Central to improving our understanding of ocean temperature change on Antarctica’s continental shelf is a better understanding of how the ocean circulation drives the onshore flux of warm deep waters across the shelf break. This study uses a primitive equation ocean model to explore how the circulation regime and changes in surface stress influence the temperature structure on Antarctica’s shelf seas. As the shelf temperature changes are largely driven by ocean circulation changes, understanding these becomes our focus. A simple barotropic model is used to describe the linear theory of the difference between throughflow and gyres regimes, and their expected response to changes in forcing. This theory informs our understanding of the barotropic circulation response of the primitive equation model where a momentum and heat budget confirm that over the simulated equilibrated timescales with surface forcing changes, the response is first-order linear. Consistent with previous findings, we find that climate change projection-like wind shifts (stronger westerlies that shift south) have a direct influence on Ekman processes across the shelf break and upwell warmer waters onto the shelf. We also find that the circulation regime (throughflow or gyre – determined by basin geometry), influences the mean shelf temperature and how susceptible the existing shelf temperatures are to changes in surface stress. While the throughflow regime can experience a complete transition in on-shelf temperatures when the transition between westerly and easterly winds shifts southward, we find relatively modest warming at the coast in a gyre regime. Combinations of these regimes also experience coastal warming under a constant positive offset in the winds.

We investigate the applicability of deep learning methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three-dimensional convolutional neural network. Our approach was developed iteratively by evaluating six modeling improvement techniques. The resulting method reduces the validation error to 48% compared to the baseline. Additionally, we demonstrate the impact of the spatial distribution of observations on reconstruction accuracy and propose a potential integration with the analogue resampling method. WeRec3D is trained and validated in a self-supervised manner using ERA5’s surface temperature and pressure data over Europe. On a hold-out set from 1950 to 1954, the validation results in an MAE of 1.11 °C and 199 Pa. As a case study, we reconstruct the 1807 heat wave and validate it using a leave-one-out method in space. Compared to the original data, the reconstructed time series exhibit a correlation of at least 0.91, with a maximum normalized RMSE and standard deviation delta of 0.58 and 0.51 respectively. To the best of our knowledge, this is the first study to investigate weather reconstruction using deep learning algorithms, proposing video inpainting as a novel approach for reconstructing missing weather information.

A document by Christopher John Roach. Click on the document to view its contents.

Widely adopted models for estimating channel geometry attributes rely on simplistic power-law (hydraulic geometry) equations. This study presents a new generation of channel geometry models based on a hybrid approach combining traditional statistical methods (Multi-Linear Regression (MLR)) and advanced tree-based Machine Learning (ML) algorithms (Random Forest Regression (RFR) and eXtreme Gradient Boosting Regression (XGBR)), utilizing novel datasets. To achieve this, a new preprocessing method was applied to refine an extensive observational dataset, namely the HYDRoacoustic dataset supporting Surface Water Oceanographic Topography (HYDRoSWOT). This process improved data quality and identified observations representing bankfull and mean-flow conditions. A compiled dataset, combining the preprocessed dataset with datasets containing additional catchment attributes like the National Hydrography Dataset Plus (NHDplusv2.1), was then used to train a suite of models to predict channel width and depth under bankfull and mean-flow conditions. The analysis shows that tree-based ML algorithms outperform traditional statistical methods in accuracy and handling the data but face limitations in prediction capabilities for streams with characteristics outside the training range. Consequently, a hybrid method was selected, combining XGBR for streams within the dataset range and MLR for those outside it. Two tiers of models were developed for each attribute using discharges derived from distinct sources (HYDRoSWOT and NHDPlusV2.1, respectively), where the second tier of models offers applicability across approximately 2.6 million streams within NHDplusv2.1. Comprehensive independent evaluations are conducted to assess the capability of the developed models in providing stream/reach-averaged (rather than at-a-station) predictions for locations outside the training and testing datasets.

