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Summary Climate change not only leads to higher air temperatures but also increases the vapour pressure deficit (VPD) of the air. Understanding the direct effect of VPD on leaf gas exchange is crucial for precise modelling of stomatal functioning. We conducted combined leaf gas exchange and online isotope discrimination measurements on four common European tree species across a VPD range of 0.8–3.6 kPa, while maintaining constant temperatures without soil water limitation. In addition to applying the standard assumption of saturated vapour pressure inside leaves (ei), we inferred ei from oxygen isotope discrimination of CO2 and water vapour. ei desaturated progressively with increasing VPD, consistently across species, resulting in an intercellular relative humidity as low as 0.73 ± 0.11 at the highest tested VPD. Assuming saturation of ei overestimated the extent of reductions in stomatal conductance and CO2 mole fraction inside leaves in response to increasing VPD compared with calculations that accounted for unsaturation. In addition, a significant decrease in mesophyll conductance with increasing VPD only occurred when the unsaturation of ei was considered. We suggest that the possibility of unsaturated ei should not be overlooked in measurements related to leaf gas exchange and in stomatal models, especially at high VPD. [ABSTRACT FROM AUTHOR]

In order to accurately and easily predict the variation of the average temperature of the concrete rectangular hollow pier. Firstly, the temperature distribution of the concrete rectangular hollow pier of Changjiahe Special Bridge was observed for 212 days. Then, based on the observation data, the functional relationship between the average concrete hollow pier temperature and the outside air temperature, and the air temperature inside the hollow pier was studied. Finally, based on this functional relationship, a prediction model for the range of variation of the mean temperature of the hollow pier was given and verified. The results of the study show that: the change of external shade temperature can be regarded as the superposition of different cyclic changes and random changes; the change rule of the average temperature of the concrete hollow pier is the same as that of the air temperature, both presenting day-by-day cyclic and step changes; the linear correlation coefficients between the daily maximum and daily minimum average hollow pier temperature and the daily average air temperature are R = 0.980 and R = 0.973, respectively; the daily average air temperature, the daily average air temperature inside the pier and daily average hollow pier temperature are R = 0.980, R = 0.998; the daily variation of the average hollow pier temperature and the daily variation of the air temperature are approximately linear, with a correlation coefficient of R = 0.899; assuming that the average temperature of the concrete hollow pier is a folding change, based on the above relationship, a method of predicting the time-by-time average temperature of the concrete hollow piers is proposed, as well as two methods of predicting the average temperature of the test piers. Average temperature change range method. Comparing the predicted values with the measured values, it is found that the predicted values are in good agreement with the measured values. [ABSTRACT FROM AUTHOR]

Impaired thermoregulatory function is a clinical feature of many health conditions that affect triathletes using wheelchairs and consequently, individual athlete performances may fluctuate according to environmental temperature. We aimed to determine the effect of 1) water temperature on wheelchair triathlon swim time and 2) air temperature on handcycle and wheelchair run (push) time. Published race records from 2017 to 2023 (n = 49 events) were extracted from the World Triathlon website. Bayesian negative binomial regression was used to separately model the nonlinear relationships between water temperature and swim time, and air temperature and handcycle and push time. Age, sex, sport class, whether wetsuits were worn (swim model), and swim time (handcycle and push model) were included as fixed effects. Over the observed water temperature range of 15.7–30.5°C, male swim time (mm:ss) improved from 14:13 (95% credible interval [CrI] = 12:27, 16:09) to 12:35 (95% CrI = 11:00, 14:19). Female swim time improved from 15:33 (95% CrI = 13:24, 17:55) to 12:46 (95% CrI = 11:03, 14:38). It was unclear whether handcycle and push time slowed over the observed air temperature range of 14–33°C. Warmer water temperatures, up to 30.5°C, were associated with faster swim times. It was unclear whether combined handcycle and push time slowed with increases in air temperature, up to 33°C. The integration of information on athlete impairment type and severity with performance data is needed to better understand the extent to which individual athlete performances fluctuate across environmental conditions. [ABSTRACT FROM AUTHOR]

The main objective of this work was to investigate the influence of environmental factors, including air temperature (AT), relative humidity (RH) and temperature–humidity index (THI), on the difference between rectal temperature (RT) and eye temperature (ET) of dairy cows. The monitoring of these parameters is important for the further possible introduction of digitalization in animal welfare, especially in dairy cattle. The mean calculated difference between rectal temperature and eye temperature (RT–ET) was 1.5 °C. The average value of AT was 16.4 °C, and the average value of RH was 59.2%. The average value of THI was 60.4. The results of the study showed that, for the temperature difference ET-RT, a low degree of correlation was found both with temperature and with the temperature-humidity index THI (R = 0.22; R = 0.23). However, the observed temperature difference of the animal (ET-RT) showed a moderate degree of dependence on the relative humidity of air (R = −0.32). Although the positive correlation coefficient for AT and THI points to the higher criticality of summer measurements, the negative correlation coefficient for RH supports the use of infrared thermography for determining the temperature of animals even in a moister barn environment. [ABSTRACT FROM AUTHOR]

The paper presents the results of cobalt corrosion tests in air at different temperature and humidity values. It was demonstrated that corrosion losses in cobalt at temperatures of 20 and 30 °C and relative humidity of 70, 80, and 95% are insignificant. However, the rate of cobalt corrosion sharply increases at an air temperature of 50 °C, especially at 95% relative humidity. A conclusion about the intensity of the cobalt corrosion processes under these conditions can also be derived from the sample appearance: after testing at 50 °C and 70% humidity, the samples turn black, while at 50 °C and 95% humidity they become covered with black, oily, and easily crumbling flakes, which represent the products of cobalt corrosion. It was found that corrosion film formed on the cobalt surface consists of the eutectic Co–CoO mixture of variable composition and CoO·Co(OH)2·H2O complex oxide. At increased corrosion test temperatures, the surface film of hydrated cobalt oxide becomes thicker, develops microcracks, and leads to the formation of cobalt hydroxide flakes, which exhibit weak adhesion to the substrate and crumble. [ABSTRACT FROM AUTHOR]

Simple Summary: The use of single-box housing for domestic horses is a matter of concern due to its potential negative effects on their social interaction and movement. Horses in tropical regions may also face challenges due to heat and humidity fluctuations affecting their thermal comfort and heat dissipation. This study sought to explore the stress responses in horses housed in different stable designs in a tropical savanna region, as little research has considered this topic. The results reveal correlations across stable designs between internal humidity, air temperature, ammonia levels, and heart rate variability (HRV) modulation in horses. We also observed distinct stress responses in horses in different stable designs. These findings suggest that daily changes in the internal environment, as well as stable design, play crucial roles in affecting horses' stress responses and overall well-being in tropical savanna regions. These results have important welfare implications for the management of horse housing in such environments. Single-confinement housing can pose welfare risks to domestic horses. This study investigated horses' stress responses when confined to single stalls in different stable designs in a tropical savanna region to address a gap in the literature. In total, 23 horses were assigned to a stable with a central corridor and solid external walls (A) (N = 8), a stable with one side corridor and solid external walls (B) (N = 6), or a stable with a central corridor and no solid external walls (C) (N = 9). Air velocity, relative humidity, air temperature, and noxious gases were measured inside the stables, and the heart rate and HRV of the horses were also determined. The relative humidity was lower in stable C than in stable A (p < 0.05), while the air temperature was higher in stable C than in stable B (p < 0.05) during the day. The airflow and ammonia levels were higher in stable C than in stables B and A (p < 0.01–0.0001). Overall, horses' HRV in stable A was lower than in those in stables B and C (p < 0.05–0.01). Horses in stable A tended to experience more stress than those in other stables. [ABSTRACT FROM AUTHOR]

