Soils of mountain regions are estimated to contain large amounts of organic matter (OM), equivalent to stocks found in high-latitude boreal and tundra soils. Mountain environments are also experiencing profound changes in land management under the influence of socio-economic pressures as well as the need to adapt to climate change, which is occurring at a faster rate than in lowland areas. These anthropogenic impacts are expected to strongly affect soil OM storage. Most studies of land-use change have however focused on topsoil OM; whether similar trends will hold true for subsoil OM remains unknown. Using Rock-Eval pyrolysis as a proxy for soil OM dynamics, we showed that the hierarchy of controls on OM properties and transformations varied greatly with increasing soil depth. In the topsoil, OM properties were related to the nature of plant inputs, their degree of in-mixing with the mineral matrix and the occurrence of seasonal water saturation. In the subsoil however, the foremost predictors of OM properties were geochemical parameters. This shift in the nature of determinants of OM dynamics indicates that shallow and deep soil OM pools should respond differently to external forcings. Podzolic profiles showed the strongest decoupling of topsoil and subsoil OM properties. We focused on this soil type to specifically investigate the effects of land use on subsoil OM. We selected field sites from the Coastal Range of British Columbia, Canada and the Pennine Alps, Switzerland representing undisturbed and managed forest, shrubland and pasture. Samples were analyzed for organic C content, OM quality and reactive mineralogy. Results showed that herbaceous cover was associated with an increase in topsoil but not subsoil OM. In the subsoil, variations in OM content and properties were associated with changes in reactive Al and Fe mineral phases. Overall, our data indicate that organo-mineral and organo-metal interactions are of prime importance to OM accumulation in the subsoil, and that understanding the response of deep soil C stocks to land use change will require consideration of the geochemical and mineralogical environment. Our results further suggest that so-called reactive mineral phases may themselves be impacted by land use, in turn affecting deep soil C stabilization and destabilization processes.

Seismological models of the mantle are routinely developed using a range of techniques applied to different data types. For quite some time, it has been recognised that on long wavelengths models of shear-wave velocity variations show a large degree of consistency. More recently, the same has been suggested for models that describe compressional-wave velocity variations. However, controversy remains regarding models of lower mantle density variations, which provide important constraints on the nature of mantle structures, e.g. whether they are caused by thermal variations or whether additional chemical heterogeneity is required. The imaging of density structure is difficult due to a small effect on seismic observables and a strong trade-off with core-mantle boundary (CMB) topography. In addition, no consistent model of CMB topography variations exists with current models differing both in amplitude and pattern. Here, I review models of lower mantle density structure and core-mantle boundary topography from the literature, with the aim to identify which structures are consistent and what we can already learn from these models. In addition, I discuss ways in which differences between existing models may be resolved in future.

Useful to Usable (U2U) was a 6-year USDA-funded research and extension project focused on improving the uptake of climate information by Midwestern U.S. farmers and agricultural advisors. Led by Purdue University, this interdisciplinary team from nine Midwestern universities developed five web-based decision support tools to examine production, financial, and environmental outcomes of different climate scenarios and management options. A dedicated team of Extension educators, marketing specialists, and program evaluators worked together to promote U2U products and collect, analyze, and communicate data to 1) help increase project impact and 2) to measure outcomes for accountability purposes. Since outreach began in July 2013, farmers and advisors have been reached at 165 outreach events and training sessions in ten Midwestern states and at least 6 regional webinars have been conducted. A four-round mailed and electronic marketing campaign reached an estimated 35,600 people from March 2015 – November 2016. Additionally, the team developed a variety of educational materials (user guides, fact sheets, presentations, etc.) to support U2U outreach and dissemination efforts. Throughout 2016-2017, the team evaluated the outcomes, impacts, and overall reach of the U2U project over the 6-year project duration. They conducted two large-scale surveys with farmers and agricultural advisors, conducted personal interviews with advisors in Iowa and Nebraska, gathered informal success stories from U2U team members and collaborators, and tracked website traffic using Google Analytics. This presentation will highlight the project’s evaluation design, results, and lessons learned, including evaluation of outreach and dissemination approaches and longer-term outcome/impact evaluation. Some key metrics include the likelihood of using U2U online tools, actual use of tools, use of tools to aid financial and environmental decisions, and willingness to consider climate information in the future (not limited to U2U tools).

