The lateral erosion of the salt marshes in Galveston Island wetlands was investigated through field UAV surveys. Using a GNSS receiver on the ground and a GNSS receiver attached to the UAV, highly accurate location information for the UAV images was derived through PPK analysis. Based on the collected UAV images with location information, Structure from Motion (SfM) by GNSS-supported aerial triangulation was conducted to generate orthomosaic images of wetlands. Drone surveys have been conducted seven times over a year, and the boundary erosion (represented by boundary retreat) for each period was calculated. The averaged error was less than 2 cm for the horizontal direction in the final orthomosaic images of wetlands. The average wave power, hypothesized to be a significant indicator of potential wetlands erosion, was calculated by investigating the wave climate of the survey area through a simplified numerical model. The relationship between wave power and erosion rate was then summarized. The average amount of erosion from 2019 DEC to 2020 MAR, where winds from north to south were dominant, was higher than from 2020 MAR to 2020 OCT. Much erosion was observed in a protruding part of the wetland. This is caused by waves coming from many different directions surrounding these parts. The erosion of this protrusion had a significant influence on the average erosion rate of the survey areas.

Subglacial models represent moulins as cylinders or cones, but field observations suggest the upper part of moulins in the Greenland Ice Sheet have more complex shapes. These more complex shapes should cause englacial water storage within moulins to vary as a function of depth, a relationship not currently accounted for in models. Here, we use a coupled englacial--subglacial conduit model to explore how moulin shape affects depth-dependent moulin water storage and water pressure dynamics within a subglacial channel. We simulate seven different moulin shapes across a range of moulin sizes. We find that the englacial storage capacity at the water level is the main control over the daily water level oscillation range and that depth-varying changes in englacial water storage control the temporal shape of this oscillation. Further, the cross-sectional area of the moulin within the daily oscillation range, but not above or below this range, controls pressures within the connected subglacial channel. Specifically, large cross-sectional areas can dampen daily to weekly oscillations that occur in the surface meltwater supply. Our findings suggest that further knowledge of the shape of moulins around the equilibrium water level would improve englacial storage parameterization in subglacial hydrological models and aid predictions of hydro-dynamic coupling.

Observations using Arecibo Observatory’s highly sensitive Incoherent Scattering Radar (AO-ISR) show ionospheric descending layers from as high as $\sim$400 km, much higher than earlier studies, with continuity down to 90 km. The AO-ISR was operated to observe the ion-line and plasma-line with coded-long-pulse for high temporal and spatial resolution of 35/10 seconds and 300 m, respectively, during 01-06 February 2019. We found multiple layering structures descending from 400 km to 90 km in all these six days. These layers are traditionally called intermediate descending layers (IDLs) ($>$130 km and below F-peak), upper semi-diurnal daytime $\&$ nighttime layers (110 km-130 km), and lower diurnal layers($<$110 km). We have denoted the new daytime descending layers above the hmF2 as top-side descending layers (TDLs). All these layers are collectively named ion descending layers (IonDLs) since all of them are connected with some discontinuity at the F1-peak (i.e., 170 km), except for the daytime lower-diurnal layer. The most pronounced IonDLs occur in the twilight times. IonDLs mainly occur in shear zones of the vertical ion drifts and are favored by downward ion drifts, and their descent speeds increase with increasing altitude. The estimated phase velocities of the waves in the F-region are comparable with the descending speed of the IonDLs. Furthermore, IonDLs/IDLs occur with and without spread-F events but intensified spread-F events raise their beginning altitude. The TDLs and IDLs are driven by gravity waves with time periods of 1.5-4 hours.

Clouds over tropical oceans are an important factor in Earth’s response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small-scale nature of the involved convective processes. We perform two 4-year-long simulations at kilometer-resolution (3.3 km horizontal grid spacing) with the limited-area model COSMO over the tropical Atlantic on a 9000x7000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo-Global Warming (PGW) approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. We find a good representation of the annual cycle of albedo, in particular for trade-wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the marine intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The ITCZ responds to warming through a vertical extension and intensification primarily at high levels, as well as a slight southward extension of the annual mean ITCZ, while the narrowing typically seen in GCMs is not visible.

