The mosquito-borne disease dengue is sensitive to climate, in part because of the influence climate has on breeding habitats of dengue’s Aedes mosquito vectors. Dengue risk assessment models currently leverage climate-dengue statistical associations, yet what remain understudied are the mechanistic pathways that yield different statistical relationships in different locations. We hypothesize that elucidating the mechanisms by which spatiotemporal variability in climate influences dengue incidence will improve dengue dynamics predictions across climatically distinct locations and beyond dengue’s well-known seasonal cycles. We test this hypothesis by investigating a key pathway in the climate-dengue process chain: climate impacts on Aedes breeding habitats. We have implemented a mechanistic modeling pipeline that simulates climatic influence on habitat water dynamics and thereby on the relative population size of the vector. We use this modeling pipeline, driven by meteorological data, to simulate monthly Aedes populations for three climatically distinct cities in Sri Lanka. We find that simulated vector abundance is plausibly associated with climate conditions and that climate drivers of vector abundance vary among locations. Moreover, tercile-tercile comparisons of dengue incidence against model variables indicate that risk assessments based on predicted vector abundance perform similarly to those based on meteorology alone—the signal of weather variability and its relationship to dengue propagates through the modeling pipeline. These results justify future assessment of this modeling pipeline’s utility within a dengue risk assessment framework, where its process-based structure may be leveraged to inform intervention strategies in high-risk years and to simulate impacts of future climate conditions on dengue dynamics.

Finite fault models using satellite radar interferometry data indicate that the Mw 6.4 Puerto Rico earthquake occurred on a N-dipping fault. Focal mechanism solutions of aftershocks show three trends of strike-slip faults including two parallel WNW-ESE left-lateral faults. The parallel strike-slip faults bound a left-stepping pull-apart basin which localizes the most concentrated area of the swarm.

Dense shelf water cascading (DSWC) is an oceanographic process occurring when dense shelf water overflows the shelf edge downslope towards the deep sea. Monitored in the Northwestern Mediterranean by moorings since 1993 in the Lacaze-Duthiers Canyon and since 2003 in the Cap de Creus Canyon, numerical modeling with reanalysis extends this timeline further into the past. This study investigates a regional reanalysis (1987 – 2021) validated against mooring observations at 750-1000 m depth. The reanalysis successfully reproduces observed Intense DSWC (IDSWC) events from 1999, 2000, 2005, 2006, 2012, 2013, and 2018, while identifying one previously unreported event in 1987, and detecting no IDSWC between 1988 and 1998. The reanalysis effectively matches 84% of observed IDSWC days within the same week and 56% on the exact date. Instead of assimilating IDSWC events from mooring observations to resolve the cascading process, the model relies solely on the seawater density on the shelf. Reanalysis data have revealed the seawater properties along the canyon that caused IDSWC and can be used to find other unreported cascading events elsewhere.

Ground-level neutron monitors (NMs) are essential tools for monitoring space weather events, including the detection and alerting of ground-level enhancement (GLE) events. This study presents findings from a neutron monitoring survey using two compact N50L neutron slab-based subsystems (N50L detectors encased in lead bricks, used as an analogue to a new NM design) deployed across various field sites in the United Kingdom (UK). Data from these N50L neutron slab-based subsystems were compared to readings from established NM-64 monitors with similar geomagnetic cutoff rigidities and accessed via the Neutron Monitor Database (NMDB). Observations showed that the N50L neutron slab-based subsystems’ cosmic ray (CR) count rates corresponded closely with NMDB trends, and count rates varied with altitude, latitude, and environmental factors within meters of the detectors. Key events observed during the campaign include Forbush decreases and GLE-74. The collected data will support the deployment of the new NM-2023 design initiative, specifically targeting the site of the first operational NM-2023 in the UK. Additionally, data from the N50L neutron slab-based subsystems were integrated with the University of Surrey’s Compact NM setup, enhancing GLE monitoring capabilities across UK cutoff rigidity. These preliminary measurements from the 4-NM-2023 prototype suggest it can achieve performance comparable to the 6-NM-64 monitor but with a reduced footprint, volume, mass, and cost, utilizing environmentally friendly, non-toxic gas-filled counters. A full 4-NM-2023 system is planned for deployment at the UK Met Office’s Camborne Observatory, with a 1-NM-2023 unit to be installed at Lancaster University.

