Search Results (270)

Significant upwelling in the equatorial ocean influences complex ocean–atmosphere interactions and contributes to diurnal variations in the lower troposphere. This study compares the temperature and relative humidity data from radiosonde observations over the tropical Indian Ocean with those from the ERA5, highlighting the underestimation of the diurnal variations in the ERA5. Radiosonde measurements were conducted at 3 h intervals for approximately 24 h, from 31 May to 1 June 2023, to investigate the diurnal variations in the lower troposphere at two fixed locations: (1) 65°E and 8°S in the upwelling region (Station 8) from 28 to 29 May 2023, and (2) 65°E and 4°S outside the upwelling region (Station 4). The radiosonde observations reveal pronounced diurnal variations in temperature and relative humidity between 950 and 650 hPa. The maximum diurnal range (maximum minus minimum) for temperature is observed above 800 hPa, with Station 8 exhibiting 4.7 °C and Station 4 exhibiting 2.7 °C. For relative humidity, Station 8 shows a diurnal range of 84%, while at Station 4, notable variations are observed only below 650 hPa, reaching 76%. However, the ERA5 underestimates the diurnal variations both within and outside the upwelling region. This underestimation is particularly evident between 850 and 750 hPa and is more pronounced within the upwelling region, where the diurnal range is larger. The diurnal ranges calculated from the ERA5 for 2004–2023 suggest that the reanalysis dataset exhibits limitations in capturing diurnal variations, particularly over the upwelling region. This report highlights the need for more in situ observations of the atmospheric variables to better represent diurnal variations in the tropical Indian Ocean. Full article

Based on the homemade Physiological and Ecological Comprehensive Analysis System for Aquatic Animals (PECA-BES01), this study compared the behavioral responses of koi carp (Cyprinus carpio) at three temperature gradients using two behavioral monitoring techniques as follows: infrared tracking and quadrupole impedance. The experiment employed comprehensive behavioral strength monitoring and infrared tracking (with tracked coordinates converted to swimming velocity data) to reflect behavioral changes. Within a certain temperature range, the behavioral strength and swimming velocity of carp increased with increasing temperature, which indicated heightened activity. The average behavioral strength and swimming velocity during light conditions (over three temperature gradients) were greater than during dark conditions. The circadian rhythm of carp becomes unstable at high temperatures, which shows abnormal periodicity with earlier occurrences of diurnal time points. Results from the system’s two behavioral monitoring methods were largely consistent and confirmed the reliability of PECA-BES01 in monitoring aquatic organism behavior. Simultaneously, each of the two technologies has its own characteristics. Quadrupole impedance can be used to monitor the behavioral response of fish to different water depths, whereas infrared tracking can be used to monitor the behavioral response of fish to different flow velocities. Therefore, both behavioral strength monitoring and infrared tracking monitoring are effective techniques for monitoring fish behavior and can be widely applied. This study provides scientific support for koi carp cultivation and other aquatic species aquaculture, while also aiming to deliver high-quality methodology for online monitoring of aquatic organisms. Full article

This study investigates the spatiotemporal dynamics of land surface temperature (LST) across five distinct land use/land cover (LULC) classes through high-resolution unmanned aerial vehicle (UAV) thermal remote sensing. Thermal orthomosaics were systematically captured at four diurnal periods (morning, afternoon, evening, and midnight) over an urban university campus environment. Using stratified random sampling in each class with spatial controls to minimize autocorrelation, we quantified thermal signatures across bare soil, buildings, grassland, paved roads, and water bodies. Statistical analyses incorporating outlier management via the Interquartile Range (IQR) method, spatial autocorrelation assessment using Moran’s I, correlation testing, and Geographically Weighted Regression (GWR) revealed substantial thermal variability across LULC classes, with temperature differentials of up to 17.7 °C between grassland (20.57 ± 5.13 °C) and water bodies (7.10 ± 1.25 °C) during afternoon periods. The Moran’s I analysis indicated notable spatial dependence in land surface temperature, justifying the use of GWR to model these spatial patterns. Impervious surfaces demonstrated pronounced heat retention capabilities, with paved roads maintaining elevated temperatures into evening (13.18 ± 3.49 °C) and midnight (2.25 ± 1.51 °C) periods despite ambient cooling. Water bodies exhibited exceptional thermal stability (SD range: 0.79–2.85 °C across all periods), while grasslands showed efficient nocturnal cooling (ΔT = 23.02 °C from afternoon to midnight). GWR models identified spatially heterogeneous relationships between LST patterns and LULC distribution, with water bodies exerting the strongest localized cooling influence ($R^2$≈ 0.62–0.68 during morning/evening periods). The findings demonstrate that surface material properties significantly modulate diurnal heat flux dynamics, with human-made surfaces contributing to prolonged thermal loading. This research advances urban microclimate monitoring methodologies by integrating high-resolution UAV thermal imagery with robust statistical frameworks, providing empirically-grounded insights for climate-adaptive urban planning and heat mitigation strategies. Future work should incorporate multi-seasonal observations, in situ validation instrumentation, and integration with human thermal comfort indices. Full article

