Search Results (196)

Understanding the physical mechanisms of compound hot-drought events (CHDEs) over the Yangtze River Basin (YRB) is essential for improving climate predictability. Using high-resolution observations and ERA5 reanalysis, this study conducted a three-dimensional comparative diagnosis of two spatially distinct CHDEs (August 2022 and 2019), deconstructing their circulation dynamics and thermodynamic budgets. Specifically, the 2022 basin-wide event was highly associated with La Niña and a negative Indian Ocean Dipole (NIOD). Anomalous latent heating in the tropical eastern Indian Ocean favored an extensive meridional Hadley circulation and a deep high-pressure belt, blocking southwest moisture transport. Thermodynamically, intense downward vertical motions produced severe descending adiabatic warming, which offset longwave radiational cooling and sustained anomalous heat accumulation throughout the deep troposphere. Conversely, the 2019 localized event occurred under a weak Central Pacific El Niño and a positive IOD. The anomalous tropical cooling weakened systematic subsidence and provided a favorable background for a Rossby wave train, featuring an offshore cyclone over the Western North Pacific that severed moisture pathways. Characterized by shallower vertical subsidence, the lack of a penetrative adiabatic heat source merely maintained a lower-level thermodynamic balance, confining extreme heat to the central–eastern YRB. Although superficially similar at the surface, these CHDEs are sustained by two distinct ocean–atmosphere interaction paradigms. Clarifying these dual paradigms provides a critical physical basis for improving sub-seasonal prediction. Full article

The Tibetan Plateau (TP) plays a crucial role in the global climate system, yet long-term trends in its top-of-atmosphere (TOA) radiation budget remain poorly understood. Using 40-year (1981–2020) CLARA-A3 satellite data and reanalysis datasets, we quantify the spatiotemporal variations of TOA reflected shortwave radiation (RSF) and outgoing longwave radiation (OLR), and decompose the RSF into atmospheric and surface contributions. Additionally, we attribute observed trends to key drivers via multiple linear regression. The TP shows a significant decrease in RSF (−11.06 W/m²) and a significant increase in OLR (+4.88 W/m²). Spatially, OLR increases are widespread and statistically significant over the central and western plateau, while RSF trends are heterogeneous and mostly non-significant except for localized negative trends. The atmospheric contribution to RSF declines markedly (−0.329 W/(m²·year)), whereas the surface contribution increases (+0.061 W/(m²·year)). Attribution analysis identifies cloud fraction change (CFC) as the dominant drivers: it explains most of the RSF decline and the OLR increase. Cloud optical thickness and vertical structure play only minor roles. Our findings demonstrate that decreasing cloud cover is reshaping the TP’s TOA energy budget, providing essential geo-information for the spatial evaluation of climate models and the refinement of satellite-derived radiation products. Full article

Surface heat sources play a critical role in shaping meteorological conditions and permafrost dynamics on the Tibetan Plateau. To better understand surface heat source variability in the permafrost region, multiyear observational data from an isolated permafrost site and a continuous permafrost site were used to analyse the variability. The results indicated that the surface heat source at the isolated permafrost site remained relatively stable, whereas that at the continuous permafrost site increased significantly, at a rate of approximately 2.2 Wm⁻²yr⁻¹. The proportions of sensible and latent heat fluxes in the surface heat source budget varied with the season. Overall, the proportion of latent heat flux was greater than that of sensible heat flux in summer and autumn, whereas the proportion of sensible heat flux was greater than that of latent heat flux in winter and spring. The peak proportion for both fluxes exceeded 80%. A random forest model effectively captured the variations in the surface heat source. And the machine learning simulation indicated that soil temperature, downward shortwave radiation and albedo were identified as major contributors to the surface heat source, collectively accounting for more than 88% of the overall variability at both sites. The impacts of snow cover on the surface heat source varied with intensity. The increasing trend of the surface heat source was closely related to climate warming in autumn and winter. A significant but weak positive correlation was observed between vegetation and the surface heat source. Full article

