Earthquakes are among the most destructive natural disasters, resulting in a huge number of fatalities and economic losses all over the world. For regions with limited number of seismic networks or for regions where there is a seismic gap to produce large magnitude and destructive earthquakes, ground motion simulation approaches provide alternative region-specific time histories for potential events. Region-specific simulations require modeling and calibration of input parameters in terms of source, path and site effects. Verification of these parameters is a challenging task and can be accomplished through comparing the real time histories of the past events against the simulated data. In this study, the recorded time histories of the 9th of July 1998 Faial earthquake (Mw=6.2) at available stations are simulated with the stochastic finite-fault ground motion simulation approach based on a dynamic corner frequency concept. For ground motion simulations, alternative region-specific source, path and site models are employed and tested. The best model is proposed through goodness of fit score which is evaluated through the complementary error function in terms of various seismological parameters between the real and simulated record sets. These seismological parameters include peak ground acceleration peak ground velocity, the ratio of peak ground velocity to peak ground acceleration, Arias intensity, cumulative absolute velocity, acceleration spectrum intensity, modified acceleration spectrum intensity for the period range of 0.1 to 2.5 seconds, velocity spectrum intensity, Housner intensity, significant duration, bracketed duration, Fourier amplitude spectra within the frequency range of 0.1 to 25 Hz and pseudo response spectra within the period range of 0 to 4 seconds. The simulation results demonstrate that for the event of interest the input parameters are verified, and the fits between the real and simulated time histories are satisfactory. Keywords: The 1998 Faial Earthquake, Azores- North Atlantic, Stochastic finite-fault Ground Motion Simulation method, Goodness of Fit Score

Near-Earth asteroids and meteoroids constitute various levels of impact danger to our planet. On the one end, billions of events associated with small-sized meteoroids have resulted in trivial effects. On the other end, the occurrences of large-sized asteroidal collisions that can cause mass extinctions and may wipe out the modern human civilization are extremely rare. In addition, large near-Earth asteroids are being monitored constantly for accurate and precise predictions of potential hazardous visits to our planet. However, small asteroids and large meteoroids can still often go under the radar and cause bolide explosions with potential of significant damage to communities on the ground. To facilitate management of bolide hazard, a number of scholarly works have been dedicated to estimation of frequencies of bolide events from a global perspective for planetary defense and mitigation. Nevertheless, few of the existing bolide frequency models were developed for local hazard management. In this presentation, the author introduces two recently developed frequency models for local management of bolide hazard. The first one, called the Dome model, computes the expected frequency of bolide explosions within a dome-shaped volume around a location. The second one, called the Coffee Cup model, is for a column-shaped volume above an area. Both models are based on empirical calibrations with historical data on energy, latitude, altitude, and frequency of bolide events. The modeling results indicate a linearly decreasing trend of frequency of bolide events from south to north latitudinally around the globe. The presented models can be applied to any location or area on Earth, including the entire surface of the planet.

One of the prominent effects of space weather is the variation of electric currents in the magnetosphere and ionosphere, which can cause localized, high amplitude Geomagnetic Disturbances (GMDs) that disrupt ground conducting systems. Because the source of localized GMDs is unresolved, we are prompted to model these effects, identify the physical drivers through examination of the model we use, and improve our prediction of these phenomena. We run a high-resolution configuration of the Space Weather Modeling Framework (SWMF) to model the September 7, 2017 event, combining three physical models: Block Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US), an ideal magnetohydrodynamic model of the magnetosphere; the Ridley Ionosphere Model (RIM), a shell ionosphere calculated by solving 2-D Ohm’s Law; and the Rice Convection Model (RCM), a kinetic drift model of the inner magnetosphere. The configuration mirrors that which is used in Space Weather Prediction Center (SWPC) operations; however, the higher grid resolution can reproduce mesoscale structure in the tail and ionosphere. We use two metrics to quantify the success of the model against observation. Regional Station Difference (RSD) is a metric that uses dB/dt or geoelectric field to pinpoint when a single magnetometer station records a significantly different value than others within a given radius, indicating a localized GMD. Regional Tail Difference (RTD) performs the same calculation using relevant variables in the magnetosphere at points that map down along field lines to the magnetometer station locations on the ground. We theorize two distinct causes of RSD, the first being small-scale structure in the tail and the second being station field lines mapping to spatially separated locations in the tail. We examine the differences between RSD spikes that we can reproduce in the model and those that we cannot. We categorize spikes by cause of localized GMDs to examine model capability for each theorized cause. We investigate the improvements in our model when we switch from empirical specification of ionosphere conductance to a physics-based one, MAGNetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Conductance Model. For small-scale effects we cannot reproduce, we explore the deficiencies in our model.

