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Satellite-based retrievals of cloud and radiation properties are available in this value-added product provided by Bill Smith’s group at NASA/Langley using the VISST (Visible Infrared Solar-Infrared Split Window Technique) algorithm. This product contains VISST-derived pixel-level products from the GOES-16 satellite, rectilinear projection 2-D array.
Current vat photopolymerization (VP) relies on UV or visible light to start the rapid crosslinking of liquid photocurable resins into 3D-printed structures. Here, we develop a self-assembly-driven photopatterning approach to photothermally generate polymeric solids by combining thermoplasmonic nanoparticles and thermoresponsive polymers, in which near-infrared (NIR) light activates thermoplasmonic heating of nanoparticles, triggering the irreversible self-assembly of thermoresponsive polymers into insoluble mesoglobules. A small amount of thermal initiator and crosslinker leads to irreversible self-assembly of polymer nanocomposites. NIR light offers deeper penetration and reduced scattering compared to UV, enabling more uniform curing of thicker or filled materials and expanded process control for composites or opaque systems. Thermoplasmonic heat generation is achieved using surface-modified gold nanorods (AuNRs) with a longitudinal localized surface plasmon resonance peak in the NIR region. Key variables such as polymer composition, molecular weight, physical interactions at the nanoparticle–polymer interface, which can be tuned by surface functionalization, AuNR concentration, and pH can be used to tailor the assembly behavior of these systems, including photothermal effect, flocculation, and cloud point temperature, and the mechanical properties of the final structures. Collectively, these results highlight a platform for photothermally-driven microlithography of polymer solids with diverse, tunable macroscopic properties, enabled by low-energy NIR light-activated self-assembly.
Efficient light management is key to maximizing power conversion efficiency (PCE) in monolithic perovskite/silicon tandem solar cells. Achieving peak efficiency requires closely matched current generation in all junctions, especially in integrated configurations. However, real-world conditions vary significantly due to factors such as sunlight spectrum, diffuse-to-direct sunlight ratio, angular distribution of light, subcell temperature coefficients, and ground reflection. This study introduces a comprehensive optical and device simulation to optimize perovskite/silicon tandem cells, considering experimental luminescent coupling (LC) efficiency and its dependence on working conditions, alongside variations in radiative recombination, effect of temperature on absorptivity spectra, and cloud cover. Our results show potential energy yield improvements of up to 1.4 % with LC, based on current perovskite radiative recombination records, and up to 4 % with direct bandgap materials. Although radiative recombination's dependence on excitation intensity reduces output power and requires thicker absorbers, LC compensates for these losses. LC also lowers the optimized bandgap for the perovskite top cell from 1.72 eV to 1.64–1.68 eV, or even lower in regions with redshifted irradiance. Additionally, optimization revealed that thinner silicon bottom cells require a higher perovskite top cell bandgap, impacting the balance between fabrication cost and cell stability.
The radiative feedback pattern effect remains a large source of uncertainty for both projections of future trends and interpretations of past trends in global temperature. The pattern effect is defined as the difference in feedbacks between transient and long-term simulations, and past work shows that is primarily attributed to changes in the marine low-cloud radiative feedback. Here we use low cloud meteorological kernels to map out both the primary cloud controlling factors through which changing surface temperature patterns drive changes in low-cloud feedback, as well as the sources of model spread. We find that the pattern effect is almost entirely driven by changes in estimated inversion strength (EIS) in the Southern Hemisphere, particularly in the South East Pacific and Southern Ocean. In both past and future simulations, inter-model spread is primarily caused by model differences in the sensitivity of low clouds to the environmental conditions, rather than differences in the simulated evolution of environmental conditions.
Climate models simulate a wide range of temperatures in the Arctic. Here we investigate one of the main drivers of changes in surface temperature: the net surface heat flux in the models. We show that in the winter months of the dark Arctic, there is a more than two-fold difference in the net surface heat fluxes among the models, and this difference is dominated by the downward infrared radiation from clouds. Owing to the small amount of water vapor in the winter Arctic, infrared radiation from clouds transmits more easily to the surface in the Arctic than at other latitudes, resulting in large cloud radiative effect at the surface. The dominant role of the cloud effect is also found in the transient variability of the net surface heat flux. Results demonstrate that accurate simulation of clouds is crucial for determining the net surface heat flux, which in turn affects surface temperature and sea ice properties in the Arctic.
Atmospheric models used for weather forecasting and climate predictions discretise the atmosphere onto a vertical grid. There are however atmospheric phenomena that occur on scales smaller than the thickness of those model layers. The formation of low-level clouds due to temperature inversions is an example. This leads to atmospheric models underestimating, or even missing, these clouds and their radiative effects. Using radiosonde observations as training data, a machine learning model is used to improve the vertical detail of modelled profiles of temperature and specific humidity. In addition, a physics-informed machine learning model is developed and compared to the traditional approach; showing improvements in the cloud fraction profiles calculated from its predictions. The vertically enhanced profiles also improve the representation of layers of convective inhibition and anomalous refractivity gradients. This work facilitates targeted improvements to the representation of certain atmospheric processes without the burden of increased memory and computational cost from increasing vertical resolution throughout the whole model.
