Basin structures in the northern Los Angeles area have been identified from a nodal seismic array along 10 lines across three basins. The dense array of 758 geophones spaced at 250-300 m apart along the lines and recorded seismic response for 30-35 days. For basin structure investigation, the teleseismic receiver functions technique was used. The primary basin concerted phases were identified from the receiver functions. A shear wave velocity model produced in a separate study using the same dataset was incorporated to convert the basin time arrivals into depth. The deepest part of the San Bernardino basin was identified near Loma Linda fault at 2.8 km. San Gabriel basin exhibit larger basin depths and Ps arrival times of all with a maximum depth of 4 km. The high lateral resolution from the dense array helped revealing more continuous structures and reducing uncertainties in the RFs. We discovered a more complex basin structure than previously identified. Our findings show the basins' core areas are not the deepest. Significant changes in basin depth were observed near some faults i.e. Rialto-Colton fault, Fontana fault, Red Hill fault and Raymond fault. Plain Language Summary Sedimentary basins can contribute to the damage caused by any earthquake. The seismic waves from an earthquake can get trapped and amplified within the basin which may result in a stronger ground shaking with longer duration. The three basins of our study area, San Bernardino, Chino and San Gabriel, are located in a seismically active region. The BASIN (Basin Amplification Seismic INvestigation) project aims to image the subsurface so that the seismic wave response within the basins for any earthquake can be assessed. As part of this project, we identified sedimentary basin shape and depth underneath this region. The P wave from an earthquake generates P to S conversions at boundaries. We used receiver functions to determine basin boundaries from those conversions. Arrival times of the P to S conversions were converted to depth using a velocity model.

Maize (Zea mays L.) is one of the most consumed grains in the world and improving maize yield is of great importance for food security, especially under global climate change and more frequent severe droughts. However, traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of unmanned aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are essential for estimating plants’ physiological and biochemical traits. In this study, we developed machine learning models to estimate grain yield and flowering time of maize breeding lines using multi-temporal UAV-based hyperspectral imagery. The performance of multiple machine learning models and the efficacy of different hyperspectral features were evaluated on Genomes to Fields (G2F) experimental sites in Wisconsin. Results showed that ridge regression is the most robust model in estimating grain yield and flowering time, compared to random forest and support vector regression models. Furthermore, the ridge regression model achieved a correlation coefficient (r) of 0.551 for yield, 0.906 for days to silking, and 0.914 for days to anthesis when using the full-bands spectra features for estimation. In addition, we assessed the modeling performance using data acquired from different growing stages. The best time of applying the UAV survey was also identified in order to reduce the data collection efforts.

Surface freshening of the Southern Ocean driven by meltwater discharge from the Antarctic ice sheet has been shown to influence global climate dynamics. However, most climate models fail to account for spatially- and temporally-varying freshwater inputs from ice sheets, introducing significant uncertainty into climate projections. Here, we present the first historically-calibrated projections of Antarctic freshwater fluxes (sub-shelf melting, calving, and surface meltwater runoff) to 2300 that can be used to force climate models lacking interactive ice sheets. Our findings indicate substantial changes in the magnitude and partitioning of Antarctic freshwater discharge over the coming decades and centuries, particularly under very-high warming scenarios, driven by the progressive collapse of the West Antarctic ice shelves. We project a shift in the form and location of Antarctic freshwater sources, as liquid sub-shelf melting increases under all climate scenarios, and surface meltwater runoff could potentially become a dominant contributor under extreme atmospheric warming.

Solar Orbiter is an ESA/NASA mission that will provide an unprecedented opportunity to discover the fundamental connections between the rapidly varying solar atmosphere and the solar wind. The Solar Wind Analyzer (SWA) plasma package shall provide comprehensive in situ measurements of the solar wind. In particular, the Proton-Alpha Sensor (PAS) will determine the high time resolution properties solar wind ion populations through the measurement of the 3D distribution function, density, bulk velocity, temperature and heat Vux. The closest approach of Solar Orbiter to the Sun is 0.28 AU. At this distance the solar wind Vow, solar UV, and solar infrared Vuxes increase by a factor 13 compared to near-Earth space. The PAS instrument will provide high cadence 3D distribution function measurements (up to 13 per second) all the way from closest approach to 1 AU. The extreme UV and thermal conditions at closest approach precluded the use of standard ion optics schemes. We thus developed a new ion sensor optic design combined with conventional robust channeltron detectors. This paper describes the PAS measurement scheme adopted for varying solar wind conditions and our approach to the fast sampling of 3D distribution functions. We also highlight the expected scienti:c return of the instrument.

