Reservoirs are a globally important source of methane (CH4) to the atmosphere, but measuring CH4 emission rates from reservoirs is difficult due to the spatial and temporal variability in emissions via the emission pathways of ebullition (bubbling) and diffusion. The dominant source of CH4 in reservoirs is production by methanogens in the reservoir sediment, a process that has been widely shown to have a positive correlation with temperature. However, oxidation of CH4 to carbon dioxide by methanotrophs, an important sink for CH4 within lakes, also scales with temperature. Understanding the relationship between reservoir CH4 emission (i.e. production – consumption) and temperature is made more complex by this dual feedback. This study presents results from multiple in-situ monitoring efforts at a small eutrophic reservoir in the Midwest US that look at how CH4 emissions vary with temperature across space and time. Using data sets from eddy covariance monitoring as well as inverted funnels, we found strong log relationships between daily average CH4 fluxes and daily average sediment temperature, with R2 values of 0.58, 0.45, and 0.7 for the eddy covariance data, the inverted funnel deployed at the 1.3-m site (“shallow”), and the inverted funnel deployed at the 8-m site (“deep”), respectively. The Q10 values for the shallow and deep site were 32 and 20, respectively, indicating a stronger dependence on temperature at the shallow site. However, both the shallow and deep sites had similar emission rates, scaling with relative maximum sediment temperature at each respective site. Sediment temperature was also found to be the second most important variable input to the artificial neural network used for gap-filling the eddy covariance CH4 fluxes (after wind speed). Improving our understanding of the temperature – methane emission feedback in freshwaters will enhance our ability to predict future global methane emissions.

Freezing rain has been normally considered to be composed of supercooled raindrops when the near surface air temperature is below freezing. However, according to a statistical survey of freezing rain events in China over the last two decades (from 2000 to 2019), we find that there were 656 cases occurring at near surface air temperature greater than 0℃ (hereafter warm freezing rain and denoted by WFR), which account for 7% of the total freezing rain events. To explain this phenomenon, a theoretical model is established by relaxing the equilibrium assumption to consider the transient heat exchange between raindrops and the surrounding atmosphere. Sensitivity analysis of the model shows that the temperature lag of raindrops to atmosphere is the main cause of WFR. The direction of raindrop temperature departure from the equilibrium depends on the sign of the temperature lapse rate Г, and the magnitude of the temperature lag is determined by the raindrop diameter D, Г, and relative humidity RH. An increase of D, an increase of Г, and a decrease of RH enhance the lag of raindrop temperature and thus the occurrence of the WFR events. Further simulations of 4 ideal and 25 real sounding profiles reveal that WFR events can form by the “melting of solid hydrometeors” or “supercooled warm rain process” when considering the temperature lag between raindrops and the atmosphere. With the assumption of initial raindrop diameter of 2mm, together the observed Г and RH, the model can diagnose more than 95% of WFR events.

Informal education can be a vital part of a person’s learning experience, thus it is important to continue studying its effectiveness and consider improvements. To that end, we studied participants who built their own AM radio at three School of Earth and Space Exploration open house events at Arizona State University. For this qualitative research study, we audio recorded and transcribed interviews of 41 adults after they completed the activity. Median duration of interviews was 3 minutes. Based on their responses, we categorized participants as either a parent who brought their children (Np = 23) or an individual (Ni = 18). This is consistent with previous studies since over 50% of participants are typically parents. We further grouped each category based on their Science, Technology, Engineering & Math (STEM) background: No STEM Background (NSB), Some STEM Background (SSB) and Expert Electronics Background (EEB). STEM backgrounds of parents were evenly distributed, with each group (i.e., NSB, SSB & EEB) accounting for about 30%. In contrast, about 60% of individuals were in the SSB group. Regardless of STEM background, the vast majority of parents stated that they came to the activity because of their children. Additionally, a majority of participants stated that they learned something from the activity, with only 4 of 41 participants saying they did not learn anything. Thus, this activity provides learning opportunities for people of all STEM backgrounds. As an example, one individual in the EEB group stated, “The design of the transmitter and the receiver itself is completely complicated based on me studying for 10 years trying to understand what a transmitter and receiver is. But just in five minutes I found that I could really built [sic] it by myself.” Yet, individuals who are either in the NSB or the EEB group may be overlooking certain informal education opportunities, since together they accounted for only 40% of individuals. To improve public understanding of science, informal learning centers should continue to consider ways to make events more accessible and more enticing for people who may not be experiencing these valuable learning opportunities.

