Commonly-occurring stratification and synoptic tendencies lead to liquid clouds and warm precipitation processes in the PBL over large portions of the globe. The climate is so sensitive to these low-level clouds that they are identified in IPCC reports as major uncertainty sources for climate prediction; their representation in GCMs thus needs improvement. PBL clouds have therefore been scrutinized in numerous field campaigns over both ocean and land. The main method for measuring clouds in field campaigns is in-situ airborne probing and, though these data are invaluable, it is widely recognized that spatial and temporal sampling is innately poor. We then turn to remote sensing as a way of drastically improving spatial sampling since it delivers cloud properties over more than a line-of-flight through 3+1D space. The obvious tradeoff is, however, generally complicated connections between remotely-measured radiances and inherent cloud properties of real interest to cloud process modelers. Active remote sensing from below or above the clouds improves vastly over in-situ sampling, but its outcome remains confined to a “ribbon” of vertical profiles ordered in time (from below) or space (from above). Passive imaging has the complimentary problem of delivering a potentially wide horizontal swath of cloud properties, but integrated along the vertical. At least that is the conventional wisdom when it comes to the solar spectrum, where observed radiances from clouds are dominated by multiple scattering. Based on recent results from AirMSPI imaging at 20 km altitude, we challenge the perceived limitation of passive shortwave radiometry to deliver only column-integrated properties. We demonstrate that multi-pixel exploitation of multi-angle spectro-polarimteric imaging at solar wavelengths can be used to extract not only maps of microphysical properties but also 3D cloud structure for both PBL-topped stratiform layers and vertically-developed 3D clouds in convective regimes. A key realization is: airborne and space-based sensors offer radically different spatial and angular sampling opportunities with unique advantages in both cases. We look forward to future PBL-specific missions in space for their global reach. At the same time, there is a clear case for deploying high-altitude imagers in all future campaigns.

On July 2, 2019, a full solar eclipse has occurred in the location of the Andes Lidar Observatory in Chile. We have recorded data utilizing the set of sensors operating at the facility, such as daytime/nighttime lidar measurements of mesosphere temperature, Na density, and winds, nightglow images of the OH(6,2), O2(0,1), O(1S), and O(1D) emissions, GPS receivers, and the new meteor radar deployed at ALO on June 2019. The data show clear effects of the solar eclipse in the mesosphere region such as deep changes in temperature and density, as well as generation of unusual gravity wave structures. In this presentation, we want to discuss the image data showing the gravity wave activity during the evening following the eclipse event and discuss the generation mechanisms of the unusual gravity wave structures in the light of data recorded by all other instruments.

Atmospheric drag is one of the primary sources of error in the orbit determination and prediction of satellites in the low altitude LEO regime. Accurate modeling of the drag force is limited by uncertainties in the atmospheric density model used in the filter and the assumption of a constant drag coefficient, the so-called ‘cannonball’ model. Over the last two decades, various advances in density and drag-coefficient modeling have been made possible through the development of empirical and physics-based dynamical calibration techniques and machine-learning methods respectively. But even with high-fidelity models for density and drag coefficient, systematic uncertainties can remain in both due to the lack of temporal and spatial resolution of data and insufficient knowledge of parameters that feed into these models. In this work, we develop an estimation-based Fourier expansion model that can provide corrections to the nominal values of density and drag coefficient during the orbit determination process. In an earlier work (Ray et al., 2018), we demonstrated improved orbit prediction performance over the standard cannonball model with Fourier series expansions of the drag coefficient in body frame and orbit frame of a satellite. Whereas a body-fixed Fourier model captures the dependence of the drag coefficient on satellite attitude, the orbit-fixed model corrects for periodic changes in the gas-surface interaction in orbit. Since changes in the gas-surface interaction parameters in orbit are highly correlated with atmospheric density, any existing errors in the density are absorbed in the estimated orbit-fixed coefficients. Here, we derive a body-orbit Fourier model such that the orbit-fixed terms provide corrections for combined error variations of density and drag coefficient in orbit while the body-fixed terms account for the drag coefficient attitude dependence. We analyze the performance of the proposed approach with various atmospheric models such as NRLMSISE-00 (Picone et al., 2002), JB08 (Bowman et al., 2008), HASDM (Storz et al., 2002) and densities derived by Mehta et al. (2017) for varying geomagnetic conditions for the GRACE satellite.

