A unique event in 2020 was the nearly complete lack of ice cover on the shallow Kara Sea and Laptev Sea continental shelves at the end of October – a necessary antecedent to hypothesized continental-shelf clathrate methane releases’ runaway-warming feedback (not well-addressed in AR5). Although the current state of coupled Atmosphere/Ocean General Circulation Models (AOGCMs) is such that they cannot reliably predict resulting weather-pattern changes in detail, a potential lower-bound for the overall impact of such releases can be explored on a decadal scale; impact is an important factor in risk-assessment. Previous work* with a prescribed-ocean, atmospheric circulation model suggested a 0.01 C global temperature increase per gigaton of additional atmospheric methane burden. The inclusion of a coupled dynamic ocean model in the present work resulted in a tripling of that estimate to about 0.03 C global warming per gigaton increase in atmospheric methane burden. Additionally, much larger Arctic responses were observed when specifying model changes chosen to approximate increased cloud brightness over the Arctic ocean. In this work, the AR5 RCP8.5 GHG scenario was chosen as the baseline in order to most faithfully represent current and anticipated trends over the relatively short time period investigated here (i.e., 2020 – 2040). After an extended model spin-up, the model global mean temperature was constant for two decades of integration using a constant model-year (2019), and closely reproduced the mean warming rate reported for this time period in the AR5 data when run in baseline RCP8.5 transient-mode over the subject time period. As is typical for the AOGCM used in this work (AR5 GISS ModelE2.0 07.50.01), the modeled Arctic sea-ice losses were less than is currently observed in the real-world; hence, the results presented here are regarded as no more than a lower bound to the magnitude of expected climatic responses to such a release. *[essoar.org/doi/10.1002/essoar.10503094.1 (2019)]

The community of Kotzebue, located on the coast of Kotzebue Sound, which is northeast of the Bering Straits adjacent to the Chukchi Sea, is reliant on the waters around Kotzebue Sound for food and economy. There have been reports of cyanobacterial blooms in these waters around Kotzebue but they have not been systematically studied yet, because the region is sparsely populated with few in-situ observations. Cyanobacteria often form surface blooms in freshwater and coastal ecosystems which can be detected using remote sensing techniques. Cyanobacteria are found to have low nutritional value and many species of cyanobacteria produce cyanotoxins, and thus can be harmful to aquatic life and cause public health hazards. In addition, consumption of decaying cyanobacterial blooms by microbes depletes oxygen level which can lead to hypoxia, adversely impacting the benthic community. As the Arctic is warming twice as fast as the rest of the planet due to climate change, thawing permafrost is releasing nutrients that might be enhancing cyanobacterial blooms in the coastal, marine and lacustrine waters of Alaska. In this study, we used remote sensing to study phytoplankton biomass, turbidity and cyanobacterial blooms between mid-June to end of September each year from 2013 to 2019 when the waters around Kotzebue are ice-free. Using images from Landsat-8 and Sentinel-2, processed using ACOLITE software, we investigated spatial and temporal changes in water quality parameters such as turbidity and chlorophyll concentration between June and September. We used a combination of true-color images and fai (floating algal index) to detect cyanobacterial blooms. There were about two scenes from Sentinel-2 and about one scene from Landsat-8, for a total of about three scenes every week between June and September. Of these, only 49% of the images were cloud-free. Of the cloud-free images, 29% were found to have a cyanobacterial bloom between August and September for an average of two to four scenes every year. Most of the cyanobacterial blooms were detected in Kobuk Lake near Kotzebue, and nearby sites in Hotham Inlet and Selawik Lake. In 2013, 68% of the images were cloudy which was the highest in the observed years and no cyanobacterial blooms were detected.

