Search Results (112)

We develop a leading-order continuum framework for thin-film hydrodynamics on inclined solid substrates, integrating capillarity, intermolecular forces, gravitational symmetry breaking, confined transport, and stochastic wetting into a single formulation. Starting from lubrication theory with capillary curvature and disjoining-pressure interactions, we obtain a lubrication-scale thin-film equation that incorporates inclination-driven advection, nanoscale stabilization, and humidity-controlled source–sink fluxes. A dimensionless analysis shows that, within the long-wave lubrication approximation, inclination induces a coordinated leading-order coupling among the Bond (Bo), Péclet (Pe), and Damköhler (Da) numbers. This coupling defines a characteristic inclination-angle-dependent scaling trajectory Γ(θ) in the (Bo, Pe, Da) space: material parameters set the system’s position along this curve, while the geometric constraint organizes the ordering of hydrodynamic, transport, and confinement regimes. We further derive leading-order crossover criteria associated with transport transitions (Pe ≃ 1) and reactive-confinement loss (Da ≃ 1), providing explicit regime boundaries that can be evaluated for representative parameter ranges. A representative parameterization of an ultrathin atmospheric electrolyte film is then used to make these crossovers explicit, yielding illustrative inclination thresholds that depend on the chosen parameter set. Coupling the deterministic structure to a minimal stochastic closure captures intermittent wet–dry dynamics under environmental forcing. In this closure, inclination selectively accelerates the drying pathway through the drainage time (and thus drying rate λ_dry), while rewetting remains primarily humidity-controlled, to leading order, providing a scaling-based description of wet-state persistence and time-of-wetness versus θ. The resulting framework provides a continuum-scale physical description of confined films under geometric asymmetry, relevant to wetting, interfacial drainage, confined transport, and thin-film systems in which symmetry breaking and coupled interfacial–transport processes coexist. Full article

Outdoor open spaces are essential for daily activities in ageing rural environments, yet the thermal effectiveness of vegetation under varying structural configurations remains unclear. Most existing Outdoor Thermal Comfort studies focus on dense urban canyons; the present study addresses this gap by examining a complexity threshold in vegetation cooling under high-SVF rural conditions and the radiation–ventilation trade-off that underlies it. An ENVI-met model was calibrated using field data from a typical village on the North China Plain and 17 vegetation scenarios were simulated. The findings reveal a non-linear relationship between vegetation complexity and cooling efficiency. A threshold of complexity was observed: the cooling performance declined with an increase in stratification from a double-layer (Scenario 12) to a triple-layer (Scenario 14) structure, with the change in mean radiant temperature (∆T_mrt) dropping from 23.16 °C to 21.10 °C. This is due to a radiation–ventilation trade-off, in which denser vegetation increases shading but reduces near-surface ventilation. Dense arrangements exhibit a heat trap effect, with the long-wave radiation flux changing from a cooling (−3.42 K/h) to a heating (+2.11 K/h) state. The results show a threshold effect in vegetation cooling and that thermal comfort is not necessarily enhanced by increased complexity. A shaded-canopy and permeable-understory structure is found to be optimal. The findings inform vegetation design in climate-adaptive rural settings. Full article

This study presents a comprehensive assessment of longwave radiation variability in the Arctic based on unique measurements collected at the North Pole drifting station SP-28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long-term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high-precision spectral pyrgeometers to identify Arctic haze. The results show that in 1987, Arctic haze layers enhanced the downward longwave flux by 15–20 W·m⁻² and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol-free model calculations, indicating a substantial decline in Arctic haze and the diminishment of radiatively significant aerosol layers. This shift is in alignment with the long-term reduction of global anthropogenic sulfur dioxide emissions across the Northern Hemisphere. Full article

