Search Results (9,152)

This modeling study investigates the impact of the 2023 Canadian wildfire aerosols (primarily black carbon and organic aerosol) on weather forecasts, concluding that incorporating real-time aerosol forcing improves model performance over using climatology. Experiments without real-time data severely underestimated aerosol optical depth (AOD), an error mitigated by including the forcing or using the coupled atmosphere–chemistry model. The aerosols exerted a strong direct radiative effect, reducing surface downward shortwave (SW) flux and generating corresponding surface cooling over the wildfire region. Furthermore, including aerosol–cloud interactions amplified this cooling and led to an increase in the overall cloud fraction and precipitation, illustrating complex indirect effects. While these physical improvements enhanced the representation of the atmosphere, the positive impact on overall medium-range forecasting performance (5–10 days) was modest, suggesting that the benefits of accurately representing wildfire feedback on the coupled Earth system are achieved through relatively slow processes, such as radiation feedback. Full article

Soil moisture (SM) in the mountains is critical for agropastoral productivity, and it is subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation and soil. However, how the climate, topography, soil and vegetation factors impact surface SM spatiotemporal dynamics remains elusive in mountainous terrains, due to their complex interactions. Based on multi-source datasets, this study employs the structural equation model to investigate the impact pathways of climate and vegetation factors on annual surface SM dynamics from the year 2000 to 2022 in the Tianshan Mountains of China (TS). We also utilize the factor and interaction detectors of Geographical Detector to explore the individual and interactive effects of climate, topography, soil and vegetation factors on the spatial pattern of the annual surface SM. Moreover, their integrated impacts on the spatiotemporal dynamics of annual surface SM were investigated based on the explanatory power from the factor detector and total effects from structural equation modeling. The results showed that the multi-year average surface SM was 0.21 m³·m⁻³ for the whole region, with greater values in areas with dense vegetation and high elevation. Annual surface SM exhibited significant increasing trends across different land cover classifications and elevation zones, which was directly influenced by vegetation greenness enhancement. Precipitation (PRE) and relative humidity (RH) also significantly influenced the temporal variations in surface SM through their indirect effect on vegetation greenness, while these indirect effects were much lower than the direct effect of vegetation greenness. RH, PRE and surface net solar radiation (SSR) showed strong individual and interactive effects on the spatial distribution of surface SM, particularly the interactive effects of RH and PRE with wind speed (WS). Surface SM was highly sensitive to RH and PRE in the central TS. Overall, vegetation greenness, PRE and RH were the main drivers of surface SM variations across both temporal and spatial scales, while SSR, total evaporation and WS primarily shaped its spatial distribution. These insights enhance our understanding of land–atmosphere interactions in mountainous areas and provide scientific references for sustainable agropastoral water resource management under global warming. Full article

Nighttime light (NTL), a crucial indicator of human activity intensity, has not been systematically analyzed for its interactive mechanisms with air pollution and climate change. This study first investigates the spatiotemporal evolution of China’s total nighttime light (TNTL) and average nighttime light (ANTL), alongside key indicators of meteorological parameters and air pollution, at the grid scale from 2000 to 2023. We then employ prefecture-level city data and a geographically and temporally weighted regression (GTWR) model to quantify the spatiotemporally heterogeneous associations of temperature (TMP), precipitation (PRE), fine particulate matter (PM_2.5), ozone (O₃), land use (LUL), topography, and socioeconomic factors with NTL. The results indicate that (1) China’s NTL exhibits a significant overall upward trend, with areas of increase or significant increase comprising 92.04% of the total study area. TNTL growth demonstrates regional heterogeneity, expanding by a factor of 4.91 in East China and 2.65 in Northeast China; (2) meteorological and air pollution indicators display spatiotemporal non-stationarity, with the synergistic effect between O₃ and PRE being the strongest; (3) among NTL drivers, LUL contributes most significantly (0.44), followed by TMP (0.14) > PM_2.5 (−0.33 × 10⁻¹) > O₃ (0.17 × 10⁻¹) > PRE (−0.33 × 10⁻⁶); (4) TMP and PRE may primarily influence NTL by altering ecological conditions and nighttime activity patterns. TMP shows a strong positive correlation with NTL in the junction zone of South, East, and Central China, whereas PRE predominantly exerts a negative influence; (5) air pollution exhibits distinct spatiotemporal effects: high PM_2.5 and O₃ generally correspond to lower NTL, though positive correlations persist in some areas due to industrial structures, highlighting the need for integrated policies that balance air quality management with sustainable urban planning; (6) the 2013 “Air Pollution Prevention and Control Action Plan” significantly strengthened the negative correlation between PM_2.5 and NTL in North China. However, O₃ concentrations increased by 28.9% after 2017, underscoring the challenge of coordinating VOC and NOx controls for long-term atmospheric sustainability. Full article

