Rapid Arctic warming and sea ice decline in recent decades might have profound impacts on midlatitude weather and climate. This study uses a high-top atmospheric general circulation model - Whole Atmosphere Community Climate Model version 6 (WACCM6) - to investigate the atmospheric responses to the Arctic sea ice reduction, including impacts on mid-latitude blocking and cyclones. The high-top configuration (top layer at about 100 km or 0.001 hPa) together with the finer vertical resolution (70 layers) in WACCM6 allow more realistic representation of stratosphere-troposphere interactions. The sea ice impact during 1979-2015 is obtained by comparing ensemble simulations forced by daily observational sea ice concentration (SIC) and sea surface temperature (SST) to simulations where the time-varying SIC is replaced by a daily SIC climatology. A series of SIC-SST adjustments are performed to minimize unrealistic SIC and SST forcings used in the simulations. Each ensemble consists of 30 members with slightly different atmospheric initial conditions in order to separate forced atmospheric responses from internal model spread.

Clouds and their associated radiative effects are a large source of uncertainty in global climate models. One region with particularly large model biases in cloud radiative effects (CRE) is the Southern Ocean. Previous research has shown that there are many dynamic “cloud controlling factors” that influence shortwave CRE, and that three important cloud controlling factors over the Southern Ocean are mid-tropospheric vertical velocity, surface thermal advection, and Estimated Inversion Strength (EIS), which have been shown to influence shortwave CRE on monthly timescales. Model errors may thus arise from biases in representing cloud controlling factors (atmospheric dynamics), in representing how clouds respond to those cloud controlling factors (cloud parameterizations), or some combination thereof. This study extends previous work by examining cloud controlling factors over the Southern Ocean on daily timescales in both observations and global climate models. This allows the cloud controlling factors to be examined in the context of transient weather systems, such as extratropical cyclones, and in the context of high pressure quiescent scenes. Composites of EIS and mid-tropospheric vertical velocity are constructed around extratropical cyclones to examine how the different dynamic cloud controlling factors influence shortwave CRE around the cyclone and how similar the model cyclones are to observations. On average, models tend to produce a realistic cyclone, when compared to observations, in terms of the dynamic cloud controlling factors. The difference between observations and models instead lies in how the models’ shortwave CRE responds to the cyclone dynamics. In particular, the models’ cloud radiative effects are too sensitive to perturbations in mid-tropospheric vertical velocity and, as a result, they tend to produce clouds that are too bright in the cold frontal region of the cyclone and too dim in the center of high pressure systems.

Reusing produced water for hydraulic fracturing simultaneously satisfies challenges of fresh water sourcing and the design/operation of an extensive disposal well infrastructure. This presentation provides an overview of a reuse program from concept through implementation including qualification of advanced water recycling technologies. We target the most prolific unconventional reservoir play in the United States – the Permian Basin. Sourcing water for full-field development therein represents a significant problem since it is in short supply in (semi)arid regions of West Texas and Southeastern New Mexico. We report results from a synergistic industry-academia collaboration wherein desalination pretreatment was first evaluated at lab scale to (i) systematically evaluate partial softening (i.e. “floc-and-drop”) versus neutral pH oxidation for iron removal (ii) investigate synergistic effects of FeCl3 and polymer addition to destabilize colloids (including particulate iron) and induce high-rate sedimentation and (iii) develop and implement robust techniques using video and image analysis to characterize process performance and floc properties (e.g. morphology, size, and settling velocity). Jar tests and associated measurements were completed in the range 4 - 44 ºC covering the range of temperatures measured in the Permian. FeCl3 in conjunction with an anionic polymer dramatically improved colloid destabilization and floc growth via enmeshment of primary colloids by amorphous iron precipitates and inter-particle bridging by the adsorbed polymer. Larger, stronger, and denser flocs thus formed settled extremely rapidly without breakage (i.e. high rate sedimentation). Bench-scale results were integrated in the design and testing a 5,000 BPD pilot scale high-rate clarifier. Pilot scale results show that the neutral pH method of clean-brine generation produced 5-10 times less sludge while achieving 15-20% higher throughput over the alternative floc-and-drop method. Both bench- and pilot-scale findings were incorporated in design and operation of a 50,000 BPD full-scale reuse facility in the Permian Basin. This presentation will share lessons learned from operating a large-scale reuse facility and how academic research can inform and be motivated by industrial practices (and vice versa).

