Search Results (216)

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

Climate variability, intensified by climate change, poses significant challenges to agriculture, affecting crop development and productivity. Integrating seasonal weather forecasts (SWF) into crop growth modelling tools is therefore essential for improving agricultural decision-making. This study assessed the uncertainties of raw (non-bias-corrected) temperature forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 seasonal (seven-month forecasts) to estimate the spring–summer maize, melon, sunflower, and tomato crops cycle from 2013 to 2022 in the Caia Irrigation Scheme, southern Portugal. AgERA5 reanalysis data, after simple bias correction using local weather station data, was used as a reference. The growing degree-day (GDD) approach was applied to estimate the crop cycle duration, which was then validated against ground truth and satellite data. The results show that SWF tend to underestimate maximum temperatures and overestimate minimum temperatures, with these biases partially offsetting to improve mean temperature accuracy. Forecast skill decreased non-linearly with lead time, especially after the second month; however, in some cases, longer lead times outperformed earlier ones. Temperature forecast biases affected GDD-based crop cycle estimates, resulting in a slight underestimation of all crop cycle durations by around a week. Nevertheless, the forecasts captured the overall increasing temperature trend, interannual variability, and anomaly signals, but with marginal added value over climatological data. This study highlights the potential of integrating ground truth and Earth observation data, together with reanalysis data and SWF, into GDD tools to support agricultural decision-making, aiming at enhancing yield and resources management. 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

This study fosters tropical hydroclimatology research by implementing a computational modeling framework based on artificial neural networks and machine learning techniques. We evaluated two models, Multilayer Perceptron (MLP) and Support Vector Machine (SVM), in their ability to simulate 20-year monthly time series (2001–2021) of minimum and maximum river stage in the Itacaiúnas River Basin (BHRI), located in the eastern Brazilian Amazon. The models were configured using explanatory variables spanning meteorological, climatological, and environmental dimensions, ensuring representation of key local and regional hydrological drivers. Both models exhibited robust performance in capturing fluviometric variability, with a comprehensive multimetric statistical evaluation indicating MLP’s superior accuracy over SVM. Notably, the MLP model reproduced the maximum river level during a sequence of extreme hydrological events linked to natural disasters (floods) across BHRI municipalities. These findings underscore the computational model’s potential for refining hydrometeorological products, thus supporting water resource management and decision-making processes in the Amazon region. Full article

This study presents a comprehensive framework for evaluating urban heat resilience, incorporating urban climatology models, their characteristics, and simulation programs. Utilizing the local climate zone (LCZ) classification method, this research explores how urban geomorphology influences the thermal characteristics of the area. This study integrates spatial data at different “levels of detail” (LOD), from the meso- to building scales, emphasizing the significance of detailed LOD 3 models acquired through 3D laser scanning. The results demonstrate the ability of these models to identify urban heat islands (UHIs) and to simulate urban planning scenarios, such as increasing green spaces and optimizing building density, to mitigate the UHI effect. The ST3D 3D model of the city of Split, represented using an LOD 2 object model, is utilized for meso- and local-scale analyses, while LOD 3 models derived from laser scanning provided in-depth insights at the building scale. The case studies included the Faculty of Civil Engineering, Architecture, and Geodesy building on the University of Split campus and the old town hall in the densely built city center. This framework highlights the advantages of integrating GIS and BIM technology with urban climate analyses, offering tools for data-driven decision-making and fostering sustainable, climate-resilient urban planning. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

Urban heat island (UHI) effects pose significant challenges to sustainable urban development, necessitating innovative modeling techniques to optimize urban morphology for thermal resilience. This study integrates the Pix2Pix and CycleGAN architectures to generate high-fidelity urban morphology models aligned with local climate zones (LCZs), enhancing their applicability to urban climate studies. This research focuses on eight major Chinese coastal cities, leveraging a robust dataset of 4712 samples to train the generative models. Quantitative evaluations demonstrated that the integration of CycleGAN with Pix2Pix substantially improved structural fidelity and realism in urban morphology synthesis, achieving a peak Structural Similarity Index Measure (SSIM) of 0.918 and a coefficient of determination (R²) of 0.987. The total adversarial loss in Pix2Pix training stabilized at 0.19 after 811 iterations, ensuring high convergence in urban structure generation. Additionally, CycleGAN-enhanced outputs exhibited a 35% reduction in relative error compared to Pix2Pix-generated images, significantly improving edge preservation and urban feature accuracy. By incorporating LCZ data, the proposed framework successfully bridges urban morphology modeling with climate-responsive urban planning, enabling adaptive design strategies for mitigating UHI effects. This study integrates Pix2Pix and CycleGAN architectures to enhance the realism and structural fidelity of urban morphology generation, while incorporating the LCZ classification framework to produce urban forms that align with specific climatological conditions. Compared to the model trained by Pix2Pix coupled with LCZ alone, the approach offers urban planners a more precise tool for designing climate-responsive cities, optimizing urban layouts to mitigate heat island effects, improve energy efficiency, and enhance resilience. Full article