Sea ice production within polynyas, an outcome of the atmosphere - ice - ocean interaction, is a major source of dense water and hence key to the global overturning circulation, but is poorly quantified over open-ocean polynyas. Using the two recent extensive open-ocean polynyas within the wider Maud Rise region of the Weddell Sea in 2016 and 2017, we here explore the surface ice energy budget and estimate their ice production based on satellite retrievals, in-situ hydrographic observations, and the Japanese 55-year Reanalysis (JRA55). We find that the oceanic heat flux amounts to 36.1 and 30.7 W m-2 within the 2016 and 2017 polynyas, respectively. We find that the 2017 open-ocean polynya produced nearly 200 km3 of new sea ice, which is comparable to the production in the largest Antarctic coastal polynyas. Finally, we find that ice production is highly correlated with the 2 m air temperature and wind speed, which affect the turbulent fluxes. It is also highly sensitive to uncertainties in the atmospheric air temperature and mixed layer depth, which urgently need to be better monitored at high latitudes.

This study explored whether the global temperature had any role in the spread and vulnerability to COVID-19 and how that knowledge can be used to arrest the fast spreading disease. It highlighted that for transmitting the virus, global temperature played an important role and a moderately cool environment was the most favourable state. Whereas, the risk from the virus was reduced significantly for warm places and countries. Based on the temperature of March and April, various degree of vulnerability was identified and countries were specified. The maximum reported case, as well as death, was noted when the temperature was in the range of around 275°K(2°C) to 290°K(17°C). Countries like the USA, UK, Italy and Spain belonged to this category. The vulnerability was moderate when the temperature was less than around 275°K(2°C) e.g., Russia, parts of Canada and a few Scandinavian countries. For temperature 300°K(27°C) and above, a significantly lesser degree of vulnerability was noted. Countries from South Asian Association for Regional Cooperation, South East Asia, the African continent and Australia fell in that category. This work discussed that based on the variability of temperature, countries can switch from one vulnerability state to another. That influence of temperature on the virus and results of previous clinical trials with similar viruses provided a useful insight that regulating the level of temperature can offer remarkable results to arrest and stop the outbreak. Based on that knowledge, some urgent and simple solutions are proposed, which are practically without side effects and very cost-effective too.

This poster presents immersion freezing efficiencies of ambient particles collected from different latitudes between 79 °N and 75 °S. We collected particles using aerosol impactors at five different geographic locations, including i) the Atlantic sector of the Arctic, ii) an urban area in Europe, iii) a rural location in the U.S., iv) a mid-latitude agricultural site in the U.S., and v) the Antarctica peninsula area around Weddell Sea, representing unique particle episodes and atmospheric conditions. Then, we used an offline droplet-freezing assay instrument to measure fine-temperature-resolved ice-nucleating particle (INP) concentrations at T > -25 °C (with a detection capability of >0.0001 per L of air) for each region. Our preliminary results show INP concentrations in polar regions are - as expected - lower compared to mid-latitudes. Low concentrations of high-latitude INPs have been reported in other previous studies (e.g., Bigg et al., 2001; Rogers, 1996; Fountain and Ohtake, 1985; Mason et al., 2015; Ardon-Dryer and Levin, 2014; Belosi and Santachiara, 2014). Another important observation is the high variability of mid-latitude INP concentrations. A difference in the aerosol episode and properties may be key for such a high variability in the mid-latitude region. The composition of INPs varies, but it typically includes dust-related minerals, pollution aerosol, biogenic nuclei and marine microlayers. It is therefore important to comprehensively study realistic representation of both INP concentration and composition (ultimately for model parameterization) and their relevance to the aerosol-cloud interactions with a better temporal resolution under different atmospheric states and a wider spatial coverage of INP sampling sites (see Fig. 1). References: Ardon-Dryer, K. and Levin, Z.: Atmos. Chem. Phys., 14, 5217-5231, 2014. Belosi, F., and Santachiara, G.: Atmos. Res., 145–146, 105–111, 2014. Bigg, E. K.: Tellus B, 48, 223–233, 1996. Fountain, A. G., and Ohtake, T.: 1985: Climate Appl. Meteor., 24, 377–382, 1985. Mason, R. H. et al.: Atmos. Chem. Phys., 16, 1637–1651, 2016. Rogers, D. C. et al.: J. Atmos. Oceanic Technol., 18, 725–741, 2001.