Amazon Basin deforestation has been driven by agricultural expansion and other developments. Interactions between deforestation, fire, climate, and drought have led to changes in precipitation patterns and river discharge. Previous work suggests that these changes are amplified by land use. Therefore, it is important to understand the degree of change in precipitation patterns and rainy season characteristics. We used long term daily gauge measurements and remote sensing data to analyze the variability and seasonality of rainfall patterns over the Amazon Basin. We focused on the PERSIANN-CDR and CHIRPS precipitation datasets from 1983 to 2018 to quantify trends in predefined indices. The indices that were analyzed to assess variability of precipitation are NDD (Number of dry days); NXE (number of extreme events) during both wet and dry seasons; ORS (Onset of Rainy Season); and ERS (End of Rainy Season). We analyzed the trends for statistical significance and spatial similarity to identify hot spots of change. To connect pattern to process, we also simulated the land-atmosphere system using WRF to assess coupling strength and causality. We are running the simulation using CFSR 2010, with grid resolution of 16 km with the convection scheme active to capture small scale convective rainfall. Previous evidence has suggested an increasing trend on NDD during the dry season, a shift to a later onset and later cessation of the rainy season window, and an increasing trend in the NXE during both wet and dry seasons. The significance and spatial distribution of changes may vary over the region, but we anticipate that in the area with the largest percent of deforestation we will see the highest amount of changes in precipitation.

Machine learning algorithms have become a powerful tool in different areas of seismology, such as phase picking/earthquake detection, earthquake early warning and focal mechanism determination. Previously convolutional neural networks (CNN) have been applied to continuous seismic waveform recordings to perform efficient phase picking and event detection with good accuracy [Zhu et al., 2018]. However, the off-line training of current CNN requires at least a few thousands of accurately picked seismic phases, which makes it difficult to be applied to regions without sufficient picked phases. In this work, we will validate the transfer learning among different geographic regions. Our tests show that the phase picker trained on manually-labeled data acquired from Sichuan, China following the 2008 M7.9 Wenchuan earthquake [Zhu et al., 2018] works equally well on the continuous waveform acquired from Oklahoma, US [Zhu et al., 2018]. Specifically, using the CNN trained on the Wenchuan dataset, together with 895 local/regional catalog events recorded in central Oklahoma, we refine part of the networks to pick the arrival times of the local seismicity in Oklahoma. The refined CNN results are compatible with the matched filter results using the same catalog events as templates. Our next step is to extend our test to waveforms from different tectonic regions to demonstrate the generality of CNN-based phase picker. We plan to further use a New Zealand seismic dataset that includes more than 20 GeoNet stations in the North Island, where the matched-filter detected results are available to be compared with (Yao et al., 2018). Alternatively dataset include a subset of events in the waveform relocated catalog in Southern California. Updated results will be presented at the meeting.

The lateral size of sea ice floes is receiving increasing attention as an important variable for the polar climate system. We have developed a model for prognostic evolution of the floe size distribution, which emerges due to five key physical processes: new ice formation, welding of floes in freezing conditions, lateral growth and melt, and fracture of floes by ocean surface waves. As a result of the model’s foundation in the governing physics, free parameters occurring in the equations can be directly constrained by observations. Initial model experiments provided insight into the relative importance of various processes, showing floe freezing processes were particularly important for simulation of the floe size distribution. Previously, physical descriptions of lateral growth and welding together of floes had not been constrained by observations. This motivated an analysis of images obtained by drifting wave buoys (SWIFTs) deployed in the autumn Arctic Ocean to quantify these processes in-situ for the first time. We separated floe area growth due to welding from that due to lateral expansion, and compared observations to our physical descriptions of the individual processes. We also found a strong limitation on floe sizes imposed by the wave field. These results have been used to inform new physical descriptions of processes important for the sea ice floe size distribution.

