We develop a Hamiltonian Monte Carlo (HMC) sampler which solves a multi-parameter elastic full-waveform inversion (FWI) in a probabilistic setting for the first time. This gives novel access to the full posterior distribution for this type of highly non-linear inverse problem. Typically, FWI has focused on using gradient descent methods with proper regularization to iteratively update models to a minimum misfit value. Non-uniqueness and uncertainties are mostly in this approach. Bayesian inversions offer an alternative by assigning a probability to each model in model space given some data and prior constraints. The drawback is the need to evaluate a very large number of models. Random walks from Markov chains counter this effect by only exploring regions of model space where probability is significant. HMC method additionally incorporates gradient information, i.e. local structure, typically available for numerical waveform tomography experiments. So far, HMC has only been implented for acoustic FWI. We implement HMC for multiple 2D elastic FWI set-ups. Using parallelized wave propagation code, wavefields and kernels are computed on an regular numerical grid and projected onto basis functions. These gradients are subsequently used to explore the posterior space of different target models using HMC. The free parameters in these experiments are P and S velocity, and density. Although simulating Hamiltonian dynamics in the resulting phase space is approximated numerically, the results of the Markov chain are nevertheless very insightful. No prior tuning of kernels, data or model space is required, under the constraint that the sampler is properly tuned. After a burn in phase during which the mass matrix is iteratively optimized, the Markov chain is run on multiple nodes. After approximately 100,000 samples (combined from all nodes) the Markov chain mixes well. The resulting samples give access to the full posterior distribution, including the mean and maximum-likelihood models, conditional probabilities, inter-parameter correlations and marginal distributions.

We explore seasonal mass oscillations by continental water storage in Southeast Asia and Himalayan arc region using continuous Global Positioning System (cGPS) measurements and satellite data from the Gravity Recovery and Climate Experiment (GRACE). While the interaction between seasonally induced non-tectonic and tectonic deformation along the Himalayan plate boundary is still debated, we propose that tectonic deformation along this plate boundary can be significantly influenced by the deformation induced by the non-tectonic hydrological loading cycles. We suggest that the substantially higher transient displacements above the base of the seismogenic zone indicate a role of changes in aseismic slip rate on the deep megathrust that may be controlled by seasonal hydrological loading. We invoke modulation of aseismic slip on the megathrust down-dip of the seismogenic zone due to a fault resonance process induced by the seasonal stress changes. This process modulates mid-crustal ramp associated micro-seismicity and influences the timing of Central Himalayan earthquakes.

We introduce a computer program for time series tuning and analysis. The well known in paleo-climatic community program AnalySeries after Paillard et al. is restricted to the Mac OS (32-bit) and, according to Apple plans to move entirely onto the 64-bit system, it will not be supported in the future upgrades of Macintosh OS. QAnalySeries is an attempt to re-implement the major functionality of AnalySeries thus providing the community with a useful tool. QAnalySeries is written using Qt SDK as a free software and can be run on Macintosh, Windows and Linux systems. Paillard, D., L. Labeyrie and P. Yiou (1996), Macintosh program performs time-series analysis, Eos Trans. AGU, 77: 379.

Extension and rifting of the lithosphere is fundamental to the evolution of the continents, but the mechanism by which the lithosphere thins remains enigmatic. Using new dense magnetotelluric array data collected within the rifted margin and adjacent areas of Southeast China, we resolve the three-dimensional electrical structure of the lithosphere to constrain the process of rifting and thinning. Our measurements discover a brittle-ductile transition zone featuring low electrical resistivity and low seismic velocity in the Cathaysia Block. A southeast-directed dip is resolved for the Jiangshan-Shaoxing Fault that documents the Neoproterozoic suturing of the Yangtze and Cathaysia Blocks, and has been reactivated by the Early Paleozoic and Early Mesozoic intracontinental orogenies. It acted as a low-angle detachment fault during the Mesozoic-Cenozoic extension and rifting. Given the asymmetries of topography, electrical resistivity, Bouguer gravity anomaly and Mesozoic volcanism across the Gan-Hang Rift, an asymmetric simple shear extension model is proposed for the South China Mesozoic-Cenozoic rift system. Water content of up to 0.1 wt% and melt fraction of up to 1% are estimated at 70 km depth beneath the central Wuyi Mountains, suggesting hydration of the mantle lithosphere. The hydration weakening of the mantle lithosphere promoted both the gravitational instability and convective removal of the lowermost lithosphere in South China.

