The Antarctic Circumpolar Current is the world's strongest ocean current and plays a disproportionate role in the climate system due to its role as a conduit for major ocean basins. This vast current system is linked to the ocean's vertical overturning circulation, and is thus pivotal to the uptake of heat and CO 2 in the ocean. The strength of the Antarctic Circumpolar Current has varied substantially across warm and cold climates in Earth's past, but the exact dynamical drivers of this change remain elusive. This is in part because ocean models were not able to adequately resolve the eddies and dense shelf water formation that control current strength. Here, we use a global ocean model which resolves such processes to diagnose the impact of future thermal, haline and wind conditions on the strength of the Antarctic Circumpolar Current. By 2050, our model suggests the strength of the Antarctic Circumpolar Current will decline by ∼ 20% in an extreme scenario. This decline is further supported by simple scaling theory, and is driven by ice shelf melting around Antarctica, which weakens the zonal density stratification historically supported by surface temperature gradients. Such a strong decline in transport would have critical implications for the entire global ocean circulation, and hence the Earth's climate system. Southern Ocean | Antarctic Circumpolar Current | Ocean Freshening | Antarctic Bottom Water

A document by Saman Aryana. Click on the document to view its contents.

A document by Saman Aryana. Click on the document to view its contents.

Current and emerging approaches to subsurface storage suffer from geographical limitations and in some cases insufficient seal integrity or lateral containment. We propose an innovative containment strategy based on time-dependent yield stress materials, namely Smectite clay suspensions, to address these challenges and make subsurface storage reliable and geographically agnostic. We outline a containment strategy designed to reinforce natural subsurface seals and engineer flow barriers. As a high-risk, high-reward approach, suspension can be injected at its initial low viscosity and elasticity into a porous medium, allowing for easy pumping and targeted delivery, once inside the target zone, it matures into a soft solid with much higher viscosity and elasticity, acting as a flow barrier. We introduce and discuss the exceptional properties of this Smectite suspensions that we believe can revolutionize subsurface containment and storage to move in tandem with the energy transition. There are no adverse effects from higher temperatures on its long-term stability, unlike most polymer aqueous dispersions used in the industry. Moreover, its thixotropic microstructure offers many advantages in operations, such as handling sudden pump shutdowns.

CO2-foams show significant potential for improving mobility control in CO2 Enhanced Oil Recovery (EOR) processes and for the geological carbon sequestration. The addition of nanoparticles (NPs), such as Fe3O4, has been shown to enhance the stability of these foams, especially when used in conjunction with surfactants. Apart from their role in stabilization, Fe3O4 NPs possess distinctive magnetic properties, making them useful in various applications. In this study, we explore the novel application of these nanoparticles, initially intended for stabilizing CO2-foams, as tracers for monitoring the migration of subsurface CO2 plumes. This monitoring is envisioned through the detection of small quantities of these particles within subsurface fluid environments. To facilitate this, we use an Induction Heating (IH) technique, involving exposing a solution with small amounts of Fe3O4 NPs to a high-frequency alternating magnetic field, then measuring the resulting temperature changes using an infrared camera variations. The results indicate a direct correlation between the NP concentration and the observed temperature increase in the solution.

A document by Alaguvalliappan Thiagarajan. Click on the document to view its contents.

A document by Collin Bode. Click on the document to view its contents.

Critical Zone (CZ) scientists study the coupled chemical, biological, physical, and geological processes operating across scales to support life at the Earth's surface. In 2020, the U.S. National Science Foundation funded a network of Thematic Cluster projects called “CZ Net” to work collaboratively in answering scientific questions related to effects of urbanization on CZ processes; CZ function in semi-arid landscapes and the role of dust in sustaining these ecosystems; deep bedrock processes and their relationship to CZ evolution; CZ recovery from disturbances such as fire and flooding; and changes in the coastal CZ related to rising sea level. Data collected by these projects are diverse, ranging from time series from in situ sensors to laboratory analysis of physical samples, geophysical measurements, and others. Thus, coordinating data collection, archival, discovery, and access for the network presents significant challenges. Given the diversity in scientific domains represented, data produced, and collaborations, no single repository fully meets the needs of CZ scientists, posing questions of which repositories to use, how to enable discovery of and access to data across different repositories, and how to develop and promote best practices for sharing research products. This presentation describes cyberinfrastructure (CI) development by the CZ Net Coordinating Hub that leverages existing, domain-specific repositories for managing, curating, disseminating, and preserving data and research products from the CZ Net projects. We have developed CI that links existing data facilities and services, including HydroShare, EarthChem, Zenodo, and other repositories via a CZ Hub that provides tools for data submission, resource registry, metadata cataloging, resource discovery/access, and links to computational resources for analysis and visualization. The CZ Hub’s goal is to make data, samples, software, and other research products created by CZ Net projects Findable, Accessible, Interoperable, and Reusable (FAIR), using existing domain-specific repositories. The repository interoperability we have demonstrated for delivering data services for an interdisciplinary science program may provide a template for future development of integrated, interdisciplinary data services.