Geologists need help classifying microscope rock images of sigma clasts; a type of mantled porphyroclasts widely used as kinematic indicators in rocks. Knowledge about the shear sense of sigma clast during formation (either CCW or CW shearing) gives insights into rock formation history. This work reports on early investigation of machine learning techniques for automatic detection and classification of sigma clasts and their rotation from photomicrographs. Convolutional Neural Networks (CNNs) are used to extract and leverage defining features of sigma clasts, such as shape, color, texture, and tail direction to improve accuracy. We leverage existing models that are pre-trained on very large collections of images, and use transfer learning techniques to apply them to microstructure images. We used YOLOv3 to identify different sigma clasts in a given image. We also experimented with other large pre-trained models such as ResNet50, VGG19, InceptionV3 with two additional layers trained specifically on our dataset. In order to facilitate exploration of different models with different settings, we are developing a computational experimentation environment to visualize different CNN network layers, classification heatmaps, and comparative metrics. Finally, since models perform better when more data are available, we are developing a web application to collect additional data from geoscientists and incentivize their participation in open science. The website allows researchers to upload images of rock microstructures, showing them the classification of the images based on the best models available, and allows them to correct any errors which can be used to improve the models.

The irregular variation of geomagnetic activity, caused by the solar wind interaction with the magnetosphere/ionosphere, occurs in a wide time interval (from seconds to annual and even larger periods) and amplitude ranges (from few to hundreds of nT). Major variation events (geomagnetic storms) can cause damaging effects and important perturbations to different human activities such as satellite communications, long distance radio broadcasting, navigation, surveying, etc. Furthermore, the induction of electric currents (geomagnetically induced currents – GICs) may affect all grounded conducting networks, in particular electrical power transmission systems. Useful proxies of GIC effects are still under debate; here, we identify the pros and cons of some different candidates. The intensity of geomagnetic activity is usually characterized by geomagnetic indices, among which the 3-hourly indices Kp and local K. In this study we compare 3-hour K and 1-hour K-derived local range indices, using geomagnetic time series from the mid-latitude Coimbra observatory (COI), in Portugal. We also compute smaller time-resolution geomagnetic and GIC indices such as the geomagnetic horizontal field components and their time derivatives, horizontal field magnitude and its time derivative and the LDi/LCi indices [Cid19]. We compare the computed indices with GIC simulations in the Portuguese transmission power grid, to evaluate which of them may be used to nowcast the induced currents. We suggest as a better GIC proxy, an index obtained from geomagnetic field components filtered by convolution with the uniform conductivity Earth filter (new EGIC index). Previous studies have considered the local ground conductivity to be an important factor to determine GIC amplitudes (e.g. [Tri07] and [Rib21]). We then obtain GIC estimations for power grid substations lying at different geological regions. Acknowledgements: This study is funded by national funds through FCT (Portuguese Foundation for Science and Technology, I.P.), under the project MAG-GIC (PTDC/CTA-GEO/31744/2017). FCT is also acknowledged for support through projects UIDB/50019/2020-IDL, PTDC/CTA-GEF/1666/2020 (MN) and PTDC/CTA-GEO/031885/2017 (MN). CITEUC is funded by FCT (UIDB/00611/2020 and UIDP/00611/2020). We acknowledge the collaboration with REN (Redes Energéticas Nacionais).