The radiometric stability of satellite thermal infrared (TIR) channels is an indispensable prerequisite for the accurate retrieval of land surface temperature (LST) and the generation of reliable climate data records. This study evaluates the on-orbit radiometric calibration stability of the Fengyun-3D (FY-3D)/MEdium Resolution Spectral Imager-II (MERSI-II) TIR channels (channels 24 and 25) over four years (2021–2024) via a rigorous cross-calibration framework against Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS). By imposing stringent spectral, spatial, temporal, and angular constraints to ensure the high fidelity of collocated pixel pairs, the cross-calibration results demonstrate that FY-3D/MERSI-II exhibits exceptional radiometric stability. Absolute brightness temperature biases are typically less than 0.1 K, with root mean square errors (RMSEs) limited to 1.20 K over a range of diurnal and seasonal conditions, demonstrating no noticeable systematic degradation. Furthermore, the downstream impact of this calibration on LST retrieval was quantified using the adapted National Oceanic and Atmospheric Administration Joint Polar Satellite System Enterprise algorithm. Validated against independent ground-based longwave radiation measurements collected from the Heihe Watershed Allied Telemetry Experimental Research network (HiWATER) and the Surface Radiation Budget Network (SURFRAD), the retrieved LST yielded overall biases of 0 K and −0.37 K, respectively, with RMSEs below 2.5 K. Cross-calibration demonstrates a limited and context-dependent impact on daytime LST, while the nighttime LST accuracy can be marginally improved using seasonal calibration coefficients derived from combined day/night matchups. Mechanistically, the integration of a soil directional emissivity model into the retrieval algorithm effectively mitigates viewing-zenith-angle (VZA)-induced uncertainties, systematically reducing biases by 0.12–0.20 K and RMSEs by 0.04–0.06 K. These findings confirm that the on-orbit radiometric calibration of FY-3D/MERSI-II meets scientific quality requirements and provide practical guidance for optimizing LST retrieval. Full article

Accurate estimation of daily net radiation (R_{n_daily}) at high spatial resolution (1 km) over the Tibetan Plateau (TP) is crucial for understanding land surface energy budgets and climate dynamics. This study proposes a densely connected multilayer perceptron (DenseMLP)-based transfer learning framework, with a two-stage strategy (coarse pre-training on GLASS R_{n_daily}, followed by fine-tuning on limited TP ground observations) using MODIS land surface parameters and auxiliary data to generate 1 km R_{n_daily}. When evaluated on the training set, the proposed model achieves an overall R² of 0.87, MAE of 16.06 W m⁻², RMSE of 21.94 W m⁻², and a near-zero bias of 0.07 W m⁻². On an independent test set, the model maintains robust performance with R² = 0.83, MAE = 17.43 W m⁻², RMSE = 22.55 W m⁻², and bias = −1.12 W m⁻². The method exhibits consistently low bias across individual sites (mostly within ±3.7 W m⁻²) and accurately captures seasonal variability. When applied to the entire TP for 2018, the 1 km R_{n_daily} product reveals clear aspect-related terrain effects and a distinct annual cycle. This framework effectively mitigates site-dependent errors, providing a useful reference for long-term R_n product development over the TP. Full article

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. 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

Net surface shortwave radiation (NSSR) is a key parameter in the Earth’s energy cycle, greatly affecting global water and heat balance. Currently, a comprehensive comparative analysis regarding the accuracy of different models remains severely lacking, and there is also a notable deficiency in the systematic exploration of seasonal radiative drivers. Therefore, we developed a machine learning-based seasonal NSSR estimation model. By integrating in-situ observational data with multi-source remote sensing datasets, we achieved precise quantification of radiative fluxes. This proposed model framework employed three cutting-edge algorithms, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to capture the non-linear interactions among radiative drivers across the four seasons. Through mechanistic sensitivity analysis, we quantified the impacts of key variables on NSSR prediction. The results unequivocally demonstrated that the RF algorithm demonstrated the best performance. Its seasonal R² were 0.95 (spring), 0.89 (summer), 0.95 (autumn), and 0.96 (winter). The Solar Zenith Angle (SZA) dominated in spring and winter; its absence reduced R² by 0.23 and raised RMSE by 20.66–26.42 W/m². Meteorological factors mattered most in summer; excluding them cut R² by 0.17 and hiked RMSE by 23.82 W/m². This study provides actionable insights for terrestrial radiation budget research. Full article

