Hurricane Nicholas was classified as a Category 1 tropical cyclone (TC) at 0000 UTC on 14 September 2021 and made landfall along the upper Texas Gulf Coast at 0530 UTC with maximum sustained winds of 33 m s-1. Much of the electrical activity during Nicholas was monitored by the Houston Lightning Mapping Array (HLMA) network. Deep convection developed in the rainband at 1700 UTC on 13 September, diminished by 2030 UTC, and re-intensified after 2200 UTC. At 2004 UTC (13 September), a curved megaflash (~220 km) was observed by the HLMA in the stratiform precipitation region of the outer rainband. By 0130 UTC on 14 September 2021, vigorous deep convective cells developed in the eastern eyewall region and propagated cyclonically to the western eyewall region. Several “jet-like” transient luminous events (TLEs) were observed by the HLMA emanating from a deep convective cell in the western eyewall region between 0230-0300 UTC with VHF source points ranging from 30-45 km in altitude. Moreover, the TLEs occurred within a region of strong wind shear, upper-level graupel-ice crystal collisions (~15 km), and strong cloud top divergence. Charge analysis of the deep convection during Nicholas revealed an overall normal dipole structure, while the megaflash and TLE deep convective cases exhibited inverted dipole charge structures. Dissipation of the upper-level screening charge layer resulting from cloud top divergence likely played a role in the observed TLE VHF sources escaping to altitudes exceeding 30 km.

Amidst the time-consuming process of conducting laboratory tests to confirm contamination incidents, utilities must promptly implement measures to mitigate the impact of potential contamination without undue overreaction. This paper proposes a novel approach for mitigating the impact of contamination threats (an unconfirmed contamination event) in smart water networks. By leveraging smart valves, the consumer flows are regulated to create a decreasing pressure gradient from the contamination source to a flushing point, facilitating efficient contamination flushing with minimal consumer inconvenience. The decreasing pressure gradients are formulated as constraints within an online optimization problem. Additionally, two methods for identifying these pressure constraints are presented. To assess the efficacy of the proposed framework, testing is conducted using a realistic water network model.

A document by Shuying Chen. Click on the document to view its contents.

Slow climate feedbacks are currently underrepresented in model assessments of climate sensitivity and their magnitudes are still poorly constrained. We combine a recently published record of Earth’s Energy Imbalance (EEI) with existing reconstructions of temperature, atmospheric composition, and sea level to estimate both the magnitude and timescale of the ice sheet-albedo feedback since the Last Glacial Maximum. This facilitates the first opportunity to quantify this feedback over the most recent deglaciation using a purely proxy data-driven approach, without the need for simulated reconstructions. We find the ice sheet-albedo feedback to be amplifying, reaching an equilibrium magnitude of 0.55 Wm-2K-1, with a 66% confidence interval of 0.45 - 0.63 Wm-2K-1. The timescale to equilibrium is estimated as 3.6Kyrs (66% confidence: 1.9 - 5.5Kyrs). These results provide new evidence for the timescale and magnitude of the amplifying ice sheet-albedo feedback that will continue to drive anthropogenic warming for millennia to come.

Accurate catchment delineation and lake connectivity assessment are crucial for understanding hydrology and managing water resources. By employing the Forest And Buildings removed Copernicus DEM (FABDEM) and a 2015 lake dataset, this study updated the delineations of 798 lake catchments on the Qiangtang Plateau. From these lake catchments, 442 whole catchments were identified based on inter-lake connectivity, with 140 encompassing multiple lakes. Based on the cascading structures of lakes identified through graph theory, we categorized these multi-lake whole catchments into three types: 86 single-linkage, 31 dendritic, and 23 hub-and-spoke, providing a refined understanding of the hierarchical organization within the lake networks. Projected reductions to 387 whole catchments by 2030 and 366 by 2050 due to global warming underscore the dynamic impacts of climate change on lake connectivity. This research provides crucial insights for managing water resources in endorheic basins under changing climatic conditions.

