Search Results (726)

The Earth Radiation Budget (ERB), a measure of the difference between incoming solar irradiance and outgoing reflected and emitted radiant energy, is a fundamental property of Earth’s climate system. The Libera satellite mission will measure the ERB’s outgoing components to continue the long-term climate data record established by NASA’s Clouds and the Earth’s Radiant Energy System (CERES) mission. In addition to ensuring data continuity, Libera will introduce a novel split-shortwave spectral channel to quantify the partitioning of the outgoing reflected solar component into visible and near-infrared sub-components. However, converting these split-shortwave radiances into the ERB-relevant irradiances requires the development of split-shortwave Angular Distribution Models (ADMs), which demand extensive angular sampling. Here, we show how Rotating Azimuthal Plane Scan (RAPS) parameters—specifically operational cadence and azimuthal scan rate—affect the observational coverage of a defined scene and angular space. Our results show that for a fixed number of azimuthal rotations, a relatively slow azimuthal scan rate of 0.5° per second, combined with more time spent in the RAPS observational mode, provides a more comprehensive sampling of the desired scene and angular space. We also show that operating the Libera instrument in RAPS mode at a cadence between every fifth day and every other day for the first year of space-based operations will provide sufficient scene and angular sampling for the observations to achieve radiance convergence for the scenes that comprise more than half of the expected Libera observations. Obtaining radiance convergence is necessary for accurate ADMs. Full article

The knowledge of the radiative properties of clouds and the atmospheric state is of fundamental importance in modelling phenomena in numerical weather predictions and climate models. In this study, we show the results of the retrieval of cloud properties, along with the atmospheric state and the surface temperature, from far-infrared spectral radiances, in the 100–1000 cm⁻¹ range, measured by the Far-Infrared Radiation Mobile Observation System-Balloon version (FIRMOS-B) spectroradiometer from a stratospheric balloon launched from Timmins (Canada) in August 2022 within the HEMERA 3 programme. The retrieval study is performed with the Optimal Estimation inversion approach, using three different forward models and retrieval codes to compare the results. Cloud optical depth, particle effective size, and cloud top height are retrieved with good accuracy, despite the relatively high measurement noise of the FIRMOS-B observations used for this study. The retrieved atmospheric profiles, computed simultaneously with cloud parameters, are in good agreement with both co-located radiosonde measurements and ERA-5 profiles, under all-sky conditions. The findings are very promising for the development of an optimised retrieval procedure to analyse the high-precision FIR spectral measurements, which will be delivered by the ESA FORUM mission. Full article

Aerosols are critical to the Earth’s atmosphere, influencing climate through interactions with solar radiation and clouds. However, accurately replicating the interactions between aerosols and clouds remains challenging due to the complexity of the physical processes involved. This study evaluated the performance of Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating aerosol optical depth (AOD), cloud fraction (CF), and liquid water path (LWP) by comparing them with satellite observations from MODIS and AMSR-E. Using 30 years of CMIP6 model simulations and available satellite observations during the satellite era, the results show that most CMIP6 models underestimate CF and LWP by 24.3% for LWP in the Northern Hemisphere. An assessment of spatial patterns indicates that models generally align more closely with observations in the Northern Hemisphere than in the Southern Hemisphere. Latitudinal profiles reveal that while most models capture the overall distribution patterns, they struggle to accurately reproduce observed magnitudes. A quantitative scoring system is applied to evaluate each model’s ability to replicate the spatial characteristics of multi-year mean aerosol and cloud properties. Overall, the findings suggest that CMIP6 models perform better in simulating AOD and CF than LWP, particularly in the Southern Hemisphere. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models. Full article

This study addresses the influence of clouds and aerosols on solar radiation and energy over Greece from September 2004 to August 2024. By leveraging Earth Observation data and radiative transfer models, the largest to date time series was constructed, in order to investigate the fluctuations in global horizontal irradiance, its rate of change, and the natural and anthropogenic factors that drive them. By incorporating simulation tools and appropriate calibration, the solar potential per region and the rate of change of the produced photovoltaic energy for 1 kWp were also quantified, highlighting the climatic effects on the production of solar energy. Additionally, two energy planning scenarios were explored: the first regarding the energy adequacy that each region can achieve, if a surface equal to 1% of its total area is covered with photovoltaics; and the latter estimating the necessary area covered with photovoltaics to fully meet each region’s energy demand. Finally, to ensure a solid and holistic approach, the research converted energy data into economic gains and avoided CO₂ emissions. The study is innovative, particularly for the Greek standards, in terms of the volume and type of information it provides. It is able to offer stakeholders and decision and policymakers, both in Greece and worldwide thanks to the use of open access data, invaluable insights regarding the impact of climate change on photovoltaic energy production, the optimization of photovoltaic installations and investments and the resulting financial and environmental benefits from proper and methodical energy planning. Full article