This study investigates the variability and forecasting ability of time-trend in mean annual surface air temperatures in Greece. Using Gaussian time-trend models, we first investigate some basic statistical characteristics associated with time-trends, such the mean and variance. This can reveal whether temperatures' mean and volatility changes are associated with time. To do so, we have used mean measures of the minimum and maximum air temperatures observed at several meteorological stations of the Hellenic Meteorological Service located in Greece for the 1960-2010 period. As a second experiment, we investigate whether temperature trends are forecastable or not using various Gaussian time-trend and no-time-trend models. The results are highly significant since they reveal the seasons, the periods and the type of models for which the inter-annual trends out-perform the no-trend ones. Moreover, they also show the statistical characteristics, such as the mean and variability of the time-trend under various seasons and sub-periods. [ABSTRACT FROM AUTHOR]

Temperature and water stress are important factors limiting the gross primary productivity (GPP) in terrestrial ecosystems, yet the extent of their influence across ecosystems remains uncertain. This study examines how surface air temperature, soil water availability (SWA) and vapor pressure deficit (VPD) influence ecosystem light use efficiency (LUE), a critical metric for assessing GPP, across different ecosystems and climatic zones at 80 flux tower sites based on in situ measurements and data assimilation products. Results indicate that LUE increases with temperature in spring, with higher correlation coefficients in colder regions (0.79–0.82) than in warmer regions (0.68–0.78). LUE reaches a plateau earlier in the season in warmer regions. LUE variations in summer are mainly driven by SWA, exhibiting a positive correlation indicative of a water‐limited regime. The relationship between the daily LUE and daytime temperature shows a clear seasonal hysteresis at many sites, with a higher LUE in spring than in fall under the same temperature, likely resulting from younger leaves being more efficient in photosynthesis. Drought stress influences LUE through SWA in all ranges of water availability; VPD variation under moderate conditions does not have a clear influence on LUE, but extremely high VPD (exceeding the threshold of 1.6 kPa, often observed during extreme drought‐heat events) causes a dramatic reduction of LUE. Our findings provide insight into how ecosystem productivities respond to climate variability and how they may change under the influence of more frequent and severe heat and drought events projected for the future. Plain Language Summary: The terrestrial ecosystem assimilates carbon through photosynthesis, and its ability to convert sunlight energy to primary production during photosynthesis is commonly measured by light use efficiency (LUE). To understand how temperature, water in the soil, and atmospheric aridity impact carbon assimilation through photosynthesis, we analyzed the drivers of LUE at 80 sites across different ecosystems and different climate regimes in the United States. We found that temperature is the main factor influencing LUE of grassland and forest in spring, and has a greater influence on LUE in colder regions than in warmer regions. Under the same temperature, LUE in the spring is higher than in the autumn season, likely because of new leaves. In summer, deep soil water availability is the primary driver of LUE variations. In addition, extreme aridity of the atmosphere contributes to a dramatic decrease in LUE during compound heat‐drought events. Our findings contribute to advancing our understanding of how climatic factors influence LUE across different ecosystems in a changing climate. Key Points: Light use efficiency (LUE) variation is generally driven by temperature in spring and by water stress in summerSoil water availability dominates the LUE response to moderate droughts, but vapor pressure deficit becomes important under extreme droughtThe primary drivers of LUE variation also depend on background climate and vegetation types [ABSTRACT FROM AUTHOR]

Seasonal variations in glycaemic patterns in children and young adults affected by type 1 diabetes are currently poorly studied. However, the spread of Flash Glucose Monitoring (FGM) and continuous glucose monitoring (CGM) systems and of dedicated platforms for the synchronization and conservation of CGM reports allows an efficient approach to the comprehension of these phenomena. Moreover, the impact that environmental parameters may have on glycaemic control takes on clinical relevance, implying a need to properly educate patients and their families. In this context, it can be investigated how blood glucose patterns in diabetic patients may have a link to outdoor temperatures. Therefore, in this study, the relationship between outdoor temperatures and glucose levels in diabetic patients, aged between 4 and 21 years old, has been analysed. For a one-year period (Autumn 2022–Summer 2023), seasonal variations in their CGM metrics (i.e., time in range (TIR), Time Above Range (TAR), Time Below Range (TBR), and coefficient of variation (CV)) were analysed with respect to atmospheric temperature. The results highlight a negative correlation between glucose in diabetic patients and temperature patterns (R value computed considering data for the entire year; Ry = −0.49), behaviour which is strongly confirmed by the analysis focused on the July 2023 heatwave (R = −0.67), which shows that during heatwave events, the anticorrelation is accentuated. The diurnal analysis shows how glucose levels fluctuate throughout the day, potentially correlating with atmospheric diurnal temperature changes in addition to the standard trend. Data captured during the July 2023 heatwave (17–21 July 2023) highlight pronounced deviations from the long-term average, signalling the rapid effects of extreme temperatures on glucose regulation. Our findings underscore the need to integrate meteorological parameters into diabetes management and clinical trial designs. These results suggest that structured diabetes self-management education of patients and their families should include adequate warnings about the effects of atmospheric temperature variations on the risk of hypoglycaemia and about the negative effects of excessive therapeutic inertia in the adjustment of insulin doses. [ABSTRACT FROM AUTHOR]

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs. [ABSTRACT FROM AUTHOR]

The winter mortality of mountain pine beetle (Dendroctonus ponderosae Hopkins) larvae caused by temperatures below survivable thresholds and sudden decreases in temperature is among the most influential factors limiting population growth. Due to the importance of winter mortality in the demise of mountain pine beetle infestations, a widely used winter mortality model was developed at the Canadian Forest Service and the United States Forest Service. It predicts lethal temperatures and survival probabilities given temperature time series over the winter season. We present a rare and possibly the first peer-reviewed validation of this winter mortality model, wherein we independently tested the model at a new region in Canada by comparing model predictions to the observed lower lethal temperature thresholds and cold-associated mortality. Model predictions were biologically reasonable but slightly biased. Bias was exacerbated by the inaccurate translation of air temperature data from weather stations to temperatures under the bark where larvae develop. The spatial prediction of relative mortality observed across the study area in Banff National Park was poor—likely because the mountainous terrain presents a difficult prediction challenge when under-bark temperatures are not directly observed. Our results will help inform users of model constraints and how to optimize the accuracy of model predictions. [ABSTRACT FROM AUTHOR]

The presence of biota in the Black Sea is mostly determined by seawater temperature and salinity. The consensus of many scientists doing research on global warming is that in 2100, the average air temperature will be increase than today's values. With climate changing, precipitation rates will change due to the Black Sea water temperature and the changing atmospheric regime, causing an unbalanced entry of nutrients into the sea, and as a result, excessive plankton proliferation may occur. This may mean that the habitats of the species in the Black Sea are further limited. The increases in sea levels may not be serious, but there is no doubt that there will be some increases. These increases will affect the coastal ecosystem, especially the rich living life in the region where the waves hit the shore, which we call the wave zone, will be adversely affected. These predictions should be followed up with regular scientific monitoring studies and it should be investigated how biota from invertebrates to fish are affected by the changes. This review explored and discussed the long-term changes and causes of biota communities on the southern coast of the Black Sea. [ABSTRACT FROM AUTHOR]

İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes. [ABSTRACT FROM AUTHOR]

Over recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region's hydroclimatic history over the last several centuries, the understanding of near surface air temperature variability is confined to the comparatively shorter observational period (1895‐present). Here, we detail the application of blue intensity (BI) methods on a network of tree‐ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28–0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct August‐September average maximum temperatures for the Southeast spanning the period 1760–2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques to improve the understanding of the multi‐centennial temperature history of the Southeast. Plain Language Summary: Tree‐ring data are important sources of paleoclimate information, which allow for the longer‐term evaluation of modern climate values and trends. Compared to much of North America, the Southeastern United States (Southeast) contains substantially fewer paleoclimate records from tree rings, and no estimates of past temperature variability which extend before the observational period. Employing a recently developed technique, which uses light reflectance properties of wood to obtain a representative metric of tree‐ring density, we develop a network of temperature‐sensitive tree‐ring records across the Southeast. These records enable us to reconstruct late summer maximum temperatures across the region spanning the period 1760–2023 CE. As few ground‐based, pre‐instrumental temperature records previously existed for this region, our reconstruction allows for an improved understanding of the region's multi‐centennial climatic history. Key Points: Maximum latewood blue intensity from tree rings can effectively be used to develop paleotemperature estimates for the southeastern USThe fidelity of tree‐ring density parameters for paleoclimate reconstruction are influenced by disturbance regimes and microsite conditionsCompared to the last 260 years, regional 20th‐century maximum late summer temperatures are not characterized by unprecedented positive trend [ABSTRACT FROM AUTHOR]

