Long-term in situ Eulerian Sea surface temperature records along the Portuguese Coast.

Monitoring ocean surface temperature is critical to infer the variability of the upper layers of the ocean, from short temporal scales to climatic change scales. Analysis of the climatological trends and anomalies is fundamental to comprehend the long-term effects of climate change on marine ecosystems and coastal regions. The original data for the dataset presented was collected by the Portuguese Hydrographic Institute (Instituto Hidrográfico) using seven Ondograph and Meteo-oceanography buoys anchored offshore along the Portuguese coast to acquire ocean surface temperatures. The original raw data was pre-processed to provide averages over 3-hour periods and daily averages, and this cleaned data constitutes the provided dataset. The 3-hour temperature averages were obtained mainly between 2011 and 2015, and the daily temperature averages were obtained in intervals that vary with the considered buoy, having an average interval of 14 years per buoy. The data gathered provides a considerable temporal window, enabling the creation of data series and the implementation of data mining algorithms to develop decision support systems. Collecting data in situ makes it possible to validate simulated results obtained using approximation models. This allows for more accurate temperature readings and facilitates testing and correcting created models.

Assessing the cooling potential of climate change adaptation measures in rural areas.

Atmospheric heat has become a major public concern in a rapidly warming world. Evapotranspiration, however, provides effective land surface cooling during the vegetation period. Adversely, modern cultural landscapes - due to both water and potential evapotranspiration pathways lacking - are increasingly incapable of offering this important benefit. We hypothesised that concerted measures for a revived landscape water retention can fuel plant transpiration, especially during dry periods, and thus contribute to climate change adaptation by stabilising the regional climate. Seeking nature-based ways to an improved landscape water retention, we used the land surface temperature (LST) as a proxy for landscape mesoclimate. For our drought-prone rural study area, we identified potential candidate environmental predictors for which we established statistical relationships to LST. We then, from a set of potential climate change adaptation measures, mapped selected items to potential locations of implementation. Building on that, we evaluated a certain measures' probable cooling effect using (i) the fitted model and (ii) the expected expression of predictors before and after a hypothetical measure implementation. In the modelling, we took into account the spatial and temporal autocorrelation of the LST data and thus achieved realistic parameter estimates. Using the candidate predictor set and the model, we were able to establish a ranking of the effectiveness of climate adaptation measures. However, due to the spatial variability of the predictors, the modelled LST is site-specific. This results in a spatial differentiation of a measure's benefit. Furthermore, seasonal variations occur, such as those caused by plant growth. On average, the afforestation of arable land or urban brownfields, and the rewetting of former wet meadows have the largest cooling capacities of up to 3.5 K. We conclude that heat countermeasures based on fostering both evapotranspiration and landscape water retention, even in rural regions, offer promising adaptation ways to atmospheric warming.

The Potential of Infrared Thermography for Early Pregnancy Diagnosis in Nili-Ravi Buffaloes.

This study was designed to explore the potential of infrared thermography (IRT) as an alternate approach for early pregnancy diagnosis in buffaloes. The surface temperature (ST) of different regions (eyes, muzzle, flanks, and vulva) was determined in 27 buffaloes using IRT from the day of artificial insemination (AI; Day 0), and measurement was repeated every fourth day until Day 24 post-AI. From all regions, the ST in each thermograph was recorded at three temperature values (maximum, average, minimum). Pregnancy status was confirmed through ultrasonography on Day 30, and animals were retrospectively grouped as pregnant or non-pregnant for analysis of thermographic data. In pregnant buffaloes, all three values of ST were significantly greater (p ≤ 0.05) for the left flank, while, in the left eye and vulva, only the maximum and average values were significantly greater. By contrast, the maximum ST of the muzzle was significantly lower (p ≤ 0.05) in pregnant buffaloes compared to non-pregnant buffaloes. However, the ST of the right eye and right flank did not show significant temperature variation at any value. These findings suggest that IRT has the potential to identify thermal changes associated with pregnancy in buffaloes at an early stage.

Using Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier.

Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

The difference in core-surface temperature indicates the prognosis of heart failure patients.