The variation in the background temperature field in aquatic environments plays a crucial role in the detection of thermal signatures of maritime moving targets. To elucidate the influence of various meteorological and hydrological parameters on the background temperature field of water bodies, this study employs the COARE 3.0 model to analyze the relationship between the net heat flux at the air–water interface and the characteristics of the cool skin layer. By examining the diurnal fluctuations of environmental parameters, the diurnal variation patterns of the cool skin layer properties are investigated. A dynamic temperature field testing platform was established in an outdoor pool to measure air–water volume variables and validate the accuracy of the water temperature field calculation model. The findings indicate that the cool skin phenomenon is indeed present in natural aquatic environments. The properties of the cool skin layer are predominantly affected by factors such as wind speed, the specific humidity gradient between the near-surface and high-altitude regions, and the temperature gradient between these regions. The temperature of the cool skin layer is typically a few tenths of K lower than that of the subsurface water, with a thickness generally ranging from 2 to 5 mm. Full article

Understanding global patterns of tree canopy height and density is essential for effective forest management and conservation planning. This study examines how these attributes vary along latitudinal gradients and identifies key climatic drivers influencing them. We utilized high-resolution remote sensing datasets, including a 10 m resolution canopy height dataset aggregated to 1 km for computational efficiency, and a 1 km resolution tree density dataset derived from ground-based measurements. To quantify the relationships between forest structure and environmental factors, we applied nonlinear regression models and climate dependency analyses, incorporating bioclimatic variables from the WorldClim dataset. Our key finding is that latitude exerts a dominant but asymmetric control on tree height and density, with tropical regions exhibiting the strongest correlations. Tree height follows a quadratic latitudinal pattern, explaining 29.3% of global variation, but this relationship is most pronounced in the tropics (−10° to 10° latitude, R² = 91.3%), where warm and humid conditions promote taller forests. Importantly, this effect differs by hemisphere, with the Southern Hemisphere (R² = 67.1%) showing stronger latitudinal dependence than the Northern Hemisphere (R² = 35.3%), indicating climatic asymmetry in forest growth dynamics. Tree density exhibits a similar quadratic trend but with weaker global predictive power (R² = 7%); however, within the tropics, latitude explains 90.6% of tree density variation, underscoring strong environmental constraints in biodiverse ecosystems. Among climatic factors, isothermality (Bio 3) is identified as the strongest determinant of tree height (R² = 50.8%), suggesting that regions with stable temperature fluctuations foster taller forests. Tree density is most strongly influenced by the mean diurnal temperature range (Bio 2, R² = 36.3%), emphasizing the role of daily thermal variability in tree distribution. Precipitation-related factors (Bio 14 and Bio 19) moderately explain tree height (~33%) and tree density (~25%), reinforcing the role of moisture availability in structuring forests. This study advances forest ecology research by integrating high-resolution canopy structure data with robust climate-driven modeling, revealing previously undocumented hemispheric asymmetries and biome-specific climate dependencies. These findings improve global forest predictive models and offer new insights for conservation strategies, particularly in tropical regions vulnerable to climate change. Full article