A document by Mark Hall. Click on the document to view its contents.

Rapid warming in the Arctic leads to increased glacier melt and freshwater runoff, especially from tidewater glaciers. Here, runoff enters the fjord at depth; induces upwelling and enhances macronutrient delivery to the fjords. However, most studies have low temporal resolutions and so the effects of low-frequency, high-amplitude events on the marine environment remain poorly known. Here, we combine glacier observations with fjord and glacier lake sampling to describe the impact of the 2021 glacier lake outburst flood (GLOF) from lake Setevatnet into Kongsfjorden (Svalbard). We demonstrate the importance of changing subglacial conditions and examine their effects upon macronutrient availability in the inner fjord. Our observations reveal that direct nutrient subsidy from the glacier is most important in early summer, providing critical nitrate (NO3-) and silicate following the routing of meltwater through an inefficient drainage system. Increasing quantities of ice melt force the establishment of an efficient drainage system, creating a plume in the inner fjord, and resulting in upwelling of nutrient-rich bottom water. When the sudden drainage of a glacier lake with high NO3- concentrations occurred, it left little imprint on the NO3- content of the inner fjord, and instead induced seasonal maximum nitrite (NO2-) concentrations. This outcome implies that NO3- was removed by denitrification at the glacier bed and its product NO2- was discharged by the flood waters into the inner fjord. Our findings show that the delivery of key, productivity-limiting nutrients from tidewater glaciers not only depends on runoff, but also on characteristics of the glacier drainage system.

Seawater intrusion poses a substantial threat to water security in coastal regions, where numerical models play a pivotal role in supporting groundwater management and protection. However, the inherent heterogeneity of coastal aquifers introduces significant uncertainties into model predictions, potentially diminishing their effectiveness in management decisions. Data assimilation (DA) offers a solution by incorporating various types of observational data to characterize these heterogeneous coastal aquifers. Traditional DA techniques, like ensemble smoother using the Kalman formula (ESK) and Markov chain Monte Carlo, face challenges when confronted with the non-linearity, non-Gaussianity, and high-dimensionality issues commonly encountered in aquifer characterization. In this study, we introduce a novel DA approach rooted in deep learning (DL), referred to as ESDL, aimed at effectively characterizing coastal aquifers with varying levels of heterogeneity. We systematically investigate a range of factors that impact the performance of ESDL, including the number and types of observations, the degree of aquifer heterogeneity, the structure and training options of the DL models, etc. Our findings reveal that ESDL excels in characterizing heterogeneous aquifers, particularly when faced with non-Gaussian conditions. Comparison between ESDL and ESK under different experimentation settings underscores the robustness of ESDL. Conversely, in certain scenarios, ESK displays noticeable biases in the characterizing results, especially when measurement data from nonlinear and discontinuous processes are used. To optimize the efficacy of ESDL, meticulous attention must be given to the design of the DL model and the selection of training options, which are crucial to ensure the universal applicability of this DA method.

A document by Javed Ali. Click on the document to view its contents.

A document by Collin Bode. Click on the document to view its contents.

A document by Florent Grasso. Click on the document to view its contents.

A document by Aidin Jabbari. Click on the document to view its contents.

Current MMRV solutions have the potential to quickly survey entire oilfields or detect methane leaks down to the component level, but also carry high price tags or, indirectly, high implementation costs. The Stanford/EDF Mobile Monitoring Challenge (MMC) conducted in 2018 was the first study to systematically evaluate methane mitigation technologies for incorporation into LDAR programs at the operator level. Three vehicle-based solutions tested in the MMC utilized a fence-line screening pattern that encompassed a production site and equipment, which we refer to as a “drive-around survey,” and showed promising results of greater than or equal to 88% true positive source identification rates for controlled releases in the 0-26 kg CH4/hr range. In this work, we evaluate a similar on-site drive-around survey as an alternative methane leak detection method under the EPA’s recent update to the Standards of Performance for New, Reconstructed, and Modified Sources and Emissions: Oil and Natural Gas Sector (NSPS). We find that a simple methane enhancement threshold binary classification system performs well with true positive rates > 0.8, though the precision of this classifier is inversely related to the magnitude of the emission rates for each class. We also describe a heuristic approach to estimating dispersion without source distance information. Incorporating this information into a linear model of emission rates regressed on survey data, we improve the model fit to R^2 > 0.9.