As the Arctic warms, winter clouds are known and expected to change. Yet the extent to which these cloud changes amplify or dampen warming (cloud feedback) remains uncertain. This uncertainty results from systemic difficulties in modeling and observing Arctic low clouds. Surface-based observations avoid many of these difficulties. Here, we use two decades of surface-based observations (1998–2023) to constrain and explain longwave flux change during winter. We find that longwave flux into the surface is increasing and that this increase cannot be explained by direct impacts of temperature and greenhouse gases alone. Only when increasing cloud radiative effect (0.96 ± 0.64 W/m 2 /K) is considered can increasing longwave flux be explained. Cloud radiative effect increases due to increasing cloud opacity, which is driven equally by ice-only and mixed-phase clouds. The direct observational constraint from this work suggests that increasing cloud opacity drives increasing net surface radiation on Alaska’s North Slope during winter.
Concentrating Solar Power (CSP) molten-salt central receivers are subject to high, transient incident flux during daily operation. The resulting creep-fatigue damage impacts the receiver’s reliability and restricts the permissible incident flux distribution for a given receiver. This paper aims to reduce CSP plants’ levelized cost of electricity by developing a methodology to predict lifetime and identifies the primary damage mechanism (creep vs fatigue) for any given fluid temperature and temperature gradient. Results are presented in the form of a damage map that serves as a valuable operation guide and design tool. Damage maps can be used to reduce maintenance costs by improving reliability and reduce receiver capital costs by better utilizing the receiver area. FEA simulation and damage modeling of tubes subject to asymmetrical flux conditions is performed in the open-source receiver design tool srlife. Parametric studies are performed over a range of inner tube temperatures and thermal gradients for A230, 316H, 740H, A282, A617, and 800H high temperature alloys. Damage maps are presented for each alloy. A parametric, FEA-based methodology is presented for comparison of fatigue-creep ratios and prediction of tube lifetime based on the critical thermal operating conditions. Fatigue is found to be negligible compared to creep for almost every case. Here, this finding suggests that fatigue effects associated with cloud events are insignificant compared to creep at these high temperature operating conditions. Additionally, lifetime predictions identify thermal conditions where small changes in operating conditions can result in large changes in predicted lifetime.
Abstract This study investigates how entrainment’s diluting effect on cumulonimbus updraft buoyancy is affected by the temperature of the troposphere, which is expected to increase by the end of the century. A parcel model framework is constructed that allows for independent variations in the temperature ( T ), the entrainment rate ε , the free-tropospheric relative humidity (RH), and the convective available potential energy (CAPE). Using this framework, dilution of buoyancy is evaluated with T and RH independently varied and with CAPE either held constant or increased with temperature. When CAPE is held constant, buoyancy decreases as T increases, with parcels in warmer environments realizing substantially smaller fractions of their CAPE as kinetic energy (KE). This occurs because the increased moisture difference between an updraft and its surroundings at warmer temperatures drives greater updraft dilution. Similar results are found in midlatitude and tropical conditions when CAPE is increased with temperature. With the expected 6%–7% increase in CAPE per kelvin of warming, KE only increases at 2%–4% K −1 in narrow updrafts but tracks more closely with CAPE at 4%–6% in wider updrafts. Interestingly, the rate of increase in the KE with T becomes larger than that of CAPE when the later quantity increases at more than 10% K −1 . These findings emphasize the importance of considering entrainment in studies of moist convection’s response to climate change, as the entrainment-driven dilution of buoyancy may partially counteract the influence of increases in CAPE on updraft intensity. Significance Statement Cumulonimbus clouds mix air with their surrounding environment through a process called entrainment, which controls how efficiently environmental energy is converted into upward speed in thunderstorm updrafts. Our research shows that warmer temperatures will exacerbate the moisture difference between cumulonimbus updrafts and their surroundings, leading to greater mixing and less efficient conversion of environmental energy into updraft speeds. This effect should be considered in future research that investigates how climate change will affect cumulonimbus clouds.
