Recent understandings regarding \textit{tresinos} in laboratory experiments and geophysical observations represents a new paradigm for Earth’s energy generation as well as a new direction toward developing \textit{tresino}-generated power reactors.

Atmospheric aerosols are widely recognized to give rise to a substantial radiative forcing to the climate by scattering and absorbing radiation and by modifying the microphysical, lifetime, and radiative properties of clouds. During the Marine ARM GPCI Investigation of Clouds (MAGIC) over the Eastern North Pacific (ENP), the ship-based measured cloud condensation nuclei (CCN) concentration at 0.2% supersaturation (NCCN,0.2) and condensation nuclei concentration (NCN) had mean values of 116.7 and 219.4 cm-3, with the highest concentrations found closest to LA due to an increase in aerosol sources. Moving westward, both NCCN,0.2 and NCN gradually decreased until stabilizing near 100 cm-3 and 200 cm-3, respectively. Using the methods proposed by Ghan and Collins (2004) and Ghan et al. (2006), NCCN,0.2 profiles are retrieved using the surface measured NCCN,0.2 as a constraint. For coupled conditions, correlations between the retrieved NCCN,0.2 profiles and cloud-droplet number concentration (NC) increase from 0.26 at the surface to 0.38 near cloud base, particularly true for non-drizzling clouds. Although the correlations are lower than expected, the percentage increase (46.2%) is encouraging. Finally, the relationships between cloud breakup (CB) and the stratocumulus to cumulus transition (SCT) with environmental conditions and associated aerosols are also studied. The decreased NCN trend east of CB is mainly caused by precipitation scavenging, while the increased NCN trend west of CB is strongly associated with the increased surface wind speed and fewer drizzle events. A further study is needed using high-resolution models to simulate these events.

A fundamental problem in turbulence is understanding how energy cascades across multiple scales. In this paper, a new weak turbulence theory is developed to explain how energy can be transferred from Langmuir and Upper-Hybrid waves (~10 MHz frequencies, 20-cm wavelengths) to ion-acoustic waves (~kHz frequencies, 3-meter wavelengths). A kinetic approach is used where the electrostatic Boltzmann equations are Fourier-Laplace transformed, and the nonlinear term is retained. A unique feature of this approach is the ability to calculate power spectra at low frequencies, for any wavelength or angle to the magnetic field. The results of this theory explain how 150-km echoes are generated in the ionosphere. First, peaks in the suprathermal electron velocity distribution drive a bump-on-tail like instability. This instability excites the Upper-Hybrid mode, and the nonlinear mode coupling theory shows that the instability generates a ~10 dB enhancement of the ion-acoustic mode: matching the observed enhancement in 150-km echoes.

A document by Matthew G Heberger. Click on the document to view its contents.

A document by dario marconi. Click on the document to view its contents.

Receiver function is important for imaging crustal and upper mantle discontinuities. However, sparsely scattered stations would introduce imaging artifacts or even lead to misinterpretation on complex structures. We regularize 3D teleseismic wavefield to reduce the artifacts using radial basis function interpolation. First, we evaluated the feasibility of the wavefield regularization with several typical models by synthetic data. The results demonstrate the high reliability of our method on recovering local 2D and 3D structures, even when seismic stations are intentionally missed 95% on a uniform fine grid. Then, we applied this method to sparsely deployed stations in Northeast China. The waveforms reconstructed from surrounding stations show good consistency with the observed waveforms; furthermore, the imaging results using the regularized data are highly comparable with the reference results obtained by a dense 2D seismic array of 60 stations (with a spacing of 10-17 km), even though our input data are mainly contributed by only 9 stations (with an averaged spacing of 80 km). Our results have better continuity of 3D topography of subsurface, compared with that obtained by traditional method. Our regularization method could significantly improve the spatial resolution of receiver function imaging both for sparse and dense distribution of seismic stations, especially for imaging relatively complex structures with lateral variations.