The evaluation of cloud distribution, properties, and their interaction with the radiation (longwave and shortwave) is of utmost importance for the proper assessment of future climate. Therefore, this study focuses on the Coupled Model Inter-Comparison Project Phase-6 (CMIP6) historical and future projections using the Shared Socio-Economic Pathways (SSPs) low- (ssp1–2.6), moderate- (ssp2–4.5), and high-emission (ssp5–8.5) scenarios along the South Asian region. For this purpose, a multi-model ensemble mean approach is employed to analyze the future projections in the low-, mid-, and high-emission scenarios. The cloud water content and cloud ice content in the CMIP6 models show an increase in upper and lower troposphere simultaneously in future projections as compared to ERA5 and historical projections. The longwave and shortwave cloud radiative effects at the top of the atmosphere are examined, as they offer a global perspective on radiation changes that influence atmospheric circulation and climate variability. The longwave cloud radiative effect (44.14 W/m²) and the shortwave cloud radiative effect (−73.43 W/m²) likely indicate an increase in cloud albedo. Similarly, there is an expansion of Hadley circulation (intensified subsidence) towards poleward, indicating the shifting of subtropical high-pressure zones, which can influence regional monsoon dynamics and cloud distributions. The impact of future projections on the tropospheric temperature (200–600 hPa) is studied, which seems to become more concentrated along the Tibetan Plateau in the moderate- and high-emission scenarios. This increase in the tropospheric temperature at 200–600 hPa reduces atmospheric stability, allowing stronger convection. Hence, the strengthening of convective activities may be favorable in future climate conditions. Thus, the correct representation of the model physics, cloud-radiative feedback, and the large-scale circulation that drives the Indian Summer Monsoon (ISM) is of critical importance in Coupled General Circulation Models (GCMs). Full article

We evaluate changes in the daily doses of surface ultraviolet radiation (UV) necessary for vitamin D production (UVpD) during the 21st century caused by the evolution of the Earth’s climate and the atmospheric ozone layer. Experiments with the Earth system model SOCOLv4 (version 4 of the Solar-Climate Ozone Links Chemistry-Climate Model) and an atmospheric radiative transfer model indicated a significant (20–80%) decrease in UVpD doses at the Earth’s surface between 2015–2024 and 2090–2099 in middle latitudes in both hemispheres and an increase of 30–40% in some areas of lower latitudes. These changes are driven by strong greenhouse gas growth and ozone-depleting substance reductions. The experiments also provided estimates of the relative contributions of the total ozone column (TOC), cloud parameters, and surface albedo changes to the corresponding variations in UVpD daily doses. Outside the tropics, the primary factor contributing to the decrease in UVpD doses (50% to 80%) is the increase in TOC. Changes in cloud parameters account for 20% to 30% of the decrease, while the decline in surface albedo contributes less than 20%. However, in the polar regions of the Northern Hemisphere, this contribution can reach up to 50%. In the lower latitudes, diminishing TOC and liquid water column of cloud (LWCC) provide the main contributions to the increase in UVpD doses. Full article

Clouds play a central role in regulating incoming solar radiation and outgoing terrestrial emission; hence, they must be internally constrained to prognose Earth’s temperature. At the same time, planetary fluids are inherently turbulent, so the climate state would tend toward maximum entropy production—a generalized second law of thermodynamics. Incorporating these requirements, I have previously formulated an aquaplanet model to demonstrate that intrinsic water properties may strongly lower the climate sensitivity to solar irradiance, thereby resolving the faint young Sun paradox (FYSP). In this paper, I extend the model to include other external forcings and show that sensitivity to the reduced outgoing longwave radiation by the elevated pCO₂ can be several times greater, but the global temperature remains capped at ~40 °C by the exponential increase in saturated vapor pressure. I further show that planetary albedo augmented by a tropical supercontinent may cool the climate sufficiently to cause tropical glaciation. And since the glacial edge is marked by above-freezing temperature, it abuts an open, co-zonal ocean, thereby obviating the “Snowball Earth” hypothesis. Our theory thus provides a unified framework for interpreting Earth’s diverse climates, including the FYSP, the warm extremes of the Cambrian and Cretaceous, and the tropical glaciations of the Precambrian. Full article