Basaltic fissure eruptions emit an array of volatile and environmentally reactive gases and particulate matter (PM) into the lower troposphere (e.g., SO2, HCl, and HF in the gas phase; Se, As, Pb as complexes in the PM phase). Lava flows from fissure eruptions can be spatially extensive, but the composition and fluxes of their emissions are poorly characterized compared to those from main vent(s). Using UAS-mounted (drone) samplers and ground-based remote FTIR spectroscopy we investigate the down-flow compositional evolution of emissions from active lava flows during the Fagradalsfjall 2021-2023 eruptions. The calculated fluxes of volatile trace metals from lava flows are considerable relative to both main vent degassing and anthropogenic fluxes in Iceland. We demonstrate a fractionation in major gas emissions, with decreasing S/halogen ratio down-flow. This S-Cl fractionation is reflected in the trace element degassing profile, where the abundance of predominantly sulfur-complexing elements (e.g., Se, Te, As, Pb) decreases more rapidly in down-flow emissions relative to elements complexing as chlorides (e.g., Cu, Rb, Cs), oxides (e.g., La, Ce) and hydroxides (e.g., Fe, Mg, Al). Using thermochemical modeling, we explain this relationship through temperature and composition dependent element speciation as the lava flow ages and cools. As a result, some chloride-complexing elements (such as Cu) become relatively more abundant in emissions further down-flow, compared to emissions from the main vent or more proximal lava flows. This variability in down-flow element fluxes suggests that the output of metals to the environment may change depending on lava flow age and thermal evolution.

The dynamics and evolution of deltas and their channel networks involve interactions between many factors, including water and sediment discharge and cohesion from fine sediment and vegetation. These interactions are likely to affect how much vegetation influences deltas, because increasing sediment discharge increases aggradation rates on the delta and may result in sediment transport processes happening on timescales that are faster than those for vegetation growth. We explore how varying water and sediment discharge changes vegetation’s effect on delta evolution. We propose two new insights into delta evolution under different discharge conditions. First, without vegetation, we observe a regime shift in avulsion dynamics with increasing water discharge, from a few active channels supplemented by overbank flow and undergoing episodic avulsion (with low discharge) to many active channels experiencing frequent partial avulsions (with high discharge). Second, with vegetation, increased aggradation results in more frequent switching of the dominant channels with increased sediment discharge, but also prevents vegetation from establishing in non-dominant channels resulting in more frequent channel reoccupation and therefore greater stability in channel network planform. These insights have important implications for understanding the distribution of water, sediment, and nutrients on deltas in the face of future changes in climate, human modifications of fluxes of sediment and water to the coast, and especially for restored or engineered deltas with controlled water or sediment discharges.

The generalized solution for the radiance equation is expanded by exploiting multiple hyperspectral image scans acquired by aerial platforms at different viewing angles. A machine learning solution based on convolutional neural networks is used to learn the relationships between the total radiance observed at the sensor, and different atmospheric components of the radiance equation. The goal is to precisely characterize the atmosphere, in order to properly solve the radiance equation, in which atmospheric components constitute important input. Traditionally, these atmospheric components are only estimated from averages of pixels, or assigned using heuristics tables. Compared to traditional image spectroscopy, this expanded radiance equation and machine learning solution integrates quantitative mathematical modeling, multiple scanned hyperspectral images and artificial intelligence. The solution is able to model and predict the transmittance, downwelling and upwelling components of the radiance equation with increased spatial and temporal dimensionality. It’s promising to use different combinations of the multiple scans to parameterize the radiance equation and improve the target detection in varying atmospheric conditions, where current solutions based on a single hyperspectral image normally fail. This works presents initial results of an expanded mathematical solution, along with the results from the convolution networks. Synthetic data were generated using the MODTRAN atmospheric software to simulate different vintage points, atmospheric models, time of the day and year, for an array of specific targets with varying reflectances. More specifically, MODTRAN was used to simulate Longwave Infrared Red between 7.5 and 12 microns with a 17.5 nanometers spectral sensitivity, which correspond to the range and resolution of the Blue Heron Longwave Hyperspectral sensor. Results from the convolutional neural network indicate our machine learning solution is computationally faster than the traditional radiative transfer (RT) model and is able to characterize the impact of varying atmospheric conditions on the at-sensor radiance components.