Urban planners increasingly require high-resolution thermal comfort maps to prioritize heat adaptation, yet validating the necessary microclimate models against standard field instruments remains methodologically fraught. This study establishes an integrated measurement–modeling framework applied to a study area in Strasbourg, France. First, we evaluate the radiative physics of the LASER/F model against net radiometer measurements at a specific sub-canopy location and against incoming shortwave radiation pyranometer records across three instrumentation sites. Results demonstrate high accuracy for longwave fluxes ($R^2>0.95$) but reveal that simplified tree geometry leads to condition-dependent shortwave discrepancies. Second, we quantify the systematic divergence between Mean Radiant Temperature derived from black globe measurements and six-directional simulations across seven sites. We analyze how these inevitable discrepancies, stemming mainly from geometric mismatch, propagate into the Universal Thermal Climate Index (UTCI), resulting in (71.5–75.5%) diurnal exact categorical agreement. Finally, spatial application of the model uncovers a “masked risk”: while temporal averaging suggests that 100% of the district remains safe (mean UTCI < 32 °C), duration-based analysis reveals that 72.8% of surfaces actually experience critical heat stress for over a quarter of the period. To address these hidden exposure risks, we propose a “Combined Risk Score” (CRS) that integrates thermal intensity and critical exposure duration on an absolute, dataset-independent scale, with a sensitivity analysis demonstrating that spatial risk prioritization is invariant to the intensity–duration weighting choice at the operational threshold. Full article

Solar radiation, longwave radiation, sensible heat flux, and latent heat flux constitute the primary forms of air–sea heat exchange, serving as crucial computational parameters in numerical simulations of thermal discharge. This study investigates a coastal nuclear power plant and employs a modified Morris screening method to quantitatively assess the contribution rates of various air–sea heat exchange processes to the spatial distribution of temperature rise under different operating conditions. The results indicate that the influence of air–sea heat exchange processes on the thermal discharge envelope exhibits a nonlinear pattern. The individual parameter sensitivity of shortwave radiation, sensible heat flux and latent heat flux is higher in the low temperature rise region ($∆\mathrm{T} \ge 1 °\mathrm{C}$) than in the high temperature rise region ($∆\mathrm{T} \ge 4 °\mathrm{C}$), with the individual parameter sensitivities of longwave radiation and latent heat flux displaying distinct threshold effects. The dominant heat exchange mechanisms vary across temperature rise regions: longwave radiation predominates in the high temperature rise region ($∆\mathrm{T} \ge 4 °\mathrm{C}$), contributing approximately 74.71%, whereas latent and sensible heat fluxes dominate in the low temperature rise region ($∆\mathrm{T} \ge 1 °\mathrm{C}$), accounting for a combined contribution of about 88.58%. These findings provide a scientific basis for model simplification and targeted parameterization. Full article

Atmospheric aerosols modulate Earth’s radiation balance through direct effects and through their role as cloud condensation nuclei (CCN), contributing to variability in near-surface temperature (NST). Galactic cosmic rays (GCRs) further influence aerosol–cloud interactions by enhancing particle formation and growth, but combined aerosol optical depth (AOD)–GCR effects on NST remain poorly constrained across climates. Using satellite and reanalysis data, we examine joint influences on NST anomalies at three neutron-monitoring stations, Oulu, Newark, and Hermanus, during 2000–2022. The sites share similar geomagnetic cutoffs but contrasting climates, enabling separation of ionization from geomagnetic shielding. Multiple linear regression (MLR) captures AOD effects and their modulation by GCR flux. Adding an interaction term (AOD × GCR) improves fit, raising adjusted R² from 0.22→0.31 (Oulu), 0.37→0.52 (Newark), and 0.69→0.78 (Hermanus). ECMWF reanalysis shows hydrophilic organic matter aerosol (OMA) dominates (0.19, 0.29, 0.41 µg kg⁻¹ at Oulu, Newark and Hermanus), with sulphate elevated at Oulu/Newark and coarse sea salt at Hermanus. Elevated OMA and sulphate at Oulu/Newark imply GCR-enhanced fine CCN and cooling, whereas humid, sea-salt-rich Hermanus favors ion-mediated growth of larger hygroscopic particles that increase longwave trapping and warming. Findings provide site-specific evidence that GCR ionization modulates aerosol processes and contributes to regional NST variability, informing improved parameterizations in climate models. Full article

Lakes are sensitive indicators of climate change and exhibit distinct responses to climatic variability. Using in situ eddy covariance and meteorological observations from Nam Co (“large lake”) and a small lake (“small lake”) adjacent to Nam Co, we evaluate the performance of the FLake model in simulating lake processes. The model generally reproduces the seasonal variations in mixed-layer depth and surface water temperature, although diurnal amplitudes are underestimated. Simulated sensible and latent heat fluxes agree well with observations when appropriate lake depth and light extinction coefficients are applied, with RMSEs of ~1 °C, 8 W m⁻², and 22 W m⁻² for lake surface temperature, sensible heat flux, and latent heat flux, respectively. For the “large lake”, latent heat flux simulations differ markedly between land-based and lake-based forcing, primarily due to differences in wind speed and air temperature. Long-term simulations (1981–2024) suggest progressive warming of lake surface waters, strengthened thermal stratification, and increasing surface heat fluxes, with downward longwave and shortwave radiation and near-surface air temperature identified as the dominant climatic drivers. Full article