Drought is a recurring and extreme hydroclimatic hazard with serious impacts on agriculture and overall society. Delineation and forecasting of agricultural and meteorological drought are essential for water resource management and sustainable crop production. Agricultural drought assessment is defined as the deficit of root-zone soil moisture (RZSM) during active crop growing season, whereas meteorological drought is defined as subnormal precipitation over months to years. Several indices have been used to characterize droughts, however, there is a lack of study focusing on comprehensive comparison among different agricultural and meteorological drought indices for their ability to delineate and forecast drought across major climate regimes and land cover types. This study evaluates the role of RZSM from Soil Moisture Active Passive (SMAP) mission along with two other soil moisture (SM) based indices (e.g., Palmer Z and SWDI) for agricultural and meteorological drought monitoring in comparison with two popular meteorological drought indices (e.g., SPEI and SPI) and a hybrid (Comprehensive Drought Index, CDI) drought index. Results demonstrate that SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought events better than meteorological (e.g., SPI, SPEI) and hybrid (CDI) drought indices, whereas the latter three performed better in delineating meteorological drought across the contiguous USA during 2015–2019. SM-based indices showed skills for forecasting agricultural drought (represented by end-of-growing season gross primary productivity) in the early growing seasons. The results further confirm the key role of SM on ecosystem dryness and corroborate the SM-memory in land-atmosphere coupling.

In this era of open data and reproducible science, graduate students need to learn where and how to publish their data and to be conversant with the challenges inherent when re-using someone else’s data. The Environmental Data Initiative partnered with UNM Libraries and Florida Coastal Everglades LTER to organize a 1-credit, semester-long distributed graduate seminar to learn if this approach could be an effective mechanism for transmitting such information. Each week during the Spring 2021 semester, an informatics specialist spoke remotely to students at University of New Mexico, Florida International University, and University of Wisconsin-Madison on topics ranging from FAIR principles to data security, team science to data provenance. Students prepared for the lecture with one or more readings, and in-class exercises reinforced the material covered. Student assignments included writing quality metadata for their own data and archiving their data in the EDI Repository. The capstone writing assignment, a data management plan for their own research project, allowed the students to integrate much of what they had learned. Student response to this class was positive, and students indicated that they learned a lot of immediately useful information without the course being a significant time-sink. The low registration numbers at UNM and FIU (6 and 7 students, respectively), however, where the seminar was not required, suggest a need to better inform both students and their advisors of the opportunity and the value provided by the training. Instructors also learned that it would be easier to create a cohesive flow to the course, without repetition, if the group of instructors took turns lecturing, rather than bringing in specialists on each subject. It was also apparent from student comments that many felt this information should be integrated, at an introductory level, into undergraduate classes or classes for new graduate students.

The high energetic plasma and the embedded magnetic field of coronal mass ejections interact with planetary magnetospheres giving rise to transient perturbations such as geomagnetic storms. Predicting the geomagnetic impact of such interplanetary coronal mass ejections (ICME) is of utmost importance for the protection of our technological infrastructure that is affected by space weather. We use 3D compressible magnetohydrodynamic simulation of a star-planet system to model and study an ICME-Earth interaction event of 20th November 2003. In the modelled interaction, we observe a change in magnetopause shape and stand-off distance on ICME impact, day and night side reconnections and induction of high currents in the magnetosphere. We also notice the formation of a ring of strong equatorial current around the Earth, leading to a reduction of the geomagnetic field. We calculate the simulated reduction in the magnetic field and compare that to the observed geomagnetic indices in order to establish a predictive approach for geomagnetic storms. These simulations are expected to illuminate the physical processes that result in space weather impacts of stellar magnetic storms in planetary and exoplanetary systems.