This study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHICL) of Kuwait City. Observations collected at 12 locations across the country of Kuwait and for the period 2010–2022 are analysed on an hourly and 3‐hourly basis to provide monthly and diurnal insights of the city's UHICL characteristics. Research on Kuwait's UHICL was first conducted by Nasrallah et al. (International Journal of Climatology, 10, 401–405). Results presented here have been afforded the benefit of additional stations and more extensive data compared with the earlier study. Mean positive UHICL intensities, ranging from 1.1°C to 3.8°C at night, are observed consistently across all months, owing to the prevalence of clear skies from winter to summer. Negative UHICL intensities, indicating a typical daytime urban cool island (UCICL), are most prominent on summer days, exhibiting a mean hourly magnitude range between 0.6°C and 2.6°C that extends into the early parts of the evening. Heat and cool island effects are maintained up to wind speeds approaching 10 m s−1 at the urban periphery. A coastal site near the city demonstrates strong influences of the Arabian Gulf temperatures and associated sea and land breeze effects on UHICL development. The results can be used for comparison with other desert locales, in the evaluation of urban climate models, for urban planning policies and improving local weather forecasts. This study honours in memoriam Dr. Hassan Nasrallah, who produced the first UHICL study in the Arab World. [ABSTRACT FROM AUTHOR]

Tomatoes are delicate and prone to damage quickly, which ultimately leads to lower quality and increased post-harvest losses. Hence, an ideal storage environment is very important to maintain the quality of tomato fruits after harvest. Therefore, this study was conducted to determine the effect of storage conditions on the quality parameters of tomato fruits. Experiments were de-signed for six storage periods (4, 8, 12, 16, 20, and 24 days) and two temperatures (12 °C and room air temperature "22 °C"). Three tomato fruit quality parameters (Brix, color, and firmness) have been selected and measured for three tomato varieties (Ghandowra-F1, Forester-F1, and Feisty-Red) grown hydroponically at three salinity levels (2.5, 6.0, and 9.5 dS m−1) of the nutrient solution. Results showed that the highest mean Brix values, for all varieties, were recorded at the highest salinity (9.5 dS m−1), and were significantly (Pr < 0.0001) higher than those at medium (6.0 dS m−1) and low (2.5 dS m−1) salinity levels. In addition, the highest fruit firmness was recorded at high salinity level (9.5 dS m−1), but there was no significant difference to that recorded at medium salinity (6.0 dS m−1). Regarding tomato fruit color, the highest average values were recorded for the Ghandowra-F1 (2.51) and Forester-F1 (2.69) varieties at medium salinity (6.0 dS m−1), while the highest average color value for the Feisty-Red variety (1.54) was obtained at high salinity (9.5 dS m−1). On the other hand, the Brix, color, and firmness of tomato fruits were significantly affected by the storage temperature. Moreover, the mean Brix values (7.66%) were slightly higher at 12 °C storage temperature compared to those at 22 °C (7.38%). In general, the fruit color values gradually increased with the storage period, especially under 22 °C storage temperature, with peak color values of 2.73, 2.70, and 1.66 recorded on the 12th day of the storage period for Ghandowra-F1, Forester-F1, and Feisty-Red, respectively. Tomato fruit firmness decreased faster with the storage period at 22 °C compared to the storage temperature of 12 °C. However, the highest average values of fruit firmness for Ghandowra-F1 (9.37 N cm−1) and Forester-F1 (9.41 N cm−1) recorded at control condition were not significantly different those recorded on the 8th day of storage at 12 °C storage temperature. By contrast, the highest average value of fruit firmness for Feisty-Red (8.85 N cm−1) recorded at control condition was not significant than that recorded on the 4th day of the storage period at 12 °C storage temperature (8.82 N cm−1). Overall, tomato fruits can be stored at 12 °C temperature for up to 20–24 days, without negative effects on fruit quality. [ABSTRACT FROM AUTHOR]

The present study focuses on characterizing the time–frequency aspects of seasonal temperatures in India by integrating the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm with the Hilbert–Huang transform (HHT) decomposition method. The investigation also explores the connections between maximum temperature (Tmax) and minimum temperature (Tmin) with global climate oscillations, such as the El Nino Southern Oscillation (ENSO), Sunspot Number (SN), and Pacific Decadal Oscillations (PDO). The findings indicate that intra and inter-decadal modes play a pivotal role in influencing temperature series across various seasons, with notable changes observed in the amplitudes of inter-decadal modes for seasonal Tmin and Tmax. The analysis of intrinsic mode functions (IMFs) reveals that IMF2 closely align to ENSO with a periodicity of 5–7 years, IMF3 to the sunspot cycle with a frequency of approximately 11 years, and IMF5 to PDO with a long periodicity exceeding 60 years. The association between the IMF components of Tmin and Tmax temperature series and the three climate indices is most evident for low-frequency modes, demonstrating a consistent evolution of trend components. [ABSTRACT FROM AUTHOR]

The Arctic is impacted by some of the fastest temperature changes observed on Earth, but the impact on the terrestrial arthropod fauna is unclear. Acute physiological thermal limits of terrestrial ectotherms from high latitudes often exceed the local air temperatures, suggesting that they may be able to cope with increasing temperatures. However, knowledge on how Arctic terrestrial arthropods cope with elevated temperatures for longer periods is lacking.Here we investigate how acclimation temperature and exposure time affect the acute physiological heat tolerance of five terrestrial arthropod species (Neomolgus littoralis, Megaphorura arctica, Nysius groenlandicus, Psammotettix lividellus and Nabis flavomarginatus) immediately after collection in Arctic and sub‐Arctic habitats.We show that although acute heat tolerances are relatively high, even exposure to moderate (temperature span assessed ca. 3–29°C) acclimation temperatures for a 24 h period have strong negative effects on heat tolerance for four of the five species. Similarly, exposure time negatively affected heat tolerance, but depending on species and temperature.Together our results suggest that exposure to even moderately elevated temperatures for periods of 24 h or even shorter can lead to lower acute heat tolerance for cold adapted terrestrial arthropod species from sub‐Arctic and Arctic regions. Consequently, climate change leading to extended periods of mildly elevated temperatures may have strong negative effects on these species. We argue that this aspect is currently overlooked when assessing the ability of arthropods from Arctic and sub‐Artic regions to cope with climate changes as such predictions are typically based on acute heat tolerance estimates and with the assumption of beneficial acclimation responses. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Permafrost warming has been observed all around the Arctic, however, variations in temperature trends and their drivers remain poorly understood. We present a comprehensive analysis of climatic changes spanning 25 years (1998–2023) at Bayelva (78.92094°N, 11.83333°E) on Spitzbergen, Svalbard. The quality controlled hourly data set includes air temperature, radiation fluxes, snow depth, rainfall, active layer temperature and moisture, and, since 2009, permafrost temperature. Our Bayesian trend analysis reveals an annual air temperature increase of 0.9 ± 0.5°C/decade and strongest warming in September and October. We observed a significant shortening of the snow cover by −14 ± 8 days/decade, coupled with reduced winter snow depth. The active layer simultaneously warmed by 0.6 ± 0.7°C/decade at the top and 0.8 ± 0.5°C/decade at the bottom. While the soil surface got drier, in particular during summer, soil moisture below increased in accordance with the longer unfrozen period and higher winter temperatures. The thawed period prolonged by 10–15 days/decade at different depths. In contrast to earlier top‐soil warming, we observed stable temperatures since 2010 and only little permafrost warming (0.14 ± 0.13°C/decade). This is likely due to recently stable winter air temperature and continuously decreasing winter snow depth. This recent development highlights a complex interplay among climate and soil variables. Our distinctive long‐term data set underscores (a) the changes in seasonal warming patterns, (b) the influential role of snow cover decline, and (c) that air temperature alone is not a sufficient indicator of change in permafrost environments, thereby highlighting the importance of investigating a wider range of parameters, such as soil moisture and snow characteristics. Plain Language Summary: Permafrost is warming across the Arctic, but it is not yet well understood why temperature trends vary and what affects them the most. Our detailed study investigates 25 years (1998–2023) of data at the Bayelva permafrost observatory on Svalbard. We analyzed a quality‐controlled data set, including hourly measurements of air temperature, radiation, snow depth, rainfall, permafrost temperature, and active layer conditions. We looked beyond annual averages, examining changes in each month. The air warmed strongly by 0.9°C/decade and even stronger in September and October. Continuous snow cover shortened by −14 days/decade and winter snow depth decreased. Simultaneously, the active layer warmed by 0.6°C/decade at the top and 0.8°C/decade at the bottom. While the surface dried in summer, deeper soil layers became moister due to a longer unfrozen period and higher temperatures in winter. The thawed period extended by 10–15 days/decade, with slightly stronger changes toward later freezing in autumn. We found that soil warming stopped in recent years and attributed this effect to the lower winter snow depth since 2010. Therefore, if we want to know how permafrost warms or cools in the future, we need to consider additional measurements such as soil moisture and snow properties. Key Points: Snow cover thinning and winter air temperature variability are the most important drivers of trends in permafrost temperatureWhile mean annual air temperature continues to increase, the top soil at Bayelva, Svalbard, stopped warming since 2010The fully snow‐covered season shortened by −14 days per decade since 1998, which led to longer unfrozen conditions in the active layer [ABSTRACT FROM AUTHOR]

The sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. It was found that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosonde stations in China and Japan over the past 60 yrs, this paper explores the probable control on the Es layer from the lower layers. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient of the intensity of Es with surface temperature is as high as 0.8204, while that with the terrain and land‐sea boundary is up to 0.6668. Based on the coupling between the lower and upper atmosphere, this paper reveals the probable controls from lower layers on the intensity of the Es in East Asia. Plain Language Summary: As an occasional ionized layer of the ionosphere, the sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. Despite extensive research on both behavior and mechanism of the Es, none have adequately explained the strong inhomogeneous of its global morphology. It is found in fact that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosondes in China and Japan over the past 60 yrs, this letter explores the formation mechanism of the strongest Es regions in the world. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient between the intensity of Es and surface temperature is highest, while that with the terrain and land‐sea boundary is also high. By analyzing the data as long as six decades, this letter reveals the probable controls from lower layers on the intensity of the Es in East Asia. Key Points: By analyzing the six‐decade data of 21 ionosondes in East Asia, it reveals probable controls on the strongest Es layer from lower layersFor the first time, it is found that the intensity of Es layer is highly correlated with the surface atmospheric temperatureAn interesting coupling from lower to upper atmosphere may be found by taking advantage of observations longer than half of a century [ABSTRACT FROM AUTHOR]

Measurements of ground and near surface atmospheric temperatures at Jezero obtained during 700 sols by the Mars Environmental Dynamics Analyzer (MEDA) characterize the thermal behavior of the near surface Martian atmosphere during a full Martian Year. The seasonal evolution of MEDA measurements is compared with predictions from the Mars Climate Database and the solar irradiance at the surface. Thermal tides observed in the daily cycle of temperatures follow a seasonal cycle with additional variations greater than 2 K on time‐scales of tens of sols. We also observe sol‐to‐sol variations of about 1 K in mean daily air temperatures in autumn and winter with periodicities of 4–7 sols that might be related to baroclinic disturbances that are frequent in those seasons at high latitudes. We examine the evolution of the vertical thermal gradient and temperature fluctuations without finding a seasonal response to irradiance and dust load. We find that the convective boundary layer becomes isothermal and collapses 1 hr before sunset except during northern hemisphere winter, when the collapse occurs closer to sunset, implying a longer duration of the daytime convective instability. Around this period, the rover was located in the delta front in a location of complex topography where we observed stronger thermal gradients and intense daytime air temperature fluctuations. We also find in this place a nighttime event of gravity waves on near‐surface air temperatures, with amplitudes of 2 K and periods of 10 min. These waves possibly propagate downward through a near isothermal stable layer. Plain Language Summary: The Perseverance rover on Mars is carrying a meteorological station that among other measurements obtains air temperatures at three heights near the surface as well as ground temperatures. We analyze seasonal and local changes in temperatures measured over one Martian year (687 Earth days) in which Perseverance moved several kilometers across Jezero crater, comparing in situ observations with the output from a Global Climate Model of the atmosphere of Mars. Our results show that the diurnal cycle of temperatures is modulated by the solar irradiance on the surface, the amount of dust in the atmosphere, local dynamics and large‐scale weather systems. The temperature difference between the ground and the atmosphere greatly determines the meteorology in the lower atmosphere. Neither this temperature difference, nor temperature fluctuations, which during daytime are a proxy of atmospheric convection driven by the heating of the surface, show seasonal variations in line with the seasonal solar irradiance or dust load. Instead, the rover path toward a more complex topography led to the highest surface‐to‐air temperature difference during the northern hemisphere winter, despite the solar irradiance being minimum at that time. Close to this complex topography, the meteorological station recorded oscillations in near‐surface temperature related to gravity waves. Key Points: Seasonal variations in irradiance at Mars' surface drive observed daily mean near‐surface air temperatures with some differences from modelsThermal tides and long‐period traveling waves present a clear seasonal cycle with autumn and winter seasons more prone to variabilityAir temperatures underwent non‐seasonal changes near Jezero's delta, with strong daytime convection and nighttime gravity waves [ABSTRACT FROM AUTHOR]

Water management strategies in the Ile River basin (Republic of Kazakhstan) have traditionally relied on historical data without fully considering the potential impacts of climate change. This gap can lead to underestimating the changes in temperature, precipitation patterns, and runoff, hindering effective water resource management. This study aimed to analyze how a changing climate is affecting the Ile River basin's water regime. Specifically, it investigated trends in temperature, precipitation, and runoff within the basin, emphasizing the importance of incorporating these intra-annual variations when planning water management strategies and hydraulic structures. A detailed analysis of the long-term data was conducted, focusing on changes in meteorological indicators. This included average air temperatures and annual precipitation for elevations above and below 1500 m during cold and warm periods. The analysis aimed to identify and quantify trends of increase or decrease. Meteorological stations were strategically chosen to represent both arid and humid areas within the basin, accounting for the region's significant altitude variations. The investigation revealed several key findings. Rising average annual air temperatures are leading to a larger area experiencing snowmelt and a longer warm period within the runoff formation zone. This directly impacts the water balance of the basin. Additionally, an increase in total annual precipitation, particularly during the cold season within the runoff formation zone, suggests a potential for future water resource growth, assuming that these trends persist. This study highlights the importance of considering intra-annual variations in water regimes when developing water management strategies. The observed changes in temperature and precipitation patterns within the Ile River basin necessitate adjustments to existing plans to ensure sustainable water resource management in a changing climate. [ABSTRACT FROM AUTHOR]

Temperature and wind observations from aircraft are regarded of major importance for aviation meteorology and numerical weather prediction (NWP). The European Meteorological Aircraft Derived Data Center (EMADDC) system processes aircraft surveillance data received from air traffic control (ATC) and converts it into observations of wind and temperature. Only so-called Mode-S Enhanced Surveillance data can be used, because this data contains the air vector and ground vector of the aircraft, from which a wind vector can be inferred. To acquire high quality observations, the data is processed in three steps: pre-processing, processing, and post-processing. The pre-processing is needed to obtain high-quality information and to calculate several correction values such as temperature corrections and heading corrections. Processing converts the aircraft data into meteorological information and finally post-processing guarantees that only high-quality information is made available. The quality of the observations produced is verified by comparing these observation to other upper air wind and temperature observations from radiosondes and comparing them with NWP data. This paper presents the EMADDC system, operational since 2019. [ABSTRACT FROM AUTHOR]