Terrestrial-aquatic interfaces such as salt marshes, mangroves, and similar wetlands provide an optimum natural environment for the sequestration and long-term storage of carbon (C) from the atmosphere, commonly known as coastal blue carbon. There are over 4 million acres of salt marsh in the US and over half of these are along the east coast of the US. Due to anthropogenic activities, this area presents the greatest nitrogen (N) pollution problem in coastal ecosystems in the U.S. as part of atmospheric N deposition, runoff, and riverine export. Ammonia (NH3) is the most abundant alkaline gas in the atmosphere. Agricultural intensification is the primary anthropogenic source of NH3 leading to a doubling of reactive nitrogen (Nr) entering the biosphere. Despite this, there are limited atmospheric measurements of NH3 concentrations in coastal areas along the east coast. The objective of this study is to advance our process-level understanding of NH3 air-surface exchange over a tidal salt marsh at the Saint Jones Reserve (DE), which is part of the National Estuarine Research Reserve System (NERRs). Continuous and high temporal resolution measurements of atmospheric NH3 concentrations were measured using a cavity ring-down spectrometer, reporting 30 min concentration averages. These high temporal resolution measurements allowed the estimation of the average diurnal cycle of NH3 fluxes using a new analytical methodology. Micrometeorological measurements were also measured using the eddy covariance system operated concurrently above the tidal marsh at the research site, which is part of the AmeriFlux network (US-StJ). This pilot study represents one of the few atmospheric measurements of NH3 over a tidal salt marsh in the eastern U.S. Such measurements are important to characterize the processes that influence the exchanges of NH3 between the atmosphere and the aquatic surface and provide baseline data to form more reliable parameterizations to simulate NH3 deposition and emissions in tidal salt marshes using surface-atmosphere transfer models.

While flagship missions such as CHAMP and GOCE have shown us with accelerometer measurements that the thermospheric density in Low Earth Orbit (LEO) can increase by more than 200% during enhanced geomagnetic activity, current empirical models, such as those of the MSISE and Jacchia families, as well as the Drag Temperature Model, fail to reproduce this behavior, limiting the ability to perform orbit prediction and space situational awareness. Several methods have been employed to address this dilemma. One is the High-Accuracy Satellite Drag Model (HASDM), which uses its Dynamic Calibration Atmosphere to employ differential correction across 75 spherical calibration satellites to generate correction parameters to the density that are related to 10.7 cm solar radio flux and ap (Storz et al. 2005). Doornbos et al. 2008 has implemented a method that estimates height-dependent scale factors to the densities from empirical models with respect to densities directly derived from two- line element sets (TLEs). HASDM’s reliance on Space Surveillance Network observations limit its accessibility and detail, and Doornbos’ methods are limited by the fact that TLEs are mean elements; densities derived from them are subject to errors due to smoothing over an entire orbit. In addition, the method of deriving densities from TLEs was initially done only to provide inputs to the SGP4 orbital propagator, which was initially developed without consideration of solar radiation pressure on the trajectory of modeled spacecraft. We present a method to generate new model densities during geomagnetic storms by using an in-house orbital propagator, the Spacecraft Orbital Characterization Kit (SpOCK). This method estimates and applies scale factors to F10.7 and a p to minimize orbit propagation errors with TLEs. The method is tested on a variety of satellites, including CHAMP, GOCE, and the CubeSats of the QB50 and FLOCK constellations. This method proposes to grant insight into storm-time thermospheric density enhancement by modeling the effects of storms on the drag of numerous LEO spacecraft, increasing our understanding of thermospheric dynamics and granting us improved tools for space traffic management and thermospheric research.

