Search Results (702)

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts. Full article

By utilizing daily precipitation data from 400 meteorological stations in the Yellow River Basin (YRB) of China, atmospheric and oceanic reanalysis data, this study investigates the climatological characteristics, leading modes, and relationships with atmospheric circulation and sea surface temperature (SST) of summer extreme precipitation in the YRB from 1981 to 2020 through the extreme precipitation metrics and Empirical Orthogonal Function (EOF) analysis. The results indicate that both the frequency and intensity of extreme precipitation exhibit an eastward and southward increasing pattern in terms of climate state, with regions of higher precipitation showing greater interannual variability. When precipitation in the YRB exhibits a spatially coherent enhancement pattern, high latitudes exhibits an Eurasian teleconnection wave train that facilitates the southward movement of cold air. Concurrently, the northward extension of the Western Pacific subtropical high (WPSH) enhances moisture transport from low latitudes to the YRB, against the backdrop of a transitioning SST pattern from El Niño to La Niña. When precipitation in the YRB shows a “south-increase, north-decrease” dipole pattern, the southward-shifted Ural high and westward-extended WPSH converge cold air and moist in the southern YRB region, with no dominant SST drivers identified. Full article

This study explores the water harvesting systems of mgouds in southern Tunisia and boqueras in southeastern Spain to understand their adaptation to semi-arid conditions and geomorphic contexts. These systems use ephemeral water through medieval-origin infrastructures to increase the water supply to rainfed crops. The hypothesis is that the diversity of these systems stems from environmental rather than cultural factors. By employing a qualitative–analytical approach, this study compares concentrated runoff diversion systems to investigate the use of boqueras/mgouds in terraces and glacis in the arid and semi-arid areas of Tunisia and the southeastern Iberian Peninsula. The research involved performing detailed geomorphological and climatological analyses and comparing structural complexities and water management strategies across different regions. The results indicate significant variability in system size and complexity. Tunisian mgouds are typically simpler and more individualised, while Spanish boqueras are larger and more complex due to more frequent and intense torrential rainfall. No common patterns were identified between the two regions. This study reveals that both types of systems reflect sophisticated adaptations to manage water scarcity and mitigate the impacts of intense rainfall, with geomorphic and climatic factors playing a decisive role. The primary conclusion is that the design and functionality of these water systems are predominantly influenced by environmental conditions rather than cultural factors. This research provides insights for developing sustainable water management strategies in other semi-arid regions. Full article

Accurate projection of future climate trends in arid regions critically depends on reliable precipitation simulations. However, most Coupled Model Intercomparison Project Phase 6 (CMIP6) models exhibit systematic overestimations of precipitation in Northwest China, a bias that undermines the credibility of climate projections for this vulnerable region. This persistent bias likely stems from the omission of key physical processes in traditional models. In this study, we incorporate a dust–ice-cloud interaction scheme into the Community Atmosphere Model version 5 (CAM5) model to investigate its role in regulating precipitation over dust-rich arid regions. This physical mechanism, which is rarely included in conventional models, is particularly relevant for Northwest China where dust aerosols are abundant. Our results show that accounting for dust-induced ice nucleation leads to a significant reduction in total precipitation, especially in the convective component, thereby alleviating the longstanding wet bias in the region. These findings underscore the critical importance of dust–ice-cloud interactions in simulating precipitation in arid environments. To improve the accuracy of future climate projections in Northwest China, climate models must incorporate realistic representations of dust-related microphysical processes. Full article

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

This study systematically reviews the development and application of remote sensing technology in monitoring and evaluating urban heat island (UHI) effects. The urban heat island effect, characterized by significantly higher temperatures in urban areas compared to surrounding rural regions, has become a widespread environmental issue globally, with impacts spanning public health, energy consumption, ecosystems, and social equity. The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. At the methodological level, the study systematically evaluates core algorithms for land surface temperature extraction and heat island intensity calculation, compares innovative developments in multi-source remote sensing data integration and fusion techniques, and establishes a framework for accuracy assessment and validation. Through analyzing the heat island differences between metropolitan areas and small–medium cities, the relationship between urban morphology and thermal environment, and regional specificity and global universal patterns, this study revealed that the proportion of impervious surfaces is the primary driving factor of heat island intensity while simultaneously finding that vegetation cover exhibits significant cooling effects under suitable conditions, with the intensity varying significantly depending on vegetation types, management levels, and climatic conditions. In terms of applications, the paper elaborates on the practical value of remote sensing technology in identifying thermally vulnerable areas, green space planning, urban material optimization, and decision support for UHI mitigation. Finally, in light of current technological limitations, the study anticipates the application prospects of artificial intelligence and emerging analytical methods, as well as trends in urban heat island monitoring against the backdrop of climate change. The research findings not only enrich the theoretical framework of urban climatology but also provide a scientific basis for urban planners, contributing to the development of more effective UHI mitigation strategies and enhanced urban climate resilience. Full article

Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover observations. Leveraging a high-resolution (500 m) daily gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset combined with reanalysis climate datasets, we systematically quantified SCP dynamics and identified the dominant controls during the 2000–2022 hydrological years using trend analysis and ridge regression. Our results reveal a significant divergence in SCP parameters: snow end dates (D_e) advanced markedly across the entire plateau (0.29 days yr⁻¹, p < 0.01), accounting for 90.39% of SCP anomalies. In contrast, snow onset date (D_o) exhibited unnoticeable changes, explaining 9.58% of SCP changes. Attribution analysis demonstrates that 47.72% of D_e variability stems from increased net shortwave radiation (+0.38 Wm⁻² yr⁻¹) and rising temperatures (+0.06 °C yr⁻¹) during the melting season, with net shortwave radiation exerting stronger control (R² = 0.73) than temperature (R² = 0.63). This study establishes the first continuous, high-resolution SCP climatology for the MP, providing mechanistic insights into cryosphere–atmosphere interactions that inform adaptive water resource strategies for climate-vulnerable arid ecosystems in this region. Full article

This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO₂ data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. The proposed approach combines geostatistical interpolation with nonlinear modeling by integrating RK with ML models—specifically comparing gradient boosting regression (GBR), random forest (RF), and K-nearest neighbors (KNN)—to determine the most suitable auxiliary predictor. This structure enables the framework to capture both spatial autocorrelation and complex relationships between NO₂ concentrations and environmental drivers. Model performance was evaluated using the coefficient of determination (r²), computed against observed TROPOMI NO₂ column values filtered by quality assurance criteria. GBR achieved the highest validation r² values of 0.83 for January and February, while RF yielded 0.82 and 0.79 in January and December, respectively. These results demonstrate the model’s robustness in capturing intra-seasonal patterns and nonlinear trends in NO₂ distribution. In contrast, models using only static land cover inputs performed poorly (r² < 0.58), emphasizing the limited predictive capacity of such variables in isolation. Interpretability analysis using the SHapley Additive exPlanations (SHAP) method revealed temperature as the most influential meteorological driver of NO₂ variation, particularly during winter, while forest cover consistently emerged as a key land-use factor mitigating NO₂ levels through dry deposition. By integrating dynamic meteorological variables and static land cover features, the hybrid RK–ML framework enhances the spatial and temporal completeness of satellite-derived air quality datasets. As the first RK–ML application for TROPOMI data in Taiwan, this study establishes a regional benchmark and offers a transferable methodology for satellite data imputation. Future research should explore ensemble-based RK variants, incorporate real-time auxiliary data, and assess transferability across diverse geographic and climatological contexts. Full article

In the actual context of climate change and increased multiannual climate variability, rainfall erosivity is one important topic linking geomorphological and climatological studies. Rainfall modeling is specific for a large part of the Romanian territory, and the estimation of rainfall erosivity is very important because it supports a better management of the arable land. The study is spatially focused on the extra-Carpathian region of Moldova, located in the northeastern part of Romania. Two rainfall erosivity indices were used: Fournier Index and Modified Fournier Index. To complete this analysis, we also used hourly data from two meteorological stations located over the most critical area of soil erosion in Romania (Cârja and Mădârjac). Our results reconfirm the extension of the critical season for soil erosion from May to July over the analyzed region, with its peak clearly defined during June. Based on the maximum hourly rainfall intensities, the synoptic aspects which led to the fall of significant amounts of precipitation in a short time interval were discussed. This analysis outlines the prevalent role of convective systems during summer, developed either within westerly flow or blocking conditions, seconded by the action of deep Mediterranean cyclones in late spring or early autumn. The results could be helpful in a very necessary attempt to develop and implement arable land management policies aiming to limit soil erosion in northeastern Romania, which is very necessary for the next decades when climate change is expected to increase this soil degradation process. Full article

Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of statistical downscaling models across different scales warrants further investigation. In this study, a stepwise downscaling method is proposed, employing multiple linear regression (MLR), Cubist regression tree, random forest (RF), and extreme gradient boosting (XGBoost) models to downscale the 3-hourly ERA5-Land reanalysis air temperature data at the resolution of 0.1° to that of 30 m. A comparative analysis was performed to evaluate the accuracy of downscaled ERA5-Land air temperature results obtained from the stepwise and the direct downscaling methods, based on observed air temperatures at meteorological stations and the spatial distribution of air temperature estimated by a remote sensing method. In addition, variations in the importance of driving factors across different spatial scales were examined. The results indicate that the stepwise downscaling method exhibits higher accuracy than the direct downscaling method, with a more pronounced performance improvement in winter. Compared with the direct downscaling method, the RMSE value of the MLR, Cubist, RF, and XGBoost models under the stepwise downscaling method were reduced by 0.48 K, 0.38 K, 0.48 K, and 0.50 K, respectively, at meteorological station locations. In terms of spatial distribution, the stepwise downscaling results demonstrate greater consistency with the estimated spatial distribution of air temperature, and it can capture air temperature variations across different land surface types more accurately. Furthermore, the stepwise downscaling method is capable of effectively capturing changes in the importance of driving factors across different spatial scales. These results generally suggest that the stepwise downscaling method can significantly improve the accuracy of air temperature downscaled from reanalysis data by adopting multiple resolutions as the intermediate downscaling process. Full article

Blocking anticyclones are important atmospheric phenomena generally associated with extreme weather (e.g., droughts and cold air surges). Blockings also constitute large-scale indicators of climate change. The study of blockings in the Southern Hemisphere (SH) has been traditionally carried out utilizing reanalysis products. This paper is aimed at presenting an updated, comprehensive climatology of blockings in the SH as extracted from the ERA5 and the NCEP/NCAR reanalysis datasets in the 1940–2022 and 1948–2022 periods, respectively. Blockings were located by means of a unidimensional index at 500 hPa. The results were stratified by season, longitude, region, persistence, and intensity, and the climatology from both datasets was compared. The primary location of blockings was close to the Date Line in every season. Additionally, depending on the season, up to fourth-rank maxima could be located. Generally, the secondary maxima were found in the south Atlantic; lower-order maxima were located in the south-eastern Pacific, west of South America, and in the south-western Indian Ocean east of South Africa. The most intense blockings were concentrated in the Pacific and in the south Atlantic in both datasets, and they were also located in the Indian Ocean, but in the ERA5 reanalysis only. The longest-lived blockings occurred in the south Pacific and in the south Atlantic during southern winter. Full article

This study presents a global assessment of the climatological seasonal variability of river discharge into the oceans, based on an expanded dataset comprising 958 gauging stations across 136 countries. Monthly discharges were compiled for 145 major rivers and tributaries, with a focus on improving the accuracy and spatial coverage of global freshwater flux estimates. Compared to previous datasets, this updated compilation includes a broader set of rivers, explicitly integrates tributary inflows, and quantifies both the absolute and relative seasonal amplitudes of discharge variability. The results reveal substantial differences among ocean basins. The Atlantic Ocean, although receiving the highest total runoff, shows relatively weak seasonal variability, with a coefficient of variation of CV = 12.6% due to asynchronous peak discharge from its major rivers (Amazon, Congo, Orinoco). In contrast, the Indian Ocean exhibits the most pronounced seasonal cycle (CV = 88.3%), driven by monsoonal rivers. The Pacific Ocean shows intermediate variability (CV = 62.1%), influenced by a combination of monsoon rains and snowmelt. At the river scale, Orinoco and Changjiang display high seasonal amplitudes, exceeding 89% of their mean flows, whereas more stable regimes are found in equatorial and temperate rivers like the Amazon and Saint Lawrence. In addition, the critical role of tributaries in altering discharge magnitude and seasonal variability is well established. This study provides high-resolution monthly discharge climatologies at global and basin scales, enhancing freshwater forcing in OGCMs. By improving the representation of land–ocean exchanges, it enables more accurate simulations of salinity, circulation, biogeochemical cycles, and climate-sensitive processes in coastal and open-ocean regions. Full article