Instrumentation plays a key role in modern horticulture. Thus, the microtensiomenter, a new plant-based sensor that continuously monitors trunk water potential (Ytrunk) can help in irrigation management decisions. To compare the response of the Ytrunk with other continuous tree water status indicators such as the sap flow rate, the difference between canopy and air temperatures, or the variations of the trunk and fruit diameter, all the sensors were installed in 2022 in a commercial orchard of 'Honeycrisp' apple trees with M.9 rootstocks in Washinton State (USA). From the daily evolution of the Ytrunk, five indicators were considered: predawn, midday, minimum, daily mean, and daily range (the difference between the daily maximum and minimum values). The daily range of Ytrunk was the most linked to the maximum daily shrinkage (MDS; R2 = 0.42), the canopy-to-air temperature (Tc-Ta; R2 = 0.32), and the sap flow rate (SF; R2 = 0.30). On the other hand, the relative fruit growth rate (FRGR) wasmore related to theminimum Ytrunk (R2 = 0.33) and the daily mean Ytrunk (R2 = 0.32) than to the daily range of Ytrunk. All indicators derived from Ytrunk identified changes in tree water status after each irrigation event and had low coefficients of variation and high sensitivity. These results encourage Ytrunk as a promising candidate for continuous monitoring of tree water status, however, more research is needed to better relate these measures with other widely studied plantbased indicators and identify good combinations of sensors and threshold values. [ABSTRACT FROM AUTHOR]

Lakes and reservoirs provide habitat for a wide range of species and are essential components of numerous ecosystems. In Spain, these water bodies are particularly important and sensitive, so studying the impact of climate change critical for their long-term management and maintenance. This study aims to identify warming trends in these water bodies and determine whether they vary by season or geographical location by processing time series data of surface temperatures from Landsat 5 images to identify changes in the surface temperature of lakes and reservoirs between 1994 and 2011. This analysis revealed that 87% of water bodies exhibit a warming trend, with an average increase in water surface temperature of þ0.037 ℃ per year. The Ebro and Guadalquivir depressions exhibited the highest temperature increases. However, temperature changes vary across the peninsula, generally associated with air surface temperatures, and are influenced by altitude in the mountain areas and Atlantic winds in the west depressions. The trends indicate a seasonal pattern: in summer, the number of water bodies with high temperatures increases, while in autumn there are more lakes and reservoirs with intermediate temperatures. In winter and spring, the number of water bodies with low temperatures decreases and they are located further north. The influence of factors such as climate and local conditions should be investigated to better understand the warming of these water bodies. [ABSTRACT FROM AUTHOR]

Using the Rescaled Adjusted Partial Sums (RAPS) and spectral analysis methods, the study aimed to extract subperiods (a break point analysis) based on data from 18 meteorological stations in Montenegro and determine the elementary periodicity using the Lomb periodogram of mean seasonal and annual air temperatures and precipitation totals for the period 1961–2020. Prior to this, trend calculations were performed, which showed a significant increase in mean seasonal and annual temperatures, with the most intense warming occurring in the summer (from 0.30 to 0.75 °C/decade). Trends in seasonal and annual precipitation totals are insignificant. According to RAPS values, two subperiods are clearly distinguished for temperatures, and a sudden increase in mean annual temperatures at 11 stations (61.1%) occurred only in 1997 and 1998. The year 1991 was a break point in 22.1% of cases. For seasonal and annual precipitation totals, a minimum of 4 subseries can be distinguished at all stations. According to the RAPS method, the years 2002 and 2003 most frequently appear as the last pivotal points leading to an increase in annual precipitation totals. In terms of precipitation, the most prominent periodicity observed is at 2.1 and 3.6 years, suggesting a quasi-biennial oscillation. Additionally, a 14.7-year periodicity is noted in winter precipitation, and a 3-year periodicity signals ENSO. Regarding average annual and seasonal temperatures, short-term (2–3 years), medium-term (4–5 years) and long-term (6, 8 and over 20 years) periodicities are distinguished. Only at two stations, was the Markowitz wave (MW) signal. [ABSTRACT FROM AUTHOR]

Background: The moisture content of litter and woody debris is a key determinant of fire potential and fire behaviour. Obtaining reliable estimates of the moisture content of dead fine fuels (i.e. 1-h and 10-h fuels) is therefore a critical requirement for effective fire management. Aims: We evaluated and compared the performance of five simple models for fuel moisture content. The models belong to two separate classes: (1) exponential functions of the vapour pressure deficit; and (2) affine functions of the (weighted) difference between air temperature and relative humidity. Methods: Model performance is assessed using error and correlation statistics, calculated using cross validation, over four empirical datasets. Key results: Overall, the best performing models were the relaxed and generalised models based on the weighted difference between temperature and relative humidity. Conclusions: Simple functions of the difference between air temperature and relative humidity can perform as well as, if not better than exponential functions of vapour pressure deficit. However, it is important to note the limitations of all these models when applied to fuels with moisture contents <10%. Implications: The moisture content of fine dead fuels and woody debris can be reliably estimated using simple models that are amenable to easy application. Five models for the moisture content of fine dead fuels were evaluated using empirical datasets. Simple functions of the difference between air temperature and relative humidity were shown to perform as well as, if not better than exponential models based on vapour pressure deficit. [ABSTRACT FROM AUTHOR]

The application of corrosion inhibitors is a more effective strategy tomitigate the impacts of aluminum corrosion. Phytochemical analysis, gravimetric technique, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) were used to investigate the inhibitory potentials of Garcinia kola aqueous leave extract for the corrosion of aluminum alloy (AA4007) in 1 M NAOH. The highest inhibition efficiency obtained was 98.5%. The result from the analyses collaborated that the leaf extract is a good inhibitor. The Langmuir, Freundlich and Temkin isotherm is in agreement that the inhibitor is physisorption. Garcinia kola showed high corrosion inhibition efficiency on aluminum alloy. [ABSTRACT FROM AUTHOR]

In this paper, the temperature dependence of single event upset (SEU) cross-section in 28 nm embedded Static Random Access Memory (SRAM) of System in Package (SiP) was investigated. An atmospheric neutron beam with an energy range of MeV~GeV was utilized. The SEU cross-section increased by 39.8% when the temperature increased from 296 K to 382 K. Further Technology Computer Aided Design (TCAD) simulation results show that the temperature has a weak impact on the peak pulse current, which is mainly caused by the change of bipolar amplification effect with temperature. As the temperature increases, the critical charge of the device decreases by about 4.8%. The impact of temperature on the SEU cross-section is determined competitively by the peak pulse current and the critical charge. The impact of temperature on critical charge is expected to become more severe as the feature size is further advanced. [ABSTRACT FROM AUTHOR]

In order to predict the shelf life of non-ferrous metals, laboratory studies into the kinetics of corrosion processes of metals under various temperature and humidity conditions were performed. The results of 12-month corrosion tests of the following non-ferrous metals are presented: aluminum, cobalt, copper, nickel, tin, lead, and zinc at various relative humidity and air temperature conditions. All the metals studied were found to be corrosion-resistant under atmospheric conditions at a relative humidity of no more than 80% and an air temperature of no more than 30 °C. An increase in air humidity from 70 to 95% and air temperature from 20 to 50 °C leads to an increase in corrosion-induced losses of metals, primarily for zinc, cobalt, and lead. During the tests, changes in the appearance of the sample surface were recorded. The chemical and phase composition of oxide films on the metal surface, as well as their morphology, were studied after different test periods. The results will be used in future works in developing methods for predicting the shelf life of non-ferrous metals. [ABSTRACT FROM AUTHOR]