A key to better constraining estimates of the ocean sink for fossil fuel emissions of carbon dioxide is reducing uncertainties in coastal carbon fluxes. A contributing factor in uncertainties in coastal carbon fluxes stems from the under sampling of seasonality and spatial heterogeneity. Our objectives were to i) assess satellite-based approaches that would expand the spatial and temporal coverage of the surface ocean pCO2 and sea-air CO2 flux for the northern Gulf of Mexico, and ii) investigate the seasonal and interannual variations in CO2 dynamics and possible environmental drivers. Regression tree analysis was effective in directly relating surface ocean pCO2 to satellite-retrieved (MODIS Aqua) products including chlorophyll, sea surface temperature, and dissolved/detrital absorption. Satellite-based assessments of sea surface pCO2 were made spanning the period from 2006-2010 and were used in conjunction with estimates of wind fields and atmospheric pCO2 to produce regional-scale estimates of air-sea fluxes. Seasonality was evident in air-sea fluxes of CO2, with an estimated annual average CO2 flux for the study region of -4.3 + 1.1 Tg C y-1, confirming prior findings that the Gulf of Mexico was a net CO2 sink. Interannual variability in fluxes was related to Mississippi River dissolved inorganic nitrogen inputs, an indication that human- and climate-related changes in river exports will impact coastal carbon budgets. This is the first multi-year assessment of pCO2 and air-sea flux of CO2 using satellite-derived environmental data for the northern Gulf of Mexico.

There is no consensus on the physical mechanisms controlling the scale at which convective activity organizes near the Equator, where the Coriolis parameter is small. High resolution cloud-permitting simulations of non-rotating convection show the emergence of a dominant length scale, which has been referred to as convective self-aggregation. Furthermore, simulations in an elongated domain of size 12228km x 192km with a 3km horizontal resolution equilibrate to a wave-like pattern in the elongated direction, where the cluster size becomes independent of the domain size. These recent findings suggest that the size of convective aggregation may be regulated by physical mechanisms, rather than artifacts of the model configuration, and thus within the reach of physical understanding. We introduce a diagnostic framework relating the evolution of the length scale of convective aggregation to the net radiative heating, the surface enthalpy flux, and horizontal energy transport. We evaluate these length scale tendencies of convective aggregation in twenty high-resolution cloud-permitting simulations of radiative-convective equilibrium. While both radiative fluxes contribute to convective aggregation, the net longwave radiative flux operates at large scales (1000-5000 km) and stretches the size of moist and dry regions, while the net shortwave flux operates at smaller scales (500-2000 km) and shrinks it. The surface flux length scale tendency is dominated by convective gustiness, which acts to aggregate convective activity at smaller scales (500-3000 km). We further investigate the scale-by-scale radiative tendencies in a suite of nine mechanism denial experiments, in which different aspects of cloud radiation are homogenized or removed across the horizontal domain, and find that liquid and ice cloud radiation can individually aggregate convection. However, only ice cloud radiation can drive the convective cluster to scales exceeding 5000 km, because of the high optical thickness of ice, and the increase in coherence between water vapor and deep convection with horizontal scale. The framework presented here focuses on the length scale tendencies rather than a static aggregated state, which is a step towards diagnosing clustering feedbacks in the real world. Overall, our work underscores the need to observe and simulate surface fluxes, radiative and advective fluxes across the 1km-1000km range of scales to better understand the characteristics of turbulent moist convection.