Aim: To evaluate the difference between core temperature and surface temperature (ΔT) as an index for the prognosis of heart failure (HF). Patients & methods: Core temperature and surface temperature were measured in 253 patients with HF. The association of ΔT with prognostic indicators of HF was analyzed. Results: Patients with ΔT ≥2°C were more likely to have lower left ventricular ejection fraction and lower estimated glomerular filtration rate, higher levels of troponin T, brain natriuretic peptide and procalcitonin, and high blood urea nitrogen/creatinine ratio. The risk of death increased by 32% for a 1°C increase in ΔT and was 4.36-times higher in the ΔT ≥2°C group than in the ΔT <2°C group. Conclusion: ΔT may be used to predict the prognosis of patients with HF.

[Box: see text].

Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery.

Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for large-scale LST retrieval. It explores the impact of Spectral indices of the surface parameters, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to reduce uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST estimations during both summer and winter seasons. All the ML models gave an R-square of above 0.8 and RF with slightly higher R-square during both summer (0.93) and winter (0.85). Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems.

Long-term effects of climate change on juvenile bull shark migratory patterns.

Seasonal variability in environmental conditions is a strong determinant of animal migrations, but warming temperatures associated with climate change are anticipated to alter this phenomenon with unknown consequences. We used a 40-year fishery-independent survey to assess how a changing climate has altered the migration timing, duration and first-year survival of juvenile bull sharks (Carcharhinus leucas). From 1982 to 2021, estuaries in the western Gulf of Mexico (Texas) experienced a mean increase of 1.55°C in autumn water temperatures, and delays in autumn cold fronts by ca. 0.5 days per year. Bull shark migrations in more northern estuaries concomitantly changed, with departures 25-36 days later in 2021 than in 1982. Later, migrations resulted in reduced overwintering durations by up to 81 days, and the relative abundance of post-overwintering age 0-1 sharks increased by >50% during the 40-year study period. Yet, reductions in prey availability were the most influential factor delaying migrations. Juvenile sharks remained in natal estuaries longer when prey were less abundant. Long-term declines in prey reportedly occurred due to reduced spawning success associated with climate change based on published reports. Consequently, warming waters likely enabled and indirectly caused the observed changes in shark migratory behaviour. As water temperatures continue to rise, bull sharks in the north-western Gulf of Mexico could forgo their winter migrations in the next 50-100 years based on current trends and physiological limits, thereby altering their ecological roles in estuarine ecosystems and recruitment into the adult population. It is unclear if estuarine food webs will be able to support changing residency patterns as climate change affects the spawning success of forage species. We expect these trends are not unique to the western Gulf of Mexico or bull sharks, and migratory patterns of predators in subtropical latitudes are similarly changing at a global scale.

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

Monitoring of temperature rise in global nuclear power plant thermal discharge from 2013 to 2022.

The development of nuclear power plants is progressing rapidly worldwide. However, there is currently a lack of dynamic monitoring of the thermal discharge temperature rise from these plants, making it unclear to governments where their nuclear power thermal discharges stand globally. We hypothesize that between 2013 and 2022, there are significant temporal and spatial differences in the thermal discharge temperature rise from nuclear power plants globally. Temporal differences are expected to reflect a country's nuclear power installed capacity and thermal discharge treatment capabilities, while spatial differences are related to the type of water bodies where nuclear power plants are located. To test these hypotheses, we utilized Landsat data to get the distribution range of thermal discharge and temperature rise levels ranging from 1 °C to 4 °C, and compared the temporal and spatial characteristics of temperature rise in different countries. The results indicate that: (1) Currently, China, the United States, and Canada rank among the top three globally in terms of the area experiencing temperature rise due to thermal discharge, which correlates with the total installed capacity of nuclear power in these countries. (2) Countries such as Russia, Finland, and Mexico exhibit larger areas with a 4 °C temperature rise level per unit installed capacity, with their thermal rise area per unit installed capacity (TRAUIC) exceeding the global average by more than 1.5 times. (3) The spatial dispersion trends of thermal discharges from nuclear power plants vary across different types of water bodies. For nuclear power plants located in bays, thermal discharges primarily disperse along the coast, while in open sea and lakes, thermal discharges tend to spread in a fan-shaped pattern. The findings of this study are crucial for understanding the efficiency of thermal discharge from nuclear power plants across different countries globally, assessing potential environmental risks during the operation of these plants, and promoting the safe and orderly development of nuclear power plants worldwide.