This study discusses the role of the aridity index in controlling the long-term fluxes of direct (Qd) and baseflow (Qd). Here we present an analytical derivation starting from similar assumptions as the one presented in the original work of Budyko that leads to two simple expressions for the control of the aridity index on Qb and Qd. These two analytical equations can be combined into a single expression with the same properties as the original Budyko equation. Our study provides a method for predicting long term baseflow and direct runoff for poorly gaged basins as well as permit the derivation of sensitivity equations for the assessment of the impact of these fluxes with respect to changes in aridity index.

Forest biodiversity has been declining across the globe due to anthropogenic activities. Losses of biodiversity have led to reduced forest health and ecosystem services. Therefore, it has become necessary to monitor changes in biodiversity over wide geographic areas. Remote sensing has the potential to monitor biodiversity changes, but the accuracy in which it can be estimated is under debate. In this study, we tested 1) the relationship among distinct metrics of biodiversity, 2) the role topographic measures have on determining biodiversity, and 3) the ability of hyperspectral remote sensing to estimate biodiversity in temperate forests of the Northeastern United States. We characterized biodiversity according to four different metrics: species, functional, structural, and phylogenetic diversity. All four metrics were quantified using species inventory data as well as Light Detecting and Ranging (LiDAR) to calculate additional indices of structural diversity. A digital elevation model was used to obtain measures of slope, aspect, and other topographic indices such as topographic wetness. Hyperspectral imagery was used to obtain reflectance, entropy, and several vegetation indices. In our analyses, species, functional, and phylogenetic diversity were shown to be moderately correlated suggesting similarities between the metrics while correlations between structural diversity and the other metrics were weak. The calculated topographic indices also showed weak correlations with the biodiversity metrics suggesting that topography does not influence measures of biodiversity at the plot level. Depending on the biodiversity metric, relationships between the hyperspectral analyses and biodiversity were weak to moderately strong. These findings suggest that hyperspectral imagery holds some potential for estimating multiple metrics of biodiversity.

Stochastic physics (SP) schemes provide a more realistic representation of the unresolved scales in global circulation models by improving both mean climate and climate variability. We study the impact of including an SP scheme in the atmospheric component of EC-Earth on the simulated climate. In particular, we analyze the evolution of the sea-ice extent in the Arctic during long-term simulations covering the historical and future periods. The experiments consist of coupled climate simulations in which three ensemble members constitute the control runs (base) and three ensemble members include stochastic physics (stoc). For the latter, the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme is incorporated in the atmospheric component of EC-Earth. The original experiments, that are part of the SPHINX project, span from 1850 to 2100. We have additionally extended each simulation for 60 years; the future scenario corresponds to the CMIP5 RCP8.5 set up. We compare both sets of experiments to investigate the climate response to a perturbed atmosphere. The simulated Arctic sea-ice extent in September and March display an overall decrease. The sea-ice loss results faster in the base experiments than in the stoc ones. The model simulates an abrupt sea-ice loss in March that takes place about 10 years earlier in the base experiments than in the stoc ones. The evolution of the global annual mean surface air temperature differs if the SP is on or off. Curves start separating by the second half of the 20th century; reach the maximum difference in 2100 and become almost indistinguishable around 2110. Our results suggest that the transient climate sensitivity is lower when the SP is on than when it is off during the 21st century. However, the opposite occurs when the Arctic is free of sea ice along the whole year. This behavior might be the consequence of the asymmetric effect of stochastic perturbations on the process of condensation. We are now investigating the differences in the albedo and cloud feedbacks between both sets of experiments and the possible influence of the mean state on the model climate sensitivity.