The lowering air pollution in the US has brought significant health benefits; however, climate change may offset the benefits by increasing the temperature and worsening air quality. This study aimed to estimate the mortality changes due to air pollution reductions and evaluate the potential climate penalty in the Mid-South Region of the US. Daily concentrations of PM_2.5 and ozone measured at local monitoring stations in 1999–2019 were extracted from the US Environmental Protection Agency’s Air Quality System. Meteorological data for the same period were obtained from the National Oceanic and Atmospheric Administration’s Local Climatological Data. Annual average age-adjusted all-cause mortality rates (MRs) were downloaded from the US Centers for Disease Control and Prevention’s WONDERS Databases. MRs attributable to exposure to PM_2.5, ozone, and high temperatures in warm months were estimated using their corresponding health impact functions. Using Year 1999 as the baseline, contributions of environmental changes to MR reductions were calculated. Results showed that annual average concentrations of PM_2.5 and ozone decreased by 46% and 23% in 2019, respectively, compared with the base year; meanwhile, the mean daily temperature in the warm season fluctuated and displayed an insignificant increasing trend (Kendall’s tau = 0.16, p = 0.30). MRs displayed a significant decreasing trend and dropped by 215 deaths/100,000 person-year in 2019. Lower PM_2.5 and ozone concentrations were estimated to reduce 59 and 30 deaths/100,000 person-year, respectively, contributing to 23% and 17% of MR reductions, respectively. The fluctuating temperatures had negligible impacts on mortality changes over the two-decade study period. This study suggests that improved air quality may have contributed to mortality reductions, while the climate penalty effects appeared to be insignificant in the Mid-South Region. Full article

Mesoscale convective systems (MCSs) are major contributors to extreme precipitation in urban agglomerations, exhibiting complex characteristics influenced by large-scale climate variability and local urban processes. This study utilizes a high-resolution MCS database spanning from 2001 to 2020 to investigate the spatiotemporal variations of MCSs in the Yangtze River Delta (YRD) urban agglomeration and assess their response to urbanization. Our analysis reveals significant spatial and temporal differences in MCS activities during the warm season (April to September), including initiation, movement, and lifespan, with notable trends observed over the study period. MCSs are found to contribute substantially to hourly extreme precipitation, accounting for approximately 60%, which exceeds their contribution to total precipitation. Furthermore, the role of MCSs in extreme precipitation has also increased, driven by the intensification of MCS-induced extreme rainfall. Additionally, MCS characteristics exhibit significant regional differences. Urban areas experience more pronounced changes in MCS activity and precipitation compared to the surrounding rural regions. Specifically, urbanization contributes approximately 16% to MCS-related precipitation and 19% to MCS initiation, highlighting its substantial role in enhancing these processes. Moreover, mountainous areas and water bodies surrounding cities show stronger trends in certain MCS characteristics than urban and rural plains. This may be attributed to climatological conditions that favor MCS activity in these regions, as well as the complex interactions between urbanization, topography, and land–sea contrasts. These complicated dynamics warrant further investigation to better understand their implications. Full article

The continuous understanding of extreme weather events in the Amazon is fundamental due to the importance of this biome for the regional and planetary climate system. Climate characterization and the identification of changes in the current climate can be key findings for adaptation and mitigation measures. This study examined climatology and trends in 20 climate extreme indices associated with air temperature and precipitation in the Brazilian Legal Amazon (BLA). Daily observed data, interpolated at grid points, were analyzed from 1961 to 2020. Statistical tests were employed to determine the trend’s significance and magnitude. The results indicate that prolonged heat, hot days, and annual temperature records have become increasingly frequent in practically all of BLA over the last decades. Warm days and nights are increasing at approximately +11 days/decade. Heat waves have gone from 10 to 20 consecutive days on average in the 1960s to around 30–40 days in recent years. Indices associated with the intensity and frequency of extreme precipitation show a reduction, especially in the rainiest portion of the BLA, the western sector. In the east/south region of BLA, where consecutive dry days reach 100 days/year, they continue to increase at a rate of +1.5 days/decade, a fact related to the delay at the beginning of the rainy season. These aspects deserve attention since they impact local circulation, reducing the convergence of humidity not only over the BLA but also in central-southern region of Brazil. Full article

Over 80% of corn on Mexico’s eastern side is sown under rainfed conditions. Therefore, drought represents a constant challenge for local producers. This study aims to determine the effects of drought on rainfed corn grain production on Mexico’s eastern side by using the North American Drought Monitor as the primary tool. Drought levels at the municipal level provided by this monitor and corn production data (surface damage, yield, and volume) of the two productive seasons (spring–summer and autumn–winter) during 20 years were correlated at two significant levels (0.05 and 0.01). The significant values (p < 0.05) were used to obtain regression curves representing corn-drought behaviors. The National Disaster Statistics and climatological stations were considered, discarding other phenomena besides drought. Results indicate that, for the significant municipalities, the years with the highest drought levels (2005, 2011, and 2019) positively correlate with reduced corn grain yield, volume, and total harvest losses. The regression curves estimated a yield reduction of 78 kg∙ha⁻¹ during the spring–summer season and 76 kg∙ha⁻¹ during the autumn–winter season. We concluded that the Drought Monitor is valuable for determining relationships between rainfed corn grain productivity and drought, considering that no other climatological phenomena affect the region. Full article