Open source in-situ environmental sensor hardware continues to expand across the geosphere to a variety of applications. These systems typically perform three fundamental tasks: sample sensors at a specified time or period, save data onto retrievable media, switch power to components on and off in between sample cycles to conserve battery energy and increase field operation time. This is commonly accomplished through integrating separate off-the-shelf components into the desired system such as: power relays, SD card hardware, Real-Time Clocks (RTCs), and coin cell batteries. To enable faster prototyping, the Openly Published Environmental Sensing Lab abstracted all of these requirements into a single PCB that can be dropped into any project to achieve these commonly-required capabilities. The hardware is laid out in a “Feather” form factor, a popular configuration in the open-source hardware community, to easily mate with other industry standard products. The onboard RTC acts as an alarm clock that wakes a user-attached micro controller from low-power sleep modes in between sample cycles. By integrating all these components into a single PCB, we save cost while significantly reducing physical system size. The design as well as a suite of code functions that enable the user to configure all the Hypnos board features are detailed. For more information, please visit open-sensing.org/projects.

Disaster risk reduction relies on quantitative estimates of the future impacts and consequences of known hazard threats in order to evaluate proposed mitigation and adaptation measures. Natural Resources Canada is collaborating with the Global Earthquake Model Foundation on the first ever national seismic risk assessment in Canada to inform disaster risk reduction planning by individuals, businesses and organizations working across all jurisdictional levels. The 2020 National Seismic Risk Model incorporates the 6th Generation National Seismic Hazard Map, a novel physical exposure model for the entire country, localized exposure models based on a machine learning approach to building categorization, and HAZUS-based earthquake building performance functions. Before results can be transmitted to end users, the model must be validated in a Canadian context using observations from real world disaster events or pre-existing catastrophic risk models. This study focuses on benchmarking the 2020 Canadian National Seismic Risk Model using shaking intensities and physical impacts recorded from the 2001 Mw 6.8 Nisqually and 2012 Mw 7.8 Haida Gwaii events, and the results of a 2013 catastrophic risk assessment performed by AIR Worldwide to evaluate the potential impact of major earthquakes in eastern Quebec and Cascadia. We compute anticipated building damage, economic loss, and fatalities for these benchmark scenario earthquakes using the OpenQuake engine and the national exposure dataset. Preliminary results indicate that the model results are largely consistent with observed or predicted impacts of these earthquakes in Canada, after adjusting for economic and population growth. Subsequently, we will evaluate the impact of running the Cascadia scenario using a regional building-level exposure database versus the national level inventory. Ultimately, this work will assess the ability of the National Seismic Risk Assessment to reproduce expected results, to ensure the applicability of this model in anticipating future outcomes at the national and local level.

Severe weather forecasting is an important tool for mitigating damages brought by intense lightning, large hail, heavy precipitation, strong winds, or tornadoes during thunderstorms, yet the reliability of such forecasts suffers from our limited understanding of the severe weather generative processes inside thunderclouds. With an increasing knowledge of the occurrence context of distinct types of lightning within storms, lightning remote sensing may elucidate the kinematic and microphysical environment where severe weather initiates. In particular, distinct energetic intra-cloud (IC) lightning discharges, compact intra-cloud lightning discharges [CID; e.g., Nag and Rakov, 2010] and energetic intra-cloud pulses [EIP; e.g., Lyu et al., 2015], have been shown to have different occurrence contexts, making them strong candidates for thunderstorm remote sensing research. In this study, observations from the RELAMPAGO field campaign in Argentina (November 1 to December 12th 2018) are used to determine lightning flash rates and the prevalence of different energetic lightning types in RELAMPAGO storms, enabling further research on the link between lightning activity and severe weather production inside thunderstorms.Lightning events during RELAMPAGO were observed by a deployed array of four Low-Frequency (LF, ~1-400 kHz) radio receivers. Using time of arrival, magnetic direction finding, and peak amplitude for each observed event at different stations, lightning source locations are estimated using a statistical least squares filter, along with clock and site errors associated with the receivers. Return stroke peak current for each event is also estimated in the filter, using an atmospheric attenuation observation model. The energetic lightning events in the campaign are then classified automatically between cloud-to-ground (CG), IC, CID or EIP, following an improved parametrization scheme originally proposed by Lyu et al. [2015]. In this paper we present the geolocation and classification of RELAMPAGO lightning events, and we also provide an analysis of lightning flash rates during the campaign. A few individual thunderstorm case studies are also discussed, which are augmented by other meteorological data from dual-polarimetric radar, hailpads, and other sources.