In recent decades, climate change has lengthened wildfire seasons globally and doubled the annual area burned. Thus, capturing fire dynamics is critical for projecting Earth system processes in warmer, drier, more fire prone future. Recent advances in fire regime modeling have linked land surface and Earth system models with fire behavior models. Such models often rely on fine surface fuels to drive fire spread, and while many models can simulate processes that control how these fuels change through time (i.e., fine fuel succession), fuel loading estimates remain highly uncertain. Uncertainties are amplified in climate change forecasts when initial conditions and feedbacks are not well represented. The goal of this review is to highlight fine fuel succession as a key uncertainty in model systems. We review the current understanding of mechanisms controlling fine fuel succession (with an emphasis on decomposition), describe how these mechanisms are incorporated into models, and evaluate the strengths and uncertainties associated with different approaches. We also use three state-of-the-art fire regime models to demonstrate the sensitivity of decomposition projections to both parameter and model structure uncertainty and show that sensitivity increases dramatically under future climate warming. Given that many of the governing decomposition equations are hard-coded in models and often based on individual case studies, substantial uncertainties are currently ignored. To understand future climate-fuel-fire feedbacks, it is essential to be transparent about model choices and uncertainty. This is particularly critical as the domain of Earth system models is expanded to include evaluation of future wildfire regimes.

The dynamic topography of the core-mantle boundary (CMB) provides important constraints on dynamic processes in the mantle and core. However, inferences on CMB topography are complicated by the uneven coverage of data with sensitivity to different length scales and strong heterogeneity in the lower mantle. Particularly, a trade-off exists with density variations, which ultimately drive mantle flow and are vital for determining the origin of mantle structures. Here, I review existing models of CMB topography and lower mantle density, focusing on seismological constraints. I develop average models and vote maps with the aim to find model consistencies and discuss what these may teach us about lower mantle structure and dynamics. While most density models image two areas of dense anomalies beneath Africa and the Pacific, their exact location and relationship to seismic velocity structure differs between studies. CMB topography strongly influences the retrieved density structure, which helps to resolve differences between recent studies based on Stoneley modes and tidal measurements. Current CMB topography models vary both in pattern and amplitude and a discrepancy exists between models based on body-wave and normal-mode data. As existing models feature elevated topography below the Large-Low-Velocity Provinces (LLVPs), very dense compositional anomalies may currently be ruled out as possibility. To achieve a similar consistency as observed in lower mantle models of S-wave and P-wave velocity, future studies should combine multiple data sets to break existing trade-offs between CMB topography and density. Important considerations in these studies should be the choice of theoretical approximation and parameterisation. Efforts to develop models of CMB topography consistent with body-wave, normal-mode and geodetic data should be intensified, which will aid in narrowing down possible explanations for the LLVPs and provide additional insights into mantle dynamics.

Earth system models currently struggle to account for the complex effects that land surface heterogeneity can have on land-atmosphere interactions. Subgrid land surface heterogeneity is currently not well accounted for in land-atmosphere interactions in earth system models. There have been attempts to include the impact of this heterogeneity on the atmosphere, but they ignore the development of coherent secondary circulations that can be driven by spatial differential surface heating. A wealth of literature, particularly large-eddy simulation (LES) based studies, shows that these circulations have significant impacts on the development and organization of clouds. In this work, we describe a two-column model with a parameterized circulation driven by atmospheric virtual potential temperature profiles, differences in near surface temperature between the two columns, patterns of surface heterogeneity, and the mean background wind. Key aspects of the proposed model structure are compared with LES output, and the model is then implemented between two otherwise independent single column models. While some avenues for improvement exist, when the circulations are parameterized, we see increased cloud development and realistic changes to the mean profiles of temperature and moisture. The proposed model qualitatively matches expectations from the literature and LES, and points to the potential success of its future implementation in coarse grid models.

Fram Strait is the primary pathway for sea ice export from the Arctic Ocean, yet estimates of volume export are constrained by observations of ice thickness and drift. Using a new year-round CryoSat-2 ice thickness product we determine an average annual export of 1,712 ± 452 km^3 from 2011-2022. 15% of the Arctic Oceans sea ice volume is exported annually, while 3.2% of the volume lost during the melt season is exported. Comparing high- and low-resolution ice drift products reveals the latter underestimate export by 30%. Comparing volume export between 82°N and 79°N reveals a high melt rate of 1 cm d-1, reducing export by 53%. September sea ice volume declines by 286 km^3 for every 100 km^3 exported during summer, highlighting how export amplifies the ice-albedo feedback. Our estimates of volume export provide new insight into Fram Straits role as a sea ice sink and freshwater source.

