Search Results (376)

To comprehensively investigate the aerosol optical properties and vertical structures over the northeastern Tibetan Plateau (TP), a field campaign was conducted from January to August 2023 in the Hainan Tibetan Autonomous Prefecture. Ground-based sunphotometer measurements yielded a mean aerosol optical depth (AOD) of 0.18 and an Ångström exponent (AE) of 1.20 over the study period. The lowest AE, observed in April alongside the highest aerosol loading, suggests a predominance of dust aerosols during this period. This finding is further supported by the elevated vertical extinction profiles derived from LiDAR measurements, indicating long-range transboundary transport of dust aerosols from northern desert regions. Ground-based AOD measurements were used to validate satellite-derived MODIS retrievals and the assimilated MERRA-2 reanalysis product. Among the aerosol types examined, dust aerosols exhibited the highest accuracy in both AOD and AE validation. MERRA-2 was found to systematically underestimate AOD by 22% and AE by 35%. Nevertheless, due to its tighter expected error envelope, lower overall errors, and superior temporal continuity and spatial coverage, MERRA-2 remains a reliable data source for subsequent analyses. A long-term analysis spanning 2006 to 2025 identifies 2011 as a turning point, after which AOD declined at a rate of 0.0022 per year. This sustained reduction highlights the effectiveness of China’s air pollution prevention and control policies. Collectively, these findings provide essential insights for refining satellite retrieval algorithms and aerosol–climate models over the TP. Full article

Evaporation is a key component of air–sea coupling processes and understanding the uncertainty in its estimation is essential for climate research and prediction. Based on four widely used datasets (OAFlux, NCEP2, MERRA2 and ERA5), this study systematically analyzes the seasonal evolution of inter-dataset uncertainty in evaporation variation over the tropical Indian Ocean using an evaporation decomposition method. Our main contribution is to show that evaporation variation uncertainty is not seasonally uniform but organized into distinct seasonal regimes with different dominant controlling factors and sensitivity structures. The results reveal significant seasonal dependence of evaporation variation uncertainty: the uncertainty is relatively small in boreal spring and autumn but larger in boreal summer and winter. The evaporation variation is primarily controlled by the relative humidity term (RH^*) in boreal summer and by the wind speed term (U^*) in other seasons. More importantly, the sources of uncertainty differ fundamentally between seasons: the large uncertainty of RH^* in boreal summer mainly originates from the high and variable sensitivity of evaporation to relative humidity, whereas the large uncertainty of U^* in boreal winter primarily stems from substantial inter-dataset discrepancies in wind speed data itself. These findings reveal that evaporation variation uncertainty arises from both input data discrepancies and the nonlinear sensitivity of evaporation processes, with their relative contributions varying seasonally. This study provides a physically based explanation for evaporation uncertainty and offers a useful basis for evaporation dataset selection and climate model evaluation. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM_2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM_2.5 variability, and precipitation is often expected to reduce particle loads through wet removal. However, humid and wet conditions may coincide with elevated PM_2.5 under specific atmospheric and compositional conditions. Here, we investigate long-term relationships between precipitation regimes and PM_2.5 concentrations in the Metropolitan Region of Belém (Eastern Amazonia) over the period 1980–2024. We combined PM_2.5 from the MERRA-2 reanalysis (including a bias-corrected product) with in situ precipitation records, and classified precipitation conditions using the Standardized Precipitation Index (SPI). We find statistically significant positive long-term tendencies in both precipitation and PM_2.5. Stratified analyses show that PM_2.5 concentrations are significantly higher under wet conditions, with a weak but significant positive relationship between SPI and PM_2.5 (r = 0.23 for the full period; r = 0.24 for the wet class, p-value < 0.01). These findings indicate that increased precipitation in a strong humid tropical urban environment does not necessarily lead to improved air quality. Instead, wet conditions may favor processes such as hygroscopic growth and secondary aerosol formation, contributing to higher PM_2.5 concentrations on a monthly scale. Overall, this study highlights the importance of considering precipitation regimes and associated atmospheric processes when assessing air quality in tropical urban environments. Full article