In the tropics, oxygen isotope signals of past climate change range from less than 1‰ to upwards of 7‰ or more. Regardless of the amplitude, these signals are often interpreted to reflect changes in local rainout. However, stable isotopes in precipitation can carry information about rainout across thousands of kilometers, making it hard to parse the local and non-local effects. Here, we present a framework that links the amplitude of tropical isotope signals to spatial patterns of climate change that cause them. Using three models of varying complexity, we show that the largest signals require coherent hydrologic change across ~1,000 to 8,000 kilometers. This pattern can be explained by the balance of vapor being rained out versus replenished as it moves over space. Within ~1,000 kilometers, upwind changes in rainout are too localized for a large isotope shift to emerge. Beyond ~8,000 kilometers, the rainout signal is overwhelmed by more locally-sourced vapor. We find that rainout in this ~1,000-8,000 km upwind window causes the largest isotope shifts in tropical paleoclimate, even when the isotope composition is strongly correlated with local precipitation amount. Our results indicate that large amplitude isotope signals are reliable tracers of large scale hydrologic change, and their link to local precipitation amount is tenuous. 

On 14 October 2023, an annular eclipse took place between 1503 UT and 2055 UT. It affected both North and South America. In this study, we used multiple ground-based and space-borne instruments, and we analysed ionospheric, thermospheric, magnetic and electrodynamic responses to this eclipse. In the vertical total electron content (VTEC) maps, at mid-latitudes, we observed a 40% depletion shortly after the Moon's shadow arrival. When the eclipse reached the low latitude region and the northern crest of the equatorial anomaly, we observed only 20-25% VTEC decrease in the vicinity of the eclipse path. However, after the Moon shadow crossed the magnetic equator, the low-latitude VTEC depletion reached 40%. The latter can be explained by the eclipse weakening the equatorial electric fields and, consequently, the equatorial fountain. In the topside VTEC and in the in-situ electron density measurements from Swarm B and C satellites we observed 40-50% decrease over the area affected by the eclipse. In the thermosphere, the eclipse produced a noticeable drop in the neutral mass density (7-18%) at 490-510 km of altitude, and changed the thermospheric composition in the vicinity of the eclipse path by 25-35%. Finally, the eclipse also clearly affected the geomagnetic field, especially at low and equatorial latitudes. Shortly after the umbra's arrival, we observed a clear decline in the total intensity F and the horizontal component H at all equatorial stations in South America. Whereas, in the variations of the vertical magnetic component Z no common pattern was observed during the eclipse. Key points:  the eclipse caused 40% depletion in total electron content, and it produced 40-50% decrease in electron density in the topside ionosphere  satellites show 7 to 20% decrease in the neutral mass density and 25-35% change in the thermospheric composition due to the eclipse  magnetic measurements show a clear decrease in the total intensity and horizontal component at low latitudes during the eclipse

The ionosphere is characterised by a large number of disturbances generated in response to a wide range of phenomena, including natural hazards, space weather and man-made events. Such disturbances are known as travelling ionospheric disturbances (TID). Identification of the origin of TID, especially in real or near-real-time (NRT), is an extremely difficult task, and it is one of the most interesting scientific questions. In this paper we present, for the first time, an approach for an automatic and NRT-compatible detection and recognition of the source of ionospheric disturbances in time series of total electron content (TEC) measured by the Global Navigation Satellite Systems (GNSS) method. The main idea is 1) to analyse main characteristics (such as spatio-temporal features and frequency content) of TID generated by known sources, and 2) in NRT, to rapidly examine TID’s, and, based on this information, recognize their source. Currently, our database contains TEC data series with response to earthquakes, volcanic eruptions, tornadoes, explosions, rocket launches and equatorial plasma bubbles. Our developments are important for the future assessment of natural hazards from the ionosphere, and also for NRT Space Weather nowcast and applications. Also, our work presents important information about the physical properties of TID of different origins.

A document by Pratik Shukla. Click on the document to view its contents.