A broad range of attempts have been made to quantify the macroeconomic impacts of climate change, such as those of intensifying weather extremes and of yield losses of major crops, which have been synthesized by the efforts to estimate the Social Cost of Carbon (e.g., the US Interagency Working Group, 2016). However, up to the present, few insights have been fed into these debates as to the economic impacts associated with climatic responses of aerosol emissions from wildfires. In this study, we shed light on the potential scale of macroeconomic impacts of Siberian wildfires’ climatic effects by drawing on results of sensitivity experiments on enhanced biomass burning (BB) emissions over the defined Siberian domain using a global aerosol climate model, MIROC-SPRINTARS, in which the model was coupled with the ocean model (i.e., Atmosphere-Ocean coupled Global Climate Model: AOGCM) – the scientific results of these simulations are also discussed in detail by our companion paper, Yasunari and Takemura (2019), in the same session at this AGU Fall Meeting 2019. We used sets of simulation results differing in the conditions of BB emissions and climate, in which three different reference levels of BB emissions over the defined Siberian domain were used under the present (RCP scenario in 2005) or future climate (RCP2.6 and RCP8.5 in 2030) conditions. Differentials of annual average temperatures estimated by the simulations were used to compute monetary-equivalent economic impacts attributable to climatic effects of BB by applying the functions of the RICE-2010 model (the 2010 version of the regional integrated model of climate and the economy model: Nordhaus, PNAS, 2010), which is a regionally disaggregated version of the most widely used climate-economy model, the DICE model. The economic impacts were estimated for the most affected countries and regions by Siberian wildfires, such as Russia, China and Europe.

As climate change adaptation is becoming a recognized policy issue, the needs are growing about quantitative economic evaluation of adaptation-related public investments, particularly in the context of climate finance. Irrigation, which enhances and stabilizes water supply for farming, is a potential means of climate change adaptation, but attempts of economic evaluation of its effectiveness as an adaptation measure are few in part because such assessment requires an integration of various types of simulation analyses. Against this background, we conduct a case study of a Kenyan irrigation development project (Mwea Irrigation Development Project), which is undertaken by the Kenyan government with a loan from the Japan International Cooperation Agency (JICA), to evaluate the effectiveness of the project for climate change adaptation by using a combination of simulation models. Specifically, we conduct a simple downscaling of CMIP5 climate simulation data, whose outputs are fed into a hydrological model (SHER model) and a yield forecasting model (DSSAT model). With these simulation data, together with data of socioeconomic parameters drawn from existing and original surveys, we compute economic variables such as farmers’ average income. Climate and other uncertainties are incorporated into analysis without probabilistic weights (conforming to the Robust Decision Making approach, e.g., Lempert et al., 2013) to highlight vulnerabilities for local farming. The results show that despite uncertainties of precipitation trends, increased temperatures due to climate change have a general tendency to reduce rice yields, and that irrigation development will mitigate that income impacts from the yield loss, i.e., it will likely be effective as a means for climate change adaptation.