To evaluate future changes in the climate system, the outputs from coarse‐resolution global climate models (GCMs) need to be downscaled to a finer scale, making them more directly applicable for impact assessment. Here we focus on examining the projected changes of three key climate variables (precipitation, air temperature, and wet bulb temperature) across Arizona (south‐western United States). We use daily outputs from GCMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and bias correct and downscale them to a 4‐km resolution. Through leave‐one‐out cross‐validation, we compare various bias correction methods and identify that the empirical quantile mapping approach performs the best regardless of the climate variable. Then, we analyse the projected bias‐corrected outputs for two future periods (Mid‐of‐Century: 2015–2048; End‐of‐Century: 2067–2100) with respect to the 1981–2014 period, under four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). Our results show that Arizona's climate is projected to become overall warmer and wetter, more so towards the end of this century and for higher emission scenarios. Additionally, our findings project an increase in wet bulb temperature and cooling degree days, implying the ongoing warming climate's potential impacts on public health and the economy. These results provide a baseline understanding of climate change impacts in the state and highlight the projected changes in the future in response to the climate scenarios. [ABSTRACT FROM AUTHOR]

Climate change has myriad impacts on ecosystems, but the mechanisms by which it affects individual species can be difficult to pinpoint. One strategy to discover such mechanisms is to identify a specific ecological factor related to survival or reproduction and determine how that factor is affected by climate. Here we used Landsat imagery to calculate water clarity for 127 lakes in northern Wisconsin from 1995 to 2021 and thus investigate the effect of clarity on the body condition of an aquatic visual predator, the common loon (Gavia immer). In addition, we examined rainfall and temperature as potential predictors of water clarity. Body mass tracked July water clarity strongly in loon chicks, which grow chiefly in that month, but weakly in adult males and females. Long‐term mean water clarity was negatively related to chick mass but positively related to adult male mass, suggesting that loons foraging in generally clear lakes enjoy good foraging conditions in the long run but might be sensitive to perturbations in clarity during chick‐rearing. Finally, chick mass was positively related to the density of docks, perhaps because angling removes large fishes and thus boosts the abundance of the small fishes on which chicks depend. Water clarity itself declined strongly from 1995 to 2021, was negatively related to July rainfall, and was positively related to July air temperature. Our findings identified both long‐term and short‐term water clarity as strong predictors of loon foraging efficiency, and suggest that climate change, through water clarity, impacts freshwater ecosystems profoundly. Moreover, our results identified the recent decrease in water clarity as a likely cause of population decline in common loons. [ABSTRACT FROM AUTHOR]

Pressure, temperature and specific humidity profiles from COSMIC-2 are compared with those from ERA5 and radiosonde data from October 2019 to September 2020. The results demonstrate that COSMIC-2 pressure and specific humidity profiles show relatively poor accuracy and stability in the lower troposphere. The 1D-Var solution effectively improves the accuracy of temperature parameters in the troposphere. Three parameters show high accuracy over one climatological year, but show slight seasonal fluctuations, especially for the specific humidity profiles in summer. Furthermore, COSMIC-2 specific humidity data show low consistency with radiosonde and ERA5 in the equatorial region. [ABSTRACT FROM AUTHOR]

Recent change in the daily temperature variation (DTV) for March in East Asia is investigated. For this purpose, the effects of atmospheric circulation and global warming on the DTV in the region are analyzed using Japanese 55-year reanalysis data. Among the high-frequency variations in surface air temperature (SAT) during spring, the DTV in March showed a significant, increasing trend in East Asia during the four decades from 1979 to 2020. Composite analysis shows that the above-normal March DTV is associated with the anomalous anticyclonic circulation in the North Pacific and the anomalous cyclonic circulation over Russia. These atmospheric circulation anomalies lead to a greater meridional SAT gradient and tend to cause more mid-latitude pressure systems to pass over East Asia. Ultimately, the SAT in March becomes more variable due to enhanced thermal advection over East Asia. In addition, this pattern of circulation anomalies associated with a large March DTV includes features of a weak East Asian winter monsoon (EAWM) system. Meanwhile, regression analysis results using the EAWM and long-term global warming trend indices suggest that both the large-scale atmospheric circulation and global warming contribute significantly to the March DTV change in East Asia. In particular, heterogeneous warming rates and localized soil drying in East Asia during the study period likely explain the role of global warming on East Asian DTV in March. [ABSTRACT FROM AUTHOR]

Current‐generation climate models project that Africa will warm by up to 5°C in the coming century, severely stressing African populations. Past and ongoing work indicates, however, that the models used to create these projections do not match proxy records of past temperature in Africa during the mid‐Holocene (MH), raising concerns that their future projections may house large uncertainties. Rather than reproducing proxy‐based reconstructions of MH warming relative to the Pre‐Industrial (PI), models instead simulate MH temperatures very similar to or slightly colder than the PI. This data‐model mismatch could be due to a variety of factors, including biases in model surface energy budgets or inaccurate representation of the feedbacks between temperature and hydrologic change during the "Green Sahara." We focus on the differences among model simulations in the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3 and PMIP4), examining surface temperature and energy budgets to investigate controls on temperature and the potential model sources of this paleoclimate data‐model mismatch. Our results suggest that colder conditions simulated by PMIP3 and PMIP4 models during the MH are in large part due to the joint impacts of feedback uncertainties in response to increased precipitation, a strengthened West African Monsoon (WAM) in the Sahel, and the Green Sahara. We extend these insights into suggestions for model physics and boundary condition changes, and discuss implications for the accuracy of future climate model projections over Africa. Key Points: We evaluate the simulation of African air temperatures in Paleoclimate Modeling Intercomparison Project Phases 3 and 4 simulations of the mid‐HoloceneEnergy balance decomposition analyses indicate the hydrologic cycle plays a key role in causing mid‐Holocene cooling in model simulations"Green Sahara" experiments show that dust and vegetation affect simulated temperatures, revealing pathways for refining model simulations [ABSTRACT FROM AUTHOR]

Humid heatwaves, characterized by high temperature and humidity combinations, challenge tropical societies. Extreme wet‐bulb temperatures (TW) over tropical land are coupled to the warmest sea surface temperatures by atmospheric convection and wave dynamics. Here, we harness this coupling for seasonal forecasts of the annual maximum of daily maximum TW (TWmax). We develop a multiple linear regression model that explains 80% of variance in tropical mean TWmax and significant regional TWmax variances. The model considers warming trends and El Niño and Southern Oscillation indices. Looking ahead, the strong‐to‐very‐strong El Niño at the end of 2023, with an Oceanic Niño Index of ∼2.0, suggests a 2024 tropical land mean TWmax of 26.2°C (25.9–26.4°C), and a 68% chance (24%–94%) of breaking existing records. This method also predicts regional TWmax in specific areas. Plain Language Summary: The heat and humidity in the tropics can be particularly challenging for people to stay comfortable and healthy. This combination of heat and moisture is described using a measure called the wet‐bulb temperature (TW). We found that these extremely humid and hot conditions on land can be predicted about 5 months in advance using a physics‐based statistical model. The forecast is possible because the peak of El Niño comes before the peak in the warmest sea surface temperatures, which affects the maximum TW on land. This prediction can help tropical societies to better prepare for extreme heat. Key Points: Tropical wet‐bulb temperatures (TW) peak around 5 months after El Niño wintersA multiple linear regression model considering the El Niño‐Southern Oscillation index and the long‐term warming trend effectively explains TWmax variabilityOur model quantifies the likelihood of strong El Niño and human‐induced warming pushing TWmax to record‐breaking levels [ABSTRACT FROM AUTHOR]