Extreme precipitation events play a pivotal role in shaping Earth’s surface through their influences on hillslope processes and sediment transport in rivers. In this study, we focus on understanding the implications of such events on sediment transport, using Réunion Island as a natural laboratory due to its intense tropical rainfall regime. Through photogrammetric techniques and subsequent sediment volume estimates spanning decades as well as cosmogenic 3He measurements, we assessed the spatio-temporal evolution of the canyon bed of the ephemeral Rivière des Remparts and the drainage of products from major landslides and/or rock avalanches between 1950 and 2011. Results show that 39.6 Mm3 of sediment was transported out of the watershed in 62 years. Furthermore, we modeled the flow dates of this ephemeral river and show that such an export of material actually happened during only 391 days over the 62 years, at an average rate of 0.1 Mm3/day. Our investigation confirms that sediment transport coincides with officially recorded extreme meteorological events such as cyclones. Moreover, our findings reveal that sediment transport predominantly occurs on days corresponding to high-percentile rank precipitation events, demonstrating that all transport is concentrated during these intense rainfall periods. Finally, this study underscores the extremely fast conveyance of material from slopes to deep-sea fans, facilitated in Réunion by the absence of a coastal platform. This rapid transfer has implications for CO2 consumption, as it should enhance the transport and burial of organic matter particles, potentially contributing significantly to the island’s overall CO2 consumption efficiency.

Neural Weather Models (NWM) are novel data-driven weather forecasting tools based on neural networks that have recently achieved comparable deterministic forecast skill to current operational approaches using significantly less real-time computational resources. The short inference times required by NWMs allow the generation of a large ensemble potentially providing benefits in quantifying the forecast uncertainty, particularly for extreme events, which is of critical importance for various socio-economic sectors. Here we propose a novel ensemble design for NWMs spanning two main sources of uncertainty: epistemic —or model uncertainty,— and aleatoric —or initial condition uncertainty. For the epistemic uncertainty, we propose an effective strategy for creating a diverse ensemble of NWMs that captures uncertainty in key model parameters. For the aleatoric, we explore the “breeding of growing modes” for the first time on NWMs, a technique traditionally used for operational numerical weather predictions as an estimate of the initial condition uncertainty. The combination of these two types of uncertainty produces an ensemble of NWM-based forecasts that is shown to improve upon benchmark probabilistic NWM and is competitive with the 51-member ensemble of the European Centre for Medium-Range Weather Forecasts based on the Integrated Forecasting System (IFS) —a gold standard in weather forecasting,— in terms of both error and calibration. In addition, we report better probabilistic skill than the IFS over land for two key variables: surface wind and air surface temperature.

Accessing velocity data in the Gulf of Mexico is critical for environmental conservation and predicting debris and oil spill movements. This data can provide valuable insights for cleaning the ocean and mitigating marine pollution. Traditionally, researchers have relied on physics models to reconstruct and predict velocity fields at desired spatial and temporal resolutions. However, obtaining this data is not only computationally expensive but also error-prone. While accurate measurements can be obtained using ocean drifters, their sparsity necessitates extensive extrapolation to create comprehensive velocity fields. We propose applying a deep learning model called Physics-Informed Neural Networks to reconstruct ocean surface velocity fields using sparse measurements obtained from drifters. With data from 200 drifters, we successfully reconstructed the surface velocity field in the Gulf of Mexico, achieving a Correlation Coefficient of more than 0.91. Notably, this performance surpasses that of classical data extrapolation methods, including Inverse Distance Weighted and Universal Kriging algorithms.

We synthesize knowledge from numerical weather prediction, inverse theory and statistics to address the problem of estimating a high-dimensional covariance matrix from a small number of samples. This problem is fundamental in statistics, machine learning/artificial intelligence, and in modern Earth science. We create several new adaptive methods for high-dimensional covariance estimation, but one method, which we call NICE (Noise-Informed Covariance Estimation), stands out because it has three important properties: (i) NICE is conceptually simple and computationally efficient; (ii) NICE guarantees symmetric positive semi-definite covariance estimates; and (iii) NICE is largely tuning-free. We illustrate the use of NICE on a large set of Earth-science-inspired numerical examples, including cycling data assimilation, geophysical inversion of field data, and training of feed-forward neural networks with time-averaged data from a chaotic dynamical system. Our theory, heuristics and numerical tests suggest that NICE may indeed be a viable option for high-dimensional covariance estimation in many Earth science problems.