Volcanic eruptions are usually not easily predictable and this poses a significant hazard not only for exposures to the local population but due to possible presence of tephra also for airline traffic. The significant investments of the last years in new monitoring techniques and networks have improved our capabilities to sense volcano health, but the path to automatically recognize signs of potentially hazardous unrest is still long. On the other hand, machine learning is currently living a period of tumultuous growth and it is possible to find its applications practically in all the contexts where there is an overflow of data to be interpreted. Our aim is to exploit the capability of some established algorithms in machine learning to test their reliability in early detecting anomalous signals from the monitoring network before eruption events on Mt. Etna (Italy). In particular, we evaluate the effectiveness of using random forest approaches to learn from the measured signals the complex dynamics of the Etnean volcanic environment without any a-priori information on the data relationships. Such models are then tested against real eruptive cases to assess their performance.

The connections between Greenland’s tidewater glaciers and the continental shelf waters are modulated by fjords that are geometrically complex with numerous modes of variability. These fjords transport heat towards the glaciers and meltwater away, with a renewal determined by a range of factors including buoyancy forcing at the surface (surface melt) and at depth (subglacial discharge), wind stresses, shelf currents, bathymetry, and mixing processes. This study aims to classify the various regimes of flow and fjord renewal over the diverse parameter space of Greenland fjords with an emphasis on investigating the role of shelf-driven variability and mixing. We conduct idealized numerical simulations using MITgcm to explore how fjord renewal is controlled by elements of the shelf and fjord geometry, and by thermodynamic and mechanical forcings, with domains consisting of idealized bays and shelves representative of Greenland. Specifically, we investigate the sensitivity of the renewal to the following dynamic/thermodynamic influences: (a) bathymetric sills and their influence on mixing, (b) horizontal constrictions (peninsulas, islands, turns), (c) ice cover, and (d) subglacial meltwater discharge. To explain our numerical model results, we develop dynamical theories for the exchange flow as a function of the geometric and forcing parameters.

Over the past several decades, the Greenland Ice Sheet has been losing mass through a combination of increased surface runoff and accelerating ice flux to the ocean. Our understanding of the surface component is drawn heavily from satellite observations and climate models. The MAR (Modèle Atmosphère Régional) model is a 3D regional climate model used extensively over Greenland. Our study focuses on the surface snow and the ice down to 15-meter in depth. A light-weighted surface model for us to integrate the local observation data and force many simulations is needed. Our goal is to implement a surface-only model, derived from MAR, as a tool for understanding the glacial surface components, correlations, and MAR biases to improve projections of surface runoff. This model includes the ability to integrate observations from surface weather stations, translate the data into a model forcing format, force different simulations with various configurations or datasets, visualize model outputs, find key correlations between atmospheric drivers and modeled firn densification. In the model development, we extract the surface code from the original MAR for the simulations initialized and forced with the following snow and atmospheric fields: snow depth, temperature, density, water volume, and grain size. We then verify that the surface model generates the same outputs as the full MAR does if fetched with the identical data. The bias is checked with snowpack time-depth plots for multiple sites around Greenland, including Summit and Swiss Camp. We have found a very small bias when compared to the fully-coupled MAR. We perform quality control for the data inputs, such as replacing missing data from the station measurements, defining the max and min for each dataset, filtering out the data outliers by statistics standard deviations. As the result, our model software can provide multiple simulations in sequential and concurrent mode with user-friendly interfaces, and run robustly. The model’s first release is currently being deployed over different sites across Greenland to understand the importance of atmospheric forcing versus snow model biases in projections of future mass loss due to surface melt.