As the problems humanity faces become ever more obvious and dangerous, the need for interdisciplinary, cross-disciplinary and trans-disciplinary research and solutions becomes ever more apparent. The problems themselves are often intertwined in complex ways - for example the impact of climate change on human health, food & water security, disasters, and so on and how all of these are exacerbated by human population growth and a general lack of recognition that humanity is part of the ecosystem upon which Earthly life depends. Underpinning our ability to understand and solve these complex problems are data of all kinds, more importantly the information that has and continues to be garnered through obtaining and analyzing these and most importantly their inclusion in the larger body of knowledge and ways of knowing that hopefully leads to the wisdom needed to directly address the root causes of each problem. Over the past decades, through involvement in projects on science topics as diverse as quasars and stellar pediatrics, solar physics, social science, and a wide variety of Earth Science topics; where informatics topics such as data management and curation, systems and framework development; and tools and methodologies such as Use Cases, Natural Language Processing, Deep Learning, Knowledge Graphs, Information Models, etc. were included; a number of principles and lessons learned which unite the technical underpinnings of these projects have come to the fore. These will be discussed in this presentation.

Ocean Worlds in our Solar System are attractive candidates in the search for extra-terrestrial life. The best chances for detecting biosignatures and biology on these bodies lie in in situ investigations of sub-ice oceans in contact with rocky interiors. The actual conditions that will confront an ice-penetrating vehicle (“cryobot”) performing such investigations are largely unknown. However, any Ocean World cryobot must be able to, at a minimum, successfully negotiate five different operating regimes to have a chance of reaching a subsurface ocean: starting at the surface in vacuum at cryogenic temperatures; brittle/cold ice transit; ductile/warm ice transit; negotiating or penetrating salt or sediment layers, and other obstacles; and detecting and transiting ice-water transitions such as voids and the final ocean entry. PROMETHEUS (nuclear-Powered RObotic MEchanism Technology for Hot-water Exploration of Under-ice Space) represents a full cryobot concept and set of key technology demonstrations that advance the capability to perform such investigations. The PROMETHEUS concept is targeted for deployment on Europa, and consists of a fully-instrumented science vehicle able to actively control descent through the ice shell and into the subsurface ocean. The concept employs closed-cycle hot water drilling (CCHWD) technology as the primary means of penetrating ice, and making forward and turning progress. A “passive” (purely conductive) heat transfer system enables penetration starting on the surface where liquid water cannot exist until hole closure is achieved and the system proceeds inside a melt water “bubble”. PROMETHEUS is compatible with a small fission reactor (the NASA Kilopower design) and employs a vertical motion control system using a trailing tether frozen into the ice to guard against falling through voids and enabling controlled entry into the sub-ice ocean. The design is capable of achieving a 20 km descent through a Europan ice profile in under a year and under 500 kg vehicle mass, including reactor mass.

Crop yield is sensitive to climate change and has been projected to be negatively affected by future climate. To reduce yield loss and ensure food security in the context of climate change, it is critical to understand how climate variables interact with crop growth in agroecosystems. One important and widely used tool to study yield responses to climate is process-based modeling. However, using process-based models to simulate the climate impacts on crops is becoming challengeable as the future climate is characterized by more and more frequent extreme events, such as heatwaves, unpredictable rainfall, and droughts. Most existing crop models may not be capable of characterizing the impacts of such extreme events on crops simply because they usually do not simulate some critical processes that climate variables directly affect crop growth such as photosynthesis. Instead, they use a simplified approach–radiation-use efficiency (RUE) which is a coefficient to describe empirical relationships between intercepted radiation and biomass. The usage of RUE has simplified computation but also limited our understanding of interactions between climate variables (e.g., temperature, CO2, rainfall) and crop growth. Thus, we developed a module combining processes of radiative transfer and photosynthesis (RP) within the canopy to account for the impacts of climate variables on crop growth dynamically. Then, we integrated the RP module into a popular agricultural system model—the Environmental Policy Integrated Climate (EPIC) to assess its performance. The results show that its capabilities of predicting crop yield are comparable to the traditional RUE method. The correlation between observed and simulated biomass is 0.77 for the RUE method, while 0.76 for the RP method. But the RP method could show responses of biomass accumulation to changes in climate factors, which is almost overwhelming for RUE. For instance, the RP module could simulate how extremely high temperatures (which usually last several hours during a day) affect crop growth and also allow the EPIC to distinguish elevated CO2 impacts on C3 and C4 crops, while the default RUE method could not. Therefore, the RP module is promising to improve capabilities and extend functionalities of current process-based models, which is not only beneficial to the community of crop modeling but also enhances our ability to evaluate the impacts of climate change on the agroecosystem.