We find evidence for multi-centennial climate cycles within the ages commonly described as the Medieval Warming Period and the Little Ice Age. We compare six proxy temperature records and find evidence for synchronicity of ~200-year cycles in the northern hemisphere. The first two data sets are (1) a reconstruction from an ice core in the Colle Gnifetti (CG) glacier on the Swiss-Italian border (Bohleber et al 2018), and (2) length records of the Great Aletsch glacier (GAG), Switzerland (Holzhauser, 2009). A third is a proxy (3) by Cabedo-Sanz et al (2016) using the biomarker IP25 in sea sediments north of Iceland, which serves as a proxy for drift ice and hence arguably for Arctic ice areal coverage. Further temperature proxies for (4) all-China (Ge et al, 2017), (5) north China and (6) central China (Wang et al, 2018) provide a distribution of coverage over the non-tropical northern hemisphere. These six regional temperature proxy data sets are also compared with the G7 global temperature reconstruction (Ludecke and Weiss, 2017). Prominent minima in temperature proxy data occur circa 1350CE (all 6 proxies), 1480-1520CE (4 proxies), 1650-1700CE (all 6 proxies) and 1800-1860CE (all 6 proxies). The last three of these are also visible in the G7 global temperature reconstruction. Over the period 1550-1949 CE the Pearson correlation coefficients for the IP25 (Arctic ice) data with the GAG and the all-China temperature proxy are 0.69 and 0.70 respectively; these high correlations in data sets from opposite sides of the globe suggest a global cause rather than regional internal variability. Since 1600CE we note that IP25 (Arctic ice) and the CG temperature proxy lag the glacier record by 17 and 10 years respectively; that lag is counterintuitive and may reflect precipitation variations in the Alps preceding the temperature drop, or it may be attributable to uncertainties in age dating between the data sets. Power spectral analysis shows the dominant centennial periods in the data sets are centered at 180, 240 and 500 which (within dating uncertainties) may relate to the ~160 year Jose cycle, the 208-year de Vries cycle and possible 350 and 500 year cycles previously recognized in solar activity via study of terrestrial cosmogenic isotopes. The consistency between the spectral maxima of temperature proxies studied here and spectral maxima of cosmogenic isotopes, supports the hypothesis of some association of these cycles with an “astronomical clock”, although the mechanism of possible forcing remains a subject for further study.

Many communities along the East and Gulf coasts of the United States are experiencing moderate to severe erosion. Decadal shoreline erosion rates determined by the North Carolina Division of Coastal Management indicate that >65% of the NC shoreline is eroding, with 20% losing shore at ~1 m per year. Chronic long-term erosion and episodic rapid impacts from storms have encouraged most coastal NC communities to conduct beach nourishments for mitigation. One concern is that there is limited knowledge of how offshore sand shoals – often sources for nourishments – are evolving in response to storm conditions and dredging activity. In September 2018, Hurricane Florence impacted southern NC and northern SC, and many communities experienced enhanced erosion. Future offshore sand removal for beach nourishment is inevitable here and elsewhere along the East Coast. A borrow area on the continental shelf seaward of Bogue Inlet (NC) offers an opportunity to see how the seabed is changing as a result of anthropogenic and oceanographic processes. Hurricane Florence made landfall as a Category 1 near Wilmington, NC on the morning of September 14, 2018. Prior to making landfall the storm was a powerful Category 2 hurricane, down from Category 4 status days prior. Hurricane-force winds were experienced over a large region, yielding powerful storm surge and waves over 8 m near the study area. Given the water depths of the ODMDS, seabed reworking was anticipated. The project collected geophysical data and sediment samples over a portion of the ODMDS offshore of Bogue Banks, NC in February 2019. Multibeam bathymetry, backscatter and seismic reflection data were obtained along with 24 sediment samples. These data add to previously collected data over the same area in 2013 and 2018. Grain-size analysis has been completed on all collected samples. Preliminary analysis of the post-hurricane data in comparison to earlier results indicate that the seabed has been reshaped since the last survey in March 2018, likely in response to Hurricane Florence. The broad dome-shape of the ODMDS remains, however, there is conspicuous morphological change. In shallower areas large sand waves (crests oriented roughly N-S, wavelength 20-30 m) differ from earlier mapping. Despite notable reworking, the anthropic signature of dredging in 2013 remains on the seascape.