Coupled climate simulations that span several hundred years cannot be run at a high-enough spatial resolution to resolve mesoscale ocean dynamics. These mesoscale dynamics backscatter to macroscales. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from idealized simulations. In this manuscript, we present a stochastic Deep Learning parameterization that is trained on data generated by CM2.6, a high-resolution state-of-the-art coupled climate model with nominal resolution 1/10° . We train a Convolutional Neural Network for the subgrid momentum forcing using macroscale surface velocities from a few selected subdomains. At each location and each time step of the coarse grid, rather than predicting a single number, we predict the mean and standard deviation of a Gaussian probability distribution. This approach requires training our neural network to minimize a negative log-likelihood loss function rather than the Mean Square Error, which has been the standard in applications of Deep Learning to the problem of parameterizations. Each prediction of the mean subgrid forcing can be associated with an uncertainty estimate and can form the basis for a stochastic subgrid parameterization. Offline tests show that our parameterization generalizes well to the global oceans, and a climate with increased CO2 levels, without further training. We test our stochastic parameterization in an idealized shallow water model. The implementation is stable and improves some statistics of the flow. Our work demonstrates the potential of combining Deep Learning tools with a probabilistic approach in parameterizing unresolved ocean dynamics.

Terrestrial hot springs have existed throughout Earth’s history and house some of the most ancient evidence of life on our planet. These settings are known for their high habitability and preservation potential, and are extensively studied as analog environments since hot spring deposits are thought to exist on the surface of Mars. Hot spring water commonly precipitates silica that coats microbial life dwelling in the hot spring outflow streams. This process can entomb microorganisms and preserve microbial remains over long timescales and with high morphological fidelity. Here we present research carried out on modern and sub-recent remains of microbial filaments from amorphous (unaltered) silica deposits in Yellowstone National Park. This work suggests that various elements sequestered by hot spring-dwelling organisms during life are preserved in microbial remains and persist over > 10,000 years. We also present findings from microfossils preserved in mid-Paleozoic terrestrial hot spring deposits which also show sequestrations of select elements in microfossil remains, suggesting that certain elements may persist even after several hundred million years and substantial host rock alteration. These elemental concentrations may be indicative of metabolic functioning during life and have application as biosignatures. Recent developments in analytical instrumentation now allow for even extremely low trace elemental abundances to be detected and mapped, regardless of sample complexity. This work is especially relevant to the search for life on Mars, as evidence of impact-induced hydrothermal activity may exist near the rim of Jezero Crater and may be sampled by the Perseverance rover. As a primary objective of the Mars 2020 mission is to search for evidence of past life on Mars, we suggest the application of this analytical technique to be valuable for potential samples returned to Earth by future Mars Sample Return missions. Distribution Statement A. Approved for public release: distribution unlimited.

Pharmaceuticals have been recognized as contaminants of emerging concern because of their ubiquitous presence in the environment and the potential they possess in causing undesirable ecological effects. Many long term ill effects of pharmaceuticals on humans are still unknown. Pharmaceuticals are recalcitrant to wastewater treatment facilities and their occurrence has been documented in almost all environmental matrices around the globe. Every third pill consumed by the world is synthesized in India, and despite that, much less attention and concern is noticed in documenting pharmaceuticals occurrence report in the Indian context. In Indian environment, pharmaceuticals have been detected in influent and effluent of wastewater treatment plants, surface waters and groundwater systems. Concentration of few pharmaceuticals such as ciprofloxacin (31 mg/L) and fluconazole (236 mg/L) reported in wastewaters of Hyderabad city in India show the highest values compared to pharmaceuticals concentration reported elsewhere in the world. Other pharmaceutical compounds that are detected in Indian matrix in mg/L range include cetirizine, losartan, and voriconazole. Metoprolol (950 µg/L), enrofloxacin (900 µg/L), citalopram (840 µg/L), moxifloxacin (694 µg/L), norfloxacin (420 µg/L), azithromycin (300 µg/L), atenolol (300 µg/L), levofloxacin (200 µg/L) are amongst other pharmaceuticals that have been detected at high concentration. Only a few research groups have paid attention on detecting pharmaceuticals in India. Disparity exists in occurrence data with most of the studies concentrated in southern India. India is amongst the top manufacturer and consumer of the pharmaceutical compounds and with pharmaceuticals occurring at alarmingly high concentration in environmental waters, much needed attention and emphasis is required to conduct nation-wide occurrence study program to fully assess the ecological risks possessed by the pharmaceuticals.