Earth’s energy imbalance (EEI) is a major indicator of climate change. Its metrics are top of the atmosphere radiation imbalance (EEI TOA) and net internal heat uptake. Both EEI and temperature are expected to respond gradually to forcing on annual timescales. This expectation was tested by analyzing regime changes in the inputs to EEI TOA along with increasing ocean heat content (OHC). Outward longwave radiation (OLR) displayed rapid shifts in three observational and two reanalysis records. The reanalysis records also contained shifts in surface fluxes and temperature. OLR, outward shortwave radiation (OSR) and TOA net radiation (Net) from the CERES Energy Balanced and Filled Ed-4.2.1 (2001–2023) record and from 27 CMIP5 historical and RCP4.5 forced simulations 1861–2100, were also analyzed. All variables from CERES contained shifts but the record was too short to confirm regime changes. Contributions of OLR and OSR to net showed high complementarity over space and time. EEI TOA was −0.47 ± 0.11 W m⁻² in 2001–2011 and −1.09 ± 0.11 W m⁻² in 2012–2023. Reduced OSR due to cloud feedback was a major contributor, coinciding with rapid increases in sea surface temperatures in 2014. Despite widely varying OLR and OSR, 26/27 climate models produced stable regimes for net radiation. EEI TOA was neutral from 1861, shifting downward in the 26 reliable records between 1963 and 1995, with 25 records showing it stabilizing by 2039. To investigate heat uptake, temperature and OHC 1955/57–2023 was analyzed for regime change in the 100 m, 700 m and 2000 m layers. The 100 m layer, about one third of total heat content, was dominated by regimes. Increases became more gradual with depth. Annual changes between the 700 m layer and 1300 m beneath were negatively correlated (−0.67), with delayed oscillations during lag years 2–9. Heat uptake at depth is dynamic. These changes reveal a complex thermodynamic response to gradual forcing. We outline a complex arrangement of naturally evolved heat engines, dominated by a dissipative heat engine nested within a radiative engine. EEI is a property of the dissipative heat engine. This far-from-equilibrium natural engine has evolved to take the path of least resistance while being constrained by its maximum power limit (~2 W m⁻²). It is open to the radiative engine, receiving solar radiation and emitting scattered shortwave and longwave radiation. Steady states maximize entropy within the dissipative engine by regulating spatial patterns in surface variables that influence outgoing OLR and OSR. Regime shifts to warmer climates balance the cost of greater irreversibility with increased energy rate density. The result is the regulation of EEI TOA through a form of thermodynamic metabolism. Full article

In this study, we evaluate aerosol, cloud, and radiation interactions in GFS.V17.p8 (Global Forecast System System Version 17 prototype 8). Two experiments were conducted for the summer of 2020. In the control experiment (EXP CTL), aerosols interact with radiation only, incorporating direct and semi-direct aerosol effects. The sensitivity experiment (EXP ACI) couples aerosols with both radiation and Thompson microphysics, accounting for aerosol indirect effects and fully interactive aerosol–cloud dynamics. Introducing aerosol and cloud interactions results in net cooling at the top of the atmosphere (TOA). Further analysis shows that the EXP ACI produces more liquid water at lower levels and less ice water at higher levels compared to the EXP CTL. The aerosol optical depth (AOD) shows a good linear relationship with cloud droplet number concentration, similar to other climate models, though with larger standard deviations. Including aerosol and cloud interactions generally enhances simulations of the Indian Summer Monsoon, stratocumulus, and diurnal cycles. Additionally, the study evaluates the impacts of aerosols on deep convection and cloud life cycles. Full article