Warming sea surface temperatures are linked to lower shorebird migratory fuel loads.

Warming sea surface temperatures (SSTs) are altering the biological structure of intertidal wetlands at a global scale, with potentially serious physiological and demographic consequences for migratory shorebird populations that depend on intertidal sites. The effects of mediating factors, such as age-related foraging skill, in shaping the consequences of warming SSTs on shorebird populations, however, remain largely unknown. Using morphological measurements of Dunlin fuelling for a >3000 km transoceanic migration, we assessed the influence of climatic conditions and age on individuals' migratory fuel loads and performance. We found that juveniles were often at risk of exhausting their fuel loads en route to primary wintering grounds, especially following high June SSTs in the previous year; the lagged nature of which suggests SSTs acted on juvenile loads by altering the availability of critical prey. Up to 45% fewer juveniles may have reached wintering grounds via a non-stop flight under recent high SSTs compared to the long-term trend. Adults, by contrast, were highly capable of reaching wintering grounds in non-stop flight across years. Our findings suggest that juveniles were disproportionately impacted by apparent SST-related declines in critical prey, and illustrate a general mechanism by which climate change may shape migratory shorebird populations worldwide.

Pacific salmon in the Canadian Arctic highlight a range-expansion pathway for sub-Arctic fishes.

Rapid climate change is altering Arctic ecosystems at unprecedented rates. These changes in the physical environment may open new corridors for species range expansions, with substantial implications for subsistence-dependent communities and sensitive ecosystems. Over the past 20 years, rising incidental harvest of Pacific salmon by subsistence fishers has been monitored across a widening range spanning multiple land claim jurisdictions in Arctic Canada. In this study, we connect Indigenous and scientific knowledges to explore potential oceanographic mechanisms facilitating this ongoing northward expansion of Pacific salmon into the western Canadian Arctic. A regression analysis was used to reveal and characterize a two-part mechanism related to thermal and sea-ice conditions in the Chukchi and Beaufort seas that explains nearly all of the variation in the relative abundance of salmon observed within this region. The results indicate that warmer late-spring temperatures in a Chukchi Sea watch-zone and persistent, suitable summer thermal conditions in a Beaufort Sea watch-zone together create a range-expansion corridor and are associated with higher salmon occurrences in subsistence harvests. Furthermore, there is a body of knowledge to suggest that these conditions, and consequently the presence and abundance of Pacific salmon, will become more persistent in the coming decades. Our collaborative approach positions us to document, explore, and explain mechanisms driving changes in fish biodiversity that have the potential to, or are already affecting, Indigenous rights-holders in a rapidly warming Arctic.

Climate change impacts on small pelagic fish distribution in Northwest Africa: trends, shifts, and risk for food security.

Climate change is recognised to lead to spatial shifts in the distribution of small pelagic fish, likely by altering their environmental optima. Fish supply along the Northwest African coast is significant at both socio-economic and cultural levels. Evaluating the impacts of climatic change on small pelagic fish is a challenge and of serious concern in the context of shared stock management. Evaluating the impact of climate change on the distribution of small pelagic fish, a trend analysis was conducted using data from 2363 trawl samplings and 170,000 km of acoustics sea surveys. Strong warming is reported across the Southern Canary Current Large Marine Ecosystem (CCLME), extending from Morocco to Senegal. Over 34 years, several trends emerged, with the southern CCLME experiencing increases in both wind speed and upwelling intensity, particularly where the coastal upwelling was already the strongest. Despite upwelling-induced cooling mechanisms, sea surface temperature (SST) increased in most areas, indicating the complex interplay of climatic-related stressors in shaping the marine ecosystem. Concomitant northward shifts in the distribution of small pelagic species were attributed to long-term warming trends in SST and a decrease in marine productivity in the south. The abundance of Sardinella aurita, the most abundant species along the coast, has increased in the subtropics and fallen in the intertropical region. Spatial shifts in biomass were observed for other exploited small pelagic species, similar to those recorded for surface isotherms. An intensification in upwelling intensity within the northern and central regions of the system is documented without a change in marine primary productivity. In contrast, upwelling intensity is stable in the southern region, while there is a decline in primary productivity. These environmental differences affected several small pelagic species across national boundaries. This adds a new threat to these recently overexploited fish stocks, making sustainable management more difficult. Such changes must motivate common regional policy considerations for food security and sovereignty in all West African countries sharing the same stocks.