Agriculture is an important part of the economy of southwestern United States (Southwest). The production of food and fiber in the Southwest is supported by irrigation, much of which is sourced from groundwater. Climate projections suggest an increasing risk of drought and heat, which can affect water supply and demand, and will challenge the future of agricultural production in the Southwest. Also, as groundwater in the Southwest is highly regulated, producers may not be able to readily rely on groundwater to meet increased demand. Climate exposure of five economically-important crops—alfalfa, cotton, pecans, pistachios, and processing tomatoes—was analyzed over twelve regulated groundwater basins by quantifying changes in a suite of both crop-specific and non-specific agroclimatic indicators between contemporary (1981–2020) and future (2045–2074, SSP2-4.5) climates. Generally, groundwater basins that are currently the most exposed to impactful climate conditions remain so under future climate. The crops with the greatest increase in exposure to their respective crop-specific indicators are cotton, which may be impacted by a ~180% increase in exposure to extreme heat days above 38 °C, and processing tomatoes, which may see a ~158% increase in exposure to high temperatures and reduced diurnal temperature range during flowering. These results improve understanding of the potential change in exposure to agroclimatic indicators, including crop-specific indicators, at the scale of regulated groundwater basins. This understanding provides useful information for the long-term implications of climate change on agriculture and agricultural water, and can inform adaptation efforts for coupled agricultural and water security in groundwater-dependent regions. These results may also be useful for assessing the adaptive potential of water conservation actions—some of which are outlined herein—or the suitability of other adaptation responses to the challenges that climate change will pose to agriculture. Full article

As a keystone predator within forest ecosystems, the dhole (Cuon alpinus) plays a pivotal role in shaping the population structure and dynamics of these biomes. In China, dhole populations have experienced a dramatic decline, primarily due to habitat loss and fragmentation, poaching, and other historical factors. However, the distribution patterns of suitable habitats and the key environmental factors influencing their suitability remained unclear. In this study, we employed the MaxEnt model to assess the habitat suitability for dholes across China. The results revealed that the primary factors influencing the distribution of potential suitable habitats for dholes were the mean diurnal range (Bio2), temperature seasonality (Bio4), minimum temperature of the coldest month (Bio6), and elevation. Potentially suitable habitats were predominantly located in the central-western and northwestern regions, with scattered distributions in the southeastern parts of China, while areas of high suitability were mainly concentrated in the central-western region. The proportion of suitable habitats varied significantly among the nine provinces analyzed. This study clarified the distribution patterns of suitable habitats and identified the key environmental constraints affecting dhole distribution in China. The findings provide critical ecological data to support the conservation and management of dholes in the region. Full article

Indoor and outdoor heat stress, which can appear during warm periods of the year, often has a negative impact on health and reduces productivity at work and study. Intense heat waves (HWs) are causing increasing rates of morbidity and mortality. This study aimed to analyze the coupling and delay of indoor and outdoor heat stress during HW events, using the example of ten workplaces (WPs) situated in different offices and buildings in the medium-sized city of Freiburg, Germany. The relationships between air temperature, humidity, and thermal stress intensity in the WPs were explored during HW periods. It was found that the level of thermal load in the investigated WPs was very different compared to that outdoors (during HWs and the entire summer). The mean physiologically equivalent temperature (PET) for the summer of 2022 inside the investigated offices was 2 °C higher than outside. All classes of thermo-physiological stress were observed outdoors at a meteorological station during the study period. While at eight of the ten workplaces, the most frequent physiological stress was slight heat stress (ranging between 62.4% and 97.4% of the time), the other two WPs were dominated by moderate heat stress (53.7% and 60.6% of the time). The daily amplitudes as well as diurnal courses of air temperature, humidity, and PET during the summer differed significantly at the ten different WPs. It is suggested to use vapor pressure instead of relative humidity to characterize and compare different HWs both outside and inside. It is proposed for future work research to analyze not only room and building characteristics but also the characteristics of the surroundings of the building for a better understanding of the key factors that influence human thermal comfort in different workplaces. A framework of the drivers affecting the coupling of outdoor and indoor heat stress is proposed. Full article

The urbanization effect (UE) on local or regional climate is a prominent research topic in the research field of urban climates. However, there is little research on the UE of Urumqi, a typical arid region city, concerning various climatic factors and their spatio–temporal characteristics. This study quantitatively investigates the UE of Urumqi on multiple climatic factors in summer based on a decade-long period of WRF–UCM (Weather Research and Forecasting model coupled with the Urban Canopy Model) simulation data. The findings reveal that the UE of Urumqi has resulted in a reduction in the diurnal temperature range (DTR) within the urban area by causing an increase in night-time minimum temperatures, with the maximum decrease reaching −2.5 °C. Additionally, the UE has also led to a decrease in the water vapor mixing ratio (WVMR) and relative humidity (RH) at 2 m, with the maximum reductions being 0.45 g kg⁻¹ and −6.5%, respectively. Furthermore, the UE of Urumqi has led to an increase in planetary boundary layer height (PBLH), with a more pronounced effect in the central part of the city than in its surroundings, reaching a maximum increase of over 750 m at 19:00 Local Solar Time (LST, i.e., UTC + 6). The UE has also resulted in an increase in precipitation in the northern part of the city by up to 7.5 mm while inhibiting precipitation in the southern part by more than 6 mm. Moreover, the UE of Urumqi has enhanced precipitation both upstream and downstream of the city, with a maximum increase of 7.9 mm. The UE of Urumqi has also suppressed precipitation during summer mornings while enhancing it in summer afternoons. The UE has exerted certain influences on the aforementioned climatic factors, with the UE varying across different directions for each factor. Except for precipitation and PBLH, the UE on the remaining factors exhibit a greater magnitude in the northern region compared to the southern region of Urumqi. Full article

Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts. Full article

Climate change has a profound impact on the phenology and growth of vegetation, which in turn influences the distribution and behavior of animal communities, including prey species. This dynamic shift significantly affects predator survival and activities. This study utilizes the MaxEnt model to explore how climate change impacts the distribution of the North China leopard (Panthera pardus japonensis) in the Ziwuling region of Gansu Province, China. As an endemic subspecies and apex predator, the North China leopard is vital for maintaining the structure and function of local ecosystems. Unfortunately, its population faces several threats, including habitat change, interspecies competition, and human encroachment, all of which are compounded by the ongoing effects of climate change. To assess the requirement and quality of habitat for this species, we conducted a population survey in the Ziwuling area from May 2020 to June 2022, utilizing 240 infrared cameras, which identified 46 active leopard sites. Using the MaxEnt model, we simulated habitat suitability and future distribution under different climate change scenarios based on nine environmental variables. Our results indicate that the population distribution of North China leopards is primarily influenced by the mean diurnal range (Bio2), with additional sensitivity to isothermal conditions (Bio3), temperature seasonality (Bio4), maximum temperature of the warmest month (Bio5), and annual temperature range (Bio7). We also evaluated habitat suitability across three socioeconomic pathways (SSP126, SSP245, and SSP585) for three time intervals: the 2050s (2041–2060), the 2070s (2061–2080), and the 2090s (2081–2100). The findings suggest a significant decline in high-suitability habitat for North China leopards, while areas of medium and low suitability are projected to increase. Understanding these distributional changes in North China leopards will enhance our comprehension of the region’s biogeography and inform conservation strategies aimed at mitigating the impacts of climate change. Full article

The increasing frequency of both singular and compound extreme climate events driven by global warming has profoundly impacted terrestrial ecosystems. Using machine learning-based Random Forest algorithms and moving correlation analysis, this study quantifies the impacts of extreme climate indices (ECIs) on two ecological indicators (EIs), the NDVI and GPP, from 1982 to 2019. The results reveal that singular extreme climate events exert a more pronounced influence on ecosystems across Southwestern China (SWC) than compound ones. Specifically, the NDVI and GPP exhibited strong correlations with summer days (SU) and diurnal temperature range (DTR), with SU contributing positively (weight = 0.275 for the GPP and 0.238 for the NDVI) and DTR negatively (weight = 0.107 for the GPP and 0.130 for the NDVI). Regional analyses highlighted distinct spatial patterns: in mid–high-altitude areas (>1 km), including the Hengduan Mountains (HDMs) and Yunnan–Guizhou Plateau (YGP), extreme temperatures and precipitation significantly promoted vegetation growth, with rainfall day index (RDI), frost days (FD), extreme temperature index (ETI), SU, and DTR all having a strong influence (>0.1) on the GPP and NDVI. These areas showed strong adaptability to extreme climate, benefiting overall vegetation health. In contrast, ecosystems in low-altitude areas (<1 km) showed more variable responses. The Guangxi Basin (GXB) exhibited strong resistance to ECIs, with vegetation being almost unaffected by extreme precipitation and benefiting from continuous warming. Only consecutive wet days (CWD) and FD were significantly negatively correlated with EIs (p < 0.05), and their correlation weights were low (weights = 0.043 and 0.013). However, the vegetation in the Sichuan Basin (SCB) is more susceptible to climate extremes, which have particularly strong effects on the NDVI. SU, tropical nights (TR), ETI, and growing season length (GSL), which have positive effects on EIs in mid–high-altitude areas, show extremely significant negative correlations in the SCB (p < 0.001), and their weights account for one-third of the total (weights = 0.15, 0.11, 0.061 and 0.012, respectively). These findings underscore the heterogeneous responses of ecosystems to ECIs and emphasize the need for region-specific strategies in ecosystem management and disaster prevention amid climate change. Full article