Field trips, camps, and courses are considered a critical experience in the education of future geoscientists. Traditional bedrock geologic mapping continues to play a prominent role in field courses and a robust body of research describes how students gain content, skills, habits of mind, social engagement, and identity as a geologist in field settings. However, little work has examined instructional practices that lead to meaningful learning of geologic mapping. During a semi-structured interview, we asked 67 novice to professional geologists to reflect on how and where they learned geologic mapping. When analyzed through the lens of situated learning theory (Lave and Wenger 1991), responses from these geologists shed light on best practices for mapping instruction in field settings. Our findings highlight two contrasting forms of instruction and the importance of a community of practice. In structured instruction, students and instructors worked together through a prescribed learning progression. Conversely, in ill-structured instruction participants described receiving little guidance on how to progress. Overall, our participants reflected positively on learning experiences that built upon sufficient prior knowledge, allowed for some struggle but included generous instructor support, and provided the opportunity to work collaboratively with other learners. Our participants found regular feedback to be highly beneficial. We suggest that appropriate activities and assessments that encourage both teamwork (collaboration) and independent studies should be incorporated into field instruction. Additionally, the results of our study suggest that the community of practice is a crucial contributor to learning to map and that the learning is defined as much by the professional domain shared between novices and experts as it is by the physical domain provided by the complex Earth environment. In order for a learner to be authentically involved in the actual practice of an expert, the learner must work with and learn from the community of practice.

Results of laboratory experimental study of the hydraulic fracture initiation, its propagation and closing, re-fracturing, the fluid pore pressure variations and related acoustic emission (AE) are considered. The experimental setup differs from common equipment designed for testing cores or cubic samples; it is designed for work with samples of disk form with diameter 430 mm and height 66 mm, which allows us to measure the pore pressure distributions along the sample boundary together with passive and active acoustic emission measurements (Fig.1). The experiments were conducted with artificial porous saturated samples made from gypsum-cement mixture in agreement with simulation criteria. The samples were created by filling the pressure chamber with the gypsum-cement-water mixture. After drying, the samples were saturated by gypsum water solution and loaded by three-axial stresses. To produce the fracture, mineral oil was injected with the constant rate 0.3 sm3/sec through the cased borehole. A set of experiments was conducted, in which the main stress axis orientation was changed after the first hydraulic fracture creation. Registration of the change in the amplitude of the waves passing through the fracture allowed to determine the fracture formations, its filling with fluid, the increase in the fracture opening as the injection continued, and the fracture closing after the injection was stopped. It was found that the influence of the fluid pore pressure diffusion from the point of injection is of great significance: the fracturing pressure was several megapascals higher than it should be expected. The fracture closing pressure also occurred to be higher in comparison with the case, when the pressure diffusion is neglected. It was obtained, that the AE source positions are closely related with the fracture position. It is remarkable, that we did not registered any AE outside the fracture. Comparison that fact with the results of the pore pressure measurements allows to suggest, that the pressure increase due to the viscous fluid injection is not enough to induce AE pulses at some distance from the borehole and the fracture. When the injection fluid has the same viscosity as the fluid saturating the sample, the pressure increase is sufficient to induce acoustic emission corresponding to the pore pressure increase.

The Arctic Ocean contains only a 1% of the world’s ocean water, but the rivers that flow out into it account for the 10% of the volume world’s rivers freshwater. The upper layer of fresher water facilitates the creation of sea ice and plays an important role in the position of the jet stream and storms over the northern hemisphere [ISBN, 978-82-7971-097-4]. Remote sensing measurements are of special importance in the Arctic since in situ data is very scarce there. SMOS and SMAP are currently providing sea surface salinity (SSS) measures, but only the product provided by Barcelona Expert Center (BEC) is a dedicated product for the Arctic region. The product that we present in this work is an improvement of the BEC Arctic v2.0. The new version 3.0 has as the primary objective the describing better the river discharges. The spatial grid used is WGS84/NSIDC EASE-Grid 2.0 North for the all stages of the processing chain. This procedure avoids spatial interpolation, favoring the definition of river mouths. The salinity retrieval is based on the Debiased non-bayesian method [doi:10.1016/j.rse.2017.02.023] and similarly to what is done in the processing of altimetric data, SMOS salinity is corrected using a reference calculated from the own SMOS data for each latitude, longitude, pass orientation and antenna measuring position. Arctic v3.0 differs from current method [doi:10.3390/rs10111772] in two important points: the reference is computed for brightness temperature instead of SSS and the antenna has been divided in a more homogeneous grid. Other improvements concern to data filtering and propagation of the radiometric errors to SSS. All these improvements provide level 3 maps less noisy, increasing the effective resolution of salinity gradients. Freshwater gradients are much better resolved than in previous version (Fig. 1). Comparison with JPL SMAP product is also planned as a first step to generate a combined product. This work is funded by ESA Arctic + project and also includes the assimilation of the resulting SSS product in the ocean-sea ice data assimilation system TOPAZ as the next version TOPAZ5. A preliminary study [doi:10.5194/os-2018-163] has been performed concluding that BEC product could be a good candidate to be assimilated by TOPAZ. Moreover, some preliminary tests with a pre-release v3.0 version will start shortly.