The aim of the research is to examine the biophysical, spatial, functional, and structural components of the pre-Hispanic terracing systems located in the ancient city of Pisaq, considering the impacts of tourism, geological instability, and cultural loss on the ecological and economic value of the terracing system. The methodology includes site analysis, climatology, and an examination of local flora and fauna, supported by digital tools such as QGIS 3.34, Google Earth Pro 2024, and Sun-Path. The results were primarily supported by the use of software tools such as QGIS, AutoCAD 2023, SketchUp 2022, 3D Sun-Path, D5 Render 2024, and Photoshop 2021. The findings include a biophysical analysis related to ecological and economic zoning (EEZ), which determines variables for preservation and reforestation; a spatial analysis measuring the cultivation terraces, with areas ranging from 4.89 ha to 110.20 ha; a functional analysis examining geophysical aspects such as seismic resistance and microclimate effects due to the greenhouse effect; and a constructive analysis that characterizes terrace typologies from an architectural perspective. In conclusion, this analysis evaluates the terracing system of the archaeological park to ensure its preservation and effective management. It also highlights that Inca culture has left a legacy of sustainable architecture, which aligns with the current Sustainable Development Goals (SDGs) (6, 11, 13, 15). Full article

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed methodology and implemented product in this study serves as a proof of concept that could be replicated by WMO members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for model input selection based on large-scale indicators leveraging artificial intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction in the wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead. Full article

Climatological variables undergo changes over time, and it is important to understand such dynamic changes at global, regional, and local levels. While global and regional studies are common in the study of climate, such studies at a local level are not as common. The aim of this article is to study temporal changes in precipitation, snowfall, and temperature variables at specific stations located on the rims of Lake Erie and Lake Michigan. The identification of changes is carried out by applying change-point analysis to precipitation, snowfall, and temperature data from Buffalo, Erie, and Cleveland stations located on the rim of Lake Erie and at Chicago, Milwaukee, and Green Bay stations located on the rim of Lake Michigan. We adopt mainly the Bayesian information criterion (BIC) method to identify the number and locations of change points, and then we apply the generalized likelihood ratio statistic to test for the statistical significance of the identified change points. We follow this up by finding 95% confidence intervals for those change points that were found to be statistically significant. The results from the analysis show that there are significant changes in precipitation, snowfall, and temperature variables at all six rim stations. Changes in precipitation show consistently significant increases, whereas there is no similar consistency in snowfall increases. Temperature increases are generally quite sharp, and they occur consistently around 1985. Overall, upon combining the amounts of changes from all six stations, the average amount of change in annual average temperature is found to be 0.96 °C, the average percentage of change in precipitation is 16%, and the average percentage of change in snowfall is 17%. The changing local climatic conditions identified in the study are important for local city planners, as well as residents, so that they can be well prepared for changing climatic scenarios. Full article

The data from thirty-one climate stations in the Canadian Prairie provinces of Alberta, Saskatchewan, and Manitoba are analyzed using a number of day-to-day thermal variability metrics. These are used to classify each climate station location using a decision tree developed previously. This is the first application of the decision tree to identify stations as having rural, urban, peri-urban, marine, island, airport, or mountain climates. Of the thirty-one, eighteen were identified as peri-urban, with fourteen of these being airports; six were identified as marine or island; four were identified as rural; one as urban was identified; and two were identified as mountain. The two climate stations at Churchill, Manitoba, located near the shores of Hudson Bay, were initially identified as peri-urban. This was re-assessed after adjusting the number of “winter” months used in the metric for identifying marine and island climates (which, for all other analyses, excluded only December, January, and February). For Churchill, to match the sea ice season, the months of November, March, April, and May were also excluded. Then, a strong marine signal was found for both stations. There is a potential to use these thermal metrics to create a sea ice climatology in Hudson Bay, particularly for pre-satellite reconnaissance (1971). Lake Louise and Banff, Alberta, are the first mountain stations to be identified as such outside of British Columbia. Five airport/non-airport pairs are examined to explore the difference between an airport site and a local site uninfluenced by the airport. In two cases, the expected outcome was not realized through the decision tree analysis. Both Jasper and Edmonton Stony Plain were classified as peri-urban. These two locations illustrated the influence of proximity to large highways. In both cases the expected outcome was replaced by peri-urban, reflective of the localized impact of the major highway. This was illustrated in both cases using a time series of the peri-urban metric before and after major highway development, which had statistically significant differences. This speaks to the importance of setting climate stations appropriately away from confounding influences. It also suggests additional metrics to assess the environmental consistency of climate time series. Full article