Access to urban natural space, including blue and greenspace, is associated with improved health. In 2021, the C40 Cities Climate Leadership Group set 2030 Urban Nature Declaration (UND) targets: “Quality Total Cover” (30% green area within each city) and “Equitable Spatial Distribution” (70% of the population living close to natural space). We evaluate progress towards these targets in the 96 C40 cities using globally available, high-resolution datasets for landcover and normalized difference vegetation index (NDVI). We use the European Space Agency (ESA)’s WorldCover dataset to define greenspace with discrete landcover categories and ESA’s Sentinel-2A to calculate NDVI, adding the ‘open water’ landcover category to characterize total natural space. We compare 2020 levels of urban green and natural space to the two UND targets and predict the city-specific NDVI level consistent with the UND targets using linear regressions. The 96-city mean NDVI was 0.538 (range: 0.148, 0.739). Most (80%) cities meet the Quality Total Cover target, and nearly half (47%) meet the Equitable Spatial Distribution target. Landcover-measured greenspace and total natural space were strong (mean R2 = 0.826) and moderate (mean R2=0.597) predictors of NDVI and our NDVI-based natural space proximity measure, respectively. The 96-city mean predicted NDVI value of meeting the UND targets was 0.478 (range: 0.352-0.565) for Quality Total Cover and 0.660 (range: 0.498-0.767) for Equitable Spatial Distribution. Our translation of the area- and access-based metrics common in urban natural space targets into the NDVI metric used in epidemiology allows for quantifying the health benefits of achieving such targets.

Wildfire smoke fine particles (PM2.5) are a growing public health threat as wildfire events become more common and intense under climate change, especially in the Western United States. Studies assessing the association between wildfire PM2.5 exposure and health typically summarize the effects over the study area. However, health responses to wildfire PM2.5 may vary spatially. We evaluated spatially-varying respiratory acute care utilization risks associated with short-term exposure to wildfire PM2.5 and explored community characteristics possibly driving spatial heterogeneity. Using ensemble-modelled daily wildfire PM2.5, we defined a wildfire smoke day to have wildfire-specific PM2.5 concentration ≥15 µg/m3. We included daily respiratory emergency department visits and unplanned hospitalizations in 1,396 California ZIP Code Tabulation Areas (ZCTAs) and 15 census-derived community characteristics. Employing a case-crossover design and conditional logistic regression, we observed increased odds of respiratory acute care utilization on wildfire smoke days at the state level (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.05, 1.07). Across air basins, ORs ranged from 0.88 to 1.57, with the highest effect estimate in San Diego. A within-community matching design and spatial Bayesian hierarchical model also revealed spatial heterogeneity in ZCTA-level rate differences. For example, communities with a higher percentage of non-Hispanic Black or Pacific Islander residents had stronger wildfire PM2.5-outcome relationships, while more air conditioning and tree canopy attenuated associations. We found an important heterogeneity in wildfire smoke-related health impacts across air basins, counties, and ZCTAs, and we identified characteristics of vulnerable communities, providing evidence to guide policy development and resource allocation.

In the past few decades, there has been an evolution in our understanding of soil organic matter (SOM) dynamics from one of inherent biochemical recalcitrance to one deriving from plant-microbe-mineral interactions. This shift in understanding has been driven, in part, by influential conceptual frameworks which put forth hypotheses about SOM dynamics. Here, we summarize several focal conceptual frameworks and derive from them six controls related to SOM formation, (de)stabilization, and loss. These include: (1) physical inaccessibility; (2) mineral stabilization; (3) abiotic environmental limitation; (4) biochemical reactivity and diversity; (5) biodegradability of plant inputs; and (6) microbial properties. We then review the empirical evidence for these controls, their model representation, and outstanding knowledge gaps. We find relatively strong empirical support and model representation of abiotic environmental limitation but disparities between data and models for biochemical reactivity and diversity, mineral stabilization, and biodegradability of plant inputs, particularly with respect to SOM destabilization for the latter two controls. More empirical research on physical inaccessibility and microbial properties is needed to deepen our understanding of these critical SOM controls and improve their model representation. The SOM controls are highly interactive and also present some inconsistencies which may be reconciled by considering methodological limitations or temporal and spatial variation. Future conceptual frameworks must simultaneously refine our understanding of these six SOM controls at various spatial and temporal scales and within a hierarchical structure, while incorporating emerging insights. This will advance our ability to accurately predict SOM dynamics.