The theory of physical degrees of freedom (DoF) developed by Franceschetti–Migliore–Minero (FMM) establishes a fundamental phase transition in the singular-value spectrum of electromagnetic radiation operators under maximal rotational symmetry. In this work, we revisit this result from a symmetry-explicit operator-theoretic perspective and extend it to scenarios with reduced and controllable symmetries, with particular emphasis on reconfigurable intelligent surfaces (RISs). We model the radiation process as a compact operator acting between admissible source and observation spaces and characterize its symmetry through group equivariance. This formulation enables a systematic decomposition of the operator into irreducible representation sectors associated with the effective symmetry group, defined as the intersection of symmetries supported jointly by the source architecture, RIS geometry and programmability, receiver configuration, and propagation environment. We show that the FMM phase transition persists within each symmetry sector and that the total DoF budget is redistributed across sectors according to symmetry constraints. A key outcome of this analysis is the distinction between physical and effective degrees of freedom. While breaking the maximal $\mathrm{SO}\left(2\right)$ symmetry does not increase the total number of electromagnetic DoF dictated by physics, symmetry reduction modifies their allocation across sectors, potentially lifting degeneracies and increasing the number of degrees of freedom that can be effectively addressed by a given excitation, RIS control, and measurement architecture, even when the total number of physical DoF remains fixed by fundamental limits. This clarifies the role of controlled symmetry breaking as a design mechanism rather than a means to surpass fundamental limits. The proposed framework bridges electromagnetic operator theory, representation theory, and RIS-enabled system design, providing both rigorous symmetry-resolved DoF accounting and actionable insights for excitation, surface programmability, and measurement strategies under practical architectural constraints. Full article

Ice clouds play a significant role in the Earth’s radiation balance due to their unique microphysical and radiative properties, which vary with formation mechanisms and regions and influence the local energy budget. In this study, six years of Ka-band Zenith Radar (KAZR) observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and the Southern Great Plains (SGP) sites, combined with the Fu–Liou radiative transfer model, were used to examine the macrophysical and microphysical properties of ice clouds, their radiative effects, and contributions to the surface energy budget. The results show that the frequency of ice cloud occurrence at SACOL is 40%, significantly higher than the 27% observed at SGP. At both sites, ice cloud altitudes exhibit an increasing trend in the context of recent warming, with a more pronounced increase at SGP. Seasonal variations are evident, with spring characterized by relatively thick and widespread ice clouds, while summer is dominated by high-altitude, optically thin clouds. Ice cloud occurrence peaks at night and decreases during the day at both sites; however, cloud diurnal variations in summer are much greater at SGP than at SACOL. Radiative analysis indicates that longwave radiation-induced warming dominates ice cloud radiative forcing. Net radiative forcing at the top of the atmosphere is 6.08 W/m² at SACOL and 3.06 W/m² at SGP, contributing to atmospheric heating within and beneath cloud layers. At the surface, sensible heat dominates the energy budget at SACOL (over 63%) due to its arid climate, whereas latent heat dominates at SGP (about 67%) because of abundant moisture; and ice clouds have the greatest impact in winter, reducing surface net radiation by 29% at SACOL and 26% at SGP, producing a cooling effect. Full article

Angular Distribution Models (ADMs) are essential for converting observed radiances from satellite sensors to the energy-budget–relevant quantity of irradiance. In preparation for the NASA Libera mission, this study presents a data-driven framework to identify optimal groupings of International Geosphere–Biosphere Programme (IGBP) surface types for Libera’s split-shortwave ADMs, in an effort to minimize the uncertainty associated with radiance-to-irradiance conversions while maintaining operational feasibility. Using data from the Clouds and the Earth’s Radiant Energy System (CERES) Flight Model 5 (FM-5), K-means clustering is applied within angular bins to capture viewing-geometry-dependent radiometric behavior. These angular clustering solutions are then assessed via hierarchical consensus clustering to derive consistent surface groups. The analysis suggests seven surface groups (K = 7) optimize the surface clustering space. The resulting classifications are broadly consistent with historical CERES–TRMM ADM surface definitions, preserving radiometrically distinct surfaces such as water bodies and snowy surfaces while highlighting opportunities to consolidate vegetative IGBP surface classes. This study provides an objective and physically grounded basis for defining Libera ADM surface groups, ensuring a robust balance between model accuracy and operational simplicity. Full article