The Congo Basin stands out as a convective hotspot and plays a crucial role in the Earth’s climate system by modulating the atmospheric circulation and carbon emissions caused by biomass burning in its southern and northern bands. Climate variability in this region is a result of interactions among various features acting on different time-scales. This presentation provides an overview of our current understanding of such features that operate at regional (e.g., Walker-like cells) and global (e.g., ENSO) scales. The distinct spatial heterogeneity of the region with respect to interannual variability will be presented and compared with the spatial variability of annual and diurnal cycles. Differences in driving factors of these variabilities will be discussed. Some challenges, such as the lack of in-situ observations, that limit the climate analysis over the region will be addressed. Finally, several aspects of future research opportunities will be highlighted. This includes interactions between local atmospheric jets, waves, precipitation-producing systems, deforestation and biomass burning, as well as potential improvements in collecting ground-based meteorological data in the region.

Analysis of topographic effects on global surface BRDF/Albedo Shengbiao Wu1,2 Jianguang Wen1,2 1Institute of Remote Sensing and Digital Earth, CAS, Beijing, 100101, China 2University of Chinese Academy of Sciences, Beijing, 100049, China Abstract: Rugged terrain significantly impacts surface reflectance anisotropy as it incurs variations in the illumination and viewing geometry, as well as the incoming irradiance. An accurate knowledge of such variation is crucial to improve the understanding land surface process and Earth’s surface radiation budget. To analyze the topographic effects on global-scale surface BRDF/Albedo, the 500 m moderate resolution imaging spectroradiometer (MODIS) daily global BRDF/Albedo Model Parameter (MCD43A1, collection 6) and MCD43A3 Albedo products, 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation (ASTER GDEM, version 2), and 500 m MODIS Land Cover Type product (MCD12Q1, collection 6) from 2000-2016 were used. The topographic characteristic (e.g., slope, aspect, sky view factor) that extracted from ASTER GDEM datasets were used to statistic surface BRDF/Albedo of different land cover types under different topography conditions. The primarily results in Figure 1-3 show that surface BRF/Albedo is significantly decreased with the mean slope angles due to the increased topographic shadow. As the mean slope increases from 0° to 60°, the maximum value of grassland red (near-infrared: NIR) BRF decreases from 0.15 (0.35) to 0.10 (0.20), evergreen broadleaf red (NIR) BRF decrease from 0.04(0.45) to 0.03(0.40). Moreover, the grassland average shortwave black-sky (white-sky) albedo decreases from 0.17 (0.18) to 0.12 (0.14), and the evergreen broadleaf average shortwave black-sky (white-sky) albedo decreases from 0.13 (0.15) to 0.11 (0.13). Fig. 1. Topographic effects on grassland BRF of h26v05 and h26v06 on DOY 245, 2016. (a) 0°-10°. (b). 10°-20°. (c). 20°-30°. (d). 30°-40°. (e). 40°-50°. (f). ≥50° Fig. 2. Topographic effects on evergreen broadleaf forest BRF of h26v05 and h26v06 on DOY 245, 2016. (a) 0°-10°. (b). 10°-20°. (c). 20°-30°. (d). 30°-40°. (e). 40°-50°. (f). 50° Fig. 3. Topographic effects on black-sky and white-sky albedos of grassland and evergreen broadleaf forest of h26v05 and h26v06 on DOY 245, 2016

An urban air temperature model is presented using GOES-16 land surface temperature. The Automated Surface Observing System (ASOS) serves as ground truth air temperature for calibration and testing of the model. The National Land Cover Database (NLCD) is used to calculate a weighted distribution of 20 land classifications for each satellite pixel surrounding a nearby ASOS station. A time-match algorithm aligns the ground and satellite measurements within 5-minutes of one another, and the resulting matched LST and air temperature are compared over nine months to investigate their cross-correlation. A model is constructed by fitting their difference using a gaussian profile. Landcover, latitude, longitude, local time, and elevation are inputted into an artificial regressive neural network to fit each unique GOES-16 pixel. Over 100 urban stations and satellite pixels throughout the continental U.S. are used to construct the diurnal gaussian model and approximate air temperature. Early statistics indicate favorable results, competing with other studies with more complicated and intensive calculations. The presentation of this model is intended to simplify the calculation of air temperature from satellite LST and create a successful model that performs well in urban environments. The improvement of urban air temperature calculations will also result in improved satellite land surface products such as relative humidity and heat index.