[Spatiotemporal Pattern and Influencing Factors of Thermal Environment Based on SEM Model: A Case Study in Xi'an Metropolitan Area].

The urban thermal environment is an important indicator for evaluating the ecological environment of a city. It directly affects the health of residents and the sustainable development of the urban economy. However, there is currently a lack of analysis on the impact pathways of the thermal environment considering both natural and human factors. Based on the MODIS MYD11A2 land surface temperature data, meteorological data, and human activity data of Xi'an metropolitan area in 2020, ArcGIS spatial geostatistical analysis was used to study the temporal and spatial distribution pattern of the thermal environment in different seasons, and redundancy analysis was utilized to select the main factors affecting the thermal environment. Then, structural equation modeling was used to quantify the direct and indirect effects of the dominant factors on the urban thermal environment. The results showed that:① The surface temperature in the Xi'an urban area showed a spatial pattern of higher temperatures in the north and lower temperatures in the south, with a decrease in temperature from the city center to the surrounding areas. The most severe heat environment pollution occurred in the summer. ② The redundancy analysis (RDA) results indicated that the main factors that affected the thermal environment were air temperature, impermeable surfaces, vegetation, and precipitation. ③ The results of the structural equation modeling (SEM) indicated that meteorological, surface, and anthropogenic factors affected the urban thermal environment mainly through direct pathways, which were much more important than all indirect pathways. Factors such as temperature, impervious surfaces, and point of interest density had a significant positive effect on the thermal environment (0.10 and 0.33). On the other hand, factors such as water bodies, precipitation, and vegetation had a significant negative effect on the thermal environment (-0.29 and -0.25). Human activities had a greater direct impact on nocturnal surface temperatures than surface and meteorological factors. Increasing economic efficiency is beneficial for mitigating the urban heat island effect. The results of the study can provide a reference for studying local climate change in urban heat islands and for the construction of green and ecologically livable urban environments.

Evaluating the impact of landscape configuration, patterns and composition on land surface temperature: an urban heat island study in the Megacity Lahore, Pakistan.

The urban heat island (UHI) phenomenon is negatively impacted by rapid urbanization, which significantly affects people's everyday lives, socioeconomic activities, and the urban thermal environment. This study focuses on the impact of composition, configuration, and landscape patterns on land surface temperature (LST) in Lahore, Pakistan. The study uses Landsat 5-TM and Landsat 8-OLI/TIRS data acquired over the years 2000, 2010 and 2020 to derive detailed information on land use, normalized difference vegetation index, LST, urban cooling islands (UCI), green cooling islands (GCI) and landscape metrics at the class and landscape level such as percentage of the landscape (PLAND), patch density (PD), class area (CA), largest patch index (LPI), number of patches (NP), aggregation index (AI), Landscape Shape Index (LSI), patch richness (PR), and mean patch shape index (SHAPE_MN). The study's results show that from the years 2000 to 2020, the built-up area increased by 17.57%, whereas vacant land, vegetation, and water bodies declined by 03.79%, 13.32% and 0.4% respectively. Furthermore, landscape metrics at the class level (PLAND, LSI, LPI, PD, AI, and NP) show that the landscape of Lahore is becoming increasingly heterogeneous and fragmented over time. The mean LST in the study area exhibited an increasing trend i.e. 18.87°C in 2000, 20.93°C in 2010, and 22.54°C in 2020. The significant contribution of green spaces is vital for reducing the effects of UHI and is highlighted by the fact that the mean LST of impervious surfaces is, on average, roughly 3°C higher than that of urban green spaces. The findings also demonstrate that there is a strong correlation between mean LST and both the amount of green space (which is negative) and impermeable surface (which is positive). The increasing trend of fragmentation and shape complexity highlighted a positive correlation with LST, while all area-related matrices including PLAND, CA and LPI displayed a negative correlation with LST. The mean LST was significantly correlated with the size, complexity of the shape, and aggregation of the patches of impervious surface and green space, although aggregation demonstrated the most constant and robust correlation. The results indicate that to create healthier and more comfortable environments in cities, the configuration and composition of urban impermeable surfaces and green spaces should be important considerations during the landscape planning and urban design processes.