Increasing awareness of the links between water-energy consumption has seen a strong focus on examining and identifying areas where reductions in greenhouse gas emissions can be achieved. We are becoming increasingly aware that a truly sustainable water and energy supply in any community must incorporate a systemic approach where community, governance, culture, economics and local environmental context are considered along with improved technological outcomes. This presentation is focussed on giving a snapshot of research projects that have been undertaken in Australia to understand the water-energy nexus through the use of digital technologies such as smart water and energy meters and big data analytics. Topics that will be highlighted will include: • the application of smart metering technology to improve our understanding of peak demand patterns and subsequent network optimization, (e.g. reduce energy demand for pumping), • assessing energy consumption of water heating systems with empirical information (e.g. measured water consumption patterns) • residential rain tank pump performance at an end use level • using smart meter enabled, water end-use data to identify the key indoor and outdoor end-uses that contribute to water-related energy demand; and • the water-energy nexus in remote and Indigenous communities – using water demand management to reduce high energy off-grid water supply costs Projects will cover urban, regional and remote communities and will. The presentation will conclude some provide insights on how using a combination of socio-technical approaches can reveal both barriers and opportunities to reducing water and water-related energy demand.

There can be few greater scientific challenges than predicting the response of the global system to anthropogenic disruption, even with the array of sensing tools available in the “digital Anthropocene”. Rather than depend on one approach, climate science thus employs a hierarchy of models, trading off the tractability of Energy Balance Models (EBMs) [1] against the detail of Global Circulation Models. Since the 70s Hasselmann-type stochastic EBMs have allowed treatment of climate fluctuations and noise. They remain topical, e.g. their use by Cox et al to propose an emergent constraint on climate sensitivity [2]. Insight comes from exploiting a mapping between Hasselmann’s EBM and the original stochastic model in physics, the Langevin equation of 1908. However, it has recently been claimed that the wide range of time scales in the global system may require a heavy-tailed response [3,4] to perturbation, instead of the familiar exponential. Evidence for this includes long range memory (LRM) in GMT, and the success of a fractional Gaussian model in predicting GMT [5]. Our line of enquiry is complementary to [3-5] and proposes mapping a model well known in statistical mechanics, the Green-Kubo “Generalised Langevin Equation” (GLE) to generalise the Hasselmann EBM [6]. If present LRM then simplifies the GLE to a fractional Langevin equation (FLE). As well as a noise term the FLM has a dissipation term not present in [3,4], generalising Hasselmann’s damping constant. We describe the corresponding EBM [7] that maps to the FLE, discuss its solutions, and relate it to existing models. References: [1] Ghil M (2019) Earth and Space Sciences, in press. [2] Cox P et al. (2018) Nature 553: 319-322 [3] Rypdal K. (2012) JGR 117: D06115 [4] Rypdal M and Rypdal K (2014) J Climate 27: 5240-5258. [5] Lovejoy et al (2015) ESDD 6:1–22 [6] Watkins N W (2013) GRL 40:1-9 [7] Watkins et al, to be submitted.