Usutu virus (USUV) is an emerging flavivirus transmitted by mosquitoes, with an increasing incidence of human infection and a geographic expansion over the past decade, thereby posing a significant threat to public health. In this study, we conducted an extensive literature search and established a comprehensive spatiotemporal database of USUV infections in vectors, animals, and humans worldwide. Based on which, we explored the distribution dynamics of USUV infections and characteristics of human infections. By employing boosted regression trees (BRT) models, we projected the distributions of three main vectors (Culex pipiens, Aedes albopictus, and Culiseta longiareolata) and three main hosts (Turdus merula, Passer domesticus, and Ardea cinerea) to obtain the mosquito index and bird index. These indices were further incorporated as predictors into the USUV infection models, which was conducted by using three different machine learning models, BRT, random forest (RF), and least absolute shrinkage and selection operator (LASSO) logistic regression model. By selecting the best models for BRT, RF, and (LASSO) logistic regression model, and integrating them into ensemble learning model, we achieved a decent model performance with an area under the curve (AUC) of 0.992. The mosquito index contributed significantly, with relative contributions estimated at 25.51%. Our estimations revealed a potential exposure area for USUV spanning 1.80 million km² globally with approximately 1.04 billion people at risk, this would guide future surveillance efforts for USUV infections, especially for countries located within high-risk areas and those that have not yet conducted surveillance activities.

Coronal mass ejections (CMEs) directed toward Earth can modulate cosmic ray fluxes detected on the ground. We provide definitive evidence that even moderately fast CMEs produce small-scale Forbush decreases (FDs) - brief ≤ 3% cosmic ray exclusions over a day. Tracking fronted halo CMEs with coordinated solar imaging and in situ monitoring reveals timing and efficiency signatures statistically linking intensity drops with transient shock passages at ejecta fronts. The reductions originate in weak sheath scattering zones featuring elliptical cross-sections preferentially oriented edge-on to Earth. Connecting properties of these subtle effects to remote CME structure and kinematics elucidates inner heliospheric shock physics below major FDs detection thresholds (CR ≥ 3%). This reveals an entirely overlooked category of minor interplanetary perturbations by common solar eruptions insufficient to spark major storms.

As a warmer climate enables an increase in atmospheric humidity, extreme precipitation events have become more frequent in the northeastern United States. Understanding the impact of evolving precipitation patterns is critical to understanding water cycling in temperate forests and moisture coupling between the atmosphere and land surface. Although the role of soil moisture in evapotranspiration has been extensively studied, few have analyzed the role of soil texture in determining ecosystem-atmosphere feedbacks. In this study, we utilized long term data associated with ecosystem water fluxes to deduce the strength of land-atmosphere coupling at Harvard Forest, Petersham, MA, USA. We found a 1.5% increase in heavy precipitation contribution per decade where high-intensity events compose upwards of 50% of total yearly precipitation in 2023. Intensifying precipitation trends were found in conjunction with a long-term soil drying at the Harvard despite no significant increase in evapotranspiration over 32 years. This suggests that soil water holding capacity is a key mediating variable controlling the supply of water to ecosystems and the atmosphere. We found that these land surface changes directly impacted the lifted condensation level (LCL) height over Harvard Forest which was found to be decreasing at a rate of 6.62 meters per year while atmospheric boundary layer (ABL) heights have fallen at a modest rate of 1.76 meters per year. This has amplified dry feedbacks between the land surface and the atmosphere such that 80% of observed summers ending in a water deficit also had an anomalously low soil water content in the spring.

Numerical weather prediction (NWP) of radiation fog, particularly over complex terrain, remains a formidable challenge. Many operational NWP models often struggle with slow or no fog formation after sunset and too rapid dissipation in the morning. This study investigates the role of physical processes in the atmospheric boundary layer (ABL) in shaping the limitations of fog and low stratus (FLS) representation within the operational ICON model. Specifically, it evaluates the effects of turbulence parameterizations and vertical resolution on fog simulations. ICON simulations were conducted for selected winter periods characterized by persistent radiation fog, nocturnal fog, low stratus, and high pollutant concentrations over the Swiss Plateau. The simulations involved different configurations of the operational turbulence scheme (ICON-TKE) and the newly developed two-energies turbulence scheme (ICON-2TE). The performance of these model configurations was assessed using ABL profiler and surface observations from the Payerne weather station in Switzerland. The results indicate that ICON-2TE, with its refined turbulence representation, allows fog to persist longer and aligns more closely with observations compared to ICON-TKE. This improvement is attributed to a more sophisticated treatment of stability dependence and turbulence length scale in the ICON-2TE scheme. Notably, an increase in vertical resolution improves fog representation in the ICON-2TE scheme, while it shows almost no effect in the ICON-TKE scheme. The lack of improvement in ICON-TKE is likely due to an overestimation of turbulence mixing, which overrides the effect of changes in vertical resolution.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Unsustainable rates of groundwater (GW) depletion make GW management a priority. Effective GW management is hindered by the uncertainty in the predictions of aquifer models, but the increase of geodetic surface deformation data can improve aquifer characterization. We integrate surface deformation measurements into a Bayesian inference framework to infer aquifer permeability in a poroelastic model. We demonstrate the applicability of this technique using a Nevada pumping test with both Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) surface deformation data. We infer the lateral permeability variations in the aquifer with high spatial resolution and identify the information content of each data set. For the Nevada case, a single InSAR surface deformation map provides significantly more information than multiple GPS time series. As the availability of global InSAR data continues to grow, geomechanical inversion of geodetic surface deformation data will become a valuable technique for aquifer characterization and management.