Sea ice pressure ridges have been recognized as important locations for both physical and biological processes. Thus, understanding the associated light-field is crucial, but their complex structure and internal geometry render them hard to study by field methods. To calculate the in- and under-ridge light field, we combined output from an ice mechanical model with a Monte-Carlo ray tracing simulation. This results in realistic light fields showing that light levels within the ridge itself are significantly higher than under the surrounding level ice. Light guided through ridge cavities and scattering in between ridge blocks also results in a more isotropic ridge-internal light field. While the true variability of light transmittance through a ridge can only be represented in ray tracing models, we show that simple parameterizations based on ice thickness and macro-porosity allow accurate estimation of mean light levels available for photosynthesis underneath ridges in field studies and large-scale models.

“Reliable water supply” does not have a clear definition in the Western United States, where water resources are limited and such a definition would be especially useful. In Utah, the three water agencies and 500 public water systems have no consistent method to define, evaluate, and report it, potentially leading to an inability to meet regulatory water demands. We propose a unified definition of reliable water supply for Utah’s public water suppliers that can also be used elsewhere. The qualitative definition we propose is necessary to precede quantitative evaluations, set policy, and provide consistency to water resources management. We derive our definition from a two-part qualitative analysis: 1) an extensive review of existing definitions in industry and academia and 2) semi-structured interviews with managers of six diverse Utah water utilities. We propose that water supply be defined by three overlapping components—hydrology, infrastructure, and governance—and that reliability be defined by the capacity of the limiting component.

Cases of water related diseases due to metal pollution are increasing over the global. The condition is serious to most of developing countries as a results of industrialization and population growth. Dissolved and particulate trace elements influence drinking water, aquatic ecosystem health and climate change. Mt. Kilimanjaro is one of the sources of water and icon in Africa but miss studies on dissolved and particulate metals. Therefore, this study was conducted to investigate geochemistry, distribution and yield of dissolved and particulate metals from Mt. Kilimanjaro to Indian Ocean. Surface water was sampled in rainy season and analyzed by high resolution inductively coupled plasma mass spectrometry in State Key Laboratory of Estuaries and Coastal Research. Health assessment revealed that level of Aluminium, iron, vanadium and Manganese in some stations were above recommended level, that can pose health impact to human and aquatic ecosystem. Correlation of Cobalt, Copper, Manganese and Vanadium with dissolved silicate, sulphate, calcium and dissolved organic carbon indicates that these elements were predominantly found in silicate, sulphide, carbonate and organic bounds. Positive relation between magnetic susceptibility with Copper and zinc reflects that magnetic susceptibility can be used as indicator of Copper and Zinc pollution. Rock weathering and anthropogenic activities were main sources of metals whereas redox reactions, pH, temperature and dissolved organic carbon were some of biogeochemical factors influencing level of metals. The basin transported more elements in particulate than dissolved form. Yield from Pangani River to Indian Ocean was lower than most of other rivers in East Africa.

While the infrastructural damage and subsequent costs associated with flood events have, and will continue to receive widespread attention, less attention is given to the adverse human health effects of these events. This is particularly significant in the ROI, which is characterised by the highest crude incidence rates of verotoxigenic E. coli (VTEC) enteritis and cryptosporidiosis in Europe. Accordingly, weekly infection incidence from July 2015 to June 2016 were employed in concurrence with weekly time-series of antecedent hydrometeorological parameters (rainfall, surface water discharge and groundwater level), and high-resolution flood risk mapping. An ensemble of statistical and time-series approaches were employed to quantify the influence and timing of flood hydrometeorology on infections. Seasonal decomposition identified a high residual infection peak (excluding seasonal pattern) during April 2016, with space-timing scanning used to identify the location, size and temporal extent of excess infection clusters. Excess cases of VTEC enteritis were geographically associated with the Shannon basin, while cryptosporidiosis excess was nationwide. Generalised linear modelling indicates that areas with a surface water body exhibited significantly higher incidence rates for both infections (OR 1.225 - 1.363 p <0.001). Non-parametric ranking identified a clear association between hydrometeorology and infection incidence, with lagged associations from 16-20 weeks proving particularly strong, thus indicating a link between infection peaks (April 2016) and the flood event which began approximately 18 weeks earlier. Findings demonstrate all three hydrometeorological variables could be used to predict the increase in cryptosporidiosis during April 2016, while only surface water discharge was associated with VTEC enteritis.