As we approach 2020, there are more well bores that require abandonment than well bores waiting to be drilled. The USA alone has ~1.7 Million well bores with an API number indicating that they are not permanently plugged, therefore will be in need of Plugging and Abandonment (P&A) at some point in the future. Some of these well bores are located in fragile ecosystems, such as the Gulf of Mexico, where it is estimated that 9,000 idle well bores are waiting permanent plugging and abandonment. The numbers of future P&As suggest requirement for an urgent improvement of current technology as many governments are preparing standards that require assurance for zero leakage over thousands of years. This paper/talk will present knowledge gaps we identified during literature review and the data obtained during first two terms of the ongoing project , under NASEM-GRP funding. Extensive literature review points to the following issues: 1) Portland-cement based materials, with their pH ~13, are geochemically incompatible with the reservoir conditions in the GoM. 2) Weak interfaces between and contamination with drilling fluids in well bore structures present high risk for Hydrocarbon leakage, which currently cannot be mitigated successfully. Placement and accessibility of well bore in offshore environment adds to technological complexity and increases the risk of leakage. 3) Monitoring and verification is currently not available for long-term assessment offshore. Some of our preliminary data suggests the following: 1) Plugging materials are impacted by contamination form drilling fluids and other well bore materials, such as fragment of dehydrated clays, oily rock fragments. 2) Casing corrosion might lead to deterioration of primary well bore cement, which can be difficult to locate and re mediate prior to plugging. 3) Geo-mimicry is a potential path forward in designing and developing barrier materials capable of serving over thousands of years.

Smart Climate Hydropower Tool is an innovative web-cloud-based service that implements a set of data-driven methods for river discharge. An application for two catchments in South America is discussed (test cases), where management of hydropower plants can benefit from knowledge of incoming discharge forecasts up to 6 months in advance. SCHT has been developed inside H2020 project “CLARA - Climate forecast enabled knowledge service” and exploits several Artificial Intelligence algorithms, evolving by R&D activity to test new available ones. Although tangible results using AI have been published (i.e. Callegari, et al., 2015, De Gregorio et. al 2017) challenges remain for seasonal lead times and rainfall dominated catchments, where forecast of meteorological variables plays a critical role. In this contribution we show results of application of different AI algorithms (from supervised learning regression techniques, to artificial neural networks). Each algorithm is trained over past decades datasets of recorded data, forecast performances are then evaluated using separate test sets with reference to benchmarks (historical average of discharge values and simpler multiparametric regressions). Major operative advantages of AI with respect to mechanistic hydrological models include limited to none a priori knowledge of involved physical phenomena, high level of flexibility when managing heterogeneous sets of variables related to discharge, and quick setup time of the forecast. Major efforts are requested to identity informative input features ranging from earth observation to gauging stations data, to public meteorological forecasts (i.e Copernicus Climate Change Service-C3S). Using AI techniques many combinations of features can be tested together, to predict river discharge to the reservoirs, choosing the best performing one and tailoring the service to the catchment of interest. Once trained, each algorithm just needs to retrieve online data to perform forecasts, with limited maintenance (i.e. annual re-training to consider new available hydrological data). For demonstrational purposes we prototyped a cloud-based service, for immediate visualization, through a common browser, of both past and forecasted data, and get on fly performance metrics calculation of the forecasts.

In recent years, the need for improved global terrestrial and space weather specification and forecast has driven the development of new commercial satellite constellations to monitor radio occultations (RO) using Global Navigation Satellite System (GNSS) signals. These signals interact with irregularities in the ionosphere, causing radio wave scintillation that is known to degrade the performance of communication and navigation systems, and may also degrade the accuracy of RO tropospheric and stratospheric retrievals. This January, PlanetiQ will launch the first two of its planned 20 microsatellites commercial constellation (to be complete by 2022), focused on weather and space weather forecasting and climate. With this constellation, PlanetiQ intends to provide over 80 million global observations per day. The focus of this paper concerns a simulation study we conducted to assess possible impacts on tracking and tropospheric retreivals due to ionospheric scintillation. We constructed a 3D, time-dependent model for the strength, orientation, and spectral characteristics of the irregularities. Our methodology generates representative realizations of irregularity structure (space weather) rather than average conditions (climatology). We integrated through the model along each RO ray-path to determine the strength and location of an equivalent phase screen, which we used to generate realizations of intensity and phase scintillation at the receiver. The phase screen calculation is a generalization of our previous algorithm (Carrano et al., Radio Sci., 2011) which now admits propagation and scanning at arbitrary angles to the magnetic field. There were approximately 50,000 RO events between the 20 PlanetiQ microsatellites and the satellites of the GPS, GLONASS, Galileo, and BeiDou GNSS constellations each day. We simulated scintillation for each carrier frequency transmitted by each GNSS satellite for a total of 39 days. We discuss potential impacts of scintillation on satellite tracking and the accuracy of tropospheric retrievals as a function of season and solar activity. We compare the scintillation index (S4) along each RO raypath with a vertical propagation path through the same irregularities. These are compared with observations from the CORISS instrument onboard the C/NOFS satellite and ground based observations from the SCINDA network.