Numerical mixing, defined here as the physically spurious diffusion of tracers due to the numerical discretization of advection, is known to contribute to biases in ocean circulation models. However, quantifying numerical mixing is non-trivial, with most studies utilizing specifically targeted experiments in idealized settings. Here, we present a precise, online water-mass transformation-based method for quantifying numerical mixing that can be applied to any conserved variable in global general circulation models. Furthermore, the method can be applied within individual fluid columns to provide a spatially-resolved metric. We apply the method to a suite of global ocean-sea ice model simulations with differing grid spacings and sub-grid scale parameterizations. In all configurations numerical mixing drives across-isotherm heat transport of comparable magnitude to that associated with explicitly-parameterized mixing. Numerical mixing is prominent at warm temperatures in the tropical thermocline, where it is sensitive to the vertical diffusivity and resolution. At colder temperatures, numerical mixing is sensitive to the presence of explicit neutral diffusion, suggesting that much of the numerical mixing in these regions acts as a proxy for neutral diffusion when it is explicitly absent. Comparison of equivalent (with respect to vertical resolution and explicit mixing parameters) $1/4^\circ$ and $1/10^\circ$ horizontal resolution configurations shows only a modest enhancement in numerical mixing at $1/4^\circ$. Our results provide a detailed view of numerical mixing in ocean models and pave the way for future improvements in numerical methods.

Current machine learning methods for discharge prediction often employ aggregated basin-wide hydrometeorological data (lumped modeling) for parametric and non-parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating temporal-spatial hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada- a large and unique basin. Importantly, discharge performance is assessed out-of-sample with k-fold replication across gauges. Results reveal a 9.6% improvement in the mean Nash-Sutcliffe Efficiency (NSE) and a 4.6% improvement in mean Kling-Gupta Efficiency (KGE) when LSTMs are trained with distributed information. Notably, the models exhibit consistently unbiased predictions, with a negligible relative bias (RBias ≈ 0.0) across all predictions. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data-driven discharge predictions.

Dyson spheres are hypothetical structures that high-level civilizations may build to harvest energy in the form of starlight. As in any thermodynamic process, the conversion of stellar energy would involve the emission of waste heat. For Dyson spheres operating at temperatures in the range 100 - 1000 K, this energy would be emitted primarily in the mid-infrared. Here we present some models to estimate upper limits on the prevalence of Dyson spheres in the Milky Way based on the number of sources with mid-infrared excess. In the present analysis, we are only using 260,000 of the most nearby stars from the Gaia DR2 and AllWISE combined dataset, but the full search will include 10^7 - 10^8 objects, making it the largest search for Dyson spheres in the Milky Way carried out so far. In this small sample, we found that less than 1 in 10,000 stars display the mid-IR excess expected for a Dyson sphere, assuming a Dyson sphere with a temperature of 300 K and a covering factor > 0.9.

Saturated hydraulic conductivity (Ks) is a crucial parameter that influences water flow in saturated soils, with applications in various fields such as surface water runoff, soil erosion, drainage, and solute transport. However, accurate estimation of Ks is challenging due to temporal and spatial uncertainties. This study addresses the knowledge gap regarding the long-term behaviour of Ks in sandy soils with less than 10% fine particles. The research investigates the changes in Ks over a long period of constant head tests and examines the factors influencing its variation. Two sandy samples were tested using a hydraulic conductivity cell, and the hydraulic head and discharge were recorded for over 50 days. The results show a general decline in Ks throughout the test, except for brief periods of increase. Furthermore, the relationship between flow rate and hydraulic head gradient does not follow the expected linear correlation from Darcy’s law, highlighting the complex nature of sandy soil hydraulic conductivity. The investigation of soil properties in three different sections of the samples before and after the tests revealed a decrease in the percentage of fine particles and a shift in specific gravity from the bottom to the top of the sample, suggesting particle migration along the flow direction. Factors such as clogging by fine particles and pore pressure variation contribute to the changes in Ks. The implications of this study have far-reaching effects on various geotechnical engineering applications. These include groundwater remediation, geotechnical stability analysis, and drainage system design.