Deep earthquakes in the lower continental lithosphere - the lower crust and uppermost mantle - are frequently too poorly located in depth to be definitively labelled as having occurred above or below the Mohorovičić discontinuity (Moho; base of the crust). Our Sn/Lg methodology utilizes two regional seismic waves to determine the depth of an earthquake relative to the Moho: Sn and Lg waves, which are phases that propagate through reflections in the mantle lid and the crust, respectively. Therefore, an analysis of Sn and Lg waves can provide a robust understanding of an earthquake’s depth relative to the Moho. We present our Sn/Lg analysis through reduced-velocity record sections, which show Sn and Lg energy in the waveform, allowing measurements of RMS amplitudes, and maps of ray-paths and Sn/Lg amplitude ratios which allow us to visualize the propagation of Sn and Lg in all directions. We demonstrate the efficacy of our approach by applying it to a well-known upper-mantle earthquake in Wyoming and a shallow earthquake with a similar epicenter. We then use our method to study other deep-crustal/upper-mantle earthquakes in North America. A cluster of earthquakes with reported depths from 10–50 km ± ~10 km, spans the border between Alberta, Canada and Montana, U.S.A. where the crustal thickness increases from ~30 km in the SW to ~45km in the NE. For an earthquake occurring in the crust-mantle transition zone, the dipping Moho should tend to block Sn (decrease the Sn/Lg ratio) in the direction of crustal thickening, and tend to block Lg (increase the Sn/Lg ratio) in the direction of crustal thinning. We studied seven earthquakes of magnitude>2.5. A m=2.7 earthquake, previously reported (USGS PDE) to be at 50±10 km where CRUST 1.0 shows a 49 km Moho depth, and a m=3.5 reported at 38 km depth above a nominal 43 km Moho, both show much stronger Sn/Lg ratios than earthquakes with nearby epicenters at nominal dots of 15 and 21 km. Hence the “50-km” and the “38-km” earthquakes must occur in the upper mantle, or so close to the upper mantle as to preferentially excite Sn. The better-recorded of these earthquakes also shows clear evidence of Sn enhancement along azimuths into regions of thinner crust.

Long-term dependencies may be one of the reasons for the spatial variability in precipitation frequencies. This study assesses the long-term dependencies in precipitation time series at a basin-scale using the wavelet-based fractal decomposition technique. The gridded precipitation datasets (0.25deg x 0.25deg) from the India Meteorological Department (IMD) for the year, 1901 to 2018 have been used. In order to find the climate change point (i.e., the year in terms of annual series) from each grid point, the mean-based change point detection is performed. Based on the change points, the input for the wavelet analysis is generated into two series, the series -1 (before change point) and the series – 2 (after change point). The results of the climate change points are different for every location, and the corresponding length of the series also gets changed. In order to handle the non-stationarity associated with the time series datasets, the method of wavelet decomposition is used. The Discrete Wavelet Transform (DWT) based fractal decomposition of time series is performed by taking the Daubechies (db1 to db10) mother wavelet along with the varying scale and translation parameters. Both the results of the series wavelet coefficients are compared using the scaled ratio method and the relative shift in the cumulative distribution functions (CDF). Comparing the time series datasets before and after the change point reveals the significance of long-term dependencies at each location. The results of the spatial variability and its patterns explain the long-term dependencies and their significance at a basin-scale, which may support various scientific studies and development.