Sea surface temperature (SST) serves as a fundamental driver of ocean–atmosphere interactions and global climate variability, exhibiting strong nonstationarity, multiscale dynamics, and cross-variable coupling. However, current deep learning models often fail to capture these complex characteristics, limiting their ability to support accurate and physically consistent long-term SST prediction. To address these issues, we propose PAMSTnet, a unified deep learning framework for physics-informed adaptive multiscale spatiotemporal prediction. PAMSTnet leverages three-dimensional empirical wavelet transform (3DEWT) to learn interpretable multiscale spatiotemporal dynamics from raw observations, and applies multivariate spatiotemporal empirical orthogonal function (MSTEOF) to identify dominant cross-variable coupled modes. These physically meaningful representations are integrated into a deep ConvLSTM predictive network (DCPN) to support coordinated multiscale dynamical learning. Furthermore, PAMSTnet introduces physics-informed consistency learning (PICL) to enforce thermodynamic and dynamic constraints, enhancing physical consistency and interpretability. Extensive experiments demonstrate that PAMSTnet achieves superior performance against state-of-the-art baselines in long-term global SST prediction, reducing RMSE by 8.1% and improving ACC by 2.8% compared with the best-performing baseline, particularly under extreme climate events. Interpretation insights further highlight PAMSTnet’s adaptive representation of variable contributions and regional physical drivers. These findings position PAMSTnet as a promising paradigm for developing intelligent ocean prediction systems with enhanced physical consistency and interpretability. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

In this work, the volumetric properties of nine binary systems composed of ethyl propanoate and n-alkylcyclohexanes (from cyclohexane to decylcyclohexane) were investigated. Densities were measured at atmospheric pressure (101 kPa) over the entire composition range and at temperatures from 288.15 K to 328.15 K. A total of 525 density data points were obtained. Excess molar volumes were derived from the experimental densities and correlated using a Redlich–Kister equation, while mixture densities were modeled with the Jouyban–Acree model. All systems exhibit positive excess molar volumes over the studied temperature and composition ranges, indicating volume expansion upon mixing due to dominant repulsive interactions. The magnitude of the excess molar volume increases with increasing alkyl chain length of the branched naphthenic compound: for an equimolar mixture, V^E is about 0.65 cm³·mol⁻¹ for the methylcylohexane + ethyl propanoate mixture and reaches 0.83 cm³·mol⁻¹ for the heptylcylohexane + ethyl propanoate binary system, although a plateau tendency is observed for longer alkyl chains. Excess molar volumes increase linearly with temperature, with a more pronounced temperature effect for shorter-chain alkylcyclohexanes. The Jouyban–Acree model provides an excellent correlation of the density data, yielding average relative deviations between 0.02% and 0.04%, and allows reliable predictions within the investigated temperature range. Full article