This study investigates low cloud feedback in a warmer climate using global simulations from the High-Resolution Multi-scale Modeling Framework (HR-MMF), which explicitly simulates small-scale eddies globally. Two 5-year simulations—one with present-day sea surface temperatures (SSTs) and a second with SSTs warmed uniformly by 4 K—reveal a positive global shortwave cloud radiative effect (SWCRE = 0.3 W/m 2 /K), comparable to estimates from CMIP models. As the climate warms, significant reductions in low cloud cover occur over stratocumulus regions. This study is the first attempt to compare HR-MMF results with predictions from idealized large-eddy simulations from the CGILS intercomparison. Despite different underlying assumptions, we find qualitative agreement in SWCRE and inversion height changes between HR-MMF and CGILS predictions. This suggests reasonable credibility for the CGILS framework in predicting cloud responses under the out-of-sample conditions found in HR-MMF. However, the HR-MMF exhibits stronger SWCRE changes than predicted by CGILS. We explore potential causes for this discrepancy, examining variations in cloud-controlling factors (CCFs) and cloud conditions. Our results show a fairly homogeneous SWCRE response, with little systematic variation tied to the variations in CCFs. This reveals a dominant role for SST forcing in modulating SWCRE.
Abstract Atmospheric models forced with observed sea surface temperatures (SSTs) suggest a trend toward a more-stabilizing cloud feedback in recent decades, partly due to the surface cooling trend in the eastern Pacific (EP) and the warming trend in the western Pacific (WP). Here, we show model evidence that the low-cloud feedback has contributions from both forced and unforced feedback components and that its time variation arises in large part through changes in the relative importance of the two over time, rather than through variations in forced or unforced feedbacks themselves. Initial-condition large ensembles (LEs) suggest that the SST patterns are dominated by unforced variations for 30-yr windows ending prior to the 1980s. In general, unforced SSTs are representative of an ENSO-like pattern, which corresponds to weak low-level stability in the tropics and less-stabilizing low-cloud feedback. Since the 1980s, the forced signals have become stronger, outweighing the unforced signals for the 30-yr windows ending after the 2010s. Forced SSTs are characterized by relatively uniform warming with an enhancement in the WP, corresponding to a more-stabilizing low-cloud feedback in most cases. The time-evolving SST pattern due to this increasing importance of forced signals is the dominant contributor to the recent stabilizing shift of low-cloud feedback in the LEs. Using single-forcing LEs, we further find that if only greenhouse gases evolve with time, the transition to the domination of forced signals occurs 10–20 years earlier compared to the LEs with full forcings, which can be understood through the compensating effect between aerosols and greenhouse gases.
The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.
The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.
Efficient management of extreme heat fluxes in the divertor region to extend the lifetime of the components remains a critical challenge for the realization of nuclear fusion-based power plants. Among the alternative concepts explored for the divertor region, the use of liquid metals, particularly lithium, is of interest due its ability to dissipate the incoming plasma heat flux through the vapour shielding effect (VS). In this work, we experimentally investigated a ‘closed’ configuration of a dedicated Vapour Box Module (VBM) in the linear plasma device Magnum-PSI. The goal of the experiments is to simulate the vapour box divertor environment conditions and assess its performance in terms of power mitigation and redistribution and lithium confinement. Initial testing without Li demonstrated the efficacy of a closed VBM structure in inducing detachment via neutral gas accumulation. Apertures which enabled non-condensing gas to be effectively pumped while ensuring lithium condensed on the inner surfaces were therefore added. With a lithium capillary porous structure target used, lithium is directly vaporized by the plasma, forming a dense lithium vapour cloud that interacts with the incoming plasma. This resulted in a significant reduction of the target temperature of at least 48%, together with a temperature locking effect, a phenomenon typically observed in the VS regime. Lithium vapour confinement within the VBM was strongly correlated with the wall temperature. Relatively cold walls promoted Li re-condensation and therefore improved Li confinement, although with the expected trade-off of increased hydrogenic retention on lithium-wetted surfaces. As the wall temperature increased, the confinement efficiency decreased, consistent with reduced Li re-condensation and thermally activated Li–H chemistry and remobilization at the walls. Diagnostic measurements through embedded thermocouples and calorimetry revealed that lithium vaporization and re-condensation processes also playedsignificant roles in plasma power dissipation. The results advance the case for a closed divertor chamber with direct lithium evaporation from the strike-points as a viable method to manage divertor heat fluxes in future fusion reactors.
Significant knowledge gaps remain in our understanding of turbulence–cloud–aerosol interactions in the Earth's atmosphere, and direct numerical simulation (DNS) has increasingly become an indispensable tool to fill such critical knowledge gaps. Here, this study is an extension of our previous DNS model [Gao et al., J. Geophys. Res.: Atmos., 123(4), 2194–2214 (2018)], with a focus on the activation of aerosol particles into cloud droplets and deactivation of cloud droplets into aerosol particles in a microscale cloud environment. The effects of turbulence intensity, particle curvature, and solute, as well as the initial distributions of the aerosol particles (monodisperse or polydisperse) are investigated. The governing equations for the flow of air, temperature, and water vapor mixing ratio are solved numerically in the Eulerian fashion, assuming homogeneous and isotropic turbulence. The dynamics of the aerosol and cloud particles are calculated with the Lagrangian particle tracking method. The results show that the deviations of the thermodynamic variables from their respective means are significantly reduced, the activation process appears to be delayed, and the deactivation process occurs more rapidly, as the turbulence intensity is increased. The inclusion of particle curvature and solute effects, as well as polydispersity, tends to retard the activation of aerosols into cloud droplets. It is also observed that fluctuations in supersaturation broaden the spread of particle radii, and the broadening is followed by a narrowing as turbulent homogenization reduces thermodynamic fluctuations over time.