We use an independent observational estimate of aerosol-cloud interactions (ACI) during the 2014 Holuhraun volcanic eruption in Iceland to evaluate 4 ACI parameterizations in a regional model. All parameterizations reproduce the observed pattern of liquid cloud droplet size reduction during the eruption, but strongly differ on its magnitude and on the resulting effective radiative forcing (ERF). Our results contradict earlier findings that this eruption could be used to constrain liquid water path (LWP) adjustments in models, except to exclude extremely high LWP adjustments of more than 20 g/m2. The modeled ERF is very sensitive to the non-volcanic background aerosol concentration: doubling the non-volcanic aerosol background weakens the ACI ERF by ~30%. Since aerosol biases in climate models can be an order of magnitude or more, these results suggest that aerosol background concentrations could be a major and under-examined source of uncertainty for modeling ACI.

Ammonia emissions from oceans are recognized as one of the most significant natural sources of ammonia globally; however, freshwater sources are rarely considered significant. The Great Lakes region, containing the largest network of freshwater lakes in the world, and a significant urbanized population exceeding 20 million, provides a unique opportunity to evaluate the potential for lacustrine surfaces to contribute to regional ammonia levels. In this work, we combine an analysis of 20 years of water quality data from the Great Lakes region and local water measurements near the Greater Toronto Area with the GEM-MACH (Global Environmental Multiscale model – Modelling Air quality and CHemistry) chemical transport model to examine the influence of the Great Lakes on atmospheric ammonia. This analysis demonstrates that while regional ammonia levels are largely controlled by known terrestrial anthropogenic sources, lacustrine emissions increase summertime (July – September) monthly average NH3 levels by 5 – 8% over the largest regional urban centers, with daily increases of up to 10 – 20%, confirming that the Great Lakes represent a regionally significant natural source of ammonia to the atmosphere.

Purpose of ReviewForests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time.This work offers a detailed examination of theories in forest growth modeling, with a focus on emergent approaches as implemented in 18 forest growth models, which vary in their approaches and goals. Recent FindingsForest modeling requires a deep understanding of forest growth theories driven by multiple, often interacting, processes. Our findings reveal distinct model clusters with varying process integrations and complexity, ranging from stand-level to terrestrial ecosystem models. Additionally, we highlight the trade-offs between model detail and scalability.SummaryOur review showcases multiple theories, such as Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth, thus providing a synthetic overview of the main frameworks for resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.

Radiative heating of clouds, particularly those in the upper troposphere, alters temperature gradients in the atmosphere, affecting circulation and precipitation in today's and future climates. However, the response of cloud radiative heating to global warming remains largely unknown. We study changes to high cloud radiative heating in a warmer climate, identify physical mechanisms responsible for these changes, and develop a theory based on well-understood physics to predict them. Our approach involves a stepwise procedure that builds upon a simple hypothesis of an upward shift in cloud radiative heating at constant temperature and gradually incorporates additional physics. We find that cloud radiative heating intensifies as clouds move upward, suggesting that the role of high clouds in controlling atmospheric circulations increases in a warmer climate.

A document by Manon Gévaudan. Click on the document to view its contents.

We measure energy spectra of electrons and gamma rays of electromagnetic avalanches developed in the electrified atmosphere as they arrive at the earth’s surface at 3200 m height where Aragats research station is located. We compare intensities and spectra shapes of 2 thunderstorm ground enhancements (TGE) observed in June and September 2020 with simulated ones. Although, the variants of electric field strength and topology assumed in the simulations are too simplified to reproduce the rather complicated and dynamic nature of the atmospheric electric field the closeness of several measured and observed parameters allows us to confirm that the relativistic runaway electron avalanches (RREA) is the origin of TGE, and to outline most probable characteristics of the atmospheric electric field for particular observed TGE events.