Atmospheric laser beam propagation is typically perturbed by the dual influences of aerosol particle systems and atmospheric turbulence. This joint perturbation induces intensity fluctuations in the transmitted optical field, which significantly degrades the performance of laser-based systems. This study integrates and improves upon existing simulation algorithms, establishing a coupled model that combines the Monte Carlo method and multi-phase screens. The model accurately characterizes optical field evolution and reveals that the impacts of scattering and turbulence on the scintillation index (SI) are not simply additive: turbulence perturbation enhances intensity fluctuations, leading to an increase in SI; however, as the energy proportion of scattered light rises, its statistical stationarity begins to dominate the optical field characteristics, stabilizing SI. Based on radiative transfer and Mie scattering theories, an analytical formula for single-scattering SI is derived, enabling direct calculation from fundamental parameters. Furthermore, a composite SI expression is established using the scattered-to-transmitted light intensity ratio. To address model deviations along the dimensions of visibility and turbulence strength, a sinusoidal compensation model and a logarithmic compensation model are proposed, respectively. Validation results verify the complementary and competitive mechanisms of scattering and turbulence in modulating intensity fluctuations. This research provides efficient theoretical tools and practical references for simulating and optimizing laser transmission in complex atmospheric environments. Full article

Vortex generators (VGs) are considered in this study as an effective means of controlling the boundary-layer structure and suppressing flow separation on the suction sides of wind turbine blades. An original geometry of a surface-mounted VG has been developed and experimentally investigated, providing a stable modification of the near-wall flow over a wide range of incoming flow velocities. The aerodynamic effect is attributed to the formation of spatially diverging vortex structures that enhance momentum transfer from the outer flow region toward the near-wall layer, thereby increasing the energy level of the boundary layer. This results in an extension of the attached-flow region and an increase in the mean flow velocity over the suction side of the airfoil by up to 6.5%. The proposed configuration enables a 15% increase in the installation spacing of surface-mounted VGs without loss of control efficiency. Experimental investigations were carried out in a subsonic aerodynamic facility using the Particle Image Velocimetry (PIV) method at free-stream velocities of up to 30 m/s. The obtained data will be used for the development and validation of a mathematical model intended for parametric studies of the influence of surface-mounted VGs on various wind turbine blade airfoils under a wide range of atmospheric turbulence conditions. Full article

Lago Argentino and Lago Viedma are large lakes fed by glaciers in Southern Patagonia, characterized by extraordinarily strong, persistent westerly winds and sharp gradients in regional relief, climate, and gravity field. We present operational models of spatio-temporal lake-level variations that represent instantaneous ellipsoidal lake-surface height as the superposition of three components: (i) a time-averaged lake-level topography derived from geoid modeling and ICESat-2 residuals, (ii) temporally varying water-volume changes in the lake estimated from tide gauge time series corrected for atmospherically driven perturbations, and (iii) a static hydrodynamic response to wind stress and air-pressure forcing. The atmospheric response is parametrized through empirically derived transfer functions obtained by regressing instantaneous lake-level anomalies against ERA5 wind and pressure fields, capturing wind-driven tilting. Standard deviations of ICESat-2 ATL13 elevations amount to 106 cm and 70 cm over Lago Argentino and Lago Viedma, respectively. The subtraction of our models reduces these standard deviations to 8 cm (Argentino) and 14 cm (Viedma). Surface waves incompletely averaged out within ICESat-2’s narrow footprint are identified as a principal source for the residual variability. A standard deviation of ATL13 elevations below 2 cm on calm days demonstrates ICESat-2’s unprecedented capability of monitoring water resources from space in a region of sparse hydrological infrastructure. Full article

The Earth’s climate is an open nonlinear system, sustained far from thermodynamic equilibrium by solar radiation and energy and matter exchange among its four major subsystems: atmosphere, ocean, land, and cryosphere. Among these four subsystems, the ocean significantly influences and sustains Earth’s climate over decadal to millennial timescales. Although modern numerical models increasingly capture intricate dynamical details, the fundamental concepts of large-scale ocean variability are less frequently explored. This study revisits ocean circulation through the lens of self-organization theory and synergetics. The key synergetic concepts of mode competition, order parameters, and the slaving principle are interpreted within the framework of general ocean circulation and the Atlantic Meridional Overturning Circulation (AMOC). The Brusselator, a simplified model of a nonlinear dynamical system initially developed in chemical kinetics, serves as a conceptual analog for ocean circulation energy conversion. Despite its high abstraction, this proxy model effectively captures essential bifurcation behaviors, such as Hopf bifurcation transitions and limit-cycle behaviors. This clarifies feedback regulation, instability, and potential regime transitions in the AMOC. The synthesis in this study is intended for an interdisciplinary readership and highlights the broader applicability of synergetic principles to the complex Earth climate system maintained far from equilibrium. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