Due to its inherent ability to estimate the background error covariances, an ensemble Kalman filter (EnKF) is thought to be a practical approach to the strongly coupled data assimilation problems, where an entire coupled model state is estimated as if it was a single integrated system. However, increased complexity and the multiple time scale of the coupled system aggravate the rank-deficiency and spurious correlation problems caused by limited ensemble size available for the analysis. To alleviate these problems, a distance-independent localization method to systematically select the observations to be assimilated into each model variable has been developed and successfully tested with a nine-variable coupled model with slow and fast modes. This method, called correlation-cutoff method, utilizes the mean squared ensemble error correlation between each observable and model variable to identify where the cross-update should be used, and we cut off the assimilation of observations when the squared error correlation becomes small. To implement the method on a more realistic model, we thoroughly investigate inter-fluid background covariances in an atmosphere-ocean coupled general circulation model where the spatiotemporal scales of coupled dynamics significantly vary by latitudes and driving processes.

Mongolia has a complex tectonic history. The lithosphere was formed from multiple plate collisions in the Neoproterozoic - Early Paleozoic associated with the Central Asian Orogenic Belt. The region has since been modified by Mesozoic rifting, Cenozoic magmatism, and major strike-slip faulting along terrane boundaries and sutures. Central and Western Mongolia are part of the larger high elevation, low-relief Mongolian Plateau. To gain deeper understanding of modern deformation within the Hangay Dome in Central Mongolia, two years of teleseismic, regional, and local seismicity, recorded by a dense array of 72 temporary broadband seismic stations, was used to determine the distribution of seismicity and crustal structure. Results from receiver function analysis indicate the Hangay Dome has a crustal thickness ranging from 41-59 km. The thickest crust resides under areas of high topography and generally thins to the east. Average Vp/Vs ratios range from 1.77-1.8. We located the 7680 events detected by the array using a local 1D velocity model. Many events outline the Bulnay and Bogd faults, where historic Mw 8 earthquakes have occurred. Considerable seismicity is observed on the South Hangay – Bayan Hongor Fault System, including a Mw 4.6 earthquake. Seismicity is also observed along the Egiin Davaa and Mogod Faults. Preliminary results from a joint tomographic inversion for earthquake location and 3D velocity structure show a relatively uniform crust, where P-wave velocities in the uppermost crust range from 5.8-6 km/s. In these preliminary inversions, large portions of the region show Vp exceeds 7.0 km/s in the lower 10-15 km of the crust. The depth to the Moho is consistent with results from the receiver function analysis. Lateral velocity variations generally align with terrane boundaries and faults, such as the South Hangay - Bayan Hongor Fault System. Seismicity relocated in the inversion outline the South Hangay, Egiin Davaa, and Bulnay Faults. In addition, a cluster of seismicity locates between the Egiin Davaa and Hag Nuur faults, where no fault has previously been mapped. Seismicity in the Hangay Dome is generally confined to the upper 20 km, suggesting a rheological transition from brittle to ductile at this depth.

Reliability of future global warming projections depends on how well climate models reproduce the observed climate change over the twentieth century. In this regard, deviations of the model simulated climate change from observations, such as a recent “pause” in global warming, have received considerable attention. Such decadal mismatches between model simulated and observed climate trends are common throughout the twentieth century, and their causes are still poorly understood. Here we show that the discrepancies between the observed and simulated climate variability on decadal and longer time scale have a coherent structure suggestive of a pronounced global multidecadal oscillation. Surface temperature anomalies associated with this variability originate in the North Atlantic and spread out to the Pacific and Southern oceans and Antarctica, with Arctic following suit in about 25–35 years. While climate models exhibit various levels of decadal climate variability and some regional similarities to observations, noneof the model simulations considered match the observed signal in terms of its magnitude, spatial patterns and their sequential time development. These results highlight a substantial degree of uncertainty in our interpretation of the observed climate change using current generation of climate models.