Microbial processing of fresh carbon inputs is recognized as a key step in the formation of mineral-associated organic matter. Low molecular weight (LMW) compounds comprise a notable fraction of these inputs and are rapidly assimilated and metabolized by the microbial community. In this work, we employ ecophysiological studies of microbial isolates to better understand the role of substrate identity as a control on preferences, uptake kinetics, and carbon use efficiencies (CUE) across a gradient of phylogenetic differences (gram negative, gram positive, and fungal). Soil-extracted, solubilized organic matter (SESOM) derived from the Oa horizon of a hemlock-hardwood forest stand and synthetic media based off of this extract were chosen as liquid media for batch growth studies. A combination of exometabolomic techniques (1H NMR, UHPLC-MS) were used to quantify 35 LMW substrates in the original extract (0.4 – 195 μM), comprising 19.5% of total C and 39.9% of total N. Consumption of these substrates by microbial isolates accounted for a substantial amount of total C and N assimilated during growth, representing 43-75% and 58-74%, respectively. Time resolved sampling allowed modeling of sigmoidal uptake curves and the comparison of the midpoint of consumption (Th, hr) and 90% usage windows (ranging from 0.18 – 2.29 hr). Complementary experiments were conducted using synthetic media with all substrates at equimolar concentrations (25 μM) to better constrain the impact of initial concentration. We use stable isotope probing to determine CUE for five different LMW substrates of interest (glucose, acetate, formate, glycine, and valine). Ultimately, we are interested in whether unifying trends can be observed across the physiological gradient and how the metabolic transformations of these inputs may impact the organo-mineral formation process.

Agriculture sector is a major consumer of available fresh water. Increasing demand for fresh water in the various sectors has necessitated the use of treated waste water for irrigation. This water has marginal amount of salts which are known to affect the soil properties and crop growth. Hence a scientific approach is necessary to model the moisture uptake by plant roots under varying saline conditions. A numerical model is developed for solving the one dimensional Richards equation for moisture movement in variably saturated vadose zone. The root water uptake is incorporated in the model as the sink term as per the Ojha and Rai (1996) model, which is a non-linear root water uptake model. Combined water and salinity stress in the root water uptake is incorporated as per Feddes et al. (1978) and Mass and Hoffman (1977). Effect of varying salinity concentrations on the water uptake by plant roots and soil moisture dynamics in the root zone is studied on a 40 day period of crop growth. This is attained by providing fresh water and saline water irrigation with 15, 30 and 50 dS/m electrical conductivity. Irrigation field experiments of winter wheat crop were carried out to measure the crop parameters used in the analysis like leaf area index and root depth. The analysis to study the effect of salinity on soil water retention (SWR), hydraulic parameters and plant root water uptake was performed for silt loam (fine textured) and sandy loam (coarse textured) soils. The results show that the roots extract water at potential rate with fresh water, with no water uptake in the case of higher salinity as osmotic stress reaches permanent wilting phase. Overall, the root water uptake reduces as the concentration of salinity increases, even if there is no water stress. However, water stress was encountered at an earlier time in the case of sandy loam soil than in the case of silt loam. As the water content falls below the available moisture content, the water uptake is affected by water stress, which affects the growth and yield of crops. The results obtained from the study are useful in the better management of available water resources for irrigation practices in crop production.

Growth and yield models are an essential tool for predicting the long-term response of forests to management and disturbance. Model evaluation and calibration are challenging, however, given data limitations for observing stand structural changes with age across heterogeneous forest landscapes. Here we present an approach for calibrating the lodgepole pine (LP) forest model in the Central Rockies variant of the Forest Vegetation Simulator (FVS-CR) using Forest Inventory and Analysis (FIA) plot data from the US Forest Service. Previous evaluation showed the FVS-CR LP model is generally successful in reproducing known patterns of stand dynamics. However, the default model settings tend to result in unrealistic stand successional behavior and over-estimate the density, basal area, and especially carbon density of mature lodgepole pine forest stands as compared to expectations. Here we develop a generalized model calibration procedure based on simulating bare-ground re-growth of a single mixed lodgepole stand and comparing to a forest growth chronosequence constructed from FIA plot measurements in Colorado and Wyoming. We set parameters such as basal area increment and maximum tree size to match corresponding characteristics observed directly in the FIA plot data. The remaining ‘free’ parameters (notably small tree height increment and tree mortality parameters) were then tuned for best fit against the FIA chronosequence in terms of five different stand characteristics: live and dead basal area, trees per hectare, quadratic mean diameter, and average height. Improving model fit against all five stand characteristics simultaneously required substantial increases to mortality-related parameters, particularly for the shade-tolerant species present in mixed stands. These parameters had to be adjusted empirically rather than based on literature values, suggesting some underlying model structural challenges. However, the resulting recalibrated FVS-CR LP model achieves much better representation of expected lodgepole stand structure and successional behavior, and can more credibly be used to evaluate carbon storage outcomes for different forest management choices in the region.