High-density attachment of Limnoperna fortunei (LF) would lead to the increase of flow resistance, which has posed big challenges to the normal operation of water conveyance projects. It is very necessary to quantify the flow resistance caused by the attachment of LF. In this study, a 3D geometric model of LF was generated based on real images. Attachment models of LF were generated with different densities and mussel size distributions, whose geometric characteristics were evaluated by some fundamental physical quantities, including attachment thickness, bed coverage, surface vertical roughness, and roughness concentration. Furtherly, a 3D numerical model with specific boundary conditions was established in OpenFOAM to simulate the flow over the LF attachment. Body-fitted mesh was generated using snappyHexMesh based on the LF attachment model. The results show that in high-density scenarios, a big wake zone formed inside LF attachment by the combined effects of each individual LF. Turbulence kinetic energy distribution indicated that LF attachment would cause viscous dissipation thus leading to more energy loss. The flow structure inside LF attachment was controlled by the size and spacing between each individual LF. Manning’s n values were calculated based on the CFD results at different densities. The results show that the flow resistance of LF attachment also followed the classic flow regimes, where in the skimming flow regime, the mussel size distribution played a non-negligible role. Higher flow speed resulted in larger flow resistance, and n could increase more than 90% compared to the scenario without LF attachment.

The ShakeOut scenario of a M7.8 northwestward rupture on the southern San Andreas fault (SSAF) (Jones et al., 2008) predicted significant long-period ground motion amplification in the greater Los Angeles, CA, area, caused by a waveguide from interconnected sedimentary basins. However, the early ShakeOut ground motion simulations omitted important model features with immature versions of the velocity structure and fault geometry. Here, we present 0-1 Hz 3D numerical wave propagation simulations for the ShakeOut scenario including surface topography, as well as updated high-resolution velocity structure and SSAF fault geometry. Spectral Accelerations at 3s are increased by the local high-resolution basin models (25-45%) but decreased from complexity in velocity and density updates outside the basins (65-100%) and inclusion of surface topography (~30%). The updated model reduces peak ground velocities in the waveguide from ~250 cm/s to ~100 cm/s, significantly closer to the values predicted by a leading NGA-West2 ground motion model.

 Bayesian calibration based estimation of proglacial lake volume  Method applied to sixty-six lakes located in the High Mountain Asia  Reduction of maximum error in modelled lake volume by >5 times as compared to existing scaling models  Requires minimal calibration with observations as compared to scaling models

Nutrient cycling in the ocean is mediated by physical mixing processes that span diverse spatial and temporal scales. New biogeochemical profiling floats (BGC-Argo) have begun to observe nutrient distributions globally, but their 10-day cycling period limits the types of processes they can capture. Small-scale dynamics, occurring on \(O\)(1) day and \(O\)(1) km, remain particularly difficult to observe in-situ. Here, we show that random forest regression (RFR) can recover high-frequency information by leveraging the sampling strategies of multiple ocean profilers. Our RFR is trained, validated, and tested on BGC-Argo and shipboard data to within ~3% accuracy, then applied to observations from two rapid-sampling Seagliders deployed during the Southern Ocean Glider Observations of the Submesoscale (SOGOS) experiment in 2019. This approach generates novel nitrate distributions at 50 times the horizontal resolution of the original float data. Using the high-resolution RFR outputs, we identify signatures of nutrient injection into the mixed layer that coincide with enhanced stirring in a turbulent region downstream of the Southwest Indian Ridge. Relating these intermittent transport events to biological time series suggests that small-scale stirring mediates additional nutrient drawdown and primary production in this region. In our exploration of high-frequency nitrate variability in the Southern Ocean, RFR extends the capabilities of the BCG-Argo array and allows for deeper understanding of biogeochemical cycling at a more comprehensive set of scales. As a flexible approach that can be generalized to suit other multi-platform observing systems, RFR presents new opportunities to maximize value from existing datasets.