We present the performance of a middle atmosphere Rayleigh lidar system that employs three receiver channels. We characterize the biases in the density and temperature profiles retrieved from each of the receiver channels as well as the combined receiver signal. We associate these biases with pulse pile-up, gain switching, and variations in the detector gain due to signal amplitude. We use a top-down temperature convergence methodology to determine the upper altitude up to which the signals should be compensated for the variations in detector gain. We find that the channels have warm biases in their temperatures of 2–8 K at 40 km. These biases decrease to between 1 K and 3 K at 60 km. Uncertainty estimates derived from the photon-counting statistics indicate temperature uncertainties on the order of 2–5 K in the 40–70 km region, which are consistent with the observed level of inter-channel variability after correction. A comparison with MERRA-2 reanalysis indicates an overall agreement in temperatures and differences that are consistent with the comparisons between the Rayleigh lidars and MERRA-02 at other sites. These results demonstrate that the proposed approach proves reliable for processing the multi-channel Rayleigh lidar data, particularly for systems employing more than two detection channels, and improves the fidelity and accuracy of the temperature retrievals. Full article

The sugarcane industry in South Africa is ranked among the top 15 producers worldwide and plays a significant role in supporting the nation’s socioeconomic development, producing approximately 2.3 million tons annually. Harvesting is largely labour-intensive and commonly involves the pre-harvest burning of sugarcane. This widespread practice is associated with (a) local air quality deterioration driven by pollutants such as carbon monoxide (CO), black carbon (BC), and sulphur dioxide (SO₂) and (b) adverse public health outcomes, including respiratory and cardiovascular diseases. This study aims to assess the air quality across KwaZulu-Natal and compare inland and coastal sugarcane-growing regions during the May–August 2023 harvest season. The CO and SO₂ concentrations are obtained from Sentinel-5P, while the BC data are sourced from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The Air Quality Index (AQI) is calculated using the CO, SO₂, PM_2.5, and NO₂ data from the Copernicus Atmosphere Monitoring Service (CAMS). The findings consistently indicate higher pollutant concentrations in inland regions, suggesting more concentrated burning activities and lower atmospheric dispersion relative to coastal areas. Overall, the results highlight the greater prevalence of poor air quality in inland sugarcane regions compared with coastal zones. Full article

This study presents an intercomparison of planetary boundary layer height (PBLH) estimates derived from three distinct approaches: the Morphological Image Processing Approach (MIPA) algorithm applied to ground-based lidar measurements, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) and Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis model outputs, and radiosonde (RS) observations, this latter being taken as reference. The intercomparison was conducted during three measurement episodes, encompassing a total of 153 h (6 days), as part of the Boundary Layer Extensive Campaign with muLti-instrumentaL Analysis (BELLA), carried out in spring and early summer 2024 at the CNR-IMAA Atmospheric Observatory (CIAO) in southern Italy (40.60N, 15.72E). The study provides insights into the performance and reliability of these PBLH estimation approaches under diverse atmospheric scenarios. Visual and statistical analyses of selected case studies indicate that MIPA often tracked the aerosol layering structure and diurnal PBLH evolution more closely than ERA5 and MERRA-2, particularly during convective growth and evening transitions. On the other hand, it is found that ERA5 provides more accurate estimates of the nighttime PBLH, where MIPA shows poor nighttime estimation capabilities. Quantitative comparison against radiosonde data reveals that MIPA reaches a weighted root mean square error ($RMS{E}_w$) of $380\pm 41$ m with a coefficient of determination ($R^2$) of $0.68\pm 0.16$, while ERA5 shows an $RMS{E}_w$ of $292\pm 72$ m and an $R^2$ of $0.81\pm 0.11$; and MERRA-2 shows an $RMS{E}_w$ of $631\pm 124$ m and an $R^2$ of $0.34\pm 0.21$. By combining MIPA daytime and ERA5 nighttime PBLH, the overall results are improved, obtaining an $R^2=0.86\pm 0.08$ and an $RMS{E}_w$ of $213\pm 40$ m. This intercomparison highlights the strengths and limitations of each method and demonstrates the benefits of combining complementary PBLH retrieval techniques. The findings contribute to refining boundary layer monitoring methodologies and provide guidance for operational atmospheric observation networks. Full article

Day-to-day temperature variability (DTD) significantly affects human health and ecosystems, yet its representation in major reanalysis datasets has not been systematically evaluated. This study assesses the ability of four widely used reanalysis datasets, namely ERA-Interim, ERA5, JRA-55, and MERRA-2, against station observations to capture DTD’s spatial and temporal characteristics. All four datasets broadly reproduce the observed spatial pattern of DTD but generally underestimate its magnitude globally, except over eastern China. JRA-55 performs better at low-to-mid latitudes, while other datasets show closer agreement with observations at high latitudes. Regarding long-term trends, the reanalyses generally capture the observed pattern of decreasing DTD at high latitudes and increasing DTD at mid-low latitudes, but they show trends opposite to observations in summer over Eurasia, the low latitudes, and the Southern Hemisphere. Skill is highest in winter and lowest in summer, with ERA5 and ERA-Interim performing the best overall. Using ERA5 for further analysis, it is suggested that the recent weakening in global extreme DTD intensity is offset by an increase in extreme-event frequency, with both exhibiting substantial regional and seasonal variability. These findings advance understanding of short-term temperature variability and provide guidance for risk assessment, early warning, and mitigation. Full article