Since its launch, in September 2018, ICESat-2’s Advanced Topographic Laser Altimeter System (ATLAS) has collected high-resolution measurements of Arctic sea ice by sampling the surface every 70 cm along-track. We utilize the high-resolution capabilities of ATLAS with a novel algorithm called the University of Maryland-Ridge Detection Algorithm (UMD-RDA) to investigate sea ice topography across a range of scales. Applying the UMD-RDA to the ATL03 Global Geolocated Photon product we measure surface roughness and derive the frequency, height, width, and angle of individual pressure ridge sails. Aggregating data from multiple orbit crossings per day we investigate ridge characteristics at length-scales varying from 1 km (individual floes) to the pan-Arctic scale (central Arctic Ocean). Here, we present an evaluation of pressure ridge characteristics during the winter seasons of 2018/19, 2019/20, and 2020/21, comparing results from distinct regions with varying ice conditions. Near-coincident, independent observations of pressure ridges with Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) lidar data, OIB near-coincident Continuous Airborne Mapping By Optical Translator (CAMBOT) high-resolution (~15 cm) optical imagery, and WorldView high-resolution (~30 cm) panchromatic satellite imagery are used to evaluate the accuracy of our ICESat-2 ridge detection scheme. There are many potential use-cases for a high-resolution sea ice topography data product within the community, ranging from navigational hazard mitigation to ecological studies of marine mammal habitats. We discuss plans for releasing these data products and discuss the improvements such data would make within high-resolution sea ice models.

Hurricanes bring heavy rain and induce catastrophic flooding. The damage and fatalities underscore the urgency for understanding and improving the hydrological forecasts. Here we build an integrated hydrological framework in support of decision making, specifically for heavy rainfall caused by tropical storms. We apply different ensemble approaches for short-lived tropical storms (e.g., Tropical Storm Imelda) and long-lasting and major hurricanes (e.g., Hurricane Harvey). To drive the WRF-Hydro/National Water Model (NWM), atmospheric inputs are derived from the dynamical ensemble prediction based on Hurricane Weather Research and Forecasting (HWRF) for Hurricane Harvey. For short-lived tropical storms, which do not have operational hurricane forecast from regional dynamical models, we manually generate an ensemble forecast from a deterministic weather forecast from the Global Forecast System (GFS) and perturb the precipitation intensity and location according to the new runs from the High-Resolution Rapid Refresh (HRRR). On top of the current operational forecast from NWM, both of our approaches generate more than 20 separate forecasts (ensemble members) to address uncertainties in atmospheric dynamics, specifically for tropical storms and hurricanes. We evaluate the storm track, precipitation, and streamflow over the hurricane-prone areas of Texas. By linking ensemble weather forecasts to hydrological forecasts, we seek to provide a more comprehensive understanding of the underlying models and support advanced research on flood resilience for critical infrastructures.

Oceanic Transform Faults (TF) comprise first order discontinuities bounded between mid-ocean ridge spreading centres. TF mainly accommodate strike slip motion, separating lithospheric plates of different age and thermal structure. Oceanic TF are intriguing in that they do not produce earthquakes as large as might be expected given their long length, with seismic slip corresponding only to a small fraction of the total tectonic slip. The relative geologic simplicity of oceanic TF means that they are an important analogue for more hazardous continental TF, with high potential for improving insights into the earthquake cycle. We investigate the earthquake properties along Chain, a ~300 km long TF in the equatorial MAR by combining both microseismic and teleseismic data. We use the ~1-year microseismicity data (total of 812 events) gathered during the PI-LAB (Passive Imaging of the Lithosphere-Asthenosphere Boundary) experiment and EURO-LAB (Experiment to Unearth the Rheological Lithosphere-Asthenosphere Boundary). We perform cluster analysis in multi-dimensional phase space, consisting of various seismic (epicentral coordinates, magnitude) and geophysical (gravity anomalies, bathymetry, tidal height) parameters. We investigate potential triggering mechanisms, including tidal, static and dynamic stresses. We extend our analysis back in time by considering stronger earthquakes (MW>~5.0) from Global Centroid Moment Tensor (GCMT) since 1976. We find three unique, 50-100 km long clusters or segments from our analysis going from east to west, separated by seismic gaps. Microseismic activity is highest at the eastern segment of Chain where there is the largest positive flower structure, negative rMBA gravity anomaly but very few M>5.5 events. The western segment has reduced seismicity rates relative to the eastern, and is associated with a positive rMBA and a few small flower structures. The central segment is bounded between two seismic gaps and demonstrates relatively high activity rates in the middle. Our result suggests that trans-pression of highly altered mantle/crust and/or high pore pressure due to hydrothermal fluid circulation in the eastern flower structure enhances seismic activity. Overall, we find the existence of consecutive locking and creeping segments, with some of the patches exhibiting hybrid behaviour, potentially causing their sporadic activation/reactivation.

