changes in river flow to the Arctic Ocean. This work was mainly supported by Russian Foundation for Basic Research, grants: 18-05-60192 and 18-05-60240.

Importance sampling is modified via {\it homotopy continuation} to improve the efficiency and success of the sampler. The homotopy will use a known distribution as a starting empirical importance sampling distribution and generate a continuous schedule which culminates with the target distribution. The focus is the estimation of the normalization constant of the target distribution. The homotopy method is extended to a Bayesian setting, for stationary and time dependent posterior distributions. The numerical implementation uses a combination of sample averages, with sampling parameter N, and homotopy stages M, where M is typically a small number. The algorithm replaces homotopy stages for sampling steps, potentially resulting in a better or more efficient importance sampler. Numerical experiments suggest this is the case. The results also suggest that the method may improve the efficiency of the sampler by concentrating the samples in regions of greater impact.

The Great Lakes create complex meteorological conditions that influence air quality throughout the region. Lake-breeze circulation, lake-induced low level jets, shoreline boundary layer processes, and photochemistry at the land-water interface affect the magnitude and timing of regional ground-level ozone episodes during the summer months. We simulated nine different Weather Research Forecasting Model (WRF) sensitivity runs for a two-week period in June 2016 during which high surface ozone concentrations were observed in the Lake Michigan region. The WRF simulations tested various combinations of physical options, forcing data, sea surface temperature integration, and nudging options for three nested modeling domains. Given the multiple model simulations, we needed a way to select the best WRF configuration for simulating the key atmospheric physical processes that drive ground level ozone formation in the region. We developed a new diagnostic approach for identifying the best WRF model configuration for our application. The approach uses statistical significance testing for comparing multiple model simulations for different periods in the diurnal cycle. We will present how this diagnostic approach is used for understanding the differences between WRF simulations and for building our confidence in selecting a configuration to support regulatory air quality modeling applications in the Midwest.

We present latest results from the Conductance Model for Extreme Events (CMEE) and the Magnetosphere-Ionosphere-Thermosphere (MAGNIT) Conductance Model. Both models have been integrated into the Space Weather Modeling Framework (SWMF) to couple dynamically with the BATS-R-US MHD model, the Rice Convection Model (RCM) of the ring current & the Ridley Ionosphere Model (RIM) to simulate the April 2010 “Galaxy15” Event. The model is used with three grid configurations: the low-resolution configuration currently employed by NOAA’s Space Weather Prediction Center and two additional configurations that decrease the minimum grid resolution from ¼ RE to ⅛ and 1/16 RE. In addition, the simulation is driven with and without the dynamic coupling with RCM to study the impact of the ring current’s pressure correction in the inner magnetospheric domain. Using this model setup for a Maxwellian distribution, aforementioned precipitation sources are progressively applied and compared against the DMSP SSUSI observations. Finally, data-model comparisons against AMPERE Field-Aligned Currents, geomagnetic indices & magnetometer measurements are shown, with additional comparison against the existing conductance model in RIM. Results show remarkable progress in auroral precipitation modeling & MI coupling layouts in global models.

The ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) is a platform designed to meet the need of the international scientific community to collaborate on the generation and analysis of increasingly massive datasets from space-based, airborne, and field observations for aboveground terrestrial carbon dynamics. The MAAP is an open-source, cloud-based platform that distinguishes itself from other science platforms by being agnostic to any science disciplines and allows scientists to write, develop, and execute their own algorithms in shared workspaces that are tailored to their specific area of research. We present an overview of the capabilities of the Pilot implementation of MAAP. Users can explore and visualize ESA and NASA data that is ingested in the MAAP data catalog, develop and test algorithms to generate new datasets and act upon existing ones, launch matured algorithms as large-scale processing jobs, and analyze the results. This allows scientists to follow the entire scientific process within the platform, from the conception of a hypothesis to the creation of scientific results in a version-controlled and reproducible environment. Users can document their process, collaborate with other users, and share algorithms and datasets. We will conclude with an outlook on the features that the next phase of development will bring to the platform.

Developing and implementing a malaria early warning system that integrates public health surveillance with monitoring of related environmental factors is the goal of the Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) project. Collaborating with our Ethiopian partners on requirements, we developed the R package epidemiar to provide a generalized set of functions for disease forecasting, plus customized code including a Google Earth Engine script for environmental data and formatting scripts for distributable reports with maps and graphs. Since 2019, a local team at Bahir Dar University in Ethiopia has been using EPIDEMIA to produce weekly malaria forecasting reports. Intensive anti-malarial efforts in the Amhara region of Ethiopia have resulted in declining malaria incidence, with a 75% decrease in cases between 2013 and 2018 (561,101 to 137,445 cases). In this context of potentially changing malaria transmission patterns, continual model evaluation past the initial model development is warranted. We built model validation and assessment tools into the epidemiar R package for on-demand evaluation for any historical period. Validation statistics included Mean Error (ME), Mean Absolute Error (MAE), and proportion of observations that fell inside the prediction intervals. Evaluation can be made for one through n-week ahead predictions, and include comparisons with two naïve models: persistence of last known value, and average cases from that week of the year. Building validation into the early warning system provides more opportunities to learn about the model via the validation results. We can identify locations where the models perform best with district-level results. With on-demand implementation and time-range flexibility, we can also investigate how accuracy changes over time, which is of particular interest in places like Ethiopia with changing patterns and declining trends due to anti-malarial programs.