We are at a unique time in the study of our place in space. On one hand, we operate in the same paradigm that has guided the study of space science for the past couple of decades, and on the other a rising dependence of our economic and social well-being on space demands a shift. Everywhere in our society ‘big data’ (defined by four V’s: volume, variety, veracity, and velocity) and the advent of sophisticated and efficient methods to explore these data (i.e., data science) present new opportunities for discovery, and the time is ripe for these methods to shift how we study the physics of space. We will first discuss the meaning of data science in the context of space science, and then demonstrate the potential for new discovery through a power use case: leveraging Global Navigation Satellite Systems (GNSS) signals for space weather prediction. In this use case, we take advantage of a large volume of data from GNSS signals, data science-driven technologies, and a machine learning algorithm known as the Support Vector Machine (SVM) to develop a novel predictive model for high-latitude ionospheric phase scintillation. This talk will conclude with a perspective on opportunities in space science through ‘big data’ and creating new scientific discovery at the intersection of traditional approaches and data science-driven innovation.

Coral reefs are one of the most diverse ecosystems on Earth and provide significant ecological, economic, and societal benefits valued at approximately $9.8 trillion U.S. dollars per year. Since 1997, NOAA’s Coral Reef Watch (CRW) has used near real-time satellite monitoring to provide ecological nowcasting of the ocean heat stress that can cause mass coral bleaching. While this benefitted coral reef managers, scientists, and other stakeholders, our users desired longer-range forecasts. In 2012, CRW launched its probabilistic, global Four-Month Coral Bleaching Outlook system based on NOAA’s operational Climate Forecast System (now CFSv2). The Outlook proved accurate in local bleaching events over the following two years. Subsequently, June 2014-May 2017 brought the longest, most widespread, and probably most damaging coral bleaching event on record. As this global event greatly threatened all tropical coral reefs, the Outlook system proved critical in helping users worldwide prepare for and respond to bleaching – including actions to reduce damage from these intense marine heatwaves. This presentation will introduce CRW’s ecoforecasting tools and focus on four “use cases” of CRW’s Outlook system during the 2014-17 global coral bleaching event. In 2015, concern over bleaching forecasted by CRW’s Outlooks prompted two actions by the State of Hawaii. First, the “Eyes of the Reef” volunteer network organized numerous training sessions and its first state-wide Bleach Watch “Bleachapalooza” event to monitor bleaching across the state. Second, State scientists collected specimens of rare corals to preserve them in onshore nurseries. One of these species is now locally extinct on Hawaii’s reefs, and these rescued specimens are being prepared for re-introduction. Next, as CRW predicted bleaching would persist for several months in the Northern Line Islands, NOAA mounted a special cruise to monitor these remote coral reefs. The record heat stress killed over 98% of the corals at Jarvis Island. Finally, in 2016, prior to peak bleaching, Thailand used CRW’s prediction of severe heat stress to close ten heavily used coral reefs to tourism as a way to reduce further stress to the reefs. These actions show the value of ecoforecasts to prepare resource managers for further climate change impacts.

Volume II of the Fourth National Climate Assessment (NCA4) - Impacts, Risks, and Adaptation in the United States - evaluates the risks American society faces as a result of climate change and highlights actions underway across the country to address those risks. NCA4 Vol. II communicates what climate change means for communities across the country, focusing on impacts and risks affecting the things and places we value. The report also elevates the visibility of successful adaptation actions throughout the ten NCA4 regions (including, for the first time, the U.S. Caribbean), with the hope that communities elsewhere in the country can learn from these experiences. This poster will: (1) present the Summary Findings for NCA4 Vol. II and (2) highlight key aspects of the report Overview, which includes core sections on Economy & Infrastructure, Natural Environment & Ecosystem Services, and Human Health & Well-Being that synthesize findings from the underlying report. The Summary Findings and Overview were developed by an author team composed of the NCA4 Director, Federal Steering Committee members, and U.S. Global Change Research Program staff and member agency experts. They were developed in parallel with the underlying report chapters and went through the same rigorous review process.