This paper describes the atmospheric component of the US Department of Energy's Energy Exascale Earth System Model (E3SM) version 3. Significant updates have been made to the atmospheric physics compared to earlier versions. Specifically, interactive gas chemistry has been implemented, along with improved representations of aerosols and dust emissions. A new stratiform cloud microphysics scheme more physically treats ice processes and aerosol‐cloud interactions. The deep convection parameterization has been largely improved with sophisticated microphysics for convective clouds, making model convection sensitive to large‐scale dynamics, and incorporating the dynamical and physical effects of organized mesoscale convection. Improvements in aerosol wet removal processes and parameter re‐tuning of key aerosol and cloud processes have improved model aerosol radiative forcing. The model's vertical resolution has increased from 72 to 80 layers with the extra eight layers added in the lower stratosphere to better simulate the Quasi‐Biennial Oscillation. These improvements have enhanced E3SM's capability to couple aerosol, chemistry, and biogeochemistry and reduced some long‐standing biases in simulating tropical variability. Compared to its predecessors, the model shows a much stronger signal for the Madden‐Julian Oscillation, Kelvin waves, mixed Rossby‐gravity waves, and eastward inertia‐gravity waves. Aerosol radiative forcing has been considerably reduced and is now better aligned with community best estimates, leading to significantly improved skill in simulating historical temperature records. Its simulated mean‐state climate is largely comparable to E3SMv2, but with some notable degradation in shortwave cloud radiative effect, precipitable water, and surface wind stress, which will be addressed in future updates.
We devise a pattern-aware feedback framework for representing the forced climate response using a suite of Green’s function experiments with solar radiation perturbations. By considering the column energy balance, a comprehensive linear response function (CLRF) for important climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, and lapse rate is learned from Green’s function data. The learned CLRF delineates the effects of the energy diffusion in both the ocean and atmosphere and the pattern-aware feedbacks from the aforementioned radiatively active processes. The CLRF can then be decomposed into forcing–response mode pairs, which are in turn used to construct a reduced-order model describing the dominant dynamics of climate responses. These mode pairs capture nonlocal effects and teleconnections in the climate and thus make the reduced-order model apt for capturing regional features of climate response. A key observation is that the CLRF captures the polar-amplified response as the most excitable mode of the climate system, and this mode is explainable in the data-learned pattern-aware feedback framework. The reduced-order model can be used for predicting the response for a given forcing and for reconstructing the forcing from a given response; we demonstrate these capabilities for multiple independent forcing scenarios.
Abstract The influence of biomass burning (BB) aerosols arising from wildfires and agricultural fires on the transient coupled evolution of El Niño–Southern Oscillation (ENSO) is explored in Community Earth System Model, version 2 (CESM2). For both El Niño and La Niña, two 20-member ensembles are generated from initial states that are predisposed to evolve into ENSO events. For each ENSO phase, one ensemble is forced with the observed BB emissions during satellite-era ENSO events while the other is forced with a climatological annual cycle, with the responses to anomalous BB emissions estimated from interensemble differences. It is found that the regional responses to anomalous BB emissions occur mainly during boreal fall, which is also the time of the climatological seasonal maximum in emissions. Transient responses are identified in precipitation, clouds, and radiation in both the tropics and extratropics. At the onset of El Niño, these include increased precipitation in the northern branch of the intertropical convergence zone (ITCZ) and an enhancement of cloud albedo and amount across the Maritime Continent and eastern subtropical Pacific Ocean. Additional responses are identified through the course of El Niño and successive La Niña events, the net effect of which is to strengthen sea surface temperature (SST) anomalies in the eastern Pacific Ocean during El Niño and warm the tropical Pacific Ocean during La Niña. These responses improve the simulation of ENSO power, diversity, and asymmetry in CESM2. Significance Statement Biomass burning emissions from both wildfires and agricultural fires during ENSO act as a climate feedback, as they are both driven by fire weather anomalies tied to ENSO and influence clouds, radiation, and precipitation on a global scale. While the largest biomass burning (BB) emissions emanate mainly from Indonesia, other regions also exhibit systematic responses. When simulated in CESM2, the emissions collectively impact the flows of energy across the tropical Pacific Ocean in a spatially and temporally complex fashion, with the net effect of improving CESM2’s simulation of ENSO events.