The Transition Zone Chlorophyll Front (TZCF) is a dynamic region of elevated chlorophyll concentrations in the Northeast Pacific that migrates from a southern winter (February) extent of approximately 30°N to a northern summer (August) extent of approximately 40°N. The transition zone has been highlighted as important habitat for marine animals and fisheries. We re-examine the physical and biological drivers of seasonal TZCF variability using a variety of remote sensing, reanalysis, and in-situ datasets. Satellite-based remote sensing estimates of chlorophyll and carbon concentrations show that seasonal TZCF migration primarily reflects a seasonal increase in the chlorophyll to carbon ratio, rather than changes in phytoplankton carbon. We use our data compilation to demonstrate how the seasonality of light and nutrient fluxes decouple chlorophyll and carbon seasonality at the transition zone latitudes. Seasonal mixed-layer-averaged light availability is positively correlated with carbon and negatively correlated with chlorophyll through the transition zone, while climatological nitrate profiles show that chlorophyll to carbon ratios are facilitated by wintertime nitrate entrainment. These empirical results are consistent with physiological data and models describing elevated chlorophyll to carbon ratios in low light, nutrient-replete environments, demonstrating the importance of latitudinal structure in interpreting seasonal chlorophyll dynamics at the basin scale.

We examine the wave coupling efficiency of solar wind to magnetospheric fluctuations in the ULF frequency range using an advanced full-wave simulation code, Petra-M. Earth’s magnetic field is tilted to the ecliptic plane; thus, compressional wave sources can be incident over a wide range of magnetic latitudes. When compressional waves are incident at a low latitude, very little wave power can reach the inner magnetosphere. On the other hand, waves incident from a high latitude source can propagate efficiently into the inner magnetosphere and reach the ground near the cusp region. The mode-conversion and polarization reversal at the crossover plays a critical role in compressional wave propagation. The mode-converted linearly polarized electromagnetic ion cyclotron waves also occur at the Alfvén and ion-ion hybrid resonances. Therefore, the results suggest that solar wind compression can drive the linearly polarized EMIC waves, and the wave occurrence can have seasonal and diurnal dependence.

This paper focuses on deriving new approximate analytical solutions in wedge-shaped aquifers. The proposed methodology is applicable to any type of aquifer namely, leaky, confined and unconfined, under both steady state and transient flow conditions. By applying the method of images and seperating the flow field into sections using physical arguements, analytical expressions are obtained for the drawdown function. In contrast to the conventional theory, the proposed solutions are applicable to arbitrary wedge angle. Comparison of the results of the derived approximate analytical solutions to numerical ones, is considered necessary to ensure its validity. MODFLOW, a well-known numerical tool is used to calculate the numerical results. The results indicate that the boundary conditions are fully observed, the drawdown is feasible to be calculated at any point of the real flow field (continuity of the drawdown function) and discrepancies compared to numerical results are considered negligible. The main advantage of the proposed procedure is that it can be easily used in conjunction with meta-heuristic algorithms to solve groundwater resources optimization problems.