With the application of lightning data in meteorology, electric power as well as public safety, massive lightning data is accumulated. Meanwhile, a synthesized and complicated problem arose, which is, how to automatically obtain the valuable information from the massive lightning data. The deep learning method provides an effective way to automatically classify the event type of lightning discharges from raw lightning data. In this paper, we propose a five-category classification model for the raw lightning waveforms in VLF/LF bands. The model is based on deep convolutional neural network, which is trained and tested by a six year (2012-2017) data set that comprised of over 30000 lightning events. We did experiment with different layers of networks and found that the 7-layer network gives the best performance. The output of the classifier is a five-element vector which shows up the results of different lightning type. Due to the multi-layer stacking of the convolutional network, higher-order features can be better extracted. Furthermore, the model we proposed can effectively identify the cloud-to-ground (CG) flash, ordinary intracloud (IC) flash, preliminary breakdown pulse (PB), narrow bipolar event (NBE), and especially the error rate on CG is less than 3%. Finally, we apply the classifier to the lightning data set for 2017 and group identified return stroke into flashes by hand to qualify the accuracy-stroke-study data of CG flashes. Based on the flashes, we present the characteristics of cloud-to-ground lightning flashes in four isolated small thunderstorms.

Coronal holes (CH) are dark areas in EUV images that are generally associated with open magnetic field regions on the Sun. They can be used to estimate the open magnetic flux in the heliosphere by overlaying them on magnetic field measurements. Accurately measuring the CH boundaries over the whole Sun remains challenging due to many factors, including limited instrument coverage, obstruction by nearby bright structures, and the assumptions of a given detection algorithm. Here we explore the effects of CH obscuration using a global thermodynamic MHD model of the corona. We generate synthetic EUV images for several sets of observer locations, and combine them into maps using current and new obscuration-mitigating strategies. CH maps are generated from each resulting EUV map (using an established CH detection algorithm) and used to estimate the open flux. The importance of synchronizing the effective EUV image height to the height of the magnetic field values is demonstrated. Comparisons of the CH contours and open flux results with the known open field in the simulation gives insight into how much CH obscuration might influence observationally detected CH maps and open flux estimates. Application of obscuration-mitigating mapping techniques to observations is also discussed.

Global maps of maximum bottom particle concentration, benthic nepheloid layer thickness, and integrated particle mass in benthic nepheloid layers (BNL) based on 2412 global profiles collected using the Lamont Thorndike nephelometer from 1964-1984 are compared with maps of those same properties compiled from 6,392 global profiles measured by transmissometers from 1979 to 2016. Outputs from both instruments were converted to particulate matter concentration (PM). We present here a visual global comparison of the location and intensity of BNLs measured with these two independent instruments over slightly overlapping decadal time periods and combine the data sets in order to expand the time scale of global in situ measurements of BNLs, and to gain insight about the factors creating/sustaining BNLs. The similarity between general locations of high and low particle concentration BNLs during the two time periods indicates that the driving forces of erosion and resuspension of bottom sediments are spatially persistent during recent decadal time spans, though in areas of strong BNLs, intensity is highly episodic. Topography and well-developed current systems play a role. These maps can be used to better understand deep ocean sediment dynamics, linkage with upper ocean dynamics, the potential for scavenging of adsorption-prone elements near the seafloor, and provide a comprehensive comparison of these data sets on a global scale. During both time periods, BNLs are weak or absent in most of the Pacific, Indian, and Atlantic basins away from continental margins. High surface eddy kinetic energy is associated with the Kuroshio Current east of Japan. Both data sets show weak BNLs south of the Kuroshio, but no transmissometer data have been collected beneath the Kuroshio itself. Sparse nephelometer data show moderate BNLs just north of the Kuroshio Extension, but with much lower concentrations than beneath the Gulf Stream. Strong BNLs are found in areas where eddy kinetic energy in overlying waters, mean kinetic energy near bottom, and energy dissipation within the bottom boundary layer are high. Areas of strongest BNLs include the Western North Atlantic, Argentine Basin, areas around South Africa tied to the Agulhas Current region, and somewhat random locations in the Antarctic Circumpolar Current of the Southern Ocean.