We propose a new approach to unambiguous determination of parameters of positive and negative electric streamer discharges (streamers). We derive several relations between streamer parameters which allow us to express them in terms of the streamer length L and the external electric field E_e, as functions of streamer radius a. In particular, we find that the streamer velocity V(a) has a maximum at a certain value of radius a_s. We interpret the streamer as a nonlinear instability, whose behavior is determined by maximizing its growth rate, proportional to V. The radius of the streamer is therefore hypothesized to be equal to a_s, which fixes all streamer parameters. Thus, for the first time (to our knowledge) we prove that the streamer parameters are completely determined by E_e and L and suggest a simple way to calculate them that does not require hydrodynamic simulations. The parameters of streamers in air at sea level conditions, calculated in the proposed way, are compared and found to be compatible with experimental data [e.g., velocities obtained by Allen and Mikropoulos, 1999, doi:10.1088/0022-3727/32/8/012 and radii seen on the photographic images of Kochkin et al, 2016, doi:10.1088/0022-3727/49/42/425203], as well as with results of hydrodynamic simulations [Lehtinen and Østgaard, 2018, doi:10.1029/2018JD028646]. We also reproduce the correct values of streamer threshold fields of ~0.45 MV/m for positive streamers and ~0.75-1.25 MV/m for negative streamers.

The spatio-temporal variability of river processes is governed by the balance between basin scale sediment supply and channel transport capacity. This balance is being altered by rampant sediment mining from the rivers which results in channel alteration and loss of physical habitat. The sediment mining guidelines are mostly based on empirical approaches and do not consider the spatio-temporal variability of river processes. This study includes hydrologic and geomorphic analysis to understand sediment dynamics and to suggest guidelines for sediment mining. The study was carried out in the 1312 km long Narmada River basin, Central India using hydrological data (1987-2015), SRTM DEM and LULC data. We identified the major aggrading river reaches (100s of km long) on the basis of reach scale sediment mass balance analysis. These reaches are dominantly aggrading at the rate of ~50,000 tons/km/yr. Sediment yield values were used to identify major erosion hot spots in the river basin and its contribution to spatial variability in aggradation-degradation processes in the river channel. This spatial variability within channel was also ascertained through stream power and sediment supply relationship. Further, temporal variability in channel processes was observed in the aggrading reaches. This variability is mostly governed by sediment concentration. Higher sediment concentration (~1.5 g/l) in a given month may change degrading reaches into aggrading reaches. Spatial variability within aggrading reaches and sediment contribution from different sub-basins was further assessed through application of semi-distributed process based Soil and Water Assessment Tool (SWAT). SWAT-CUP yielded good calibration results (~ 40% of the measured data bracketed under the 95PPU envelope, for discharge and sediment load). SWAT was found to be suitable to analyze sediment yield distribution at HRU scale in the Narmada River basin, except for smaller and steeper subbasins where hillslope processes dominate. Further, spatial variability within these reaches was identified by analysis of bar dynamics. Satellite data based change detection analysis was used to identify aggrading channel bars. These bars may be focused for sand mining operations within the aggrading reaches, although limited to ~20% change in the sediment bar area.