Forecasting hydroclimatic extremes holds significant importance considering the increasing trends in natural cascading climate-induced hazards such as wildfires, floods, and droughts. This study evaluates the performance of five Copernicus Climate Change Service (C3S) seasonal forecast models (i.e., CMCC, DWD, ECCC, UK-Met, and Météo-France) in predicting extreme precipitation events from 1993 to 2016 using 28 extreme precipitation indices reflecting timing and intensity of precipitation in a seasonal timescale. We design indices using various precipitation thresholds to reflect model skill in capturing the distribution and intensity of precipitation over a season. We use percentage bias, the Kendall Tau rank correlation, and ROC scores for skill evaluation. We introduce an impact-based framework to evaluate model skill in capturing extreme events over regions prone to natural disasters such as floods and wildfires. The performance of models varies across regions and seasons. The model skill is highlighted primarily in the tropical and inter-tropical regions, while skill in extra-tropical regions is markedly lower. Elevated precipitation thresholds correlate with heightened model bias, revealing deficiencies in modelling severe precipitation events. The impact-based framework analysis highlights the superior predictive capabilities of the UK-Met and Météo-France models for extreme event forecasting across many regions and seasons. In contrast, other models exhibit strong performance in specific regions and seasons. These results advance our understanding of an impact-based framework in capturing a broad spectrum of extreme climatic events through the strategic amalgamation of diverse models across different regions and seasons, offering valuable insights for disaster management and risk analysis.

Lakes are the most prevalent and predominant water repositories on land surface. A primary objective of the Surface Water and Ocean Topography (SWOT) satellite mission is to monitor the surface water elevation, area, and storage change in Earth’s lakes. To meet this objective, prior information of global lakes, such as locations and benchmark extents, is required to organize SWOT’s KaRIn observations over time for computing lake storage variation. Here, we present the SWOT mission Prior Lake Database (PLD) to fulfill this requirement. This paper emphasizes the development of the “operational PLD”, which consists of (1) a high-resolution mask of ~6 million lakes and reservoirs with a minimum area of 1 ha, and (2) multiple operational auxiliaries to assist the lake mask in generating SWOT’s standard vector lake products. We built the prior lake mask by harmonizing the UCLA Circa-2015 Global Lake Dataset and several state-of-the-art reservoir databases. Operational auxiliaries were produced from multi-theme geospatial data to provide information necessary to embody the PLD function, including lake catchments and influence areas, ice phenology, relationship with SWOT-visible rivers, and spatiotemporal coverage by SWOT overpasses. Globally, over three quarters of the prior lakes are smaller than 10 ha. Nearly 96% of the lakes, constituting over half of the global lake area, are fully observed at least once per orbit cycle. The PLD will be recursively improved during the mission period and serves as a critical framework for organizing, processing, and interpreting SWOT observations over lacustrine environments with fundamental significance to lake system science.

Understanding seasonality in outlet glacier dynamics reveals insight into long-term retreat and acceleration. Leveraging recent high-resolution satellite data, we examine changes in surface elevation, velocity, and terminus position for five glaciers in Central Western Greenland over the past ~6 years. We employ an approach that examines the stress imbalance at the ice-ocean terminus and models the expected response in upstream velocity caused by the observed terminus changes. The model shows that some glaciers’ seasonal velocity changes can be largely explained by terminus changes, while others can be compounded by multiple processes. Additionally, we test the sensitivity of the results by including seasonally varying and artificially modified surface topography. We find surface slope changes impact velocity response to terminus changes more than spatially uniform changes in along-flow elevation. Our approach provides a scalable framework to comprehend the compounded nature of glacier seasonal velocity variations across the Greenland Ice Sheet outlet glaciers.

A document by Shenyue Jia. Click on the document to view its contents.

One of the outstanding questions in lightning research is how dart leaders (also called recoil leaders or K-leaders) initiate and develop during a lightning flash. Dart leaders travel quickly (106-107 m/s) along previously ionized channels and occur intermittently in the later stage of a flash. We have recently reported some insights into dart leader initiation and development based on our BIMAP-3D observations. In this presentation we will expand on that work by combining observations and modeling to try to understand the observed dart leader behaviors. BIMAP-3D consists of two broadband interferometric mapping and polarization (BIMAP) systems that are separated by 11.5km at Los Alamos National Laboratory. Each station maps the lightning VHF sources in a 2D space, and the combination of the 2-station measurements provides a detailed 3D source map. A fast antenna is also included at each station for electric field change measurements. Our previously reported observations suggest dart leaders commonly exhibit an initial acceleration, followed by a more gradual deceleration to a stop. We also modeled the dart leader electric field change with a simple configuration of two point-charges, finding that the modeled tip charge increased in magnitude during the initial acceleration in some simple cases. We now employ a more sophisticated model to better understand the distribution of charge along the dart leader channel, and the background electric field in which the dart leader develops.Presented at the AGU 2023 Fall Meeting