Evapotranspiration (ET) is a critical component of the hydrological cycle. The eddy covariance data at 40 flux stations in different climatic regions in China were used to evaluate the accuracy of five reanalysis actual ET datasets (ERA5, ERA5-LAND, GLDAS-2.1, MERRA-2, TerraClimate) and four remote sensing estimation ET datasets (ETMonitor, GLEAM4.2a, PML_V2, SiTHv2), which are widely used by the hydrometeorological and climatological communities, in terms of the root mean square error, Pearson correlation coefficient, mean absolute deviation, and Taylor skill score. The results show that remote sensing products outperform reanalysis datasets. Among them, ETMonitor has the highest accuracy, followed by PML_V2 and SiTHv2. TerraClimate and MERRA-2 have the least agreement with the observations at flux sites across nearly all evaluation metrics. All products can capture the seasonality of ET in China, but underestimate ET in northwest China and overestimate ET in southern China throughout the year. We tried to merge three optimal data products (ETMonitor, PML_V2, and SiTHv2) using the triple collocation analysis method to improve the ET estimation, but the results showed that the improvement by the data fusion approach is marginal. The estimation of the multi-year average evapotranspiration during the period from 2001 to 2020 ranges from 397.8 mm/year (GLEAM4.2a) to 504.8 mm/year (ERA5-Land) in China. From 2001 to 2020, annual evapotranspiration in China generally increased, but with varying rates across different products. MERRA-2 showed the largest annual increase rate (3.71 mm/year), while SiTHv2 had the smallest (0.17 mm/year). There are no significant changes in the seasonality of ET by most ET products from 2001 to 2020, except for PML_V2 and SiTHv2, which indicate an increase in seasonality in terms of the evapotranspiration concentration index. This ET intercomparison addresses a key knowledge gap in terrestrial water flux quantification, aiding climate and hydrological research. Full article

A seasonal climatology of the diurnal cycle of precipitation (DCP) and the assessment of its observed trend since the beginning of the 21st century using the IMERG product are performed for South America (SA). Its high spatial–temporal resolution ($\mathsf{\Delta }x=0.1^{\circ }$, $\mathsf{\Delta }t=0.5$ h) enables the examination of the fine-scale features of the DCP associated with the complex physical characteristics of SA. Using 20 years of precipitation rate data, diurnal and semi-diurnal scale processes are analyzed through harmonic analysis. Diurnal metrics—including the hourly mean precipitation rate, normalized amplitude, and phase—are employed to quantify the DCP. The results indicate that large-scale mechanisms, such as the South American Monsoon System (SAMS), seasonally modulate the DCP. These mechanisms in combination with local factors (e.g., land use, topography, and water bodies) influence the timing of peak and intensity of precipitation rates. Cluster analysis identifies regions with homogeneous DCP; however, some distant regions are classified as homogeneous, suggesting that local-scale physical processes triggering precipitation onset operate similarly across these regions (e.g., thermally induced local circulations). The trend analysis of the DCP reveals that, over the past 20 years, the tropical region of SA has undergone changes in the intensity and hourly distribution of this fine-scale climate variability mode. This trend is heterogeneous in space and time and is possibly associated with land-use changes. Full article

Using multi-source precipitation datasets including NASA GPM (IMERG), GPCP, ECMWF ERA5, and station precipitation data from the China Meteorological Administration (CMA), along with ERA5 reanalysis fields for atmospheric circulation analysis, this study investigates the extreme precipitation events during the “Dragon-Boat Precipitation” period from 20 May to 21 June over South China in 2022 using the synoptic diagnostic method. The results indicate that the total precipitation during this period significantly exceeded the climatological average, with multiple large-scale extreme rainfall events characterized by high intensity, extensive coverage, and prolonged duration. The spatial distribution of precipitation exhibited a north-more-south-less pattern, with the maximum rainfall center located in the Nanling Mountains, particularly in the Shaoguan–Qingyuan–Heyuan region of Guangdong Province, where peak precipitation exceeded 1100 mm, and the mean precipitation was approximately 1.7 times the climatology from the GPM data. The average daily precipitation throughout the period was 17.5 mm/day, which was 6 mm/day higher than the climatological mean, while the heaviest rainfall on 13 June reached 39 mm/day above the average, exceeding two standard deviations. The extreme precipitation during the “Dragon-Boat Precipitation” period in 2022 was associated with an anomalous deep East Asian trough, an intensified South Asian High, a stronger-than-usual Western Pacific Subtropical High, an enhanced South Asian monsoon and South China Sea monsoon, and the dominance of a strong Southwesterly Low-Level Jet (SLLJ) over South China. Two major moisture transport pathways were established: one from the Bay of Bengal to South China and another from the South China Sea, with the latter contributing a little higher amount of water vapor transport than the former. The widespread extreme precipitation on 13 June 2022 was triggered by the anomalous atmospheric circulation conditions. In the upper levels, South China was located at the northwestern periphery of the slightly stronger-than-normal Western Pacific Subtropical High, intersecting with the base of a deep trough associated with an anomalous intense Northeast China Cold Vortex (NCCV). At lower levels, the region was positioned along a shear line formed by anomalous southwesterly and northerly winds, where exceptionally strong southwesterly moisture transport, significant moisture convergence, and intense vertical updraft led to the widespread extreme rainfall event on that day. Full article