Nitrous oxide (N2O), a potent greenhouse gas and ozone depleting substance, plays a crucial role in the atmosphere. Anthropogenic emissions from agriculture contribute to a rising trend in global N2O emissions and atmospheric concentrations. However, due to insufficient direct observations, regional N2O emissions derived in bottom-up and top-down studies are highly uncertain. The U.S. Midwest is one of the most intensive agriculture areas worldwide and hence may contribute significantly to the observed trend. Recent top-down studies suggest that bottom-up estimates underestimate agricultural emissions in that area by up to an order of magnitude. Here we quantify nitrous oxide emissions in the Midwest in October 2017 and June-July 2019 with a top-down approach. Unique continuous aircraft-based measurements of N2O conducted during the ACT-America campaign together with forward WRF-Chem model simulations are used to scale the EDGAR inventory thus quantifying emissions. On average we had to upscale October 2017 and June-July 2019 agricultural EDGAR 4.3.2/5.0 emissions by a factor of 6.3/3.5 and 11.4/9.9, resulting in 0.42 nmol m-2 s-1 and 1.06 nmol m-2 s-1 emissions in the Midwest, respectively. Finally, calculations of direct soil N2O emissions from the DayCent biogeochemical model are compared to our estimates.

Publicly accessible data has been used to construct a county-scale supply chain model of United States gasoline consumption and quantify the scope 3 CO2; emissions from gasoline consumption. Our model tracks the movement of refined fuels from county of refinement to county of blending and eventually to county of consumption via multiple infrastructure networks – pipelines, tankers, trains, and trucks. Where quantities of the fuel moved across different linkages and different transportation modes are known, they are used as is. However, for the vast majority of the country, the exact quantities of fuel moved between county of refining and county of blending or county of blending and county of consumption, as well as the mode of transportation, is not known with certainty. Linear optimization is used to model those links with constraints related to total supply and demand at lower spatial resolutions (State-level and Petroleum Administration for Defense (PAD) Districts). This is the first real attempt at a spatially-resolved scope 3 style CO2 emissions data product specific to United States gasoline consumption. This model can improve understanding of the complex liquid fuel supply chain, and has significant implications for local policy. With a complete model of scope 3 CO2 emissions, it is also possible to analyze how the differences between scope 1 and scope 3 emissions vary across the country. Finally, this model lays the foundation to model the evolution of the U.S. gasoline supply chain – its dependencies, critical linkages, and pinch points – and the evolution of scope 1 and scope 3 CO2 emissions using the full extent of available public data.