A careful characterization of moisture fluxes and saturation-ratio statistics in atmospheric convection is significant for cloud microphysical processes and dynamics. The saturation-ratio of water vapor is defined as the ratio of actual water vapor pressure and its equilibrium value at a given air temperature. Therefore, it is a function of two scalars (water vapor and temperature) and is coupled through the nonlinear Clausius-Clapeyron equation. Participation of both scalar fields in the convection process and the nonlinear coupling of both scalars in saturation-ratio make this problem more complex, as compared to its dry-convection counterpart. We have explored heat and water vapor fluxes and saturation-ratio statistic in the moist Rayleigh-Bénard convection case, using the one-dimensional-turbulence (ODT) model developed by Wunsch et al. JFM 2005. This idealized small-scale simulation is a step toward understanding the full atmospheric convection problem at a more fundamental level. We have obtained the thermal and moisture fluxes as a function of the non-dimensional buoyancy parameter, also known as moist Rayleigh number, and compared it with the scaling relations. Moreover, we have examined the mean and variance profiles of saturation-ratio, and analyzed the different contributing terms for saturation-ratio fluctuations. Based on the scaling analysis, a simplified relation between saturation-ratio variance and moist Rayleigh number has been derived and compared with the simulation results. Additionally, we found that different values of water vapor and thermal diffusivities make the saturation-ratio pdf broader than the case when they are considered equal.

Hydro climate services, such as early warning systems and monitoring and forecasting services for floods and droughts, are required to deal with climate risk management and adaptation. Those tools also contribute to achieving international goals and commitments such as Sustainable Development Goals (SDGs), the Paris Agreement and the Sendai Framework for Disaster Risk Reductions. Recognizing these needs, UNESCO’s International Hydrological Programme (IHP) has fostered the creation and application of many such products in recent years. This has been accomplished through unique formal and informal partnerships among IHP programs, such as the Global Network on Water and Development Information for Arid Lands (G-WADI) and the International Flood Initiative (IFI); UNESCO regional and country offices in Africa, Latin America and Asia; university engineering and hydrology departments; and UNESCO’s network of affiliated (“category 2”) centers, such as the International Center for Integrated Water Resources Management (ICIWaRM) and the International Centre for Water Hazard and Risk Management (ICHARM). The presentation will include an overview of the products, tools and services developed through these collaborations among IHP and its “water family” and their effectiveness in providing such services to Member States.

Ash-producing volcanic eruptions are a major natural hazard and the magnitude of the explosivity of these eruptions have been indicated for decades by a semi-quantitative “Volcanic Explosivity Index (VEI)” based on a number of interdependent factors, (eruption duration, volume of ejected material, eruptive column height, tropospheric and stratospheric injection, and qualitative descriptors). We present a simple empirical formulation for a fully quantitative VEI (𝑉𝐸𝐼Hc), based solely on eruption column height. This method directly translates the height of the eruption column into VEI, a useful formulation for immediate understanding of the explosivity of eruptions as they are taking place. This new scale is calibrated for convenience and familiarity to span the same range of values as the existing, semi-quantitative, VEI scale. In addition to being a simple and quantitative approach, this new method, translating column height directly into VEI, has the advantage of being calculated in real time during an eruption. This will improve immediate response by aviation, hazard management, and others to mitigate societal impact of hazardous eruptions.

Computational geoscience courses often combine domain science, math, programming, and hardware instrumentation. Ensuring students master all those skills can be daunting for professors, as well as for students who tackle these hybrid classes. Through a series of 3-day in-person workshops, faculty across the geoscience disciplines and allied science fields have collaborated to produce on-line teaching resources and a community of peers to support these multi-faceted but essential Geoscience courses. These resources support Geoscience and Science educators seeking to update their curriculum and even create whole new courses. Topics addressed include approaches to teaching, best practices for working group design, empowering students to self-advocate, building computational skills optimally, and coordinating curriculum across a department and even cross-departments. This e-lightning talk will show the resources available to educators – teaching activities including MATLAB code, presentations on teaching approaches, and course curriculum, among others. It will also highlight relevant MathWorks tools for learning and teaching, from online videos, to free, interactive MATLAB tutorials (MATLAB Onramp and more), to autograding software for MATLAB code (MATLAB Grader), with associated publicly available homework problem sets. Attendees will learn where and how to access these online resources, share their teaching challenges, and participate in future workshops.