Uturuncu volcano in southern Bolivia is something of a “zombie” volcano – presumed dead, but showing signs of life. The volcano has not erupted in 250 kyr, but is exhibiting unrest in the form of ground deformation, seismicity, and active fumaroles. Elucidating the subsurface structure of the volcano is key for interpreting this recent unrest. Magnetotelluric measurements revealed alternating high and low resistivity anomalies at depths <10 km beneath the volcano, with a low-resistivity anomaly directly beneath Uturuncu. A key question is, what is the nature of this anomaly? To what extent is it partial melt, a hydrothermal brine reservoir, or a mature ore body? Knowing the density of this anomaly could distinguish between these scenarios, but existing density models of the area lack sufficient resolution. To address this issue, we collected additional gravity measurements on the Uturuncu edifice with 1.5 km spacing in November 2018. Gradient analysis and geophysical inversion of these data revealed several features: a 5 km diameter, high density anomaly beneath the summit of Uturuncu (1 – 3 km elev.), a 20 km diameter ring-shaped negative density anomaly around the volcano (-3 – 4 km elev.), a NNE trending, positive density anomaly northwest of the volcano (0 – 4 km elev.), and a NW trending, negative density anomaly to the southeast. These structures often (but not always) align with resistivity anomalies, features in new seismic tomography models, and relocated earthquake hypocenters. Based on a joint analysis of these data, we interpret the positive density anomaly as a crystallizing dacite pluton, and the negative density ring anomaly as a zone of hydrothermal alteration. Earthquakes around the edges of the crystallizing pluton may represent escaping fluids as the magma cools. The high density anomaly to the northwest likely represents a solidified pluton, and the low density anomaly to the southeast may represent a fractured fault zone. We posit that the alternating zones of high and low resistivity anomalies represent zones of low and high fluid/brine content, respectively. Based on this analysis we suggest that the unrest at Uturuncu is unlikely to be pre-eruptive. This study shows the value of joint analysis of multiple types of geophysical data in evaluating volcanic subsurface structure.

Snow is among the most reflective of natural surfaces on Earth and in its reflectance, also known as snow surface albedo (SSA), a small SSA decrease can increase its absorption of solar energy. Also, climate models indicate that the reduction of SSA contributes to global warming and near-worldwide melting of cryosphere. The snowpack through South America (SA) is mainly associated to the Andes mountain range, which geologically extends 9000 km approx. from the tropical region (11°N) to the Antarctandes (76°S) in the Antarctic. There, snow is an important source of fresh water for more than 80 million people who live in the 9 countries where this mountain passes. In this study we have analyzed the SSA trend. For that, in accordance with the division proposed by Dussaillant et al. (2019), we have divided the study area into 8 zones: Inner Tropics (11°N-5°S), Outer Tropics (5°S-18°S), Desert Andes (18° S-31°S), Central Andes (31°S-37°S), North Patagonia (37°S-46°S), South Patagonia (46°S-54°S), Fuegian Andes (54°S- 56°S), and Antarctandes (63°S- 76°S). We used daily data of SSA available from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Terra and Aqua satellites for the last 20 years (Mar 2000 to Feb 2020). The results show that the greatest negative inter and intra-annual trends (i.e. is a decrease in SSA) during the last 20 years were observed in the Central Andes (April, -5.76%; p <0.001), North Patagonia (Nov, -2.33%; p <0.05) and Fuegian Andes (Oct, -1.73%; p <0.05). While the greatest positive variations were detected in Antarctandes (May, 4.76%; p <0.001), Fuegian Andes (Dec, 4%; p <0.05), and Outer Tropics (Dec, 1.67%; p <0.01). The highest SSA decrease observed in the Central Andes is consistent with previous studies carried out by the authors that have shown an association between light-absorbing particles with SSA decrease in various basins there. Our results could serve to better understand the radiative forcing changes generated in the SA cryosphere and its effects on climate change.