Spectrally dependent emission by the surface (i.e., surface spectral emissivity) is commonly ignored by current climate models. Surface spectral emissivity matters more in cold and dry environments than in hot and humid environments. Recent modeling studies confirmed that, for current climate simulations, this process affects the polar climate more than the extra-polar climate. As for the Snowball Earth, a period characterized by global polar-like conditions of extreme cold and low humidity, including surface spectral emissivity could alter the simulated global radiation budget. This, in turn, could affect the simulated climate of the Snowball Earth. Here, we use anaqua-planet slab-ocean simulation of Snowball Earth by the ICON model to perform offline radiative transfer calculations to quantify such impact on the outgoing longwave radiation (OLR). The offline radiative transfer model is used to compute the clear-sky OLR for two surfaces that would be present in the extremely cold simulation: ice and snow. Compared to the results with assumed blackbody surface, the globalmean OLR decreases by 3.4 and 1.1 W m-2 for ice and snow surfaces, respectively. The impact of surface spectral emissivity on the OLR is strongest at the equator and weakens towards the poles, presenting a noticeable meridional gradient. The seasonal variation of the impact is also obvious. The radiative effects of this often-neglected process warrant further scrutiny for the Snowball Earth simulations, particularly the Jormungand mechanism, as well as simulations of other cold and dry climates.

 Title: Assessing Tidal Marsh Dynamics: A Multi-Model Synthesis Approach for Climate Resilience Authors: Ramin Familkhalili, Davina Passeri, Kevin Buffington, Nate Herold Tidal marshes are vital ecosystems that provide significant benefits to human communities and aquatic environments but face substantial threats from ongoing habitat loss and degradation, exacerbated by accelerating sea-level rise (SLR) driven by climate change. While effective models for predicting marsh dynamics exist, their usage by resource managers in guiding management decisions is often limited due to a lack of confidence and understanding. This research addresses these challenges by employing a multimodel synthesis approach, highlighting areas of agreement among models and exploring the impact of different modeling approaches on predicted outcomes. We investigated a multi-model synthesis approach to assess marshland distributions as a proof-ofconcept within the Plum Island Sound Estuary, MA. Ensuring that no single model is prioritized, we integrated existing model predictions from the Hydrodynamic-Marsh Equilibrium Model (Hydro-MEM) and the Sea Level Rise Wetland Impacts and Migration Model (SLRWIMM) alongside two newly generated model outputs from the Sea Level Affecting Marshes Model (SLAMM) and the Wetland Accretion Rate Model of Ecosystem Resilience (WARMER). Integrating outputs from various models, we created a cohesive representation of tidal marsh evolution across different model classifications. This talk will describe our approach, preliminary results, and next steps for this initial multi-model comparison using two SLR scenarios (1.16 and 1.51m by 2100). By collectively analyzing model predictions, this approach enhances our understanding of marsh dynamics and bolsters our confidence in applying models to predict areas of potential marsh expansion and loss from inundation and erosion.

Arizona State University, Earth Genome, and USACE' Engineer Research and Development Center has been working together under a Network for Engineering With Nature project to develop a Web Application tool for regional screening of Managed Aquifer Recharge (MAR) Projects. The objective of the project is to provide relative, regional indices for appropriate siting of MAR, which would be an innovative approach to flood management and restore hydrological and ecological processes linked to aquifer recharge. Developing a rigorous decision-making framework includes physical characteristics such as surface water runoff, connection with groundwater aquifers; as well as social considerations, such as land use, engaging stakeholders in floodplain management, and distribution of risks and benefits. A suite of analyses, from CONUS-wide Machine Learning for groundwater attributes and statistical estimation of flood volumes to surveys about perceptions of MAR were implemented to inform development of a decision support tool and supplemental guidance in an effort to increase the accessibility of this innovative flood management approach to floodplain managers. This research is funded by the Engineering With Nature ® and Network for Engineering With Nature® programs. https://ewn.erdc.dren.mil/

A document by Dani Jones. Click on the document to view its contents.