Photovoltaic double-skin façades (PV-DSFs) can block solar radiation heat, mitigate air heat transfer, facilitate ventilation cooling, and generate electricity, making them a high-performance building envelope suitable for hot southern regions in summer. The thermal performance of DSFs is relatively well understood; however, with the addition of photovoltaic glass panels, the influence of design parameters is altered due to thermoelectric coupling effects. Then, the influence of design parameters on their thermoelectric performance remains unclear, hindering their design optimization. This paper establishes a mathematical model for DSFs with MATLAB (R2023a) to analyze their thermoelectric performance and the impact of design parameters. The results indicate that the daily power generation of PV-DSFs is primarily influenced by the solar radiation on the west-facing vertical surface. The wall exterior surface gains heat via longwave radiation during the day and loses heat at night, while convective heat dissipation occurs throughout the entire day, with radiative heat flux being the dominant mechanism. The power generation of photovoltaic cells is significantly influenced by their coverage ratio, while the impact of other factors can be neglected. The temperature of the wall’s exterior surface is significantly influenced by the heat storage of the outer cladding panel, the solar absorptivity of the exterior surface, and the emissivity of the interior surface. Among these factors, the heat storage of the outer cladding panel primarily affects the attenuation and delay of peak values and temperature fluctuations on the exterior surface. Meanwhile, the solar absorptivity of the exterior surface and the emissivity of the interior surface mainly influence the peak temperature of the wall’s exterior surface, with the effect becoming more pronounced when the interior surface emissivity is lower. Full article

We develop a minimal thermodynamic model to predict Titan’s surface temperature based on radiative–convective equilibrium and the principle of maximum entropy production (MEP). The model retains only the essential atmospheric constituents: gaseous methane, which absorbs both longwave and near-infrared radiation, and stratospheric haze, which scatters and absorbs solar flux. Subject to Clausius–Clapeyron scaling of methane vapor pressure together with energy balances at the surface, tropopause, and stratopause, the model links the convective flux to the surface temperature, which exhibits a pronounced maximum due to competing radiative effects of tropospheric methane. As the surface warms, enhanced greenhouse effect would strengthen the convection, whereas the rising anti-greenhouse effect would suppress convection. The resulting convective peak corresponds to MEP, which thus selects a surface temperature slightly above methane’s triple point. To assess its long-term evolution, we consider a 20% dimmer early Sun and a hypothetical 20% enrichment of the oceanic methane. Even in combination, they only cool the surface by ~2 K, in sharp contrast to the ~20 K cooling inferred in studies that prescribe haze abundance. This study suggests a critical role of self-adjusting haze in providing the internal degree of freedom necessary for MEP closure, thereby stabilizing Titan’s temperature. Full article

This study assessed the performance of the Clouds and the Earth’s Radiant Energy System (CERES) surface longwave downward radiation (SLDR) products under the atmospheric temperature inversion (ATI) conditions for the first time. Three years of ground-measured SLDRs from 409 globally distributed stations across four flux networks were employed, and the collocated MODIS atmospheric profile product was used to identify the ATI profiles at each flux station. All three SLDR estimate algorithms (Models A, B, and C) show a pronounced accuracy decline under ATI conditions, regardless of region (polar or non-polar) or time of day (daytime or nighttime). Under ATI conditions, the Bias/RMSE increases by approximately 10.0/5.0 W/m² for Models A and B, 5.0/1.0 W/m² for Model C. Sensitivity analysis reveals that the concurrent atmospheric moisture inversion (AMI) compounds this degradation; both the Bias and RMSE increase with the AMI intensity. These results underscore the need to refine CERES SLDR algorithms in the future. Full article

