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Abstract:

Limiting global warming to 1.5 °C above pre-industrial levels requires a rapid and sustained transition to renewable energy systems, with photovoltaic (PV) solar energy playing a central role due to its scalability and declining costs. However, PV power generation is inherently sensitive to atmospheric conditions such as aerosols, cloud cover, and temperature, which vary spatially and are expected to evolve under climate change. While global PV capacity has expanded rapidly, climate-related impacts on PV energy generation, particularly at the facility level, remain insufficiently quantified. Many existing assessments rely on generalized assumptions, overlooking the heterogeneity of PV deployment and local environmental conditions, which limits their relevance for integrated energy system modelling and planning.This study combines machine learning and satellite-based observations to improve the representation of PV systems and climate-related performance losses in global-scale assessments. A machine learning model is trained on diverse geospatial datasets to identify PV installations across a range of geographic and land-use contexts, including complex terrains. Facility-level PV data are then integrated with satellite and reanalysis products to quantify the influence of aerosols, cloud variability, and temperature on solar energy generation over the past decade.Results reveal pronounced regional variability in PV energy losses, driven by differences in atmospheric composition, cloud dynamics, and thermal stress. Elevated aerosol loads are associated with significant reductions in surface solar irradiance, while cloud variability affects both average generation and short-term reliability. Extreme temperatures further reduce PV efficiency in certain regions. These findings highlight the importance of incorporating site-specific climate sensitivities into energy system models to better assess performance, resilience, and trade-offs in renewable energy deployment.By shifting the focus from installed capacity to climate-related energy losses, this work contributes to integrated assessments of sustainable energy transitions. The approach provides actionable insights for system planning, model improvement, and policy development, 91̽��ing more robust and environmentally informed strategies for scaling solar energy within diversified and resilient energy systems.

Abstract:

The eruption of Hunga volcano on 15 January 2022 was an exceptional event in the satellite era. Record-breaking heights of the volcanic plume were reported, a large amount of water was injected into the stratosphere and a broad spectrum of atmospheric waves were detected. Here, we use satellite measurements to show that a transient ring of small ice particles (~2 μm) formed around the plume. We hypothesize that the ice ring was generated by the passage of an atmospheric wave triggered by a pressure pulse at the surface corresponding to a violent explosion that occurred during the 15 January 2022 eruption sequence. The passage of the atmospheric wave produced a transient rarefaction in the upper troposphere-lower stratosphere, which in turn led to oscillations in ambient temperature. Due to the supersaturated state of the atmosphere with respect to ice, ice particles formed in the wake of the radially propagating atmospheric wave, allowing an exceptional opportunity to study ice particle growth via vapour deposition. This atmospheric phenomenon serves as an important natural experiment that reveals the time scale on which ice particles nucleate and grow given an abrupt perturbation in ambient temperature.

Abstract:

300–400 km in diameter. Previous reports showed that one of these entities was traceable for 3 months. Anticyclonic circulation was also previously reported. We present multiple lines of evidence to characterize these cloud subelements by their spatial confinement, morphology, and sulfate-dominated aerosol aspect, which was evident from plume onset. In addition, we show that they were ably identifiable in geostationary satellite “cirrus channel” reflectance imagery and had an enduring signal of window infrared absorption, detectable for at least 1 month. The term we apply to this phenomenon is “sulfate/SO₂ anticyclonic contained circulation,” abbreviated SSACC. Anticyclonic circulation is first detectable on 24 June, 2 days posteruption. Two SSACCs persist beyond June. One is traceable until mid-August over Canada. The other SSACC was discernible until 5 October after having completed three global circumnavigations. The internal SSACC circulation aspect is gleaned from geostationary-based visible image animations and confirmed in situ via a novel application of high-resolution radiosonde wind direction and balloon position data. We also examine diabatic lofting of both SSACCs in relation to their individual geographic and constituent morphologies. Thermal infrared observations show that SSACC aerosols produce brightness temperature depressions of ~2.6 K, opening a new line of investigation into the source of heating that contributes to diabatic rise.

Abstract:

The Arctic is experiencing heightened precipitation, affected by aerosols impacting rainfall and snowfall. However, sparse aerosol observations in the central Arctic cryosphere contribute to uncertainties in simulating aerosol-precipitation two-way interaction. This study examines aerosol-precipitation co-variation in various climate models during the Arctic spring and summer seasons from 2003 to 2011, leveraging satellite-based aerosol data and various CMIP6 climate models. Findings reveal significant spatio-temporal biases between models and observations. Snowfall dominance occurs in models where total AOD surpasses the observation by 121% (57–186%, confidence interval), intensifying simulated snowfall by two times compared to rainfall during summer. Consequently, climate models tend to underestimate central Arctic rainfall to the total precipitation ratio, suggesting a positive bias towards snowfall dominance. This highlights the importance of constraining total AOD and associated aerosol schemes in climate models using satellite measurements, which potentially could lead to a substantial reduction in snowfall contribution to the total precipitation ratio in the central Arctic, contrary to current multi-model simulations across various spatiotemporal scales.

Abstract:

Analysis of thermal infrared satellite measurements of umbrella clouds generated by volcanic eruptions suggests that asymptotic gravity current models of the temporal (C) radial (A) spreading (A ⇠ C 5 , 5 < 1) of the umbrella-shaped intrusion do not adequately explain the observations. Umbrella clouds from 13 volcanic eruptions are studied using satellite data that have spatial resolutions of ⇠4–25 km2 and temporal resolutions of 1–60 minutes. The umbrella cloud morphology is evaluated using 15 digital image processing tools in a Lagrangian frame of reference. At the onset of neutral buoyancy, the radial spreading is better explained by a stronger dependence on time of A ⇠ C, rather than C 2/3, C 3/4 or C 2/9. This flow regime exists on the order of minutes and has not been observed previously in satellite data. This may be of significance as it provides a means to rapidly (within the first 2-3 observations) determine the volumetric eruption rate. A hyperbolic tangent model, A ⇠ tanh (C) is presented that matches the entire radial spreading time history and has a conserved torus-shaped volume in which the intrusion depth is 20 proportional to sech (C). This model also predicts the the observed radial velocities. The data and the model estimates of the volumetric flow rate for the 15 January 2022 Hunga eruption are found to be 3.6–5 x 1011 m3s1, the largest ever measured.