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for simulating snowpack dynamics. This is challenging at spatial resolutions smaller than common gridded meteorological datasets (e.g., resolutions smaller than 5 to 250 km) due to sparse long-term temperature measurements at those resolutions and local factors like cool air pooling and inversions. Near-surface air temperatures (Ta) are often assumed to decrease with elevation at a constant rate of 6.5°C km-1, leading to significant model errors in snow evolution and other key processes. This study evaluated the impact of local dynamical adjustments to downscaled Ta on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization land surface model. Forcings were derived from remote sensing and reanalysis precipitation products and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric products at the downscaled 1-km resolution. Hourly lapse rates at each grid cell were calculated by applying linear regression to Ta and elevation from neighbor grids (one grid length in the x or y direction) at the native MERRA-2 resolution and applied to the downscaled 1-km Ta product. We assessed the impact on simulated snow cover and depth across simulations forced with the downscaled Ta (1) without lapse rate correction, (2) corrected with a static lapse rate (6.5°C km-1), and (3) corrected with the dynamic hourly lapse rate. We evaluated model skill improvement with dynamic or static lapse rate correction, and no correction against satellite-derived products. Both lapse rate correction methods led to similar improvements on average, relative to no correction. However, dynamic lapse rate correction showed more pronounced improvements in simulating perennial snowpacks at mid-elevations and in deficit years, indicating the method can better resolve heterogeneous snowpack conditions that are key for water resource management.

Effusive volcanic eruptions have been common in Iceland throughout the Holocene with the largest ones happening prior to the industrial revolution. Such eruptions can affect climate through the formation of sulfate aerosols and subsequent impacts on clouds. As different atmospheric conditions modulate the cloud and climate responses to aerosol perturbations, a pre-industrial effusive eruption might have different climate impacts were it to happen today or in the future. Here we use an Earth system model to simulate the surface climate response to Icelandic effusive volcanic eruptions under pre-industrial, present day, and end of the 21st century climate conditions. For the last case, high anthropogenic greenhouse gas and aerosol emissions are assumed. We find that the climate state significantly modulates the climate response, especially in the Arctic where we model amplified surface warming during winter under pre-industrial conditions and stronger surface cooling during summer in warmer climates.

A document by Federico. Click on the document to view its contents.

Paleomagnetic data are usually retrieved by subjecting bulk samples, e.g. lavas, to laboratory measurement protocols. In many instances, these protocols yield uninterpretable results caused by the presence of particles with adverse magnetic properties that blur the signal of the reliable magnetic particles. With Micromagnetic Tomography (MMT) we focus on identifying the signal of particles with reliable properties. Their individual magnetic moments are computed by scanning the surface of a ~3 mm2 thin section with a quantum diamond microscope (QDM) and locating the magnetic recorders with computed tomography. Currently, almost 80% of all resolved magnetic moments is discarded due to numerical instability, making it difficult to obtain statistically relevant paleointensities and paleodirections based on the remaining magnetic moments. We improve the number of reliable magnetic moments from MMT experiments by making a QDM scan of both sides of the sample. Here, we conduct a combined numerical and empirical study to investigate the benefits and difficulties of adding this double-sided scanning (DSS) protocol to MMT. By investigating the theoretical gain of DSS for varying sample thicknesses, we show that DSS returns three times more numerically stable magnetic moments compared to single sided scanning for a sample thickness of 50 μm. However, careful sample preparation and handling is required: best results are obtained when both sample surfaces are measured as parallel as possible, while keeping the sample intact when flipping it to the other side for the bottom measurement. By overcoming these difficulties, DSS will provide a significant boost in stable magnetic moments.

Rock-magnetic properties are widely used to resolve the composition, concentration, size, shape and alignment of iron oxides in rock samples. Although anisotropy of magnetic susceptibility is measured routinely, acquisition of remanent magnetization as well as hysteresis loops are usually measured in one direction only. This may lead to biased interpretation of the results. In our study, observation of strong directional dependence of remanence acquisition curves and hysteresis loops in experimentally deformed samples of hematite ore is reported. Subpopulations of grains with different magnetic coercivities are expressed in different way, depending on the measurement direction, the anisotropy of induced magnetization being of several tens of per cent. These findings have significant impact on the remanent and induced magnetization measurement protocols and interpretation of the data. Anisotropy of these parameters should be checked at least in cases when hematite is assumed to be present in the samples, or the samples underwent deformation.