Quantifying the time and space scale variability in air–sea fluxes is challenging. This study adopts tower-based in situ observations in the northern South China Sea (SCS) to evaluate widely used reanalysis and CO₂ flux products. For heat and momentum fluxes, three reanalysis products were considered: the fifth-generation European Centre for Medium-Range Weather Forecast reanalysis (ERA5), the NCEP Climate Forecast System Version 2 reanalysis (CFSv2), and third-generation Japanese Meteorological Agency reanalysis (JRA55). Comparisons of surface state variables show that these three reanalysis products generally agree well with observations on both the daily and monthly scales. On the daily scale, the correlation coefficients between observations and ERA5 exceed 0.93 for wind, air temperature, relative humidity, and longwave radiation. On the monthly scale, seasonal variations in wind, air temperature, and relative humidity are well captured. Nevertheless, the three reanalysis products all overestimate (underestimate) the latent (sensible) heat flux, with a root mean square error above 90.50 (33.35) ${\mathrm{W}/\mathrm{m}}^2$. For momentum fluxes, the three reanalysis datasets tend to underestimate 0.07∼0.08 ${\mathrm{N}/\mathrm{m}}^2$ with a high correlation coefficient above 0.71. In terms of CO₂ fluxes, the Multi-observation Carbon Assimilation System (MCAS), Surface Ocean CO₂ Atlas (SOCAT), and Global ObservatioN-based system for monitoring Greenhouse GAs (GONGGA) inversion CO₂ flux datasets were evaluated. SOCAT performs best with a correlation coefficient of 0.75, and GONGGA follows with 0.64, while MCAS demonstrates the lowest performance with a value of 0.36. In addition, the spatial patterns of the monthly mean surface CO₂ flux in the northern SCS illustrate significant discrepancies between MCAS, SOCAT, and GONGGA. These results can provide valuable insights for reducing uncertainties in air–sea flux products over coastal areas in the future. Full article

Land surface temperature (LST) is a critical variable for understanding energy exchanges and water balance at the Earth’s surface, as well as for calculating turbulent heat flux and long-wave radiation at the surface–atmosphere interface. Remote sensing techniques, particularly using satellite platforms like Landsat 8 OLI/TIRS and Sentinel-2A, have facilitated detailed LST mapping. Sentinel-2 offers high spatial and temporal resolution multispectral data, but it lacks thermal infrared bands, which Landsat 8 can provide a 30 m resolution with less frequent revisits compared to Sentinel-2. This study employs Sentinel-2 spectral indices as independent variables and Landsat 8-derived LST data as the target variable within a machine-learning framework, enabling LST prediction at a 10 m resolution. This method applies grid search-based hyperparameter-tuned machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and k-Nearest Neighbours (kNN)—to model complex nonlinear relationships between the spectral indices (NDVI, NDWI, NDBI, and BSI) and LST. Grid search, combined with cross-validation, enhanced the model’s prediction accuracy for both pre- and post-monsoon seasons. This approach surpasses earlier methods that either employed untuned models or failed to integrate Sentinel-2 data. This study demonstrates that capturing urban thermal dynamics at fine spatial and temporal scales, combined with tuned machine learning models, can enhance the capability of urban heat island monitoring, climate adaptation planning, and sustainable environmental management models. Full article

Dust plays a significant role in the atmospheric radiative balance by altering both shortwave and longwave radiation fluxes. While deserts are the primary sources of dust emissions, atmospheric circulation can transport dust over long distances, impacting air quality and climate in remote regions. These transport episodes, commonly known as dust events, vary in intensity and effects. Despite extensive research, uncertainties persist regarding their precise radiative impacts. This study examines the direct radiative effects of dust events in 2024 (a year marked by heightened dust activity) over Limassol, Cyprus. A comprehensive approach is employed, integrating radiative transfer modelling, ground-based solar radiation measurements, and dust optical property analysis. The LibRadtran radiative transfer package is used to simulate atmospheric radiative transfer under dust-laden conditions, incorporating key dust optical properties such as Aerosol Optical Depth, Single Scattering Albedo, and the Asymmetry Parameter retrieved from the Limassol’s AERONET station. Observations from solar radiation station at the ERATOSTHENES Centre of Excellence serve as validation for the model. This study quantifies the radiative impact of dust by evaluating changes in surface irradiance, providing valuable insights into the role of dust in atmospheric energy balance. Full article

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets. Full article

This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K_60x, and the neural network emulator, RRTMG-K_NN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth’s Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-K_NN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K_60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K_60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. Full article