Evidence of syntectonic magmatism associated with onset extension and unroofing of the Menderes Massif metamorphic core complex, western Turkey, is well documented. The Salihli and Turgutlu plutons, located along the Alasehir detachment in the Central Menderes Massif (CMM) and the Koyunoba and Eğrigöz Plutons located in the Northern Menderes Massif (NMM) are common targets for understanding the dynamics and timing of this Cenozoic activity. To this end, here we report new potassium feldspar 40Ar/39Ar ages from samples collected from each pluton and compare these to available zircon U-Pb and monazite Th-Pb crystallization ages. Argon age spectra were collected by incrementally heating bulk concentrates with a CO2 laser and analyzing the gas released at each step. The peraluminous granite samples from the Koyunoba (AT17) and Eğrigöz (WA12) plutons both have effectively flat spectra with average plateau ages of 20.12±0.05 Ma and 19.86±0.05 Ma, respectively. The U-Pb age of zircon from WA12 is 20.5±1.1 Ma [Catlos et al., 2012; doi: 10.2475/05.2012.03 ]; although a zircon U-Pb age from AT17 has not been reported, zircon from other Koyunoba rocks have U-Pb ages between 21.1 Ma and 23.2 Ma [1]. K-feldspar from sample EB06 (Turgutlu Granite) steadily increases in age from 10.62±0.03 Ma to a plateau age of 14.06±0.03 Ma, with similar inverse isochron (13.66±0.29 Ma) and total gas ages (13.36±0.2 Ma). Sample EB05 (Salihli Granite) increases in age from 3.27±0.10 Ma (step 3, 0.5% 39Ar released) to a maximum of 6.05±0.09 Ma (step 33, 96.6% 39Ar released). A plateau age could not be estimated for this sample, but two inverse isochron ages from different degassing steps are calculated (3.02±0.09 Ma for the initial 19 steps and 3.29±0.22 Ma, for the final steps 19-31). Regarding their crystallization histories, the oldest reported monazite Th-Pb age for EB06 is 15.5±1.2 Ma [2] and reported monazite Th-Pb ages for Salhili granite ranges from 9.6±1.6 Ma to 21.7±4.5 Ma [Catlos et al., 2010; https://doi.org/10.1016/j.tecto.2009.06.001]. These 40Ar/39Ar ages suggest NMM plutons rapidly cooled whereas CMM Salihli and Turgutlu plutons not only remained at depth below the argon retention window for a prolonged period following emplacement, but each experienced unique thermal (exhumation) histories despite their geographic proximity.

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat-2/ATLAS data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m, and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat-2 employs a year-round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat-2 observations may be used to investigate time-varying, dynamic and thermodynamic sea ice processes.