Solar radiation is a first-order essential climate variable like temperature and precipitation. Its significant spatiotemporal variability, mainly due to atmospheric conditions, makes modelling particularly challenging, especially in regions with complex atmospheric dynamics and sparse meteorological stations. This study evaluates 6 solar radiation models (SARAH, PVGIS, Constant Atmospheric Conditions, Physical Solar Model, CAMS Worldwide, and InsolMets) using monthly measurements from 141 ground-based stations across the Iberian Peninsula from 2004–2020. Although all models consistently captured intra-annual variability, discrepancies in absolute values arise due to factors such as the differences in their functional designs and input parameters. InsolMets, which integrates cloud optical thickness, cloud fractional cover, the diffuse radiation component, and enhanced solar illumination geometry, was the most robust model, showing relevant improvements (61.5% in January, 59.7% in November, and 52.0% in December) compared to the worst-performing model (constant atmospheric conditions). Using as a threshold three times the root-mean-square error (RMSE) proposed by the Global Climate Observing System, InsolMets achieved the highest number of months (10) under this limit, also achieving the best overall result, with only 1 month showing non-significant correlations over the same time span. Nevertheless, SARAH and PVGIS matched InsolMets’ performance during March, November, and December. The results provide insights for selecting and improving solar radiation estimations, highlighting the need to incorporate remote sensing atmospheric data to minimize uncertainties. While all models that account for atmospheric effects enhance accuracy, InsolMets stands out as the most accurate model for estimating solar radiation across the Iberian Peninsula throughout the year, achieving the lowest RMSE and normalized RMSE values. Full article

Aerosols and clouds influence Earth’s radiative energy budget, but their regional radiative impacts remain insufficiently understood. This study investigates the spatial distribution patterns and long-term trends of radiative fluxes over China from March 2000 to February 2023 using CERES-SYN data. Notable decreasing trends in the net radiative fluxes over China at the top of the atmosphere (−0.38 W m⁻² year⁻¹) and the surface (−0.35 W m⁻² year⁻¹) during the study period have been observed. Cloud properties from CERES-SYN and aerosol properties from MERRA-2 are used to assess the impacts of aerosols and clouds on radiative flux variations. Results show that aerosols are the primary drivers of radiative flux variations across China, while cloud changes exert notable but regionally dependent influences. In southern China, reductions in black carbon and organic carbon aerosols substantially influence radiative flux variations, along with contributions from changes in mid-high, mid-low, and low clouds. In northern China, decreases in dust and organic carbon aerosols primarily drive radiative flux trends. Over the Tibetan Plateau, variations in mid-high clouds predominantly affect radiative flux changes. In Xinjiang and Inner Mongolia, fluctuations in high, mid-high, and mid-low clouds, along with dust and sulfate aerosols, jointly contribute to the radiative flux variations, although the overall impacts remain relatively small. Full article

Over recent decades, China has achieved significant progress in reducing haze pollution, yet photochemical pollution, primarily characterized by elevated ozone (O₃) levels, has intensified, posing severe ecological and public health risks. The “holiday effect”—periodic changes in human activities during holidays such as weekends and the Spring Festival—provides a critical lens to evaluate anthropogenic influences on O₃ pollution, particularly under scenarios of reduced anthropogenic emissions driven by the carbon neutrality pledge. However, a comprehensive summary of this phenomenon remains lacking. This review systematically examines spatial–temporal variations and driving factors, including precursor emissions, atmospheric oxidation capacity (AOC), and meteorological conditions, of the holiday effects across China. Key findings reveal that reduced nitrogen monoxide during holidays weakens its titration effect on O₃. Additionally, decreased particulate matter on holidays leads to improved atmospheric visibility and a corresponding enhancement in AOC and biogenic volatile organic compound emissions, all of which boost the photochemical formation capacity of O₃. Furthermore, solar radiation and temperature positively correlate with O₃ pollution during holidays, whereas rainfall and cloud cover suppress it. This review also provides suggestions for future research regarding mechanistic studies on photochemistry, machine learning for pollution drivers, radical roles in O₃ formation, and health impact assessments. Full article

The accurate measurement of solar radiation is essential for applications in agriculture, renewable energy, and environmental monitoring. Traditional pyranometers provide high-precision readings but are often costly and inaccessible for large-scale deployment. This study explores the feasibility of using low-cost ambient light sensors combined with statistical and machine learning models based on linear, random forest, and support vector regressions to estimate solar irradiance. To achieve this, an Internet of Things-based system was developed, integrating the light sensors with cloud storage and processing capabilities. A dedicated solar radiation sensor (Davis 6450) served as a reference, and results were validated against meteorological API data. Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². These results suggest that low-cost light sensors, when combined with data-driven models, offer a viable and scalable solution for solar radiation monitoring, particularly in resource-limited regions. Full article