Sustainable aviation fuels (SAF) produced from lipid feedstocks are an increasingly mature and low-cost option for aviation sector decarbonization. Ethiopian mustard (Brassica carinata) is a non-food oilseed crop that can be grown on winter fallow land in the southeastern US and used as a feedstock for SAF, with a high-protein livestock feed co-product. Integrating carinata into existing annual crop rotations produces an additional revenue stream for landowners, with potential co-benefits for soil carbon and other ecosystem services. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a USDA-funded research consortium to advance carinata production and associated SAF and bioproduct supply chains in the region. A SPARC research team used the DayCent ecosystem model to estimate the potential production of carinata across the tri-state region of Alabama, Florida, and Georgia, and assess associated changes in soil carbon storage and emissions of nitrous oxide (N2O), the main biogenic greenhouse gas (GHG) emissions from agriculture. First, we calibrated DayCent to reproduce the phenology, harvest index, productivity response to nitrogen application, root-to-shoot biomass ratio, and tissue nitrogen content data observed for a set of carinata field trials in the region. Next, we simulated the integration of carinata into a typical cotton/peanut rotation across the 2.3 million hectares of annual cropland within the climate suitability range for this crop, grown once every third winter. We show an annual production potential of greater than 1 billion liters of SAF from this feedstock in the region. Our base carinata management case is approximately neutral in biogenic GHG emissions, with modest soil carbon sequestration that offsets the associated small increase in N2O emissions. However, adopting conservation management practices such as no-till establishment or poultry litter soil amendments results in a more substantial net soil carbon sink, reducing the GHG footprint of carinata-derived SAF by up to 20 grams of CO2-equivalent per megajoule of fuel. This work supports SPARC’s ongoing efforts to develop improved crop varieties and management practices that simultaneously improve the economics and ecosystem service value of carinata production.

Present work deals with paleogeographic reconstruction through sequence modelling, facies and micro-facies analysis and bio-geochemical investigations of late Archaean Banded Iron Formations (BIFs) from Kushtagi-Hungund Schist Belt (KHSB) of Eastern Dharwar Craton (EDC), South Indian Shield (SIS). This comparatively less metamorphosed schist belt of EDC is correlated to the Bababudan Group of Dharwar Supergroup, Western Dharwar Craton (WDC). The lower and upper age constraints have been established from dating of 3.4-3.1 Ga old underlying Sargur Group and TTG gneisses, and 2.5 Ga old younger granites, intruding the KHSB metasediments, respectively. Further, metavolcanics intercalating with the metasediments of KHSB have been dated at 2750-2670 Ma. In this work, special emphasis has been given to the shaley-BIFs of this schist belt which is comprised of thin (few millimeters to a maximum of 1.2-1.5 cm thick) alternating units of iron rich shale, chert and hematite. Facies analysis and sequence model reveal deep water, offshore paleogeography, where proximal outer shelf is dominated by shaley siliciclastics and distal outer shelf and further deep oceanic succession is occupied by chert-hematite-dominated chemogenic sediment suits. Micro-facies analysis of the shaley-BIFs elucidated the interaction between chemogenic and deep water siliciclastic and volcaniclastic shaley sediments within micro domains. Primary chert layers contain several permineralized structures associated with carbonaceous matters. Recent RAMAN spectrometric analysis (compared to previous data in provided figure) and Carbon-isotopic values (d13C values ranging from -22.08 to -30.84 ‰ -VPDB, n= 12) of corresponding Total Organic Carbon (TOC) (ranging from 0.03 to 0.14 %) from cherts and shales indicate preservation of Archean biogenic remnants. Recent elemental (major and trace) and oxygen isotopic data, associated with the micro-facies systematics of shale-chert-iron oxide units, have been compared to previously published geochemical data sets derived from KHSB and other BIFs of SIS to provide important clues and new insights regarding late Archean ocean water chemistry, redox state, paleoclimate and control of tectonics and provenance on sedimentation pattern, prior to Great Oxygenation Event (GOE).