Grassland degradation is a serious ecological issue in the farming–pastoral ecotone of northern China. Utilizing native grasses for the restoration of degraded grasslands is an effective technological approach. Leymus secalinus is a superior indigenous grass species for grassland ecological restoration in northern China. Therefore, the excavation of potential distribution areas of L. secalinus and important ecological factors affecting its distribution is crucial for grassland conservation and restoration of degraded grasslands. Based on 357 data points collected on the natural distribution of L. secalinus, this study employs the jackknife method and Pearson correlation analysis to screen out 23 variables affecting its spatial distribution. The MaxEnt model was used herein to predict the current suitable distribution area of L. secalinus and the suitable distribution of L. secalinus under different SSP scenarios (SSP1-26, SSP2-45, and SSP5-85) for future climate. The results showed the following: (1) Mean diurnal temperature range, annual mean temperature, precipitation of the wettest quarter, and elevation are the major factors impacting the distribution of L. secalinus. (2) Under the current climatic conditions, L. secalinus is mainly distributed in the farming–pastoral ecotone of northern China; in addition, certain suitable areas also exist in parts of Xinjiang, Tibet, Sichuan, Heilongjiang, and Jilin. (3) Under future climate change scenarios, the suitable areas for L. secalinus are generally the same as at present, with slight changes in area under different scenarios, with the largest expansion of 97,222 km² of suitable area in 2021–2040 under the SSP1-26 scenario and the largest shrinkage of potential suitable area in 2061–2080 under the SSP2-45 scenario, with 87,983 km². Notably, the northern boundary of the middle- and high-suitability areas is reduced, while the northeastern boundary and some areas of Heilongjiang and Jilin are expanded. The results of this study revealed the suitable climatic conditions and potential distribution range of L. secalinus, which can provide a reference for the conservation, introduction, and cultivation of L. secalinus in new ecological zones, avoiding the blind introduction of inappropriate habitats, and is also crucial for sustaining the economic benefits associated with L. secalinus ecological services. Full article

Addressing global warming and adapting to the impacts of climate change is a primary focus of climate change adaptation strategies at both European and national levels. Land surface temperature (LST) is a widely used proxy for investigating climate-change-induced phenomena, providing insights into the surface radiative properties of different land cover types and the impact of urbanization on local climate characteristics. Accurate and continuous estimation across large spatial regions is crucial for the implementation of LST as an essential parameter in climate change mitigation strategies. Here, we propose a deep-learning-based methodology for LST estimation using multi-source data including Sentinel-2 imagery, land cover, and meteorological data. Our approach addresses common challenges in satellite-derived LST data, such as gaps caused by cloud cover, image border limitations, grid-pattern sensor artifacts, and temporal discontinuities due to infrequent sensor overpasses. We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills data gaps and enhances spatial and temporal coverage. In non-gap areas validated against ground truth ECOSTRESS data, the model achieves LST predictions with at least 80% of all pixel errors falling within a ±3 °C range. Unlike traditional satellite-based techniques, our model leverages high-temporal-resolution meteorological data to capture diurnal variations, allowing for more robust LST predictions across different regions and time periods. The model’s performance demonstrates the potential for integrating LST into urban planning, climate resilience strategies, and near-real-time heat stress monitoring, providing a valuable resource to assess and visualize the impact of urban development and land use and land cover changes. Full article

Remote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heterogeneous areas such as cities. Polar orbiting sensors have the advantage of a higher spatial resolution, enabling a better characterization of the surface while only providing one to two observations per day. This work aims at using a multi-layer perceptron-based method to downscale geostationary-derived LST based on a polar-orbit-derived one. The model is trained on a pixel-by-pixel basis, which reduces the complexity of the model while requiring fewer auxiliary data to characterize the surface conditions. Results show that the model is able to successfully downscale LST for the city of Madrid, from approximately 4.5 km to 750 m. Performance metrics between training and validation datasets show no overfitting. The model was applied to a different time period and compared to data derived from three additional sensors, which were not used in any stage of the training process, yielding a R² of 0.99, root mean square errors between 1.45 and 1.58 and mean absolute errors ranging from 1.07 to 1.15. The downscaled LST is shown to improve the representation of both the temporal variability and spatial heterogeneity of temperature, when compared to geostationary- and polar-orbit-derived LST individually. The resulting downscaled data take advantage of the high observation frequency of geostationary data, combined with the spatial resolution of polar orbiting sensors and may be of added value for the study of diurnal and seasonal patterns of LST in urban environments. Full article