We consider the transient electrokinetic response of an aquifer-aquitard system to groundwater abstraction from the aquifer. The system was instrumented with 18 non-polarizable copper/copper sulphate electrodes installed at three different depths in the aquitard above the aquifer. The sensing electrodes were installed at depth of 1, 2, and 4 m below ground surface along three overlapping transects. The differential voltages relative to a single permanent electrode were measured with a Campbell Scientific CR1000 datalogger with a single multiplexer. Additionally, six piezometers screened in the top 1.5 m of the confined aquifer, were installed by direct-push. All the piezometers were instrumented with pressure transducers to measure directly the hydraulic response of the aquifer. The vertical variation of resistivity in the aquitard was measured on sediment cores recovered from one of the boreholes used to install the deepest piezometer. The resistivity distribution at antecedent sediment wetness (moisture content) was measured using the MC Miller resistivity boxes and meter, with wetness measured gravimetrically. Previous exploratory drilling and sampling activities at the site indicate that the aquifer is fractured greywacke sandstone overlain with clayey aquitard of semi-consolidated alluvial sediment, with the aquifer-aquitard contact at a depth of 10.3 m below the ground surface. We report the results of the site instrumentation, monitoring, characterization, hydraulic testing, and compare the results of parameter estimation using streaming potential and hydraulic data separately and jointly. We explore the effect of depth of installation of the electrodes in the aquitard on signal strength and quality and compare this to model predicted behavior using semi-analytical models from the literature. The results suggest the need for deep sensing of electrokinetic signals generated by groundwater flow to improve signal-to-noise ratios and the usefulness of self-potential data for hydrogeophysical characterization of aquifer-aquitard systems.

The role of waves in the propagation, scattering and energization of electrons in the solar wind has long been a topic of interest. Conversely, understanding the excitation of waves by energetic electrons can provide us with a diagnostic for the processes that accelerate the electrons. We will discuss two different processes: (1) the interaction of narrowband whistler-mode waves with solar wind electrons, and (2) how periodic Type III radio bursts yield clues to small-scale acceleration of energetic electrons in the solar corona. Waveform captures in the solar wind at 1 AU obtained by the STEREO revealed the existence of narrowband large amplitude whistler mode waves, propagating at highly oblique angles to the magnetic field. Similar waves are less commonly seen inside .2 AU by Parker Solar Probe. The differences provide clues for understanding electron propagation, scattering and energization. Type III radio bursts have long been used as remote probes of electron acceleration in the solar corona. The occurrence of periodic behavior in Type III bursts observed by Parker Solar Probe, Wind and STEREO when there are no observable flares provides a unique opportunity to diagnose small-scale acceleration of electrons in the corona. Periodicities of ~ 5 minutes in the Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA) Extreme Ultraviolet data in several areas of an active region are well correlated with the repetition rate of the Type III radio bursts. Similar periods occur in the Helioseismic and Magnetic Imager (HMI )data. These results provide evidence for acceleration by wave-modulated reconnection or small-scale size waves, such as kinetic Alfven waves, even during intervals with no observable flares. The possible connections between these two phenomena will be addressed.

Data maturity describes the degree of the formalisation/standardisation of a data object with respect to FAIRness and quality of the (meta-) data. Therefore, a high (meta-) data maturity increases the reusability of data. Moreover, it is an important topic in data management, which is reflected by a growing number of tools and theories trying to measure it, e.g. the FAIR testing tools assessed by RDA(1) or the NOAA maturity matrix(2). If the results of stewardship tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data is easy to reuse. For example, the DataCite Metadata Schema does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (3, Atmospheric Model Data) aims to improve the reusability of published atmospheric model data by scientists, the public sector, companies, and other stakeholders. These data are valuable because they form the basis to understand and predict natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data from other fields of research would also benefit from easily findable maturity information. Therefore, we developed a Maturity Indicator concept and propose to introduce it as a new property in the DataCite Metadata Schema. This indicator is generic and independent of any scientific discipline and data stewardship tool. Hence, it can be used in a variety of research fields. 1 https://doi.org/10.15497/RDA00034 2 Peng et al., 2015: https://doi.org/10.2481/dsj.14-049 3 www.atmodat.de

In 2019 sprite observations appeared on You-Tube showing green emissions in the top of some sprites and after the main sprite. The emissions were named green ghosts. We present here similar color camera observations made on 24 and 25 May 2020 from Lamy, NM. The recordings were made at 30 fps. The green ghost increased in brightness for 3 to 4 frames after the main ‘jellyfish’ sprite and decayed thereafter. The delayed green emissions are likely from atomic oxygen at 557.7 nm, the auroral green line, which is a forbidden atomic oxygen emission with a radiative life time of 0.7 s. In the atmosphere the emissions are affected by quenching and at the altitude of the green ghost the decay time constant is substantially less. We suggest that the green ghost is generated by super thermal electrons exciting ambient atomic oxygen (only 4.1 eV required). The electrons are energized by the large-scale sprite electric field, which initially caused the sprite, and decays over some time after the sprite. We present a simple model based on this idea. The model assumes an exponentially decaying sprite E-field and reproduces the observed delayed peak in the green emissions and the subsequent decay of the emissions.