To better understand the role projected land-use changes (LUC) may play in future regional climate projections, we assess the combined effects of greenhouse-gas (GHG)-forced climate change and LUCs in regional climate model (RCM) simulations. To do so, we produced RCM simulations that are complementary to the North-American Coordinated Regional Downscaling Experiment (NA-CORDEX) simulations, but with future LUCs that are consistent with particular Shared Socioeconomic Pathways (SSPs) and related to a specific Representative Concentration Pathway (RCP). We examine the state of the climate at the end of the 21st Century with and without two urban and agricultural LUC scenarios that follow SSP3 and SSP5 using the Weather Research and Forecasting model (WRF) forced by one global climate model, the MPI-ESM, under the RCP8.5 scenario. We find that LUCs following different societal trends under the SSPs can significantly affect climate projections in different ways. In regions of significant cropland expansion over previously forested area, projected annual mean temperature increases are diminished by around 0.5-1.0℃. Across all seasons, where urbanization is high, projected temperature increases are magnified. In particular, summer mean temperature projections are up to 4-5℃ greater and minimum and maximum temperature projections are increased by 2.5-6℃, amounts that are on par with the warming due to GHG-forced climate change. Warming is also enhanced in the urban surroundings. Future urbanization also has a large influence on precipitation projections during summer, increasing storm intensity, event length, and the overall amount over urbanized areas, and decreasing precipitation in surrounding areas.

Today with the advancement in remote sensing technology, very high resolution imaging together with the capability of stereo data acquisition has greatly enhanced the disaster management of high hazardous and inaccessible mountainous terrain. The Rishiganga valley in the Chamoli district of Uttarakhand (North India) was impacted by sudden flash floods triggered due to a massive rockslide, caused by wedge failure on 7th February, 2021. This event caused loss of more than 200 lives besides widespread damage to hydropower projects downstream. Due to the high altitude of the terrain, huge flow of mud and debris down the valley and disruption of road connectivity, made the disaster site inaccessible through conventional means. Therefore most of the preliminary and subsequent studies carried out to assess the impact and the causes of the rockslide have heavily relied on the space based observations. The Indian Space Agency (ISRO) activated (Call Id-803) the International Charter “Space and Major Disasters” (ICSMD) to image the area of the disaster in Uttarakhand. The ICSMD is a consortium of space faring members having a constellation (61 as on date) of highly agile, multi-sensor and multi-resolution of satellite resources, which can be tasked based on type of disaster and requirement, very rapidly over the disaster affected areas to collect information on being activated. These satellite datasets are provided freely by the space agencies under ICSMD platform to serve humanitarian response for the disaster affected sites, keeping aside the commercial interests. The present study is carried out with two objectives first highlighting the role of space data in quick satellite based disaster response for this particular event which assisted the Uttarakhand State Disaster Management Agency (USDMA), Govt. of Uttarakhand to take immediate steps to mitigate the problem. The second objective tries to do an assessment and review of major studies published in peer reviewed journal between February and July, 2021 on this disaster globally to have a synthesised understanding of the event. The pre and post multi-temporal and multi-sensor and multi-resolution satellite data from ICSMD over Rishiganga and Dhauliganga valleys provided first-hand information on the chronology of the events, causes and process mechanism of this unique event and identifying locations impacted downstream. The high resolution images were useful in providing in depth understanding of the damage to hydropower projects, changes in the river geomorphology and the river impoundment due to the obstruction in the flow of one of the tributaries of the Rishiganga joining from the northeast. The satellite based inputs proved to be valuable source of information during this event for the state machinery. From all the analysis it has emerged that the satellite images have unequivocally allowed researchers to determine and quantify various terrain parameters (volume, ele

A system capable of reliably detecting catastrophic landslides and centimeter movement in land mass could save lives and offers landowners valuable information about gradual changes in soil displacement on their land. With precise acceleration and relative positioning data collected from an accelerometer and a set of GPS receivers, a system can be designed to detect subtle changes in sensor position due to land movement. With the rapid production of new microprocessors and greater memory storage capabilities the limits of microcontroller systems are continually expanding. The Slide Sentinel project offers landowners a low-cost alternative to commercial equipment consisting of a network of remote low power sensors that detect fast linear slides and eventually lower soil movements such as creep. Long range low-power (LoRa) radio connections on these sensor nodes wirelessly transmit three-dimensional acceleration, Real Time Kinematic (RTK) GPS coordinates, and sudden shift alerts to a common base station where they are exported to an online spreadsheet to be processed remotely.