One of the next giant leaps for humanity—inhabiting our neighbor planet Mars—requires enough water to support multi-year human survival and to create rocket fuel for the nearly 150-million-mile return trip to Earth. Water that is already on Mars, in the form of ice, is one of the leading in situ resources being considered in preparation for human exploration. Human missions will need to land in locations with relatively warm temperatures and consistent sunlight. But in these locations, ice (if present) is buried underground. Much of the ice known to exist in mid-latitude locations was likely emplaced under climate conditions (and orbital parameters) different from today. So in addition to providing an in-situ resource for human exploration, Martian ice also provides a crucial record of planetary climate change and the effects of orbital forcing.This presentation will highlight techniques and recent activities to characterize Mars’ underground ice, such as the Subsurface Water Ice Mapping (SWIM) Project (Morgan et al. 2021, Nature Astro.; Putzig et al. In Press, Handbook of Space Resources; Putzig et al. this AGU; Morgan et al. this AGU). We present outstanding questions that will be vital to address in the context of ISRU (in situ resource utilization) and connections between these questions and the climate in which the ice was emplaced and evolved (e.g., Bramson et al. 2020, Decadal White Paper). Lastly, we discuss how these science activities intersect with future exploration, particularly that enabled by collaborations between space agencies as well as industry partners (Heldmann et al. 2020, Decadal White Paper; Golombek et al. 2021, LPSC).High-priority future work includes better orbital characterization of shallow ice deposits, such as radar sounding at shallower scales (<~10m) than that of SHARAD, as proposed for the International Mars Ice Mapper. Also needed are detailed studies of the engineering required to build potential settlements at specific candidate locations; this includes characterization of the nature of the overburden above the ice, which will inform future resource extraction technology development efforts. Ideally, initial landing sites would be chosen with a long-term vision which includes preparation and development of the basic technologies and designs needed for human landing on Mars.

To stabilize global warming within the 1.5-2.0°C Paris Agreement goal, greenhouse gas (GHG) emissions need to reach net-zero. Offsetting is required for net-zero emissions (emissions minus offsets), as achieving zero emissions is unrealistic. Offset schemes differ widely in their capacity to mitigate anthropogenic global warming (AGW). Rayer et al. (2021) provide offsetting guidelines to strengthen their long-term climate benefit, this chapter further develops this topic. We propose a framework for rating different offset types based on their climate risk, moving beyond the usual criteria of verification, audit, and additionality. We apply our offset climate ratings to some offsetting types and case studies including the sorts of schemes proposed by major oil companies, and the PAS 2060 carbon-neutrality standard. Some offsets provide little more than economic nudges towards emissions reduction. Climate offsetting requirements are more exacting. The proposed framework grades offsets from most to least benefit. Grades help classify offsets’ climate risks based on: helping terminate it, slowing warming, or only offering economic incentives with little climate benefit. Ultimately, to stabilize global warming, it will be necessary for policy to encourage the highest rated offsets under the proposed framework. Reference Rayer, Q. G., Jenkins, S., & Walton, P. (2021). Defining net-zero and climate recommendations for carbon offsetting. In Walker, T., Wendt, S., Goubran, S., & Schwartz, T. (Eds.), Beyond the 2ºC: Business and Policy Trajectories to Climate Change Adaptation (chapter 2). Palgrave Macmillan.