Flow separation has been observed and studied in sinuous laboratory channels and natural meanders, but the effects of flow separation on along-channel drag are not well understood. Motivated by observations of large drag coefficients from a shallow, sinuous estuary, we found in idealized numerical models representative of that system that flow separation in tidal channels with curvature can create form drag that increases the total drag to more than twice that from bottom friction alone. In the momentum budget, the pressure gradient is balanced by the combined effects of bottom friction and form drag, which is calculated directly. The effective increase in total drag coefficient depends on two geometric parameters: dimensionless water depth and bend sharpness, or the bend radius of curvature to channel width ratio. We introduce a theoretical boundary layer separation model to explain this parameter dependence and to predict flow separation and the increased drag. The drag coefficient can increase by a factor of 2 - 7 in “sharp” and “deep” sinuous channels where flow separation is most likely. Flow separation also enhances energy dissipation due to increased velocities, resulting in greater loss of tidal energy and weakened stratification.Flow separation and the associated drag increase are expected to be more common in meanders of tidal channels than rivers, where point bars that inhibit flow separation are more commonly found. The increased drag due to flow separation affects the tidal amplitude and phasing along the estuary and creates potential morphological feedbacks.

Aquifer remediation with in situ soil washing techniques and enhanced oil removal typically involve the injection of liquid solutions into the geological formation to displace and mobilize non-aqueous phase liquids (NAPLs). The efficiency of these systems is oftentimes low because the displacing fluid bypasses large quantities of NAPL due to the inherent complexity of a heterogeneous natural system. Here, chaotic advection generated by a rotating periodic injection pulse is proposed as a method to enhance NAPL removal and mixing. To evaluate the method, we perform two-phase flow simulations in multiple realizations of random permeability fields with different correlation structures and connectivity between injection and extraction wells embedded in a five-spot pattern. Results show that chaotic advection can significantly improve removal efficiency and mixing depending on several controlling factors. Chaotic advection effects are more significant under unfavorable conditions, i.e., when injection and extraction wells are well-connected through preferential channels, permeabilities are highly heterogeneous, and/or the mobility ratio between the wetting and the non-wetting fluid is larger than one. Removal efficiency reaches its maximum value when the Kubo number is close to one, i.e., when the saturation front travels one range of the permeability field in an injection pulse. These effects can develop in just a few cycles. However, removal efficiency should undergo first an early stage with detrimental effects in order to maximize removal in the long term. Chaotic advection not only enhances NAPL removal and mixing, but also reduces the uncertainty, making the system more reliable and less dependent on heterogeneity.

The application of a distributed energy balance model over 9 years at an hourly time step to a 20 x 20 m grid cell over Glacier Zongo (Bolivia, 16°S) enabled assessment of the climate factors that control the interseasonal and interannual variability of its surface mass balance. The model was validated by comparing the measured and simulated discharge at the outlet, albedo at the AWS, surface types and annual mass balance both glacier wide and as a function of the altitudinal range. Analysis of the mean monthly energy fluxes highlighted the importance of the meteorological conditions during the period between September and November on the variability of the annual surface mass balance. Two sensitivity analyses are presented, one of the distribution of precipitation over time which maintains a physical coherence between the different meteorological variables and one of the impact of prolonged periods of intense cloud radiative forcing on the surface mass balance. The distribution of precipitation events over time and their associated amounts are the main drivers of the interannual variability of the surface mass balance via an albedo feedback effect. Additionally, prolonged periods of negative cloud radiative forcing, specifically over the month of November, have notable ability to reduce the melt rate.

A document by Brook Tozer. Click on the document to view its contents.

Summer ice in the Arctic is diminishing at an alarming rate. Increase in radiative forcing due to the loss of sea ice is contributing to global warming. Artificially enhancing the sea ice albedo could be a possible lever to restoring and rebuilding the Arctic sea ice. Using reflective hollow glass spheres with low environmental impact, Field et al (2018) reported that such a technology could be promising towards restoring Arctic Sea Ice. Here we present the preliminary climate modeling and impact assessment of such a technology both regionally and globally. We seek to answer the scientific question of whether an enhanced sea ice albedo over the whole of Arctic sea ice provides a large enough perturbation to the climate in the Arctic and if so what is its likely impact over the rest of the globe. The study shows that the climate impact of such a method results in more than 1.5°C cooler temperatures over a large part of the Arctic and about 3°C reduction over regions north of Barents and Kara Seas. We also see notable increases in sea ice thickness (20–50 cm Arctic wide) and (>15–20%) increases in sea ice concentration across large parts of the central Arctic. These preliminary results suggest that such a technology may be a viable instrument for restoring Arctic ice. However, practicality dictates that a localized targeted deployment of the technology may be more desirable. We are extending this work to evaluate targeted deployment of materials in key areas and will present the climate modeling results on efficiency of targeted deployment for at least one such targeted area. Reference: Field, L., Ivanova, D., Bhattacharyya, S., Mlaker, V., Sholtz, A., Decca, R., et al.(2018). Increasing Arctic sea ice albedo using localized reversible geoengineering. Earth’s Future, 6. https://doi.org/10.1029/2018EF000820