Tsunamis generated by large earthquake-induced displacements of the ocean floor can lead to tragic consequences for coastal communities. Ionospheric measurements of Co-Seismic Disturbances (CIDs) offer a unique solution to characterize an earthquake’s tsunami potential in Near-Real-Time (NRT) since CIDs can be detected within 15 min of a seismic event. However, the detection of CIDs relies on human experts, which currently prevents the deployment of ionospheric methods in NRT. To address this critical lack of automatic procedure, we designed a machine-learning based framework to (1) classify ionospheric waveforms into CIDs and noise, (2) pick CID arrival times, and (3) associate arrivals across a satellite network in NRT. Machine-learning models (random forests) trained over an extensive ionospheric waveform dataset show excellent classification and arrival-time picking performances compared to existing detection procedures, which paves the way for the NRT imaging of surface displacements from the ionosphere.

For designing of Ground Source Heating Systems (GSHS) Thermal Response Tests (TRT) are used for a long time. TRTs generally provide a profound basis for the final implementation of a GSHS. They obtain the effective thermal conductivity and thermal resistance of a borehole. Until today several TRT types have been introduced and defined as Geothermal Response Test (GRT), Enhanced GRT (EGRT), Constant Heat Flux TRT (chTRT), Constant temperature (ctTRT ) etc.. These models mainly focus on closed heat exchangers in shallow boreholes. However, recent developments in medium depth drilling technologies make deeper boreholes more economic. Furthermore, advanced methodologies for obtaining thermal conductivities in deep and open boreholes are progressing. In this study, we introduce a new simple thermal conductivity obtaining methodology that can be used for deep and open boreholes. Generally, TRTs are applied with the circulation of water inside pipes or immersing probes in water. In case of water circulation in deep boreholes, different heat flux values will be attributed to different layers. Moreover, by introducing a constant heat flux similar to conventional TRT into water-filled boreholes or water-filled pipes, convective movements of water will occur and thermal conductivities cannot be obtained. However, immersed probes as a heat source might prevent water convection cells inside a pipe or a borehole. In deep boreholes, the heat flux is depended on the thermal conductivity of each layer. Thus, deep boreholes require a discrete heat introduction into every single layer of different thermal conductivity in order to keep the temperature constant. The thermal conductivity of each layer can be approximated and integrated over the total length of a deep borehole. This effect is analytically and numerically investigated in this study.

The WIVERN (WInd VElocity Radar Nephoscope) mission, currently under the Phase-0 of the ESA Earth Explorer program, promises to provide new insight in the coupling between winds and microphysics by globally observing, for the first time, vertical profiles of horizontal winds in cloudy areas. The objective of this work is to explore the potential of the WIVERN conically scanning Doppler 94GHz radar for filling the wind observation gap inside tropical cyclones. To this aim, realistic WIVERN notional observations of TCs are produced by combining the CloudSat 94GHz radar reflectivity observations from 2007 to 2009 with ECMWF co-located winds. Despite the short wavelength of the radar (3mm), which causes strong attenuation in presence of large amount of liquid hydrometeors, the system can profile most of the tropical cyclones, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. The statistical analysis of the results shows that, (i) because of its lower sensitivity, a nadir pointing WIVERN would detect 80% of the clouds (60% of winds with 3m/s accuracy) observed by CloudSat, (ii) but thanks to its scanning capability, WIVERN would actually provide 52 times more observations of clouds (40~times more observations of horizontal winds) than CloudSat in TCs, (iii) this corresponds to more than 400 (300) million observations of clouds (accurate winds) every year. Such observations could be used in data assimilation models in order to improve numerical weather prediction and by modellers in order to shed light on the physical processes underpinning the evolution of tropical cyclones.