Sea ice leads are produced from deformational forces, which break apart the ice surface and expose open water areas or leads. Leads are the primary regulators of heat in Arctic sea ice during the polar night. Partially-frozen and re-frozen leads produce smaller heat exchanges than open leads due to the absence of a warm water surface. Thus detecting leads is important because they may be used as an indirect way of estimating air-sea heat fluxes. To quantify winter-time leads, we utilize Sentinel-1 C-Band Synthetic Aperture Radar (SAR) data to examine sea ice images through heavy cloud cover. We employ a support vector machine learning technique in a cloud computation environment (Google Earth Engine) to detect and quantify lead areas. With the use of dual-polarization data, we improve the separation of leads from other elongated features (e.g., ridges) in the Sentinel-1 dataset by adding altimetry information from ICESat-2. In addition to typical texture analysis to assess surface roughness, the ICESat-2 ATL-10 data allows us to train the algorithm by discretizing leads and ridges by their freeboard values. Performing this method in a cloud environment allows processing of a large volume of satellite data and converting it into a time series of leads properties. Overall, our method improves lead detection with dual-polarization SAR data while simultaneously providing a big data solution for SAR image processing. The interannual variability of leads and newly formed ice fractions were found for the winters of 2017-2020. Finally, we compare the results from previous studies to validate our cloud-derived sea ice lead detection maps.

One of the most important steps in any AI/ML application is the pre-processing of the data. The objective of this step is to project the original data in a new basis, or in a new latent space, where the different features of the problem are comparable and where their distribution covers a large range of values. Using the data in its natural basis can lead to under-performing AI/ML models. While almost all papers in our domain are careful to normalize or standardize the data, it is less frequent to see the use of simple linear PCA transformations, and even less frequent the use of more complex non-linear projections in latent spaces. Here we show how our research team is using autoencoder neural networks to perform non-linear transformations of images, simulations and time-series used in heliophyisical applications. Autoencoder transformations allow to parametrize any type of data by projecting it onto a latent space of higher or lower dimension. In these latent spaces the transformed data commonly presents better statistical properties allowing improvements in the AI/ML modeling. In addition, autoencoders are also known as generative techniques, i.e. they can be used to produce “artificial” or “synthetic” data. We will present three particular examples of the use of autoencoders: 1) parametrization of solar wind observations using standard feed forward autoencoders, 2) parametrization of magnetosphere simulations using convolutional autoencoders, and 3) parametrization and generation of solar active regions using variational convolutional autoencoders. We will show how these parametrizations can then be used for AI/ML classification and forecasting. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA).

Data of the Earth’s magnetic field strength is diversely applicable from dating archeological artefacts or lava flows to understanding early earth evolution and the mechanisms of the geodynamo. Lava flows are commonly used as a means to obtain records of this paleointensity. Understanding the underlying paleomagnetic and rock magnetic properties and how they vary across a flow is crucial to ensure collection of good quality samples for analysis. In fact, the success of paleointensity as well as paleomagnetic analysis is strongly dependent on the rock-magnetic properties of the samples, and large variations may exist between samples even of the same unit, related mainly to varying cooling rates. The active Tungurahua volcano is one of the most prominent features in the Ecuadorian Eastern Cordillera with numerous basaltic andesite and andesitic lava flows exposed along its flanks. To appraise the relation between volcanic emplacement processes and rock-magnetic properties, we sampled a vertical transect in a ~20 m thick lava flow at Tungurahua volcano. A total of 55 oriented in situ sample from six sites distributed across Ulba Cascada lava flow were collected for this purpose. We will present petrographical analysis of each sample as a function of the depth within the lava flow, with an emphasis on the textural and structural characteristics of magnetic minerals as observed with transmitted, reflected light and scanning electron microscopes. Moreover, a detailed analysis of rock-magnetic properties such as magnetic susceptibility, hysteresis (remanent magnetization, saturation remanent magnetization, coercivity and back-field coercivity), accompanied with detailed stepwise alternating field and thermal demagnetizations will allow us to determine the direction of the magnetic field and assess the variations of magnetic properties with respect to the position within a lava flow. We will also discuss the correlations that may exist between grain size, oxidation state of the magnetic minerals and the emplacement processes of a lava flow, as well as the implications of all these results to paleointensity determinations.