Aerosols acting as cloud condensation nuclei (CCN) play a fundamental role in droplet formation and cloud development in the atmosphere. Several studies have focused on the representation of aerosol-cloud interactions to improve cloud parametrizations and deep moist convection representation in numerical models. However, the effect of aerosol concentration on mixed-phase clouds is complex as it can not be directly measured and it is far from being fully understood. Then, observation of CCN distributions, variability, and exploration of possible sources can help go deeper into the effects of aerosols on cloud and precipitation processes. From October 2018 to April 2019, Cloud, Aerosol, and Complex Terrain Interactions field campaign (CACTI) took place in central Argentina. An ARM-DOE Mobility Facility was installed in Villa Yacanto, Cordoba to understand the processes affecting orographic deep convective initiation in the region. CCNs in situ observations from a Mobile Aerosol Observation System were analyzed. Amazon biomass burning is one of the known sources of aerosols in central and northern Argentina however during CACTI measurement period low rate emissions were observed from this source. Then, local aerosol source regions were investigated by backward trajectories with lagrangian dispersion model FLEXPART to determine their possible interaction with extreme precipitation events.

The occurrence of a large number of earthquakes in the inter and intraplate settings of the Japan Trench leads to ruptures with varying frequencies. To capture the temporal distribution of energy and their ranges of frequencies, we have used the Intrinsic Mode Functions (IMFs) derived from the vertical components of the strong-motion records. Here we present an “energy release function”, which is yet another way of representing frequency-dependent energy release. Without the assumptions of the area of slip and elastic moduli, this provides a new representation of the energy released at the source. Choice of the appropriate IMF and thus the range of frequencies representing the source was based on the best fitting teleseismic model for the same earthquake. We analysed the strong-motion records for three earthquakes (all in the magnitude range of 7.1 to 7.3), representing interplate, intraplate, and intraslab settings and used borehole data from the KiK-net. These were the Miyagi 2005 (Interplate), Tohoku 2011 (Intraslab), and Honshu 2012 (intraplate). We used the Hilbert-Huang Transform, a combination of Empirical Mode Decomposition (EMD) and Hilbert Transform (HT) to develop the spectra for vertical components of each of these earthquakes. A combination of the IMFs within the frequency band (0.1 to 3 Hz) that mostly represent the frequency range used for teleseismic source inversion (0.01 to 2 Hz) was used to develop the spectra in each case. The shape of the spectra generally mimics that of the moment rate function. Where the moment-rate function follows a single pulse, the spectrum is able to generate its shape, and the sub-events are represented through independent pulses of energy. We believe that the representation of an earthquake source based on its frequency content and temporal pattern has important applications in predicting the shaking effects of an earthquake.

Impact craters in the Pluto system as observed by New Horizons in July of 2015 revealed a previously-unknown deficit of small Kuiper belt objects (KBOs) less than ~1–2 km in diameter (Singer et al., 2019, Science) . New Horizons was poised to test this observation 3.5 years later with its next KBO close flyby of the cold classical KBO (486958) 2014 MU69 (nicknamed Ultima Thule). The images returned by New Horizons in early 2019 show MU69 is only modestly cratered, and potential craters on the surface show a shallow size-frequency distribution (SFD) similar to that of craters on Pluto and Charon (Stern et al. 2019, Science; Singer et al., 2019, EPSC-DPS; Spencer et al. 2020, Science). Some features on MU69 do have multiple morphologic indicators suggesting they are impact craters. There are regions on MU69 with few-to-no craters, even where the lighting is favorable for recognizing topographic features. The ~7-km-diameter depression (informally called Maryland) on the small lobe of MU69 is the largest easily observed feature that is likely to be an impact crater. There are also craters under 1 km in diameter, but no obvious craters between 1 and 7 km in diameter. The possible impact craters < ~1 km in diameter were divided into subgroups based on morphology, likelihood of being a crater , lighting geometry, and also into one possible geologic unit. Measurements/ratings were provided by several team members. All subgroups produced shallow-sloped SFDs (differential power-law slope less than approximately −2) similar to those seen on Pluto and Charon for the same size craters (when scaled for gravity and impact velocity). Whole-sale resurfacing through geologic processes is not expected for MU69. Thus many more craters and at least some heavily cratered terrains would be predicted if the impactor SFD slope were steeper (an average differential slope closer to −3; Greenstreet et al. 2019, ApJ). Both the apparent lack of craters overall, and the shallow SFD slopes, are consistent with a relatively benign collisional environment for MU69 (McKinnon et al., 2020, Science).