• Fruit temperatures across the supply chain varied between 16 °C and 31 °C. • Temperature variations of fruits in upper and base cartons differed during cruising. • Carton positions in the ULD container showed no impact on product quality. • Superior product quality was obtained during storage at 16 °C compared to 21 °C. • Estimation of product mass loss using model based on temperature and humidity history. An air cargo supply chain for fresh mango was investigated from Thailand to France. Measurements of air temperature, air relative humidity, and fruit (peel) temperature were conducted using thermo-hygrometer data loggers in cartons. In a Unit Load Device (ULD) containing 148 cartons, twenty-seven instrumented cartons were placed at different positions to observe spatial and temporal temperature variations during 12 h cruising. At the destination, mango quality attributes were assessed at different time-points during 15-day storage at 16.1 °C and 21.6 °C. Throughout the supply chain, fruit temperatures ranged from 16.6 °C to 30.5 °C. During cruising, fruit temperatures in upper cartons remained almost constant, whereas base carton temperatures continuously decreased from 26.1 °C to 18.1 °C. Because of a short cruising period in comparison to a storage duration at the destination and proper ambient temperature condition during the experiment, carton position in the ULD had no discernible impact on fruit quality during storage. Fruit stored at 16.1 °C preserved superior fruit quality compared to 21.6 °C storage. For the same storage temperature, better quality of fruits stored in Thailand demonstrated the impact of air shipment on product degradation. The measured temperature and relative humidity evolutions were used as input parameters of a mass loss evolution model. A comparison with the measured data exhibited good agreement (RMSE < 1.8 % mass loss). Considering 10 % mass loss as a critical value, this model allows an estimation of the product shelf life based on a supply chain scenario. [ABSTRACT FROM AUTHOR]

Maize is one of the multipurpose crop whose yields can be affected by climate change. Climate change includes increasing air temperature, increasing sea levels, and changes in rainfall can be a threat and cause a decrease in maize yields. This could be caused by climate change factors. This research aims to identify climate change and analyze the correlation between climate change and maize production in the Majalengka Regency. Maize plantations in the 26 sub-districts are still fluctuative. The method used in this research is quantitative descriptive using trend analysis and correlation. The data used include temperature, rainfall, harvested area, production, and productivity of maize obtained from (i) the Meteorological, Climatological, and Geophysical Agency of Majalengka Regency, (ii) Statistics Indonesia, (iii) Food Security, Agriculture, and Fisheries Office of Majalengka Regency, and other related sources. The results of this research showed that climate change in Majalengka Regency was marked by an increase in temperature trend and a decrease in rainfall trend. The correlation between temperature and changes in maize production in Majalengka Regency was significant, while rainfall was not significant. [ABSTRACT FROM AUTHOR]

Studying atmospheric temperature characteristics is crucial under climate change, as it helps us to understand the changing patterns in temperature that have significant implications for the environment, ecosystems, and human well-being. This study presents the comprehensive analysis of the spatiotemporal variability of scaling behavior of daily temperature series across the whole Indian mainland, using a Multifractal Detrended Fluctuation Analysis (MFDFA). The analysis considered 1° × 1° datasets of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR) (TDTR = Tmax − Tmin) from 1951 to 2016 to compare their scaling behavior for the first time. Our results indicate that the Tmin series exhibits the highest persistence (with the Hurst exponent ranging from 0.849 to unity, and a mean of 0.971), and all four-temperature series display long-term persistence and multifractal characteristics. The variability of the multifractal characteristics is less significant in North–Central India, while it is highest along the western coast of India. Moreover, the assessment of multifractal characteristics of different temperature series during the pre- and post-1976–1977 period of the Pacific climate shift reveals a notable decrease in multifractal strength and persistence in the post-1976–1977 series across all regions. Moreover, for the detection of climate change and its dominant driver, we propose a new rolling window multifractal (RWM) framework by evaluating the temporal evolution of the spectral exponents and the Hurst exponent. This study successfully captured the regime shifts during the periods of 1976–1977 and 1997–1998. Interestingly, the earlier climatic shift primarily mitigated the persistence of the Tmax series, whereas the latter shift significantly influenced the persistence of the Tmean series in the majority of temperature-homogeneous regions in India. [ABSTRACT FROM AUTHOR]

Crop water stress index (CWSI) has been widely used in soil moisture monitoring. However, the influence of the time lag effect between canopy temperature and air temperature on the accuracy of soil moisture monitoring with different CWSI models has not been further investigated. Therefore, based on the continuous record of canopy temperature and air temperature, this study explored the influence of canopy-air temperature hysteresis on the diagnosis of soil moisture with three CWSI models (CWSIT—theoretical, CWSIE—empirical, CWSIH—hybrid). The results show (1) the peak time of canopy temperature was ahead of that of air temperature, and the lag time varied under different soil moisture conditions. When the soil moisture was seriously deficient, the lag time decreased. However, from jointing-heading period to filling-ripening period, the lag time became longer. (2) The values of CWSIT, CWSIE, and CWSIH decreased when the time lag effect was considered. In jointing-heading period, heading-filling period, and filling-ripening period, CWSIT had the highest accuracy in soil moisture monitoring without the consideration of the time lag effect. When the time lag effect was considered, the monitoring accuracy of CWSIE and CWSIH was greatly improved and higher than that of CWSIT, while that of CWSIT was reduced. The findings provided a basis for further improving the accuracy of soil moisture monitoring with CWSI models. [ABSTRACT FROM AUTHOR]

The number of chilling hours is responsible for maximizing budding and flowering in temperate climate fruit trees, and this number varies widely between years. An important source of variability is the occurrence of the ENSO - El Niño Southern Oscillation. With the intent of further understanding, the objective set was to analyze the influence of ENSO on chilling hours in Rio Grande do Sul State. To perform the analysis it was used data of air temperature - below 7.2 o C - from 24 meteorological stations. The results showed that when La Niña occurs, the percentage of chilling hours was above average for the period analyzed in all locations, and when El Niño occurs, the values were below average in 79% of the locations. The highest number of chilling hours, observed in each location, mainly occurred during the presence of La Niña and the lowest number was mainly observed during the occurrence of El Niño. The largest deviation (%) of chilling hours from the average occurred in May. Despite the month of July having the highest number of chilling hours, the occurrence of El Niño or La Niña does not show any difference between them. The month of September also shows no difference when comparing instances of El Niño and La Niña. [ABSTRACT FROM AUTHOR]

Results from eight regional climate models (RCMs) participating in the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) initiative of the Global Energy and Water Exchanges (GEWEX) are examined and compared with observations over the Tibetan Plateau (TP). The RCM common domain covers most areas of East Asia with a horizontal resolution of 20–30 km. The model simulation covers a period from April to September in each year between 1991 and 2015. This study explores the RCMs' ability for seasonal climate simulation over the TP, focusing on the summer monsoon climate as part of the LS4P initiative. An intercomparison is made among eight RCMs for precipitation, surface air temperature, mid-troposphere atmospheric circulation, moisture conditions, and surface energy fluxes. It shows that the downscaling characteristics differ significantly between two major RCM types. The RegCM4 models show positive precipitation biases over the entire TP, especially over the south and southeast TP, while the WRF models mostly show both positive and negative precipitation biases over the TP with relatively high spatial correlation between simulated and observed precipitation. The multi-model ensemble mean produces overall smaller precipitation biases than most individual RCMs, with the largest biases over the southeastern TP, and smaller surface air temperature biases over most areas of the TP, especially over the central and southwestern TP. Moreover, the ensemble mean can better reproduce the inter-annual variation of precipitation and surface air temperature than most RCMs with proper magnitude. Sensitivity analyses using RegCM4 with different physics parameterizations show that varying land and cumulus schemes may induce large precipitation differences over the TP by affecting moisture and atmospheric circulation conditions in the lower and upper troposphere, respectively. Moreover, turbulent heat and radiation fluxes differences are associated with the temperature differences between different RegCM4 models. [ABSTRACT FROM AUTHOR]

• Filtering OH rotational temperature estimates perturbations having periods of 0.7–8.2 h. • Statistical approaches allow subtracting uncorrelated random noise. • Seasonal and interannual variations of residuals reflect climatology of correlated wave processes. Ground-based observations of the natural hydroxyl (OH) nightglow at altitudes of 85–90 km are used for deriving the rotational temperature of excited OH, which is close to the neutral atmospheric temperature. For filtering of mesoscale perturbations, we use differences between pairs of measured values of OH rotational temperature separated with fixed time intervals in the range of 0.5–2 h. The filtering is applied for studying mesoscale variations of temperature near the mesopause according to the data of spectral OH nightglow measurements at observatories of Zvenigorod (56°N, 37°E.) in the years 2004–2016, Tory (52°N, 103°E) in 2012–2017 and Maymaga (63°N, 130°E) in 2000–2015. Monthly-mean values and variances of temperature disturbances with periods 0.7–8.2 h are determined. Semiempirical and statistical approaches are used to estimate and subtract variances of the instrumental dark current noise and uncorrelated in time fluctuations. Seasonal and interannual variations in standard deviations of correlated in time mesoscale perturbations of the OH rotational temperature at the considered observational sites are studied. They can give information about multiyear changes in the activity of atmospheric acoustic-gravity waves propagating through the OH layer near the mesopause. [ABSTRACT FROM AUTHOR]