There are records of past Earth climates that were ice-free all the way to the poles (Barron 1983), which can be described as “hothouse” climates. These hothouse climates can be contrasted with an “all-tropics” planet, where the tropics are defined by the atmospheric dynamics, i.e. the Hadley Cell extent (Faulk et al. 2017). This classification is thus primarily dependent on a planet’s rotation, rather than its ice-free extent or surface temperatures. We investigate the parameter space between Earth and an all-tropics world using the open-source GCM Isca, developed by Vallis et al (2018). We take an Earth analog and perform a parameter sweep in two dimensions: global reservoir depth (10m, 1m, 1cm) and rotation period (8 days, 4 days, 1 day). The sweep will allow us to explore the effects of surface liquid coverage and large-scale atmospheric circulation on an Earth-like climate. To better represent the distribution of surface water, we utilize the surface hydrology scheme developed by Faulk et al. (2020) for the Titan Atmosphere Model. In this presentation we provide a status report and analysis of initial findings.

Groundwater is the largest source of Earth’s liquid freshwater and plays a critical role in global food security. With the rising global demand for drinking water and increased agricultural production, overuse of groundwater resources is a major concern. Because groundwater withdrawals are not monitored in most regions with the highest use, methods are needed to monitor withdrawals at a scale suitable for implementing sustainable management practices. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. This extends a previous study in which we estimated groundwater withdrawals in Kansas, where the climatic conditions and aquifer characteristics are significantly different. Datasets used in our model include energy-balance (SSEBop) and crop coefficient evapotranspiration estimates, precipitation(PRISM), and land-use (USDA-NASS Cropland Data Layer), and a watershed stress metric. Random forests, a widely popular machine learning algorithm, are employed for predicting groundwater withdrawals from 2002-2018 at 5 km spatial resolution. We used in-situ groundwater withdrawals available over the Arizona Active Management Area (AMA) and Irrigation Non-Expansion Area (INA) from 2002-2010 for training and 2011-2018 for validating the model respectively. The results show high training (R2 ≈ 0.98) and good testing (R2 ≈ 0.82) scores with low normalized mean absolute error ≈ 0.28 and root mean square error ≈ 1.28 for the AMA/INA region. Using this method, we are able to spatially extend estimates of groundwater withdrawals to the whole state of Arizona. We also observed that land subsidence in Arizona is predominantly occurring in areas having high yearly groundwater withdrawals of at least 100 mm per unit area. Our model shows promising results in sub-humid and semi-arid (Kansas) and arid regions (Arizona), which proves the robustness and extensibility of our integrated approach combining remote sensing and machine learning into a holistic, automated, and fully-reproducible workflow. The success of this method indicates that it could be extended to areas with more limited groundwater withdrawal data under different climatic conditions and aquifer properties.