This poster presents field-scale numerical compositional simulations of CO2 storage mechanisms in the Morrow B sandstone of the Farnsworth Unit (FWU) located in Ochiltree County, Texas. The study examines structural-stratigraphic, residual, solubility and mineral trapping mechanisms. The reactive transport modeling incorporated evaluates the field’s potential for long-term CO2 sequestration and predicts the CO2 injection effects on the pore fluid composition, mineralogy, porosity and permeability. The dynamic CO2 sequestration simulation model was built from an upscaled geocellar model for the Morrow B. This model incorporated geological, geophysical, and engineering data including well logs, core, 3D surface seismic and fluid analysis. We calibrated the model with historical CO2-WAG miscible flood data and used it to evaluate the feasibility and mechanisms for CO2 sequestration. We used the maximum residual phase saturations to estimate the effect of gas trapped due to hysteresis. In addition, gas solubility in the aqueous phase was modelled as function of pressure, temperature and salinity. Lastly, the coupled geochemical reactions, i.e., the characteristic intra-aqueous and mineral dissolution/precipitation reactions were assimilated numerically as chemical equilibrium and rate-dependent reactions respectively. Additional scenarios that involve shut-in of wells were performed and the reservoir monitored for over 1000 years to understand possible mineralization. Changes in permeability as a function of changes in porosity caused by mineral precipitation/dissolution were calibrated to the laboratory chemo-mechanical responses. The study validates the effects of Morrow B petrophysical properties on CO2 storage potential within the FWU. Study results shows: EOR at the tertiary stage of field operations, total amount of CO2 stored in aqueous-gaseous-mineral phases, evolution and dissolution/precipitation of the principal accessory minerals and the time scale over which mineral sequestration took place in the FWU. This study relates the important physics and mechanisms for CO2 storage in the FWU and illustrates the use of the coupled reactive flow. The study serves as a is benchmark for future field-scale reactive transport CO2-EOR projects in similar fields throughout the world.

During the last few years, the debate over the changes in the global average temperature has been one of the most important political issues. A better understanding of the effects of the temperature increase on other variables is one of the main challenges in climate. In the last IPCC report, due in part to the limited rainfall data in the tropics, there is no reliable conclusion about the observed and expected long-term precipitation changes in association with the global temperature increase. Since water resources are essential for energy generation and food production in the Department of Antioquia, Colombia, a better understanding of changes in rainfall in the long term becomes a vital tool for decision making. This research presents an assessment of rain variability at different temporal and spatial scales over Colombia, and more specifically over Antioquia. The data used was corresponds to long-term records of 86 rain gauges, in addition to 9 temperature stations, and TRMM precipitation products. The use of in-situ rain gauge information allows focusing on a spatial scale useful not only for a general understanding of precipitation changes but also for engineering and other practical applications. Analyses reveal that while there are no long-term trends in precipitation at the monthly or longer timescales, relatively short-lived extreme events show long-term changes in intensity and frequency. Results show that the shorter the duration of the intense events, the higher the magnitude of the increasing intensity trend. Similarly, for more intense events, the trends are also larger and more significant from a statistical point of view. Analysis of temperatures shows a clear relationship with extreme precipitation events with scaling features explained via the Clausius-Clapeyron relation, controlling the intensification of precipitation. The long-term rainfall trends are compared with modeling results from the different scenarios of a small set of CMIP runs given that most models do not adequately represent Colombia´s precipitation climatology. The results indicate a substantial reduction of return period of extreme events with implications in engineering: the current hydraulic designs would be obsolete in less than 50 years if the increment in the frequency of intense events is not considered in the design.

HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling individual researchers to more easily share products resulting from their research. The community platform supports, not just the scientific publication summarizing a study, but also the data, models and workflow scripts used to create the scientific publication and reproduce the results therein. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps) and that allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe the capabilities developed for HydroShare to support the full research data management life cycle. Data can be entered into HydroShare as soon as it is collected, and initially shared only with the team directly working on the data. As analysis proceeds, tools, scripts and models that act on the data to produce research results may be stored in HydroShare resources alongside the data. At the time of publication these resources may be permanently published and receive digital object identifiers and cited in research papers. Resources may themselves include citations to the research papers, thereby linking the publications to the supporting data, scripts and models. HydroShare design choices and capabilities for establishing relationships and versioning, based on simplicity, and ease of use, and some of the challenges encountered, will be discussed.