In many regions globally, snowmelt-recharged mountainous karst aquifers serve as crucial sources for municipal and agricultural water supplies. In these watersheds, complex interplay of meteorological, topographical, and hydrogeological factors leads to intricate recharge-discharge pathways. This study introduces a spatially distributed deep learning precipitation-runoff model that combines Convolutional Long Short-Term Memory (ConvLSTM) with a spatial attention mechanism. The effectiveness of the deep learning model was evaluated using data from the Logan River watershed and subwatersheds, a characteristically karst-dominated hydrological system in northern Utah. Compared to the ConvLSTM baseline, the inclusion of a spatial attention mechanism improved performance for simulating discharge at the watershed outlet. Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. Furthermore, fine-tuning the model at subwatershed scales provided insights into cross-subwatershed subsurface connectivity. These findings align with results obtained from detailed hydrogeochemical tracer studies. Results highlight the potential of the proposed deep learning approach to unravel the complexities of karst aquifer systems, offering valuable insights for water resource management under future climate conditions. Furthermore, results suggest that the proposed explainable, spatially distributed, deep learning approach to hydrologic modeling holds promise for non-karstic watersheds.

A document by Jewel A. Abbate. Click on the document to view its contents.

Nares Strait, situated between northwest Greenland and Ellesmere Island is a major sea ice export path from the Arctic Ocean. One of the narrowest parts of the strait --- Kennedy Channel --- is host to several islands. These obstacles' impact on the ice flow along the Strait has yet to be investigated. Here we show that during autumn, these islands can interrupt the relatively homogenous sea ice flow through Kennedy Channel, carried by wind and water currents, and shield downstream regions from becoming ice-covered, resulting in the formation of hitherto unknown transient polynyas, whose size and extent were correlated with the wind's strength and direction along Kennedy Channel. These polynyas likely impact the regional meteorology and oceanography through enhanced air-sea fluxes of heat, moisture, momentum, and carbon dioxide. The presence of open water may also impact the complex and productive regional ecosystem.

Carbon export driven by submesoscale, eddy-associated vertical velocities (“eddy subduction”) remains understudied, leaving a gap in our understanding of ocean carbon sequestration. Here, we assess mechanisms controlling eddy subduction’s spatial and seasonal patterns using 15 years of observations from BGC-Argo floats in the Southern Ocean. We identify signatures of eddy subduction as subsurface anomalies in temperature-salinity and oxygen. The anomalies’ spatial distribution is concentrated near weakly stratified areas and strong lateral buoyancy gradients diagnosed from satellite altimetry, particularly in the Antarctic Circumpolar Current’s standing meander regions. Meanwhile, vertical stratification drives seasonal variability. Bio-optical proxies associated with subsurface anomalies (such as the Chlorophyll a to particulate backscatter ratio: Chl/bbp), indicate that eddy subduction is most active in the spring and early summer, with freshly exported material associated with seasonally weak vertical stratification. Climate change is increasing ocean stratification globally, which may weaken eddy subduction’s carbon export potential.

Investigating the Organic Chemical Evolution of Enceladus Recorded by the Macromolecular Organic Matter from Its PlumeJennifer L. Eigenbrode (1), Alfonso Davila (2), Sarinah Z. Wahl (1,3), Jennifer C. Stern (1), and Andrej Grubisic (1)(1) NASA Goddard Space Flight Center, Solar System Exploration Division, Greenbelt, MD, USA; (2) NASA Ames Research Center, Moffett Field, CA, USA; (3) Center for Research and Exploration in Space Science and Technology, Southeastern Universities Research Association, Washington, DC, USACitation: Eigenbrode, J. L., Davila, A., Wahl, S. Z., Stern, J. C., and Grubisic, A. (2024), Investigating the Organic Chemical Evolution of Enceladus Recorded by the Macromolecular Organic Matter from Its Plume, Abstract 418-02 presented at AbSciCon 2024, Providence, RI, USA, 5-10 May 2024.