With the expansion of science and technology worldwide, various satellite products of meteorological parameters have been developed, which can compensate for the lack of observational data. However, to use these products effectively, their accuracy needs to be verified. Therefore, this study investigates the precision of NASA POWER satellite minimum and maximum air temperature data in 70 synoptic stations in Iran from 1987 to 2018. The study examines the data on three-time scales, with 0.5 degree spatial resolution. The findings indicate that the degree of exactness in the NASA POWER temperature data was greater during the monthly interval. The performance of the minimum and maximum air temperature data of the NASA POWER database has been relatively constant in different latitudes. However, changes in the performance of NASA POWER products in stations with different altitudes indicate that the performance of maximum air temperature is better in stations with an altitude of > 1900 m.a.s.l. On the other hand, altitude greatly affects the accuracy of the performance of the satellite model's maximum air temperature. On a daily and monthly time scale, altitude does not significantly impact the minimum temperature data, but on an annual scale, NASA POWER products perform better at low-altitude stations. Hence, the RMSE error values are lower in stations with an altitude of 400–700 m.a.s.l (RMSE = 2.8 °C) than in stations with an altitude of > 1900 m.a.s.l (RMSE = 6.9 °C). NASA-POWER products perform better in summer than in winter. The RMSE error values in the hot months of the year were between 2.64 and 2.81 °C all over Iran, while the minimum temperature in the cold months of the year was between 6.26 and 7.48 °C, which is significantly different. The research also showed that NASA POWER data are more accurate at maximum temperature than at minimum temperature, and these products have less accuracy at minimum temperatures in cold regions. Additionally, the results showed that the error rate in minimum and maximum data in dry and hot regions was less than in other regions, and this satellite product had a more acceptable performance in detecting the temperature of hot and desert regions. The investigation of diverse return periods of minimum and maximum air temperatures elucidates that in arid and parched areas, the maximum air temperature escalates as the return period amplifies; conversely, in hilly and moist regions, the minimum air temperature heightens with the upsurge of the return period. The consequences garnered from this exploration can be employed in meteorological and climatologic analyses and drought inquiries. [ABSTRACT FROM AUTHOR]

Background: Snow forms when tiny ice crystals in clouds stick together to become snowflakes. If enough crystals stick together, they become heavy enough to fall to the ground. Where background includes Precipitation falls as snow when the air temperature is below 2 °C (275.15 K). The falling snow does begin to melt as soon as the temperature rises above freezing, but as the melting process begins, the air around the snowflake is cold. It is a myth that it needs to be below 0 °C (273.15) K to snow. Objective: In Iraq, the heaviest snowfalls tend to occur when the air temperature is between (273.15-275.15) K (0-2) °C. Methods: The data for this study, which includes Temperature (T), Snow Albedo (SA), and Snow Density (SD) as monthly-daily mean, taken from the European Center for Medium-Range Weather Forecasts (ECMWF) for fifteen years from 2008 to 2022 for several selected stations over northern Iraq. The method was to take the monthly rates of snow density, snow albedo, and temperature for the stations of Erbil, Sulaymaniyah, Zakho, Dohuk, and Amadiyah, and the type of relationship and strength of the connection between them was also known. Results: The study found an inverse relationship between snow albedo and snow density across the selected stations, indicating that an increase in snow density leads to a decrease in snow albedo. Notably, Duhok City exhibited the strongest relationship between snow albedo and density, with a regression coefficient of 0.9699 compared to other regions. Conclusions: This study highlights the complex relationship between snow albedo and density in northern Iraq. The strong correlation observed in Duhok City suggests the importance of further research to understand the factors influencing snow properties in this region. [ABSTRACT FROM AUTHOR]

Simple Summary: As climate change intensifies, understanding the impact of environmental factors on bat activity becomes essential for their conservation. This study, centered in central Chile, utilizes data from automatic bat detectors and climatological stations to explore the effects of air temperature and relative humidity on the timing of activity and abundance of five bat species. Temperature exerts influence over the timing of activity and relative abundance, revealing species-specific responses. Relative humidity emerges as a significant factor affecting abundance, with drier nights exhibiting higher bat pass rates. These findings suggest that global warming has the potential to reshape bat behaviors, impacting foraging patterns and activity levels. Further studies are essential to provide deeper insights that can guide effective conservation efforts in the context of climate change. Insectivorous bats play a crucial role in agroecosystems by providing invaluable pest control services. With the escalating impacts of climate change, a comprehensive understanding of the environmental factors influencing bat activity becomes imperative for their conservation in agricultural landscapes. This study investigates the influence of weather conditions, specifically air temperature and relative humidity, on the timing activity and the relative abundance of five insectivorous bat species in central Chile. Data from automatic bat detectors and climatological stations are utilized for analysis. Our results unveil species-specific behaviors, with Tadarida brasiliensis exhibiting early emergence and extended activity periods compared to other bat species. Histiotus montanus and Lasiurus villosissimus display delayed onsets on more humid evenings, whereas Lasiurus varius and T. brasiliensis initiate activity earlier on colder nights compared to warmer ones. Relative humidity emerges as a key factor influencing relative abundance for all species, with more minutes with bat passes detected on drier nights. These findings suggest that global warming may influence observed bat behaviors, potentially altering foraging patterns and activity levels of these bat species. Moreover, as climate change continues, understanding the long-term impact on bat populations and their adaptive strategies is crucial for effective conservation measures. Further studies exploring these dynamics can provide valuable insights for shaping conservation efforts in the face of evolving environmental challenges. [ABSTRACT FROM AUTHOR]

We assess the temperature stability requirements of unseeded Nd:YAG lasers in lidar systems for atmospheric temperature profiling through the rotational Raman technique. Taking as a reference a system using a seeded laser assumed to emit pulses of negligible spectral width and wavelength-drift free, we estimate first the effect of the pulse spectral widening of the unseeded laser on the output of the interference filters, then we derive the limits of the allowable wavelength drift for a given bias in the temperature measurement that would add to the noise-induced uncertainty. Finally, using spectroscopic data, we relate the allowable wavelength drift to allowable temperature variations of the YAG rod. We find that, in order to keep the bias affecting atmospheric temperature measurements smaller than 1 K, the Nd:YAG rod temperature should also be kept within 1 K. [ABSTRACT FROM AUTHOR]

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today's world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature. [ABSTRACT FROM AUTHOR]

Task scheduling based on temperature perception is beneficial for avoiding hotspots and optimizing the internal temperature distribution of data centers. However, the accuracy of task scheduling largely depends on the accuracy of temperature prediction. There are many features that affect the accuracy of temperature prediction in data centers, and the variation periods of these features vary greatly. Traditional machine learning models are difficult to accurately fit them. Therefore, this article proposes a step-by-step temperature prediction algorithm based on Gated Recurrent Unit (GRU). This algorithm establishes prediction models for important parameters such as CPU utilization and air conditioning temperature that affect temperature prediction, and uses the outputs of these two models as inputs for the server temperature prediction model to better fit the changes of feature values. The model combines the principle of thermal locality and integrates the temperature of upper and lower servers for joint modeling. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. RSME reaches 0.278 and the average prediction temperature difference is 0.633, which is much higher than the traditional model. In addition, this article also propose a minimum temperature difference scheduling algorithm based on temperature prediction model, which can effectively reduce the number of servers running at high temperature and low temperature in the data center, make the temperature of the data center more balanced and achieve better energy-saving compared with other baseline algorithms. [ABSTRACT FROM AUTHOR]