Numerical simulations of (1) two aerosol types such as organic carbon (i.e., spherical) and dust (i.e., non-spherical) particles, and (2) their mixtures are carried out. Optical and microphysical parameters of these aerosols in our simulations are provided by MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2). The inversion routine is performed with TiARA (Tikhonov Advanced Regularization Algorithm) using the Lorenz–Mie (i.e., spherical) light-scattering model in unsupervised and automated, i.e., autonomous mode. The results of our numerical simulations show that the accuracy of the inversion results for the aerosol mixtures from synthetic optical data perturbed by ±10% random error is comparable to the accuracy observed for the inversion results of the “pure” spherical particles. In particular, the retrieval uncertainties of effective radius, and number, surface-area, and volume concentrations of these mixtures are ±30%, ±10%, between −50% and +100% and ±30%, respectively. However, we need to apply a modified version of the gradient correlation method (GCM) to stabilize the inversion results. The results of this study will form the baseline for future work, where we plan to apply TiARA to optical data products obtained from real lidar observations in the framework of the SCC (Single Calculus Chain) of EARLINET (European Aerosol Research Lidar Network). Full article

Through a comprehensive analysis of urban vegetation summer seasonal and interannual patterns in the Bucharest metropolis in Romania, this study explored the response of urban vegetation to heat waves’ (HWs) impact in relation to multi-climatic parameters variability from a spatiotemporal perspective during 2000–2024, with a focus on summer HWs periods (June–August), and particularly on the hottest summer 2024. Statistical correlation, regression, and linear trend analysis were applied to multiple long-term MODIS Terra/Aqua and MERRA-2 Reanalysis satellite and in situ climate data time series. To support the decline in urban vegetation during summer hot periods due to heat stress, this study found strong negative correlations between vegetation biophysical observables and urban thermal environment parameters at both the city center and metropolitan scales. In contrast, during the autumn–winter–spring seasons (September–May), positive correlations have been identified between vegetation biophysical observables and a few climate parameters, indicating their beneficial role for vegetation growth from 2000 to 2024. The recorded decreasing trend in evapotranspiration from 2000 to 2024 during summer HW periods in Bucharest’s metropolis was associated with a reduction in the evaporative cooling capacity of urban vegetation at high air temperatures, diminishing vegetation’s key function in mitigating urban heat stress. The slight decline in land surface albedo in the Bucharest metropolis due to increased urbanization may explain the enhanced air temperatures and the severity of HWs, as evidenced by 41 heat wave events (HWEs) and 222 heat wave days (HWDs) recorded during the summer (June–August) period from 2000 to 2024. During the severe 2024 summer heat wave episodes in the south-eastern part of Romania, a rise of 5.89 °C in the mean annual land surface temperature and a rise of 6.76 °C in the mean annual air temperature in the Bucharest metropolitan region were observed. The findings of this study provide a refined understanding of heat stress’s impact on urban vegetation, essential for developing effective mitigation strategies and prioritizing interventions in vulnerable areas. Full article

We introduce the Semiparametric Time Series Regression with Mixed Additive Spline Fourier (STSR–MASF) model as an innovative approach for analyzing time series data with complex patterns. The model combines the flexibility of the spline estimator in capturing nonlinear variations across specific sub-intervals and the strength of the Fourier series in representing periodically recurring patterns. Within the semiparametric regression framework, STSR–MASF integrates both linear parametric and nonparametric components, with the optimal number of knots and oscillations determined using the Generalized Cross-Validation (GCV) criterion. The model was trained and tested using Earth’s skin temperature data from the National Aeronautics and Space Administration (NASA) MERRA-2 for East Kalimantan, Indonesia, a tropical rainforest region. The results demonstrate that the STSR–MASF model provides more accurate estimations and forecasts compared to six previous methods proposed in earlier studies with highly accurate predictions. This innovation not only offers methodological advancements in nonlinear time series modeling, but also contributes practical insights into understanding variations in Earth’s skin temperature in tropical regions, supporting broader efforts toward global climate change mitigation. Full article