Unravelling the signatures of submarine groundwater discharge and seawater intrusion along the coastal plains of Odisha, India: a multi-proxy approach.

Submarine Groundwater Discharge (SGD) and Seawater Intrusion (SWI) are two contrary hydrological processes that occur across the land-sea continuum and understanding their nature is essential for management and development of coastal groundwater resource. Present study has attempted to demarcate probable zones of SGD and SWI along highly populated Odisha coastal plains which is water stressed due to indiscriminate-exploitation of groundwater leading to salinization and fresh groundwater loss from the alluvial aquifers. A multi-proxy investigation approach including decadal groundwater level dynamics, LANDSAT derived sea surface temperature (SST) anomalies and in-situ physicochemical analysis (pH, EC, TDS, salinity and temperature) of porewater, groundwater and seawater were used to locate the SGD and SWI sites. A total of 340 samples for four seasons (85 samples i.e., 30 porewater, 30 seawater and 25 groundwater in each season) were collected and their in-situ parameters were measured at every 1-2 km gap along ~ 145 km coastline of central Odisha (excluding the estuarine region). Considering high groundwater EC values (> 3000 µS/cm), three probable SWI and low porewater salinities (< 32 ppt in pre- and < 25 ppt in post-monsoons), four probable SGD zones were identified. The identified zones were validated with observed high positive hydraulic gradient (> 10 m) at SGD and negative hydraulic gradient (< 0 m) at SWI sites along with anomalous SST (colder in pre- and warmer in post-monsoon) near probable SGD locations. This study is first of its kind along the Odisha coast and may act as initial basis for subsequent investigations on fresh-saline interaction along the coastal plains where environmental integrity supports the livelihood of coastal communities and the ecosystem.

Increasing marine heatwaves in the Gulf of Thailand after the global warming hiatus.

Marine heatwaves (MHWs) have been reported often throughout the world, producing severe effects on marine ecosystems. However, the spatial pattern and trend of MHWs in the Gulf of Thailand (GOT) is still unknown. Based on high-resolution daily satellite data over a 40-year period from 1982 to 2021, changes in annual mean SST and MHW occurrences across the GOT are explored here. The results demonstrate that during a warming hiatus (1998-2009), annual mean SST in the GOT encountered a dropping trend, followed by an increasing trend during a warming reacceleration period (2010-2021). Although a warming hiatus and a warming reacceleration occurred in the annual mean SST after 1998, regional averaged SSTs were still 0.18 °C-0.42 °C higher than that for 1982-1997. Statistical distributions reveal that there was a significant shift in both annual mean SSTs and annual extreme hot SSTs. These changes have the potential to increase the frequency of MHWs. Further analysis reveals that MHW frequency has increased at a rate of 1.11 events per decade from 1982 to 2021, which is 2.5 times the global mean rate. For the period 2010-2021, the frequency and intensity of MHWs in the GOT have never dropped, but have instead been more frequent, longer lasting and extreme than those metrics of MHWs between 1982 and 2009. Furthermore, the findings highlight significant changes in the SST over the GOT that may lead us to change or modify the reference period of the MHW definition. The findings also suggest that heat transport and redistribution mechanisms in the GOT sea are changing. This study contributes to our understanding of MHW features in the GOT and the implications for marine ecosystems.

The Application of Infrared Thermography in the Assessment of BEMER Physical Vascular Therapy on Body Surface Temperature in Racing Thoroughbreds: A Preliminary Study.