The impacts of climate change are being felt across the country, with wildfire seasons getting longer and more severe and flooding occurring more frequently. Colorado has experienced significant extreme weather events in the last ten years and, consequently, has begun a statewide effort to incorporate resilience into short- and long-term planning across state and local governments. As cities and counties undergo resilience planning processes, today’s students (tomorrow’s leaders) are often unaware of these efforts and are left out of the planning process. The HEART Force curriculum empowers students with the knowledge needed to participate (and lead) the resilience conversation in their own community, with place-based hazard education that includes a scenario-based role-play game and design thinking to create resilience strategies in their community. The curricular unit culminates with a resilience expo, where students engage with community members as resilience experts and share their ideas. HEART is a novel approach in that it uses several current instructional strategies (place-based learning, project-based learning, gamification, and design thinking) to empower students to engage with their community. If students want to implement their resilience projects that arise from the curriculum, mini-grants are available to fund projects. The HEART program is currently in its second year of piloting in rural and urban Colorado schools. We will present preliminary evaluation findings and share curriculum and program design strategies.

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).

In this study, we implement a mobile monitoring methodology in order to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) in Philadelphia, PA, USA. Over the course of 12 days between June 27, 2019 and July 29, 2019, we measured air pollution concentrations across two replicated 150-mile long routes. Mean concentrations for each pollutant were 11.25 ± 5.43 ug/m3 (PM1), 11.08 ± 6.25 ug/m3 (PM2.5), 15.57 ± 8.51 ug/m3 (PM10), and 1.27 ± 0.80 µg/m3 (BC). We find that finer PM size fractions (PM2.5 and smaller) constitute approximately 71% of PM10. Air pollution hotspots across three size fractions of PM (PM1, PM2.5, and PM10) and BC were present throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of air pollution hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM – 9:00 AM. Despite significant temporal variation, pollutant concentrations, except for PM10, clustered temporally with a separation before 12 PM. Our approach and findings identify times and places where pollutant concentrations are highest, which is integral to effective air pollution reduction in urban environments.

Thermal pressure is an inevitable thermodynamic consequence of heating a volumetrically constrained sample in the diamond anvil cell. Its possible influences on experimentally determined density-mineralogy correlations are widely appreciated, yet the effect itself has never been experimentally measured. We present here the first quantitative measurements of the spatial distribution of thermal pressure in a laser heated diamond anvil cell (LHDAC) in both olivine and AgI. The observed thermal pressure is strongly localized and closely follows the distribution of the laser hotspot. The magnitude of the thermal pressure is of the order of the thermodynamic thermal pressure (αKTT) with gradients between 0.5 – 1.0 GPa/10 μm. Remarkably, we measure a steep gradient in thermal pressure even in a sample that is heated close to its melting line. This generates consequences for pressure determinations in pressure-volume-temperature (PVT) equation of state measurements when using an LHDAC. We show that an incomplete account of thermal pressure in PVT experiments can lead to biases in the coveted depth versus mineralogy correlation. However, the ability to spatially resolve thermal pressure in an LHDAC opens avenues to measure difficult-to-constrain thermodynamic derivative properties, which are important for comprehensive thermodynamic descriptions of the interior of planets.

The properties of ice-giant normal mode oscillations, including their periods, spatial structure, stratospheric amplitudes, and relative influence on the external gravity field, are surveyed for the purpose of formulating the best strategy for their eventual detection. Measurement requirements for detecting a normal mode's periodic pressure and temperature variations, including a possible stratospheric signal, and its effect on the external gravity field, are discussed in terms of its radial velocity amplitude at the 1-bar pressure level. It is found that for reasonable amplitudes, detection of the pressure and temperature variations of ice-giant normal modes presents an extraordinary technical challenge. The prospects for detecting their gravitational influence on an orbiting spacecraft are more promising, with requirements that lie within the range of current technology.

Hurricane activity has been higher since 1995 than in the 1970s and 1980s. This rise in activity has been linked to a warming Atlantic. In this study, we consider variability of the volume of water warmer than 26.5 ºC, taken as the temperature threshold crucial to hurricane development, through the Water Mass Transformation framework. The volume of water transformed by surface heat fluxes to temperatures of 26.5 ºC is calculated, and compared with the year to year changes in the volume of water of this temperature. Variability of transformed volume is largely due to latent heat flux processes, associated in turn with anomalies in cloud fraction and surface winds. In some years, there is correspondence between transformed and observed volume anomalies, but in other years, alternative processes must drive observed volume anomalies. Coordinated physical mechanisms are thus responsible for anomalous ocean heat, providing fuel for larger numbers of intense hurricanes.