The Proterozoic Kayad Zn-Pb deposit in Ajmer, Rajasthan is located within the Aravalli-Delhi Fold Belt in western India. Mineralization of sphalerite and galena, commonly associated with chalcopyrite and pyrrhotite, is hosted primarily by graphitic quartz mica schist (QMS) and subordinately by calc-silicate, quartzite and pegmatite. In QMS, both massive ore, evidently formed by remobilization, and laminated ore are present. Calc-silicate and quartzite contain disseminated sulfides while pegmatites contain sulfide veins that cause massive mineralization. Mineral replacement textures such as replacement of albite and muscovite by K-feldspars and biotite by chlorite suggest that Fe-Cu sulfides, represented by chalcopyrite and pyrrhotite, formed during potassic and acidic alteration. Trace element characterization demonstrates that between co-existing chalcopyrite and pyrrhotite, Ag, Zn, Sn, In, Cd, Ga are strongly portioned in chalcopyrite whereas Co and Ni are partitioned in pyrrhotite. High concentration of Ag (up to ~9000 ppm) in chalcopyrite adds to economic potential of the deposit. Barring the exception of massive ore in QMS, the trace element compositions of chalcopyrite and pyrrhotite are host-rock-independent suggesting profound control of the parental hydrothermal fluid on their composition. In laminated QMS, pegmatite and quartzite, higher Co, Cd, Mn and In in sphalerite and higher Ag, Sb, Bi, Se in galena compared to chalcopyrite suggest that these phases co-crystallized during their formation. In contrast in remobilized massive ore in QMS, higher Co, Cd and Mn in sphalerite over chalcopyrite and higher Ga, Sn and In in chalcopyrite over sphalerite and galena possibly indicate co-crystallization followed by recrystallization. Furthermore, constant yet distinct Cd:Zn ratios of chalcopyrite and co-existing sphalerite may indicate involvement of two different fluids. The observed trace element characteristics can be best explained by co-crystallization of chalcopyrite and sphalerite by fluid-mixing and subsequent recrystallization leading to the formation of part of the massive ore.

We examine long-term energetic heavy ion measurements including three planets’ magnetospheres, focusing on Fe ions (specifically, but not exclusively, Fe+) in and near Earth’s magnetosphere. We compare Fe data to that of other energetic ion species with masses greater than C (carbon) and consider the relationship(s) of energetic Fe ion measurements at the three planets to internal (ionospheres, exospheres, moons, rings, and trapped radiation) and external (solar wind and interplanetary dust) source candidates. Fe+ has been observed at Earth and Saturn, but not yet at Jupiter, as our observations there were brief. The measurements are from two functionally identical charge-energy-mass ion spectrometers: one on Geotail (~87-212 keV/e), orbiting Earth at ~9-30 Re; and the other on Cassini (~83-167 keV/e), in interplanetary space, during Jupiter flyby, and at ~4-20 Rs on its constantly varying orbits around Saturn. These ion spectrometers efficiently separate energetic light and heavy ions by mass, as well as lower charge state ions from higher charge state ions by mass-per-charge. Energetic low charge state ions often derive from magnetospheric sources, while energetic high charge state ions most often derive from the solar wind. We also enlist heavy ion measurements closer to the Earth from AMPTE/CCE which are used for C and Fe radiation-belt- modeling content, consideration, and estimation.

In the Arctic, the spatial distribution of boreal forest cover and soil profile transition characterizing the taiga-tundra ecological transition zone (TTE) is experiencing an alarming transformation. The SIBBORK-TTE model provides a unique opportunity to predict the spatiotemporal distribution patterns of vegetation heterogeneity, forest structure change, arctic-boreal forest interactions, and ecosystem transitions with high resolution scaling across broad domains. Within the TTE, evolving climatological and biogeochemical dynamics facilitate moisture signaling and nutrient cycle disruption, i.e. permafrost thaw and nutrient decomposition, thereby catalyzing land cover change and ecosystem instability. To demonstrate these trends, in situ ground measurements for active layer depth were collected to cross-validate below-ground-enhanced modeled simulations from 1996-2017. Shifting trends in permafrost variability (i.e. active layer depth) and seasonality were derived from model results and compared statistically to the in situ data. The SIBBORK-TTE model was then run to project future below-ground conditions utilizing CMIP6 scenarios. Upon visualization and curve-integrated analysis of the simulated freeze-thaw dynamics, the calculated performance metric associated with annual maximum active layer depth rate of change yielded 76.19%. Future climatic conditions indicate an increase in active layer depth and shifting seasonality across the TTE. With this novel approach, spatiotemporal variation of active layer depth provides an opportunity for identifying climate and topographic drivers and forecasting permafrost variability and earth system feedback mechanisms.