Systematically characterizing the time series deformation evolution of open-pit mine slopes is key to revealing their potential instability development and supporting subsequent deformation-level classification. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale approximately every ten days, may hold the key to those interactions. However, atmospheric propagation delays still have a significant impact on deformation calculations, and open-pit mine slopes monitored by InSAR often suffer from low coherence. This noise can obscure nonlinear and transient precursory signatures in deformation time series, reducing the identifiability of key temporal patterns required for automated interpretation. Here, we present a Coherence-conditioned Encoder–Decoder Long Short-Term Memory (CED-LSTM) denoising network for deformation time series. We generate a physics-aware synthetic dataset by modeling coherence-dependent measurement noise and temporally correlated atmospheric delays. The network jointly models deformation time series and coherence, using residual learning and adaptive gated composite loss to preserve deformation trends. It is designed to autonomously extract ground deformation signals from noise in InSAR time series without prior knowledge of where deformation occurs or how it evolves. On the synthetic validation set, the network achieved a root mean square error (RMSE) of 2.2 mm across the validation sequences. Applied to three InSAR datasets over an open-pit mine from March 2019 to March 2022, denoising suppresses noise and stabilizes deformation boundaries, enabling extraction of trend and transient indicators and a data-driven deformation-level score. Using quantile-based thresholds, these scores are then used to produce multi-year deformation-level classification maps. Full article

Sulfur dioxide is a trace constituent of the upper atmosphere of Venus but plays a dominant role in the photochemistry above the cloud tops. Because SO₂ undergoes indirect dissociation to a relatively long-lived excited state, it has a line-type absorption spectrum in the dissociation region (~190–220 nm). This leads to strong isotopic fractionation under optically thick conditions, a process referred to as self-shielding. Here, I use SO₂ cross-sections, shielding functions, and a simple steady-state photochemical model to estimate sulfur isotope ratios in SO₂. The results indicate that large isotope depletion relative to SO₂ in the deep atmosphere is expected in SO₂ below 70 km altitude, with δ³⁴S ~ −100 to −200 permil. This is readily detectable by the VTLS tunable laser spectrometer planned for the NASA DAVINCI mission. Full article

Biogeochemical processes, including the oxygen cycle, were investigated in Lake Baikal during the spring thermal bar using a coupled numerical model that takes into account the intraday variability of atmospheric parameters and contains the following variables: nitrate, ammonium, phosphate, oxygen, chlorophyll a, phytoplankton, zooplankton, and small and large detritus. Nitrification, photosynthesis, remineralization, and respiration processes describe the biochemical dynamics of oxygen in the model. As a study area, the deep-water cross-section of Lake Baikal, Boldakov River–Maloye More Strait, was considered using meteorological data for June 2024 at the lake surface. Numerical results show that the thermal bar can contribute to the transport of dissolved oxygen and phyto- and zooplankton to the deeper layers of the lake. Full article

The vertical distribution of atmospheric aerosol layers plays a fundamental role in understanding their climatic and environmental effects. Using one year of lidar observations in Jinhua, together with ground-based meteorological measurements and ERA5 reanalysis data, this study develops an integrated analytical framework to investigate the structural characteristics of aerosol layers in Mie-scattering profiles and their meteorological driving factors. K-means clustering identifies three representative aerosol layer structure types: single-layer concave, single-layer convex, and multi-layer profiles. By combining the Boruta algorithm with a random forest model, the dominant meteorological factors associated with each structure type are quantified across four boundary-layer stages (00–06, 06–12, 12–18, 18–24 LT). Temperature, humidity, wind speed, wind direction, divergence, and vertical velocity exhibit distinct influences across different boundary-layer conditions, revealing differentiated regulatory mechanisms governing aerosol layer structure change. The proposed framework establishes a coupled perspective between atmospheric dynamic/thermodynamic processes and aerosol layer structure formation, providing a basis for refined modeling of aerosol evolution and improved understanding of aerosol–meteorology interactions. Full article