Low clouds significantly influence Earth’s radiation budget, but their climate feedback remains highly uncertain due to complex interactions with meteorological conditions across spatial and temporal scales. The cloud controlling factor framework is widely used to link meteorological variables with cloud properties. However, most studies assume a static, linear relationship, potentially obscuring the timescale-dependent responses. In this study, we apply the Ensemble Empirical Mode Decomposition method to ISCCP-H cloud observations and ERA5 data (1987–2016) to isolate low cloud amount across multiple intrinsic timescales and trends over global land and ocean. The trends show a nonlinear increase in stratocumulus (Sc) and a significant nonlinear decline in cumulus (Cu), while stratus (St) exhibits weaker trends. We categorize timescales short (≤1 year) for annual variations, medium (1–8 years) for interannual variability such as ENSO, and long (>8 years) for decadal and longer-term climate changes. It is found that Sc and Cu over land are primarily influenced by near-surface heating, while sea surface temperature and surface sensible heat flux (SHF) dominate over ocean at short timescales. SHF becomes dominant over land at medium timescales, largely reflecting ENSO-related induced surface anomalies. At long timescales, atmospheric stability and wind speed influence continental clouds, while SHF remains dominant over ocean. Trend components reveal that Sc and Cu are most sensitive to temperature changes, whereas St responds to mid-level humidity over ocean and SHF over land. These findings underscore the importance of timescale-dependent cloud–meteorology relationships to improve cloud parameterizations and reduce climate projection uncertainties. Overall, our results demonstrate that low cloud variability and trends cannot be explained by a single linear mechanism but instead arise from distinct meteorological controls that change across timescales, cloud types, and surface regimes. 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

The surface albedo, $\alpha$, is one of the key climate parameters since it regulates the shortwave radiation absorbed by the Earth’s surface. An accurate determination of the albedo is crucial in the polar regions due to its variations associated with climate change and its role in the strong feedback mechanisms. In this work, satellite and in situ measurements of broadband surface albedo at the Thule High Arctic Atmospheric Observatory (THAAO) on the northwestern coast of Greenland (76.5°N, 68.8°W) are compared. Measurements of surface albedo were started at THAAO in 2016. They show a large variability mainly in the transition seasons, suggesting that THAAO is a very interesting site for verifying the satellite capabilities in challenging conditions. The comparison of daily ground-based and MODIS-derived albedo covers the period July 2016–October 2024. The analysis has been conducted for all-sky and cloud-free conditions. The mean bias and mean squared difference between the two datasets are −0.02 and 0.09, respectively, for all sky conditions and −0.03 and 0.06 for cloud-free conditions. Very good agreement is found in summer in snow-free conditions, when the mean albedo is 0.17 in both datasets under cloud-free conditions. On the contrary, the capability to determine the surface albedo from space is largely reduced in the transition seasons, when significant differences between ground- and satellite-based albedo estimates are found. Differences for all-sky conditions may be as large as 0.3 in spring and autumn. These maximum differences are significantly reduced for cloud-free conditions, although a negative bias of MODIS data with respect to measurements at THAAO is generally found in spring. The combined analysis of the albedo, cloudiness, air temperature, and precipitation characteristics during two periods in 2023 and 2024 shows that, although satellite observations provide a reasonable picture of the long-term albedo evolution, they are not capable of following fast changes in albedo values induced by precipitation of snow/rain or temperature variations. Moreover, as expected, cloudiness plays a large role in affecting the satellite capabilities. The use of MODIS albedo data with the best value of the quality assurance flag (equal to 0) is recommended for studies aimed at determining the daily evolution of the surface radiation and energy budget. Full article

Total column water vapor (TCWV) is essential for assessing Earth’s radiation budget and hydrological cycle and plays a crucial role in accurate Land Surface Temperature (LST) retrieval from thermal infrared (TIR) imagery. Although TCWV is commonly estimated using near-infrared or microwave observations, TIR-based methods offer an efficient alternative; however, their long-term validation remains limited. This study evaluates TCWV retrieval from Landsat 8/9 Thermal Infrared Sensor (TIRS) using an updated version of the Modified Split-Window Covariance-Variance Ratio (MSWCVR) method, implemented on the Google Earth Engine platform, across Europe. Validation is conducted using AERONET sun photometer measurements (2013–2024) and GPS-based TCWV estimates enhanced with meteorological inputs (2020). Retrieval accuracy is evaluated analyzed in relation to seasonal variations, surface characteristics (e.g., land cover, altitude) and background climate. Results demonstrate robust performance of the TIR-based method, with an average Mean Absolute Error (MAE) of 0.6 gr/cm² across stations and datasets, supporting its applicability for LST retrieval and broader environmental monitoring applications. Full article