The Canadian Lake Ice Model has been distributed and adapted to run at a fine spatial resolution (~50 m) for simulating lake ice thickness and phenology on small to medium-sized lakes for this study. The model’s capabilities are extended to simulate the spatial variability of Lake ice thickness (LIT), Ice Cover Duration (ICD), and Lake Ice Phenology (LIP) across 500 predominantly small to medium-sized lakes in the North Slave Region (NSR) of the Northwest Territories (NWT), Canada, from 1984 to 2022. The model utilizes 30 m grid lake surface temperature (LST) data derived from the North Slave LST dataset, along with climate inputs from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 and ECMWF (ERA5), including wind speed, mean air temperature, relative humidity, snow depth, and cloud cover. These inputs provide surface fluxes to the model, driving its unsteady heat equation to produce daily LIT, annual ICD, and annual freeze-up and break-up dates on a ~50 m grid. Validation against in-situ measurements showed a root mean square deviation of 2.7 cm to 7 cm for LIT. Trend analysis revealed a significant decline in LIT (-0.26 cm/year to -0.10 cm/year) and ICD (-0.40 day/year to -0.15 day/year) over the study period. The findings highlight the sensitivity of LIT and freeze-up dates to lake morphometry. In contrast, ice break-up dates are primarily influenced by geographic factors such as latitude and elevation. The distributed model comprehensively assesses lake ice variability and trends across 500 lakes under changing climate and weather conditions.

A document by Sushel Unninayar. Click on the document to view its contents.

Geospatial intelligence (GEOINT) has become an indispensable tool in modern military operations since it considerably increases situational awareness, strategic decision-making, and operational success. Military force collecting, analysis, and spatial data interpretation have been changed by advanced geospatial technologies, including satellite imagery, remote sensing, geographic information systems (GIS), and unmanned aerial vehicles (UAVs). Emphasizing its strategic relevance in both conventional and asymmetric warfare, this paper explores the evolution, uses, and challenges of GEOINT inside modern military defense. By means of multiple case studies, the paper demonstrates how GEOINT assists targeting, mission planning, reconnaissance, and disaster response. The ethical implications and limitations of GEOINT are also assessed, particularly with regard to international law, data privacy, and security. The final section of the essay examines GEOINT's future in the context of emerging technologies like artificial intelligence (AI) and machine learning that should have an even bigger influence on military operations

Electromagnetic whistler-mode waves play a crucial role in the acceleration and precipitation of radiation belt electrons. Statistical surveys of wave characteristics suggest that these waves should preferentially scatter and precipitate relativistic electrons on the day side. However, the night-side region is expected to be primarily associated with electron acceleration. The recent low-altitude observations reveal relativistic electron precipitation in the night-side region. In this paper, we present statistical surveys of night-side relativistic electron losses due to intense precipitation bursts. We demonstrate that such bursts are associated with storm time substorm injections and are likely related to relativistic electron scattering by ducted whistler-mode waves. We also speculate on the role of injections in creating conditions favorable for relativistic electron precipitation.

More frequent extreme rainfall events is one worrisome aspect of climate change, likely driven by an increase in atmospheric heat which boosts the atmospheric water content, subsequently altering precipitation. Urban areas are particularly affected by weather extremes, although the impact of urbanization on intense rainfall remains difficult to quantify. This study investigates changes in extreme precipitation events using daily data from 6028 weather stations worldwide, extracted from the Global Historical Climatological Network (GHCN). While previous studies examined similar trends, and found the largest frequency increase for the most intense events, this work also incorporates urbanization factors. The analysis focuses on the 60 most intense precipitation events within the 1962-2021 timeframe, evaluating trends in frequency at each station. Two urbanization indices were used to categorize stations, revealing correlations with changes in extreme event frequency, indicating that the occurrence of extreme precipitations is increasing significantly more in densely populated urban areas.