Submesoscale currents, comprising fronts and mixed-layer eddies, exhibit a dual cascade of kinetic energy: a forward cascade to dissipation scales at fronts and an inverse cascade from mixed-layer eddies to mesoscale eddies. Within a coarse-graining framework using both spatial and temporal filters, we show that this dual cascade can be captured in simple mathematical form obtained by writing the cross-scale energy flux in the local principal strain coordinate system, wherein the flux reduces to the the sum of two terms, one proportional to the convergence and the other proportional to the strain. The strain term is found to cause the inverse energy flux to larger scales while an approximate equipartition of the convergent and strain terms capture the forward energy flux, demonstrated through model-based analysis and asymptotic theory. A consequence of this equipartition is that the frontal forward energy flux is simply proportional to the frontal convergence. In a recent study, it was shown that the Lagrangian rate of change of quantities like the divergence, vorticity and horizontal buoyancy gradient are proportional to convergence at fronts implying that horizontal convergence drives frontogenesis. We show that these two results imply that the primary mechanism for the forward energy flux at fronts is frontogenesis. We also analyze the energy flux through a Helmholtz decomposition and show that the rotational components are primarily responsible for the inverse cascade while a mix of the divergent and rotational components cause the forward cascade, consistent with our asymptotic analysis based on the principal strain framework.

A document by Michael Lockwood. Click on the document to view its contents.

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite 2 precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

In the Archean Gorumahishani greenstone belt (~120 km) of the Singhbhum Craton, the komatiitic suite of rocks (~3.51Ga) is present at the lower part of the metamorphosed volcano-sedimentary sequence. The lower adcumulate zone (~28 m) of the komatiitic sequence is represented by meta-dunite whereas meta-peridotite is manifested in the spinifex zone (~200 m) that terminates with the komatiitic meta-basalt (~250 m). Serpentine, tremolite, chlorite, carbonate, hornblende, and epidote are the major minerals in the komatiites indicating a greenschist-amphibolite transition to amphibolite facies metamorphism. The modal% of chromite is higher in the spinifex zone than in the lower adcumulates. A gradual decrease in serpentine and an increase in tremolite and chlorite modal% is observed across the komatiitic sequence. Thus, bulk-rock geochemistry shows a decrease in Ni and MgO together with an increase in CaO, Al2O3, Fe2O3 (total), TiO2, Sc, Sr, and V from the lower to the upper part of the sequence. Pd/Ru and Pd/Ir show a negative relation with MgO and Cr which is due to the early removal of Ru and Ir by chromite from the komatiitic melt. Compositionally the komatiites are Ti-depleted in the western part and Al-depleted in the middle and eastern parts of the greenstone belt. A non-modal batch melting model shows that the Al-depleted komatiitic parental melt might be generated by ~27% partial melting of a rising mantle plume at the garnet peridotite stability field with ~15% garnet in the residue. The Ti-depleted komatiitic melt might be generated by ~45% partial melting of a depleted mantle peridotite of the same rising plume at the spinel peridotite stability field. The early Archean voluminous komatiitic magma of the Singhbhum Craton erupted in an intracontinental rift setting where the magma interacted with the primordial crust. High-temperature of the ascending komatiitic magma caused the melting of the primordial crust at different depths and in different degrees that produced the parental melt of the contemporaneous (ca. 3.5-3.3 Ga) tonalite-trondhjemite granodiorite-granite. The intracontinental rift basin gradually transformed into the ocean basin and a tectonic shift through the Meso to Late Archean produced the younger granite-greenstone association (ca. 3.2-2.8 Ga) within active continental margin settings.  

Recent advances in regional climate modelling include finer-scale simulations that can partially resolve deep convective processes. These convection-permitting models can help provide insight on how regional distributions of hazardous convection could evolve in possible future climates. However, the use of these models for representing climatologies of severe wind gusts related to convection has not been explored in detail, including for Australian regional climate. As a result, future projections of this hazard have mostly been estimated using changes in the large-scale environment from global climate models, with significant uncertainties related to this method. Here, we present findings on the ability of a regional, convection-permitting climate model in representing severe wind gusts associated with convection in southeastern Australia. We also examine future changes in the frequency and intensity of severe convective wind events as represented by this convection-permitting model, and compare these with changes in the large-scale environment from the global climate model that forces these simulations.