Since we discovered the newly morphological transpolar arc (TPA), whose nightside end got distorted toward pre- or post-midnight, identified as “nightside distorted TPAs”, their fundamental characteristics have been revealed based on investigations of the space-borne auroral imager data and corresponding solar wind conditions. Nightside distorted TPAs had two types; “J”- and “L”-shaped TPAs, and their locations of appearance (dawn or duskside of the polar cap) were governed by the polarity of the By component of the Interplanetary Magnetic Field (IMF). Furthermore, we found that the nightside distorted TPAs have antisymmetric morphologies in the Northern and Southern hemispheres, also depending on the IMF-By orientation. In this presentation, we show that that the electric currents flowing aligned to the magnetic field lines which connect between the magnetotail and the ionosphere, that is, Field-aligned currents (FACs) play an essential role in the formations of the “J”- and “L”-shaped TPAs. They are induced by significant plasma flow velocity difference (plasma flow shear) between the fast plasma flows associated with nightside magnetic reconnection and slower background plasma flows in the magnetotail. The current vortex structures with the counterclockwise rotation are also clearly seen in the ionospheric current vectors derived from fluctuations of the geomagnetic field measured at the ground observatories beneath and in close proximity of the growth regions of the nightside distorted TPA. This result suggests that the FACs were flowing out of the ionosphere toward the magnetotail (upward FACs) near the TPA. Furthermore, based on the geomagnetic field variations and the SuperDARN HF radar data, we obtained evidence in which the locations of magnetotail magnetic reconnection, which persisted even during northward IMF-Bz intervals, that is, the TPA durations, retreated further down tail as the TPA grew to the dayside. Taking into account these observational results, we finally show a model to illustrate the nightside distorted TPA (particularly, “L”-shaped TPA) formation.

Global food supply has substantial impacts on nature including environmental degradation from chemicals, carbon emissions and biodiversity loss through agricultural land conversion. Over the past decade, public demand for information on sustainable consumption choices has increased. Meanwhile, development and expansion of the life cycle assessment literature has improved scientific evidence on supply-chain impacts on the environment. However, data gaps and biases lead to uncertainty and undermine development of effective impact mitigation actions or behavior-change policies. This study evaluates whether scientific research into the nature-related impacts of agri-food systems aligns with the needs of the public, as indicated by patterns of information seeking. We compare the relative volume of public Google queries to scientific articles related to agri-food systems and three major impacts: chemical pollution, greenhouse gas emissions or biodiversity loss. We discover that biodiversity is systematically overlooked in scientific studies on agri-food system impacts in favor of research on emissions. In contrast, the relative volumes of public queries on agri-food systems and biodiversity equal those for emissions impacts at global and Australian scales. Public interest in biodiversity impacts of agri-food systems increased significantly between 2009 and 2020, despite no significant change in the relative volume of biodiversity-focused scientific articles. Both public and scientific attention on chemical impacts declined significantly over this time period. We recommend strategic investment into the biodiversity impacts of agri-food systems to build a knowledge base that allows the public to learn about the impacts of their choices and be inspired to change to more sustainable behaviors.

The Cape Verde archipelago is a group of Ocean Islands in the Central Atlantic that forms two chains of islands trending Northwest and Southwest. Several of the islands are considered to be volcanically active, with frequent eruptions on Fogo. We examine the mineral chemistry and thermobarometry of the southern islands; Santiago, Fogo and Brava together with the Cadamosto Seamount. Our objective is to explore the magmatic storage system and implications for volcanic eruptions and associated hazards at Cape Verde. The volcanic rocks at Cape Verde are alkaline and dominantly mafic, whereas the island of Brava and the Cadamosto Seamount are unusually felsic. Clinopyroxene compositions range from 60 to 90 Mg# at Santiago and Fogo. In contrast, at Brava and the Cadamosto Seamount the clinopyroxene compositions are 5 to 75 Mg#. Mineral chemistry and zonation records fractional crystallization, recharge, aggregation of crystals, magma mixing and variations in thermal conditions of the magma at temperatures from 925 to 1250C. Magma storage depths at Santiago, Fogo, Brava and the Cadamosto Seamount are between 12 and 40 km, forming deep sub-Moho magma storage zones. Transient magma storage in the crust is suggested by fluid inclusion re-equilibration and pre-eruption seismicity. A global compilation of magma storage at Ocean Islands suggests deep magma storage is a common feature and volcanic eruptions are often associated with rapid magma ascent through the crust. Shallow magma storage is more variable and likely reflects local variations in crustal structure, sediment supply and tectonics. Petrological constraints on the magma plumbing system at Cape Verde and elsewhere are vital to integrate with deformation models and seismicity in order to improve understanding and mitigation of the volcanic hazards.