Historical and future simulated temperature data from five climate models in the Coupled Model Intercomparing Project Phase 6 (CMIP6) are used to understand how climate change might alter cold air outbreaks (CAOs) in the future. Three different Shared Socioeconomic Pathways (SSPs), SSP 1 – 2.6, SSP 2 – 4.5, and SSP 5 – 8.5 are examined to identify potential fluctuations in CAOs across the globe between 2015 and 2054. Though CAOs may remain persistent or even increase in some regions through 2040, all five climate models show CAOs disappearing by 2054. Climate models were able to accurately simulate the spatial distribution and trends of historical CAOs, but there were large errors in the simulated interannual frequency of CAOs in the North Atlantic and North Pacific. Fluctuations in complex processes, such as Atlantic Meridional Overturning Circulation, may be contributing to each model’s inability to simulate historical CAOs in these regions.

Energetic ash-producing volcanic eruptions are driven by the diffusive and decompressive growth of bubbles (mostly water) during ascent in a magma conduit. The spatial distribution of bubble nucleation sites is one of the primary controls on ash-forming fragmentation. However, the initial formation of bubbles in a supersaturated magma is problematical, especially for homogeneous nucleation. Excessive surface tension pressure should preclude the existence of small bubbles, because exsolved water is driven back into the melt. This is the “tiny bubble paradox.” We suggest that—under special circumstances—the tiny bubble paradox may be circumvented by spinodal decomposition, a process in which uphill diffusion enables spontaneous unmixing of phases to reduce the free energy of the system. As spinodal decomposition progresses, three dimensional, quasi-spherical, zones of water-rich magma develop. These zones are characterized by an increasingly high concentration of dissolved water at the centers and reducing concentration at the margins. Bubbles are born when the concentration of water in the interior of the water-rich zones goes to 100% and the concentration of melt goes to zero. The small, nascent, bubbles that emerge will be buffered from melt by water-rich shells with increasing melt concentration away from newly formed bubbles. This diffuse concentration gradient of water means that there is no surface, per se, for surface tension to arise. This is the crux of the solution of the tiny bubble paradox. Particle morphology may be used to distinguish ash with spinodal origins from ash associated with typical (metastable) bubble nucleation. Spinodal decomposition occurs at a wavelength determined by the pressure, temperature, and viscosity of the magmatic system. This wavelength should create bubbles of uniform size and bubble walls of equal strength in a fragmenting magmatic foam, leading to sharply mono-modal vesicle and ash particle size distributions. Classical bubble nucleation should create more-variable bubble sizes and bubble wall strengths, leading to a broader particle size distribution. Better understanding the mechanism of bubble formation in magmatic systems will, in turn, enable better understanding of hazardous, explosive, eruptions.

Marine low clouds are one of the greatest sources of uncertainty for climate projection. We present an observed climatology of cloud albedo susceptibility to cloud droplet number concentration perturbations (S0) with changing sea surface temperature (SST) and estimated inversion strength for single-layer warm clouds over the North Atlantic Ocean, using eight years of satellite and reanalysis data. The key findings are that SST has a dominant control on S0 in the presence of co-varying synoptic conditions and aerosol perturbations. Regions conducive to aerosol-induced darkening (brightening) clouds occur with high (low) local SST. Higher SST significantly hastens cloud-top evaporation with increasing aerosol loading, by accelerating entrainment and facilitating entrainment drying. In a global-warming-like scenario, cloud darkening is expected, mainly as a result of increased entrainment drying via Clausius-Clapeyron scaling. Our results imply a more (less) positive low-cloud liquid water path feedback in a warmer climate with increasing (decreasing) aerosol loading.