The study aimed to evaluate the impact of BEMER (Physical Vascular Therapy) on body surface temperature using infrared thermography (IRT) in the distal parts of the forelimbs in Thoroughbreds. The study tested the hypothesis that BEMER therapy leads to an increase in body surface temperature and blood vessel diameter in the distal parts of the forelimbs. The study involved 16 horses, split into 2 groups: active BEMER (n = 8) and sham (n = 8). The active BEMER group had BEMER boots applied to the distal parts of the forelimbs, whereas the sham group had BEMER boots applied without activation of the device. Both groups underwent IRT examination to detect changes in body surface temperature, followed by ultrasonographic examination to assess changes in vein and artery diameter before (BT) and just after (JAT) therapy. The IRT examination was repeated 15 min after BEMER therapy (15AT). There were no significant body surface temperature differences between BT and JAT in any regions of interest (ROIs) in either group. In the active BEMER group, the ROIs did not change significantly at 15AT, compared to the temperatures measured at BT (except for the hooves). At 15AT the temperature of all the ROIs (except the fetlock bone) dropped significantly in the sham group. In the ultrasonographic examination, there was a significant increase in vein and artery diameter in the study group JAT, whereas the sham group had a significant increase only in artery diameter JAT. These results suggest an effect of BEMER on stimulating blood circulation in the distal parts of the forelimbs in clinically healthy horses. IRT did not identify changes in skin surface temperature after BEMER therapy at the distal parts of the forelimbs.

Estimation on the hourly near-surface temperature lapse rate and its time-varying characteristics.

Since much of the current researches have focused on daily, monthly or annual near-surface (2 m) temperature lapse rate (NSTLR), there is little guidance on best estimation practices and analyses of time-varying characteristics for the hourly NSTLR. To estimate hourly NSTLR and identify its time-varying characteristics accurately and objectively, this study proposed a robust estimation strategy based on IGGIII equivalent weight using multiple linear regression models. The accuracy and reliability of the proposed method was verified. The results show that the robust estimation strategy can further improve the hourly NSTLR solution accuracy relative to the least square (LSQ) method, especially in the time period of relatively high temperature. The hourly NSTLR was positively correlated with temperature, with a 24-h average maximum of 0.604 °C/100 m at universal time coordinated (UTC) 7.2 h and minimum of 0.284 °C/100 m at UTC 20.5 h, respectively. Throughout the year, the NSTLR was the largest from June to August, with an average median of around 0.492 °C/100 m. However, from November to the following January, the NSTLR value was the smallest, with a mean median of about 0.323 °C/100 m. In addition, the hourly NSTLR values were essentially less than the constant value of 0.65 °C/100 m. When the hourly NSTLR estimated based on the proposed method was applied to the temperature interpolation, the interpolation accuracies at the highest altitude (1545 m) and other meteorological stations (below 310 m) can increase by 22.4 % and 8.1 %, respectively, relative to the hourly NSTLR calculated by the LSQ method, and increased by 55.6 % and 13.0 %, respectively, relative to the no-NSTLR correction. The results are important for the fine establishment of high spatiotemporal resolution temperature fields and for the study of climatic phenomena characterized with rapid spatiotemporal variation.

Stress response to social isolation followed by exposure to a novel object in aged ewes born to undernourished mothers.

Fetal programming by subnutrition affects offspring's behaviour, metabolism, and sensitivity to stressors in sheep. The objective was to determine the stress response of ewes born to mothers nutritionally restricted during gestation to social isolation followed by exposure to a novel object. Twenty-six-year-old Corriedale ewes born to mothers who grazed high or low pasture allowances (HPA and LPA groups) from 23 days before conception until 122 days of gestation were used. Ewes were individually isolated in a novel place for 10 min, and 5 min after its beginning, an orange ball was dropped into the test pen. The ewes' behaviours were recorded during the test. Blood proteins, glucose and cortisol concentrations, heart and respiratory rates and rectal and surface temperatures were determined. The number of times looking at the ball tended to be greater in HPA ewes than LPA (6.7 ± 1.0 vs 4.7 ± 0.8, P = 0.08). The LPA ewes had greater serum albumin concentration than HPA ewes (3.2 ± 0.1 g/dL vs 3.0 ± 0.1 g/dL, P = 0.02), regardless of the applied stressors. Overall, the nutritional treatments applied to ewes during their intrauterine development did not modify the stress responses to social isolation followed by exposure to a novel object.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning.

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 µmol/kg to 0.99 and 4.01 µmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.