The COVID-19 pandemic led to an unprecedented slowdown in global economic and transportation activities, offering a unique opportunity to assess the relationship between human activity and atmospheric pollution. This study analyzes global variations in major air pollutants and meteorological conditions during the pandemic period using multi-satellite and reanalysis datasets. Nitrogen dioxide (NO₂) data were obtained from the OMI sensor aboard NASA’s Aura satellite, while carbon monoxide (CO) observations were taken from the MOPITT instrument on Terra. Reanalysis products from MERRA-2 were used to assess CO, sulfur dioxide (SO₂), black carbon (BC), organic carbon (OC), and key meteorological variables, including temperature, precipitation, evaporation, wind speed, and direction. Average concentrations of pollutants for April, May, and June 2020, representing the lockdown phase, were compared with the average values of the same months during 2017–2019, representing pre-pandemic conditions. The difference between these multi-year means was used to quantify spatial changes in pollutant levels. Results reveal widespread reductions in NO₂, CO, SO₂, and BC concentrations across major industrial and urban regions worldwide, consistent with decreased anthropogenic activity during lockdowns. Meteorological analysis indicates that the observed reductions were not primarily driven by short-term weather variability, confirming that the declines are largely attributable to reduced emissions. Unlike most previous studies, which examined local or regional air-quality changes, this work provides a consistent global-scale assessment using harmonized multi-sensor datasets and uniform temporal baselines. These findings highlight the strong influence of human activities on atmospheric composition and demonstrate how large-scale behavioral and economic shifts can rapidly alter air quality on a global scale. The results also provide valuable baseline information for understanding emission–climate interactions and for guiding post-pandemic strategies aimed at sustainable air-quality management. Full article

Evapotranspiration (ET) data products has greatly facilitated the hydrological research in complex basins, and various ET datasets have been produced and applied. The applicability and reliability of ET dataset is significant for regional studies. Therefore, this study compared ET datasets from multisource remote sensing (GLEAM, MOD16, GLASS, PML-V2, Han, Chen and Ma), machine learning (Jung) and reanalysis products (ERA5-Land, MERRA2) for the Yarlung Zangbo River basin (YZB). ET was estimated using the terrestrial water balance (TWB) and was taken as baseline for comparisons of different ET datasets in terms of spatial distribution and temporal variation. Results indicate that (1) the TWB-based ET estimates are rational with acceptable uncertainties; (2) the multi-source ET datasets exhibit good correlations with TWB-ET across the entire basin (r = 0.78–0.90) in term of annual variation, with GLEAM-ET performing the best (r = 0.88, RMSE = 14.24 mm, Rbias = 18.55%); (3) Spatially, PML-ET and Ma-ET show higher consistency with TWB-ET, and temporally, MOD16-ET and GLASS-ET better capture the changing trend; (4) A comprehensive evaluation using the linear weighted method reveals that GLASS-ET and GLEAM-ET perform relatively well in all aspects and are reliable datasets for ET research in the YZB. These findings provide a scientific basis for ET estimation and data selection in the YZB, offering important references for ET analysis and hydrological research. Full article

The Arctic is warming rapidly, with atmospheric rivers (ARs) amplifying ice melt, extreme precipitation, and abrupt temperature shifts. Detecting ARs in the Arctic remains challenging, because AR detection algorithms designed for mid-latitudes perform poorly in polar regions. This study introduces a regional deep learning (DL) image segmentation model for Arctic AR detection, leveraging large-ensemble (LE) climate simulations. We analyse historical simulations from the Climate Change in the Arctic and North Atlantic Region and Impacts on the UK (CANARI) project, which provides a large, internally consistent sample of AR events at 6-hourly resolution and enables a close comparison of AR climatology across model and reanalysis data. A polar-specific, rule-based AR detection algorithm was adapted to label ARs in simulated data using multiple thresholds, providing training data for the segmentation model and supporting sensitivity analyses. U-Net-based models are trained on integrated water vapour transport, total column water vapour, and 850 hPa wind speed fields. We quantify how AR identification depends on threshold choices in the rule-based algorithm and show how these propagate to the U-Net-based models. This study represents the first use of the CANARI-LE for Arctic AR detection and introduces a unified framework combining rule-based and DL methods to evaluate model sensitivity and detection robustness. Our results demonstrate that DL segmentation achieves robust skill and eliminates the need for threshold tuning, providing a consistent and transferable framework for detecting Arctic ARs. This unified approach advances high-latitude moisture transport assessment and supports improved evaluation of Arctic extremes under climate change. Full article