Dr. Jennifer Gannon passed away suddenly on 2024 May 2 in Greenbelt, MD. Dr. Gannon had served as an editor for the Space Weather journal since April 2019, and she was the longest-serving editor on the current board, having started under the previous editor-in-chief, Dr. Delores Knipp. Abstract content goes here

Boreal wildfires impact surface climates with consequences for plant physiology, permafrost thaw, and carbon fluxes. Post-fire temperatures vary over decades due to successional changes in vegetation structure and composition. Yet, the underlying biophysical drivers remain uncertain. Here, we quantify surface climate changes following fire disturbances in the North American boreal forest and identify its dominant biophysical drivers. To do so, we analyse multi-year land-atmosphere energy exchange and satellite observations from across Canada and Alaska. We find post-fire daytime land surface temperatures to be substantially warmer for about five decades while winter temperatures are slightly cooler. Post-fire decadal changes are characterised by a decrease in leaf area index during the first decade, a sharp increase in snow cover period surface albedo, and a decrease in the efficiency of heat transfer for about 2-3 decades. Evaporative fraction increases in the first three decades before returning to lower values again. We find that warming is mainly explained by a decrease in the efficiency of heat transfer while cooling is additionally explained by increasing surface albedo. We estimate that current daytime surface temperatures of the boreal biome of Canada are 0.18 °C warmer in the summer and 0.04 °C cooler during the winter due to fire. For a scenario with a strong increase in burned area, we estimate doubled warming from fire until 2050. Our study highlights the potential for accelerated surface warming in the boreal biome with increasing wildfire activity and disentangles the biophysical drivers of fire-related surface climate impacts.

Marine heatwaves (MHWs) have devastating effects on ecosystems and impact regional air-sea CO2 exchange. Yet a global assessment of these regional impacts of MHWs on the air-sea CO2 exchange is missing. Here, we analyze thirty global observation-based air-sea CO2 flux datasets from 1990 to 2019. We observe minimal reduction in global oceanic CO2 uptake during MHWs. Regional variations are evident with the equatorial Pacific experiencing a 31% (spread across datasets: 3-49%) reduction in carbon release, suggesting that MHWs are the dominant drivers of strong air-sea CO2 flux anomalies in this region. In low-to-mid latitudes, MHWs cause a 29% (19-37%) decrease in air-sea CO2 uptake. Reduced dissolved inorganic carbon in the tropics weakens outgassing, while high ocean temperatures diminish uptake in the low-to-mid latitudes. In the North Pacific and Southern Ocean, enhanced carbon uptake occurs during MHWs, but uncertainties in pCO2 datasets limit a comprehensive assessment in these regions.

Numerous submarine landslides have been reported in the last two decades due to Gas hydrate dissociation and fluid migration. In various basins across the globe, the dissociation of hydrates into free gas and water has been observed due to a rise in sea surface temperature over the course of the last century. To have an insight into the process, we have studied the effect of temperature variations and the associated impact on gas hydrate dissociation in two gas hydrate provinces in the East Indian Margin of India, namely the Krishna-Godavari (KG) and Cauvery basins. The 3D seismic data of the Krishna Godavari and Cauvery basin has shown occurrences of Bottom Simulating Reflectors (BSR) at ~160 meters below seafloor (mbsf) and ~110 mbsf, respectively. In the Last Glacial Maximum (LGM), the sea surface temperature increased by 3 ºC in the Bay of Bengal and the Indian Ocean. The modelling of the base of the hydrate stability zone (BHSZ) at ~1000m water depth has shown that the BSR is shifted by ~80m in the KG and ~60 m in the Cauvery basin Post LGM. The difference in the shift is due to the Lithology, high sea bottom temperature, Low Geothermal Gradients and less sedimentation rates in the Cauvery basin compared to the KG. The slope angle in both study areas is less than 2 degrees however, we have identified a large slump of ~21sq. km in the KG that occurred due to the gas hydrate dissociation and fluid migration. It appears that there has been no observed slumping in the Cauvery region, possibly due to lower sedimentation rates and lithology. Another reason is that the seismic data is available in limited area i.e., ~20 sq.km in the Cauvery region. However, significant Mass Transport Deposits and mud diapers were observed which might have formed due to the hydrate dissociation beneath the BSR. Hydrate dissociation due to an increase in the bottom water temperatures is also recorded in the carbon isotopes of benthic (Miliolida) foraminifera in the Indian margin. The first ever detected gas flares in the multibeam echo sounder data of the Cauvery basin lends support to our argument that the gas hydrate dissociation is actively occurring in the East Indian margin.

• A dilation-based algorithm adds sub-resolution porosity, quantified by differential imaging, as microlinks to an extracted pore network. • Empirical models are used to characterize the flow properties of microlinks. • The tuned multiscale network, using permeability, formation factor, and drainage capillary pressure, matches experimental relative permeability.

Paleosecular variation analysis is a primary tool for characterizing ancient geomagnetic behavior and its evolution through time. This study presents a new high-quality directional dataset, paleosecular variation of the Paleogene (PSVP), with and without correction for serial correlation (SC), compiled from 1,667 sites from 45 different localities from the Paleogene and late Cretaceous (84 – 23 Ma). The dataset is used to study the variability, structure, and latitude dependence of the geomagnetic field during that period by varying selection criteria and PSV models. Modeled values for the equatorial virtual geomagnetic pole (VGP) dispersion have over-lapping uncertainty intervals within their uncertainty bounds between 8.3° and 18.6° 30 for the past 250 Ma. We investigate the suitability of two descriptive models of PSV, Model G-style quadratic fits and covariant GGP models, and find that both styles of model fail to satisfactorily reproduce the latitude dependent morphology of PSV,  but suggest that estimates of the equatorial VGP dispersion may still robustly characterize aspects of Earth’s long-term field morphology. During this time where the PSV behavior has not changed substantially, the reversal frequency has varied widely. The lack of a clear relationship between PSV behavior and reversal frequency is not trivially explained in the context of published findings regarding numerical geodynamo simulations.This manuscript is the version accepted for publication in G-Cubed on 16th May, 2024.

We explore 幸运飞行艇 168开奖网 官方开奖网站查询 幸运飞行艇官方开奖历史记录 168飞艇官方开奖记录历史查询 幸运168飞艇开奖官网查询结果 幸运飞行艇官方开奖历史记录查询结果 deep learning for the Next Generation Water Resources Modeling Framework (NextGen). We present results from Random forest-based multi-model ensembles, Long Short-Term Memory (LSTM) and differentiable parameter learning hydrological models (δ conceptual models) and attribute sensitivity for ungauged basins.This poster was presented at the CIROH Developers Conference May 29 – June 1, 2024, at the University of Utah in Salt Lake City.

Mountain ecosystems are distributed across all continents, covering about 27% of the world's land surface. These regions support 1.1 billion people worldwide, about 15% of the global population, with 91% in developing countries. Mountains are social-ecological systems characterized by vertical gradients of climate and cultural and natural diversity. Rapid shifts in topography and climatic regimes make mountain ecosystems susceptible to climate change. People in these regions use rangelands for extensive livestock production. However, many rangelands previously used by local communities are currently under protection with different use levels. Although it is anticipated that the effects of climate change will be significant in the tropical Andes, the extent of these impacts is still not well understood. This research explores climate change's potential ecological and land management impacts on two agropastoral communities neighboring Huascarán National Park in the Peruvian Andes. For this, we created the Agent-based model of laNd management Dynamics and Ecosystem Services (ANDES) linked to L-Range, an ecosystem-process model. L-Range simulates biogeochemical processes and vegetation dynamics, e.g., monthly primary production, N and C cycling, and plant population dynamics for rangeland ecosystems. Implementing L-Range required parametrizing 54 climatic, edaphic, and plant variables and using landscape and climatic information, e.g., fractional vegetation cover, topographic features, soil properties, temperature, and precipitation. The ANDES model is a spatially explicit model of livestock grazing behavior, land and livestock management, and households' economies. The assessment of the model will involve using a pattern-oriented approach, such as comparing simulated responses of the daily fraction of biomass consumed by livestock and daily distance traveled with information from reports and peerreview journals. The validation of the L-Range model results also will require comparing the model outputs, such as net primary production and vegetation cover, to historic remotely sensed data. This research is in the phase of model validation and data analysis. Thus, we present the methodology for building the coupled model and preliminary findings.

• We present a two-dimensional Discrete Element Method model that simulates host rock fracturing during the inflation of a magmatic laccolith. • The host rock toughness and stiffness along with source depth control spatial fracture distribution between two end-members. • At a constant injection rate, the vertical displacement maximum and lateral extent show sudden changes in response to host rock fracturing.